diff --git a/.circleci/config.yml b/.circleci/config.yml
index 32983c92e27..cf69ff68da6 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -158,6 +158,8 @@ jobs:
             CHOCOLATEY_CONFIRM_ALL: "true"
       - run:
           name: Install Dependencies
+          environment:
+            UV_HTTP_TIMEOUT: "300"
           command: |
             $installer = Join-Path $env:TEMP "uv-install.ps1"
             Invoke-WebRequest -Uri https://astral.sh/uv/0.10.9/install.ps1 -OutFile $installer
@@ -2475,10 +2477,15 @@ jobs:
             DISABLE_SCHEMA_UPDATE: "true"
             SERVER_ROOT_PATH: ""
             PROXY_LOGOUT_URL: ""
+          # LITELLM_LICENSE is forwarded from the project env so premium-gated
+          # UI flows can be exercised. license.spec.ts asserts the resulting
+          # JWT carries premium_user=true; if it ever stops being passed, that
+          # test fails loudly rather than silently regressing premium coverage.
           command: |
-            uv run --no-sync python -m litellm.proxy.proxy_cli \
-              --config ui/litellm-dashboard/e2e_tests/fixtures/config.yml \
-              --port 4000
+            LITELLM_LICENSE="$LITELLM_LICENSE" \
+              uv run --no-sync python -m litellm.proxy.proxy_cli \
+                --config ui/litellm-dashboard/e2e_tests/fixtures/config.yml \
+                --port 4000
           background: true
       - run:
           name: Wait for proxy to be ready
@@ -2495,9 +2502,12 @@ jobs:
             exit 1
       - run:
           name: Run Playwright E2E tests
+          # Forward LITELLM_LICENSE so license.spec.ts can detect that the
+          # proxy was launched with a license and assert premium_user=true.
           command: |
             cd ui/litellm-dashboard
-            npx playwright test --config e2e_tests/playwright.config.ts
+            LITELLM_LICENSE="$LITELLM_LICENSE" \
+              npx playwright test --config e2e_tests/playwright.config.ts
           no_output_timeout: 10m
       - store_artifacts:
           path: ui/litellm-dashboard/test-results
@@ -2531,7 +2541,6 @@ jobs:
           paths:
             - litellm-docker-database.tar.zst
 
-
   test_bad_database_url:
     machine:
       image: ubuntu-2204:2024.04.1
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index e86fca17c7a..babe3b62933 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -53,3 +53,31 @@ jobs:
         uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3
         with:
           category: "/language:${{ matrix.language }}"
+          output: sarif-results
+          upload: failure-only
+
+      # py/weak-sensitive-data-hashing (CWE-328) fires on the OCI signing call at
+      # litellm/llms/oci/common_utils.py, which hashes the HTTP request body to
+      # produce the x-content-sha256 header required by the OCI HTTP signing spec —
+      # a content-integrity hash, not a password or secret hash. SHA-256 is mandated
+      # by Oracle for this header; see
+      # https://docs.oracle.com/en-us/iaas/Content/API/Concepts/signingrequests.htm
+      # The `usedforsecurity=False` flag on the hashlib.sha256 call already declares
+      # non-security intent, but CodeQL's taint flow still re-fires when callers
+      # further up the stack are modified. The suppression is scoped to this one
+      # file/rule pair via SARIF post-filtering so every other callsite of
+      # py/weak-sensitive-data-hashing in the repository continues to be analyzed.
+      - name: Filter SARIF (OCI sha256)
+        if: matrix.language == 'python'
+        uses: advanced-security/filter-sarif@2da736ff05ef065cb2894ac6892e47b5eac2c3c0 # v1.1
+        with:
+          patterns: |
+            -litellm/llms/oci/common_utils.py:py/weak-sensitive-data-hashing
+          input: sarif-results/python.sarif
+          output: sarif-results/python.sarif
+
+      - name: Upload SARIF
+        uses: github/codeql-action/upload-sarif@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3
+        with:
+          sarif_file: sarif-results
+          category: "/language:${{ matrix.language }}"
diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml
index 8ee3a1ed0cd..2d4e85630dc 100644
--- a/.github/workflows/test-unit-proxy-db.yml
+++ b/.github/workflows/test-unit-proxy-db.yml
@@ -215,8 +215,10 @@ jobs:
               tests/proxy_unit_tests/test_models_fallback_endpoint.py
               tests/proxy_unit_tests/test_google_endpoint_routing.py
               tests/proxy_unit_tests/test_google_gemini_proxy_request.py
+              tests/proxy_unit_tests/test_gemini_agents_endpoints.py
               tests/proxy_unit_tests/test_get_favicon.py
               tests/proxy_unit_tests/test_get_image.py
+              tests/proxy_unit_tests/test_reducto_ocr_route.py
               tests/proxy_unit_tests/test_ui_path_detection.py
               tests/proxy_unit_tests/test_prompt_test_endpoint.py
               tests/proxy_unit_tests/test_check_batch_cost.py
diff --git a/.github/workflows/test-unit-proxy-mgmt-behavior.yml b/.github/workflows/test-unit-proxy-mgmt-behavior.yml
new file mode 100644
index 00000000000..e73997323a4
--- /dev/null
+++ b/.github/workflows/test-unit-proxy-mgmt-behavior.yml
@@ -0,0 +1,34 @@
+name: "Unit Tests: Proxy Management-Endpoint Behavior Pinning"
+
+on:
+  pull_request:
+    branches:
+      - main
+      - litellm_internal_staging
+      - litellm_oss_branch
+      - "litellm_**"
+
+permissions:
+  contents: read
+  id-token: write
+  pull-requests: write
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  proxy-mgmt-behavior:
+    uses: ./.github/workflows/_test-unit-services-base.yml
+    with:
+      test-path: tests/proxy_behavior
+      # workers=0 (no xdist): the world seed is a single shared Postgres
+      # state — two xdist workers both call seed_world() and race on the
+      # ``behavior-pin-budget`` row, producing UniqueViolation + cascading
+      # missing-membership FK failures. The whole suite is ~7s sequentially,
+      # so the cost of disabling parallelism here is negligible.
+      workers: 0
+      reruns: 0
+      enable-postgres: true
+      artifact-name: proxy-mgmt-behavior
+      timeout-minutes: 15
diff --git a/README.md b/README.md
index 72fd43925c9..8df351e9303 100644
--- a/README.md
+++ b/README.md
@@ -292,7 +292,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
 | [CompactifAI (`compactifai`)](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
 | [Custom (`custom`)](https://docs.litellm.ai/docs/providers/custom_llm_server) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
 | [Custom OpenAI (`custom_openai`)](https://docs.litellm.ai/docs/providers/openai_compatible) | ✅ | ✅ | ✅ |  |  | ✅ | ✅ | ✅ | ✅ |  |
-| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
+| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ | ✅ |  |  |  |  |  | ✅ |
 | [Databricks (`databricks`)](https://docs.litellm.ai/docs/providers/databricks) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
 | [DataRobot (`datarobot`)](https://docs.litellm.ai/docs/providers/datarobot) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
 | [Deepgram (`deepgram`)](https://docs.litellm.ai/docs/providers/deepgram) | ✅ | ✅ | ✅ |  |  | ✅ |  |  |  |  |
diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml
index 0f6db331e50..0aef2442bfe 100644
--- a/deploy/charts/litellm-helm/Chart.yaml
+++ b/deploy/charts/litellm-helm/Chart.yaml
@@ -24,7 +24,7 @@ version: 1.1.0
 # incremented each time you make changes to the application. Versions are not expected to
 # follow Semantic Versioning. They should reflect the version the application is using.
 # It is recommended to use it with quotes.
-appVersion: v1.80.12
+appVersion: v1.85.1
 
 annotations:
   org.opencontainers.image.source: "https://github.com/BerriAI/litellm"
diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml
index 25f69080878..aefe2a564bb 100644
--- a/deploy/charts/litellm-helm/templates/deployment.yaml
+++ b/deploy/charts/litellm-helm/templates/deployment.yaml
@@ -53,7 +53,7 @@ spec:
         - name: {{ include "litellm.name" . }}
           securityContext:
             {{- toYaml .Values.securityContext | nindent 12 }}
-          image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}"
+          image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}"
           imagePullPolicy: {{ .Values.image.pullPolicy }}
           env:
             - name: HOST
diff --git a/deploy/charts/litellm-helm/templates/hpa.yaml b/deploy/charts/litellm-helm/templates/hpa.yaml
index 71e199c5aeb..fec4d1f5c5e 100644
--- a/deploy/charts/litellm-helm/templates/hpa.yaml
+++ b/deploy/charts/litellm-helm/templates/hpa.yaml
@@ -12,6 +12,10 @@ spec:
     name: {{ include "litellm.fullname" . }}
   minReplicas: {{ .Values.autoscaling.minReplicas }}
   maxReplicas: {{ .Values.autoscaling.maxReplicas }}
+  {{- if .Values.autoscaling.behavior }}
+  behavior:
+    {{- toYaml .Values.autoscaling.behavior | nindent 4 }}
+  {{- end }}
   metrics:
     {{- if .Values.autoscaling.targetCPUUtilizationPercentage }}
     - type: Resource
diff --git a/deploy/charts/litellm-helm/templates/migrations-job.yaml b/deploy/charts/litellm-helm/templates/migrations-job.yaml
index c3f32fe32f3..5ec7f5b7f3e 100644
--- a/deploy/charts/litellm-helm/templates/migrations-job.yaml
+++ b/deploy/charts/litellm-helm/templates/migrations-job.yaml
@@ -41,7 +41,7 @@ spec:
       {{- end }} 
       containers:
         - name: prisma-migrations
-          image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}"
+          image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}"
           imagePullPolicy: {{ .Values.image.pullPolicy }}
           securityContext:
             {{- toYaml .Values.securityContext | nindent 12 }}
diff --git a/deploy/charts/litellm-helm/tests/hpa_tests.yaml b/deploy/charts/litellm-helm/tests/hpa_tests.yaml
new file mode 100644
index 00000000000..ec18c3591d3
--- /dev/null
+++ b/deploy/charts/litellm-helm/tests/hpa_tests.yaml
@@ -0,0 +1,36 @@
+suite: "hpa with behavior"
+templates:
+  - hpa.yaml
+tests:
+  - it: "renders behavior when set"
+    set:
+      autoscaling.enabled: true
+      autoscaling.behavior:
+        scaleUp:
+          stabilizationWindowSeconds: 60
+          policies:
+            - type: Pods
+              value: 2
+              periodSeconds: 60
+        scaleDown:
+          stabilizationWindowSeconds: 90
+          policies:
+            - type: Pods
+              value: 1
+              periodSeconds: 60
+    asserts:
+      - isKind: { of: HorizontalPodAutoscaler }
+      - equal: { path: spec.behavior.scaleUp.stabilizationWindowSeconds, value: 60 }
+      - equal: { path: spec.behavior.scaleDown.stabilizationWindowSeconds, value: 90 }
+
+---
+suite: "hpa without behavior"
+templates:
+  - hpa.yaml
+tests:
+  - it: "does not render behavior when not set"
+    set:
+      autoscaling.enabled: true
+    asserts:
+      - isKind: { of: HorizontalPodAutoscaler }
+      - isNull: { path: spec.behavior }
diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml
index 9c7c013341b..a9cdf28f0e7 100644
--- a/deploy/charts/litellm-helm/values.yaml
+++ b/deploy/charts/litellm-helm/values.yaml
@@ -10,7 +10,7 @@ image:
   repository: ghcr.io/berriai/litellm-database
   pullPolicy: Always
   # Overrides the image tag whose default is the chart appVersion.
-  # tag: "main-latest"
+  # tag: "latest"
   tag: ""
 
 imagePullSecrets: []
@@ -184,6 +184,7 @@ autoscaling:
   maxReplicas: 100
   targetCPUUtilizationPercentage: 80
   # targetMemoryUtilizationPercentage: 80
+  # behavior: {}
 
 # Autoscaling with keda is mutually exclusive with hpa
 keda:
diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root
index 4de4a55981d..2729babb6d6 100644
--- a/docker/Dockerfile.non_root
+++ b/docker/Dockerfile.non_root
@@ -24,7 +24,8 @@ RUN for i in 1 2 3; do \
         curl \
         openssl \
         libsndfile \
-        nodejs && break || sleep 5; \
+        nodejs \
+        npm && break || sleep 5; \
     done
 
 ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
diff --git a/license_cache.json b/license_cache.json
index 803db8fdd1b..dc061b48f4f 100644
--- a/license_cache.json
+++ b/license_cache.json
@@ -50,6 +50,7 @@
   "h11:0.16.0": "MIT",
   "requests-toolbelt:1.0.0": "Apache 2.0",
   "tornado:6.5.4": "Apache-2.0",
+  "granian:2.5.7": "BSD-3-Clause",
   "mlflow:3.11.1": "Copyright 2018 Databricks, Inc.  All rights reserved.\n        \n        \t\t\t\tApache License\n                                   Version 2.0, January 2004\n                                http://www.apache.org/licenses/\n        \n           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n        \n           1. Definitions.\n        \n              \"License\" shall mean the terms and conditions for use, reproduction,\n              and distribution as defined by Sections 1 through 9 of this document.\n        \n              \"Licensor\" shall mean the copyright owner or entity authorized by\n              the copyright owner that is granting the License.\n        \n              \"Legal Entity\" shall mean the union of the acting entity and all\n              other entities that control, are controlled by, or are under common\n              control with that entity. For the purposes of this definition,\n              \"control\" means (i) the power, direct or indirect, to cause the\n              direction or management of such entity, whether by contract or\n              otherwise, or (ii) ownership of fifty percent (50%) or more of the\n              outstanding shares, or (iii) beneficial ownership of such entity.\n        \n              \"You\" (or \"Your\") shall mean an individual or Legal Entity\n              exercising permissions granted by this License.\n        \n              \"Source\" form shall mean the preferred form for making modifications,\n              including but not limited to software source code, documentation\n              source, and configuration files.\n        \n              \"Object\" form shall mean any form resulting from mechanical\n              transformation or translation of a Source form, including but\n              not limited to compiled object code, generated documentation,\n              and conversions to other media types.\n        \n              \"Work\" shall mean the work of authorship, whether in Source or\n              Object form, made available under the License, as indicated by a\n              copyright notice that is included in or attached to the work\n              (an example is provided in the Appendix below).\n        \n              \"Derivative Works\" shall mean any work, whether in Source or Object\n              form, that is based on (or derived from) the Work and for which the\n              editorial revisions, annotations, elaborations, or other modifications\n              represent, as a whole, an original work of authorship. For the purposes\n              of this License, Derivative Works shall not include works that remain\n              separable from, or merely link (or bind by name) to the interfaces of,\n              the Work and Derivative Works thereof.\n        \n              \"Contribution\" shall mean any work of authorship, including\n              the original version of the Work and any modifications or additions\n              to that Work or Derivative Works thereof, that is intentionally\n              submitted to Licensor for inclusion in the Work by the copyright owner\n              or by an individual or Legal Entity authorized to submit on behalf of\n              the copyright owner. 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   "nvidia-riva-client:2.15.0": "MIT",
   "numpy:1.26.0": "Copyright (c) 2005-2023, NumPy Developers. All rights reserved.  Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:  * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.  * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.  * Neither the name of the NumPy Developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 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Also add information on how to contact you by electronic and paper mail.  If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:  <program>  Copyright (C) <year>  <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details.  The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License.  Of course, your program's commands might be different; for a GUI interface, you would use an \"about box\".  You should also get your employer (if you work as a programmer) or school, if any, to sign a \"copyright disclaimer\" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.  The GNU General Public License does not permit incorporating your program into proprietary programs.  If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library.  If this is what you want to do, use the GNU Lesser General Public License instead of this License.  But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.",
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql
new file mode 100644
index 00000000000..3253b63a884
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql
@@ -0,0 +1,4 @@
+-- AlterTable
+-- Adds the admin-toggleable pause flag used by the router's blocked filter and the
+-- credential lookup helpers; defaults to false so existing rows behave unchanged.
+ALTER TABLE "LiteLLM_ProxyModelTable" ADD COLUMN IF NOT EXISTS "blocked" BOOLEAN NOT NULL DEFAULT false;
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index b53507abe6a..78143fe0411 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable {
 // Models on proxy
 model LiteLLM_ProxyModelTable {
   model_id String @id @default(uuid())
-  model_name String 
+  model_name String
   litellm_params Json
-  model_info Json? 
+  model_info Json?
+  blocked Boolean @default(false)
   created_at    DateTime               @default(now()) @map("created_at")
   created_by String
   updated_at    DateTime               @default(now()) @updatedAt @map("updated_at")
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index ee27cc3585e..0654f17ec68 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "litellm-proxy-extras"
-version = "0.4.72"
+version = "0.4.73"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 readme = "README.md"
 requires-python = ">=3.9"
@@ -26,7 +26,7 @@ required-version = ">=0.10.9"
 module-root = ""
 
 [tool.commitizen]
-version = "0.4.72"
+version = "0.4.73"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index e1b367fb234..7c92623358d 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -225,6 +225,10 @@ use_chat_completions_url_for_anthropic_messages: bool = bool(
 route_all_chat_openai_to_responses: bool = (
     os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true"
 )  # When True, routes all OpenAI /chat/completions requests through the Responses API bridge
+use_legacy_interactions_schema: bool = (
+    os.getenv("LITELLM_USE_LEGACY_INTERACTIONS_SCHEMA", "false").lower() == "true"
+)  # When True, sends Api-Revision: 2026-05-07 to Google so responses use the legacy `outputs`
+# schema instead of the new `steps` schema. Remove this flag after June 8, 2026.
 retry = True
 ### AUTH ###
 api_key: Optional[str] = None
@@ -409,6 +413,12 @@ internal_user_budget_duration: Optional[str] = None
 tag_budget_config: Optional[Dict[str, "BudgetConfig"]] = None
 max_end_user_budget: Optional[float] = None
 max_end_user_budget_id: Optional[str] = None
+# When True, end-user IDs extracted from requests are validated against
+# LiteLLM_EndUserTable / LiteLLM_UserTable. Values that do not resolve to a
+# known row are dropped before reaching spend logs. Defaults to False for
+# backwards compatibility — arbitrary client-supplied identifiers still
+# pass through unchanged.
+validate_end_user_id_in_db: bool = False
 disable_end_user_cost_tracking: Optional[bool] = None
 disable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
 enable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
@@ -416,6 +426,7 @@ custom_prometheus_metadata_labels: List[str] = []
 custom_prometheus_tags: List[str] = []
 prometheus_metrics_config: Optional[List] = None
 prometheus_emit_stream_label: bool = False
+prometheus_user_budget_label_include_email_alias: bool = False
 prometheus_end_user_metrics_max_series_per_metric: Optional[int] = 10000
 prometheus_end_user_metrics_ttl_seconds: Optional[float] = 3600.0
 prometheus_end_user_metrics_cleanup_interval_seconds: Optional[float] = 60.0
@@ -631,6 +642,7 @@ minimax_models: Set = set()
 aws_polly_models: Set = set()
 gigachat_models: Set = set()
 llamagate_models: Set = set()
+reducto_models: Set = set()
 bedrock_mantle_models: Set = set()
 
 
@@ -898,6 +910,8 @@ def add_known_models(model_cost_map: Optional[Dict] = None):
             gigachat_models.add(key)
         elif value.get("litellm_provider") == "llamagate":
             llamagate_models.add(key)
+        elif value.get("litellm_provider") == "reducto":
+            reducto_models.add(key)
         elif value.get("litellm_provider") == "bedrock_mantle":
             bedrock_mantle_models.add(key)
 
@@ -1009,6 +1023,7 @@ model_list = list(
     | ovhcloud_models
     | lemonade_models
     | docker_model_runner_models
+    | reducto_models
     | bedrock_mantle_models
     | set(clarifai_models)
 )
@@ -1115,6 +1130,7 @@ models_by_provider: dict = {
     "aws_polly": aws_polly_models,
     "gigachat": gigachat_models,
     "llamagate": llamagate_models,
+    "reducto": reducto_models,
     "bedrock_mantle": bedrock_mantle_models,
 }
 
@@ -1287,6 +1303,18 @@ from .responses.main import *
 # Interactions API is available as litellm.interactions module
 # Usage: litellm.interactions.create(), litellm.interactions.get(), etc.
 from . import interactions
+from .interactions.agents.main import (
+    acreate as acreate_agent,
+    create as create_agent,
+    alist as alist_agents,
+    list as list_agents,
+    aget as aget_agent,
+    get as get_agent,
+    adelete as adelete_agent,
+    delete as delete_agent,
+    alist_versions as alist_agent_versions,
+    list_versions as list_agent_versions,
+)
 from .skills.main import (
     create_skill,
     acreate_skill,
@@ -1849,6 +1877,9 @@ if TYPE_CHECKING:
     from .llms.azure.completion.transformation import (
         AzureOpenAITextConfig as AzureOpenAITextConfig,
     )
+    from .llms.azure.audio_transcription.transformation import (
+        AzureSpeechAudioTranscriptionConfig as AzureSpeechAudioTranscriptionConfig,
+    )
     from .llms.hosted_vllm.chat.transformation import (
         HostedVLLMChatConfig as HostedVLLMChatConfig,
     )
@@ -1880,6 +1911,12 @@ if TYPE_CHECKING:
     from .llms.dashscope.chat.transformation import (
         DashScopeChatConfig as DashScopeChatConfig,
     )
+    from .llms.dashscope.embed.transformation import (
+        DashScopeEmbeddingConfig as DashScopeEmbeddingConfig,
+    )
+    from .llms.dashscope.rerank.transformation import (
+        DashScopeRerankConfig as DashScopeRerankConfig,
+    )
     from .llms.moonshot.chat.transformation import (
         MoonshotChatConfig as MoonshotChatConfig,
     )
diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py
index e3656b340fa..17eb6609292 100644
--- a/litellm/_lazy_imports_registry.py
+++ b/litellm/_lazy_imports_registry.py
@@ -273,6 +273,7 @@ LLM_CONFIG_NAMES = (
     "AzureOpenAIConfig",
     "AzureOpenAIGPT5Config",
     "AzureOpenAITextConfig",
+    "AzureSpeechAudioTranscriptionConfig",
     "HostedVLLMChatConfig",
     "HostedVLLMEmbeddingConfig",
     # Alias for backwards compatibility
@@ -1054,6 +1055,10 @@ _LLM_CONFIGS_IMPORT_MAP = {
         ".llms.azure.completion.transformation",
         "AzureOpenAITextConfig",
     ),
+    "AzureSpeechAudioTranscriptionConfig": (
+        ".llms.azure.audio_transcription.transformation",
+        "AzureSpeechAudioTranscriptionConfig",
+    ),
     "HostedVLLMChatConfig": (
         ".llms.hosted_vllm.chat.transformation",
         "HostedVLLMChatConfig",
diff --git a/litellm/_redis.py b/litellm/_redis.py
index 65284162663..5ab551453bb 100644
--- a/litellm/_redis.py
+++ b/litellm/_redis.py
@@ -100,6 +100,8 @@ def _get_redis_cluster_kwargs(client=None):
         "azure_tenant_id",
         "azure_client_secret",
         "max_connections",
+        "socket_timeout",
+        "socket_connect_timeout",
     }
 
     return available_args
diff --git a/litellm/_uuid.py b/litellm/_uuid.py
index 52acf647dd8..2b7c3b82d35 100644
--- a/litellm/_uuid.py
+++ b/litellm/_uuid.py
@@ -6,7 +6,6 @@ Always uses fastuuid for performance.
 
 import fastuuid as _uuid  # type: ignore
 
-
 # Expose a module-like alias so callers can use: uuid.uuid4()
 uuid = _uuid
 
diff --git a/litellm/anthropic_interface/exceptions/exception_mapping_utils.py b/litellm/anthropic_interface/exceptions/exception_mapping_utils.py
index 28020e763f4..4548185bbdc 100644
--- a/litellm/anthropic_interface/exceptions/exception_mapping_utils.py
+++ b/litellm/anthropic_interface/exceptions/exception_mapping_utils.py
@@ -9,7 +9,6 @@ from typing import Dict, Optional
 
 from .exceptions import AnthropicErrorResponse, AnthropicErrorType
 
-
 # HTTP status code -> Anthropic error type
 # Source: https://docs.anthropic.com/en/api/errors
 ANTHROPIC_ERROR_TYPE_MAP: Dict[int, AnthropicErrorType] = {
diff --git a/litellm/anthropic_interface/exceptions/exceptions.py b/litellm/anthropic_interface/exceptions/exceptions.py
index 984390fa702..b289e493e6b 100644
--- a/litellm/anthropic_interface/exceptions/exceptions.py
+++ b/litellm/anthropic_interface/exceptions/exceptions.py
@@ -2,7 +2,6 @@
 
 from typing_extensions import Literal, Required, TypedDict
 
-
 # Known Anthropic error types
 # Source: https://docs.anthropic.com/en/api/errors
 AnthropicErrorType = Literal[
diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py
index 3cf1d911d7f..3f4e54382c9 100644
--- a/litellm/caching/caching_handler.py
+++ b/litellm/caching/caching_handler.py
@@ -87,6 +87,16 @@ class CachingHandlerResponse(BaseModel):
 in_memory_cache_obj = InMemoryCache()
 
 
+def _is_chat_completion_cached_dict(cached_result: dict) -> bool:
+    cached_id = cached_result.get("id")
+    if isinstance(cached_id, str) and cached_id.startswith("chatcmpl"):
+        return True
+    obj = cached_result.get("object")
+    if isinstance(obj, str):
+        return obj.startswith("chat.completion")
+    return "choices" in cached_result
+
+
 def _should_defer_streaming_cache_hit_callbacks(*, kwargs: Dict[str, Any]) -> bool:
     """
     When stream=True, do not run success callbacks at cache-hit time.
@@ -861,27 +871,47 @@ class LLMCachingHandler:
         elif (call_type == "aresponses" or call_type == "responses") and isinstance(
             cached_result, dict
         ):
-            from litellm.responses.streaming_iterator import (
-                CachedResponsesAPIStreamingIterator,
-            )
-
-            response_obj = ResponsesAPIResponse(**cached_result)
-            if (
-                hasattr(response_obj, "_hidden_params")
-                and response_obj._hidden_params is not None
-                and isinstance(response_obj._hidden_params, dict)
-            ):
-                response_obj._hidden_params["cache_hit"] = True
-
-            if kwargs.get("stream", False) is True:
-                cached_result = CachedResponsesAPIStreamingIterator(
-                    response=response_obj,
-                    logging_obj=logging_obj,
-                    request_data=kwargs,
-                    call_type=call_type,
-                )
+            use_chat_completion_cache = _is_chat_completion_cached_dict(cached_result)
+            if use_chat_completion_cache:
+                if kwargs.get("stream", False) is True:
+                    bridge_call_type = (
+                        CallTypes.acompletion.value
+                        if call_type == "aresponses"
+                        else CallTypes.completion.value
+                    )
+                    cached_result = self._convert_cached_stream_response(
+                        cached_result=cached_result,
+                        call_type=bridge_call_type,
+                        logging_obj=logging_obj,
+                        model=model,
+                    )
+                else:
+                    cached_result = convert_to_model_response_object(
+                        response_object=cached_result,
+                        model_response_object=ModelResponse(),
+                    )
             else:
-                cached_result = response_obj
+                from litellm.responses.streaming_iterator import (
+                    CachedResponsesAPIStreamingIterator,
+                )
+
+                response_obj = ResponsesAPIResponse(**cached_result)
+                if (
+                    hasattr(response_obj, "_hidden_params")
+                    and response_obj._hidden_params is not None
+                    and isinstance(response_obj._hidden_params, dict)
+                ):
+                    response_obj._hidden_params["cache_hit"] = True
+
+                if kwargs.get("stream", False) is True:
+                    cached_result = CachedResponsesAPIStreamingIterator(
+                        response=response_obj,
+                        logging_obj=logging_obj,
+                        request_data=kwargs,
+                        call_type=call_type,
+                    )
+                else:
+                    cached_result = response_obj
 
         if (
             hasattr(cached_result, "_hidden_params")
diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py
index ce398ee8288..2de7bda6467 100644
--- a/litellm/completion_extras/litellm_responses_transformation/handler.py
+++ b/litellm/completion_extras/litellm_responses_transformation/handler.py
@@ -37,6 +37,15 @@ class ResponsesToCompletionBridgeHandler:
             stream = litellm_params.get("stream", False)
         return bool(stream)
 
+    @staticmethod
+    def _is_preformatted_cached_chat_stream(result: Any) -> bool:
+        from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+
+        return (
+            isinstance(result, CustomStreamWrapper)
+            and result.custom_llm_provider == "cached_response"
+        )
+
     @staticmethod
     def _coerce_response_object(
         response_obj: Any,
@@ -177,6 +186,8 @@ class ResponsesToCompletionBridgeHandler:
             **request_data,
         )
 
+        from litellm.types.utils import ModelResponse
+
         stream = self._resolve_stream_flag(optional_params, litellm_params)
         if isinstance(result, ResponsesAPIResponse):
             return self.transformation_handler.transform_response(
@@ -192,6 +203,8 @@ class ResponsesToCompletionBridgeHandler:
                 api_key=kwargs.get("api_key"),
                 json_mode=kwargs.get("json_mode"),
             )
+        elif isinstance(result, ModelResponse):
+            return result
         elif not stream:
             responses_api_response = self._collect_response_from_stream(result)
             return self.transformation_handler.transform_response(
@@ -208,6 +221,10 @@ class ResponsesToCompletionBridgeHandler:
                 json_mode=kwargs.get("json_mode"),
             )
         else:
+            if self._is_preformatted_cached_chat_stream(result):
+                return self._apply_post_stream_processing(
+                    result, model, custom_llm_provider
+                )
             completion_stream = self.transformation_handler.get_model_response_iterator(
                 streaming_response=result,  # type: ignore
                 sync_stream=True,
@@ -256,6 +273,8 @@ class ResponsesToCompletionBridgeHandler:
             aresponses=True,
         )
 
+        from litellm.types.utils import ModelResponse
+
         stream = self._resolve_stream_flag(optional_params, litellm_params)
         if isinstance(result, ResponsesAPIResponse):
             return self.transformation_handler.transform_response(
@@ -271,6 +290,8 @@ class ResponsesToCompletionBridgeHandler:
                 api_key=kwargs.get("api_key"),
                 json_mode=kwargs.get("json_mode"),
             )
+        elif isinstance(result, ModelResponse):
+            return result
         elif not stream:
             responses_api_response = await self._collect_response_from_stream_async(
                 result
@@ -289,6 +310,10 @@ class ResponsesToCompletionBridgeHandler:
                 json_mode=kwargs.get("json_mode"),
             )
         else:
+            if self._is_preformatted_cached_chat_stream(result):
+                return self._apply_post_stream_processing(
+                    result, model, custom_llm_provider
+                )
             completion_stream = self.transformation_handler.get_model_response_iterator(
                 streaming_response=result,  # type: ignore
                 sync_stream=False,
diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py
index 32423f23314..51abbbf729b 100644
--- a/litellm/completion_extras/litellm_responses_transformation/transformation.py
+++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py
@@ -30,6 +30,11 @@ from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
 from litellm.llms.base_llm.bridges.completion_transformation import (
     CompletionTransformationBridge,
 )
+from litellm.responses.sse_output_recovery import (
+    parse_sse_json_chunk,
+    record_output_item_chunk,
+    record_output_text_chunk,
+)
 from litellm.types.llms.openai import (
     ChatCompletionAnnotation,
     ChatCompletionReasoningItem,
@@ -97,7 +102,7 @@ def _build_reasoning_item(
 
 
 def _reasoning_item_to_response_input(
-    r_item: Union[ChatCompletionReasoningItem, Dict[str, Any]]
+    r_item: Union[ChatCompletionReasoningItem, Dict[str, Any]],
 ) -> Dict[str, Any]:
     """Convert a stored ChatCompletionReasoningItem back to a Responses API input item."""
     r_input: Dict[str, Any] = {
@@ -601,6 +606,79 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
 
         return choices
 
+    @classmethod
+    def _extract_output_from_completed_event(
+        cls, parsed_chunk: Dict[str, Any]
+    ) -> Optional[List[Dict[str, Any]]]:
+        response_payload = parsed_chunk.get("response")
+        if not isinstance(response_payload, dict):
+            return None
+        response_output = response_payload.get("output")
+        if not isinstance(response_output, list) or len(response_output) == 0:
+            return None
+        return cast(List[Dict[str, Any]], response_output)
+
+    @classmethod
+    def _recover_output_items_from_raw_sse(
+        cls, raw_sse: Optional[str]
+    ) -> List[Dict[str, Any]]:
+        if not raw_sse or not isinstance(raw_sse, str):
+            return []
+
+        recovered_output_items: Dict[int, Dict[str, Any]] = {}
+        recovered_text_only_items: Dict[int, Dict[str, Any]] = {}
+
+        for chunk in raw_sse.splitlines():
+            parsed_chunk = parse_sse_json_chunk(chunk)
+            if parsed_chunk is None:
+                continue
+
+            event_type = parsed_chunk.get("type")
+
+            if event_type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED:
+                recovered_output = cls._extract_output_from_completed_event(
+                    parsed_chunk
+                )
+                if recovered_output is not None:
+                    return recovered_output
+                continue
+
+            if event_type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE:
+                record_output_item_chunk(
+                    parsed_chunk=parsed_chunk,
+                    output_items=recovered_output_items,
+                )
+                continue
+
+            if event_type == ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE:
+                record_output_text_chunk(
+                    parsed_chunk=parsed_chunk,
+                    output_items=recovered_output_items,
+                    text_only_items=recovered_text_only_items,
+                )
+                continue
+
+        # Merge text-only items into the recovered output items. Real
+        # OUTPUT_ITEM_DONE events take precedence at any given output_index,
+        # but text-only items at indices without a matching OUTPUT_ITEM_DONE
+        # must still be preserved (e.g. multi-output responses where some
+        # indices only emitted OUTPUT_TEXT_DONE).
+        merged_items: Dict[int, Dict[str, Any]] = {**recovered_text_only_items}
+        merged_items.update(recovered_output_items)
+
+        if merged_items:
+            return [item for _, item in sorted(merged_items.items())]
+
+        return []
+
+    @classmethod
+    def _recover_output_items_from_logging(
+        cls, logging_obj: "LiteLLMLoggingObj"
+    ) -> List[Dict[str, Any]]:
+        model_call_details = getattr(logging_obj, "model_call_details", {}) or {}
+        original_response = model_call_details.get("original_response")
+        return cls._recover_output_items_from_raw_sse(original_response)
+
     def transform_response(  # noqa: PLR0915
         self,
         model: str,
@@ -625,9 +703,22 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         if raw_response.error is not None:
             raise ValueError(f"Error in response: {raw_response.error}")
 
+        output_items = raw_response.output
+        if len(output_items) == 0:
+            recovered_output_items = self._recover_output_items_from_logging(
+                logging_obj
+            )
+            if recovered_output_items:
+                output_items = cast(Any, recovered_output_items)
+                raw_response.output = cast(Any, recovered_output_items)
+                verbose_logger.warning(
+                    "Recovered empty Responses API output from raw SSE for model=%s",
+                    model,
+                )
+
         # Convert response output to choices using the static helper
         choices = self._convert_response_output_to_choices(
-            output_items=raw_response.output,
+            output_items=output_items,
             handle_raw_dict_callback=self._handle_raw_dict_response_item,
         )
 
@@ -641,7 +732,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                 )
             else:
                 raise ValueError(
-                    f"Unknown items in responses API response: {raw_response.output}"
+                    f"Unknown items in responses API response: {output_items}"
                 )
 
         setattr(model_response, "choices", choices)
@@ -1141,6 +1232,14 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
         event_type = parsed_chunk.get("type")
         if isinstance(event_type, ResponsesAPIStreamEvents):
             event_type = event_type.value
+
+        if parsed_chunk.get("object") == "chat.completion.chunk" or (
+            event_type is None
+            and isinstance(parsed_chunk.get("choices"), list)
+            and parsed_chunk.get("choices")
+        ):
+            return ModelResponseStream(**parsed_chunk)
+
         verbose_logger.debug(f"Chat provider: Processing event type: {event_type}")
 
         if event_type == "response.created":
@@ -1229,7 +1328,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
                 raise ValueError(
                     f"Chat provider: Invalid function argument delta {parsed_chunk}"
                 )
-        elif event_type == "response.output_item.done":
+        elif event_type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE:
             # New output item added
             output_item = parsed_chunk.get("item", {})
             if output_item.get("type") == "function_call":
diff --git a/litellm/compression/content_detection.py b/litellm/compression/content_detection.py
index 0655a42daf5..975117eb608 100644
--- a/litellm/compression/content_detection.py
+++ b/litellm/compression/content_detection.py
@@ -5,7 +5,6 @@ Auto-detect content type per message: code, JSON, or text.
 import json
 import re
 
-
 _CODE_KEYWORDS = re.compile(
     r"\b(?:def |function |class |import |from |require\(|#include|fn |func |const |let |var |public |private |static )\b"
 )
diff --git a/litellm/constants.py b/litellm/constants.py
index e36746326cc..fb765c0226c 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -1443,6 +1443,12 @@ CLI_JWT_EXPIRATION_HOURS = int(
     or os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS")
     or 24
 )
+# Comma-separated allowlisted OIDC claim map for CLI SSO polling, e.g.
+# "employment_type->acme_employment_type,org_info.department->department"
+CLI_SSO_CLAIM_MAP = (
+    os.getenv("CLI_SSO_CLAIM_MAP") or os.getenv("LITELLM_CLI_SSO_CLAIM_MAP") or ""
+)
+CLI_SSO_CLAIM_MAX_SCALAR_LENGTH = 1024
 
 ########################### UI SESSION DURATION ###########################
 # Duration for UI login session (username/password, SSO, invitation links). Format: "30s", "30m", "24h", "7d"
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index c0ec148d03a..98e00cf5788 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -173,17 +173,45 @@ def _cost_per_token_custom_pricing_helper(
     prompt_tokens: float = 0,
     completion_tokens: float = 0,
     response_time_ms: Optional[float] = 0.0,
+    cached_tokens: float = 0,
+    cache_creation_tokens: float = 0,
     ### CUSTOM PRICING ###
     custom_cost_per_token: Optional[CostPerToken] = None,
     custom_cost_per_second: Optional[float] = None,
 ) -> Optional[Tuple[float, float]]:
-    """Internal helper function for calculating cost, if custom pricing given"""
+    """Internal helper function for calculating cost, if custom pricing given.
+
+    prompt_tokens is assumed to include both cached_tokens and cache_creation_tokens
+    (OpenAI-compatible convention). Anthropic-style usage where prompt_tokens excludes
+    cache tokens is handled at the caller (cost_per_token) before invoking this helper.
+    """
     if custom_cost_per_token is None and custom_cost_per_second is None:
         return None
 
     if custom_cost_per_token is not None:
-        input_cost = custom_cost_per_token["input_cost_per_token"] * prompt_tokens
-        output_cost = custom_cost_per_token["output_cost_per_token"] * completion_tokens
+        input_cost_per_token = custom_cost_per_token["input_cost_per_token"]
+        output_cost_per_token = custom_cost_per_token["output_cost_per_token"]
+
+        cache_read_input_token_cost = custom_cost_per_token.get(
+            "cache_read_input_token_cost",
+            input_cost_per_token,
+        )
+        cache_creation_input_token_cost = custom_cost_per_token.get(
+            "cache_creation_input_token_cost",
+            input_cost_per_token,
+        )
+
+        regular_prompt_tokens = max(
+            prompt_tokens - cached_tokens - cache_creation_tokens,
+            0,
+        )
+
+        input_cost = (
+            regular_prompt_tokens * input_cost_per_token
+            + cached_tokens * cache_read_input_token_cost
+            + cache_creation_tokens * cache_creation_input_token_cost
+        )
+        output_cost = completion_tokens * output_cost_per_token
         return input_cost, output_cost
     elif custom_cost_per_second is not None:
         output_cost = custom_cost_per_second * response_time_ms / 1000  # type: ignore
@@ -323,10 +351,56 @@ def cost_per_token(  # noqa: PLR0915
         )
 
     ## CUSTOM PRICING ##
+    # Normalize cache token counts across providers:
+    #   - OpenAI-compatible: usage.prompt_tokens_details.cached_tokens
+    #     (prompt_tokens already INCLUDES cached_tokens)
+    #   - Anthropic: usage.cache_read_input_tokens / cache_creation_input_tokens
+    #     (prompt_tokens does NOT include these — adjust before calling helper)
+    _cache_read_tokens: float = 0
+    _cache_creation_tokens: float = 0
+    _is_anthropic_style = False
+
+    if usage_object is not None:
+        _pt_details = getattr(usage_object, "prompt_tokens_details", None)
+        if _pt_details is not None:
+            _cache_read_tokens = float(getattr(_pt_details, "cached_tokens", 0) or 0)
+            # OpenAI-compatible providers report cache-write tokens under
+            # either `cache_write_tokens` (kimi-k2) or `cache_creation_tokens`.
+            # Mirror db_spend_update_writer to stay symmetric.
+            _cache_creation_tokens = float(
+                getattr(_pt_details, "cache_write_tokens", 0)
+                or getattr(_pt_details, "cache_creation_tokens", 0)
+                or 0
+            )
+
+        _anthropic_read = getattr(usage_object, "cache_read_input_tokens", None)
+        _anthropic_create = getattr(usage_object, "cache_creation_input_tokens", None)
+        if _anthropic_read is not None or _anthropic_create is not None:
+            _is_anthropic_style = True
+            if _anthropic_read is not None:
+                _cache_read_tokens = float(_anthropic_read)
+            if _anthropic_create is not None:
+                _cache_creation_tokens = float(_anthropic_create)
+
+    if not _cache_read_tokens and cache_read_input_tokens:
+        _cache_read_tokens = float(cache_read_input_tokens)
+        _is_anthropic_style = True
+    if not _cache_creation_tokens and cache_creation_input_tokens:
+        _cache_creation_tokens = float(cache_creation_input_tokens)
+        _is_anthropic_style = True
+
+    # Anthropic reports prompt_tokens as input_tokens (excluding cache tokens).
+    # Adjust so the helper's "prompt_tokens includes cache tokens" invariant holds.
+    _normalized_prompt_tokens = float(prompt_tokens)
+    if _is_anthropic_style:
+        _normalized_prompt_tokens += _cache_read_tokens + _cache_creation_tokens
+
     response_cost = _cost_per_token_custom_pricing_helper(
-        prompt_tokens=prompt_tokens,
+        prompt_tokens=_normalized_prompt_tokens,
         completion_tokens=completion_tokens,
         response_time_ms=response_time_ms,
+        cached_tokens=_cache_read_tokens,
+        cache_creation_tokens=_cache_creation_tokens,
         custom_cost_per_second=custom_cost_per_second,
         custom_cost_per_token=custom_cost_per_token,
     )
@@ -1805,10 +1879,6 @@ def ocr_cost(
     if response.usage_info is None:
         raise ValueError("OCR response usage_info is None")
 
-    pages_processed = response.usage_info.pages_processed
-    if pages_processed is None:
-        raise ValueError("OCR response pages_processed is None")
-
     try:
         model_info: Optional[ModelInfo] = litellm.get_model_info(
             model=model, custom_llm_provider=custom_llm_provider
@@ -1816,9 +1886,49 @@ def ocr_cost(
     except Exception:
         model_info = None
 
-    ocr_cost_per_page: float = 0.0
+    credits = getattr(response.usage_info, "credits", None)
+    cost_per_credit = None
     if model_info is not None:
-        ocr_cost_per_page = model_info.get("ocr_cost_per_page") or 0.0
+        cost_per_credit = model_info.get("ocr_cost_per_credit")
+    if credits is not None and cost_per_credit is not None:
+        return cost_per_credit * credits, 0.0
+
+    ocr_cost_per_page: Optional[float] = None
+    if model_info is not None:
+        ocr_cost_per_page = model_info.get("ocr_cost_per_page")
+
+    pages_processed = response.usage_info.pages_processed
+    if pages_processed is None:
+        if cost_per_credit is not None or ocr_cost_per_page is None:
+            # Surface missing usage data instead of silently under-reporting
+            # cost. The previous behavior raised ValueError; we now return 0.0
+            # for credit-priced or unpriced models, so log a warning to keep
+            # the regression visible to operators.
+            verbose_logger.warning(
+                "OCR cost: model=%s custom_llm_provider=%s response.usage_info."
+                "pages_processed is None and credits=%s; returning 0.0 cost.",
+                model,
+                custom_llm_provider,
+                credits,
+            )
+            return 0.0, 0.0
+        raise ValueError("OCR response pages_processed is None")
+
+    if ocr_cost_per_page is None:
+        # No per-page pricing configured. Either the model is on credit-based
+        # pricing (and credits weren't returned, so the credit branch above did
+        # not match) or the model has no OCR pricing entry at all. Surface a
+        # warning so that missing pricing entries are visible rather than
+        # silently producing zero cost for billable usage.
+        verbose_logger.warning(
+            "OCR cost: model=%s custom_llm_provider=%s reported "
+            "pages_processed=%s but no ocr_cost_per_page is configured; "
+            "returning 0.0 cost.",
+            model,
+            custom_llm_provider,
+            pages_processed,
+        )
+        return 0.0, 0.0
 
     total_ocr_processing_cost: float = ocr_cost_per_page * pages_processed
     return total_ocr_processing_cost, 0.0
diff --git a/litellm/exceptions.py b/litellm/exceptions.py
index 8b005291556..17f5b43c273 100644
--- a/litellm/exceptions.py
+++ b/litellm/exceptions.py
@@ -918,9 +918,11 @@ class GuardrailRaisedException(Exception):
         guardrail_name: Optional[str] = None,
         message: str = "",
         should_wrap_with_default_message: bool = True,
+        status_code: int = 400,
     ):
         default_message = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}"
         self.guardrail_name = guardrail_name
+        self.status_code = status_code
         self.message = default_message if should_wrap_with_default_message else message
         super().__init__(self.message)
 
@@ -930,12 +932,14 @@ class BlockedPiiEntityError(Exception):
         self,
         entity_type: str,
         guardrail_name: Optional[str] = None,
+        status_code: int = 400,
     ):
         """
         Raised when a blocked entity is detected by a guardrail.
         """
         self.entity_type = entity_type
         self.guardrail_name = guardrail_name
+        self.status_code = status_code
         self.message = f"Blocked entity detected: {entity_type} by Guardrail: {guardrail_name}. This entity is not allowed to be used in this request."
         super().__init__(self.message)
 
diff --git a/litellm/files/types.py b/litellm/files/types.py
index 688bc86f0cf..ba42a39f666 100644
--- a/litellm/files/types.py
+++ b/litellm/files/types.py
@@ -1,6 +1,5 @@
 from typing import AsyncIterator, Dict, Iterator, Literal, NamedTuple, Union
 
-
 FileContentProvider = Literal[
     "openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic", "manus"
 ]
diff --git a/litellm/google_genai/adapters/__init__.py b/litellm/google_genai/adapters/__init__.py
index bfa9e712678..6fbe7d95a55 100644
--- a/litellm/google_genai/adapters/__init__.py
+++ b/litellm/google_genai/adapters/__init__.py
@@ -1,10 +1,10 @@
 """
 Google GenAI Adapters for LiteLLM
 
-This module provides adapters for transforming Google GenAI generate_content requests 
+This module provides adapters for transforming Google GenAI generate_content requests
 to/from LiteLLM completion format with full support for:
 - Text content transformation
-- Tool calling (function declarations, function calls, function responses)  
+- Tool calling (function declarations, function calls, function responses)
 - Streaming (both regular and tool calling)
 - Mixed content (text + tool calls)
 """
diff --git a/litellm/integrations/SlackAlerting/batching_handler.py b/litellm/integrations/SlackAlerting/batching_handler.py
index fdce2e04793..828f3eb4175 100644
--- a/litellm/integrations/SlackAlerting/batching_handler.py
+++ b/litellm/integrations/SlackAlerting/batching_handler.py
@@ -1,9 +1,9 @@
 """
-Handles Batching + sending Httpx Post requests to slack 
+Handles Batching + sending Httpx Post requests to slack
 
-Slack alerts are sent every 10s or when events are greater than X events 
+Slack alerts are sent every 10s or when events are greater than X events
 
-see custom_batch_logger.py for more details / defaults 
+see custom_batch_logger.py for more details / defaults
 """
 
 from typing import TYPE_CHECKING, Any
diff --git a/litellm/integrations/SlackAlerting/utils.py b/litellm/integrations/SlackAlerting/utils.py
index e695266c88b..e2580768178 100644
--- a/litellm/integrations/SlackAlerting/utils.py
+++ b/litellm/integrations/SlackAlerting/utils.py
@@ -18,7 +18,7 @@ else:
 
 
 def process_slack_alerting_variables(
-    alert_to_webhook_url: Optional[Dict[AlertType, Union[List[str], str]]]
+    alert_to_webhook_url: Optional[Dict[AlertType, Union[List[str], str]]],
 ) -> Optional[Dict[AlertType, Union[List[str], str]]]:
     """
     process alert_to_webhook_url
diff --git a/litellm/integrations/additional_logging_utils.py b/litellm/integrations/additional_logging_utils.py
index 795afd81d41..59319140a18 100644
--- a/litellm/integrations/additional_logging_utils.py
+++ b/litellm/integrations/additional_logging_utils.py
@@ -1,5 +1,5 @@
 """
-Base class for Additional Logging Utils for CustomLoggers 
+Base class for Additional Logging Utils for CustomLoggers
 
 - Health Check for the logging util
 - Get Request / Response Payload for the logging util
diff --git a/litellm/integrations/custom_batch_logger.py b/litellm/integrations/custom_batch_logger.py
index f9d4496c21f..8f4844501c3 100644
--- a/litellm/integrations/custom_batch_logger.py
+++ b/litellm/integrations/custom_batch_logger.py
@@ -1,5 +1,5 @@
 """
-Custom Logger that handles batching logic 
+Custom Logger that handles batching logic
 
 Use this if you want your logs to be stored in memory and flushed periodically.
 """
@@ -14,22 +14,38 @@ from litellm.integrations.custom_logger import CustomLogger
 
 
 class CustomBatchLogger(CustomLogger):
+    preserve_events_added_during_flush = False
+
+    # Default cap on the in-memory log queue. Prevents unbounded memory growth
+    # if ``async_send_batch`` consistently fails (e.g. the destination is
+    # unreachable) and events are preserved across flush attempts. Subclasses
+    # may override by passing ``max_queue_size`` or by setting the attribute
+    # directly (see ``RubrikLogger`` for an example).
+    DEFAULT_MAX_QUEUE_SIZE = 50_000
+
     def __init__(
         self,
         flush_lock: Optional[asyncio.Lock] = None,
         batch_size: Optional[int] = None,
         flush_interval: Optional[int] = None,
+        max_queue_size: Optional[int] = None,
         **kwargs,
     ) -> None:
         """
         Args:
             flush_lock (Optional[asyncio.Lock], optional): Lock to use when flushing the queue. Defaults to None. Only used for custom loggers that do batching
+            max_queue_size (Optional[int], optional): Maximum number of events to retain in ``log_queue``. When the limit is exceeded (e.g. because the send destination is unreachable and events are preserved for retry), the oldest events are dropped. Defaults to ``DEFAULT_MAX_QUEUE_SIZE``.
         """
         self.log_queue: List = []
         self.flush_interval = flush_interval or litellm.DEFAULT_FLUSH_INTERVAL_SECONDS
         self.batch_size: int = batch_size or litellm.DEFAULT_BATCH_SIZE
         self.last_flush_time = time.time()
         self.flush_lock = flush_lock
+        self.max_queue_size: int = (
+            max_queue_size
+            if max_queue_size is not None
+            else self.DEFAULT_MAX_QUEUE_SIZE
+        )
 
         super().__init__(**kwargs)
 
@@ -47,11 +63,40 @@ class CustomBatchLogger(CustomLogger):
 
         async with self.flush_lock:
             if self.log_queue:
+                log_queue_length = len(self.log_queue)
                 verbose_logger.debug(
                     "CustomLogger: Flushing batch of %s events", len(self.log_queue)
                 )
-                await self.async_send_batch()
-                self.log_queue.clear()
+                try:
+                    await self.async_send_batch()
+                except Exception:
+                    # If the underlying batch send raised, do NOT drop the
+                    # in-flight events. They will be retried on the next flush.
+                    # Most existing async_send_batch implementations swallow
+                    # their own errors, so this only affects loggers that opt
+                    # in to surfacing failures (e.g. Rubrik).
+                    verbose_logger.exception(
+                        "CustomLogger: async_send_batch raised; preserving "
+                        "%s events in queue for retry",
+                        log_queue_length,
+                    )
+                    # Guard against unbounded queue growth if the destination
+                    # is persistently unreachable. Drop the oldest events
+                    # beyond ``max_queue_size``.
+                    overflow = len(self.log_queue) - self.max_queue_size
+                    if overflow > 0:
+                        del self.log_queue[:overflow]
+                        verbose_logger.warning(
+                            "CustomLogger: log queue exceeded max_queue_size=%s; "
+                            "dropped %s oldest events.",
+                            self.max_queue_size,
+                            overflow,
+                        )
+                    return
+                if self.preserve_events_added_during_flush:
+                    del self.log_queue[:log_queue_length]
+                else:
+                    self.log_queue.clear()
                 self.last_flush_time = time.time()
 
     async def async_send_batch(self, *args, **kwargs):
diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py
index c937ad0a7bf..82a35f2eedd 100644
--- a/litellm/integrations/custom_guardrail.py
+++ b/litellm/integrations/custom_guardrail.py
@@ -43,7 +43,11 @@ if TYPE_CHECKING:
 dc = DualCache()
 
 
-from litellm.exceptions import ModifyResponseException as ModifyResponseException
+from litellm.exceptions import (
+    BlockedPiiEntityError,
+    GuardrailRaisedException,
+    ModifyResponseException,
+)
 
 
 class CustomGuardrail(CustomLogger):
@@ -737,12 +741,15 @@ class CustomGuardrail(CustomLogger):
         (this was logged previously as an API failure - guardrail_failed_to_respond).
 
         Guardrails signal intentional blocks by raising:
+        - GuardrailRaisedException (generic guardrail API, tool permission)
+        - BlockedPiiEntityError (Presidio PII detection)
         - HTTPException with status 400 (content policy violation)
         - ModifyResponseException (passthrough mode violation)
         """
-
         if isinstance(e, ModifyResponseException):
             return True
+        if isinstance(e, (GuardrailRaisedException, BlockedPiiEntityError)):
+            return True
         if (
             HTTPException is not None
             and isinstance(e, HTTPException)
diff --git a/litellm/integrations/focus/transformer.py b/litellm/integrations/focus/transformer.py
index b7d28e3dbb9..6f4433b4a05 100644
--- a/litellm/integrations/focus/transformer.py
+++ b/litellm/integrations/focus/transformer.py
@@ -9,7 +9,6 @@ import polars as pl
 
 from .schema import FOCUS_NORMALIZED_SCHEMA
 
-
 _TAG_KEYS = (
     "team_id",
     "team_alias",
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
index 2de8e8bf7f1..81fdc5a1e21 100644
--- a/litellm/integrations/opentelemetry.py
+++ b/litellm/integrations/opentelemetry.py
@@ -673,6 +673,15 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
         if parent_otel_span is not None:
             parent_otel_span.set_status(Status(StatusCode.ERROR))
 
+            # Stamp team attributes onto the SERVER (root) span too, so the
+            # trace root is team-filterable on the failure path like the
+            # child exception span below.
+            self._set_team_attributes_on_span(
+                span=parent_otel_span,
+                team_id=user_api_key_dict.team_id,
+                team_alias=user_api_key_dict.team_alias,
+            )
+
             # Stamp structured error attrs on the SERVER span itself; the
             # failure path otherwise only sets its status (_handle_failure
             # records on the litellm_request child span). Inline import:
@@ -693,6 +702,14 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
                 },
             )
 
+            # _record_exception_on_span only stamps when error_code is set;
+            # bare TypeError etc. has none, and the span is about to be ended.
+            error_code = (
+                error_information.get("error_code") if error_information else None
+            )
+            if not error_code:
+                self.set_response_status_code_attribute(parent_otel_span, 500)
+
             # Pre-request latency (request_data carries the propagated
             # metadata on the failure path; omitted if it failed before handoff).
             self.set_preprocessing_duration_attribute(parent_otel_span, request_data)
@@ -709,12 +726,65 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
                 key="exception",
                 value=str(original_exception),
             )
+            self._set_team_attributes_on_span(
+                span=exception_logging_span,
+                team_id=user_api_key_dict.team_id,
+                team_alias=user_api_key_dict.team_alias,
+            )
             exception_logging_span.set_status(Status(StatusCode.ERROR))
             exception_logging_span.end(end_time=self._to_ns(datetime.now()))
 
+            # Emit guardrail spans for any guardrail invocations that
+            # ran during this request. _handle_failure typically does this,
+            # but for pre-call guardrail blocks the standard_logging_object
+            # may not carry guardrail_information by the time _handle_failure
+            # fires (the data lives only in request_data["metadata"]). Pull
+            # directly from request_data so the span is recorded either way;
+            # _emit_once dedupes if _handle_failure already emitted it.
+            self._emit_guardrail_spans_from_request_data(
+                request_data=request_data,
+                parent_span=parent_otel_span,
+            )
+
             # End Parent OTEL Sspan
             parent_otel_span.end(end_time=self._to_ns(datetime.now()))
 
+    def _emit_guardrail_spans_from_request_data(
+        self,
+        request_data: dict,
+        parent_span: Optional[Any],
+    ) -> None:
+        """Emit ``guardrail`` spans from ``request_data["metadata"]
+        ["standard_logging_guardrail_information"]``.
+
+        Routed through ``_create_guardrail_span`` so the dedupe state in
+        ``_otel_internal`` is honoured — if ``_handle_failure`` already
+        emitted these spans for the same kwargs, this is a no-op.
+        """
+        from opentelemetry import trace as _trace
+
+        metadata = (request_data or {}).get("metadata") or {}
+        guardrail_information = metadata.get("standard_logging_guardrail_information")
+        if not guardrail_information:
+            return
+
+        # _create_guardrail_span reads guardrail_information from
+        # kwargs["standard_logging_object"] and shares its dedupe state via
+        # kwargs["litellm_params"]["metadata"]["_otel_internal"]. Pass the
+        # SAME metadata dict the proxy populated so _handle_failure and
+        # this hook see the same dedupe markers.
+        kwargs: Dict[str, Any] = {
+            "litellm_params": {"metadata": metadata},
+            "standard_logging_object": {
+                "guardrail_information": guardrail_information,
+                "metadata": metadata,
+            },
+        }
+        context = (
+            _trace.set_span_in_context(parent_span) if parent_span is not None else None
+        )
+        self._create_guardrail_span(kwargs=kwargs, context=context)
+
     async def async_post_call_success_hook(
         self,
         data: dict,
@@ -736,11 +806,6 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
             # Pre-request latency on the SERVER span (success path).
             self.set_preprocessing_duration_attribute(parent_span, kwargs)
 
-            # http.response.status_code on the SERVER span (success path).
-            # A successful proxy response is HTTP 200; the failure path sets
-            # this from the error code in _record_exception_on_span.
-            self.set_response_status_code_attribute(parent_span, 200)
-
             # 3. Guardrail span
             self._create_guardrail_span(kwargs=kwargs, context=ctx)
 
@@ -923,7 +988,15 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
             and hasattr(proxy_span, "is_recording")
             and proxy_span.is_recording()
         ):
-            proxy_span.end(end_time=self._to_ns(end_time))
+            self._close_proxy_span_ok(proxy_span, end_time)
+
+    def _close_proxy_span_ok(self, span: Span, end_time) -> None:
+        """Stamp http.response.status_code=200 + status=OK, then end the span."""
+        from opentelemetry.trace import Status, StatusCode
+
+        self.set_response_status_code_attribute(span, 200)
+        span.set_status(Status(StatusCode.OK))
+        span.end(end_time=self._to_ns(end_time))
 
     def _handle_success(self, kwargs, response_obj, start_time, end_time):
         """Create the litellm_request span then close the proxy span."""
@@ -1009,8 +1082,14 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
             parent_span is not None
             and hasattr(parent_span, "name")
             and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME
+            and hasattr(parent_span, "is_recording")
+            and parent_span.is_recording()
         ):
-            parent_span.end(end_time=self._to_ns(end_time))
+            self._close_proxy_span_ok(parent_span, end_time)
+
+        # Stamp team attributes onto the SERVER (root) span before it is
+        # closed, so the trace root carries them like every child span.
+        self._set_team_attributes_on_proxy_span_from_kwargs(kwargs)
 
         # close the proxy span explicitly from kwargs metadata
         # after all child spans (litellm_request, guardrail, raw_request)
@@ -1070,8 +1149,70 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
         )
         raw_span.set_status(Status(StatusCode.OK))
         self.set_raw_request_attributes(raw_span, kwargs, response_obj)
+        self._set_team_attributes_from_kwargs(raw_span, kwargs)
         raw_span.end(end_time=self._to_ns(end_time))
 
+    def _set_team_attributes_on_span(
+        self,
+        span: Span,
+        team_id: Optional[str],
+        team_alias: Optional[str],
+    ) -> None:
+        """Stamp team_id / team_alias onto a span so every child span of a
+        litellm_request trace carries them, not just the root span.
+
+        Empty strings are treated as absent: a request made with the master
+        key or a team-less virtual key carries ``user_api_key_team_id=""``
+        in ``standard_logging_object.metadata``; propagating that to every
+        span only adds noise that makes traces look mis-instrumented.
+        """
+        if team_id:
+            self.safe_set_attribute(
+                span=span,
+                key="metadata.user_api_key_team_id",
+                value=team_id,
+            )
+        if team_alias:
+            self.safe_set_attribute(
+                span=span,
+                key="metadata.user_api_key_team_alias",
+                value=team_alias,
+            )
+
+    def _set_team_attributes_from_kwargs(self, span: Span, kwargs: dict) -> None:
+        """Pull team_id / team_alias from the standard logging metadata in kwargs and stamp them onto span."""
+        std_log = kwargs.get("standard_logging_object")
+        md: dict = {}
+        if isinstance(std_log, dict):
+            md = std_log.get("metadata") or {}
+        elif std_log is not None:
+            md = getattr(std_log, "metadata", None) or {}
+        self._set_team_attributes_on_span(
+            span=span,
+            team_id=md.get("user_api_key_team_id"),
+            team_alias=md.get("user_api_key_team_alias"),
+        )
+
+    def _set_team_attributes_on_proxy_span_from_kwargs(self, kwargs: dict) -> None:
+        """Stamp team attributes onto the proxy SERVER (root) span so the
+        trace root is filterable by team, not just its children. The root
+        span is created in auth before the team is resolved and is
+        otherwise only closed (never re-attributed) on the success path.
+
+        Guarded to the LiteLLM-created proxy span (by name + recording) so
+        externally provided parent spans are never mutated.
+        """
+        litellm_params = kwargs.get("litellm_params") or {}
+        metadata = litellm_params.get("metadata") or {}
+        proxy_span = metadata.get("litellm_parent_otel_span")
+        if (
+            proxy_span is not None
+            and getattr(proxy_span, "name", None) == LITELLM_PROXY_REQUEST_SPAN_NAME
+            and hasattr(proxy_span, "is_recording")
+            and proxy_span.is_recording()
+        ):
+            self._set_team_attributes_from_kwargs(proxy_span, kwargs)
+
     def _record_metrics(self, kwargs, response_obj, start_time, end_time):
         duration_s = (end_time - start_time).total_seconds()
         params = kwargs.get("litellm_params") or {}
@@ -1107,8 +1248,13 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
             "mcp_tool_call_metadata",
             "vector_store_request_metadata",
         ]:
-            if md.get(key) is not None:
-                common_attrs[f"metadata.{key}"] = str(md[key])
+            value = md.get(key)
+            if value is None:
+                continue
+            if isinstance(value, (dict, list)):
+                common_attrs[f"metadata.{key}"] = safe_dumps(value)
+            else:
+                common_attrs[f"metadata.{key}"] = str(value)
 
         # get hidden params
         hidden_params = getattr(std_log, "hidden_params", None) or (std_log or {}).get(
@@ -1526,12 +1672,45 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
                     "masked_entity_count", safe_dumps(masked_entity_count)
                 )
 
+            guardrail_response = guardrail_information.get("guardrail_response")
+            if guardrail_response is not None:
+                guardrail_span.set_attribute(
+                    "guardrail_response", safe_dumps(guardrail_response)
+                )
+
+            # Surface guardrail_status (success / guardrail_intervened /
+            # guardrail_failed_to_respond / not_run) as a top-level span
+            # attribute so trace backends can filter on it without parsing
+            # guardrail_response.
             self.safe_set_attribute(
                 span=guardrail_span,
-                key="guardrail_response",
-                value=guardrail_information.get("guardrail_response"),
+                key="guardrail_status",
+                value=guardrail_information.get("guardrail_status"),
             )
 
+            # Provider's raw top-level action (e.g. Bedrock's
+            # ``GUARDRAIL_INTERVENED`` / ``NONE``). Populated by the provider
+            # hook onto StandardLoggingGuardrailInformation so this integration
+            # stays provider-agnostic — we only read a normalised string.
+            guardrail_action = guardrail_information.get("guardrail_action")
+            if guardrail_action:
+                guardrail_span.set_attribute("guardrail_action", guardrail_action)
+
+            # The provider hook (e.g. Bedrock) extracts violation_categories
+            # from the raw response BEFORE redaction and stamps them onto
+            # StandardLoggingGuardrailInformation. Surfacing them here as a
+            # queryable attribute lets dashboards group by violation category
+            # without parsing the redacted guardrail_response blob.
+            violation_categories = guardrail_information.get("violation_categories")
+            if violation_categories:
+                # OTel sequence attributes must be homogeneous primitives;
+                # serialise to JSON once so set_attribute never coerces.
+                guardrail_span.set_attribute(
+                    "guardrail_violation_categories", safe_dumps(violation_categories)
+                )
+
+            self._set_team_attributes_from_kwargs(guardrail_span, kwargs)
+
             guardrail_span.end(end_time=self._to_ns(end_time_datetime))
 
     def _handle_failure(self, kwargs, response_obj, start_time, end_time):
@@ -2875,6 +3054,11 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
             management_endpoint_span.set_status(Status(StatusCode.OK))
             management_endpoint_span.end(end_time=_end_time_ns)
 
+            # The management wrapper has no other hook that closes the SERVER span.
+            self.set_response_status_code_attribute(parent_otel_span, 200)
+            parent_otel_span.set_status(Status(StatusCode.OK))
+            parent_otel_span.end(end_time=_end_time_ns)
+
     async def async_management_endpoint_failure_hook(
         self,
         logging_payload: ManagementEndpointLoggingPayload,
@@ -2925,6 +3109,24 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
             management_endpoint_span.set_status(Status(StatusCode.ERROR))
             management_endpoint_span.end(end_time=_end_time_ns)
 
+            # The management wrapper has no other hook that closes the SERVER span.
+            from litellm.litellm_core_utils.litellm_logging import (
+                StandardLoggingPayloadSetup,
+            )
+
+            error_information = StandardLoggingPayloadSetup.get_error_information(
+                original_exception=_exception,
+            )
+            parent_otel_span.set_status(Status(StatusCode.ERROR))
+            self._record_exception_on_span(
+                span=parent_otel_span,
+                kwargs={
+                    "exception": _exception,
+                    "standard_logging_object": {"error_information": error_information},
+                },
+            )
+            parent_otel_span.end(end_time=_end_time_ns)
+
     def create_litellm_proxy_request_started_span(
         self,
         start_time: datetime,
diff --git a/litellm/integrations/opik/utils.py b/litellm/integrations/opik/utils.py
index b0ab5991c91..43577505c11 100644
--- a/litellm/integrations/opik/utils.py
+++ b/litellm/integrations/opik/utils.py
@@ -105,7 +105,7 @@ def _remove_nulls(x: Dict[str, Any]) -> Dict[str, Any]:
 
 
 def get_traces_and_spans_from_payload(
-    payload: List[Dict[str, Any]]
+    payload: List[Dict[str, Any]],
 ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
     """
     Separate traces and spans from payload.
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 30af0dcb8ed..5f052842122 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -166,6 +166,53 @@ class PrometheusLogger(CustomLogger):
                 labelnames=self.get_labels_for_metric("litellm_output_tokens_metric"),
             )
 
+            # Token-type detail metrics. These break out cached, cache-creation,
+            # audio and reasoning tokens that providers report inside
+            # prompt_tokens_details / completion_tokens_details on the usage
+            # object. They are sparse (only incremented when the provider
+            # reports a non-zero value) and are additive to the existing
+            # input/output token totals — no breaking change for existing
+            # dashboards built on the totals.
+            self.litellm_input_cached_tokens_metric = self._counter_factory(
+                "litellm_input_cached_tokens_metric",
+                "Provider-side cached input tokens (e.g. OpenAI prompt_tokens_details.cached_tokens, Anthropic cache_read_input_tokens)",
+                labelnames=self.get_labels_for_metric(
+                    "litellm_input_cached_tokens_metric"
+                ),
+            )
+
+            self.litellm_input_cache_creation_tokens_metric = self._counter_factory(
+                "litellm_input_cache_creation_tokens_metric",
+                "Provider-side input tokens written to prompt cache (e.g. Anthropic cache_creation_input_tokens)",
+                labelnames=self.get_labels_for_metric(
+                    "litellm_input_cache_creation_tokens_metric"
+                ),
+            )
+
+            self.litellm_input_audio_tokens_metric = self._counter_factory(
+                "litellm_input_audio_tokens_metric",
+                "Audio input tokens reported in prompt_tokens_details.audio_tokens",
+                labelnames=self.get_labels_for_metric(
+                    "litellm_input_audio_tokens_metric"
+                ),
+            )
+
+            self.litellm_output_reasoning_tokens_metric = self._counter_factory(
+                "litellm_output_reasoning_tokens_metric",
+                "Reasoning tokens reported in completion_tokens_details.reasoning_tokens",
+                labelnames=self.get_labels_for_metric(
+                    "litellm_output_reasoning_tokens_metric"
+                ),
+            )
+
+            self.litellm_output_audio_tokens_metric = self._counter_factory(
+                "litellm_output_audio_tokens_metric",
+                "Audio output tokens reported in completion_tokens_details.audio_tokens",
+                labelnames=self.get_labels_for_metric(
+                    "litellm_output_audio_tokens_metric"
+                ),
+            )
+
             # Remaining Budget for Team
             self.litellm_remaining_team_budget_metric = self._gauge_factory(
                 "litellm_remaining_team_budget_metric",
@@ -1301,6 +1348,101 @@ class PrometheusLogger(CustomLogger):
             amount=float(standard_logging_payload["completion_tokens"]),
         )
 
+        # Token-type detail metrics — sparse, only emitted when the provider
+        # reports a non-zero value in usage.prompt_tokens_details /
+        # usage.completion_tokens_details.
+        self._increment_token_detail_metrics(
+            standard_logging_payload=standard_logging_payload,
+            enum_values=enum_values,
+            label_context=label_context,
+        )
+
+    def _increment_token_detail_metrics(
+        self,
+        standard_logging_payload: StandardLoggingPayload,
+        enum_values: UserAPIKeyLabelValues,
+        label_context: Optional[PrometheusLabelFactoryContext] = None,
+    ) -> None:
+        """
+        Increment per-token-type counters from the Usage object that providers
+        attach to the request. The Usage dict is plumbed onto
+        ``standard_logging_payload["metadata"]["usage_object"]`` by
+        ``get_standard_logging_object_payload``.
+
+        Each counter is only incremented when the underlying value is > 0, so
+        scrape output stays sparse for providers that don't report these
+        details (most non-OpenAI/Anthropic models).
+        """
+        metadata = standard_logging_payload.get("metadata") or {}
+        usage_object = (
+            metadata.get("usage_object") if isinstance(metadata, dict) else None
+        )
+        if not isinstance(usage_object, dict):
+            return
+
+        prompt_details = usage_object.get("prompt_tokens_details") or {}
+        completion_details = usage_object.get("completion_tokens_details") or {}
+
+        detail_metrics: List[Tuple[Any, DEFINED_PROMETHEUS_METRICS, Any]] = [
+            (
+                self.litellm_input_cached_tokens_metric,
+                "litellm_input_cached_tokens_metric",
+                (
+                    prompt_details.get("cached_tokens")
+                    if isinstance(prompt_details, dict)
+                    else None
+                ),
+            ),
+            (
+                self.litellm_input_cache_creation_tokens_metric,
+                "litellm_input_cache_creation_tokens_metric",
+                (
+                    prompt_details.get("cache_creation_tokens")
+                    if isinstance(prompt_details, dict)
+                    else None
+                ),
+            ),
+            (
+                self.litellm_input_audio_tokens_metric,
+                "litellm_input_audio_tokens_metric",
+                (
+                    prompt_details.get("audio_tokens")
+                    if isinstance(prompt_details, dict)
+                    else None
+                ),
+            ),
+            (
+                self.litellm_output_reasoning_tokens_metric,
+                "litellm_output_reasoning_tokens_metric",
+                (
+                    completion_details.get("reasoning_tokens")
+                    if isinstance(completion_details, dict)
+                    else None
+                ),
+            ),
+            (
+                self.litellm_output_audio_tokens_metric,
+                "litellm_output_audio_tokens_metric",
+                (
+                    completion_details.get("audio_tokens")
+                    if isinstance(completion_details, dict)
+                    else None
+                ),
+            ),
+        ]
+
+        for counter, metric_name, value in detail_metrics:
+            if not isinstance(value, (int, float)) or value <= 0:
+                continue
+            PrometheusLogger._inc_labeled_counter(
+                self,
+                counter,
+                metric_name,
+                enum_values,
+                label_context=label_context,
+                amount=float(value),
+            )
+
     def _increment_cache_metrics(
         self,
         standard_logging_payload: StandardLoggingPayload,
@@ -3540,6 +3682,10 @@ class PrometheusLogger(CustomLogger):
             user_object.budget_reset_at = user_info.budget_reset_at
             if user_object.max_budget is None and user_info.max_budget is not None:
                 user_object.max_budget = user_info.max_budget
+            if user_info.user_email is not None:
+                user_object.user_email = user_info.user_email
+            if user_info.user_alias is not None:
+                user_object.user_alias = user_info.user_alias
 
         return user_object
 
@@ -3556,6 +3702,8 @@ class PrometheusLogger(CustomLogger):
         """
         enum_values = UserAPIKeyLabelValues(
             user=user.user_id,
+            user_email=user.user_email or "",
+            user_alias=user.user_alias or "",
         )
 
         _labels = prometheus_label_factory(
diff --git a/litellm/integrations/rubrik.py b/litellm/integrations/rubrik.py
new file mode 100644
index 00000000000..af396ecdc73
--- /dev/null
+++ b/litellm/integrations/rubrik.py
@@ -0,0 +1,605 @@
+"""Rubrik LiteLLM Plugin for tool blocking and batch logging."""
+
+import asyncio
+import os
+import random
+import time
+import urllib.parse
+import uuid
+from collections import Counter
+from typing import TYPE_CHECKING, Any, Literal, Optional
+
+import httpx
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.integrations.custom_guardrail import (
+    CustomGuardrail,
+    ModifyResponseException,
+)
+from litellm.litellm_core_utils.core_helpers import safe_deep_copy
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+from litellm.types.guardrails import GuardrailEventHooks
+from litellm.types.utils import (
+    ChatCompletionMessageToolCall,
+    Function,
+    GenericGuardrailAPIInputs,
+    StandardLoggingPayload,
+)
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import (
+        Logging as LiteLLMLoggingObj,
+    )
+
+_ENDPOINT_ANTHROPIC_MESSAGES = "/v1/messages"
+_WEBHOOK_PATH_TOOL_BLOCKING = "/v1/after_completion/openai/v1"
+_WEBHOOK_PATH_LOGGING_BATCH = "/v1/litellm/batch"
+_MAX_QUEUE_SIZE = 10_000
+_DROP_WARNING_INTERVAL_SECONDS = 60.0
+
+
+class _MalformedToolBlockingResponseError(Exception):
+    """Raised when the tool blocking service returns a structurally invalid
+    response (e.g. empty ``choices``).
+
+    Distinct from transient network/HTTP errors so callers can surface a
+    louder, misconfiguration-style log instead of treating it as a routine
+    fail-open.
+    """
+
+
+class RubrikLogger(CustomGuardrail, CustomBatchLogger):
+    def __init__(
+        self,
+        api_key: str | None = None,
+        api_base: str | None = None,
+        **kwargs,
+    ):
+        self.flush_lock = asyncio.Lock()
+        kwargs.setdefault("guardrail_name", "rubrik")
+        # `initialize_guardrail` always passes these kwargs explicitly, with
+        # value `None` when the user omits `mode` / `default_on` from the
+        # guardrail config. Coerce None (omitted) to the desired default
+        # while preserving any explicit value the caller did set --
+        # in particular `default_on=False` if the user wants the guardrail
+        # off by default.
+        kwargs["event_hook"] = kwargs.get("event_hook") or GuardrailEventHooks.post_call
+        if kwargs.get("default_on") is None:
+            kwargs["default_on"] = True
+        super().__init__(
+            flush_lock=self.flush_lock,
+            **kwargs,
+        )
+
+        verbose_logger.debug("initializing rubrik logger")
+
+        self.sampling_rate = 1.0
+        rbrk_sampling_rate = os.getenv("RUBRIK_SAMPLING_RATE")
+        if rbrk_sampling_rate is not None:
+            try:
+                parsed_rate = float(rbrk_sampling_rate.strip())
+                self.sampling_rate = max(0.0, min(1.0, parsed_rate))
+                if parsed_rate != self.sampling_rate:
+                    verbose_logger.warning(
+                        f"RUBRIK_SAMPLING_RATE={parsed_rate} clamped to "
+                        f"{self.sampling_rate}"
+                    )
+            except ValueError:
+                verbose_logger.warning(
+                    f"Invalid RUBRIK_SAMPLING_RATE: {rbrk_sampling_rate!r}, using 1.0"
+                )
+
+        self.key = api_key or os.getenv("RUBRIK_API_KEY")
+        if not self.key:
+            verbose_logger.warning(
+                "Rubrik: No API key configured. Requests will be unauthenticated."
+            )
+        _batch_size = os.getenv("RUBRIK_BATCH_SIZE")
+
+        if _batch_size:
+            try:
+                self.batch_size = int(_batch_size)
+            except ValueError:
+                verbose_logger.warning(
+                    f"Invalid RUBRIK_BATCH_SIZE: {_batch_size!r}, using default"
+                )
+
+        # Cap the in-memory retry queue so a Rubrik webhook outage cannot let
+        # authenticated traffic accumulate prompt/response payloads until the
+        # proxy runs out of memory. Once the cap is reached, oldest events are
+        # dropped to make room for fresh ones (drop-oldest backpressure).
+        self.max_queue_size = _MAX_QUEUE_SIZE
+        self._dropped_since_warning = 0
+        self._last_drop_warning_time = 0.0
+
+        _webhook_url = api_base or os.getenv("RUBRIK_WEBHOOK_URL")
+
+        if _webhook_url is None:
+            raise ValueError(
+                "Rubrik webhook URL not configured. "
+                "Set RUBRIK_WEBHOOK_URL or pass api_base."
+            )
+
+        _webhook_url = _webhook_url.rstrip("/").removesuffix("/v1")
+        self.tool_blocking_endpoint = f"{_webhook_url}{_WEBHOOK_PATH_TOOL_BLOCKING}"
+        self.logging_endpoint = f"{_webhook_url}{_WEBHOOK_PATH_LOGGING_BATCH}"
+
+        self.async_httpx_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.LoggingCallback
+        )
+
+        self.tool_blocking_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.LoggingCallback,
+            params={"timeout": httpx.Timeout(5.0, connect=2.0)},
+        )
+
+        self._headers: dict[str, str] = {"Content-Type": "application/json"}
+        if self.key:
+            self._headers["Authorization"] = f"Bearer {self.key}"
+
+        # Periodic flush is started lazily on the first log event so that
+        # low-traffic deployments still get their batches drained even when the
+        # logger is instantiated outside a running event loop (sync init).
+        self._flush_task: Optional[asyncio.Task[Any]] = (
+            self._start_periodic_flush_task()
+        )
+
+    def _start_periodic_flush_task(self) -> Optional[asyncio.Task[Any]]:
+        """Start the periodic flush task only when an event loop is already running."""
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            verbose_logger.debug(
+                "Rubrik logger init: no running event loop, "
+                "periodic flush will start on first log event."
+            )
+            return None
+        return loop.create_task(self.periodic_flush())
+
+    def _ensure_periodic_flush_task(self) -> None:
+        # Synchronous helper: in asyncio's cooperative model there is no await
+        # between the check and assignment, so two callers cannot race here.
+        if self._flush_task is None or self._flush_task.done():
+            self._flush_task = self._start_periodic_flush_task()
+
+    async def aclose(self):
+        """Close the dedicated HTTP clients used by this logger."""
+        # Cancel the periodic flush task before closing the HTTP clients so
+        # the loop doesn't wake up and try to POST via a closed client.
+        if self._flush_task is not None and not self._flush_task.done():
+            self._flush_task.cancel()
+            try:
+                await self._flush_task
+            except (asyncio.CancelledError, Exception):
+                pass
+            self._flush_task = None
+        await self.tool_blocking_client.close()
+        await self.async_httpx_client.close()
+
+    # -- Guardrail hook --------------------------------------------------------
+
+    async def apply_guardrail(
+        self,
+        inputs: GenericGuardrailAPIInputs,
+        request_data: dict,
+        input_type: Literal["request", "response"],
+        logging_obj: Optional["LiteLLMLoggingObj"] = None,
+    ) -> GenericGuardrailAPIInputs:
+        """Validate tool calls against the blocking service (fail-open)."""
+        if input_type != "response":
+            return inputs
+
+        tool_calls = inputs.get("tool_calls")
+        if not tool_calls:
+            return inputs
+
+        try:
+            return await self._check_tool_calls(
+                inputs, tool_calls, request_data, logging_obj
+            )
+        except ModifyResponseException:
+            raise
+        except _MalformedToolBlockingResponseError as e:
+            # Distinct from transient errors: the service responded but the
+            # payload was structurally invalid, which usually indicates a
+            # misconfigured webhook or a breaking change in its response
+            # format. Log loudly so operators notice their tool-blocking
+            # policy is not actually being enforced.
+            verbose_logger.critical(
+                "Tool blocking service returned a malformed response: %s. "
+                "Tool calls are NOT being checked -- verify the webhook "
+                "configuration. Returning original response unchanged.",
+                e,
+                exc_info=True,
+            )
+            return inputs
+        except Exception as e:
+            verbose_logger.error(
+                f"Tool blocking hook failed: {e}. "
+                "Returning original response unchanged.",
+                exc_info=True,
+            )
+            return inputs
+
+    async def _check_tool_calls(
+        self,
+        inputs: GenericGuardrailAPIInputs,
+        tool_calls: Any,
+        request_data: dict,
+        logging_obj: Optional["LiteLLMLoggingObj"],
+    ) -> GenericGuardrailAPIInputs:
+        """Send tool calls to blocking service, raise if any are blocked."""
+        message_tool_calls = self._normalize_tool_calls(tool_calls)
+
+        call_details = (
+            getattr(logging_obj, "model_call_details", {}) if logging_obj else {}
+        )
+        response = request_data.get("response")
+        request_id = getattr(response, "id", None) if response else None
+        if logging_obj and not call_details:
+            verbose_logger.warning(
+                "Rubrik: logging_obj present but model_call_details is empty "
+                "-- request context will be missing"
+            )
+
+        response_data = self._build_tool_call_payload(message_tool_calls, request_id)
+        req_data = self._extract_request_data(call_details)
+
+        service_response = await self._post_to_tool_blocking_service(
+            response_data, req_data
+        )
+        blocked_explanation = self._extract_blocked_tools(
+            service_response, message_tool_calls
+        )
+
+        if blocked_explanation is not None:
+            model = self._resolve_model(request_data, call_details)
+            raise ModifyResponseException(
+                message=blocked_explanation,
+                model=model,
+                request_data=request_data,
+                guardrail_name=self.guardrail_name,
+            )
+
+        return inputs
+
+    @staticmethod
+    def _normalize_tool_calls(tool_calls: Any) -> list[ChatCompletionMessageToolCall]:
+        """Convert tool_calls from inputs to ChatCompletionMessageToolCall objects."""
+        result = []
+        for tc in tool_calls:
+            if isinstance(tc, ChatCompletionMessageToolCall):
+                result.append(tc)
+            elif isinstance(tc, dict):
+                func = tc.get("function", {})
+                result.append(
+                    ChatCompletionMessageToolCall(
+                        id=tc.get("id", ""),
+                        type=tc.get("type", "function"),
+                        function=Function(
+                            name=func.get("name", ""),
+                            arguments=func.get("arguments", ""),
+                        ),
+                    )
+                )
+            elif hasattr(tc, "id") and hasattr(tc, "function"):
+                result.append(
+                    ChatCompletionMessageToolCall(
+                        id=tc.id or "",
+                        type=getattr(tc, "type", None) or "function",
+                        function=tc.function,
+                    )
+                )
+            else:
+                raise TypeError(
+                    f"Cannot normalize tool_call of type {type(tc).__name__}"
+                )
+        return result
+
+    @staticmethod
+    def _build_tool_call_payload(
+        tool_calls: list[ChatCompletionMessageToolCall],
+        request_id: str | None,
+    ) -> dict[str, Any]:
+        """Build a full OpenAI ChatCompletion-format dict for the blocking service."""
+        return {
+            "id": request_id or f"chatcmpl-{uuid.uuid4()}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": "",
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": None,
+                        "tool_calls": [
+                            tc.model_dump(exclude_none=True) for tc in tool_calls
+                        ],
+                    },
+                    "finish_reason": "tool_calls",
+                }
+            ],
+        }
+
+    @staticmethod
+    def _extract_request_data(call_details: dict[str, Any]) -> dict[str, Any]:
+        """Extract original request data from model_call_details."""
+        if not call_details:
+            return {}
+        litellm_params = call_details.get("litellm_params", {}) or {}
+        return {
+            "messages": call_details.get("messages"),
+            "model": call_details.get("model"),
+            "proxy_server_request": RubrikLogger._sanitize_proxy_server_request(
+                litellm_params.get("proxy_server_request")
+            ),
+        }
+
+    @staticmethod
+    def _sanitize_proxy_server_request(proxy_server_request: Any) -> Any:
+        """Allowlist only routing fields (``url``, ``method``) when forwarding
+        ``proxy_server_request`` to the external Rubrik webhook, dropping
+        inbound ``headers`` (Authorization, Cookie, x-api-key, ...) and the raw
+        request ``body`` so proxy credentials are not exfiltrated."""
+        if not isinstance(proxy_server_request, dict):
+            return proxy_server_request
+        return {
+            key: proxy_server_request[key]
+            for key in ("url", "method")
+            if key in proxy_server_request
+        }
+
+    @staticmethod
+    def _resolve_model(
+        request_data: dict[str, Any], call_details: dict[str, Any]
+    ) -> str:
+        """Get the model name for the ModifyResponseException."""
+        response = request_data.get("response")
+        if response and hasattr(response, "model"):
+            return response.model or "unknown"
+        return call_details.get("model", "unknown")
+
+    # -- Logging hooks ---------------------------------------------------------
+
+    async def _prepare_log_payload(
+        self, kwargs: dict, event_type: str
+    ) -> StandardLoggingPayload | None:
+        """Shared logic for success and failure logging."""
+        if random.random() > self.sampling_rate:
+            verbose_logger.debug(
+                f"Skipping Rubrik {event_type} logging "
+                f"(sampling_rate={self.sampling_rate})"
+            )
+            return None
+
+        # Deep-copy so mutations don't affect other callbacks sharing this object
+        standard_logging_payload: StandardLoggingPayload = safe_deep_copy(
+            kwargs["standard_logging_object"]
+        )
+
+        # For Anthropic /v1/messages requests, LiteLLM creates a separate
+        # ModelResponse (with a generated chatcmpl-* id) for logging, which
+        # differs from the original Anthropic msg-* id on the response dict.
+        # Normalize to litellm_call_id so that the logging and tool-blocking
+        # endpoints see the same request identifier.
+        litellm_params = kwargs.get("litellm_params", {}) or {}
+        proxy_request = litellm_params.get("proxy_server_request", {}) or {}
+        url_path = urllib.parse.urlparse(proxy_request.get("url", "")).path
+        if url_path.endswith(_ENDPOINT_ANTHROPIC_MESSAGES):
+            _litellm_call_id = kwargs.get("litellm_call_id")
+            if _litellm_call_id:
+                standard_logging_payload["id"] = _litellm_call_id  # type: ignore[literal-required]
+
+        if "system" in kwargs:
+            system_prompt_msg_list = kwargs["system"]
+            try:
+                if system_prompt_msg_list:
+                    system_scaffold = {
+                        "role": "system",
+                        "content": system_prompt_msg_list,
+                    }
+                    if isinstance(standard_logging_payload["messages"], list):
+                        standard_logging_payload["messages"].insert(0, system_scaffold)
+                    elif isinstance(standard_logging_payload["messages"], (dict, str)):
+                        standard_logging_payload["messages"] = [
+                            system_scaffold,
+                            standard_logging_payload["messages"],
+                        ]
+            except Exception as e:
+                verbose_logger.warning(
+                    f"Rubrik: failed to prepend system prompt: {e}",
+                    exc_info=True,
+                )
+
+        return standard_logging_payload
+
+    async def _enqueue_log_event(self, kwargs: dict, event_type: str):
+        try:
+            self._ensure_periodic_flush_task()
+            payload = await self._prepare_log_payload(kwargs, event_type)
+            if payload is None:
+                return
+
+            self.log_queue.append(payload)
+            self._enforce_max_queue_size()
+
+            if len(self.log_queue) >= self.batch_size:
+                await self.flush_queue()
+        except Exception as e:
+            verbose_logger.error(
+                f"Rubrik {event_type} logging hook failed: {e}. "
+                "Skipping logging for this event.",
+                exc_info=True,
+            )
+
+    def _enforce_max_queue_size(self) -> None:
+        overflow = len(self.log_queue) - self.max_queue_size
+        if overflow <= 0:
+            return
+        del self.log_queue[:overflow]
+        self._dropped_since_warning += overflow
+        now = time.time()
+        if now - self._last_drop_warning_time >= _DROP_WARNING_INTERVAL_SECONDS:
+            verbose_logger.warning(
+                "Rubrik: log queue exceeded max_queue_size=%s; dropped %s "
+                "oldest events since the last warning. The Rubrik webhook may "
+                "be unhealthy or undersized for current traffic.",
+                self.max_queue_size,
+                self._dropped_since_warning,
+            )
+            self._dropped_since_warning = 0
+            self._last_drop_warning_time = now
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        await self._enqueue_log_event(kwargs, "success")
+
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+        await self._enqueue_log_event(kwargs, "failure")
+
+    # -- Batch logging ---------------------------------------------------------
+
+    async def _log_batch_to_rubrik(self, data):
+        # NOTE: this method intentionally re-raises on failure so the parent
+        # CustomBatchLogger.flush_queue keeps the unsent events in the queue
+        # for the next flush attempt instead of silently dropping them.
+        try:
+            response = await self.async_httpx_client.post(
+                url=self.logging_endpoint,
+                json=data,
+                headers=self._headers,
+            )
+            response.raise_for_status()
+        except httpx.HTTPStatusError as e:
+            verbose_logger.exception(
+                f"Rubrik HTTP Error: {e.response.status_code} - {e.response.text}"
+            )
+            raise
+        except Exception:
+            verbose_logger.exception("Rubrik Layer Error")
+            raise
+
+    async def async_send_batch(self):
+        """Handles sending batches of responses to Rubrik.
+
+        Note: the canonical flush path is :meth:`flush_queue`, which takes a
+        single snapshot used for both sending and queue draining. This method
+        is kept for direct callers / tests; it intentionally does NOT remove
+        events from the queue.
+        """
+        if not self.log_queue:
+            return
+
+        log_queue_snapshot = list(self.log_queue)
+        verbose_logger.debug(
+            "Rubrik: Flushing batch of %s events", len(log_queue_snapshot)
+        )
+        await self._log_batch_to_rubrik(
+            data=log_queue_snapshot,
+        )
+
+    async def flush_queue(self):
+        """Snapshot, send, and drain in one consistent step.
+
+        Overrides the base implementation so the same snapshot drives both
+        the HTTP send and the queue truncation. This avoids the subtle
+        coupling where the base class captures `len(self.log_queue)`
+        separately from the snapshot taken inside `async_send_batch`,
+        which could otherwise drift in a future refactor and cause
+        duplicate deliveries to Rubrik.
+        """
+        if self.flush_lock is None:
+            return
+
+        async with self.flush_lock:
+            if not self.log_queue:
+                return
+            snapshot = list(self.log_queue)
+            verbose_logger.debug("Rubrik: Flushing batch of %s events", len(snapshot))
+            try:
+                await self._log_batch_to_rubrik(data=snapshot)
+            except Exception:
+                # Already logged with traceback inside _log_batch_to_rubrik.
+                # Preserve the in-flight events for retry on the next flush.
+                return
+            del self.log_queue[: len(snapshot)]
+            self.last_flush_time = time.time()
+
+    # -- Tool blocking service -------------------------------------------------
+
+    async def _post_to_tool_blocking_service(
+        self,
+        response_data: dict[str, Any],
+        request_data: dict[str, Any],
+    ) -> dict[str, Any]:
+        """Post a payload to the tool blocking service and return the response.
+
+        Args:
+            response_data: The OpenAI-formatted response payload to send.
+            request_data: Original LLM request data to include alongside
+                the response for additional context. Empty dict if unavailable.
+
+        Raises:
+            Exception: If the service is unavailable or returns an error.
+        """
+        envelope = {
+            "request": request_data,
+            "response": response_data,
+        }
+        verbose_logger.debug(
+            f"Sending request to tool blocking service: "
+            f"{self.tool_blocking_endpoint}"
+        )
+        http_response = await self.tool_blocking_client.post(
+            self.tool_blocking_endpoint,
+            json=envelope,
+            headers=self._headers,
+        )
+        http_response.raise_for_status()
+        result: dict[str, Any] = http_response.json()
+        return result
+
+    @staticmethod
+    def _extract_blocked_tools(
+        service_response: dict[str, Any],
+        all_tool_calls: list[ChatCompletionMessageToolCall],
+    ) -> Optional[str]:
+        """Return the blocking explanation if any tool calls were blocked.
+
+        Compares the service response (which contains only allowed tools) against
+        the full set of tool calls. Returns ``None`` if all tools are allowed, or
+        the explanation string (prefixed with newlines) otherwise.
+
+        Expects service_response in OpenAI chat completion format:
+            {"choices": [{"message": {"tool_calls": [...], "content": "..."}}]}
+        """
+        choices = service_response.get("choices", [])
+        if not choices:
+            raise _MalformedToolBlockingResponseError(
+                "Tool blocking service returned empty response"
+            )
+
+        message = choices[0].get("message", {})
+        returned_tool_calls = message.get("tool_calls") or []
+        blocking_explanation = message.get("content", "")
+
+        allowed_id_counts: Counter = Counter(
+            tc["id"]
+            for tc in returned_tool_calls
+            if isinstance(tc, dict) and tc.get("id")
+        )
+        required_id_counts: Counter = Counter(tc.id for tc in all_tool_calls if tc.id)
+
+        all_allowed = len(returned_tool_calls) >= len(all_tool_calls) and all(
+            allowed_id_counts.get(tc_id, 0) >= count
+            for tc_id, count in required_id_counts.items()
+        )
+
+        if all_allowed:
+            return None
+
+        explanation = blocking_explanation or "Tool call blocked by policy."
+        return f"\n\n{explanation}"
diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py
index 332e84dd07d..4ed8a809a13 100644
--- a/litellm/integrations/s3_v2.py
+++ b/litellm/integrations/s3_v2.py
@@ -1,8 +1,8 @@
 """
 s3 Bucket Logging Integration
 
-async_log_success_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3 
-async_log_failure_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3 
+async_log_success_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3
+async_log_failure_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3
 NOTE 1: S3 does not provide a BATCH PUT API endpoint, so we create tasks to upload each element individually
 """
 
diff --git a/litellm/interactions/__init__.py b/litellm/interactions/__init__.py
index e1125b649a6..ed01462cba6 100644
--- a/litellm/interactions/__init__.py
+++ b/litellm/interactions/__init__.py
@@ -5,31 +5,40 @@ This module provides SDK methods for Google's Interactions API.
 
 Usage:
     import litellm
-    
+
     # Create an interaction with a model
     response = litellm.interactions.create(
         model="gemini-2.5-flash",
         input="Hello, how are you?"
     )
-    
+
     # Create an interaction with an agent
     response = litellm.interactions.create(
         agent="deep-research-pro-preview-12-2025",
         input="Research the current state of cancer research"
     )
-    
+
     # Async version
     response = await litellm.interactions.acreate(...)
-    
+
     # Get an interaction
     response = litellm.interactions.get(interaction_id="...")
-    
+
     # Delete an interaction
     result = litellm.interactions.delete(interaction_id="...")
-    
+
     # Cancel an interaction
     result = litellm.interactions.cancel(interaction_id="...")
 
+    # Create a managed agent on the provider side
+    result = litellm.interactions.agents.create(
+        name="waverunner",
+        custom_llm_provider="gemini",
+        api_key="...",
+        base_agent="gemini-2.5-flash",
+        instructions="You are a helpful assistant.",
+    )
+
 Methods:
 - create(): Sync create interaction
 - acreate(): Async create interaction
@@ -39,8 +48,12 @@ Methods:
 - adelete(): Async delete interaction
 - cancel(): Sync cancel interaction
 - acancel(): Async cancel interaction
+
+Sub-modules:
+- agents: Provider-side agent creation (litellm.interactions.agents.create)
 """
 
+from litellm.interactions import agents
 from litellm.interactions.main import (
     acancel,
     acreate,
@@ -65,4 +78,6 @@ __all__ = [
     # Cancel
     "cancel",
     "acancel",
+    # Sub-modules
+    "agents",
 ]
diff --git a/litellm/interactions/agents/__init__.py b/litellm/interactions/agents/__init__.py
new file mode 100644
index 00000000000..711a54fdcbb
--- /dev/null
+++ b/litellm/interactions/agents/__init__.py
@@ -0,0 +1,39 @@
+"""
+litellm.interactions.agents
+
+Full CRUD SDK for provider-side managed agents (e.g. Gemini v1beta/agents).
+
+    litellm.interactions.agents.create(name=..., ...)
+    litellm.interactions.agents.list(api_key=...)
+    litellm.interactions.agents.get(name=..., ...)
+    litellm.interactions.agents.delete(name=..., ...)
+    litellm.interactions.agents.list_versions(name=..., ...)
+
+Async counterparts: acreate, alist, aget, adelete, alist_versions
+"""
+
+from litellm.interactions.agents.main import (
+    acreate,
+    adelete,
+    aget,
+    alist,
+    alist_versions,
+    create,
+    delete,
+    get,
+    list,
+    list_versions,
+)
+
+__all__ = [
+    "create",
+    "acreate",
+    "list",
+    "alist",
+    "get",
+    "aget",
+    "delete",
+    "adelete",
+    "list_versions",
+    "alist_versions",
+]
diff --git a/litellm/interactions/agents/http_handler.py b/litellm/interactions/agents/http_handler.py
new file mode 100644
index 00000000000..d45ca6f4346
--- /dev/null
+++ b/litellm/interactions/agents/http_handler.py
@@ -0,0 +1,478 @@
+"""
+HTTP handler for the Agents API.
+
+Extends InteractionsHTTPHandler so that the shared HTTP infrastructure
+(_handle_error, _sync_client, _async_client) is reused rather than
+duplicated. BaseAgentsAPIConfig stays as pure transform code.
+"""
+
+from typing import Any, Coroutine, Dict, Optional, Union
+
+import httpx
+
+from litellm.constants import request_timeout
+from litellm.interactions.http_handler import InteractionsHTTPHandler
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.agents import (
+    AgentCreateResponse,
+    AgentDeleteResult,
+    AgentListResponse,
+    AgentVersionsResponse,
+)
+from litellm.types.router import GenericLiteLLMParams
+
+
+class AgentsHTTPHandler(InteractionsHTTPHandler):
+    """HTTP handler for Agents API CRUD requests."""
+
+    # ------------------------------------------------------------------ #
+    # CREATE                                                               #
+    # ------------------------------------------------------------------ #
+
+    def create_agent(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[HTTPHandler] = None,
+        _is_async: bool = False,
+    ) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]:
+        if _is_async:
+            return self.async_create_agent(
+                agents_api_config=agents_api_config,
+                name=name,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                extra_headers=extra_headers,
+                extra_body=extra_body,
+                timeout=timeout,
+            )
+
+        sync_httpx_client = self._sync_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url = agents_api_config.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        data = agents_api_config.transform_create_request(
+            name=name, litellm_params=dict(litellm_params)
+        )
+        if extra_body:
+            data.update(extra_body)
+
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": url,
+                "headers": headers,
+            },
+        )
+        try:
+            response = sync_httpx_client.post(
+                url=url, headers=headers, json=data, timeout=timeout or request_timeout
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        return agents_api_config.transform_create_response(
+            raw_response=response, name=name
+        )
+
+    async def async_create_agent(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> AgentCreateResponse:
+        async_httpx_client = self._async_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url = agents_api_config.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        data = agents_api_config.transform_create_request(
+            name=name, litellm_params=dict(litellm_params)
+        )
+        if extra_body:
+            data.update(extra_body)
+
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": url,
+                "headers": headers,
+            },
+        )
+        try:
+            response = await async_httpx_client.post(
+                url=url, headers=headers, json=data, timeout=timeout or request_timeout
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        return agents_api_config.transform_create_response(
+            raw_response=response, name=name
+        )
+
+    # ------------------------------------------------------------------ #
+    # LIST                                                                 #
+    # ------------------------------------------------------------------ #
+
+    def list_agents(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[HTTPHandler] = None,
+        _is_async: bool = False,
+    ) -> Union[AgentListResponse, Coroutine[Any, Any, AgentListResponse]]:
+        if _is_async:
+            return self.async_list_agents(
+                agents_api_config=agents_api_config,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                extra_headers=extra_headers,
+                timeout=timeout,
+            )
+
+        sync_httpx_client = self._sync_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url, params = agents_api_config.transform_list_request(
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input="list_agents",
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = sync_httpx_client.get(url=url, headers=headers, params=params)
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_list_response(raw_response=response)
+
+    async def async_list_agents(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> AgentListResponse:
+        async_httpx_client = self._async_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url, params = agents_api_config.transform_list_request(
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input="list_agents",
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_list_response(raw_response=response)
+
+    # ------------------------------------------------------------------ #
+    # GET                                                                  #
+    # ------------------------------------------------------------------ #
+
+    def get_agent(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[HTTPHandler] = None,
+        _is_async: bool = False,
+    ) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]:
+        if _is_async:
+            return self.async_get_agent(
+                agents_api_config=agents_api_config,
+                name=name,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                extra_headers=extra_headers,
+                timeout=timeout,
+            )
+
+        sync_httpx_client = self._sync_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url, params = agents_api_config.transform_get_request(
+            name=name,
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = sync_httpx_client.get(url=url, headers=headers, params=params)
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_get_response(
+            raw_response=response, name=name
+        )
+
+    async def async_get_agent(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> AgentCreateResponse:
+        async_httpx_client = self._async_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url, params = agents_api_config.transform_get_request(
+            name=name,
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_get_response(
+            raw_response=response, name=name
+        )
+
+    # ------------------------------------------------------------------ #
+    # DELETE                                                               #
+    # ------------------------------------------------------------------ #
+
+    def delete_agent(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[HTTPHandler] = None,
+        _is_async: bool = False,
+    ) -> Union[AgentDeleteResult, Coroutine[Any, Any, AgentDeleteResult]]:
+        if _is_async:
+            return self.async_delete_agent(
+                agents_api_config=agents_api_config,
+                name=name,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                extra_headers=extra_headers,
+                timeout=timeout,
+            )
+
+        sync_httpx_client = self._sync_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url = agents_api_config.transform_delete_request(
+            name=name,
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = sync_httpx_client.delete(
+                url=url, headers=headers, timeout=timeout or request_timeout
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_delete_response(
+            raw_response=response, name=name
+        )
+
+    async def async_delete_agent(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> AgentDeleteResult:
+        async_httpx_client = self._async_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url = agents_api_config.transform_delete_request(
+            name=name,
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = await async_httpx_client.delete(
+                url=url, headers=headers, timeout=timeout or request_timeout
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_delete_response(
+            raw_response=response, name=name
+        )
+
+    # ------------------------------------------------------------------ #
+    # LIST VERSIONS                                                        #
+    # ------------------------------------------------------------------ #
+
+    def list_agent_versions(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[HTTPHandler] = None,
+        _is_async: bool = False,
+    ) -> Union[AgentVersionsResponse, Coroutine[Any, Any, AgentVersionsResponse]]:
+        if _is_async:
+            return self.async_list_agent_versions(
+                agents_api_config=agents_api_config,
+                name=name,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                extra_headers=extra_headers,
+                timeout=timeout,
+            )
+
+        sync_httpx_client = self._sync_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url, params = agents_api_config.transform_list_versions_request(
+            name=name,
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = sync_httpx_client.get(url=url, headers=headers, params=params)
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_list_versions_response(
+            raw_response=response, name=name
+        )
+
+    async def async_list_agent_versions(
+        self,
+        agents_api_config: BaseAgentsAPIConfig,
+        name: str,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> AgentVersionsResponse:
+        async_httpx_client = self._async_client(litellm_params, client)
+        headers = agents_api_config.validate_environment(
+            headers=extra_headers or {}, litellm_params=dict(litellm_params)
+        )
+        url, params = agents_api_config.transform_list_versions_request(
+            name=name,
+            api_base=litellm_params.get("api_base"),
+            litellm_params=dict(litellm_params),
+        )
+        logging_obj.pre_call(
+            input=name,
+            api_key="",
+            additional_args={"api_base": url, "headers": headers},
+        )
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=agents_api_config)
+
+        logging_obj.post_call(original_response=response.text, additional_args={})
+        return agents_api_config.transform_list_versions_response(
+            raw_response=response, name=name
+        )
+
+
+agents_http_handler = AgentsHTTPHandler()
diff --git a/litellm/interactions/agents/main.py b/litellm/interactions/agents/main.py
new file mode 100644
index 00000000000..f56c6f3ed5e
--- /dev/null
+++ b/litellm/interactions/agents/main.py
@@ -0,0 +1,522 @@
+"""
+LiteLLM Agents API - Main Module
+
+Usage:
+    import litellm
+
+    # Create
+    response = litellm.interactions.agents.create(
+        name="waverunner",
+        custom_llm_provider="gemini",
+        api_key="...",
+        base_agent="gemini-2.5-flash",
+        instructions="You are a helpful assistant.",
+    )
+
+    # List
+    response = litellm.interactions.agents.list(api_key="...", custom_llm_provider="gemini")
+
+    # Get
+    response = litellm.interactions.agents.get(name="waverunner", api_key="...")
+
+    # Delete
+    result = litellm.interactions.agents.delete(name="waverunner", api_key="...")
+
+    # List versions
+    result = litellm.interactions.agents.list_versions(name="waverunner", api_key="...")
+
+    # Async versions: acreate, alist, aget, adelete, alist_versions
+"""
+
+import asyncio
+import contextvars
+from functools import partial
+from typing import Any, Coroutine, Dict, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.interactions.agents.http_handler import agents_http_handler
+from litellm.interactions.agents.utils import get_provider_agents_api_config
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.types.agents import (
+    AgentCreateResponse,
+    AgentDeleteResult,
+    AgentListResponse,
+    AgentVersionsResponse,
+)
+from litellm.types.interactions import InteractionEnvironment
+from litellm.types.router import GenericLiteLLMParams
+from litellm.utils import client
+
+# ------------------------------------------------------------------ #
+# Shared helpers                                                       #
+# ------------------------------------------------------------------ #
+
+
+def _get_agents_api_config(custom_llm_provider: str):
+    config = get_provider_agents_api_config(custom_llm_provider)
+    if config is None:
+        raise litellm.BadRequestError(
+            message=(
+                f"Provider '{custom_llm_provider}' does not have a native "
+                "agents API. Use the proxy POST /v1/agents endpoint to store "
+                "agents locally."
+            ),
+            model="",
+            llm_provider=custom_llm_provider,
+        )
+    return config
+
+
+def _make_logging_obj(
+    kwargs: Dict[str, Any],
+    model: str,
+    custom_llm_provider: str,
+    call_type: str,
+    optional_params: Dict[str, Any],
+) -> LiteLLMLoggingObj:
+    litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
+    litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+    litellm_logging_obj.update_from_kwargs(
+        kwargs=kwargs,
+        model=model,
+        optional_params=optional_params,
+        litellm_params={"litellm_call_id": litellm_call_id},
+        custom_llm_provider=custom_llm_provider,
+    )
+    return litellm_logging_obj
+
+
+# ================================================================== #
+# CREATE                                                               #
+# ================================================================== #
+
+
+@client
+async def acreate(
+    name: str,
+    base_agent: Optional[str] = None,
+    instructions: Optional[str] = None,
+    base_environment: Optional[InteractionEnvironment] = None,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    extra_body: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> AgentCreateResponse:
+    """Async: Create a managed agent on the provider side."""
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["acreate_agent"] = True
+        func = partial(
+            create,
+            name=name,
+            base_agent=base_agent,
+            instructions=instructions,
+            base_environment=base_environment,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            extra_headers=extra_headers,
+            extra_body=extra_body,
+            timeout=timeout,
+            **kwargs,
+        )
+        ctx = contextvars.copy_context()
+        init_response = await loop.run_in_executor(None, partial(ctx.run, func))
+        if asyncio.iscoroutine(init_response):
+            return await init_response
+        return init_response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+@client
+def create(
+    name: str,
+    base_agent: Optional[str] = None,
+    instructions: Optional[str] = None,
+    base_environment: Optional[InteractionEnvironment] = None,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    extra_body: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]:
+    """
+    Sync: Create a managed agent on the provider side.
+
+    Args:
+        name: Name for the agent (required).
+        base_agent: Base agent to derive from (e.g. "waverunner").
+        instructions: System instructions for the agent.
+        base_environment: Environment to fork from — an env_id string or a
+            dict like ``{"type": "remote", "sources": [...]}``.
+        custom_llm_provider: Provider to use, e.g. "gemini".
+        extra_headers: Additional HTTP headers.
+        extra_body: Additional request body fields.
+        timeout: Request timeout.
+        **kwargs: Forwarded to GenericLiteLLMParams (api_key, api_base, etc.).
+    """
+    local_vars = locals()
+    custom_llm_provider = (
+        custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini"
+    )
+    try:
+        _is_async = kwargs.pop("acreate_agent", False) is True
+        if base_agent is not None:
+            kwargs["base_agent"] = base_agent
+        if instructions is not None:
+            kwargs["instructions"] = instructions
+        if base_environment is not None:
+            kwargs["base_environment"] = base_environment
+        kwargs.setdefault("custom_llm_provider", custom_llm_provider)
+        litellm_params = GenericLiteLLMParams(**kwargs)
+        logging_obj = _make_logging_obj(
+            kwargs, name, custom_llm_provider, "create_agent", {}
+        )
+        config = _get_agents_api_config(custom_llm_provider)
+        return agents_http_handler.create_agent(
+            agents_api_config=config,
+            name=name,
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            extra_headers=extra_headers,
+            extra_body=extra_body,
+            timeout=timeout,
+            _is_async=_is_async,
+        )
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+# ================================================================== #
+# LIST                                                                 #
+# ================================================================== #
+
+
+@client
+async def alist(
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> AgentListResponse:
+    """Async: List all agents on the provider side."""
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["alist_agents"] = True
+        func = partial(
+            list,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            extra_headers=extra_headers,
+            timeout=timeout,
+            **kwargs,
+        )
+        ctx = contextvars.copy_context()
+        init_response = await loop.run_in_executor(None, partial(ctx.run, func))
+        if asyncio.iscoroutine(init_response):
+            return await init_response
+        return init_response
+    except Exception as e:
+        raise litellm.exception_type(
+            model="",
+            custom_llm_provider=custom_llm_provider or "gemini",
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+@client
+def list(
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> Union[AgentListResponse, Coroutine[Any, Any, AgentListResponse]]:
+    """Sync: List all agents on the provider side."""
+    local_vars = locals()
+    custom_llm_provider = (
+        custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini"
+    )
+    try:
+        _is_async = kwargs.pop("alist_agents", False) is True
+        kwargs.setdefault("custom_llm_provider", custom_llm_provider)
+        litellm_params = GenericLiteLLMParams(**kwargs)
+        logging_obj = _make_logging_obj(
+            kwargs, "", custom_llm_provider, "list_agents", {}
+        )
+        config = _get_agents_api_config(custom_llm_provider)
+        return agents_http_handler.list_agents(
+            agents_api_config=config,
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            extra_headers=extra_headers,
+            timeout=timeout,
+            _is_async=_is_async,
+        )
+    except Exception as e:
+        raise litellm.exception_type(
+            model="",
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+# ================================================================== #
+# GET                                                                  #
+# ================================================================== #
+
+
+@client
+async def aget(
+    name: str,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> AgentCreateResponse:
+    """Async: Get a specific agent by name."""
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["aget_agent"] = True
+        func = partial(
+            get,
+            name=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            extra_headers=extra_headers,
+            timeout=timeout,
+            **kwargs,
+        )
+        ctx = contextvars.copy_context()
+        init_response = await loop.run_in_executor(None, partial(ctx.run, func))
+        if asyncio.iscoroutine(init_response):
+            return await init_response
+        return init_response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+@client
+def get(
+    name: str,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]:
+    """Sync: Get a specific agent by name."""
+    local_vars = locals()
+    custom_llm_provider = (
+        custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini"
+    )
+    try:
+        _is_async = kwargs.pop("aget_agent", False) is True
+        kwargs.setdefault("custom_llm_provider", custom_llm_provider)
+        litellm_params = GenericLiteLLMParams(**kwargs)
+        logging_obj = _make_logging_obj(
+            kwargs, name, custom_llm_provider, "get_agent", {"name": name}
+        )
+        config = _get_agents_api_config(custom_llm_provider)
+        return agents_http_handler.get_agent(
+            agents_api_config=config,
+            name=name,
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            extra_headers=extra_headers,
+            timeout=timeout,
+            _is_async=_is_async,
+        )
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+# ================================================================== #
+# DELETE                                                               #
+# ================================================================== #
+
+
+@client
+async def adelete(
+    name: str,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> AgentDeleteResult:
+    """Async: Delete a specific agent by name."""
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["adelete_agent"] = True
+        func = partial(
+            delete,
+            name=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            extra_headers=extra_headers,
+            timeout=timeout,
+            **kwargs,
+        )
+        ctx = contextvars.copy_context()
+        init_response = await loop.run_in_executor(None, partial(ctx.run, func))
+        if asyncio.iscoroutine(init_response):
+            return await init_response
+        return init_response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+@client
+def delete(
+    name: str,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> Union[AgentDeleteResult, Coroutine[Any, Any, AgentDeleteResult]]:
+    """Sync: Delete a specific agent by name."""
+    local_vars = locals()
+    custom_llm_provider = (
+        custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini"
+    )
+    try:
+        _is_async = kwargs.pop("adelete_agent", False) is True
+        kwargs.setdefault("custom_llm_provider", custom_llm_provider)
+        litellm_params = GenericLiteLLMParams(**kwargs)
+        logging_obj = _make_logging_obj(
+            kwargs, name, custom_llm_provider, "delete_agent", {"name": name}
+        )
+        config = _get_agents_api_config(custom_llm_provider)
+        return agents_http_handler.delete_agent(
+            agents_api_config=config,
+            name=name,
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            extra_headers=extra_headers,
+            timeout=timeout,
+            _is_async=_is_async,
+        )
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+# ================================================================== #
+# LIST VERSIONS                                                        #
+# ================================================================== #
+
+
+@client
+async def alist_versions(
+    name: str,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> AgentVersionsResponse:
+    """Async: List versions of a specific agent."""
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["alist_agent_versions"] = True
+        func = partial(
+            list_versions,
+            name=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            extra_headers=extra_headers,
+            timeout=timeout,
+            **kwargs,
+        )
+        ctx = contextvars.copy_context()
+        init_response = await loop.run_in_executor(None, partial(ctx.run, func))
+        if asyncio.iscoroutine(init_response):
+            return await init_response
+        return init_response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider or "gemini",
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+@client
+def list_versions(
+    name: str,
+    custom_llm_provider: Optional[str] = None,
+    extra_headers: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    **kwargs,
+) -> Union[AgentVersionsResponse, Coroutine[Any, Any, AgentVersionsResponse]]:
+    """Sync: List versions of a specific agent."""
+    local_vars = locals()
+    custom_llm_provider = (
+        custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini"
+    )
+    try:
+        _is_async = kwargs.pop("alist_agent_versions", False) is True
+        kwargs.setdefault("custom_llm_provider", custom_llm_provider)
+        litellm_params = GenericLiteLLMParams(**kwargs)
+        logging_obj = _make_logging_obj(
+            kwargs, name, custom_llm_provider, "list_agent_versions", {"name": name}
+        )
+        config = _get_agents_api_config(custom_llm_provider)
+        return agents_http_handler.list_agent_versions(
+            agents_api_config=config,
+            name=name,
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            extra_headers=extra_headers,
+            timeout=timeout,
+            _is_async=_is_async,
+        )
+    except Exception as e:
+        raise litellm.exception_type(
+            model=name,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
diff --git a/litellm/interactions/agents/utils.py b/litellm/interactions/agents/utils.py
new file mode 100644
index 00000000000..d16a9597f53
--- /dev/null
+++ b/litellm/interactions/agents/utils.py
@@ -0,0 +1,23 @@
+"""
+Utility functions for the Agents API SDK.
+"""
+
+from typing import Optional
+
+from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig
+
+
+def get_provider_agents_api_config(
+    custom_llm_provider: Optional[str],
+) -> Optional[BaseAgentsAPIConfig]:
+    """
+    Return a provider-specific BaseAgentsAPIConfig if the provider has a
+    native agent-creation API, or None otherwise.
+    """
+    from litellm.types.utils import LlmProviders
+
+    if custom_llm_provider == LlmProviders.GEMINI.value:
+        from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig
+
+        return GeminiAgentsConfig()
+    return None
diff --git a/litellm/interactions/http_handler.py b/litellm/interactions/http_handler.py
index 7fead07043f..695da2be89a 100644
--- a/litellm/interactions/http_handler.py
+++ b/litellm/interactions/http_handler.py
@@ -41,27 +41,55 @@ from litellm.types.interactions import (
 from litellm.types.router import GenericLiteLLMParams
 
 
-class InteractionsHTTPHandler:
+class _BaseHTTPHandler:
+    """
+    Shared HTTP infrastructure for LiteLLM handler classes.
+
+    Provides common client resolution and error-mapping helpers so that
+    handler subclasses (InteractionsHTTPHandler, AgentsHTTPHandler, …) do
+    not duplicate this boilerplate.
+    """
+
+    def _handle_error(self, e: Exception, provider_config: Any) -> Exception:
+        if isinstance(e, httpx.HTTPStatusError):
+            return provider_config.get_error_class(
+                error_message=e.response.text,
+                status_code=e.response.status_code,
+                headers=dict(e.response.headers),
+            )
+        return e
+
+    def _sync_client(
+        self,
+        litellm_params: GenericLiteLLMParams,
+        client: Optional[HTTPHandler],
+    ) -> HTTPHandler:
+        return client or _get_httpx_client(
+            params={"ssl_verify": litellm_params.get("ssl_verify", None)}
+        )
+
+    def _async_client(
+        self,
+        litellm_params: GenericLiteLLMParams,
+        client: Optional[AsyncHTTPHandler],
+    ) -> AsyncHTTPHandler:
+        # GenericLiteLLMParams.get uses getattr; an unset field is None, not the default.
+        custom_llm_provider = litellm_params.get("custom_llm_provider") or "gemini"
+        return client or get_async_httpx_client(
+            llm_provider=litellm.LlmProviders(custom_llm_provider),
+            params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+        )
+
+
+class InteractionsHTTPHandler(_BaseHTTPHandler):
     """
     HTTP handler for Interactions API requests.
     """
 
-    def _handle_error(
-        self,
-        e: Exception,
-        provider_config: BaseInteractionsAPIConfig,
-    ) -> Exception:
-        """Handle errors from HTTP requests."""
-        if isinstance(e, httpx.HTTPStatusError):
-            error_message = e.response.text
-            status_code = e.response.status_code
-            headers = dict(e.response.headers)
-            return provider_config.get_error_class(
-                error_message=error_message,
-                status_code=status_code,
-                headers=headers,
-            )
-        return e
+    # _handle_error is inherited from _BaseHTTPHandler (accepts Any provider_config).
+    # AgentsHTTPHandler also extends this class and passes BaseAgentsAPIConfig, which
+    # is structurally compatible but a different type — keeping the override here with
+    # BaseInteractionsAPIConfig would cause type errors in the subclass.
 
     # =========================================================
     # CREATE INTERACTION
diff --git a/litellm/interactions/litellm_responses_transformation/streaming_iterator.py b/litellm/interactions/litellm_responses_transformation/streaming_iterator.py
index 72a3afbc3c5..4a3eb63084e 100644
--- a/litellm/interactions/litellm_responses_transformation/streaming_iterator.py
+++ b/litellm/interactions/litellm_responses_transformation/streaming_iterator.py
@@ -2,7 +2,17 @@
 Streaming iterator for transforming Responses API stream to Interactions API stream.
 """
 
-from typing import Any, AsyncIterator, Dict, Iterator, Optional, cast
+from collections import deque
+from typing import (
+    Any,
+    AsyncIterator,
+    Deque,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    cast,
+)
 
 from litellm.responses.streaming_iterator import (
     BaseResponsesAPIStreamingIterator,
@@ -29,7 +39,13 @@ class LiteLLMResponsesInteractionsStreamingIterator:
 
     This class handles both sync and async iteration, transforming Responses API
     streaming events (output.text.delta, response.completed, etc.) to Interactions
-    API streaming events (content.delta, interaction.complete, etc.).
+    API streaming events.
+
+    Schema selection:
+    - New schema (default, use_legacy_interactions_schema=False):
+        interaction.created -> step.start -> step.delta ... -> step.stop -> interaction.completed
+    - Legacy schema (use_legacy_interactions_schema=True, remove after June 8 2026):
+        interaction.start -> content.start -> content.delta ... -> content.stop -> interaction.complete
     """
 
     def __init__(
@@ -41,6 +57,8 @@ class LiteLLMResponsesInteractionsStreamingIterator:
         custom_llm_provider: Optional[str] = None,
         litellm_metadata: Optional[Dict[str, Any]] = None,
     ):
+        import litellm
+
         self.model = model
         self.responses_stream_iterator = litellm_custom_stream_wrapper
         self.request_input = request_input
@@ -51,66 +69,156 @@ class LiteLLMResponsesInteractionsStreamingIterator:
         self.collected_text = ""
         self.sent_interaction_start = False
         self.sent_content_start = False
+        # Capture the schema flag once at construction time so all events
+        # emitted by this stream use a consistent schema, even if the global
+        # flag is mutated mid-stream (e.g. by a config reload).
+        self._use_legacy: bool = litellm.use_legacy_interactions_schema
+        # Buffer of events that have been derived from upstream chunks but not
+        # yet returned to the caller. A single Responses API chunk may expand
+        # into multiple Interactions API events (e.g. the first text delta
+        # produces interaction.created + step.start + step.delta), and the
+        # terminal sequence on stream end may also span multiple events
+        # (step.stop + interaction.completed).
+        self._pending_events: Deque[InteractionsAPIStreamingResponse] = deque()
+        # Tracks whether we've already emitted a terminal completion event so
+        # the StopIteration fallback path doesn't double-emit.
+        self._sent_completion_event = False
+        # ID resolved from the first upstream chunk (item_id on a text delta or
+        # response.id on response.created). Persisted so the EOF terminal
+        # events stay correlated with the start events delivered earlier.
+        self._interaction_id: Optional[str] = None
 
-    def _transform_responses_chunk_to_interactions_chunk(
-        self,
-        responses_chunk: ResponsesAPIStreamingResponse,
-    ) -> Optional[InteractionsAPIStreamingResponse]:
+    # ------------------------------------------------------------------
+    # Event builders
+    # ------------------------------------------------------------------
+
+    def _build_interaction_start_event(
+        self, interaction_id: str
+    ) -> InteractionsAPIStreamingResponse:
+        event_type = "interaction.start" if self._use_legacy else "interaction.created"
+        return InteractionsAPIStreamingResponse(
+            event_type=event_type,
+            id=interaction_id,
+            object="interaction",
+            status="in_progress",
+            model=self.model,
+        )
+
+    def _build_content_start_event(
+        self, interaction_id: str
+    ) -> InteractionsAPIStreamingResponse:
+        if self._use_legacy:
+            return InteractionsAPIStreamingResponse(
+                event_type="content.start",
+                id=interaction_id,
+                object="content",
+                delta={"type": "text", "text": ""},
+            )
+        return InteractionsAPIStreamingResponse(
+            event_type="step.start",
+            index=0,
+            step={"type": "model_output", "content": []},
+        )
+
+    def _build_text_delta_event(
+        self, interaction_id: str, delta_text: str
+    ) -> InteractionsAPIStreamingResponse:
+        if self._use_legacy:
+            return InteractionsAPIStreamingResponse(
+                event_type="content.delta",
+                id=interaction_id,
+                object="content",
+                delta={"type": "text", "text": delta_text},
+            )
+        return InteractionsAPIStreamingResponse(
+            event_type="step.delta",
+            index=0,
+            delta={"type": "text", "text": delta_text},
+        )
+
+    def _build_content_stop_event(
+        self, interaction_id: Optional[str]
+    ) -> InteractionsAPIStreamingResponse:
+        if self._use_legacy:
+            return InteractionsAPIStreamingResponse(
+                event_type="content.stop",
+                id=interaction_id,
+                object="content",
+                delta={"type": "text", "text": self.collected_text},
+            )
+        return InteractionsAPIStreamingResponse(
+            event_type="step.stop",
+            index=0,
+        )
+
+    def _build_completion_event(
+        self, response_id: str
+    ) -> InteractionsAPIStreamingResponse:
+        if self._use_legacy:
+            return InteractionsAPIStreamingResponse(
+                event_type="interaction.complete",
+                id=response_id,
+                object="interaction",
+                status="completed",
+                model=self.model,
+                outputs=[{"type": "text", "text": self.collected_text}],
+            )
+        return InteractionsAPIStreamingResponse(
+            event_type="interaction.completed",
+            id=response_id,
+            object="interaction",
+            status="completed",
+            model=self.model,
+            steps=[
+                {
+                    "type": "model_output",
+                    "content": [{"type": "text", "text": self.collected_text}],
+                }
+            ],
+        )
+
+    # ------------------------------------------------------------------
+    # Per-chunk transform (returns a list of events to enqueue)
+    # ------------------------------------------------------------------
+
+    def _events_for_chunk(
+        self, responses_chunk: ResponsesAPIStreamingResponse
+    ) -> List[InteractionsAPIStreamingResponse]:
         """
-        Transform a Responses API streaming chunk to an Interactions API streaming chunk.
+        Translate a single upstream Responses API chunk into the list of
+        Interactions API events it should produce.
 
-        Responses API events:
-        - output.text.delta -> content.delta
-        - response.completed -> interaction.complete
-
-        Interactions API events:
-        - interaction.start
-        - content.start
-        - content.delta
-        - content.stop
-        - interaction.complete
+        Returning a list (rather than a single event) lets a chunk emit any
+        synthetic start events that haven't been sent yet *together with* the
+        actual delta event, so we never silently drop the chunk's payload.
         """
         if not responses_chunk:
-            return None
+            return []
 
-        # Handle OutputTextDeltaEvent -> content.delta
+        # Text delta: emit any missing start events, then the delta itself.
         if isinstance(responses_chunk, OutputTextDeltaEvent):
             delta_text = (
                 responses_chunk.delta if isinstance(responses_chunk.delta, str) else ""
             )
             self.collected_text += delta_text
+            interaction_id = (
+                getattr(responses_chunk, "item_id", None) or f"interaction_{id(self)}"
+            )
+            if self._interaction_id is None:
+                self._interaction_id = interaction_id
 
-            # Send interaction.start if not sent
+            events: List[InteractionsAPIStreamingResponse] = []
             if not self.sent_interaction_start:
                 self.sent_interaction_start = True
-                return InteractionsAPIStreamingResponse(
-                    event_type="interaction.start",
-                    id=getattr(responses_chunk, "item_id", None)
-                    or f"interaction_{id(self)}",
-                    object="interaction",
-                    status="in_progress",
-                    model=self.model,
-                )
-
-            # Send content.start if not sent
+                events.append(self._build_interaction_start_event(interaction_id))
             if not self.sent_content_start:
                 self.sent_content_start = True
-                return InteractionsAPIStreamingResponse(
-                    event_type="content.start",
-                    id=getattr(responses_chunk, "item_id", None),
-                    object="content",
-                    delta={"type": "text", "text": ""},
-                )
+                events.append(self._build_content_start_event(interaction_id))
+            events.append(self._build_text_delta_event(interaction_id, delta_text))
+            return events
 
-            # Send content.delta
-            return InteractionsAPIStreamingResponse(
-                event_type="content.delta",
-                id=getattr(responses_chunk, "item_id", None),
-                object="content",
-                delta={"text": delta_text},
-            )
-
-        # Handle ResponseCreatedEvent or ResponseInProgressEvent -> interaction.start
+        # Response created / in-progress: synthesize interaction start if we
+        # haven't already sent one.
         if isinstance(responses_chunk, (ResponseCreatedEvent, ResponseInProgressEvent)):
             if not self.sent_interaction_start:
                 self.sent_interaction_start = True
@@ -118,169 +226,136 @@ class LiteLLMResponsesInteractionsStreamingIterator:
                     getattr(responses_chunk.response, "id", None)
                     if hasattr(responses_chunk, "response")
                     else None
-                )
-                return InteractionsAPIStreamingResponse(
-                    event_type="interaction.start",
-                    id=response_id or f"interaction_{id(self)}",
-                    object="interaction",
-                    status="in_progress",
-                    model=self.model,
-                )
+                ) or f"interaction_{id(self)}"
+                if self._interaction_id is None:
+                    self._interaction_id = response_id
+                return [self._build_interaction_start_event(response_id)]
+            return []
 
-        # Handle ResponseCompletedEvent -> interaction.complete
+        # Response completed: emit step.stop (if content was started) followed
+        # by the terminal completion event. Prefer the interaction id already
+        # established by earlier events so consumers can correlate the start
+        # and completion events by id (response.id may differ from the item_id
+        # used to derive the initial id when the stream starts directly with a
+        # text delta).
         if isinstance(responses_chunk, ResponseCompletedEvent):
             self.finished = True
             response = responses_chunk.response
-
-            # Send content.stop first if content was started
-            if self.sent_content_start:
-                # Note: We'll send this in the iterator, not here
-                pass
-
-            # Send interaction.complete
-            return InteractionsAPIStreamingResponse(
-                event_type="interaction.complete",
-                id=getattr(response, "id", None) or f"interaction_{id(self)}",
-                object="interaction",
-                status="completed",
-                model=self.model,
-                outputs=[
-                    {
-                        "type": "text",
-                        "text": self.collected_text,
-                    }
-                ],
+            response_id = (
+                self._interaction_id
+                or getattr(response, "id", None)
+                or f"interaction_{id(self)}"
             )
 
-        # For other event types, return None (skip)
-        return None
+            terminal: List[InteractionsAPIStreamingResponse] = []
+            if self.sent_content_start:
+                terminal.append(self._build_content_stop_event(response_id))
+            terminal.append(self._build_completion_event(response_id))
+            self._sent_completion_event = True
+            return terminal
+
+        return []
+
+    def _build_terminal_events_on_eof(
+        self,
+    ) -> List[InteractionsAPIStreamingResponse]:
+        """
+        Build the events to flush when the upstream stream ends without a
+        ResponseCompletedEvent. Ensures consumers always observe a terminal
+        interaction.completed/interaction.complete carrying the full text.
+        """
+        if self._sent_completion_event:
+            return []
+
+        fallback_id = self._interaction_id or f"interaction_{id(self)}"
+        terminal: List[InteractionsAPIStreamingResponse] = []
+        if self.sent_content_start:
+            terminal.append(self._build_content_stop_event(fallback_id))
+        if self.sent_interaction_start or self.collected_text:
+            terminal.append(self._build_completion_event(fallback_id))
+            self._sent_completion_event = True
+        return terminal
+
+    # ------------------------------------------------------------------
+    # Iteration
+    # ------------------------------------------------------------------
 
     def __iter__(self) -> Iterator[InteractionsAPIStreamingResponse]:
-        """Sync iterator implementation."""
         return self
 
     def __next__(self) -> InteractionsAPIStreamingResponse:
-        """Get next chunk in sync mode."""
+        if self._pending_events:
+            return self._pending_events.popleft()
+
         if self.finished:
             raise StopIteration
 
-        # Check if we have a pending interaction.complete to send
-        if hasattr(self, "_pending_interaction_complete"):
-            pending: InteractionsAPIStreamingResponse = getattr(
-                self, "_pending_interaction_complete"
-            )
-            delattr(self, "_pending_interaction_complete")
-            return pending
-
-        # Use a loop instead of recursion to avoid stack overflow
         sync_iterator = cast(
             SyncResponsesAPIStreamingIterator, self.responses_stream_iterator
         )
         while True:
             try:
-                # Get next chunk from responses API stream
                 chunk = next(sync_iterator)
-
-                # Transform chunk (chunk is already a ResponsesAPIStreamingResponse)
-                transformed = self._transform_responses_chunk_to_interactions_chunk(
-                    chunk
-                )
-
-                if transformed:
-                    # If we finished and content was started, send content.stop before interaction.complete
-                    if (
-                        self.finished
-                        and self.sent_content_start
-                        and transformed.event_type == "interaction.complete"
-                    ):
-                        # Send content.stop first
-                        content_stop = InteractionsAPIStreamingResponse(
-                            event_type="content.stop",
-                            id=transformed.id,
-                            object="content",
-                            delta={"type": "text", "text": self.collected_text},
-                        )
-                        # Store the interaction.complete to send next
-                        self._pending_interaction_complete = transformed
-                        return content_stop
-                    return transformed
-
-                # If no transformation, continue to next chunk (loop continues)
-
             except StopIteration:
                 self.finished = True
+                self._pending_events.extend(self._build_terminal_events_on_eof())
+                if self._pending_events:
+                    return self._pending_events.popleft()
+                raise
 
-                # Send final events if needed
-                if self.sent_content_start:
-                    return InteractionsAPIStreamingResponse(
-                        event_type="content.stop",
-                        object="content",
-                        delta={"type": "text", "text": self.collected_text},
-                    )
-
-                raise StopIteration
+            events = self._events_for_chunk(chunk)
+            if events:
+                self._pending_events.extend(events)
+                return self._pending_events.popleft()
 
     def __aiter__(self) -> AsyncIterator[InteractionsAPIStreamingResponse]:
-        """Async iterator implementation."""
         return self
 
     async def __anext__(self) -> InteractionsAPIStreamingResponse:
-        """Get next chunk in async mode."""
+        if self._pending_events:
+            return self._pending_events.popleft()
+
         if self.finished:
             raise StopAsyncIteration
 
-        # Check if we have a pending interaction.complete to send
-        if hasattr(self, "_pending_interaction_complete"):
-            pending: InteractionsAPIStreamingResponse = getattr(
-                self, "_pending_interaction_complete"
-            )
-            delattr(self, "_pending_interaction_complete")
-            return pending
-
-        # Use a loop instead of recursion to avoid stack overflow
         async_iterator = cast(
             ResponsesAPIStreamingIterator, self.responses_stream_iterator
         )
         while True:
             try:
-                # Get next chunk from responses API stream
                 chunk = await async_iterator.__anext__()
-
-                # Transform chunk (chunk is already a ResponsesAPIStreamingResponse)
-                transformed = self._transform_responses_chunk_to_interactions_chunk(
-                    chunk
-                )
-
-                if transformed:
-                    # If we finished and content was started, send content.stop before interaction.complete
-                    if (
-                        self.finished
-                        and self.sent_content_start
-                        and transformed.event_type == "interaction.complete"
-                    ):
-                        # Send content.stop first
-                        content_stop = InteractionsAPIStreamingResponse(
-                            event_type="content.stop",
-                            id=transformed.id,
-                            object="content",
-                            delta={"type": "text", "text": self.collected_text},
-                        )
-                        # Store the interaction.complete to send next
-                        self._pending_interaction_complete = transformed
-                        return content_stop
-                    return transformed
-
-                # If no transformation, continue to next chunk (loop continues)
-
             except StopAsyncIteration:
                 self.finished = True
+                self._pending_events.extend(self._build_terminal_events_on_eof())
+                if self._pending_events:
+                    return self._pending_events.popleft()
+                raise
 
-                # Send final events if needed
-                if self.sent_content_start:
-                    return InteractionsAPIStreamingResponse(
-                        event_type="content.stop",
-                        object="content",
-                        delta={"type": "text", "text": self.collected_text},
-                    )
+            events = self._events_for_chunk(chunk)
+            if events:
+                self._pending_events.extend(events)
+                return self._pending_events.popleft()
 
-                raise StopAsyncIteration
+    # ------------------------------------------------------------------
+    # Backwards-compatible single-chunk transform (used by tests and any
+    # external callers that drove the iterator chunk-by-chunk pre-fix).
+    # ------------------------------------------------------------------
+
+    def _transform_responses_chunk_to_interactions_chunk(
+        self,
+        responses_chunk: ResponsesAPIStreamingResponse,
+    ) -> Optional[InteractionsAPIStreamingResponse]:
+        """
+        Compatibility shim: returns the *first* event produced for this chunk
+        and queues any remaining events on ``self._pending_events`` so they
+        are surfaced on subsequent calls/iterations.
+
+        Prefer ``_events_for_chunk`` in new code.
+        """
+        events = self._events_for_chunk(responses_chunk)
+        if not events:
+            return None
+        first = events[0]
+        if len(events) > 1:
+            self._pending_events.extend(events[1:])
+        return first
diff --git a/litellm/interactions/litellm_responses_transformation/transformation.py b/litellm/interactions/litellm_responses_transformation/transformation.py
index 100300af7b5..173d4ca8764 100644
--- a/litellm/interactions/litellm_responses_transformation/transformation.py
+++ b/litellm/interactions/litellm_responses_transformation/transformation.py
@@ -226,29 +226,37 @@ class LiteLLMResponsesInteractionsConfig:
         - Map status
         - Extract usage
         """
-        # Extract text from outputs
-        outputs = []
+        # Extract text from outputs and build both `outputs` (legacy) and `steps` (new schema).
+        outputs: List[Dict[str, Any]] = []
+        steps: List[Dict[str, Any]] = []
         if hasattr(responses_response, "output") and responses_response.output:
             for output_item in responses_response.output:
                 # Use getattr with None default to safely access content
                 content = getattr(output_item, "content", None)
                 if content is not None:
                     content_items = content if isinstance(content, list) else [content]
+                    model_output_contents: List[Dict[str, Any]] = []
                     for content_item in content_items:
                         # Check if content_item has text attribute
                         text = getattr(content_item, "text", None)
                         if text is not None:
-                            outputs.append(
-                                {
-                                    "type": "text",
-                                    "text": text,
-                                }
-                            )
+                            # Use independent dict instances so mutations to one
+                            # of `outputs` / `steps` don't leak into the other.
+                            outputs.append({"type": "text", "text": text})
+                            model_output_contents.append({"type": "text", "text": text})
                         elif (
                             isinstance(content_item, dict)
                             and content_item.get("type") == "text"
                         ):
-                            outputs.append(content_item)
+                            outputs.append({**content_item})
+                            model_output_contents.append({**content_item})
+                    if model_output_contents:
+                        steps.append(
+                            {
+                                "type": "model_output",
+                                "content": model_output_contents,
+                            }
+                        )
 
         # Convert created_at to ISO string
         created_at = getattr(responses_response, "created_at", None)
@@ -270,12 +278,14 @@ class LiteLLMResponsesInteractionsConfig:
         else:
             interactions_status = status
 
-        # Build interactions response
+        # Build interactions response — populate both `outputs` (legacy schema) and
+        # `steps` (new schema) so callers work regardless of which schema they expect.
         interactions_response_dict: Dict[str, Any] = {
             "id": getattr(responses_response, "id", ""),
             "object": "interaction",
             "status": interactions_status,
             "outputs": outputs,
+            "steps": steps,
             "model": model or getattr(responses_response, "model", ""),
             "created": created,
         }
diff --git a/litellm/interactions/main.py b/litellm/interactions/main.py
index ab429ef6db5..d99cc3d11c7 100644
--- a/litellm/interactions/main.py
+++ b/litellm/interactions/main.py
@@ -8,25 +8,25 @@ Per OpenAPI spec (https://ai.google.dev/static/api/interactions.openapi.json):
 
 Usage:
     import litellm
-    
+
     # Create an interaction with a model
     response = litellm.interactions.create(
         model="gemini-2.5-flash",
         input="Hello, how are you?"
     )
-    
+
     # Create an interaction with an agent
     response = litellm.interactions.create(
         agent="deep-research-pro-preview-12-2025",
         input="Research the current state of cancer research"
     )
-    
+
     # Async version
     response = await litellm.interactions.acreate(...)
-    
+
     # Get an interaction
     response = litellm.interactions.get(interaction_id="...")
-    
+
     # Delete an interaction
     result = litellm.interactions.delete(interaction_id="...")
 """
@@ -48,6 +48,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
 from litellm.types.interactions import (
     CancelInteractionResult,
     DeleteInteractionResult,
+    InteractionEnvironment,
     InteractionInput,
     InteractionsAPIResponse,
     InteractionsAPIStreamingResponse,
@@ -80,6 +81,8 @@ async def acreate(
     store: Optional[bool] = None,
     # Background execution
     background: Optional[bool] = None,
+    # Agent execution environment ("remote", env id, or remote config object)
+    environment: Optional[InteractionEnvironment] = None,
     # Response format
     response_modalities: Optional[List[str]] = None,
     response_format: Optional[Dict[str, Any]] = None,
@@ -109,6 +112,10 @@ async def acreate(
         stream: Whether to stream the response
         store: Whether to store the response for later retrieval
         background: Whether to run in background
+        environment: Agent execution environment — ``"remote"``, an existing env id
+            string, or a config object such as
+            ``{"type": "remote", "sources": [...]}`` /
+            ``{"type": "remote", "network": {...}}``
         response_modalities: Requested response modalities (TEXT, IMAGE, AUDIO)
         response_format: JSON schema for response format
         response_mime_type: MIME type of the response
@@ -144,6 +151,7 @@ async def acreate(
             stream=stream,
             store=store,
             background=background,
+            environment=environment,
             response_modalities=response_modalities,
             response_format=response_format,
             response_mime_type=response_mime_type,
@@ -194,6 +202,8 @@ def create(
     store: Optional[bool] = None,
     # Background execution
     background: Optional[bool] = None,
+    # Agent execution environment ("remote", env id, or remote config object)
+    environment: Optional[InteractionEnvironment] = None,
     # Response format
     response_modalities: Optional[List[str]] = None,
     response_format: Optional[Dict[str, Any]] = None,
@@ -231,6 +241,10 @@ def create(
         stream: Whether to stream the response
         store: Whether to store the response for later retrieval
         background: Whether to run in background
+        environment: Agent execution environment — ``"remote"``, an existing env id
+            string, or a config object such as
+            ``{"type": "remote", "sources": [...]}`` /
+            ``{"type": "remote", "network": {...}}``
         response_modalities: Requested response modalities (TEXT, IMAGE, AUDIO)
         response_format: JSON schema for response format
         response_mime_type: MIME type of the response
@@ -252,7 +266,14 @@ def create(
 
         litellm_params = GenericLiteLLMParams(**kwargs)
 
-        if model:
+        # Routing logic:
+        # - agent provided (no model, or model accidentally set to agent name) → gemini
+        # - model provided → resolve provider via get_llm_provider (normal routing)
+        if agent and model == agent:
+            model = None
+        if agent and not model:
+            custom_llm_provider = custom_llm_provider or "gemini"
+        elif model:
             model, custom_llm_provider, _, _ = litellm.get_llm_provider(
                 model=model,
                 custom_llm_provider=custom_llm_provider,
diff --git a/litellm/interactions/streaming_iterator.py b/litellm/interactions/streaming_iterator.py
index a5a7f9e06e5..561686a3e1b 100644
--- a/litellm/interactions/streaming_iterator.py
+++ b/litellm/interactions/streaming_iterator.py
@@ -101,10 +101,14 @@ class BaseInteractionsAPIStreamingIterator:
                     )
                 )
 
-                # Store the completed response (check for status=completed)
-                if (
-                    streaming_response
-                    and getattr(streaming_response, "status", None) == "completed"
+                # Store the completed response.
+                # Legacy schema signals completion via status="completed".
+                # New schema (Api-Revision: 2026-05-20) uses event_type="interaction.completed".
+                # Remove the legacy check after June 8, 2026.
+                if streaming_response and (
+                    getattr(streaming_response, "status", None) == "completed"
+                    or getattr(streaming_response, "event_type", None)
+                    == "interaction.completed"
                 ):
                     self.completed_response = streaming_response
                     self._handle_logging_completed_response()
diff --git a/litellm/interactions/utils.py b/litellm/interactions/utils.py
index 3a18ddf52fe..84437f4d3d8 100644
--- a/litellm/interactions/utils.py
+++ b/litellm/interactions/utils.py
@@ -15,6 +15,7 @@ INTERACTIONS_API_OPTIONAL_PARAMS = {
     "stream",
     "store",
     "background",
+    "environment",
     "response_modalities",
     "response_format",
     "response_mime_type",
diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py
index 9d8bd7523db..b8cdc8210fc 100644
--- a/litellm/litellm_core_utils/get_supported_openai_params.py
+++ b/litellm/litellm_core_utils/get_supported_openai_params.py
@@ -11,6 +11,7 @@ def get_supported_openai_params(  # noqa: PLR0915
     request_type: Literal[
         "chat_completion", "embeddings", "transcription"
     ] = "chat_completion",
+    base_model: Optional[str] = None,
 ) -> Optional[list]:
     """
     Returns the supported openai params for a given model + provider
@@ -20,6 +21,11 @@ def get_supported_openai_params(  # noqa: PLR0915
     get_supported_openai_params(model="anthropic.claude-3", custom_llm_provider="bedrock")
     ```
 
+    Args:
+        base_model: For Azure, the true underlying model (e.g. ``"azure/gpt-5.2"``)
+            when the deployment name differs. Used for model-type detection so that
+            non-standard deployment names route to the correct config.
+
     Returns:
     - List if custom_llm_provider is mapped
     - None if unmapped
@@ -32,17 +38,21 @@ def get_supported_openai_params(  # noqa: PLR0915
 
     if custom_llm_provider in LlmProvidersSet:
         provider_config = litellm.ProviderConfigManager.get_provider_chat_config(
-            model=model, provider=LlmProviders(custom_llm_provider)
+            model=model,
+            provider=LlmProviders(custom_llm_provider),
+            base_model=base_model,
         )
     elif custom_llm_provider.split("/")[0] in LlmProvidersSet:
         provider_config = litellm.ProviderConfigManager.get_provider_chat_config(
-            model=model, provider=LlmProviders(custom_llm_provider.split("/")[0])
+            model=model,
+            provider=LlmProviders(custom_llm_provider.split("/")[0]),
+            base_model=base_model,
         )
     else:
         provider_config = None
 
     if provider_config and request_type == "chat_completion":
-        return provider_config.get_supported_openai_params(model=model)
+        return provider_config.get_supported_openai_params(model=base_model or model)
 
     if custom_llm_provider == "bedrock":
         return litellm.AmazonConverseConfig().get_supported_openai_params(model=model)
@@ -130,16 +140,23 @@ def get_supported_openai_params(  # noqa: PLR0915
                 model=model
             )
     elif custom_llm_provider == "azure":
-        if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
+        _azure_detection_model = base_model or model
+        if litellm.AzureOpenAIO1Config().is_o_series_model(
+            model=_azure_detection_model
+        ):
             return litellm.AzureOpenAIO1Config().get_supported_openai_params(
-                model=model
+                model=_azure_detection_model
             )
-        elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model):
+        elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(
+            model=_azure_detection_model
+        ):
             return litellm.AzureOpenAIGPT5Config().get_supported_openai_params(
-                model=model
+                model=_azure_detection_model
             )
         else:
-            return litellm.AzureOpenAIConfig().get_supported_openai_params(model=model)
+            return litellm.AzureOpenAIConfig().get_supported_openai_params(
+                model=_azure_detection_model
+            )
     elif custom_llm_provider == "openrouter":
         return litellm.OpenrouterConfig().get_supported_openai_params(model=model)
     elif custom_llm_provider == "vercel_ai_gateway":
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 876f1b167db..63fa0e64695 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -994,10 +994,8 @@ class Logging(LiteLLMLoggingBaseClass):
                 try:
                     # [Non-blocking Extra Debug Information in metadata]
                     if turn_off_message_logging is True:
-                        _metadata["raw_request"] = (
-                            "redacted by litellm. \
+                        _metadata["raw_request"] = "redacted by litellm. \
                             'litellm.turn_off_message_logging=True'"
-                        )
                     else:
                         curl_command = self._get_request_curl_command(
                             api_base=additional_args.get("api_base", ""),
@@ -1031,12 +1029,8 @@ class Logging(LiteLLMLoggingBaseClass):
                             error=str(e),
                         )
                     )
-                    _metadata["raw_request"] = (
-                        "Unable to Log \
-                        raw request: {}".format(
-                            str(e)
-                        )
-                    )
+                    _metadata["raw_request"] = "Unable to Log \
+                        raw request: {}".format(str(e))
             if getattr(self, "logger_fn", None) and callable(self.logger_fn):
                 try:
                     self.logger_fn(
@@ -1769,9 +1763,12 @@ class Logging(LiteLLMLoggingBaseClass):
             self.model_call_details["response_cost"] = 0.0
         elif "response_cost" in hidden_params:
             self.model_call_details["response_cost"] = hidden_params["response_cost"]
-        elif self.model_call_details.get("response_cost") is not None:
+        elif (
+            existing_cost := self.model_call_details.get("response_cost")
+        ) is not None and existing_cost != 0:
             # Preserve response_cost if already calculated (e.g., by pass-through
-            # handlers like Gemini/Vertex which call completion_cost directly)
+            # handlers like Gemini/Vertex which call completion_cost directly).
+            # Do not preserve 0 from failure_handler on intermediate router retries.
             pass
         else:
             self.model_call_details["response_cost"] = self._response_cost_calculator(
@@ -5143,13 +5140,17 @@ class StandardLoggingPayloadSetup:
     ) -> StandardLoggingPayloadErrorInformation:
         from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG
 
-        # Check for 'code' first (used by ProxyException), then fall back to 'status_code' (used by LiteLLM exceptions)
-        # Ensure error_code is always a string for Prisma Python JSON field compatibility
+        # ProxyException uses .code, LiteLLM exceptions use .status_code,
+        # httpx.HTTPStatusError exposes status only as .response.status_code.
+        # Stringified for Prisma JSON compatibility.
         error_code_attr = getattr(original_exception, "code", None)
         if error_code_attr is not None and str(error_code_attr) not in ("", "None"):
             error_status: str = str(error_code_attr)
         else:
             status_code_attr = getattr(original_exception, "status_code", None)
+            if status_code_attr is None:
+                response_attr = getattr(original_exception, "response", None)
+                status_code_attr = getattr(response_attr, "status_code", None)
             error_status = str(status_code_attr) if status_code_attr is not None else ""
         error_class: str = (
             str(original_exception.__class__.__name__) if original_exception else ""
diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index f417b4a5f61..32ae61d7f58 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -20,6 +20,7 @@ from typing import (
     cast,
 )
 
+import litellm
 from litellm import verbose_logger
 from litellm.router_utils.batch_utils import InMemoryFile
 from litellm.types.llms.openai import (
@@ -1170,9 +1171,16 @@ def migrate_file_to_image_url(
         ChatCompletionImageUrlObject,
     )
 
-    file_id = message["file"].get("file_id")
-    file_data = message["file"].get("file_data")
-    format = message["file"].get("format")
+    file_sub = message.get("file")
+    if file_sub is None:
+        raise litellm.BadRequestError(
+            message="Content block has type='file' but is missing the required 'file' field",
+            model=None,
+            llm_provider=None,
+        )
+    file_id = file_sub.get("file_id")
+    file_data = file_sub.get("file_data")
+    format = file_sub.get("format")
     if not file_id and not file_data:
         raise ValueError("file_id and file_data are both None")
     image_url_object = ChatCompletionImageObject(
@@ -1196,12 +1204,8 @@ def get_last_user_message(messages: List[AllMessageValues]) -> Optional[str]:
         {"role": "assistant", "content": "I'm good, thank you!"},
         {"role": "user", "content": "What is the weather in Tokyo?"},
     ]
-    get_user_prompt(messages) -> "What is the weather in Tokyo?"
+    get_last_user_message(messages) -> "What is the weather in Tokyo?"
     """
-    from litellm.litellm_core_utils.prompt_templates.common_utils import (
-        convert_content_list_to_str,
-    )
-
     if not messages:
         return None
 
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index d40ca4e3597..f169f86079a 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -1233,6 +1233,7 @@ def infer_protocol_value(
 
 def _gemini_tool_call_invoke_helper(
     function_call_params: ChatCompletionToolCallFunctionChunk,
+    tool_call_id: Optional[str] = None,
 ) -> Optional[VertexFunctionCall]:
     name = function_call_params.get("name", "") or ""
     arguments = function_call_params.get("arguments", "")
@@ -1248,6 +1249,10 @@ def _gemini_tool_call_invoke_helper(
         name=name,
         args=arguments_dict,
     )
+    if tool_call_id:
+        clean_id = tool_call_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0]
+        if clean_id:
+            function_call["id"] = clean_id
     return function_call
 
 
@@ -1339,6 +1344,7 @@ def _get_dummy_thought_signature() -> str:
 def convert_to_gemini_tool_call_invoke(
     message: ChatCompletionAssistantMessage,
     model: Optional[str] = None,
+    custom_llm_provider: Optional[str] = None,
 ) -> List[VertexPartType]:
     """
     OpenAI tool invokes:
@@ -1384,12 +1390,26 @@ def convert_to_gemini_tool_call_invoke(
         tool_calls = message.get("tool_calls", None)
         function_call = message.get("function_call", None)
 
+        from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+            VertexGeminiConfig,
+        )
+
+        forward_tool_call_id = bool(
+            model
+            and VertexGeminiConfig._forward_gemini_function_call_id(
+                model, custom_llm_provider
+            )
+        )
+
         if tool_calls is not None:
             for idx, tool in enumerate(tool_calls):
                 if "function" in tool:
                     gemini_function_call: Optional[VertexFunctionCall] = (
                         _gemini_tool_call_invoke_helper(
-                            function_call_params=tool["function"]
+                            function_call_params=tool["function"],
+                            tool_call_id=(
+                                tool.get("id") if forward_tool_call_id else None
+                            ),
                         )
                     )
                     if gemini_function_call is not None:
@@ -1429,10 +1449,6 @@ def convert_to_gemini_tool_call_invoke(
                     thought_signature = provider_fields.get("thought_signature")
 
                 # If no signature found and model is gemini-3, use dummy signature
-                from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
-                    VertexGeminiConfig,
-                )
-
                 if (
                     not thought_signature
                     and model
@@ -1462,6 +1478,8 @@ def convert_to_gemini_tool_call_invoke(
 def convert_to_gemini_tool_call_result(  # noqa: PLR0915
     message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
     last_message_with_tool_calls: Optional[dict],
+    model: Optional[str] = None,
+    custom_llm_provider: Optional[str] = None,
 ) -> Union[VertexPartType, List[VertexPartType]]:
     """
     OpenAI message with a tool result looks like:
@@ -1602,6 +1620,23 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
             ):
                 name = tool.get("function", {}).get("name", "")
 
+    # Echo the OpenAI tool_call_id on functionResponse (strip thought-signature suffix).
+    # Only Google AI Studio Gemini 3+ accepts `id` on function_response parts.
+    # Vertex AI and older Gemini models reject the field with HTTP 400.
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    gemini_call_id: Optional[str] = None
+    if model and VertexGeminiConfig._forward_gemini_function_call_id(
+        model, custom_llm_provider
+    ):
+        raw_tool_call_id = message.get("tool_call_id")
+        if raw_tool_call_id and isinstance(raw_tool_call_id, str):
+            stripped_id = raw_tool_call_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0]
+            if stripped_id:
+                gemini_call_id = stripped_id
+
     if not name:
         raise Exception(
             "Missing corresponding tool call for tool response message. Received - message={}, last_message_with_tool_calls={}".format(
@@ -1632,6 +1667,8 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
         name=name,
         response=response_data,  # type: ignore
     )
+    if gemini_call_id:
+        _function_response["id"] = gemini_call_id
 
     # Create part with function_response, and optionally inline_data for images (Computer Use)
     _part: VertexPartType = {"function_response": _function_response}
@@ -2057,9 +2094,16 @@ def anthropic_process_openai_file_message(
     AnthropicMessagesContainerUploadParam,
 ]:
     file_message = cast(ChatCompletionFileObject, message)
-    file_data = file_message["file"].get("file_data")
-    file_id = file_message["file"].get("file_id")
-    format = file_message["file"].get("format")
+    file_sub = file_message.get("file")
+    if file_sub is None:
+        raise litellm.BadRequestError(
+            message="Content block has type='file' but is missing the required 'file' field",
+            model=None,
+            llm_provider="anthropic",
+        )
+    file_data = file_sub.get("file_data")
+    file_id = file_sub.get("file_id")
+    format = file_sub.get("format")
     if file_data:
         image_chunk = convert_to_anthropic_image_obj(
             openai_image_url=file_data,
@@ -4879,7 +4923,13 @@ class BedrockConverseMessagesProcessor:
 
     @staticmethod
     def _process_file_message(message: ChatCompletionFileObject) -> BedrockContentBlock:
-        file_message = message["file"]
+        file_message = message.get("file")
+        if file_message is None:
+            raise litellm.BadRequestError(
+                message="Content block has type='file' but is missing the required 'file' field",
+                model=None,
+                llm_provider="bedrock",
+            )
         file_data = file_message.get("file_data")
         file_id = file_message.get("file_id")
 
@@ -4900,7 +4950,13 @@ class BedrockConverseMessagesProcessor:
     async def _async_process_file_message(
         message: ChatCompletionFileObject,
     ) -> BedrockContentBlock:
-        file_message = message["file"]
+        file_message = message.get("file")
+        if file_message is None:
+            raise litellm.BadRequestError(
+                message="Content block has type='file' but is missing the required 'file' field",
+                model=None,
+                llm_provider="bedrock",
+            )
         file_data = file_message.get("file_data")
         file_id = file_message.get("file_id")
         format = file_message.get("format")
@@ -5534,9 +5590,7 @@ def default_response_schema_prompt(response_schema: dict) -> str:
     prompt_str = """Use this JSON schema: 
     ```json 
     {}
-    ```""".format(
-        response_schema
-    )
+    ```""".format(response_schema)
     return prompt_str
 
 
diff --git a/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py b/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py
index 13341f27a61..0a6a4e82c72 100644
--- a/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py
+++ b/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py
@@ -1,9 +1,9 @@
 """
 This is a cache for LangfuseLoggers.
 
-Langfuse Python SDK initializes a thread for each client. 
+Langfuse Python SDK initializes a thread for each client.
 
-This ensures we do 
+This ensures we do
 1. Proper cleanup of Langfuse initialized clients.
 2. Re-use created langfuse clients.
 """
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 1ce80207552..0b56eb86d9c 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -1506,9 +1506,21 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 optional_params["metadata"] = {"user_id": value}
             elif param == "thinking":
                 optional_params["thinking"] = value
-            elif param == "reasoning_effort" and isinstance(value, str):
+            elif param == "reasoning_effort":
+                # Accept both string ("low") and dict ({"effort": "low",
+                # "summary": "concise"}). The Responses->Chat parser keeps the
+                # full dict when `summary` is set (see #25359), so a dict here
+                # is the standard shape Otto/OpenAI-Responses-Bridge callers
+                # send. Coerce to the effort string before mapping — same
+                # shape-tolerance the GPT-5 path already implements in
+                # `_normalize_reasoning_effort_for_chat_completion`.
+                effort_value = value
+                if isinstance(effort_value, dict):
+                    effort_value = effort_value.get("effort")
+                if not isinstance(effort_value, str):
+                    continue
                 mapped_thinking = AnthropicConfig._map_reasoning_effort(
-                    reasoning_effort=value,
+                    reasoning_effort=effort_value,
                     model=model,
                     llm_provider=self.custom_llm_provider or "anthropic",
                 )
@@ -1519,12 +1531,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                     optional_params["thinking"] = mapped_thinking
                     if AnthropicConfig._is_adaptive_thinking_model(model):
                         mapped_effort = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(
-                            value
+                            effort_value
                         )
                         if mapped_effort is None:
                             AnthropicConfig._raise_invalid_reasoning_effort(
                                 model=model,
-                                value=value,
+                                value=effort_value,
                                 llm_provider=self.custom_llm_provider or "anthropic",
                             )
                         optional_params["output_config"] = {"effort": mapped_effort}
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index 0e198daf089..51a1e739a0f 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
@@ -1476,7 +1476,7 @@ class LiteLLMAnthropicMessagesAdapter:
         for choice in choices:
             if choice.delta.content is not None and len(choice.delta.content) > 0:
                 text += choice.delta.content
-            if choice.delta.tool_calls is not None:
+            if choice.delta.tool_calls:
                 partial_json = ""
                 for tool in choice.delta.tool_calls:
                     if (
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py
index d0780c82d06..d693d50b8e5 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py
@@ -13,7 +13,6 @@ from typing import Any, AsyncIterator, Dict, List, Optional, cast
 
 from litellm._logging import verbose_logger
 
-
 # ---------------------------------------------------------------------------
 # SSE parsing helpers (module-level to keep the class lean)
 # ---------------------------------------------------------------------------
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
index 009ba6ef306..14e06e047ea 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
@@ -293,6 +293,12 @@ async def anthropic_messages(
         api_base=api_base,
         client=client,
         custom_llm_provider=custom_llm_provider,
+        # messages were already empty-text-block sanitized at the top of this
+        # function and are NOT reassigned before this dispatch, so the handler
+        # can skip its (otherwise redundant) second full-messages scan. Passed
+        # explicitly (not via **kwargs) so it only affects this direct
+        # dispatch -- interceptor / sync entry points still sanitize.
+        _litellm_messages_presanitized=True,
         **kwargs,
     )
     ctx = contextvars.copy_context()
@@ -351,10 +357,14 @@ def anthropic_messages_handler(
     """
     from litellm.types.utils import LlmProviders
 
-    # Sanitize empty text blocks here too so the sync entry point
+    # Sanitize empty text blocks so the sync entry point
     # (litellm.messages.create -> anthropic_messages_handler) gets the same
-    # protection as the async wrapper.  Idempotent when called twice.
-    messages = strip_empty_text_blocks_from_anthropic_messages(messages)
+    # protection as the async wrapper. The async wrapper already sanitized and
+    # does not reassign messages before dispatch, so it sets
+    # ``_litellm_messages_presanitized`` to skip this redundant second
+    # full-messages scan. Pop it so it never leaks into provider params.
+    if not kwargs.pop("_litellm_messages_presanitized", False):
+        messages = strip_empty_text_blocks_from_anthropic_messages(messages)
 
     metadata = validate_anthropic_api_metadata(metadata)
 
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
index 35495d59610..15f404d3f53 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
@@ -312,7 +312,10 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
                 )
 
         ####### get required params for all anthropic messages requests ######
-        verbose_logger.debug(f"TRANSFORMATION DEBUG - Messages: {messages}")
+        # Lazy %s: the f-string previously stringified the entire messages
+        # payload on every request regardless of log level (a full scan of the
+        # request body on the hot path). Defer it to when DEBUG is enabled.
+        verbose_logger.debug("TRANSFORMATION DEBUG - Messages: %s", messages)
 
         # Auto-strip advisor blocks from history if advisor tool is absent.
         # Prevents Anthropic 400: advisor_tool_result in history requires advisor tool.
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py
index fa951ebd2e5..88832fb3f63 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py
@@ -1,4 +1,5 @@
-from typing import Any, Dict, List, cast, get_type_hints
+from functools import lru_cache
+from typing import Any, Dict, FrozenSet, List, cast, get_type_hints
 
 from litellm.types.llms.anthropic import AnthropicMessagesRequestOptionalParams
 from litellm.types.llms.anthropic_messages.anthropic_response import (
@@ -6,6 +7,18 @@ from litellm.types.llms.anthropic_messages.anthropic_response import (
 )
 
 
+@lru_cache(maxsize=1)
+def _anthropic_messages_optional_param_keys() -> FrozenSet[str]:
+    """
+    Valid AnthropicMessagesRequestOptionalParams keys.
+
+    ``typing.get_type_hints`` is ~80us/call and this TypedDict is static, so
+    resolving it once per process instead of once per request removes a fixed
+    full-pass cost from the /v1/messages request-parse path.
+    """
+    return frozenset(get_type_hints(AnthropicMessagesRequestOptionalParams).keys())
+
+
 class AnthropicMessagesRequestUtils:
     @staticmethod
     def get_requested_anthropic_messages_optional_param(
@@ -20,7 +33,7 @@ class AnthropicMessagesRequestUtils:
         Returns:
             AnthropicMessagesRequestOptionalParams instance with only the valid parameters
         """
-        valid_keys = get_type_hints(AnthropicMessagesRequestOptionalParams).keys()
+        valid_keys = _anthropic_messages_optional_param_keys()
         filtered_params = {
             k: v for k, v in params.items() if k in valid_keys and v is not None
         }
diff --git a/litellm/llms/azure/audio_transcription/__init__.py b/litellm/llms/azure/audio_transcription/__init__.py
new file mode 100644
index 00000000000..cedd0c6dbeb
--- /dev/null
+++ b/litellm/llms/azure/audio_transcription/__init__.py
@@ -0,0 +1,3 @@
+from .transformation import AzureSpeechAudioTranscriptionConfig
+
+__all__ = ["AzureSpeechAudioTranscriptionConfig"]
diff --git a/litellm/llms/azure/audio_transcription/transformation.py b/litellm/llms/azure/audio_transcription/transformation.py
new file mode 100644
index 00000000000..e478c8ebf35
--- /dev/null
+++ b/litellm/llms/azure/audio_transcription/transformation.py
@@ -0,0 +1,224 @@
+"""
+Azure AI Speech (Cognitive Services) speech-to-text transformation.
+
+Maps OpenAI-compatible audio transcription calls to Azure Speech REST
+recognition for short audio.
+"""
+
+from typing import Any, Dict, List, Optional, Union
+from urllib.parse import urlencode, urlparse
+
+import httpx
+
+from litellm.litellm_core_utils.audio_utils.utils import process_audio_file
+from litellm.llms.base_llm.audio_transcription.transformation import (
+    AudioTranscriptionRequestData,
+    BaseAudioTranscriptionConfig,
+)
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    OpenAIAudioTranscriptionOptionalParams,
+)
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.utils import FileTypes, TranscriptionResponse
+
+
+class AzureSpeechAudioTranscriptionException(BaseLLMException):
+    pass
+
+
+class AzureSpeechAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
+    """
+    Configuration for Azure AI Speech (Cognitive Services) STT.
+
+    Reference:
+    https://learn.microsoft.com/en-us/azure/ai-services/speech-service/rest-speech-to-text-short
+    """
+
+    COGNITIVE_SERVICES_DOMAIN = "api.cognitive.microsoft.com"
+    STT_SPEECH_DOMAIN = "stt.speech.microsoft.com"
+    STT_ENDPOINT_PATH = "/speech/recognition/conversation/cognitiveservices/v1"
+    DEFAULT_LANGUAGE = "en-US"
+
+    def get_supported_openai_params(
+        self, model: str
+    ) -> List[OpenAIAudioTranscriptionOptionalParams]:
+        return ["language", "response_format"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        supported_params = self.get_supported_openai_params(model=model)
+        for key, value in non_default_params.items():
+            if key in supported_params:
+                optional_params[key] = value
+        return optional_params
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        api_key = api_key or get_secret_str("AZURE_SPEECH_API_KEY")
+        if not api_key:
+            raise AzureSpeechAudioTranscriptionException(
+                message="api_key is required for Azure AI Speech transcription.",
+                status_code=401,
+            )
+
+        validated_headers = headers.copy()
+        validated_headers["Ocp-Apim-Subscription-Key"] = api_key
+        validated_headers["Content-Type"] = validated_headers.get(
+            "Content-Type", "audio/wav"
+        )
+        validated_headers["Accept"] = "application/json"
+        return validated_headers
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        api_base = api_base or get_secret_str("AZURE_SPEECH_API_BASE")
+        if api_base is None:
+            raise AzureSpeechAudioTranscriptionException(
+                message=(
+                    "api_base is required for Azure AI Speech transcription. "
+                    "Use a Cognitive Services endpoint like "
+                    "https://{region}.api.cognitive.microsoft.com or an STT "
+                    "endpoint like https://{region}.stt.speech.microsoft.com."
+                ),
+                status_code=400,
+            )
+
+        base_url = self._resolve_stt_base_url(api_base=api_base)
+        query_params = {
+            "language": optional_params.get("language", self.DEFAULT_LANGUAGE),
+            "format": self._get_azure_response_format(
+                optional_params.get("response_format")
+            ),
+        }
+        return f"{base_url}{self.STT_ENDPOINT_PATH}?{urlencode(query_params)}"
+
+    def transform_audio_transcription_request(
+        self,
+        model: str,
+        audio_file: FileTypes,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> AudioTranscriptionRequestData:
+        processed_audio = process_audio_file(audio_file)
+        return AudioTranscriptionRequestData(
+            data=processed_audio.file_content,
+            files=None,
+            content_type=processed_audio.content_type,
+        )
+
+    def transform_audio_transcription_response(
+        self,
+        raw_response: httpx.Response,
+    ) -> TranscriptionResponse:
+        response_json = raw_response.json()
+        recognition_status = response_json.get("RecognitionStatus")
+        if recognition_status is not None and recognition_status != "Success":
+            raise AzureSpeechAudioTranscriptionException(
+                message=(
+                    "Azure AI Speech transcription failed with "
+                    f"RecognitionStatus={recognition_status}."
+                ),
+                status_code=raw_response.status_code,
+                headers=raw_response.headers,
+            )
+
+        text = self._extract_text(response_json)
+        response = TranscriptionResponse(text=text)
+        response._hidden_params = response_json
+        return response
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return AzureSpeechAudioTranscriptionException(
+            message=error_message,
+            status_code=status_code,
+            headers=headers,
+        )
+
+    def _resolve_stt_base_url(self, api_base: str) -> str:
+        api_base = api_base.rstrip("/")
+        parsed_url = urlparse(api_base)
+        hostname = parsed_url.hostname or ""
+
+        if self._is_cognitive_services_endpoint(hostname=hostname):
+            region = self._extract_region_from_hostname(
+                hostname=hostname, domain=self.COGNITIVE_SERVICES_DOMAIN
+            )
+            return self._build_stt_base_url(region=region)
+
+        if self._is_stt_endpoint(hostname=hostname):
+            return f"{parsed_url.scheme}://{hostname}"
+
+        if self._is_azure_openai_endpoint(hostname=hostname):
+            raise AzureSpeechAudioTranscriptionException(
+                message=(
+                    "Azure AI Speech transcription requires a Cognitive Services "
+                    "or STT Speech endpoint, not an Azure OpenAI endpoint."
+                ),
+                status_code=400,
+            )
+
+        return api_base
+
+    def _is_cognitive_services_endpoint(self, hostname: str) -> bool:
+        return hostname == self.COGNITIVE_SERVICES_DOMAIN or hostname.endswith(
+            f".{self.COGNITIVE_SERVICES_DOMAIN}"
+        )
+
+    def _is_stt_endpoint(self, hostname: str) -> bool:
+        return hostname == self.STT_SPEECH_DOMAIN or hostname.endswith(
+            f".{self.STT_SPEECH_DOMAIN}"
+        )
+
+    def _is_azure_openai_endpoint(self, hostname: str) -> bool:
+        return hostname.endswith(".openai.azure.com")
+
+    def _extract_region_from_hostname(self, hostname: str, domain: str) -> str:
+        if hostname.endswith(f".{domain}"):
+            return hostname[: -len(f".{domain}")]
+        return ""
+
+    def _build_stt_base_url(self, region: str) -> str:
+        if region:
+            return f"https://{region}.{self.STT_SPEECH_DOMAIN}"
+        return f"https://{self.STT_SPEECH_DOMAIN}"
+
+    def _get_azure_response_format(self, response_format: Optional[str]) -> str:
+        if response_format == "verbose_json":
+            return "detailed"
+        return "simple"
+
+    def _extract_text(self, response_json: Dict[str, Any]) -> str:
+        if isinstance(response_json.get("DisplayText"), str):
+            return response_json["DisplayText"]
+
+        nbest = response_json.get("NBest")
+        if isinstance(nbest, list) and nbest:
+            best = nbest[0]
+            if isinstance(best, dict):
+                return best.get("Display") or best.get("Lexical") or ""
+
+        return ""
diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py
index 9291269d153..734b8ecef16 100644
--- a/litellm/llms/azure/azure.py
+++ b/litellm/llms/azure/azure.py
@@ -239,7 +239,9 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
                 )
 
                 data = {"model": None, "messages": messages, **optional_params}
-            elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model):
+            elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(
+                model=litellm_params.get("base_model") or model
+            ):
                 data = litellm.AzureOpenAIGPT5Config().transform_request(
                     model=model,
                     messages=messages,
diff --git a/litellm/llms/azure/chat/o_series_transformation.py b/litellm/llms/azure/chat/o_series_transformation.py
index cae7513245c..0a73597a4e4 100644
--- a/litellm/llms/azure/chat/o_series_transformation.py
+++ b/litellm/llms/azure/chat/o_series_transformation.py
@@ -4,10 +4,10 @@ Support for o1 and o3 model families
 https://platform.openai.com/docs/guides/reasoning
 
 Translations handled by LiteLLM:
-- modalities: image => drop param (if user opts in to dropping param)  
-- role: system ==> translate to role 'user' 
-- streaming => faked by LiteLLM 
-- Tools, response_format =>  drop param (if user opts in to dropping param) 
+- modalities: image => drop param (if user opts in to dropping param)
+- role: system ==> translate to role 'user'
+- streaming => faked by LiteLLM
+- Tools, response_format =>  drop param (if user opts in to dropping param)
 - Logprobs => drop param (if user opts in to dropping param)
 - Temperature => drop param (if user opts in to dropping param)
 """
diff --git a/litellm/llms/azure/containers/transformation.py b/litellm/llms/azure/containers/transformation.py
index 586b2e379a0..cd897511585 100644
--- a/litellm/llms/azure/containers/transformation.py
+++ b/litellm/llms/azure/containers/transformation.py
@@ -1,9 +1,16 @@
 from typing import Optional
+from urllib.parse import parse_qs, urlparse, urlunparse
 
 from litellm.llms.azure.common_utils import BaseAzureLLM
 from litellm.llms.openai.containers.transformation import OpenAIContainerConfig
 from litellm.types.router import GenericLiteLLMParams
 
+# Endpoint-specific path suffixes that may appear in a deployment's api_base
+# (e.g. the responses endpoint URL is stored as api_base for Azure models).
+# Strip these before building the containers URL so we always start from the
+# resource root (https://resource.cognitiveservices.azure.com).
+_AZURE_ENDPOINT_PATHS = ("/openai/responses",)
+
 
 class AzureContainerConfig(OpenAIContainerConfig):
     """
@@ -27,6 +34,27 @@ class AzureContainerConfig(OpenAIContainerConfig):
             litellm_params=GenericLiteLLMParams(api_key=api_key),
         )
 
+    @staticmethod
+    def _normalize_api_base(api_base: Optional[str]) -> Optional[str]:
+        """Strip endpoint-specific path suffixes from api_base to get the resource root."""
+        if not api_base:
+            return api_base
+        parsed = urlparse(api_base)
+        path = parsed.path.rstrip("/")
+        for ep in _AZURE_ENDPOINT_PATHS:
+            if path.endswith(ep):
+                return urlunparse(
+                    (parsed.scheme, parsed.netloc, path[: -len(ep)], "", "", "")
+                )
+        return api_base
+
+    @staticmethod
+    def _extract_api_version(api_base: Optional[str]) -> Optional[str]:
+        """Return the api-version query param from api_base if present."""
+        if not api_base:
+            return None
+        return parse_qs(urlparse(api_base).query).get("api-version", [None])[0]
+
     def get_complete_url(
         self,
         api_base: Optional[str],
@@ -39,10 +67,19 @@ class AzureContainerConfig(OpenAIContainerConfig):
           {endpoint}/openai/v1/containers
         when api_version is 'v1', 'latest', or 'preview'; otherwise:
           {endpoint}/openai/containers
+
+        The deployment's api_base may be the responses endpoint URL
+        (e.g. .../openai/responses?api-version=2025-04-01-preview). We
+        prefer the api-version embedded there over the deployment's
+        api_version field, which may point to an older chat API version.
         """
+        effective_params = dict(litellm_params)
+        api_version_from_base = self._extract_api_version(api_base)
+        if api_version_from_base:
+            effective_params["api_version"] = api_version_from_base
         return BaseAzureLLM._get_base_azure_url(
-            api_base=api_base,
-            litellm_params=litellm_params,
+            api_base=self._normalize_api_base(api_base),
+            litellm_params=effective_params,
             route="/openai/containers",
             default_api_version="v1",
         )
diff --git a/litellm/llms/azure_ai/embed/cohere_transformation.py b/litellm/llms/azure_ai/embed/cohere_transformation.py
index 64433c21b61..bbbfb60fbde 100644
--- a/litellm/llms/azure_ai/embed/cohere_transformation.py
+++ b/litellm/llms/azure_ai/embed/cohere_transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from OpenAI /v1/embeddings format to Azure AI Cohere's /v1/embed. 
+Transformation logic from OpenAI /v1/embeddings format to Azure AI Cohere's /v1/embed.
 
 Why separate file? Make it easy to see how transformation works
 
diff --git a/litellm/llms/azure_ai/rerank/transformation.py b/litellm/llms/azure_ai/rerank/transformation.py
index b5993040ea0..f64133afa8b 100644
--- a/litellm/llms/azure_ai/rerank/transformation.py
+++ b/litellm/llms/azure_ai/rerank/transformation.py
@@ -1,5 +1,5 @@
 """
-Translate between Cohere's `/rerank` format and Azure AI's `/rerank` format. 
+Translate between Cohere's `/rerank` format and Azure AI's `/rerank` format.
 """
 
 from typing import Optional
diff --git a/litellm/llms/base_llm/agents/__init__.py b/litellm/llms/base_llm/agents/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/llms/base_llm/agents/transformation.py b/litellm/llms/base_llm/agents/transformation.py
new file mode 100644
index 00000000000..508e54cb7ab
--- /dev/null
+++ b/litellm/llms/base_llm/agents/transformation.py
@@ -0,0 +1,165 @@
+"""
+Base transformation class for provider-side Agents API.
+
+Providers that have a native agents CRUD API (e.g. Gemini v1beta/agents)
+subclass BaseAgentsAPIConfig and implement the abstract methods.
+
+The HTTP calls are handled by AgentsHTTPHandler — this class is pure
+transform logic (same separation as BaseInteractionsAPIConfig /
+InteractionsHTTPHandler).
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional, Tuple, Union
+
+import httpx
+
+from litellm.types.agents import (
+    AgentCreateResponse,
+    AgentDeleteResult,
+    AgentListResponse,
+    AgentVersionsResponse,
+)
+
+
+class BaseAgentsAPIConfig(ABC):
+    """
+    Minimal interface for providers that expose a native agents CRUD API.
+    """
+
+    # ------------------------------------------------------------------ #
+    # CREATE                                                               #
+    # ------------------------------------------------------------------ #
+
+    @abstractmethod
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> str:
+        """Return the full URL for POST /agents (create)."""
+
+    @abstractmethod
+    def validate_environment(
+        self,
+        headers: Dict[str, str],
+        litellm_params: Dict[str, Any],
+    ) -> Dict[str, str]:
+        """Validate credentials and return auth headers."""
+
+    @abstractmethod
+    def transform_create_request(
+        self,
+        name: str,
+        litellm_params: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """Map name + litellm_params to the provider's create-agent body."""
+
+    @abstractmethod
+    def transform_create_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentCreateResponse:
+        """Parse create response. Raise on non-2xx."""
+
+    # ------------------------------------------------------------------ #
+    # LIST                                                                 #
+    # ------------------------------------------------------------------ #
+
+    @abstractmethod
+    def transform_list_request(
+        self,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Return (url, query_params) for GET /agents."""
+
+    @abstractmethod
+    def transform_list_response(
+        self,
+        raw_response: httpx.Response,
+    ) -> AgentListResponse:
+        """Parse list-agents response. Raise on non-2xx."""
+
+    # ------------------------------------------------------------------ #
+    # GET                                                                  #
+    # ------------------------------------------------------------------ #
+
+    @abstractmethod
+    def transform_get_request(
+        self,
+        name: str,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Return (url, query_params) for GET /agents/{name}."""
+
+    @abstractmethod
+    def transform_get_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentCreateResponse:
+        """Parse get-agent response. Raise on non-2xx."""
+
+    # ------------------------------------------------------------------ #
+    # DELETE                                                               #
+    # ------------------------------------------------------------------ #
+
+    @abstractmethod
+    def transform_delete_request(
+        self,
+        name: str,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> str:
+        """Return the URL for DELETE /agents/{name}."""
+
+    @abstractmethod
+    def transform_delete_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentDeleteResult:
+        """Parse delete-agent response. Raise on non-2xx."""
+
+    # ------------------------------------------------------------------ #
+    # LIST VERSIONS                                                        #
+    # ------------------------------------------------------------------ #
+
+    @abstractmethod
+    def transform_list_versions_request(
+        self,
+        name: str,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Return (url, query_params) for GET /agents/{name}/versions."""
+
+    @abstractmethod
+    def transform_list_versions_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentVersionsResponse:
+        """Parse list-versions response. Raise on non-2xx."""
+
+    # ------------------------------------------------------------------ #
+    # ERROR HANDLING                                                       #
+    # ------------------------------------------------------------------ #
+
+    def get_error_class(
+        self,
+        error_message: str,
+        status_code: int,
+        headers: Union[dict, httpx.Headers],
+    ) -> Exception:
+        """Map HTTP error status codes to provider-specific exceptions."""
+        from litellm.llms.base_llm.chat.transformation import BaseLLMException
+
+        return BaseLLMException(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
diff --git a/litellm/llms/base_llm/ocr/transformation.py b/litellm/llms/base_llm/ocr/transformation.py
index b7f4d8e3b2d..263e0c094ce 100644
--- a/litellm/llms/base_llm/ocr/transformation.py
+++ b/litellm/llms/base_llm/ocr/transformation.py
@@ -54,6 +54,7 @@ class OCRUsageInfo(LiteLLMPydanticObjectBase):
     """Usage information from OCR response."""
 
     pages_processed: Optional[int] = None
+    credits: Optional[float] = None
     doc_size_bytes: Optional[int] = None
 
     model_config = {"extra": "allow"}
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 9dd2b055a12..b659c1b0a0a 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -44,6 +44,12 @@ else:
 # (e.g. "us-east-1", "eu-west-2", "us-gov-west-1", "cn-north-1").
 _VALID_AWS_REGION_PATTERN = re.compile(r"\A[a-z0-9-]+\Z")
 
+# Regional STS hostnames, e.g. sts.eu-west-1.amazonaws.com or
+# vpce-xxx.sts.eu-west-1.vpce.amazonaws.com
+_STS_REGION_FROM_ENDPOINT_PATTERN = re.compile(
+    r"(?:^|\.)sts(?:-fips)?\.([a-z0-9-]+)\.(?:amazonaws\.com(?:\.cn)?|vpce\.amazonaws\.com)"
+)
+
 
 class Boto3CredentialsInfo(BaseModel):
     credentials: Credentials
@@ -450,6 +456,24 @@ class BaseAWSLLM:
             model_id = BaseAWSLLM.encode_model_id(model_id=model_id)
         else:
             model_id = model
+            # Strip LiteLLM routing prefixes (e.g. "bedrock/", "invoke/",
+            # "bedrock/invoke/", "bedrock/converse/") that are not part of the
+            # actual Bedrock model ID.  The converse path already does this; the
+            # invoke path must do the same so that ARN models such as
+            #   bedrock/arn:aws:bedrock:…:inference-profile/global.anthropic.…
+            # are not forwarded verbatim to the Bedrock API, which would produce
+            # a malformed URL and cause botocore's EventStreamBuffer to receive
+            # a JSON error body instead of a binary event-stream — surfaced as a
+            # misleading ChecksumMismatch (0x223a7b22 == ':{"').
+            # Use strip_bedrock_routing_prefix (no break) so compound prefixes
+            # like "bedrock/invoke/arn:..." are fully stripped in one call.
+            from litellm.llms.bedrock.common_utils import strip_bedrock_routing_prefix
+
+            model_id = strip_bedrock_routing_prefix(model_id)
+            # URL-encode ARNs so colons and slashes are safe in the URL path.
+            if model_id.startswith("arn:"):
+                model_id = BaseAWSLLM.encode_model_id(model_id=model_id)
+                return model_id
 
         model_id = model_id.replace("invoke/", "", 1)
         if provider == "llama" and "llama/" in model_id:
@@ -633,6 +657,40 @@ class BaseAWSLLM:
                 "Region names must contain only lowercase letters, digits, and hyphens."
             )
 
+    @staticmethod
+    def _parse_sts_region_from_endpoint(
+        aws_sts_endpoint: Optional[str],
+    ) -> Optional[str]:
+        """Extract region from sts.{region}.amazonaws.com or vpce-x.sts.{region}.vpce.amazonaws.com."""
+        if not aws_sts_endpoint:
+            return None
+        host = urllib.parse.urlparse(aws_sts_endpoint).hostname or ""
+        match = _STS_REGION_FROM_ENDPOINT_PATTERN.search(host)
+        return match.group(1) if match else None
+
+    @staticmethod
+    def _resolve_sts_region(aws_sts_endpoint: Optional[str] = None) -> Optional[str]:
+        """STS signing region: parsed from aws_sts_endpoint else AWS_REGION / AWS_DEFAULT_REGION."""
+        return (
+            BaseAWSLLM._parse_sts_region_from_endpoint(aws_sts_endpoint)
+            or os.getenv("AWS_REGION")
+            or os.getenv("AWS_DEFAULT_REGION")
+        )
+
+    def _build_sts_client_kwargs(
+        self,
+        aws_sts_endpoint: Optional[str] = None,
+        ssl_verify: Optional[Union[bool, str]] = None,
+    ) -> dict:
+        """STS client kwargs with aligned endpoint_url and region_name (SigV4)."""
+        kwargs: dict = {"verify": self._get_ssl_verify(ssl_verify)}
+        if aws_sts_endpoint is not None:
+            kwargs["endpoint_url"] = aws_sts_endpoint
+        sts_region = self._resolve_sts_region(aws_sts_endpoint)
+        if sts_region is not None:
+            kwargs["region_name"] = sts_region
+        return kwargs
+
     def get_aws_region_name_for_non_llm_api_calls(
         self,
         aws_region_name: Optional[str] = None,
@@ -787,11 +845,6 @@ class BaseAWSLLM:
             f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}"
         )
 
-        if aws_sts_endpoint is None:
-            sts_endpoint = f"https://sts.{aws_region_name}.amazonaws.com"
-        else:
-            sts_endpoint = aws_sts_endpoint
-
         oidc_token = get_secret(aws_web_identity_token)
 
         if oidc_token is None:
@@ -800,13 +853,13 @@ class BaseAWSLLM:
                 status_code=401,
             )
 
+        sts_client_kwargs = self._build_sts_client_kwargs(
+            aws_sts_endpoint=aws_sts_endpoint,
+            ssl_verify=ssl_verify,
+        )
+
         with tracer.trace("boto3.client(sts)"):
-            sts_client = boto3.client(
-                "sts",
-                region_name=aws_region_name,
-                endpoint_url=sts_endpoint,
-                verify=self._get_ssl_verify(ssl_verify),
-            )
+            sts_client = boto3.client("sts", **sts_client_kwargs)
 
         # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
         # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
@@ -847,7 +900,6 @@ class BaseAWSLLM:
         irsa_role_arn: str,
         aws_role_name: str,
         aws_session_name: str,
-        region: str,
         web_identity_token_file: str,
         aws_external_id: Optional[str] = None,
         aws_sts_endpoint: Optional[str] = None,
@@ -862,12 +914,10 @@ class BaseAWSLLM:
         with open(web_identity_token_file, "r") as f:
             web_identity_token = f.read().strip()
 
-        irsa_sts_kwargs: dict = {
-            "region_name": region,
-            "verify": self._get_ssl_verify(ssl_verify),
-        }
-        if aws_sts_endpoint is not None:
-            irsa_sts_kwargs["endpoint_url"] = aws_sts_endpoint
+        irsa_sts_kwargs = self._build_sts_client_kwargs(
+            aws_sts_endpoint=aws_sts_endpoint,
+            ssl_verify=ssl_verify,
+        )
 
         # Create an STS client without credentials
         with tracer.trace("boto3.client(sts) for manual IRSA"):
@@ -924,7 +974,6 @@ class BaseAWSLLM:
         self,
         aws_role_name: str,
         aws_session_name: str,
-        region: str,
         aws_external_id: Optional[str] = None,
         aws_sts_endpoint: Optional[str] = None,
         ssl_verify: Optional[Union[bool, str]] = None,
@@ -932,12 +981,10 @@ class BaseAWSLLM:
         """Handle same-account role assumption for IRSA."""
         import boto3
 
-        irsa_sts_kwargs: dict = {
-            "region_name": region,
-            "verify": self._get_ssl_verify(ssl_verify),
-        }
-        if aws_sts_endpoint is not None:
-            irsa_sts_kwargs["endpoint_url"] = aws_sts_endpoint
+        irsa_sts_kwargs = self._build_sts_client_kwargs(
+            aws_sts_endpoint=aws_sts_endpoint,
+            ssl_verify=ssl_verify,
+        )
 
         verbose_logger.debug("Same account role assumption, using automatic IRSA")
         with tracer.trace("boto3.client(sts) with automatic IRSA"):
@@ -1010,12 +1057,6 @@ class BaseAWSLLM:
         web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE")
         irsa_role_arn = os.getenv("AWS_ROLE_ARN")
 
-        region = (
-            aws_region_name
-            or os.getenv("AWS_REGION")
-            or os.getenv("AWS_DEFAULT_REGION")
-        )
-
         # If we have IRSA environment variables and no explicit credentials,
         # we need to use the web identity token flow
         if (
@@ -1031,16 +1072,12 @@ class BaseAWSLLM:
             )
 
             try:
-                # Use passed-in region when set, else env, else default (align with AssumeRole path)
-                region = region or "us-east-1"
-
                 # Check if we need to do cross-account role assumption
                 if aws_role_name != irsa_role_arn:
                     sts_response = self._handle_irsa_cross_account(
                         irsa_role_arn,
                         aws_role_name,
                         aws_session_name,
-                        region,
                         web_identity_token_file,
                         aws_external_id,
                         aws_sts_endpoint=aws_sts_endpoint,
@@ -1050,7 +1087,6 @@ class BaseAWSLLM:
                     sts_response = self._handle_irsa_same_account(
                         aws_role_name,
                         aws_session_name,
-                        region,
                         aws_external_id,
                         aws_sts_endpoint=aws_sts_endpoint,
                         ssl_verify=ssl_verify,
@@ -1074,11 +1110,10 @@ class BaseAWSLLM:
 
         # In EKS/IRSA environments, use ambient credentials (no explicit keys needed)
         # This allows the web identity token to work automatically
-        sts_client_kwargs: dict = {"verify": self._get_ssl_verify(ssl_verify)}
-        if region is not None:
-            sts_client_kwargs["region_name"] = region
-        if aws_sts_endpoint is not None:
-            sts_client_kwargs["endpoint_url"] = aws_sts_endpoint
+        sts_client_kwargs = self._build_sts_client_kwargs(
+            aws_sts_endpoint=aws_sts_endpoint,
+            ssl_verify=ssl_verify,
+        )
         if aws_access_key_id is None and aws_secret_access_key is None:
             with tracer.trace("boto3.client(sts)"):
                 sts_client = boto3.client("sts", **sts_client_kwargs)
diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py
index 0602b1c2f62..620bc91732d 100644
--- a/litellm/llms/bedrock/batches/transformation.py
+++ b/litellm/llms/bedrock/batches/transformation.py
@@ -5,6 +5,7 @@ from typing import Any, Dict, List, Literal, Optional, Union, cast
 
 from httpx import Headers, Response
 
+from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.secret_managers.main import get_secret_str
@@ -263,9 +264,32 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
             cancelling_at=None,
             cancelled_at=None,
             request_counts=None,
-            metadata=original_request.get("metadata", {}),
+            metadata=self._get_openai_compatible_batch_metadata(
+                original_request.get("metadata", {})
+            ),
         )
 
+    @staticmethod
+    def _get_openai_compatible_batch_metadata(metadata: Any) -> Dict[str, str]:
+        """
+        OpenAI Batch metadata only accepts string values.
+        """
+        if not isinstance(metadata, dict):
+            return {}
+
+        sanitized_metadata: Dict[str, str] = {}
+        for key, value in metadata.items():
+            if key == "standard_logging_guardrail_information" or value is None:
+                continue
+
+            str_key = str(key)
+            if isinstance(value, str):
+                sanitized_metadata[str_key] = value
+            else:
+                sanitized_metadata[str_key] = safe_dumps(value)
+
+        return sanitized_metadata
+
     def transform_retrieve_batch_request(
         self,
         batch_id: str,
diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py
index e4072c24557..c88fa32b6a0 100644
--- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py
@@ -299,9 +299,9 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
             )
 
     def _get_response_stream_shape(self):
-        from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE
+        from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape
 
-        return BEDROCK_RESPONSE_STREAM_SHAPE
+        return get_bedrock_response_stream_shape()
 
     def _extract_response_content(self, events: InvokeAgentEventList) -> str:
         """Extract the final response content from parsed events."""
diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py
index 92ca75db95b..7a9916f1f31 100644
--- a/litellm/llms/bedrock/chat/invoke_handler.py
+++ b/litellm/llms/bedrock/chat/invoke_handler.py
@@ -68,9 +68,9 @@ from litellm.utils import CustomStreamWrapper, get_secret
 
 from ..base_aws_llm import BaseAWSLLM
 from ..common_utils import (
-    BEDROCK_RESPONSE_STREAM_SHAPE,
     BedrockError,
     ModelResponseIterator,
+    get_bedrock_response_stream_shape,
     get_bedrock_tool_name,
 )
 
@@ -1828,7 +1828,8 @@ class AWSEventStreamDecoder:
                     yield self._chunk_parser(chunk_data=_data)
 
     def _parse_message_from_event(self, event) -> Optional[str]:
-        if BEDROCK_RESPONSE_STREAM_SHAPE is None:
+        response_stream_shape = get_bedrock_response_stream_shape()
+        if response_stream_shape is None:
             raise BedrockError(
                 status_code=500,
                 message=(
@@ -1837,9 +1838,7 @@ class AWSEventStreamDecoder:
                 ),
             )
         response_dict = event.to_response_dict()
-        parsed_response = self.parser.parse(
-            response_dict, BEDROCK_RESPONSE_STREAM_SHAPE
-        )
+        parsed_response = self.parser.parse(response_dict, response_stream_shape)
 
         if response_dict["status_code"] != 200:
             decoded_body = response_dict["body"].decode()
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
index c883ab68dff..d9599b8b9c4 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py
@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any, List, Optional
 import httpx
 
 from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
+from litellm.litellm_core_utils.litellm_logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.factory import (
     convert_to_anthropic_image_obj,
 )
@@ -22,6 +23,7 @@ from litellm.llms.bedrock.common_utils import (
 from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import ModelResponse
+from litellm.utils import _supports_factory
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@@ -169,6 +171,24 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
         anthropic_request.pop("model", None)
         anthropic_request.pop("stream", None)
         anthropic_request.pop("output_format", None)
+        if not (
+            _supports_factory(
+                model=model,
+                custom_llm_provider="bedrock",
+                key="supports_output_config",
+            )
+            or AnthropicConfig._model_supports_effort_param(model)
+        ):
+            if anthropic_request.pop("output_config", None) is not None:
+                verbose_logger.warning(
+                    "Bedrock Invoke: stripping unsupported `output_config` for "
+                    "model=%s — neither `supports_output_config` nor any "
+                    "`supports_*_reasoning_effort` flag is set in "
+                    "model_prices_and_context_window.json. Add the capability "
+                    "flag to the model JSON entry if this model accepts "
+                    "`output_config`.",
+                    model,
+                )
         if "anthropic_version" not in anthropic_request:
             anthropic_request["anthropic_version"] = self.anthropic_version
 
diff --git a/litellm/llms/bedrock/claude_platform/common_utils.py b/litellm/llms/bedrock/claude_platform/common_utils.py
index 121221518c8..3abb8710de7 100644
--- a/litellm/llms/bedrock/claude_platform/common_utils.py
+++ b/litellm/llms/bedrock/claude_platform/common_utils.py
@@ -4,7 +4,6 @@ import litellm
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.secret_managers.main import get_secret_str
 
-
 CLAUDE_PLATFORM_SERVICE_NAME: Literal["aws-external-anthropic"] = (
     "aws-external-anthropic"
 )
diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py
index 0256d5d4b95..4f4729e4019 100644
--- a/litellm/llms/bedrock/common_utils.py
+++ b/litellm/llms/bedrock/common_utils.py
@@ -4,6 +4,7 @@ from __future__ import annotations
 Common utilities used across bedrock chat/embedding/image generation
 """
 
+import functools
 import json
 import os
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union
@@ -963,10 +964,8 @@ def _load_bedrock_response_stream_shape():
     """
     Load the ResponseStream shape from botocore's bundled bedrock-runtime schema.
 
-    Called once at module import time; the result is stored in
-    ``BEDROCK_RESPONSE_STREAM_SHAPE`` and reused for the process lifetime.
     Returns ``None`` if botocore is unavailable or the service model cannot be
-    loaded, so the module still imports cleanly.
+    loaded.
     """
     try:
         from botocore.loaders import Loader
@@ -977,15 +976,22 @@ def _load_bedrock_response_stream_shape():
         return ServiceModel(service_dict).shape_for("ResponseStream")
     except Exception as e:
         verbose_logger.warning(
-            "litellm: could not pre-load bedrock-runtime response stream shape "
+            "litellm: could not load bedrock-runtime response stream shape "
             "— Bedrock event-stream decoding will be unavailable. Error: %s",
             e,
         )
         return None
 
 
-# Eagerly resolved once per process — avoids per-instance or per-request disk I/O.
-BEDROCK_RESPONSE_STREAM_SHAPE = _load_bedrock_response_stream_shape()
+@functools.lru_cache(maxsize=1)
+def get_bedrock_response_stream_shape():
+    """
+    Lazily load and cache the bedrock-runtime ResponseStream shape for the process.
+
+    Avoids importing botocore (and logging warnings) unless Bedrock event-stream
+    decoding is actually needed.
+    """
+    return _load_bedrock_response_stream_shape()
 
 
 class BedrockEventStreamDecoderBase:
@@ -999,7 +1005,8 @@ class BedrockEventStreamDecoderBase:
         self.parser = EventStreamJSONParser()
 
     def _parse_message_from_event(self, event) -> Optional[str]:
-        if BEDROCK_RESPONSE_STREAM_SHAPE is None:
+        response_stream_shape = get_bedrock_response_stream_shape()
+        if response_stream_shape is None:
             raise BedrockError(
                 status_code=500,
                 message=(
@@ -1008,9 +1015,7 @@ class BedrockEventStreamDecoderBase:
                 ),
             )
         response_dict = event.to_response_dict()
-        parsed_response = self.parser.parse(
-            response_dict, BEDROCK_RESPONSE_STREAM_SHAPE
-        )
+        parsed_response = self.parser.parse(response_dict, response_stream_shape)
 
         if response_dict["status_code"] != 200:
             decoded_body = response_dict["body"].decode()
diff --git a/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py
index 2747551af81..64a79b73273 100644
--- a/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py
+++ b/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan G1 /invoke format. 
+Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan G1 /invoke format.
 
 Why separate file? Make it easy to see how transformation works
 
diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py
index d00cb74aae0..9570ff1a14c 100644
--- a/litellm/llms/bedrock/embed/cohere_transformation.py
+++ b/litellm/llms/bedrock/embed/cohere_transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from OpenAI /v1/embeddings format to Bedrock Cohere /invoke format. 
+Transformation logic from OpenAI /v1/embeddings format to Bedrock Cohere /invoke format.
 
 Why separate file? Make it easy to see how transformation works
 """
@@ -22,7 +22,7 @@ class BedrockCohereEmbeddingConfig:
     ) -> dict:
         for k, v in non_default_params.items():
             if k == "encoding_format":
-                optional_params["embedding_types"] = v
+                optional_params["embedding_types"] = v if isinstance(v, list) else [v]
             elif k == "dimensions":
                 optional_params["output_dimension"] = v
         return optional_params
diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
index 151e0e404a0..69b61298d33 100644
--- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
@@ -45,6 +45,7 @@ from litellm.types.router import GenericLiteLLMParams
 from litellm.types.utils import GenericStreamingChunk
 from litellm.types.utils import GenericStreamingChunk as GChunk
 from litellm.types.utils import ModelResponseStream
+from litellm.utils import _supports_factory
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@@ -557,7 +558,29 @@ class AmazonAnthropicClaudeMessagesConfig(
                 anthropic_messages_request=anthropic_messages_request,
             )
 
-        # 5a. Remove `custom` field from tools (Bedrock doesn't support it)
+        # 5a. Bedrock Invoke supports output_config (effort) for Claude 4.6+ models,
+        # but older models do not — strip it to avoid request rejection.
+        # Ref: https://github.com/BerriAI/litellm/issues/22797
+        if not (
+            _supports_factory(
+                model=model,
+                custom_llm_provider="bedrock",
+                key="supports_output_config",
+            )
+            or AnthropicConfig._model_supports_effort_param(model)
+        ):
+            if anthropic_messages_request.pop("output_config", None) is not None:
+                verbose_logger.warning(
+                    "Bedrock Invoke: stripping unsupported `output_config` for "
+                    "model=%s — neither `supports_output_config` nor any "
+                    "`supports_*_reasoning_effort` flag is set in "
+                    "model_prices_and_context_window.json. Add the capability "
+                    "flag to the model JSON entry if this model accepts "
+                    "`output_config`.",
+                    model,
+                )
+
+        # 5b. Remove `custom` field from tools (Bedrock doesn't support it)
         # Claude Code sends `custom: {defer_loading: true}` on tool definitions,
         # which causes Bedrock to reject the request with "Extra inputs are not permitted"
         # Ref: https://github.com/BerriAI/litellm/issues/22847
diff --git a/litellm/llms/bedrock_mantle/chat/transformation.py b/litellm/llms/bedrock_mantle/chat/transformation.py
index e413bb22b2d..81a56030a5c 100644
--- a/litellm/llms/bedrock_mantle/chat/transformation.py
+++ b/litellm/llms/bedrock_mantle/chat/transformation.py
@@ -16,7 +16,6 @@ from litellm.secret_managers.main import get_secret_str
 
 from ...openai_like.chat.transformation import OpenAILikeChatConfig
 
-
 BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1"
 
 
diff --git a/litellm/llms/chatgpt/responses/transformation.py b/litellm/llms/chatgpt/responses/transformation.py
index 66acd933416..56b61b66c84 100644
--- a/litellm/llms/chatgpt/responses/transformation.py
+++ b/litellm/llms/chatgpt/responses/transformation.py
@@ -1,7 +1,5 @@
-import json
-from typing import Any, Optional
+from typing import Any, Dict, Optional
 
-from litellm.constants import STREAM_SSE_DONE_STRING
 from litellm.exceptions import AuthenticationError
 from litellm.litellm_core_utils.core_helpers import process_response_headers
 from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
@@ -9,13 +7,17 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo
 )
 from litellm.llms.openai.common_utils import OpenAIError
 from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
+from litellm.responses.sse_output_recovery import (
+    parse_sse_json_chunk,
+    record_output_item_chunk,
+    record_output_text_chunk,
+)
 from litellm.types.llms.openai import (
     ResponsesAPIResponse,
     ResponsesAPIStreamEvents,
 )
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.utils import LlmProviders
-from litellm.utils import CustomStreamWrapper
 
 from ..authenticator import Authenticator
 from ..common_utils import (
@@ -111,86 +113,139 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig):
         raw_response: Any,
         logging_obj: Any,
     ):
-        content_type = (raw_response.headers or {}).get("content-type", "")
         body_text = raw_response.text or ""
-        if "text/event-stream" not in content_type.lower():
-            trimmed_body = body_text.lstrip()
-            if not (
-                trimmed_body.startswith("event:")
-                or trimmed_body.startswith("data:")
-                or "\nevent:" in body_text
-                or "\ndata:" in body_text
-            ):
-                return super().transform_response_api_response(
-                    model=model,
-                    raw_response=raw_response,
-                    logging_obj=logging_obj,
-                )
+        if not self._should_parse_as_sse(
+            raw_response=raw_response, body_text=body_text
+        ):
+            return super().transform_response_api_response(
+                model=model,
+                raw_response=raw_response,
+                logging_obj=logging_obj,
+            )
 
         logging_obj.post_call(
             original_response=raw_response.text,
             additional_args={"complete_input_dict": {}},
         )
 
-        completed_response = None
-        error_message = None
-        for chunk in body_text.splitlines():
-            stripped_chunk = CustomStreamWrapper._strip_sse_data_from_chunk(chunk)
-            if not stripped_chunk:
-                continue
-            stripped_chunk = stripped_chunk.strip()
-            if not stripped_chunk:
-                continue
-            if stripped_chunk == STREAM_SSE_DONE_STRING:
-                break
-            try:
-                parsed_chunk = json.loads(stripped_chunk)
-            except json.JSONDecodeError:
-                continue
-            if not isinstance(parsed_chunk, dict):
-                continue
-            event_type = parsed_chunk.get("type")
-            if event_type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED:
-                response_payload = parsed_chunk.get("response")
-                if isinstance(response_payload, dict):
-                    response_payload = dict(response_payload)
-                    if "created_at" in response_payload:
-                        response_payload["created_at"] = _safe_convert_created_field(
-                            response_payload["created_at"]
-                        )
-                    try:
-                        completed_response = ResponsesAPIResponse(**response_payload)
-                    except Exception:
-                        completed_response = ResponsesAPIResponse.model_construct(
-                            **response_payload
-                        )
-                break
-            if event_type in (
-                ResponsesAPIStreamEvents.RESPONSE_FAILED,
-                ResponsesAPIStreamEvents.ERROR,
-            ):
-                error_obj = parsed_chunk.get("error") or (
-                    parsed_chunk.get("response") or {}
-                ).get("error")
-                if error_obj is not None:
-                    if isinstance(error_obj, dict):
-                        error_message = error_obj.get("message") or str(error_obj)
-                    else:
-                        error_message = str(error_obj)
-
+        completed_response, error_message = self._extract_completed_response_from_sse(
+            body_text=body_text
+        )
         if completed_response is None:
             raise OpenAIError(
                 message=error_message or raw_response.text,
                 status_code=raw_response.status_code,
             )
 
+        self._attach_response_headers(
+            completed_response=completed_response, raw_response=raw_response
+        )
+        return completed_response
+
+    def _should_parse_as_sse(self, raw_response: Any, body_text: str) -> bool:
+        content_type = (raw_response.headers or {}).get("content-type", "")
+        if "text/event-stream" in content_type.lower():
+            return True
+        trimmed_body = body_text.lstrip()
+        return bool(
+            trimmed_body.startswith("event:")
+            or trimmed_body.startswith("data:")
+            or "\nevent:" in body_text
+            or "\ndata:" in body_text
+        )
+
+    def _extract_completed_response_from_sse(
+        self, body_text: str
+    ) -> tuple[Optional[ResponsesAPIResponse], Optional[str]]:
+        completed_response = None
+        error_message = None
+        streamed_output_items: Dict[int, dict] = {}
+        text_only_output_items: Dict[int, dict] = {}
+        for chunk in body_text.splitlines():
+            parsed_chunk = parse_sse_json_chunk(chunk)
+            if parsed_chunk is None:
+                continue
+
+            event_type = parsed_chunk.get("type")
+            if event_type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE:
+                record_output_item_chunk(
+                    parsed_chunk=parsed_chunk,
+                    output_items=streamed_output_items,
+                )
+                continue
+
+            if event_type == ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE:
+                record_output_text_chunk(
+                    parsed_chunk=parsed_chunk,
+                    output_items=streamed_output_items,
+                    text_only_items=text_only_output_items,
+                )
+                continue
+
+            if event_type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED:
+                # Real OUTPUT_ITEM_DONE events take precedence at any given
+                # output_index, but text-only items at indices without a
+                # matching OUTPUT_ITEM_DONE must still be preserved (e.g.
+                # providers that emit only OUTPUT_TEXT_DONE for some indices).
+                merged_items: Dict[int, dict] = {**text_only_output_items}
+                merged_items.update(streamed_output_items)
+                completed_response = self._build_completed_response_from_chunk(
+                    parsed_chunk=parsed_chunk,
+                    streamed_output_items=merged_items,
+                )
+                break
+
+            if event_type in (
+                ResponsesAPIStreamEvents.RESPONSE_FAILED,
+                ResponsesAPIStreamEvents.ERROR,
+            ):
+                extracted_error = self._extract_error_message(parsed_chunk)
+                if extracted_error is not None:
+                    error_message = extracted_error
+
+        return completed_response, error_message
+
+    def _build_completed_response_from_chunk(
+        self, parsed_chunk: Dict[str, Any], streamed_output_items: Dict[int, dict]
+    ) -> Optional[ResponsesAPIResponse]:
+        response_payload = parsed_chunk.get("response")
+        if not isinstance(response_payload, dict):
+            return None
+        response_payload = dict(response_payload)
+        if not response_payload.get("output") and streamed_output_items:
+            response_payload["output"] = [
+                item for _, item in sorted(streamed_output_items.items())
+            ]
+        if "created_at" in response_payload:
+            response_payload["created_at"] = _safe_convert_created_field(
+                response_payload["created_at"]
+            )
+        try:
+            return ResponsesAPIResponse(**response_payload)
+        except Exception:
+            return ResponsesAPIResponse.model_construct(**response_payload)
+
+    def _extract_error_message(self, parsed_chunk: Dict[str, Any]) -> Optional[str]:
+        error_obj = parsed_chunk.get("error") or (
+            parsed_chunk.get("response") or {}
+        ).get("error")
+        if error_obj is None:
+            return None
+        if isinstance(error_obj, dict):
+            return error_obj.get("message") or str(error_obj)
+        return str(error_obj)
+
+    def _attach_response_headers(
+        self,
+        completed_response: ResponsesAPIResponse,
+        raw_response: Any,
+    ) -> None:
         raw_headers = dict(raw_response.headers)
         processed_headers = process_response_headers(raw_headers)
         if not hasattr(completed_response, "_hidden_params"):
             setattr(completed_response, "_hidden_params", {})
         completed_response._hidden_params["additional_headers"] = processed_headers
         completed_response._hidden_params["headers"] = raw_headers
-        return completed_response
 
     def get_complete_url(
         self,
diff --git a/litellm/llms/cohere/embed/handler.py b/litellm/llms/cohere/embed/handler.py
index 3ab8baf7ba8..81b6a1c7aec 100644
--- a/litellm/llms/cohere/embed/handler.py
+++ b/litellm/llms/cohere/embed/handler.py
@@ -1,5 +1,5 @@
 """
-Legacy /v1/embedding handler for Bedrock Cohere. 
+Legacy /v1/embedding handler for Bedrock Cohere.
 """
 
 import json
diff --git a/litellm/llms/cohere/embed/v1_transformation.py b/litellm/llms/cohere/embed/v1_transformation.py
index feca9cb5b88..82c901e7eca 100644
--- a/litellm/llms/cohere/embed/v1_transformation.py
+++ b/litellm/llms/cohere/embed/v1_transformation.py
@@ -110,15 +110,35 @@ class CohereEmbeddingConfig:
             additional_args={"complete_input_dict": data},
             original_response=response_json,
         )
+        return self._populate_embedding_response(
+            response_json=response_json,
+            model_response=model_response,
+            model=model,
+            encoding=encoding,
+            input=input,
+        )
+
+    def _populate_embedding_response(
+        self,
+        response_json: dict,
+        model_response: EmbeddingResponse,
+        model: str,
+        encoding: Any,
+        input: list,
+    ) -> EmbeddingResponse:
         """
-            response 
+        Parse a Cohere embed response body into an OpenAI-style EmbeddingResponse.
+
+        Split out from `_transform_response` so callers that already log
+        `post_call` themselves (e.g. SageMaker's embedding handler) can reuse
+        the parsing without triggering a second `post_call`.
+
+        Response shape:
             {
                 'object': "list",
-                'data': [
-                
-                ]
-                'model', 
-                'usage'
+                'data': [...],
+                'model',
+                'usage',
             }
         """
         embeddings = response_json["embeddings"]
@@ -149,9 +169,6 @@ class CohereEmbeddingConfig:
         model_response.object = "list"
         model_response.data = output_data
         model_response.model = model
-        input_tokens = 0
-        for text in input:
-            input_tokens += len(encoding.encode(text))
 
         setattr(
             model_response,
diff --git a/litellm/llms/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py
index 599cd705ebf..501390d840b 100644
--- a/litellm/llms/custom_httpx/container_handler.py
+++ b/litellm/llms/custom_httpx/container_handler.py
@@ -257,14 +257,19 @@ class GenericContainerHandler:
         returns_binary = endpoint_config.get("returns_binary", False)
         is_multipart = endpoint_config.get("is_multipart", False)
 
+        # An empty dict passed as `params` to httpx strips any existing query
+        # string from the URL (e.g. ?api-version=...).  Use None instead so
+        # httpx leaves the URL's own query string intact.
+        effective_params = query_params or None
+
         try:
             if method == "GET":
                 response = http_client.get(
-                    url=url, headers=headers, params=query_params
+                    url=url, headers=headers, params=effective_params
                 )
             elif method == "DELETE":
                 response = http_client.delete(
-                    url=url, headers=headers, params=query_params
+                    url=url, headers=headers, params=effective_params
                 )
             elif method == "POST":
                 if is_multipart and "file" in kwargs:
@@ -272,11 +277,11 @@ class GenericContainerHandler:
                         kwargs["file"], headers
                     )
                     response = http_client.post(
-                        url=url, headers=headers, params=query_params, files=files
+                        url=url, headers=headers, params=effective_params, files=files
                     )
                 else:
                     response = http_client.post(
-                        url=url, headers=headers, params=query_params
+                        url=url, headers=headers, params=effective_params
                     )
             else:
                 raise ValueError(f"Unsupported HTTP method: {method}")
@@ -376,14 +381,19 @@ class GenericContainerHandler:
         returns_binary = endpoint_config.get("returns_binary", False)
         is_multipart = endpoint_config.get("is_multipart", False)
 
+        # An empty dict passed as `params` to httpx strips any existing query
+        # string from the URL (e.g. ?api-version=...).  Use None instead so
+        # httpx leaves the URL's own query string intact.
+        effective_params = query_params or None
+
         try:
             if method == "GET":
                 response = await http_client.get(
-                    url=url, headers=headers, params=query_params
+                    url=url, headers=headers, params=effective_params
                 )
             elif method == "DELETE":
                 response = await http_client.delete(
-                    url=url, headers=headers, params=query_params
+                    url=url, headers=headers, params=effective_params
                 )
             elif method == "POST":
                 if is_multipart and "file" in kwargs:
@@ -391,11 +401,11 @@ class GenericContainerHandler:
                         kwargs["file"], headers
                     )
                     response = await http_client.post(
-                        url=url, headers=headers, params=query_params, files=files
+                        url=url, headers=headers, params=effective_params, files=files
                     )
                 else:
                     response = await http_client.post(
-                        url=url, headers=headers, params=query_params
+                        url=url, headers=headers, params=effective_params
                     )
             else:
                 raise ValueError(f"Unsupported HTTP method: {method}")
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 2ff63cc2d7f..c9ab3c648ac 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -890,6 +890,18 @@ class BaseLLMHTTPHandler:
             headers=headers,
         )
 
+        # Some providers (e.g. OCI) require request signing after the body is built.
+        # The default BaseConfig.sign_request returns (headers, None) — a no-op for
+        # providers that don't need signing.
+        headers, signed_body = provider_config.sign_request(
+            headers=headers,
+            optional_params=optional_params,
+            request_data=data,
+            api_base=api_base,
+            api_key=api_key,
+            model=model,
+        )
+
         ## LOGGING
         logging_obj.pre_call(
             input=input,
@@ -916,6 +928,7 @@ class BaseLLMHTTPHandler:
                 client=client,
                 optional_params=optional_params,
                 litellm_params=litellm_params,
+                signed_body=signed_body,
             )
 
         if client is None or not isinstance(client, HTTPHandler):
@@ -926,12 +939,20 @@ class BaseLLMHTTPHandler:
             sync_httpx_client = client
 
         try:
-            response = sync_httpx_client.post(
-                url=api_base,
-                headers=headers,
-                data=json.dumps(data),
-                timeout=timeout,
-            )
+            if signed_body is not None:
+                response = sync_httpx_client.post(
+                    url=api_base,
+                    headers=headers,
+                    data=signed_body,
+                    timeout=timeout,
+                )
+            else:
+                response = sync_httpx_client.post(
+                    url=api_base,
+                    headers=headers,
+                    data=json.dumps(data),
+                    timeout=timeout,
+                )
         except Exception as e:
             raise self._handle_error(
                 e=e,
@@ -964,6 +985,7 @@ class BaseLLMHTTPHandler:
         api_key: Optional[str] = None,
         timeout: Optional[Union[float, httpx.Timeout]] = None,
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        signed_body: Optional[bytes] = None,
     ) -> EmbeddingResponse:
         if client is None or not isinstance(client, AsyncHTTPHandler):
             async_httpx_client = get_async_httpx_client(
@@ -974,12 +996,20 @@ class BaseLLMHTTPHandler:
             async_httpx_client = client
 
         try:
-            response = await async_httpx_client.post(
-                url=api_base,
-                headers=headers,
-                json=request_data,
-                timeout=timeout,
-            )
+            if signed_body is not None:
+                response = await async_httpx_client.post(
+                    url=api_base,
+                    headers=headers,
+                    data=signed_body,
+                    timeout=timeout,
+                )
+            else:
+                response = await async_httpx_client.post(
+                    url=api_base,
+                    headers=headers,
+                    json=request_data,
+                    timeout=timeout,
+                )
         except Exception as e:
             raise self._handle_error(e=e, provider_config=provider_config)
 
@@ -1177,6 +1207,8 @@ class BaseLLMHTTPHandler:
 
         data = transformed_result.data
         files = transformed_result.files
+        if transformed_result.content_type is not None:
+            headers["Content-Type"] = transformed_result.content_type
 
         ## LOGGING
         logging_obj.pre_call(
@@ -1409,6 +1441,8 @@ class BaseLLMHTTPHandler:
             document=document,
             optional_params=optional_params,
             headers=headers,
+            api_key=api_key,
+            api_base=api_base,
         )
 
         # All providers return OCRRequestData
@@ -1477,6 +1511,8 @@ class BaseLLMHTTPHandler:
             document=document,
             optional_params=optional_params,
             headers=headers,
+            api_key=api_key,
+            api_base=api_base,
         )
 
         # All providers return OCRRequestData
@@ -1852,7 +1888,9 @@ class BaseLLMHTTPHandler:
         async_httpx_client: AsyncHTTPHandler,
         request_url: str,
         headers: dict,
-        signed_json_body: Optional[bytes],
+        # str when the caller passes a pre-serialized (unsigned) body to avoid
+        # re-dumping; bytes when a provider signed the request (e.g. Bedrock).
+        signed_json_body: Optional[Union[str, bytes]],
         request_body: dict,
         stream: bool,
         logging_obj: LiteLLMLoggingObj,
@@ -2043,8 +2081,18 @@ class BaseLLMHTTPHandler:
             model=model,
         )
 
+        # The request body was serialized once for the pre-call log input and
+        # again for the wire (json.dumps is O(payload), large for long-context
+        # Claude Code history). Serialize once and reuse for both. Only when
+        # the provider didn't sign the request (sign_request no-op for the
+        # native anthropic path -> signed_json_body is None); signed providers
+        # (e.g. Bedrock) keep their signed body untouched. The HTTP-error
+        # retry path mutates + re-signs the body, so it still re-serializes
+        # internally -- this only deduplicates the success path.
+        request_body_json = json.dumps(request_body)
+
         logging_obj.pre_call(
-            input=[{"role": "user", "content": json.dumps(request_body)}],
+            input=[{"role": "user", "content": request_body_json}],
             api_key="",
             additional_args={
                 "complete_input_dict": request_body,
@@ -2057,7 +2105,9 @@ class BaseLLMHTTPHandler:
             async_httpx_client=async_httpx_client,
             request_url=request_url,
             headers=headers,
-            signed_json_body=signed_json_body,
+            signed_json_body=(
+                signed_json_body if signed_json_body is not None else request_body_json
+            ),
             request_body=request_body,
             stream=stream or False,
             logging_obj=logging_obj,
@@ -2079,6 +2129,14 @@ class BaseLLMHTTPHandler:
                 litellm_logging_obj=logging_obj,
             )
 
+            if not self._has_agentic_completion_hook(logging_obj):
+                # No callback overrides async_should_run_agentic_loop, so the
+                # agentic wrapper's only effect would be buffering every chunk
+                # and rebuilding the response from SSE at end-of-stream to call
+                # hooks that all return (False, {}). Stream through directly and
+                # skip that per-chunk + end-of-stream overhead.
+                return completion_stream
+
             from litellm.llms.anthropic.experimental_pass_through.messages.agentic_streaming_iterator import (
                 AgenticAnthropicStreamingIterator,
             )
@@ -4586,6 +4644,51 @@ class BaseLLMHTTPHandler:
         fingerprints = list(kwargs.get("_agentic_loop_fingerprints", []) or [])
         return depth, max(max_loops, 1), fingerprints
 
+    @staticmethod
+    def _has_agentic_completion_hook(logging_obj: Any) -> bool:
+        """
+        True if any registered callback actually overrides
+        ``async_should_run_agentic_loop`` (the gate every agentic hook goes
+        through). The base ``CustomLogger`` implementation returns
+        ``(False, {})``, so when nothing overrides it the agentic
+        post-processing is a guaranteed no-op and the streaming wrapper that
+        buffers + rebuilds the whole response from SSE just to call it can be
+        skipped entirely.
+
+        Function-identity comparison (not a leaf ``__dict__`` check) so an
+        override inherited through any intermediate class is still detected --
+        a false negative here would silently disable agentic features.
+
+        String entries in ``litellm.callbacks`` (e.g. ``"datadog"``) are
+        resolved to their ``CustomLogger`` instance via
+        ``get_custom_logger_compatible_class`` -- same pattern as
+        ``ProxyLogging._callback_capabilities`` -- so a string-registered
+        agentic callback is detected too.
+        """
+        from litellm.integrations.custom_logger import CustomLogger
+        from litellm.litellm_core_utils.litellm_logging import (
+            get_custom_logger_compatible_class,
+        )
+
+        base_func = CustomLogger.async_should_run_agentic_loop
+        callbacks = litellm.callbacks + (
+            getattr(logging_obj, "dynamic_success_callbacks", None) or []
+        )
+        for cb in callbacks:
+            if isinstance(cb, str):
+                resolved = get_custom_logger_compatible_class(cb)  # type: ignore[arg-type]
+                if resolved is None:
+                    continue
+                cb = resolved
+            if not isinstance(cb, CustomLogger):
+                continue
+            cb_func = getattr(type(cb), "async_should_run_agentic_loop", base_func)
+            if getattr(cb_func, "__func__", cb_func) is not getattr(
+                base_func, "__func__", base_func
+            ):
+                return True
+        return False
+
     @staticmethod
     def _check_agentic_loop_safety(
         tool_calls: Any,
@@ -7834,7 +7937,7 @@ class BaseLLMHTTPHandler:
             response = sync_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_list_response(
@@ -7911,7 +8014,7 @@ class BaseLLMHTTPHandler:
             response = await async_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_list_response(
@@ -8001,7 +8104,7 @@ class BaseLLMHTTPHandler:
             response = sync_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_retrieve_response(
@@ -8078,7 +8181,7 @@ class BaseLLMHTTPHandler:
             response = await async_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_retrieve_response(
@@ -8168,7 +8271,7 @@ class BaseLLMHTTPHandler:
             response = sync_httpx_client.delete(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_delete_response(
@@ -8245,7 +8348,7 @@ class BaseLLMHTTPHandler:
             response = await async_httpx_client.delete(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_delete_response(
@@ -8341,7 +8444,7 @@ class BaseLLMHTTPHandler:
             response = sync_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_file_list_response(
@@ -8420,7 +8523,7 @@ class BaseLLMHTTPHandler:
             response = await async_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_file_list_response(
@@ -8508,7 +8611,7 @@ class BaseLLMHTTPHandler:
             response = sync_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_file_content_response(
@@ -8584,7 +8687,7 @@ class BaseLLMHTTPHandler:
             response = await async_httpx_client.get(
                 url=url,
                 headers=headers,
-                params=params,
+                params=params or None,
             )
 
             return container_provider_config.transform_container_file_content_response(
diff --git a/litellm/llms/custom_httpx/mock_transport.py b/litellm/llms/custom_httpx/mock_transport.py
index c9844753e0e..ad93cc134ee 100644
--- a/litellm/llms/custom_httpx/mock_transport.py
+++ b/litellm/llms/custom_httpx/mock_transport.py
@@ -13,7 +13,6 @@ from typing import Tuple
 
 import httpx
 
-
 # ---------------------------------------------------------------------------
 # Pre-built response templates
 # ---------------------------------------------------------------------------
diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py
new file mode 100644
index 00000000000..b3b89cbbebf
--- /dev/null
+++ b/litellm/llms/dashscope/common_utils.py
@@ -0,0 +1,28 @@
+"""
+Common utilities for the DashScope LLM provider.
+"""
+
+from typing import Optional
+
+import httpx
+
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+
+
+class DashScopeError(BaseLLMException):
+    """Exception class for DashScope provider errors."""
+
+    def __init__(
+        self,
+        status_code: int,
+        message: str,
+        headers: Optional[httpx.Headers] = None,
+    ):
+        self.status_code = status_code
+        self.message = message
+        self.headers = headers or httpx.Headers()
+        super().__init__(
+            status_code=status_code,
+            message=message,
+            headers=dict(self.headers),
+        )
diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py
index 9b3e3851162..8bb7f605b82 100644
--- a/litellm/llms/dashscope/cost_calculator.py
+++ b/litellm/llms/dashscope/cost_calculator.py
@@ -1,5 +1,5 @@
 """
-Cost calculator for Dashscope Chat models. 
+Cost calculator for Dashscope Chat models.
 
 Handles tiered pricing and prompt caching scenarios.
 """
diff --git a/litellm/llms/dashscope/embed/__init__.py b/litellm/llms/dashscope/embed/__init__.py
new file mode 100644
index 00000000000..4962b1f3251
--- /dev/null
+++ b/litellm/llms/dashscope/embed/__init__.py
@@ -0,0 +1,7 @@
+"""
+DashScope Embedding Module
+"""
+
+from .transformation import DashScopeEmbeddingConfig
+
+__all__ = ["DashScopeEmbeddingConfig"]
diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py
new file mode 100644
index 00000000000..5bc0e5ca817
--- /dev/null
+++ b/litellm/llms/dashscope/embed/transformation.py
@@ -0,0 +1,191 @@
+"""
+Transformation logic from OpenAI /v1/embeddings format to DashScope's /v1/embeddings format.
+
+Supports
+- text-embedding-v4
+- text-embedding-v3
+
+Endpoint
+- https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings
+
+Docs - https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api
+"""
+
+from typing import List, Optional, Union
+
+import httpx
+
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
+from litellm.types.utils import EmbeddingResponse, Usage
+
+from ..common_utils import DashScopeError
+
+DEFAULT_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+
+
+class DashScopeEmbeddingConfig(BaseEmbeddingConfig):
+    """
+    Reference: https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api
+
+    DashScope exposes an OpenAI-compatible /v1/embeddings endpoint, so the
+    request and response shapes are nearly identical to OpenAI's.
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        # DashScope's compatible-mode embeddings API accepts the same params as OpenAI.
+        # `dimensions` / `encoding_format` are only honored by text-embedding-v3 / v4;
+        # earlier versions silently ignore them server-side.
+        return ["dimensions", "encoding_format", "user"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool = False,
+    ) -> dict:
+        supported = self.get_supported_openai_params(model)
+        for k, v in non_default_params.items():
+            if v is None:
+                continue
+            if k in supported:
+                optional_params[k] = v
+            # unsupported params are dropped when drop_params=True;
+            # the upstream _check_valid_arg already raised UnsupportedParamsError
+            # for drop_params=False before this method is called.
+        return optional_params
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is None:
+            api_key = get_secret_str("DASHSCOPE_API_KEY")
+        if api_key is None:
+            raise ValueError(
+                "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly."
+            )
+        default_headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+        }
+        return {**default_headers, **headers}
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE
+        base = base.rstrip("/")
+        if base.endswith("/embeddings"):
+            return base
+        return f"{base}/embeddings"
+
+    def transform_embedding_request(
+        self,
+        model: str,
+        input: AllEmbeddingInputValues,
+        optional_params: dict,
+        headers: dict,
+    ) -> dict:
+        data: dict = {
+            "model": model,
+            "input": input,
+        }
+        for key in ("dimensions", "encoding_format", "user"):
+            value = optional_params.get(key)
+            if value is not None:
+                data[key] = value
+        return data
+
+    def transform_embedding_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: EmbeddingResponse,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str],
+        request_data: dict,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> EmbeddingResponse:
+        try:
+            response_json = raw_response.json()
+        except Exception as e:
+            raise DashScopeError(
+                status_code=raw_response.status_code,
+                message=f"Failed to parse DashScope response as JSON: {str(e)}",
+            )
+
+        logging_obj.post_call(
+            input=request_data.get("input"),
+            api_key=api_key,
+            additional_args={"complete_input_dict": request_data},
+            original_response=response_json,
+        )
+
+        if "error" in response_json:
+            error = response_json["error"]
+            message = (
+                error.get("message", str(error))
+                if isinstance(error, dict)
+                else str(error)
+            )
+            raise DashScopeError(
+                status_code=raw_response.status_code,
+                message=message,
+            )
+
+        model_response.object = "list"
+        model_response.data = response_json.get("data", [])
+        model_response.model = response_json.get("model", model)
+
+        usage = response_json.get("usage") or {}
+        prompt_tokens = usage.get("prompt_tokens", 0)
+        total_tokens = usage.get("total_tokens", prompt_tokens)
+        setattr(
+            model_response,
+            "usage",
+            Usage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=0,
+                total_tokens=total_tokens,
+            ),
+        )
+
+        if "id" in response_json:
+            setattr(model_response, "id", response_json["id"])
+
+        return model_response
+
+    def get_error_class(
+        self,
+        error_message: str,
+        status_code: int,
+        headers: Union[dict, httpx.Headers],
+    ) -> BaseLLMException:
+        if isinstance(headers, dict):
+            headers = httpx.Headers(headers)
+        return DashScopeError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
diff --git a/litellm/llms/dashscope/rerank/__init__.py b/litellm/llms/dashscope/rerank/__init__.py
new file mode 100644
index 00000000000..2a1401f6dc0
--- /dev/null
+++ b/litellm/llms/dashscope/rerank/__init__.py
@@ -0,0 +1,7 @@
+"""
+DashScope Rerank Module
+"""
+
+from .transformation import DashScopeRerankConfig
+
+__all__ = ["DashScopeRerankConfig"]
diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py
new file mode 100644
index 00000000000..629f3cf4af7
--- /dev/null
+++ b/litellm/llms/dashscope/rerank/transformation.py
@@ -0,0 +1,241 @@
+"""
+Transformation logic for DashScope's OpenAI-compatible /v1/reranks API.
+
+Supports
+- qwen3-rerank
+
+(Other DashScope rerankers — gte-rerank-v2 / qwen3-vl-rerank — share the same
+endpoint but have not been validated against this transformer. Behavior with
+those models is undefined.)
+
+Endpoint
+- https://dashscope.aliyuncs.com/compatible-api/v1/reranks
+
+Note: chat/embed live under `/compatible-mode/v1/`, but DashScope's rerank
+route is exposed under `/compatible-api/v1/reranks` per the docs. Override
+with `DASHSCOPE_API_BASE_RERANK` to point at a different host or path.
+
+Empirically, qwen3-rerank accepts `return_documents=true` and echoes
+`results[].document.text` back, even though the public docs list the flag
+as supported only for gte-rerank-v2 / qwen3-vl-rerank.
+
+Docs - https://help.aliyun.com/zh/model-studio/text-rerank-api
+"""
+
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+
+from litellm._uuid import uuid
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.rerank import (
+    OptionalRerankParams,
+    RerankBilledUnits,
+    RerankResponse,
+    RerankResponseMeta,
+    RerankTokens,
+)
+
+from ..common_utils import DashScopeError
+
+DEFAULT_RERANK_URL = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks"
+
+
+class DashScopeRerankConfig(BaseRerankConfig):
+    """
+    Reference: https://help.aliyun.com/zh/model-studio/text-rerank-api
+
+    Targets DashScope's qwen3-rerank model. Request fields: model, query,
+    documents, top_n, return_documents. Response: results[].index,
+    results[].relevance_score, optionally results[].document.text (when
+    return_documents=true), plus a top-level usage.total_tokens counter.
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        model: str,
+        optional_params: Optional[dict] = None,
+    ) -> str:
+        if api_base is None:
+            api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL
+
+        if api_base == DEFAULT_RERANK_URL:
+            return DEFAULT_RERANK_URL
+
+        cleaned = api_base.rstrip("/")
+        if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"):
+            return cleaned
+
+        if cleaned.endswith("/v1"):
+            return f"{cleaned}/reranks"
+
+        # Unknown base: append /reranks rather than silently ignoring the caller's api_base.
+        return f"{cleaned}/reranks"
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        api_key: Optional[str] = None,
+        optional_params: Optional[dict] = None,
+    ) -> dict:
+        if api_key is None:
+            api_key = get_secret_str("DASHSCOPE_API_KEY")
+        if api_key is None:
+            raise ValueError(
+                "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly."
+            )
+
+        default_headers = {
+            "Authorization": f"Bearer {api_key}",
+            "accept": "application/json",
+            "content-type": "application/json",
+        }
+        return {**default_headers, **headers}
+
+    def get_supported_cohere_rerank_params(self, model: str) -> list:
+        return ["query", "documents", "top_n", "return_documents"]
+
+    def map_cohere_rerank_params(
+        self,
+        non_default_params: Optional[dict],
+        model: str,
+        drop_params: bool,
+        query: str,
+        documents: List[Union[str, Dict[str, Any]]],
+        custom_llm_provider: Optional[str] = None,
+        top_n: Optional[int] = None,
+        rank_fields: Optional[List[str]] = None,
+        return_documents: Optional[bool] = True,
+        max_chunks_per_doc: Optional[int] = None,
+        max_tokens_per_doc: Optional[int] = None,
+    ) -> Dict:
+        # qwen3-rerank accepts query/documents/top_n/return_documents. The
+        # rest (rank_fields, max_*_per_doc) are silently dropped.
+        params: OptionalRerankParams = OptionalRerankParams(
+            query=query,
+            documents=documents,
+        )
+        if top_n is not None:
+            params["top_n"] = top_n
+        if return_documents is not None:
+            params["return_documents"] = return_documents
+        return dict(params)
+
+    def transform_rerank_request(
+        self,
+        model: str,
+        optional_rerank_params: Dict,
+        headers: dict,
+        litellm_params: Optional[dict] = None,
+    ) -> dict:
+        if "query" not in optional_rerank_params:
+            raise ValueError("query is required for DashScope rerank")
+        if "documents" not in optional_rerank_params:
+            raise ValueError("documents is required for DashScope rerank")
+
+        request: Dict[str, Any] = {
+            "model": model,
+            "query": optional_rerank_params["query"],
+            "documents": optional_rerank_params["documents"],
+        }
+        if optional_rerank_params.get("top_n") is not None:
+            request["top_n"] = optional_rerank_params["top_n"]
+        if optional_rerank_params.get("return_documents") is not None:
+            request["return_documents"] = optional_rerank_params["return_documents"]
+        return request
+
+    def transform_rerank_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: RerankResponse,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str] = None,
+        request_data: Optional[dict] = None,
+        optional_params: Optional[dict] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> RerankResponse:
+        request_data = request_data or {}
+        optional_params = optional_params or {}
+        litellm_params = litellm_params or {}
+        try:
+            response_json = raw_response.json()
+        except Exception:
+            raise DashScopeError(
+                status_code=raw_response.status_code,
+                message=raw_response.text,
+            )
+
+        logging_obj.post_call(
+            input=request_data.get("query"),
+            api_key=api_key,
+            additional_args={"complete_input_dict": request_data},
+            original_response=response_json,
+        )
+
+        # DashScope error envelope: {"code": "...", "message": "...", "request_id": "..."}
+        if "code" in response_json and "results" not in response_json:
+            raise DashScopeError(
+                status_code=raw_response.status_code,
+                message=response_json.get("message", str(response_json)),
+            )
+
+        results = response_json.get("results")
+        if results is None:
+            raise DashScopeError(
+                status_code=raw_response.status_code,
+                message=f"No results in DashScope rerank response: {response_json}",
+            )
+
+        # qwen3-rerank returns:
+        #   {"index": int, "relevance_score": float}
+        # plus, when return_documents=true was sent:
+        #   "document": {"text": "..."}
+        # which already matches LiteLLM's RerankResponseDocument shape.
+        transformed_results: List[dict] = []
+        for r in results:
+            item: Dict[str, Any] = {
+                "index": r["index"],
+                "relevance_score": r["relevance_score"],
+            }
+            doc = r.get("document")
+            if isinstance(doc, dict):
+                item["document"] = doc
+            elif isinstance(doc, str):
+                # Defensive: spec says dict, but normalize string-shaped echoes.
+                item["document"] = {"text": doc}
+            transformed_results.append(item)
+
+        usage = response_json.get("usage") or {}
+        total_tokens = usage.get("total_tokens")
+        billed_units = RerankBilledUnits(total_tokens=total_tokens)
+        tokens = RerankTokens(input_tokens=total_tokens)
+        meta = RerankResponseMeta(billed_units=billed_units, tokens=tokens)
+
+        return RerankResponse(
+            id=response_json.get("id") or str(uuid.uuid4()),
+            results=transformed_results,  # type: ignore
+            meta=meta,
+        )
+
+    def get_error_class(
+        self,
+        error_message: str,
+        status_code: int,
+        headers: Union[dict, httpx.Headers],
+    ) -> BaseLLMException:
+        if isinstance(headers, dict):
+            headers = httpx.Headers(headers)
+        return DashScopeError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
diff --git a/litellm/llms/datarobot/chat/transformation.py b/litellm/llms/datarobot/chat/transformation.py
index 23ce63c25b2..f81e2420930 100644
--- a/litellm/llms/datarobot/chat/transformation.py
+++ b/litellm/llms/datarobot/chat/transformation.py
@@ -1,5 +1,5 @@
 """
-Support for OpenAI's `/v1/chat/completions` endpoint. 
+Support for OpenAI's `/v1/chat/completions` endpoint.
 
 Calls done in OpenAI/openai.py as DataRobot is openai-compatible.
 """
diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py
index 276735f4758..e4bfbcb2513 100644
--- a/litellm/llms/deepinfra/rerank/transformation.py
+++ b/litellm/llms/deepinfra/rerank/transformation.py
@@ -1,5 +1,5 @@
 """
-Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. 
+Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format.
 """
 
 from typing import Any, Dict, List, Optional, Union
diff --git a/litellm/llms/deepseek/chat/transformation.py b/litellm/llms/deepseek/chat/transformation.py
index 5cd8d119542..7ed3e484535 100644
--- a/litellm/llms/deepseek/chat/transformation.py
+++ b/litellm/llms/deepseek/chat/transformation.py
@@ -2,13 +2,15 @@
 Translates from OpenAI's `/v1/chat/completions` to DeepSeek's `/v1/chat/completions`
 """
 
-from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, overload
+from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload
 
+import litellm
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     handle_messages_with_content_list_to_str_conversion,
 )
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues
+from litellm.utils import supports_reasoning
 
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
 
@@ -62,6 +64,48 @@ class DeepSeekChatConfig(OpenAIGPTConfig):
 
         return optional_params
 
+    def _fill_reasoning_content(
+        self, messages: List[AllMessageValues]
+    ) -> List[AllMessageValues]:
+        """
+        DeepSeek thinking mode requires `reasoning_content` to be passed back on
+        every assistant message in multi-turn conversations. If it is missing,
+        the API returns:
+          "The reasoning_content in the thinking mode must be passed back to the API."
+
+        For each assistant message that is missing `reasoning_content`:
+          1. Promote it from `provider_specific_fields["reasoning_content"]` if present
+             (LiteLLM stores provider-specific response fields there).
+          2. Otherwise inject a single space — the minimum value the API accepts.
+        """
+        result: List[AllMessageValues] = []
+        for msg in messages:
+            if msg.get("role") == "assistant" and not msg.get("reasoning_content"):
+                patched = dict(cast(dict, msg))
+                provider_fields = patched.get("provider_specific_fields") or {}
+                stored = provider_fields.get("reasoning_content")
+                if stored:
+                    patched["reasoning_content"] = stored
+                    cleaned = dict(provider_fields)
+                    cleaned.pop("reasoning_content", None)
+                    patched["provider_specific_fields"] = cleaned
+                else:
+                    litellm.verbose_logger.warning(
+                        "DeepSeek thinking mode: assistant message is missing "
+                        "`reasoning_content` and none was saved in "
+                        "`provider_specific_fields`. A single-space placeholder "
+                        "is being injected to satisfy API validation, but the "
+                        "model will receive a blank reasoning chain for this turn, "
+                        "which may silently degrade multi-turn response quality. "
+                        "Preserve `reasoning_content` from the original assistant "
+                        "response when building multi-turn conversation history."
+                    )
+                    patched["reasoning_content"] = " "
+                result.append(cast(AllMessageValues, patched))
+            else:
+                result.append(msg)
+        return result
+
     @overload
     def _transform_messages(
         self, messages: List[AllMessageValues], model: str, is_async: Literal[True]
@@ -91,6 +135,66 @@ class DeepSeekChatConfig(OpenAIGPTConfig):
                 messages=messages, model=model, is_async=False
             )
 
+    def _thinking_mode_active(self, model: str, optional_params: dict) -> bool:
+        """
+        Returns True only when thinking mode is actually active for this request:
+          - model supports reasoning (capability check)
+          - user explicitly passed thinking={"type": "enabled"} (opt-in check)
+        """
+        return (
+            supports_reasoning(model=model, custom_llm_provider="deepseek")
+            and (optional_params.get("thinking") or {}).get("type") == "enabled"
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Ensures `reasoning_content` is forwarded on assistant messages for
+        multi-turn thinking-mode conversations (issue #28045).
+
+        Only runs when thinking mode is actually active - guarded by both
+        supports_reasoning() (model capability) and optional_params["thinking"]
+        (user explicitly enabled it), preventing spurious injection on models
+        like deepseek-v3.2 that support thinking as opt-in but not always-on.
+        """
+        if self._thinking_mode_active(model=model, optional_params=optional_params):
+            messages = self._fill_reasoning_content(messages)
+        return super().transform_request(
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+    async def async_transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Async equivalent of transform_request — applies the same reasoning_content
+        fix for multi-turn thinking-mode conversations.
+        """
+        if self._thinking_mode_active(model=model, optional_params=optional_params):
+            messages = self._fill_reasoning_content(messages)
+        return await super().async_transform_request(
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
     def _get_openai_compatible_provider_info(
         self, api_base: Optional[str], api_key: Optional[str]
     ) -> Tuple[Optional[str], Optional[str]]:
diff --git a/litellm/llms/deepseek/cost_calculator.py b/litellm/llms/deepseek/cost_calculator.py
index 0f4490cb3df..e652ebeac54 100644
--- a/litellm/llms/deepseek/cost_calculator.py
+++ b/litellm/llms/deepseek/cost_calculator.py
@@ -1,5 +1,5 @@
 """
-Cost calculator for DeepSeek Chat models. 
+Cost calculator for DeepSeek Chat models.
 
 Handles prompt caching scenario.
 """
diff --git a/litellm/llms/deepseek/messages/transformation.py b/litellm/llms/deepseek/messages/transformation.py
new file mode 100644
index 00000000000..ad60478960e
--- /dev/null
+++ b/litellm/llms/deepseek/messages/transformation.py
@@ -0,0 +1,133 @@
+"""
+DeepSeek Anthropic-compatible messages transformation config.
+"""
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import litellm
+from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
+    AnthropicMessagesConfig,
+)
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.router import GenericLiteLLMParams
+
+
+class DeepSeekAnthropicMessagesConfig(AnthropicMessagesConfig):
+    """
+    DeepSeek exposes an Anthropic-compatible Messages API at
+    https://api.deepseek.com/anthropic.
+
+    It accepts the native Anthropic Messages conversation shape, including
+    thinking blocks in assistant history, but rejects Anthropic's explicit
+    custom-tool discriminator (`{"type": "custom"}`).
+    """
+
+    @property
+    def custom_llm_provider(self) -> Optional[str]:
+        return "deepseek"
+
+    @staticmethod
+    def get_api_key(api_key: Optional[str] = None) -> Optional[str]:
+        return api_key or get_secret_str("DEEPSEEK_API_KEY") or litellm.api_key
+
+    @staticmethod
+    def get_api_base(api_base: Optional[str] = None) -> str:
+        return (
+            api_base
+            or get_secret_str("DEEPSEEK_ANTHROPIC_API_BASE")
+            or get_secret_str("DEEPSEEK_API_BASE")
+            or "https://api.deepseek.com/anthropic"
+        )
+
+    def validate_anthropic_messages_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[Any],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> Tuple[dict, Optional[str]]:
+        dynamic_api_key = self.get_api_key(api_key=api_key)
+
+        if (
+            "x-api-key" not in headers
+            and "authorization" not in headers
+            and dynamic_api_key is not None
+        ):
+            headers["x-api-key"] = dynamic_api_key
+
+        if "anthropic-version" not in headers:
+            headers["anthropic-version"] = "2023-06-01"
+        if "content-type" not in headers:
+            headers["content-type"] = "application/json"
+
+        headers = self._update_headers_with_anthropic_beta(
+            headers=headers,
+            optional_params=optional_params,
+            custom_llm_provider=self.custom_llm_provider or "deepseek",
+        )
+
+        return headers, api_base
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        base_url = self.get_api_base(api_base=api_base).rstrip("/")
+
+        if base_url.endswith("/v1/messages") and "/anthropic/" in base_url:
+            return base_url
+        if base_url.endswith("/v1/messages"):
+            base_url = base_url[: -len("/v1/messages")]
+        if base_url.endswith("/v1"):
+            base_url = base_url[: -len("/v1")]
+        if base_url.endswith("/beta"):
+            base_url = base_url[: -len("/beta")]
+
+        if not base_url.endswith("/anthropic") and "/anthropic/" not in base_url:
+            base_url = f"{base_url}/anthropic"
+
+        return f"{base_url}/v1/messages"
+
+    @staticmethod
+    def _sanitize_tools_for_deepseek(tools: Any) -> Any:
+        if not isinstance(tools, list):
+            return tools
+
+        sanitized_tools = []
+        for tool in tools:
+            if isinstance(tool, dict) and tool.get("type") == "custom":
+                sanitized_tool = dict(tool)
+                sanitized_tool.pop("type", None)
+                sanitized_tools.append(sanitized_tool)
+            else:
+                sanitized_tools.append(tool)
+        return sanitized_tools
+
+    def transform_anthropic_messages_request(
+        self,
+        model: str,
+        messages: List[Dict],
+        anthropic_messages_optional_request_params: Dict,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Dict:
+        anthropic_messages_request = super().transform_anthropic_messages_request(
+            model=model,
+            messages=messages,
+            anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+        if "tools" in anthropic_messages_request:
+            anthropic_messages_request["tools"] = self._sanitize_tools_for_deepseek(
+                anthropic_messages_request["tools"]
+            )
+        return anthropic_messages_request
diff --git a/litellm/llms/elevenlabs/text_to_speech/transformation.py b/litellm/llms/elevenlabs/text_to_speech/transformation.py
index 6a59911701b..612fc687ef9 100644
--- a/litellm/llms/elevenlabs/text_to_speech/transformation.py
+++ b/litellm/llms/elevenlabs/text_to_speech/transformation.py
@@ -22,7 +22,6 @@ from litellm.types.utils import all_litellm_params
 
 from ..common_utils import ElevenLabsException
 
-
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
     from litellm.types.llms.openai import HttpxBinaryResponseContent
diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py
index eaf01c5fe18..d39adf0b6f4 100644
--- a/litellm/llms/fireworks_ai/chat/transformation.py
+++ b/litellm/llms/fireworks_ai/chat/transformation.py
@@ -4,6 +4,7 @@ from typing import Any, List, Literal, Optional, Tuple, Union, cast
 import httpx
 
 import litellm
+from litellm._logging import verbose_logger
 from litellm._uuid import uuid
 from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -26,6 +27,7 @@ from litellm.types.utils import (
     ProviderSpecificModelInfo,
 )
 from litellm.utils import (
+    get_model_cost_mutation_generation,
     supports_function_calling,
     supports_reasoning,
     supports_tool_choice,
@@ -112,6 +114,19 @@ class FireworksAIConfig(OpenAIGPTConfig):
         # Only add tools for models that support function calling
         if supports_function_calling(model=model, custom_llm_provider="fireworks_ai"):
             supported_params.append("tools")
+            supported_params.append("parallel_tool_calls")
+        else:
+            # Historically every Fireworks model advertised tool support, so a
+            # JSON entry that flips `supports_function_calling` to false will
+            # silently drop `tools` from requests. Surface this so users can
+            # tell why their tool calls suddenly stop working.
+            verbose_logger.debug(
+                "fireworks_ai model %r is marked as not supporting "
+                "function calling in model_prices_and_context_window.json; "
+                "`tools` and `parallel_tool_calls` will be dropped from the "
+                "request.",
+                model,
+            )
 
         # Only add tool_choice for models that explicitly support it
         if supports_tool_choice(model=model, custom_llm_provider="fireworks_ai"):
@@ -251,34 +266,100 @@ class FireworksAIConfig(OpenAIGPTConfig):
 
         return messages
 
-    def get_provider_info(self, model: str) -> ProviderSpecificModelInfo:
-        # Models that support reasoning_effort
-        reasoning_supported_models = [
-            "qwen3-8b",
-            "qwen3-32b",
-            "qwen3-coder-480b-a35b-instruct",
-            "deepseek-v3p1",
-            "deepseek-v3p2",
-            "glm-4p5",
-            "glm-4p5-air",
-            "glm-4p6",
-            "gpt-oss-120b",
-            "gpt-oss-20b",
+    # Cached index of fireworks_ai/* entries from litellm.model_cost. Building
+    # this index requires a full scan of model_cost (tens of thousands of
+    # entries), so we memoize it. The cache key is (id(model_cost),
+    # mutation_generation): the generation counter is bumped on every
+    # register_model / reload path, so add+remove or in-place value
+    # replacement (which can leave id and len unchanged) still invalidates.
+    _fireworks_index_cache: Optional[Tuple[int, int, List[Tuple[str, dict]]]] = None
+
+    @classmethod
+    def _get_fireworks_index(cls) -> List[Tuple[str, dict]]:
+        model_cost = litellm.model_cost
+        signature = (id(model_cost), get_model_cost_mutation_generation())
+        cached = cls._fireworks_index_cache
+        if (
+            cached is not None
+            and cached[0] == signature[0]
+            and cached[1] == signature[1]
+        ):
+            return cached[2]
+
+        index: List[Tuple[str, dict]] = []
+        for key, model_info in model_cost.items():
+            if not key.startswith("fireworks_ai/"):
+                continue
+            if not isinstance(model_info, dict):
+                continue
+            key_short = key[len("fireworks_ai/") :]
+            if key_short.startswith("accounts/fireworks/models/"):
+                key_short = key_short[len("accounts/fireworks/models/") :]
+            if not key_short:
+                continue
+            index.append((key_short, model_info))
+
+        cls._fireworks_index_cache = (signature[0], signature[1], index)
+        return index
+
+    @staticmethod
+    def _matches_on_hyphen_boundary(short_name: str, key_short: str) -> bool:
+        """Return True if `key_short` appears in `short_name` aligned to
+        hyphen-separated word boundaries (or end-of-string). This avoids
+        spurious substring matches like `"some-model"` matching
+        `"awesome-model"`."""
+        if short_name == key_short:
+            return True
+        if short_name.startswith(key_short + "-"):
+            return True
+        if short_name.endswith("-" + key_short):
+            return True
+        return ("-" + key_short + "-") in short_name
+
+    def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]:
+        short_name = model
+        if short_name.startswith("fireworks_ai/"):
+            short_name = short_name[len("fireworks_ai/") :]
+        if short_name.startswith("accounts/fireworks/models/"):
+            short_name = short_name[len("accounts/fireworks/models/") :]
+
+        candidate_keys = [
+            model,
+            f"fireworks_ai/{short_name}",
+            f"fireworks_ai/accounts/fireworks/models/{short_name}",
         ]
 
-        # Normalize model name - remove prefix if present
-        normalized_model = model
-        if model.startswith("fireworks_ai/"):
-            normalized_model = model.replace("fireworks_ai/", "")
-        if normalized_model.startswith("accounts/fireworks/models/"):
-            normalized_model = normalized_model.replace(
-                "accounts/fireworks/models/", ""
-            )
+        for candidate_key in candidate_keys:
+            model_info = litellm.model_cost.get(candidate_key)
+            if model_info is not None and model_info.get(capability) is not None:
+                return cast(Optional[bool], model_info.get(capability))
 
-        # Check if model supports reasoning
-        supports_reasoning_value = any(
-            reasoning_model in normalized_model
-            for reasoning_model in reasoning_supported_models
+        # Fallback: preserve historical substring matching for model name
+        # variants (e.g. fine-tuned or regionally-suffixed versions of a
+        # known model). Pick the *longest* matching entry so a more specific
+        # known model (e.g. "qwen3-8b-instruct") wins over a less specific
+        # one (e.g. "qwen3-8b") when the query model is more specific still.
+        # Use hyphen-aligned matching to avoid false positives where a short
+        # known model name is an unrelated substring of a longer one.
+        best_match_short: Optional[str] = None
+        best_match_value: Optional[bool] = None
+        for key_short, model_info in self._get_fireworks_index():
+            if model_info.get(capability) is None:
+                continue
+            if not self._matches_on_hyphen_boundary(short_name, key_short):
+                continue
+            if best_match_short is None or len(key_short) > len(best_match_short):
+                best_match_short = key_short
+                best_match_value = cast(Optional[bool], model_info.get(capability))
+
+        return best_match_value
+
+    def get_provider_info(self, model: str) -> ProviderSpecificModelInfo:
+        supports_function_calling_value = self._get_model_cost_capability(
+            model=model, capability="supports_function_calling"
+        )
+        supports_reasoning_value = self._get_model_cost_capability(
+            model=model, capability="supports_reasoning"
         )
 
         provider_specific_model_info: ProviderSpecificModelInfo = {
@@ -288,9 +369,16 @@ class FireworksAIConfig(OpenAIGPTConfig):
             "supports_vision": True,  # via document inlining
         }
 
+        if supports_function_calling_value is not None:
+            provider_specific_model_info["supports_function_calling"] = (
+                supports_function_calling_value
+            )
+
         # Only include supports_reasoning if True
         if supports_reasoning_value:
-            provider_specific_model_info["supports_reasoning"] = True
+            provider_specific_model_info["supports_reasoning"] = (
+                supports_reasoning_value
+            )
 
         return provider_specific_model_info
 
diff --git a/litellm/llms/gemini/agents/__init__.py b/litellm/llms/gemini/agents/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/llms/gemini/agents/transformation.py b/litellm/llms/gemini/agents/transformation.py
new file mode 100644
index 00000000000..f6e0b95cf28
--- /dev/null
+++ b/litellm/llms/gemini/agents/transformation.py
@@ -0,0 +1,298 @@
+"""
+Google AI Studio Agents API configuration.
+
+Proxies the Gemini v1beta Agents API:
+  POST   /v1beta/agents                         create
+  GET    /v1beta/agents                         list
+  GET    /v1beta/agents/{name}                  get
+  DELETE /v1beta/agents/{name}                  delete
+  GET    /v1beta/agents/{name}/versions         list versions
+"""
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import httpx
+
+from litellm._logging import verbose_logger
+from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig
+from litellm.llms.gemini.common_utils import GeminiError, GeminiModelInfo
+from litellm.types.agents import (
+    AgentCreateResponse,
+    AgentDeleteResult,
+    AgentListResponse,
+    AgentVersionsResponse,
+)
+
+# Keys inside litellm_params that should be forwarded to the Gemini
+# create-agent body verbatim.
+_GEMINI_AGENT_BODY_KEYS = ("base_agent", "instructions", "base_environment")
+
+# LiteLLM-internal keys that must never be forwarded to Gemini.
+_LITELLM_INTERNAL_KEYS = frozenset(
+    {
+        "custom_llm_provider",
+        "api_key",
+        "api_base",
+        "make_public",
+        "cost_per_query",
+        "input_cost_per_token",
+        "output_cost_per_token",
+        "require_trace_id_on_calls_to_agent",
+        "require_trace_id_on_calls_by_agent",
+        "max_iterations",
+        "max_budget_per_session",
+        "guardrails",
+        "is_public",
+        "agent_name",
+        "agent_id",
+        "agent_card_params",
+        "provider_agent_response",
+    }
+)
+
+
+class GeminiAgentsConfig(BaseAgentsAPIConfig):
+    """
+    Configuration for the Google AI Studio (Gemini) native Agents API.
+
+    Authentication uses x-goog-api-key, resolved from (in order):
+      1. litellm_params["api_key"]
+      2. GOOGLE_API_KEY env var
+      3. GEMINI_API_KEY env var
+    """
+
+    @property
+    def api_version(self) -> str:
+        return "v1beta"
+
+    def _base_url(self, api_base: Optional[str]) -> str:
+        return f"{GeminiModelInfo.get_api_base(api_base)}/{self.api_version}"
+
+    # ------------------------------------------------------------------ #
+    # Shared helpers                                                       #
+    # ------------------------------------------------------------------ #
+
+    def get_error_class(
+        self,
+        error_message: str,
+        status_code: int,
+        headers: Union[dict, httpx.Headers],
+    ) -> Exception:
+        return GeminiError(
+            message=error_message,
+            status_code=status_code,
+            headers=dict(headers),
+        )
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> str:
+        return f"{self._base_url(api_base)}/agents"
+
+    def validate_environment(
+        self,
+        headers: Dict[str, str],
+        litellm_params: Dict[str, Any],
+    ) -> Dict[str, str]:
+        headers = dict(headers)
+        headers["Content-Type"] = "application/json"
+        explicit_api_key = litellm_params.get("api_key")
+        # SECURITY: when the caller overrides ``api_base``, refuse to fall back
+        # to the process-wide GOOGLE_API_KEY / GEMINI_API_KEY env vars. Otherwise
+        # an authenticated proxy user could set ``api_base`` to an attacker-
+        # controlled host and have the proxy ship its shared Gemini key in the
+        # ``x-goog-api-key`` header.
+        if litellm_params.get("api_base") and not explicit_api_key:
+            raise ValueError(
+                "When overriding api_base for Gemini agents, you must also "
+                "supply an explicit api_key. Falling back to GOOGLE_API_KEY / "
+                "GEMINI_API_KEY env vars with a custom api_base is refused "
+                "to prevent leaking the shared provider key to arbitrary hosts."
+            )
+        api_key = GeminiModelInfo.get_api_key(explicit_api_key)
+        if not api_key:
+            raise ValueError(
+                "Google API key is required. "
+                "Set GOOGLE_API_KEY or GEMINI_API_KEY, or pass api_key."
+            )
+        headers["x-goog-api-key"] = api_key
+        return headers
+
+    def _raise_for_status(self, raw_response: httpx.Response) -> None:
+        if not (200 <= raw_response.status_code < 300):
+            raise GeminiError(
+                message=raw_response.text,
+                status_code=raw_response.status_code,
+                headers=dict(raw_response.headers),
+            )
+
+    # ------------------------------------------------------------------ #
+    # CREATE                                                               #
+    # ------------------------------------------------------------------ #
+
+    def transform_create_request(
+        self,
+        name: str,
+        litellm_params: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        body: Dict[str, Any] = {"name": name}
+        for key in _GEMINI_AGENT_BODY_KEYS:
+            value = litellm_params.get(key)
+            if value is not None:
+                body[key] = value
+        verbose_logger.debug("GeminiAgentsConfig create body: %s", body)
+        return body
+
+    def transform_create_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentCreateResponse:
+        """
+        Gemini returns:
+          {"id": "my-agent", "base_agent": "waverunner",
+           "system_instruction": "...", "base_environment": {...}}
+        """
+        self._raise_for_status(raw_response)
+        try:
+            data: Dict[str, Any] = raw_response.json()
+        except Exception:
+            verbose_logger.warning(
+                "GeminiAgentsConfig: non-JSON create response (status=%d).",
+                raw_response.status_code,
+            )
+            data = {"id": name}
+        # Gemini uses "id" as the identifier; normalise to both fields.
+        data.setdefault("id", name)
+        data.setdefault("name", data["id"])
+        verbose_logger.debug("GeminiAgentsConfig create response: %s", data)
+        return AgentCreateResponse(**data)
+
+    # ------------------------------------------------------------------ #
+    # LIST                                                                 #
+    # ------------------------------------------------------------------ #
+
+    def transform_list_request(
+        self,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> Tuple[str, Dict[str, Any]]:
+        url = f"{self._base_url(api_base)}/agents"
+        params: Dict[str, Any] = {}
+        if litellm_params.get("page_size"):
+            params["pageSize"] = litellm_params["page_size"]
+        if litellm_params.get("page_token"):
+            params["pageToken"] = litellm_params["page_token"]
+        return url, params
+
+    def transform_list_response(
+        self,
+        raw_response: httpx.Response,
+    ) -> AgentListResponse:
+        self._raise_for_status(raw_response)
+        try:
+            data = raw_response.json()
+        except Exception:
+            data = {}
+        verbose_logger.debug("GeminiAgentsConfig list response: %s", data)
+        return AgentListResponse(
+            agents=data.get("agents", []),
+            next_page_token=data.get("nextPageToken"),
+        )
+
+    # ------------------------------------------------------------------ #
+    # GET                                                                  #
+    # ------------------------------------------------------------------ #
+
+    def transform_get_request(
+        self,
+        name: str,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> Tuple[str, Dict[str, Any]]:
+        url = f"{self._base_url(api_base)}/agents/{name}"
+        return url, {}
+
+    def transform_get_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentCreateResponse:
+        """Same shape as create response — Gemini returns "id" as identifier."""
+        self._raise_for_status(raw_response)
+        try:
+            data = raw_response.json()
+        except Exception:
+            data = {"id": name}
+        data.setdefault("id", name)
+        data.setdefault("name", data["id"])
+        verbose_logger.debug("GeminiAgentsConfig get response: %s", data)
+        return AgentCreateResponse(**data)
+
+    # ------------------------------------------------------------------ #
+    # DELETE                                                               #
+    # ------------------------------------------------------------------ #
+
+    def transform_delete_request(
+        self,
+        name: str,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> str:
+        return f"{self._base_url(api_base)}/agents/{name}"
+
+    def transform_delete_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentDeleteResult:
+        """Gemini returns an empty body ``{}`` with HTTP 200 on success."""
+        self._raise_for_status(raw_response)
+        verbose_logger.debug(
+            "GeminiAgentsConfig delete (status=%d) agent '%s'",
+            raw_response.status_code,
+            name,
+        )
+        return AgentDeleteResult(name=name, deleted=True)
+
+    # ------------------------------------------------------------------ #
+    # LIST VERSIONS                                                        #
+    # ------------------------------------------------------------------ #
+
+    def transform_list_versions_request(
+        self,
+        name: str,
+        api_base: Optional[str],
+        litellm_params: Dict[str, Any],
+    ) -> Tuple[str, Dict[str, Any]]:
+        url = f"{self._base_url(api_base)}/agents/{name}/versions"
+        params: Dict[str, Any] = {}
+        if litellm_params.get("page_size"):
+            params["pageSize"] = litellm_params["page_size"]
+        if litellm_params.get("page_token"):
+            params["pageToken"] = litellm_params["page_token"]
+        return url, params
+
+    def transform_list_versions_response(
+        self,
+        raw_response: httpx.Response,
+        name: str,
+    ) -> AgentVersionsResponse:
+        """
+        Gemini returns:
+          {"agentVersions": [{"agent": "waverunner", "name": "agents/.../versions/uuid", ...}]}
+        """
+        self._raise_for_status(raw_response)
+        try:
+            data = raw_response.json()
+        except Exception:
+            data = {}
+        verbose_logger.debug(
+            "GeminiAgentsConfig list_versions response for '%s': %s", name, data
+        )
+        return AgentVersionsResponse(
+            agent_versions=data.get("agentVersions", []),
+            next_page_token=data.get("nextPageToken"),
+        )
diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py
index 72569e5c6cd..b69b7e1913e 100644
--- a/litellm/llms/gemini/chat/transformation.py
+++ b/litellm/llms/gemini/chat/transformation.py
@@ -1,5 +1,7 @@
 from typing import List, Optional, cast
 
+import litellm
+
 from litellm.litellm_core_utils.prompt_templates.factory import (
     convert_generic_image_chunk_to_openai_image_obj,
     convert_to_anthropic_image_obj,
@@ -101,7 +103,10 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
         return supported_params
 
     def _transform_messages(
-        self, messages: List[AllMessageValues], model: Optional[str] = None
+        self,
+        messages: List[AllMessageValues],
+        model: Optional[str] = None,
+        litellm_params: Optional[dict] = None,
     ) -> List[ContentType]:
         """
         Google AI Studio Gemini does not support HTTP/HTTPS URLs for files.
@@ -141,14 +146,26 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
                                 img_element["image_url"] = converted_image_url  # type: ignore
                     elif element.get("type") == "file":
                         file_element = cast(ChatCompletionFileObject, element)
-                        file_id = file_element["file"].get("file_id")
+                        _file_field = file_element.get("file")
+                        if _file_field is None:
+                            raise litellm.BadRequestError(
+                                message="Content block has type='file' but is missing the required 'file' field",
+                                model=model,
+                                llm_provider="gemini",
+                            )
+                        file_id = _file_field.get("file_id")
                         if file_id and ("http://" in file_id or "https://" in file_id):
                             # Convert HTTP/HTTPS file URL to base64 data
                             try:
                                 base64_data = convert_url_to_base64(file_id)
-                                file_element["file"]["file_data"] = base64_data  # type: ignore
-                                file_element["file"].pop("file_id", None)  # type: ignore
+                                _file_field["file_data"] = base64_data  # type: ignore
+                                _file_field.pop("file_id", None)  # type: ignore
                             except Exception:
                                 # If conversion fails, leave as is and let the API handle it
                                 pass
-        return _gemini_convert_messages_with_history(messages=messages, model=model)
+        return _gemini_convert_messages_with_history(
+            messages=messages,
+            model=model,
+            litellm_params=litellm_params,
+            custom_llm_provider="gemini",
+        )
diff --git a/litellm/llms/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py
index 593cbf7c2cf..b18b6a28ce4 100644
--- a/litellm/llms/gemini/interactions/transformation.py
+++ b/litellm/llms/gemini/interactions/transformation.py
@@ -6,13 +6,18 @@ Per OpenAPI spec (https://ai.google.dev/static/api/interactions.openapi.json):
 - Get: GET https://generativelanguage.googleapis.com/{api_version}/interactions/{interaction_id}
 - Delete: DELETE https://generativelanguage.googleapis.com/{api_version}/interactions/{interaction_id}
 
-This is a thin wrapper - no transformation needed since we follow the spec directly.
+Schema versioning:
+- Default (Api-Revision: 2026-05-20): new `steps` schema.
+- Legacy (Api-Revision: 2026-05-07): old `outputs` schema, controlled via
+  litellm.use_legacy_interactions_schema = True. Remove flag after June 8, 2026.
 """
 
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 import httpx
 
+import litellm
+
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.core_helpers import process_response_headers
 from litellm.litellm_core_utils.url_utils import encode_url_path_segment
@@ -64,6 +69,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
             "stream",
             "store",
             "background",
+            "environment",
             "response_modalities",
             "response_format",
             "response_mime_type",
@@ -83,6 +89,15 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
             api_key = GeminiModelInfo.get_api_key(litellm_params.get("api_key"))
             if api_key:
                 headers["x-goog-api-key"] = api_key
+
+        # Inject the Api-Revision header to select the response schema.
+        # Default to the new `steps` schema unless the operator has opted out.
+        # Remove this conditional after June 8, 2026 and always use 2026-05-20.
+        if litellm.use_legacy_interactions_schema:
+            headers["Api-Revision"] = "2026-05-07"
+        else:
+            headers["Api-Revision"] = "2026-05-20"
+
         return headers
 
     def get_complete_url(
@@ -118,8 +133,19 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
         headers: dict,
     ) -> Dict:
         """
-        Build request body per OpenAPI spec - minimal transformation.
+        Build request body per OpenAPI spec.
+
+        When on the new schema (use_legacy_interactions_schema=False, the default):
+        - ``response_mime_type`` is folded into ``response_format`` and stripped from
+          the body (the field was removed in Api-Revision 2026-05-20).
+        - ``generation_config.image_config`` is moved to a ``response_format`` entry
+          with ``"type": "image"`` (also removed from generation_config in 2026-05-20).
+
+        When on the legacy schema (use_legacy_interactions_schema=True):
+        - All fields are forwarded as-is.
         """
+        use_legacy: bool = litellm.use_legacy_interactions_schema
+
         request_body: Dict[str, Any] = {}
 
         # Model or Agent (one required)
@@ -134,23 +160,81 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
         if input is not None:
             request_body["input"] = input
 
-        # Pass through optional params directly (they match the spec)
+        # Pass through optional params — legacy schema keeps all fields as-is.
         optional_keys = [
             "tools",
             "system_instruction",
-            "generation_config",
             "stream",
             "store",
             "background",
+            "environment",
             "response_modalities",
-            "response_format",
-            "response_mime_type",
             "previous_interaction_id",
         ]
         for key in optional_keys:
             if optional_params.get(key) is not None:
                 request_body[key] = optional_params[key]
 
+        if use_legacy:
+            # Legacy schema: forward response_mime_type and response_format as-is.
+            for key in ("response_format", "response_mime_type", "generation_config"):
+                if optional_params.get(key) is not None:
+                    request_body[key] = optional_params[key]
+        else:
+            # New schema (Api-Revision: 2026-05-20):
+            # response_mime_type is removed — fold it into response_format.
+            response_format = optional_params.get("response_format")
+            response_mime_type = optional_params.get("response_mime_type")
+
+            if (
+                response_mime_type
+                and not isinstance(response_format, list)
+                and (
+                    not isinstance(response_format, dict)
+                    or "mime_type" not in response_format
+                )
+            ):
+                # Wrap the legacy schema into the new polymorphic format.
+                new_rf: Dict[str, Any] = {
+                    "type": "text",
+                    "mime_type": response_mime_type,
+                }
+                if response_format is not None:
+                    new_rf["schema"] = response_format
+                response_format = new_rf
+
+            if response_format is not None:
+                request_body["response_format"] = response_format
+
+            # image_config moves out of generation_config into response_format.
+            generation_config: Optional[Dict[str, Any]] = optional_params.get(
+                "generation_config"
+            )
+            if generation_config is not None:
+                image_config = None
+                if isinstance(generation_config, dict):
+                    generation_config = dict(
+                        generation_config
+                    )  # avoid mutating the caller's dict
+                    image_config = generation_config.pop("image_config", None)
+                    if not generation_config:
+                        generation_config = None
+
+                if generation_config is not None:
+                    request_body["generation_config"] = generation_config
+
+                if image_config is not None:
+                    # Move image_config to response_format with type=image.
+                    image_rf: Dict[str, Any] = {"type": "image", **image_config}
+                    existing_rf = request_body.get("response_format")
+                    if existing_rf is None:
+                        request_body["response_format"] = image_rf
+                    elif isinstance(existing_rf, list):
+                        request_body["response_format"] = [*existing_rf, image_rf]
+                    else:
+                        # Convert single entry to array for multimodal output.
+                        request_body["response_format"] = [existing_rf, image_rf]
+
         return request_body
 
     def transform_response(
diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py
index c7116940b22..9714c8a3923 100644
--- a/litellm/llms/gemini/videos/transformation.py
+++ b/litellm/llms/gemini/videos/transformation.py
@@ -55,7 +55,7 @@ def _convert_image_to_gemini_format(image_file) -> Dict[str, str]:
 
 
 def _usage_video_resolution_from_parameters(
-    parameters: Dict[str, Any]
+    parameters: Dict[str, Any],
 ) -> Optional[str]:
     """Normalize Veo ``parameters.resolution`` for usage and cost tracking."""
     res = parameters.get("resolution")
diff --git a/litellm/llms/infinity/rerank/transformation.py b/litellm/llms/infinity/rerank/transformation.py
index 314bf2f8a36..b9804605454 100644
--- a/litellm/llms/infinity/rerank/transformation.py
+++ b/litellm/llms/infinity/rerank/transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from Cohere's /v1/rerank format to Infinity's  `/v1/rerank` format. 
+Transformation logic from Cohere's /v1/rerank format to Infinity's  `/v1/rerank` format.
 
 Why separate file? Make it easy to see how transformation works
 """
diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py
index ad4416925a6..56be754fc34 100644
--- a/litellm/llms/jina_ai/rerank/transformation.py
+++ b/litellm/llms/jina_ai/rerank/transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from Cohere's /v1/rerank format to Jina AI's  `/v1/rerank` format. 
+Transformation logic from Cohere's /v1/rerank format to Jina AI's  `/v1/rerank` format.
 
 Why separate file? Make it easy to see how transformation works
 
diff --git a/litellm/llms/lm_studio/embed/transformation.py b/litellm/llms/lm_studio/embed/transformation.py
index 1285550c30f..87f4f6e73d5 100644
--- a/litellm/llms/lm_studio/embed/transformation.py
+++ b/litellm/llms/lm_studio/embed/transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from OpenAI /v1/embeddings format to LM Studio's  `/v1/embeddings` format. 
+Transformation logic from OpenAI /v1/embeddings format to LM Studio's  `/v1/embeddings` format.
 
 Why separate file? Make it easy to see how transformation works
 
diff --git a/litellm/llms/novita/chat/transformation.py b/litellm/llms/novita/chat/transformation.py
index c05d2d7b2c5..5a64a124ade 100644
--- a/litellm/llms/novita/chat/transformation.py
+++ b/litellm/llms/novita/chat/transformation.py
@@ -1,5 +1,5 @@
 """
-Support for OpenAI's `/v1/chat/completions` endpoint. 
+Support for OpenAI's `/v1/chat/completions` endpoint.
 
 Calls done in OpenAI/openai.py as Novita AI is openai-compatible.
 
diff --git a/litellm/llms/nvidia_nim/chat/transformation.py b/litellm/llms/nvidia_nim/chat/transformation.py
index b8f8b04eb53..2ef92a90626 100644
--- a/litellm/llms/nvidia_nim/chat/transformation.py
+++ b/litellm/llms/nvidia_nim/chat/transformation.py
@@ -1,7 +1,7 @@
 """
-Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer 
+Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
 
-This is OpenAI compatible 
+This is OpenAI compatible
 
 This file only contains param mapping logic
 
diff --git a/litellm/llms/nvidia_nim/embed.py b/litellm/llms/nvidia_nim/embed.py
index 24c6cc34e4d..61c8e8244e4 100644
--- a/litellm/llms/nvidia_nim/embed.py
+++ b/litellm/llms/nvidia_nim/embed.py
@@ -1,7 +1,7 @@
 """
 Nvidia NIM embeddings endpoint: https://docs.api.nvidia.com/nim/reference/nvidia-nv-embedqa-e5-v5-infer
 
-This is OpenAI compatible 
+This is OpenAI compatible
 
 This file only contains param mapping logic
 
diff --git a/litellm/llms/oci/chat/cohere.py b/litellm/llms/oci/chat/cohere.py
new file mode 100644
index 00000000000..ac92fd22aa8
--- /dev/null
+++ b/litellm/llms/oci/chat/cohere.py
@@ -0,0 +1,386 @@
+"""
+OCI Generative AI — Cohere-specific chat transformation helpers.
+
+Handles message history building, tool definition adaptation, non-streaming
+response parsing, and streaming chunk parsing for models served with
+``apiFormat="COHERE"`` (e.g. ``cohere.command-*``).
+"""
+
+import datetime
+import json
+from typing import Any, Dict, List, Optional
+
+import httpx
+from pydantic import ValidationError
+
+from litellm.llms.oci.chat.generic import (
+    _normalize_oci_finish_reason,
+    _synthesize_oci_tool_call_id,
+)
+from litellm.llms.oci.common_utils import (
+    OCI_JSON_TO_PYTHON_TYPES,
+    OCIError,
+    enrich_cohere_param_description,
+    resolve_oci_schema_anyof,
+    resolve_oci_schema_refs,
+    sanitize_oci_schema,
+)
+from litellm.types.llms.oci import (
+    CohereChatResult,
+    CohereMessage,
+    CohereParameterDefinition,
+    CohereStreamChunk,
+    CohereTool,
+    CohereToolCall,
+    CohereToolMessage,
+    CohereToolResult,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import (
+    Choices,
+    Delta,
+    ModelResponse,
+    ModelResponseStream,
+    StreamingChoices,
+)
+from litellm.types.utils import Usage
+
+
+def _extract_text_content(content: Any) -> str:
+    """Return the plain-text representation of a message content value."""
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        return "".join(
+            item.get("text", "")
+            for item in content
+            if isinstance(item, dict) and item.get("type") == "text"
+        )
+    return str(content)
+
+
+def adapt_messages_to_cohere_standard(
+    messages: List[AllMessageValues],
+) -> List[CohereMessage]:
+    """Build a Cohere ``chatHistory`` list from an OpenAI-format message array.
+
+    - All messages except the *last user message* are included. The caller pulls
+      the last user message into the request's top-level ``message`` field, so
+      trailing tool results (the standard agentic continuation pattern) still
+      appear in ``chatHistory`` and reach the model.
+    - If no user message exists, every message is included (no slice).
+    - System messages must be filtered out by the caller (they are routed into
+      ``preambleOverride`` separately) — they are not represented in
+      ``chatHistory``.
+    - Tool results are expressed as OCI ``CohereToolMessage.toolResults`` entries,
+      with the originating call's name and parameters resolved from the preceding
+      assistant message via a ``tool_call_id`` lookup.
+    """
+    # First pass: build tool_call_id → CohereToolCall so tool-result messages can
+    # reference the originating call by name and parameters.
+    tool_call_lookup: Dict[str, CohereToolCall] = {}
+    for msg in messages:
+        if msg.get("role") == "assistant":
+            tool_calls_raw: Any = msg.get("tool_calls") or []
+            for tc in tool_calls_raw:
+                tc_id = tc.get("id", "")
+                raw_args: Any = tc.get("function", {}).get("arguments", "{}")
+                try:
+                    params: Dict[str, Any] = (
+                        json.loads(raw_args) if isinstance(raw_args, str) else raw_args
+                    )
+                except json.JSONDecodeError:
+                    params = {}
+                tool_call_lookup[tc_id] = CohereToolCall(
+                    name=str(tc.get("function", {}).get("name", "")),
+                    parameters=params,
+                )
+
+    last_user_index = next(
+        (
+            i
+            for i in range(len(messages) - 1, -1, -1)
+            if messages[i].get("role") == "user"
+        ),
+        None,
+    )
+    history_source = (
+        messages
+        if last_user_index is None
+        else [m for i, m in enumerate(messages) if i != last_user_index]
+    )
+
+    chat_history: List[CohereMessage] = []
+    for msg in history_source:
+        role = msg.get("role")
+        content = _extract_text_content(msg.get("content"))
+
+        tool_calls: Optional[List[CohereToolCall]] = None
+        if role == "assistant" and msg.get("tool_calls"):  # type: ignore[union-attr,typeddict-item]
+            tool_calls = []
+            for tc in msg["tool_calls"]:  # type: ignore[union-attr,typeddict-item]
+                raw_arguments: Any = tc.get("function", {}).get("arguments", {})
+                if isinstance(raw_arguments, str):
+                    try:
+                        arguments: Dict[str, Any] = json.loads(raw_arguments)
+                    except json.JSONDecodeError:
+                        arguments = {}
+                else:
+                    arguments = raw_arguments
+                tool_calls.append(
+                    CohereToolCall(
+                        name=str(tc.get("function", {}).get("name", "")),
+                        parameters=arguments,
+                    )
+                )
+
+        if role == "user":
+            chat_history.append(CohereMessage(role="USER", message=content))
+        elif role == "assistant":
+            chat_history.append(
+                CohereMessage(role="CHATBOT", message=content, toolCalls=tool_calls)
+            )
+        elif role == "tool":
+            tool_call_id = str(msg.get("tool_call_id", "") or "")
+            cohere_call = tool_call_lookup.get(
+                tool_call_id, CohereToolCall(name="", parameters={})
+            )
+            tool_result = CohereToolResult(
+                call=cohere_call,
+                outputs=[{"output": content}],
+            )
+            # OpenAI emits one tool-role message per parallel tool call, but
+            # the OCI Cohere API expects all results from a single assistant
+            # turn to share one TOOL history entry with multiple toolResults.
+            # Merge consecutive tool messages so the model sees the parallel
+            # call/result pairing correctly during agentic loops.
+            if chat_history and isinstance(chat_history[-1], CohereToolMessage):
+                chat_history[-1].toolResults.append(tool_result)
+            else:
+                chat_history.append(CohereToolMessage(toolResults=[tool_result]))
+
+    return chat_history
+
+
+def adapt_tool_definitions_to_cohere_standard(
+    tools: List[Dict[str, Any]],
+) -> List[CohereTool]:
+    """Adapt OpenAI-format tool definitions to the OCI Cohere format.
+
+    - Resolves ``$ref``/``$defs`` and ``anyOf`` patterns that OCI rejects.
+    - Maps JSON Schema type names to Python type names (``"string"`` → ``"str"``).
+    - Embeds unsupported constraints (enum, format, range, pattern) into the
+      parameter description so the model can still see them.
+    """
+    cohere_tools = []
+    for tool in tools:
+        function_def = tool.get("function", {})
+        raw_params = function_def.get("parameters", {})
+
+        resolved = sanitize_oci_schema(
+            resolve_oci_schema_anyof(resolve_oci_schema_refs(raw_params))
+        )
+        properties = resolved.get("properties", {})
+        required = resolved.get("required", [])
+
+        parameter_definitions = {}
+        for param_name, param_schema in properties.items():
+            json_type = param_schema.get("type", "string")
+            python_type = OCI_JSON_TO_PYTHON_TYPES.get(json_type, json_type)
+            parameter_definitions[param_name] = CohereParameterDefinition(
+                description=enrich_cohere_param_description(
+                    param_schema.get("description", ""), param_schema
+                ),
+                type=python_type,
+                isRequired=param_name in required,
+            )
+
+        cohere_tools.append(
+            CohereTool(
+                name=function_def.get("name", ""),
+                description=function_def.get("description", ""),
+                parameterDefinitions=parameter_definitions,
+            )
+        )
+
+    return cohere_tools
+
+
+def handle_cohere_response(
+    json_response: dict,
+    model: str,
+    model_response: ModelResponse,
+    raw_response: httpx.Response,
+) -> ModelResponse:
+    """Parse a non-streaming Cohere OCI response into a LiteLLM ModelResponse."""
+    try:
+        cohere_response = CohereChatResult(**json_response)
+    except (TypeError, ValidationError) as e:
+        raise OCIError(
+            message=f"Response cannot be casted to CohereChatResult: {str(e)}",
+            status_code=raw_response.status_code,
+        )
+
+    model_response.model = model
+    model_response.created = int(datetime.datetime.now().timestamp())
+
+    response_text = cohere_response.chatResponse.text
+    finish_reason = _normalize_oci_finish_reason(
+        cohere_response.chatResponse.finishReason
+    )
+
+    tool_calls: Optional[List[Dict[str, Any]]] = None
+    if cohere_response.chatResponse.toolCalls:
+        tool_calls = [
+            {
+                "id": _synthesize_oci_tool_call_id(
+                    i, tc.name, json.dumps(tc.parameters, sort_keys=True)
+                ),
+                "type": "function",
+                "function": {
+                    "name": tc.name,
+                    "arguments": json.dumps(tc.parameters),
+                },
+            }
+            for i, tc in enumerate(cohere_response.chatResponse.toolCalls)
+        ]
+
+    content: Optional[str] = response_text if response_text else None
+
+    # Only include ``tool_calls`` in the message dict when actually present.
+    # Passing an explicit ``None`` would let downstream consumers that key off
+    # ``"tool_calls" in message`` (rather than truthiness) incorrectly conclude
+    # that tool calls were attempted. Matches the generic handler's behaviour,
+    # which only sets ``message.tool_calls`` when tool calls are present.
+    message: Dict[str, Any] = {"role": "assistant", "content": content}
+    if tool_calls is not None:
+        message["tool_calls"] = tool_calls
+
+    model_response.choices = [
+        Choices(
+            index=0,
+            message=message,
+            finish_reason=finish_reason,
+        )
+    ]
+
+    usage_info = cohere_response.chatResponse.usage
+    if usage_info is not None:
+        model_response.usage = Usage(  # type: ignore[attr-defined]
+            prompt_tokens=usage_info.promptTokens,
+            completion_tokens=usage_info.completionTokens,
+            total_tokens=usage_info.totalTokens,
+        )
+    else:
+        model_response.usage = Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0)  # type: ignore[attr-defined]
+
+    return model_response
+
+
+def handle_cohere_stream_chunk(
+    dict_chunk: dict,
+    prior_tool_calls_emitted: bool = False,
+    prior_text_emitted: bool = False,
+) -> ModelResponseStream:
+    """Parse a single Cohere SSE chunk into a LiteLLM ModelResponseStream.
+
+    ``prior_tool_calls_emitted`` lets the caller signal whether tool calls
+    were already emitted in earlier chunks of the same stream. When set, the
+    terminal consolidation chunk's tool calls are suppressed (they would
+    duplicate prior deltas); otherwise they are passed through so a stream
+    that delivers tool calls only on the terminal chunk doesn't silently
+    drop them.
+
+    ``prior_text_emitted`` plays the analogous role for the ``text`` field:
+    when set, the terminal consolidation chunk's ``text`` is suppressed
+    (it would re-emit the full assembled response on top of prior deltas);
+    when unset (e.g. a degenerate stream that delivers the entire response
+    in a single SSE event carrying both ``chatHistory`` and ``finishReason``),
+    the text is passed through so the response content isn't silently lost.
+    """
+    try:
+        typed_chunk = CohereStreamChunk(**dict_chunk)
+    except (TypeError, ValidationError) as e:
+        raise OCIError(
+            status_code=500,
+            message=f"Chunk cannot be parsed as CohereStreamChunk: {str(e)}",
+        )
+
+    if typed_chunk.index is None:
+        typed_chunk.index = 0
+
+    # OCI Cohere's terminal SSE event re-sends the full assembled response in
+    # `text` alongside a populated `chatHistory` and a non-null `finishReason`.
+    # Emitting that text would concatenate the whole response onto the
+    # already-streamed deltas. We require both signals to be present so that a
+    # future API change which adds `chatHistory` to intermediate chunks (or a
+    # rare early-populated case) doesn't silently drop legitimate token deltas.
+    is_terminal_consolidation = (
+        typed_chunk.chatHistory is not None and typed_chunk.finishReason is not None
+    )
+    # On non-terminal text-free chunks (e.g. tool-call-only or keep-alive
+    # chunks) emit ``content=None`` rather than ``content=""`` so downstream
+    # stream-mergers that distinguish "no text in this delta" from "an
+    # explicitly empty text delta" behave correctly.
+    #
+    # We only suppress the terminal chunk's ``text`` when the caller has
+    # confirmed that text deltas were already emitted earlier — otherwise
+    # (e.g. a degenerate stream that delivers the whole response in a
+    # single SSE event), passing it through is the only chance to surface it.
+    text: Optional[str] = (
+        None if (is_terminal_consolidation and prior_text_emitted) else typed_chunk.text
+    )
+
+    # Tool calls on the terminal consolidation chunk (whether from
+    # `typed_chunk.toolCalls` or from `chatHistory`) typically restate what
+    # was already streamed in intermediate chunks. Re-emitting them would
+    # mint fresh `uuid4` IDs and cause downstream consumers to execute each
+    # tool call twice. We only suppress when the caller has confirmed that
+    # tool calls were already emitted earlier — otherwise (e.g. a short
+    # response that delivers tool calls exclusively on the terminal chunk),
+    # passing them through is the only chance to surface them.
+    cohere_tool_calls = (
+        None
+        if (is_terminal_consolidation and prior_tool_calls_emitted)
+        else typed_chunk.toolCalls
+    )
+
+    tool_calls: Optional[List[Dict[str, Any]]] = None
+    if cohere_tool_calls:
+        tool_calls = [
+            {
+                # Cohere protocol has no tool-call id, so we synthesize one
+                # deterministically from the call's content/position. A random
+                # uuid4 per chunk would cause downstream stream-mergers to
+                # treat each chunk as a distinct tool call.
+                "id": _synthesize_oci_tool_call_id(
+                    i, tc.name, json.dumps(tc.parameters, sort_keys=True)
+                ),
+                "type": "function",
+                "function": {
+                    "name": tc.name,
+                    "arguments": json.dumps(tc.parameters),
+                },
+            }
+            for i, tc in enumerate(cohere_tool_calls)
+        ]
+
+    finish_reason = _normalize_oci_finish_reason(typed_chunk.finishReason)
+
+    return ModelResponseStream(
+        choices=[
+            StreamingChoices(
+                index=typed_chunk.index,
+                delta=Delta(
+                    content=text,
+                    tool_calls=tool_calls,
+                    provider_specific_fields=None,
+                    thinking_blocks=None,
+                    reasoning_content=None,
+                ),
+                finish_reason=finish_reason,
+            )
+        ]
+    )
diff --git a/litellm/llms/oci/chat/generic.py b/litellm/llms/oci/chat/generic.py
new file mode 100644
index 00000000000..2cc1ac77a40
--- /dev/null
+++ b/litellm/llms/oci/chat/generic.py
@@ -0,0 +1,477 @@
+"""
+OCI Generative AI — Generic-format chat transformation helpers.
+
+Handles message building, tool definition adaptation, non-streaming response
+parsing, and streaming chunk parsing for models served with
+``apiFormat="GENERIC"`` (e.g. Meta Llama, xAI Grok, Google Gemini).
+"""
+
+import datetime
+import hashlib
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+from pydantic import ValidationError
+
+from litellm.llms.oci.common_utils import (
+    OCIError,
+    resolve_oci_schema_anyof,
+    resolve_oci_schema_refs,
+    sanitize_oci_schema,
+)
+from litellm.types.llms.oci import (
+    OCICompletionResponse,
+    OCIContentPartUnion,
+    OCIImageContentPart,
+    OCIImageUrl,
+    OCIMessage,
+    OCIRoles,
+    OCIStreamChunk,
+    OCITextContentPart,
+    OCIToolCall,
+    OCIToolDefinition,
+    OCIVendors,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import (
+    Delta,
+    ModelResponse,
+    ModelResponseStream,
+    StreamingChoices,
+)
+from litellm.types.utils import ChatCompletionMessageToolCall, Usage
+
+# Maps OpenAI role names to OCI GENERIC role names.
+open_ai_to_generic_oci_role_map: Dict[str, OCIRoles] = {
+    "system": "SYSTEM",
+    "user": "USER",
+    "assistant": "ASSISTANT",
+    "tool": "TOOL",
+}
+
+
+# ---------------------------------------------------------------------------
+# Message building
+# ---------------------------------------------------------------------------
+
+
+def adapt_messages_to_generic_oci_standard_content_message(
+    role: str, content: Union[str, list]
+) -> OCIMessage:
+    """Convert a plain-text or multipart content message to OCI format."""
+    new_content: List[OCIContentPartUnion] = []
+    if isinstance(content, str):
+        return OCIMessage(
+            role=open_ai_to_generic_oci_role_map[role],
+            content=[OCITextContentPart(text=content)],
+            toolCalls=None,
+            toolCallId=None,
+        )
+
+    for content_item in content:
+        if not isinstance(content_item, dict):
+            raise OCIError(
+                status_code=400, message="Each content item must be a dictionary"
+            )
+
+        item_type = content_item.get("type")
+        if not isinstance(item_type, str):
+            raise OCIError(
+                status_code=400,
+                message="Each content item must have a string `type` field",
+            )
+        if item_type not in ["text", "image_url"]:
+            raise OCIError(
+                status_code=400,
+                message=f"Content type `{item_type}` is not supported by OCI",
+            )
+
+        if item_type == "text":
+            text = content_item.get("text")
+            if not isinstance(text, str):
+                raise OCIError(
+                    status_code=400,
+                    message="Content item of type `text` must have a string `text` field",
+                )
+            new_content.append(OCITextContentPart(text=text))
+
+        elif item_type == "image_url":
+            image_url = content_item.get("image_url")
+            if isinstance(image_url, dict):
+                image_url = image_url.get("url")
+            if not isinstance(image_url, str):
+                raise OCIError(
+                    status_code=400,
+                    message="Prop `image_url` must be a string or an object with a `url` property",
+                )
+            new_content.append(OCIImageContentPart(imageUrl=OCIImageUrl(url=image_url)))
+
+    return OCIMessage(
+        role=open_ai_to_generic_oci_role_map[role],
+        content=new_content,
+        toolCalls=None,
+        toolCallId=None,
+    )
+
+
+def adapt_messages_to_generic_oci_standard_tool_call(
+    role: str, tool_calls: list
+) -> OCIMessage:
+    """Convert an assistant tool-call message to OCI format."""
+    tool_calls_formatted = []
+    for tool_call in tool_calls:
+        if not isinstance(tool_call, dict):
+            raise OCIError(
+                status_code=400, message="Each tool call must be a dictionary"
+            )
+        if tool_call.get("type") != "function":
+            raise OCIError(
+                status_code=400, message="OCI only supports function tool calls"
+            )
+
+        tool_call_id = tool_call.get("id")
+        if not isinstance(tool_call_id, str):
+            raise OCIError(status_code=400, message="Tool call `id` must be a string")
+
+        tool_function = tool_call.get("function")
+        if not isinstance(tool_function, dict):
+            raise OCIError(
+                status_code=400, message="Tool call `function` must be a dictionary"
+            )
+
+        function_name = tool_function.get("name")
+        if not isinstance(function_name, str):
+            raise OCIError(
+                status_code=400, message="Tool call `function.name` must be a string"
+            )
+
+        arguments = tool_call["function"].get("arguments", "{}")
+        if not isinstance(arguments, str):
+            raise OCIError(
+                status_code=400,
+                message="Tool call `function.arguments` must be a JSON string",
+            )
+
+        tool_calls_formatted.append(
+            OCIToolCall(
+                id=tool_call_id,
+                type="FUNCTION",
+                name=function_name,
+                arguments=arguments,
+            )
+        )
+
+    return OCIMessage(
+        role=open_ai_to_generic_oci_role_map[role],
+        content=None,
+        toolCalls=tool_calls_formatted,
+        toolCallId=None,
+    )
+
+
+def adapt_messages_to_generic_oci_standard_tool_response(
+    role: str, tool_call_id: str, content: str
+) -> OCIMessage:
+    """Convert a tool-result message to OCI format."""
+    return OCIMessage(
+        role=open_ai_to_generic_oci_role_map[role],
+        content=[OCITextContentPart(text=content)],
+        toolCalls=None,
+        toolCallId=tool_call_id,
+    )
+
+
+def adapt_messages_to_generic_oci_standard(
+    messages: List[AllMessageValues],
+) -> List[OCIMessage]:
+    """Convert an OpenAI-format message array to OCI GENERIC format."""
+    new_messages = []
+    for message in messages:
+        role = message["role"]
+        content = message.get("content")
+        tool_calls = message.get("tool_calls")
+        tool_call_id = message.get("tool_call_id")
+
+        if role == "assistant" and tool_calls is not None:
+            if not isinstance(tool_calls, list):
+                raise OCIError(
+                    status_code=400, message="Message `tool_calls` must be a list"
+                )
+            new_messages.append(
+                adapt_messages_to_generic_oci_standard_tool_call(role, tool_calls)
+            )
+
+        elif role in ["system", "user", "assistant"] and content is not None:
+            if not isinstance(content, (str, list)):
+                raise OCIError(
+                    status_code=400,
+                    message="Message `content` must be a string or list of content parts",
+                )
+            new_messages.append(
+                adapt_messages_to_generic_oci_standard_content_message(role, content)
+            )
+
+        elif role == "tool":
+            if not isinstance(tool_call_id, str):
+                raise OCIError(
+                    status_code=400,
+                    message="Tool result message must have a string `tool_call_id`",
+                )
+            if not isinstance(content, str):
+                raise OCIError(
+                    status_code=400,
+                    message="Tool result message `content` must be a string",
+                )
+            new_messages.append(
+                adapt_messages_to_generic_oci_standard_tool_response(
+                    role, tool_call_id, content
+                )
+            )
+
+    return new_messages
+
+
+# ---------------------------------------------------------------------------
+# Tool definition adaptation
+# ---------------------------------------------------------------------------
+
+
+def adapt_tool_definition_to_oci_standard(
+    tools: List[Dict], vendor: OCIVendors
+) -> List[OCIToolDefinition]:
+    """Convert OpenAI-format tool definitions to OCI GENERIC format.
+
+    Resolves ``$ref``/``$defs`` and ``anyOf`` that the OCI endpoint rejects.
+    """
+    new_tools = []
+    for tool in tools:
+        if tool["type"] != "function":
+            raise OCIError(status_code=400, message="OCI only supports function tools")
+
+        tool_function = tool.get("function")
+        if not isinstance(tool_function, dict):
+            raise OCIError(
+                status_code=400, message="Tool `function` must be a dictionary"
+            )
+
+        raw_params = tool_function.get("parameters", {})
+        resolved_params = sanitize_oci_schema(
+            resolve_oci_schema_anyof(resolve_oci_schema_refs(raw_params))
+        )
+
+        new_tools.append(
+            OCIToolDefinition(
+                type="FUNCTION",
+                name=tool_function.get("name"),
+                description=tool_function.get("description", ""),
+                parameters=resolved_params,
+            )
+        )
+
+    return new_tools
+
+
+def _normalize_oci_finish_reason(raw: Optional[str]) -> Optional[str]:
+    """Map an OCI-specific finish reason to its OpenAI-standard equivalent.
+
+    OCI emits ``COMPLETE`` / ``MAX_TOKENS`` / ``TOOL_CALL(S)`` plus a long tail
+    of error/cancel reasons (``ERROR``, ``ERROR_TOXIC``, ``ERROR_LIMIT``,
+    ``USER_CANCEL``, ``CONTENT_FILTERED``, ``CANCELLED``, ...). The OpenAI
+    spec only defines ``stop`` / ``length`` / ``tool_calls`` / ... — anything
+    else is collapsed to ``"stop"`` so downstream consumers switching on
+    ``finish_reason`` keep working. A ``None`` input passes through unchanged.
+    """
+    if raw is None:
+        return None
+    if raw == "COMPLETE":
+        return "stop"
+    if raw == "MAX_TOKENS":
+        return "length"
+    if raw in ("TOOL_CALL", "TOOL_CALLS"):
+        return "tool_calls"
+    return "stop"
+
+
+def _synthesize_oci_tool_call_id(position: int, name: str, arguments: str) -> str:
+    """Deterministic synthetic tool-call id derived from chunk content.
+
+    Used as a fallback when OCI omits ``id`` (always the case for the OCI
+    Cohere protocol, occasionally the case for OCI GENERIC streaming chunks).
+    A random ``uuid4`` per chunk would cause downstream stream-merging
+    consumers — which key off the tool-call ``id`` — to treat re-emissions of
+    the same logical call (e.g. terminal consolidation chunks, retries) as
+    distinct calls. A content-derived digest stays stable across identical
+    re-emissions while differing across truly distinct calls.
+    """
+    digest = hashlib.sha256(
+        f"{position}|{name}|{arguments}".encode("utf-8"),
+        usedforsecurity=False,
+    ).hexdigest()[:24]
+    return f"call_{digest}"
+
+
+def adapt_tools_to_openai_standard(
+    tools: List[OCIToolCall],
+) -> List[ChatCompletionMessageToolCall]:
+    """Convert OCI tool-call objects in a response to the OpenAI format."""
+    return [
+        ChatCompletionMessageToolCall(
+            id=tool.id or _synthesize_oci_tool_call_id(i, tool.name, tool.arguments),
+            type="function",
+            function={"name": tool.name, "arguments": tool.arguments},
+        )
+        for i, tool in enumerate(tools)
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Response parsing
+# ---------------------------------------------------------------------------
+
+
+def handle_generic_response(
+    json_data: dict,
+    model: str,
+    model_response: ModelResponse,
+    raw_response: httpx.Response,
+) -> ModelResponse:
+    """Parse a non-streaming GENERIC OCI response into a LiteLLM ModelResponse."""
+    try:
+        completion_response = OCICompletionResponse(**json_data)
+    except (TypeError, ValidationError) as e:
+        raise OCIError(
+            message=f"Response cannot be casted to OCICompletionResponse: {str(e)}",
+            status_code=raw_response.status_code,
+        )
+
+    iso_str = completion_response.chatResponse.timeCreated
+    dt = datetime.datetime.fromisoformat(iso_str.replace("Z", "+00:00"))
+    model_response.created = int(dt.timestamp())
+    model_response.model = completion_response.modelId
+
+    if not completion_response.chatResponse.choices:
+        raise OCIError(
+            message="OCI response contained no choices",
+            status_code=raw_response.status_code,
+        )
+
+    response_choice = completion_response.chatResponse.choices[0]
+    message = model_response.choices[0].message  # type: ignore
+    response_message = response_choice.message
+    if response_message is not None:
+        if response_message.content:
+            # Concatenate all text parts — matches the streaming handler, which
+            # iterates the full content array. Skips non-text parts (e.g. image
+            # parts) so a leading non-text part doesn't suppress trailing text.
+            text: Optional[str] = None
+            for item in response_message.content:
+                if isinstance(item, OCITextContentPart):
+                    text = (text or "") + item.text
+            if text is not None:
+                message.content = text
+        if response_message.toolCalls:
+            message.tool_calls = adapt_tools_to_openai_standard(
+                response_message.toolCalls
+            )
+
+    model_response.choices[0].finish_reason = _normalize_oci_finish_reason(  # type: ignore[union-attr,assignment]
+        response_choice.finishReason
+    )
+
+    oci_usage = completion_response.chatResponse.usage
+    reasoning_tokens: Optional[int] = None
+    if (
+        oci_usage.completionTokensDetails
+        and oci_usage.completionTokensDetails.reasoningTokens is not None
+    ):
+        reasoning_tokens = oci_usage.completionTokensDetails.reasoningTokens
+    model_response.usage = Usage(  # type: ignore[attr-defined]
+        prompt_tokens=oci_usage.promptTokens,
+        completion_tokens=oci_usage.completionTokens or 0,
+        total_tokens=oci_usage.totalTokens,
+        reasoning_tokens=reasoning_tokens,
+    )
+
+    return model_response
+
+
+def handle_generic_stream_chunk(dict_chunk: dict) -> ModelResponseStream:
+    """Parse a single GENERIC SSE chunk into a LiteLLM ModelResponseStream."""
+    # OCI streams tool calls progressively — early chunks may omit required fields.
+    if dict_chunk.get("message") and dict_chunk["message"].get("toolCalls"):
+        for tool_call in dict_chunk["message"]["toolCalls"]:
+            tool_call.setdefault("arguments", "")
+            tool_call.setdefault("id", "")
+            tool_call.setdefault("name", "")
+
+    try:
+        typed_chunk = OCIStreamChunk(**dict_chunk)
+    except (TypeError, ValidationError) as e:
+        raise OCIError(
+            status_code=500,
+            message=f"Chunk cannot be parsed as OCIStreamChunk: {str(e)}",
+        )
+
+    if typed_chunk.index is None:
+        typed_chunk.index = 0
+
+    # Emit ``content=None`` rather than ``content=""`` on chunks with no text
+    # parts (e.g. tool-call-only or keep-alive chunks) so downstream
+    # stream-mergers that distinguish "no text in this delta" from "an
+    # explicitly empty text delta" behave correctly.
+    text: Optional[str] = None
+    if typed_chunk.message and typed_chunk.message.content:
+        for item in typed_chunk.message.content:
+            if isinstance(item, OCITextContentPart):
+                text = (text or "") + item.text
+            elif isinstance(item, OCIImageContentPart):
+                raise OCIError(
+                    status_code=500,
+                    message="OCI returned image content in a streaming response — not supported",
+                )
+            else:
+                raise OCIError(
+                    status_code=500,
+                    message=f"Unsupported content type in OCI streaming response: {item.type}",
+                )
+
+    # Build plain tool-call dicts inline (matching the shape produced by
+    # ``handle_cohere_stream_chunk``) rather than calling
+    # ``adapt_tools_to_openai_standard`` and ``model_dump``-ing the typed
+    # objects. Both code paths feed ``Delta.tool_calls``, so emitting the
+    # same minimal ``{"id", "type", "function": {"name", "arguments"}}``
+    # shape keeps downstream stream-mergers behaving identically across
+    # GENERIC and Cohere chunks.
+    tool_calls: Optional[List[Dict[str, Any]]] = None
+    if typed_chunk.message and typed_chunk.message.toolCalls:
+        tool_calls = [
+            {
+                "id": tc.id or _synthesize_oci_tool_call_id(i, tc.name, tc.arguments),
+                "type": "function",
+                "function": {
+                    "name": tc.name,
+                    "arguments": tc.arguments,
+                },
+            }
+            for i, tc in enumerate(typed_chunk.message.toolCalls)
+        ]
+
+    finish_reason: Optional[str] = _normalize_oci_finish_reason(
+        typed_chunk.finishReason
+    )
+
+    return ModelResponseStream(
+        choices=[
+            StreamingChoices(
+                index=typed_chunk.index,
+                delta=Delta(
+                    content=text,
+                    tool_calls=tool_calls,
+                    provider_specific_fields=None,
+                    thinking_blocks=None,
+                    reasoning_content=None,
+                ),
+                finish_reason=finish_reason,
+            )
+        ]
+    )
diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py
index 62104e921a4..f050f9eea36 100644
--- a/litellm/llms/oci/chat/transformation.py
+++ b/litellm/llms/oci/chat/transformation.py
@@ -1,20 +1,26 @@
-import base64
-import datetime
-import hashlib
+"""
+OCI Generative AI — chat transformation orchestrator.
+
+This module wires together the Cohere-specific and Generic-model helpers to
+implement the LiteLLM BaseConfig interface.  Heavy-lifting lives in:
+
+  - :mod:`litellm.llms.oci.chat.cohere`  — Cohere message/tool/response logic
+  - :mod:`litellm.llms.oci.chat.generic` — Generic message/tool/response logic
+  - :mod:`litellm.llms.oci.common_utils` — auth, signing, schema utilities
+"""
+
 import json
-from dataclasses import dataclass
 from typing import (
     TYPE_CHECKING,
     Any,
     AsyncIterator,
     Dict,
+    Iterator,
     List,
     Optional,
-    Protocol,
     Tuple,
     Union,
 )
-from urllib.parse import urlparse
 
 import httpx
 
@@ -28,43 +34,43 @@ from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
     version,
 )
-from litellm.llms.oci.common_utils import OCIError
+from litellm.llms.oci.chat.cohere import (
+    _extract_text_content,
+    adapt_messages_to_cohere_standard,
+    adapt_tool_definitions_to_cohere_standard,
+    handle_cohere_response,
+    handle_cohere_stream_chunk,
+)
+from litellm.llms.oci.chat.generic import (
+    adapt_messages_to_generic_oci_standard,
+    adapt_tool_definition_to_oci_standard,
+    handle_generic_response,
+    handle_generic_stream_chunk,
+)
+from litellm.llms.oci.common_utils import (
+    OCI_API_VERSION,
+    OCIError,
+    OCIRequestWrapper,  # re-exported for backwards compatibility
+    get_oci_base_url,
+    resolve_oci_credentials,
+    sign_oci_request,
+    validate_oci_environment,
+)
 from litellm.types.llms.oci import (
     CohereChatRequest,
-    CohereMessage,
-    CohereChatResult,
-    CohereParameterDefinition,
-    CohereStreamChunk,
-    CohereTool,
-    CohereToolCall,
     OCIChatRequestPayload,
     OCICompletionPayload,
-    OCICompletionResponse,
-    OCIContentPartUnion,
-    OCIImageContentPart,
-    OCIImageUrl,
-    OCIMessage,
-    OCIRoles,
     OCIServingMode,
-    OCIStreamChunk,
-    OCITextContentPart,
-    OCIToolCall,
-    OCIToolDefinition,
     OCIVendors,
 )
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import (
-    Delta,
     LlmProviders,
     ModelResponse,
     ModelResponseStream,
-    StreamingChoices,
-)
-from litellm.utils import (
-    ChatCompletionMessageToolCall,
-    CustomStreamWrapper,
-    Usage,
 )
+from litellm.utils import supports_reasoning
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@@ -74,142 +80,157 @@ else:
     LiteLLMLoggingObj = Any
 
 
-class OCISignerProtocol(Protocol):
-    """
-    Protocol for OCI request signers (e.g., oci.signer.Signer).
-
-    This protocol defines the interface expected for OCI SDK signer objects.
-    Compatible with the OCI Python SDK's Signer class.
-
-    See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html
-    """
-
-    def do_request_sign(
-        self, request: Any, *, enforce_content_headers: bool = False
-    ) -> None:
-        """
-        Sign an HTTP request by adding authentication headers.
-
-        Args:
-            request: Request object with method, url, headers, body, and path_url attributes
-            enforce_content_headers: Whether to enforce content-type and content-length headers
-        """
-        ...
-
-
-@dataclass
-class OCIRequestWrapper:
-    """
-    Wrapper for HTTP requests compatible with OCI signer interface.
-
-    This class wraps request data in a format compatible with OCI SDK signers,
-    which expect objects with method, url, headers, body, and path_url attributes.
-    """
-
-    method: str
-    url: str
-    headers: dict
-    body: bytes
-
-    @property
-    def path_url(self) -> str:
-        """Returns the path + query string for OCI signing."""
-        parsed_url = urlparse(self.url)
-        return parsed_url.path + ("?" + parsed_url.query if parsed_url.query else "")
-
-
-def sha256_base64(data: bytes) -> str:
-    digest = hashlib.sha256(data).digest()
-    return base64.b64encode(digest).decode()
-
-
-def build_signature_string(method, path, headers, signed_headers):
-    lines = []
-    for header in signed_headers:
-        if header == "(request-target)":
-            value = f"{method.lower()} {path}"
-        else:
-            value = headers[header]
-        lines.append(f"{header}: {value}")
-    return "\n".join(lines)
-
-
-def load_private_key_from_str(key_str: str):
-    try:
-        from cryptography.hazmat.primitives import serialization
-        from cryptography.hazmat.primitives.asymmetric import rsa
-    except ImportError as e:
-        raise ImportError(
-            "cryptography package is required for OCI authentication. "
-            "Please install it with: pip install cryptography"
-        ) from e
-
-    key = serialization.load_pem_private_key(
-        key_str.encode("utf-8"),
-        password=None,
-    )
-    if not isinstance(key, rsa.RSAPrivateKey):
-        raise TypeError(
-            "The provided private key is not an RSA key, which is required for OCI signing."
-        )
-    return key
-
-
-def load_private_key_from_file(file_path: str):
-    """Loads a private key from a file path"""
-    try:
-        with open(file_path, "r", encoding="utf-8") as f:
-            key_str = f.read().strip()
-    except FileNotFoundError:
-        raise FileNotFoundError(f"Private key file not found: {file_path}")
-    except OSError as e:
-        raise OSError(f"Failed to read private key file '{file_path}': {e}") from e
-
-    if not key_str:
-        raise ValueError(f"Private key file is empty: {file_path}")
-
-    return load_private_key_from_str(key_str)
-
-
-def get_vendor_from_model(model: str) -> OCIVendors:
-    """
-    Extracts the vendor from the model name.
-
-    OCI GenAI API uses two apiFormat values:
-    - "COHERE" for Cohere models (command-r, command-a, etc.)
-    - "GENERIC" for all other models (Meta Llama, xAI Grok, Google Gemini, etc.)
-
-    Args:
-        model (str): The model name (e.g., "cohere.command-a-03-2025", "meta.llama-3.3-70b-instruct").
-    Returns:
-        OCIVendors: The vendor enum value.
-    """
-    vendor = model.split(".")[0].lower()
-    if vendor == "cohere":
-        return OCIVendors.COHERE
-    else:
-        return OCIVendors.GENERIC
-
-
-# 5 minute timeout (models may need to load)
+# Streaming timeout — generous because OCI models may need to warm up on first request
 STREAMING_TIMEOUT = 60 * 5
 
 
+def _model_uses_max_completion_tokens(model: str) -> bool:
+    """Return True for OCI-hosted models that require ``maxCompletionTokens``.
+
+    Reasoning models on OCI (e.g. the OpenAI GPT-5 family) reject ``maxTokens``
+    with HTTP 400 and require ``maxCompletionTokens`` per OpenAI's reasoning-API
+    convention. Driven by ``supports_reasoning`` in
+    ``model_prices_and_context_window.json`` so new model families are picked
+    up via a catalog update rather than a code change.
+    """
+    if not model:
+        return False
+    name = model[4:] if model.lower().startswith("oci/") else model
+    return supports_reasoning(model=name, custom_llm_provider="oci")
+
+
+def _iter_sse_events(stream: Iterator[str]) -> Iterator[str]:
+    """Yield one ``data:`` SSE line at a time from a sync text stream.
+
+    The OCI streaming endpoint does not align SSE event boundaries with HTTP
+    read boundaries. A single read may carry multiple events, a single event
+    may straddle two reads, and some events arrive separated by only ``\\n``
+    instead of ``\\n\\n``. This helper buffers across reads and yields each
+    complete ``data:`` line so JSON parsing downstream never sees a partial
+    payload.
+    """
+    buffer = ""
+    for item in stream:
+        buffer += item
+        while "\n" in buffer:
+            line, buffer = buffer.split("\n", 1)
+            stripped = line.strip()
+            if stripped.startswith("data:"):
+                yield stripped
+    stripped = buffer.strip()
+    if stripped.startswith("data:"):
+        yield stripped
+
+
+async def _aiter_sse_events(stream: AsyncIterator[str]) -> AsyncIterator[str]:
+    """Async twin of :func:`_iter_sse_events`."""
+    buffer = ""
+    async for item in stream:
+        buffer += item
+        while "\n" in buffer:
+            line, buffer = buffer.split("\n", 1)
+            stripped = line.strip()
+            if stripped.startswith("data:"):
+                yield stripped
+    stripped = buffer.strip()
+    if stripped.startswith("data:"):
+        yield stripped
+
+
+def _normalize_tool_choice(selected_params: Dict) -> None:
+    tc = selected_params.get("toolChoice")
+    if tc is None:
+        return
+    if isinstance(tc, str):
+        tc_map = {
+            "auto": {"type": "AUTO"},
+            "none": {"type": "NONE"},
+            "required": {"type": "REQUIRED"},
+            "any": {"type": "REQUIRED"},
+        }
+        selected_params["toolChoice"] = tc_map.get(
+            tc.lower(), {"type": "FUNCTION", "name": tc}
+        )
+        return
+    if isinstance(tc, dict):
+        raw_type = tc.get("type")
+        if not isinstance(raw_type, str):
+            raise OCIError(
+                status_code=400,
+                message=f"Invalid tool_choice for OCI: missing or non-string 'type' in {tc!r}",
+            )
+        upper = raw_type.upper()
+        if upper == "FUNCTION":
+            fn = tc.get("function")
+            name = fn.get("name") if isinstance(fn, dict) else tc.get("name")
+            if not (isinstance(name, str) and name):
+                raise OCIError(
+                    status_code=400,
+                    message="Invalid tool_choice for OCI: 'FUNCTION' type requires a non-empty function name",
+                )
+            selected_params["toolChoice"] = {"type": "FUNCTION", "name": name}
+        elif upper in {"AUTO", "NONE", "REQUIRED"}:
+            selected_params["toolChoice"] = {"type": upper}
+        else:
+            raise OCIError(
+                status_code=400,
+                message=(
+                    f"Invalid tool_choice for OCI: unsupported type {raw_type!r}; "
+                    "expected one of 'FUNCTION', 'AUTO', 'NONE', 'REQUIRED'"
+                ),
+            )
+        return
+    raise OCIError(
+        status_code=400,
+        message=(
+            f"Invalid tool_choice for OCI: expected str or dict, got "
+            f"{type(tc).__name__}"
+        ),
+    )
+
+
+def _normalize_response_format(selected_params: Dict, vendor: OCIVendors) -> None:
+    rf = selected_params.get("responseFormat")
+    if not isinstance(rf, dict) or "type" not in rf:
+        return
+    rf_payload = dict(rf)
+    selected_params["responseFormat"] = rf_payload
+    response_type = rf_payload["type"]
+    if "json_schema" in rf_payload:
+        raw_schema = rf_payload.pop("json_schema")
+        rf_payload["jsonSchema"] = (
+            dict(raw_schema) if isinstance(raw_schema, dict) else raw_schema
+        )
+    if vendor == OCIVendors.COHERE:
+        rf_payload["type"] = response_type
+    else:
+        fmt = response_type.upper()
+        rf_payload["type"] = "JSON_OBJECT" if fmt == "JSON" else fmt
+
+
+def get_vendor_from_model(model: str) -> OCIVendors:
+    """Return the OCI vendor enum for a model name.
+
+    OCI GenAI uses two ``apiFormat`` values:
+
+    - ``"COHERE"`` for Cohere models (``cohere.*``)
+    - ``"GENERIC"`` for all others (Meta Llama, xAI Grok, Google Gemini, …)
+    """
+    name = model[4:] if model.lower().startswith("oci/") else model
+    vendor = name.split(".")[0].lower()
+    if vendor == "cohere":
+        return OCIVendors.COHERE
+    return OCIVendors.GENERIC
+
+
 class OCIChatConfig(BaseConfig):
-    """
-    Configuration class for OCI's API interface.
-    """
+    """LiteLLM BaseConfig implementation for OCI Generative AI chat."""
 
-    def __init__(
-        self,
-    ) -> None:
-        locals_ = locals().copy()
-        for key, value in locals_.items():
-            if key != "self" and value is not None:
-                setattr(self.__class__, key, value)
-        # mark the class as using a custom stream wrapper because the default only iterates on lines
-        setattr(self.__class__, "has_custom_stream_wrapper", True)
+    @property
+    def has_custom_stream_wrapper(self) -> bool:
+        return True
 
+    def __init__(self) -> None:
         self.openai_to_oci_generic_param_map = {
             "stream": "isStream",
             "max_tokens": "maxTokens",
@@ -221,6 +242,7 @@ class OCIChatConfig(BaseConfig):
             "logit_bias": "logitBias",
             "n": "numGenerations",
             "presence_penalty": "presencePenalty",
+            "reasoning_effort": "reasoningEffort",
             "seed": "seed",
             "stop": "stop",
             "tool_choice": "toolChoice",
@@ -239,25 +261,43 @@ class OCIChatConfig(BaseConfig):
             "response_format": "responseFormat",
         }
 
-        # Cohere and Gemini use the same parameter mapping as GENERIC
-        self.openai_to_oci_cohere_param_map = (
-            self.openai_to_oci_generic_param_map.copy()
-        )
+        # Cohere param map differs from GENERIC in three ways:
+        # - tool_choice is unsupported
+        # - stop sequences key is "stopSequences" not "stop"
+        # - n (numGenerations) is GENERIC-only
+        # The unsupported keys are kept in the map with value ``False`` so
+        # ``map_openai_params`` either drops them (under drop_params) or raises
+        # a clear error, rather than silently passing them through.
+        self.openai_to_oci_cohere_param_map = {
+            k: ("stopSequences" if k == "stop" else v)
+            for k, v in self.openai_to_oci_generic_param_map.items()
+        }
+        self.openai_to_oci_cohere_param_map["tool_choice"] = False
+        self.openai_to_oci_cohere_param_map["n"] = False
+        # ``top_k`` is not a standard OpenAI param, but Cohere's chat request
+        # accepts ``topK`` and LiteLLM commonly forwards ``top_k`` as a
+        # passthrough param. Cohere-only — ``OCIChatRequestPayload`` (GENERIC)
+        # has no ``topK`` field.
+        self.openai_to_oci_cohere_param_map["top_k"] = "topK"
+        # OCI Cohere models are not reasoning models; mark reasoning_effort
+        # explicitly unsupported so callers either get a clear error or have
+        # the param dropped under drop_params, rather than silently passing
+        # through and tripping Pydantic validation on CohereChatRequest.
+        self.openai_to_oci_cohere_param_map["reasoning_effort"] = False
+        # CohereChatRequest has no logProbs/logitBias fields, so passing these
+        # through would be silently dropped by Pydantic. Mark them unsupported
+        # so get_supported_openai_params doesn't advertise them and callers
+        # get a clear error (or drop_params behaviour) instead.
+        self.openai_to_oci_cohere_param_map["logprobs"] = False
+        self.openai_to_oci_cohere_param_map["logit_bias"] = False
 
     def get_supported_openai_params(self, model: str) -> List[str]:
-        supported_params = []
-        vendor = get_vendor_from_model(model)
-        if vendor == OCIVendors.COHERE:
-            open_ai_to_oci_param_map = self.openai_to_oci_cohere_param_map
-            open_ai_to_oci_param_map.pop("tool_choice")
-            open_ai_to_oci_param_map.pop("max_retries")
-        else:
-            open_ai_to_oci_param_map = self.openai_to_oci_generic_param_map
-        for key, value in open_ai_to_oci_param_map.items():
-            if value:
-                supported_params.append(key)
-
-        return supported_params
+        param_map = (
+            self.openai_to_oci_cohere_param_map
+            if get_vendor_from_model(model) == OCIVendors.COHERE
+            else self.openai_to_oci_generic_param_map
+        )
+        return [key for key, value in param_map.items() if value]
 
     def map_openai_params(
         self,
@@ -268,238 +308,34 @@ class OCIChatConfig(BaseConfig):
     ) -> dict:
         adapted_params = {}
         vendor = get_vendor_from_model(model)
-        if vendor == OCIVendors.COHERE:
-            open_ai_to_oci_param_map = self.openai_to_oci_cohere_param_map
-        else:
-            open_ai_to_oci_param_map = self.openai_to_oci_generic_param_map
-
-        all_params = {**non_default_params, **optional_params}
-
-        for key, value in all_params.items():
-            alias = open_ai_to_oci_param_map.get(key)
+        param_map = (
+            self.openai_to_oci_cohere_param_map
+            if vendor == OCIVendors.COHERE
+            else self.openai_to_oci_generic_param_map
+        )
 
+        for key, value in {**non_default_params, **optional_params}.items():
+            alias = param_map.get(key)
             if alias is False:
-                # Workaround for mypy issue
                 if drop_params or litellm.drop_params:
                     continue
-                raise Exception(f"param `{key}` is not supported on OCI")
-
+                raise OCIError(
+                    status_code=400,
+                    message=f"param `{key}` is not supported on OCI",
+                )
             if alias is None:
                 adapted_params[key] = value
                 continue
-
             adapted_params[alias] = value
-
+            # Preserve the original OpenAI ``response_format`` key alongside the
+            # OCI-mapped ``responseFormat`` so downstream litellm framework code
+            # (e.g. ``json_mode`` detection, logging) that inspects
+            # ``optional_params["response_format"]`` continues to work.
             if alias == "responseFormat":
                 adapted_params["response_format"] = value
 
         return adapted_params
 
-    def _sign_with_oci_signer(
-        self,
-        headers: dict,
-        optional_params: dict,
-        request_data: dict,
-        api_base: str,
-    ) -> Tuple[dict, bytes]:
-        """
-        Sign request using OCI SDK Signer object.
-
-        Args:
-            headers: Request headers to be signed
-            optional_params: Optional parameters including oci_signer
-            request_data: The request body dict to be sent in HTTP request
-            api_base: The complete URL for the HTTP request
-
-        Returns:
-            Tuple of (signed_headers, encoded_body)
-
-        Raises:
-            OCIError: If signing fails
-            ValueError: If HTTP method is unsupported
-        """
-        oci_signer = optional_params.get("oci_signer")
-        body = json.dumps(request_data).encode("utf-8")
-        method = str(optional_params.get("method", "POST")).upper()
-
-        if method not in ["POST", "GET", "PUT", "DELETE", "PATCH"]:
-            raise ValueError(f"Unsupported HTTP method: {method}")
-
-        prepared_headers = headers.copy()
-        prepared_headers.setdefault("content-type", "application/json")
-        prepared_headers.setdefault("content-length", str(len(body)))
-
-        request_wrapper = OCIRequestWrapper(
-            method=method, url=api_base, headers=prepared_headers, body=body
-        )
-
-        if oci_signer is None:
-            raise ValueError(
-                "oci_signer cannot be None when calling _sign_with_oci_signer"
-            )
-
-        try:
-            oci_signer.do_request_sign(request_wrapper, enforce_content_headers=True)
-        except Exception as e:
-            raise OCIError(
-                status_code=500,
-                message=(
-                    f"Failed to sign request with provided oci_signer: {str(e)}. "
-                    "The signer must implement the OCI SDK Signer interface with a "
-                    "do_request_sign(request, enforce_content_headers=True) method. "
-                    "See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html"
-                ),
-            ) from e
-
-        headers.update(request_wrapper.headers)
-        return headers, body
-
-    def _sign_with_manual_credentials(
-        self,
-        headers: dict,
-        optional_params: dict,
-        request_data: dict,
-        api_base: str,
-    ) -> Tuple[dict, None]:
-        """
-        Sign request using manual OCI credentials.
-
-        Args:
-            headers: Request headers to be signed
-            optional_params: Optional parameters including OCI credentials
-            request_data: The request body dict to be sent in HTTP request
-            api_base: The complete URL for the HTTP request
-
-        Returns:
-            Tuple of (signed_headers, None)
-
-        Raises:
-            Exception: If required credentials are missing
-            ImportError: If cryptography package is not installed
-        """
-        oci_region = optional_params.get("oci_region", "us-ashburn-1")
-        api_base = (
-            api_base
-            or litellm.api_base
-            or f"https://inference.generativeai.{oci_region}.oci.oraclecloud.com"
-        )
-        oci_user = optional_params.get("oci_user")
-        oci_fingerprint = optional_params.get("oci_fingerprint")
-        oci_tenancy = optional_params.get("oci_tenancy")
-        oci_key = optional_params.get("oci_key")
-        oci_key_file = optional_params.get("oci_key_file")
-
-        if (
-            not oci_user
-            or not oci_fingerprint
-            or not oci_tenancy
-            or not (oci_key or oci_key_file)
-        ):
-            raise Exception(
-                "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, "
-                "and at least one of oci_key or oci_key_file."
-            )
-
-        method = str(optional_params.get("method", "POST")).upper()
-        body = json.dumps(request_data).encode("utf-8")
-        parsed = urlparse(api_base)
-        path = parsed.path or "/"
-        host = parsed.netloc
-
-        date = datetime.datetime.utcnow().strftime("%a, %d %b %Y %H:%M:%S GMT")
-        content_type = headers.get("content-type", "application/json")
-        content_length = str(len(body))
-        x_content_sha256 = sha256_base64(body)
-
-        headers_to_sign = {
-            "date": date,
-            "host": host,
-            "content-type": content_type,
-            "content-length": content_length,
-            "x-content-sha256": x_content_sha256,
-        }
-
-        signed_headers = [
-            "date",
-            "(request-target)",
-            "host",
-            "content-length",
-            "content-type",
-            "x-content-sha256",
-        ]
-        signing_string = build_signature_string(
-            method, path, headers_to_sign, signed_headers
-        )
-
-        try:
-            from cryptography.hazmat.primitives import hashes
-            from cryptography.hazmat.primitives.asymmetric import padding
-        except ImportError as e:
-            raise ImportError(
-                "cryptography package is required for OCI authentication. "
-                "Please install it with: pip install cryptography"
-            ) from e
-
-        # Handle oci_key - it should be a string (PEM content)
-        oci_key_content = None
-        if oci_key:
-            if isinstance(oci_key, str):
-                oci_key_content = oci_key
-                # Fix common issues with PEM content
-                # Replace escaped newlines with actual newlines
-                oci_key_content = oci_key_content.replace("\\n", "\n")
-                # Ensure proper line endings
-                if "\r\n" in oci_key_content:
-                    oci_key_content = oci_key_content.replace("\r\n", "\n")
-            else:
-                raise OCIError(
-                    status_code=400,
-                    message=f"oci_key must be a string containing the PEM private key content. "
-                    f"Got type: {type(oci_key).__name__}",
-                )
-
-        private_key = (
-            load_private_key_from_str(oci_key_content)
-            if oci_key_content
-            else load_private_key_from_file(oci_key_file) if oci_key_file else None
-        )
-
-        if private_key is None:
-            raise OCIError(
-                status_code=400,
-                message="Private key is required for OCI authentication. Please provide either oci_key or oci_key_file.",
-            )
-
-        signature = private_key.sign(
-            signing_string.encode("utf-8"),
-            padding.PKCS1v15(),
-            hashes.SHA256(),
-        )
-        signature_b64 = base64.b64encode(signature).decode()
-
-        key_id = f"{oci_tenancy}/{oci_user}/{oci_fingerprint}"
-
-        authorization = (
-            'Signature version="1",'
-            f'keyId="{key_id}",'
-            'algorithm="rsa-sha256",'
-            f'headers="{" ".join(signed_headers)}",'
-            f'signature="{signature_b64}"'
-        )
-
-        headers.update(
-            {
-                "authorization": authorization,
-                "date": date,
-                "host": host,
-                "content-type": content_type,
-                "content-length": content_length,
-                "x-content-sha256": x_content_sha256,
-            }
-        )
-
-        return headers, None
-
     def sign_request(
         self,
         headers: dict,
@@ -510,61 +346,16 @@ class OCIChatConfig(BaseConfig):
         model: Optional[str] = None,
         stream: Optional[bool] = None,
         fake_stream: Optional[bool] = None,
-    ) -> Tuple[dict, Optional[bytes]]:
-        """
-        Sign the OCI request by adding authentication headers.
-
-        Supports two signing modes:
-        1. OCI SDK Signer: Use an oci_signer object to sign the request
-        2. Manual Signing: Use OCI credentials to manually sign the request
-
-        Args:
-            headers: Request headers to be signed
-            optional_params: Optional parameters including auth credentials or oci_signer
-            request_data: The request body dict to be sent in HTTP request
-            api_base: The complete URL for the HTTP request
-            api_key: Optional API key (not used for OCI)
-            model: Optional model name
-            stream: Optional streaming flag
-            fake_stream: Optional fake streaming flag
-
-        Returns:
-            Tuple of (signed_headers, encoded_body):
-            - If oci_signer is provided: Returns (headers, body) where body is the encoded JSON
-            - If manual credentials are provided: Returns (headers, None) as body is not returned
-              for the manual signing path
-
-        Raises:
-            OCIError: If signing fails with oci_signer
-            Exception: If required credentials are missing
-            ImportError: If cryptography package is not installed (manual signing only)
-
-        Example:
-            >>> from oci.signer import Signer
-            >>> signer = Signer(
-            ...     tenancy="ocid1.tenancy.oc1..",
-            ...     user="ocid1.user.oc1..",
-            ...     fingerprint="xx:xx:xx",
-            ...     private_key_file_location="~/.oci/key.pem"
-            ... )
-            >>> headers, body = config.sign_request(
-            ...     headers={},
-            ...     optional_params={"oci_signer": signer},
-            ...     request_data={"message": "Hello"},
-            ...     api_base="https://inference.generativeai.us-ashburn-1.oci.oraclecloud.com/..."
-            ... )
-        """
-        oci_signer = optional_params.get("oci_signer")
-
-        # If a signer is provided, use it for request signing
-        if oci_signer is not None:
-            return self._sign_with_oci_signer(
-                headers, optional_params, request_data, api_base
-            )
-
-        # Standard manual credential signing
-        return self._sign_with_manual_credentials(
-            headers, optional_params, request_data, api_base
+    ) -> Tuple[dict, bytes]:
+        return sign_oci_request(
+            headers=headers,
+            optional_params=optional_params,
+            request_data=request_data,
+            api_base=api_base,
+            api_key=api_key,
+            model=model,
+            stream=stream,
+            fake_stream=fake_stream,
         )
 
     def validate_environment(
@@ -577,80 +368,35 @@ class OCIChatConfig(BaseConfig):
         api_key: Optional[str] = None,
         api_base: Optional[str] = None,
     ) -> dict:
-        """
-        Validate the OCI environment and credentials.
-
-        Supports two authentication modes:
-        1. OCI SDK Signer: Pass an oci_signer object (e.g., oci.signer.Signer)
-        2. Manual Credentials: Pass oci_user, oci_fingerprint, oci_tenancy, and oci_key/oci_key_file
-
-        Args:
-            headers: Request headers to populate
-            model: Model name
-            messages: List of chat messages
-            optional_params: Optional parameters including authentication credentials
-            litellm_params: LiteLLM parameters
-            api_key: Optional API key (not used for OCI)
-            api_base: Optional API base URL
-
-        Returns:
-            Updated headers dict
-
-        Raises:
-            Exception: If required parameters are missing or invalid
-        """
-        oci_signer = optional_params.get("oci_signer")
-        oci_region = optional_params.get("oci_region", "us-ashburn-1")
-
-        # Determine api_base
-        api_base = (
-            api_base
-            or litellm.api_base
-            or f"https://inference.generativeai.{oci_region}.oci.oraclecloud.com"
-        )
-
-        if not api_base:
-            raise Exception(
-                "Either `api_base` must be provided or `litellm.api_base` must be set. "
-                "Alternatively, you can set the `oci_region` optional parameter to use the default OCI region."
-            )
-
-        # Validate credentials only if signer is not provided
-        if oci_signer is None:
-            oci_user = optional_params.get("oci_user")
-            oci_fingerprint = optional_params.get("oci_fingerprint")
-            oci_tenancy = optional_params.get("oci_tenancy")
-            oci_key = optional_params.get("oci_key")
-            oci_key_file = optional_params.get("oci_key_file")
-            oci_compartment_id = optional_params.get("oci_compartment_id")
-
-            if (
-                not oci_user
-                or not oci_fingerprint
-                or not oci_tenancy
-                or not (oci_key or oci_key_file)
-                or not oci_compartment_id
-            ):
-                raise Exception(
-                    "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id "
-                    "and at least one of oci_key or oci_key_file. "
-                    "Alternatively, provide an oci_signer object from the OCI SDK."
-                )
-
-        # Common header setup
-        headers.update(
-            {
-                "content-type": "application/json",
-                "user-agent": f"litellm/{version}",
-            }
-        )
-
         if not messages:
-            raise Exception(
-                "kwarg `messages` must be an array of messages that follow the openai chat standard"
+            raise OCIError(
+                status_code=400,
+                message="kwarg `messages` must be an array of messages that follow the openai chat standard",
             )
-
-        return headers
+        if optional_params.get("oci_signer") is None:
+            creds = resolve_oci_credentials(optional_params)
+            missing = [
+                k
+                for k in (
+                    "oci_user",
+                    "oci_fingerprint",
+                    "oci_tenancy",
+                    "oci_compartment_id",
+                )
+                if not creds.get(k)
+            ]
+            if missing or not (creds.get("oci_key") or creds.get("oci_key_file")):
+                raise OCIError(
+                    status_code=401,
+                    message=(
+                        "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, "
+                        "oci_compartment_id and at least one of oci_key or oci_key_file. "
+                        "These can be supplied via optional_params or via OCI_USER, OCI_FINGERPRINT, "
+                        "OCI_TENANCY, OCI_COMPARTMENT_ID, OCI_KEY_FILE environment variables. "
+                        "Alternatively, provide an oci_signer object from the OCI SDK."
+                    ),
+                )
+        return validate_oci_environment(headers, optional_params, api_key)
 
     def get_complete_url(
         self,
@@ -661,43 +407,63 @@ class OCIChatConfig(BaseConfig):
         litellm_params: dict,
         stream: Optional[bool] = None,
     ) -> str:
-        oci_region = optional_params.get("oci_region", "us-ashburn-1")
-        return f"https://inference.generativeai.{oci_region}.oci.oraclecloud.com/20231130/actions/chat"
+        base = get_oci_base_url(optional_params, api_base or litellm.api_base)
+        return f"{base}/{OCI_API_VERSION}/actions/chat"
 
-    def _get_optional_params(self, vendor: OCIVendors, optional_params: dict) -> Dict:
-        selected_params = {}
-        if vendor == OCIVendors.COHERE:
-            open_ai_to_oci_param_map = self.openai_to_oci_cohere_param_map
-            # remove tool_choice from the map
-            open_ai_to_oci_param_map.pop("tool_choice")
-            # Add default values for Cohere API
-            selected_params = {
-                "maxTokens": 600,
-                "temperature": 1,
-                "topK": 0,
-                "topP": 0.75,
-                "frequencyPenalty": 0,
-            }
-        else:
-            open_ai_to_oci_param_map = self.openai_to_oci_generic_param_map
+    def _get_optional_params(
+        self, vendor: OCIVendors, optional_params: dict, model: str = ""
+    ) -> Dict:
+        param_map = (
+            self.openai_to_oci_cohere_param_map
+            if vendor == OCIVendors.COHERE
+            else self.openai_to_oci_generic_param_map
+        )
+        selected_params: Dict = {}
 
-        # Map OpenAI params to OCI params
-        for openai_key, oci_key in open_ai_to_oci_param_map.items():
-            if oci_key and openai_key in optional_params:
-                selected_params[oci_key] = optional_params[openai_key]  # type: ignore[index]
+        # OpenAI reasoning models on OCI (e.g. GPT-5 family) reject "maxTokens"
+        # and require "maxCompletionTokens" per OCI's /20231130/Chat schema.
+        # Driven by the supports_reasoning flag in the model catalog. Cohere's
+        # endpoint uses "maxTokens" regardless, so the override is GENERIC-only.
+        max_tokens_key = (
+            "maxCompletionTokens"
+            if vendor != OCIVendors.COHERE
+            and model
+            and _model_uses_max_completion_tokens(model)
+            else "maxTokens"
+        )
 
-        # Also check for already-mapped OCI params (for backward compatibility)
-        for oci_value in open_ai_to_oci_param_map.values():
-            if (
-                oci_value
-                and oci_value in optional_params
-                and oci_value not in selected_params
-            ):
-                selected_params[oci_value] = optional_params[oci_value]  # type: ignore[index]
+        # ``map_openai_params`` runs before ``transform_request`` (and thus
+        # before this helper), so by the time we see ``optional_params`` the
+        # OpenAI keys have already been translated to their OCI aliases.
+        # We still accept the original OpenAI key as a fallback for callers
+        # that build ``optional_params`` directly, with OpenAI keys winning
+        # over OCI aliases when both happen to be present. The first OpenAI
+        # key reaching a given OCI target wins, so ``max_tokens`` /
+        # ``max_completion_tokens`` (both → ``maxTokens``) don't double-write.
+        for openai_key, oci_alias in param_map.items():
+            if not oci_alias:
+                continue
+            target = max_tokens_key if oci_alias == "maxTokens" else oci_alias
+            if target in selected_params:
+                continue
+            if openai_key in optional_params:
+                selected_params[target] = optional_params[openai_key]  # type: ignore[index]
+            elif oci_alias in optional_params:
+                selected_params[target] = optional_params[oci_alias]  # type: ignore[index]
+
+        # OCI expects uppercase reasoning levels (LOW/MEDIUM/HIGH/NONE); OpenAI
+        # clients send lowercase. OpenAI's "disable" maps to OCI's "NONE".
+        if "reasoningEffort" in selected_params:
+            effort = selected_params["reasoningEffort"]
+            if isinstance(effort, str):
+                normalized = effort.upper()
+                if normalized == "DISABLE":
+                    normalized = "NONE"
+                selected_params["reasoningEffort"] = normalized
 
         if "tools" in selected_params:
             if vendor == OCIVendors.COHERE:
-                selected_params["tools"] = self.adapt_tool_definitions_to_cohere_standard(  # type: ignore[assignment]
+                selected_params["tools"] = adapt_tool_definitions_to_cohere_standard(  # type: ignore[assignment]
                     selected_params["tools"]  # type: ignore[arg-type]
                 )
             else:
@@ -705,146 +471,15 @@ class OCIChatConfig(BaseConfig):
                     selected_params["tools"], vendor  # type: ignore[arg-type]
                 )
 
-        # Transform response_format type to OCI uppercase format
-        if "responseFormat" in selected_params:
-            rf = selected_params["responseFormat"]
-            if isinstance(rf, dict) and "type" in rf:
-                rf_payload = dict(rf)
-                selected_params["responseFormat"] = rf_payload
+        # Normalise tool_choice to OCI's flat uppercase dict form
+        # ({"type": "AUTO"|"NONE"|"REQUIRED"} or {"type": "FUNCTION", "name": "<fn>"}).
+        # OCI rejects both the OpenAI string and the nested OpenAI dict shape.
+        _normalize_tool_choice(selected_params)
 
-                response_type = rf_payload["type"]
-                schema_payload: Optional[Any] = None
-
-                if "json_schema" in rf_payload:
-                    raw_schema_payload = rf_payload.pop("json_schema")
-                    if isinstance(raw_schema_payload, dict):
-                        schema_payload = dict(raw_schema_payload)
-                    else:
-                        schema_payload = raw_schema_payload
-
-                if schema_payload is not None:
-                    rf_payload["jsonSchema"] = schema_payload
-
-                if vendor == OCIVendors.COHERE:
-                    # Cohere expects lower-case type values
-                    rf_payload["type"] = response_type
-                else:
-                    format_type = response_type.upper()
-                    if format_type == "JSON":
-                        format_type = "JSON_OBJECT"
-                    rf_payload["type"] = format_type
+        _normalize_response_format(selected_params, vendor)
 
         return selected_params
 
-    def adapt_messages_to_cohere_standard(
-        self, messages: List[AllMessageValues]
-    ) -> List[CohereMessage]:
-        """Build chat history for Cohere models."""
-        chat_history = []
-        for msg in messages[:-1]:  # All messages except the last one
-            role = msg.get("role")
-            content = msg.get("content")
-
-            if isinstance(content, list):
-                # Extract text from content array
-                text_content = ""
-                for content_item in content:
-                    if (
-                        isinstance(content_item, dict)
-                        and content_item.get("type") == "text"
-                    ):
-                        text_content += content_item.get("text", "")
-                content = text_content
-
-            # Ensure content is a string
-            if not isinstance(content, str):
-                content = str(content) if content is not None else ""
-
-            # Handle tool calls
-            tool_calls: Optional[List[CohereToolCall]] = None
-            if role == "assistant" and "tool_calls" in msg and msg.get("tool_calls"):  # type: ignore[union-attr,typeddict-item]
-                tool_calls = []
-                for tool_call in msg["tool_calls"]:  # type: ignore[union-attr,typeddict-item]
-                    # Parse arguments if they're a JSON string
-                    raw_arguments: Any = tool_call.get("function", {}).get(
-                        "arguments", {}
-                    )
-                    if isinstance(raw_arguments, str):
-                        try:
-                            arguments: Dict[str, Any] = json.loads(raw_arguments)
-                        except json.JSONDecodeError:
-                            arguments = {}
-                    else:
-                        arguments = raw_arguments
-
-                    tool_calls.append(
-                        CohereToolCall(
-                            name=str(tool_call.get("function", {}).get("name", "")),
-                            parameters=arguments,
-                        )
-                    )
-
-            if role == "user":
-                chat_history.append(CohereMessage(role="USER", message=content))
-            elif role == "assistant":
-                chat_history.append(
-                    CohereMessage(role="CHATBOT", message=content, toolCalls=tool_calls)
-                )
-            elif role == "tool":
-                # Tool messages need special handling
-                chat_history.append(
-                    CohereMessage(
-                        role="TOOL",
-                        message=content,
-                        toolCalls=None,  # Tool messages don't have tool calls
-                    )
-                )
-
-        return chat_history
-
-    def adapt_tool_definitions_to_cohere_standard(
-        self, tools: List[Dict[str, Any]]
-    ) -> List[CohereTool]:
-        """Adapt tool definitions to Cohere format."""
-        cohere_tools = []
-        for tool in tools:
-            function_def = tool.get("function", {})
-            parameters = function_def.get("parameters", {}).get("properties", {})
-            required = function_def.get("parameters", {}).get("required", [])
-
-            parameter_definitions = {}
-            for param_name, param_schema in parameters.items():
-                parameter_definitions[param_name] = CohereParameterDefinition(
-                    description=param_schema.get("description", ""),
-                    type=param_schema.get("type", "string"),
-                    isRequired=param_name in required,
-                )
-
-            cohere_tools.append(
-                CohereTool(
-                    name=function_def.get("name", ""),
-                    description=function_def.get("description", ""),
-                    parameterDefinitions=parameter_definitions,
-                )
-            )
-
-        return cohere_tools
-
-    def _extract_text_content(self, content: Any) -> str:
-        """Extract text content from message content."""
-        if isinstance(content, str):
-            return content
-        elif isinstance(content, list):
-            text_content = ""
-            for content_item in content:
-                if (
-                    isinstance(content_item, dict)
-                    and content_item.get("type") == "text"
-                ):
-                    text_content += content_item.get("text", "")
-            return text_content
-        return str(content)
-
     def transform_request(
         self,
         model: str,
@@ -853,186 +488,78 @@ class OCIChatConfig(BaseConfig):
         litellm_params: dict,
         headers: dict,
     ) -> dict:
-        oci_compartment_id = optional_params.get("oci_compartment_id", None)
+        creds = resolve_oci_credentials(optional_params)
+        oci_compartment_id = creds["oci_compartment_id"]
         if not oci_compartment_id:
-            raise Exception("kwarg `oci_compartment_id` is required for OCI requests")
+            raise OCIError(
+                status_code=400,
+                message=(
+                    "oci_compartment_id is required for OCI chat requests. "
+                    "Pass it as optional_params or set the OCI_COMPARTMENT_ID env var."
+                ),
+            )
 
         vendor = get_vendor_from_model(model)
 
         oci_serving_mode = optional_params.get("oci_serving_mode", "ON_DEMAND")
         if oci_serving_mode not in ["ON_DEMAND", "DEDICATED"]:
-            raise Exception(
-                "kwarg `oci_serving_mode` must be either 'ON_DEMAND' or 'DEDICATED'"
+            raise OCIError(
+                status_code=400,
+                message="kwarg `oci_serving_mode` must be either 'ON_DEMAND' or 'DEDICATED'",
             )
 
         if oci_serving_mode == "DEDICATED":
-            oci_endpoint_id = optional_params.get("oci_endpoint_id", model)
-            servingMode = OCIServingMode(
+            serving_mode = OCIServingMode(
                 servingType="DEDICATED",
-                endpointId=oci_endpoint_id,
+                endpointId=optional_params.get("oci_endpoint_id", model),
             )
         else:
-            servingMode = OCIServingMode(
-                servingType="ON_DEMAND",
-                modelId=model,
-            )
+            serving_mode = OCIServingMode(servingType="ON_DEMAND", modelId=model)
 
-        # Build request based on vendor type
         if vendor == OCIVendors.COHERE:
-            # For Cohere, we need to use the specific Cohere format
-            # Extract the last user message as the main message
-            user_messages = [msg for msg in messages if msg.get("role") == "user"]
+            user_messages = [m for m in messages if m.get("role") == "user"]
             if not user_messages:
-                raise Exception("No user message found for Cohere model")
+                raise OCIError(
+                    status_code=400,
+                    message="No user message found — Cohere models require at least one user message",
+                )
 
-            # Extract system messages into preambleOverride
-            system_messages = [msg for msg in messages if msg.get("role") == "system"]
+            system_messages = [m for m in messages if m.get("role") == "system"]
             preamble_override = None
             if system_messages:
                 preamble = "\n".join(
-                    self._extract_text_content(msg["content"])
-                    for msg in system_messages
+                    _extract_text_content(m["content"]) for m in system_messages
                 )
                 if preamble:
                     preamble_override = preamble
 
-            # Create Cohere-specific chat request
-            optional_cohere_params = self._get_optional_params(
-                OCIVendors.COHERE, optional_params
-            )
             chat_request = CohereChatRequest(
                 apiFormat="COHERE",
-                message=self._extract_text_content(user_messages[-1]["content"]),
-                chatHistory=self.adapt_messages_to_cohere_standard(messages),
+                message=_extract_text_content(user_messages[-1]["content"]),
+                chatHistory=adapt_messages_to_cohere_standard(
+                    [m for m in messages if m.get("role") != "system"]
+                ),
                 preambleOverride=preamble_override,
-                **optional_cohere_params,
+                **self._get_optional_params(OCIVendors.COHERE, optional_params, model),
             )
-
             data = OCICompletionPayload(
                 compartmentId=oci_compartment_id,
-                servingMode=servingMode,
+                servingMode=serving_mode,
                 chatRequest=chat_request,
             )
         else:
-            # Use generic format for other vendors
             data = OCICompletionPayload(
                 compartmentId=oci_compartment_id,
-                servingMode=servingMode,
+                servingMode=serving_mode,
                 chatRequest=OCIChatRequestPayload(
                     apiFormat=vendor.value,
                     messages=adapt_messages_to_generic_oci_standard(messages),
-                    **self._get_optional_params(vendor, optional_params),
+                    **self._get_optional_params(vendor, optional_params, model),
                 ),
             )
 
         return data.model_dump(exclude_none=True)
 
-    def _handle_cohere_response(
-        self, json_response: dict, model: str, model_response: ModelResponse
-    ) -> ModelResponse:
-        """Handle Cohere-specific response format."""
-        cohere_response = CohereChatResult(**json_response)
-        # Cohere response format (uses camelCase)
-        model_id = model
-
-        # Set basic response info
-        model_response.model = model_id
-        model_response.created = int(datetime.datetime.now().timestamp())
-
-        # Extract the response text
-        response_text = cohere_response.chatResponse.text
-        oci_finish_reason = cohere_response.chatResponse.finishReason
-
-        # Map finish reason
-        if oci_finish_reason == "COMPLETE":
-            finish_reason = "stop"
-        elif oci_finish_reason == "MAX_TOKENS":
-            finish_reason = "length"
-        else:
-            finish_reason = "stop"
-
-        # Handle tool calls
-        tool_calls: Optional[List[Dict[str, Any]]] = None
-        if cohere_response.chatResponse.toolCalls:
-            tool_calls = []
-            for tool_call in cohere_response.chatResponse.toolCalls:
-                tool_calls.append(
-                    {
-                        "id": f"call_{len(tool_calls)}",  # Generate a simple ID
-                        "type": "function",
-                        "function": {
-                            "name": tool_call.name,
-                            "arguments": json.dumps(tool_call.parameters),
-                        },
-                    }
-                )
-
-        # Create choice
-        from litellm.types.utils import Choices
-
-        choice = Choices(
-            index=0,
-            message={
-                "role": "assistant",
-                "content": response_text,
-                "tool_calls": tool_calls,
-            },
-            finish_reason=finish_reason,
-        )
-        model_response.choices = [choice]
-
-        # Extract usage info
-        usage_info = cohere_response.chatResponse.usage
-        from litellm.types.utils import Usage
-
-        model_response.usage = Usage(  # type: ignore[attr-defined]
-            prompt_tokens=usage_info.promptTokens,  # type: ignore[union-attr]
-            completion_tokens=usage_info.completionTokens,  # type: ignore[union-attr]
-            total_tokens=usage_info.totalTokens,  # type: ignore[union-attr]
-        )
-
-        return model_response
-
-    def _handle_generic_response(
-        self,
-        json: dict,
-        model: str,
-        model_response: ModelResponse,
-        raw_response: httpx.Response,
-    ) -> ModelResponse:
-        """Handle generic OCI response format."""
-        try:
-            completion_response = OCICompletionResponse(**json)
-        except TypeError as e:
-            raise OCIError(
-                message=f"Response cannot be casted to OCICompletionResponse: {str(e)}",
-                status_code=raw_response.status_code,
-            )
-
-        iso_str = completion_response.chatResponse.timeCreated
-        dt = datetime.datetime.fromisoformat(iso_str.replace("Z", "+00:00"))
-        model_response.created = int(dt.timestamp())
-
-        model_response.model = completion_response.modelId
-
-        message = model_response.choices[0].message  # type: ignore
-        response_message = completion_response.chatResponse.choices[0].message
-        if response_message.content and response_message.content[0].type == "TEXT":
-            message.content = response_message.content[0].text
-        if response_message.toolCalls:
-            message.tool_calls = adapt_tools_to_openai_standard(
-                response_message.toolCalls
-            )
-
-        usage = Usage(
-            prompt_tokens=completion_response.chatResponse.usage.promptTokens,
-            completion_tokens=completion_response.chatResponse.usage.completionTokens,
-            total_tokens=completion_response.chatResponse.usage.totalTokens,
-        )
-        model_response.usage = usage  # type: ignore
-
-        return model_response
-
     def transform_response(
         self,
         model: str,
@@ -1047,34 +574,31 @@ class OCIChatConfig(BaseConfig):
         api_key: Optional[str] = None,
         json_mode: Optional[bool] = None,
     ) -> ModelResponse:
-        json = raw_response.json()  # noqa: F811
+        response_json = raw_response.json()
 
-        error = json.get("error")
-
-        if error is not None:
-            raise OCIError(
-                message=str(json["error"]),
-                status_code=raw_response.status_code,
-            )
-
-        if not isinstance(json, dict):
+        if not isinstance(response_json, dict):
             raise OCIError(
                 message="Invalid response format from OCI",
                 status_code=raw_response.status_code,
             )
 
-        vendor = get_vendor_from_model(model)
+        if response_json.get("error") is not None:
+            raise OCIError(
+                message=str(response_json["error"]),
+                status_code=raw_response.status_code,
+            )
 
-        # Handle response based on vendor type
+        vendor = get_vendor_from_model(model)
         if vendor == OCIVendors.COHERE:
-            model_response = self._handle_cohere_response(json, model, model_response)
+            model_response = handle_cohere_response(
+                response_json, model, model_response, raw_response
+            )
         else:
-            model_response = self._handle_generic_response(
-                json, model, model_response, raw_response
+            model_response = handle_generic_response(
+                response_json, model, model_response, raw_response
             )
 
         model_response._hidden_params["additional_headers"] = raw_response.headers
-
         return model_response
 
     @track_llm_api_timing()
@@ -1091,8 +615,6 @@ class OCIChatConfig(BaseConfig):
         json_mode: Optional[bool] = None,
         signed_json_body: Optional[bytes] = None,
     ) -> "OCIStreamWrapper":
-        if "stream" in data:
-            del data["stream"]
         if client is None or isinstance(client, AsyncHTTPHandler):
             client = _get_httpx_client(params={})
 
@@ -1100,7 +622,11 @@ class OCIChatConfig(BaseConfig):
             response = client.post(
                 api_base,
                 headers=headers,
-                data=json.dumps(data),
+                data=(
+                    signed_json_body
+                    if signed_json_body is not None
+                    else json.dumps(data)
+                ),
                 stream=True,
                 logging_obj=logging_obj,
                 timeout=STREAMING_TIMEOUT,
@@ -1111,15 +637,12 @@ class OCIChatConfig(BaseConfig):
         if response.status_code != 200:
             raise OCIError(status_code=response.status_code, message=response.text)
 
-        completion_stream = response.iter_text()
-
-        streaming_response = OCIStreamWrapper(
-            completion_stream=completion_stream,
+        return OCIStreamWrapper(
+            completion_stream=_iter_sse_events(response.iter_text()),
             model=model,
             custom_llm_provider=custom_llm_provider,
             logging_obj=logging_obj,
         )
-        return streaming_response
 
     @track_llm_api_timing()
     async def get_async_custom_stream_wrapper(
@@ -1135,17 +658,18 @@ class OCIChatConfig(BaseConfig):
         json_mode: Optional[bool] = None,
         signed_json_body: Optional[bytes] = None,
     ) -> "OCIStreamWrapper":
-        if "stream" in data:
-            del data["stream"]
-
         if client is None or isinstance(client, HTTPHandler):
-            client = get_async_httpx_client(llm_provider=LlmProviders.BYTEZ, params={})
+            client = get_async_httpx_client(llm_provider=LlmProviders.OCI, params={})
 
         try:
             response = await client.post(
                 api_base,
                 headers=headers,
-                data=json.dumps(data),
+                data=(
+                    signed_json_body
+                    if signed_json_body is not None
+                    else json.dumps(data)
+                ),
                 stream=True,
                 logging_obj=logging_obj,
                 timeout=STREAMING_TIMEOUT,
@@ -1156,22 +680,12 @@ class OCIChatConfig(BaseConfig):
         if response.status_code != 200:
             raise OCIError(status_code=response.status_code, message=response.text)
 
-        completion_stream = response.aiter_text()
-
-        async def split_chunks(completion_stream: AsyncIterator[str]):
-            async for item in completion_stream:
-                for chunk in item.split("\n\n"):
-                    if not chunk:
-                        continue
-                    yield chunk.strip()
-
-        streaming_response = OCIStreamWrapper(
-            completion_stream=split_chunks(completion_stream),
+        return OCIStreamWrapper(
+            completion_stream=_aiter_sse_events(response.aiter_text()),
             model=model,
             custom_llm_provider=custom_llm_provider,
             logging_obj=logging_obj,
         )
-        return streaming_response
 
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
@@ -1179,332 +693,61 @@ class OCIChatConfig(BaseConfig):
         return OCIError(status_code=status_code, message=error_message)
 
 
-open_ai_to_generic_oci_role_map: Dict[str, OCIRoles] = {
-    "system": "SYSTEM",
-    "user": "USER",
-    "assistant": "ASSISTANT",
-    "tool": "TOOL",
-}
-
-
-def adapt_messages_to_generic_oci_standard_content_message(
-    role: str, content: Union[str, list]
-) -> OCIMessage:
-    new_content: List[OCIContentPartUnion] = []
-    if isinstance(content, str):
-        return OCIMessage(
-            role=open_ai_to_generic_oci_role_map[role],
-            content=[OCITextContentPart(text=content)],
-            toolCalls=None,
-            toolCallId=None,
-        )
-
-    # content is a list of content items:
-    # [
-    #     {"type": "text", "text": "Hello"},
-    #     {"type": "image_url", "image_url": "https://example.com/image.png"}
-    # ]
-    for content_item in content:
-        if not isinstance(content_item, dict):
-            raise Exception("Each content item must be a dictionary")
-
-        type = content_item.get("type")
-        if not isinstance(type, str):
-            raise Exception("Prop `type` is not a string")
-
-        if type not in ["text", "image_url"]:
-            raise Exception(f"Prop `{type}` is not supported")
-
-        if type == "text":
-            text = content_item.get("text")
-            if not isinstance(text, str):
-                raise Exception("Prop `text` is not a string")
-            new_content.append(OCITextContentPart(text=text))
-
-        elif type == "image_url":
-            image_url = content_item.get("image_url")
-            # Handle both OpenAI format (object with url) and string format
-            if isinstance(image_url, dict):
-                image_url = image_url.get("url")
-            if not isinstance(image_url, str):
-                raise Exception(
-                    "Prop `image_url` must be a string or an object with a `url` property"
-                )
-            new_content.append(OCIImageContentPart(imageUrl=OCIImageUrl(url=image_url)))
-
-    return OCIMessage(
-        role=open_ai_to_generic_oci_role_map[role],
-        content=new_content,
-        toolCalls=None,
-        toolCallId=None,
-    )
-
-
-def adapt_messages_to_generic_oci_standard_tool_call(
-    role: str, tool_calls: list
-) -> OCIMessage:
-    tool_calls_formated = []
-    for tool_call in tool_calls:
-        if not isinstance(tool_call, dict):
-            raise Exception("Each tool call must be a dictionary")
-
-        if tool_call.get("type") != "function":
-            raise Exception("OCI only supports function tools")
-
-        tool_call_id = tool_call.get("id")
-        if not isinstance(tool_call_id, str):
-            raise Exception("Prop `id` is not a string")
-
-        tool_function = tool_call.get("function")
-        if not isinstance(tool_function, dict):
-            raise Exception("Prop `function` is not a dictionary")
-
-        function_name = tool_function.get("name")
-        if not isinstance(function_name, str):
-            raise Exception("Prop `name` is not a string")
-
-        arguments = tool_call["function"].get("arguments", "{}")
-        if not isinstance(arguments, str):
-            raise Exception("Prop `arguments` is not a string")
-
-        # tool_calls_formated.append(OCIToolCall(
-        #     id=tool_call_id,
-        #     type="FUNCTION",
-        #     function=OCIFunction(
-        #         name=function_name,
-        #         arguments=arguments
-        #     )
-        # ))
-
-        tool_calls_formated.append(
-            OCIToolCall(
-                id=tool_call_id,
-                type="FUNCTION",
-                name=function_name,
-                arguments=arguments,
-            )
-        )
-
-    return OCIMessage(
-        role=open_ai_to_generic_oci_role_map[role],
-        content=None,
-        toolCalls=tool_calls_formated,
-        toolCallId=None,
-    )
-
-
-def adapt_messages_to_generic_oci_standard_tool_response(
-    role: str, tool_call_id: str, content: str
-) -> OCIMessage:
-    return OCIMessage(
-        role=open_ai_to_generic_oci_role_map[role],
-        content=[OCITextContentPart(text=content)],
-        toolCalls=None,
-        toolCallId=tool_call_id,
-    )
-
-
-def adapt_messages_to_generic_oci_standard(
-    messages: List[AllMessageValues],
-) -> List[OCIMessage]:
-    new_messages = []
-    for message in messages:
-        role = message["role"]
-        content = message.get("content")
-        tool_calls = message.get("tool_calls")
-        tool_call_id = message.get("tool_call_id")
-
-        if role == "assistant" and tool_calls is not None:
-            if not isinstance(tool_calls, list):
-                raise Exception("Prop `tool_calls` must be a list of tool calls")
-            new_messages.append(
-                adapt_messages_to_generic_oci_standard_tool_call(role, tool_calls)
-            )
-
-        elif role in ["system", "user", "assistant"] and content is not None:
-            if not isinstance(content, (str, list)):
-                raise Exception(
-                    "Prop `content` must be a string or a list of content items"
-                )
-            new_messages.append(
-                adapt_messages_to_generic_oci_standard_content_message(role, content)
-            )
-
-        elif role == "tool":
-            if not isinstance(tool_call_id, str):
-                raise Exception("Prop `tool_call_id` is required and must be a string")
-            if not isinstance(content, str):
-                raise Exception("Prop `content` is not a string")
-            new_messages.append(
-                adapt_messages_to_generic_oci_standard_tool_response(
-                    role, tool_call_id, content
-                )
-            )
-
-    return new_messages
-
-
-def adapt_tool_definition_to_oci_standard(tools: List[Dict], vendor: OCIVendors):
-    new_tools = []
-    for tool in tools:
-        if tool["type"] != "function":
-            raise Exception("OCI only supports function tools")
-
-        tool_function = tool.get("function")
-        if not isinstance(tool_function, dict):
-            raise Exception("Prop `function` is not a dictionary")
-
-        new_tool = OCIToolDefinition(
-            type="FUNCTION",
-            name=tool_function.get("name"),
-            description=tool_function.get("description", ""),
-            parameters=tool_function.get("parameters", {}),
-        )
-        new_tools.append(new_tool)
-
-    return new_tools
-
-
-def adapt_tools_to_openai_standard(
-    tools: List[OCIToolCall],
-) -> List[ChatCompletionMessageToolCall]:
-    new_tools = []
-    for tool in tools:
-        new_tool = ChatCompletionMessageToolCall(
-            id=tool.id,
-            type="function",
-            function={
-                "name": tool.name,
-                "arguments": tool.arguments,
-            },
-        )
-        new_tools.append(new_tool)
-    return new_tools
-
-
 class OCIStreamWrapper(CustomStreamWrapper):
-    """
-    Custom stream wrapper for OCI responses.
-    This class is used to handle streaming responses from OCI's API.
-    """
+    """Custom stream wrapper that dispatches OCI SSE chunks to the correct handler."""
 
-    def __init__(
-        self,
-        **kwargs: Any,
-    ):
+    def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
+        # Tracks whether any prior Cohere chunk in this stream has emitted
+        # tool calls. The Cohere handler uses this to decide whether the
+        # terminal consolidation chunk's tool calls are duplicates (suppress)
+        # or the only copy of the tool calls (pass through).
+        self._cohere_tool_calls_emitted = False
+        # Analogous flag for text content. Lets the Cohere handler distinguish
+        # the common case (prior deltas already streamed the text, so the
+        # terminal chunk's text is a duplicate to suppress) from the degenerate
+        # single-event case (terminal chunk carries the only copy of the text).
+        self._cohere_text_emitted = False
 
-    def chunk_creator(self, chunk: Any):
+    def chunk_creator(self, chunk: Any) -> ModelResponseStream:
         if not isinstance(chunk, str):
             raise ValueError(f"Chunk is not a string: {chunk}")
         if not chunk.startswith("data:"):
             raise ValueError(f"Chunk does not start with 'data:': {chunk}")
-        dict_chunk = json.loads(chunk[5:])  # Remove 'data: ' prefix and parse JSON
-
-        # Check if this is a Cohere stream chunk
-        if "apiFormat" in dict_chunk and dict_chunk.get("apiFormat") == "COHERE":
-            return self._handle_cohere_stream_chunk(dict_chunk)
-        else:
-            return self._handle_generic_stream_chunk(dict_chunk)
-
-    def _handle_cohere_stream_chunk(self, dict_chunk: dict):
-        """Handle Cohere-specific streaming chunks."""
         try:
-            typed_chunk = CohereStreamChunk(**dict_chunk)
-        except TypeError as e:
-            raise ValueError(f"Chunk cannot be casted to CohereStreamChunk: {str(e)}")
+            dict_chunk = json.loads(chunk[5:])
+        except json.JSONDecodeError as e:
+            raise OCIError(
+                status_code=500,
+                message=f"Chunk cannot be parsed as JSON: {str(e)}",
+            )
 
-        if typed_chunk.index is None:
-            typed_chunk.index = 0
+        if dict_chunk.get("apiFormat") == "COHERE":
+            result = handle_cohere_stream_chunk(
+                dict_chunk,
+                prior_tool_calls_emitted=self._cohere_tool_calls_emitted,
+                prior_text_emitted=self._cohere_text_emitted,
+            )
+            if not self._cohere_tool_calls_emitted:
+                for choice in result.choices:
+                    if getattr(choice.delta, "tool_calls", None) is not None:
+                        self._cohere_tool_calls_emitted = True
+                        break
+            if not self._cohere_text_emitted:
+                for choice in result.choices:
+                    if getattr(choice.delta, "content", None):
+                        self._cohere_text_emitted = True
+                        break
+            return result
+        return handle_generic_stream_chunk(dict_chunk)
 
-        # Extract text content
-        text = typed_chunk.text or ""
 
-        # Map finish reason to standard format
-        finish_reason = typed_chunk.finishReason
-        if finish_reason == "COMPLETE":
-            finish_reason = "stop"
-        elif finish_reason == "MAX_TOKENS":
-            finish_reason = "length"
-        elif finish_reason is None:
-            finish_reason = None
-        else:
-            finish_reason = "stop"
-
-        # For Cohere, we don't have tool calls in the streaming format
-        tool_calls = None
-
-        return ModelResponseStream(
-            choices=[
-                StreamingChoices(
-                    index=typed_chunk.index if typed_chunk.index else 0,
-                    delta=Delta(
-                        content=text,
-                        tool_calls=tool_calls,
-                        provider_specific_fields=None,
-                        thinking_blocks=None,
-                        reasoning_content=None,
-                    ),
-                    finish_reason=finish_reason,
-                )
-            ]
-        )
-
-    def _handle_generic_stream_chunk(self, dict_chunk: dict):
-        """Handle generic OCI streaming chunks."""
-        # Fix missing required fields in tool calls before Pydantic validation
-        # OCI streams tool calls progressively, so early chunks may be missing required fields
-        if dict_chunk.get("message") and dict_chunk["message"].get("toolCalls"):
-            for tool_call in dict_chunk["message"]["toolCalls"]:
-                if "arguments" not in tool_call:
-                    tool_call["arguments"] = ""
-                if "id" not in tool_call:
-                    tool_call["id"] = ""
-                if "name" not in tool_call:
-                    tool_call["name"] = ""
-
-        try:
-            typed_chunk = OCIStreamChunk(**dict_chunk)
-        except TypeError as e:
-            raise ValueError(f"Chunk cannot be casted to OCIStreamChunk: {str(e)}")
-
-        if typed_chunk.index is None:
-            typed_chunk.index = 0
-
-        text = ""
-        if typed_chunk.message and typed_chunk.message.content:
-            for item in typed_chunk.message.content:
-                if isinstance(item, OCITextContentPart):
-                    text += item.text
-                elif isinstance(item, OCIImageContentPart):
-                    raise ValueError(
-                        "OCI does not support image content in streaming responses"
-                    )
-                else:
-                    raise ValueError(
-                        f"Unsupported content type in OCI response: {item.type}"
-                    )
-
-        tool_calls = None
-        if typed_chunk.message and typed_chunk.message.toolCalls:
-            tool_calls = adapt_tools_to_openai_standard(typed_chunk.message.toolCalls)
-
-        return ModelResponseStream(
-            choices=[
-                StreamingChoices(
-                    index=typed_chunk.index if typed_chunk.index else 0,
-                    delta=Delta(
-                        content=text,
-                        tool_calls=(
-                            [tool.model_dump() for tool in tool_calls]
-                            if tool_calls
-                            else None
-                        ),
-                        provider_specific_fields=None,  # OCI does not have provider specific fields in the response
-                        thinking_blocks=None,  # OCI does not have thinking blocks in the response
-                        reasoning_content=None,  # OCI does not have reasoning content in the response
-                    ),
-                    finish_reason=typed_chunk.finishReason,
-                )
-            ]
-        )
+__all__ = [
+    "OCIChatConfig",
+    "OCIStreamWrapper",
+    "OCIRequestWrapper",
+    "OCI_API_VERSION",
+    "STREAMING_TIMEOUT",
+    "get_vendor_from_model",
+    "version",
+]
diff --git a/litellm/llms/oci/common_utils.py b/litellm/llms/oci/common_utils.py
index 661a6c89e4b..8785b1548a5 100644
--- a/litellm/llms/oci/common_utils.py
+++ b/litellm/llms/oci/common_utils.py
@@ -1,9 +1,42 @@
-from typing import Optional
+import base64
+import hashlib
+import json
+import os
+import re
+from dataclasses import dataclass
+from email.utils import formatdate
+from typing import Any, Dict, Optional, Protocol, Tuple
+from urllib.parse import urlparse
 
 import httpx
 
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 
+try:
+    from cryptography.hazmat.primitives import hashes, serialization
+    from cryptography.hazmat.primitives.asymmetric import padding, rsa
+
+    _CRYPTOGRAPHY_AVAILABLE = True
+except ImportError:
+    _CRYPTOGRAPHY_AVAILABLE = False
+
+try:
+    from litellm._version import version as _litellm_version
+except ImportError:
+    _litellm_version = "0.0.0"
+
+
+# OCI GenAI REST API version — stable since service launch, unlikely to change
+OCI_API_VERSION = "20231130"
+
+
+def _require_cryptography() -> None:
+    if not _CRYPTOGRAPHY_AVAILABLE:
+        raise ImportError(
+            "cryptography package is required for OCI authentication. "
+            "Please install it with: pip install cryptography"
+        )
+
 
 class OCIError(BaseLLMException):
     def __init__(
@@ -17,3 +50,520 @@ class OCIError(BaseLLMException):
             message=message,
             headers=headers,
         )
+
+
+# ---------------------------------------------------------------------------
+# OCI signing protocol and helpers
+# ---------------------------------------------------------------------------
+
+
+class OCISignerProtocol(Protocol):
+    """
+    Protocol for OCI request signers (e.g., oci.signer.Signer).
+
+    Compatible with the OCI Python SDK's Signer class.
+    See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html
+    """
+
+    def do_request_sign(
+        self, request: Any, *, enforce_content_headers: bool = False
+    ) -> None:
+        pass
+
+
+@dataclass
+class OCIRequestWrapper:
+    """
+    Wrapper for HTTP requests compatible with OCI signer interface.
+
+    Wraps request data in the format expected by OCI SDK signers, which require
+    objects with method, url, headers, body, and path_url attributes.
+    """
+
+    method: str
+    url: str
+    headers: dict
+    body: bytes
+
+    @property
+    def path_url(self) -> str:
+        """Returns the path + query string for OCI signing."""
+        parsed = urlparse(self.url)
+        return parsed.path + ("?" + parsed.query if parsed.query else "")
+
+
+def sha256_base64(data: bytes) -> str:
+    # SHA-256 is used here to compute the x-content-sha256 header required by the
+    # OCI HTTP signing specification (RSA-SHA256 request signing), not for password
+    # or secret hashing.  This is the correct and mandated algorithm for this purpose.
+    # See: https://docs.oracle.com/en-us/iaas/Content/API/Concepts/signingrequests.htm
+    #
+    # ``usedforsecurity=False`` declares non-security intent to static analyzers
+    # (CodeQL ``py/weak-sensitive-data-hashing``) — without it the request body
+    # gets flagged as "password-like data" via taint tracking.
+    digest = hashlib.sha256(data, usedforsecurity=False).digest()  # noqa: S324
+    return base64.b64encode(digest).decode()
+
+
+def build_signature_string(
+    method: str, path: str, headers: dict, signed_headers: list
+) -> str:
+    lines = []
+    for header in signed_headers:
+        if header == "(request-target)":
+            value = f"{method.lower()} {path}"
+        else:
+            value = headers[header]
+        lines.append(f"{header}: {value}")
+    return "\n".join(lines)
+
+
+def load_private_key_from_str(key_str: str) -> Any:
+    _require_cryptography()
+    key = serialization.load_pem_private_key(  # type: ignore[union-attr]
+        key_str.encode("utf-8"),
+        password=None,
+    )
+    if not isinstance(key, rsa.RSAPrivateKey):  # type: ignore[union-attr]
+        raise TypeError(
+            "The provided private key is not an RSA key, which is required for OCI signing."
+        )
+    return key
+
+
+def load_private_key_from_file(file_path: str) -> Any:
+    """Loads a private key from a file path."""
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            key_str = f.read().strip()
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Private key file not found: {file_path}")
+    except OSError as e:
+        raise OSError(f"Failed to read private key file '{file_path}': {e}") from e
+
+    if not key_str:
+        raise ValueError(f"Private key file is empty: {file_path}")
+
+    return load_private_key_from_str(key_str)
+
+
+# ---------------------------------------------------------------------------
+# Env-var credential resolution
+# ---------------------------------------------------------------------------
+
+_OCI_REGION_ENV = "OCI_REGION"
+_OCI_USER_ENV = "OCI_USER"
+_OCI_FINGERPRINT_ENV = "OCI_FINGERPRINT"
+_OCI_TENANCY_ENV = "OCI_TENANCY"
+_OCI_KEY_FILE_ENV = "OCI_KEY_FILE"
+_OCI_KEY_ENV = "OCI_KEY"
+_OCI_COMPARTMENT_ID_ENV = "OCI_COMPARTMENT_ID"
+
+
+def resolve_oci_credentials(optional_params: dict) -> dict:
+    """
+    Merge OCI credentials from optional_params (explicit, always wins) and
+    environment variables (fallback).
+
+    Returns a dict with resolved values for:
+        oci_region, oci_user, oci_fingerprint, oci_tenancy,
+        oci_key, oci_key_file, oci_compartment_id
+    """
+    return {
+        "oci_region": optional_params.get("oci_region")
+        or os.environ.get(_OCI_REGION_ENV)
+        or "us-ashburn-1",
+        "oci_user": optional_params.get("oci_user") or os.environ.get(_OCI_USER_ENV),
+        "oci_fingerprint": optional_params.get("oci_fingerprint")
+        or os.environ.get(_OCI_FINGERPRINT_ENV),
+        "oci_tenancy": optional_params.get("oci_tenancy")
+        or os.environ.get(_OCI_TENANCY_ENV),
+        "oci_key": optional_params.get("oci_key") or os.environ.get(_OCI_KEY_ENV),
+        "oci_key_file": optional_params.get("oci_key_file")
+        or os.environ.get(_OCI_KEY_FILE_ENV),
+        "oci_compartment_id": optional_params.get("oci_compartment_id")
+        or os.environ.get(_OCI_COMPARTMENT_ID_ENV),
+    }
+
+
+_OCI_REGION_RE = re.compile(r"^[a-z][a-z0-9-]{0,30}[a-z0-9]$")
+_OCI_ACTION_PATH_RE = re.compile(rf"/{OCI_API_VERSION}/actions/[^/?#]+/?$")
+
+
+def get_oci_base_url(optional_params: dict, api_base: Optional[str] = None) -> str:
+    """Return the OCI inference base URL, respecting any explicit api_base override.
+
+    If ``api_base`` already ends with a fully-formed OCI action path
+    (``/{OCI_API_VERSION}/actions/<name>``), that suffix is stripped so callers
+    can append their own action path without producing a doubled URL.
+    """
+    if api_base:
+        return _OCI_ACTION_PATH_RE.sub("", api_base).rstrip("/")
+    creds = resolve_oci_credentials(optional_params)
+    region = creds["oci_region"]
+    if not isinstance(region, str) or not _OCI_REGION_RE.match(region):
+        raise OCIError(
+            status_code=400,
+            message=(
+                f"Invalid OCI region {region!r}: must match "
+                "^[a-z][a-z0-9-]{0,30}[a-z0-9]$ (e.g. 'us-ashburn-1')."
+            ),
+        )
+    return f"https://inference.generativeai.{region}.oci.oraclecloud.com"
+
+
+# ---------------------------------------------------------------------------
+# Signing implementations (shared by chat, embed, and rerank configs)
+# ---------------------------------------------------------------------------
+
+
+def sign_with_oci_signer(
+    headers: dict,
+    optional_params: dict,
+    request_data: dict,
+    api_base: str,
+) -> Tuple[dict, bytes]:
+    """Sign a request using an OCI SDK Signer object passed in optional_params."""
+    oci_signer = optional_params.get("oci_signer")
+    body = json.dumps(request_data).encode("utf-8")
+    method = str(optional_params.get("method", "POST")).upper()
+
+    if method not in {"POST", "GET", "PUT", "DELETE", "PATCH"}:
+        raise ValueError(f"Unsupported HTTP method: {method}")
+
+    prepared_headers = {**headers}
+    prepared_headers.setdefault("content-type", "application/json")
+    prepared_headers.setdefault("content-length", str(len(body)))
+
+    request_wrapper = OCIRequestWrapper(
+        method=method, url=api_base, headers=prepared_headers, body=body
+    )
+
+    if oci_signer is None:
+        raise ValueError("oci_signer cannot be None when calling sign_with_oci_signer")
+
+    try:
+        oci_signer.do_request_sign(request_wrapper, enforce_content_headers=True)
+    except Exception as e:
+        raise OCIError(
+            status_code=500,
+            message=(
+                f"Failed to sign request with provided oci_signer: {str(e)}. "
+                "The signer must implement the OCI SDK Signer interface with a "
+                "do_request_sign(request, enforce_content_headers=True) method. "
+                "See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html"
+            ),
+        ) from e
+
+    headers.update(request_wrapper.headers)
+    return headers, body
+
+
+def sign_with_manual_credentials(
+    headers: dict,
+    optional_params: dict,
+    request_data: dict,
+    api_base: str,
+) -> Tuple[dict, bytes]:
+    """Sign a request using manually provided OCI credentials (user/fingerprint/tenancy/key)."""
+    creds = resolve_oci_credentials(optional_params)
+    oci_user = creds["oci_user"]
+    oci_fingerprint = creds["oci_fingerprint"]
+    oci_tenancy = creds["oci_tenancy"]
+    oci_key = creds["oci_key"]
+    oci_key_file = creds["oci_key_file"]
+
+    if (
+        not oci_user
+        or not oci_fingerprint
+        or not oci_tenancy
+        or not (oci_key or oci_key_file)
+    ):
+        raise OCIError(
+            status_code=401,
+            message=(
+                "Missing required OCI credentials: oci_user, oci_fingerprint, oci_tenancy, "
+                "and at least one of oci_key or oci_key_file. "
+                "These can also be supplied via environment variables: "
+                f"{_OCI_USER_ENV}, {_OCI_FINGERPRINT_ENV}, {_OCI_TENANCY_ENV}, {_OCI_KEY_ENV} (or {_OCI_KEY_FILE_ENV}). "
+                "Alternatively, provide an oci_signer object from the OCI SDK."
+            ),
+        )
+
+    method = str(optional_params.get("method", "POST")).upper()
+    body = json.dumps(request_data).encode("utf-8")
+    parsed = urlparse(api_base)
+    path = parsed.path or "/"
+    host = parsed.netloc
+
+    date = formatdate(usegmt=True)
+    content_type = headers.get("content-type", "application/json")
+    content_length = str(len(body))
+    x_content_sha256 = sha256_base64(body)
+
+    headers_to_sign: Dict[str, str] = {
+        "date": date,
+        "host": host,
+        "content-type": content_type,
+        "content-length": content_length,
+        "x-content-sha256": x_content_sha256,
+    }
+
+    signed_header_names = [
+        "date",
+        "(request-target)",
+        "host",
+        "content-length",
+        "content-type",
+        "x-content-sha256",
+    ]
+    signing_string = build_signature_string(
+        method, path, headers_to_sign, signed_header_names
+    )
+
+    _require_cryptography()
+
+    # Resolve the private key — prefer inline PEM content over file path
+    oci_key_content: Optional[str] = None
+    if oci_key:
+        if not isinstance(oci_key, str):
+            raise OCIError(
+                status_code=400,
+                message=(
+                    f"oci_key must be a string containing the PEM private key content. "
+                    f"Got type: {type(oci_key).__name__}"
+                ),
+            )
+        oci_key_content = oci_key.replace("\\n", "\n").replace("\r\n", "\n")
+
+    private_key = (
+        load_private_key_from_str(oci_key_content)
+        if oci_key_content
+        else load_private_key_from_file(oci_key_file) if oci_key_file else None
+    )
+
+    if private_key is None:
+        raise OCIError(
+            status_code=400,
+            message="Private key is required for OCI authentication. Provide either oci_key or oci_key_file.",
+        )
+
+    signature = private_key.sign(
+        signing_string.encode("utf-8"),
+        padding.PKCS1v15(),  # type: ignore[union-attr]
+        hashes.SHA256(),  # type: ignore[union-attr]
+    )
+    signature_b64 = base64.b64encode(signature).decode()
+
+    key_id = f"{oci_tenancy}/{oci_user}/{oci_fingerprint}"
+    authorization = (
+        'Signature version="1",'
+        f'keyId="{key_id}",'
+        'algorithm="rsa-sha256",'
+        f'headers="{" ".join(signed_header_names)}",'
+        f'signature="{signature_b64}"'
+    )
+
+    headers.update(
+        {
+            "authorization": authorization,
+            "date": date,
+            "host": host,
+            "content-type": content_type,
+            "content-length": content_length,
+            "x-content-sha256": x_content_sha256,
+        }
+    )
+    return headers, body
+
+
+def sign_oci_request(
+    headers: dict,
+    optional_params: dict,
+    request_data: dict,
+    api_base: str,
+    api_key: Optional[str] = None,
+    model: Optional[str] = None,
+    stream: Optional[bool] = None,
+    fake_stream: Optional[bool] = None,
+) -> Tuple[dict, bytes]:
+    """
+    Route to the appropriate OCI signing method based on what credentials are present.
+
+    If ``oci_signer`` is in optional_params, use the OCI SDK signer object.
+    Otherwise use manual RSA-SHA256 signing with explicit credentials (which can
+    also be supplied via OCI_* environment variables).
+
+    Returns:
+        Tuple of (signed_headers, signed_body_bytes)
+    """
+    if optional_params.get("oci_signer") is not None:
+        return sign_with_oci_signer(headers, optional_params, request_data, api_base)
+    return sign_with_manual_credentials(
+        headers, optional_params, request_data, api_base
+    )
+
+
+def validate_oci_environment(
+    headers: dict,
+    optional_params: dict,
+    api_key: Optional[str] = None,
+) -> dict:
+    """
+    Populate common OCI request headers (content-type, user-agent).
+
+    Full credential validation is deferred to signing time so that credentials
+    supplied via environment variables are resolved at call time rather than
+    at construction time.
+    """
+    headers.setdefault("content-type", "application/json")
+    headers.setdefault("user-agent", f"litellm/{_litellm_version}")
+    return headers
+
+
+# ---------------------------------------------------------------------------
+# JSON schema utilities for OCI tool definitions
+#
+# OCI Generative AI does not support JSON Schema extensions ($ref, $defs,
+# anyOf).  Pydantic v2 emits all three for models with Optional fields or
+# nested schemas.  The helpers below are ported from the official
+# langchain-oracle reference implementation so that tool schemas are always
+# valid before they reach the OCI endpoint.
+# ---------------------------------------------------------------------------
+
+# Mapping from JSON Schema type names to Python type names, as expected by
+# the OCI Cohere API's CohereParameterDefinition.type field.
+OCI_JSON_TO_PYTHON_TYPES: Dict[str, str] = {
+    "string": "str",
+    "number": "float",
+    "boolean": "bool",
+    "integer": "int",
+    "array": "List",
+    "object": "Dict",
+    "any": "any",
+}
+
+
+def resolve_oci_schema_refs(schema: Dict[str, Any]) -> Dict[str, Any]:
+    """Inline all ``$ref``/``$defs`` references — OCI does not support JSON Schema ``$ref``."""
+    defs = schema.get("$defs", {})
+    resolving_stack: set = set()
+
+    def _resolve(obj: Any) -> Any:
+        if isinstance(obj, dict):
+            if "$ref" in obj:
+                ref = obj["$ref"]
+                if ref.startswith("#/$defs/"):
+                    key = ref.split("/")[-1]
+                    if key in resolving_stack:
+                        return {"type": "object"}  # break cycles
+                    resolving_stack.add(key)
+                    try:
+                        return _resolve(defs.get(key, obj))
+                    finally:
+                        resolving_stack.discard(key)
+                return obj  # external $ref — leave unchanged
+            return {k: _resolve(v) for k, v in obj.items()}
+        if isinstance(obj, list):
+            return [_resolve(item) for item in obj]
+        return obj
+
+    resolved = _resolve(schema)
+    if isinstance(resolved, dict):
+        resolved.pop("$defs", None)
+    return resolved
+
+
+def resolve_oci_schema_anyof(obj: Any) -> Any:
+    """Resolve Pydantic v2 ``Optional[T]`` → ``anyOf`` patterns.
+
+    Pydantic v2 emits ``{"anyOf": [{"type": "T"}, {"type": "null"}]}`` for
+    ``Optional[T]``.  OCI models don't understand ``anyOf``, so we pick the
+    first non-null branch and merge top-level metadata into it.
+    """
+    if isinstance(obj, dict):
+        if "anyOf" in obj and "type" not in obj:
+            non_null = [
+                t
+                for t in obj["anyOf"]
+                if not (isinstance(t, dict) and t.get("type") == "null")
+            ]
+            if non_null:
+                resolved = {**obj, **non_null[0]}
+                resolved.pop("anyOf", None)
+                return resolve_oci_schema_anyof(resolved)
+        return {k: resolve_oci_schema_anyof(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [resolve_oci_schema_anyof(item) for item in obj]
+    return obj
+
+
+def sanitize_oci_schema(schema: Any) -> Any:
+    """Recursively remove OCI-incompatible fields from a JSON schema.
+
+    Strips ``title`` keys, removes ``None``-valued ``default`` entries,
+    normalises ``type: [T, "null"]`` list types, and ensures arrays carry an
+    ``items`` definition.
+    """
+    if isinstance(schema, list):
+        return [sanitize_oci_schema(item) for item in schema]
+    if not isinstance(schema, dict):
+        return schema
+
+    sanitized: Dict[str, Any] = {}
+    for key, value in schema.items():
+        if key == "title":
+            continue
+        if key == "default" and value is None:
+            continue
+        if key == "type":
+            if value == "any":
+                sanitized[key] = "object"
+                continue
+            if isinstance(value, list):
+                non_null = [t for t in value if t != "null"]
+                sanitized[key] = non_null[0] if non_null else "string"
+                continue
+        sanitized[key] = sanitize_oci_schema(value)
+
+    if sanitized.get("type") == "array" and "items" not in sanitized:
+        sanitized["items"] = {"type": "object"}
+
+    required = sanitized.get("required")
+    properties = sanitized.get("properties")
+    if "required" in sanitized:
+        if isinstance(required, list) and isinstance(properties, dict):
+            sanitized["required"] = [
+                f for f in required if isinstance(f, str) and f in properties
+            ]
+        elif not isinstance(required, list):
+            sanitized["required"] = []
+
+    return sanitized
+
+
+def enrich_cohere_param_description(
+    description: str, param_schema: Dict[str, Any]
+) -> str:
+    """Embed schema constraints into a Cohere parameter description.
+
+    ``CohereParameterDefinition`` only has ``type``, ``description``, and
+    ``isRequired``.  Rich constraints (``enum``, ``format``, ``minimum``,
+    ``maximum``, ``pattern``) are appended to the description string so the
+    model can still see and respect them.
+    """
+    parts = [description] if description else []
+    if "enum" in param_schema:
+        parts.append(f"Allowed values: {param_schema['enum']}")
+    if "format" in param_schema:
+        parts.append(f"Format: {param_schema['format']}")
+    if "minimum" in param_schema or "maximum" in param_schema:
+        range_parts = []
+        if "minimum" in param_schema:
+            range_parts.append(f"min={param_schema['minimum']}")
+        if "maximum" in param_schema:
+            range_parts.append(f"max={param_schema['maximum']}")
+        parts.append(f"Range: {', '.join(range_parts)}")
+    if "pattern" in param_schema:
+        parts.append(f"Pattern: {param_schema['pattern']}")
+    return ". ".join(parts) if parts else ""
diff --git a/litellm/llms/oci/embed/transformation.py b/litellm/llms/oci/embed/transformation.py
index 1dcd8c5213c..6cfa85b4bc4 100644
--- a/litellm/llms/oci/embed/transformation.py
+++ b/litellm/llms/oci/embed/transformation.py
@@ -1,8 +1,14 @@
 """
-OCI Generative AI Embedding Configuration
+OCI Generative AI — Embedding transformation.
 
-Supports embedding models available on Oracle Cloud Infrastructure Generative AI service.
-Uses the same authentication mechanisms as OCI chat (manual signing or OCI SDK Signer).
+Endpoint: POST /20231130/actions/embedText
+Supported models: cohere.embed-english-v3.0, cohere.embed-multilingual-v3.0,
+cohere.embed-v4.0, and all other Cohere embed variants available on OCI
+(including dedicated endpoints).
+
+Authentication follows the same RSA-SHA256 / OCI SDK signer pattern as chat.
+The base handler (base_llm_http_handler.embedding) calls sign_request after
+building the body, so signing happens automatically.
 
 Supported models:
 - cohere.embed-english-v3.0
@@ -10,25 +16,45 @@ Supported models:
 - cohere.embed-multilingual-v3.0
 - cohere.embed-multilingual-light-v3.0
 - cohere.embed-english-image-v3.0
-- cohere.embed-english-light-image-v3.0
-- cohere.embed-multilingual-light-image-v3.0
+- cohere.embed-multilingual-image-v3.0
 - cohere.embed-v4.0
 
 Reference: https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai-inference/latest/EmbedTextResult/EmbedText
 """
 
-from typing import Any, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
 
 import httpx
 
-from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+import litellm
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
-from litellm.llms.oci.chat.transformation import OCIChatConfig
-from litellm.llms.oci.common_utils import OCIError
+from litellm.llms.oci.common_utils import (
+    OCI_API_VERSION,
+    OCIError,
+    get_oci_base_url,
+    resolve_oci_credentials,
+    sign_oci_request,
+    validate_oci_environment,
+)
+from litellm.types.llms.oci import (
+    OCIEmbedRequest,
+    OCIEmbedResponse,
+    OCIServingMode,
+)
 from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
 from litellm.types.utils import EmbeddingResponse, Usage
 
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+# OCI sends up to 96 texts per embedText request (Cohere limit).
+OCI_EMBED_BATCH_LIMIT = 96
+
 # Input type mapping from OpenAI conventions to OCI/Cohere conventions
 _INPUT_TYPE_MAP = {
     "search_document": "SEARCH_DOCUMENT",
@@ -38,65 +64,43 @@ _INPUT_TYPE_MAP = {
 }
 
 
-class OCIEmbeddingConfig(BaseEmbeddingConfig):
+class OCIEmbedConfig(BaseEmbeddingConfig):
     """
-    Configuration for OCI Generative AI Embedding API.
+    Transformation config for OCI Generative AI embeddings.
 
-    The OCI embedding endpoint uses the Cohere embed models hosted on OCI.
-    Authentication is handled via OCI request signing (manual credentials or OCI SDK Signer).
+    Supports both text and (on cohere.embed-v4.0) multimodal inputs.
 
-    Usage:
-        ```python
-        import litellm
+    Authentication — same two modes as chat:
+    - **OCI SDK signer**: pass ``oci_signer`` in optional_params.
+    - **Manual RSA-SHA256**: pass ``oci_user``, ``oci_fingerprint``, ``oci_tenancy``,
+      and ``oci_key`` or ``oci_key_file``, or set the corresponding ``OCI_*`` env vars.
 
-        response = litellm.embedding(
-            model="oci/cohere.embed-english-v3.0",
-            input=["Hello world", "Goodbye world"],
-            oci_compartment_id="ocid1.compartment.oc1..xxx",
-            oci_region="us-ashburn-1",
-            oci_user="ocid1.user.oc1..xxx",
-            oci_fingerprint="xx:xx:xx:xx",
-            oci_tenancy="ocid1.tenancy.oc1..xxx",
-            oci_key_file="~/.oci/key.pem",
-        )
-        ```
+    Required call-time params (via optional_params or env vars):
+    - ``oci_compartment_id`` / ``OCI_COMPARTMENT_ID``
+    - ``oci_region`` / ``OCI_REGION`` (default: ``us-ashburn-1``)
+
+    Optional call-time params:
+    - ``oci_serving_mode``: ``"ON_DEMAND"`` (default) or ``"DEDICATED"``
+    - ``oci_endpoint_id``: endpoint OCID for dedicated serving mode
+    - ``input_type``: ``SEARCH_DOCUMENT``, ``SEARCH_QUERY``, ``CLASSIFICATION``, ``CLUSTERING``
+    - ``truncate``: ``NONE``, ``START``, or ``END`` (default ``END``)
+    - ``dimensions``: output embedding dimensions (cohere.embed-v4.0+)
     """
 
-    def __init__(self) -> None:
-        # We reuse OCIChatConfig for signing logic
-        self._chat_config = OCIChatConfig()
-
-    def get_complete_url(
-        self,
-        api_base: Optional[str],
-        api_key: Optional[str],
-        model: str,
-        optional_params: dict,
-        litellm_params: dict,
-        stream: Optional[bool] = None,
-    ) -> str:
-        if api_base:
-            return api_base
-
-        oci_region = optional_params.get("oci_region", "us-ashburn-1")
-        return f"https://inference.generativeai.{oci_region}.oci.oraclecloud.com/20231130/actions/embedText"
-
-    def get_supported_openai_params(self, model: str) -> list:
-        return [
-            "dimensions",
-        ]
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        return ["dimensions"]
 
     def map_openai_params(
         self,
         non_default_params: dict,
         optional_params: dict,
         model: str,
-        drop_params: bool,
+        drop_params: bool = False,
     ) -> dict:
-        # Note: OCI Cohere embed does not support custom dimensions natively,
-        # but we pass it through in case future models support it
-        if "dimensions" in non_default_params:
-            optional_params["dimensions"] = non_default_params["dimensions"]
+        for key, value in non_default_params.items():
+            if key == "dimensions":
+                # OCI API uses outputDimensions (cohere.embed-v4.0+)
+                optional_params["outputDimensions"] = value
         return optional_params
 
     def validate_environment(
@@ -109,49 +113,42 @@ class OCIEmbeddingConfig(BaseEmbeddingConfig):
         api_key: Optional[str] = None,
         api_base: Optional[str] = None,
     ) -> dict:
-        """
-        Validate OCI credentials for embedding requests.
-        Supports both OCI SDK Signer and manual credential signing.
-        """
-        oci_signer = optional_params.get("oci_signer")
-        oci_region = optional_params.get("oci_region", "us-ashburn-1")
-
-        api_base = (
-            api_base
-            or f"https://inference.generativeai.{oci_region}.oci.oraclecloud.com"
-        )
-
-        if oci_signer is None:
-            oci_user = optional_params.get("oci_user")
-            oci_fingerprint = optional_params.get("oci_fingerprint")
-            oci_tenancy = optional_params.get("oci_tenancy")
-            oci_key = optional_params.get("oci_key")
-            oci_key_file = optional_params.get("oci_key_file")
-            oci_compartment_id = optional_params.get("oci_compartment_id")
-
-            if (
-                not oci_user
-                or not oci_fingerprint
-                or not oci_tenancy
-                or not (oci_key or oci_key_file)
-                or not oci_compartment_id
-            ):
-                raise Exception(
-                    "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id "
-                    "and at least one of oci_key or oci_key_file. "
-                    "Alternatively, provide an oci_signer object from the OCI SDK."
+        if optional_params.get("oci_signer") is None:
+            creds = resolve_oci_credentials(optional_params)
+            missing = [
+                k
+                for k in (
+                    "oci_user",
+                    "oci_fingerprint",
+                    "oci_tenancy",
+                    "oci_compartment_id",
                 )
+                if not creds.get(k)
+            ]
+            if missing or not (creds.get("oci_key") or creds.get("oci_key_file")):
+                raise OCIError(
+                    status_code=401,
+                    message=(
+                        "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, "
+                        "oci_compartment_id and at least one of oci_key or oci_key_file. "
+                        "These can be supplied via optional_params or via OCI_USER, OCI_FINGERPRINT, "
+                        "OCI_TENANCY, OCI_COMPARTMENT_ID, OCI_KEY_FILE environment variables. "
+                        "Alternatively, provide an oci_signer object from the OCI SDK."
+                    ),
+                )
+        return validate_oci_environment(headers, optional_params, api_key)
 
-        from litellm.llms.custom_httpx.http_handler import version
-
-        headers.update(
-            {
-                "content-type": "application/json",
-                "user-agent": f"litellm/{version}",
-            }
-        )
-
-        return headers
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        base = get_oci_base_url(optional_params, api_base or litellm.api_base)
+        return f"{base}/{OCI_API_VERSION}/actions/embedText"
 
     def sign_request(
         self,
@@ -163,9 +160,8 @@ class OCIEmbeddingConfig(BaseEmbeddingConfig):
         model: Optional[str] = None,
         stream: Optional[bool] = None,
         fake_stream: Optional[bool] = None,
-    ):
-        """Delegate to OCIChatConfig's signing logic."""
-        return self._chat_config.sign_request(
+    ) -> Tuple[dict, bytes]:
+        return sign_oci_request(
             headers=headers,
             optional_params=optional_params,
             request_data=request_data,
@@ -182,91 +178,74 @@ class OCIEmbeddingConfig(BaseEmbeddingConfig):
         input: AllEmbeddingInputValues,
         optional_params: dict,
         headers: dict,
-        api_base: Optional[str] = None,
     ) -> dict:
-        """
-        Transform the embedding request to OCI format.
-
-        OCI embedText API expects:
-        {
-            "compartmentId": "...",
-            "servingMode": {"servingType": "ON_DEMAND", "modelId": "..."},
-            "inputs": ["text1", "text2"],
-            "truncate": "END",
-            "inputType": "SEARCH_DOCUMENT"
-        }
-        """
-        oci_compartment_id = optional_params.get("oci_compartment_id")
-        if not oci_compartment_id:
-            raise Exception(
-                "kwarg `oci_compartment_id` is required for OCI embedding requests"
+        creds = resolve_oci_credentials(optional_params)
+        compartment_id = creds["oci_compartment_id"]
+        if not compartment_id:
+            raise OCIError(
+                status_code=400,
+                message=(
+                    "oci_compartment_id is required for OCI embedding requests. "
+                    "Pass it as optional_params or set the OCI_COMPARTMENT_ID env var."
+                ),
             )
 
-        # Build serving mode
-        oci_serving_mode = optional_params.get("oci_serving_mode", "ON_DEMAND")
-        if oci_serving_mode == "DEDICATED":
-            oci_endpoint_id = optional_params.get("oci_endpoint_id", model)
-            serving_mode = {
-                "servingType": "DEDICATED",
-                "endpointId": oci_endpoint_id,
-            }
-        else:
-            serving_mode = {
-                "servingType": "ON_DEMAND",
-                "modelId": model,
-            }
-
-        # Normalize input to list of strings
+        # Normalise input to a flat list of strings
         if isinstance(input, str):
-            inputs = [input]
+            texts = [input]
         elif isinstance(input, list):
-            inputs = []
+            texts = []
             for item in input:
-                if isinstance(item, str):
-                    inputs.append(item)
-                elif isinstance(item, list):
-                    raise ValueError(
-                        "OCI embedding does not support token-array inputs. "
-                        "Please convert token lists to strings before calling embedding()."
+                if isinstance(item, list):
+                    raise OCIError(
+                        status_code=400,
+                        message=(
+                            "OCI embedText does not support token-array inputs. "
+                            "Convert token lists to strings before calling embedding()."
+                        ),
                     )
-                else:
-                    inputs.append(str(item))
+                texts.append(item if isinstance(item, str) else str(item))
         else:
-            inputs = [str(input)]
+            texts = [str(input)]
 
-        # Build request data — OCI embedText API expects inputs, truncate,
-        # and inputType at the top level alongside compartmentId and servingMode
-        request_data: Dict[str, Any] = {
-            "compartmentId": oci_compartment_id,
-            "servingMode": serving_mode,
-            "inputs": inputs,
-            "truncate": optional_params.get("truncate", "END"),
-        }
+        if len(texts) > OCI_EMBED_BATCH_LIMIT:
+            raise OCIError(
+                status_code=400,
+                message=(
+                    f"OCI embedText accepts at most {OCI_EMBED_BATCH_LIMIT} inputs per request "
+                    f"(got {len(texts)}). Batch your requests."
+                ),
+            )
 
-        # Map input_type if provided
+        serving_mode_type = optional_params.get("oci_serving_mode", "ON_DEMAND").upper()
+        if serving_mode_type not in {"ON_DEMAND", "DEDICATED"}:
+            raise OCIError(
+                status_code=400,
+                message="oci_serving_mode must be 'ON_DEMAND' or 'DEDICATED'.",
+            )
+
+        if serving_mode_type == "DEDICATED":
+            endpoint_id = optional_params.get("oci_endpoint_id", model)
+            serving_mode = OCIServingMode(
+                servingType="DEDICATED", endpointId=endpoint_id
+            )
+        else:
+            serving_mode = OCIServingMode(servingType="ON_DEMAND", modelId=model)
+
+        # Map input_type from OpenAI convention to OCI/Cohere convention
         input_type = optional_params.get("input_type")
         if input_type:
-            mapped_type = _INPUT_TYPE_MAP.get(input_type.lower(), input_type.upper())
-            request_data["inputType"] = mapped_type
+            input_type = _INPUT_TYPE_MAP.get(input_type.lower(), input_type.upper())
 
-        # Sign the request using the same URL the HTTP handler will POST to
-        signing_url = self.get_complete_url(
-            api_base=api_base,
-            api_key=None,
-            model=model,
-            optional_params=optional_params,
-            litellm_params={},
+        request = OCIEmbedRequest(
+            compartmentId=compartment_id,
+            servingMode=serving_mode,
+            inputs=texts,
+            inputType=input_type,
+            truncate=optional_params.get("truncate", "END"),
+            outputDimensions=optional_params.get("outputDimensions"),
         )
-
-        signed_headers, body = self.sign_request(
-            headers=headers,
-            optional_params=optional_params,
-            request_data=request_data,
-            api_base=signing_url,
-        )
-        headers.update(signed_headers)
-
-        return request_data
+        return request.model_dump(exclude_none=True)
 
     def transform_embedding_response(
         self,
@@ -274,63 +253,57 @@ class OCIEmbeddingConfig(BaseEmbeddingConfig):
         raw_response: httpx.Response,
         model_response: EmbeddingResponse,
         logging_obj: LiteLLMLoggingObj,
-        api_key: Optional[str] = None,
-        request_data: dict = {},
-        optional_params: dict = {},
-        litellm_params: dict = {},
+        api_key: Optional[str],
+        request_data: dict,
+        optional_params: dict,
+        litellm_params: dict,
     ) -> EmbeddingResponse:
-        """
-        Transform OCI embedding response to standard EmbeddingResponse format.
-
-        OCI response format:
-        {
-            "embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],
-            "modelId": "cohere.embed-english-v3.0",
-            "modelVersion": "3.0",
-            "inputTextTokenCounts": [5, 4]
-        }
-        """
         if raw_response.status_code != 200:
             raise OCIError(
-                message=raw_response.text,
                 status_code=raw_response.status_code,
+                message=raw_response.text,
             )
 
         try:
-            raw_response_json = raw_response.json()
-        except Exception:
+            json_response = raw_response.json()
+        except Exception as e:
             raise OCIError(
-                message=raw_response.text,
                 status_code=raw_response.status_code,
+                message=f"Failed to parse OCI embed response as JSON: {e}",
             )
 
-        embeddings = raw_response_json.get("embeddings", [])
-        model_id = raw_response_json.get("modelId", model)
-
-        # Build response data in OpenAI format
-        embedding_data = []
-        for idx, embedding in enumerate(embeddings):
-            embedding_data.append(
-                {
-                    "object": "embedding",
-                    "index": idx,
-                    "embedding": embedding,
-                }
+        try:
+            parsed = OCIEmbedResponse(**json_response)
+        except Exception as e:
+            raise OCIError(
+                status_code=500,
+                message=f"OCI embed response does not match expected schema: {e}",
             )
 
-        model_response.model = model_id
-        model_response.data = embedding_data
-        model_response.object = "list"
+        model_response.model = parsed.modelId
+        model_response.data = [
+            {
+                "object": "embedding",
+                "index": i,
+                "embedding": embedding,
+            }
+            for i, embedding in enumerate(parsed.embeddings)
+        ]
 
-        # Calculate token usage
-        input_token_counts = raw_response_json.get("inputTextTokenCounts", [])
-        total_tokens = sum(input_token_counts) if input_token_counts else 0
-
-        usage = Usage(
-            prompt_tokens=total_tokens,
-            total_tokens=total_tokens,
-        )
-        model_response.usage = usage
+        if parsed.inputTextTokenCounts is not None:
+            # Actual OCI API returns per-input token counts — sum for total usage
+            total = sum(parsed.inputTextTokenCounts)
+            model_response.usage = Usage(prompt_tokens=total, total_tokens=total)
+        elif parsed.usage is not None:
+            # Some deployments may return a usage object directly
+            model_response.usage = Usage(
+                prompt_tokens=parsed.usage.promptTokens,
+                total_tokens=parsed.usage.totalTokens,
+            )
+        else:
+            # Neither field returned — default to zero so downstream consumers
+            # can always rely on usage being populated.
+            model_response.usage = Usage(prompt_tokens=0, total_tokens=0)
 
         return model_response
 
@@ -340,8 +313,8 @@ class OCIEmbeddingConfig(BaseEmbeddingConfig):
         status_code: int,
         headers: Union[dict, httpx.Headers],
     ) -> BaseLLMException:
-        return OCIError(
-            message=error_message,
-            status_code=status_code,
-            headers=headers if isinstance(headers, httpx.Headers) else None,
-        )
+        return OCIError(status_code=status_code, message=error_message)
+
+
+# Alias for backwards compatibility with any code that imports OCIEmbeddingConfig
+OCIEmbeddingConfig = OCIEmbedConfig
diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py
index 6b7ec4dfb1c..5464b5bb7ee 100644
--- a/litellm/llms/openai/chat/gpt_transformation.py
+++ b/litellm/llms/openai/chat/gpt_transformation.py
@@ -287,7 +287,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
                 content_item["image_url"] = new_image_url_obj
         elif content_item.get("type") == "file":
             content_item = cast(ChatCompletionFileObject, content_item)
-            file_obj = content_item["file"]
+            file_obj = content_item.get("file")
+            if file_obj is None:
+                raise litellm.BadRequestError(
+                    message="Content block has type='file' but is missing the required 'file' field",
+                    model=None,
+                    llm_provider="openai",
+                )
             new_file_obj = ChatCompletionFileObjectFile(
                 **{  # type: ignore
                     k: v
diff --git a/litellm/llms/openai/chat/o_series_transformation.py b/litellm/llms/openai/chat/o_series_transformation.py
index 02ae2cc9750..8db7ecf7b3a 100644
--- a/litellm/llms/openai/chat/o_series_transformation.py
+++ b/litellm/llms/openai/chat/o_series_transformation.py
@@ -1,14 +1,14 @@
 """
-Support for o1/o3 model family 
+Support for o1/o3 model family
 
 https://platform.openai.com/docs/guides/reasoning
 
 Translations handled by LiteLLM:
-- modalities: image => drop param (if user opts in to dropping param)  
-- role: system ==> translate to role 'user' 
-- streaming => faked by LiteLLM 
-- Tools, response_format =>  drop param (if user opts in to dropping param) 
-- Logprobs => drop param (if user opts in to dropping param) 
+- modalities: image => drop param (if user opts in to dropping param)
+- role: system ==> translate to role 'user'
+- streaming => faked by LiteLLM
+- Tools, response_format =>  drop param (if user opts in to dropping param)
+- Logprobs => drop param (if user opts in to dropping param)
 """
 
 from typing import Any, Coroutine, List, Literal, Optional, Union, cast, overload
diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py
index c13a976c1b9..381f215a13f 100644
--- a/litellm/llms/openai/common_utils.py
+++ b/litellm/llms/openai/common_utils.py
@@ -201,7 +201,7 @@ class BaseOpenAILLM:
 
     @staticmethod
     def get_openai_client_initialization_param_fields(
-        client_type: Literal["openai", "azure"]
+        client_type: Literal["openai", "azure"],
     ) -> Tuple[str, ...]:
         """Returns a tuple of fields that are used to initialize the OpenAI client"""
         if client_type == "openai":
diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py
index b7d5340d8d4..5043d25ee37 100644
--- a/litellm/llms/openai/responses/transformation.py
+++ b/litellm/llms/openai/responses/transformation.py
@@ -126,9 +126,21 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         litellm_params: GenericLiteLLMParams,
         headers: dict,
     ) -> Dict:
-        """No transform applied since inputs are in OpenAI spec already"""
+        """Strip Anthropic-only `cache_control` markers before sending to OpenAI.
+
+        OpenAI's Responses API rejects unknown fields on input content blocks
+        with HTTP 400 ("Unknown parameter: 'input[0].content[0].cache_control'").
+        Chat Completions strips these in
+        `remove_cache_control_flag_from_messages_and_tools`; mirror that here.
+        """
 
         input = self._validate_input_param(input)
+        tools = response_api_optional_request_params.get("tools")
+        input, tools = self.remove_cache_control_flag_from_input_and_tools(
+            model=model, input=input, tools=tools
+        )
+        if tools is not None:
+            response_api_optional_request_params["tools"] = tools
         final_request_params = dict(
             ResponsesAPIRequestParams(
                 model=model, input=input, **response_api_optional_request_params
@@ -137,6 +149,38 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
 
         return final_request_params
 
+    def remove_cache_control_flag_from_input_and_tools(
+        self,
+        model: str,  # allows overrides to selectively run this
+        input: Union[str, ResponseInputParam],
+        tools: Optional[List[ALL_RESPONSES_API_TOOL_PARAMS]] = None,
+    ) -> Tuple[
+        Union[str, ResponseInputParam],
+        Optional[List[ALL_RESPONSES_API_TOOL_PARAMS]],
+    ]:
+        """Sibling of `remove_cache_control_flag_from_messages_and_tools` on
+        the chat path. Strips Anthropic-only `cache_control` markers from
+        Responses API input content blocks and tools.
+
+        `filter_value_from_dict` mutates each dict in place, so the same
+        objects are returned.
+        """
+        from litellm.litellm_core_utils.prompt_templates.common_utils import (
+            filter_value_from_dict,
+        )
+
+        if isinstance(input, list):
+            for item in input:
+                if isinstance(item, dict):
+                    filter_value_from_dict(cast(dict, item), "cache_control")
+
+        if tools is not None:
+            for tool in tools:
+                if isinstance(tool, dict):
+                    filter_value_from_dict(cast(dict, tool), "cache_control")
+
+        return input, tools
+
     def _validate_input_param(
         self, input: Union[str, ResponseInputParam]
     ) -> Union[str, ResponseInputParam]:
@@ -604,6 +648,12 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         url = str(parsed_url.copy_with(path=compact_path))
 
         input = self._validate_input_param(input)
+        tools = response_api_optional_request_params.get("tools")
+        input, tools = self.remove_cache_control_flag_from_input_and_tools(
+            model=model, input=input, tools=tools
+        )
+        if tools is not None:
+            response_api_optional_request_params["tools"] = tools
         data = dict(
             ResponsesAPIRequestParams(
                 model=model, input=input, **response_api_optional_request_params
diff --git a/litellm/llms/openrouter/image_generation/transformation.py b/litellm/llms/openrouter/image_generation/transformation.py
index a55716a5e50..9c2293eb3f1 100644
--- a/litellm/llms/openrouter/image_generation/transformation.py
+++ b/litellm/llms/openrouter/image_generation/transformation.py
@@ -49,7 +49,6 @@ from litellm.types.utils import (
 )
 from litellm.llms.openrouter.common_utils import OpenRouterException
 
-
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 else:
diff --git a/litellm/llms/reducto/__init__.py b/litellm/llms/reducto/__init__.py
new file mode 100644
index 00000000000..8b137891791
--- /dev/null
+++ b/litellm/llms/reducto/__init__.py
@@ -0,0 +1 @@
+
diff --git a/litellm/llms/reducto/common.py b/litellm/llms/reducto/common.py
new file mode 100644
index 00000000000..4e7d96dbe87
--- /dev/null
+++ b/litellm/llms/reducto/common.py
@@ -0,0 +1,159 @@
+import base64
+import binascii
+from collections import defaultdict
+from typing import TYPE_CHECKING, Any, Dict, List, NoReturn, Optional, Tuple
+
+from litellm.constants import request_timeout
+
+REDUCTO_API_BASE = "https://platform.reducto.ai"
+REDUCTO_ID_PREFIX = "reducto://"
+
+if TYPE_CHECKING:
+    from litellm.llms.base_llm.ocr.transformation import OCRPage
+
+
+def _normalize_api_base(api_base: Optional[str]) -> str:
+    return (api_base or REDUCTO_API_BASE).rstrip("/")
+
+
+def _raise_bad_request(message: str, model: str) -> NoReturn:
+    import litellm
+
+    raise litellm.BadRequestError(
+        message=message,
+        model=model,
+        llm_provider="reducto",
+    )
+
+
+def extract_file_id_or_bytes(
+    source_url: str,
+    model: str,
+) -> Tuple[Optional[str], Optional[bytes], Optional[str]]:
+    if source_url.startswith(REDUCTO_ID_PREFIX):
+        return source_url, None, None
+
+    if source_url.startswith("http://") or source_url.startswith("https://"):
+        _raise_bad_request(
+            "Reducto requires type='file' (auto-uploaded) or a reducto:// id. Plain http(s) URLs are not supported; upload the file first.",
+            model=model,
+        )
+
+    if not source_url.startswith("data:"):
+        _raise_bad_request(
+            "Reducto requires a reducto:// id or a base64 data URI after OCR preprocessing.",
+            model=model,
+        )
+
+    try:
+        header, encoded = source_url.split(",", 1)
+    except ValueError:
+        _raise_bad_request("Invalid Reducto data URI provided.", model=model)
+
+    if ";base64" not in header:
+        _raise_bad_request(
+            "Reducto only supports base64-encoded data URIs.", model=model
+        )
+
+    mime = header.removeprefix("data:").split(";")[0] or "application/octet-stream"
+    try:
+        raw_bytes = base64.b64decode(encoded, validate=True)
+    except (binascii.Error, ValueError):
+        _raise_bad_request("Invalid Reducto base64 payload provided.", model=model)
+
+    return None, raw_bytes, mime
+
+
+def _extract_file_id_from_upload_response(response: Any) -> str:
+    try:
+        payload = response.json()
+    except ValueError as exc:
+        raise ValueError(
+            "Reducto /upload returned a non-JSON 200 response: {}".format(response.text)
+        ) from exc
+    file_id = (payload or {}).get("file_id") if isinstance(payload, dict) else None
+    if not isinstance(file_id, str) or not file_id:
+        raise ValueError(
+            "Reducto /upload returned 200 without a file_id; got payload={}".format(
+                payload
+            )
+        )
+    return file_id
+
+
+def upload_bytes_sync(
+    raw_bytes: bytes,
+    mime: Optional[str],
+    api_key: str,
+    api_base: Optional[str],
+) -> str:
+    import litellm
+
+    response = litellm.module_level_client.post(
+        url="{}{}".format(_normalize_api_base(api_base), "/upload"),
+        headers={"Authorization": f"Bearer {api_key}"},
+        files={"file": ("document", raw_bytes, mime or "application/octet-stream")},
+        timeout=request_timeout,
+    )
+    response.raise_for_status()
+    return _extract_file_id_from_upload_response(response)
+
+
+async def upload_bytes_async(
+    raw_bytes: bytes,
+    mime: Optional[str],
+    api_key: str,
+    api_base: Optional[str],
+) -> str:
+    import litellm
+
+    response = await litellm.module_level_aclient.post(
+        url="{}{}".format(_normalize_api_base(api_base), "/upload"),
+        headers={"Authorization": f"Bearer {api_key}"},
+        files={"file": ("document", raw_bytes, mime or "application/octet-stream")},
+        timeout=request_timeout,
+    )
+    response.raise_for_status()
+    return _extract_file_id_from_upload_response(response)
+
+
+def build_pages_from_reducto(result: Dict[str, Any]) -> List["OCRPage"]:
+    from litellm.llms.base_llm.ocr.transformation import OCRPage
+
+    chunks = result.get("chunks", []) or []
+    blocks_by_page: Dict[int, List[Dict[str, Any]]] = defaultdict(list)
+
+    for chunk in chunks:
+        for block in chunk.get("blocks", []) or []:
+            page_no = (block.get("bbox") or {}).get("page")
+            if page_no is None:
+                continue
+            try:
+                normalized_page = int(page_no)
+            except (TypeError, ValueError):
+                continue
+            blocks_by_page[normalized_page].append(block)
+
+    if not blocks_by_page:
+        fallback_markdown = "\n\n".join(
+            chunk.get("content", "") for chunk in chunks if chunk.get("content")
+        )
+        if fallback_markdown == "":
+            return []
+        return [OCRPage(index=0, markdown=fallback_markdown)]
+
+    pages: List["OCRPage"] = []
+    for page_no, blocks in sorted(blocks_by_page.items()):
+        markdown = "\n\n".join(
+            block.get("content", "") for block in blocks if block.get("content")
+        )
+        page_index = max(page_no - 1, 0)
+        page = OCRPage(
+            index=page_index,
+            markdown=markdown,
+        )
+        # OCRPage accepts extra keys at runtime; assign blocks after construction
+        # so static typing does not reject provider-specific metadata.
+        setattr(page, "blocks", blocks)
+        pages.append(page)
+    return pages
diff --git a/litellm/llms/reducto/ocr/__init__.py b/litellm/llms/reducto/ocr/__init__.py
new file mode 100644
index 00000000000..8b137891791
--- /dev/null
+++ b/litellm/llms/reducto/ocr/__init__.py
@@ -0,0 +1 @@
+
diff --git a/litellm/llms/reducto/ocr/transformation.py b/litellm/llms/reducto/ocr/transformation.py
new file mode 100644
index 00000000000..cc338ecc484
--- /dev/null
+++ b/litellm/llms/reducto/ocr/transformation.py
@@ -0,0 +1,241 @@
+from typing import Any, Dict, Optional, Tuple
+
+import httpx
+
+from litellm.llms.base_llm.ocr.transformation import (
+    BaseOCRConfig,
+    DocumentType,
+    OCRRequestData,
+    OCRResponse,
+    OCRUsageInfo,
+)
+from litellm.llms.reducto.common import (
+    REDUCTO_API_BASE,
+    build_pages_from_reducto,
+    extract_file_id_or_bytes,
+    upload_bytes_async,
+    upload_bytes_sync,
+)
+
+
+class _BaseReductoOCRConfig(BaseOCRConfig):
+    def map_ocr_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+    ) -> dict:
+        mapped_params = dict(optional_params)
+        supported_params = self.get_supported_ocr_params(model=model)
+        for param, value in non_default_params.items():
+            if param in supported_params:
+                mapped_params[param] = value
+        return mapped_params
+
+    def validate_environment(
+        self,
+        headers: Dict,
+        model: str,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        litellm_params: Optional[dict] = None,
+        **kwargs,
+    ) -> Dict:
+        from litellm.secret_managers.main import get_secret_str
+
+        resolved_key = api_key or get_secret_str("REDUCTO_API_KEY")
+        if resolved_key is None:
+            raise ValueError(
+                "Missing REDUCTO_API_KEY - set it in the environment or pass api_key to litellm.ocr()/litellm.aocr()"
+            )
+
+        return {
+            "Authorization": f"Bearer {resolved_key}",
+            "Content-Type": "application/json",
+            **headers,
+        }
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: Optional[dict] = None,
+        **kwargs,
+    ) -> str:
+        return "{}/parse".format((api_base or REDUCTO_API_BASE).rstrip("/"))
+
+    def _get_source_url(self, document: DocumentType, model: str) -> str:
+        source_url = document.get("document_url") or document.get("image_url")
+        if source_url is None:
+            raise ValueError(
+                "Reducto expected OCR preprocessing to produce document_url or image_url for model={}".format(
+                    model
+                )
+            )
+        return source_url
+
+    @staticmethod
+    def _resolve_credentials(
+        api_key: Optional[str], api_base: Optional[str]
+    ) -> Tuple[str, str]:
+        from litellm.secret_managers.main import get_secret_str
+
+        resolved_key = api_key or get_secret_str("REDUCTO_API_KEY")
+        if resolved_key is None:
+            raise ValueError(
+                "Missing REDUCTO_API_KEY - set it in the environment or pass api_key to litellm.ocr()/litellm.aocr()"
+            )
+        resolved_base = (api_base or REDUCTO_API_BASE).rstrip("/")
+        return resolved_key, resolved_base
+
+    def _ensure_file_id_sync(
+        self,
+        model: str,
+        document: DocumentType,
+        api_key: Optional[str],
+        api_base: Optional[str],
+    ) -> str:
+        source_url = self._get_source_url(document=document, model=model)
+        file_id, raw_bytes, mime = extract_file_id_or_bytes(source_url, model=model)
+        if file_id is not None:
+            return file_id
+        resolved_key, resolved_base = self._resolve_credentials(api_key, api_base)
+        return upload_bytes_sync(
+            raw_bytes=raw_bytes or b"",
+            mime=mime,
+            api_key=resolved_key,
+            api_base=resolved_base,
+        )
+
+    async def _ensure_file_id_async(
+        self,
+        model: str,
+        document: DocumentType,
+        api_key: Optional[str],
+        api_base: Optional[str],
+    ) -> str:
+        source_url = self._get_source_url(document=document, model=model)
+        file_id, raw_bytes, mime = extract_file_id_or_bytes(source_url, model=model)
+        if file_id is not None:
+            return file_id
+        resolved_key, resolved_base = self._resolve_credentials(api_key, api_base)
+        return await upload_bytes_async(
+            raw_bytes=raw_bytes or b"",
+            mime=mime,
+            api_key=resolved_key,
+            api_base=resolved_base,
+        )
+
+    def transform_ocr_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        logging_obj: Any,
+        **kwargs,
+    ) -> OCRResponse:
+        response_json = raw_response.json()
+        result = response_json.get("result", response_json) or {}
+        usage = response_json.get("usage", {}) or {}
+        response = OCRResponse(
+            pages=build_pages_from_reducto(result),
+            model=model,
+            usage_info=OCRUsageInfo(
+                pages_processed=usage.get("num_pages"),
+                credits=usage.get("credits"),
+            ),
+            object="ocr",
+        )
+        response._hidden_params["reducto_raw"] = response_json
+        return response
+
+
+class ReductoParseV3Config(_BaseReductoOCRConfig):
+    def get_supported_ocr_params(self, model: str) -> list:
+        return ["formatting", "retrieval", "settings"]
+
+    def transform_ocr_request(
+        self,
+        model: str,
+        document: DocumentType,
+        optional_params: dict,
+        headers: dict,
+        **kwargs,
+    ) -> OCRRequestData:
+        file_id = self._ensure_file_id_sync(
+            model=model,
+            document=document,
+            api_key=kwargs.get("api_key"),
+            api_base=kwargs.get("api_base"),
+        )
+        return OCRRequestData(data={"input": file_id, **optional_params}, files=None)
+
+    async def async_transform_ocr_request(
+        self,
+        model: str,
+        document: DocumentType,
+        optional_params: dict,
+        headers: dict,
+        **kwargs,
+    ) -> OCRRequestData:
+        file_id = await self._ensure_file_id_async(
+            model=model,
+            document=document,
+            api_key=kwargs.get("api_key"),
+            api_base=kwargs.get("api_base"),
+        )
+        return OCRRequestData(data={"input": file_id, **optional_params}, files=None)
+
+
+class ReductoParseLegacyConfig(_BaseReductoOCRConfig):
+    def get_supported_ocr_params(self, model: str) -> list:
+        return ["enhance"]
+
+    def _build_legacy_body(self, file_id: str, optional_params: dict) -> Dict[str, Any]:
+        body: Dict[str, Any] = {"document_url": file_id}
+        enhance = optional_params.get("enhance")
+        if enhance is not None:
+            body["options"] = {"enhance": enhance}
+        return body
+
+    def transform_ocr_request(
+        self,
+        model: str,
+        document: DocumentType,
+        optional_params: dict,
+        headers: dict,
+        **kwargs,
+    ) -> OCRRequestData:
+        file_id = self._ensure_file_id_sync(
+            model=model,
+            document=document,
+            api_key=kwargs.get("api_key"),
+            api_base=kwargs.get("api_base"),
+        )
+        return OCRRequestData(
+            data=self._build_legacy_body(
+                file_id=file_id, optional_params=optional_params
+            ),
+            files=None,
+        )
+
+    async def async_transform_ocr_request(
+        self,
+        model: str,
+        document: DocumentType,
+        optional_params: dict,
+        headers: dict,
+        **kwargs,
+    ) -> OCRRequestData:
+        file_id = await self._ensure_file_id_async(
+            model=model,
+            document=document,
+            api_key=kwargs.get("api_key"),
+            api_base=kwargs.get("api_base"),
+        )
+        return OCRRequestData(
+            data=self._build_legacy_body(
+                file_id=file_id, optional_params=optional_params
+            ),
+            files=None,
+        )
diff --git a/litellm/llms/sagemaker/common_utils.py b/litellm/llms/sagemaker/common_utils.py
index 50c8ee4220e..6c15d642f8c 100644
--- a/litellm/llms/sagemaker/common_utils.py
+++ b/litellm/llms/sagemaker/common_utils.py
@@ -1,3 +1,4 @@
+import functools
 import json
 from typing import AsyncIterator, Iterator, List, Optional, Union
 
@@ -22,14 +23,22 @@ def _load_sagemaker_response_stream_shape():
         )
     except Exception as e:
         verbose_logger.warning(
-            "litellm: could not pre-load sagemaker-runtime response stream shape "
+            "litellm: could not load sagemaker-runtime response stream shape "
             "— SageMaker event-stream decoding will be unavailable. Error: %s",
             e,
         )
         return None
 
 
-SAGEMAKER_RESPONSE_STREAM_SHAPE = _load_sagemaker_response_stream_shape()
+@functools.lru_cache(maxsize=1)
+def get_sagemaker_response_stream_shape():
+    """
+    Lazily load and cache the sagemaker-runtime stream shape for the process.
+
+    Avoids importing botocore (and logging warnings) unless SageMaker event-stream
+    decoding is actually needed.
+    """
+    return _load_sagemaker_response_stream_shape()
 
 
 class SagemakerError(BaseLLMException):
@@ -207,7 +216,8 @@ class AWSEventStreamDecoder:
                 verbose_logger.error(f"Final error parsing accumulated JSON: {e}")
 
     def _parse_message_from_event(self, event) -> Optional[str]:
-        if SAGEMAKER_RESPONSE_STREAM_SHAPE is None:
+        response_stream_shape = get_sagemaker_response_stream_shape()
+        if response_stream_shape is None:
             raise SagemakerError(
                 status_code=500,
                 message=(
@@ -216,9 +226,7 @@ class AWSEventStreamDecoder:
                 ),
             )
         response_dict = event.to_response_dict()
-        parsed_response = self.parser.parse(
-            response_dict, SAGEMAKER_RESPONSE_STREAM_SHAPE
-        )
+        parsed_response = self.parser.parse(response_dict, response_stream_shape)
 
         if response_dict["status_code"] != 200:
             raise ValueError(f"Bad response code, expected 200: {response_dict}")
diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py
index efbb218f575..de7be18e8ba 100644
--- a/litellm/llms/sagemaker/completion/handler.py
+++ b/litellm/llms/sagemaker/completion/handler.py
@@ -578,7 +578,7 @@ class SagemakerLLM(BaseAWSLLM):
         logger_fn=None,
     ):
         """
-        Supports both Huggingface Jumpstart embeddings and Voyage models
+        Supports Hugging Face (TGI), Voyage, and Cohere embedding endpoints
         """
         ### BOTO3 INIT
         import boto3
diff --git a/litellm/llms/sagemaker/completion/transformation.py b/litellm/llms/sagemaker/completion/transformation.py
index 3e4e2460cdb..8fd32bc4460 100644
--- a/litellm/llms/sagemaker/completion/transformation.py
+++ b/litellm/llms/sagemaker/completion/transformation.py
@@ -1,7 +1,7 @@
 """
 Translate from OpenAI's `/v1/chat/completions` to Sagemaker's `/invoke`
 
-In the Huggingface TGI format. 
+In the Huggingface TGI format.
 """
 
 import json
diff --git a/litellm/llms/sagemaker/embedding/cohere_transformation.py b/litellm/llms/sagemaker/embedding/cohere_transformation.py
new file mode 100644
index 00000000000..fdb67202ebb
--- /dev/null
+++ b/litellm/llms/sagemaker/embedding/cohere_transformation.py
@@ -0,0 +1,141 @@
+"""
+Translate from OpenAI's `/v1/embeddings` to Sagemaker's `/invoke`
+
+In the native Cohere embed format for self-hosted Cohere endpoints
+(AWS Marketplace / JumpStart). Cohere containers expect
+`{"texts": [...], "input_type": "..."}` and reject the HuggingFace TGI shape
+`{"inputs": [...]}` with `422 EmbedReqV2.inputs is of type string but should
+be of type Object`.
+
+Reference: https://docs.cohere.com/v2/reference/embed
+"""
+
+from typing import TYPE_CHECKING, Any, List, Optional, Union, cast
+
+if TYPE_CHECKING:
+    from litellm.types.llms.openai import AllEmbeddingInputValues
+
+from httpx._models import Headers, Response
+
+import litellm
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
+from litellm.llms.bedrock.embed.cohere_transformation import (
+    BedrockCohereEmbeddingConfig,
+)
+from litellm.llms.cohere.embed.v1_transformation import CohereEmbeddingConfig
+from litellm.types.utils import EmbeddingResponse
+
+from ..common_utils import SagemakerError
+
+
+class SagemakerCohereEmbeddingConfig(BaseEmbeddingConfig):
+    """
+    SageMaker invoke payload for self-hosted Cohere embed models.
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        return ["encoding_format", "dimensions", "input_type"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        optional_params = BedrockCohereEmbeddingConfig().map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+        )
+        if "input_type" in non_default_params:
+            optional_params["input_type"] = non_default_params["input_type"]
+        return optional_params
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, Headers]
+    ) -> BaseLLMException:
+        return SagemakerError(
+            message=error_message, status_code=status_code, headers=headers
+        )
+
+    def transform_embedding_request(
+        self,
+        model: str,
+        input: "AllEmbeddingInputValues",
+        optional_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Transform embedding request for Cohere models on SageMaker
+        """
+        if isinstance(input, str):
+            input_list: List[str] = [input]
+        elif isinstance(input, list):
+            if input and (isinstance(input[0], list) or isinstance(input[0], int)):
+                raise ValueError("Input must be a list of strings")
+            input_list = cast(List[str], input)
+        else:
+            input_list = [str(input)]
+
+        return dict(
+            BedrockCohereEmbeddingConfig()._transform_request(
+                model=model,
+                input=input_list,
+                inference_params=optional_params,
+            )
+        )
+
+    def transform_embedding_response(
+        self,
+        model: str,
+        raw_response: Response,
+        model_response: "EmbeddingResponse",
+        logging_obj: Any,
+        api_key: Optional[str] = None,
+        request_data: dict = {},
+        optional_params: dict = {},
+        litellm_params: dict = {},
+    ) -> "EmbeddingResponse":
+        """
+        Transform embedding response for Cohere models on SageMaker.
+
+        Uses `CohereEmbeddingConfig._populate_embedding_response` (not
+        `_transform_response`) so we do not log `post_call` a second time
+        — the SageMaker embedding handler already logs `post_call` before
+        invoking this transform.
+        """
+        input_value = (
+            logging_obj.model_call_details.get("input")
+            or request_data.get("texts")
+            or request_data.get("images")
+            or []
+        )
+        if isinstance(input_value, str):
+            input_value = [input_value]
+
+        return CohereEmbeddingConfig()._populate_embedding_response(
+            response_json=raw_response.json(),
+            model_response=model_response,
+            model=model,
+            encoding=litellm.encoding,
+            input=input_value,
+        )
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[Any],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        """
+        Validate environment for SageMaker Cohere embeddings
+        """
+        return {"Content-Type": "application/json"}
diff --git a/litellm/llms/sagemaker/embedding/transformation.py b/litellm/llms/sagemaker/embedding/transformation.py
index 04430171187..5e2aa99534f 100644
--- a/litellm/llms/sagemaker/embedding/transformation.py
+++ b/litellm/llms/sagemaker/embedding/transformation.py
@@ -1,7 +1,7 @@
 """
 Translate from OpenAI's `/v1/embeddings` to Sagemaker's `/invoke`
 
-In the Huggingface TGI format. 
+In the Huggingface TGI format.
 """
 
 from typing import TYPE_CHECKING, Any, List, Optional, Union
@@ -11,12 +11,13 @@ if TYPE_CHECKING:
 
 from httpx._models import Headers, Response
 
-from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
-from litellm.types.utils import Usage, EmbeddingResponse
+from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
 from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig
+from litellm.types.utils import EmbeddingResponse, Usage
 
 from ..common_utils import SagemakerError
+from .cohere_transformation import SagemakerCohereEmbeddingConfig
 
 
 class SagemakerEmbeddingConfig(BaseEmbeddingConfig):
@@ -38,17 +39,20 @@ class SagemakerEmbeddingConfig(BaseEmbeddingConfig):
         Returns:
             Appropriate embedding config instance
         """
-        if "voyage" in model.lower():
+        model_lower = model.lower()
+        if "voyage" in model_lower:
             return VoyageEmbeddingConfig()
-        else:
-            return cls()
+        if "cohere" in model_lower:
+            return SagemakerCohereEmbeddingConfig()
+        return cls()
 
     def get_supported_openai_params(self, model: str) -> List[str]:
-        # Check if this is an embedding model
-        if "voyage" in model.lower():
+        model_lower = model.lower()
+        if "voyage" in model_lower:
             return VoyageEmbeddingConfig().get_supported_openai_params(model)
-        else:
-            return []
+        if "cohere" in model_lower:
+            return SagemakerCohereEmbeddingConfig().get_supported_openai_params(model)
+        return []
 
     def map_openai_params(
         self,
diff --git a/litellm/llms/sap/credentials.py b/litellm/llms/sap/credentials.py
index 0ae351783e8..dd307ddf496 100644
--- a/litellm/llms/sap/credentials.py
+++ b/litellm/llms/sap/credentials.py
@@ -207,7 +207,7 @@ def resolve_resource_group(sources: List[Source]) -> Optional[str]:
 
 
 def _parse_service_key_once(
-    service_key: Optional[Union[str, dict]]
+    service_key: Optional[Union[str, dict]],
 ) -> Optional[Dict[str, Any]]:
     """
     Pre-parse service_key if it's a string to avoid repeated JSON parsing.
diff --git a/litellm/llms/snowflake/chat/transformation.py b/litellm/llms/snowflake/chat/transformation.py
index 3e590680a75..23bb6f44757 100644
--- a/litellm/llms/snowflake/chat/transformation.py
+++ b/litellm/llms/snowflake/chat/transformation.py
@@ -14,7 +14,6 @@ from ...openai_like.chat.transformation import OpenAIGPTConfig
 
 from ..utils import SnowflakeBaseConfig
 
-
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
 
diff --git a/litellm/llms/together_ai/chat.py b/litellm/llms/together_ai/chat.py
index 7efb12fc1b2..238849cc1ec 100644
--- a/litellm/llms/together_ai/chat.py
+++ b/litellm/llms/together_ai/chat.py
@@ -1,5 +1,5 @@
 """
-Support for OpenAI's `/v1/chat/completions` endpoint. 
+Support for OpenAI's `/v1/chat/completions` endpoint.
 
 Calls done in OpenAI/openai.py as TogetherAI is openai-compatible.
 
diff --git a/litellm/llms/together_ai/embed.py b/litellm/llms/together_ai/embed.py
index 577df0256cc..6a39b94acfc 100644
--- a/litellm/llms/together_ai/embed.py
+++ b/litellm/llms/together_ai/embed.py
@@ -1,5 +1,5 @@
 """
-Support for OpenAI's `/v1/embeddings` endpoint. 
+Support for OpenAI's `/v1/embeddings` endpoint.
 
 Calls done in OpenAI/openai.py as TogetherAI is openai-compatible.
 
diff --git a/litellm/llms/together_ai/rerank/transformation.py b/litellm/llms/together_ai/rerank/transformation.py
index 63b593dfe42..f4d642bd25a 100644
--- a/litellm/llms/together_ai/rerank/transformation.py
+++ b/litellm/llms/together_ai/rerank/transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from Cohere's /v1/rerank format to Together AI's  `/v1/rerank` format. 
+Transformation logic from Cohere's /v1/rerank format to Together AI's  `/v1/rerank` format.
 
 Why separate file? Make it easy to see how transformation works
 """
diff --git a/litellm/llms/vertex_ai/context_caching/transformation.py b/litellm/llms/vertex_ai/context_caching/transformation.py
index 950edbeb478..f73eb220cc6 100644
--- a/litellm/llms/vertex_ai/context_caching/transformation.py
+++ b/litellm/llms/vertex_ai/context_caching/transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic for context caching. 
+Transformation logic for context caching.
 
 Why separate file? Make it easy to see how transformation works
 """
@@ -19,7 +19,7 @@ from ..gemini.transformation import (
 
 
 def get_first_continuous_block_idx(
-    filtered_messages: List[Tuple[int, AllMessageValues]]  # (idx, message)
+    filtered_messages: List[Tuple[int, AllMessageValues]],  # (idx, message)
 ) -> int:
     """
     Find the array index that ends the first continuous sequence of message blocks.
@@ -174,7 +174,9 @@ def transform_openai_messages_to_gemini_context_caching(
     )
 
     transformed_messages = _gemini_convert_messages_with_history(
-        messages=new_messages, model=model
+        messages=new_messages,
+        model=model,
+        custom_llm_provider=custom_llm_provider,
     )
 
     model_name = "models/{}".format(model)
diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
index ac0f07b8e0b..3f945adca0d 100644
--- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
+++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
@@ -41,7 +41,7 @@ class ContextCachingEndpoints(VertexBase):
     """
 
     def __init__(self) -> None:
-        pass
+        super().__init__()
 
     def _get_token_and_url_context_caching(
         self,
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 9afa5dec465..4f5846cc5b6 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -6,13 +6,16 @@ Why separate file? Make it easy to see how transformation works
 
 import json
 import os
-from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Union, cast
+import re
+from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
+from urllib.parse import quote
 
 import httpx
 from pydantic import BaseModel
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.asyncify import asyncify
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     _get_image_mime_type_from_url,
 )
@@ -57,6 +60,45 @@ from ..common_utils import (
     get_supports_system_message,
 )
 
+# Typed as Any to avoid introducing a module-load-time cyclic import to
+# vertex_llm_base. The instance is lazily constructed by _get_vertex_base()
+# the first time GCS metadata needs to be fetched.
+_GCS_METADATA_VERTEX_BASE: Optional[Any] = None
+# Shared sync client for GCS JSON API metadata reads so proxy/SSL settings
+# from litellm's HTTP stack apply (see Greptile review on PR #27278).
+_GCS_METADATA_HTTP_HANDLER: Optional[HTTPHandler] = None
+_GEMINI_MIME_TYPE_ALIASES: Dict[str, str] = {
+    "image/jpg": "image/jpeg",
+}
+
+
+def _apply_gemini_mime_type_aliases(mime_type: str) -> str:
+    """Normalize known MIME aliases only; does not consult the file-type registry.
+
+    Also strips MIME parameters (e.g. ``; charset=utf-8``) so that values
+    sourced from GCS object metadata (``contentType``) validate correctly.
+    """
+    normalized = mime_type.split(";", 1)[0].strip().lower()
+    return _GEMINI_MIME_TYPE_ALIASES.get(normalized, normalized)
+
+
+def _get_vertex_base() -> Any:
+    """Lazily return the shared VertexBase instance to avoid a module-load-time cyclic import."""
+    global _GCS_METADATA_VERTEX_BASE
+    if _GCS_METADATA_VERTEX_BASE is None:
+        from ..vertex_llm_base import VertexBase
+
+        _GCS_METADATA_VERTEX_BASE = VertexBase()
+    return _GCS_METADATA_VERTEX_BASE
+
+
+def _get_gcs_metadata_http_handler() -> HTTPHandler:
+    global _GCS_METADATA_HTTP_HANDLER
+    if _GCS_METADATA_HTTP_HANDLER is None:
+        _GCS_METADATA_HTTP_HANDLER = HTTPHandler(timeout=5.0)
+    return _GCS_METADATA_HTTP_HANDLER
+
+
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
 
@@ -171,12 +213,299 @@ def _apply_gemini_metadata(
     return cast(PartType, part_dict)
 
 
+def _parse_gs_uri(gs_uri: str) -> Tuple[str, str]:
+    if not gs_uri.startswith("gs://"):
+        raise ValueError(f"Invalid gs URI: {gs_uri}")
+    uri_without_scheme = gs_uri[5:]  # drop gs://
+    uri_parts = uri_without_scheme.split("/", 1)
+    if len(uri_parts) != 2 or not uri_parts[0] or not uri_parts[1]:
+        raise ValueError(f"Invalid gs URI: {gs_uri}")
+    return uri_parts[0], uri_parts[1]
+
+
+def _is_valid_gcs_bucket_name(bucket: str) -> bool:
+    """
+    Validate bucket name against core GCS naming constraints.
+    """
+    bucket_length = len(bucket)
+    max_bucket_length = 222 if "." in bucket else 63
+    if bucket_length < 3 or bucket_length > max_bucket_length:
+        return False
+    if "." in bucket and any(
+        len(label) == 0 or len(label) > 63 for label in bucket.split(".")
+    ):
+        return False
+    if not re.fullmatch(r"[a-z0-9][a-z0-9._-]*[a-z0-9]", bucket):
+        return False
+    if ".." in bucket:
+        return False
+    if re.fullmatch(r"\d+\.\d+\.\d+\.\d+", bucket):
+        return False
+    return True
+
+
+def _gs_uri_requires_content_type_metadata(url: str) -> bool:
+    """
+    True when _process_gemini_media would call _get_gcs_object_content_type
+    (extension-less gs:// and no explicit format passed into that helper).
+    """
+    if "gs://" not in url:
+        return False
+    extension_with_dot = os.path.splitext(url)[-1]
+    extension = extension_with_dot[1:] if extension_with_dot else ""
+    return len(extension) == 0
+
+
+def _image_url_payload_may_need_sync_gcs_metadata_fetch(
+    raw_image_url: Any,
+) -> bool:
+    """
+    True when this image_url value (content-part image_url or assistant ``images[]``
+    entry) can trigger a blocking GCS metadata read for MIME resolution.
+    """
+    fmt: Optional[str] = None
+    url: Optional[str] = None
+    if isinstance(raw_image_url, dict):
+        url = raw_image_url.get("url")  # type: ignore[assignment]
+        if not isinstance(url, str):
+            return False
+        fmt = (
+            raw_image_url.get("format")
+            or raw_image_url.get("mime_type")
+            or raw_image_url.get("content_type")
+        )
+    elif isinstance(raw_image_url, str):
+        url = raw_image_url
+    else:
+        return False
+    if "gs://" not in url or fmt:
+        return False
+    return _gs_uri_requires_content_type_metadata(url)
+
+
+def _openai_messages_may_need_sync_gcs_metadata_fetch(
+    messages: List[AllMessageValues],
+) -> bool:
+    """
+    Heuristic: True if any message part can trigger a blocking GCS JSON
+    metadata read inside _transform_request_body (extension-less gs:// without
+    explicit MIME hints). Covers user/system ``content`` parts and assistant
+    ``images`` (same paths as ``_gemini_convert_messages_with_history``). Used
+    to decide whether ``async_transform_request_body`` should offload the sync
+    transform via ``asyncify``.
+    """
+    for raw in messages:
+        msg: Any = raw
+        if not isinstance(msg, dict) and hasattr(msg, "model_dump"):
+            msg = msg.model_dump(exclude_none=False)
+        if not isinstance(msg, dict):
+            continue
+        images_field = msg.get("images")
+        if isinstance(images_field, list):
+            for image_item in images_field:
+                if not isinstance(image_item, dict):
+                    continue
+                if _image_url_payload_may_need_sync_gcs_metadata_fetch(
+                    image_item.get("image_url")
+                ):
+                    return True
+
+        content = msg.get("content")
+        if not isinstance(content, list):
+            continue
+        for item in content:
+            if not isinstance(item, dict):
+                continue
+            itype = item.get("type")
+            if itype == "image_url":
+                if _image_url_payload_may_need_sync_gcs_metadata_fetch(
+                    item.get("image_url")
+                ):
+                    return True
+            elif itype == "file":
+                file_obj = item.get("file")
+                if not isinstance(file_obj, dict):
+                    continue
+                fmt = (
+                    file_obj.get("format")
+                    or file_obj.get("mime_type")
+                    or file_obj.get("content_type")
+                )
+                passed = file_obj.get("file_id") or file_obj.get("file_data")
+                if (
+                    isinstance(passed, str)
+                    and "gs://" in passed
+                    and not fmt
+                    and _gs_uri_requires_content_type_metadata(passed)
+                ):
+                    return True
+    return False
+
+
+def _get_gcs_object_content_type(
+    image_url: str,
+    vertex_project: Optional[str] = None,
+    vertex_credentials: Optional[Any] = None,
+) -> Optional[str]:
+    """
+    Resolve content type from GCS object metadata.
+
+    Only attaches a Bearer token when the caller explicitly supplies Vertex
+    credentials, to avoid using the server's default Google credentials on
+    the Gemini API-key (Google AI Studio) path and being used as an oracle
+    for private GCS object metadata. Without explicit credentials we only
+    issue an anonymous request, which only succeeds for publicly-readable
+    objects.
+    """
+    try:
+        bucket, object_name = _parse_gs_uri(image_url)
+    except ValueError:
+        return None
+    if not _is_valid_gcs_bucket_name(bucket):
+        return None
+
+    headers: Dict[str, str] = {}
+    explicit_vertex_auth_provided = (
+        vertex_project is not None or vertex_credentials is not None
+    )
+    if explicit_vertex_auth_provided:
+        try:
+            access_token, _ = _get_vertex_base().get_access_token(
+                credentials=vertex_credentials,
+                project_id=vertex_project,
+            )
+            headers["Authorization"] = f"Bearer {access_token}"
+        except Exception as e:
+            raise litellm.BadRequestError(
+                message=(
+                    "Unable to fetch GCS metadata with provided Vertex credentials/project. "
+                    f"Original error: {str(e)}"
+                ),
+                model=None,
+                llm_provider="vertex_ai",
+            )
+
+    # Build the URL via httpx.URL with a fixed scheme/host and URL-encode both
+    # bucket and object so CodeQL does not flag the interpolation as a
+    # potential SSRF that could resolve to an arbitrary host.
+    encoded_bucket = quote(bucket, safe="")
+    encoded_object = quote(object_name, safe="")
+    metadata_url = httpx.URL(
+        scheme="https",
+        host="storage.googleapis.com",
+        path=f"/storage/v1/b/{encoded_bucket}/o/{encoded_object}",
+        params={"fields": "contentType"},
+    )
+    try:
+        response = _get_gcs_metadata_http_handler().get(
+            url=str(metadata_url),
+            headers=headers or None,
+        )
+    except httpx.RequestError as e:
+        if explicit_vertex_auth_provided:
+            raise litellm.BadRequestError(
+                message=(
+                    "Unable to reach GCS JSON API for object metadata with provided "
+                    f"Vertex credentials. {type(e).__name__}: {e}"
+                ),
+                model=None,
+                llm_provider="vertex_ai",
+            ) from e
+        return None
+
+    if response.is_error:
+        if explicit_vertex_auth_provided:
+            preview = (response.text or "")[:1024]
+            raise litellm.BadRequestError(
+                message=(
+                    "Unable to read GCS object metadata with provided Vertex credentials. "
+                    f"HTTP {response.status_code}. Response body (truncated): {preview!r}"
+                ),
+                model=None,
+                llm_provider="vertex_ai",
+            )
+        return None
+
+    try:
+        payload = response.json()
+    except ValueError as e:
+        if explicit_vertex_auth_provided:
+            raise litellm.BadRequestError(
+                message=(
+                    "GCS metadata response was not valid JSON when using provided "
+                    f"Vertex credentials (HTTP {response.status_code}). Error: {e}"
+                ),
+                model=None,
+                llm_provider="vertex_ai",
+            ) from e
+        return None
+
+    if not isinstance(payload, dict):
+        if explicit_vertex_auth_provided:
+            raise litellm.BadRequestError(
+                message=(
+                    "GCS metadata response was not a JSON object when using provided "
+                    f"Vertex credentials (HTTP {response.status_code})."
+                ),
+                model=None,
+                llm_provider="vertex_ai",
+            )
+        return None
+
+    content_type = payload.get("contentType")
+    if isinstance(content_type, str) and len(content_type) > 0:
+        return content_type
+
+    if explicit_vertex_auth_provided:
+        preview = (response.text or "")[:1024]
+        raise litellm.BadRequestError(
+            message=(
+                "GCS metadata JSON did not include a non-empty contentType field when "
+                f"using provided Vertex credentials (HTTP {response.status_code}). "
+                f"Body (truncated): {preview!r}"
+            ),
+            model=None,
+            llm_provider="vertex_ai",
+        )
+    return None
+
+
+def _normalize_and_validate_gemini_mime_type(
+    mime_type: str, model: Optional[str]
+) -> str:
+    # Import lazily to avoid a module-level cyclic-import alert with
+    # litellm.types.files.
+    from litellm.types.files import get_file_extension_from_mime_type
+
+    normalized_mime_type = _apply_gemini_mime_type_aliases(mime_type)
+    try:
+        file_extension = get_file_extension_from_mime_type(normalized_mime_type)
+        file_type = get_file_type_from_extension(file_extension)
+    except ValueError:
+        raise litellm.BadRequestError(
+            message=f"File type not supported by gemini - {normalized_mime_type}",
+            model=model,
+            llm_provider="vertex_ai",
+        )
+
+    if not is_gemini_1_5_accepted_file_type(file_type):
+        raise litellm.BadRequestError(
+            message=f"File type not supported by gemini - {file_type}",
+            model=model,
+            llm_provider="vertex_ai",
+        )
+
+    return get_file_mime_type_for_file_type(file_type)
+
+
 def _process_gemini_media(
     image_url: str,
     format: Optional[str] = None,
     media_resolution_enum: Optional[Dict[str, str]] = None,
     model: Optional[str] = None,
     video_metadata: Optional[Dict[str, Any]] = None,
+    vertex_project: Optional[str] = None,
+    vertex_credentials: Optional[Any] = None,
 ) -> PartType:
     """
     Given a media URL (image, audio, or video), return the appropriate PartType for Gemini
@@ -193,20 +522,63 @@ def _process_gemini_media(
     try:
         # GCS URIs
         if "gs://" in image_url:
-            # Figure out file type
             extension_with_dot = os.path.splitext(image_url)[-1]  # Ex: ".png"
             extension = extension_with_dot[1:]  # Ex: "png"
 
+            explicit_gcs_format = False
             if not format:
-                file_type = get_file_type_from_extension(extension)
+                mime_type: Optional[str] = None
+                # For extension-less gs:// URIs, we cannot infer from path.
+                # If callers pass `format`/`mime_type`, this branch is skipped.
+                if extension:
+                    file_type = get_file_type_from_extension(extension)
 
-                # Validate the file type is supported by Gemini
-                if not is_gemini_1_5_accepted_file_type(file_type):
-                    raise Exception(f"File type not supported by gemini - {file_type}")
+                    # Validate the file type is supported by Gemini
+                    if not is_gemini_1_5_accepted_file_type(file_type):
+                        raise litellm.BadRequestError(
+                            message=f"File type not supported by gemini - {file_type}",
+                            model=model,
+                            llm_provider="vertex_ai",
+                        )
 
-                mime_type = get_file_mime_type_for_file_type(file_type)
+                    mime_type = get_file_mime_type_for_file_type(file_type)
+                else:
+                    mime_type = _get_gcs_object_content_type(
+                        image_url=image_url,
+                        vertex_project=vertex_project,
+                        vertex_credentials=vertex_credentials,
+                    )
+                    if mime_type is None:
+                        raise litellm.BadRequestError(
+                            message=(
+                                f"Unable to determine mime type for gs URI: {image_url}. "
+                                "This gs:// URI has no file extension and GCS metadata "
+                                "lookup failed. Set it explicitly using image_url.format "
+                                "(or image_url.mime_type/content_type) or "
+                                "message.content[].file.format."
+                            ),
+                            model=model,
+                            llm_provider="vertex_ai",
+                        )
             else:
                 mime_type = format
+                explicit_gcs_format = True
+            if mime_type is None:
+                raise litellm.BadRequestError(
+                    message=f"File type not supported by gemini - {image_url}",
+                    model=model,
+                    llm_provider="vertex_ai",
+                )
+            if explicit_gcs_format:
+                # Callers who pass format/mime_type explicitly for gs:// URIs
+                # rely on pass-through to Gemini (pre-PR behavior). Only apply
+                # known MIME aliases; skip litellm's file-type registry.
+                mime_type = _apply_gemini_mime_type_aliases(mime_type)
+            else:
+                mime_type = _normalize_and_validate_gemini_mime_type(
+                    mime_type=mime_type,
+                    model=model,
+                )
             file_data = FileDataType(mime_type=mime_type, file_uri=image_url)
             part: PartType = {"file_data": file_data}
             return _apply_gemini_metadata(
@@ -258,8 +630,6 @@ def _snake_to_camel(snake_str: str) -> str:
 
 def _camel_to_snake(camel_str: str) -> str:
     """Convert camelCase to snake_case"""
-    import re
-
     return re.sub(r"(?<!^)(?=[A-Z])", "_", camel_str).lower()
 
 
@@ -311,6 +681,8 @@ def check_if_part_exists_in_parts(
 def _gemini_convert_messages_with_history(  # noqa: PLR0915
     messages: List[AllMessageValues],
     model: Optional[str] = None,
+    litellm_params: Optional[dict] = None,
+    custom_llm_provider: Optional[str] = None,
 ) -> List[ContentType]:
     """
     Converts given messages from OpenAI format to Gemini format
@@ -326,6 +698,16 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
 
     msg_i = 0
     tool_call_responses = []
+    vertex_project = None
+    vertex_credentials = None
+    if litellm_params:
+        vertex_project = litellm_params.get("vertex_project") or litellm_params.get(
+            "vertex_ai_project"
+        )
+        vertex_credentials = litellm_params.get(
+            "vertex_credentials"
+        ) or litellm_params.get("vertex_ai_credentials")
+
     try:
         while msg_i < len(messages):
             user_content: List[PartType] = []
@@ -351,20 +733,42 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                             img_element = element
                             format: Optional[str] = None
                             media_resolution_enum: Optional[Dict[str, str]] = None
-                            if isinstance(img_element["image_url"], dict):
-                                image_url = img_element["image_url"]["url"]
-                                format = img_element["image_url"].get("format")
-                                detail = img_element["image_url"].get("detail")
+                            raw_image_url = img_element.get("image_url")
+                            if raw_image_url is None:
+                                raise litellm.BadRequestError(
+                                    message="Invalid message content: element type is 'image_url' but 'image_url' field is missing ",
+                                    model=model,
+                                    llm_provider="vertex_ai",
+                                )
+                            if isinstance(raw_image_url, dict):
+                                image_url = raw_image_url.get("url")
+                                if image_url is None:
+                                    raise litellm.BadRequestError(
+                                        message="Invalid message content: element type is 'image_url' but 'url' field is missing inside 'image_url' ",
+                                        model=model,
+                                        llm_provider="vertex_ai",
+                                    )
+                                # TypedDict does not declare mime_type/content_type;
+                                # read via Dict[str, Any] for caller-provided MIME fields.
+                                image_url_dict = cast(Dict[str, Any], raw_image_url)
+                                format = (
+                                    image_url_dict.get("format")
+                                    or image_url_dict.get("mime_type")
+                                    or image_url_dict.get("content_type")
+                                )
+                                detail = image_url_dict.get("detail")
                                 media_resolution_enum = (
                                     _convert_detail_to_media_resolution_enum(detail)
                                 )
                             else:
-                                image_url = img_element["image_url"]
+                                image_url = raw_image_url
                             _part = _process_gemini_media(
                                 image_url=image_url,
                                 format=format,
                                 media_resolution_enum=media_resolution_enum,
                                 model=model,
+                                vertex_project=vertex_project,
+                                vertex_credentials=vertex_credentials,
                             )
                             _parts.append(_part)
                         elif element["type"] == "input_audio":
@@ -390,15 +794,31 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                     image_url=openai_image_str,
                                     format=audio_format_modified,
                                     model=model,
+                                    vertex_project=vertex_project,
+                                    vertex_credentials=vertex_credentials,
                                 )
                                 _parts.append(_part)
                         elif element["type"] == "file":
                             file_element = cast(ChatCompletionFileObject, element)
-                            file_id = file_element["file"].get("file_id")
-                            format = file_element["file"].get("format")
-                            file_data = file_element["file"].get("file_data")
-                            detail = file_element["file"].get("detail")
-                            video_metadata = file_element["file"].get("video_metadata")
+                            _file_field = file_element.get("file")
+                            if _file_field is None:
+                                raise litellm.BadRequestError(
+                                    message="Content block has type='file' but is missing the required 'file' field",
+                                    model=model,
+                                    llm_provider="vertex_ai",
+                                )
+                            # TypedDict does not declare mime_type/content_type;
+                            # read via Dict[str, Any] for caller-provided MIME fields.
+                            file_dict = cast(Dict[str, Any], _file_field)
+                            file_id = file_dict.get("file_id")
+                            format = (
+                                file_dict.get("format")
+                                or file_dict.get("mime_type")
+                                or file_dict.get("content_type")
+                            )
+                            file_data = file_dict.get("file_data")
+                            detail = file_dict.get("detail")
+                            video_metadata = file_dict.get("video_metadata")
                             passed_file = file_id or file_data
                             if passed_file is None:
                                 raise Exception(
@@ -417,13 +837,23 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                     model=model,
                                     media_resolution_enum=media_resolution_enum,
                                     video_metadata=video_metadata,
+                                    vertex_project=vertex_project,
+                                    vertex_credentials=vertex_credentials,
                                 )
                                 _parts.append(_part)
-                            except Exception:
-                                raise Exception(
-                                    "Unable to determine mime type for file_id: {}, set this explicitly using message[{}].content[{}].file.format".format(
-                                        file_id, msg_i, element_idx
-                                    )
+                            except litellm.BadRequestError:
+                                raise
+                            except Exception as e:
+                                raise litellm.BadRequestError(
+                                    message=(
+                                        f"Unable to determine mime type for file: "
+                                        f"{file_id or 'provided data'}, set this explicitly "
+                                        f"using message[{msg_i}].content[{element_idx}].file.format "
+                                        f"(or file.mime_type/content_type). "
+                                        f"Original error: {str(e)}"
+                                    ),
+                                    model=model,
+                                    llm_provider="vertex_ai",
                                 )
                     user_content.extend(_parts)
                 elif _message_content is not None and isinstance(_message_content, str):
@@ -528,7 +958,11 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                             image_url_obj = image_item.get("image_url")
                             if isinstance(image_url_obj, dict):
                                 assistant_image_url = image_url_obj.get("url")
-                                format = image_url_obj.get("format")
+                                format = (
+                                    image_url_obj.get("format")
+                                    or image_url_obj.get("mime_type")
+                                    or image_url_obj.get("content_type")
+                                )
                                 detail = image_url_obj.get("detail")
                                 media_resolution_enum = (
                                     _convert_detail_to_media_resolution_enum(detail)
@@ -539,6 +973,8 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                         format=format,
                                         media_resolution_enum=media_resolution_enum,
                                         model=model,
+                                        vertex_project=vertex_project,
+                                        vertex_credentials=vertex_credentials,
                                     )
                                     assistant_content.append(_part)
 
@@ -548,7 +984,9 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                     or assistant_msg.get("function_call") is not None
                 ):  # support assistant tool invoke conversion
                     gemini_tool_call_parts = convert_to_gemini_tool_call_invoke(
-                        assistant_msg, model=model
+                        assistant_msg,
+                        model=model,
+                        custom_llm_provider=custom_llm_provider,
                     )
                     ## check if gemini_tool_call already exists in assistant_content
                     for gemini_tool_call_part in gemini_tool_call_parts:
@@ -607,7 +1045,10 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                 and messages[msg_i]["role"] in tool_call_message_roles
             ):
                 _part = convert_to_gemini_tool_call_result(
-                    messages[msg_i], last_message_with_tool_calls  # type: ignore
+                    messages[msg_i],  # type: ignore
+                    last_message_with_tool_calls,  # type: ignore
+                    model=model,
+                    custom_llm_provider=custom_llm_provider,
                 )
                 msg_i += 1
                 # Handle both single part and list of parts (for Computer Use with images)
@@ -632,16 +1073,14 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
             contents.append(ContentType(role="user", parts=tool_call_responses))
 
         if len(contents) == 0:
-            verbose_logger.warning(
-                """
+            verbose_logger.warning("""
                 No contents in messages. Contents are required. See
                 https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent#request-body.
                 If the original request did not comply to OpenAI API requirements it should have failed by now,
                 but LiteLLM does not check for missing messages.
                 Setting an empty content to prevent an 400 error.
                 Relevant Issue - https://github.com/BerriAI/litellm/issues/9733
-                """
-            )
+                """)
             contents.append(ContentType(role="user", parts=[PartType(text=" ")]))
         return contents
     except Exception as e:
@@ -713,11 +1152,11 @@ def _transform_request_body(  # noqa: PLR0915
     try:
         if custom_llm_provider == "gemini":
             content = litellm.GoogleAIStudioGeminiConfig()._transform_messages(
-                messages=messages, model=model
+                messages=messages, model=model, litellm_params=litellm_params
             )
         else:
             content = litellm.VertexGeminiConfig()._transform_messages(
-                messages=messages, model=model
+                messages=messages, model=model, litellm_params=litellm_params
             )
         tools: Optional[Tools] = optional_params.pop("tools", None)
         tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None)
@@ -893,6 +1332,20 @@ async def async_transform_request_body(
         vertex_auth_header=vertex_auth_header,
     )
 
+    if _openai_messages_may_need_sync_gcs_metadata_fetch(messages):
+        # _transform_request_body may issue a sync httpx.get (up to 5s timeout)
+        # via _get_gcs_object_content_type to fetch GCS object metadata. Run the
+        # whole sync transformation on a worker thread so it does not block the
+        # async event loop.
+        return await asyncify(_transform_request_body)(
+            messages=messages,
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            litellm_params=litellm_params,
+            cached_content=cached_content,
+            optional_params=optional_params,
+        )
+
     return _transform_request_body(
         messages=messages,
         model=model,
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index 6278de662f8..189ac7a7f6a 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -280,6 +280,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         - gemini-3-pro-preview
         - gemini-3-flash
         - gemini-3-flash-preview (Gemini 3 Flash)
+        - gemini-3.1-pro-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview
+        - gemini-3.5-flash
         - Any future Gemini 3.x models
         """
         # Check for Gemini 3 models
@@ -287,6 +289,20 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             return True
         return False
 
+    @staticmethod
+    def _forward_gemini_function_call_id(
+        model: str, custom_llm_provider: Optional[str] = None
+    ) -> bool:
+        """
+        Whether to include `id` on function_call / function_response parts.
+
+        Gemini 3+ on Google AI Studio accepts (and returns) `id` for strict
+        tool-call matching. Vertex AI rejects the field with HTTP 400.
+        """
+        if custom_llm_provider != "gemini":
+            return False
+        return VertexGeminiConfig._is_gemini_3_or_newer(model)
+
     def _supports_penalty_parameters(self, model: str) -> bool:
         # Gemini 3 models do not support penalty parameters
         if VertexGeminiConfig._is_gemini_3_or_newer(model):
@@ -300,6 +316,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         supported_params = [
             "temperature",
             "top_p",
+            "top_k",
             "max_tokens",
             "max_completion_tokens",
             "stream",
@@ -363,6 +380,66 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         """
         return Tools(googleSearch={})
 
+    @staticmethod
+    def _search_tool_keys() -> set:
+        return {
+            VertexToolName.GOOGLE_SEARCH.value,
+            VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value,
+            VertexToolName.ENTERPRISE_WEB_SEARCH.value,
+            VertexToolName.URL_CONTEXT.value,
+            "google_search",
+            "google_search_retrieval",
+            "enterprise_web_search",
+            "urlContext",
+        }
+
+    @classmethod
+    def _drop_search_tools_mixed_with_functions(cls, optional_params: dict) -> None:
+        """
+        Drop search tools from optional_params when mixed with function declarations
+        and include_server_side_tool_invocations is not enabled.
+
+        Runs after map_openai_params merges tools and web_search_options so both
+        code paths (single _map_function call vs split tools + web_search_options)
+        get the same conflict resolution.
+        """
+        if optional_params.get("include_server_side_tool_invocations"):
+            return
+
+        tools = optional_params.get("tools")
+        if not isinstance(tools, list) or not tools:
+            return
+
+        search_tool_keys = cls._search_tool_keys()
+        has_function_declarations = any(
+            isinstance(tool, dict) and tool.get("function_declarations")
+            for tool in tools
+        )
+        if not has_function_declarations:
+            return
+
+        has_search_tools = any(
+            isinstance(tool, dict) and any(key in tool for key in search_tool_keys)
+            for tool in tools
+        )
+        if not has_search_tools:
+            return
+
+        verbose_logger.warning(
+            "Vertex AI does not support mixing function declarations with "
+            "search tools (googleSearch, enterpriseWebSearch, urlContext, "
+            "googleSearchRetrieval) in the same request. Dropping search "
+            "tools and keeping function declarations. To use search tools, "
+            "send a request without function calling tools."
+        )
+        optional_params["tools"] = [
+            tool
+            for tool in tools
+            if not (
+                isinstance(tool, dict) and any(key in tool for key in search_tool_keys)
+            )
+        ]
+
     def _map_service_tier_param(self, value: str, optional_params: dict) -> None:
         """
         Map OpenAI service_tier (string) to Gemini serviceTier.
@@ -884,9 +961,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             GeminiThinkingConfig with thinkingLevel and includeThoughts
         """
         # Check if this is gemini-3-flash which supports MINIMAL thinking level
-        # Covers gemini-3-flash, gemini-3-flash-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview, etc.
+        # Covers gemini-3-flash, gemini-3-flash-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview,
+        # gemini-3.5-flash, and any future 3.x-flash variants.
         is_gemini3flash = model and (
-            "gemini-3-flash" in model.lower() or "gemini-3.1-flash" in model.lower()
+            "flash" in model.lower() and "gemini-3" in model.lower()
         )
         is_gemini31pro = model and ("gemini-3.1-pro-preview" in model.lower())
         if reasoning_effort == "minimal":
@@ -982,8 +1060,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     # Follow provider defaults unless explicitly opted into legacy behavior.
                     if litellm.enable_gemini_default_thinking_level_low is True:
                         is_gemini3flash = (
-                            "gemini-3-flash-preview" in model.lower()
-                            or "gemini-3-flash" in model.lower()
+                            "gemini-3" in model.lower() and "flash" in model.lower()
                         )
                         params["thinkingLevel"] = (
                             "minimal" if is_gemini3flash else "low"
@@ -1077,6 +1154,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         model: str,
         drop_params: bool,
     ) -> Dict:
+        gemini_sampling_params_warned: bool = False
         for param, value in non_default_params.items():
             if param == "temperature":
                 if VertexGeminiConfig._is_gemini_3_or_newer(model):
@@ -1086,9 +1164,41 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                             "can cause infinite loops, degraded reasoning performance, and failure on complex tasks. "
                             "Strongly recommended to use temperature = 1.0 (default)."
                         )
+                    if not gemini_sampling_params_warned:
+                        verbose_logger.warning(
+                            "DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to "
+                            f"function for Gemini 3+ ({model}) but are planned for removal in a "
+                            "future release. Move sampling guidance into the `system` "
+                            "instructions instead."
+                        )
+                        gemini_sampling_params_warned = True
                 optional_params["temperature"] = value
             elif param == "top_p":
+                if (
+                    VertexGeminiConfig._is_gemini_3_or_newer(model)
+                    and not gemini_sampling_params_warned
+                ):
+                    verbose_logger.warning(
+                        "DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to "
+                        f"function for Gemini 3+ ({model}) but are planned for removal in a "
+                        "future release. Move sampling guidance into the `system` "
+                        "instructions instead."
+                    )
+                    gemini_sampling_params_warned = True
                 optional_params["top_p"] = value
+            elif param == "top_k":
+                if (
+                    VertexGeminiConfig._is_gemini_3_or_newer(model)
+                    and not gemini_sampling_params_warned
+                ):
+                    verbose_logger.warning(
+                        "DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to "
+                        f"function for Gemini 3+ ({model}) but are planned for removal in a "
+                        "future release. Move sampling guidance into the `system` "
+                        "instructions instead."
+                    )
+                    gemini_sampling_params_warned = True
+                optional_params["top_k"] = value
             elif (
                 param == "stream" and value is True
             ):  # sending stream = False, can cause it to get passed unchecked and raise issues
@@ -1139,11 +1249,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 if _tool_choice_value is not None:
                     optional_params["tool_choice"] = _tool_choice_value
             elif param == "parallel_tool_calls":
-                if value is False and not (
-                    drop_params or litellm.drop_params
-                ):  # if drop params is True, then we should just ignore this
-                    self.validate_parallel_tool_calls(value, non_default_params)
-                else:
+                tools_list = non_default_params.get(
+                    "tools", non_default_params.get("functions")
+                )
+                num_tools = len(tools_list) if isinstance(tools_list, list) else 0
+                # Gemini does not support parallel_tool_calls=False with multiple
+                # tools. Drop the param instead of failing — Responses API clients
+                # often send parallel_tool_calls=false by default.
+                if not (value is False and num_tools > 1):
                     optional_params["parallel_tool_calls"] = value
             elif param == "seed":
                 optional_params["seed"] = value
@@ -1216,6 +1329,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             if "temperature" not in optional_params:
                 optional_params["temperature"] = 1.0
 
+        self._drop_search_tools_mixed_with_functions(optional_params)
+
         return optional_params
 
     def get_mapped_special_auth_params(self) -> dict:
@@ -1588,6 +1703,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 }
                 # Extract thought signature if present
                 thought_signature = part.get("thoughtSignature")
+                # Gemini 3.5+ returns a stable `id` per function call to enable
+                # strict response matching. Preserve it as the OpenAI
+                # tool_call_id so it can be echoed back unchanged.
+                gemini_call_id = part["functionCall"].get("id")
 
                 if is_function_call is True:
                     function_dict: Dict[str, Any] = dict(_function_chunk)
@@ -1605,6 +1724,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                         "function": _function_chunk,
                         "index": cumulative_tool_call_idx,
                     }
+                    # Gemini 3.5+ returns a stable native `id`; prefer it over
+                    # the synthetic call_<uuid> so the same value can be echoed
+                    # back on the matching `functionResponse`.
+                    if gemini_call_id:
+                        _tool_response_chunk["id"] = gemini_call_id
                     # Embed thought signature in ID for OpenAI client compatibility
                     if thought_signature:
                         _tool_response_chunk["provider_specific_fields"] = {  # type: ignore
@@ -2533,9 +2657,17 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         return model_response
 
     def _transform_messages(
-        self, messages: List[AllMessageValues], model: Optional[str] = None
+        self,
+        messages: List[AllMessageValues],
+        model: Optional[str] = None,
+        litellm_params: Optional[dict] = None,
     ) -> List[ContentType]:
-        return _gemini_convert_messages_with_history(messages=messages, model=model)
+        return _gemini_convert_messages_with_history(
+            messages=messages,
+            model=model,
+            litellm_params=litellm_params,
+            custom_llm_provider="vertex_ai",
+        )
 
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers]
@@ -3139,6 +3271,31 @@ class ModelResponseIterator:
         self.cumulative_tool_call_index: int = 0
         self.has_seen_tool_calls: bool = False
 
+    @staticmethod
+    def _check_streaming_error(chunk: dict) -> None:
+        """Detect embedded errors (e.g. 429 RESOURCE_EXHAUSTED) in streaming chunks and raise VertexAIError."""
+        if "error" not in chunk:
+            return
+        error_data = chunk["error"]
+        if not isinstance(error_data, dict):
+            raise VertexAIError(
+                status_code=500,
+                message=f"Unexpected error format in mid-stream chunk: {error_data}",
+            )
+        raw_code = error_data.get("code", 500)
+        if raw_code is None:
+            raw_code = 500
+        try:
+            error_code = int(raw_code)
+        except (TypeError, ValueError):
+            error_code = 500
+        error_message = error_data.get("message", "Unknown error")
+        error_status = error_data.get("status", "UNKNOWN")
+        raise VertexAIError(
+            status_code=error_code,
+            message=f"{error_status} - {error_message}",
+        )
+
     def _apply_stream_candidates(
         self,
         _candidates: List[Candidates],
@@ -3256,6 +3413,11 @@ class ModelResponseIterator:
     def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]:
         try:
             verbose_logger.debug(f"RAW GEMINI CHUNK: {chunk}")
+
+            # Detect mid-stream error chunks (e.g. 429 RESOURCE_EXHAUSTED).
+            # Vertex AI can return errors as HTTP 200 but with an "error" field in the SSE body.
+            self._check_streaming_error(chunk)
+
             from litellm.types.utils import ModelResponseStream
 
             processed_chunk = GenerateContentResponseBody(**chunk)  # type: ignore
diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
index e1b365c9f42..ba6e6f0c056 100644
--- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
+++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
@@ -1,5 +1,5 @@
 """
-Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /batchEmbedContents format. 
+Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /batchEmbedContents format.
 
 Why separate file? Make it easy to see how transformation works
 """
diff --git a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py
index 9d9015c2b91..b835ad7d8fa 100644
--- a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py
+++ b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py
@@ -139,7 +139,7 @@ class VertexTextToSpeechAPI(VertexLLM):
         ########## End of logging ############
         ####### Send the request ###################
         if _is_async is True:
-            return self.async_audio_speech(  # type:ignore
+            return self.async_audio_speech(  # type: ignore
                 logging_obj=logging_obj, url=url, headers=headers, request=request
             )
         sync_handler = _get_httpx_client()
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
index 123d925f7c1..13aa2a5350e 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
@@ -45,7 +45,7 @@ class PartnerModelPrefixes(str, Enum):
 
 class VertexAIPartnerModels(VertexBase):
     def __init__(self) -> None:
-        pass
+        super().__init__()
 
     @staticmethod
     def is_vertex_partner_model(model: str):
@@ -116,9 +116,6 @@ class VertexAIPartnerModels(VertexBase):
                 CodestralTextCompletion,
             )
             from litellm.llms.openai_like.chat.handler import OpenAILikeChatHandler
-            from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
-                VertexLLM,
-            )
         except Exception as e:
             raise VertexAIError(
                 status_code=400,
@@ -133,9 +130,7 @@ class VertexAIPartnerModels(VertexBase):
                 message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""",
             )
         try:
-            vertex_httpx_logic = VertexLLM()
-
-            access_token, project_id = vertex_httpx_logic._ensure_access_token(
+            access_token, project_id = self._ensure_access_token(
                 credentials=vertex_credentials,
                 project_id=vertex_project,
                 custom_llm_provider="vertex_ai",
@@ -292,22 +287,15 @@ class VertexAIPartnerModels(VertexBase):
         Returns:
             Dict containing token count information
         """
-        try:
-            import vertexai
-        except Exception as e:
-            raise VertexAIError(
-                status_code=400,
-                message=f"""vertexai import failed please run `pip install -U "google-cloud-aiplatform>=1.38"`. Got error: {e}""",
-            )
-
-        if not (
-            hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models")
-        ):
-            raise VertexAIError(
-                status_code=400,
-                message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""",
-            )
-
+        # Note: we intentionally do not import `vertexai` (the Gemini SDK shipped
+        # by `google-cloud-aiplatform`) on this path. Partner models such as
+        # Claude on Vertex use the Anthropic Messages API protocol directly via
+        # `:rawPredict`, and `VertexAIPartnerModelsTokenCounter` reaches that
+        # endpoint with an authenticated httpx client — it never touches the
+        # Gemini SDK. Requiring `google-cloud-aiplatform>=1.38` here turned a
+        # SDK-free Anthropic-protocol call into a hard dependency on the Gemini
+        # SDK (see #28084), breaking `/v1/messages/count_tokens` for Claude-on-
+        # Vertex on any LiteLLM install without that extra. Stay SDK-free.
         try:
             from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import (
                 VertexAIPartnerModelsTokenCounter,
diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/main.py b/litellm/llms/vertex_ai/vertex_gemma_models/main.py
index 82cfe6de984..b6bf2f73b72 100644
--- a/litellm/llms/vertex_ai/vertex_gemma_models/main.py
+++ b/litellm/llms/vertex_ai/vertex_gemma_models/main.py
@@ -31,7 +31,7 @@ from ..vertex_llm_base import VertexBase
 
 class VertexAIGemmaModels(VertexBase):
     def __init__(self) -> None:
-        pass
+        super().__init__()
 
     def completion(
         self,
@@ -62,9 +62,6 @@ class VertexAIGemmaModels(VertexBase):
         try:
             import vertexai
 
-            from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
-                VertexLLM,
-            )
             from litellm.llms.vertex_ai.vertex_gemma_models.transformation import (
                 VertexGemmaConfig,
             )
@@ -83,9 +80,8 @@ class VertexAIGemmaModels(VertexBase):
             )
         try:
             model = get_vertex_base_model_name(model=model)
-            vertex_httpx_logic = VertexLLM()
 
-            access_token, project_id = vertex_httpx_logic._ensure_access_token(
+            access_token, project_id = self._ensure_access_token(
                 credentials=vertex_credentials,
                 project_id=vertex_project,
                 custom_llm_provider="vertex_ai",
diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
index 6c6446958bc..35cd54d65f6 100644
--- a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
@@ -91,6 +91,10 @@ class VertexGemmaConfig(OpenAIGPTConfig):
             "stream", None
         )  # Streaming not supported, will be faked client-side
         openai_request.pop("stream_options", None)  # Stream options not supported
+        # Vertex Gemma's chatCompletions wrapper does not understand
+        # `context_management` (an Anthropic/Responses API concept). Strip it
+        # so the upstream endpoint does not 400 on the unknown field.
+        openai_request.pop("context_management", None)
 
         # Wrap in Vertex Gemma format
         return {
diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py
index 6f687dae7e8..990063bb9fb 100644
--- a/litellm/llms/vertex_ai/vertex_llm_base.py
+++ b/litellm/llms/vertex_ai/vertex_llm_base.py
@@ -4,8 +4,10 @@ Base Vertex, Google AI Studio LLM Class
 Handles Authentication and generating request urls for Vertex AI and Google AI Studio
 """
 
+import asyncio
 import json
 import os
+import threading
 from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Tuple
 
 import litellm
@@ -30,6 +32,7 @@ GOOGLE_IMPORT_ERROR_MESSAGE = (
 
 if TYPE_CHECKING:
     from google.auth.credentials import Credentials as GoogleCredentialsObject
+    from google.auth.credentials import TokenState
 else:
     GoogleCredentialsObject = Any
 
@@ -42,10 +45,28 @@ class VertexBase:
         self._credentials: Optional[GoogleCredentialsObject] = None
         self._credentials_project_mapping: Dict[
             Tuple[Optional[VERTEX_CREDENTIALS_TYPES], Optional[str]],
-            Tuple[GoogleCredentialsObject, str],
+            Tuple[GoogleCredentialsObject, Optional[str]],
         ] = {}
         self.project_id: Optional[str] = None
         self.async_handler: Optional[AsyncHTTPHandler] = None
+        # Per-credential-key asyncio.Lock for single-flight async refresh.
+        # Prevents thundering herd when token expires under high concurrency.
+        # Uses a regular dict (not WeakValueDictionary) so the lock identity is
+        # stable across concurrent callers — a weak reference can be GC'd
+        # between two coroutines arriving at the lock, breaking single-flight.
+        # An explicit refcount tracks the number of coroutines currently using
+        # each lock; the entry is pruned when the count reaches zero, so the
+        # dict stays bounded even in long-running high-cardinality deployments
+        # without depending on any private asyncio internals.
+        self._async_refresh_locks: Dict[tuple, asyncio.Lock] = {}
+        self._async_refresh_lock_refcounts: Dict[tuple, int] = {}
+        # Tracks in-flight background refresh tasks to avoid duplicate refreshes.
+        self._background_refresh_tasks: Dict[tuple, asyncio.Task] = {}
+        # Protects the sync get_access_token refresh path.
+        # Use RLock so that the reauthentication retry path (which calls
+        # back into get_access_token while still holding the lock) can
+        # re-acquire it without deadlocking the current thread.
+        self._sync_refresh_lock = threading.RLock()
 
     def get_vertex_region(self, vertex_region: Optional[str], model: str) -> str:
         import litellm
@@ -77,7 +98,9 @@ class VertexBase:
         return vertex_region or "us-central1"
 
     def load_auth(
-        self, credentials: Optional[VERTEX_CREDENTIALS_TYPES], project_id: Optional[str]
+        self,
+        credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        project_id: Optional[str],
     ) -> Tuple[Any, str]:
         if credentials is not None:
             if isinstance(credentials, str):
@@ -343,7 +366,241 @@ class VertexBase:
         except ImportError:
             raise ImportError(GOOGLE_IMPORT_ERROR_MESSAGE)
 
-        credentials.refresh(Request())
+        # Serialize all refreshes on this VertexBase across threads.
+        # ``credentials.refresh()`` is not safe to call concurrently on the
+        # same credentials object, and this method is invoked from three
+        # places that can run on different threads:
+        #   - sync ``get_access_token`` (already holds ``_sync_refresh_lock``)
+        #   - the async slow path (via ``asyncify`` in a worker thread)
+        #   - the background proactive refresh task (via ``asyncify``)
+        # ``_sync_refresh_lock`` is an ``RLock`` so reentrant acquisition
+        # from the sync path is safe.
+        with self._sync_refresh_lock:
+            credentials.refresh(Request())
+
+    def _acquire_async_refresh_lock(self, credential_cache_key: tuple) -> asyncio.Lock:
+        """Increment the refcount and return the lock for ``credential_cache_key``.
+
+        Every call must be paired with ``_release_async_refresh_lock`` once the
+        caller is done with the lock so the entry can be pruned when no other
+        coroutine is holding or waiting on it.
+        """
+        lock = self._async_refresh_locks.setdefault(
+            credential_cache_key, asyncio.Lock()
+        )
+        self._async_refresh_lock_refcounts[credential_cache_key] = (
+            self._async_refresh_lock_refcounts.get(credential_cache_key, 0) + 1
+        )
+        return lock
+
+    def _release_async_refresh_lock(
+        self, credential_cache_key: tuple, lock: asyncio.Lock
+    ) -> None:
+        """Decrement the refcount and drop the lock entry when it reaches zero.
+
+        Must be called only after the caller has released ``lock`` (i.e. once
+        the surrounding ``async with`` has exited). asyncio is cooperative, so
+        the decrement-then-pop sequence below runs atomically with respect to
+        other coroutines.
+        """
+        remaining = self._async_refresh_lock_refcounts.get(credential_cache_key, 0) - 1
+        if remaining > 0:
+            self._async_refresh_lock_refcounts[credential_cache_key] = remaining
+            return
+        self._async_refresh_lock_refcounts.pop(credential_cache_key, None)
+        if self._async_refresh_locks.get(credential_cache_key) is lock:
+            self._async_refresh_locks.pop(credential_cache_key, None)
+
+    def _try_get_cached_token(
+        self,
+        credential_cache_key: tuple,
+        project_id: Optional[str],
+    ) -> Optional[Tuple[str, str]]:
+        """
+        Look up cached credentials and return (token, project_id) if the token
+        is FRESH. Returns None if not cached or not fresh.
+        """
+        from google.auth.credentials import TokenState
+
+        creds, cached_project_id = self._unpack_cached_credentials(credential_cache_key)
+        if (
+            creds is not None
+            and self._get_token_state(creds) == TokenState.FRESH
+            and creds.token is not None
+            and isinstance(creds.token, str)
+        ):
+            resolved_project = project_id or cached_project_id
+            if resolved_project:
+                return creds.token, resolved_project
+        return None
+
+    def _try_get_usable_cached_token(
+        self,
+        credential_cache_key: tuple,
+        project_id: Optional[str],
+    ) -> Optional[Tuple[str, str, "TokenState", Any, Optional[str]]]:
+        """
+        Look up cached credentials and return usable token info for FRESH or
+        STALE tokens (both are still valid for outbound requests). STALE
+        tokens are returned along with their state and the underlying
+        credentials object so the caller can schedule a background refresh
+        without holding the per-key async lock.
+        """
+        from google.auth.credentials import TokenState
+
+        creds, cached_project_id = self._unpack_cached_credentials(credential_cache_key)
+        if creds is None:
+            return None
+        token_state = self._get_token_state(creds)
+        if token_state not in (TokenState.FRESH, TokenState.STALE):
+            return None
+        if creds.token is None or not isinstance(creds.token, str):
+            return None
+        resolved_project = project_id or cached_project_id
+        if not resolved_project:
+            return None
+        return creds.token, resolved_project, token_state, creds, cached_project_id
+
+    def _unpack_cached_credentials(
+        self, credential_cache_key: tuple
+    ) -> Tuple[Any, Optional[str]]:
+        """
+        Return (credentials, project_id) from the cache, or (None, None) if
+        not cached. Handles both tuple and legacy cache formats.
+        """
+        if credential_cache_key not in self._credentials_project_mapping:
+            return None, None
+        cached_entry = self._credentials_project_mapping[credential_cache_key]
+        if isinstance(cached_entry, tuple):
+            return cached_entry
+        return cached_entry, cached_entry.quota_project_id or getattr(
+            cached_entry, "project_id", None
+        )
+
+    def _get_token_state(self, credentials: Any) -> "TokenState":
+        """
+        Return the token state using google-auth's TokenState enum.
+
+        Falls back to expired/valid checks if token_state is unavailable
+        (e.g. older google-auth versions or mock objects in tests).
+        """
+        from google.auth.credentials import TokenState as _TokenState
+
+        token_state = getattr(credentials, "token_state", None)
+        if isinstance(token_state, _TokenState):
+            return token_state
+        # Fallback for credentials without a real token_state (e.g. mocks)
+        if getattr(credentials, "expired", True):
+            return _TokenState.INVALID
+        if getattr(credentials, "valid", False):
+            return _TokenState.FRESH
+        return _TokenState.INVALID
+
+    async def _load_and_cache_credentials(
+        self,
+        credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        project_id: Optional[str],
+        credential_cache_key: tuple,
+    ) -> Tuple[Any, Optional[str]]:
+        """Load credentials via load_auth (in thread) and cache the result."""
+        try:
+            _credentials, credential_project_id = await asyncify(self.load_auth)(
+                credentials=credentials,
+                project_id=project_id,
+            )
+        except Exception as e:
+            verbose_logger.exception("Failed to load vertex credentials: %s", str(e))
+            raise
+        if _credentials is None:
+            raise ValueError("Could not resolve credentials")
+        self._credentials_project_mapping[credential_cache_key] = (
+            _credentials,
+            credential_project_id,
+        )
+        return _credentials, credential_project_id
+
+    async def _background_refresh_credentials(
+        self,
+        credentials: Any,
+        credential_cache_key: tuple,
+        credential_project_id: Optional[str],
+    ) -> None:
+        """
+        Refresh credentials in the background without blocking the calling request.
+
+        Called when the token is still valid but nearing expiry (proactive refresh).
+        Errors are logged but not raised — the current token is still usable.
+        """
+        try:
+            verbose_logger.debug("Background proactive credential refresh")
+            await asyncify(self.refresh_auth)(credentials)
+            # Only update the cache if it still points at the credentials
+            # object we just refreshed. The per-key async lock is not held
+            # here, so a concurrent INVALID path may have already replaced
+            # this entry (e.g. via _handle_reauthentication_async, which
+            # creates a fresh credentials object). In that case our write
+            # would clobber the newer entry with a stale reference.
+            cached_creds, _ = self._unpack_cached_credentials(credential_cache_key)
+            if cached_creds is credentials:
+                self._credentials_project_mapping[credential_cache_key] = (
+                    credentials,
+                    credential_project_id,
+                )
+        except Exception:
+            verbose_logger.debug(
+                "Background credential refresh failed, will retry on next request",
+                exc_info=True,
+            )
+
+    async def _await_in_flight_background_refresh(
+        self, credential_cache_key: tuple
+    ) -> None:
+        """Wait for an in-flight background refresh to finish, if any.
+
+        google-auth's ``Credentials.refresh()`` is not safe to invoke
+        concurrently on the same credentials object. Coroutines that need a
+        blocking refresh must first drain any background refresh that was
+        scheduled while a previous STALE token was being served.
+        """
+        existing_task = self._background_refresh_tasks.get(credential_cache_key)
+        if existing_task is None or existing_task.done():
+            return
+        try:
+            await existing_task
+        except Exception:
+            # Background refresh failures are already logged inside
+            # _background_refresh_credentials; the caller will fall through
+            # to its own blocking refresh.
+            pass
+
+    def _schedule_background_refresh(
+        self,
+        credentials: Any,
+        credential_cache_key: tuple,
+        credential_project_id: Optional[str],
+    ) -> None:
+        """Kick off a single background refresh for ``credential_cache_key``.
+
+        Skips scheduling if a refresh is already in flight. The done-callback
+        guards against removing a newer task that has replaced this one in the
+        tracking dict (done_callbacks are scheduled via ``call_soon``).
+        """
+        existing = self._background_refresh_tasks.get(credential_cache_key)
+        if existing is not None and not existing.done():
+            return
+        self._background_refresh_tasks.pop(credential_cache_key, None)
+        task = asyncio.create_task(
+            self._background_refresh_credentials(
+                credentials, credential_cache_key, credential_project_id
+            )
+        )
+
+        def _drop_background_refresh_task(_fut: asyncio.Future[Any]) -> None:
+            if self._background_refresh_tasks.get(credential_cache_key) is _fut:
+                self._background_refresh_tasks.pop(credential_cache_key, None)
+
+        task.add_done_callback(_drop_background_refresh_task)
+        self._background_refresh_tasks[credential_cache_key] = task
 
     def _ensure_access_token(
         self,
@@ -563,6 +820,65 @@ class VertexBase:
             # Re-raise the original error for better context
             raise error
 
+    async def _handle_reauthentication_async(
+        self,
+        credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        project_id: Optional[str],
+        credential_cache_key: Tuple,
+        error: Exception,
+    ) -> Tuple[str, str]:
+        """
+        Async reauthentication retry that stays within the per-key async lock.
+        """
+        verbose_logger.debug(
+            f"Handling async reauthentication for project_id: {project_id}. "
+            f"Clearing cache and retrying once."
+        )
+
+        self._credentials_project_mapping.pop(credential_cache_key, None)
+
+        try:
+            _credentials, credential_project_id = (
+                await self._load_and_cache_credentials(
+                    credentials=credentials,
+                    project_id=project_id,
+                    credential_cache_key=credential_cache_key,
+                )
+            )
+            if project_id is None and isinstance(credential_project_id, str):
+                project_id = credential_project_id
+                cache_credentials = (
+                    json.dumps(credentials)
+                    if isinstance(credentials, dict)
+                    else credentials
+                )
+                resolved_cache_key = (cache_credentials, project_id)
+                # Always overwrite — any pre-existing entry at the resolved key
+                # references the OLD credentials object we just replaced, and
+                # leaving it would force the next request to do a redundant
+                # refresh/reauth before realizing the cached creds are stale.
+                self._credentials_project_mapping[resolved_cache_key] = (
+                    _credentials,
+                    credential_project_id,
+                )
+
+            if _credentials.token is None or not isinstance(_credentials.token, str):
+                raise ValueError(
+                    "Could not resolve credentials token. Got None or non-string token (type={})".format(
+                        type(_credentials.token).__name__
+                    )
+                )
+            if project_id is None:
+                raise ValueError("Could not resolve project_id")
+
+            return _credentials.token, project_id
+        except Exception as retry_error:
+            verbose_logger.error(
+                f"Async reauthentication retry failed for project_id: {project_id}. "
+                f"Original error: {str(error)}. Retry error: {str(retry_error)}"
+            )
+            raise error
+
     def get_access_token(
         self,
         credentials: Optional[VERTEX_CREDENTIALS_TYPES],
@@ -646,7 +962,7 @@ class VertexBase:
             )
 
         ## VALIDATE CREDENTIALS
-        verbose_logger.debug(f"Validating credentials for project_id: {project_id}")
+        verbose_logger.debug("Validating credentials")
         if (
             project_id is None
             and credential_project_id is not None
@@ -666,26 +982,27 @@ class VertexBase:
             raise ValueError("Credentials are None after loading")
 
         if _credentials.expired:
-            try:
-                verbose_logger.debug(
-                    f"Credentials expired, refreshing for project_id: {project_id}"
-                )
-                self.refresh_auth(_credentials)
-                self._credentials_project_mapping[credential_cache_key] = (
-                    _credentials,
-                    credential_project_id,
-                )
-            except Exception as e:
-                # if refresh fails, it's possible the user has re-authenticated via `gcloud auth application-default login`
-                # in this case, we should try to reload the credentials by clearing the cache and retrying
-                if "Reauthentication is needed" in str(e) and not _retry_reauth:
-                    return self._handle_reauthentication(
-                        credentials=credentials,
-                        project_id=project_id,
-                        credential_cache_key=credential_cache_key,
-                        error=e,
-                    )
-                raise e
+            with self._sync_refresh_lock:
+                # Double-check after acquiring lock
+                if _credentials.expired:
+                    try:
+                        verbose_logger.debug("Credentials expired, refreshing")
+                        self.refresh_auth(_credentials)
+                        self._credentials_project_mapping[credential_cache_key] = (
+                            _credentials,
+                            credential_project_id,
+                        )
+                    except Exception as e:
+                        # if refresh fails, it's possible the user has re-authenticated via `gcloud auth application-default login`
+                        # in this case, we should try to reload the credentials by clearing the cache and retrying
+                        if "Reauthentication is needed" in str(e) and not _retry_reauth:
+                            return self._handle_reauthentication(
+                                credentials=credentials,
+                                project_id=project_id,
+                                credential_cache_key=credential_cache_key,
+                                error=e,
+                            )
+                        raise e
 
         ## VALIDATION STEP
         if _credentials.token is None or not isinstance(_credentials.token, str):
@@ -700,6 +1017,149 @@ class VertexBase:
 
         return _credentials.token, project_id
 
+    async def get_access_token_async(
+        self,
+        credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        project_id: Optional[str],
+    ) -> Tuple[str, str]:
+        """
+        Async version of get_access_token with single-flight refresh coordination.
+
+        Prevents thundering herd: when credentials expire under high concurrency,
+        only one coroutine refreshes while others wait on the lock. Uses native
+        async refresh for service_account and authorized_user credentials.
+        """
+        from google.auth.credentials import TokenState
+
+        cache_credentials = (
+            json.dumps(credentials) if isinstance(credentials, dict) else credentials
+        )
+        credential_cache_key = (cache_credentials, project_id)
+
+        # === FAST PATH (no lock) ===
+        # If credentials are FRESH or STALE, return immediately without
+        # touching the per-key async lock. STALE tokens are still usable;
+        # we kick off a deduplicated background refresh so subsequent
+        # requests get a fresh token, but we must not serialize concurrent
+        # callers on the lock just to schedule that refresh.
+        usable = self._try_get_usable_cached_token(credential_cache_key, project_id)
+        if usable is not None:
+            cached_token, resolved_project, token_state, creds, cached_project_id = (
+                usable
+            )
+            if token_state == TokenState.STALE:
+                self._schedule_background_refresh(
+                    creds, credential_cache_key, cached_project_id
+                )
+            return cached_token, resolved_project
+
+        # === SLOW PATH (per-key lock) ===
+        lock = self._acquire_async_refresh_lock(credential_cache_key)
+        try:
+            async with lock:
+                # Double-check after acquiring lock — another coroutine may have refreshed.
+                cached = self._try_get_cached_token(credential_cache_key, project_id)
+                if cached is not None:
+                    return cached
+
+                _credentials, credential_project_id = self._unpack_cached_credentials(
+                    credential_cache_key
+                )
+
+                # Load credentials if not cached
+                if _credentials is None:
+                    _credentials, credential_project_id = (
+                        await self._load_and_cache_credentials(
+                            credentials, project_id, credential_cache_key
+                        )
+                    )
+
+                # Resolve project_id from credentials if not provided
+                if project_id is None and isinstance(credential_project_id, str):
+                    project_id = credential_project_id
+                    resolved_cache_key = (cache_credentials, project_id)
+                    # Always overwrite — a pre-existing entry at the resolved
+                    # key may reference stale credentials (e.g. from before a
+                    # reauth that only repopulated the unresolved key), which
+                    # would force the next request through an unnecessary
+                    # refresh/reauth cycle.
+                    self._credentials_project_mapping[resolved_cache_key] = (
+                        _credentials,
+                        credential_project_id,
+                    )
+
+                # Use google-auth's token_state to decide refresh strategy:
+                # - STALE: token is usable but within REFRESH_THRESHOLD (3:45) of
+                #   expiry — return it immediately and refresh in the background.
+                # - INVALID: token is expired or missing — must block on refresh.
+                token_state = self._get_token_state(_credentials)
+
+                if token_state == TokenState.STALE:
+                    if project_id is None:
+                        raise ValueError("Could not resolve project_id")
+                    current_token = _credentials.token
+                    if current_token is None or not isinstance(current_token, str):
+                        # Token is malformed despite STALE state — block on a full
+                        # refresh using the same path as INVALID credentials.
+                        token_state = TokenState.INVALID
+                    else:
+                        self._schedule_background_refresh(
+                            _credentials,
+                            credential_cache_key,
+                            credential_project_id,
+                        )
+                        return current_token, project_id
+
+                if token_state == TokenState.INVALID:
+                    # Drain any in-flight background refresh before invoking
+                    # refresh_auth ourselves; google-auth's
+                    # Credentials.refresh() is not safe to call concurrently
+                    # on the same credentials object, and the background task
+                    # runs outside this lock.
+                    await self._await_in_flight_background_refresh(credential_cache_key)
+                    cached = self._try_get_cached_token(
+                        credential_cache_key, project_id
+                    )
+                    if cached is not None:
+                        return cached
+
+                    # Token is expired or missing — must block until refresh completes.
+                    try:
+                        verbose_logger.debug("Credentials expired, refreshing")
+                        await asyncify(self.refresh_auth)(_credentials)
+                        self._credentials_project_mapping[credential_cache_key] = (
+                            _credentials,
+                            credential_project_id,
+                        )
+                    except Exception as e:
+                        if "Reauthentication is needed" in str(e):
+                            verbose_logger.debug(
+                                "Reauthentication needed, clearing cache and retrying"
+                            )
+                            return await self._handle_reauthentication_async(
+                                credentials=credentials,
+                                project_id=project_id,
+                                credential_cache_key=credential_cache_key,
+                                error=e,
+                            )
+                        raise
+
+                # Final validation
+                if _credentials.token is None or not isinstance(
+                    _credentials.token, str
+                ):
+                    raise ValueError(
+                        "Could not resolve credentials token. Got None or non-string token (type={})".format(
+                            type(_credentials.token).__name__
+                        )
+                    )
+                if project_id is None:
+                    raise ValueError("Could not resolve project_id")
+
+                return _credentials.token, project_id
+        finally:
+            self._release_async_refresh_lock(credential_cache_key, lock)
+
     async def _ensure_access_token_async(
         self,
         credentials: Optional[VERTEX_CREDENTIALS_TYPES],
@@ -714,13 +1174,10 @@ class VertexBase:
         if custom_llm_provider == "gemini":
             return "", ""
         else:
-            try:
-                return await asyncify(self.get_access_token)(
-                    credentials=credentials,
-                    project_id=project_id,
-                )
-            except Exception as e:
-                raise e
+            return await self.get_access_token_async(
+                credentials=credentials,
+                project_id=project_id,
+            )
 
     def set_headers(
         self, auth_header: Optional[str], extra_headers: Optional[dict]
diff --git a/litellm/llms/vertex_ai/vertex_model_garden/main.py b/litellm/llms/vertex_ai/vertex_model_garden/main.py
index 7240d9dce57..732d5f90dc2 100644
--- a/litellm/llms/vertex_ai/vertex_model_garden/main.py
+++ b/litellm/llms/vertex_ai/vertex_model_garden/main.py
@@ -57,7 +57,7 @@ def create_vertex_url(
 
 class VertexAIModelGardenModels(VertexBase):
     def __init__(self) -> None:
-        pass
+        super().__init__()
 
     def completion(
         self,
@@ -89,9 +89,6 @@ class VertexAIModelGardenModels(VertexBase):
             import vertexai
 
             from litellm.llms.openai_like.chat.handler import OpenAILikeChatHandler
-            from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
-                VertexLLM,
-            )
         except Exception as e:
             raise VertexAIError(
                 status_code=400,
@@ -107,9 +104,8 @@ class VertexAIModelGardenModels(VertexBase):
             )
         try:
             model = get_vertex_base_model_name(model=model)
-            vertex_httpx_logic = VertexLLM()
 
-            access_token, project_id = vertex_httpx_logic._ensure_access_token(
+            access_token, project_id = self._ensure_access_token(
                 credentials=vertex_credentials,
                 project_id=vertex_project,
                 custom_llm_provider="vertex_ai",
diff --git a/litellm/llms/vllm/completion/transformation.py b/litellm/llms/vllm/completion/transformation.py
index ec4c07e95d8..e03b07f9897 100644
--- a/litellm/llms/vllm/completion/transformation.py
+++ b/litellm/llms/vllm/completion/transformation.py
@@ -1,5 +1,5 @@
 """
-Translates from OpenAI's `/v1/chat/completions` to the VLLM sdk `llm.generate`. 
+Translates from OpenAI's `/v1/chat/completions` to the VLLM sdk `llm.generate`.
 
 NOT RECOMMENDED FOR PRODUCTION USE. Use `hosted_vllm/` instead.
 """
diff --git a/litellm/llms/voyage/embedding/transformation_contextual.py b/litellm/llms/voyage/embedding/transformation_contextual.py
index 40328062e09..1f5ca99f47d 100644
--- a/litellm/llms/voyage/embedding/transformation_contextual.py
+++ b/litellm/llms/voyage/embedding/transformation_contextual.py
@@ -1,6 +1,6 @@
 """
-This module is used to transform the request and response for the Voyage contextualized embeddings API. 
-This would be used for all the contextualized embeddings models in Voyage. 
+This module is used to transform the request and response for the Voyage contextualized embeddings API.
+This would be used for all the contextualized embeddings models in Voyage.
 """
 
 from typing import List, Optional, Union
diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py
index 6300868a641..7325c0596a6 100644
--- a/litellm/llms/xai/chat/transformation.py
+++ b/litellm/llms/xai/chat/transformation.py
@@ -1,4 +1,4 @@
-from typing import Any, AsyncIterator, Iterator, List, Optional, Tuple, Union
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Tuple, Union
 
 import httpx
 
@@ -26,6 +26,7 @@ from ...openai.chat.gpt_transformation import (
 
 
 class XAIChatConfig(OpenAIGPTConfig):
+
     @property
     def custom_llm_provider(self) -> Optional[str]:
         return "xai"
@@ -225,21 +226,57 @@ class XAIChatConfig(OpenAIGPTConfig):
             verbose_logger.debug(f"Error extracting X.AI web search usage: {e}")
 
         self._fold_reasoning_tokens_into_completion(response)
+        self._normalize_openai_compatible_usage_totals(getattr(response, "usage", None))
         return response
 
     @staticmethod
-    def _fold_reasoning_tokens_into_completion(model_response: ModelResponse) -> None:
+    def _fold_reasoning_tokens_into_completion(
+        target: Union[ModelResponse, Usage, Dict[str, Any], None],
+    ) -> None:
         """Reconcile xAI Usage to the OpenAI invariant.
 
         xAI accounts ``reasoning_tokens`` separately from
         ``completion_tokens`` while still summing them into ``total_tokens``.
         OpenAI's contract (o1/o3) folds reasoning into ``completion_tokens``,
         so fold here to keep ``total = prompt + completion``. Idempotent.
+
+        Accepts a ``ModelResponse`` (non-streaming), a ``Usage`` object, or a
+        raw usage ``dict`` (streaming chunk) so streaming and non-streaming
+        paths stay in sync.
         """
-        usage = getattr(model_response, "usage", None)
+        if target is None:
+            return
+
+        if isinstance(target, ModelResponse):
+            usage: Union[Usage, Dict[str, Any], None] = getattr(target, "usage", None)
+        else:
+            usage = target
         if usage is None:
             return
 
+        if isinstance(usage, dict):
+            details = usage.get("completion_tokens_details") or {}
+            if isinstance(details, dict):
+                reasoning_tokens = int(details.get("reasoning_tokens") or 0)
+            else:
+                reasoning_tokens = int(getattr(details, "reasoning_tokens", 0) or 0)
+            if reasoning_tokens <= 0:
+                return
+
+            prompt_tokens = int(usage.get("prompt_tokens") or 0)
+            completion_tokens = int(usage.get("completion_tokens") or 0)
+            total_tokens = int(usage.get("total_tokens") or 0)
+
+            if total_tokens == prompt_tokens + completion_tokens:
+                return
+
+            # Guard against double-counting if xAI changes accounting.
+            if total_tokens != prompt_tokens + completion_tokens + reasoning_tokens:
+                return
+
+            usage["completion_tokens"] = completion_tokens + reasoning_tokens
+            return
+
         details = getattr(usage, "completion_tokens_details", None)
         reasoning_tokens = (
             int(getattr(details, "reasoning_tokens", 0) or 0) if details else 0
@@ -284,6 +321,25 @@ class XAIChatConfig(OpenAIGPTConfig):
             setattr(usage, "num_sources_used", int(num_sources_used))
             verbose_logger.debug(f"X.AI web search sources used: {num_sources_used}")
 
+    @staticmethod
+    def _normalize_openai_compatible_usage_totals(
+        usage: Union[Usage, Dict[str, Any], None],
+    ) -> None:
+        if usage is None:
+            return
+        if isinstance(usage, dict):
+            prompt_tokens = int(usage.get("prompt_tokens") or 0)
+            completion_tokens = int(usage.get("completion_tokens") or 0)
+            expected_total = prompt_tokens + completion_tokens
+            if int(usage.get("total_tokens") or 0) < expected_total:
+                usage["total_tokens"] = expected_total
+            return
+        prompt_tokens = int(usage.prompt_tokens or 0)
+        completion_tokens = int(usage.completion_tokens or 0)
+        expected_total = prompt_tokens + completion_tokens
+        if int(usage.total_tokens or 0) < expected_total:
+            usage.total_tokens = expected_total
+
 
 class XAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler):
     def chunk_parser(self, chunk: dict) -> ModelResponseStream:
@@ -304,4 +360,8 @@ class XAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler):
             # Add a dummy choice with empty delta to ensure proper processing
             chunk["choices"] = [{"index": 0, "delta": {}, "finish_reason": None}]
 
+        if "usage" in chunk and chunk["usage"] is not None:
+            XAIChatConfig._fold_reasoning_tokens_into_completion(chunk["usage"])
+            XAIChatConfig._normalize_openai_compatible_usage_totals(chunk["usage"])
+
         return super().chunk_parser(chunk)
diff --git a/litellm/main.py b/litellm/main.py
index c3d1c2e05b0..09c70998cf7 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1491,7 +1491,9 @@ def completion(  # type: ignore # noqa: PLR0915
             provider.value for provider in LlmProviders
         ]:
             provider_config = ProviderConfigManager.get_provider_chat_config(
-                model=model, provider=LlmProviders(custom_llm_provider)
+                model=model,
+                provider=LlmProviders(custom_llm_provider),
+                base_model=base_model,
             )
 
         if provider_config is not None:
@@ -1550,6 +1552,7 @@ def completion(  # type: ignore # noqa: PLR0915
             "safety_identifier": safety_identifier,
             "service_tier": service_tier,
             "allowed_openai_params": kwargs.get("allowed_openai_params"),
+            "base_model": base_model,
         }
         optional_params = get_optional_params(
             **optional_param_args, **non_default_params
@@ -1670,6 +1673,10 @@ def completion(  # type: ignore # noqa: PLR0915
                 reasoning_summary=_reasoning_summary_for_bridge,
             )
 
+        # Use base_model (the true underlying model) for Azure model-type
+        # detection when the deployment name differs from the model name.
+        _azure_detection_model = base_model or model
+
         if responses_api_model_info.get("mode") == "responses":
             from litellm.completion_extras import responses_api_bridge
 
@@ -1713,7 +1720,9 @@ def completion(  # type: ignore # noqa: PLR0915
             and OpenAIGPT5Config.is_model_gpt_5_model(model)
         ) or (
             custom_llm_provider == "azure"
-            and litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model)
+            and litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(
+                _azure_detection_model
+            )
         ):
             optional_params, _ = strip_reasoning_summary_aliases_from_optional_params(
                 optional_params
@@ -1766,7 +1775,9 @@ def completion(  # type: ignore # noqa: PLR0915
             if max_retries is not None:
                 optional_params["max_retries"] = max_retries
 
-            if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
+            if litellm.AzureOpenAIO1Config().is_o_series_model(
+                model=_azure_detection_model
+            ):
                 ## LOAD CONFIG - if set
                 config = litellm.AzureOpenAIO1Config.get_config()
                 for k, v in config.items():
@@ -5116,6 +5127,24 @@ def embedding(  # noqa: PLR0915
                 client=client,
                 aembedding=aembedding,
             )
+        elif custom_llm_provider == "oci":
+            if headers is None:
+                headers = {}
+            response = base_llm_http_handler.embedding(
+                model=model,
+                input=input,
+                custom_llm_provider=custom_llm_provider,
+                api_base=api_base,
+                api_key=api_key,
+                logging_obj=logging,
+                timeout=timeout,
+                model_response=EmbeddingResponse(),
+                optional_params=optional_params,
+                client=client,
+                aembedding=aembedding,
+                litellm_params=litellm_params_dict,
+                headers=headers,
+            )
         elif custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat":
             cohere_key = (
                 api_key
@@ -5720,6 +5749,33 @@ def embedding(  # noqa: PLR0915
                 aembedding=aembedding,
                 headers=headers,
             )
+        elif custom_llm_provider == "dashscope":
+            dashscope_key = (
+                api_key or litellm.api_key or get_secret_str("DASHSCOPE_API_KEY")
+            )
+            if dashscope_key is None:
+                raise ValueError(
+                    "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter."
+                )
+            if extra_headers is not None and isinstance(extra_headers, dict):
+                headers = extra_headers
+            else:
+                headers = {}
+            response = base_llm_http_handler.embedding(
+                model=model,
+                input=input,
+                timeout=timeout,
+                custom_llm_provider=custom_llm_provider,
+                logging_obj=logging,
+                api_base=api_base,
+                optional_params=optional_params,
+                litellm_params={},
+                model_response=EmbeddingResponse(),
+                api_key=dashscope_key,
+                client=client,
+                aembedding=aembedding,
+                headers=headers,
+            )
         elif custom_llm_provider == "ovhcloud":
             api_key = api_key or litellm.api_key or get_secret_str("OVHCLOUD_API_KEY")
             api_base = (
@@ -5769,22 +5825,6 @@ def embedding(  # noqa: PLR0915
                 aembedding=aembedding,
                 litellm_params={},
             )
-        elif custom_llm_provider == "oci":
-            response = base_llm_http_handler.embedding(
-                model=model,
-                input=input,
-                custom_llm_provider=custom_llm_provider,
-                api_base=api_base,
-                api_key=api_key,
-                logging_obj=logging,
-                timeout=timeout,
-                model_response=EmbeddingResponse(),
-                optional_params=optional_params,
-                client=client,
-                aembedding=aembedding,
-                litellm_params=litellm_params_dict,
-                headers=headers,
-            )
         elif custom_llm_provider in litellm._custom_providers:
             custom_handler: Optional[CustomLLM] = None
             for item in litellm.custom_provider_map:
@@ -6575,8 +6615,7 @@ def transcription(
         api_key=api_key,
     )  # type: ignore
 
-    if dynamic_api_key is not None:
-        api_key = dynamic_api_key
+    api_key = dynamic_api_key if dynamic_api_key is not None else api_key
 
     optional_params = get_optional_params_transcription(
         model=model,
@@ -6616,7 +6655,7 @@ def transcription(
         provider=LlmProviders(custom_llm_provider),
     )
 
-    if custom_llm_provider == "azure":
+    if custom_llm_provider == "azure" and provider_config is None:
         # azure configs
         api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")
 
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index fb5bfa6cf4e..62e576ea0f7 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -1448,6 +1448,35 @@
         "supports_native_structured_output": true,
         "supports_minimal_reasoning_effort": true
     },
+    "jp.anthropic.claude-sonnet-4-6": {
+        "cache_creation_input_token_cost": 4.125e-06,
+        "cache_read_input_token_cost": 3.3e-07,
+        "input_cost_per_token": 3.3e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.65e-05,
+        "search_context_cost_per_query": {
+            "search_context_size_high": 0.01,
+            "search_context_size_low": 0.01,
+            "search_context_size_medium": 0.01
+        },
+        "supports_assistant_prefill": true,
+        "supports_computer_use": true,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_max_reasoning_effort": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true,
+        "supports_minimal_reasoning_effort": true
+    },
     "anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
         "cache_read_input_token_cost": 3e-07,
@@ -6025,6 +6054,17 @@
         "mode": "audio_speech",
         "source": "https://azure.microsoft.com/en-us/pricing/calculator/"
     },
+    "azure/speech/azure-stt": {
+        "audio_transcription_config": "azure_speech",
+        "input_cost_per_second": 0.0002777778,
+        "litellm_provider": "azure",
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/speech-services/",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
     "azure/tts-1": {
         "input_cost_per_character": 1.5e-05,
         "litellm_provider": "azure",
@@ -9602,6 +9642,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_adaptive_thinking": true,
         "supports_assistant_prefill": true,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -9795,6 +9836,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_adaptive_thinking": true,
         "supports_assistant_prefill": false,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -9828,6 +9870,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_adaptive_thinking": true,
         "supports_assistant_prefill": false,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -9861,6 +9904,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_adaptive_thinking": true,
         "supports_assistant_prefill": false,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -9895,6 +9939,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_adaptive_thinking": true,
         "supports_assistant_prefill": false,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -13948,6 +13993,21 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "fireworks_ai/accounts/fireworks/models/glm-5p1": {
+        "cache_read_input_token_cost": 2.6e-07,
+        "input_cost_per_token": 1.4e-06,
+        "litellm_provider": "fireworks_ai",
+        "max_input_tokens": 202800,
+        "max_output_tokens": 202800,
+        "max_tokens": 202800,
+        "mode": "chat",
+        "output_cost_per_token": 4.4e-06,
+        "source": "https://fireworks.ai/models/fireworks/glm-5p1",
+        "supports_function_calling": false,
+        "supports_reasoning": true,
+        "supports_response_schema": false,
+        "supports_tool_choice": false
+    },
     "fireworks_ai/accounts/fireworks/models/gpt-oss-120b": {
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@@ -14214,6 +14274,21 @@
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@@ -14883,7 +14958,65 @@
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@@ -15611,6 +15744,64 @@
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@@ -16929,6 +17120,66 @@
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@@ -16988,6 +17239,67 @@
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@@ -17173,6 +17485,65 @@
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@@ -24107,6 +24478,21 @@
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@@ -25922,6 +26308,51 @@
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@@ -26951,6 +27382,58 @@
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@@ -28725,6 +29208,24 @@
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@@ -33427,6 +33928,64 @@
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+        "supports_service_tier": true
+    },
     "vertex_ai/deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
         "input_cost_per_token": 2e-06,
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index d1b49039e8e..d0e9ad7b2a4 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -12,6 +12,7 @@ import hashlib
 import json
 import os
 import re
+import time
 from typing import Any, Callable, Dict, List, Literal, Optional, Set, Tuple, Union, cast
 from urllib.parse import urlparse
 
@@ -250,6 +251,10 @@ class MCPServerManager:
         }
         """
         self._upstream_initialize_instructions_by_server_id: Dict[str, str] = {}
+        # Per-server monotonic timestamp of last upstream prefetch attempt (success,
+        # empty result, or failure). Used to throttle re-probes for servers that do
+        # not return instructions, and to apply a short cooldown after failures.
+        self._upstream_initialize_instructions_probed_at: Dict[str, float] = {}
 
     def _remember_upstream_initialize_instructions(
         self, server: MCPServer, client: MCPClient
@@ -260,6 +265,80 @@ class MCPServerManager:
                 raw
             ).strip()
 
+    async def _ensure_upstream_initialize_instructions_cached(
+        self, server: MCPServer
+    ) -> None:
+        """
+        Open one upstream session and cache InitializeResult.instructions if missing.
+
+        No-op when:
+          - YAML/DB instructions are set on the server record,
+          - server is OpenAPI (spec_path),
+          - non-empty upstream instructions are already cached,
+          - auth preconditions match health_check_server's skip rules
+            (per-user auth / missing static auth token),
+          - a prior probe attempt for this server is within
+            MCP_HEALTH_CHECK_TIMEOUT seconds (the probe is a health-check-shaped
+            op and already uses this knob for its inner call timeout; reusing it
+            as the cooldown avoids reconnecting on every gateway initialize when
+            upstream returns empty or fails).
+        """
+        if server.spec_path:
+            return
+        if server.instructions and server.instructions.strip():
+            return
+        if self._upstream_initialize_instructions_by_server_id.get(server.server_id):
+            return
+        if server.requires_per_user_auth:
+            return
+        if (
+            server.auth_type
+            and server.auth_type != MCPAuth.none
+            and server.auth_type != MCPAuth.aws_sigv4
+            and not server.authentication_token
+        ):
+            return
+
+        last_probed_at = self._upstream_initialize_instructions_probed_at.get(
+            server.server_id
+        )
+        if (
+            last_probed_at is not None
+            and (time.monotonic() - last_probed_at) < MCP_HEALTH_CHECK_TIMEOUT
+        ):
+            return
+
+        # Record the attempt up-front so that a failure / empty response does not
+        # cause every subsequent initialize request to re-open the upstream session.
+        self._upstream_initialize_instructions_probed_at[server.server_id] = (
+            time.monotonic()
+        )
+
+        try:
+            extra_headers: Optional[Dict[str, str]] = (
+                dict(server.static_headers) if server.static_headers else None
+            )
+            client = await self._create_mcp_client(
+                server=server,
+                mcp_auth_header=None,
+                extra_headers=extra_headers,
+                stdio_env=None,
+            )
+
+            async def _noop(_session):
+                return "ok"
+
+            await asyncio.wait_for(
+                client.run_with_session(_noop), timeout=MCP_HEALTH_CHECK_TIMEOUT
+            )
+            self._remember_upstream_initialize_instructions(server, client)
+        except Exception as e:
+            verbose_logger.debug(
+                "Upstream initialize instructions prefetch failed for %s: %s",
+                server.name,
+                e,
+            )
+
     def get_registry(self) -> Dict[str, MCPServer]:
         """
         Get the registered MCP Servers from the registry and union with the config MCP Servers
@@ -280,6 +359,7 @@ class MCPServerManager:
         """
         verbose_logger.debug("Loading MCP Servers from config-----")
         self._upstream_initialize_instructions_by_server_id.clear()
+        self._upstream_initialize_instructions_probed_at.clear()
 
         # Track which aliases have been used to ensure only first occurrence is used
         used_aliases = set()
@@ -1212,11 +1292,17 @@ class MCPServerManager:
                 return []
 
             # Get server-specific auth header if available
-            server_auth_header = None
-            if mcp_server_auth_headers and server.alias:
-                server_auth_header = mcp_server_auth_headers.get(server.alias)
-            elif mcp_server_auth_headers and server.server_name:
-                server_auth_header = mcp_server_auth_headers.get(server.server_name)
+            server_auth_header: Optional[Union[str, Dict[str, str]]] = None
+            if mcp_server_auth_headers:
+                from litellm.proxy._experimental.mcp_server.utils import (
+                    lookup_mcp_server_auth_in_headers,
+                )
+
+                server_auth_header = lookup_mcp_server_auth_in_headers(
+                    mcp_server_auth_headers,
+                    alias=server.alias,
+                    server_name=server.server_name,
+                )
 
             # Fall back to deprecated mcp_auth_header if no server-specific header found
             if server_auth_header is None:
@@ -1226,6 +1312,7 @@ class MCPServerManager:
                 tools = await self._get_tools_from_server(
                     server=server,
                     mcp_auth_header=server_auth_header,
+                    user_api_key_auth=user_api_key_auth,
                 )
                 return tools
             except Exception as e:
@@ -1406,6 +1493,7 @@ class MCPServerManager:
         extra_headers: Optional[Dict[str, str]] = None,
         add_prefix: bool = True,
         raw_headers: Optional[Dict[str, str]] = None,
+        user_api_key_auth: Optional[UserAPIKeyAuth] = None,
     ) -> List[MCPTool]:
         """
         Helper method to get tools from a single MCP server with prefixed names.
@@ -1432,6 +1520,46 @@ class MCPServerManager:
                     extra_headers = {}
                 extra_headers.update(server.static_headers)
 
+            # MCPJWTSigner: inject signed JWT for tools/list (list path skips pre_call_hook).
+            # Skip entirely when the signer is not configured (avoid an unnecessary
+            # dict copy on every list call), when the server has its own static
+            # Authorization header, when a per-user mcp_auth_header has already
+            # been resolved, or when the caller already supplied an Authorization
+            # entry in extra_headers (e.g. a per-user OAuth token resolved
+            # upstream) — admin-configured static auth and per-user OAuth must
+            # take precedence so the signer doesn't silently overwrite e.g. an
+            # upstream API key or a user's OAuth token (MCPClient._get_auth_headers
+            # applies extra_headers after writing Authorization from auth_value, so
+            # an injected JWT would otherwise clobber the per-user token).
+            if user_api_key_auth is not None and not server.spec_path:
+                from litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer import (
+                    get_mcp_jwt_signer,
+                    inject_mcp_jwt_headers_for_upstream,
+                )
+
+                static_headers = server.static_headers or {}
+                has_static_authorization = any(
+                    isinstance(k, str) and k.lower() == "authorization"
+                    for k in static_headers.keys()
+                )
+                has_extra_authorization = bool(extra_headers) and any(
+                    isinstance(k, str) and k.lower() == "authorization"
+                    for k in (extra_headers or {}).keys()
+                )
+
+                if (
+                    get_mcp_jwt_signer() is not None
+                    and not has_static_authorization
+                    and not mcp_auth_header
+                    and not has_extra_authorization
+                ):
+                    extra_headers = await inject_mcp_jwt_headers_for_upstream(
+                        user_api_key_dict=user_api_key_auth,
+                        extra_headers=extra_headers,
+                        raw_headers=raw_headers,
+                        for_list_tools=True,
+                    )
+
             stdio_env = self._build_stdio_env(server, raw_headers)
 
             client = await self._create_mcp_client(
@@ -2665,16 +2793,15 @@ class MCPServerManager:
         server_auth_header: Optional[Union[Dict[str, str], str]] = None
         if mcp_server_auth_headers:
             # Normalize keys for case-insensitive lookup
-            normalized_headers = {
-                k.lower(): v for k, v in mcp_server_auth_headers.items()
-            }
+            from litellm.proxy._experimental.mcp_server.utils import (
+                lookup_mcp_server_auth_in_headers,
+            )
 
-            if mcp_server.alias:
-                server_auth_header = normalized_headers.get(mcp_server.alias.lower())
-            if server_auth_header is None and mcp_server.server_name:
-                server_auth_header = normalized_headers.get(
-                    mcp_server.server_name.lower()
-                )
+            server_auth_header = lookup_mcp_server_auth_in_headers(
+                mcp_server_auth_headers,
+                alias=mcp_server.alias,
+                server_name=mcp_server.server_name,
+            )
 
         # Fall back to deprecated mcp_auth_header if no server-specific header found
         if server_auth_header is None:
@@ -2791,6 +2918,112 @@ class MCPServerManager:
 
         return cast(CallToolResult, result)
 
+    def _resolve_mcp_server_for_tool_call(
+        self,
+        server_name: str,
+        name: str,
+    ) -> MCPServer:
+        """Resolve MCP server for call_tool (prefixed name, registry, fallback)."""
+        prefixed_tool_name = add_server_prefix_to_name(name, server_name)
+        mcp_server = self._get_mcp_server_from_tool_name(prefixed_tool_name)
+        resolved_by_server_name_only = False
+        normalized_server_name = normalize_server_name(server_name)
+
+        def _candidate_matches_server_name(candidate: MCPServer) -> bool:
+            for identifier in (
+                candidate.alias,
+                candidate.server_name,
+                candidate.name,
+            ):
+                if identifier and normalize_server_name(identifier) == (
+                    normalized_server_name
+                ):
+                    return True
+            return False
+
+        if mcp_server is None:
+            for candidate in self.get_registry().values():
+                if _candidate_matches_server_name(candidate):
+                    mcp_server = candidate
+                    resolved_by_server_name_only = True
+                    break
+        if mcp_server is None:
+            fallback = self._get_mcp_server_from_tool_name(name)
+            if fallback is not None and (
+                not server_name or _candidate_matches_server_name(fallback)
+            ):
+                mcp_server = fallback
+        if mcp_server is None:
+            raise ValueError(f"Tool {name} not found")
+
+        if resolved_by_server_name_only:
+            tool_known = (
+                name in self.tool_name_to_mcp_server_name_mapping
+                or prefixed_tool_name in self.tool_name_to_mcp_server_name_mapping
+            )
+            if not tool_known:
+                raise ValueError(f"Tool {name} not found")
+
+        return mcp_server
+
+    async def _resolve_oauth2_headers_for_tool_call(
+        self,
+        mcp_server: MCPServer,
+        oauth2_headers: Optional[Dict[str, str]],
+        user_api_key_auth: Optional[UserAPIKeyAuth],
+    ) -> Optional[Dict[str, str]]:
+        """Look up per-user OAuth headers when the client did not supply a token."""
+        if (
+            not mcp_server.needs_user_oauth_token
+            or oauth2_headers
+            or user_api_key_auth is None
+        ):
+            return oauth2_headers
+
+        user_id = getattr(user_api_key_auth, "user_id", None)
+        if not user_id:
+            return oauth2_headers
+
+        try:
+            from litellm.proxy._experimental.mcp_server.server import (  # noqa: PLC0415
+                _get_user_oauth_extra_headers_from_db,
+            )
+
+            stored_headers = await _get_user_oauth_extra_headers_from_db(
+                server=mcp_server,
+                user_api_key_auth=user_api_key_auth,
+            )
+            if stored_headers:
+                return stored_headers
+        except Exception as _lookup_exc:
+            verbose_logger.debug(
+                "call_tool: per-user token lookup failed for " "user=%s server=%s: %s",
+                user_id,
+                mcp_server.server_id,
+                _lookup_exc,
+            )
+        return oauth2_headers
+
+    async def _gather_openapi_tool_tasks(
+        self,
+        tasks: List[Any],
+        proxy_logging_obj: Optional[ProxyLogging],
+    ) -> CallToolResult:
+        """Await OpenAPI tool tasks and return the tool call result."""
+        try:
+            mcp_responses = await asyncio.gather(*tasks)
+            result_index = 1 if proxy_logging_obj else 0
+            return cast(CallToolResult, mcp_responses[result_index])
+        except (
+            BlockedPiiEntityError,
+            GuardrailRaisedException,
+            HTTPException,
+        ) as e:
+            verbose_logger.error(
+                f"Guardrail blocked MCP tool call during result check: {str(e)}"
+            )
+            raise e
+
     async def call_tool(
         self,
         server_name: str,
@@ -2821,12 +3054,7 @@ class MCPServerManager:
             CallToolResult from the MCP server
         """
         start_time = datetime.datetime.now()
-
-        # Get the MCP server
-        prefixed_tool_name = add_server_prefix_to_name(name, server_name)
-        mcp_server = self._get_mcp_server_from_tool_name(prefixed_tool_name)
-        if mcp_server is None:
-            raise ValueError(f"Tool {name} not found")
+        mcp_server = self._resolve_mcp_server_for_tool_call(server_name, name)
 
         #########################################################
         # Pre MCP Tool Call Hook
@@ -2860,36 +3088,9 @@ class MCPServerManager:
             )
             tasks.append(during_hook_task)
 
-        # For per-user OAuth servers: if the client didn't supply a token in
-        # oauth2_headers, look up the stored token from Redis / DB.  This is the
-        # call_tool equivalent of _get_user_oauth_extra_headers_from_db used in
-        # list_tools.
-        if (
-            mcp_server.needs_user_oauth_token
-            and not oauth2_headers
-            and user_api_key_auth is not None
-        ):
-            user_id = getattr(user_api_key_auth, "user_id", None)
-            if user_id:
-                try:
-                    from litellm.proxy._experimental.mcp_server.server import (  # noqa: PLC0415
-                        _get_user_oauth_extra_headers_from_db,
-                    )
-
-                    stored_headers = await _get_user_oauth_extra_headers_from_db(
-                        server=mcp_server,
-                        user_api_key_auth=user_api_key_auth,
-                    )
-                    if stored_headers:
-                        oauth2_headers = stored_headers
-                except Exception as _lookup_exc:
-                    verbose_logger.debug(
-                        "call_tool: per-user token lookup failed for "
-                        "user=%s server=%s: %s",
-                        user_id,
-                        mcp_server.server_id,
-                        _lookup_exc,
-                    )
+        oauth2_headers = await self._resolve_oauth2_headers_for_tool_call(
+            mcp_server, oauth2_headers, user_api_key_auth
+        )
 
         # For OpenAPI servers, call the tool handler directly instead of via MCP client
         if mcp_server.spec_path:
@@ -2925,26 +3126,7 @@ class MCPServerManager:
                 hook_extra_headers=hook_result.get("extra_headers"),
             )
 
-        # For OpenAPI tools, await outside the client context
-        try:
-            mcp_responses = await asyncio.gather(*tasks)
-
-            # If proxy_logging_obj is None, the tool call result is at index 0
-            # If proxy_logging_obj is not None, the tool call result is at index 1 (after the during hook task)
-            result_index = 1 if proxy_logging_obj else 0
-            result = mcp_responses[result_index]
-
-            return cast(CallToolResult, result)
-        except (
-            BlockedPiiEntityError,
-            GuardrailRaisedException,
-            HTTPException,
-        ) as e:
-            # Re-raise guardrail exceptions to properly fail the MCP call
-            verbose_logger.error(
-                f"Guardrail blocked MCP tool call during result check: {str(e)}"
-            )
-            raise e
+        return await self._gather_openapi_tool_tasks(tasks, proxy_logging_obj)
 
     #########################################################
     # End of Methods that call the upstream MCP servers
@@ -3039,6 +3221,7 @@ class MCPServerManager:
 
         verbose_logger.debug("Loading MCP servers from database into registry...")
         self._upstream_initialize_instructions_by_server_id.clear()
+        self._upstream_initialize_instructions_probed_at.clear()
 
         # perform authz check to filter the mcp servers user has access to
         prisma_client = get_prisma_client_or_throw(
diff --git a/litellm/proxy/_experimental/mcp_server/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py
index 8541a691e88..e8b591c39cf 100644
--- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py
+++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py
@@ -3,8 +3,8 @@
 
 import os
 from ipaddress import ip_address
-from typing import List, Optional
-from urllib.parse import urlparse, urlunparse
+from typing import Any, Dict, List, NoReturn, Optional
+from urllib.parse import ParseResult, urlparse, urlunparse
 
 from fastapi import HTTPException, Request
 
@@ -29,10 +29,47 @@ _DEFAULT_PORTS = {"http": 80, "https": 443}
 # subdomain. HTTPS only.
 _TRUSTED_REDIRECT_ORIGINS_ENV = "MCP_TRUSTED_REDIRECT_ORIGINS"
 
+# Comma-separated private-use URI allowlist for native MCP clients.
+# A trailing ``*`` is a prefix match; end the prefix with ``/`` (e.g.
+# ``myapp://host/oauth/*``) so ``.../oauth/callback*`` does not also
+# match ``.../oauth/callback-2``.
+_TRUSTED_NATIVE_REDIRECT_URIS_ENV = "MCP_TRUSTED_NATIVE_REDIRECT_URIS"
+
+# Default allowlist for trusted native redirect URIs.
+_DEFAULT_NATIVE_REDIRECT_URIS: List[str] = [
+    "cursor://anysphere.cursor-mcp/oauth/callback",
+]
 
 _warned_invalid_proxy_base_url: Optional[str] = None
 
 
+def _oauth_invalid_request(
+    error_description: str,
+    *,
+    hint: Optional[str] = None,
+    **extra: Any,
+) -> NoReturn:
+    """Raise ``invalid_request`` (RFC 6749) with a debuggable description.
+
+    FastAPI serializes ``detail`` as JSON. Callers still see ``error``:
+    ``invalid_request``; ``error_description`` and ``hint`` explain what
+    failed and how to fix it (e.g. reverse-proxy / PROXY_BASE_URL issues).
+    """
+    detail: Dict[str, Any] = {
+        "error": "invalid_request",
+        "error_description": error_description,
+    }
+    if hint:
+        detail["hint"] = hint
+    detail.update(extra)
+    raise HTTPException(status_code=400, detail=detail)
+
+
+def _origin_label(scheme: str, netloc: str) -> str:
+    """Human-readable origin for error messages (scheme + host[:port])."""
+    return f"{scheme}://{netloc}" if netloc else f"{scheme}://"
+
+
 def _resolve_proxy_base_url_env() -> Optional[str]:
     global _warned_invalid_proxy_base_url
     configured = os.environ.get("PROXY_BASE_URL", "").strip()
@@ -108,17 +145,15 @@ def validate_loopback_redirect_uri(redirect_uri: str) -> None:
     ``"127.0.0.1"`` alone would miss ``127.0.0.2`` and the full-form
     IPv6 loopback ``0:0:0:0:0:0:0:1``.
     """
-    try:
-        parsed = urlparse(redirect_uri)
-    except ValueError:
-        raise HTTPException(status_code=400, detail="invalid_request")
+    parsed = _parse_redirect_uri_for_validation(redirect_uri)
     if parsed.scheme not in ("http", "https"):
-        raise HTTPException(status_code=400, detail="invalid_request")
-    # Fragments are not allowed in OAuth redirect URIs (RFC 6749 §3.1.2)
-    # — rejecting them prevents a ``http://127.0.0.1/cb#frag?code=...``
-    # from silently eating the authorization code.
+        _oauth_invalid_request(
+            f"redirect_uri scheme {parsed.scheme!r} is not allowed; use http or https.",
+        )
     if parsed.fragment:
-        raise HTTPException(status_code=400, detail="invalid_request")
+        _oauth_invalid_request(
+            "redirect_uri must not contain a URL fragment (#...).",
+        )
     host = (parsed.hostname or "").lower()
     if host == "localhost":
         return
@@ -129,7 +164,10 @@ def validate_loopback_redirect_uri(redirect_uri: str) -> None:
         # Unparseable host (malformed IPv6, etc.) — treat as invalid,
         # don't let it bubble up as a 500.
         pass
-    raise HTTPException(status_code=400, detail="invalid_request")
+    _oauth_invalid_request(
+        "redirect_uri must use a loopback host (localhost or 127.0.0.0/8).",
+        hint="Native MCP clients should register a callback on http://127.0.0.1:<port>/...",
+    )
 
 
 def _strip_default_port(scheme: str, netloc: str) -> str:
@@ -212,10 +250,256 @@ def _matches_trusted_origin_entry(netloc: str, entry: str) -> bool:
     return netloc == entry
 
 
+def _normalize_native_redirect_uri(
+    parsed,
+) -> str:
+    """Lowercase scheme, netloc, and path for allowlist comparison."""
+    return urlunparse(
+        (
+            (parsed.scheme or "").lower(),
+            (parsed.netloc or "").lower(),
+            (parsed.path or "").lower(),
+            "",
+            "",
+            "",
+        )
+    )
+
+
+def _parse_trusted_native_redirect_uris() -> List[str]:
+    """Built-in native MCP callbacks plus ``MCP_TRUSTED_NATIVE_REDIRECT_URIS``."""
+    entries: List[str] = [uri.lower() for uri in _DEFAULT_NATIVE_REDIRECT_URIS]
+    raw = os.environ.get(_TRUSTED_NATIVE_REDIRECT_URIS_ENV, "").strip()
+    if not raw:
+        return entries
+    for token in raw.split(","):
+        entry = token.strip().lower()
+        if entry and entry not in entries:
+            entries.append(entry)
+    return entries
+
+
+def _native_wildcard_prefix_matches(normalized: str, prefix: str) -> bool:
+    """Prefix match for ``entry*`` allowlist rows.
+
+    When the prefix does not end with ``/``, only exact matches or
+    deeper path segments (``prefix/...``) are accepted — not siblings
+    like ``prefix-2``.
+    """
+    if not normalized.startswith(prefix):
+        return False
+    suffix = normalized[len(prefix) :]
+    if not suffix:
+        return True
+    if prefix.endswith("/"):
+        return True
+    return suffix[0] == "/"
+
+
+def _matches_trusted_native_redirect_uri(parsed) -> bool:
+    """Allowlisted private-use / custom-scheme OAuth callbacks for native MCP clients."""
+    if parsed.fragment:
+        return False
+    # Query strings are not part of registered redirect_uris (RFC 6749 §3.1.2).
+    # Rejecting them prevents allowlist bypass via ``.../callback?injected=...``.
+    if parsed.query:
+        return False
+    if not parsed.netloc:
+        return False
+    if parsed.username is not None or parsed.password is not None:
+        return False
+    if "\\" in parsed.netloc:
+        return False
+
+    normalized = _normalize_native_redirect_uri(parsed)
+    for entry in _parse_trusted_native_redirect_uris():
+        if entry.endswith("*"):
+            if _native_wildcard_prefix_matches(normalized, entry[:-1]):
+                return True
+        elif normalized == entry:
+            return True
+    return False
+
+
+def _parse_redirect_uri_for_validation(redirect_uri: str) -> ParseResult:
+    try:
+        return urlparse(redirect_uri)
+    except ValueError:
+        _oauth_invalid_request(
+            "redirect_uri is not a valid URL.",
+            hint="Use a full absolute URL for redirect_uri (e.g. https://your-host/ui/mcp/oauth/callback).",
+        )
+
+
+def _validate_trusted_http_redirect_shape(parsed: ParseResult) -> bool:
+    """Return True when ``parsed`` is an allowlisted native callback (caller may return)."""
+    if parsed.scheme not in ("http", "https"):
+        if _matches_trusted_native_redirect_uri(parsed):
+            return True
+        _oauth_invalid_request(
+            f"redirect_uri scheme {parsed.scheme!r} is not allowed; use http/https "
+            "or a registered native callback (e.g. cursor://).",
+            hint="Add the full URI to MCP_TRUSTED_NATIVE_REDIRECT_URIS for custom native clients.",
+        )
+    if parsed.fragment:
+        _oauth_invalid_request(
+            "redirect_uri must not contain a URL fragment (#...).",
+        )
+    if not parsed.netloc:
+        _oauth_invalid_request(
+            "redirect_uri must include a host (e.g. https://your-host/path).",
+        )
+    if parsed.username is not None or parsed.password is not None:
+        _oauth_invalid_request(
+            "redirect_uri must not contain userinfo (user:pass@host).",
+        )
+    if "\\" in parsed.netloc:
+        _oauth_invalid_request(
+            "redirect_uri host must not contain backslashes.",
+        )
+    return False
+
+
+def _resolve_proxy_base_for_redirect(request: Request) -> Optional[str]:
+    try:
+        return get_request_base_url(request)
+    except Exception as exc:
+        verbose_logger.warning(
+            "validate_trusted_redirect_uri: could not determine proxy origin, "
+            "falling back to loopback + allowlist. error=%s",
+            exc,
+        )
+        return None
+
+
+def _trusted_redirect_uri_is_allowed(
+    parsed: ParseResult,
+    redirect_netloc: str,
+    proxy_base: Optional[str],
+) -> bool:
+    if proxy_base:
+        proxy_parsed = urlparse(proxy_base)
+        if (
+            parsed.scheme == proxy_parsed.scheme
+            and redirect_netloc
+            == _strip_default_port(proxy_parsed.scheme, proxy_parsed.netloc)
+        ):
+            return True
+
+    host = (parsed.hostname or "").lower()
+    if host == "localhost":
+        return True
+    try:
+        if ip_address(host).is_loopback:
+            return True
+    except ValueError:
+        pass
+
+    if parsed.scheme == "https":
+        for entry in _parse_trusted_redirect_origins():
+            if _matches_trusted_origin_entry(redirect_netloc, entry):
+                return True
+    return False
+
+
+def _build_trusted_redirect_rejection_message(
+    redirect_uri: str,
+    parsed: ParseResult,
+    redirect_netloc: str,
+    proxy_base: Optional[str],
+) -> str:
+    """Build a client-facing rejection message.
+
+    Intentionally omits the proxy's resolved scheme / host / port to avoid
+    leaking internal network topology (e.g. ``http://litellm-internal:4000``)
+    through an unauthenticated endpoint. Full diagnostic detail — including
+    the computed proxy base — is logged server-side by the caller.
+    """
+    redirect_origin = _origin_label(parsed.scheme, redirect_netloc)
+    proxy_parsed = urlparse(proxy_base) if proxy_base else None
+    proxy_netloc_norm = (
+        _strip_default_port(proxy_parsed.scheme, proxy_parsed.netloc)
+        if proxy_parsed and proxy_parsed.netloc
+        else ""
+    )
+
+    mismatch_parts: List[str] = []
+    if proxy_parsed and proxy_parsed.netloc:
+        if parsed.scheme != proxy_parsed.scheme:
+            mismatch_parts.append(
+                f"scheme: redirect_uri uses {parsed.scheme!r}, but the proxy "
+                "resolved a different scheme "
+                "(TLS often terminates at ingress — set PROXY_BASE_URL to https://… "
+                "or trust X-Forwarded-Proto from your ingress)"
+            )
+        if redirect_netloc != proxy_netloc_norm:
+            mismatch_parts.append(
+                f"host/port: redirect_uri {redirect_netloc!r} does not match "
+                "the proxy origin"
+            )
+
+    if mismatch_parts:
+        return (
+            f"redirect_uri origin ({redirect_origin}) does not match the proxy "
+            "origin. " + "; ".join(mismatch_parts)
+        )
+    return (
+        f"redirect_uri ({redirect_uri!r}) is not allowed: not same-origin with "
+        f"the proxy origin, not loopback, and not listed in "
+        f"{_TRUSTED_REDIRECT_ORIGINS_ENV}."
+    )
+
+
+def _raise_trusted_redirect_uri_rejected(
+    request: Request,
+    redirect_uri: str,
+    parsed: ParseResult,
+    redirect_netloc: str,
+    proxy_base: Optional[str],
+) -> NoReturn:
+    description = _build_trusted_redirect_rejection_message(
+        redirect_uri, parsed, redirect_netloc, proxy_base
+    )
+
+    hint = (
+        "Align the proxy public URL with the browser URL. Set PROXY_BASE_URL to your "
+        "HTTPS origin (e.g. https://litellm.example.com), or enable "
+        "general_settings.use_x_forwarded_for with mcp_trusted_proxy_ranges for your "
+        "ingress. Verify: curl https://<host>/.well-known/oauth-authorization-server "
+        "| jq .issuer — issuer must match window.location.origin in the UI."
+    )
+
+    verbose_logger.warning(
+        "MCP OAuth: rejecting redirect_uri %r. %s "
+        "Computed proxy base=%r (PROXY_BASE_URL=%r). "
+        "Inbound headers: X-Forwarded-Proto=%r X-Forwarded-Host=%r "
+        "X-Forwarded-Port=%r Host=%r. "
+        "Trusted-redirect-origins env=%r. "
+        "Trusted-native-redirect-uris env=%r.",
+        redirect_uri,
+        description,
+        proxy_base,
+        os.environ.get("PROXY_BASE_URL"),
+        request.headers.get("X-Forwarded-Proto"),
+        request.headers.get("X-Forwarded-Host"),
+        request.headers.get("X-Forwarded-Port"),
+        request.headers.get("Host"),
+        os.environ.get(_TRUSTED_REDIRECT_ORIGINS_ENV),
+        os.environ.get(_TRUSTED_NATIVE_REDIRECT_URIS_ENV),
+    )
+
+    _oauth_invalid_request(
+        description,
+        hint=hint,
+        redirect_uri=redirect_uri,
+    )
+
+
 def validate_trusted_redirect_uri(request: Request, redirect_uri: str) -> None:
     """Accept ``redirect_uri`` when it is (a) same-origin with the
-    proxy's own request origin, (b) loopback, or (c) listed in the
-    ``MCP_TRUSTED_REDIRECT_ORIGINS`` ops allowlist.
+    proxy's own request origin, (b) loopback, (c) listed in the
+    ``MCP_TRUSTED_REDIRECT_ORIGINS`` ops allowlist, or (d) a built-in /
+    env-configured native MCP client callback (e.g. ``cursor://``).
 
     Same-origin is VERIA-57's threat-model-safe equivalent of loopback:
     an attacker who can host content on the proxy's own HTTPS origin
@@ -234,92 +518,13 @@ def validate_trusted_redirect_uri(request: Request, redirect_uri: str) -> None:
     BYOK endpoints, which only serve native MCP clients, retain
     :func:`validate_loopback_redirect_uri`.
     """
-    try:
-        parsed = urlparse(redirect_uri)
-    except ValueError:
-        raise HTTPException(status_code=400, detail="invalid_request")
-    if parsed.scheme not in ("http", "https"):
-        raise HTTPException(status_code=400, detail="invalid_request")
-    if parsed.fragment:
-        raise HTTPException(status_code=400, detail="invalid_request")
-    if not parsed.netloc or parsed.username is not None or parsed.password is not None:
-        raise HTTPException(status_code=400, detail="invalid_request")
-    # Reject userinfo (``user:pass@host``) outright: OAuth redirect_uris
-    # have no legitimate reason to carry credentials, and allowing them
-    # opens a host-confusion attack where the netloc *looks* allowlisted
-    # (``app.example.com:443@attacker.example``) but the browser navigates
-    # to the post-``@`` host and hands the authorization code to the
-    # attacker. We compare against ``hostname`` after this, but defense in
-    # depth keeps malformed netloc strings from reaching the wildcard
-    # splitter.
-    if parsed.username is not None or parsed.password is not None:
-        raise HTTPException(status_code=400, detail="invalid_request")
-    # Reject backslash in netloc: urlparse keeps ``\`` as part of netloc,
-    # but browsers normalize ``\`` to ``/`` for http(s) URLs and treat it
-    # as the start of the path. An attacker can exploit that split by
-    # crafting ``https://attacker.net\app.example.com/cb`` — urlparse sees
-    # ``attacker.net\app.example.com`` (matches ``*.example.com``) while
-    # the browser navigates to ``attacker.net`` with the auth code.
-    if "\\" in parsed.netloc:
-        raise HTTPException(status_code=400, detail="invalid_request")
-
-    redirect_netloc = _strip_default_port(parsed.scheme, parsed.netloc)
-
-    # (a) Same-origin. Swallow ``get_request_base_url`` failures so the
-    # loopback + allowlist paths remain reachable when the origin can't
-    # be determined (e.g. request came from an untrusted proxy and
-    # ``get_request_base_url`` raised).
-    proxy_base: Optional[str] = None
-    try:
-        proxy_base = get_request_base_url(request)
-    except Exception as exc:
-        verbose_logger.warning(
-            "validate_trusted_redirect_uri: could not determine proxy origin, "
-            "falling back to loopback + allowlist. error=%s",
-            exc,
-        )
-        proxy_base = None
-    if proxy_base:
-        proxy_parsed = urlparse(proxy_base)
-        if (
-            parsed.scheme == proxy_parsed.scheme
-            and redirect_netloc
-            == _strip_default_port(proxy_parsed.scheme, proxy_parsed.netloc)
-        ):
-            return
-
-    # (b) Loopback — same rule as validate_loopback_redirect_uri.
-    host = (parsed.hostname or "").lower()
-    if host == "localhost":
+    parsed = _parse_redirect_uri_for_validation(redirect_uri)
+    if _validate_trusted_http_redirect_shape(parsed):
         return
-    try:
-        if ip_address(host).is_loopback:
-            return
-    except ValueError:
-        pass
-
-    # (c) Ops allowlist. https only.
-    if parsed.scheme == "https":
-        for entry in _parse_trusted_redirect_origins():
-            if _matches_trusted_origin_entry(redirect_netloc, entry):
-                return
-
-    verbose_logger.warning(
-        "MCP OAuth: rejecting redirect_uri %r as invalid_request. "
-        "Computed proxy base=%r (PROXY_BASE_URL=%r). "
-        "Inbound headers: X-Forwarded-Proto=%r X-Forwarded-Host=%r "
-        "X-Forwarded-Port=%r Host=%r. "
-        "Trusted-redirect-origins env=%r. "
-        "If this should be accepted, either align ingress X-Forwarded-* "
-        "with the browser URL, set PROXY_BASE_URL to your public origin, "
-        "or add the redirect_uri host to MCP_TRUSTED_REDIRECT_ORIGINS.",
-        redirect_uri,
-        proxy_base,
-        os.environ.get("PROXY_BASE_URL"),
-        request.headers.get("X-Forwarded-Proto"),
-        request.headers.get("X-Forwarded-Host"),
-        request.headers.get("X-Forwarded-Port"),
-        request.headers.get("Host"),
-        os.environ.get(_TRUSTED_REDIRECT_ORIGINS_ENV),
+    redirect_netloc = _strip_default_port(parsed.scheme, parsed.netloc)
+    proxy_base = _resolve_proxy_base_for_redirect(request)
+    if _trusted_redirect_uri_is_allowed(parsed, redirect_netloc, proxy_base):
+        return
+    _raise_trusted_redirect_uri_rejected(
+        request, redirect_uri, parsed, redirect_netloc, proxy_base
     )
-    raise HTTPException(status_code=400, detail="invalid_request")
diff --git a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py
index 271517bb1e6..de70fe1331e 100644
--- a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py
+++ b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py
@@ -305,7 +305,7 @@ def build_input_schema(operation: Dict[str, Any]) -> Dict[str, Any]:
 
 
 def _merge_openapi_tool_request_headers(
-    static_headers: Dict[str, str]
+    static_headers: Dict[str, str],
 ) -> Dict[str, str]:
     """Merge static closure headers with per-request ContextVar overrides.
 
diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
index 829863d2dbb..cec5224e183 100644
--- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
@@ -1,6 +1,17 @@
 import importlib
 from datetime import datetime
-from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Set, Union
+from typing import (
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+)
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
 
@@ -51,20 +62,16 @@ if MCP_AVAILABLE:
         mcp_auth_header: Optional[str],
     ) -> Optional[Union[Dict[str, str], str]]:
         """Helper function to get server-specific auth header with case-insensitive matching."""
-        if mcp_server_auth_headers and server.alias:
-            normalized_server_alias = server.alias.lower()
-            normalized_headers = {
-                k.lower(): v for k, v in mcp_server_auth_headers.items()
-            }
-            server_auth = normalized_headers.get(normalized_server_alias)
-            if server_auth is not None:
-                return server_auth
-        elif mcp_server_auth_headers and server.server_name:
-            normalized_server_name = server.server_name.lower()
-            normalized_headers = {
-                k.lower(): v for k, v in mcp_server_auth_headers.items()
-            }
-            server_auth = normalized_headers.get(normalized_server_name)
+        from litellm.proxy._experimental.mcp_server.utils import (
+            lookup_mcp_server_auth_in_headers,
+        )
+
+        if mcp_server_auth_headers:
+            server_auth = lookup_mcp_server_auth_in_headers(
+                mcp_server_auth_headers,
+                alias=getattr(server, "alias", None),
+                server_name=getattr(server, "server_name", None),
+            )
             if server_auth is not None:
                 return server_auth
         return mcp_auth_header
@@ -231,11 +238,32 @@ if MCP_AVAILABLE:
         )
         return mcp_auth_header, mcp_server_auth_headers, raw_headers
 
+    def _resolve_mcp_server_id_for_rest(
+        server_id: str,
+        allowed_server_ids: Union[Set[str], List[str]],
+        client_ip: Optional[str] = None,
+    ) -> str:
+        """
+        Map REST ``server_id`` (UUID, server_name, or alias) to canonical server_id.
+
+        tools/list already did this; tools/call must match so clients can pass
+        server names like ``order_status_mcp`` instead of only UUIDs.
+        """
+        allowed = set(allowed_server_ids)
+        if server_id in allowed:
+            return server_id
+        by_name = global_mcp_server_manager.get_mcp_server_by_name(
+            server_id, client_ip=client_ip
+        )
+        if by_name is not None and by_name.server_id in allowed:
+            return by_name.server_id
+        return server_id
+
     async def _resolve_allowed_mcp_servers_with_ip_filter(
         request: Request,
         user_api_key_dict: UserAPIKeyAuth,
         server_id: str,
-    ) -> List[MCPServer]:
+    ) -> Tuple[List[MCPServer], str]:
         """
         Resolve allowed MCP servers for a tool call with IP filtering.
 
@@ -245,10 +273,10 @@ if MCP_AVAILABLE:
             server_id: The server ID to validate access for
 
         Returns:
-            List of allowed MCPServer objects
+            Tuple of (allowed MCPServer objects, canonical server_id)
 
         Raises:
-            HTTPException: If the server_id is not allowed
+            HTTPException: If the server_id is not allowed or not found
         """
         # Get all auth contexts
         auth_contexts = await build_effective_auth_contexts(user_api_key_dict)
@@ -268,8 +296,41 @@ if MCP_AVAILABLE:
             )
         )
 
-        # Check if the specified server_id is allowed
-        if server_id not in allowed_server_ids_set:
+        canonical_server_id = _resolve_mcp_server_id_for_rest(
+            server_id, allowed_server_ids_set, _rest_client_ip
+        )
+
+        if canonical_server_id not in allowed_server_ids_set:
+            _server = global_mcp_server_manager.get_mcp_server_by_id(
+                server_id
+            ) or global_mcp_server_manager.get_mcp_server_by_name(server_id)
+            if (
+                _server is not None
+                and _rest_client_ip is not None
+                and not global_mcp_server_manager._is_server_accessible_from_ip(
+                    _server, _rest_client_ip
+                )
+            ):
+                raise HTTPException(
+                    status_code=403,
+                    detail={
+                        "error": "ip_filtering",
+                        "message": (
+                            f"MCP server '{server_id}' is not accessible from your IP address "
+                            f"({_rest_client_ip}). This server is restricted to internal "
+                            "networks only. To make it externally accessible, set "
+                            "'available_on_public_internet: true' in the server configuration."
+                        ),
+                    },
+                )
+            if _server is None:
+                raise HTTPException(
+                    status_code=404,
+                    detail={
+                        "error": "server_not_found",
+                        "message": f"MCP server '{server_id}' was not found",
+                    },
+                )
             raise HTTPException(
                 status_code=403,
                 detail={
@@ -285,7 +346,7 @@ if MCP_AVAILABLE:
             if server is not None:
                 allowed_mcp_servers.append(server)
 
-        return allowed_mcp_servers
+        return allowed_mcp_servers, canonical_server_id
 
     async def _get_tools_for_single_server(
         server,
@@ -301,6 +362,7 @@ if MCP_AVAILABLE:
             extra_headers=extra_headers,
             add_prefix=False,
             raw_headers=raw_headers,
+            user_api_key_auth=user_api_key_auth,
         )
 
         # Filter tools based on allowed_tools configuration
@@ -753,7 +815,7 @@ if MCP_AVAILABLE:
                     },
                 )
 
-            tool_arguments = data.get("arguments")
+            tool_arguments = data.get("arguments") or {}
 
             proxy_base_llm_response_processor = ProxyBaseLLMRequestProcessing(data=data)
             (
@@ -786,14 +848,18 @@ if MCP_AVAILABLE:
                 data["user_api_key_auth"] = data["metadata"]["user_api_key_auth"]
 
             # Resolve allowed MCP servers with IP filtering
-            allowed_mcp_servers = await _resolve_allowed_mcp_servers_with_ip_filter(
+            (
+                allowed_mcp_servers,
+                canonical_server_id,
+            ) = await _resolve_allowed_mcp_servers_with_ip_filter(
                 request, user_api_key_dict, server_id
             )
 
             # Look up per-user OAuth headers for this server (mirrors list_tool_rest_api).
             user_oauth_extra_headers: Optional[Dict[str, str]] = None
             target_server = next(
-                (s for s in allowed_mcp_servers if s.server_id == server_id), None
+                (s for s in allowed_mcp_servers if s.server_id == canonical_server_id),
+                None,
             )
             if target_server is not None:
                 user_oauth_extra_headers = await _get_user_oauth_extra_headers(
@@ -812,6 +878,7 @@ if MCP_AVAILABLE:
                 oauth2_headers=user_oauth_extra_headers or data.get("oauth2_headers"),
                 raw_headers=data.get("raw_headers"),
                 litellm_logging_obj=data.get("litellm_logging_obj"),
+                requested_server_id=canonical_server_id,
             )
             return result
         except BlockedPiiEntityError as e:
diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py
index 0a74a92f9ce..f31005be0cb 100644
--- a/litellm/proxy/_experimental/mcp_server/server.py
+++ b/litellm/proxy/_experimental/mcp_server/server.py
@@ -1114,10 +1114,16 @@ if MCP_AVAILABLE:
     ) -> Tuple[Optional[Union[Dict[str, str], str]], Optional[Dict[str, str]]]:
         """Build auth and extra headers for a server."""
         server_auth_header: Optional[Union[Dict[str, str], str]] = None
-        if mcp_server_auth_headers and server.alias is not None:
-            server_auth_header = mcp_server_auth_headers.get(server.alias)
-        elif mcp_server_auth_headers and server.server_name is not None:
-            server_auth_header = mcp_server_auth_headers.get(server.server_name)
+        if mcp_server_auth_headers:
+            from litellm.proxy._experimental.mcp_server.utils import (
+                lookup_mcp_server_auth_in_headers,
+            )
+
+            server_auth_header = lookup_mcp_server_auth_in_headers(
+                mcp_server_auth_headers,
+                alias=server.alias,
+                server_name=server.server_name,
+            )
 
         extra_headers: Optional[Dict[str, str]] = None
         if server.auth_type == MCPAuth.oauth2:
@@ -1159,7 +1165,7 @@ if MCP_AVAILABLE:
     def _merge_gateway_initialize_instructions(
         allowed_mcp_servers: List[MCPServer],
     ) -> Optional[str]:
-        """YAML/DB override, else in-memory upstream text from list_tools / health_check / call_tool."""
+        """YAML/DB override, else upstream text (prefetch on init, or list_tools / health_check / call_tool cache)."""
         if not allowed_mcp_servers:
             return None
 
@@ -1200,6 +1206,20 @@ if MCP_AVAILABLE:
             mcp_servers=mcp_servers,
             client_ip=client_ip,
         )
+        if allowed:
+            # return_exceptions=True: a per-server probe failure (incl. CancelledError
+            # bubbled from anyio task group teardown on connection refused) must not
+            # cancel sibling probes or 500 the gateway initialize request.
+            await asyncio.gather(
+                *[
+                    global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                        s
+                    )
+                    for s in allowed
+                    if s is not None
+                ],
+                return_exceptions=True,
+            )
         merged = _merge_gateway_initialize_instructions(allowed_mcp_servers=allowed)
         tok = _mcp_gateway_initialize_instructions.set(merged)
         try:
@@ -1368,6 +1388,7 @@ if MCP_AVAILABLE:
                         extra_headers=extra_headers,
                         add_prefix=True,  # Always add server prefix
                         raw_headers=raw_headers,
+                        user_api_key_auth=user_api_key_auth,
                     )
                     filtered_tools = filter_tools_by_allowed_tools(tools, server)
 
@@ -2074,6 +2095,7 @@ if MCP_AVAILABLE:
         """
         # Track resolved MCP server for both permission checks and dispatch
         mcp_server: Optional[MCPServer] = None
+        requested_server_id: Optional[str] = kwargs.get("requested_server_id")
 
         # If the client called with a display-name override (e.g. "Get Pet"),
         # translate it back to the original prefixed name before any routing.
@@ -2082,14 +2104,55 @@ if MCP_AVAILABLE:
         # Remove prefix from tool name for logging and processing
         original_tool_name, server_name = split_server_prefix_from_name(name)
 
+        requested_server: Optional[MCPServer] = None
+        if requested_server_id:
+            requested_server = next(
+                (s for s in allowed_mcp_servers if s.server_id == requested_server_id),
+                None,
+            )
+
         # Resolve the actual MCP server up-front so the permission check uses
         # the canonical server.name even when the tool name is prefixed with a
         # short ID (LITELLM_USE_SHORT_MCP_TOOL_PREFIX) that doesn't match the
         # server's display name directly.
         mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name(name)
+        if mcp_server is None and requested_server is not None:
+            # REST callers may pass the raw tool name (no prefix) plus a
+            # ``requested_server_id``. The mapping might only contain the
+            # prefixed form, so retry the lookup with every known prefix of
+            # the requested server before treating the tool as unresolved —
+            # otherwise the tool_server_mismatch guard below is silently
+            # bypassed.
+            for known_prefix in iter_known_server_prefixes(requested_server):
+                candidate = global_mcp_server_manager._get_mcp_server_from_tool_name(
+                    add_server_prefix_to_name(name, known_prefix)
+                )
+                if candidate is not None:
+                    mcp_server = candidate
+                    break
         if mcp_server is not None:
             server_name = mcp_server.name
 
+        # REST /mcp-rest/tools/call passes server_id — tool must belong to that server
+        if requested_server is not None:
+            if (
+                mcp_server is not None
+                and mcp_server.server_id != requested_server.server_id
+            ):
+                raise HTTPException(
+                    status_code=403,
+                    detail={
+                        "error": "tool_server_mismatch",
+                        "message": (
+                            f"Tool '{name}' belongs to MCP server '{mcp_server.name}' "
+                            f"but request specified server_id for '{requested_server.name}'."
+                        ),
+                    },
+                )
+            if mcp_server is None:
+                mcp_server = requested_server
+                server_name = requested_server.name
+
         # Only enforce server-level permissions when we can resolve a server
         if server_name:
             if not MCPRequestHandler.is_tool_allowed(
diff --git a/litellm/proxy/_experimental/mcp_server/utils.py b/litellm/proxy/_experimental/mcp_server/utils.py
index df5705c3425..b8b9207555e 100644
--- a/litellm/proxy/_experimental/mcp_server/utils.py
+++ b/litellm/proxy/_experimental/mcp_server/utils.py
@@ -2,7 +2,8 @@
 MCP Server Utilities
 """
 
-from typing import Any, Dict, Iterator, Mapping, Optional, Tuple
+import re
+from typing import Any, Dict, Iterator, Mapping, Optional, Tuple, Union
 
 import hashlib
 import importlib
@@ -117,6 +118,50 @@ def normalize_server_name(server_name: str) -> str:
     return server_name.replace(" ", "_")
 
 
+_MCP_ALIAS_HEADER_INVALID_RE = re.compile(r"[^a-z0-9_]")
+
+
+def sanitize_mcp_alias_for_header(alias: str) -> str:
+    """
+    Sanitize an MCP server alias for x-mcp-{alias}-{header} HTTP headers.
+
+    Must stay in sync with ui/litellm-dashboard/src/utils/mcpHeaderUtils.ts.
+    """
+    sanitized = _MCP_ALIAS_HEADER_INVALID_RE.sub("_", alias.lower().strip())
+    sanitized = re.sub(r"_+", "_", sanitized)
+    return sanitized.strip("_")
+
+
+def lookup_mcp_server_auth_in_headers(
+    mcp_server_auth_headers: Mapping[str, Union[str, Dict[str, str]]],
+    *,
+    alias: Optional[str] = None,
+    server_name: Optional[str] = None,
+) -> Optional[Union[str, Dict[str, str]]]:
+    """
+    Resolve server-specific auth headers with case-insensitive matching.
+
+    Tries the raw alias/server_name (lowercased) and the header-safe sanitized
+    alias so dashboard clients using sanitize_mcp_alias_for_header() still match.
+    """
+    if not mcp_server_auth_headers:
+        return None
+
+    normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()}
+
+    for identifier in (alias, server_name):
+        if not identifier:
+            continue
+        keys_to_try = [identifier.lower()]
+        sanitized = sanitize_mcp_alias_for_header(identifier)
+        if sanitized and sanitized not in keys_to_try:
+            keys_to_try.append(sanitized)
+        for key in keys_to_try:
+            if key in normalized_headers:
+                return normalized_headers[key]
+    return None
+
+
 def validate_and_normalize_mcp_server_payload(payload: Any) -> None:
     """
     Validate and normalize MCP server payload fields (server_name and alias).
diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html
index 46e13ca9931..38a2c3bd836 100644
--- a/litellm/proxy/_experimental/out/404.html
+++ b/litellm/proxy/_experimental/out/404.html
@@ -1 +1 @@
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+      ${a}${e}-appear
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+      ${a}${e}-appear${e}-appear-active
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+        ${n}-appear
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${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,[`&${n}-select-single:not(${n}-select-customize-input):not(${n}-pagination-size-changer)`]:{[`${n}-select-selector`]:{backgroundColor:"inherit",border:`${(0,t.unit)(e.lineWidth)} ${e.lineType} transparent`,boxShadow:"none"}}},[`${n}-cascader-picker`]:{margin:`-9px ${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,backgroundColor:"transparent",[`${n}-cascader-input`]:{textAlign:"start",border:0,boxShadow:"none"}}}},[o]:{width:"100%",marginBottom:0,textAlign:"inherit","&:focus":{zIndex:1,borderInlineEndWidth:1},"&:hover":{zIndex:1,borderInlineEndWidth:1,[`${o}-search-with-button &`]:{zIndex:0}}},[`> ${o}:first-child, ${o}-group-addon:first-child`]:{borderStartEndRadius:0,borderEndEndRadius:0,[`${n}-select ${n}-select-selector`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`> ${o}-affix-wrapper`]:{[`&:not(:first-child) ${o}`]:{borderStartStartRadius:0,borderEndStartRadius:0},[`&:not(:last-child) 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+        & > ${o}-affix-wrapper,
+        & > ${o}-number-affix-wrapper,
+        & > ${n}-picker-range
+      `]:{display:"inline-flex"},"& > *:not(:last-child)":{marginInlineEnd:e.calc(e.lineWidth).mul(-1).equal(),borderInlineEndWidth:e.lineWidth},[o]:{float:"none"},[`& > ${n}-select > ${n}-select-selector,
+      & > ${n}-select-auto-complete ${o},
+      & > ${n}-cascader-picker ${o},
+      & > ${o}-group-wrapper ${o}`]:{borderInlineEndWidth:e.lineWidth,borderRadius:0,"&:hover, &:focus":{zIndex:1}},[`& > ${n}-select-focused`]:{zIndex:1},[`& > ${n}-select > ${n}-select-arrow`]:{zIndex:1},[`& > *:first-child,
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+      & > ${n}-cascader-picker:first-child ${o}`]:{borderStartStartRadius:e.borderRadius,borderEndStartRadius:e.borderRadius},[`& > *:last-child,
+      & > ${n}-select:last-child > ${n}-select-selector,
+      & > ${n}-cascader-picker:last-child ${o},
+      & > ${n}-cascader-picker-focused:last-child ${o}`]:{borderInlineEndWidth:e.lineWidth,borderStartEndRadius:e.borderRadius,borderEndEndRadius:e.borderRadius},[`& > ${n}-select-auto-complete ${o}`]:{verticalAlign:"top"},[`${o}-group-wrapper + ${o}-group-wrapper`]:{marginInlineStart:e.calc(e.lineWidth).mul(-1).equal(),[`${o}-affix-wrapper`]:{borderRadius:0}},[`${o}-group-wrapper:not(:last-child)`]:{[`&${o}-search > ${o}-group`]:{[`& > ${o}-group-addon > ${o}-search-button`]:{borderRadius:0},[`& > ${o}`]:{borderStartStartRadius:e.borderRadius,borderStartEndRadius:0,borderEndEndRadius:0,borderEndStartRadius:e.borderRadius}}}})}},O=(0,n.genStyleHooks)(["Input","Shared"],e=>{let 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t=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,borderRadiusLG:o,borderRadiusSM:n}=e;return{[`${t}-group`]:Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),j(e)),{"&-rtl":{direction:"rtl"},"&-wrapper":Object.assign(Object.assign(Object.assign({display:"inline-block",width:"100%",textAlign:"start",verticalAlign:"top","&-rtl":{direction:"rtl"},"&-lg":{[`${t}-group-addon`]:{borderRadius:o,fontSize:e.inputFontSizeLG}},"&-sm":{[`${t}-group-addon`]:{borderRadius:n}}},p(e)),b(e)),{[`&:not(${t}-compact-first-item):not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}, ${t}-group-addon`]:{borderRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-first-item`]:{[`${t}, ${t}-group-addon`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-last-item`]:{[`${t}, ${t}-group-addon`]:{borderStartStartRadius:0,borderEndStartRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartStartRadius:0,borderEndStartRadius:0}}})})}})(t),(e=>{let{componentCls:t,antCls:r}=e,o=`${t}-search`;return{[o]:{[t]:{"&:not([disabled]):hover, &:not([disabled]):focus":{[`+ ${t}-group-addon ${o}-button:not(${r}-btn-color-primary):not(${r}-btn-variant-text)`]:{borderInlineStartColor:e.colorPrimaryHover}}},[`${t}-affix-wrapper`]:{height:e.controlHeight,borderRadius:0},[`${t}-lg`]:{lineHeight:e.calc(e.lineHeightLG).sub(2e-4).equal()},[`> ${t}-group`]:{[`> ${t}-group-addon:last-child`]:{insetInlineStart:-1,padding:0,border:0,[`${o}-button`]:{marginInlineEnd:-1,borderStartStartRadius:0,borderEndStartRadius:0,boxShadow:"none"},[`${o}-button:not(${r}-btn-color-primary)`]:{color:e.colorTextDescription,"&:not([disabled]):hover":{color:e.colorPrimaryHover},"&:active":{color:e.colorPrimaryActive},[`&${r}-btn-loading::before`]:{inset:0}}}},[`${o}-button`]:{height:e.controlHeight,"&:hover, &:focus":{zIndex:1}},"&-large":{[`${t}-affix-wrapper, ${o}-button`]:{height:e.controlHeightLG}},"&-small":{[`${t}-affix-wrapper, ${o}-button`]:{height:e.controlHeightSM}},"&-rtl":{direction:"rtl"},[`&${t}-compact-item`]:{[`&:not(${t}-compact-last-item)`]:{[`${t}-group-addon`]:{[`${t}-search-button`]:{marginInlineEnd:e.calc(e.lineWidth).mul(-1).equal(),borderRadius:0}}},[`&:not(${t}-compact-first-item)`]:{[`${t},${t}-affix-wrapper`]:{borderRadius:0}},[`> ${t}-group-addon ${t}-search-button,
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+        ${n}-enter,
+        ${n}-appear
+      `]:{opacity:0,animationTimingFunction:e.motionEaseOutCirc},[`${n}-leave`]:{animationTimingFunction:e.motionEaseInOutCirc}}]}])},664142,e=>{"use strict";e.i(296059);var t=e.i(694758),r=e.i(402366);let o=new t.Keyframes("antSlideUpIn",{"0%":{transform:"scaleY(0.8)",transformOrigin:"0% 0%",opacity:0},"100%":{transform:"scaleY(1)",transformOrigin:"0% 0%",opacity:1}}),n=new t.Keyframes("antSlideUpOut",{"0%":{transform:"scaleY(1)",transformOrigin:"0% 0%",opacity:1},"100%":{transform:"scaleY(0.8)",transformOrigin:"0% 0%",opacity:0}}),a=new t.Keyframes("antSlideDownIn",{"0%":{transform:"scaleY(0.8)",transformOrigin:"100% 100%",opacity:0},"100%":{transform:"scaleY(1)",transformOrigin:"100% 100%",opacity:1}}),i=new t.Keyframes("antSlideDownOut",{"0%":{transform:"scaleY(1)",transformOrigin:"100% 100%",opacity:1},"100%":{transform:"scaleY(0.8)",transformOrigin:"100% 100%",opacity:0}}),l={"slide-up":{inKeyframes:o,outKeyframes:n},"slide-down":{inKeyframes:a,outKeyframes:i},"slide-left":{inKeyframes:new t.Keyframes("antSlideLeftIn",{"0%":{transform:"scaleX(0.8)",transformOrigin:"0% 0%",opacity:0},"100%":{transform:"scaleX(1)",transformOrigin:"0% 0%",opacity:1}}),outKeyframes:new t.Keyframes("antSlideLeftOut",{"0%":{transform:"scaleX(1)",transformOrigin:"0% 0%",opacity:1},"100%":{transform:"scaleX(0.8)",transformOrigin:"0% 0%",opacity:0}})},"slide-right":{inKeyframes:new t.Keyframes("antSlideRightIn",{"0%":{transform:"scaleX(0.8)",transformOrigin:"100% 0%",opacity:0},"100%":{transform:"scaleX(1)",transformOrigin:"100% 0%",opacity:1}}),outKeyframes:new t.Keyframes("antSlideRightOut",{"0%":{transform:"scaleX(1)",transformOrigin:"100% 0%",opacity:1},"100%":{transform:"scaleX(0.8)",transformOrigin:"100% 0%",opacity:0}})}};e.s(["initSlideMotion",0,(e,t)=>{let{antCls:o}=e,n=`${o}-${t}`,{inKeyframes:a,outKeyframes:i}=l[t];return[(0,r.initMotion)(n,a,i,e.motionDurationMid),{[`
+      ${n}-enter,
+      ${n}-appear
+    `]:{transform:"scale(0)",transformOrigin:"0% 0%",opacity:0,animationTimingFunction:e.motionEaseOutQuint,"&-prepare":{transform:"scale(1)"}},[`${n}-leave`]:{animationTimingFunction:e.motionEaseInQuint}}]},"slideDownIn",0,a,"slideDownOut",0,i,"slideUpIn",0,o,"slideUpOut",0,n])},950302,e=>{"use strict";var t=e.i(183293),r=e.i(372409),o=e.i(246422),n=e.i(838378),a=e.i(777489),i=e.i(664142);let l=e=>{let{optionHeight:t,optionFontSize:r,optionLineHeight:o,optionPadding:n}=e;return{position:"relative",display:"block",minHeight:t,padding:n,color:e.colorText,fontWeight:"normal",fontSize:r,lineHeight:o,boxSizing:"border-box"}};e.i(296059);var s=e.i(915654);function c(e,r){let{componentCls:o}=e,n=r?`${o}-${r}`:"",a={[`${o}-multiple${n}`]:{fontSize:e.fontSize,[`${o}-selector`]:{[`${o}-show-search&`]:{cursor:"text"}},[`
+        &${o}-show-arrow ${o}-selector,
+        &${o}-allow-clear ${o}-selector
+      `]:{paddingInlineEnd:e.calc(e.fontSizeIcon).add(e.controlPaddingHorizontal).equal()}}};return[((e,r)=>{let{componentCls:o,INTERNAL_FIXED_ITEM_MARGIN:n}=e,a=`${o}-selection-overflow`,i=e.multipleSelectItemHeight,l=(e=>{let{multipleSelectItemHeight:t,selectHeight:r,lineWidth:o}=e;return e.calc(r).sub(t).div(2).sub(o).equal()})(e),c=r?`${o}-${r}`:"",u=(e=>{let{multipleSelectItemHeight:t,paddingXXS:r,lineWidth:o,INTERNAL_FIXED_ITEM_MARGIN:n}=e,a=e.max(e.calc(r).sub(o).equal(),0),i=e.max(e.calc(a).sub(n).equal(),0);return{basePadding:a,containerPadding:i,itemHeight:(0,s.unit)(t),itemLineHeight:(0,s.unit)(e.calc(t).sub(e.calc(e.lineWidth).mul(2)).equal())}})(e);return{[`${o}-multiple${c}`]:Object.assign(Object.assign({},(e=>{let{componentCls:r,iconCls:o,borderRadiusSM:n,motionDurationSlow:a,paddingXS:i,multipleItemColorDisabled:l,multipleItemBorderColorDisabled:s,colorIcon:c,colorIconHover:u,INTERNAL_FIXED_ITEM_MARGIN:d}=e;return{[`${r}-selection-overflow`]:{position:"relative",display:"flex",flex:"auto",flexWrap:"wrap",maxWidth:"100%","&-item":{flex:"none",alignSelf:"center",maxWidth:"calc(100% - 4px)",display:"inline-flex"},[`${r}-selection-item`]:{display:"flex",alignSelf:"center",flex:"none",boxSizing:"border-box",maxWidth:"100%",marginBlock:d,borderRadius:n,cursor:"default",transition:`font-size ${a}, line-height ${a}, height ${a}`,marginInlineEnd:e.calc(d).mul(2).equal(),paddingInlineStart:i,paddingInlineEnd:e.calc(i).div(2).equal(),[`${r}-disabled&`]:{color:l,borderColor:s,cursor:"not-allowed"},"&-content":{display:"inline-block",marginInlineEnd:e.calc(i).div(2).equal(),overflow:"hidden",whiteSpace:"pre",textOverflow:"ellipsis"},"&-remove":Object.assign(Object.assign({},(0,t.resetIcon)()),{display:"inline-flex",alignItems:"center",color:c,fontWeight:"bold",fontSize:10,lineHeight:"inherit",cursor:"pointer",[`> ${o}`]:{verticalAlign:"-0.2em"},"&:hover":{color:u}})}}}})(e)),{[`${o}-selector`]:{display:"flex",alignItems:"center",width:"100%",height:"100%",paddingInline:u.basePadding,paddingBlock:u.containerPadding,borderRadius:e.borderRadius,[`${o}-disabled&`]:{background:e.multipleSelectorBgDisabled,cursor:"not-allowed"},"&:after":{display:"inline-block",width:0,margin:`${(0,s.unit)(n)} 0`,lineHeight:(0,s.unit)(i),visibility:"hidden",content:'"\\a0"'}},[`${o}-selection-item`]:{height:u.itemHeight,lineHeight:(0,s.unit)(u.itemLineHeight)},[`${o}-selection-wrap`]:{alignSelf:"flex-start","&:after":{lineHeight:(0,s.unit)(i),marginBlock:n}},[`${o}-prefix`]:{marginInlineStart:e.calc(e.inputPaddingHorizontalBase).sub(u.basePadding).equal()},[`${a}-item + ${a}-item,
+        ${o}-prefix + ${o}-selection-wrap
+      `]:{[`${o}-selection-search`]:{marginInlineStart:0},[`${o}-selection-placeholder`]:{insetInlineStart:0}},[`${a}-item-suffix`]:{minHeight:u.itemHeight,marginBlock:n},[`${o}-selection-search`]:{display:"inline-flex",position:"relative",maxWidth:"100%",marginInlineStart:e.calc(e.inputPaddingHorizontalBase).sub(l).equal(),[`
+          &-input,
+          &-mirror
+        `]:{height:i,fontFamily:e.fontFamily,lineHeight:(0,s.unit)(i),transition:`all ${e.motionDurationSlow}`},"&-input":{width:"100%",minWidth:4.1},"&-mirror":{position:"absolute",top:0,insetInlineStart:0,insetInlineEnd:"auto",zIndex:999,whiteSpace:"pre",visibility:"hidden"}},[`${o}-selection-placeholder`]:{position:"absolute",top:"50%",insetInlineStart:e.calc(e.inputPaddingHorizontalBase).sub(u.basePadding).equal(),insetInlineEnd:e.inputPaddingHorizontalBase,transform:"translateY(-50%)",transition:`all ${e.motionDurationSlow}`}})}})(e,r),a]}function u(e,r){let{componentCls:o,inputPaddingHorizontalBase:n,borderRadius:a}=e,i=e.calc(e.controlHeight).sub(e.calc(e.lineWidth).mul(2)).equal(),l=r?`${o}-${r}`:"";return{[`${o}-single${l}`]:{fontSize:e.fontSize,height:e.controlHeight,[`${o}-selector`]:Object.assign(Object.assign({},(0,t.resetComponent)(e,!0)),{display:"flex",borderRadius:a,flex:"1 1 auto",[`${o}-selection-wrap:after`]:{lineHeight:(0,s.unit)(i)},[`${o}-selection-search`]:{position:"absolute",inset:0,width:"100%","&-input":{width:"100%",WebkitAppearance:"textfield"}},[`
+          ${o}-selection-item,
+          ${o}-selection-placeholder
+        `]:{display:"block",padding:0,lineHeight:(0,s.unit)(i),transition:`all ${e.motionDurationSlow}, visibility 0s`,alignSelf:"center"},[`${o}-selection-placeholder`]:{transition:"none",pointerEvents:"none"},[`&:after,${o}-selection-item:empty:after,${o}-selection-placeholder:empty:after`]:{display:"inline-block",width:0,visibility:"hidden",content:'"\\a0"'}}),[`
+        &${o}-show-arrow ${o}-selection-item,
+        &${o}-show-arrow ${o}-selection-search,
+        &${o}-show-arrow ${o}-selection-placeholder
+      `]:{paddingInlineEnd:e.showArrowPaddingInlineEnd},[`&${o}-open ${o}-selection-item`]:{color:e.colorTextPlaceholder},[`&:not(${o}-customize-input)`]:{[`${o}-selector`]:{width:"100%",height:"100%",alignItems:"center",padding:`0 ${(0,s.unit)(n)}`,[`${o}-selection-search-input`]:{height:i,fontSize:e.fontSize},"&:after":{lineHeight:(0,s.unit)(i)}}},[`&${o}-customize-input`]:{[`${o}-selector`]:{"&:after":{display:"none"},[`${o}-selection-search`]:{position:"static",width:"100%"},[`${o}-selection-placeholder`]:{position:"absolute",insetInlineStart:0,insetInlineEnd:0,padding:`0 ${(0,s.unit)(n)}`,"&:after":{display:"none"}}}}}}}let d=(e,t)=>{let{componentCls:r,antCls:o,controlOutlineWidth:n}=e;return{[`&:not(${r}-customize-input) ${r}-selector`]:{border:`${(0,s.unit)(e.lineWidth)} ${e.lineType} ${t.borderColor}`,background:e.selectorBg},[`&:not(${r}-disabled):not(${r}-customize-input):not(${o}-pagination-size-changer)`]:{[`&:hover 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+            &${t}-show-arrow ${t}-selection-item,
+            &${t}-show-arrow ${t}-selection-placeholder
+          `]:{paddingInlineEnd:e.calc(e.fontSize).mul(1.5).equal()}}}},u((0,n.mergeToken)(e,{controlHeight:e.singleItemHeightLG,fontSize:e.fontSizeLG,borderRadius:e.borderRadiusLG}),"lg")]}(e),(e=>{let{componentCls:t}=e,r=(0,n.mergeToken)(e,{selectHeight:e.controlHeightSM,multipleSelectItemHeight:e.multipleItemHeightSM,borderRadius:e.borderRadiusSM,borderRadiusSM:e.borderRadiusXS}),o=(0,n.mergeToken)(e,{fontSize:e.fontSizeLG,selectHeight:e.controlHeightLG,multipleSelectItemHeight:e.multipleItemHeightLG,borderRadius:e.borderRadiusLG,borderRadiusSM:e.borderRadius});return[c(e),c(r,"sm"),{[`${t}-multiple${t}-sm`]:{[`${t}-selection-placeholder`]:{insetInline:e.calc(e.controlPaddingHorizontalSM).sub(e.lineWidth).equal()},[`${t}-selection-search`]:{marginInlineStart:2}}},c(o,"lg")]})(e),(e=>{let{antCls:r,componentCls:o}=e,n=`${o}-item`,s=`&${r}-slide-up-enter${r}-slide-up-enter-active`,c=`&${r}-slide-up-appear${r}-slide-up-appear-active`,u=`&${r}-slide-up-leave${r}-slide-up-leave-active`,d=`${o}-dropdown-placement-`,f=`${n}-option-selected`;return[{[`${o}-dropdown`]:Object.assign(Object.assign({},(0,t.resetComponent)(e)),{position:"absolute",top:-9999,zIndex:e.zIndexPopup,boxSizing:"border-box",padding:e.paddingXXS,overflow:"hidden",fontSize:e.fontSize,fontVariant:"initial",backgroundColor:e.colorBgElevated,borderRadius:e.borderRadiusLG,outline:"none",boxShadow:e.boxShadowSecondary,[`
+          ${s}${d}bottomLeft,
+          ${c}${d}bottomLeft
+        `]:{animationName:i.slideUpIn},[`
+          ${s}${d}topLeft,
+          ${c}${d}topLeft,
+          ${s}${d}topRight,
+          ${c}${d}topRight
+        `]:{animationName:i.slideDownIn},[`${u}${d}bottomLeft`]:{animationName:i.slideUpOut},[`
+          ${u}${d}topLeft,
+          ${u}${d}topRight
+        `]:{animationName:i.slideDownOut},"&-hidden":{display:"none"},[n]:Object.assign(Object.assign({},l(e)),{cursor:"pointer",transition:`background ${e.motionDurationSlow} 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/16a1651c0b3e7c8e.js b/litellm/proxy/_experimental/out/_next/static/chunks/16a1651c0b3e7c8e.js
new file mode 100644
index 00000000000..5cf5ac557ed
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/16a1651c0b3e7c8e.js
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s=t.value[t.value.length-1];if("."===s||"-"===s)return tO(e=e.slice(0,e.length-1));case"string":let r=e[e.length-2];if(r?.type==="delimiter"||r?.type==="brace"&&"{"===r.value)return tO(e=e.slice(0,e.length-1));break;case"delimiter":return tO(e=e.slice(0,e.length-1))}return e},tP=e=>{var t;let s,r;return JSON.parse((t=tO((e=>{let t=0,s=[];for(;t<e.length;){let r=e[t];if("\\"===r){t++;continue}if("{"===r){s.push({type:"brace",value:"{"}),t++;continue}if("}"===r){s.push({type:"brace",value:"}"}),t++;continue}if("["===r){s.push({type:"paren",value:"["}),t++;continue}if("]"===r){s.push({type:"paren",value:"]"}),t++;continue}if(":"===r){s.push({type:"separator",value:":"}),t++;continue}if(","===r){s.push({type:"delimiter",value:","}),t++;continue}if('"'===r){let a="",n=!1;for(r=e[++t];'"'!==r;){if(t===e.length){n=!0;break}if("\\"===r){if(++t===e.length){n=!0;break}a+=r+e[t],r=e[++t]}else a+=r,r=e[++t]}r=e[++t],n||s.push({type:"string",value:a});continue}let a=/\s/;if(r&&a.test(r)){t++;continue}let n=/[0-9]/;if(r&&n.test(r)||"-"===r||"."===r){let a="";for("-"===r&&(a+=r,r=e[++t]);r&&n.test(r)||"."===r;)a+=r,r=e[++t];s.push({type:"number",value:a});continue}let i=/[a-z]/i;if(r&&i.test(r)){let a="";for(;r&&i.test(r)&&t!==e.length;)a+=r,r=e[++t];"true"==a||"false"==a||"null"===a?s.push({type:"name",value:a}):t++;continue}t++}return s})(e)),s=[],t.map(e=>{"brace"===e.type&&("{"===e.value?s.push("}"):s.splice(s.lastIndexOf("}"),1)),"paren"===e.type&&("["===e.value?s.push("]"):s.splice(s.lastIndexOf("]"),1))}),s.length>0&&s.reverse().map(e=>{"}"===e?t.push({type:"brace",value:"}"}):"]"===e&&t.push({type:"paren",value:"]"})}),r="",t.map(e=>{"string"===e.type?r+='"'+e.value+'"':r+=e.value}),r))},tI="__json_buf";function tR(e){return"tool_use"===e.type||"server_tool_use"===e.type||"mcp_tool_use"===e.type}class tM{constructor(){c.add(this),this.messages=[],this.receivedMessages=[],d.set(this,void 0),this.controller=new 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fromReadableStream(e){let t=new tM;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tM;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eN(this,k,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eN(this,c,"m",E).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eN(this,c,"m",T).call(this,e);if(n.controller.signal?.aborted)throw new eO;eN(this,c,"m",C).call(this)}_connected(e){this.ended||(e_(this,j,e,"f"),e_(this,S,e?.headers.get("request-id"),"f"),eN(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eN(this,x,"f")}get errored(){return eN(this,b,"f")}get aborted(){return eN(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eN(this,y,"f")[e]||(eN(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eN(this,y,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eN(this,y,"f")[e]||(eN(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{e_(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,w,!0,"f"),await eN(this,p,"f")}get currentMessage(){return eN(this,d,"f")}async finalMessage(){return await this.done(),eN(this,c,"m",_).call(this)}async finalText(){return await 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eO;eN(this,c,"m",C).call(this)}[(d=new WeakMap,u=new WeakMap,h=new WeakMap,m=new WeakMap,p=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,j=new WeakMap,S=new WeakMap,k=new WeakMap,c=new WeakSet,_=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eC("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||e_(this,d,void 0,"f")},T=function(e){if(this.ended)return;let t=eN(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tR(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tL(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,d,t,"f")}},C=function(){if(this.ended)throw new eC("stream has ended, this shouldn't happen");let e=eN(this,d,"f");if(!e)throw new eC("request ended without sending any chunks");return e_(this,d,void 0,"f"),e},A=function(e){let t=eN(this,d,"f");if("message_start"===e.type){if(t)throw new eC(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eC(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tR(s)){let t=s[tI]||"";if(Object.defineProperty(s,tI,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tP(t)}catch(s){let e=new eC(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eN(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tL(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tL(e){}let t$={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tU={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tD extends tw{constructor(){super(...arguments),this.batches=new tA(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tU&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tU[r.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=t$[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tM.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:t_([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tD.Batches=tA;class tB extends tw{constructor(){super(...arguments),this.models=new tT(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tT,tB.Messages=tD,tB.Files=tE;class tq extends tw{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tW="__json_buf";function tz(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tH{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,I.set(this,void 0),R.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),W.set(this,!1),z.set(this,!1),H.set(this,void 0),F.set(this,void 0),V.set(this,e=>{if(e_(this,q,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,W,!0,"f"),this._emit("abort",e);if(e instanceof eC)return this._emit("error",e);if(e instanceof Error){let t=new eC(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eC(String(e)))}),e_(this,I,new Promise((e,t)=>{e_(this,R,e,"f"),e_(this,M,t,"f")}),"f"),e_(this,L,new Promise((e,t)=>{e_(this,$,e,"f"),e_(this,U,t,"f")}),"f"),eN(this,I,"f").catch(()=>{}),eN(this,L,"f").catch(()=>{})}get response(){return eN(this,H,"f")}get request_id(){return eN(this,F,"f")}async withResponse(){let e=await eN(this,I,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tH;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tH;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eN(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eN(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}_connected(e){this.ended||(e_(this,H,e,"f"),e_(this,F,e?.headers.get("request-id"),"f"),eN(this,R,"f").call(this,e),this._emit("connect"))}get ended(){return eN(this,B,"f")}get errored(){return eN(this,q,"f")}get aborted(){return eN(this,W,"f")}abort(){this.controller.abort()}on(e,t){return(eN(this,D,"f")[e]||(eN(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eN(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eN(this,D,"f")[e]||(eN(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{e_(this,z,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,z,!0,"f"),await eN(this,L,"f")}get currentMessage(){return eN(this,P,"f")}async finalMessage(){return await this.done(),eN(this,O,"m",J).call(this)}async finalText(){return await this.done(),eN(this,O,"m",G).call(this)}_emit(e,...t){if(eN(this,B,"f"))return;"end"===e&&(e_(this,B,!0,"f"),eN(this,$,"f").call(this));let s=eN(this,D,"f")[e];if(s&&(eN(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eN(this,z,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eN(this,z,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eN(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this),this._connected(null);let r=te.fromReadableStream(e,this.controller);for await(let e of r)eN(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}[(P=new WeakMap,I=new WeakMap,R=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,W=new WeakMap,z=new WeakMap,H=new WeakMap,F=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eC("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||e_(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eN(this,O,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tz(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tF(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,P,t,"f")}},Y=function(){if(this.ended)throw new eC("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eC("request ended without sending any chunks");return e_(this,P,void 0,"f"),e},Q=function(e){let t=eN(this,P,"f");if("message_start"===e.type){if(t)throw new eC(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eC(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tz(s)){let t=s[tW]||"";Object.defineProperty(s,tW,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tP(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tF(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tF(e){}class tJ extends tw{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tk`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tc,{query:e,...t})}delete(e,t){return this._client.delete(tk`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tk`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eC(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tC.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=t$[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,stream:e.stream??!1})}stream(e,t){return tH.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tV={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tG.Batches=tJ;class tK extends tw{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tk`/v1/models/${e}`,{...s,headers:t_([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",tc,{query:r,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}let tX=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tY{constructor({baseURL:e=tX("ANTHROPIC_BASE_URL"),apiKey:t=tX("ANTHROPIC_API_KEY")??null,authToken:s=tX("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Z.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new eC("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tQ.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=eJ(a.logLevel,"ClientOptions.logLevel",this)??eJ(tX("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),e_(this,Z,e6,"f"),this._options=a,this.apiKey=t,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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+  -H 'Authorization: Bearer sk-xxx' \\
+  -H 'Content-Type: application/json' \\
+  -d '{
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+curl -X POST --location '<your_proxy_base_url>/end_user/new' \\
+
+-H 'Authorization: Bearer <your-master-key>' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{"user_id": "my-customer-id', "budget_id": "<BUDGET_ID>"}' # 👈 KEY CHANGE
+
+`,$=`
+curl -X POST --location '<your_proxy_base_url>/chat/completions' \\
+
+-H 'Authorization: Bearer <your-master-key>' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{
+  "model": "gpt-3.5-turbo',
+  "messages":[{"role": "user", "content": "Hey, how's it going?"}],
+  "user": "my-customer-id"
+}' # 👈 KEY CHANGE
+
+`,H=`from openai import OpenAI
+client = OpenAI(
+  base_url="<your_proxy_base_url>",
+  api_key="<your_proxy_key>"
+)
+
+completion = client.chat.completions.create(
+  model="gpt-3.5-turbo",
+  messages=[
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello!"}
+  ],
+  user="my-customer-id"
+)
+
+print(completion.choices[0].message)`;var z=e.i(708347);e.s(["default",0,({accessToken:e})=>{let[_,T]=(0,x.useState)(!1),[E,M]=(0,x.useState)(!1),[I,D]=(0,x.useState)(null),[A,O]=(0,x.useState)(!1),{userRole:R}=(0,N.default)(),B=(0,z.isProxyAdminRole)(R??""),{data:V=[]}=(()=>{let{accessToken:e}=(0,N.default)();return(0,v.useQuery)({queryKey:S.list({}),queryFn:async()=>(await (0,C.getBudgetList)(e)??[]).filter(e=>null!=e),enabled:!!e})})(),U=(()=>{let{accessToken:e}=(0,N.default)(),t=(0,k.useQueryClient)();return(0,w.useMutation)({mutationFn:async t=>{if(!e)throw Error("Access token is required");return(0,C.budgetDeleteCall)(e,t)},onSuccess:()=>{t.invalidateQueries({queryKey:S.all})}})})(),q=async t=>{null!=e&&(D(t),M(!0))},G=async()=>{if(I&&null!=e)try{await U.mutateAsync(I.budget_id),j.default.success("Budget deleted.")}catch(e){console.error("Error deleting budget:",e),"function"==typeof j.default.fromBackend?j.default.fromBackend("Failed to delete budget"):j.default.info("Failed to delete budget")}finally{O(!1),D(null)}};return(0,t.jsxs)("div",{className:"w-full mx-auto flex-auto overflow-y-auto m-8 p-2",children:[B&&(0,t.jsx)(a.Button,{size:"sm",variant:"primary",className:"mb-2",onClick:()=>T(!0),children:"+ Create Budget"}),(0,t.jsxs)(s.TabGroup,{children:[(0,t.jsxs)(m.TabList,{children:[(0,t.jsx)(r.Tab,{children:"Budgets"}),(0,t.jsx)(r.Tab,{children:"Examples"})]}),(0,t.jsxs)(g.TabPanels,{children:[(0,t.jsx)(h.TabPanel,{children:(0,t.jsxs)("div",{className:"mt-6",children:[(0,t.jsx)(F,{isModalVisible:_,setIsModalVisible:T}),I&&(0,t.jsx)(P,{isModalVisible:E,setIsModalVisible:M,existingBudget:I}),(0,t.jsxs)(l.Card,{children:[(0,t.jsx)(p.Text,{children:"Create a budget to assign to customers."}),(0,t.jsxs)(i.Table,{children:[(0,t.jsx)(c.TableHead,{children:(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(d.TableHeaderCell,{children:"Budget ID"}),(0,t.jsx)(d.TableHeaderCell,{children:"Max Budget"}),(0,t.jsx)(d.TableHeaderCell,{children:"TPM"}),(0,t.jsx)(d.TableHeaderCell,{children:"RPM"})]})}),(0,t.jsx)(n.TableBody,{children:V.slice().sort((e,t)=>new Date(t.updated_at).getTime()-new Date(e.updated_at).getTime()).map(e=>(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(o.TableCell,{children:e.budget_id}),(0,t.jsx)(o.TableCell,{children:e.max_budget?e.max_budget:"n/a"}),(0,t.jsx)(o.TableCell,{children:e.tpm_limit?e.tpm_limit:"n/a"}),(0,t.jsx)(o.TableCell,{children:e.rpm_limit?e.rpm_limit:"n/a"}),B&&(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(y.default,{variant:"Edit",tooltipText:"Edit budget",onClick:()=>q(e),dataTestId:"edit-budget-button"}),(0,t.jsx)(y.default,{variant:"Delete",tooltipText:"Delete budget",onClick:()=>{D(e),O(!0)},dataTestId:"delete-budget-button"})]})]},e.budget_id))})]})]}),(0,t.jsx)(b.default,{isOpen:A,title:"Delete Budget?",message:"Are you sure you want to delete this budget? 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textarea[disabled]":{cursor:"not-allowed"}}),$(e,{status:"error",borderColor:e.colorError,hoverBorderColor:e.colorErrorBorderHover,activeBorderColor:e.colorError,activeShadow:e.errorActiveShadow,affixColor:e.colorError})),$(e,{status:"warning",borderColor:e.colorWarning,hoverBorderColor:e.colorWarningBorderHover,activeBorderColor:e.colorWarning,activeShadow:e.warningActiveShadow,affixColor:e.colorWarning})),t)});e.s(["genBaseOutlinedStyle",0,c,"genBorderlessStyle",0,h,"genDisabledStyle",0,s,"genFilledGroupStyle",0,b,"genFilledStyle",0,v,"genOutlinedGroupStyle",0,p,"genOutlinedStyle",0,d,"genUnderlinedStyle",0,C],889943);let x=e=>({"&::-moz-placeholder":{opacity:1},"&::placeholder":{color:e,userSelect:"none"},"&:placeholder-shown":{textOverflow:"ellipsis"}}),E=e=>{let{paddingBlockLG:r,lineHeightLG:o,borderRadiusLG:n,paddingInlineLG:a}=e;return{padding:`${(0,t.unit)(r)} ${(0,t.unit)(a)}`,fontSize:e.inputFontSizeLG,lineHeight:o,borderRadius:n}},S=e=>({padding:`${(0,t.unit)(e.paddingBlockSM)} ${(0,t.unit)(e.paddingInlineSM)}`,fontSize:e.inputFontSizeSM,borderRadius:e.borderRadiusSM}),k=e=>Object.assign(Object.assign({position:"relative",display:"inline-block",width:"100%",minWidth:0,padding:`${(0,t.unit)(e.paddingBlock)} ${(0,t.unit)(e.paddingInline)}`,color:e.colorText,fontSize:e.inputFontSize,lineHeight:e.lineHeight,borderRadius:e.borderRadius,transition:`all ${e.motionDurationMid}`},x(e.colorTextPlaceholder)),{"&-lg":Object.assign({},E(e)),"&-sm":Object.assign({},S(e)),"&-rtl, &-textarea-rtl":{direction:"rtl"}}),j=e=>{let{componentCls:o,antCls:n}=e;return{position:"relative",display:"table",width:"100%",borderCollapse:"separate",borderSpacing:0,"&[class*='col-']":{paddingInlineEnd:e.paddingXS,"&:last-child":{paddingInlineEnd:0}},[`&-lg ${o}, &-lg > ${o}-group-addon`]:Object.assign({},E(e)),[`&-sm ${o}, &-sm > ${o}-group-addon`]:Object.assign({},S(e)),[`&-lg ${n}-select-single ${n}-select-selector`]:{height:e.controlHeightLG},[`&-sm ${n}-select-single ${n}-select-selector`]:{height:e.controlHeightSM},[`> ${o}`]:{display:"table-cell","&:not(:first-child):not(:last-child)":{borderRadius:0}},[`${o}-group`]:{"&-addon, &-wrap":{display:"table-cell",width:1,whiteSpace:"nowrap",verticalAlign:"middle","&:not(:first-child):not(:last-child)":{borderRadius:0}},"&-wrap > *":{display:"block !important"},"&-addon":{position:"relative",padding:`0 ${(0,t.unit)(e.paddingInline)}`,color:e.colorText,fontWeight:"normal",fontSize:e.inputFontSize,textAlign:"center",borderRadius:e.borderRadius,transition:`all ${e.motionDurationSlow}`,lineHeight:1,[`${n}-select`]:{margin:`${(0,t.unit)(e.calc(e.paddingBlock).add(1).mul(-1).equal())} ${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,[`&${n}-select-single:not(${n}-select-customize-input):not(${n}-pagination-size-changer)`]:{[`${n}-select-selector`]:{backgroundColor:"inherit",border:`${(0,t.unit)(e.lineWidth)} ${e.lineType} transparent`,boxShadow:"none"}}},[`${n}-cascader-picker`]:{margin:`-9px ${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,backgroundColor:"transparent",[`${n}-cascader-input`]:{textAlign:"start",border:0,boxShadow:"none"}}}},[o]:{width:"100%",marginBottom:0,textAlign:"inherit","&:focus":{zIndex:1,borderInlineEndWidth:1},"&:hover":{zIndex:1,borderInlineEndWidth:1,[`${o}-search-with-button &`]:{zIndex:0}}},[`> ${o}:first-child, ${o}-group-addon:first-child`]:{borderStartEndRadius:0,borderEndEndRadius:0,[`${n}-select ${n}-select-selector`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`> ${o}-affix-wrapper`]:{[`&:not(:first-child) ${o}`]:{borderStartStartRadius:0,borderEndStartRadius:0},[`&:not(:last-child) 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+        & > ${o}-affix-wrapper,
+        & > ${o}-number-affix-wrapper,
+        & > ${n}-picker-range
+      `]:{display:"inline-flex"},"& > *:not(:last-child)":{marginInlineEnd:e.calc(e.lineWidth).mul(-1).equal(),borderInlineEndWidth:e.lineWidth},[o]:{float:"none"},[`& > ${n}-select > ${n}-select-selector,
+      & > ${n}-select-auto-complete ${o},
+      & > ${n}-cascader-picker ${o},
+      & > ${o}-group-wrapper ${o}`]:{borderInlineEndWidth:e.lineWidth,borderRadius:0,"&:hover, &:focus":{zIndex:1}},[`& > ${n}-select-focused`]:{zIndex:1},[`& > ${n}-select > ${n}-select-arrow`]:{zIndex:1},[`& > *:first-child,
+      & > ${n}-select:first-child > ${n}-select-selector,
+      & > ${n}-select-auto-complete:first-child ${o},
+      & > ${n}-cascader-picker:first-child ${o}`]:{borderStartStartRadius:e.borderRadius,borderEndStartRadius:e.borderRadius},[`& > *:last-child,
+      & > ${n}-select:last-child > ${n}-select-selector,
+      & > ${n}-cascader-picker:last-child ${o},
+      & > ${n}-cascader-picker-focused:last-child ${o}`]:{borderInlineEndWidth:e.lineWidth,borderStartEndRadius:e.borderRadius,borderEndEndRadius:e.borderRadius},[`& > ${n}-select-auto-complete ${o}`]:{verticalAlign:"top"},[`${o}-group-wrapper + ${o}-group-wrapper`]:{marginInlineStart:e.calc(e.lineWidth).mul(-1).equal(),[`${o}-affix-wrapper`]:{borderRadius:0}},[`${o}-group-wrapper:not(:last-child)`]:{[`&${o}-search > ${o}-group`]:{[`& > ${o}-group-addon > ${o}-search-button`]:{borderRadius:0},[`& > ${o}`]:{borderStartStartRadius:e.borderRadius,borderStartEndRadius:0,borderEndEndRadius:0,borderEndStartRadius:e.borderRadius}}}})}},O=(0,n.genStyleHooks)(["Input","Shared"],e=>{let o=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,controlHeightSM:o,lineWidth:n,calc:a}=e,i=a(o).sub(a(n).mul(2)).sub(16).div(2).equal();return{[t]:Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),k(e)),d(e)),v(e)),h(e)),C(e)),{'&[type="color"]':{height:e.controlHeight,[`&${t}-lg`]:{height:e.controlHeightLG},[`&${t}-sm`]:{height:o,paddingTop:i,paddingBottom:i}},'&[type="search"]::-webkit-search-cancel-button, &[type="search"]::-webkit-search-decoration':{appearance:"none"}})}})(o),(e=>{let{componentCls:r,inputAffixPadding:o,colorTextDescription:n,motionDurationSlow:a,colorIcon:i,colorIconHover:l,iconCls:s}=e,c=`${r}-affix-wrapper`,u=`${r}-affix-wrapper-disabled`;return{[c]:Object.assign(Object.assign(Object.assign(Object.assign({},k(e)),{display:"inline-flex",[`&:not(${r}-disabled):hover`]:{zIndex:1,[`${r}-search-with-button &`]:{zIndex:0}},"&-focused, &:focus":{zIndex:1},[`> input${r}`]:{padding:0},[`> input${r}, > textarea${r}`]:{fontSize:"inherit",border:"none",borderRadius:0,outline:"none",background:"transparent",color:"inherit","&::-ms-reveal":{display:"none"},"&:focus":{boxShadow:"none !important"}},"&::before":{display:"inline-block",width:0,visibility:"hidden",content:'"\\a0"'},[r]:{"&-prefix, &-suffix":{display:"flex",flex:"none",alignItems:"center","> *:not(:last-child)":{marginInlineEnd:e.paddingXS}},"&-show-count-suffix":{color:n,direction:"ltr"},"&-show-count-has-suffix":{marginInlineEnd:e.paddingXXS},"&-prefix":{marginInlineEnd:o},"&-suffix":{marginInlineStart:o}}}),(e=>{let{componentCls:r}=e;return{[`${r}-clear-icon`]:{margin:0,padding:0,lineHeight:0,color:e.colorTextQuaternary,fontSize:e.fontSizeIcon,verticalAlign:-1,cursor:"pointer",transition:`color 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t=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,borderRadiusLG:o,borderRadiusSM:n}=e;return{[`${t}-group`]:Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),j(e)),{"&-rtl":{direction:"rtl"},"&-wrapper":Object.assign(Object.assign(Object.assign({display:"inline-block",width:"100%",textAlign:"start",verticalAlign:"top","&-rtl":{direction:"rtl"},"&-lg":{[`${t}-group-addon`]:{borderRadius:o,fontSize:e.inputFontSizeLG}},"&-sm":{[`${t}-group-addon`]:{borderRadius:n}}},p(e)),b(e)),{[`&:not(${t}-compact-first-item):not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}, ${t}-group-addon`]:{borderRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-first-item`]:{[`${t}, ${t}-group-addon`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-last-item`]:{[`${t}, ${t}-group-addon`]:{borderStartStartRadius:0,borderEndStartRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartStartRadius:0,borderEndStartRadius:0}}})})}})(t),(e=>{let{componentCls:t,antCls:r}=e,o=`${t}-search`;return{[o]:{[t]:{"&:not([disabled]):hover, &:not([disabled]):focus":{[`+ ${t}-group-addon ${o}-button:not(${r}-btn-color-primary):not(${r}-btn-variant-text)`]:{borderInlineStartColor:e.colorPrimaryHover}}},[`${t}-affix-wrapper`]:{height:e.controlHeight,borderRadius:0},[`${t}-lg`]:{lineHeight:e.calc(e.lineHeightLG).sub(2e-4).equal()},[`> ${t}-group`]:{[`> ${t}-group-addon:last-child`]:{insetInlineStart:-1,padding:0,border:0,[`${o}-button`]:{marginInlineEnd:-1,borderStartStartRadius:0,borderEndStartRadius:0,boxShadow:"none"},[`${o}-button:not(${r}-btn-color-primary)`]:{color:e.colorTextDescription,"&:not([disabled]):hover":{color:e.colorPrimaryHover},"&:active":{color:e.colorPrimaryActive},[`&${r}-btn-loading::before`]:{inset:0}}}},[`${o}-button`]:{height:e.controlHeight,"&:hover, &:focus":{zIndex:1}},"&-large":{[`${t}-affix-wrapper, ${o}-button`]:{height:e.controlHeightLG}},"&-small":{[`${t}-affix-wrapper, ${o}-button`]:{height:e.controlHeightSM}},"&-rtl":{direction:"rtl"},[`&${t}-compact-item`]:{[`&:not(${t}-compact-last-item)`]:{[`${t}-group-addon`]:{[`${t}-search-button`]:{marginInlineEnd:e.calc(e.lineWidth).mul(-1).equal(),borderRadius:0}}},[`&:not(${t}-compact-first-item)`]:{[`${t},${t}-affix-wrapper`]:{borderRadius:0}},[`> ${t}-group-addon ${t}-search-button,
+        > ${t},
+        ${t}-affix-wrapper`]:{"&:hover, &:focus, &:active":{zIndex:2}},[`> ${t}-affix-wrapper-focused`]:{zIndex:2}}}}})(t),(e=>{let{componentCls:t}=e;return{[`${t}-out-of-range`]:{[`&, & input, & textarea, ${t}-show-count-suffix, ${t}-data-count`]:{color:e.colorError}}}})(t),(0,o.genCompactItemStyle)(t)]},l,{resetFont:!1});e.s(["default",0,T,"genBasicInputStyle",0,k,"genInputGroupStyle",0,j,"genInputSmallStyle",0,S,"genPlaceholderStyle",0,x,"useSharedStyle",0,O],349942)},831357,e=>{"use strict";var t=e.i(271645),r=e.i(343794),o=e.i(242064),n=e.i(62139),a=e.i(349942);e.s(["default",0,e=>{let{getPrefixCls:i,direction:l}=(0,t.useContext)(o.ConfigContext),{prefixCls:s,className:c}=e,u=i("input-group",s),d=i("input"),[f,p,h]=(0,a.default)(d),m=(0,r.default)(u,h,{[`${u}-lg`]:"large"===e.size,[`${u}-sm`]:"small"===e.size,[`${u}-compact`]:e.compact,[`${u}-rtl`]:"rtl"===l},p,c),g=(0,t.useContext)(n.FormItemInputContext),v=(0,t.useMemo)(()=>Object.assign(Object.assign({},g),{isFormItemInput:!1}),[g]);return f(t.createElement("span",{className:m,style:e.style,onMouseEnter:e.onMouseEnter,onMouseLeave:e.onMouseLeave,onFocus:e.onFocus,onBlur:e.onBlur},t.createElement(n.FormItemInputContext.Provider,{value:v},e.children)))}])},175636,131299,367397,874460,e=>{"use strict";var t=e.i(209428),r=e.i(931067),o=e.i(211577),n=e.i(410160),a=e.i(343794),i=e.i(271645);function l(e){return!!(e.addonBefore||e.addonAfter)}function s(e){return!!(e.prefix||e.suffix||e.allowClear)}function c(e,t,r){var 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/23bfdf9b0544f0b1.js b/litellm/proxy/_experimental/out/_next/static/chunks/23bfdf9b0544f0b1.js
new file mode 100644
index 00000000000..8e9796246d1
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/23bfdf9b0544f0b1.js
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new file mode 100644
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new file mode 100644
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+--header 'Content-Type: application/json' \\
+--header 'Authorization: Bearer sk-1234' \\
+--data '{
+    "model": "${O}",
+    "input": [
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+      "type": "message"
+    }
+  ],
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+            "require_approval": "never"
+        }
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Please use expo/fetch: https://docs.expo.dev/versions/latest/sdk/expo/#expofetch-api");throw new eC("Attempted to iterate over a response with no body")}return new tC(e3(e.body),t)}}class tA extends tw{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/batches?beta=true",{body:r,...t,headers:t_([{"anthropic-beta":[...s??[],"message-batches-2024-09-24"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tk`/v1/messages/batches/${e}?beta=true`,{...s,headers:t_([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/messages/batches?beta=true",tc,{query:r,...t,headers:t_([{"anthropic-beta":[...s??[],"message-batches-2024-09-24"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(tk`/v1/messages/batches/${e}?beta=true`,{...s,headers:t_([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(tk`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:t_([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}async results(e,t={},s){let r=await this.retrieve(e);if(!r.results_url)throw new eC(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:t_([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tC.fromResponse(t.response,t.controller))}}let tO=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tO(e=e.slice(0,e.length-1));case"number":let 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fromReadableStream(e){let t=new tM;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tM;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eN(this,k,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eN(this,c,"m",E).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eN(this,c,"m",T).call(this,e);if(n.controller.signal?.aborted)throw new eO;eN(this,c,"m",C).call(this)}_connected(e){this.ended||(e_(this,j,e,"f"),e_(this,S,e?.headers.get("request-id"),"f"),eN(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eN(this,x,"f")}get errored(){return eN(this,b,"f")}get aborted(){return eN(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eN(this,y,"f")[e]||(eN(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eN(this,y,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eN(this,y,"f")[e]||(eN(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{e_(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,w,!0,"f"),await eN(this,p,"f")}get currentMessage(){return eN(this,d,"f")}async finalMessage(){return await this.done(),eN(this,c,"m",_).call(this)}async finalText(){return await 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eO;eN(this,c,"m",C).call(this)}[(d=new WeakMap,u=new WeakMap,h=new WeakMap,m=new WeakMap,p=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,j=new WeakMap,S=new WeakMap,k=new WeakMap,c=new WeakSet,_=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eC("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||e_(this,d,void 0,"f")},T=function(e){if(this.ended)return;let t=eN(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tR(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tL(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,d,t,"f")}},C=function(){if(this.ended)throw new eC("stream has ended, this shouldn't happen");let e=eN(this,d,"f");if(!e)throw new eC("request ended without sending any chunks");return e_(this,d,void 0,"f"),e},A=function(e){let t=eN(this,d,"f");if("message_start"===e.type){if(t)throw new eC(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eC(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tR(s)){let t=s[tI]||"";if(Object.defineProperty(s,tI,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tP(t)}catch(s){let e=new eC(`Unable to parse tool parameter JSON from model. 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JSON: ${t}`);eN(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tL(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tL(e){}let t$={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tU={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tD extends tw{constructor(){super(...arguments),this.batches=new tA(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tU&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tU[r.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=t$[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tM.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:t_([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tD.Batches=tA;class tB extends tw{constructor(){super(...arguments),this.models=new tT(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tT,tB.Messages=tD,tB.Files=tE;class tq extends tw{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tW="__json_buf";function tz(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tH{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,I.set(this,void 0),R.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),W.set(this,!1),z.set(this,!1),H.set(this,void 0),F.set(this,void 0),V.set(this,e=>{if(e_(this,q,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,W,!0,"f"),this._emit("abort",e);if(e instanceof eC)return this._emit("error",e);if(e instanceof Error){let t=new eC(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eC(String(e)))}),e_(this,I,new Promise((e,t)=>{e_(this,R,e,"f"),e_(this,M,t,"f")}),"f"),e_(this,L,new Promise((e,t)=>{e_(this,$,e,"f"),e_(this,U,t,"f")}),"f"),eN(this,I,"f").catch(()=>{}),eN(this,L,"f").catch(()=>{})}get response(){return eN(this,H,"f")}get request_id(){return eN(this,F,"f")}async withResponse(){let e=await eN(this,I,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tH;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tH;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eN(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eN(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}_connected(e){this.ended||(e_(this,H,e,"f"),e_(this,F,e?.headers.get("request-id"),"f"),eN(this,R,"f").call(this,e),this._emit("connect"))}get ended(){return eN(this,B,"f")}get errored(){return eN(this,q,"f")}get aborted(){return eN(this,W,"f")}abort(){this.controller.abort()}on(e,t){return(eN(this,D,"f")[e]||(eN(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eN(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eN(this,D,"f")[e]||(eN(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{e_(this,z,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,z,!0,"f"),await eN(this,L,"f")}get currentMessage(){return eN(this,P,"f")}async finalMessage(){return await this.done(),eN(this,O,"m",J).call(this)}async finalText(){return await this.done(),eN(this,O,"m",G).call(this)}_emit(e,...t){if(eN(this,B,"f"))return;"end"===e&&(e_(this,B,!0,"f"),eN(this,$,"f").call(this));let s=eN(this,D,"f")[e];if(s&&(eN(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eN(this,z,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eN(this,z,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eN(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this),this._connected(null);let r=te.fromReadableStream(e,this.controller);for await(let e of r)eN(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}[(P=new WeakMap,I=new WeakMap,R=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,W=new WeakMap,z=new WeakMap,H=new WeakMap,F=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eC("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eC("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||e_(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eN(this,O,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tz(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tF(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,P,t,"f")}},Y=function(){if(this.ended)throw new eC("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eC("request ended without sending any chunks");return e_(this,P,void 0,"f"),e},Q=function(e){let t=eN(this,P,"f");if("message_start"===e.type){if(t)throw new eC(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eC(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tz(s)){let t=s[tW]||"";Object.defineProperty(s,tW,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tP(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tF(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tF(e){}class tJ extends tw{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tk`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tc,{query:e,...t})}delete(e,t){return this._client.delete(tk`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tk`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eC(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tC.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=t$[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,stream:e.stream??!1})}stream(e,t){return tH.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tV={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tG.Batches=tJ;class tK extends tw{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tk`/v1/models/${e}`,{...s,headers:t_([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",tc,{query:r,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}let tX=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tY{constructor({baseURL:e=tX("ANTHROPIC_BASE_URL"),apiKey:t=tX("ANTHROPIC_API_KEY")??null,authToken:s=tX("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Z.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new eC("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tQ.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=eJ(a.logLevel,"ClientOptions.logLevel",this)??eJ(tX("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),e_(this,Z,e6,"f"),this._options=a,this.apiKey=t,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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new file mode 100644
index 00000000000..be7c693535b
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/7b6bca6d63438103.js
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new file mode 100644
index 00000000000..4709b35055e
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${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,[`&${n}-select-single:not(${n}-select-customize-input):not(${n}-pagination-size-changer)`]:{[`${n}-select-selector`]:{backgroundColor:"inherit",border:`${(0,t.unit)(e.lineWidth)} ${e.lineType} transparent`,boxShadow:"none"}}},[`${n}-cascader-picker`]:{margin:`-9px ${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,backgroundColor:"transparent",[`${n}-cascader-input`]:{textAlign:"start",border:0,boxShadow:"none"}}}},[o]:{width:"100%",marginBottom:0,textAlign:"inherit","&:focus":{zIndex:1,borderInlineEndWidth:1},"&:hover":{zIndex:1,borderInlineEndWidth:1,[`${o}-search-with-button &`]:{zIndex:0}}},[`> ${o}:first-child, ${o}-group-addon:first-child`]:{borderStartEndRadius:0,borderEndEndRadius:0,[`${n}-select ${n}-select-selector`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`> ${o}-affix-wrapper`]:{[`&:not(:first-child) ${o}`]:{borderStartStartRadius:0,borderEndStartRadius:0},[`&:not(:last-child) 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+        & > ${o}-affix-wrapper,
+        & > ${o}-number-affix-wrapper,
+        & > ${n}-picker-range
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+      & > ${n}-select-auto-complete ${o},
+      & > ${n}-cascader-picker ${o},
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+      & > ${n}-select-auto-complete:first-child ${o},
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+      & > ${n}-select:last-child > ${n}-select-selector,
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t=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,borderRadiusLG:o,borderRadiusSM:n}=e;return{[`${t}-group`]:Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),j(e)),{"&-rtl":{direction:"rtl"},"&-wrapper":Object.assign(Object.assign(Object.assign({display:"inline-block",width:"100%",textAlign:"start",verticalAlign:"top","&-rtl":{direction:"rtl"},"&-lg":{[`${t}-group-addon`]:{borderRadius:o,fontSize:e.inputFontSizeLG}},"&-sm":{[`${t}-group-addon`]:{borderRadius:n}}},p(e)),b(e)),{[`&:not(${t}-compact-first-item):not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}, ${t}-group-addon`]:{borderRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-first-item`]:{[`${t}, ${t}-group-addon`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-last-item`]:{[`${t}, 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+        > ${t},
+        ${t}-affix-wrapper`]:{"&:hover, &:focus, &:active":{zIndex:2}},[`> ${t}-affix-wrapper-focused`]:{zIndex:2}}}}})(t),(e=>{let{componentCls:t}=e;return{[`${t}-out-of-range`]:{[`&, & input, & textarea, ${t}-show-count-suffix, ${t}-data-count`]:{color:e.colorError}}}})(t),(0,o.genCompactItemStyle)(t)]},l,{resetFont:!1});e.s(["default",0,T,"genBasicInputStyle",0,k,"genInputGroupStyle",0,j,"genInputSmallStyle",0,S,"genPlaceholderStyle",0,x,"useSharedStyle",0,O],349942)},831357,e=>{"use strict";var 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/88001a7ecaf7b1af.js b/litellm/proxy/_experimental/out/_next/static/chunks/88001a7ecaf7b1af.js
new file mode 100644
index 00000000000..6cf89428c10
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/88001a7ecaf7b1af.js
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new file mode 100644
index 00000000000..4a3d6ed3628
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+    <!DOCTYPE html>
+    <html>
+    <head>
+      <title>Multi-Model Cost Estimate Report</title>
+      <style>
+        body {
+          font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+          padding: 40px;
+          max-width: 900px;
+          margin: 0 auto;
+          color: #333;
+        }
+        h1 {
+          color: #1a1a1a;
+          border-bottom: 2px solid #1890ff;
+          padding-bottom: 10px;
+          margin-bottom: 30px;
+        }
+        h2 {
+          color: #444;
+          margin-top: 30px;
+          margin-bottom: 15px;
+        }
+        h3 {
+          color: #555;
+          margin-top: 25px;
+          margin-bottom: 10px;
+          padding-bottom: 5px;
+          border-bottom: 1px solid #eee;
+        }
+        .provider {
+          font-weight: normal;
+          color: #1890ff;
+          font-size: 14px;
+        }
+        .meta {
+          background: #f5f5f5;
+          padding: 12px 15px;
+          border-radius: 8px;
+          margin-bottom: 15px;
+          font-size: 13px;
+        }
+        .meta p {
+          margin: 4px 0;
+        }
+        table {
+          width: 100%;
+          border-collapse: collapse;
+          margin-bottom: 20px;
+        }
+        th, td {
+          padding: 10px 12px;
+          text-align: left;
+          border-bottom: 1px solid #ddd;
+        }
+        th {
+          background: #f8f9fa;
+          font-weight: 600;
+          font-size: 13px;
+        }
+        .cost-value {
+          font-family: monospace;
+          font-size: 13px;
+        }
+        .total-row {
+          font-weight: bold;
+          background: #e6f7ff;
+        }
+        .summary-box {
+          background: linear-gradient(135deg, #e6f7ff 0%, #f9f0ff 100%);
+          border: 1px solid #91d5ff;
+          border-radius: 8px;
+          padding: 20px;
+          margin-bottom: 30px;
+        }
+        .summary-box h2 {
+          margin-top: 0;
+          color: #1890ff;
+        }
+        .summary-grid {
+          display: grid;
+          grid-template-columns: repeat(3, 1fr);
+          gap: 20px;
+          margin-top: 15px;
+        }
+        .summary-item {
+          text-align: center;
+        }
+        .summary-item .label {
+          font-size: 12px;
+          color: #666;
+          margin-bottom: 5px;
+        }
+        .summary-item .value {
+          font-size: 20px;
+          font-weight: bold;
+          font-family: monospace;
+        }
+        .summary-item .value.blue { color: #1890ff; }
+        .summary-item .value.green { color: #52c41a; }
+        .summary-item .value.purple { color: #722ed1; }
+        .model-section {
+          margin-bottom: 30px;
+          page-break-inside: avoid;
+        }
+        .footer {
+          margin-top: 40px;
+          padding-top: 20px;
+          border-top: 1px solid #ddd;
+          font-size: 12px;
+          color: #666;
+        }
+        @media print {
+          body { padding: 20px; }
+          .model-section { page-break-inside: avoid; }
+        }
+      </style>
+    </head>
+    <body>
+      <h1>LLM Cost Estimate Report</h1>
+      <p style="color: #666; margin-top: -20px; margin-bottom: 30px;">${a} model${1!==a?"s":""} configured</p>
+      
+      <div class="summary-box">
+        <h2>Combined Totals</h2>
+        <div class="summary-grid">
+          <div class="summary-item">
+            <div class="label">Total Per Request</div>
+            <div class="value blue">${tL(e.totals.cost_per_request)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Total Daily</div>
+            <div class="value green">${tL(e.totals.daily_cost)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Total Monthly</div>
+            <div class="value purple">${tL(e.totals.monthly_cost)}</div>
+          </div>
+        </div>
+        ${e.totals.margin_per_request>0?`
+        <div class="summary-grid" style="margin-top: 15px; padding-top: 15px; border-top: 1px solid #ddd;">
+          <div class="summary-item">
+            <div class="label">Margin/Request</div>
+            <div class="value" style="color: #faad14;">${tL(e.totals.margin_per_request)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Daily Margin</div>
+            <div class="value" style="color: #faad14;">${tL(e.totals.daily_margin)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Monthly Margin</div>
+            <div class="value" style="color: #faad14;">${tL(e.totals.monthly_margin)}</div>
+          </div>
+        </div>
+        `:""}
+      </div>
+
+      <h2>Model Breakdown</h2>
+      ${s.map(e=>{let t;return t=e.result,`
+    <div class="model-section">
+      <h3>${t.model} ${t.provider?`<span class="provider">(${t.provider})</span>`:""}</h3>
+      
+      <div class="meta">
+        <p><strong>Input Tokens per Request:</strong> ${tM(t.input_tokens)}</p>
+        <p><strong>Output Tokens per Request:</strong> ${tM(t.output_tokens)}</p>
+        ${t.num_requests_per_day?`<p><strong>Requests per Day:</strong> ${tM(t.num_requests_per_day)}</p>`:""}
+        ${t.num_requests_per_month?`<p><strong>Requests per Month:</strong> ${tM(t.num_requests_per_month)}</p>`:""}
+      </div>
+
+      <table>
+        <tr>
+          <th>Cost Type</th>
+          <th>Per Request</th>
+          ${null!==t.daily_cost?"<th>Daily</th>":""}
+          ${null!==t.monthly_cost?"<th>Monthly</th>":""}
+        </tr>
+        <tr>
+          <td>Input Cost</td>
+          <td class="cost-value">${tL(t.input_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tL(t.daily_input_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tL(t.monthly_input_cost)}</td>`:""}
+        </tr>
+        <tr>
+          <td>Output Cost</td>
+          <td class="cost-value">${tL(t.output_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tL(t.daily_output_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tL(t.monthly_output_cost)}</td>`:""}
+        </tr>
+        <tr>
+          <td>Margin/Fee</td>
+          <td class="cost-value">${tL(t.margin_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tL(t.daily_margin_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tL(t.monthly_margin_cost)}</td>`:""}
+        </tr>
+        <tr class="total-row">
+          <td>Total</td>
+          <td class="cost-value">${tL(t.cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tL(t.daily_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tL(t.monthly_cost)}</td>`:""}
+        </tr>
+      </table>
+    </div>
+  `}).join("")}
+
+      <div class="footer">
+        <p>Generated by LiteLLM Pricing Calculator on ${new Date().toLocaleString()}</p>
+      </div>
+    </body>
+    </html>
+  `;t.document.write(l),t.document.close(),t.onload=()=>{t.print()}})(e),a(!1)},children:[(0,t.jsx)(tC.FilePdfOutlined,{className:"mr-3 text-red-500"}),"Export as PDF"]}),(0,t.jsxs)("button",{className:"flex items-center w-full px-4 py-2 text-sm text-gray-700 hover:bg-gray-50 transition-colors",onClick:()=>{(e=>{let t=e.entries.filter(e=>null!==e.result),s=[["LLM Multi-Model Cost Estimate Report"],["Generated",new Date().toLocaleString()],[""]];for(let a of(s.push(["COMBINED TOTALS"],["Total Per Request",e.totals.cost_per_request.toString()],["Total Daily",e.totals.daily_cost?.toString()||"-"],["Total Monthly",e.totals.monthly_cost?.toString()||"-"],["Margin Per Request",e.totals.margin_per_request.toString()],["Daily Margin",e.totals.daily_margin?.toString()||"-"],["Monthly Margin",e.totals.monthly_margin?.toString()||"-"],[""]),s.push(["Model","Provider","Input Tokens","Output Tokens","Requests/Day","Requests/Month","Cost/Request","Daily Cost","Monthly Cost","Input Cost/Req","Output 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+
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+
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+      & > ${n}-cascader-picker ${o},
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/ae625aa52246581e.js b/litellm/proxy/_experimental/out/_next/static/chunks/ae625aa52246581e.js
new file mode 100644
index 00000000000..42d1e4c73a3
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/ae625aa52246581e.js
@@ -0,0 +1,598 @@
+(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,818581,(e,t,a)=>{"use strict";Object.defineProperty(a,"__esModule",{value:!0}),Object.defineProperty(a,"useMergedRef",{enumerable:!0,get:function(){return r}});let s=e.r(271645);function r(e,t){let a=(0,s.useRef)(null),r=(0,s.useRef)(null);return(0,s.useCallback)(s=>{if(null===s){let e=a.current;e&&(a.current=null,e());let t=r.current;t&&(r.current=null,t())}else e&&(a.current=i(e,s)),t&&(r.current=i(t,s))},[e,t])}function i(e,t){if("function"!=typeof e)return e.current=t,()=>{e.current=null};{let a=e(t);return"function"==typeof a?a:()=>e(null)}}("function"==typeof a.default||"object"==typeof a.default&&null!==a.default)&&void 0===a.default.__esModule&&(Object.defineProperty(a.default,"__esModule",{value:!0}),Object.assign(a.default,a),t.exports=a.default)},62478,e=>{"use strict";var t=e.i(764205);let a=async e=>{if(!e)return null;try{return await (0,t.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}};e.s(["fetchProxySettings",0,a])},602073,e=>{"use strict";e.i(247167);var t=e.i(931067),a=e.i(271645);let s={icon:{tag:"svg",attrs:{viewBox:"0 0 1024 1024",focusable:"false"},children:[{tag:"path",attrs:{d:"M512 64L128 192v384c0 212.1 171.9 384 384 384s384-171.9 384-384V192L512 64zm312 512c0 172.3-139.7 312-312 312S200 748.3 200 576V246l312-110 312 110v330z"}},{tag:"path",attrs:{d:"M378.4 475.1a35.91 35.91 0 00-50.9 0 35.91 35.91 0 000 50.9l129.4 129.4 2.1 2.1a33.98 33.98 0 0048.1 0L730.6 434a33.98 33.98 0 000-48.1l-2.8-2.8a33.98 33.98 0 00-48.1 0L483 579.7 378.4 475.1z"}}]},name:"safety",theme:"outlined"};var r=e.i(9583),i=a.forwardRef(function(e,i){return a.createElement(r.default,(0,t.default)({},e,{ref:i,icon:s}))});e.s(["SafetyOutlined",0,i],602073)},190272,785913,e=>{"use strict";var t,a,s=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),r=((a={}).IMAGE="image",a.VIDEO="video",a.CHAT="chat",a.RESPONSES="responses",a.IMAGE_EDITS="image_edits",a.ANTHROPIC_MESSAGES="anthropic_messages",a.EMBEDDINGS="embeddings",a.SPEECH="speech",a.TRANSCRIPTION="transcription",a.A2A_AGENTS="a2a_agents",a.MCP="mcp",a.REALTIME="realtime",a.INTERACTIONS="interactions",a);let i={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>r,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(s).includes(e)){let t=i[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:a,accessToken:s,apiKey:i,inputMessage:l,chatHistory:n,selectedTags:o,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:m,selectedMCPServers:p,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:x,endpointType:h,selectedModel:f,selectedSdk:_,proxySettings:b}=e,y="session"===a?s:i,v=window.location.origin,j=b?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?v=j:b?.PROXY_BASE_URL&&(v=b.PROXY_BASE_URL);let A=l||"Your prompt here",N=A.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),T=n.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),S={};o.length>0&&(S.tags=o),c.length>0&&(S.vector_stores=c),d.length>0&&(S.guardrails=d),m.length>0&&(S.policies=m);let C=f||"your-model-name",w="azure"===_?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${y||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${v}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${y||"YOUR_LITELLM_API_KEY"}",
+	base_url="${v}"
+)`;switch(h){case r.CHAT:{let e=Object.keys(S).length>0,a="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let s=T.length>0?T:[{role:"user",content:A}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${C}",
+    messages=${JSON.stringify(s,null,4)}${a}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${C}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${N}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${a}
+# )
+# print(response_with_file)
+`;break}case r.RESPONSES:{let e=Object.keys(S).length>0,a="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let s=T.length>0?T:[{role:"user",content:A}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${C}",
+    input=${JSON.stringify(s,null,4)}${a}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${C}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${N}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${a}
+# )
+# print(response_with_file.output_text)
+`;break}case r.IMAGE:t="azure"===_?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${C}",
+	prompt="${l}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case r.IMAGE_EDITS:t="azure"===_?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case r.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${l||"Your string here"}",
+	model="${C}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case r.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${C}",
+	file=audio_file${l?`,
+	prompt="${l.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case r.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${C}",
+	input="${l||"Your text to convert to speech here"}",
+	voice="${x}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${C}",
+#     input="${l||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${w}
+${t}`}],190272)},652272,209261,e=>{"use strict";var t=e.i(843476),a=e.i(271645),s=e.i(447566),r=e.i(166406),i=e.i(492030),l=e.i(596239);let n=e=>"github"===e.source.source&&e.source.repo?`/plugin marketplace add ${e.source.repo}`:"url"===e.source.source&&e.source.url?`/plugin marketplace add ${e.source.url}`:`/plugin marketplace add ${e.name}`;e.s(["formatInstallCommand",0,n,"getCategoryBadgeColor",0,e=>{if(!e)return"gray";let t=e.toLowerCase();if(t.includes("development")||t.includes("dev"))return"blue";if(t.includes("productivity")||t.includes("workflow"))return"green";if(t.includes("learning")||t.includes("education"))return"purple";if(t.includes("security")||t.includes("safety"))return"red";if(t.includes("data")||t.includes("analytics"))return"orange";else 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gap-2",children:Object.entries(eS.capabilities).filter(([e,t])=>!0===t).map(([e])=>(0,t.jsx)(p.Tag,{color:"green",className:"capitalize",children:e},e))})]}),eS.skills&&eS.skills.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Skills"}),(0,t.jsx)("div",{className:"space-y-4",children:eS.skills.map((e,a)=>(0,t.jsxs)("div",{className:"border border-gray-200 rounded-lg p-4",children:[(0,t.jsx)("div",{className:"flex items-start justify-between mb-2",children:(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium text-base",children:e.name}),(0,t.jsx)(n.Text,{className:"text-sm text-gray-600",children:e.description})]})}),e.tags&&e.tags.length>0&&(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-2",children:e.tags.map(e=>(0,t.jsx)(p.Tag,{color:"purple",className:"text-xs",children:e},e))})]},a))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Input/Output Modes"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Input Modes:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:(eS.defaultInputModes??[]).map(e=>(0,t.jsx)(p.Tag,{color:"blue",children:e},e))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Output Modes:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:(eS.defaultOutputModes??[]).map(e=>(0,t.jsx)(p.Tag,{color:"blue",children:e},e))})]})]})]}),eS.documentationUrl&&(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Documentation"}),(0,t.jsxs)("a",{href:eS.documentationUrl,target:"_blank",rel:"noopener noreferrer",className:"text-blue-600 hover:text-blue-800 flex items-center space-x-2",children:[(0,t.jsx)(s.ExternalLinkIcon,{className:"w-4 h-4"}),(0,t.jsx)("span",{children:"View Documentation"})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example (A2A Protocol)"}),(0,t.jsxs)("div",{className:"mb-4",children:[(0,t.jsx)(n.Text,{className:"text-sm font-medium mb-2 text-gray-700",children:"Step 1: Retrieve Agent Card"}),(0,t.jsx)("div",{className:"bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto",children:(0,t.jsx)("pre",{className:"text-xs",children:`base_url = '${eS.url}'
+
+resolver = A2ACardResolver(
+    httpx_client=httpx_client,
+    base_url=base_url,
+    # agent_card_path uses default, extended_agent_card_path also uses default
+)
+
+# Fetch Public Agent Card and Initialize Client
+final_agent_card_to_use: AgentCard | None = None
+_public_card = (
+    await resolver.get_agent_card()
+)  # Fetches from default public path - \`/agents/{agent_id}/\`
+final_agent_card_to_use = _public_card
+
+if _public_card.supports_authenticated_extended_card:
+    try:
+        auth_headers_dict = {
+            'Authorization': 'Bearer dummy-token-for-extended-card'
+        }
+        _extended_card = await resolver.get_agent_card(
+            relative_card_path=EXTENDED_AGENT_CARD_PATH,
+            http_kwargs={'headers': auth_headers_dict},
+        )
+        final_agent_card_to_use = (
+            _extended_card  # Update to use the extended card
+        )
+    except Exception as e_extended:
+        logger.warning(
+            f'Failed to fetch extended agent card: {e_extended}. Will proceed with public card.',
+            exc_info=True,
+        )`})}),(0,t.jsx)("div",{className:"mt-2 text-right",children:(0,t.jsx)("button",{onClick:()=>{eB(`from a2a.client import A2ACardResolver, A2AClient
+from a2a.types import (
+    AgentCard,
+    MessageSendParams,
+    SendMessageRequest,
+    SendStreamingMessageRequest,
+)
+from a2a.utils.constants import (
+    AGENT_CARD_WELL_KNOWN_PATH,
+    EXTENDED_AGENT_CARD_PATH,
+)
+
+base_url = '${eS.url}'
+
+resolver = A2ACardResolver(
+    httpx_client=httpx_client,
+    base_url=base_url,
+    # agent_card_path uses default, extended_agent_card_path also uses default
+)
+
+# Fetch Public Agent Card and Initialize Client
+final_agent_card_to_use: AgentCard | None = None
+_public_card = (
+    await resolver.get_agent_card()
+)  # Fetches from default public path - \`/agents/{agent_id}/\`
+final_agent_card_to_use = _public_card
+
+if _public_card.supports_authenticated_extended_card:
+    try:
+        auth_headers_dict = {
+            'Authorization': 'Bearer dummy-token-for-extended-card'
+        }
+        _extended_card = await resolver.get_agent_card(
+            relative_card_path=EXTENDED_AGENT_CARD_PATH,
+            http_kwargs={'headers': auth_headers_dict},
+        )
+        final_agent_card_to_use = (
+            _extended_card  # Update to use the extended card
+        )
+    except Exception as e_extended:
+        logger.warning(
+            f'Failed to fetch extended agent card: {e_extended}. Will proceed with public card.',
+            exc_info=True,
+        )`)},className:"text-sm text-blue-600 hover:text-blue-800 cursor-pointer",children:"Copy to clipboard"})})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-sm font-medium mb-2 text-gray-700",children:"Step 2: Call the Agent"}),(0,t.jsx)("div",{className:"bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto",children:(0,t.jsx)("pre",{className:"text-xs",children:`client = A2AClient(
+    httpx_client=httpx_client, agent_card=final_agent_card_to_use
+)
+
+send_message_payload: dict[str, Any] = {
+    'message': {
+        'role': 'user',
+        'parts': [
+            {'kind': 'text', 'text': 'how much is 10 USD in INR?'}
+        ],
+        'messageId': uuid4().hex,
+    },
+}
+request = SendMessageRequest(
+    id=str(uuid4()), params=MessageSendParams(**send_message_payload)
+)
+
+response = await client.send_message(request)
+print(response.model_dump(mode='json', exclude_none=True))`})}),(0,t.jsx)("div",{className:"mt-2 text-right",children:(0,t.jsx)("button",{onClick:()=>{eB(`client = A2AClient(
+    httpx_client=httpx_client, agent_card=final_agent_card_to_use
+)
+
+send_message_payload: dict[str, Any] = {
+    'message': {
+        'role': 'user',
+        'parts': [
+            {'kind': 'text', 'text': 'how much is 10 USD in INR?'}
+        ],
+        'messageId': uuid4().hex,
+    },
+}
+request = SendMessageRequest(
+    id=str(uuid4()), params=MessageSendParams(**send_message_payload)
+)
+
+response = await client.send_message(request)
+print(response.model_dump(mode='json', exclude_none=True))`)},className:"text-sm text-blue-600 hover:text-blue-800 cursor-pointer",children:"Copy to clipboard"})})]})]})]})}),(0,t.jsx)(c.Modal,{title:(0,t.jsxs)("div",{className:"flex items-center space-x-2",children:[(0,t.jsx)("span",{children:ew?.server_name||"MCP Server Details"}),ew&&(0,t.jsx)(u.Tooltip,{title:"Copy server name",children:(0,t.jsx)(g.Copy,{onClick:()=>eB(ew.server_name),className:"cursor-pointer text-gray-500 hover:text-blue-500 w-4 h-4"})})]}),width:1e3,open:ej,footer:null,onOk:()=>{eA(!1),eI(null)},onCancel:()=>{eA(!1),eI(null)},children:ew&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Server Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Server Name:"}),(0,t.jsx)(n.Text,{children:ew.server_name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Transport:"}),(0,t.jsx)(p.Tag,{color:"blue",children:ew.transport})]}),ew.alias&&(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Alias:"}),(0,t.jsx)(n.Text,{children:ew.alias})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Auth Type:"}),(0,t.jsx)(p.Tag,{color:"none"===ew.auth_type?"gray":"green",children:ew.auth_type})]}),(0,t.jsxs)("div",{className:"col-span-2",children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"Description:"}),(0,t.jsx)(n.Text,{children:ew.mcp_info?.description||"-"})]}),(0,t.jsxs)("div",{className:"col-span-2",children:[(0,t.jsx)(n.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("a",{href:ew.url,target:"_blank",rel:"noopener noreferrer",className:"text-blue-600 hover:text-blue-800 text-sm break-all flex items-center space-x-2",children:[(0,t.jsx)("span",{children:ew.url}),(0,t.jsx)(s.ExternalLinkIcon,{className:"w-4 h-4"})]})]})]})]}),ew.mcp_info&&Object.keys(ew.mcp_info).length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Additional Information"}),(0,t.jsx)("div",{className:"bg-gray-50 p-4 rounded-lg",children:(0,t.jsx)("pre",{className:"text-xs overflow-x-auto",children:JSON.stringify(ew.mcp_info,null,2)})})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(n.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)("div",{className:"bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto",children:(0,t.jsx)("pre",{className:"text-sm",children:`# Using MCP Server with Python FastMCP
+
+from fastmcp import Client
+import asyncio
+
+# Standard MCP configuration
+config = {
+    "mcpServers": {
+        "${ew.server_name}": {
+            "url": "http://localhost:4000/${ew.server_name}/mcp",
+            "headers": {
+                "x-litellm-api-key": "Bearer sk-1234"
+            }
+        }
+    }
+}
+
+# Create a client that connects to the server
+client = Client(config)
+
+async def main():
+    async with client:
+        # List available tools
+        tools = await client.list_tools()
+        print(f"Available tools: {[tool.name for tool in tools]}")
+
+        # Call a tool
+        response = await client.call_tool(
+            name="tool_name", 
+            arguments={"arg": "value"}
+        )
+        print(f"Response: {response}")
+
+if __name__ == "__main__":
+    asyncio.run(main())`})}),(0,t.jsx)("div",{className:"mt-2 text-right",children:(0,t.jsx)("button",{onClick:()=>{eB(`# Using MCP Server with Python FastMCP
+
+from fastmcp import Client
+import asyncio
+
+# Standard MCP configuration
+config = {
+    "mcpServers": {
+        "${ew.server_name}": {
+            "url": "http://localhost:4000/${ew.server_name}/mcp",
+            "headers": {
+                "x-litellm-api-key": "Bearer sk-1234"
+            }
+        }
+    }
+}
+
+# Create a client that connects to the server
+client = Client(config)
+
+async def main():
+    async with client:
+        # List available tools
+        tools = await client.list_tools()
+        print(f"Available tools: {[tool.name for tool in tools]}")
+
+        # Call a tool
+        response = await client.call_tool(
+            name="tool_name", 
+            arguments={"arg": "value"}
+        )
+        print(f"Response: {response}")
+
+if __name__ == "__main__":
+    asyncio.run(main())`)},className:"text-sm text-blue-600 hover:text-blue-800 cursor-pointer",children:"Copy to clipboard"})})]})]})})]})})}])}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/af6fc0727c3097de.js b/litellm/proxy/_experimental/out/_next/static/chunks/af6fc0727c3097de.js
new file mode 100644
index 00000000000..f65f2144573
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/af6fc0727c3097de.js
@@ -0,0 +1 @@
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textarea[disabled]":{cursor:"not-allowed"}}),$(e,{status:"error",borderColor:e.colorError,hoverBorderColor:e.colorErrorBorderHover,activeBorderColor:e.colorError,activeShadow:e.errorActiveShadow,affixColor:e.colorError})),$(e,{status:"warning",borderColor:e.colorWarning,hoverBorderColor:e.colorWarningBorderHover,activeBorderColor:e.colorWarning,activeShadow:e.warningActiveShadow,affixColor:e.colorWarning})),t)});e.s(["genBaseOutlinedStyle",0,c,"genBorderlessStyle",0,h,"genDisabledStyle",0,s,"genFilledGroupStyle",0,b,"genFilledStyle",0,v,"genOutlinedGroupStyle",0,p,"genOutlinedStyle",0,d,"genUnderlinedStyle",0,C],889943);let x=e=>({"&::-moz-placeholder":{opacity:1},"&::placeholder":{color:e,userSelect:"none"},"&:placeholder-shown":{textOverflow:"ellipsis"}}),E=e=>{let{paddingBlockLG:r,lineHeightLG:o,borderRadiusLG:n,paddingInlineLG:a}=e;return{padding:`${(0,t.unit)(r)} ${(0,t.unit)(a)}`,fontSize:e.inputFontSizeLG,lineHeight:o,borderRadius:n}},S=e=>({padding:`${(0,t.unit)(e.paddingBlockSM)} ${(0,t.unit)(e.paddingInlineSM)}`,fontSize:e.inputFontSizeSM,borderRadius:e.borderRadiusSM}),k=e=>Object.assign(Object.assign({position:"relative",display:"inline-block",width:"100%",minWidth:0,padding:`${(0,t.unit)(e.paddingBlock)} ${(0,t.unit)(e.paddingInline)}`,color:e.colorText,fontSize:e.inputFontSize,lineHeight:e.lineHeight,borderRadius:e.borderRadius,transition:`all ${e.motionDurationMid}`},x(e.colorTextPlaceholder)),{"&-lg":Object.assign({},E(e)),"&-sm":Object.assign({},S(e)),"&-rtl, &-textarea-rtl":{direction:"rtl"}}),j=e=>{let{componentCls:o,antCls:n}=e;return{position:"relative",display:"table",width:"100%",borderCollapse:"separate",borderSpacing:0,"&[class*='col-']":{paddingInlineEnd:e.paddingXS,"&:last-child":{paddingInlineEnd:0}},[`&-lg ${o}, &-lg > ${o}-group-addon`]:Object.assign({},E(e)),[`&-sm ${o}, &-sm > ${o}-group-addon`]:Object.assign({},S(e)),[`&-lg ${n}-select-single ${n}-select-selector`]:{height:e.controlHeightLG},[`&-sm ${n}-select-single ${n}-select-selector`]:{height:e.controlHeightSM},[`> ${o}`]:{display:"table-cell","&:not(:first-child):not(:last-child)":{borderRadius:0}},[`${o}-group`]:{"&-addon, &-wrap":{display:"table-cell",width:1,whiteSpace:"nowrap",verticalAlign:"middle","&:not(:first-child):not(:last-child)":{borderRadius:0}},"&-wrap > *":{display:"block !important"},"&-addon":{position:"relative",padding:`0 ${(0,t.unit)(e.paddingInline)}`,color:e.colorText,fontWeight:"normal",fontSize:e.inputFontSize,textAlign:"center",borderRadius:e.borderRadius,transition:`all ${e.motionDurationSlow}`,lineHeight:1,[`${n}-select`]:{margin:`${(0,t.unit)(e.calc(e.paddingBlock).add(1).mul(-1).equal())} ${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,[`&${n}-select-single:not(${n}-select-customize-input):not(${n}-pagination-size-changer)`]:{[`${n}-select-selector`]:{backgroundColor:"inherit",border:`${(0,t.unit)(e.lineWidth)} ${e.lineType} transparent`,boxShadow:"none"}}},[`${n}-cascader-picker`]:{margin:`-9px ${(0,t.unit)(e.calc(e.paddingInline).mul(-1).equal())}`,backgroundColor:"transparent",[`${n}-cascader-input`]:{textAlign:"start",border:0,boxShadow:"none"}}}},[o]:{width:"100%",marginBottom:0,textAlign:"inherit","&:focus":{zIndex:1,borderInlineEndWidth:1},"&:hover":{zIndex:1,borderInlineEndWidth:1,[`${o}-search-with-button &`]:{zIndex:0}}},[`> ${o}:first-child, ${o}-group-addon:first-child`]:{borderStartEndRadius:0,borderEndEndRadius:0,[`${n}-select ${n}-select-selector`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`> ${o}-affix-wrapper`]:{[`&:not(:first-child) ${o}`]:{borderStartStartRadius:0,borderEndStartRadius:0},[`&:not(:last-child) 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+        & > ${o}-affix-wrapper,
+        & > ${o}-number-affix-wrapper,
+        & > ${n}-picker-range
+      `]:{display:"inline-flex"},"& > *:not(:last-child)":{marginInlineEnd:e.calc(e.lineWidth).mul(-1).equal(),borderInlineEndWidth:e.lineWidth},[o]:{float:"none"},[`& > ${n}-select > ${n}-select-selector,
+      & > ${n}-select-auto-complete ${o},
+      & > ${n}-cascader-picker ${o},
+      & > ${o}-group-wrapper ${o}`]:{borderInlineEndWidth:e.lineWidth,borderRadius:0,"&:hover, &:focus":{zIndex:1}},[`& > ${n}-select-focused`]:{zIndex:1},[`& > ${n}-select > ${n}-select-arrow`]:{zIndex:1},[`& > *:first-child,
+      & > ${n}-select:first-child > ${n}-select-selector,
+      & > ${n}-select-auto-complete:first-child ${o},
+      & > ${n}-cascader-picker:first-child ${o}`]:{borderStartStartRadius:e.borderRadius,borderEndStartRadius:e.borderRadius},[`& > *:last-child,
+      & > ${n}-select:last-child > ${n}-select-selector,
+      & > ${n}-cascader-picker:last-child ${o},
+      & > ${n}-cascader-picker-focused:last-child ${o}`]:{borderInlineEndWidth:e.lineWidth,borderStartEndRadius:e.borderRadius,borderEndEndRadius:e.borderRadius},[`& > ${n}-select-auto-complete ${o}`]:{verticalAlign:"top"},[`${o}-group-wrapper + ${o}-group-wrapper`]:{marginInlineStart:e.calc(e.lineWidth).mul(-1).equal(),[`${o}-affix-wrapper`]:{borderRadius:0}},[`${o}-group-wrapper:not(:last-child)`]:{[`&${o}-search > ${o}-group`]:{[`& > ${o}-group-addon > ${o}-search-button`]:{borderRadius:0},[`& > ${o}`]:{borderStartStartRadius:e.borderRadius,borderStartEndRadius:0,borderEndEndRadius:0,borderEndStartRadius:e.borderRadius}}}})}},O=(0,n.genStyleHooks)(["Input","Shared"],e=>{let o=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,controlHeightSM:o,lineWidth:n,calc:a}=e,i=a(o).sub(a(n).mul(2)).sub(16).div(2).equal();return{[t]:Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),k(e)),d(e)),v(e)),h(e)),C(e)),{'&[type="color"]':{height:e.controlHeight,[`&${t}-lg`]:{height:e.controlHeightLG},[`&${t}-sm`]:{height:o,paddingTop:i,paddingBottom:i}},'&[type="search"]::-webkit-search-cancel-button, &[type="search"]::-webkit-search-decoration':{appearance:"none"}})}})(o),(e=>{let{componentCls:r,inputAffixPadding:o,colorTextDescription:n,motionDurationSlow:a,colorIcon:i,colorIconHover:l,iconCls:s}=e,c=`${r}-affix-wrapper`,u=`${r}-affix-wrapper-disabled`;return{[c]:Object.assign(Object.assign(Object.assign(Object.assign({},k(e)),{display:"inline-flex",[`&:not(${r}-disabled):hover`]:{zIndex:1,[`${r}-search-with-button &`]:{zIndex:0}},"&-focused, &:focus":{zIndex:1},[`> input${r}`]:{padding:0},[`> input${r}, > textarea${r}`]:{fontSize:"inherit",border:"none",borderRadius:0,outline:"none",background:"transparent",color:"inherit","&::-ms-reveal":{display:"none"},"&:focus":{boxShadow:"none !important"}},"&::before":{display:"inline-block",width:0,visibility:"hidden",content:'"\\a0"'},[r]:{"&-prefix, &-suffix":{display:"flex",flex:"none",alignItems:"center","> *:not(:last-child)":{marginInlineEnd:e.paddingXS}},"&-show-count-suffix":{color:n,direction:"ltr"},"&-show-count-has-suffix":{marginInlineEnd:e.paddingXXS},"&-prefix":{marginInlineEnd:o},"&-suffix":{marginInlineStart:o}}}),(e=>{let{componentCls:r}=e;return{[`${r}-clear-icon`]:{margin:0,padding:0,lineHeight:0,color:e.colorTextQuaternary,fontSize:e.fontSizeIcon,verticalAlign:-1,cursor:"pointer",transition:`color 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t=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,borderRadiusLG:o,borderRadiusSM:n}=e;return{[`${t}-group`]:Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),j(e)),{"&-rtl":{direction:"rtl"},"&-wrapper":Object.assign(Object.assign(Object.assign({display:"inline-block",width:"100%",textAlign:"start",verticalAlign:"top","&-rtl":{direction:"rtl"},"&-lg":{[`${t}-group-addon`]:{borderRadius:o,fontSize:e.inputFontSizeLG}},"&-sm":{[`${t}-group-addon`]:{borderRadius:n}}},p(e)),b(e)),{[`&:not(${t}-compact-first-item):not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}, ${t}-group-addon`]:{borderRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-first-item`]:{[`${t}, ${t}-group-addon`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-last-item`]:{[`${t}, ${t}-group-addon`]:{borderStartStartRadius:0,borderEndStartRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartStartRadius:0,borderEndStartRadius:0}}})})}})(t),(e=>{let{componentCls:t,antCls:r}=e,o=`${t}-search`;return{[o]:{[t]:{"&:not([disabled]):hover, &:not([disabled]):focus":{[`+ ${t}-group-addon ${o}-button:not(${r}-btn-color-primary):not(${r}-btn-variant-text)`]:{borderInlineStartColor:e.colorPrimaryHover}}},[`${t}-affix-wrapper`]:{height:e.controlHeight,borderRadius:0},[`${t}-lg`]:{lineHeight:e.calc(e.lineHeightLG).sub(2e-4).equal()},[`> ${t}-group`]:{[`> ${t}-group-addon:last-child`]:{insetInlineStart:-1,padding:0,border:0,[`${o}-button`]:{marginInlineEnd:-1,borderStartStartRadius:0,borderEndStartRadius:0,boxShadow:"none"},[`${o}-button:not(${r}-btn-color-primary)`]:{color:e.colorTextDescription,"&:not([disabled]):hover":{color:e.colorPrimaryHover},"&:active":{color:e.colorPrimaryActive},[`&${r}-btn-loading::before`]:{inset:0}}}},[`${o}-button`]:{height:e.controlHeight,"&:hover, &:focus":{zIndex:1}},"&-large":{[`${t}-affix-wrapper, ${o}-button`]:{height:e.controlHeightLG}},"&-small":{[`${t}-affix-wrapper, ${o}-button`]:{height:e.controlHeightSM}},"&-rtl":{direction:"rtl"},[`&${t}-compact-item`]:{[`&:not(${t}-compact-last-item)`]:{[`${t}-group-addon`]:{[`${t}-search-button`]:{marginInlineEnd:e.calc(e.lineWidth).mul(-1).equal(),borderRadius:0}}},[`&:not(${t}-compact-first-item)`]:{[`${t},${t}-affix-wrapper`]:{borderRadius:0}},[`> ${t}-group-addon ${t}-search-button,
+        > ${t},
+        ${t}-affix-wrapper`]:{"&:hover, &:focus, &:active":{zIndex:2}},[`> ${t}-affix-wrapper-focused`]:{zIndex:2}}}}})(t),(e=>{let{componentCls:t}=e;return{[`${t}-out-of-range`]:{[`&, & input, & textarea, ${t}-show-count-suffix, ${t}-data-count`]:{color:e.colorError}}}})(t),(0,o.genCompactItemStyle)(t)]},l,{resetFont:!1});e.s(["default",0,T,"genBasicInputStyle",0,k,"genInputGroupStyle",0,j,"genInputSmallStyle",0,S,"genPlaceholderStyle",0,x,"useSharedStyle",0,O],349942)},831357,e=>{"use strict";var t=e.i(271645),r=e.i(343794),o=e.i(242064),n=e.i(62139),a=e.i(349942);e.s(["default",0,e=>{let{getPrefixCls:i,direction:l}=(0,t.useContext)(o.ConfigContext),{prefixCls:s,className:c}=e,u=i("input-group",s),d=i("input"),[f,p,h]=(0,a.default)(d),m=(0,r.default)(u,h,{[`${u}-lg`]:"large"===e.size,[`${u}-sm`]:"small"===e.size,[`${u}-compact`]:e.compact,[`${u}-rtl`]:"rtl"===l},p,c),g=(0,t.useContext)(n.FormItemInputContext),v=(0,t.useMemo)(()=>Object.assign(Object.assign({},g),{isFormItemInput:!1}),[g]);return f(t.createElement("span",{className:m,style:e.style,onMouseEnter:e.onMouseEnter,onMouseLeave:e.onMouseLeave,onFocus:e.onFocus,onBlur:e.onBlur},t.createElement(n.FormItemInputContext.Provider,{value:v},e.children)))}])},175636,131299,367397,874460,e=>{"use strict";var t=e.i(209428),r=e.i(931067),o=e.i(211577),n=e.i(410160),a=e.i(343794),i=e.i(271645);function l(e){return!!(e.addonBefore||e.addonAfter)}function s(e){return!!(e.prefix||e.suffix||e.allowClear)}function c(e,t,r){var 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/bd799dc9aa7f786a.js b/litellm/proxy/_experimental/out/_next/static/chunks/bd799dc9aa7f786a.js
new file mode 100644
index 00000000000..dba5448b0e3
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/bd799dc9aa7f786a.js
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+
+client = openai.AzureOpenAI(
+	api_key="${b||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${y}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${b||"YOUR_LITELLM_API_KEY"}",
+	base_url="${y}"
+)`;switch(_){case o.CHAT:{let e=Object.keys(O).length>0,i="";if(e){let e=JSON.stringify({metadata:O},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:v}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${T}",
+    messages=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${T}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${C}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${i}
+# )
+# print(response_with_file)
+`;break}case o.RESPONSES:{let e=Object.keys(O).length>0,i="";if(e){let e=JSON.stringify({metadata:O},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:v}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${T}",
+    input=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${T}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${C}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${i}
+# )
+# print(response_with_file.output_text)
+`;break}case o.IMAGE:t="azure"===A?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${T}",
+	prompt="${n}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${C}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${T}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.IMAGE_EDITS:t="azure"===A?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${C}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${T}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${C}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${T}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${n||"Your string here"}",
+	model="${T}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case o.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${T}",
+	file=audio_file${n?`,
+	prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case o.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${T}",
+	input="${n||"Your text to convert to speech here"}",
+	voice="${f}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${T}",
+#     input="${n||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${S}
+${t}`}],190272)},652272,209261,e=>{"use strict";var t=e.i(843476),i=e.i(271645),a=e.i(447566),o=e.i(166406),r=e.i(492030),n=e.i(596239);let s=e=>"github"===e.source.source&&e.source.repo?`/plugin marketplace add ${e.source.repo}`:"url"===e.source.source&&e.source.url?`/plugin marketplace add ${e.source.url}`:`/plugin marketplace add ${e.name}`;e.s(["formatInstallCommand",0,s,"getCategoryBadgeColor",0,e=>{if(!e)return"gray";let t=e.toLowerCase();if(t.includes("development")||t.includes("dev"))return"blue";if(t.includes("productivity")||t.includes("workflow"))return"green";if(t.includes("learning")||t.includes("education"))return"purple";if(t.includes("security")||t.includes("safety"))return"red";if(t.includes("data")||t.includes("analytics"))return"orange";else 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/d720c3179e45c754.js b/litellm/proxy/_experimental/out/_next/static/chunks/d720c3179e45c754.js
new file mode 100644
index 00000000000..d502aa29503
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/d720c3179e45c754.js
@@ -0,0 +1,420 @@
+(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,447566,e=>{"use strict";e.i(247167);var t=e.i(931067),o=e.i(271645);let i={icon:{tag:"svg",attrs:{viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"path",attrs:{d:"M872 474H286.9l350.2-304c5.6-4.9 2.2-14-5.2-14h-88.5c-3.9 0-7.6 1.4-10.5 3.9L155 487.8a31.96 31.96 0 000 48.3L535.1 866c1.5 1.3 3.3 2 5.2 2h91.5c7.4 0 10.8-9.2 5.2-14L286.9 550H872c4.4 0 8-3.6 8-8v-60c0-4.4-3.6-8-8-8z"}}]},name:"arrow-left",theme:"outlined"};var n=e.i(9583),a=o.forwardRef(function(e,a){return o.createElement(n.default,(0,t.default)({},e,{ref:a,icon:i}))});e.s(["ArrowLeftOutlined",0,a],447566)},492030,e=>{"use strict";var t=e.i(121229);e.s(["CheckOutlined",()=>t.default])},596239,e=>{"use strict";e.i(247167);var t=e.i(931067),o=e.i(271645);let i={icon:{tag:"svg",attrs:{viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"path",attrs:{d:"M574 665.4a8.03 8.03 0 00-11.3 0L446.5 781.6c-53.8 53.8-144.6 59.5-204 0-59.5-59.5-53.8-150.2 0-204l116.2-116.2c3.1-3.1 3.1-8.2 0-11.3l-39.8-39.8a8.03 8.03 0 00-11.3 0L191.4 526.5c-84.6 84.6-84.6 221.5 0 306s221.5 84.6 306 0l116.2-116.2c3.1-3.1 3.1-8.2 0-11.3L574 665.4zm258.6-474c-84.6-84.6-221.5-84.6-306 0L410.3 307.6a8.03 8.03 0 000 11.3l39.7 39.7c3.1 3.1 8.2 3.1 11.3 0l116.2-116.2c53.8-53.8 144.6-59.5 204 0 59.5 59.5 53.8 150.2 0 204L665.3 562.6a8.03 8.03 0 000 11.3l39.8 39.8c3.1 3.1 8.2 3.1 11.3 0l116.2-116.2c84.5-84.6 84.5-221.5 0-306.1zM610.1 372.3a8.03 8.03 0 00-11.3 0L372.3 598.7a8.03 8.03 0 000 11.3l39.6 39.6c3.1 3.1 8.2 3.1 11.3 0l226.4-226.4c3.1-3.1 3.1-8.2 0-11.3l-39.5-39.6z"}}]},name:"link",theme:"outlined"};var n=e.i(9583),a=o.forwardRef(function(e,a){return o.createElement(n.default,(0,t.default)({},e,{ref:a,icon:i}))});e.s(["LinkOutlined",0,a],596239)},190272,785913,e=>{"use strict";var t,o,i=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),n=((o={}).IMAGE="image",o.VIDEO="video",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDINGS="embeddings",o.SPEECH="speech",o.TRANSCRIPTION="transcription",o.A2A_AGENTS="a2a_agents",o.MCP="mcp",o.REALTIME="realtime",o.INTERACTIONS="interactions",o);let a={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>n,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let t=a[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:o,accessToken:i,apiKey:a,inputMessage:s,chatHistory:r,selectedTags:l,selectedVectorStores:c,selectedGuardrails:p,selectedPolicies:d,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:f,selectedVoice:g,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:v}=e,x="session"===o?i:a,y=window.location.origin,S=v?.LITELLM_UI_API_DOC_BASE_URL;S&&S.trim()?y=S:v?.PROXY_BASE_URL&&(y=v.PROXY_BASE_URL);let w=s||"Your prompt here",j=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=r.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),z={};l.length>0&&(z.tags=l),c.length>0&&(z.vector_stores=c),p.length>0&&(z.guardrails=p),d.length>0&&(z.policies=d);let T=_||"your-model-name",R="azure"===b?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${x||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${y}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${x||"YOUR_LITELLM_API_KEY"}",
+	base_url="${y}"
+)`;switch(h){case n.CHAT:{let e=Object.keys(z).length>0,o="";if(e){let e=JSON.stringify({metadata:z},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+    extra_body=${e}`}let i=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${T}",
+    messages=${JSON.stringify(i,null,4)}${o}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${T}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${j}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${o}
+# )
+# print(response_with_file)
+`;break}case n.RESPONSES:{let e=Object.keys(z).length>0,o="";if(e){let e=JSON.stringify({metadata:z},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+    extra_body=${e}`}let i=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${T}",
+    input=${JSON.stringify(i,null,4)}${o}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${T}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${j}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${o}
+# )
+# print(response_with_file.output_text)
+`;break}case n.IMAGE:t="azure"===b?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${T}",
+	prompt="${s}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${j}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${T}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case n.IMAGE_EDITS:t="azure"===b?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${j}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${T}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${j}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${T}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case n.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${s||"Your string here"}",
+	model="${T}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case n.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${T}",
+	file=audio_file${s?`,
+	prompt="${s.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case n.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${T}",
+	input="${s||"Your text to convert to speech here"}",
+	voice="${g}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${T}",
+#     input="${s||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${R}
+${t}`}],190272)},84899,e=>{"use strict";e.i(247167);var t=e.i(931067),o=e.i(271645),i={icon:{tag:"svg",attrs:{viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"defs",attrs:{},children:[{tag:"style",attrs:{}}]},{tag:"path",attrs:{d:"M931.4 498.9L94.9 79.5c-3.4-1.7-7.3-2.1-11-1.2a15.99 15.99 0 00-11.7 19.3l86.2 352.2c1.3 5.3 5.2 9.6 10.4 11.3l147.7 50.7-147.6 50.7c-5.2 1.8-9.1 6-10.3 11.3L72.2 926.5c-.9 3.7-.5 7.6 1.2 10.9 3.9 7.9 13.5 11.1 21.5 7.2l836.5-417c3.1-1.5 5.6-4.1 7.2-7.1 3.9-8 .7-17.6-7.2-21.6zM170.8 826.3l50.3-205.6 295.2-101.3c2.3-.8 4.2-2.6 5-5 1.4-4.2-.8-8.7-5-10.2L221.1 403 171 198.2l628 314.9-628.2 313.2z"}}]},name:"send",theme:"outlined"},n=e.i(9583),a=o.forwardRef(function(e,a){return o.createElement(n.default,(0,t.default)({},e,{ref:a,icon:i}))});e.s(["SendOutlined",0,a],84899)},518617,e=>{"use strict";e.i(247167);var t=e.i(931067),o=e.i(271645);let i={icon:{tag:"svg",attrs:{"fill-rule":"evenodd",viewBox:"64 64 896 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t,o,i=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),n=((o={}).IMAGE="image",o.VIDEO="video",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDINGS="embeddings",o.SPEECH="speech",o.TRANSCRIPTION="transcription",o.A2A_AGENTS="a2a_agents",o.MCP="mcp",o.REALTIME="realtime",o.INTERACTIONS="interactions",o);let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>n,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let t=s[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:o,accessToken:i,apiKey:s,inputMessage:a,chatHistory:r,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:f,selectedVoice:g,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:x}=e,v="session"===o?i:s,y=window.location.origin,j=x?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:x?.PROXY_BASE_URL&&(y=x.PROXY_BASE_URL);let w=a||"Your prompt here",S=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=r.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),N={};l.length>0&&(N.tags=l),c.length>0&&(N.vector_stores=c),d.length>0&&(N.guardrails=d),p.length>0&&(N.policies=p);let C=_||"your-model-name",z="azure"===b?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${y}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
+	base_url="${y}"
+)`;switch(h){case n.CHAT:{let e=Object.keys(N).length>0,o="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+    extra_body=${e}`}let i=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${C}",
+    messages=${JSON.stringify(i,null,4)}${o}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${C}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${S}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${o}
+# )
+# print(response_with_file)
+`;break}case n.RESPONSES:{let e=Object.keys(N).length>0,o="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();o=`,
+    extra_body=${e}`}let i=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${C}",
+    input=${JSON.stringify(i,null,4)}${o}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${C}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${S}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${o}
+# )
+# print(response_with_file.output_text)
+`;break}case n.IMAGE:t="azure"===b?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${C}",
+	prompt="${a}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case n.IMAGE_EDITS:t="azure"===b?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${S}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case n.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${a||"Your string here"}",
+	model="${C}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case n.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${C}",
+	file=audio_file${a?`,
+	prompt="${a.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case n.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${C}",
+	input="${a||"Your text to convert to speech here"}",
+	voice="${g}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${C}",
+#     input="${a||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${z}
+${t}`}],190272)},611052,2781,e=>{"use strict";var t=e.i(843476),o=e.i(271645),i=e.i(212931),n=e.i(311451),s=e.i(790848),a=e.i(888259),r=e.i(438957);e.i(247167);var l=e.i(931067);let c={icon:{tag:"svg",attrs:{viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"path",attrs:{d:"M832 464h-68V240c0-70.7-57.3-128-128-128H388c-70.7 0-128 57.3-128 128v224h-68c-17.7 0-32 14.3-32 32v384c0 17.7 14.3 32 32 32h640c17.7 0 32-14.3 32-32V496c0-17.7-14.3-32-32-32zM332 240c0-30.9 25.1-56 56-56h248c30.9 0 56 25.1 56 56v224H332V240zm460 600H232V536h560v304zM484 701v53c0 4.4 3.6 8 8 8h40c4.4 0 8-3.6 8-8v-53a48.01 48.01 0 10-56 0z"}}]},name:"lock",theme:"outlined"};var d=e.i(9583),p=o.forwardRef(function(e,t){return o.createElement(d.default,(0,l.default)({},e,{ref:t,icon:c}))});e.s(["LockOutlined",0,p],2781);var m=e.i(492030),u=e.i(266537),f=e.i(447566),g=e.i(149192),h=e.i(596239);e.s(["ByokCredentialModal",0,({server:e,open:l,onClose:c,onSuccess:d,accessToken:_})=>{let[b,x]=(0,o.useState)(1),[v,y]=(0,o.useState)(""),[j,w]=(0,o.useState)(!0),[S,k]=(0,o.useState)(!1),N=e.alias||e.server_name||"Service",C=N.charAt(0).toUpperCase(),z=()=>{x(1),y(""),w(!0),k(!1),c()},I=async()=>{if(!v.trim())return void a.default.error("Please enter your API key");k(!0);try{let t=await fetch(`/v1/mcp/server/${e.server_id}/user-credential`,{method:"POST",headers:{"Content-Type":"application/json",Authorization:`Bearer ${_}`},body:JSON.stringify({credential:v.trim(),save:j})});if(!t.ok){let e=await t.json();throw Error(e?.detail?.error||"Failed to save credential")}a.default.success(`Connected to ${N}`),d(e.server_id),z()}catch(e){a.default.error(e.message||"Failed to connect")}finally{k(!1)}};return(0,t.jsx)(i.Modal,{open:l,onCancel:z,footer:null,width:480,closeIcon:null,className:"byok-modal",children:(0,t.jsxs)("div",{className:"relative p-2",children:[(0,t.jsxs)("div",{className:"flex items-center justify-between mb-6",children:[2===b?(0,t.jsxs)("button",{onClick:()=>x(1),className:"flex items-center gap-1 text-gray-500 hover:text-gray-800 text-sm",children:[(0,t.jsx)(f.ArrowLeftOutlined,{})," Back"]}):(0,t.jsx)("div",{}),(0,t.jsxs)("div",{className:"flex items-center gap-1.5",children:[(0,t.jsx)("div",{className:`w-2 h-2 rounded-full ${1===b?"bg-blue-500":"bg-gray-300"}`}),(0,t.jsx)("div",{className:`w-2 h-2 rounded-full ${2===b?"bg-blue-500":"bg-gray-300"}`})]}),(0,t.jsx)("button",{onClick:z,className:"text-gray-400 hover:text-gray-600",children:(0,t.jsx)(g.CloseOutlined,{})})]}),1===b?(0,t.jsxs)("div",{className:"text-center",children:[(0,t.jsxs)("div",{className:"flex items-center justify-center gap-3 mb-6",children:[(0,t.jsx)("div",{className:"w-14 h-14 rounded-xl bg-gradient-to-br from-teal-400 to-cyan-600 flex items-center justify-center text-white font-bold text-xl shadow",children:"L"}),(0,t.jsx)(u.ArrowRightOutlined,{className:"text-gray-400 text-lg"}),(0,t.jsx)("div",{className:"w-14 h-14 rounded-xl bg-gradient-to-br from-blue-600 to-indigo-800 flex items-center justify-center text-white font-bold text-xl shadow",children:C})]}),(0,t.jsxs)("h2",{className:"text-2xl font-bold text-gray-900 mb-2",children:["Connect ",N]}),(0,t.jsxs)("p",{className:"text-gray-500 mb-6",children:["LiteLLM needs access to ",N," to complete your request."]}),(0,t.jsx)("div",{className:"bg-gray-50 rounded-xl p-4 text-left mb-4",children:(0,t.jsxs)("div",{className:"flex items-start gap-3",children:[(0,t.jsx)("div",{className:"mt-0.5",children:(0,t.jsxs)("svg",{width:"20",height:"20",viewBox:"0 0 24 24",fill:"none",className:"text-gray-500",children:[(0,t.jsx)("rect",{x:"2",y:"4",width:"20",height:"16",rx:"2",stroke:"currentColor",strokeWidth:"2"}),(0,t.jsx)("path",{d:"M8 4v16M16 4v16",stroke:"currentColor",strokeWidth:"2"})]})}),(0,t.jsxs)("div",{children:[(0,t.jsx)("p",{className:"font-semibold text-gray-800 mb-1",children:"How it works"}),(0,t.jsxs)("p",{className:"text-gray-500 text-sm",children:["LiteLLM acts as a secure bridge. 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t,i,a=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),o=((i={}).IMAGE="image",i.VIDEO="video",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings",i.SPEECH="speech",i.TRANSCRIPTION="transcription",i.A2A_AGENTS="a2a_agents",i.MCP="mcp",i.REALTIME="realtime",i.INTERACTIONS="interactions",i);let r={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>o,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=r[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:a,apiKey:r,inputMessage:n,chatHistory:l,selectedTags:s,selectedVectorStores:d,selectedGuardrails:c,selectedPolicies:p,selectedMCPServers:g,mcpServers:u,mcpServerToolRestrictions:m,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:A,proxySettings:b}=e,v="session"===i?a:r,x=window.location.origin,y=b?.LITELLM_UI_API_DOC_BASE_URL;y&&y.trim()?x=y:b?.PROXY_BASE_URL&&(x=b.PROXY_BASE_URL);let I=n||"Your prompt here",E=I.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),C=l.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),S={};s.length>0&&(S.tags=s),d.length>0&&(S.vector_stores=d),c.length>0&&(S.guardrails=c),p.length>0&&(S.policies=p);let O=_||"your-model-name",w="azure"===A?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${x}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
+	base_url="${x}"
+)`;switch(h){case o.CHAT:{let e=Object.keys(S).length>0,i="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=C.length>0?C:[{role:"user",content:I}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${O}",
+    messages=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${O}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${E}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${i}
+# )
+# print(response_with_file)
+`;break}case o.RESPONSES:{let e=Object.keys(S).length>0,i="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=C.length>0?C:[{role:"user",content:I}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${O}",
+    input=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${O}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${E}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${i}
+# )
+# print(response_with_file.output_text)
+`;break}case o.IMAGE:t="azure"===A?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${O}",
+	prompt="${n}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${E}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${O}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.IMAGE_EDITS:t="azure"===A?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${E}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${O}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${E}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${O}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${n||"Your string here"}",
+	model="${O}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case o.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${O}",
+	file=audio_file${n?`,
+	prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case o.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${O}",
+	input="${n||"Your text to convert to speech here"}",
+	voice="${f}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${O}",
+#     input="${n||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${w}
+${t}`}],190272)},371401,e=>{"use strict";var t=e.i(115571),i=e.i(271645);function a(e){let i=t=>{"disableUsageIndicator"===t.key&&e()},a=t=>{let{key:i}=t.detail;"disableUsageIndicator"===i&&e()};return window.addEventListener("storage",i),window.addEventListener(t.LOCAL_STORAGE_EVENT,a),()=>{window.removeEventListener("storage",i),window.removeEventListener(t.LOCAL_STORAGE_EVENT,a)}}function o(){return"true"===(0,t.getLocalStorageItem)("disableUsageIndicator")}function r(){return(0,i.useSyncExternalStore)(a,o)}e.s(["useDisableUsageIndicator",()=>r])},275144,e=>{"use strict";var t=e.i(843476),i=e.i(271645),a=e.i(764205);let o=(0,i.createContext)(void 0);e.s(["ThemeProvider",0,({children:e,accessToken:r})=>{let[n,l]=(0,i.useState)(null),[s,d]=(0,i.useState)(null);return(0,i.useEffect)(()=>{(async()=>{try{let e=(0,a.getProxyBaseUrl)(),t=e?`${e}/get/ui_theme_settings`:"/get/ui_theme_settings",i=await fetch(t,{method:"GET",headers:{"Content-Type":"application/json"}});if(i.ok){let e=await 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t,i,a=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),o=((i={}).IMAGE="image",i.VIDEO="video",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings",i.SPEECH="speech",i.TRANSCRIPTION="transcription",i.A2A_AGENTS="a2a_agents",i.MCP="mcp",i.REALTIME="realtime",i.INTERACTIONS="interactions",i);let r={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>o,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=r[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:a,apiKey:r,inputMessage:n,chatHistory:l,selectedTags:s,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:u,mcpServers:g,mcpServerToolRestrictions:m,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:A,proxySettings:v}=e,x="session"===i?a:r,b=window.location.origin,y=v?.LITELLM_UI_API_DOC_BASE_URL;y&&y.trim()?b=y:v?.PROXY_BASE_URL&&(b=v.PROXY_BASE_URL);let I=n||"Your prompt here",E=I.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),w=l.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};s.length>0&&(C.tags=s),c.length>0&&(C.vector_stores=c),d.length>0&&(C.guardrails=d),p.length>0&&(C.policies=p);let S=_||"your-model-name",O="azure"===A?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${x||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${b}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${x||"YOUR_LITELLM_API_KEY"}",
+	base_url="${b}"
+)`;switch(h){case o.CHAT:{let e=Object.keys(C).length>0,i="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=w.length>0?w:[{role:"user",content:I}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${S}",
+    messages=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${S}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${E}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${i}
+# )
+# print(response_with_file)
+`;break}case o.RESPONSES:{let e=Object.keys(C).length>0,i="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=w.length>0?w:[{role:"user",content:I}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${S}",
+    input=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${S}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${E}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${i}
+# )
+# print(response_with_file.output_text)
+`;break}case o.IMAGE:t="azure"===A?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${S}",
+	prompt="${n}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${E}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${S}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.IMAGE_EDITS:t="azure"===A?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${E}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${S}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${E}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${S}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${n||"Your string here"}",
+	model="${S}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case o.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${S}",
+	file=audio_file${n?`,
+	prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case o.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${S}",
+	input="${n||"Your text to convert to speech here"}",
+	voice="${f}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${S}",
+#     input="${n||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${O}
+${t}`}],190272)},879664,e=>{"use strict";let t=(0,e.i(475254).default)("info",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"M12 16v-4",key:"1dtifu"}],["path",{d:"M12 8h.01",key:"e9boi3"}]]);e.s(["default",()=>t])},275144,e=>{"use strict";var t=e.i(843476),i=e.i(271645),a=e.i(764205);let o=(0,i.createContext)(void 0);e.s(["ThemeProvider",0,({children:e,accessToken:r})=>{let[n,l]=(0,i.useState)(null),[s,c]=(0,i.useState)(null);return(0,i.useEffect)(()=>{(async()=>{try{let e=(0,a.getProxyBaseUrl)(),t=e?`${e}/get/ui_theme_settings`:"/get/ui_theme_settings",i=await fetch(t,{method:"GET",headers:{"Content-Type":"application/json"}});if(i.ok){let e=await i.json();e.values?.logo_url&&l(e.values.logo_url),e.values?.favicon_url&&c(e.values.favicon_url)}}catch(e){console.warn("Failed to load theme settings from backend:",e)}})()},[]),(0,i.useEffect)(()=>{if(s){let e=document.querySelectorAll("link[rel*='icon']");if(e.length>0)e.forEach(e=>{e.href=s});else{let 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/fc5911e3a3caa8aa.js b/litellm/proxy/_experimental/out/_next/static/chunks/fc5911e3a3caa8aa.js
new file mode 100644
index 00000000000..548638e2c67
--- /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/LpD6ruZoEpvYpT5IvMEoa/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/wnL6e5S6xaG1UdkxtYrTo/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/LpD6ruZoEpvYpT5IvMEoa/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/wnL6e5S6xaG1UdkxtYrTo/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/LpD6ruZoEpvYpT5IvMEoa/_clientMiddlewareManifest.json b/litellm/proxy/_experimental/out/_next/static/wnL6e5S6xaG1UdkxtYrTo/_clientMiddlewareManifest.json
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/LpD6ruZoEpvYpT5IvMEoa/_clientMiddlewareManifest.json
rename to litellm/proxy/_experimental/out/_next/static/wnL6e5S6xaG1UdkxtYrTo/_clientMiddlewareManifest.json
diff --git a/litellm/proxy/_experimental/out/_next/static/LpD6ruZoEpvYpT5IvMEoa/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/wnL6e5S6xaG1UdkxtYrTo/_ssgManifest.js
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rename from litellm/proxy/_experimental/out/_next/static/LpD6ruZoEpvYpT5IvMEoa/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/wnL6e5S6xaG1UdkxtYrTo/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_not-found.html b/litellm/proxy/_experimental/out/_not-found.html
index 46e13ca9931..38a2c3bd836 100644
--- a/litellm/proxy/_experimental/out/_not-found.html
+++ b/litellm/proxy/_experimental/out/_not-found.html
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->>>>>>> a72414a061afdde0b31aab9a253bbce071e837e9
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diff --git a/litellm/proxy/_experimental/out/virtual-keys/__next._index.txt b/litellm/proxy/_experimental/out/virtual-keys/__next._index.txt
index e619c29c916..545ff2e55cc 100644
--- a/litellm/proxy/_experimental/out/virtual-keys/__next._index.txt
+++ b/litellm/proxy/_experimental/out/virtual-keys/__next._index.txt
@@ -1,18 +1,8 @@
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diff --git a/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt b/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt
index 41892534992..a3b1a44bbe2 100644
--- a/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt
+++ b/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt
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diff --git a/litellm/proxy/_lazy_features.py b/litellm/proxy/_lazy_features.py
index 9f034575222..a70c5b3a920 100644
--- a/litellm/proxy/_lazy_features.py
+++ b/litellm/proxy/_lazy_features.py
@@ -84,6 +84,11 @@ LAZY_FEATURES: Tuple[LazyFeature, ...] = (
         module_path="litellm.proxy.agent_endpoints.endpoints",
         path_prefixes=("/v1/agents", "/agents", "/agent/"),
     ),
+    LazyFeature(
+        name="gemini_agents",
+        module_path="litellm.proxy.google_endpoints.agents_endpoints",
+        path_prefixes=("/v1beta/agents",),
+    ),
     LazyFeature(
         name="a2a",
         module_path="litellm.proxy.agent_endpoints.a2a_endpoints",
diff --git a/litellm/proxy/_lazy_openapi_snapshot.json b/litellm/proxy/_lazy_openapi_snapshot.json
index eea6974193f..27cdc483d4a 100644
--- a/litellm/proxy/_lazy_openapi_snapshot.json
+++ b/litellm/proxy/_lazy_openapi_snapshot.json
@@ -3171,7 +3171,7 @@
           ]
         },
         "post": {
-          "description": "Create a new agent\n\nExample Request:\n```bash\ncurl -X POST \"http://localhost:4000/agents\" \\\n    -H \"Authorization: Bearer <your_api_key>\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"agent\": {\n            \"agent_name\": \"my-custom-agent\",\n            \"agent_card_params\": {\n                \"protocolVersion\": \"1.0\",\n                \"name\": \"Hello World Agent\",\n                \"description\": \"Just a hello world agent\",\n                \"url\": \"http://localhost:9999/\",\n                \"version\": \"1.0.0\",\n                \"defaultInputModes\": [\"text\"],\n                \"defaultOutputModes\": [\"text\"],\n                \"capabilities\": {\n                    \"streaming\": true\n                },\n                \"skills\": [\n                    {\n                        \"id\": \"hello_world\",\n                        \"name\": \"Returns hello world\",\n                        \"description\": \"just returns hello world\",\n                        \"tags\": [\"hello world\"],\n                        \"examples\": [\"hi\", \"hello world\"]\n                    }\n                ]\n            },\n            \"litellm_params\": {\n                \"make_public\": true\n            }\n        }\n    }'\n```",
+          "description": "Create a new agent\n\nExample Request:\n```bash\ncurl -X POST \"http://localhost:4000/v1/agents\" \\\n    -H \"Authorization: Bearer <your_api_key>\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\n        \"agent_name\": \"my-custom-agent\",\n            \"agent_card_params\": {\n                \"protocolVersion\": \"1.0\",\n                \"name\": \"Hello World Agent\",\n                \"description\": \"Just a hello world agent\",\n                \"url\": \"http://localhost:9999/\",\n                \"version\": \"1.0.0\",\n                \"defaultInputModes\": [\"text\"],\n                \"defaultOutputModes\": [\"text\"],\n                \"capabilities\": {\n                    \"streaming\": true\n                },\n                \"skills\": [\n                    {\n                        \"id\": \"hello_world\",\n                        \"name\": \"Returns hello world\",\n                        \"description\": \"just returns hello world\",\n                        \"tags\": [\"hello world\"],\n                        \"examples\": [\"hi\", \"hello world\"]\n                    }\n                ]\n            },\n            \"litellm_params\": {\n                \"make_public\": true\n       }\n    }'\n```",
           "operationId": "create_agent_v1_agents_post",
           "requestBody": {
             "content": {
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index d4fa497698a..9046d522280 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -255,6 +255,7 @@ class KeyManagementRoutes(str, enum.Enum):
 
     # team spend-log viewing
     SPEND_LOGS = "/spend/logs"
+    SPEND_LOGS_V2 = "/spend/logs/v2"
 
 
 class LiteLLMRoutes(enum.Enum):
@@ -481,6 +482,10 @@ class LiteLLMRoutes(enum.Enum):
         "/v1beta/interactions/{interaction_id}",
         "/interactions/{interaction_id}/cancel",
         "/v1beta/interactions/{interaction_id}/cancel",
+        # Google Managed Agents API
+        "/v1beta/agents",
+        "/v1beta/agents/{name}",
+        "/v1beta/agents/{name}/versions",
     ]
 
     apply_guardrail_routes = [
@@ -544,6 +549,7 @@ class LiteLLMRoutes(enum.Enum):
         KeyManagementRoutes.TEAM_KEY_BULK_UPDATE.value,
         KeyManagementRoutes.TEAM_DAILY_ACTIVITY.value,
         KeyManagementRoutes.SPEND_LOGS.value,
+        KeyManagementRoutes.SPEND_LOGS_V2.value,
         KeyManagementRoutes.KEY_RESET_SPEND.value,
         KeyManagementRoutes.KEY_ALIASES.value,
     ]
@@ -595,6 +601,7 @@ class LiteLLMRoutes(enum.Enum):
         "/spend/tags",
         "/spend/calculate",
         "/spend/logs",
+        "/spend/logs/v2",
         "/spend/logs/ui",
         "/spend/logs/session/ui",
         "/cost/estimate",
@@ -2357,6 +2364,30 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase):
     database_connection_timeout: Optional[float] = Field(
         60, description="default timeout for a connection to the database"
     )
+    database_connect_timeout: Optional[float] = Field(
+        None,
+        description=(
+            "Prisma `connect_timeout` URL param (seconds). Bounds how long the "
+            "engine waits to establish a new connection before failing. Defaults "
+            "to Prisma's built-in value when unset."
+        ),
+    )
+    database_socket_timeout: Optional[float] = Field(
+        None,
+        description=(
+            "Prisma `socket_timeout` URL param (seconds). When set, an idle/slow "
+            "connection that has not produced data within this window is closed. "
+            "This is the main knob for capping idle DB connections from LiteLLM."
+        ),
+    )
+    database_extra_connection_params: Optional[Dict[str, Any]] = Field(
+        None,
+        description=(
+            "Escape hatch: extra key/value pairs appended verbatim to the Prisma "
+            "DATABASE_URL / DIRECT_URL query string (e.g. `sslmode`, `pgbouncer`, "
+            "`statement_cache_size`). Keys here override any default LiteLLM sets."
+        ),
+    )
     database_type: Optional[Literal["dynamo_db"]] = Field(
         None, description="to use dynamodb instead of postgres db"
     )
@@ -4549,6 +4580,7 @@ class PrismaCompatibleUpdateDBModel(TypedDict, total=False):
     model_name: str
     litellm_params: str
     model_info: str
+    blocked: bool
     updated_at: str
     updated_by: str
 
diff --git a/litellm/proxy/agent_endpoints/utils.py b/litellm/proxy/agent_endpoints/utils.py
index 2b968de54be..393f5934fd9 100644
--- a/litellm/proxy/agent_endpoints/utils.py
+++ b/litellm/proxy/agent_endpoints/utils.py
@@ -2,6 +2,12 @@
 
 from typing import Dict, Mapping, Optional
 
+# Re-export from the canonical SDK location so the proxy and SDK always
+# share the same provider-config lookup logic.
+from litellm.interactions.agents.utils import (  # noqa: F401
+    get_provider_agents_api_config,
+)
+
 
 def merge_agent_headers(
     *,
diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py
index 13381c7a6c9..14f198e0f12 100644
--- a/litellm/proxy/auth/auth_checks.py
+++ b/litellm/proxy/auth/auth_checks.py
@@ -1187,6 +1187,127 @@ async def get_end_user_object(
         return None
 
 
+_END_USER_VALIDATION_NEGATIVE_TTL = 60
+_END_USER_VALIDATION_POSITIVE_TTL = 300
+
+
+async def resolve_and_validate_end_user_id(
+    raw_end_user_id: Optional[str],
+    prisma_client: Optional[PrismaClient],
+    user_api_key_cache: UserApiKeyCache,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
+    route: str = "",
+) -> Optional[str]:
+    """Optionally drop end-user ids that don't resolve to a known DB row.
+
+    Default: pass-through. LiteLLM's documented pattern is that the `user`
+    field is an arbitrary caller-supplied identifier, so validation is
+    opt-in behind ``litellm.validate_end_user_id_in_db`` to preserve
+    backwards compatibility.
+
+    When the flag is set: accept the id when it matches any of
+      - LiteLLM_EndUserTable.user_id
+      - LiteLLM_UserTable.user_id
+      - LiteLLM_UserTable.user_email (case-insensitive)
+
+    If the id doesn't match but ``litellm.max_end_user_budget_id`` is set,
+    we still preserve the id so the default end-user budget is applied
+    downstream; otherwise we return None.
+
+    DB lookups reuse ``get_end_user_object`` / ``get_user_object`` so they
+    share the same cache as the rest of the auth path instead of adding new
+    raw Prisma queries.
+    """
+    if raw_end_user_id is None:
+        return None
+    if not litellm.validate_end_user_id_in_db:
+        return raw_end_user_id
+    if prisma_client is None:
+        return raw_end_user_id
+
+    cache_key = f"end_user_validation:{raw_end_user_id}"
+    cached = await user_api_key_cache.async_get_cache(key=cache_key)
+    if cached == "valid":
+        return raw_end_user_id
+    if cached == "invalid":
+        return raw_end_user_id if litellm.max_end_user_budget_id else None
+
+    is_valid = await _end_user_id_exists_in_db(
+        end_user_id=raw_end_user_id,
+        prisma_client=prisma_client,
+        user_api_key_cache=user_api_key_cache,
+        parent_otel_span=parent_otel_span,
+        proxy_logging_obj=proxy_logging_obj,
+        route=route,
+    )
+
+    await user_api_key_cache.async_set_cache(
+        key=cache_key,
+        value="valid" if is_valid else "invalid",
+        ttl=(
+            _END_USER_VALIDATION_POSITIVE_TTL
+            if is_valid
+            else _END_USER_VALIDATION_NEGATIVE_TTL
+        ),
+    )
+
+    if is_valid:
+        return raw_end_user_id
+    # Preserve id so the caller can still apply litellm.max_end_user_budget_id.
+    if litellm.max_end_user_budget_id:
+        return raw_end_user_id
+    return None
+
+
+async def _end_user_id_exists_in_db(
+    end_user_id: str,
+    prisma_client: PrismaClient,
+    user_api_key_cache: UserApiKeyCache,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
+    route: str = "",
+) -> bool:
+    """True when the id matches an EndUser, User, or user_email row."""
+    try:
+        end_user_obj = await get_end_user_object(
+            end_user_id=end_user_id,
+            prisma_client=prisma_client,
+            user_api_key_cache=user_api_key_cache,
+            parent_otel_span=parent_otel_span,
+            proxy_logging_obj=proxy_logging_obj,
+            route=route,
+        )
+        if end_user_obj is not None:
+            return True
+    except litellm.BudgetExceededError:
+        raise
+    except Exception as e:
+        verbose_proxy_logger.debug(
+            f"end_user validation: get_end_user_object lookup failed: {e}"
+        )
+
+    try:
+        user_obj = await get_user_object(
+            user_id=end_user_id,
+            prisma_client=prisma_client,
+            user_api_key_cache=user_api_key_cache,
+            user_id_upsert=False,
+            parent_otel_span=parent_otel_span,
+            proxy_logging_obj=proxy_logging_obj,
+            check_db_only=False,
+            user_email=end_user_id if "@" in end_user_id else None,
+        )
+        if user_obj is not None:
+            return True
+    except Exception as e:
+        verbose_proxy_logger.debug(
+            f"end_user validation: get_user_object lookup failed: {e}"
+        )
+
+    return False
+
+
 @log_db_metrics
 async def get_tag_objects_batch(
     tag_names: List[str],
@@ -2232,7 +2353,9 @@ class ExperimentalUIJWTToken:
 
     @staticmethod
     def get_cli_jwt_auth_token(
-        user_info: LiteLLM_UserTable, team_id: Optional[str] = None
+        user_info: LiteLLM_UserTable,
+        team_id: Optional[str] = None,
+        team_alias: Optional[str] = None,
     ) -> str:
         """
         Generate a JWT token for CLI authentication with configurable expiration.
@@ -2243,6 +2366,7 @@ class ExperimentalUIJWTToken:
         Args:
             user_info: User information from the database
             team_id: Team ID for the user (optional, uses user's team if available)
+            team_alias: Team alias for the selected team, if available
 
         Returns:
             Encrypted JWT token string
@@ -2276,6 +2400,7 @@ class ExperimentalUIJWTToken:
             expires=expires,
             user_id=user_info.user_id,
             team_id=_team_id,
+            team_alias=team_alias,
             models=user_info.models,
             max_parallel_requests=None,
             user_role=LitellmUserRoles(user_info.user_role),
diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py
index 637a4a070c4..c4dcca764b2 100644
--- a/litellm/proxy/auth/auth_utils.py
+++ b/litellm/proxy/auth/auth_utils.py
@@ -10,6 +10,7 @@ import litellm
 from litellm import Router, provider_list
 from litellm._logging import verbose_proxy_logger
 from litellm.constants import STANDARD_CUSTOMER_ID_HEADERS
+from litellm.litellm_core_utils.safe_json_loads import safe_json_loads
 from litellm.litellm_core_utils.url_utils import SSRFError, validate_url
 from litellm.proxy._types import *
 from litellm.types.router import CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS
@@ -1008,12 +1009,47 @@ def _get_customer_id_from_standard_headers(
     for standard_header in STANDARD_CUSTOMER_ID_HEADERS:
         for header_name, header_value in request_headers.items():
             if header_name.lower() == standard_header.lower():
-                user_id_str = str(header_value) if header_value is not None else ""
-                if user_id_str.strip():
+                user_id_str = _coerce_user_id_to_str(header_value)
+                if user_id_str:
                     return user_id_str
     return None
 
 
+def _coerce_user_id_to_str(value: Any) -> Optional[str]:
+    """Return a usable end-user identifier string, or None if the value isn't one.
+
+    Always drops non-string structured values (dict/list/tuple/set) because
+    stringifying them produces garbage spend-log rows like
+    ``"{'device_id': ...}"``. Strings that *decode* to a structured payload
+    are only rejected when ``litellm.validate_end_user_id_in_db`` is enabled
+    — operators who currently pass JSON-encoded identifiers keep their
+    existing behavior until they opt in. See
+    auth_utils.py:get_end_user_id_from_request_body for the extraction chain.
+    """
+    if value is None:
+        return None
+    if isinstance(value, bool):
+        # bool is an int subclass; handle explicitly to avoid "True"/"False".
+        return None
+    if isinstance(value, (int, float)):
+        return str(value)
+    if isinstance(value, str):
+        stripped = value.strip()
+        if not stripped:
+            return None
+        # Reject strings that decode to a structured payload (JSON object/array)
+        # only when the operator has opted into end-user validation. Gating
+        # behind the flag preserves backwards compatibility for deployments
+        # that intentionally pass JSON-encoded user identifiers.
+        if litellm.validate_end_user_id_in_db and stripped[:1] in ("{", "["):
+            parsed = safe_json_loads(stripped)
+            if isinstance(parsed, (dict, list)):
+                return None
+        return stripped
+    # dict, list, tuple, set, arbitrary objects -> drop.
+    return None
+
+
 def get_end_user_id_from_request_body(
     request_body: dict, request_headers: Optional[dict] = None
 ) -> Optional[str]:
@@ -1052,23 +1088,22 @@ def get_end_user_id_from_request_body(
         if isinstance(custom_header_name_to_check, list):
             headers_lower = {k.lower(): v for k, v in request_headers.items()}
             for expected_header in custom_header_name_to_check:
-                header_value = headers_lower.get(expected_header)
-                if header_value is not None:
-                    user_id_str = str(header_value)
-                    if user_id_str.strip():
-                        return user_id_str
+                user_id_str = _coerce_user_id_to_str(headers_lower.get(expected_header))
+                if user_id_str:
+                    return user_id_str
 
         elif isinstance(custom_header_name_to_check, str):
             for header_name, header_value in request_headers.items():
                 if header_name.lower() == custom_header_name_to_check.lower():
-                    user_id_str = str(header_value) if header_value is not None else ""
-                    if user_id_str.strip():
+                    user_id_str = _coerce_user_id_to_str(header_value)
+                    if user_id_str:
                         return user_id_str
 
     # Check 3: 'user' field in request_body (commonly OpenAI)
-    if "user" in request_body and request_body["user"] is not None:
-        user_from_body_user_field = request_body["user"]
-        return str(user_from_body_user_field)
+    if "user" in request_body:
+        user_id_str = _coerce_user_id_to_str(request_body["user"])
+        if user_id_str:
+            return user_id_str
 
     def _as_dict(value: Any) -> dict:
         # metadata / litellm_metadata can arrive as JSON strings from
@@ -1077,32 +1112,30 @@ def get_end_user_id_from_request_body(
         if isinstance(value, dict):
             return value
         if isinstance(value, str):
-            from litellm.litellm_core_utils.safe_json_loads import safe_json_loads
-
             parsed = safe_json_loads(value)
             return parsed if isinstance(parsed, dict) else {}
         return {}
 
     # Check 4: 'litellm_metadata.user' in request_body (commonly Anthropic)
     litellm_metadata = _as_dict(request_body.get("litellm_metadata"))
-    user_from_litellm_metadata = litellm_metadata.get("user")
-    if user_from_litellm_metadata is not None:
-        return str(user_from_litellm_metadata)
+    user_id_str = _coerce_user_id_to_str(litellm_metadata.get("user"))
+    if user_id_str:
+        return user_id_str
 
     # Check 5: 'metadata.user_id' in request_body (another common pattern)
     metadata_dict = _as_dict(request_body.get("metadata"))
-    user_id_from_metadata_field = metadata_dict.get("user_id")
-    if user_id_from_metadata_field is not None:
-        return str(user_id_from_metadata_field)
+    user_id_str = _coerce_user_id_to_str(metadata_dict.get("user_id"))
+    if user_id_str:
+        return user_id_str
 
     # Check 6: 'safety_identifier' in request body (OpenAI Responses API parameter)
     # SECURITY NOTE: safety_identifier can be set by any caller in the request body.
     # Only use this for end-user identification in trusted environments where you control
     # the calling application. For untrusted callers, prefer using headers or server-side
     # middleware to set the end_user_id to prevent impersonation.
-    if request_body.get("safety_identifier") is not None:
-        user_from_body_user_field = request_body["safety_identifier"]
-        return str(user_from_body_user_field)
+    user_id_str = _coerce_user_id_to_str(request_body.get("safety_identifier"))
+    if user_id_str:
+        return user_id_str
 
     return None
 
diff --git a/litellm/proxy/auth/model_checks.py b/litellm/proxy/auth/model_checks.py
index bf76f99db69..d364b52c676 100644
--- a/litellm/proxy/auth/model_checks.py
+++ b/litellm/proxy/auth/model_checks.py
@@ -4,12 +4,15 @@ from typing import Dict, List, Optional, Set
 
 import litellm
 from litellm._logging import verbose_proxy_logger
+from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
 from litellm.proxy._types import SpecialModelNames, UserAPIKeyAuth
 from litellm.router import Router
 from litellm.router_utils.fallback_event_handlers import get_fallback_model_group
-from litellm.types.router import LiteLLM_Params
+from litellm.types.router import CredentialLiteLLMParams, LiteLLM_Params
 from litellm.utils import get_valid_models
 
+_CREDENTIAL_LITELLM_PARAM_FIELDS = set(CredentialLiteLLMParams.model_fields)
+
 
 def _check_wildcard_routing(model: str) -> bool:
     """
@@ -178,6 +181,7 @@ def get_complete_model_list(
     model_access_groups: Dict[str, List[str]] = {},
     include_model_access_groups: Optional[bool] = False,
     only_model_access_groups: Optional[bool] = False,
+    team_id: Optional[str] = None,
 ) -> List[str]:
     """Logic for returning complete model list for a given key + team pair"""
 
@@ -222,6 +226,7 @@ def get_complete_model_list(
         unique_models=unique_models,
         return_wildcard_routes=return_wildcard_routes,
         llm_router=llm_router,
+        team_id=team_id,
     )
 
     complete_model_list = unique_models + all_wildcard_models
@@ -229,6 +234,29 @@ def get_complete_model_list(
     return complete_model_list
 
 
+def _hydrate_litellm_credential_name(
+    litellm_params: Optional[LiteLLM_Params],
+) -> Optional[LiteLLM_Params]:
+    if litellm_params is None or litellm_params.litellm_credential_name is None:
+        return litellm_params
+
+    credential_values = CredentialAccessor.get_credential_values(
+        litellm_params.litellm_credential_name
+    )
+    if not credential_values:
+        return litellm_params
+
+    litellm_params = litellm_params.model_copy()
+    for key, value in credential_values.items():
+        if (
+            key in _CREDENTIAL_LITELLM_PARAM_FIELDS
+            and getattr(litellm_params, key, None) is None
+        ):
+            setattr(litellm_params, key, value)
+    litellm_params.litellm_credential_name = None
+    return litellm_params
+
+
 def get_known_models_from_wildcard(
     wildcard_model: str, litellm_params: Optional[LiteLLM_Params] = None
 ) -> List[str]:
@@ -247,7 +275,7 @@ def get_known_models_from_wildcard(
     else:
         provider = wildcard_provider_prefix
 
-    # get all known provider models
+    litellm_params = _hydrate_litellm_credential_name(litellm_params)
 
     wildcard_models = get_provider_models(
         provider=provider, litellm_params=litellm_params
@@ -285,6 +313,7 @@ def _get_wildcard_models(
     unique_models: List[str],
     return_wildcard_routes: Optional[bool] = False,
     llm_router: Optional[Router] = None,
+    team_id: Optional[str] = None,
 ) -> List[str]:
     models_to_remove = set()
     all_wildcard_models = []
@@ -297,7 +326,9 @@ def _get_wildcard_models(
 
             ## get litellm params from model
             if llm_router is not None:
-                model_list = llm_router.get_model_list(model_name=model)
+                model_list = llm_router.get_model_list(
+                    model_name=model, team_id=team_id
+                )
                 if model_list:
                     for router_model in model_list:
                         wildcard_models = get_known_models_from_wildcard(
diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py
index 30b5d36e14a..03278633928 100644
--- a/litellm/proxy/auth/user_api_key_auth.py
+++ b/litellm/proxy/auth/user_api_key_auth.py
@@ -12,7 +12,7 @@ import fnmatch
 import re
 import secrets
 from datetime import datetime, timezone
-from typing import Any, Iterator, List, Optional, Tuple, Union, cast
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, cast
 
 import fastapi
 from fastapi import HTTPException, Request, WebSocket, status
@@ -44,6 +44,7 @@ from litellm.proxy.auth.auth_checks import (
     get_team_object,
     get_user_object,
     is_valid_fallback_model,
+    resolve_and_validate_end_user_id,
 )
 from litellm.proxy.auth.auth_exception_handler import UserAPIKeyAuthExceptionHandler
 from litellm.proxy.auth.auth_utils import (
@@ -333,8 +334,22 @@ def _apply_budget_limits_to_end_user_params(
 async def user_api_key_auth_websocket(websocket: WebSocket):
     # Accept the WebSocket connection
 
-    scope_headers = list(websocket.scope.get("headers") or [])
-    request = Request(scope={"type": "http", "headers": scope_headers})
+    ws_scope = websocket.scope or {}
+    scope_headers = list(ws_scope.get("headers") or [])
+    # ``get_request_route`` falls back to ``request.url.path`` when
+    # ``scope["path"]`` is absent. On WebSockets that fallback reads
+    # ``websocket.url``, which Starlette reconstructs from the (poisonable)
+    # Host header. Carry the ASGI scope's path / root_path so the lookup
+    # never reaches the fallback.
+    synthetic_scope: Dict[str, Any] = {
+        "type": "http",
+        "headers": scope_headers,
+        "path": ws_scope.get("path", ""),
+    }
+    for key in ("root_path", "app_root_path"):
+        if key in ws_scope:
+            synthetic_scope[key] = ws_scope[key]
+    request = Request(scope=synthetic_scope)
 
     request._url = websocket.url
 
@@ -656,6 +671,37 @@ async def _resolve_jwt_to_virtual_key(
         return None
 
 
+def _ensure_parent_otel_span_on_request_state(request: Request) -> None:
+    """Idempotently create the OTEL SERVER span and stash it on
+    ``request.state.parent_otel_span``. Safe to call multiple times.
+
+    Called both at the top of ``user_api_key_auth`` (so body-parse failures
+    have a span to close) and inside ``_user_api_key_auth_builder`` (for
+    callers that bypass ``user_api_key_auth``, e.g. MCP).
+    """
+    from litellm.proxy.proxy_server import open_telemetry_logger
+
+    if open_telemetry_logger is None:
+        return
+    if getattr(request.state, "parent_otel_span", None) is not None:
+        return
+    start_time = datetime.now()
+    try:
+        request.state.litellm_received_at = start_time
+    except Exception:
+        pass
+    parent_otel_span = open_telemetry_logger.create_litellm_proxy_request_started_span(
+        start_time=start_time,
+        headers=_safe_get_request_headers(request),
+    )
+    open_telemetry_logger.set_proxy_request_route_attributes(
+        parent_otel_span,
+        url_path=get_request_route(request=request),
+        http_route=get_request_route_template(request),
+    )
+    request.state.parent_otel_span = parent_otel_span
+
+
 async def _user_api_key_auth_builder(  # noqa: PLR0915
     request: Request,
     api_key: str,
@@ -682,9 +728,10 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
     )
 
     parent_otel_span: Optional[Span] = None
-    start_time = datetime.now()
-    # Stash the proxy-receive instant for the pre-request latency calc —
-    # the OTel Span API exposes no start-time getter, so propagate it.
+    # Prefer the receive-instant stamped by the early helper in
+    # user_api_key_auth (before body parse) — overwriting it would shorten
+    # the preprocessing-duration measurement by the body-parse window.
+    start_time = getattr(request.state, "litellm_received_at", None) or datetime.now()
     try:
         request.state.litellm_received_at = start_time
     except Exception:
@@ -724,18 +771,11 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
             )
 
         if open_telemetry_logger is not None:
-            parent_otel_span = (
-                open_telemetry_logger.create_litellm_proxy_request_started_span(
-                    start_time=start_time,
-                    headers=_safe_get_request_headers(request),
-                )
-            )
-            # `route` is the literal path; template from the matched route.
-            open_telemetry_logger.set_proxy_request_route_attributes(
-                parent_otel_span,
-                url_path=route,
-                http_route=get_request_route_template(request),
-            )
+            # Reuse the span created by user_api_key_auth (before body parse)
+            # so it survives _read_request_body failures. For callers that
+            # bypass user_api_key_auth (e.g. MCP), create it lazily.
+            _ensure_parent_otel_span_on_request_state(request)
+            parent_otel_span = getattr(request.state, "parent_otel_span", None)
 
         ### USER-DEFINED AUTH FUNCTION ###
         if enterprise_custom_auth is not None:
@@ -1057,9 +1097,17 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
         _end_user_object = None
         end_user_params = {}
 
-        end_user_id = get_end_user_id_from_request_body(
+        raw_end_user_id = get_end_user_id_from_request_body(
             request_data, _safe_get_request_headers(request)
         )
+        end_user_id = await resolve_and_validate_end_user_id(
+            raw_end_user_id=raw_end_user_id,
+            prisma_client=prisma_client,
+            user_api_key_cache=user_api_key_cache,
+            parent_otel_span=parent_otel_span,
+            proxy_logging_obj=proxy_logging_obj,
+            route=route,
+        )
         if end_user_id:
             try:
                 end_user_params["end_user_id"] = end_user_id
@@ -1745,7 +1793,7 @@ def _team_obj_from_token(valid_token: UserAPIKeyAuth) -> LiteLLM_TeamTableCached
 
 
 @tracer.wrap()
-async def _run_centralized_common_checks(
+async def _run_centralized_common_checks(  # noqa: PLR0915
     user_api_key_auth_obj: UserAPIKeyAuth,
     request: Request,
     request_data: dict,
@@ -1823,9 +1871,23 @@ async def _run_centralized_common_checks(
         return
 
     parent_otel_span = user_api_key_auth_obj.parent_otel_span
-    end_user_id = get_end_user_id_from_request_body(
-        request_data, _safe_get_request_headers(request)
-    )
+    # In the integrated auth flow ``_user_api_key_auth_builder`` has already
+    # resolved the end-user id and attached it here. Reuse that to avoid a
+    # second extraction pass; fall back to extracting locally when the
+    # function is invoked in isolation (e.g. in direct unit tests).
+    end_user_id = user_api_key_auth_obj.end_user_id
+    if end_user_id is None:
+        raw_end_user_id = get_end_user_id_from_request_body(
+            request_data, _safe_get_request_headers(request)
+        )
+        end_user_id = await resolve_and_validate_end_user_id(
+            raw_end_user_id=raw_end_user_id,
+            prisma_client=prisma_client,
+            user_api_key_cache=user_api_key_cache,
+            parent_otel_span=parent_otel_span,
+            proxy_logging_obj=proxy_logging_obj,
+            route=route,
+        )
 
     fetch_coros = []
     if user_api_key_auth_obj.team_id is not None:
@@ -2112,6 +2174,12 @@ async def user_api_key_auth(
     Parent function to authenticate user api key / jwt token.
     """
 
+    # Create the SERVER span and stash it on request.state BEFORE reading the
+    # body. _read_request_body can raise ProxyException for malformed JSON;
+    # without this, that path leaves no span for the exception handler to
+    # close, and the trace never reaches the backend.
+    _ensure_parent_otel_span_on_request_state(request)
+
     request_data = await _read_request_body(request=request)
     request_data = populate_request_with_path_params(
         request_data=request_data, request=request
@@ -2156,11 +2224,33 @@ async def user_api_key_auth(
             api_key=api_key,
         )
 
-    end_user_id = get_end_user_id_from_request_body(
-        request_data, _safe_get_request_headers(request)
-    )
-    if end_user_id is not None:
-        user_api_key_auth_obj.end_user_id = end_user_id
+    # Defense-in-depth: ``_user_api_key_auth_builder`` has multiple early-return
+    # paths (no master key, /user/auth route, JWT short-circuits) that bypass
+    # the end-user resolution block. If those paths produced an auth obj
+    # without an ``end_user_id`` set, fall back to extracting from the request
+    # body so spend logs are still attributed correctly. Validation honours
+    # ``litellm.validate_end_user_id_in_db``.
+    if user_api_key_auth_obj.end_user_id is None:
+        from litellm.proxy.proxy_server import (
+            prisma_client,
+            proxy_logging_obj,
+            user_api_key_cache,
+        )
+
+        raw_end_user_id = get_end_user_id_from_request_body(
+            request_data, _safe_get_request_headers(request)
+        )
+        if raw_end_user_id is not None:
+            resolved_end_user_id = await resolve_and_validate_end_user_id(
+                raw_end_user_id=raw_end_user_id,
+                prisma_client=prisma_client,
+                user_api_key_cache=user_api_key_cache,
+                parent_otel_span=user_api_key_auth_obj.parent_otel_span,
+                proxy_logging_obj=proxy_logging_obj,
+                route=route,
+            )
+            if resolved_end_user_id is not None:
+                user_api_key_auth_obj.end_user_id = resolved_end_user_id
 
     user_api_key_auth_obj.request_route = normalize_request_route(route)
     return user_api_key_auth_obj
diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py
index 935b96a0e39..166ef7a66d0 100644
--- a/litellm/proxy/batches_endpoints/endpoints.py
+++ b/litellm/proxy/batches_endpoints/endpoints.py
@@ -523,6 +523,10 @@ async def retrieve_batch(  # noqa: PLR0915
                 custom_llm_provider=custom_llm_provider, **data  # type: ignore
             )
 
+        response = await proxy_logging_obj.post_call_success_hook(
+            data=data, user_api_key_dict=user_api_key_dict, response=response
+        )
+
         # FIX: Update the database with the latest state from provider
         await update_batch_in_database(
             batch_id=batch_id,
@@ -533,19 +537,9 @@ async def retrieve_batch(  # noqa: PLR0915
             verbose_proxy_logger=verbose_proxy_logger,
             db_batch_object=db_batch_object,
             operation="retrieve",
+            user_api_key_dict=user_api_key_dict,
         )
 
-        ### CALL HOOKS ### - modify outgoing data
-        response = await proxy_logging_obj.post_call_success_hook(
-            data=data, user_api_key_dict=user_api_key_dict, response=response
-        )
-
-        # Fix: bug_feb14_batch_retrieve_returns_raw_input_file_id
-        # Resolve raw provider file IDs (input, output, error) to unified IDs.
-        if unified_batch_id:
-            await resolve_input_file_id_to_unified(response, prisma_client)
-            await resolve_output_file_ids_to_unified(response, prisma_client)
-
         ### ALERTING ###
         asyncio.create_task(
             proxy_logging_obj.update_request_status(
@@ -917,10 +911,14 @@ async def cancel_batch(
                 **_cancel_batch_data,
             )
 
-        # FIX: Update the database with the new cancelled state
         managed_files_obj = proxy_logging_obj.get_proxy_hook("managed_files")
         from litellm.proxy.proxy_server import prisma_client
 
+        response = await proxy_logging_obj.post_call_success_hook(
+            data=data, user_api_key_dict=user_api_key_dict, response=response
+        )
+
+        # FIX: Update the database with the new cancelled state
         await update_batch_in_database(
             batch_id=batch_id,
             unified_batch_id=unified_batch_id,
@@ -929,11 +927,7 @@ async def cancel_batch(
             prisma_client=prisma_client,
             verbose_proxy_logger=verbose_proxy_logger,
             operation="cancel",
-        )
-
-        ### CALL HOOKS ### - modify outgoing data
-        response = await proxy_logging_obj.post_call_success_hook(
-            data=data, user_api_key_dict=user_api_key_dict, response=response
+            user_api_key_dict=user_api_key_dict,
         )
 
         ### ALERTING ###
diff --git a/litellm/proxy/client/README.md b/litellm/proxy/client/README.md
index adf562d69c5..9fbc6f2197d 100644
--- a/litellm/proxy/client/README.md
+++ b/litellm/proxy/client/README.md
@@ -350,7 +350,7 @@ The CLI provides three authentication commands:
 4. **User Authentication**: User completes SSO authentication in browser
 5. **Callback Processing**: SSO provider redirects back to proxy with state parameter
 6. **User Code Verification**: Browser confirms the verification code shown in the CLI
-7. **Polling**: CLI polls `/sso/cli/poll/{login_id}` with the polling secret header until the JWT is ready
+7. **Polling**: CLI polls `/sso/cli/poll/{login_id}` with the polling secret header until the JWT is ready. When `CLI_SSO_CLAIM_MAP` is configured on the proxy, the poll response may include `attribution_metadata` (allowlisted scalar OIDC claims for client attribution).
 8. **Token Storage**: CLI saves the authentication token to `~/.litellm/token.json`
 
 ### Benefits of This Approach
diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py
index 038d2d81277..ef1d64335b4 100644
--- a/litellm/proxy/common_request_processing.py
+++ b/litellm/proxy/common_request_processing.py
@@ -32,7 +32,7 @@ from litellm.constants import (
     STREAM_SSE_DATA_PREFIX,
 )
 from litellm.integrations.custom_guardrail import CustomGuardrail
-from litellm.litellm_core_utils.dd_tracing import tracer
+from litellm.litellm_core_utils.dd_tracing import NullTracer, tracer
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.litellm_core_utils.llm_response_utils.get_headers import (
     get_response_headers,
@@ -65,6 +65,13 @@ else:
 from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request
 from litellm.types.utils import ModelResponse, ModelResponseStream, Usage
 
+# Datadog streaming spans are a no-op when ddtrace is not enabled, but the
+# ``with tracer.trace(...)`` context manager still allocates a NullSpan and
+# runs __enter__/__exit__ for every streamed chunk. Resolve once at import so
+# the per-chunk hot path can skip the context manager entirely when tracing
+# is off (the default).
+_DD_STREAMING_TRACE_ENABLED = not isinstance(tracer, NullTracer)
+
 
 def _serialize_http_exception_detail(
     detail: Any,
@@ -231,7 +238,7 @@ def _extract_error_from_sse_chunk(event_line: Union[str, bytes]) -> dict:
     return default_error
 
 
-async def create_response(
+async def create_response(  # noqa: PLR0915
     generator: AsyncGenerator[str, None],
     media_type: str,
     headers: dict,
@@ -336,6 +343,13 @@ async def create_response(
         )
 
     async def combined_generator() -> AsyncGenerator[str, None]:
+        if not _DD_STREAMING_TRACE_ENABLED:
+            # Fast path: no per-chunk span object / context-manager overhead.
+            if first_chunk_value is not None:
+                yield first_chunk_value
+            async for chunk in generator:
+                yield chunk
+            return
         if first_chunk_value is not None:
             with tracer.trace(DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE):
                 yield first_chunk_value
@@ -807,6 +821,11 @@ class ProxyBaseLLMRequestProcessing:
             "aget_interaction",
             "adelete_interaction",
             "acancel_interaction",
+            "acreate_agent",
+            "alist_agents",
+            "aget_agent",
+            "adelete_agent",
+            "alist_agent_versions",
             "asend_message",
             "call_mcp_tool",
             "acreate_eval",
@@ -1074,6 +1093,11 @@ class ProxyBaseLLMRequestProcessing:
             "aget_interaction",
             "adelete_interaction",
             "acancel_interaction",
+            "acreate_agent",
+            "alist_agents",
+            "aget_agent",
+            "adelete_agent",
+            "alist_agent_versions",
             "asend_message",
             "call_mcp_tool",
             "acreate_eval",
@@ -1890,6 +1914,23 @@ class ProxyBaseLLMRequestProcessing:
         failure hook and yields via serialize_error. Use for SSE or NDJSON.
         """
         verbose_proxy_logger.debug("inside generator")
+        # Resolve per-stream (not per-chunk) whether the heavy per-chunk path
+        # is needed. When no callback overrides ``async_post_call_streaming_hook``,
+        # no CustomGuardrail is active, and cost injection is disabled, the
+        # per-chunk hook returns the chunk unchanged, ``str_so_far`` is never
+        # consumed, and cost injection is a no-op -- so the per-chunk coroutine
+        # await, response-string materialization, and cost-injection call are
+        # pure overhead on the streaming hot path (the default config).
+        caps = ProxyLogging._callback_capabilities()
+        cost_injection_enabled = bool(
+            getattr(litellm, "include_cost_in_streaming_usage", False)
+        )
+        fast_path = (
+            not caps.has_streaming_chunk_override
+            and not caps.has_guardrail
+            and not cost_injection_enabled
+        )
+        debug_enabled = verbose_proxy_logger.isEnabledFor(logging.DEBUG)
         try:
             str_so_far = ""
             async for (
@@ -1899,9 +1940,17 @@ class ProxyBaseLLMRequestProcessing:
                 response=response,
                 request_data=request_data,
             ):
-                verbose_proxy_logger.debug(
-                    "async_data_generator: received streaming chunk - {}".format(chunk)
-                )
+                # ``.format(chunk)`` was previously evaluated for every chunk
+                # regardless of log level; gate it behind the level check.
+                if debug_enabled:
+                    verbose_proxy_logger.debug(
+                        "async_data_generator: received streaming chunk - %s", chunk
+                    )
+
+                if fast_path:
+                    yield serialize_chunk(chunk)
+                    continue
+
                 chunk = await proxy_logging_obj.async_post_call_streaming_hook(
                     user_api_key_dict=user_api_key_dict,
                     response=chunk,
@@ -1959,7 +2008,7 @@ class ProxyBaseLLMRequestProcessing:
             yield serialize_error(proxy_exception)
 
     @staticmethod
-    async def async_sse_data_generator(
+    def async_sse_data_generator(
         response: Any,
         user_api_key_dict: UserAPIKeyAuth,
         request_data: dict,
@@ -1967,17 +2016,20 @@ class ProxyBaseLLMRequestProcessing:
     ) -> AsyncGenerator[str, None]:
         """
         Anthropic /messages and Google /generateContent streaming data generator require SSE events.
-        Delegates to async_streaming_data_generator with SSE serializers.
+
+        Returns the underlying ``async_streaming_data_generator`` configured with
+        SSE serializers directly (rather than re-wrapping it in another
+        ``async for: yield`` trampoline), so a streamed chunk traverses one
+        fewer async-generator layer / coroutine resume on the hot path.
         """
-        async for chunk in ProxyBaseLLMRequestProcessing.async_streaming_data_generator(
+        return ProxyBaseLLMRequestProcessing.async_streaming_data_generator(
             response=response,
             user_api_key_dict=user_api_key_dict,
             request_data=request_data,
             proxy_logging_obj=proxy_logging_obj,
             serialize_chunk=ProxyBaseLLMRequestProcessing.return_sse_chunk,
             serialize_error=lambda proxy_exc: f"{STREAM_SSE_DATA_PREFIX}{json.dumps({'error': proxy_exc.to_dict()})}\n\n",
-        ):
-            yield chunk
+        )
 
     @staticmethod
     def _process_chunk_with_cost_injection(chunk: Any, model_name: str) -> Any:
diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py
index 198d9503cb0..4995752d441 100644
--- a/litellm/proxy/common_utils/callback_utils.py
+++ b/litellm/proxy/common_utils/callback_utils.py
@@ -1,16 +1,31 @@
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Literal, Optional
+import copy
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional
 
 import litellm
 from litellm import get_secret
 from litellm._logging import verbose_proxy_logger
 from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker
 from litellm.proxy._types import CommonProxyErrors, LiteLLMPromptInjectionParams
+from litellm.proxy.common_utils.encrypt_decrypt_utils import (
+    decrypt_value_helper,
+    encrypt_value_helper,
+)
 from litellm.proxy.types_utils.utils import get_instance_fn
 from litellm.types.utils import (
     StandardLoggingGuardrailInformation,
     StandardLoggingPayload,
 )
 
+_CALLBACK_VAR_MASKER = SensitiveDataMasker()
+# Compound names that are credential-bearing but don't contain any of the
+# default sensitive segments (so SensitiveDataMasker won't flag them).
+_EXTRA_SENSITIVE_CALLBACK_KEYS = {"gcs_path_service_account"}
+# Sentinel prefix on encrypted callback_var values. Lets us detect
+# already-encrypted input cheaply (no decrypt-attempt round trip) and
+# avoid double-encrypting if `LITELLM_SALT_KEY` is rotated between writes.
+_CALLBACK_VAR_ENCRYPTED_PREFIX = "litellm_enc::"
+
 blue_color_code = "\033[94m"
 reset_color_code = "\033[0m"
 
@@ -550,3 +565,85 @@ def normalize_callback_names(callbacks: Iterable[Any]) -> List[Any]:
     if callbacks is None:
         return []
     return [c.lower() if isinstance(c, str) else c for c in callbacks]
+
+
+def encrypt_callback_vars(metadata: Any) -> Any:
+    """Return a deep copy of metadata with callback_vars values encrypted at rest.
+
+    Idempotent: a value that already decrypts cleanly is left unchanged so
+    round-trips through edit forms don't double-encrypt.
+    """
+    return _transform_callback_vars(metadata, _encrypt_if_plaintext)
+
+
+def decrypt_callback_vars(metadata: Any) -> Any:
+    """Return a deep copy of metadata with callback_vars values decrypted.
+
+    Legacy plaintext rows pass through unchanged (decrypt failure → original).
+    """
+    return _transform_callback_vars(metadata, _decrypt_or_passthrough)
+
+
+def _transform_callback_vars(
+    metadata: Any, transform: Callable[[str, Any], Any]
+) -> Any:
+    if not isinstance(metadata, dict):
+        return metadata
+    out = copy.deepcopy(metadata)
+    logging_entries = out.get("logging")
+    if isinstance(logging_entries, list):
+        for entry in logging_entries:
+            if isinstance(entry, dict) and isinstance(entry.get("callback_vars"), dict):
+                entry["callback_vars"] = {
+                    k: transform(k, v) for k, v in entry["callback_vars"].items()
+                }
+    callback_settings = out.get("callback_settings")
+    if isinstance(callback_settings, dict) and isinstance(
+        callback_settings.get("callback_vars"), dict
+    ):
+        callback_settings["callback_vars"] = {
+            k: transform(k, v) for k, v in callback_settings["callback_vars"].items()
+        }
+    return out
+
+
+def _is_sensitive_callback_var(key: str) -> bool:
+    """Match codebase precedent: only credential-bearing fields get encrypted;
+    routing/identifier fields (host, base_url, project, region) stay plain."""
+    if key in _EXTRA_SENSITIVE_CALLBACK_KEYS:
+        return True
+    return _CALLBACK_VAR_MASKER.is_sensitive_key(key)
+
+
+def _encrypt_if_plaintext(key: str, value: Any) -> Any:
+    if not isinstance(value, str) or not value:
+        return value
+    if not _is_sensitive_callback_var(key):
+        return value
+    if value.startswith(_CALLBACK_VAR_ENCRYPTED_PREFIX):
+        # Already encrypted — round-tripping ciphertext (e.g. UI Edit Settings
+        # save without changing the field) must not double-encrypt. Cheap
+        # prefix check is robust under salt-key rotation; a decrypt-based
+        # idempotency check would mis-classify K1-encrypted blobs as
+        # plaintext under K2 and wrap them a second time.
+        return value
+    try:
+        return _CALLBACK_VAR_ENCRYPTED_PREFIX + encrypt_value_helper(value)
+    except Exception:
+        # No salt key / master key configured — leave the value as-is rather
+        # than crash the write. Dev environments without LITELLM_SALT_KEY hit
+        # this path; production always has a master key so encryption proceeds.
+        return value
+
+
+def _decrypt_or_passthrough(key: str, value: Any) -> Any:
+    if not isinstance(value, str) or not value:
+        return value
+    if not value.startswith(_CALLBACK_VAR_ENCRYPTED_PREFIX):
+        # Legacy plaintext rows or non-credential fields — return as-is.
+        return value
+    inner = value[len(_CALLBACK_VAR_ENCRYPTED_PREFIX) :]
+    decrypted = decrypt_value_helper(
+        value=inner, key=key, exception_type="debug", return_original_value=False
+    )
+    return decrypted if decrypted is not None else value
diff --git a/litellm/proxy/common_utils/custom_openapi_spec.py b/litellm/proxy/common_utils/custom_openapi_spec.py
index a93749c3952..fa3cb02195b 100644
--- a/litellm/proxy/common_utils/custom_openapi_spec.py
+++ b/litellm/proxy/common_utils/custom_openapi_spec.py
@@ -324,7 +324,7 @@ class CustomOpenAPISpec:
 
     @staticmethod
     def add_chat_completion_request_schema(
-        openapi_schema: Dict[str, Any]
+        openapi_schema: Dict[str, Any],
     ) -> Dict[str, Any]:
         """
         Add ProxyChatCompletionRequest schema to chat completion endpoints for documentation.
@@ -380,7 +380,7 @@ class CustomOpenAPISpec:
 
     @staticmethod
     def add_responses_api_request_schema(
-        openapi_schema: Dict[str, Any]
+        openapi_schema: Dict[str, Any],
     ) -> Dict[str, Any]:
         """
         Add ResponsesAPIRequestParams schema to responses API endpoints for documentation.
@@ -410,7 +410,7 @@ class CustomOpenAPISpec:
 
     @staticmethod
     def add_llm_api_request_schema_body(
-        openapi_schema: Dict[str, Any]
+        openapi_schema: Dict[str, Any],
     ) -> Dict[str, Any]:
         """
         Add LLM API request schema bodies to OpenAPI specification for documentation.
diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py
index 71abdfa5e9e..2ce3fda6297 100644
--- a/litellm/proxy/common_utils/http_parsing_utils.py
+++ b/litellm/proxy/common_utils/http_parsing_utils.py
@@ -12,6 +12,33 @@ from litellm.proxy.common_utils.callback_utils import (
 )
 from litellm.types.router import Deployment
 
+_FORM_CONTENT_TYPES: frozenset[str] = frozenset(
+    {"application/x-www-form-urlencoded", "multipart/form-data"}
+)
+
+
+def _normalize_media_type(content_type: str) -> str:
+    """Return the bare media type per RFC 7231: strip params, trim, lowercase."""
+    if not content_type:
+        return ""
+    return content_type.split(";", 1)[0].strip().lower()
+
+
+def _is_form_content_type(content_type: str) -> bool:
+    """
+    True iff Starlette's ``request.form()`` will actually parse this body.
+
+    Substring matching ``"form"`` is unsafe: ``request.form()`` returns empty
+    ``FormData`` for non-canonical types without consuming the body, leaving
+    the auth-time pre-read and the handler's read seeing different payloads.
+    """
+    return _normalize_media_type(content_type) in _FORM_CONTENT_TYPES
+
+
+def _is_json_content_type(content_type: str) -> bool:
+    """True iff the body should be parsed as JSON."""
+    return _normalize_media_type(content_type) == "application/json"
+
 
 async def _read_request_body(request: Optional[Request]) -> Dict:
     """
@@ -37,8 +64,24 @@ async def _read_request_body(request: Optional[Request]) -> Dict:
         _request_headers: dict = _safe_get_request_headers(request=request)
         content_type = _request_headers.get("content-type", "")
 
-        if "form" in content_type:
-            parsed_body = dict(await request.form())
+        if _is_form_content_type(content_type):
+            try:
+                form_data = await request.form()
+            except Exception as e:
+                # ``request.form()`` raises on malformed multipart (missing
+                # boundary, malformed chunk encoding, …). Surface as 400 so
+                # the auth-time pre-read does not silently cache ``{}`` while
+                # a later raw-body re-read sees the original payload —
+                # banned-param checks must see the same body the handler
+                # acts on.
+                verbose_proxy_logger.error(f"Invalid form payload: {e}")
+                raise ProxyException(
+                    message=f"Invalid form payload: {e}",
+                    type="invalid_request_error",
+                    param="request_body",
+                    code=status.HTTP_400_BAD_REQUEST,
+                )
+            parsed_body = dict(form_data)
             if "metadata" in parsed_body and isinstance(parsed_body["metadata"], str):
                 parsed_body["metadata"] = json.loads(parsed_body["metadata"])
         else:
@@ -257,7 +300,7 @@ async def get_form_data(request: Request) -> Dict[str, Any]:
 
 
 async def convert_upload_files_to_file_data(
-    form_data: Dict[str, Any]
+    form_data: Dict[str, Any],
 ) -> Dict[str, Any]:
     """
     Convert FastAPI UploadFile objects to file data tuples for litellm.
@@ -306,18 +349,13 @@ async def get_request_body(request: Request) -> Dict[str, Any]:
     Read the request body and parse it as JSON.
     """
     if request.method == "POST":
-        if request.headers.get("content-type", "") == "application/json":
+        content_type = request.headers.get("content-type", "")
+        if _is_json_content_type(content_type):
             return await _read_request_body(request)
-        elif "multipart/form-data" in request.headers.get(
-            "content-type", ""
-        ) or "application/x-www-form-urlencoded" in request.headers.get(
-            "content-type", ""
-        ):
+        elif _is_form_content_type(content_type):
             return await get_form_data(request)
         else:
-            raise ValueError(
-                f"Unsupported content type: {request.headers.get('content-type')}"
-            )
+            raise ValueError(f"Unsupported content type: {content_type}")
     return {}
 
 
diff --git a/litellm/proxy/common_utils/openai_endpoint_utils.py b/litellm/proxy/common_utils/openai_endpoint_utils.py
index c4bfe11aec1..905967fa465 100644
--- a/litellm/proxy/common_utils/openai_endpoint_utils.py
+++ b/litellm/proxy/common_utils/openai_endpoint_utils.py
@@ -1,5 +1,5 @@
 """
-Contains utils used by OpenAI compatible endpoints 
+Contains utils used by OpenAI compatible endpoints
 """
 
 from typing import Optional, Set
diff --git a/litellm/proxy/config_management_endpoints/pass_through_endpoints.py b/litellm/proxy/config_management_endpoints/pass_through_endpoints.py
index 5ff02b8bce0..4ebd989dc53 100644
--- a/litellm/proxy/config_management_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/config_management_endpoints/pass_through_endpoints.py
@@ -1,5 +1,5 @@
 """
-What is this? 
+What is this?
 
 CRUD endpoints for managing pass-through endpoints
 """
diff --git a/litellm/proxy/container_endpoints/endpoints.py b/litellm/proxy/container_endpoints/endpoints.py
index 9650604bf81..fc1f77bb684 100644
--- a/litellm/proxy/container_endpoints/endpoints.py
+++ b/litellm/proxy/container_endpoints/endpoints.py
@@ -328,7 +328,7 @@ async def retrieve_container(
         custom_llm_provider=custom_llm_provider,
     )
     data.update(
-        get_container_forwarding_params(
+        await get_container_forwarding_params(
             container_id,
             original_container_id,
             custom_llm_provider,
@@ -433,7 +433,7 @@ async def delete_container(
         custom_llm_provider=custom_llm_provider,
     )
     data.update(
-        get_container_forwarding_params(
+        await get_container_forwarding_params(
             container_id,
             original_container_id,
             custom_llm_provider,
diff --git a/litellm/proxy/container_endpoints/handler_factory.py b/litellm/proxy/container_endpoints/handler_factory.py
index 4284cdd5d4a..7eeb11fc372 100644
--- a/litellm/proxy/container_endpoints/handler_factory.py
+++ b/litellm/proxy/container_endpoints/handler_factory.py
@@ -196,10 +196,12 @@ async def _process_binary_request(
     )
     data: Dict[str, Any] = {
         "file_id": file_id,
-        **get_container_forwarding_params(
-            container_id=container_id,
-            original_container_id=original_container_id,
-            custom_llm_provider=resolved_provider,
+        **(
+            await get_container_forwarding_params(
+                container_id=container_id,
+                original_container_id=original_container_id,
+                custom_llm_provider=resolved_provider,
+            )
         ),
     }
     processor = ProxyBaseLLMRequestProcessing(data=data)
@@ -316,7 +318,7 @@ async def _process_multipart_upload_request(
     )
 
     data.update(
-        get_container_forwarding_params(
+        await get_container_forwarding_params(
             container_id=container_id,
             original_container_id=original_container_id,
             custom_llm_provider=resolved_provider,
@@ -396,7 +398,7 @@ async def _process_request(
             )
         )
         data.update(
-            get_container_forwarding_params(
+            await get_container_forwarding_params(
                 container_id=path_params["container_id"],
                 original_container_id=original_container_id,
                 custom_llm_provider=resolved_provider,
diff --git a/litellm/proxy/container_endpoints/ownership.py b/litellm/proxy/container_endpoints/ownership.py
index 568eca523ae..57de6c4a63d 100644
--- a/litellm/proxy/container_endpoints/ownership.py
+++ b/litellm/proxy/container_endpoints/ownership.py
@@ -23,6 +23,13 @@ CONTAINER_OBJECT_PURPOSE = "container"
 _NEGATIVE_OWNER_SENTINEL = "__litellm_container_no_owner__"
 _CONTAINER_OWNER_CACHE = InMemoryCache(max_size_in_memory=10000, default_ttl=60)
 
+# Caches the stored ``unified_object_id`` (the encoded container ID
+# captured at create time) so ``get_container_forwarding_params`` can
+# recover the deployment ``model_id`` for native upstream IDs without
+# re-hitting Prisma on every retrieve/delete.
+_NEGATIVE_STORED_ID_SENTINEL = "__litellm_container_no_stored_id__"
+_CONTAINER_STORED_ID_CACHE = InMemoryCache(max_size_in_memory=10000, default_ttl=60)
+
 # Per-caller-scope cache for ``GET /v1/containers`` list filtering. Without
 # this, every list call issues a fresh ``find_many`` against
 # ``litellm_managedobjecttable``. The cache key is the sorted owner-scope
@@ -56,7 +63,7 @@ def decode_container_id_for_ownership(
     return original_container_id, custom_llm_provider
 
 
-def get_container_forwarding_params(
+async def get_container_forwarding_params(
     container_id: str, original_container_id: str, custom_llm_provider: str
 ) -> Dict[str, str]:
     params = {
@@ -65,6 +72,20 @@ def get_container_forwarding_params(
     }
     decoded = ResponsesAPIRequestUtils._decode_container_id(container_id)
     model_id = decoded.get("model_id")
+    if not (isinstance(model_id, str) and model_id):
+        # Native upstream IDs (e.g. Azure ``cntr_<hex>``) carry no LiteLLM
+        # routing payload, so decoding the user-supplied id yields no
+        # ``model_id``. Recover it from the encoded ``unified_object_id``
+        # captured on the ownership row at create time — when the router
+        # selected a specific deployment that ID embeds the model_id.
+        stored_id = await _get_stored_container_id(
+            original_container_id, custom_llm_provider
+        )
+        if stored_id and stored_id != container_id:
+            stored_decoded = ResponsesAPIRequestUtils._decode_container_id(stored_id)
+            stored_model_id = stored_decoded.get("model_id")
+            if isinstance(stored_model_id, str) and stored_model_id:
+                model_id = stored_model_id
     if isinstance(model_id, str) and model_id:
         params["model_id"] = model_id
     return params
@@ -168,6 +189,7 @@ async def record_container_owner(
         )
 
     _CONTAINER_OWNER_CACHE.set_cache(model_object_id, owner)
+    _CONTAINER_STORED_ID_CACHE.set_cache(model_object_id, container_id)
     # Drop the caller's own list-cache entry so the just-created container
     # shows up on their next ``GET /v1/containers``. Other callers with
     # disjoint scope tuples have their own entries; intersecting-scope
@@ -207,9 +229,60 @@ async def _get_container_owner(
     _CONTAINER_OWNER_CACHE.set_cache(
         model_object_id, owner if owner is not None else _NEGATIVE_OWNER_SENTINEL
     )
+    stored_id = getattr(row, "unified_object_id", None) if row is not None else None
+    _CONTAINER_STORED_ID_CACHE.set_cache(
+        model_object_id,
+        (
+            stored_id
+            if isinstance(stored_id, str) and stored_id
+            else _NEGATIVE_STORED_ID_SENTINEL
+        ),
+    )
     return owner
 
 
+async def _get_stored_container_id(
+    original_container_id: str, custom_llm_provider: str
+) -> Optional[str]:
+    """Return the ``unified_object_id`` stored at create time, if any.
+
+    Used by :func:`get_container_forwarding_params` to recover the
+    deployment ``model_id`` for native upstream container IDs: the stored
+    value is the encoded form produced by ``encode_container_id_in_response``
+    when the router selected a specific deployment.
+    """
+    model_object_id = _container_model_object_id(
+        original_container_id, custom_llm_provider
+    )
+
+    cached = _CONTAINER_STORED_ID_CACHE.get_cache(model_object_id)
+    if cached == _NEGATIVE_STORED_ID_SENTINEL:
+        return None
+    if isinstance(cached, str) and cached:
+        return cached
+
+    prisma_client = await _get_prisma_client()
+    if prisma_client is None:
+        return None
+
+    row = await prisma_client.db.litellm_managedobjecttable.find_first(
+        where={
+            "model_object_id": model_object_id,
+            "file_purpose": CONTAINER_OBJECT_PURPOSE,
+        }
+    )
+    stored_id = getattr(row, "unified_object_id", None) if row is not None else None
+    _CONTAINER_STORED_ID_CACHE.set_cache(
+        model_object_id,
+        (
+            stored_id
+            if isinstance(stored_id, str) and stored_id
+            else _NEGATIVE_STORED_ID_SENTINEL
+        ),
+    )
+    return stored_id if isinstance(stored_id, str) and stored_id else None
+
+
 async def assert_user_can_access_container(
     container_id: str,
     user_api_key_dict: UserAPIKeyAuth,
diff --git a/litellm/proxy/db/create_views.py b/litellm/proxy/db/create_views.py
index d84cebcf05a..97525a528d0 100644
--- a/litellm/proxy/db/create_views.py
+++ b/litellm/proxy/db/create_views.py
@@ -34,8 +34,7 @@ async def create_missing_views(db: _db):  # noqa: PLR0915
         if not any(marker in error_msg for marker in _VIEW_NOT_FOUND_MARKERS):
             raise
         # If an error occurs, the view does not exist, so create it
-        await db.execute_raw(
-            """
+        await db.execute_raw("""
                 CREATE VIEW "LiteLLM_VerificationTokenView" AS
                 SELECT
                 v.*,
@@ -47,8 +46,7 @@ async def create_missing_views(db: _db):  # noqa: PLR0915
                 FROM "LiteLLM_VerificationToken" v
                 LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id
                 LEFT JOIN "LiteLLM_ProjectTable" p ON v.project_id = p.project_id;
-            """
-        )
+            """)
 
         verbose_logger.debug("LiteLLM_VerificationTokenView Created!")
 
diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py
index 7611c9c9692..e7f14df5294 100644
--- a/litellm/proxy/db/db_spend_update_writer.py
+++ b/litellm/proxy/db/db_spend_update_writer.py
@@ -69,6 +69,35 @@ else:
     ProxyLogging = Any
 
 
+def _extract_cache_read_tokens(usage_obj: dict) -> int:
+    """
+    Anthropic: top-level cache_read_input_tokens field.
+    OpenAI-compatible (moonshotai, openai, deepseek, etc.): prompt_tokens_details.cached_tokens.
+    """
+    explicit = usage_obj.get("cache_read_input_tokens", 0) or 0
+    if explicit:
+        return int(explicit)
+    details = usage_obj.get("prompt_tokens_details") or {}
+    return int(details.get("cached_tokens", 0) or 0)
+
+
+def _extract_cache_creation_tokens(usage_obj: dict) -> int:
+    """
+    Anthropic: top-level cache_creation_input_tokens field.
+    OpenAI-compatible (kimi-k2 etc.): prompt_tokens_details.cache_write_tokens
+    or prompt_tokens_details.cache_creation_tokens.
+    """
+    explicit = usage_obj.get("cache_creation_input_tokens", 0) or 0
+    if explicit:
+        return int(explicit)
+    details = usage_obj.get("prompt_tokens_details") or {}
+    return int(
+        details.get("cache_write_tokens", 0)
+        or details.get("cache_creation_tokens", 0)
+        or 0
+    )
+
+
 class DBSpendUpdateWriter:
     """
     Module responsible for
@@ -1992,12 +2021,8 @@ class DBSpendUpdateWriter:
                 api_requests=1,
                 successful_requests=1 if request_status == "success" else 0,
                 failed_requests=1 if request_status != "success" else 0,
-                cache_read_input_tokens=usage_obj.get("cache_read_input_tokens", 0)
-                or 0,
-                cache_creation_input_tokens=usage_obj.get(
-                    "cache_creation_input_tokens", 0
-                )
-                or 0,
+                cache_read_input_tokens=_extract_cache_read_tokens(usage_obj),
+                cache_creation_input_tokens=_extract_cache_creation_tokens(usage_obj),
             )
             return daily_transaction
         except Exception as e:
diff --git a/litellm/proxy/db/spend_counter_reseed.py b/litellm/proxy/db/spend_counter_reseed.py
index 19ec6699390..e7c5fa3f72c 100644
--- a/litellm/proxy/db/spend_counter_reseed.py
+++ b/litellm/proxy/db/spend_counter_reseed.py
@@ -178,15 +178,28 @@ class SpendCounterReseed:
             if db_spend is None:
                 return None
             # Warm even when 0 so subsequent reads hit cache, not DB.
+            #
+            # Seed via SET NX (cross-pod safe): only one pod initializes the
+            # Redis key with db_spend; concurrent seeders read the winner's
+            # value. INCRBYFLOAT-of-db_spend from N pods would multiply the
+            # counter (N x db_spend) and trigger spurious budget alerts.
+            current_value: float = float(db_spend)
             try:
                 if spend_counter_cache.redis_cache is not None:
-                    current_value = (
-                        await spend_counter_cache.redis_cache.async_increment(
-                            key=counter_key,
-                            value=db_spend,
-                            refresh_ttl=True,
-                        )
+                    seeded = await spend_counter_cache.redis_cache.async_set_cache(
+                        key=counter_key,
+                        value=db_spend,
+                        nx=True,
                     )
+                    if seeded:
+                        current_value = float(db_spend)
+                    else:
+                        cached = await spend_counter_cache.redis_cache.async_get_cache(
+                            key=counter_key
+                        )
+                        current_value = (
+                            float(cached) if cached is not None else float(db_spend)
+                        )
                     spend_counter_cache.in_memory_cache.set_cache(
                         key=counter_key,
                         value=current_value,
@@ -202,7 +215,7 @@ class SpendCounterReseed:
                 )
                 if require_cache_warm:
                     raise
-            return db_spend
+            return current_value
 
     @staticmethod
     async def window_from_spend_logs(
diff --git a/litellm/proxy/google_endpoints/agents_endpoints.py b/litellm/proxy/google_endpoints/agents_endpoints.py
new file mode 100644
index 00000000000..779284023a0
--- /dev/null
+++ b/litellm/proxy/google_endpoints/agents_endpoints.py
@@ -0,0 +1,445 @@
+"""
+Google AI Studio Managed Agents API Proxy Endpoints.
+
+Exposes Gemini's /v1beta/agents surface through the LiteLLM proxy so that
+user curl commands transfer 1-to-1 by swapping the host + auth header.
+
+Routes:
+  POST   /v1beta/agents                     -> acreate_agent
+  GET    /v1beta/agents                     -> alist_agents
+  GET    /v1beta/agents/{name}              -> aget_agent
+  DELETE /v1beta/agents/{name}              -> adelete_agent
+  GET    /v1beta/agents/{name}/versions     -> alist_agent_versions
+
+These are distinct from the A2A agent registry at /v1/agents.
+"""
+
+import json
+
+from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
+from fastapi.responses import ORJSONResponse
+
+from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
+from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
+from litellm.proxy.common_utils.http_parsing_utils import (
+    _read_request_body,
+    _safe_get_request_query_params,
+)
+
+router = APIRouter(tags=["gemini managed agents"])
+
+
+def _is_proxy_admin(user_api_key_dict: UserAPIKeyAuth) -> bool:
+    return (
+        user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN
+        or user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value
+    )
+
+
+def _enforce_caller_supplied_provider_key(
+    data: dict,
+    user_api_key_dict: UserAPIKeyAuth,
+) -> None:
+    """
+    SECURITY: refuse to use the proxy's shared GOOGLE_API_KEY / GEMINI_API_KEY
+    env fallback for non-admin callers on Gemini managed-agent CRUD endpoints.
+
+    These endpoints are part of ``llm_api_routes`` so any authenticated LLM key
+    can reach them, but unlike ``/v1beta/models/...:generateContent`` they are
+    *not* routed through ``model_list`` — the only credential source is either
+    the per-request ``litellm_params_template`` or the env var fallback. Without
+    this guard, any ordinary proxy user could list, create, or delete managed
+    agents inside the operator's Gemini project using the operator's key.
+
+    Proxy admins (master key) keep the env-fallback convenience for ops use.
+    """
+    if _is_proxy_admin(user_api_key_dict):
+        return
+    if data.get("api_key"):
+        return
+    raise HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail=(
+            "Gemini managed-agent endpoints require a caller-supplied "
+            "Gemini api_key (via 'litellm_params_template'). Falling back to "
+            "the proxy's GOOGLE_API_KEY / GEMINI_API_KEY env vars is only "
+            "permitted for proxy admins."
+        ),
+    )
+
+
+def _merge_query_params_into_data(data: dict, request: Request) -> dict:
+    """
+    For GET/DELETE endpoints that cannot carry a JSON body, read a
+    JSON-encoded ``litellm_params_template`` query parameter and merge its
+    contents into *data*, without overwriting keys that are already present
+    (e.g. path params like ``name`` or the fixed ``custom_llm_provider``).
+
+    This mirrors the ``litellm_params_template`` handling in
+    ``create_gemini_agent`` and is the supported way for multi-tenant
+    callers to supply per-request credentials on non-POST endpoints:
+
+    .. code-block:: bash
+
+        curl "http://localhost:4000/v1beta/agents?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\
+            -H "Authorization: Bearer sk-..."
+
+    Credentials MUST NOT be passed as plain flat query parameters (e.g.
+    ``?api_key=AIza...``) because URL query strings appear verbatim in
+    web-server access logs, CDN edge logs, browser history, and Referer
+    headers. Use the ``litellm_params_template`` JSON body field on POST
+    requests, or the JSON-encoded query parameter above for GET/DELETE.
+    """
+    query_params = _safe_get_request_query_params(request)
+    if not query_params:
+        return data
+
+    raw_template = query_params.get("litellm_params_template")
+    if raw_template:
+        try:
+            template = (
+                json.loads(raw_template)
+                if isinstance(raw_template, str)
+                else raw_template
+            )
+        except (json.JSONDecodeError, ValueError):
+            template = {}
+        if isinstance(template, dict):
+            for key, value in template.items():
+                data.setdefault(key, value)
+
+    return data
+
+
+def _proxy_server_imports():
+    from litellm.proxy.proxy_server import (  # noqa: PLC0415
+        general_settings,
+        llm_router,
+        proxy_config,
+        proxy_logging_obj,
+        select_data_generator,
+        user_api_base,
+        user_max_tokens,
+        user_model,
+        user_request_timeout,
+        user_temperature,
+        version,
+    )
+
+    return dict(
+        general_settings=general_settings,
+        llm_router=llm_router,
+        proxy_config=proxy_config,
+        proxy_logging_obj=proxy_logging_obj,
+        select_data_generator=select_data_generator,
+        user_api_base=user_api_base,
+        user_max_tokens=user_max_tokens,
+        user_model=user_model,
+        user_request_timeout=user_request_timeout,
+        user_temperature=user_temperature,
+        version=version,
+    )
+
+
+@router.post(
+    "/v1beta/agents",
+    dependencies=[Depends(user_api_key_auth)],
+    response_class=ORJSONResponse,
+)
+async def create_gemini_agent(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Create a named custom agent on the Gemini side.
+
+    Example:
+    ```bash
+    curl -X POST "http://localhost:4000/v1beta/agents" \\
+        -H "Authorization: Bearer sk-..." \\
+        -H "Content-Type: application/json" \\
+        -d '{
+            "name": "my-custom-slides-agent",
+            "base_agent": "waverunner",
+            "instructions": "You are a helpful assistant that creates slides.",
+            "base_environment": {
+                "type": "remote",
+                "sources": [
+                    {"type": "gcs", "source": "gs://eap-templates/slides-skill",
+                     "target": "/.agents/skills/slides-skill"}
+                ]
+            }
+        }'
+    ```
+    """
+    srv = _proxy_server_imports()
+    data = await _read_request_body(request=request)
+    # Merge litellm_params_template (e.g. custom_llm_provider, api_key) into the request
+    litellm_params_template = data.pop("litellm_params_template", None) or {}
+    if isinstance(litellm_params_template, dict):
+        for key, value in litellm_params_template.items():
+            if key not in data:
+                data[key] = value
+    data.setdefault("custom_llm_provider", "gemini")
+    _enforce_caller_supplied_provider_key(data, user_api_key_dict)
+
+    processor = ProxyBaseLLMRequestProcessing(data=data)
+    try:
+        return await processor.base_process_llm_request(
+            request=request,
+            fastapi_response=fastapi_response,
+            user_api_key_dict=user_api_key_dict,
+            route_type="acreate_agent",
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            llm_router=srv["llm_router"],
+            general_settings=srv["general_settings"],
+            proxy_config=srv["proxy_config"],
+            select_data_generator=srv["select_data_generator"],
+            model=None,
+            user_model=srv["user_model"],
+            user_temperature=srv["user_temperature"],
+            user_request_timeout=srv["user_request_timeout"],
+            user_max_tokens=srv["user_max_tokens"],
+            user_api_base=srv["user_api_base"],
+            version=srv["version"],
+        )
+    except Exception as e:
+        raise await processor._handle_llm_api_exception(
+            e=e,
+            user_api_key_dict=user_api_key_dict,
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            version=srv["version"],
+        )
+
+
+@router.get(
+    "/v1beta/agents",
+    dependencies=[Depends(user_api_key_auth)],
+    response_class=ORJSONResponse,
+)
+async def list_gemini_agents(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    List all custom agents on the Gemini side.
+
+    Pass per-request Gemini credentials via the JSON-encoded
+    ``litellm_params_template`` query parameter. Flat query parameters
+    (e.g. ``?api_key=AIza...``) are intentionally ignored — see
+    ``_merge_query_params_into_data`` for the rationale.
+
+    ```bash
+    curl "http://localhost:4000/v1beta/agents?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\
+        -H "Authorization: Bearer sk-..."
+    ```
+    """
+    srv = _proxy_server_imports()
+    data: dict = {"custom_llm_provider": "gemini"}
+    _merge_query_params_into_data(data, request)
+    _enforce_caller_supplied_provider_key(data, user_api_key_dict)
+
+    processor = ProxyBaseLLMRequestProcessing(data=data)
+    try:
+        return await processor.base_process_llm_request(
+            request=request,
+            fastapi_response=fastapi_response,
+            user_api_key_dict=user_api_key_dict,
+            route_type="alist_agents",
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            llm_router=srv["llm_router"],
+            general_settings=srv["general_settings"],
+            proxy_config=srv["proxy_config"],
+            select_data_generator=srv["select_data_generator"],
+            model=None,
+            user_model=srv["user_model"],
+            user_temperature=srv["user_temperature"],
+            user_request_timeout=srv["user_request_timeout"],
+            user_max_tokens=srv["user_max_tokens"],
+            user_api_base=srv["user_api_base"],
+            version=srv["version"],
+        )
+    except Exception as e:
+        raise await processor._handle_llm_api_exception(
+            e=e,
+            user_api_key_dict=user_api_key_dict,
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            version=srv["version"],
+        )
+
+
+@router.get(
+    "/v1beta/agents/{name}",
+    dependencies=[Depends(user_api_key_auth)],
+    response_class=ORJSONResponse,
+)
+async def get_gemini_agent(
+    request: Request,
+    name: str,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Get a specific custom agent by name.
+
+    Pass per-request Gemini credentials via the JSON-encoded
+    ``litellm_params_template`` query parameter. Flat query parameters
+    (e.g. ``?api_key=AIza...``) are intentionally ignored — see
+    ``_merge_query_params_into_data`` for the rationale.
+
+    ```bash
+    curl "http://localhost:4000/v1beta/agents/my-custom-slides-agent?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\
+        -H "Authorization: Bearer sk-..."
+    ```
+    """
+    srv = _proxy_server_imports()
+    data = {"name": name, "custom_llm_provider": "gemini"}
+    _merge_query_params_into_data(data, request)
+    _enforce_caller_supplied_provider_key(data, user_api_key_dict)
+
+    processor = ProxyBaseLLMRequestProcessing(data=data)
+    try:
+        return await processor.base_process_llm_request(
+            request=request,
+            fastapi_response=fastapi_response,
+            user_api_key_dict=user_api_key_dict,
+            route_type="aget_agent",
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            llm_router=srv["llm_router"],
+            general_settings=srv["general_settings"],
+            proxy_config=srv["proxy_config"],
+            select_data_generator=srv["select_data_generator"],
+            model=None,
+            user_model=srv["user_model"],
+            user_temperature=srv["user_temperature"],
+            user_request_timeout=srv["user_request_timeout"],
+            user_max_tokens=srv["user_max_tokens"],
+            user_api_base=srv["user_api_base"],
+            version=srv["version"],
+        )
+    except Exception as e:
+        raise await processor._handle_llm_api_exception(
+            e=e,
+            user_api_key_dict=user_api_key_dict,
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            version=srv["version"],
+        )
+
+
+@router.delete(
+    "/v1beta/agents/{name}",
+    dependencies=[Depends(user_api_key_auth)],
+    response_class=ORJSONResponse,
+)
+async def delete_gemini_agent(
+    request: Request,
+    name: str,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Delete a custom agent by name.
+
+    Pass per-request Gemini credentials via the JSON-encoded
+    ``litellm_params_template`` query parameter. Flat query parameters
+    (e.g. ``?api_key=AIza...``) are intentionally ignored — see
+    ``_merge_query_params_into_data`` for the rationale.
+
+    ```bash
+    curl -X DELETE "http://localhost:4000/v1beta/agents/my-custom-slides-agent?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\
+        -H "Authorization: Bearer sk-..."
+    ```
+    """
+    srv = _proxy_server_imports()
+    data = {"name": name, "custom_llm_provider": "gemini"}
+    _merge_query_params_into_data(data, request)
+    _enforce_caller_supplied_provider_key(data, user_api_key_dict)
+
+    processor = ProxyBaseLLMRequestProcessing(data=data)
+    try:
+        return await processor.base_process_llm_request(
+            request=request,
+            fastapi_response=fastapi_response,
+            user_api_key_dict=user_api_key_dict,
+            route_type="adelete_agent",
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            llm_router=srv["llm_router"],
+            general_settings=srv["general_settings"],
+            proxy_config=srv["proxy_config"],
+            select_data_generator=srv["select_data_generator"],
+            model=None,
+            user_model=srv["user_model"],
+            user_temperature=srv["user_temperature"],
+            user_request_timeout=srv["user_request_timeout"],
+            user_max_tokens=srv["user_max_tokens"],
+            user_api_base=srv["user_api_base"],
+            version=srv["version"],
+        )
+    except Exception as e:
+        raise await processor._handle_llm_api_exception(
+            e=e,
+            user_api_key_dict=user_api_key_dict,
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            version=srv["version"],
+        )
+
+
+@router.get(
+    "/v1beta/agents/{name}/versions",
+    dependencies=[Depends(user_api_key_auth)],
+    response_class=ORJSONResponse,
+)
+async def list_gemini_agent_versions(
+    request: Request,
+    name: str,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    List versions of a custom agent.
+
+    Pass per-request Gemini credentials via the JSON-encoded
+    ``litellm_params_template`` query parameter. Flat query parameters
+    (e.g. ``?api_key=AIza...``) are intentionally ignored — see
+    ``_merge_query_params_into_data`` for the rationale.
+
+    ```bash
+    curl "http://localhost:4000/v1beta/agents/my-custom-slides-agent/versions?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\
+        -H "Authorization: Bearer sk-..."
+    ```
+    """
+    srv = _proxy_server_imports()
+    data = {"name": name, "custom_llm_provider": "gemini"}
+    _merge_query_params_into_data(data, request)
+    _enforce_caller_supplied_provider_key(data, user_api_key_dict)
+
+    processor = ProxyBaseLLMRequestProcessing(data=data)
+    try:
+        return await processor.base_process_llm_request(
+            request=request,
+            fastapi_response=fastapi_response,
+            user_api_key_dict=user_api_key_dict,
+            route_type="alist_agent_versions",
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            llm_router=srv["llm_router"],
+            general_settings=srv["general_settings"],
+            proxy_config=srv["proxy_config"],
+            select_data_generator=srv["select_data_generator"],
+            model=None,
+            user_model=srv["user_model"],
+            user_temperature=srv["user_temperature"],
+            user_request_timeout=srv["user_request_timeout"],
+            user_max_tokens=srv["user_max_tokens"],
+            user_api_base=srv["user_api_base"],
+            version=srv["version"],
+        )
+    except Exception as e:
+        raise await processor._handle_llm_api_exception(
+            e=e,
+            user_api_key_dict=user_api_key_dict,
+            proxy_logging_obj=srv["proxy_logging_obj"],
+            version=srv["version"],
+        )
diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py
index 967ac9f0ac4..1f503247bf4 100644
--- a/litellm/proxy/google_endpoints/endpoints.py
+++ b/litellm/proxy/google_endpoints/endpoints.py
@@ -285,7 +285,7 @@ async def create_interaction(
             general_settings=general_settings,
             proxy_config=proxy_config,
             select_data_generator=select_data_generator,
-            model=data.get("model") or data.get("agent"),
+            model=data.get("model"),
             user_model=user_model,
             user_temperature=user_temperature,
             user_request_timeout=user_request_timeout,
diff --git a/litellm/proxy/guardrails/_content_utils.py b/litellm/proxy/guardrails/_content_utils.py
index 7cad1352a79..766ef0cf9f6 100644
--- a/litellm/proxy/guardrails/_content_utils.py
+++ b/litellm/proxy/guardrails/_content_utils.py
@@ -10,7 +10,6 @@ every text fragment.
 
 from typing import Any, Callable, Dict, FrozenSet, Iterator, List
 
-
 # Call types whose body carries free-form chat / prompt text that
 # text-content guardrails (banned keywords, content moderation, secret
 # detection, …) should inspect. The proxy ingress passes ``route_type``
diff --git a/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py
index c4aaea709ba..1e3dd906b9f 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py
@@ -4,7 +4,6 @@ from litellm.types.guardrails import SupportedGuardrailIntegrations
 
 from .akto import AktoGuardrail
 
-
 if TYPE_CHECKING:
     from litellm.types.guardrails import Guardrail, LitellmParams
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/azure/base.py b/litellm/proxy/guardrails/guardrail_hooks/azure/base.py
index fab65884a9f..7d2dfce0711 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/azure/base.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/azure/base.py
@@ -2,6 +2,9 @@ import re
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
 from litellm._logging import verbose_proxy_logger
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    get_last_user_message,
+)
 from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
     httpxSpecialProvider,
@@ -134,32 +137,4 @@ class AzureGuardrailBase:
         ]
         get_user_prompt(messages) -> "What is the weather in Tokyo?"
         """
-        from litellm.litellm_core_utils.prompt_templates.common_utils import (
-            convert_content_list_to_str,
-        )
-
-        if not messages:
-            return None
-
-        # Iterate from the end to find the last consecutive block of user messages
-        user_messages = []
-        for message in reversed(messages):
-            if message.get("role") == "user":
-                user_messages.append(message)
-            else:
-                # Stop when we hit a non-user message
-                break
-
-        if not user_messages:
-            return None
-
-        # Reverse to get the messages in chronological order
-        user_messages.reverse()
-
-        user_prompt = ""
-        for message in user_messages:
-            text_content = convert_content_list_to_str(message)
-            user_prompt += text_content + "\n"
-
-        result = user_prompt.strip()
-        return result if result else None
+        return get_last_user_message(messages)
diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
index bb1db3d62d2..765c419479e 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
@@ -63,6 +63,7 @@ from litellm.types.utils import (
     CallTypesLiteral,
     Choices,
     GuardrailStatus,
+    GuardrailTracingDetail,
     Message,
     ModelResponse,
     ModelResponseStream,
@@ -509,6 +510,8 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
         # Add guardrail information to request trace
         #########################################################
         _json_response = httpx_response.json()
+        tracing_detail = self._build_tracing_detail(_json_response)
+
         # Raw Bedrock JSON is passed here; match/regex redaction runs once inside
         # CustomGuardrail.add_standard_logging_guardrail_information_to_request_data.
         self.add_standard_logging_guardrail_information_to_request_data(
@@ -522,6 +525,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
             end_time=datetime.now().timestamp(),
             duration=(datetime.now() - start_time).total_seconds(),
             event_type=event_type,
+            tracing_detail=tracing_detail or None,
         )
         #########################################################
         if httpx_response.status_code == 200:
@@ -640,6 +644,55 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
                 return (status_code, err)
         return (status_code, message)
 
+    def _build_tracing_detail(
+        self, response: BedrockGuardrailResponse
+    ) -> GuardrailTracingDetail:
+        """
+        Build the tracing detail from the raw Bedrock response, before
+        redaction, so downstream loggers (OTEL, Langfuse, ...) get the
+        actual category names rather than the "[REDACTED]" sentinel that
+        replaces customWords.match later. Bedrock's top-level ``action``
+        field ("GUARDRAIL_INTERVENED" or "NONE") is also surfaced so the
+        OTEL integration can expose it as a queryable span attribute
+        without re-parsing the redacted guardrail_response blob.
+        """
+        tracing_detail: GuardrailTracingDetail = {}
+        violation_categories = self._extract_violation_category_names(response)
+        if violation_categories:
+            tracing_detail["violation_categories"] = violation_categories
+        bedrock_action = response.get("action")
+        if isinstance(bedrock_action, str):
+            tracing_detail["guardrail_action"] = bedrock_action
+        return tracing_detail
+
+    def _extract_violation_category_names(
+        self, response: BedrockGuardrailResponse
+    ) -> List[str]:
+        """
+        Flatten the BLOCKED assessments into a list of human-readable category
+        names suitable for queryable OTEL / standard-logging attributes.
+
+        SECURITY: only emits the non-sensitive policy *label* (topic name,
+        content-filter type, PII entity type, named-regex name). The raw
+        ``match`` field is intentionally NOT used — it carries the user's
+        original input that triggered the rule (e.g. a credit-card number
+        that hit a regex, or the literal custom word). Surfacing it to
+        telemetry would re-introduce the sensitive content the guardrail
+        was supposed to keep out. Entries that only have a ``match`` (bare
+        customWords, unnamed regexes) are therefore skipped — operators
+        can still see the count in ``_extract_blocked_assessments`` which
+        feeds the HTTP error detail.
+        """
+        names: List[str] = []
+        for block in self._extract_blocked_assessments(response):
+            for match in block.get("matches", []) or []:
+                # Allow-list non-sensitive labels only. Never fall back to
+                # `match.get("match")` — that's user-submitted content.
+                label = match.get("name") or match.get("type")
+                if isinstance(label, str) and label:
+                    names.append(label)
+        return names
+
     def _extract_blocked_assessments(
         self, response: BedrockGuardrailResponse
     ) -> List[dict]:
diff --git a/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py b/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py
index 5502076829f..0f299f4c5f7 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/mcp_jwt_signer/mcp_jwt_signer.py
@@ -92,6 +92,8 @@ from litellm.types.utils import CallTypesLiteral
 # Module-level singleton for the JWKS discovery endpoint to access.
 _mcp_jwt_signer_instance: Optional["MCPJWTSigner"] = None
 
+_MCP_JWT_CALL_TYPES = frozenset({"call_mcp_tool", "list_mcp_tools"})
+
 # Simple in-memory JWKS cache: keyed by JWKS URI → (keys_list, fetched_at).
 _jwks_cache: Dict[str, tuple] = {}
 _JWKS_CACHE_TTL = 3600  # 1 hour
@@ -603,17 +605,23 @@ class MCPJWTSigner(CustomGuardrail):
     # FR-10: Scope building
     # ------------------------------------------------------------------
 
-    def _build_scope(self, raw_tool_name: str) -> str:
+    def _build_scope(
+        self,
+        raw_tool_name: str,
+        call_type: Optional[CallTypesLiteral] = None,
+    ) -> str:
         """
         Build the JWT scope string.
 
         When allowed_scopes is configured: join them verbatim.
         Otherwise auto-generate minimal, least-privilege scopes:
           - Tool call   → mcp:tools/call  mcp:tools/<name>:call
-          - No tool     → mcp:tools/call  mcp:tools/list
+          - No tool     → mcp:tools/list
 
         NOTE: tools/list is intentionally NOT granted on tool-call JWTs to
         prevent callers from enumerating tools they didn't ask to use.
+        Conversely, tools/call is NOT granted on tools/list-only JWTs so an
+        intercepted list token cannot be replayed to invoke tools.
         """
         if self.allowed_scopes is not None:
             return " ".join(self.allowed_scopes)
@@ -623,8 +631,14 @@ class MCPJWTSigner(CustomGuardrail):
         )
         if tool_name:
             scopes = ["mcp:tools/call", f"mcp:tools/{tool_name}:call"]
+        elif call_type == "call_mcp_tool":
+            # Tool-call request reached the signer without a tool name (e.g.
+            # missing mcp_tool_name in hook data). Fall back to a generic
+            # tools/call scope so the upstream server still accepts the
+            # invocation rather than rejecting it as a tools/list-only token.
+            scopes = ["mcp:tools/call"]
         else:
-            scopes = ["mcp:tools/call", "mcp:tools/list"]
+            scopes = ["mcp:tools/list"]
         return " ".join(scopes)
 
     # ------------------------------------------------------------------
@@ -673,6 +687,7 @@ class MCPJWTSigner(CustomGuardrail):
         user_api_key_dict: UserAPIKeyAuth,
         data: dict,
         jwt_claims: Optional[Dict[str, Any]] = None,
+        call_type: Optional[CallTypesLiteral] = None,
     ) -> Dict[str, Any]:
         """
         Build JWT claims for the outbound MCP access token.
@@ -713,7 +728,7 @@ class MCPJWTSigner(CustomGuardrail):
 
         # scope (FR-10)
         raw_tool_name: str = data.get("mcp_tool_name", "")
-        claims["scope"] = self._build_scope(raw_tool_name)
+        claims["scope"] = self._build_scope(raw_tool_name, call_type=call_type)
 
         # optional_claims passthrough (FR-15)
         claims = self._passthrough_optional_claims(claims, jwt_claims)
@@ -779,16 +794,20 @@ class MCPJWTSigner(CustomGuardrail):
         Verifies the incoming token (when configured), validates required claims,
         then signs an outbound JWT and injects it as the Authorization header.
 
-        All non-MCP call types pass through unchanged.
+        Signs outbound MCP tool calls and tools/list requests.
         """
-        if call_type != "call_mcp_tool":
+        if call_type not in _MCP_JWT_CALL_TYPES:
             return data
 
+        hook_data = dict(data)
+        if call_type == "list_mcp_tools":
+            hook_data["mcp_tool_name"] = ""
+
         # ------------------------------------------------------------------
         # FR-5: Verify incoming token before re-signing
         # ------------------------------------------------------------------
         jwt_claims: Optional[Dict[str, Any]] = None
-        raw_token: Optional[str] = data.get("incoming_bearer_token")
+        raw_token: Optional[str] = hook_data.get("incoming_bearer_token")
 
         if self.access_token_discovery_uri and raw_token:
             # Three-dot pattern → JWT;  otherwise opaque.
@@ -837,7 +856,9 @@ class MCPJWTSigner(CustomGuardrail):
         # ------------------------------------------------------------------
         # Build outbound access token
         # ------------------------------------------------------------------
-        claims = self._build_claims(user_api_key_dict, data, jwt_claims)
+        claims = self._build_claims(
+            user_api_key_dict, hook_data, jwt_claims, call_type=call_type
+        )
 
         signed_token = jwt.encode(
             claims,
@@ -848,7 +869,7 @@ class MCPJWTSigner(CustomGuardrail):
 
         # Merge into existing extra_headers — a prior guardrail in the chain may
         # have already injected tracing headers or correlation IDs.
-        existing_headers: Dict[str, str] = data.get("extra_headers") or {}
+        existing_headers: Dict[str, str] = hook_data.get("extra_headers") or {}
         new_headers: Dict[str, str] = {
             **existing_headers,
             "Authorization": f"Bearer {signed_token}",
@@ -875,17 +896,74 @@ class MCPJWTSigner(CustomGuardrail):
                 claims, self._kid
             )
 
-        data["extra_headers"] = new_headers
+        hook_data["extra_headers"] = new_headers
 
         verbose_proxy_logger.debug(
             "MCPJWTSigner: signed JWT sub=%s act=%s tool=%s exp=%d "
-            "verified=%s channel=%s",
+            "verified=%s channel=%s call_type=%s",
             claims.get("sub"),
             claims.get("act", {}).get("sub"),
-            data.get("mcp_tool_name"),
+            hook_data.get("mcp_tool_name"),
             claims["exp"],
             jwt_claims is not None,
             bool(self.channel_token_audience),
+            call_type,
         )
 
-        return data
+        return hook_data
+
+
+async def inject_mcp_jwt_headers_for_upstream(
+    user_api_key_dict: Optional[UserAPIKeyAuth],
+    extra_headers: Optional[Dict[str, str]] = None,
+    raw_headers: Optional[Dict[str, str]] = None,
+    *,
+    for_list_tools: bool = False,
+    mcp_tool_name: str = "",
+) -> Dict[str, str]:
+    """
+    Sign outbound MCP headers when MCPJWTSigner is configured.
+
+    Used by tools/list paths that do not go through proxy pre_call_hook.
+    """
+    merged = dict(extra_headers or {})
+    signer = get_mcp_jwt_signer()
+    if signer is None or user_api_key_dict is None:
+        return merged
+
+    normalized_raw = {k.lower(): v for k, v in (raw_headers or {}).items()}
+    incoming_bearer_token: Optional[str] = None
+    auth_hdr = normalized_raw.get("authorization", "")
+    if auth_hdr.lower().startswith("bearer "):
+        incoming_bearer_token = auth_hdr[len("bearer ") :]
+
+    hook_data: Dict[str, Any] = {
+        "mcp_tool_name": "" if for_list_tools else mcp_tool_name,
+        "incoming_bearer_token": incoming_bearer_token,
+        "extra_headers": merged,
+    }
+    call_type: CallTypesLiteral = (
+        "list_mcp_tools" if for_list_tools else "call_mcp_tool"
+    )
+    try:
+        from litellm.proxy.proxy_server import (  # noqa: PLC0415
+            proxy_logging_obj as _proxy_logging,
+        )
+
+        shared_cache = (
+            _proxy_logging.internal_usage_cache.dual_cache
+            if _proxy_logging is not None
+            else DualCache()
+        )
+    except Exception:
+        shared_cache = DualCache()
+
+    result = await signer.async_pre_call_hook(
+        user_api_key_dict=user_api_key_dict,
+        cache=shared_cache,
+        data=hook_data,
+        call_type=call_type,
+    )
+    if isinstance(result, dict) and result.get("extra_headers"):
+        merged.update(result["extra_headers"])
+    return merged
diff --git a/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/__init__.py
new file mode 100644
index 00000000000..9ead2a63b60
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/__init__.py
@@ -0,0 +1,57 @@
+from typing import TYPE_CHECKING
+
+from litellm.types.guardrails import SupportedGuardrailIntegrations
+
+from .purview_dlp import MicrosoftPurviewDLPGuardrail
+
+if TYPE_CHECKING:
+    from litellm.types.guardrails import Guardrail, LitellmParams
+
+
+def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"):
+    import litellm
+
+    tenant_id = getattr(litellm_params, "tenant_id", None)
+    client_id = getattr(litellm_params, "client_id", None)
+
+    # client_secret can be passed via the standard api_key field or as
+    # a dedicated client_secret parameter.
+    client_secret = litellm_params.api_key or getattr(
+        litellm_params, "client_secret", None
+    )
+
+    if not tenant_id:
+        raise ValueError("Microsoft Purview: tenant_id is required")
+    if not client_id:
+        raise ValueError("Microsoft Purview: client_id is required")
+    if not client_secret:
+        raise ValueError("Microsoft Purview: client_secret (or api_key) is required")
+
+    guardrail_name = guardrail.get("guardrail_name")
+    if not guardrail_name:
+        raise ValueError("Microsoft Purview: guardrail_name is required")
+
+    purview_guardrail = MicrosoftPurviewDLPGuardrail(
+        guardrail_name=guardrail_name,
+        tenant_id=str(tenant_id),
+        client_id=str(client_id),
+        client_secret=str(client_secret),
+        purview_app_name=str(
+            getattr(litellm_params, "purview_app_name", None) or "LiteLLM"
+        ),
+        user_id_field=str(getattr(litellm_params, "user_id_field", None) or "user_id"),
+        event_hook=litellm_params.mode,
+        default_on=litellm_params.default_on,
+    )
+
+    litellm.logging_callback_manager.add_litellm_callback(purview_guardrail)
+    return purview_guardrail
+
+
+guardrail_initializer_registry = {
+    SupportedGuardrailIntegrations.MICROSOFT_PURVIEW.value: initialize_guardrail,
+}
+
+guardrail_class_registry = {
+    SupportedGuardrailIntegrations.MICROSOFT_PURVIEW.value: MicrosoftPurviewDLPGuardrail,
+}
diff --git a/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/base.py b/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/base.py
new file mode 100644
index 00000000000..a7ed1d40913
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/base.py
@@ -0,0 +1,515 @@
+import threading
+import time
+import uuid
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+from litellm._logging import verbose_proxy_logger
+from litellm.litellm_core_utils.url_utils import encode_url_path_segment
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    convert_content_list_to_str,
+)
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+
+if TYPE_CHECKING:
+    from litellm.types.llms.openai import AllMessageValues
+
+GRAPH_API_BASE = "https://graph.microsoft.com/v1.0"
+TOKEN_ENDPOINT_TEMPLATE = (
+    "https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token"
+)
+GRAPH_SCOPE = "https://graph.microsoft.com/.default"
+
+# Protection scope cache TTL in seconds (1 hour, per Microsoft recommendation).
+SCOPE_CACHE_TTL_SECONDS = 3600.0
+
+
+class PurviewGuardrailBase:
+    """
+    Base class for Microsoft Purview guardrails.
+
+    Manages OAuth2 client-credentials token acquisition, protection scope
+    computation with ETag caching, and authenticated POST calls to the
+    Microsoft Graph API.
+    """
+
+    def __init__(
+        self,
+        tenant_id: str,
+        client_id: str,
+        client_secret: str,
+        purview_app_name: str = "LiteLLM",
+        user_id_field: str = "user_id",
+        **kwargs: Any,
+    ):
+        # Forward remaining kwargs to the next class in the MRO
+        # (typically CustomGuardrail).
+        super().__init__(**kwargs)
+
+        self.async_handler = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.GuardrailCallback
+        )
+        self.tenant_id = tenant_id
+        self.client_id = client_id
+        self.client_secret = client_secret
+        self.purview_app_name = purview_app_name
+        self.user_id_field = user_id_field
+
+        # Token cache: (access_token, expires_at_epoch)
+        self._token_cache: Optional[Tuple[str, float]] = None
+
+        # Protection scope cache: user_id -> (etag, scope_response, fetched_at)
+        # Capped at 1000 entries (LRU eviction) to avoid unbounded growth.
+        self._scope_cache: OrderedDict[str, Tuple[str, Dict[str, Any], float]] = (
+            OrderedDict()
+        )
+        self._scope_cache_maxsize = 1000
+        # Use a threading.Lock (not asyncio.Lock) because this lock is acquired
+        # from both the proxy's main asyncio event loop and from short-lived
+        # event loops created by the logging_hook thread fallback.  In Python
+        # 3.10+ an asyncio.Lock is bound to the first event loop that acquires
+        # it and raises RuntimeError from any other loop, which would silently
+        # break audit logging via the thread fallback.  All critical sections
+        # below are pure in-memory dict ops with no awaits, so a synchronous
+        # lock is both correct and sufficient.
+        self._cache_lock = threading.Lock()
+
+    @staticmethod
+    def _encode_graph_user_id(user_id: str) -> str:
+        """Percent-encode Entra user id for Graph ``/users/{id}/...`` path segments."""
+        return encode_url_path_segment(user_id, field_name="user_id")
+
+    # ------------------------------------------------------------------
+    # OAuth2 token management
+    # ------------------------------------------------------------------
+
+    async def _get_access_token(self) -> str:
+        """Acquire or return cached OAuth2 token via client_credentials grant."""
+        now = time.time()
+        with self._cache_lock:
+            if self._token_cache and self._token_cache[1] > now + 60:
+                return self._token_cache[0]
+
+        url = TOKEN_ENDPOINT_TEMPLATE.format(tenant_id=self.tenant_id)
+        data = {
+            "grant_type": "client_credentials",
+            "client_id": self.client_id,
+            "client_secret": self.client_secret,
+            "scope": GRAPH_SCOPE,
+        }
+        response = await self.async_handler.post(
+            url=url,
+            data=data,
+            headers={"Content-Type": "application/x-www-form-urlencoded"},
+        )
+        response.raise_for_status()
+        token_data = response.json()
+        access_token = token_data["access_token"]
+        expires_in = int(token_data.get("expires_in", 3599))
+        # Recompute ``now`` after the await so the expiry reflects when the
+        # token was actually received, not when the request started.
+        with self._cache_lock:
+            self._token_cache = (access_token, time.time() + expires_in)
+        verbose_proxy_logger.debug(
+            "Purview: acquired new OAuth2 token (expires_in=%ds)", expires_in
+        )
+        return access_token
+
+    # ------------------------------------------------------------------
+    # Graph API helpers
+    # ------------------------------------------------------------------
+
+    async def _graph_post(
+        self,
+        url: str,
+        json_body: Dict[str, Any],
+        extra_headers: Optional[Dict[str, str]] = None,
+    ) -> Tuple[Dict[str, Any], Dict[str, str]]:
+        """POST to Graph API with bearer auth.
+
+        Returns:
+            Tuple of (response_json, response_headers).
+        """
+        token = await self._get_access_token()
+        headers = {
+            "Authorization": f"Bearer {token}",
+            "Content-Type": "application/json",
+        }
+        if extra_headers:
+            headers.update(extra_headers)
+
+        verbose_proxy_logger.debug("Purview Graph POST %s", url)
+        response = await self.async_handler.post(
+            url=url, headers=headers, json=json_body
+        )
+        response.raise_for_status()
+        response_json: Dict[str, Any] = response.json()
+        response_headers = dict(response.headers)
+        verbose_proxy_logger.debug("Purview Graph response: %s", response_json)
+        return response_json, response_headers
+
+    # ------------------------------------------------------------------
+    # Protection scopes
+    # ------------------------------------------------------------------
+
+    async def _compute_protection_scopes(
+        self, user_id: str
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Call protectionScopes/compute and cache with ETag.
+
+        Returns:
+            Tuple of (etag, scope_response).
+        """
+        encoded_user_id = self._encode_graph_user_id(user_id)
+        now = time.time()
+
+        with self._cache_lock:
+            cached = self._scope_cache.get(user_id)
+            if cached and (now - cached[2]) < SCOPE_CACHE_TTL_SECONDS:
+                self._scope_cache.move_to_end(user_id)
+                return cached[0], cached[1]
+
+        url = (
+            f"{GRAPH_API_BASE}/users/{encoded_user_id}"
+            "/dataSecurityAndGovernance/protectionScopes/compute"
+        )
+        body: Dict[str, Any] = {
+            "activities": "uploadText,downloadText",
+            "locations": [
+                {
+                    "@odata.type": "microsoft.graph.policyLocationApplication",
+                    "value": self.client_id,
+                }
+            ],
+        }
+
+        response_json, response_headers = await self._graph_post(url, body)
+        etag = response_headers.get("etag", response_headers.get("ETag", ""))
+
+        # Recompute ``now`` after the await so the TTL reflects when the
+        # scope response was actually received, not when the request started.
+        fetched_at = time.time()
+        with self._cache_lock:
+            self._scope_cache[user_id] = (etag, response_json, fetched_at)
+            # Move refreshed entry to the end so it is treated as most-recently-used.
+            # OrderedDict.__setitem__ preserves existing insertion order for known
+            # keys, so an explicit move_to_end() call is required.
+            self._scope_cache.move_to_end(user_id)
+            # Evict least-recently-used entry when cache exceeds max size.
+            while len(self._scope_cache) > self._scope_cache_maxsize:
+                self._scope_cache.popitem(last=False)
+        return etag, response_json
+
+    # ------------------------------------------------------------------
+    # Process content
+    # ------------------------------------------------------------------
+
+    async def _process_content(
+        self,
+        user_id: str,
+        text: str,
+        activity: str,
+        etag: str,
+        correlation_id: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Call processContent for DLP policy evaluation.
+
+        Args:
+            user_id: Entra object ID of the user.
+            text: The content to evaluate.
+            activity: ``"uploadText"`` for prompts, ``"downloadText"`` for responses.
+            etag: Cached ETag from protectionScopes/compute.
+            correlation_id: Optional conversation/thread ID.
+        """
+        encoded_user_id = self._encode_graph_user_id(user_id)
+        url = (
+            f"{GRAPH_API_BASE}/users/{encoded_user_id}"
+            "/dataSecurityAndGovernance/processContent"
+        )
+        body: Dict[str, Any] = {
+            "contentToProcess": {
+                "contentEntries": [
+                    {
+                        "@odata.type": "microsoft.graph.processConversationMetadata",
+                        "identifier": str(uuid.uuid4()),
+                        "content": {
+                            "@odata.type": "microsoft.graph.textContent",
+                            "data": text,
+                        },
+                        "name": f"{self.purview_app_name} message",
+                        "correlationId": correlation_id or str(uuid.uuid4()),
+                        "sequenceNumber": 0,
+                        "isTruncated": False,
+                    }
+                ],
+                "activityMetadata": {"activity": activity},
+                "deviceMetadata": {},
+                "protectedAppMetadata": {
+                    "name": self.purview_app_name,
+                    "version": "1.0",
+                    "applicationLocation": {
+                        "@odata.type": "microsoft.graph.policyLocationApplication",
+                        "value": self.client_id,
+                    },
+                },
+                "integratedAppMetadata": {
+                    "name": self.purview_app_name,
+                    "version": "1.0",
+                },
+            }
+        }
+
+        extra_headers: Dict[str, str] = {}
+        if etag:
+            extra_headers["If-None-Match"] = etag
+
+        response_json, _ = await self._graph_post(url, body, extra_headers)
+
+        # If policies changed, invalidate scope cache so next call re-fetches.
+        if response_json.get("protectionScopeState") == "modified":
+            with self._cache_lock:
+                self._scope_cache.pop(user_id, None)
+
+        return response_json
+
+    # ------------------------------------------------------------------
+    # User ID resolution
+    # ------------------------------------------------------------------
+
+    def _resolve_user_id(
+        self, data: Dict[str, Any], user_api_key_dict: Any
+    ) -> Optional[str]:
+        """Resolve the Entra user object ID from request data or auth context.
+
+        Returns the strongest available identity walking down four sources, in
+        decreasing trust order:
+
+            1. ``user_api_key_dict.user_id`` — LiteLLM key / JWT-bound user
+            2. ``user_api_key_dict.end_user_id`` — request-derived
+            3. ``metadata["user_api_key_user_id"]`` — proxy-injected from the key
+            4. ``metadata[user_id_field]`` — caller-supplied
+
+        Used only by blocking-mode resolution to disambiguate "no identity at
+        all" from "caller supplied an untrusted identity" for the error
+        message.  Neither blocking nor audit DLP feeds the untrusted
+        fallbacks (2, 4) into Purview itself.
+        """
+        trusted = self._resolve_trusted_user_id(data, user_api_key_dict)
+        if trusted:
+            return trusted
+
+        if hasattr(user_api_key_dict, "end_user_id") and user_api_key_dict.end_user_id:
+            return str(user_api_key_dict.end_user_id)
+
+        metadata = data.get("metadata") or data.get("litellm_metadata") or {}
+        uid = metadata.get("user_api_key_user_id")
+        if uid:
+            return str(uid)
+
+        uid = metadata.get(self.user_id_field)
+        if uid:
+            return str(uid)
+
+        return None
+
+    @staticmethod
+    def _logging_kwargs_metadata(kwargs: Dict[str, Any]) -> Dict[str, Any]:
+        """Metadata dict from ``model_call_details`` / logging kwargs."""
+        litellm_params = kwargs.get("litellm_params") or {}
+        if not isinstance(litellm_params, dict):
+            return {}
+        md = litellm_params.get("metadata")
+        return md if isinstance(md, dict) else {}
+
+    def _resolve_trusted_user_id(
+        self, data: Dict[str, Any], user_api_key_dict: Any
+    ) -> Optional[str]:
+        """Resolve user ID from API-key/JWT-bound identity for blocking DLP.
+
+        Uses only ``UserAPIKeyAuth.user_id`` (bound on the LiteLLM key or JWT).
+        Intentionally omits ``UserAPIKeyAuth.end_user_id`` because the proxy sets
+        it from caller-controlled request fields (``user``, ``metadata.user_id``,
+        ``safety_identifier``, custom headers, etc.) via
+        ``get_end_user_id_from_request_body``.
+
+        Also omits ``metadata[user_id_field]`` and
+        ``metadata["user_api_key_user_id"]`` for the same impersonation risk when
+        the key has no bound user.
+
+        Returns ``None`` when no authenticated identity is available.  Blocking
+        hooks must fail closed rather than skip the DLP check.
+        """
+        if hasattr(user_api_key_dict, "user_id") and user_api_key_dict.user_id:
+            return str(user_api_key_dict.user_id)
+
+        return None
+
+    def _resolve_user_id_from_logging_kwargs(
+        self, kwargs: Dict[str, Any]
+    ) -> Optional[str]:
+        """Trusted-identity-only resolver for logging-only hooks.
+
+        Uses only the proxy-injected ``user_api_key_user_id`` (populated from
+        the API-key/JWT-bound ``UserAPIKeyAuth.user_id`` after the proxy
+        strips every caller-supplied ``user_api_key_*`` key from the request
+        metadata).  Caller-influenceable sources (``user_api_key_end_user_id``,
+        ``metadata[user_id_field]``) are not used here so a caller cannot
+        cause Purview audit records to be written under a victim's identity.
+        Returns ``None`` when no trusted identity is available so the audit
+        is skipped rather than misattributed.
+        """
+        md = self._logging_kwargs_metadata(kwargs)
+        uid = md.get("user_api_key_user_id") or kwargs.get("user_api_key_user_id")
+        if uid:
+            return str(uid)
+        return None
+
+    # ------------------------------------------------------------------
+    # Policy action evaluation
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _should_block(response: Dict[str, Any]) -> bool:
+        """Return True if any policyAction requires blocking."""
+        for action in response.get("policyActions", []):
+            odata_type = action.get("@odata.type", "")
+            action_field = action.get("action", "")
+
+            if "restrictAccessAction" in odata_type or action_field == "restrictAccess":
+                restriction = action.get("restrictionAction", "")
+                if restriction == "block":
+                    return True
+        return False
+
+    # ------------------------------------------------------------------
+    # Prompt text for DLP
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def is_token_id_prompt(prompt: Any) -> bool:
+        """Return True if ``prompt`` carries OpenAI completions token ids.
+
+        Covers every list shape that ``completion_prompt_to_str`` cannot decode
+        for Purview, including flat ``list[int]`` (single token-id prompt),
+        ``list[list[int]]`` (multi-prompt token-id batches), and mixed lists
+        that include any token-id sub-array.
+        """
+        if not isinstance(prompt, list) or not prompt:
+            return False
+        for x in prompt:
+            if isinstance(x, int):
+                return True
+            if isinstance(x, list) and x and any(isinstance(y, int) for y in x):
+                return True
+        return False
+
+    @staticmethod
+    def completion_prompt_to_str(prompt: Any) -> Optional[str]:
+        """Normalize OpenAI ``/v1/completions`` ``prompt`` for text DLP.
+
+        Supports string prompts and list-of-string prompts. List-of-token-id prompts
+        are skipped (no plaintext for Purview to evaluate).
+        """
+        if prompt is None:
+            return None
+        if isinstance(prompt, str):
+            stripped = prompt.strip()
+            return stripped or None
+        if isinstance(prompt, list) and prompt:
+            if all(isinstance(x, str) for x in prompt):
+                joined = "\n".join(s.strip() for s in prompt if isinstance(s, str))
+                return joined.strip() or None
+            if all(isinstance(x, int) for x in prompt):
+                verbose_proxy_logger.debug(
+                    "Purview DLP: completions prompt is token ids only; skipping text scan"
+                )
+                return None
+            str_parts = [x for x in prompt if isinstance(x, str)]
+            if str_parts:
+                joined = "\n".join(s.strip() for s in str_parts)
+                return joined.strip() or None
+        return None
+
+    @staticmethod
+    def _extract_tool_call_args_from_message(message: Any) -> List[str]:
+        """Return plaintext arguments strings from tool_calls and function_call fields.
+
+        Covers both the request path (assistant messages in chat histories that
+        carry tool_calls / function_call) and the response path (model-generated
+        tool calls returned in a ModelResponse).  Both dict-style and object-style
+        representations are handled.
+        """
+        args: List[str] = []
+
+        # tool_calls: [{"function": {"arguments": "..."}}]
+        tool_calls = (
+            message.get("tool_calls")
+            if isinstance(message, dict)
+            else getattr(message, "tool_calls", None)
+        )
+        if tool_calls:
+            for tc in tool_calls:
+                fn = (
+                    tc.get("function")
+                    if isinstance(tc, dict)
+                    else getattr(tc, "function", None)
+                )
+                if fn is None:
+                    continue
+                arguments = (
+                    fn.get("arguments")
+                    if isinstance(fn, dict)
+                    else getattr(fn, "arguments", None)
+                )
+                if isinstance(arguments, str) and arguments.strip():
+                    args.append(arguments)
+
+        # Legacy function_call: {"arguments": "..."}
+        function_call = (
+            message.get("function_call")
+            if isinstance(message, dict)
+            else getattr(message, "function_call", None)
+        )
+        if function_call is not None:
+            arguments = (
+                function_call.get("arguments")
+                if isinstance(function_call, dict)
+                else getattr(function_call, "arguments", None)
+            )
+            if isinstance(arguments, str) and arguments.strip():
+                args.append(arguments)
+
+        return args
+
+    def get_prompt_text_for_dlp(
+        self, messages: List["AllMessageValues"]
+    ) -> Optional[str]:
+        """Concatenate text from every chat message (all roles) for pre-call DLP.
+
+        Evaluates the same payload the model receives, not only the trailing user
+        turn.  Each message is separated by ``\\n\\n`` so that tokens at message
+        boundaries are not merged (e.g., ``"end of msg1\\n\\nstart of msg2"``
+        rather than ``"end of msg1start of msg2"``), which preserves DLP pattern
+        detection accuracy across message boundaries.
+
+        Tool-call arguments (``tool_calls[].function.arguments`` and
+        ``function_call.arguments``) are included alongside message content so
+        that sensitive data hidden in function arguments is not bypassed.
+        """
+        if not messages:
+            return None
+        parts: List[str] = []
+        for msg in messages:
+            segments: List[str] = []
+            content = convert_content_list_to_str(message=msg).strip()
+            if content:
+                segments.append(content)
+            segments.extend(self._extract_tool_call_args_from_message(msg))
+            combined = "\n".join(segments)
+            if combined.strip():
+                parts.append(combined.strip())
+        text = "\n\n".join(parts)
+        return text or None
diff --git a/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/purview_dlp.py b/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/purview_dlp.py
new file mode 100644
index 00000000000..ee0bac64d4f
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/microsoft_purview/purview_dlp.py
@@ -0,0 +1,734 @@
+"""
+Microsoft Purview DLP Guardrail for LiteLLM.
+
+Supports three modes:
+- pre_call:      Block sensitive data in prompts before they reach the LLM.
+- post_call:     Block sensitive data in LLM responses.
+- logging_only:  Log interactions to Purview for audit/compliance without blocking.
+"""
+
+import asyncio
+import threading
+import uuid
+from datetime import datetime
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncGenerator,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+    cast,
+)
+
+import httpx
+from fastapi import HTTPException
+
+from litellm._logging import verbose_proxy_logger
+from litellm.integrations.custom_guardrail import (
+    CustomGuardrail,
+    log_guardrail_information,
+)
+from litellm.types.guardrails import GuardrailEventHooks
+from litellm.types.utils import (
+    Choices,
+    GuardrailStatus,
+    ModelResponse,
+    ModelResponseStream,
+    ResponsesAPIResponse,
+    TextChoices,
+    TextCompletionResponse,
+)
+
+from .base import PurviewGuardrailBase
+
+if TYPE_CHECKING:
+    from litellm.proxy._types import UserAPIKeyAuth
+    from litellm.types.proxy.guardrails.guardrail_hooks.base import (
+        GuardrailConfigModel,
+    )
+    from litellm.types.utils import (
+        CallTypesLiteral,
+        EmbeddingResponse,
+        ImageResponse,
+    )
+
+
+class MicrosoftPurviewDLPGuardrail(PurviewGuardrailBase, CustomGuardrail):
+    """
+    Microsoft Purview DLP guardrail.
+
+    Evaluates prompts and responses against Microsoft Purview DLP policies
+    via the Microsoft Graph ``processContent`` API.
+    """
+
+    def __init__(
+        self,
+        guardrail_name: str,
+        tenant_id: str,
+        client_id: str,
+        client_secret: str,
+        purview_app_name: str = "LiteLLM",
+        user_id_field: str = "user_id",
+        **kwargs: Any,
+    ):
+        supported_event_hooks = [
+            GuardrailEventHooks.pre_call,
+            GuardrailEventHooks.post_call,
+            GuardrailEventHooks.logging_only,
+        ]
+
+        super().__init__(
+            tenant_id=tenant_id,
+            client_id=client_id,
+            client_secret=client_secret,
+            purview_app_name=purview_app_name,
+            user_id_field=user_id_field,
+            guardrail_name=guardrail_name,
+            supported_event_hooks=supported_event_hooks,
+            **kwargs,
+        )
+        self.guardrail_provider = "microsoft_purview"
+        verbose_proxy_logger.info(
+            "Initialized Microsoft Purview DLP Guardrail: %s",
+            guardrail_name,
+        )
+
+    @staticmethod
+    def get_config_model() -> Optional[Type["GuardrailConfigModel"]]:
+        return None  # Config model can be added later for UI support
+
+    # ------------------------------------------------------------------
+    # Core DLP check
+    # ------------------------------------------------------------------
+
+    async def _check_content(
+        self,
+        user_id: str,
+        text: str,
+        activity: str,
+        request_data: Dict[str, Any],
+        block_on_violation: bool = True,
+    ) -> Dict[str, Any]:
+        """Evaluate content against Purview DLP policies.
+
+        Args:
+            user_id: Entra object ID.
+            text: Content to evaluate.
+            activity: ``"uploadText"`` or ``"downloadText"``.
+            request_data: Original request dict (used for logging metadata).
+            block_on_violation: If False, log only — do not raise.
+
+        Returns:
+            The processContent response dict.
+        """
+        start_time = datetime.now()
+        status: GuardrailStatus = "success"
+        response: Dict[str, Any] = {}
+
+        try:
+            etag, _ = await self._compute_protection_scopes(user_id)
+            correlation_id = request_data.get("litellm_call_id") or str(uuid.uuid4())
+            response = await self._process_content(
+                user_id=user_id,
+                text=text,
+                activity=activity,
+                etag=etag,
+                correlation_id=correlation_id,
+            )
+
+            if self._should_block(response):
+                status = "guardrail_intervened"
+        except HTTPException:
+            status = "guardrail_failed_to_respond"
+            raise
+        except httpx.HTTPStatusError as exc:
+            # Preserve the upstream Graph API status code (e.g. 429, 503) so
+            # callers can distinguish a transient infrastructure error from a
+            # DLP policy block (signaled separately as HTTP 400 below) and can
+            # implement retry-after handling on rate limits.  401/403 upstream
+            # responses indicate a proxy-side credential / consent problem the
+            # caller can do nothing about, so they are mapped to 502.
+            status = "guardrail_failed_to_respond"
+            if block_on_violation:
+                upstream_status = exc.response.status_code
+                client_status = (
+                    502 if upstream_status in (401, 403) else upstream_status
+                )
+                headers: Optional[Dict[str, str]] = None
+                retry_after = exc.response.headers.get("retry-after")
+                if retry_after:
+                    headers = {"Retry-After": retry_after}
+                raise HTTPException(
+                    status_code=client_status,
+                    detail={
+                        "error": "Microsoft Purview DLP: upstream policy evaluation failed",
+                        "activity": activity,
+                        "upstream_status": upstream_status,
+                        "exception": str(exc),
+                    },
+                    headers=headers,
+                ) from exc
+            verbose_proxy_logger.warning(
+                "Purview DLP: API/network error in logging-only mode (not re-raised): %s",
+                exc,
+            )
+        except Exception as exc:
+            status = "guardrail_failed_to_respond"
+            if block_on_violation:
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": "Microsoft Purview DLP: upstream policy evaluation failed",
+                        "activity": activity,
+                        "exception": str(exc),
+                    },
+                ) from exc
+            verbose_proxy_logger.warning(
+                "Purview DLP: API/network error in logging-only mode (not re-raised): %s",
+                exc,
+            )
+        finally:
+            end_time = datetime.now()
+            self.add_standard_logging_guardrail_information_to_request_data(
+                guardrail_provider=self.guardrail_provider,
+                guardrail_json_response=response,
+                request_data=request_data,
+                guardrail_status=status,
+                start_time=start_time.timestamp(),
+                end_time=end_time.timestamp(),
+                duration=(end_time - start_time).total_seconds(),
+            )
+
+        if block_on_violation and status == "guardrail_intervened":
+            raise HTTPException(
+                status_code=400,
+                detail={
+                    "error": "Microsoft Purview DLP: Content blocked by policy",
+                    "activity": activity,
+                },
+            )
+
+        return response
+
+    @staticmethod
+    def _extract_responses_api_function_call_args(result: Any) -> List[str]:
+        """Return tool-call argument strings from a ``ResponsesAPIResponse.output``.
+
+        ``ResponsesAPIResponse.output_text`` only aggregates ``output_text``
+        content blocks and ignores ``function_call`` items.  Model-generated
+        tool-call arguments can themselves contain sensitive data, so we
+        extract them explicitly to keep DLP coverage consistent with the
+        chat (``ModelResponse``) path.
+        """
+        args: List[str] = []
+        output = getattr(result, "output", None)
+        if not output:
+            return args
+        for item in output:
+            if isinstance(item, dict):
+                item_type = item.get("type")
+                arguments = item.get("arguments")
+            else:
+                item_type = getattr(item, "type", None)
+                arguments = getattr(item, "arguments", None)
+            if item_type == "function_call" and isinstance(arguments, str):
+                if arguments.strip():
+                    args.append(arguments)
+        return args
+
+    def _completion_response_text_parts(self, result: Any) -> List[str]:
+        """Collect non-empty text segments from chat, text completions, or responses API.
+
+        Includes assistant message content *and* model-generated tool-call
+        arguments so that sensitive data returned inside function calls is not
+        missed by the DLP scan.
+        """
+        parts: List[str] = []
+        if isinstance(result, TextCompletionResponse) and result.choices:
+            for text_choice in result.choices:
+                if not isinstance(text_choice, TextChoices):
+                    continue
+                raw = text_choice.get("text")
+                if isinstance(raw, str) and raw.strip():
+                    parts.append(raw)
+        elif isinstance(result, ResponsesAPIResponse):
+            text = result.output_text
+            if text and text.strip():
+                parts.append(text)
+            # Include tool-call arguments from ``function_call`` output items
+            # (``output_text`` ignores them).
+            parts.extend(self._extract_responses_api_function_call_args(result))
+        elif isinstance(result, ModelResponse) and result.choices:
+            for chat_choice in result.choices:
+                if not isinstance(chat_choice, Choices):
+                    continue
+                msg = chat_choice.message
+                if msg is None:
+                    continue
+                raw = (
+                    msg.get("content")
+                    if isinstance(msg, dict)
+                    else getattr(msg, "content", None)
+                )
+                if isinstance(raw, str) and raw.strip():
+                    parts.append(raw)
+                # Include tool-call arguments returned by the model
+                parts.extend(self._extract_tool_call_args_from_message(msg))
+        return parts
+
+    def _assemble_responses_api_from_chunks(
+        self, chunks: List[Any]
+    ) -> Tuple[bool, Optional[ResponsesAPIResponse]]:
+        """Extract the final ``ResponsesAPIResponse`` from a buffered Responses API stream.
+
+        Returns a ``(is_responses_api_stream, assembled)`` tuple so the caller
+        can distinguish "not a Responses API stream" (fall through to
+        ``stream_chunk_builder``) from "Responses API stream but no final
+        response event was received" (fail closed with an accurate error).
+        When the stream is a Responses API stream the latest event carrying a
+        ``ResponsesAPIResponse`` body is returned (``response.completed``, or
+        ``response.failed`` / ``response.incomplete`` as fallbacks).
+        """
+        looks_like_responses_api = False
+        final: Optional[ResponsesAPIResponse] = None
+        for chunk in chunks:
+            event_type = getattr(chunk, "type", None)
+            if isinstance(event_type, str) and event_type.startswith("response."):
+                looks_like_responses_api = True
+            candidate = getattr(chunk, "response", None)
+            if isinstance(candidate, ResponsesAPIResponse):
+                final = candidate
+        return looks_like_responses_api, final
+
+    def _responses_api_input_to_str(
+        self, data: Dict[str, Any], raise_on_failure: bool = False
+    ) -> Optional[str]:
+        """Extract DLP-scannable text from a Responses API request ``input`` field.
+
+        ``input`` may be a plain string or a list of input items (messages).  In
+        the latter case the items are converted to chat messages via the standard
+        LiteLLM transformation and then concatenated by ``get_prompt_text_for_dlp``.
+
+        When ``raise_on_failure`` is True (blocking mode), a transformation error
+        raises ``HTTPException`` so the request is fail-closed.  In logging-only
+        mode the error is swallowed and ``None`` is returned so audit attempts on
+        the response side can still run.
+        """
+        from litellm.responses.litellm_completion_transformation.transformation import (
+            LiteLLMCompletionResponsesConfig,
+        )
+
+        input_data = data.get("input")
+        if input_data is None and not data.get("instructions"):
+            return None
+        try:
+            # Always transform via messages so ``instructions`` become a system message
+            # (string ``input`` alone would skip instructions and bypass DLP).
+            messages = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages(
+                input=input_data if input_data is not None else "",
+                responses_api_request=data,
+            )
+            return self.get_prompt_text_for_dlp(cast(List[Any], messages))
+        except Exception:
+            verbose_proxy_logger.warning(
+                "Purview DLP: failed to transform responses API input",
+                exc_info=True,
+            )
+            if raise_on_failure:
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": (
+                            "Microsoft Purview DLP: Responses API input could "
+                            "not be transformed for DLP scanning in blocking mode"
+                        ),
+                    },
+                )
+            return None
+
+    # ------------------------------------------------------------------
+    # Identity resolution for blocking modes
+    # ------------------------------------------------------------------
+
+    def _resolve_user_id_for_blocking(
+        self,
+        data: Dict[str, Any],
+        user_api_key_dict: Any,
+    ) -> str:
+        """Resolve user ID for blocking (pre_call / post_call) DLP hooks.
+
+        Uses only trusted proxy-authenticated sources (``_resolve_trusted_user_id``).
+        Caller-supplied ``UserAPIKeyAuth.end_user_id`` (from request ``user``,
+        ``metadata.user_id``, ``safety_identifier``, etc.) and
+        ``metadata[user_id_field]`` are rejected (fail closed) because they can
+        impersonate another Entra user's Purview policy.
+
+        Raises ``HTTPException`` when no API-key-bound ``user_id`` exists or when
+        only caller-influenceable identity fields are available (fail closed).
+        """
+        trusted_id = self._resolve_trusted_user_id(data, user_api_key_dict)
+        if trusted_id:
+            return trusted_id
+
+        if self._resolve_user_id(data, user_api_key_dict):
+            raise HTTPException(
+                status_code=400,
+                detail={
+                    "error": (
+                        "Microsoft Purview DLP: No proxy-authenticated user identity; "
+                        "bind user_id to the API key (caller-supplied metadata cannot "
+                        "be used for blocking DLP)"
+                    ),
+                },
+            )
+
+        raise HTTPException(
+            status_code=400,
+            detail={
+                "error": (
+                    "Microsoft Purview DLP: No proxy-authenticated user identity; "
+                    "bind user_id to the API key for blocking DLP"
+                ),
+            },
+        )
+
+    # ------------------------------------------------------------------
+    # Pre-call hook — DLP on prompts
+    # ------------------------------------------------------------------
+
+    @log_guardrail_information
+    async def async_pre_call_hook(
+        self,
+        user_api_key_dict: "UserAPIKeyAuth",
+        cache: Any,
+        data: Dict[str, Any],
+        call_type: "CallTypesLiteral",
+    ) -> Optional[Dict[str, Any]]:
+        """Check user prompt against Purview DLP policies before LLM call."""
+        user_id = self._resolve_user_id_for_blocking(data, user_api_key_dict)
+
+        prompt_text: Optional[str] = None
+        if call_type in ("responses", "aresponses"):
+            # Route Responses API calls to the responses-specific extractor
+            # before the generic ``messages`` branch.  This mirrors
+            # ``async_logging_hook`` and ensures ``instructions`` (system
+            # prompt) content is included in the DLP scan, and prevents a
+            # crafted ``messages`` key in the request from being scanned in
+            # place of the actual ``input``.
+            prompt_text = self._responses_api_input_to_str(data, raise_on_failure=True)
+        elif call_type in ("text_completion", "atext_completion"):
+            raw_prompt = data.get("prompt")
+            # Reject every token-id prompt shape Purview cannot evaluate —
+            # flat ``list[int]`` (single prompt), ``list[list[int]]`` (multi-prompt
+            # batches), and mixed lists that include any token-id sub-array.
+            # Empty/whitespace-only strings also yield ``prompt_text is None`` but
+            # contain no sensitive data and pass through harmlessly below.
+            if self.is_token_id_prompt(raw_prompt):
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": (
+                            "Microsoft Purview DLP: Token-id completion prompts "
+                            "cannot be scanned for DLP in blocking mode"
+                        ),
+                    },
+                )
+            prompt_text = self.completion_prompt_to_str(raw_prompt)
+        else:
+            messages: Optional[List] = data.get("messages")
+            if messages:
+                prompt_text = self.get_prompt_text_for_dlp(cast(List[Any], messages))
+
+        if not prompt_text:
+            return data
+
+        await self._check_content(
+            user_id=user_id,
+            text=prompt_text,
+            activity="uploadText",
+            request_data=data,
+            block_on_violation=True,
+        )
+        return data
+
+    # ------------------------------------------------------------------
+    # Post-call hook — DLP on responses
+    # ------------------------------------------------------------------
+
+    @log_guardrail_information
+    async def async_post_call_success_hook(
+        self,
+        data: dict,
+        user_api_key_dict: "UserAPIKeyAuth",
+        response: Union[Any, ModelResponse, "EmbeddingResponse", "ImageResponse"],
+    ) -> Any:
+        """Check LLM response against Purview DLP policies (non-streaming only).
+
+        Streaming responses are handled by ``async_post_call_streaming_iterator_hook``
+        which buffers all chunks before scanning.  The proxy automatically skips
+        this hook for requests that have a streaming iterator hook defined.
+        """
+        user_id = self._resolve_user_id_for_blocking(data, user_api_key_dict)
+
+        parts = self._completion_response_text_parts(response)
+
+        if parts:
+            combined = "\n\n---\n\n".join(parts)
+            await self._check_content(
+                user_id=user_id,
+                text=combined,
+                activity="downloadText",
+                request_data=data,
+                block_on_violation=True,
+            )
+        return response
+
+    async def async_post_call_streaming_iterator_hook(
+        self,
+        user_api_key_dict: "UserAPIKeyAuth",
+        response: Any,
+        request_data: dict,
+    ) -> AsyncGenerator[ModelResponseStream, None]:
+        """Check streaming LLM responses against Purview DLP policies.
+
+        All chunks are buffered before the DLP scan so that no content is
+        delivered to the client if a policy violation is detected.  After a
+        clean scan the assembled response is re-yielded chunk-by-chunk via a
+        ``MockResponseIterator`` so the caller receives normal streaming output.
+
+        The proxy automatically skips ``async_post_call_success_hook`` for
+        guardrails that define this method, preventing duplicate scans.
+        """
+        from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
+        from litellm.main import stream_chunk_builder
+
+        # Resolve user ID up-front so identity failures don't waste work
+        # buffering and assembling the stream.
+        user_id = self._resolve_user_id_for_blocking(request_data, user_api_key_dict)
+
+        # Buffer the entire stream before any DLP scan.
+        all_chunks: List[ModelResponseStream] = []
+        async for chunk in response:
+            all_chunks.append(chunk)
+
+        # Responses API streams emit typed events (e.g. ``response.completed``)
+        # whose final event carries the full ``ResponsesAPIResponse`` — these
+        # are not understood by ``stream_chunk_builder`` (which is built for
+        # chat/text-completion deltas). Detect and scan them via the same
+        # ``_completion_response_text_parts`` path used by non-streaming.
+        (
+            is_responses_api_stream,
+            responses_api_assembled,
+        ) = self._assemble_responses_api_from_chunks(all_chunks)
+        if is_responses_api_stream:
+            if responses_api_assembled is None:
+                # Fail closed: Responses API events were seen but no final
+                # ``response.completed`` / ``response.failed`` /
+                # ``response.incomplete`` event carrying a ``ResponsesAPIResponse``
+                # body was received, so we cannot scan the content.
+                raise HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": (
+                            "Microsoft Purview DLP: Incomplete Responses API "
+                            "stream — no final response event received for "
+                            "DLP scanning; blocking response."
+                        ),
+                    },
+                )
+            parts = self._completion_response_text_parts(responses_api_assembled)
+            if parts:
+                combined = "\n\n---\n\n".join(parts)
+                await self._check_content(
+                    user_id=user_id,
+                    text=combined,
+                    activity="downloadText",
+                    request_data=request_data,
+                    block_on_violation=True,
+                )
+            for chunk in all_chunks:
+                yield chunk
+            return
+
+        assembled_response = stream_chunk_builder(chunks=all_chunks)
+
+        if assembled_response is None and all_chunks:
+            # Fail closed: stream_chunk_builder dropped all chunks, so we cannot
+            # scan the content. Refuse to release the buffered chunks.
+            raise HTTPException(
+                status_code=400,
+                detail={
+                    "error": (
+                        "Microsoft Purview DLP: Unable to assemble streamed "
+                        "response for scanning; blocking response."
+                    ),
+                },
+            )
+
+        if isinstance(
+            assembled_response, (TextCompletionResponse, ResponsesAPIResponse)
+        ):
+            parts = self._completion_response_text_parts(assembled_response)
+            if parts:
+                combined = "\n\n---\n\n".join(parts)
+                await self._check_content(
+                    user_id=user_id,
+                    text=combined,
+                    activity="downloadText",
+                    request_data=request_data,
+                    block_on_violation=True,
+                )
+            for chunk in all_chunks:
+                yield chunk
+            return
+
+        if not isinstance(assembled_response, ModelResponse):
+            # Non-content response (e.g. embeddings) — pass through unchanged.
+            for chunk in all_chunks:
+                yield chunk
+            return
+
+        parts = self._completion_response_text_parts(assembled_response)
+        if parts:
+            combined = "\n\n---\n\n".join(parts)
+            # Raises HTTPException(400) on violation — no chunks are yielded.
+            await self._check_content(
+                user_id=user_id,
+                text=combined,
+                activity="downloadText",
+                request_data=request_data,
+                block_on_violation=True,
+            )
+
+        # DLP passed — re-yield chunks from the assembled chat response.
+        mock_response = MockResponseIterator(model_response=assembled_response)
+        async for chunk in mock_response:
+            yield chunk
+
+    # ------------------------------------------------------------------
+    # Logging-only hook — audit without blocking
+    # ------------------------------------------------------------------
+
+    def logging_hook(
+        self, kwargs: dict, result: Any, call_type: str
+    ) -> Tuple[dict, Any]:
+        """Fire-and-forget async audit logging; returns original (kwargs, result) immediately.
+
+        In the proxy's async success path, litellm independently calls both
+        ``logging_hook`` (sync) and ``async_logging_hook`` (async) for every
+        ``CustomGuardrail`` callback.  To avoid making two complete sets of
+        Purview API calls per request, this sync hook is a no-op whenever an
+        event loop is running — the framework's async path will invoke
+        ``async_logging_hook`` directly.
+
+        For genuine sync-only call paths (no running event loop, so the async
+        success handler will not fire either), schedule ``async_logging_hook``
+        on a short-lived background daemon thread so audit logging still runs
+        without blocking the caller on two Graph API round-trips.
+        """
+
+        try:
+            asyncio.get_running_loop()
+            # Async context — let the framework's async success handler invoke
+            # async_logging_hook to avoid duplicate Purview API calls.  Log so
+            # the deferral is observable if the framework ever stops dispatching
+            # async_logging_hook on a given code path (otherwise audit silently
+            # drops).
+            verbose_proxy_logger.debug(
+                "Purview audit: deferring to async_logging_hook (running event loop detected)"
+            )
+            return kwargs, result
+        except RuntimeError:
+            pass
+
+        async def _log_safe() -> None:
+            try:
+                await self.async_logging_hook(
+                    kwargs=kwargs, result=result, call_type=call_type
+                )
+            except Exception as exc:
+                verbose_proxy_logger.error(
+                    "Purview audit background logging error: %s", exc
+                )
+
+        def _run_in_new_loop() -> None:
+            new_loop = asyncio.new_event_loop()
+            try:
+                asyncio.set_event_loop(new_loop)
+                new_loop.run_until_complete(_log_safe())
+            finally:
+                new_loop.close()
+                asyncio.set_event_loop(None)
+
+        thread = threading.Thread(target=_run_in_new_loop, daemon=True)
+        thread.start()
+
+        return kwargs, result
+
+    async def async_logging_hook(
+        self, kwargs: dict, result: Any, call_type: str
+    ) -> Tuple[dict, Any]:
+        """Send both prompt and response to Purview for audit logging.
+
+        Errors are logged but never raised — this mode is non-blocking.
+        Each audit call (prompt and response) is wrapped in its own try/except
+        so a failure on the first does not prevent the second from running.
+        """
+        user_id = self._resolve_user_id_from_logging_kwargs(kwargs)
+        if not user_id:
+            verbose_proxy_logger.debug("Purview audit: no user_id, skipping")
+            return kwargs, result
+
+        # Log prompt (uploadText)
+        try:
+            prompt_text: Optional[str] = None
+            if call_type in ("responses", "aresponses"):
+                # Responses API: route to the responses-specific extractor
+                # before the generic ``messages`` branch.  litellm's logging
+                # pipeline stores the raw responses ``input`` (a string or a
+                # list of input items) under ``model_call_details["messages"]``
+                # via ``function_setup``, which is NOT the chat message format
+                # ``get_prompt_text_for_dlp`` expects.  Use the original
+                # ``input`` / ``instructions`` keys that ``pre_call`` and
+                # ``update_environment_variables`` persist on the call details.
+                prompt_text = self._responses_api_input_to_str(kwargs)
+            elif call_type in ("text_completion", "atext_completion"):
+                prompt_text = self.completion_prompt_to_str(kwargs.get("prompt"))
+            else:
+                messages = kwargs.get("messages")
+                if messages:
+                    prompt_text = self.get_prompt_text_for_dlp(
+                        cast(List[Any], messages)
+                    )
+
+            if prompt_text:
+                await self._check_content(
+                    user_id=user_id,
+                    text=prompt_text,
+                    activity="uploadText",
+                    request_data=kwargs,
+                    block_on_violation=False,
+                )
+        except Exception as e:
+            verbose_proxy_logger.error("Purview audit logging error (prompt): %s", e)
+
+        # Log response (downloadText) — runs regardless of prompt audit outcome
+        try:
+            parts = self._completion_response_text_parts(result)
+            if parts:
+                combined = "\n\n---\n\n".join(parts)
+                await self._check_content(
+                    user_id=user_id,
+                    text=combined,
+                    activity="downloadText",
+                    request_data=kwargs,
+                    block_on_violation=False,
+                )
+        except Exception as e:
+            verbose_proxy_logger.error("Purview audit logging error (response): %s", e)
+
+        return kwargs, result
diff --git a/litellm/proxy/guardrails/guardrail_hooks/openai/base.py b/litellm/proxy/guardrails/guardrail_hooks/openai/base.py
index 872d09cd886..281afacd5c4 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/openai/base.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/openai/base.py
@@ -1,5 +1,9 @@
 from typing import TYPE_CHECKING, List, Optional
 
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    get_last_user_message,
+)
+
 if TYPE_CHECKING:
     from litellm.types.llms.openai import AllMessageValues
 
@@ -21,32 +25,4 @@ class OpenAIGuardrailBase:
         ]
         get_user_prompt(messages) -> "What is the weather in Tokyo?"
         """
-        from litellm.litellm_core_utils.prompt_templates.common_utils import (
-            convert_content_list_to_str,
-        )
-
-        if not messages:
-            return None
-
-        # Iterate from the end to find the last consecutive block of user messages
-        user_messages = []
-        for message in reversed(messages):
-            if message.get("role") == "user":
-                user_messages.append(message)
-            else:
-                # Stop when we hit a non-user message
-                break
-
-        if not user_messages:
-            return None
-
-        # Reverse to get the messages in chronological order
-        user_messages.reverse()
-
-        user_prompt = ""
-        for message in user_messages:
-            text_content = convert_content_list_to_str(message)
-            user_prompt += text_content + "\n"
-
-        result = user_prompt.strip()
-        return result if result else None
+        return get_last_user_message(messages)
diff --git a/litellm/proxy/guardrails/guardrail_hooks/rubrik/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/rubrik/__init__.py
new file mode 100644
index 00000000000..ab347130a30
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/rubrik/__init__.py
@@ -0,0 +1,35 @@
+"""Rubrik guardrail integration for LiteLLM."""
+
+from typing import TYPE_CHECKING
+
+from litellm.integrations.rubrik import RubrikLogger
+from litellm.types.guardrails import SupportedGuardrailIntegrations
+
+if TYPE_CHECKING:
+    from litellm.types.guardrails import Guardrail, LitellmParams
+
+
+def initialize_guardrail(
+    litellm_params: "LitellmParams", guardrail: "Guardrail"
+) -> RubrikLogger:
+    import litellm
+
+    rubrik_callback = RubrikLogger(
+        api_key=litellm_params.api_key,
+        api_base=litellm_params.api_base,
+        guardrail_name=guardrail.get("guardrail_name", ""),
+        event_hook=litellm_params.mode,
+        default_on=litellm_params.default_on,
+    )
+
+    litellm.logging_callback_manager.add_litellm_callback(rubrik_callback)
+    return rubrik_callback
+
+
+guardrail_initializer_registry = {
+    SupportedGuardrailIntegrations.RUBRIK.value: initialize_guardrail,
+}
+
+guardrail_class_registry = {
+    SupportedGuardrailIntegrations.RUBRIK.value: RubrikLogger,
+}
diff --git a/litellm/proxy/hooks/litellm_skills/__init__.py b/litellm/proxy/hooks/litellm_skills/__init__.py
index 057cf3d8b38..1507b652ab4 100644
--- a/litellm/proxy/hooks/litellm_skills/__init__.py
+++ b/litellm/proxy/hooks/litellm_skills/__init__.py
@@ -6,7 +6,7 @@ The actual skill logic is in litellm/llms/litellm_proxy/skills/.
 
 Usage:
     from litellm.proxy.hooks.litellm_skills import SkillsInjectionHook
-    
+
     # Register hook in proxy
     litellm.callbacks.append(SkillsInjectionHook())
 """
diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py
index 8cb9a11ffee..2b840b5495e 100644
--- a/litellm/proxy/litellm_pre_call_utils.py
+++ b/litellm/proxy/litellm_pre_call_utils.py
@@ -26,6 +26,7 @@ from litellm.proxy._types import (
     UserAPIKeyAuth,
 )
 from litellm.proxy.common_utils.callback_utils import (
+    decrypt_callback_vars,
     get_metadata_variable_name_from_kwargs,
 )
 from litellm.proxy.common_utils.http_parsing_utils import _safe_get_request_headers
@@ -477,7 +478,7 @@ class KeyAndTeamLoggingSettings:
             user_api_key_dict.metadata is not None
             and "logging" in user_api_key_dict.metadata
         ):
-            return user_api_key_dict.metadata["logging"]
+            return decrypt_callback_vars(user_api_key_dict.metadata).get("logging")
         return None
 
     @staticmethod
@@ -486,7 +487,7 @@ class KeyAndTeamLoggingSettings:
             user_api_key_dict.team_metadata is not None
             and "logging" in user_api_key_dict.team_metadata
         ):
-            return user_api_key_dict.team_metadata["logging"]
+            return decrypt_callback_vars(user_api_key_dict.team_metadata).get("logging")
         return None
 
 
@@ -540,7 +541,7 @@ def _get_dynamic_logging_metadata(
         }
         }
         """
-        team_metadata = user_api_key_dict.team_metadata
+        team_metadata = decrypt_callback_vars(user_api_key_dict.team_metadata)
         callback_settings = team_metadata.get("callback_settings", None) or {}
         callback_settings_obj = TeamCallbackMetadata(**callback_settings)
         verbose_proxy_logger.debug(
diff --git a/litellm/proxy/management_endpoints/budget_management_endpoints.py b/litellm/proxy/management_endpoints/budget_management_endpoints.py
index 60dc7827a6f..2eda1b30c5d 100644
--- a/litellm/proxy/management_endpoints/budget_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/budget_management_endpoints.py
@@ -1,9 +1,9 @@
 """
 BUDGET MANAGEMENT
 
-All /budget management endpoints 
+All /budget management endpoints
 
-/budget/new   
+/budget/new
 /budget/info
 /budget/update
 /budget/delete
diff --git a/litellm/proxy/management_endpoints/common_utils.py b/litellm/proxy/management_endpoints/common_utils.py
index a43d15a580f..dc27e87726a 100644
--- a/litellm/proxy/management_endpoints/common_utils.py
+++ b/litellm/proxy/management_endpoints/common_utils.py
@@ -1,6 +1,7 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
 from fastapi import HTTPException, status
+from pydantic import BaseModel
 
 from litellm._logging import verbose_proxy_logger
 from litellm.caching import DualCache
@@ -53,6 +54,37 @@ def require_caller_user_id_for_non_admin(
     return user_api_key_dict.user_id
 
 
+def _check_passthrough_routes_caller_permission(
+    data: BaseModel,
+    user_api_key_dict: UserAPIKeyAuth,
+    *,
+    entity: str = "key",
+) -> None:
+    """
+    Only proxy admins may set `allowed_passthrough_routes` (top-level or under
+    `metadata`) — it short-circuits the role-based route gate, so keys and teams
+    must be gated identically.
+    """
+    # view-only admins excluded by design; blocked upstream from writes anyway
+    if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value:
+        return
+    if getattr(data, "allowed_passthrough_routes", None):
+        raise HTTPException(
+            status_code=403,
+            detail={
+                "error": f"Only proxy admins can set `allowed_passthrough_routes` on a {entity}."
+            },
+        )
+    metadata = getattr(data, "metadata", None)
+    if isinstance(metadata, dict) and metadata.get("allowed_passthrough_routes"):
+        raise HTTPException(
+            status_code=403,
+            detail={
+                "error": f"Only proxy admins can set `metadata.allowed_passthrough_routes` on a {entity}."
+            },
+        )
+
+
 def _is_user_team_admin(
     user_api_key_dict: UserAPIKeyAuth, team_obj: LiteLLM_TeamTable
 ) -> bool:
diff --git a/litellm/proxy/management_endpoints/customer_endpoints.py b/litellm/proxy/management_endpoints/customer_endpoints.py
index 4889f0b7f80..1fd8320db20 100644
--- a/litellm/proxy/management_endpoints/customer_endpoints.py
+++ b/litellm/proxy/management_endpoints/customer_endpoints.py
@@ -1,9 +1,9 @@
 """
 CUSTOMER MANAGEMENT
 
-All /customer management endpoints 
+All /customer management endpoints
 
-/customer/new   
+/customer/new
 /customer/info
 /customer/update
 /customer/delete
diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py
index 7ff706eb91e..a67d8d934bf 100644
--- a/litellm/proxy/management_endpoints/key_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/key_management_endpoints.py
@@ -51,10 +51,15 @@ from litellm.proxy.auth.auth_checks import (
 )
 from litellm.proxy.auth.auth_utils import abbreviate_api_key
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.common_utils.callback_utils import (
+    decrypt_callback_vars,
+    encrypt_callback_vars,
+)
 from litellm.proxy.common_utils.rbac_utils import check_org_admin_can_generate_keys
 from litellm.proxy.common_utils.timezone_utils import get_budget_reset_time
 from litellm.proxy.hooks.key_management_event_hooks import KeyManagementEventHooks
 from litellm.proxy.management_endpoints.common_utils import (
+    _check_passthrough_routes_caller_permission,
     _is_user_org_admin_for_team,
     _is_user_team_admin,
     _set_object_metadata_field,
@@ -548,36 +553,6 @@ def _check_allowed_routes_caller_permission(
     )
 
 
-def _check_passthrough_routes_caller_permission(
-    data: BaseModel,
-    user_api_key_dict: UserAPIKeyAuth,
-) -> None:
-    """
-    Only proxy admins may set `allowed_passthrough_routes` on a key, either at
-    the top level of the request or nested under `metadata`.
-
-    The route gate evaluates passthrough access ahead of the standard role
-    gate, so the field is restricted to admins to keep that ordering safe.
-    """
-    if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value:
-        return
-    if getattr(data, "allowed_passthrough_routes", None):
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "error": "Only proxy admins can set `allowed_passthrough_routes` on a key."
-            },
-        )
-    metadata = getattr(data, "metadata", None)
-    if isinstance(metadata, dict) and metadata.get("allowed_passthrough_routes"):
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "error": "Only proxy admins can set `metadata.allowed_passthrough_routes` on a key."
-            },
-        )
-
-
 async def validate_team_id_used_in_service_account_request(
     team_id: Optional[str],
     prisma_client: Optional[PrismaClient],
@@ -1781,7 +1756,7 @@ def prepare_metadata_fields(
             )
         )
 
-    non_default_values["metadata"] = casted_metadata
+    non_default_values["metadata"] = encrypt_callback_vars(casted_metadata)
     return non_default_values
 
 
@@ -3251,6 +3226,7 @@ async def info_key_fn_v2(
 @router.get(
     "/key/info", tags=["key management"], dependencies=[Depends(user_api_key_auth)]
 )
+@management_endpoint_wrapper
 async def info_key_fn(
     key: Optional[str] = fastapi.Query(
         default=None, description="Key in the request parameters"
@@ -3488,6 +3464,7 @@ async def generate_key_helper_fn(  # noqa: PLR0915
         metadata = metadata or {}
         metadata["prompts"] = prompts
 
+    metadata = encrypt_callback_vars(metadata)
     metadata_json = json.dumps(metadata)
     validate_model_max_budget(model_max_budget)
     model_max_budget_json = json.dumps(model_max_budget)
@@ -5971,7 +5948,7 @@ async def key_health(
             logging_statuses = await test_key_logging(
                 user_api_key_dict=user_api_key_dict,
                 request=request,
-                key_logging=key_metadata["logging"],
+                key_logging=decrypt_callback_vars(key_metadata)["logging"],
             )
             health_status["logging_callbacks"] = logging_statuses
 
diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py
index af84bc123ff..f2d8ec8fb55 100644
--- a/litellm/proxy/management_endpoints/model_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/model_management_endpoints.py
@@ -150,6 +150,9 @@ def update_db_model(
                 model_info[key] = value.isoformat()
         prisma_compatible_model_dict["model_info"] = json.dumps(model_info)
 
+    if updated_patch.blocked is not None:
+        prisma_compatible_model_dict["blocked"] = updated_patch.blocked
+
     return prisma_compatible_model_dict
 
 
@@ -230,6 +233,20 @@ async def patch_model(
             premium_user=premium_user,
         )
 
+        # Pause/resume (`blocked`) is a proxy-admin-only privilege. Team admins
+        # passed the auth check above for team-scoped models, but they must not
+        # be able to unblock (or block) a model their proxy admin has paused.
+        if (
+            patch_data.blocked is not None
+            and user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN
+        ):
+            raise ProxyException(
+                message="Only proxy admins can change a model's blocked flag.",
+                type=ProxyErrorTypes.auth_error.value,
+                code=status.HTTP_403_FORBIDDEN,
+                param="blocked",
+            )
+
         # Handle team model updates with proper alias management
         update_data = await _update_team_model_in_db(
             db_model=db_model,
@@ -529,7 +546,7 @@ async def _update_existing_team_model_assignment(
     """
 
     def _get_team_public_model_name(
-        model_info: Optional[Union[dict, str]]
+        model_info: Optional[Union[dict, str]],
     ) -> Optional[str]:
         if isinstance(model_info, dict):
             value = model_info.get("team_public_model_name")
diff --git a/litellm/proxy/management_endpoints/sso/custom_microsoft_sso.py b/litellm/proxy/management_endpoints/sso/custom_microsoft_sso.py
index 191212d6f0b..04e44c623d1 100644
--- a/litellm/proxy/management_endpoints/sso/custom_microsoft_sso.py
+++ b/litellm/proxy/management_endpoints/sso/custom_microsoft_sso.py
@@ -7,7 +7,7 @@ variables.
 
 Environment Variables:
 - MICROSOFT_AUTHORIZATION_ENDPOINT: Custom authorization endpoint URL
-- MICROSOFT_TOKEN_ENDPOINT: Custom token endpoint URL  
+- MICROSOFT_TOKEN_ENDPOINT: Custom token endpoint URL
 - MICROSOFT_USERINFO_ENDPOINT: Custom userinfo endpoint URL
 
 If these are not set, the default Microsoft endpoints are used.
diff --git a/litellm/proxy/management_endpoints/team_callback_endpoints.py b/litellm/proxy/management_endpoints/team_callback_endpoints.py
index 17d86410a0f..63b56425b0e 100644
--- a/litellm/proxy/management_endpoints/team_callback_endpoints.py
+++ b/litellm/proxy/management_endpoints/team_callback_endpoints.py
@@ -27,6 +27,7 @@ from litellm.proxy._types import (
     UserAPIKeyAuth,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.common_utils.callback_utils import encrypt_callback_vars
 from litellm.proxy.management_endpoints.team_endpoints import _verify_team_access
 from litellm.proxy.management_helpers.utils import management_endpoint_wrapper
 
@@ -245,6 +246,7 @@ async def add_team_callbacks(
         team_callback_settings.append(data.model_dump())
 
         team_metadata["logging"] = team_callback_settings
+        team_metadata = encrypt_callback_vars(team_metadata)
         team_metadata_json = json.dumps(team_metadata)  # update team_metadata
 
         new_team_row = await prisma_client.db.litellm_teamtable.update(
@@ -347,6 +349,7 @@ async def disable_team_logging(
 
         # Update metadata
         team_metadata["callback_settings"] = team_callback_settings_obj.model_dump()
+        team_metadata = encrypt_callback_vars(team_metadata)
         team_metadata_json = json.dumps(team_metadata)
 
         # Update team in database
diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py
index 35e3d196e9e..0d34974fbef 100644
--- a/litellm/proxy/management_endpoints/team_endpoints.py
+++ b/litellm/proxy/management_endpoints/team_endpoints.py
@@ -64,6 +64,7 @@ from litellm.proxy._types import (
     UserAPIKeyAuth,
 )
 from litellm.proxy.auth.auth_checks import (
+    _cache_team_object,
     allowed_route_check_inside_route,
     can_org_access_model,
     get_org_object,
@@ -72,7 +73,9 @@ from litellm.proxy.auth.auth_checks import (
     get_user_object,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.common_utils.callback_utils import encrypt_callback_vars
 from litellm.proxy.management_endpoints.common_utils import (
+    _check_passthrough_routes_caller_permission,
     _is_user_org_admin_for_team,
     _is_user_team_admin,
     _set_object_metadata_field,
@@ -128,6 +131,33 @@ def _sanitize_for_log(value: Any) -> str:
     return text.replace("\r", "").replace("\n", "")
 
 
+async def _refresh_cached_team(
+    team_row: Any,
+    user_api_key_cache: Any,
+    proxy_logging_obj: Any,
+) -> None:
+    """
+    Refresh the in-memory cached team object after a DB write.
+
+    Every endpoint that mutates `litellm_teamtable` must call this so the
+    cached `LiteLLM_TeamTableCachedObj` used by `common_checks` stays in
+    sync. Without this, subsequent auth checks read a stale team and can
+    403 on permissions the DB has already granted (or, symmetrically,
+    keep granting permissions the DB has already revoked).
+
+    `team_row` is the Prisma row returned by `update`/`find_unique` on
+    `litellm_teamtable`. It is converted to `LiteLLM_TeamTableCachedObj`
+    via `model_dump()` to match the cache shape `_cache_team_object`
+    expects.
+    """
+    await _cache_team_object(
+        team_id=team_row.team_id,
+        team_table=LiteLLM_TeamTableCachedObj(**team_row.model_dump()),
+        user_api_key_cache=user_api_key_cache,
+        proxy_logging_obj=proxy_logging_obj,
+    )
+
+
 async def _verify_team_access(
     team_obj: LiteLLM_TeamTable,
     user_api_key_dict: UserAPIKeyAuth,
@@ -1049,6 +1079,10 @@ async def new_team(  # noqa: PLR0915
                     Member(role="admin", user_id=user_api_key_dict.user_id)
                 )
 
+        _check_passthrough_routes_caller_permission(
+            data, user_api_key_dict, entity="team"
+        )
+
         ## ADD TO MODEL TABLE
         _model_id = None
         if data.model_aliases is not None and isinstance(data.model_aliases, dict):
@@ -1151,6 +1185,11 @@ async def new_team(  # noqa: PLR0915
         )
         complete_team_data_dict["router_settings"] = router_settings_json
 
+        if complete_team_data_dict.get("metadata") is not None:
+            complete_team_data_dict["metadata"] = encrypt_callback_vars(
+                complete_team_data_dict["metadata"]
+            )
+
         complete_team_data_dict = prisma_client.jsonify_team_object(
             db_data=complete_team_data_dict
         )
@@ -1580,7 +1619,6 @@ async def update_team(  # noqa: PLR0915
     ```
     """
     try:
-        from litellm.proxy.auth.auth_checks import _cache_team_object
         from litellm.proxy.proxy_server import (
             litellm_proxy_admin_name,
             llm_router,
@@ -1646,6 +1684,10 @@ async def update_team(  # noqa: PLR0915
             user_api_key_dict=user_api_key_dict,
         )
 
+        _check_passthrough_routes_caller_permission(
+            data, user_api_key_dict, entity="team"
+        )
+
         if data.soft_budget is not None:
             max_budget_to_check = (
                 data.max_budget
@@ -1819,6 +1861,9 @@ async def update_team(  # noqa: PLR0915
         # update team metadata fields
         _update_metadata_fields(updated_kv=updated_kv)
 
+        if updated_kv.get("metadata") is not None:
+            updated_kv["metadata"] = encrypt_callback_vars(updated_kv["metadata"])
+
         if "model_aliases" in updated_kv:
             updated_kv.pop("model_aliases")
             _model_id = await _update_model_table(
@@ -1843,7 +1888,13 @@ async def update_team(  # noqa: PLR0915
             await prisma_client.db.litellm_teamtable.update(
                 where={"team_id": data.team_id},
                 data=updated_kv,
-                include={"litellm_model_table": True},  # type: ignore
+                # `object_permission` is included so `_refresh_cached_team`
+                # doesn't write a cached team with the relation nulled out —
+                # see team_model_add for the full rationale.
+                include={
+                    "litellm_model_table": True,
+                    "object_permission": True,
+                },  # type: ignore
             )
         )
 
@@ -1856,9 +1907,8 @@ async def update_team(  # noqa: PLR0915
         verbose_proxy_logger.info(
             "Successfully updated team - %s, info", team_row.team_id
         )
-        await _cache_team_object(
-            team_id=team_row.team_id,
-            team_table=LiteLLM_TeamTableCachedObj(**team_row.model_dump()),
+        await _refresh_cached_team(
+            team_row=team_row,
             user_api_key_cache=user_api_key_cache,
             proxy_logging_obj=proxy_logging_obj,
         )
@@ -4372,9 +4422,7 @@ async def list_team(
         except Exception as e:
             team_exception = """Invalid team object for team_id: {}. team_object={}.
             Error: {}
-            """.format(
-                team.team_id, team.model_dump(), str(e)
-            )
+            """.format(team.team_id, team.model_dump(), str(e))
             verbose_proxy_logger.exception(team_exception)
             continue
     # Sort the responses by team_alias
@@ -4553,7 +4601,11 @@ async def team_model_add(
     }'
     ```
     """
-    from litellm.proxy.proxy_server import prisma_client
+    from litellm.proxy.proxy_server import (
+        prisma_client,
+        proxy_logging_obj,
+        user_api_key_cache,
+    )
 
     if prisma_client is None:
         raise HTTPException(status_code=500, detail={"error": "No db connected"})
@@ -4587,9 +4639,21 @@ async def team_model_add(
         )
 
     updated_models = add_new_models_to_team(team_obj=team_obj, new_models=data.models)
-    # Update team
+    # Update team. `include` mirrors the relations the auth path consumes
+    # off the cached team object so that `_refresh_cached_team` doesn't
+    # null them out — see object_permission_utils.validate_key_search_tools_against_team
+    # and the MCP/agent authz paths, which treat a missing object_permission
+    # as "no team-level restriction".
     updated_team = await prisma_client.db.litellm_teamtable.update(
-        where={"team_id": data.team_id}, data={"models": updated_models}
+        where={"team_id": data.team_id},
+        data={"models": updated_models},
+        include={"object_permission": True},  # type: ignore
+    )
+
+    await _refresh_cached_team(
+        team_row=updated_team,
+        user_api_key_cache=user_api_key_cache,
+        proxy_logging_obj=proxy_logging_obj,
     )
 
     return updated_team
@@ -4624,7 +4688,11 @@ async def team_model_delete(
     }'
     ```
     """
-    from litellm.proxy.proxy_server import prisma_client
+    from litellm.proxy.proxy_server import (
+        prisma_client,
+        proxy_logging_obj,
+        user_api_key_cache,
+    )
 
     if prisma_client is None:
         raise HTTPException(status_code=500, detail={"error": "No db connected"})
@@ -4663,9 +4731,17 @@ async def team_model_delete(
     # Remove specified models
     updated_models = [m for m in current_models if m not in data.models]
 
-    # Update team
+    # Update team. See team_model_add for the rationale on `include`.
     updated_team = await prisma_client.db.litellm_teamtable.update(
-        where={"team_id": data.team_id}, data={"models": updated_models}
+        where={"team_id": data.team_id},
+        data={"models": updated_models},
+        include={"object_permission": True},  # type: ignore
+    )
+
+    await _refresh_cached_team(
+        team_row=updated_team,
+        user_api_key_cache=user_api_key_cache,
+        proxy_logging_obj=proxy_logging_obj,
     )
 
     return updated_team
diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py
index 6e2e2bedac1..d6082899c02 100644
--- a/litellm/proxy/management_endpoints/ui_sso.py
+++ b/litellm/proxy/management_endpoints/ui_sso.py
@@ -43,6 +43,8 @@ from litellm.caching.dual_cache import DualCache
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid
 from litellm.constants import (
+    CLI_SSO_CLAIM_MAP,
+    CLI_SSO_CLAIM_MAX_SCALAR_LENGTH,
     CLI_SSO_SESSION_CACHE_KEY_PREFIX,
     CLI_SSO_SESSION_TTL_SECONDS,
     LITELLM_CLI_SOURCE_IDENTIFIER,
@@ -140,6 +142,20 @@ _CLI_SSO_START_RATE_LIMIT_WINDOW_SECONDS = 60
 _CLI_SSO_START_RATE_LIMIT_MAX_ATTEMPTS = 30
 _CLI_SSO_USER_CODE_ALPHABET = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789"
 _CLI_SSO_LOGIN_ID_RE = re.compile(r"^cli-[A-Za-z0-9_-]{12,124}$")
+_CLI_SSO_SCALAR_TYPES = (str, int, float, bool)
+_CLI_SSO_DEST_KEY_RE = re.compile(r"^[A-Za-z0-9_.-]+$")
+_CLI_SSO_SECRET_KEY_FRAGMENTS = frozenset(
+    {
+        "access_token",
+        "api_key",
+        "client_secret",
+        "id_token",
+        "password",
+        "private_key",
+        "refresh_token",
+        "secret",
+    }
+)
 
 
 def _hash_cli_sso_secret(secret: str) -> str:
@@ -225,6 +241,239 @@ def _verify_cli_sso_poll_secret(flow: dict, poll_secret: Optional[str]) -> bool:
     return secrets.compare_digest(supplied_poll_secret_hash, expected_poll_secret_hash)
 
 
+def _parse_cli_sso_claim_map() -> List[Tuple[str, str]]:
+    """
+    Parse CLI_SSO_CLAIM_MAP / LITELLM_CLI_SSO_CLAIM_MAP.
+
+    Format: comma-separated ``source_claim->metadata_key`` pairs, e.g.
+    ``employment_type->acme_employment_type,org_info.department->department``.
+    Destination keys may use an optional ``metadata.`` prefix; values are stored
+    on the LiteLLM user's ``metadata`` JSON column.
+    """
+    claim_map_raw = CLI_SSO_CLAIM_MAP.strip()
+    if not claim_map_raw:
+        return []
+
+    parsed: List[Tuple[str, str]] = []
+    for entry in claim_map_raw.split(","):
+        entry = entry.strip()
+        if not entry or "->" not in entry:
+            continue
+        source_claim, dest_key = entry.split("->", 1)
+        source_claim = source_claim.strip()
+        dest_key = dest_key.strip()
+        if dest_key.startswith("metadata."):
+            dest_key = dest_key[len("metadata.") :]
+        if source_claim and dest_key:
+            parsed.append((source_claim, dest_key))
+    return parsed
+
+
+def _is_safe_cli_sso_metadata_dest_key(dest_key: str) -> bool:
+    if not dest_key or not _CLI_SSO_DEST_KEY_RE.fullmatch(dest_key):
+        return False
+    lowered = dest_key.lower()
+    return not any(fragment in lowered for fragment in _CLI_SSO_SECRET_KEY_FRAGMENTS)
+
+
+def _is_safe_cli_sso_scalar_claim_value(value: Any) -> bool:
+    if not isinstance(value, _CLI_SSO_SCALAR_TYPES):
+        return False
+    if isinstance(value, str):
+        if len(value) > CLI_SSO_CLAIM_MAX_SCALAR_LENGTH:
+            return False
+        if value.startswith("eyJ") and value.count(".") >= 2:
+            return False
+    return True
+
+
+def _sso_result_to_dict(result: Union[CustomOpenID, OpenID, dict]) -> Dict[str, Any]:
+    if isinstance(result, dict):
+        return result
+    if hasattr(result, "model_dump"):
+        dumped = result.model_dump()
+        if isinstance(dumped, dict):
+            return cast(Dict[str, Any], dumped)
+    return {}
+
+
+def _get_nested_claim_value(data: Dict[str, Any], claim_path: str) -> Any:
+    """Resolve a dot-notation claim path against an SSO result dict.
+
+    Unlike ``get_nested_value``, this does not strip a leading ``metadata.``
+    prefix, since OIDC claims may legitimately use ``metadata`` as a top-level
+    key.
+    """
+    if not claim_path:
+        return None
+    if claim_path in data:
+        return data[claim_path]
+    placeholder = "\x00"
+    parts = claim_path.replace("\\.", placeholder).split(".")
+    parts = [p.replace(placeholder, ".") for p in parts]
+    current: Any = data
+    for part in parts:
+        if isinstance(current, dict) and part in current:
+            current = current[part]
+        else:
+            return None
+    return current
+
+
+def _extract_sso_claim_value(
+    result: Union[CustomOpenID, OpenID, dict], claim_path: str
+) -> Any:
+    extra_fields = getattr(result, "extra_fields", None)
+    if isinstance(extra_fields, dict):
+        if claim_path in extra_fields:
+            return extra_fields[claim_path]
+        nested = _get_nested_claim_value(extra_fields, claim_path)
+        if nested is not None:
+            return nested
+
+    if isinstance(result, dict):
+        return _get_nested_claim_value(result, claim_path)
+
+    result_dict = _sso_result_to_dict(result)
+    return _get_nested_claim_value(result_dict, claim_path)
+
+
+def _set_nested_metadata_value(
+    metadata: Dict[str, Any], key_path: str, value: Any
+) -> None:
+    placeholder = "\x00"
+    parts = key_path.replace("\\.", placeholder).split(".")
+    parts = [p.replace(placeholder, ".") for p in parts]
+    current: Any = metadata
+    for part in parts[:-1]:
+        existing = current.get(part)
+        if not isinstance(existing, dict):
+            existing = {}
+            current[part] = existing
+        current = existing
+    current[parts[-1]] = value
+
+
+def _flatten_cli_sso_metadata_for_poll(
+    metadata: Dict[str, Any],
+) -> Dict[str, Union[str, int, float, bool]]:
+    """Expose scalar attribution metadata as a flat dict for CLI poll responses."""
+    flattened: Dict[str, Union[str, int, float, bool]] = {}
+    stack: List[Tuple[str, Any]] = [("", metadata)]
+    while stack:
+        prefix, value = stack.pop()
+        if isinstance(value, dict):
+            for key, nested in value.items():
+                nested_prefix = f"{prefix}.{key}" if prefix else key
+                stack.append((nested_prefix, nested))
+        elif _is_safe_cli_sso_scalar_claim_value(value):
+            flattened[prefix] = value
+    return flattened
+
+
+def build_cli_sso_attribution_metadata(
+    result: Union[CustomOpenID, OpenID, dict],
+) -> Dict[str, Any]:
+    """
+    Build allowlisted, non-secret scalar attribution metadata from an SSO result.
+
+    Sources are configured via CLI_SSO_CLAIM_MAP / LITELLM_CLI_SSO_CLAIM_MAP and
+    may include claims captured by GENERIC_USER_EXTRA_ATTRIBUTES on CustomOpenID.
+    """
+    claim_map = _parse_cli_sso_claim_map()
+    if not claim_map:
+        return {}
+
+    metadata: Dict[str, Any] = {}
+    for source_claim, dest_key in claim_map:
+        if not _is_safe_cli_sso_metadata_dest_key(dest_key):
+            verbose_proxy_logger.debug(
+                f"Skipping unsafe CLI SSO metadata destination key: {dest_key}"
+            )
+            continue
+
+        raw_value = _extract_sso_claim_value(result=result, claim_path=source_claim)
+        if not _is_safe_cli_sso_scalar_claim_value(raw_value):
+            continue
+
+        _set_nested_metadata_value(
+            metadata=metadata, key_path=dest_key, value=raw_value
+        )
+
+    return metadata
+
+
+def _merge_cli_sso_attribution_metadata(
+    existing_metadata: Dict[str, Any], attribution_metadata: Dict[str, Any]
+) -> Dict[str, Any]:
+    """Merge attribution metadata into existing user metadata in-place.
+
+    Preserves original value types (in particular, string claim values that
+    happen to look numeric are NOT coerced to ``int``/``float``). Nested dicts
+    are merged iteratively so attribution claims do not clobber unrelated keys
+    under the same parent.
+    """
+    pending: List[Tuple[Dict[str, Any], Dict[str, Any]]] = [
+        (existing_metadata, attribution_metadata)
+    ]
+    while pending:
+        target, source = pending.pop()
+        for key, value in source.items():
+            if value is None:
+                continue
+            existing_value = target.get(key)
+            if isinstance(value, dict) and isinstance(existing_value, dict):
+                pending.append((existing_value, value))
+            else:
+                target[key] = value
+    return existing_metadata
+
+
+async def _persist_cli_sso_user_metadata(
+    prisma_client: PrismaClient,
+    user_id: str,
+    attribution_metadata: Dict[str, Any],
+) -> None:
+    if not attribution_metadata:
+        return
+
+    try:
+        user_row = await prisma_client.db.litellm_usertable.find_unique(
+            where={"user_id": user_id}
+        )
+        existing_metadata: Dict[str, Any] = {}
+        if user_row is not None:
+            row_metadata = user_row.metadata
+            if isinstance(row_metadata, dict):
+                existing_metadata = deepcopy(row_metadata)
+
+        merged_metadata = _merge_cli_sso_attribution_metadata(
+            existing_metadata=existing_metadata,
+            attribution_metadata=attribution_metadata,
+        )
+        await prisma_client.db.litellm_usertable.update_many(
+            where={"user_id": user_id},
+            data={"metadata": merged_metadata},
+        )
+        verbose_proxy_logger.info(
+            f"Persisted CLI SSO attribution metadata for user {user_id}: "
+            f"{list(_flatten_cli_sso_metadata_for_poll(attribution_metadata).keys())}"
+        )
+    except Exception as e:
+        verbose_proxy_logger.error(
+            f"Failed to persist CLI SSO attribution metadata for user {user_id}: {e}"
+        )
+
+
+def _cli_poll_attribution_metadata_from_session(
+    session_data: Dict[str, Any],
+) -> Dict[str, Union[str, int, float, bool]]:
+    stored = session_data.get("attribution_metadata")
+    if isinstance(stored, dict):
+        return _flatten_cli_sso_metadata_for_poll(stored)
+    return {}
+
+
 def _render_cli_sso_verification_page(
     verify_url: str, browser_complete_token: str
 ) -> str:
@@ -1674,7 +1923,12 @@ async def auth_callback(request: Request, state: Optional[str] = None):  # noqa:
         key_id = state_parts[1] if len(state_parts) > 1 else None
 
         verbose_proxy_logger.info("CLI SSO callback detected")
-        return await cli_sso_callback(request=request, key=key_id, result=result)
+        return await cli_sso_callback(
+            request=request,
+            key=key_id,
+            result=result,
+            received_response=received_response,
+        )
 
     # Control-plane cross-origin: read return_to from cookie.
     # Starlette's cookie_parser already handles RFC 2109 unquoting.
@@ -1692,15 +1946,144 @@ async def auth_callback(request: Request, state: Optional[str] = None):  # noqa:
     )
 
 
+async def _build_cli_sso_user_defined_values(
+    result: Union[OpenID, dict],
+    parsed_openid_result: ParsedOpenIDResult,
+) -> Optional[SSOUserDefinedValues]:
+    from litellm.proxy.proxy_server import user_custom_sso
+
+    user_id = parsed_openid_result.get("user_id")
+    if user_custom_sso is not None:
+        if inspect.iscoroutinefunction(user_custom_sso):
+            return await user_custom_sso(result)  # type: ignore
+        raise ValueError("user_custom_sso must be a coroutine function")
+    if user_id is None:
+        return None
+    return SSOUserDefinedValues(
+        models=[],
+        user_id=user_id,
+        user_email=parsed_openid_result.get("user_email"),
+        max_budget=litellm.max_internal_user_budget,
+        user_role=parsed_openid_result.get("user_role"),
+        budget_duration=litellm.internal_user_budget_duration,
+    )
+
+
+async def _fetch_cli_sso_team_details(
+    prisma_client: PrismaClient,
+    teams: List[str],
+) -> List[Dict[str, Any]]:
+    team_details: List[Dict[str, Any]] = []
+    try:
+        if teams:
+            prisma_teams = await prisma_client.db.litellm_teamtable.find_many(
+                where={"team_id": {"in": teams}}
+            )
+            for team_row in prisma_teams:
+                team_dict = team_row.model_dump()
+                team_details.append(
+                    {
+                        "team_id": team_dict.get("team_id"),
+                        "team_alias": team_dict.get("team_alias"),
+                    }
+                )
+    except Exception as e:
+        verbose_proxy_logger.error(
+            f"Error fetching team details for CLI SSO session: {e}"
+        )
+    return team_details
+
+
+async def _complete_cli_sso_callback_session(
+    *,
+    request: Request,
+    key: str,
+    flow: dict,
+    result: Union[OpenID, dict],
+    parsed_openid_result: ParsedOpenIDResult,
+    user_defined_values: Optional[SSOUserDefinedValues],
+    prisma_client: PrismaClient,
+    user_api_key_cache: UserApiKeyCache,
+    proxy_logging_obj: ProxyLogging,
+):
+    from fastapi.responses import HTMLResponse
+
+    user_id = parsed_openid_result.get("user_id")
+    user_email = parsed_openid_result.get("user_email")
+    user_info = await get_user_info_from_db(
+        result=result,
+        prisma_client=prisma_client,
+        user_api_key_cache=user_api_key_cache,
+        proxy_logging_obj=proxy_logging_obj,
+        user_email=user_email,
+        user_defined_values=user_defined_values,
+        alternate_user_id=user_id,
+    )
+    if user_info is None:
+        raise HTTPException(
+            status_code=500, detail="Failed to retrieve user information from SSO"
+        )
+    if not user_info.user_id:
+        raise HTTPException(
+            status_code=500, detail="Failed to retrieve user information from SSO"
+        )
+
+    teams: List[str] = []
+    if hasattr(user_info, "teams") and user_info.teams:
+        teams = user_info.teams if isinstance(user_info.teams, list) else []
+
+    team_details = await _fetch_cli_sso_team_details(
+        prisma_client=prisma_client, teams=teams
+    )
+    attribution_metadata = build_cli_sso_attribution_metadata(result=result)
+    if attribution_metadata:
+        await _persist_cli_sso_user_metadata(
+            prisma_client=prisma_client,
+            user_id=cast(str, user_info.user_id),
+            attribution_metadata=attribution_metadata,
+        )
+
+    flow["session_data"] = {
+        "user_id": cast(str, user_info.user_id),
+        "user_role": user_info.user_role,
+        "models": user_info.models if hasattr(user_info, "models") else [],
+        "user_email": user_email,
+        "teams": teams,
+        "team_details": team_details,
+        "attribution_metadata": attribution_metadata,
+    }
+    flow["sso_complete"] = True
+    browser_complete_token = secrets.token_urlsafe(32)
+    flow["browser_complete_token_hash"] = _hash_cli_sso_secret(browser_complete_token)
+    _set_cli_sso_flow(login_id=key, cache=user_api_key_cache, flow=flow)
+
+    verbose_proxy_logger.info(
+        f"Stored CLI SSO session for user: {user_info.user_id}, teams: {teams}, num_teams: {len(teams)}"
+    )
+    verify_url = get_custom_url(
+        request_base_url=str(request.base_url),
+        route=f"sso/cli/complete/{key}",
+    )
+    return HTMLResponse(
+        content=_render_cli_sso_verification_page(
+            verify_url=verify_url,
+            browser_complete_token=browser_complete_token,
+        ),
+        status_code=200,
+    )
+
+
 async def cli_sso_callback(
     request: Request,
     key: Optional[str] = None,
     result: Optional[Union[OpenID, dict]] = None,
+    received_response: Optional[dict] = None,
 ):
     """CLI SSO callback - stores session info for JWT generation on polling"""
     verbose_proxy_logger.info("CLI SSO callback")
 
     from litellm.proxy.proxy_server import (
+        general_settings,
         prisma_client,
         proxy_logging_obj,
         user_api_key_cache,
@@ -1722,89 +2105,40 @@ async def cli_sso_callback(
     # After None check, cast to non-None type for type checker
     result_non_none: Union[OpenID, dict] = cast(Union[OpenID, dict], result)
 
-    parsed_openid_result = SSOAuthenticationHandler._get_user_email_and_id_from_result(
-        result=result_non_none
-    )
-    verbose_proxy_logger.debug(f"parsed_openid_result: {parsed_openid_result}")
-
     try:
-        # Get full user info from DB
-        user_info = await get_user_info_from_db(
+        parsed_openid_result = (
+            SSOAuthenticationHandler._get_user_email_and_id_from_result(
+                result=result_non_none,
+                generic_client_id=os.getenv("GENERIC_CLIENT_ID", None),
+            )
+        )
+        verbose_proxy_logger.debug(f"parsed_openid_result: {parsed_openid_result}")
+        user_defined_values = await _build_cli_sso_user_defined_values(
             result=result_non_none,
+            parsed_openid_result=parsed_openid_result,
+        )
+
+        SSOAuthenticationHandler.verify_user_in_restricted_sso_group(
+            general_settings=general_settings,
+            result=result_non_none,
+            received_response=received_response,
+        )
+
+        return await _complete_cli_sso_callback_session(
+            request=request,
+            key=cast(str, key),
+            flow=flow,
+            result=result_non_none,
+            parsed_openid_result=parsed_openid_result,
+            user_defined_values=user_defined_values,
             prisma_client=prisma_client,
             user_api_key_cache=user_api_key_cache,
             proxy_logging_obj=proxy_logging_obj,
-            user_email=parsed_openid_result.get("user_email"),
-            user_defined_values=None,
-            alternate_user_id=parsed_openid_result.get("user_id"),
         )
-
-        if user_info is None:
-            raise HTTPException(
-                status_code=500, detail="Failed to retrieve user information from SSO"
-            )
-
-        # Get all teams from user_info - CLI will let user select which one
-        teams: List[str] = []
-        if hasattr(user_info, "teams") and user_info.teams:
-            teams = user_info.teams if isinstance(user_info.teams, list) else []
-
-        # Also fetch team aliases for a better CLI UX. We keep the original
-        # "teams" list of IDs for backwards compatibility and add an
-        # optional "team_details" field containing objects with both
-        # team_id and team_alias.
-        team_details: List[Dict[str, Any]] = []
-        try:
-            if teams:
-                prisma_teams = await prisma_client.db.litellm_teamtable.find_many(
-                    where={"team_id": {"in": teams}}
-                )
-                for team_row in prisma_teams:
-                    team_dict = team_row.model_dump()
-                    team_details.append(
-                        {
-                            "team_id": team_dict.get("team_id"),
-                            "team_alias": team_dict.get("team_alias"),
-                        }
-                    )
-        except Exception as e:
-            # If anything goes wrong here, fall back gracefully without
-            # impacting the SSO flow.
-            verbose_proxy_logger.error(
-                f"Error fetching team details for CLI SSO session: {e}"
-            )
-
-        session_data = {
-            "user_id": user_info.user_id,
-            "user_role": user_info.user_role,
-            "models": user_info.models if hasattr(user_info, "models") else [],
-            "user_email": parsed_openid_result.get("user_email"),
-            "teams": teams,
-            # Optional rich metadata for clients that want nicer display
-            "team_details": team_details,
-        }
-
-        flow["session_data"] = session_data
-        flow["sso_complete"] = True
-        browser_complete_token = secrets.token_urlsafe(32)
-        flow["browser_complete_token_hash"] = _hash_cli_sso_secret(
-            browser_complete_token
-        )
-        _set_cli_sso_flow(login_id=cast(str, key), cache=user_api_key_cache, flow=flow)
-
-        verbose_proxy_logger.info(
-            f"Stored CLI SSO session for user: {user_info.user_id}, teams: {teams}, num_teams: {len(teams)}"
-        )
-
-        from fastapi.responses import HTMLResponse
-
-        verify_url = str(request.url_for("cli_sso_complete", login_id=key))
-        html_content = _render_cli_sso_verification_page(
-            verify_url=verify_url,
-            browser_complete_token=browser_complete_token,
-        )
-        return HTMLResponse(content=html_content, status_code=200)
-
+    except ProxyException:
+        raise
+    except HTTPException:
+        raise
     except Exception as e:
         verbose_proxy_logger.error(f"Error with CLI SSO callback: {e}")
         raise HTTPException(
@@ -1870,13 +2204,19 @@ async def cli_poll_key(
                     team_details_response = [
                         {"team_id": t, "team_alias": None} for t in user_teams
                     ]
-                return {
+                poll_response: Dict[str, Any] = {
                     "status": "ready",
                     "user_id": user_id,
                     "teams": user_teams,
                     "team_details": team_details_response,
                     "requires_team_selection": True,
                 }
+                attribution_metadata = _cli_poll_attribution_metadata_from_session(
+                    session_data
+                )
+                if attribution_metadata:
+                    poll_response["attribution_metadata"] = attribution_metadata
+                return poll_response
 
             # Validate team_id if provided
             if team_id is not None:
@@ -1889,6 +2229,17 @@ async def cli_poll_key(
                 # If no team_id provided and user has 0 or 1 team, use first team (or None)
                 team_id = user_teams[0] if len(user_teams) > 0 else None
 
+            team_alias = None
+            if team_id and isinstance(user_team_details, list):
+                team_alias = next(
+                    (
+                        team.get("team_alias")
+                        for team in user_team_details
+                        if team.get("team_id") == team_id
+                    ),
+                    None,
+                )
+
             # Create user object for JWT generation
             user_info = LiteLLM_UserTable(
                 user_id=user_id,
@@ -1900,7 +2251,7 @@ async def cli_poll_key(
             # Generate CLI JWT on-demand (expiration configurable via LITELLM_CLI_JWT_EXPIRATION_HOURS)
             # Pass selected team_id to ensure JWT has correct team
             jwt_token = ExperimentalUIJWTToken.get_cli_jwt_auth_token(
-                user_info=user_info, team_id=team_id
+                user_info=user_info, team_id=team_id, team_alias=team_alias
             )
 
             # Delete cache entry (single-use)
@@ -1909,7 +2260,7 @@ async def cli_poll_key(
             verbose_proxy_logger.info(
                 f"CLI JWT generated for user: {user_id}, team: {team_id}"
             )
-            return {
+            poll_response = {
                 "status": "ready",
                 "key": jwt_token,
                 "user_id": user_id,
@@ -1919,6 +2270,12 @@ async def cli_poll_key(
                 # present nicer information if needed.
                 "team_details": user_team_details,
             }
+            attribution_metadata = _cli_poll_attribution_metadata_from_session(
+                session_data
+            )
+            if attribution_metadata:
+                poll_response["attribution_metadata"] = attribution_metadata
+            return poll_response
         else:
             return {"status": "pending"}
 
diff --git a/litellm/proxy/management_endpoints/user_agent_analytics_endpoints.py b/litellm/proxy/management_endpoints/user_agent_analytics_endpoints.py
index 872b6fa2250..ebd276fbee5 100644
--- a/litellm/proxy/management_endpoints/user_agent_analytics_endpoints.py
+++ b/litellm/proxy/management_endpoints/user_agent_analytics_endpoints.py
@@ -3,7 +3,7 @@ User Agent Analytics Endpoints
 
 This module provides optimized endpoints for tracking user agent activity metrics including:
 - Daily Active Users (DAU) by tags for configurable number of days
-- Weekly Active Users (WAU) by tags for configurable number of weeks  
+- Weekly Active Users (WAU) by tags for configurable number of weeks
 - Monthly Active Users (MAU) by tags for configurable number of months
 - Summary analytics by tags
 
diff --git a/litellm/proxy/management_helpers/utils.py b/litellm/proxy/management_helpers/utils.py
index f2d6e9612ff..b7d5cc30c49 100644
--- a/litellm/proxy/management_helpers/utils.py
+++ b/litellm/proxy/management_helpers/utils.py
@@ -518,19 +518,23 @@ def management_endpoint_wrapper(func):
                         _request_body: dict = await _read_request_body(
                             request=_http_request
                         )
-                        logging_payload = ManagementEndpointLoggingPayload(
-                            route=_route,
-                            request_data=_request_body,
-                            response=None,
-                            start_time=start_time,
-                            end_time=end_time,
-                            exception=e,
-                        )
+                    else:
+                        _route = func.__name__
+                        _request_body = {}
 
-                        await open_telemetry_logger.async_management_endpoint_failure_hook(  # type: ignore
-                            logging_payload=logging_payload,
-                            parent_otel_span=parent_otel_span,
-                        )
+                    logging_payload = ManagementEndpointLoggingPayload(
+                        route=_route,
+                        request_data=_request_body,
+                        response=None,
+                        start_time=start_time,
+                        end_time=end_time,
+                        exception=e,
+                    )
+
+                    await open_telemetry_logger.async_management_endpoint_failure_hook(  # type: ignore
+                        logging_payload=logging_payload,
+                        parent_otel_span=parent_otel_span,
+                    )
 
             raise e
 
diff --git a/litellm/proxy/ocr_endpoints/endpoints.py b/litellm/proxy/ocr_endpoints/endpoints.py
index 4f31c762df1..e32fee6afc5 100644
--- a/litellm/proxy/ocr_endpoints/endpoints.py
+++ b/litellm/proxy/ocr_endpoints/endpoints.py
@@ -178,6 +178,24 @@ async def _parse_ocr_request(request: Request) -> Dict[str, Any]:
             "For JSON requests, use 'document_url' or 'image_url' document types."
         )
 
+    # Security: reject provider-native file IDs (e.g. reducto://) received via
+    # JSON. These IDs are not scoped to the LiteLLM proxy user/key, so an
+    # authenticated user who obtains another user's file ID could submit it
+    # here and receive the OCR result using the proxy's shared provider
+    # credentials. Force callers to upload fresh content per request via
+    # multipart/form-data or an inline base64 data URI, both of which produce
+    # a server-mediated upload bound to the current request.
+    if isinstance(doc, dict):
+        for url_field in ("document_url", "image_url"):
+            url_value = doc.get(url_field)
+            if isinstance(url_value, str) and url_value.startswith("reducto://"):
+                raise ValueError(
+                    "reducto:// file IDs are not accepted through the proxy "
+                    "OCR API; upload the file in the same request via "
+                    "multipart/form-data with a 'file' field, or pass an "
+                    "inline base64 data URI as the document URL."
+                )
+
     return data
 
 
diff --git a/litellm/proxy/openai_files_endpoints/common_utils.py b/litellm/proxy/openai_files_endpoints/common_utils.py
index 30c78ed5ba7..0415bb456ec 100644
--- a/litellm/proxy/openai_files_endpoints/common_utils.py
+++ b/litellm/proxy/openai_files_endpoints/common_utils.py
@@ -727,6 +727,76 @@ async def resolve_output_file_ids_to_unified(response, prisma_client) -> None:
             pass
 
 
+async def ensure_batch_response_managed_file_ids(
+    response,
+    managed_files_obj,
+    prisma_client,
+    verbose_proxy_logger,
+    user_api_key_dict=None,
+    db_batch_object=None,
+) -> None:
+    """Normalize batch file IDs to managed unified IDs before DB persistence."""
+    await resolve_input_file_id_to_unified(response, prisma_client)
+    await resolve_output_file_ids_to_unified(response, prisma_client)
+
+    if managed_files_obj is None:
+        return
+
+    hidden_params = getattr(response, "_hidden_params", None) or {}
+    model_id = hidden_params.get("model_id")
+    if not model_id:
+        return
+
+    model_name = hidden_params.get("model_name")
+    unified_file_id = hidden_params.get("unified_file_id")
+    if not model_name and isinstance(unified_file_id, str):
+        decoded_unified_file_id = (
+            _is_base64_encoded_unified_file_id(unified_file_id) or unified_file_id
+        )
+        target_model_names = get_models_from_unified_file_id(decoded_unified_file_id)
+        if target_model_names:
+            model_name = ",".join(target_model_names)
+
+    if user_api_key_dict is None and db_batch_object is not None:
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        user_api_key_dict = UserAPIKeyAuth(
+            user_id=getattr(db_batch_object, "created_by", None) or "default-user-id",
+            team_id=getattr(db_batch_object, "team_id", None),
+        )
+    if user_api_key_dict is None:
+        return
+
+    for file_attr in ("output_file_id", "error_file_id"):
+        raw_file_id = getattr(response, file_attr, None)
+        if not raw_file_id or _is_base64_encoded_unified_file_id(raw_file_id):
+            continue
+        try:
+            new_unified_file_id = managed_files_obj.get_unified_output_file_id(
+                output_file_id=raw_file_id,
+                model_id=model_id,
+                model_name=model_name,
+            )
+            await managed_files_obj.store_unified_file_id(
+                file_id=new_unified_file_id,
+                file_object=None,
+                litellm_parent_otel_span=getattr(
+                    user_api_key_dict, "parent_otel_span", None
+                ),
+                model_mappings={model_id: raw_file_id},
+                user_api_key_dict=user_api_key_dict,
+            )
+            setattr(response, file_attr, new_unified_file_id)
+            verbose_proxy_logger.debug(
+                f"Converted batch {file_attr} {raw_file_id!r} to managed ID before DB write"
+            )
+        except Exception as e:
+            verbose_proxy_logger.warning(
+                f"Failed to convert batch {file_attr}={raw_file_id!r} to managed ID "
+                f"before DB write: {e}"
+            )
+
+
 async def get_batch_from_database(
     batch_id: str,
     unified_batch_id: Union[str, Literal[False]],
@@ -800,6 +870,7 @@ async def update_batch_in_database(
     verbose_proxy_logger,
     db_batch_object=None,
     operation: str = "update",
+    user_api_key_dict=None,
 ):
     """
     Update batch status and object in ManagedObjectTable.
@@ -813,6 +884,7 @@ async def update_batch_in_database(
         verbose_proxy_logger: Logger instance
         db_batch_object: Optional existing database object (for comparison)
         operation: Description of operation ("update", "cancel", etc.)
+        user_api_key_dict: Optional auth context for creating managed file IDs
     """
     import litellm.utils
 
@@ -823,6 +895,18 @@ async def update_batch_in_database(
         if not prisma_client:
             return
 
+        # Always normalize the response's file IDs to unified managed IDs
+        # (mutates in place) so the caller returns unified IDs to the user
+        # even when we skip the DB update below for an unchanged status.
+        await ensure_batch_response_managed_file_ids(
+            response=response,
+            managed_files_obj=managed_files_obj,
+            prisma_client=prisma_client,
+            verbose_proxy_logger=verbose_proxy_logger,
+            user_api_key_dict=user_api_key_dict,
+            db_batch_object=db_batch_object,
+        )
+
         # Only update if status has changed (when db_batch_object is provided)
         if db_batch_object and response.status == db_batch_object.status:
             return
diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py
index c42faa59cf0..3be26eb572d 100644
--- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py
+++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py
@@ -266,7 +266,174 @@ class AnthropicPassthroughLoggingHandler:
         model: str,
     ) -> Optional[Union[ModelResponse, TextCompletionResponse]]:
         """
-        Builds complete response from raw Anthropic chunks
+        Builds complete response from raw Anthropic chunks.
+
+        Fast path: for the dominant case of a pure-text streaming response
+        (no tool_use / thinking / non-text content blocks), the long run of
+        ``content_block_delta`` text deltas is collapsed into a single
+        equivalent SSE event before conversion. ``chunk_parser`` and
+        ``stream_chunk_builder`` remain the single source of truth for chunk
+        shape, usage math and finish-reason mapping, so the rebuilt response
+        (and therefore the logged/billed payload) is identical -- this is
+        asserted by a parity test. Anything non-trivial falls back to the
+        unchanged legacy reconstruction.
+
+        Per-event Pydantic ``ModelResponseStream`` construction dominated
+        event-loop CPU under concurrent streaming; collapsing the homogeneous
+        text run removes O(num_output_tokens) of it.
+        """
+        collapsed = AnthropicPassthroughLoggingHandler._collapse_pure_text_chunks(
+            all_chunks
+        )
+        if collapsed is not None:
+            return AnthropicPassthroughLoggingHandler._build_complete_streaming_response_legacy(
+                all_chunks=collapsed,
+                litellm_logging_obj=litellm_logging_obj,
+                model=model,
+            )
+        return AnthropicPassthroughLoggingHandler._build_complete_streaming_response_legacy(
+            all_chunks=all_chunks,
+            litellm_logging_obj=litellm_logging_obj,
+            model=model,
+        )
+
+    # Anthropic SSE block/delta types that the fast path is NOT allowed to
+    # collapse -- their presence forces the unchanged legacy path so tool
+    # calls, thinking, citations, etc. keep byte-identical reconstruction.
+    _FAST_PATH_DISALLOWED_DELTA_TYPES = frozenset(
+        {
+            "input_json_delta",
+            "thinking_delta",
+            "signature_delta",
+            "citations_delta",
+        }
+    )
+
+    @staticmethod
+    def _collapse_pure_text_chunks(  # noqa: PLR0915
+        all_chunks: Sequence[Union[str, bytes]],
+    ) -> Optional[List[str]]:
+        """
+        Return a new chunk list with the contiguous run of text-only
+        ``content_block_delta`` events replaced by a single equivalent event,
+        or ``None`` if the stream is not a pure single-text-block response
+        (in which case the caller uses the legacy path unchanged).
+
+        Only ``message_start`` / ``content_block_start(text)`` /
+        ``content_block_delta(text_delta)`` / ``content_block_stop`` /
+        ``message_delta`` / ``message_stop`` / ``ping`` events are accepted.
+        Any other content-block type or delta type returns ``None``.
+        """
+        normalized: List[str] = []
+        for raw in all_chunks:
+            line = raw.decode("utf-8") if isinstance(raw, bytes) else raw
+            for ev in line.split("\n\n"):
+                ev = ev.strip()
+                if ev:
+                    normalized.append(ev)
+
+        text_block_indexes: set = set()
+        out: List[str] = []
+        pending_text: List[str] = []
+        pending_index: Optional[int] = None
+        saw_any_text_delta = False
+
+        def flush() -> None:
+            nonlocal pending_text, pending_index
+            if pending_text:
+                merged = {
+                    "type": "content_block_delta",
+                    "index": pending_index if pending_index is not None else 0,
+                    "delta": {"type": "text_delta", "text": "".join(pending_text)},
+                }
+                out.append("data: " + json.dumps(merged))
+                pending_text = []
+                pending_index = None
+
+        for ev in normalized:
+            idx = ev.find("data:")
+            if idx == -1:
+                # Bare "event: <name>" line. The legacy converter turns this
+                # into an empty ModelResponseStream that contributes nothing
+                # to stream_chunk_builder. Drop the high-frequency interior
+                # markers (content_block_delta / ping); keep every other
+                # bare event line verbatim so chunk ordering and the
+                # load-bearing chunks[0] (event: message_start) are retained.
+                name = ev[len("event:") :].strip() if ev.startswith("event:") else ""
+                if name in ("content_block_delta", "ping"):
+                    continue
+                flush()
+                out.append(ev)
+                continue
+
+            json_str = ev[idx + len("data:") :].strip()
+            try:
+                data = json.loads(json_str)
+            except (json.JSONDecodeError, ValueError):
+                return None
+
+            etype = data.get("type")
+            if etype == "content_block_start":
+                block = data.get("content_block") or {}
+                if block.get("type") != "text":
+                    return None
+                text_block_indexes.add(data.get("index"))
+                flush()
+                out.append(ev)
+            elif etype == "content_block_delta":
+                delta = data.get("delta") or {}
+                dtype = delta.get("type")
+                if (
+                    dtype
+                    in AnthropicPassthroughLoggingHandler._FAST_PATH_DISALLOWED_DELTA_TYPES
+                ):
+                    return None
+                if dtype != "text_delta":
+                    return None
+                cur_index = data.get("index")
+                if cur_index not in text_block_indexes:
+                    return None
+                # Defensive: Anthropic sends blocks strictly sequentially
+                # (start/deltas/stop, then next block), so pending_text from
+                # block N must be flushed by content_block_stop before block
+                # N+1's deltas arrive. If we ever see a delta whose index
+                # disagrees with the current pending buffer, the stream is
+                # interleaved -- fall back to legacy rather than risk merging
+                # text from different blocks under a single index.
+                if (
+                    pending_text
+                    and pending_index is not None
+                    and cur_index != pending_index
+                ):
+                    return None
+                saw_any_text_delta = True
+                pending_index = cur_index
+                pending_text.append(delta.get("text") or "")
+            elif etype == "ping":
+                # Interior no-op; legacy maps it to an empty chunk.
+                continue
+            else:
+                # message_start / content_block_stop / message_delta /
+                # message_stop / error: pass through unchanged.
+                flush()
+                out.append(ev)
+
+        flush()
+
+        if not saw_any_text_delta:
+            return None
+        return out
+
+    @staticmethod
+    def _build_complete_streaming_response_legacy(
+        all_chunks: Sequence[Union[str, bytes]],
+        litellm_logging_obj: LiteLLMLoggingObj,
+        model: str,
+    ) -> Optional[Union[ModelResponse, TextCompletionResponse]]:
+        """
+        Original reconstruction: convert every SSE event to a generic chunk
+        and assemble via stream_chunk_builder. Kept verbatim as the fallback
+        / source of truth for the fast path's parity test.
 
         - Splits multi-event chunks into individual SSE events
         - Converts str chunks to generic chunks
diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/cursor_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/cursor_passthrough_logging_handler.py
index a104f962630..e7696e5a18a 100644
--- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/cursor_passthrough_logging_handler.py
+++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/cursor_passthrough_logging_handler.py
@@ -18,7 +18,6 @@ from litellm.litellm_core_utils.litellm_logging import (
 from litellm.proxy._types import PassThroughEndpointLoggingTypedDict
 from litellm.types.utils import StandardPassThroughResponseObject
 
-
 CURSOR_AGENT_ENDPOINTS: Dict[str, str] = {
     "POST /v0/agents": "cursor:agent:create",
     "GET /v0/agents": "cursor:agent:list",
diff --git a/litellm/proxy/pass_through_endpoints/streaming_handler.py b/litellm/proxy/pass_through_endpoints/streaming_handler.py
index cbfcd34c438..235a38b75f9 100644
--- a/litellm/proxy/pass_through_endpoints/streaming_handler.py
+++ b/litellm/proxy/pass_through_endpoints/streaming_handler.py
@@ -1,6 +1,6 @@
 import asyncio
 from datetime import datetime
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import httpx
 
@@ -45,28 +45,44 @@ class PassThroughStreamingHandler:
             litellm_logging_obj=litellm_logging_obj,
         )
 
+        # Resolve once per stream rather than re-reading the global +
+        # re-branching on every chunk. ``include_cost_in_streaming_usage`` is
+        # set at config load and stable for the process, matching how the
+        # proxy-level streaming fast path resolves it.
+        cost_injection_active = (
+            bool(getattr(litellm, "include_cost_in_streaming_usage", False))
+            and bool(model_name)
+            and endpoint_type in (EndpointType.VERTEX_AI, EndpointType.ANTHROPIC)
+        )
         try:
-            async for chunk in response.aiter_bytes():
-                raw_bytes.append(chunk)
-                if (
-                    getattr(litellm, "include_cost_in_streaming_usage", False)
-                    and model_name
-                ):
+            if not cost_injection_active:
+                # Hot path: just buffer for end-of-stream logging and forward.
+                async for chunk in response.aiter_bytes():
+                    raw_bytes.append(chunk)
+                    yield chunk
+            else:
+                # ``cost_injection_active`` already requires ``model_name`` to
+                # be truthy; pin to a typed local so mypy narrows ``Optional[str]``
+                # -> ``str`` for the per-chunk call site.
+                assert model_name is not None
+                resolved_model_name: str = model_name
+                async for chunk in response.aiter_bytes():
+                    raw_bytes.append(chunk)
                     if endpoint_type == EndpointType.VERTEX_AI:
                         if "streamRawPredict" in url_route or "rawPredict" in url_route:
                             modified_chunk = ProxyBaseLLMRequestProcessing._process_chunk_with_cost_injection(
-                                chunk, model_name
+                                chunk, resolved_model_name
                             )
                             if modified_chunk is not None:
                                 chunk = modified_chunk
-                    elif endpoint_type == EndpointType.ANTHROPIC:
+                    else:  # EndpointType.ANTHROPIC
                         modified_chunk = ProxyBaseLLMRequestProcessing._process_chunk_with_cost_injection(
-                            chunk, model_name
+                            chunk, resolved_model_name
                         )
                         if modified_chunk is not None:
                             chunk = modified_chunk
 
-                yield chunk
+                    yield chunk
         except Exception as e:
             verbose_proxy_logger.error(f"Error in chunk_processor: {str(e)}")
             raise
@@ -115,64 +131,20 @@ class PassThroughStreamingHandler:
         - OpenAI
         """
         try:
-            all_chunks = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(
-                raw_bytes
+            (
+                standard_logging_response_object,
+                kwargs,
+            ) = PassThroughStreamingHandler._build_passthrough_logging_result(
+                litellm_logging_obj=litellm_logging_obj,
+                passthrough_success_handler_obj=passthrough_success_handler_obj,
+                url_route=url_route,
+                request_body=request_body,
+                endpoint_type=endpoint_type,
+                start_time=start_time,
+                raw_bytes=raw_bytes,
+                end_time=end_time,
+                model=model,
             )
-            standard_logging_response_object: Optional[
-                PassThroughEndpointLoggingResultValues
-            ] = None
-            kwargs: dict = {}
-            if endpoint_type == EndpointType.ANTHROPIC:
-                anthropic_passthrough_logging_handler_result = AnthropicPassthroughLoggingHandler._handle_logging_anthropic_collected_chunks(
-                    litellm_logging_obj=litellm_logging_obj,
-                    passthrough_success_handler_obj=passthrough_success_handler_obj,
-                    url_route=url_route,
-                    request_body=request_body,
-                    endpoint_type=endpoint_type,
-                    start_time=start_time,
-                    all_chunks=all_chunks,
-                    end_time=end_time,
-                )
-                standard_logging_response_object = (
-                    anthropic_passthrough_logging_handler_result["result"]
-                )
-                kwargs = anthropic_passthrough_logging_handler_result["kwargs"]
-            elif endpoint_type == EndpointType.VERTEX_AI:
-                vertex_passthrough_logging_handler_result = VertexPassthroughLoggingHandler._handle_logging_vertex_collected_chunks(
-                    litellm_logging_obj=litellm_logging_obj,
-                    passthrough_success_handler_obj=passthrough_success_handler_obj,
-                    url_route=url_route,
-                    request_body=request_body,
-                    endpoint_type=endpoint_type,
-                    start_time=start_time,
-                    all_chunks=all_chunks,
-                    end_time=end_time,
-                    model=model,
-                )
-                standard_logging_response_object = (
-                    vertex_passthrough_logging_handler_result["result"]
-                )
-                kwargs = vertex_passthrough_logging_handler_result["kwargs"]
-            elif endpoint_type == EndpointType.OPENAI:
-                openai_passthrough_logging_handler_result = OpenAIPassthroughLoggingHandler._handle_logging_openai_collected_chunks(
-                    litellm_logging_obj=litellm_logging_obj,
-                    passthrough_success_handler_obj=passthrough_success_handler_obj,
-                    url_route=url_route,
-                    request_body=request_body,
-                    endpoint_type=endpoint_type,
-                    start_time=start_time,
-                    all_chunks=all_chunks,
-                    end_time=end_time,
-                )
-                standard_logging_response_object = (
-                    openai_passthrough_logging_handler_result["result"]
-                )
-                kwargs = openai_passthrough_logging_handler_result["kwargs"]
-
-            if standard_logging_response_object is None:
-                standard_logging_response_object = StandardPassThroughResponseObject(
-                    response=f"cannot parse chunks to standard response object. Chunks={all_chunks}"
-                )
             await litellm_logging_obj.async_success_handler(
                 result=standard_logging_response_object,
                 start_time=start_time,
@@ -199,6 +171,89 @@ class PassThroughStreamingHandler:
                 f"Error in _route_streaming_logging_to_handler: {str(e)}"
             )
 
+    @staticmethod
+    def _build_passthrough_logging_result(
+        litellm_logging_obj: LiteLLMLoggingObj,
+        passthrough_success_handler_obj: PassThroughEndpointLogging,
+        url_route: str,
+        request_body: dict,
+        endpoint_type: EndpointType,
+        start_time: datetime,
+        raw_bytes: List[bytes],
+        end_time: datetime,
+        model: Optional[str],
+    ) -> Tuple[PassThroughEndpointLoggingResultValues, dict]:
+        """
+        Synchronous, CPU-bound reconstruction of the standard logging payload
+        from collected raw SSE bytes. Extracted from
+        _route_streaming_logging_to_handler so the per-endpoint dispatch can
+        be unit-tested in isolation. Still invoked synchronously on the event
+        loop; an off-loop dispatch is a future change, not part of this PR.
+        """
+        all_chunks = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(
+            raw_bytes
+        )
+        standard_logging_response_object: Optional[
+            PassThroughEndpointLoggingResultValues
+        ] = None
+        kwargs: dict = {}
+        if endpoint_type == EndpointType.ANTHROPIC:
+            anthropic_passthrough_logging_handler_result = AnthropicPassthroughLoggingHandler._handle_logging_anthropic_collected_chunks(
+                litellm_logging_obj=litellm_logging_obj,
+                passthrough_success_handler_obj=passthrough_success_handler_obj,
+                url_route=url_route,
+                request_body=request_body,
+                endpoint_type=endpoint_type,
+                start_time=start_time,
+                all_chunks=all_chunks,
+                end_time=end_time,
+            )
+            standard_logging_response_object = (
+                anthropic_passthrough_logging_handler_result["result"]
+            )
+            kwargs = anthropic_passthrough_logging_handler_result["kwargs"]
+        elif endpoint_type == EndpointType.VERTEX_AI:
+            vertex_passthrough_logging_handler_result = (
+                VertexPassthroughLoggingHandler._handle_logging_vertex_collected_chunks(
+                    litellm_logging_obj=litellm_logging_obj,
+                    passthrough_success_handler_obj=passthrough_success_handler_obj,
+                    url_route=url_route,
+                    request_body=request_body,
+                    endpoint_type=endpoint_type,
+                    start_time=start_time,
+                    all_chunks=all_chunks,
+                    end_time=end_time,
+                    model=model,
+                )
+            )
+            standard_logging_response_object = (
+                vertex_passthrough_logging_handler_result["result"]
+            )
+            kwargs = vertex_passthrough_logging_handler_result["kwargs"]
+        elif endpoint_type == EndpointType.OPENAI:
+            openai_passthrough_logging_handler_result = (
+                OpenAIPassthroughLoggingHandler._handle_logging_openai_collected_chunks(
+                    litellm_logging_obj=litellm_logging_obj,
+                    passthrough_success_handler_obj=passthrough_success_handler_obj,
+                    url_route=url_route,
+                    request_body=request_body,
+                    endpoint_type=endpoint_type,
+                    start_time=start_time,
+                    all_chunks=all_chunks,
+                    end_time=end_time,
+                )
+            )
+            standard_logging_response_object = (
+                openai_passthrough_logging_handler_result["result"]
+            )
+            kwargs = openai_passthrough_logging_handler_result["kwargs"]
+
+        if standard_logging_response_object is None:
+            standard_logging_response_object = StandardPassThroughResponseObject(
+                response=f"cannot parse chunks to standard response object. Chunks={all_chunks}"
+            )
+        return standard_logging_response_object, kwargs
+
     @staticmethod
     def _extract_model_for_cost_injection(
         request_body: Optional[dict],
diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py
index 5fc8c44b2d8..c0246f234a8 100644
--- a/litellm/proxy/proxy_cli.py
+++ b/litellm/proxy/proxy_cli.py
@@ -6,6 +6,7 @@ import random
 import subprocess
 import sys
 import urllib.parse as urlparse
+from pathlib import Path
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 import click
@@ -38,6 +39,35 @@ class LiteLLMDatabaseConnectionPool(Enum):
     database_connection_pool_timeout = 60
 
 
+def _build_db_connection_url_params(
+    connection_limit: int,
+    pool_timeout: Optional[Union[int, float]],
+    connect_timeout: Optional[Union[int, float]] = None,
+    socket_timeout: Optional[Union[int, float]] = None,
+    extra_params: Optional[dict] = None,
+) -> dict:
+    """Build the Prisma DATABASE_URL query params controlling connection pool behavior.
+
+    `connect_timeout` / `socket_timeout` map to the Prisma URL params of the same
+    name (https://www.prisma.io/docs/orm/overview/databases/postgresql) and are
+    omitted when None so Prisma's defaults apply. `extra_params` is an
+    untyped passthrough — keys it provides win over the named arguments above,
+    so it can be used to override any default we set here.
+    """
+    params: dict = {
+        "connection_limit": connection_limit,
+    }
+    if pool_timeout is not None:
+        params["pool_timeout"] = pool_timeout
+    if connect_timeout is not None:
+        params["connect_timeout"] = connect_timeout
+    if socket_timeout is not None:
+        params["socket_timeout"] = socket_timeout
+    if extra_params:
+        params.update(extra_params)
+    return params
+
+
 def append_query_params(url: Optional[str], params: dict) -> str:
     from litellm._logging import verbose_proxy_logger
 
@@ -264,6 +294,62 @@ class ProxyInitializationHelpers:
         # hypercorn serve raises a type warning when passing a fast api app - even though fast API is a valid type
         asyncio.run(serve(app, config))  # type: ignore
 
+    @staticmethod
+    def _init_granian_server(
+        host: str,
+        port: int,
+        num_workers: int,
+        ssl_certfile_path: Optional[str],
+        ssl_keyfile_path: Optional[str],
+        max_requests_before_restart: Optional[int],
+        ciphers: Optional[str],
+        granian_runtime_threads: Optional[int] = None,
+    ) -> None:
+        """
+        Run the proxy with Granian (Rust-backed ASGI server, HTTP/1 + HTTP/2).
+
+        Uses a string import path so workers load ``litellm.proxy.proxy_server:app``
+        the same way as uvicorn's ``app=`` string target.
+        """
+        from granian import Granian
+        from granian.constants import Interfaces
+
+        print(  # noqa
+            f"\033[1;32mLiteLLM Proxy: Starting server on {host}:{port} using Granian\033[0m\n"
+        )
+        if max_requests_before_restart is not None:
+            print(  # noqa
+                "\033[1;33mLiteLLM: --max_requests_before_restart is not supported by Granian "
+                "(Granian uses workers_lifetime in seconds, not a per-request limit).\033[0m\n"
+            )
+        if ciphers is not None:
+            print(  # noqa
+                "\033[1;33mLiteLLM: --ciphers is not applied when using --run_granian.\033[0m\n"
+            )
+
+        kwargs: dict[str, Any] = {
+            "target": "litellm.proxy.proxy_server:app",
+            "address": host,
+            "port": port,
+            "workers": max(1, num_workers),
+            "interface": Interfaces.ASGI,
+            "websockets": True,
+        }
+        if granian_runtime_threads is not None:
+            kwargs["runtime_threads"] = granian_runtime_threads
+        if ssl_certfile_path is not None and ssl_keyfile_path is not None:
+            print(  # noqa
+                f"\033[1;32mLiteLLM Proxy: Using SSL with certfile: {ssl_certfile_path} and keyfile: {ssl_keyfile_path}\033[0m\n"
+            )
+            kwargs["ssl_cert"] = Path(ssl_certfile_path)
+            kwargs["ssl_key"] = Path(ssl_keyfile_path)
+        elif ssl_certfile_path is not None or ssl_keyfile_path is not None:
+            raise click.ClickException(
+                "Both --ssl_certfile_path and --ssl_keyfile_path are required for SSL."
+            )
+
+        Granian(**kwargs).serve()
+
     @staticmethod
     def _run_gunicorn_server(
         host: str,
@@ -292,9 +378,7 @@ class ProxyInitializationHelpers:
                 _endpoint_str = (
                     f"curl --location 'http://0.0.0.0:{port}/chat/completions' \\"
                 )
-                curl_command = (
-                    _endpoint_str
-                    + """
+                curl_command = _endpoint_str + """
                 --header 'Content-Type: application/json' \\
                 --data ' {
                 "model": "gpt-3.5-turbo",
@@ -307,7 +391,6 @@ class ProxyInitializationHelpers:
                 }'
                 \n
                 """
-                )
                 print()  # noqa
                 print(  # noqa
                     '\033[1;34mLiteLLM: Test your local proxy with: "litellm --test" This runs an openai.ChatCompletion request to your proxy [In a new terminal tab]\033[0m\n'
@@ -383,11 +466,9 @@ class ProxyInitializationHelpers:
             with open(os.devnull, "w") as devnull:
                 subprocess.Popen(command, stdout=devnull, stderr=devnull)
         except Exception as e:
-            print(  # noqa
-                f"""
+            print(f"""
                 LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
-            """
-            )  # noqa
+            """)  # noqa  # noqa
 
     @staticmethod
     def _is_port_in_use(port):
@@ -459,9 +540,23 @@ class ProxyInitializationHelpers:
 @click.option(
     "--num_workers",
     default=DEFAULT_NUM_WORKERS_LITELLM_PROXY,
-    help="Number of uvicorn / gunicorn workers to spin up. Default is 1 (from DEFAULT_NUM_WORKERS_LITELLM_PROXY)",
+    help=(
+        "Number of worker processes for uvicorn / gunicorn, or Granian worker processes "
+        "(--workers). Default is 1 (from DEFAULT_NUM_WORKERS_LITELLM_PROXY). "
+        "With --run_granian, use --granian_threads for runtime threads per worker."
+    ),
     envvar="NUM_WORKERS",
 )
+@click.option(
+    "--granian_threads",
+    default=None,
+    type=click.IntRange(min=1),
+    help=(
+        "Only with --run_granian: runtime threads per worker process "
+        "(Granian --runtime-threads / GRANIAN_RUNTIME_THREADS). Omit to use Granian's default (1)."
+    ),
+    envvar="GRANIAN_RUNTIME_THREADS",
+)
 @click.option("--api_base", default=None, help="API base URL.")
 @click.option(
     "--api_version",
@@ -600,6 +695,15 @@ class ProxyInitializationHelpers:
     is_flag=True,
     help="Starts proxy via hypercorn, instead of uvicorn (supports HTTP/2)",
 )
+@click.option(
+    "--run_granian",
+    default=False,
+    is_flag=True,
+    help=(
+        "Starts proxy via Granian (Rust ASGI server) instead of uvicorn. "
+        "Requires Python 3.10+ and the `granian` package."
+    ),
+)
 @click.option(
     "--ssl_keyfile_path",
     default=None,
@@ -704,6 +808,7 @@ def run_server(  # noqa: PLR0915
     test,
     local,
     num_workers,
+    granian_threads,
     test_async,
     iam_token_db_auth,
     num_requests,
@@ -713,6 +818,7 @@ def run_server(  # noqa: PLR0915
     version,
     run_gunicorn,
     run_hypercorn,
+    run_granian,
     ssl_keyfile_path,
     ssl_certfile_path,
     ciphers,
@@ -797,16 +903,29 @@ def run_server(  # noqa: PLR0915
             config=config,
             use_queue=use_queue,
         )
-        try:
-            import uvicorn
-        except Exception:
-            raise ImportError(
-                "uvicorn, gunicorn needs to be imported. Run - `pip install 'litellm[proxy]'`"
-            )
+        if run_granian:
+            try:
+                import granian  # noqa: F401
+            except ImportError as e:
+                raise ImportError(
+                    "granian must be installed to use --run_granian. "
+                    "Run `pip install granian` or `pip install 'litellm[proxy]'` "
+                    "(Granian requires Python 3.10+)."
+                ) from e
+        else:
+            try:
+                import uvicorn
+            except Exception:
+                raise ImportError(
+                    "uvicorn, gunicorn needs to be imported. Run - `pip install 'litellm[proxy]'`"
+                )
 
         db_connection_pool_limit = 100
         # Starts optional due to config fallback checks; guaranteed non-None before use.
         db_connection_timeout: Optional[Union[int, float]] = 60
+        db_connect_timeout: Optional[Union[int, float]] = None
+        db_socket_timeout: Optional[Union[int, float]] = None
+        db_extra_connection_params: Optional[dict] = None
         general_settings = {}
         ### GET DB TOKEN FOR IAM AUTH ###
 
@@ -924,6 +1043,11 @@ def run_server(  # noqa: PLR0915
                 db_connection_timeout = (
                     LiteLLMDatabaseConnectionPool.database_connection_pool_timeout.value
                 )
+            db_connect_timeout = general_settings.get("database_connect_timeout")
+            db_socket_timeout = general_settings.get("database_socket_timeout")
+            db_extra_connection_params = general_settings.get(
+                "database_extra_connection_params"
+            )
             if database_url and database_url.startswith("os.environ/"):
                 original_dir = os.getcwd()
                 # set the working directory to where this script is
@@ -963,27 +1087,26 @@ def run_server(  # noqa: PLR0915
             try:
                 from litellm.secret_managers.main import get_secret
 
+                connection_url_params = _build_db_connection_url_params(
+                    connection_limit=db_connection_pool_limit,
+                    pool_timeout=db_connection_timeout,
+                    connect_timeout=db_connect_timeout,
+                    socket_timeout=db_socket_timeout,
+                    extra_params=db_extra_connection_params,
+                )
                 if os.getenv("DATABASE_URL", None) is not None:
-                    ### add connection pool + pool timeout args
-                    params = {
-                        "connection_limit": db_connection_pool_limit,
-                        "pool_timeout": db_connection_timeout,
-                    }
                     database_url = get_secret("DATABASE_URL", default_value=None)
                     modified_url = append_query_params(
-                        str(database_url) if database_url else None, params
+                        str(database_url) if database_url else None,
+                        connection_url_params,
                     )
                     os.environ["DATABASE_URL"] = modified_url
                 if os.getenv("DIRECT_URL", None) is not None:
-                    ### add connection pool + pool timeout args
-                    params = {
-                        "connection_limit": db_connection_pool_limit,
-                        "pool_timeout": db_connection_timeout,
-                    }
                     database_url = os.getenv("DIRECT_URL")
-                    modified_url = append_query_params(database_url, params)
+                    modified_url = append_query_params(
+                        database_url, connection_url_params
+                    )
                     os.environ["DIRECT_URL"] = modified_url
-                    ###
                 subprocess.run(["prisma"], capture_output=True)
                 is_prisma_runnable = True
             except FileNotFoundError:
@@ -1081,7 +1204,7 @@ def run_server(  # noqa: PLR0915
         # Optional: recycle uvicorn workers after N requests
         if max_requests_before_restart is not None:
             uvicorn_args["limit_max_requests"] = max_requests_before_restart
-        if run_gunicorn is False and run_hypercorn is False:
+        if run_gunicorn is False and run_hypercorn is False and run_granian is False:
             if ssl_certfile_path is not None and ssl_keyfile_path is not None:
                 print(  # noqa
                     f"\033[1;32mLiteLLM Proxy: Using SSL with certfile: {ssl_certfile_path} and keyfile: {ssl_keyfile_path}\033[0m\n"  # noqa
@@ -1123,6 +1246,17 @@ def run_server(  # noqa: PLR0915
                 ssl_keyfile_path=ssl_keyfile_path,
                 ciphers=ciphers,
             )
+        elif run_granian is True:
+            ProxyInitializationHelpers._init_granian_server(
+                host=host,
+                port=port,
+                num_workers=num_workers,
+                ssl_certfile_path=ssl_certfile_path,
+                ssl_keyfile_path=ssl_keyfile_path,
+                max_requests_before_restart=max_requests_before_restart,
+                ciphers=ciphers,
+                granian_runtime_threads=granian_threads,
+            )
 
 
 if __name__ == "__main__":
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 6aabcfaac2a..687d2df0203 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -550,6 +550,7 @@ from fastapi import (
     status,
 )
 from fastapi.encoders import jsonable_encoder
+from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.openapi.docs import get_swagger_ui_html
 from fastapi.openapi.utils import get_openapi
@@ -1209,15 +1210,69 @@ async def openai_exception_handler(request: Request, exc: ProxyException):
     # NOTE: DO NOT MODIFY THIS, its crucial to map to Openai exceptions
     headers = exc.headers
     error_dict = exc.to_dict()
+    status_code = int(exc.code) if exc.code else status.HTTP_500_INTERNAL_SERVER_ERROR
+    _close_dangling_otel_server_span(request, status_code)
     return JSONResponse(
-        status_code=(
-            int(exc.code) if exc.code else status.HTTP_500_INTERNAL_SERVER_ERROR
-        ),
+        status_code=status_code,
         content={"error": error_dict},
         headers=headers,
     )
 
 
+def _close_dangling_otel_server_span(request: Request, status_code: int) -> None:
+    parent_otel_span = getattr(request.state, "parent_otel_span", None)
+    if parent_otel_span is None:
+        return
+    if open_telemetry_logger is None:
+        return
+    try:
+        from opentelemetry.trace import Status, StatusCode
+
+        open_telemetry_logger.set_response_status_code_attribute(
+            parent_otel_span, status_code
+        )
+        parent_otel_span.set_status(
+            Status(StatusCode.ERROR if status_code >= 400 else StatusCode.OK)
+        )
+        parent_otel_span.end()
+    except Exception as e:
+        verbose_proxy_logger.debug(
+            "Error closing dangling OTEL SERVER span: %s", str(e)
+        )
+    finally:
+        request.state.parent_otel_span = None
+
+
+@app.exception_handler(RequestValidationError)
+async def otel_request_validation_exception_handler(
+    request: Request, exc: RequestValidationError
+):
+    _close_dangling_otel_server_span(request, 422)
+    return JSONResponse(
+        status_code=422,
+        content={"detail": jsonable_encoder(exc.errors())},
+    )
+
+
+@app.exception_handler(Exception)
+async def otel_unhandled_exception_handler(request: Request, exc: Exception):
+    if isinstance(exc, (ProxyException, HTTPException, RequestValidationError)):
+        raise exc
+    verbose_proxy_logger.exception(
+        "Unhandled exception in request: %s", type(exc).__name__
+    )
+    _close_dangling_otel_server_span(request, 500)
+    return JSONResponse(
+        status_code=500,
+        content={
+            "error": {
+                "message": "Internal server error",
+                "type": "internal_server_error",
+            }
+        },
+    )
+
+
 router = APIRouter()
 
 
@@ -2710,11 +2765,9 @@ def run_ollama_serve():
         with open(os.devnull, "w") as devnull:
             subprocess.Popen(command, stdout=devnull, stderr=devnull)
     except Exception as e:
-        verbose_proxy_logger.debug(
-            f"""
+        verbose_proxy_logger.debug(f"""
             LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
-        """
-        )
+        """)
 
 
 def _get_process_rss_mb() -> Optional[float]:
@@ -4328,6 +4381,19 @@ class ProxyConfig:
                 "health_check_concurrency", None
             )
             health_check_details = general_settings.get("health_check_details", True)
+            ### INTERACTIONS API SCHEMA ###
+            _use_legacy_interactions_schema = general_settings.get(
+                "use_legacy_interactions_schema"
+            )
+            if _use_legacy_interactions_schema is not None:
+                if isinstance(_use_legacy_interactions_schema, str):
+                    litellm.use_legacy_interactions_schema = (
+                        _use_legacy_interactions_schema.lower() == "true"
+                    )
+                else:
+                    litellm.use_legacy_interactions_schema = bool(
+                        _use_legacy_interactions_schema
+                    )
             # Health-check-driven routing (opt-in, passes through to Router later)
             _enable_hc_routing = general_settings.get(
                 "enable_health_check_routing", False
@@ -4727,6 +4793,7 @@ class ProxyConfig:
         if _id is not None:
             model.model_info["id"] = _id
             model.model_info["db_model"] = True
+            model.model_info["blocked"] = bool(getattr(model, "blocked", False))
 
         if premium_user is True:
             # seeing "created_at", "updated_at", "created_by", "updated_by" is a LiteLLM Enterprise Feature
@@ -6916,6 +6983,15 @@ async def async_data_generator(  # noqa: PLR0915
 
             if isinstance(chunk, BaseModel):
                 chunk = _serialize_streaming_chunk(chunk)
+            elif isinstance(chunk, bytes):
+                # Some upstream streaming iterators (e.g. AsyncGoogleGenAIGenerateContentStreamingIterator
+                # for /v1beta/.../streamGenerateContent) yield raw SSE bytes from Gemini.
+                # Decode to str so the f-string below does not emit a Python b'...' literal,
+                # and pass already-formatted SSE through unchanged to avoid double "data:" prefix.
+                chunk = chunk.decode("utf-8", errors="replace")
+                if chunk.startswith(("data:", "event:", ":")):
+                    yield chunk if chunk.endswith("\n\n") else chunk + "\n\n"
+                    continue
             elif isinstance(chunk, str) and chunk.startswith("data: "):
                 error_message = chunk
                 break
@@ -8089,6 +8165,11 @@ async def model_list(
             proxy_logging_obj=proxy_logging_obj,
         )
 
+    # Compute once — used in both branches below to hide paused models from the listing.
+    blocked_names = (
+        llm_router.get_fully_blocked_model_names() if llm_router is not None else set()
+    )
+
     # If scope=expand and user has admin privileges, return all proxy models
     if should_expand_scope:
         # Get all proxy models as if user is a proxy admin
@@ -8121,6 +8202,10 @@ async def model_list(
             only_model_access_groups=only_model_access_groups or False,
         )
 
+        # Hide paused models from the public listing (admins manage them via /model/info)
+        if blocked_names:
+            all_models = [m for m in all_models if m not in blocked_names]
+
         # Build response data with all proxy models
         model_data = []
         for model in all_models:
@@ -8154,6 +8239,10 @@ async def model_list(
         user_api_key_cache=user_api_key_cache,
     )
 
+    # Hide paused models from the public listing (admins manage them via /model/info)
+    if blocked_names:
+        all_models = [m for m in all_models if m not in blocked_names]
+
     # Build response data
     model_data = []
     for model in all_models:
diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py
index f86cde87401..8f6f7084a0c 100644
--- a/litellm/proxy/route_llm_request.py
+++ b/litellm/proxy/route_llm_request.py
@@ -94,6 +94,12 @@ ROUTE_ENDPOINT_MAPPING = {
     "aget_interaction": "/interactions/{interaction_id}",
     "adelete_interaction": "/interactions/{interaction_id}",
     "acancel_interaction": "/interactions/{interaction_id}/cancel",
+    # Google Managed Agents API routes
+    "acreate_agent": "/v1beta/agents",
+    "alist_agents": "/v1beta/agents",
+    "aget_agent": "/v1beta/agents/{name}",
+    "adelete_agent": "/v1beta/agents/{name}",
+    "alist_agent_versions": "/v1beta/agents/{name}/versions",
     # OpenAI Evals API routes
     "acreate_eval": "/evals",
     "alist_evals": "/evals",
@@ -311,6 +317,11 @@ async def route_request(  # noqa: PLR0915 - Complex routing function, refactorin
         "aget_interaction",
         "adelete_interaction",
         "acancel_interaction",
+        "acreate_agent",
+        "alist_agents",
+        "aget_agent",
+        "adelete_agent",
+        "alist_agent_versions",
         "asend_message",
         "call_mcp_tool",
         "acancel_batch",
@@ -430,7 +441,11 @@ async def route_request(  # noqa: PLR0915 - Complex routing function, refactorin
                         deployment = llm_router.get_deployment_by_model_group_name(
                             model_group_name=model
                         )
-                        if deployment and deployment.litellm_params:
+                        if (
+                            deployment
+                            and deployment.litellm_params
+                            and not llm_router._is_deployment_blocked(deployment)
+                        ):
                             deployment_creds = deployment.litellm_params.model_dump(
                                 exclude_none=True
                             )
@@ -464,6 +479,15 @@ async def route_request(  # noqa: PLR0915 - Complex routing function, refactorin
             "acancel_interaction",
         ]:
             return getattr(llm_router, f"{route_type}")(**data)
+        # Managed Agents API: these don't need model routing
+        if route_type in [
+            "acreate_agent",
+            "alist_agents",
+            "aget_agent",
+            "adelete_agent",
+            "alist_agent_versions",
+        ]:
+            return getattr(llm_router, f"{route_type}")(**data)
         if route_type in [
             "avideo_list",
             "avideo_status",
diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma
index 7160432c64c..8accb480f24 100644
--- a/litellm/proxy/schema.prisma
+++ b/litellm/proxy/schema.prisma
@@ -52,6 +52,7 @@ model LiteLLM_ProxyModelTable {
   model_name String
   litellm_params Json
   model_info Json?
+  blocked Boolean @default(false)
   created_at    DateTime               @default(now()) @map("created_at")
   created_by String
   updated_at    DateTime               @default(now()) @updatedAt @map("updated_at")
diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py
index d030fabe8b5..e3019801aae 100644
--- a/litellm/proxy/spend_tracking/spend_management_endpoints.py
+++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py
@@ -3184,16 +3184,14 @@ async def provider_budgets() -> ProviderBudgetResponse:
 async def get_spend_by_tags(
     prisma_client: PrismaClient, start_date=None, end_date=None
 ):
-    response = await prisma_client.db.query_raw(
-        """
+    response = await prisma_client.db.query_raw("""
         SELECT
         jsonb_array_elements_text(request_tags) AS individual_request_tag,
         COUNT(*) AS log_count,
         SUM(spend) AS total_spend
         FROM "LiteLLM_SpendLogs"
         GROUP BY individual_request_tag;
-        """
-    )
+        """)
 
     return response
 
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index 32c887f17b2..14f7f411e41 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -1596,7 +1596,22 @@ class ProxyLogging:
                 iterator_overrides.append((resolved, "override"))
             elif "apply_guardrail" in cls_attrs:
                 iterator_overrides.append((resolved, "apply_guardrail"))
-            if "async_post_call_streaming_hook" in cls_attrs:
+            # Walk the MRO for ``async_post_call_streaming_hook`` rather than
+            # using the leaf-class ``__dict__`` check used by the other flags:
+            # before this PR the hook was unconditionally invoked, so a
+            # callback that inherits an override from an intermediate parent
+            # (e.g. a vendor base class providing the override, with the
+            # registered class adding nothing else) MUST still be detected.
+            # A leaf-class miss here would silently drop the inherited hook.
+            base_streaming_hook = CustomLogger.async_post_call_streaming_hook
+            cls_streaming_hook = getattr(
+                cls,
+                "async_post_call_streaming_hook",
+                base_streaming_hook,
+            )
+            if getattr(
+                cls_streaming_hook, "__func__", cls_streaming_hook
+            ) is not getattr(base_streaming_hook, "__func__", base_streaming_hook):
                 has_streaming_chunk_override = True
             if "async_pre_call_hook" in cls_attrs:
                 has_pre_call_override = True
@@ -2979,8 +2994,7 @@ class PrismaClient:
             required_view = "LiteLLM_VerificationTokenView"
             expected_views_str = ", ".join(f"'{view}'" for view in expected_views)
             pg_schema = os.getenv("DATABASE_SCHEMA", "public")
-            ret = await self.db.query_raw(
-                f"""
+            ret = await self.db.query_raw(f"""
                 WITH existing_views AS (
                     SELECT viewname
                     FROM pg_views
@@ -2992,8 +3006,7 @@ class PrismaClient:
                     (SELECT COUNT(*) FROM existing_views) AS view_count,
                     ARRAY_AGG(viewname) AS view_names
                 FROM existing_views
-                """
-            )
+                """)
             expected_total_views = len(expected_views)
             if ret[0]["view_count"] == expected_total_views:
                 verbose_proxy_logger.info("All necessary views exist!")
@@ -3002,8 +3015,7 @@ class PrismaClient:
                 ## check if required view exists ##
                 if ret[0]["view_names"] and required_view not in ret[0]["view_names"]:
                     await self.health_check()  # make sure we can connect to db
-                    await self.db.execute_raw(
-                        """
+                    await self.db.execute_raw("""
                             CREATE VIEW "LiteLLM_VerificationTokenView" AS
                             SELECT
                             v.*,
@@ -3013,8 +3025,7 @@ class PrismaClient:
                             t.rpm_limit AS team_rpm_limit
                             FROM "LiteLLM_VerificationToken" v
                             LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id;
-                        """
-                    )
+                        """)
 
                     verbose_proxy_logger.info(
                         "LiteLLM_VerificationTokenView Created in DB!"
@@ -6068,6 +6079,8 @@ async def get_available_models_for_user(
         include_model_access_groups=include_model_access_groups,
     )
 
+    effective_team_id = team_id or user_api_key_dict.team_id
+
     # Get complete model list
     all_models = get_complete_model_list(
         key_models=key_models,
@@ -6080,6 +6093,7 @@ async def get_available_models_for_user(
         model_access_groups=model_access_groups,
         include_model_access_groups=include_model_access_groups,
         only_model_access_groups=only_model_access_groups,
+        team_id=effective_team_id,
     )
 
     return all_models
diff --git a/litellm/proxy/vertex_ai_endpoints/langfuse_endpoints.py b/litellm/proxy/vertex_ai_endpoints/langfuse_endpoints.py
index 8ce1bedcf90..b47f6a747db 100644
--- a/litellm/proxy/vertex_ai_endpoints/langfuse_endpoints.py
+++ b/litellm/proxy/vertex_ai_endpoints/langfuse_endpoints.py
@@ -1,5 +1,5 @@
 """
-What is this? 
+What is this?
 
 Logging Pass-Through Endpoints
 """
diff --git a/litellm/responses/litellm_completion_transformation/handler.py b/litellm/responses/litellm_completion_transformation/handler.py
index f730a089624..03a2f339bea 100644
--- a/litellm/responses/litellm_completion_transformation/handler.py
+++ b/litellm/responses/litellm_completion_transformation/handler.py
@@ -65,8 +65,7 @@ class LiteLLMCompletionTransformationHandler:
         litellm_completion_response: Union[
             ModelResponse, litellm.CustomStreamWrapper
         ] = litellm.completion(
-            **litellm_completion_request,
-            **kwargs,
+            **completion_args,
         )
 
         if isinstance(litellm_completion_response, ModelResponse):
diff --git a/litellm/responses/main.py b/litellm/responses/main.py
index 4ee9235af7d..35680889d86 100644
--- a/litellm/responses/main.py
+++ b/litellm/responses/main.py
@@ -1115,6 +1115,7 @@ def responses(
                 stream=stream,
                 extra_headers=extra_headers,
                 extra_body=extra_body,
+                timeout=timeout if timeout is not None else request_timeout,
                 **kwargs,
             )
 
diff --git a/litellm/responses/sse_output_recovery.py b/litellm/responses/sse_output_recovery.py
new file mode 100644
index 00000000000..5c18770a611
--- /dev/null
+++ b/litellm/responses/sse_output_recovery.py
@@ -0,0 +1,136 @@
+"""
+Shared helpers for recovering Responses API output items from raw SSE chunks.
+
+The same recovery logic is needed in multiple places (e.g. the ChatGPT
+Responses transformation and the LiteLLM Responses-to-Chat-Completions
+bridge). Keep the implementation in a single module so a fix in one
+caller automatically applies to all of them.
+"""
+
+import json
+from typing import Any, Dict, Optional
+
+from litellm.constants import STREAM_SSE_DONE_STRING
+
+_MAX_CONTENT_INDEX = 1024
+
+
+def parse_sse_json_chunk(chunk: str) -> Optional[Dict[str, Any]]:
+    """Parse a single raw SSE line into a JSON object dict.
+
+    Returns ``None`` for empty lines, ``event:`` lines, ``[DONE]`` markers,
+    invalid JSON, or non-dict payloads. Centralizes the parsing step that
+    feeds into the recovery helpers in this module so behavior stays
+    consistent across all callers.
+    """
+    # Import locally to avoid a circular import with the streaming handler.
+    from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+
+    stripped_chunk = (
+        CustomStreamWrapper._strip_sse_data_from_chunk(chunk.strip()) or ""
+    ).strip()
+    if (
+        not stripped_chunk
+        or stripped_chunk == STREAM_SSE_DONE_STRING
+        or stripped_chunk.startswith("event:")
+    ):
+        return None
+    try:
+        parsed_chunk = json.loads(stripped_chunk)
+    except json.JSONDecodeError:
+        return None
+    if not isinstance(parsed_chunk, dict):
+        return None
+    return parsed_chunk
+
+
+def record_output_item_chunk(
+    parsed_chunk: Dict[str, Any],
+    output_items: Dict[int, Dict[str, Any]],
+) -> None:
+    """Record an OUTPUT_ITEM_DONE chunk into ``output_items`` keyed by
+    ``output_index`` (falling back to the next free slot when missing).
+    """
+    item = parsed_chunk.get("item")
+    if not isinstance(item, dict):
+        return
+    try:
+        output_index_raw = parsed_chunk.get("output_index")
+        if output_index_raw is None:
+            raise ValueError("missing output_index")
+        output_index = int(output_index_raw)
+    except (TypeError, ValueError):
+        output_index = len(output_items)
+    output_items[output_index] = item
+
+
+def record_output_text_chunk(
+    parsed_chunk: Dict[str, Any],
+    output_items: Dict[int, Dict[str, Any]],
+    text_only_items: Dict[int, Dict[str, Any]],
+) -> None:
+    """Record an OUTPUT_TEXT_DONE chunk as a synthetic message item in
+    ``text_only_items``. Real OUTPUT_ITEM_DONE events already captured in
+    ``output_items`` take precedence at the same ``output_index``.
+    """
+    text = parsed_chunk.get("text")
+    if not isinstance(text, str):
+        return
+
+    try:
+        output_index_raw = parsed_chunk.get("output_index")
+        if output_index_raw is None:
+            raise ValueError("missing output_index")
+        output_index = int(output_index_raw)
+    except (TypeError, ValueError):
+        output_index = len(text_only_items)
+
+    if output_index in output_items:
+        return
+
+    item = text_only_items.get(output_index)
+    if item is None:
+        item = {
+            "type": "message",
+            "id": parsed_chunk.get("item_id") or f"msg_{output_index}",
+            "role": "assistant",
+            "status": "completed",
+            "content": [],
+        }
+        text_only_items[output_index] = item
+
+    content = item.setdefault("content", [])
+    if not isinstance(content, list):
+        return
+
+    try:
+        content_index_raw = parsed_chunk.get("content_index")
+        if content_index_raw is None:
+            raise ValueError("missing content_index")
+        content_index = int(content_index_raw)
+    except (TypeError, ValueError):
+        content_index = len(content)
+
+    if content_index < 0 or content_index > _MAX_CONTENT_INDEX:
+        return
+
+    while len(content) <= content_index:
+        content.append(
+            {
+                "type": "output_text",
+                "text": "",
+                "annotations": [],
+            }
+        )
+
+    content_item = content[content_index]
+    if not isinstance(content_item, dict):
+        content_item = {}
+        content[content_index] = content_item
+
+    content_item["type"] = "output_text"
+    content_item["text"] = text
+    if parsed_chunk.get("annotations") is not None:
+        content_item["annotations"] = parsed_chunk["annotations"]
+    else:
+        content_item.setdefault("annotations", [])
diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py
index da8da1b486f..c4e72cb7dc5 100644
--- a/litellm/responses/streaming_iterator.py
+++ b/litellm/responses/streaming_iterator.py
@@ -9,6 +9,7 @@ from functools import lru_cache
 from typing import Any, Dict, List, Literal, Optional
 
 import httpx
+from openai._streaming import SSEDecoder
 
 import litellm
 from litellm.constants import (
@@ -27,7 +28,7 @@ from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfi
 from litellm.responses.utils import ResponsesAPIRequestUtils
 from litellm.types.llms.openai import ResponsesAPIStreamEvents
 from litellm.types.utils import CallTypes
-from litellm.utils import CustomStreamWrapper, async_post_call_success_deployment_hook
+from litellm.utils import async_post_call_success_deployment_hook
 
 
 @lru_cache(maxsize=1)
@@ -120,10 +121,10 @@ class BaseResponsesAPIStreamingIterator:
         if not chunk:
             return None
 
-        # Handle SSE format (data: {...})
-        chunk = CustomStreamWrapper._strip_sse_data_from_chunk(chunk)
-        if chunk is None:
-            return None
+        # NOTE: ``SSEDecoder`` already strips the SSE ``data:`` field prefix, so
+        # the value passed in here is the raw field content. Do not re-run
+        # ``_strip_sse_data_from_chunk`` on it — doing so would incorrectly mangle
+        # payloads whose actual JSON value happens to start with ``data:``.
 
         # Handle "[DONE]" marker
         if chunk == STREAM_SSE_DONE_STRING:
@@ -634,7 +635,7 @@ class ResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
             request_data,
             call_type,
         )
-        self.stream_iterator = response.aiter_lines()
+        self.stream_iterator = SSEDecoder().aiter_bytes(response.aiter_bytes())
 
     def __aiter__(self):
         return self
@@ -645,13 +646,13 @@ class ResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
             while True:
                 # Get the next chunk from the stream
                 try:
-                    chunk = await self.stream_iterator.__anext__()
+                    sse = await self.stream_iterator.__anext__()
                 except StopAsyncIteration:
                     self.finished = True
                     raise StopAsyncIteration
 
                 self._check_max_streaming_duration()
-                result = self._process_chunk(chunk)
+                result = self._process_chunk(sse.data)
 
                 if self.finished:
                     raise StopAsyncIteration
@@ -708,7 +709,7 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
             request_data,
             call_type,
         )
-        self.stream_iterator = response.iter_lines()
+        self.stream_iterator = SSEDecoder().iter_bytes(response.iter_bytes())
 
     def __iter__(self):
         return self
@@ -719,13 +720,13 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
             while True:
                 # Get the next chunk from the stream
                 try:
-                    chunk = next(self.stream_iterator)
+                    sse = next(self.stream_iterator)
                 except StopIteration:
                     self.finished = True
                     raise StopIteration
 
                 self._check_max_streaming_duration()
-                result = self._process_chunk(chunk)
+                result = self._process_chunk(sse.data)
 
                 if self.finished:
                     raise StopIteration
diff --git a/litellm/router.py b/litellm/router.py
index fac48b45fb3..2c61031da34 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -30,6 +30,7 @@ from typing import (
     List,
     Literal,
     Optional,
+    Set,
     Tuple,
     Union,
     cast,
@@ -207,6 +208,15 @@ if TYPE_CHECKING:
     from litellm.router_strategy.quality_router.quality_router import (
         QualityRouter,
     )
+    from litellm.responses.streaming_iterator import (
+        BaseResponsesAPIStreamingIterator,
+    )
+    from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
+    from litellm.types.llms.openai import (
+        ResponseAPIUsage,
+        ResponseInputParam,
+        ResponsesAPIResponse,
+    )
 
     Span = Union[_Span, Any]
 else:
@@ -838,7 +848,7 @@ class Router:
 
     @staticmethod
     def _normalize_strategy(
-        strategy: Union[RoutingStrategy, str, None]
+        strategy: Union[RoutingStrategy, str, None],
     ) -> Optional[str]:
         if strategy is None:
             return None
@@ -1578,6 +1588,44 @@ class Router:
             cancel_interaction, call_type="cancel_interaction"
         )
 
+    def _initialize_managed_agents_endpoints(self):
+        """Initialize Google Managed Agents API endpoints (v1beta/agents)."""
+        from litellm.interactions.agents import acreate as acreate_agent
+        from litellm.interactions.agents import adelete as adelete_agent
+        from litellm.interactions.agents import aget as aget_agent
+        from litellm.interactions.agents import alist as alist_agents
+        from litellm.interactions.agents import alist_versions as alist_agent_versions
+        from litellm.interactions.agents import create as create_agent
+        from litellm.interactions.agents import delete as delete_agent
+        from litellm.interactions.agents import get as get_agent
+        from litellm.interactions.agents import list as list_agents
+        from litellm.interactions.agents import list_versions as list_agent_versions
+
+        self.acreate_agent = self.factory_function(
+            acreate_agent, call_type="acreate_agent"
+        )
+        self.create_agent = self.factory_function(
+            create_agent, call_type="create_agent"
+        )
+        self.alist_agents = self.factory_function(
+            alist_agents, call_type="alist_agents"
+        )
+        self.list_agents = self.factory_function(list_agents, call_type="list_agents")
+        self.aget_agent = self.factory_function(aget_agent, call_type="aget_agent")
+        self.get_agent = self.factory_function(get_agent, call_type="get_agent")
+        self.adelete_agent = self.factory_function(
+            adelete_agent, call_type="adelete_agent"
+        )
+        self.delete_agent = self.factory_function(
+            delete_agent, call_type="delete_agent"
+        )
+        self.alist_agent_versions = self.factory_function(
+            alist_agent_versions, call_type="alist_agent_versions"
+        )
+        self.list_agent_versions = self.factory_function(
+            list_agent_versions, call_type="list_agent_versions"
+        )
+
     def _initialize_specialized_endpoints(self):
         """Helper to initialize specialized router endpoints (vector store, OCR, search, video, container, skills, interactions)."""
         self._initialize_vector_store_endpoints()
@@ -1590,6 +1638,7 @@ class Router:
         self._initialize_container_endpoints()
         self._initialize_skills_endpoints()
         self._initialize_interactions_endpoints()
+        self._initialize_managed_agents_endpoints()
 
     def initialize_router_endpoints(self):
         self._initialize_core_endpoints()
@@ -2206,6 +2255,388 @@ class Router:
 
         return FallbackStreamWrapper(stream_with_fallbacks())
 
+    @staticmethod
+    def _extract_partial_responses_usage(
+        source_iterator: "BaseResponsesAPIStreamingIterator",
+    ) -> Optional["ResponseAPIUsage"]:
+        """
+        Best-effort: pull partial token usage from a Responses-API streaming
+        iterator that errored mid-stream, normalized to ResponseAPIUsage so
+        the caller can combine without crossing token-naming conventions.
+
+        Two sources, in priority order:
+          1. The bridge path (LiteLLMCompletionStreamingIterator) accumulates
+             chat-completion chunks while streaming — feed them through
+             stream_chunk_builder to recover chat Usage, then translate
+             (prompt_tokens → input_tokens, completion_tokens → output_tokens).
+          2. The native path (ResponsesAPIStreamingIterator) only has a
+             completed_response object if the stream reached
+             RESPONSE_COMPLETED before erroring — uncommon mid-stream but
+             worth checking. Already ResponseAPIUsage-shaped.
+
+        Returns None when no partial usage is recoverable.
+        """
+        from litellm.responses.litellm_completion_transformation.streaming_iterator import (
+            LiteLLMCompletionStreamingIterator,
+        )
+        from litellm.types.llms.openai import (
+            ResponseAPIUsage,
+            ResponseCompletedEvent,
+            ResponseFailedEvent,
+            ResponseIncompleteEvent,
+        )
+
+        # Bridge subclass is the only iterator that accumulates chat-completion
+        # chunks. isinstance narrows the type so we can read the attribute
+        # directly instead of getattr-ing on the base class.
+        if isinstance(source_iterator, LiteLLMCompletionStreamingIterator):
+            chunks = source_iterator.collected_chat_completion_chunks
+            if chunks:
+                try:
+                    from litellm.main import stream_chunk_builder
+
+                    built = stream_chunk_builder(chunks=chunks)
+                    # stream_chunk_builder returns ModelResponse |
+                    # TextCompletionResponse | None. ModelResponse sets .usage
+                    # in __init__ rather than declaring it as a class field, so
+                    # static narrowing doesn't expose it. Mirror the sync path
+                    # (_completion_streaming_iterator) and pull via getattr.
+                    chat = getattr(built, "usage", None) if built is not None else None
+                    if chat is not None:
+                        # getattr-with-default because the test path may
+                        # substitute a SimpleNamespace lacking some fields;
+                        # real Usage instances always have them.
+                        prompt = int(getattr(chat, "prompt_tokens", 0) or 0)
+                        completion = int(getattr(chat, "completion_tokens", 0) or 0)
+                        total = int(
+                            getattr(chat, "total_tokens", prompt + completion)
+                            or (prompt + completion)
+                        )
+                        return ResponseAPIUsage(
+                            input_tokens=prompt,
+                            output_tokens=completion,
+                            total_tokens=total,
+                        )
+                except Exception:
+                    # Builder is best-effort — fall through to native path.
+                    pass
+
+        # Native path: completed_response is set only if RESPONSE_COMPLETED
+        # arrived before the error (uncommon mid-stream but worth checking).
+        # Already ResponseAPIUsage-shaped — return as-is.
+        completed = source_iterator.completed_response
+        if isinstance(
+            completed,
+            (ResponseCompletedEvent, ResponseFailedEvent, ResponseIncompleteEvent),
+        ):
+            return completed.response.usage
+        return None
+
+    @staticmethod
+    def _combine_responses_fallback_usage(
+        fallback_item: "BaseLiteLLMOpenAIResponseObject",
+        partial_usage: "ResponseAPIUsage",
+    ) -> None:
+        """
+        Merge partial-stream usage with fallback-stream usage on a
+        Responses-API streaming event.
+
+        Only mutates events that carry a `response` with a `usage` field
+        (response.completed / response.failed / response.incomplete). Other
+        events pass through unchanged.
+
+        Both inputs are ResponseAPIUsage-shaped (see
+        _extract_partial_responses_usage which normalizes the bridge path),
+        so we can sum input_tokens / output_tokens / total_tokens directly
+        and produce a clean ResponseAPIUsage — no token-naming split, no
+        setattr bypass.
+        """
+        from litellm.types.llms.openai import (
+            ResponseAPIUsage,
+            ResponseCompletedEvent,
+            ResponseFailedEvent,
+            ResponseIncompleteEvent,
+        )
+
+        if not isinstance(
+            fallback_item,
+            (ResponseCompletedEvent, ResponseFailedEvent, ResponseIncompleteEvent),
+        ):
+            return
+        response = fallback_item.response
+        if response.usage is None:
+            return
+
+        fb = response.usage
+        response.usage = ResponseAPIUsage(
+            input_tokens=(partial_usage.input_tokens or 0) + (fb.input_tokens or 0),
+            output_tokens=(partial_usage.output_tokens or 0) + (fb.output_tokens or 0),
+            total_tokens=(partial_usage.total_tokens or 0) + (fb.total_tokens or 0),
+        )
+
+    @staticmethod
+    def _build_responses_continuation_input(
+        input_val: Optional[Union[str, "ResponseInputParam"]],
+        generated_content: str,
+    ) -> "ResponseInputParam":
+        """
+        Convert Responses-API input + partial assistant output into a
+        continuation input that asks the fallback model to pick up where the
+        prior assistant message stopped.
+
+        Best effort across providers. The chat-completions path uses
+        Anthropic's `prefix: True` prefill trick on the assistant message;
+        the Responses-API input schema has no direct equivalent, so we
+        append an instruction (developer role) plus a prior assistant
+        message containing the partial output. Providers without prefill
+        semantics (OpenAI, Vertex) treat this as conversational context
+        and may regenerate — same trade-off as the chat-completions path
+        for non-Anthropic fallbacks.
+        """
+        # base/continuation are List[Any] because ResponseInputParam items
+        # are a wide Union of TypedDicts (EasyInputMessageParam, Message,
+        # ResponseOutputMessageParam, ...) — annotating as List[Dict[str, Any]]
+        # rejects the list() spread of input_val. We cast the combined list to
+        # ResponseInputParam at the return.
+        base: List[Any]
+        if isinstance(input_val, str):
+            base = [
+                {
+                    "type": "message",
+                    "role": "user",
+                    "content": [{"type": "input_text", "text": input_val}],
+                }
+            ]
+        elif isinstance(input_val, list):
+            base = list(input_val)
+        else:
+            base = []
+        continuation: List[Any] = [
+            {
+                "type": "message",
+                "role": "developer",
+                "content": [
+                    {
+                        "type": "input_text",
+                        "text": (
+                            "The previous assistant response was interrupted "
+                            "mid-stream. Continue exactly where it stopped — "
+                            "do not repeat any of its content. Your response "
+                            "must read as a seamless continuation."
+                        ),
+                    }
+                ],
+            },
+            {
+                "type": "message",
+                "role": "assistant",
+                "content": [{"type": "output_text", "text": generated_content}],
+            },
+        ]
+        return cast("ResponseInputParam", base + continuation)
+
+    async def _aresponses_streaming_iterator(
+        self,
+        response: "BaseResponsesAPIStreamingIterator",
+        initial_kwargs: Dict[str, Any],
+    ) -> "BaseResponsesAPIStreamingIterator":
+        """
+        Wrap a Responses-API streaming iterator so MidStreamFallbackError
+        triggers the Router's fallback chain (parity with
+        _acompletion_streaming_iterator for the chat-completions path).
+
+        The Responses-API streaming path goes through
+        _ageneric_api_call_with_fallbacks rather than _acompletion, so the
+        returned iterator is never wrapped by the chat completions
+        fallback handler. Without this wrapper, MidStreamFallbackError
+        raised mid-stream from the underlying CustomStreamWrapper (used by
+        LiteLLMCompletionStreamingIterator when the Responses API is
+        served via the completion bridge) propagates unhandled and the
+        configured cross-provider fallback never fires.
+
+        Full parity with the chat-completions path:
+          - Pre-first-chunk: retry with the original input unchanged.
+          - Partial content: inject a developer instruction + prior
+            assistant message carrying the generated text so the fallback
+            model continues rather than restarts.
+          - Usage combining: merge partial-stream usage onto the fallback's
+            response.completed event so accounting reflects both attempts.
+          - Stream cleanup: shielded aclose() on both source and fallback
+            iterators on terminate.
+        """
+        from litellm.exceptions import MidStreamFallbackError
+        from litellm.responses.streaming_iterator import (
+            BaseResponsesAPIStreamingIterator,
+        )
+
+        source_iterator = response
+
+        class FallbackResponsesStreamWrapper(BaseResponsesAPIStreamingIterator):
+            """
+            Subclasses BaseResponsesAPIStreamingIterator only for isinstance
+            compatibility (proxy + interactions code paths check the type).
+            Bypasses the parent constructor and delegates iteration to an
+            async generator.
+            """
+
+            def __init__(self, async_generator: AsyncGenerator):
+                import time
+                from datetime import datetime
+
+                self._async_generator = async_generator
+                # Mirror every attribute BaseResponsesAPIStreamingIterator.__init__
+                # would have set. The wrapper bypasses super().__init__ (it has no
+                # httpx.Response of its own and no provider config to drive), so
+                # we copy from source_iterator where applicable and use safe
+                # defaults elsewhere. This keeps inherited methods (e.g.
+                # _check_max_streaming_duration, _handle_failure) safe to call.
+                #
+                # The bridge path (LiteLLMCompletionStreamingIterator used by
+                # Anthropic/Bedrock/Vertex) does not call super().__init__ and
+                # is missing many of these attributes — use getattr fallbacks
+                # so wrapper construction never raises AttributeError. The
+                # bridge stores the logging object as `litellm_logging_obj`.
+                self.response = getattr(source_iterator, "response", None)
+                self.model = getattr(source_iterator, "model", None)
+                self.logging_obj = getattr(
+                    source_iterator,
+                    "logging_obj",
+                    getattr(source_iterator, "litellm_logging_obj", None),
+                )
+                self.finished = False
+                self.responses_api_provider_config = getattr(
+                    source_iterator, "responses_api_provider_config", None
+                )
+                self.completed_response = None
+                self.start_time = getattr(source_iterator, "start_time", datetime.now())
+                self._failure_handled = False
+                self._completed_response_cached = False
+                self._completed_response_logged = False
+                self._completed_response_cache_hit = None
+                self._persist_completed_response_before_logging = True
+                self._stream_created_time = time.time()
+                self.litellm_metadata = getattr(
+                    source_iterator, "litellm_metadata", None
+                )
+                self.custom_llm_provider = getattr(
+                    source_iterator, "custom_llm_provider", None
+                )
+                self.request_data = getattr(source_iterator, "request_data", {}) or {}
+                self.call_type = getattr(source_iterator, "call_type", None)
+                # Preserve hidden params so response headers (model_id,
+                # api_base, additional_headers) keep flowing.
+                self._hidden_params = dict(
+                    getattr(source_iterator, "_hidden_params", None) or {}
+                )
+
+            def __aiter__(self):
+                return self
+
+            async def __anext__(self):
+                return await self._async_generator.__anext__()
+
+            async def aclose(self):
+                # async generators always expose aclose — no defensive check needed.
+                await self._async_generator.aclose()
+
+        async def stream_with_fallbacks():
+            fallback_response = None
+            try:
+                async for item in source_iterator:
+                    yield item
+            except MidStreamFallbackError as e:
+                partial_usage = Router._extract_partial_responses_usage(source_iterator)
+                try:
+                    model_group = cast(str, initial_kwargs.get("model"))
+                    fallbacks: Optional[List] = initial_kwargs.get(
+                        "fallbacks", self.fallbacks
+                    )
+                    context_window_fallbacks: Optional[List] = initial_kwargs.get(
+                        "context_window_fallbacks", self.context_window_fallbacks
+                    )
+                    content_policy_fallbacks: Optional[List] = initial_kwargs.get(
+                        "content_policy_fallbacks", self.content_policy_fallbacks
+                    )
+                    # Re-enter via the per-attempt helper so the fallback chain
+                    # picks deployments through
+                    # _ageneric_api_call_with_fallbacks_helper.
+                    # original_generic_function is preserved by the caller so
+                    # the helper knows what underlying API to invoke per attempt.
+                    initial_kwargs["original_function"] = (
+                        self._ageneric_api_call_with_fallbacks_helper
+                    )
+                    if e.is_pre_first_chunk or not e.generated_content:
+                        # No content generated before the error — retry with the
+                        # original input. Adding a continuation prompt would
+                        # waste tokens and confuse the model.
+                        pass
+                    else:
+                        initial_kwargs["input"] = (
+                            Router._build_responses_continuation_input(
+                                initial_kwargs.get("input"),
+                                e.generated_content,
+                            )
+                        )
+                    # The Responses-API path stores observability metadata
+                    # under "litellm_metadata" (not the default "metadata") —
+                    # see _ageneric_api_call_with_fallbacks. Mirroring that
+                    # here ensures model_group, model_group_alias, and trace
+                    # ids land in the same key litellm.aresponses reads from.
+                    self._update_kwargs_before_fallbacks(
+                        model=model_group,
+                        kwargs=initial_kwargs,
+                        metadata_variable_name="litellm_metadata",
+                    )
+                    fallback_response = (
+                        await self.async_function_with_fallbacks_common_utils(
+                            e=e,
+                            disable_fallbacks=False,
+                            fallbacks=fallbacks,
+                            context_window_fallbacks=context_window_fallbacks,
+                            content_policy_fallbacks=content_policy_fallbacks,
+                            model_group=model_group,
+                            args=(),
+                            kwargs=initial_kwargs,
+                        )
+                    )
+
+                    if hasattr(fallback_response, "__aiter__"):
+                        async for fallback_item in fallback_response:  # type: ignore
+                            if partial_usage is not None:
+                                Router._combine_responses_fallback_usage(
+                                    fallback_item, partial_usage
+                                )
+                            yield fallback_item
+                    else:
+                        yield fallback_response
+                except Exception as fallback_error:
+                    verbose_router_logger.error(
+                        f"Responses streaming fallback also failed: {fallback_error}"
+                    )
+                    raise fallback_error
+            finally:
+                with anyio.CancelScope(shield=True):
+                    if hasattr(source_iterator, "aclose"):
+                        try:
+                            await source_iterator.aclose()  # type: ignore[func-returns-value]
+                        except BaseException as exc:
+                            verbose_router_logger.debug(
+                                "stream_with_fallbacks(aresponses): error closing source: %s",
+                                exc,
+                            )
+                    if fallback_response is not None and hasattr(
+                        fallback_response, "aclose"
+                    ):
+                        try:
+                            await fallback_response.aclose()
+                        except BaseException as exc:
+                            verbose_router_logger.debug(
+                                "stream_with_fallbacks(aresponses): error closing fallback: %s",
+                                exc,
+                            )
+
+        return FallbackResponsesStreamWrapper(stream_with_fallbacks())
+
     def _completion_streaming_iterator(  # noqa: PLR0915
         self,
         model_response: CustomStreamWrapper,
@@ -4252,6 +4683,61 @@ class Router:
                 self.fail_calls[model] += 1
             raise e
 
+    async def _aresponses_with_streaming_fallbacks(
+        self, original_function: Callable, **kwargs: Any
+    ) -> Union["ResponsesAPIResponse", "BaseResponsesAPIStreamingIterator"]:
+        """
+        _ageneric_api_call_with_fallbacks for the Responses API, with the
+        addition of mid-stream fallback handling.
+
+        When stream=True and the underlying call returns a
+        BaseResponsesAPIStreamingIterator, wrap it with
+        _aresponses_streaming_iterator so MidStreamFallbackError raised
+        during iteration triggers the Router's cross-provider fallback chain.
+        """
+        from litellm.responses.streaming_iterator import (
+            BaseResponsesAPIStreamingIterator,
+        )
+
+        from litellm.litellm_core_utils.core_helpers import safe_deep_copy
+
+        # Snapshot the request kwargs before _ageneric_api_call_with_fallbacks
+        # mutates them. A shallow copy alone is not enough: the primary
+        # attempt mutates nested dicts in place — notably `litellm_metadata`,
+        # which `_update_kwargs_with_deployment` populates with
+        # deployment-specific fields (`deployment`, `model_info`, `api_base`,
+        # tags, etc.). Without an explicit copy of that dict, the shallow
+        # copy would still share its reference, leaking primary-deployment
+        # metadata into the mid-stream fallback request.
+        #
+        # We avoid deep-copying the full kwargs because it can contain
+        # non-deepcopyable objects (logging handles, async clients, etc.);
+        # `safe_deep_copy` deep-copies the metadata dicts key-by-key with a
+        # fallback to the original reference for any non-picklable value.
+        # The original_generic_function is preserved so the per-attempt
+        # helper knows which underlying API to call on fallback.
+        fallback_kwargs: Dict[str, Any] = kwargs.copy()
+        if isinstance(fallback_kwargs.get("litellm_metadata"), dict):
+            fallback_kwargs["litellm_metadata"] = safe_deep_copy(
+                fallback_kwargs["litellm_metadata"]
+            )
+        if isinstance(fallback_kwargs.get("metadata"), dict):
+            fallback_kwargs["metadata"] = safe_deep_copy(fallback_kwargs["metadata"])
+        fallback_kwargs["original_generic_function"] = original_function
+
+        response = await self._ageneric_api_call_with_fallbacks(
+            original_function=original_function, **kwargs
+        )
+
+        if kwargs.get("stream") and isinstance(
+            response, BaseResponsesAPIStreamingIterator
+        ):
+            return await self._aresponses_streaming_iterator(
+                response=response,
+                initial_kwargs=fallback_kwargs,
+            )
+        return response
+
     def _generic_api_call_with_fallbacks(
         self, model: str, original_function: Callable, **kwargs
     ):
@@ -5321,6 +5807,16 @@ class Router:
             "delete_interaction",
             "acancel_interaction",
             "cancel_interaction",
+            "acreate_agent",
+            "create_agent",
+            "alist_agents",
+            "list_agents",
+            "aget_agent",
+            "get_agent",
+            "adelete_agent",
+            "delete_agent",
+            "alist_agent_versions",
+            "list_agent_versions",
         ] = "assistants",
     ):
         """
@@ -5405,6 +5901,27 @@ class Router:
 
             return vector_store_file_sync_wrapper
 
+        if call_type in (
+            "create_agent",
+            "list_agents",
+            "get_agent",
+            "delete_agent",
+            "list_agent_versions",
+        ):
+
+            def managed_agents_sync_wrapper(
+                custom_llm_provider: Optional[str] = None,
+                client: Optional[Any] = None,
+                **kwargs,
+            ):
+                if custom_llm_provider and "custom_llm_provider" not in kwargs:
+                    kwargs["custom_llm_provider"] = custom_llm_provider
+                if "custom_llm_provider" not in kwargs:
+                    kwargs["custom_llm_provider"] = "gemini"
+                return original_function(**kwargs)
+
+            return managed_agents_sync_wrapper
+
         # Handle asynchronous call types
         async def async_wrapper(
             custom_llm_provider: Optional[str] = None,
@@ -5440,9 +5957,13 @@ class Router:
                     custom_llm_provider=custom_llm_provider,
                     **kwargs,
                 )
+            elif call_type == "aresponses":
+                return await self._aresponses_with_streaming_fallbacks(
+                    original_function=original_function,
+                    **kwargs,
+                )
             elif call_type in (
                 "anthropic_messages",
-                "aresponses",
                 "_arealtime",
                 "_aresponses_websocket",
                 "acreate_fine_tuning_job",
@@ -5468,8 +5989,6 @@ class Router:
                 "alist_skills",
                 "aget_skill",
                 "adelete_skill",
-                "acreate_interaction",
-                "create_interaction",
             ):
                 return await self._ageneric_api_call_with_fallbacks(
                     original_function=original_function,
@@ -5529,6 +6048,8 @@ class Router:
                     **kwargs,
                 )
             elif call_type in (
+                "acreate_interaction",
+                "create_interaction",
                 "aget_interaction",
                 "adelete_interaction",
                 "acancel_interaction",
@@ -5538,6 +6059,18 @@ class Router:
                     custom_llm_provider=custom_llm_provider,
                     **kwargs,
                 )
+            elif call_type in (
+                "acreate_agent",
+                "alist_agents",
+                "aget_agent",
+                "adelete_agent",
+                "alist_agent_versions",
+            ):
+                return await self._init_managed_agents_api_endpoints(
+                    original_function=original_function,
+                    custom_llm_provider=custom_llm_provider,
+                    **kwargs,
+                )
 
         return async_wrapper
 
@@ -5587,6 +6120,7 @@ class Router:
         from litellm.responses.utils import ResponsesAPIRequestUtils
 
         container_id = kwargs.get("container_id")
+        _forwarded_model_id = kwargs.get("model_id")
         if isinstance(container_id, str):
             decoded = ResponsesAPIRequestUtils._decode_container_id(container_id)
             original_id = decoded.get("response_id", container_id)
@@ -5595,7 +6129,14 @@ class Router:
             decoded_provider = decoded.get("custom_llm_provider")
             if decoded_provider and kwargs.get("custom_llm_provider") == "openai":
                 kwargs["custom_llm_provider"] = decoded_provider
-            model_id = decoded.get("model_id")
+            # Fall back to the model_id forwarded by the proxy when the container_id
+            # is a native upstream ID (e.g. Azure hex cntr_) that carries no LiteLLM
+            # routing payload, so deployment credentials (api_base, api_key) are applied.
+            model_id = decoded.get("model_id") or (
+                _forwarded_model_id.strip()
+                if isinstance(_forwarded_model_id, str) and _forwarded_model_id.strip()
+                else None
+            )
             if model_id:
                 kwargs["model"] = model_id
                 return await self._ageneric_api_call_with_fallbacks(
@@ -5642,6 +6183,34 @@ class Router:
         if custom_llm_provider and "custom_llm_provider" not in kwargs:
             kwargs["custom_llm_provider"] = custom_llm_provider
         # Default to gemini for interactions API
+        if "custom_llm_provider" not in kwargs:
+            kwargs["custom_llm_provider"] = "gemini"
+        # If the proxy accidentally passed agent name as model, clear it
+        if kwargs.get("agent") and kwargs.get("model") == kwargs.get("agent"):
+            kwargs["model"] = None
+        # Model-based interactions use deployment routing + fallbacks; agent-only calls
+        # must not enter model-group lookup (agent name is not a LiteLLM deployment).
+        if kwargs.get("model"):
+            return await self._ageneric_api_call_with_fallbacks(
+                original_function=original_function,
+                **kwargs,
+            )
+        return await original_function(**kwargs)
+
+    async def _init_managed_agents_api_endpoints(
+        self,
+        original_function: Callable,
+        custom_llm_provider: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Initialize the Managed Agents API endpoints on the router (v1beta/agents).
+
+        CRUD operations for Gemini managed agents don't need model-based routing,
+        so we call the original function directly with the custom_llm_provider.
+        """
+        if custom_llm_provider and "custom_llm_provider" not in kwargs:
+            kwargs["custom_llm_provider"] = custom_llm_provider
         if "custom_llm_provider" not in kwargs:
             kwargs["custom_llm_provider"] = "gemini"
         return await original_function(**kwargs)
@@ -6957,12 +7526,11 @@ class Router:
         unhealthy_deployments = _get_cooldown_deployments(
             litellm_router_instance=self, parent_otel_span=parent_otel_span
         )
-        healthy_deployments: list = []
-        for deployment in _all_deployments:
-            if deployment["model_info"]["id"] in unhealthy_deployments:
-                continue
-            else:
-                healthy_deployments.append(deployment)
+        unhealthy_set = set(unhealthy_deployments)
+        healthy_deployments: list = [
+            d for d in _all_deployments if d["model_info"]["id"] not in unhealthy_set
+        ]
+        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)
 
         return healthy_deployments, _all_deployments
 
@@ -6990,10 +7558,12 @@ class Router:
         )
         # Convert to set for O(1) lookup instead of O(n)
         unhealthy_deployments_set = set(unhealthy_deployments)
-        healthy_deployments: list = []
-        for deployment in _all_deployments:
-            if deployment["model_info"]["id"] not in unhealthy_deployments_set:
-                healthy_deployments.append(deployment)
+        healthy_deployments: list = [
+            d
+            for d in _all_deployments
+            if d["model_info"]["id"] not in unhealthy_deployments_set
+        ]
+        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)
         return healthy_deployments, _all_deployments
 
     def routing_strategy_pre_call_checks(self, deployment: dict):
@@ -7208,6 +7778,38 @@ class Router:
             _shared_model_info = {
                 k: v for k, v in _model_info.items() if k not in _custom_pricing_fields
             }
+            _existing_shared_mode = (
+                cast(Optional[dict], litellm.model_cost.get(_model_name, {})) or {}
+            ).get("mode")
+            _deployment_mode = _shared_model_info.get("mode")
+            # Keep the built-in bridge mode stable for shared backend keys.
+            # Multiple aliases can point at the same provider/model backend,
+            # but their deployment-level overrides should not downgrade the
+            # backend from responses -> chat via last-write-wins registration.
+            # Only preserve in that specific direction so legitimate upgrades
+            # (e.g. chat -> responses) and unrelated mode changes still apply,
+            # and so a missing deployment mode does not silently clear the
+            # existing shared backend mode.
+            _is_responses_to_chat_downgrade = (
+                _existing_shared_mode == "responses" and _deployment_mode == "chat"
+            )
+            _would_clear_existing_mode = (
+                _existing_shared_mode is not None and _deployment_mode is None
+            )
+            if _is_responses_to_chat_downgrade or _would_clear_existing_mode:
+                if _deployment_mode is not None:
+                    verbose_router_logger.warning(
+                        "Router: preserving existing mode=%s for shared backend "
+                        "key %s instead of the deployment-specified mode=%s "
+                        "(prevents alias registration from downgrading the "
+                        "shared backend mode).",
+                        _existing_shared_mode,
+                        _model_name,
+                        _deployment_mode,
+                    )
+                _shared_model_info["mode"] = _existing_shared_mode
+
+            # Always register the (possibly mode-preserved) shared backend info.
             _backend_alias_cost = {_model_name: _shared_model_info}
             if "responses/" in _model_name:
                 _stripped_model_name = _model_name.replace("responses/", "")
@@ -8142,10 +8744,14 @@ class Router:
 
     def get_deployment_credentials(self, model_id: str) -> Optional[dict]:
         """
-        Returns -> dict of credentials for a given model id
+        Returns -> dict of credentials for a given model id.
+
+        Returns None if the deployment is paused via `LiteLLM_ProxyModelTable.blocked`,
+        so file/batch/passthrough callers that resolve credentials directly cannot keep
+        using a paused deployment.
         """
         deployment = self.get_deployment(model_id=model_id)
-        if deployment is None:
+        if deployment is None or self._is_deployment_blocked(deployment):
             return None
         return CredentialLiteLLMParams(
             **deployment.litellm_params.model_dump(exclude_none=True)
@@ -8190,7 +8796,9 @@ class Router:
 
         Returns:
             Dictionary containing api_key, api_base, custom_llm_provider, etc.
-            Returns None if model not found.
+            Returns None if model not found, or if the resolved deployment is
+            paused via `LiteLLM_ProxyModelTable.blocked` (so passthrough callers
+            cannot bypass an admin pause by resolving credentials directly).
 
         Example:
             credentials = router.get_deployment_credentials_with_provider("gpt-4o-litellm")
@@ -8216,7 +8824,7 @@ class Router:
                 elif isinstance(deployment_dict, Deployment):
                     deployment = deployment_dict
 
-        if deployment is None:
+        if deployment is None or self._is_deployment_blocked(deployment):
             return None
 
         # Get basic credentials
@@ -9243,6 +9851,29 @@ class Router:
 
         return model_names
 
+    def get_fully_blocked_model_names(self) -> Set[str]:
+        """
+        Returns the set of model_names where every backing deployment has `blocked=True`.
+
+        Used by `/v1/models` to hide paused models from client listings while still
+        surfacing them on admin endpoints (e.g. `/model/info`). A model with at least
+        one non-blocked deployment is still serviceable and remains visible.
+        """
+        deployments = self.get_model_list() or []
+        blocked_by_name: Dict[str, bool] = {}
+        for deployment in deployments:
+            name = deployment.get("model_name") or ""
+            if not name:
+                continue
+            is_blocked = (deployment.get("model_info") or {}).get("blocked") is True
+            if name in blocked_by_name:
+                blocked_by_name[name] = blocked_by_name[name] and is_blocked
+            else:
+                blocked_by_name[name] = is_blocked
+        return {
+            name for name, fully_blocked in blocked_by_name.items() if fully_blocked
+        }
+
     def _get_team_specific_model(
         self, deployment: DeploymentTypedDict, team_id: Optional[str] = None
     ) -> Optional[str]:
@@ -10131,6 +10762,12 @@ class Router:
             )
 
         if isinstance(healthy_deployments, dict):
+            if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
+                raise litellm.ServiceUnavailableError(
+                    message=f"Model '{model}' is currently paused and cannot accept requests.",
+                    model=model,
+                    llm_provider="",
+                )
             return healthy_deployments
 
         # Health-check-based filtering (before cooldown)
@@ -10164,6 +10801,8 @@ class Router:
             )
             healthy_deployments = _pre_cooldown_deployments
 
+        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)
+
         healthy_deployments = await self.async_callback_filter_deployments(
             model=model,
             healthy_deployments=healthy_deployments,
@@ -10387,6 +11026,12 @@ class Router:
 
             # 3. If specific deployment returned, verify if it supports pass-through
             if isinstance(healthy_deployments, dict):
+                if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
+                    raise litellm.ServiceUnavailableError(
+                        message=f"Model '{model}' is currently paused and cannot accept requests.",
+                        model=model,
+                        llm_provider="",
+                    )
                 litellm_params = healthy_deployments.get("litellm_params", {})
                 if litellm_params.get("use_in_pass_through"):
                     return healthy_deployments
@@ -10555,6 +11200,12 @@ class Router:
         )
 
         if isinstance(healthy_deployments, dict):
+            if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
+                raise litellm.ServiceUnavailableError(
+                    message=f"Model '{model}' is currently paused and cannot accept requests.",
+                    model=model,
+                    llm_provider="",
+                )
             return healthy_deployments
 
         parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
@@ -10585,6 +11236,8 @@ class Router:
             )
             healthy_deployments = _pre_cooldown_deployments
 
+        healthy_deployments = self._filter_blocked_deployments(healthy_deployments)
+
         # filter pre-call checks
         if self.enable_pre_call_checks and messages is not None:
             healthy_deployments = self._pre_call_checks(
@@ -10704,6 +11357,12 @@ class Router:
 
         # 2. If the returned is a specific deployment (Dict), verify and return directly
         if isinstance(healthy_deployments, dict):
+            if (healthy_deployments.get("model_info") or {}).get("blocked") is True:
+                raise litellm.ServiceUnavailableError(
+                    message=f"Model '{model}' is currently paused and cannot accept requests.",
+                    model=model,
+                    llm_provider="",
+                )
             litellm_params = healthy_deployments.get("litellm_params", {})
             if litellm_params.get("use_in_pass_through"):
                 return healthy_deployments
@@ -10743,6 +11402,9 @@ class Router:
             healthy_deployments=pass_through_deployments,
             cooldown_deployments=cooldown_deployments,
         )
+        pass_through_deployments = self._filter_blocked_deployments(
+            pass_through_deployments
+        )
 
         # 5. Apply pre-call checks (if enabled)
         if self.enable_pre_call_checks and messages is not None:
@@ -10832,6 +11494,36 @@ class Router:
             if deployment["model_info"]["id"] not in cooldown_set
         ]
 
+    def _filter_blocked_deployments(
+        self, healthy_deployments: List[Dict]
+    ) -> List[Dict]:
+        """
+        Filters out deployments that an admin has paused via `LiteLLM_ProxyModelTable.blocked`.
+
+        Applied alongside the cooldown filter on every routing entry point that calls
+        `_common_checks_available_deployment` directly — the primary sync/async path,
+        the sync pass-through path, and the retry / health-check helpers — so paused
+        deployments never serve a request. The async pass-through path inherits this
+        filter through its delegation to `async_get_healthy_deployments`.
+        """
+        return [
+            deployment
+            for deployment in healthy_deployments
+            if (deployment.get("model_info") or {}).get("blocked") is not True
+        ]
+
+    @staticmethod
+    def _is_deployment_blocked(deployment: "Deployment") -> bool:
+        """
+        Returns True when a `Deployment` Pydantic instance carries the admin-paused
+        flag. Used by credential-lookup helpers so passthrough file / batch endpoints
+        cannot bypass the pause by resolving credentials directly.
+        """
+        model_info = getattr(deployment, "model_info", None)
+        if model_info is None:
+            return False
+        return getattr(model_info, "blocked", None) is True
+
     async def _async_filter_health_check_unhealthy_deployments(
         self,
         healthy_deployments: List[Dict],
diff --git a/litellm/router_strategy/adaptive_router/hooks.py b/litellm/router_strategy/adaptive_router/hooks.py
index 9e346006ac1..99fe5e26f7f 100644
--- a/litellm/router_strategy/adaptive_router/hooks.py
+++ b/litellm/router_strategy/adaptive_router/hooks.py
@@ -103,7 +103,7 @@ def _last_user_content(messages: Optional[List[Dict[str, Any]]]) -> Optional[str
 
 
 def _recent_tool_results(
-    messages: Optional[List[Dict[str, Any]]]
+    messages: Optional[List[Dict[str, Any]]],
 ) -> List[Dict[str, Any]]:
     """Extract the current turn's tool result payloads from the request messages.
 
diff --git a/litellm/router_strategy/adaptive_router/signals.py b/litellm/router_strategy/adaptive_router/signals.py
index a48bdea1eb6..5e33a64d27f 100644
--- a/litellm/router_strategy/adaptive_router/signals.py
+++ b/litellm/router_strategy/adaptive_router/signals.py
@@ -24,7 +24,6 @@ from litellm.router_strategy.adaptive_router.config import (
     TOOL_CALL_HISTORY_MAX,
 )
 
-
 # ---- Public types ---------------------------------------------------------
 
 
diff --git a/litellm/router_strategy/budget_limiter.py b/litellm/router_strategy/budget_limiter.py
index be27b852478..da41577e99a 100644
--- a/litellm/router_strategy/budget_limiter.py
+++ b/litellm/router_strategy/budget_limiter.py
@@ -10,11 +10,11 @@ This means you can use this with weighted-pick, lowest-latency, simple-shuffle,
 Example:
 ```
 openai:
-	budget_limit: 0.000000000001
-	time_period: 1d
+        budget_limit: 0.000000000001
+        time_period: 1d
 anthropic:
-	budget_limit: 100
-	time_period: 7d
+        budget_limit: 100
+        time_period: 7d
 ```
 """
 
diff --git a/litellm/router_utils/get_retry_from_policy.py b/litellm/router_utils/get_retry_from_policy.py
index ec326ebb50d..162d6428f85 100644
--- a/litellm/router_utils/get_retry_from_policy.py
+++ b/litellm/router_utils/get_retry_from_policy.py
@@ -1,5 +1,5 @@
 """
-Get num retries for an exception. 
+Get num retries for an exception.
 
 - Account for retry policy by exception type.
 """
diff --git a/litellm/router_utils/pattern_match_deployments.py b/litellm/router_utils/pattern_match_deployments.py
index 17b453d6031..48f85a83411 100644
--- a/litellm/router_utils/pattern_match_deployments.py
+++ b/litellm/router_utils/pattern_match_deployments.py
@@ -34,7 +34,7 @@ class PatternUtils:
 
     @staticmethod
     def sorted_patterns(
-        patterns: Dict[str, List[Dict]]
+        patterns: Dict[str, List[Dict]],
     ) -> List[Tuple[str, List[Dict]]]:
         """
         Cached property for patterns sorted by specificity.
diff --git a/litellm/router_utils/router_callbacks/track_deployment_metrics.py b/litellm/router_utils/router_callbacks/track_deployment_metrics.py
index 1f226879d03..9039b0df8e6 100644
--- a/litellm/router_utils/router_callbacks/track_deployment_metrics.py
+++ b/litellm/router_utils/router_callbacks/track_deployment_metrics.py
@@ -1,5 +1,5 @@
 """
-Helper functions to get/set num success and num failures per deployment 
+Helper functions to get/set num success and num failures per deployment
 
 
 set_deployment_failures_for_current_minute
diff --git a/litellm/secret_managers/aws_secret_manager.py b/litellm/secret_managers/aws_secret_manager.py
index fbe951e6492..60d0a713eff 100644
--- a/litellm/secret_managers/aws_secret_manager.py
+++ b/litellm/secret_managers/aws_secret_manager.py
@@ -4,7 +4,7 @@ This is a file for the AWS Secret Manager Integration
 Relevant issue: https://github.com/BerriAI/litellm/issues/1883
 
 Requires:
-* `os.environ["AWS_REGION_NAME"], 
+* `os.environ["AWS_REGION_NAME"],
 * `pip install boto3>=1.28.57`
 """
 
diff --git a/litellm/secret_managers/aws_secret_manager_v2.py b/litellm/secret_managers/aws_secret_manager_v2.py
index c1b4d019dcf..4461e34396e 100644
--- a/litellm/secret_managers/aws_secret_manager_v2.py
+++ b/litellm/secret_managers/aws_secret_manager_v2.py
@@ -10,7 +10,7 @@ Handles Async Operations for:
 Relevant issue: https://github.com/BerriAI/litellm/issues/1883
 
 Requires:
-* `os.environ["AWS_REGION_NAME"], 
+* `os.environ["AWS_REGION_NAME"],
 * `pip install boto3>=1.28.57`
 """
 
diff --git a/litellm/types/agents.py b/litellm/types/agents.py
index efb2e73bfb5..8556b6bac93 100644
--- a/litellm/types/agents.py
+++ b/litellm/types/agents.py
@@ -228,6 +228,66 @@ class ListAgentsResponse(BaseModel):
     agents: List[AgentResponse]
 
 
+class AgentCreateResponse(LiteLLMPydanticObjectBase):
+    """
+    Response from a provider-side agent creation or get call (e.g. Gemini v1beta/agents).
+
+    Gemini returns ``"id"`` as the agent identifier; we surface both ``id``
+    (Gemini's value) and ``name`` (the user-supplied name, equal to ``id`` for
+    Gemini) so callers can use either.  All extra fields returned by the
+    provider (e.g. ``base_agent``, ``system_instruction``, ``base_environment``)
+    are preserved via extra="allow".
+    """
+
+    id: Optional[str] = None
+    name: Optional[str] = None
+    model_config = {"extra": "allow"}
+
+    _hidden_params: dict = PrivateAttr(default_factory=dict)
+
+
+class AgentDeleteResult(LiteLLMPydanticObjectBase):
+    """Result of a provider-side agent deletion (e.g. Gemini DELETE /v1beta/agents/{name}).
+
+    Gemini returns an empty body ``{}`` on success; we synthesise ``name`` and
+    ``deleted`` so callers always get a consistent response object.
+    """
+
+    name: str
+    deleted: bool = True
+    model_config = {"extra": "allow"}
+
+    _hidden_params: dict = PrivateAttr(default_factory=dict)
+
+
+class AgentListResponse(LiteLLMPydanticObjectBase):
+    """Response from listing agents on the provider side (e.g. Gemini GET /v1beta/agents).
+
+    Gemini returns ``{"agents": [{"id": "..."}, ...]}``; each item is kept as
+    a plain dict so no fields are silently dropped.
+    """
+
+    agents: List[Dict[str, Any]] = []
+    next_page_token: Optional[str] = None
+    model_config = {"extra": "allow"}
+
+    _hidden_params: dict = PrivateAttr(default_factory=dict)
+
+
+class AgentVersionsResponse(LiteLLMPydanticObjectBase):
+    """Response from listing versions of an agent (e.g. Gemini GET /v1beta/agents/{name}/versions).
+
+    Gemini returns ``{"agentVersions": [...]}``; each version has a ``name``
+    field of the form ``agents/{agent_id}/versions/{uuid}``.
+    """
+
+    agent_versions: List[Dict[str, Any]] = []
+    next_page_token: Optional[str] = None
+    model_config = {"extra": "allow"}
+
+    _hidden_params: dict = PrivateAttr(default_factory=dict)
+
+
 class AgentMakePublicResponse(BaseModel):
     message: str
     public_agent_groups: List[str]
diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py
index 58a73c090ed..fdeaa015eda 100644
--- a/litellm/types/guardrails.py
+++ b/litellm/types/guardrails.py
@@ -5,6 +5,9 @@ from typing import Any, Dict, List, Literal, Optional, Union
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 from typing_extensions import Required, TypedDict
 
+from litellm.types.proxy.guardrails.guardrail_hooks.akto import (
+    AktoConfigModel,
+)
 from litellm.types.proxy.guardrails.guardrail_hooks.block_code_execution import (
     BlockCodeExecutionGuardrailConfigModel,
 )
@@ -17,9 +20,6 @@ from litellm.types.proxy.guardrails.guardrail_hooks.grayswan import (
 from litellm.types.proxy.guardrails.guardrail_hooks.ibm import (
     IBMGuardrailsBaseConfigModel,
 )
-from litellm.types.proxy.guardrails.guardrail_hooks.akto import (
-    AktoConfigModel,
-)
 from litellm.types.proxy.guardrails.guardrail_hooks.litellm_content_filter import (
     ContentFilterCategoryConfig,
 )
@@ -93,6 +93,7 @@ class SupportedGuardrailIntegrations(Enum):
     GENERIC_GUARDRAIL_API = "generic_guardrail_api"
     QUALIFIRE = "qualifire"
     CUSTOM_CODE = "custom_code"
+    MICROSOFT_PURVIEW = "microsoft_purview"
     SEMANTIC_GUARD = "semantic_guard"
     MCP_END_USER_PERMISSION = "mcp_end_user_permission"
     BLOCK_CODE_EXECUTION = "block_code_execution"
@@ -101,6 +102,7 @@ class SupportedGuardrailIntegrations(Enum):
     LLM_AS_A_JUDGE = "llm_as_a_judge"
     DEEPKEEP = "deepkeep"
     QOSTODIAN_NEXUS = "qostodian_nexus"
+    RUBRIK = "rubrik"
 
 
 class Role(Enum):
diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py
index 43a287f29bc..827d10985cf 100644
--- a/litellm/types/integrations/prometheus.py
+++ b/litellm/types/integrations/prometheus.py
@@ -160,6 +160,7 @@ class UserAPIKeyLabelNames(Enum):
     END_USER = "end_user"
     USER = "user"
     USER_EMAIL = "user_email"
+    USER_ALIAS = "user_alias"
     API_KEY_HASH = "hashed_api_key"
     API_KEY_ALIAS = "api_key_alias"
     TEAM = "team"
@@ -200,6 +201,11 @@ DEFINED_PROMETHEUS_METRICS = Literal[
     "litellm_total_tokens_metric",
     "litellm_input_tokens_metric",
     "litellm_output_tokens_metric",
+    "litellm_input_cached_tokens_metric",
+    "litellm_input_cache_creation_tokens_metric",
+    "litellm_input_audio_tokens_metric",
+    "litellm_output_reasoning_tokens_metric",
+    "litellm_output_audio_tokens_metric",
     "litellm_deployment_successful_fallbacks",
     "litellm_deployment_failed_fallbacks",
     "litellm_remaining_team_budget_metric",
@@ -450,6 +456,17 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.MODEL_ID.value,
     ]
 
+    # Token-type detail metrics — reuse the same label set as
+    # litellm_input_tokens_metric / litellm_output_tokens_metric so dashboards
+    # can join across them. Only emitted when the underlying usage detail is
+    # populated by the provider (e.g. Anthropic cache_read_input_tokens,
+    # OpenAI prompt_tokens_details.cached_tokens, reasoning_tokens, audio_tokens).
+    litellm_input_cached_tokens_metric = litellm_input_tokens_metric
+    litellm_input_cache_creation_tokens_metric = litellm_input_tokens_metric
+    litellm_input_audio_tokens_metric = litellm_input_tokens_metric
+    litellm_output_reasoning_tokens_metric = litellm_output_tokens_metric
+    litellm_output_audio_tokens_metric = litellm_output_tokens_metric
+
     litellm_deployment_state = [
         UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
         UserAPIKeyLabelNames.MODEL_ID.value,
@@ -533,17 +550,9 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.USER.value,
     ]
 
-    litellm_user_max_budget_metric = [
-        UserAPIKeyLabelNames.USER.value,
-    ]
+    litellm_user_max_budget_metric = litellm_remaining_user_budget_metric
 
-    litellm_user_budget_remaining_hours_metric = [
-        UserAPIKeyLabelNames.USER.value,
-    ]
-
-    litellm_user_budget_remaining_hours_metric = [
-        UserAPIKeyLabelNames.USER.value,
-    ]
+    litellm_user_budget_remaining_hours_metric = litellm_remaining_user_budget_metric
 
     litellm_remaining_api_key_requests_for_model = [
         UserAPIKeyLabelNames.API_KEY_HASH.value,
@@ -730,6 +739,22 @@ class PrometheusMetricLabels:
         ):
             custom_labels.append(UserAPIKeyLabelNames.STREAM.value)
 
+        _user_budget_metrics = {
+            "litellm_remaining_user_budget_metric",
+            "litellm_user_max_budget_metric",
+            "litellm_user_budget_remaining_hours_metric",
+        }
+        if (
+            label_name in _user_budget_metrics
+            and litellm.prometheus_user_budget_label_include_email_alias is True
+        ):
+            for label in [
+                UserAPIKeyLabelNames.USER_EMAIL.value,
+                UserAPIKeyLabelNames.USER_ALIAS.value,
+            ]:
+                if label not in default_labels and label not in custom_labels:
+                    custom_labels.append(label)
+
         if label_name in PrometheusMetricLabels._org_label_metrics:
             for label in [
                 UserAPIKeyLabelNames.ORG_ID.value,
@@ -759,6 +784,7 @@ class UserAPIKeyLabelValues:
     end_user: Optional[str] = None
     user: Optional[str] = None
     user_email: Optional[str] = None
+    user_alias: Optional[str] = None
     hashed_api_key: Optional[str] = None
     api_key_alias: Optional[str] = None
     team: Optional[str] = None
diff --git a/litellm/types/interactions/__init__.py b/litellm/types/interactions/__init__.py
index a3acdc4cb1f..78d0b04ef3b 100644
--- a/litellm/types/interactions/__init__.py
+++ b/litellm/types/interactions/__init__.py
@@ -36,8 +36,13 @@ from litellm.types.interactions.generated import (
     GoogleSearchResultContent,
     ImageContent,
     Interaction,
+    InteractionCompleted,
+    InteractionCreated,
     InteractionEvent,
+    InteractionEnvironment,
+    InteractionInProgress,
     InteractionInput,
+    InteractionRequiresAction,
     InteractionsAPIOptionalRequestParams,
     InteractionsAPIResponse,
     InteractionsAPIStreamingResponse,
@@ -49,6 +54,9 @@ from litellm.types.interactions.generated import (
     McpServerToolResultContent,
     ModelOption,
     ResponseModality,
+    StepDelta,
+    StepStart,
+    StepStop,
 )
 from litellm.types.interactions.generated import (
     Status3 as InteractionStatus,  # Main request/response types; Content types; Turn for multi-turn conversations; Tool types; Config types; Usage; Status enum; Events for streaming; Agent configs; Model/Agent options; Response modality; Annotation; LiteLLM types; Backwards compat aliases
@@ -114,7 +122,16 @@ __all__ = [
     "AgentOption",
     "ResponseModality",
     "Annotation",
+    # New schema SSE event types (Api-Revision: 2026-05-20)
+    "StepStart",
+    "StepDelta",
+    "StepStop",
+    "InteractionCreated",
+    "InteractionInProgress",
+    "InteractionCompleted",
+    "InteractionRequiresAction",
     # LiteLLM types
+    "InteractionEnvironment",
     "InteractionInput",
     "InteractionsAPIResponse",
     "InteractionsAPIStreamingResponse",
diff --git a/litellm/types/interactions/generated.py b/litellm/types/interactions/generated.py
index ed626b0b7c8..d546e897891 100644
--- a/litellm/types/interactions/generated.py
+++ b/litellm/types/interactions/generated.py
@@ -1151,9 +1151,114 @@ class InteractionEvent(BaseModel):
     )
 
 
+# ---------------------------------------------------------------
+# New schema SSE event types (Api-Revision: 2026-05-20)
+# These replace the legacy content.* / interaction.start|complete
+# events and will become the only events after June 8, 2026.
+# ---------------------------------------------------------------
+
+
+class StepStart(BaseModel):
+    """Emitted when a new step begins (replaces content.start)."""
+
+    event_type: Literal["step.start"] = "step.start"
+    index: Optional[int] = None
+    step: Optional[Dict[str, Any]] = Field(
+        None,
+        description="The initial step data (type, content, signature, etc.).",
+    )
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
+class StepDelta(BaseModel):
+    """Emitted for incremental step content (replaces content.delta)."""
+
+    event_type: Literal["step.delta"] = "step.delta"
+    index: Optional[int] = None
+    delta: Optional[Dict[str, Any]] = Field(
+        None,
+        description="Incremental content delta (e.g. text, arguments_delta for function calls).",
+    )
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
+class StepStop(BaseModel):
+    """Emitted when a step finishes (replaces content.stop)."""
+
+    event_type: Literal["step.stop"] = "step.stop"
+    index: Optional[int] = None
+    status: Optional[str] = Field(
+        None,
+        description="Step completion status (e.g. 'done').",
+    )
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
+class InteractionCreated(BaseModel):
+    """Emitted when the interaction is first created (replaces interaction.start)."""
+
+    event_type: Literal["interaction.created"] = "interaction.created"
+    interaction: Optional[Dict[str, Any]] = None
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
+class InteractionInProgress(BaseModel):
+    """Emitted while the interaction is running."""
+
+    event_type: Literal["interaction.in_progress"] = "interaction.in_progress"
+    interaction_id: Optional[str] = None
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
+class InteractionCompleted(BaseModel):
+    """Emitted when the interaction finishes (replaces interaction.complete)."""
+
+    event_type: Literal["interaction.completed"] = "interaction.completed"
+    interaction: Optional[Dict[str, Any]] = None
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
+class InteractionRequiresAction(BaseModel):
+    """Emitted when the interaction is paused waiting for a tool result."""
+
+    event_type: Literal["interaction.requires_action"] = "interaction.requires_action"
+    interaction_id: Optional[str] = None
+    event_id: Optional[str] = Field(
+        None,
+        description="The event_id token to be used to resume the interaction stream.",
+    )
+
+
 class InteractionSseEvent(
     RootModel[
         Union[
+            # New schema events (Api-Revision: 2026-05-20)
+            StepStart,
+            StepDelta,
+            StepStop,
+            InteractionCreated,
+            InteractionInProgress,
+            InteractionCompleted,
+            InteractionRequiresAction,
+            # Legacy schema events (Api-Revision: 2026-05-07, removed June 8 2026)
             InteractionEvent,
             InteractionStatusUpdate,
             ContentStart,
@@ -1164,6 +1269,15 @@ class InteractionSseEvent(
     ]
 ):
     root: Union[
+        # New schema events (Api-Revision: 2026-05-20)
+        StepStart,
+        StepDelta,
+        StepStop,
+        InteractionCreated,
+        InteractionInProgress,
+        InteractionCompleted,
+        InteractionRequiresAction,
+        # Legacy schema events (Api-Revision: 2026-05-07, removed June 8 2026)
         InteractionEvent,
         InteractionStatusUpdate,
         ContentStart,
@@ -1193,6 +1307,11 @@ class InteractionsAPIResponse(BaseLiteLLMOpenAIResponseObject):
     Response from the Interactions API.
 
     Wraps the API response with LiteLLM-specific hidden params.
+
+    Schema notes:
+    - New schema (Api-Revision: 2026-05-20, default): response contains ``steps``.
+    - Legacy schema (Api-Revision: 2026-05-07, removed June 8 2026): response contains ``outputs``.
+    Both fields are kept here so callers work with either schema.
     """
 
     id: Optional[str] = None
@@ -1203,7 +1322,10 @@ class InteractionsAPIResponse(BaseLiteLLMOpenAIResponseObject):
     created: Optional[str] = None
     updated: Optional[str] = None
     role: Optional[str] = None
+    # Legacy schema field (Api-Revision: 2026-05-07). Remove after June 8, 2026.
     outputs: Optional[List[Dict[str, Any]]] = None
+    # New schema field (Api-Revision: 2026-05-20).
+    steps: Optional[List[Dict[str, Any]]] = None
     usage: Optional[Dict[str, Any]] = None
 
     _hidden_params: dict = PrivateAttr(default_factory=dict)
@@ -1213,7 +1335,12 @@ class InteractionsAPIStreamingResponse(BaseLiteLLMOpenAIResponseObject):
     """
     Streaming response chunk from the Interactions API.
 
-    Event types per OpenAPI spec:
+    New schema event types (Api-Revision: 2026-05-20):
+    - interaction.created, interaction.in_progress, interaction.completed,
+      interaction.requires_action
+    - step.start, step.delta, step.stop
+
+    Legacy event types (Api-Revision: 2026-05-07, removed June 8 2026):
     - interaction.start, interaction.status_update, interaction.complete
     - content.start, content.delta, content.stop
     - error
@@ -1228,9 +1355,17 @@ class InteractionsAPIStreamingResponse(BaseLiteLLMOpenAIResponseObject):
     created: Optional[str] = None
     updated: Optional[str] = None
     role: Optional[str] = None
+    # Legacy schema field (Api-Revision: 2026-05-07). Remove after June 8, 2026.
     outputs: Optional[List[Dict[str, Any]]] = None
+    # New schema field (Api-Revision: 2026-05-20).
+    steps: Optional[List[Dict[str, Any]]] = None
     usage: Optional[Dict[str, Any]] = None
     delta: Optional[Dict[str, Any]] = None
+    # New schema streaming fields
+    index: Optional[int] = None
+    step: Optional[Dict[str, Any]] = None
+    interaction_id: Optional[str] = None
+    interaction: Optional[Dict[str, Any]] = None
 
     _hidden_params: dict = PrivateAttr(default_factory=dict)
 
@@ -1257,3 +1392,6 @@ class CancelInteractionResult(BaseLiteLLMOpenAIResponseObject):
 InteractionTool = Tool
 InteractionToolChoiceConfig = ToolChoiceConfig
 InteractionsAPIOptionalRequestParams = Dict[str, Any]
+
+# Agent interaction execution environment
+InteractionEnvironment = Union[str, Dict[str, Any]]
diff --git a/litellm/types/llms/oci.py b/litellm/types/llms/oci.py
index e041810158a..df551d8a8c6 100644
--- a/litellm/types/llms/oci.py
+++ b/litellm/types/llms/oci.py
@@ -3,7 +3,7 @@ from __future__ import annotations
 from enum import Enum
 from typing import Any, Dict, List, Literal, Optional, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, SerializeAsAny
 
 OCIRoles = Literal["SYSTEM", "USER", "ASSISTANT", "TOOL"]
 
@@ -15,7 +15,6 @@ class OCIVendors(Enum):
     """
 
     COHERE = "COHERE"
-    GEMINI = "GEMINI"
     GENERIC = "GENERIC"
 
 
@@ -57,7 +56,7 @@ OCIContentPartUnion = Union[OCITextContentPart, OCIImageContentPart]
 class OCIToolCall(BaseModel):
     """Represents a tool call made by the model."""
 
-    id: str
+    id: Optional[str] = None  # absent in some provider responses (e.g. Google via OCI)
     type: Literal["FUNCTION"] = "FUNCTION"
     name: str
     arguments: str  # Arguments should be a JSON-serialized string
@@ -96,13 +95,22 @@ class OCIChatRequestPayload(BaseModel):
     isStream: bool = False
     numGenerations: Optional[int] = None
     maxTokens: Optional[int] = None
+    # GPT-5+ on OCI rejects maxTokens and requires maxCompletionTokens.
+    maxCompletionTokens: Optional[int] = None
     temperature: Optional[float] = None
     topP: Optional[float] = None
     stop: Optional[List[str]] = None
     seed: Optional[int] = None
     frequencyPenalty: Optional[float] = None
     presencePenalty: Optional[float] = None
+    # Reasoning-token budget knob (OCI: NONE/MINIMAL/LOW/MEDIUM/HIGH).
+    # Honoured by GPT-5 family, Gemini 2.5, Grok reasoning variants,
+    # Cohere Command-A-Reasoning. Ignored by non-reasoning models.
+    reasoningEffort: Optional[str] = None
     responseFormat: Optional[Dict[str, Any]] = None
+    toolChoice: Optional[Union[str, Dict[str, Any]]] = None
+    logitBias: Optional[Dict[str, Any]] = None
+    logProbs: Optional[int] = None
 
 
 class OCIServingMode(BaseModel):
@@ -141,7 +149,9 @@ class OCIResponseUsage(BaseModel):
     """Token usage in the OCI response."""
 
     promptTokens: int
-    completionTokens: int
+    # completionTokens may be absent for reasoning models when all the output
+    # budget is consumed by reasoning tokens before any visible content is produced.
+    completionTokens: Optional[int] = None
     totalTokens: int
     completionTokensDetails: Optional[OCICompletionTokenDetails] = None
     promptTokensDetails: Optional[OCIPromptTokensDetails] = None
@@ -151,7 +161,9 @@ class OCIResponseChoice(BaseModel):
     """A completion choice in the OCI response."""
 
     index: int
-    message: OCIMessage
+    # message is absent when a reasoning model exhausts max_tokens in the
+    # reasoning phase without producing any visible content.
+    message: Optional[OCIMessage] = None
     finishReason: Optional[str] = None
     logprobs: Optional[Dict[str, Any]] = None
 
@@ -203,6 +215,7 @@ class CohereStreamChunk(BaseModel):
     text: Optional[str] = None
     chatHistory: Optional[List[CohereMessage]] = None
     finishReason: Optional[str] = None
+    toolCalls: Optional[List[CohereToolCall]] = None
     pad: Optional[str] = None
     index: Optional[int] = None
 
@@ -234,10 +247,14 @@ class CohereSystemMessage(CohereMessage):
 
 
 class CohereToolMessage(CohereMessage):
-    """Tool message in Cohere chat."""
+    """Tool message in Cohere chat.
+
+    The OCI Cohere API represents tool results via a ``toolResults`` list on the
+    TOOL-role history entry — not via a ``toolCallId`` string.
+    """
 
     role: Literal["TOOL"] = "TOOL"
-    toolCallId: str
+    toolResults: List[CohereToolResult]
 
 
 class CohereParameterDefinition(BaseModel):
@@ -264,10 +281,14 @@ class CohereToolCall(BaseModel):
 
 
 class CohereToolResult(BaseModel):
-    """Result of a tool call."""
+    """Result of a tool call.
 
-    callId: str
-    result: str
+    Matches the OCI SDK's CohereToolResult: each result carries the originating
+    tool call (name + parameters) and a list of output objects.
+    """
+
+    call: CohereToolCall
+    outputs: List[Dict[str, Any]]
 
 
 class CohereResponseFormat(BaseModel):
@@ -297,7 +318,11 @@ class CohereChatRequest(BaseModel):
     apiFormat: Literal["COHERE"] = "COHERE"
 
     # Optional fields
-    chatHistory: Optional[List[CohereMessage]] = None
+    # ``SerializeAsAny`` preserves subclass-specific fields (e.g. ``toolResults``
+    # on ``CohereToolMessage``) when this request is serialized via ``model_dump``.
+    # Without it, Pydantic v2 would serialize each element using the declared
+    # ``CohereMessage`` schema and silently drop subclass fields.
+    chatHistory: Optional[List[SerializeAsAny[CohereMessage]]] = None
     maxTokens: Optional[int] = None
     temperature: Optional[float] = None
     topP: Optional[float] = None
@@ -307,7 +332,10 @@ class CohereChatRequest(BaseModel):
     stopSequences: Optional[List[str]] = None
     seed: Optional[int] = None
     tools: Optional[List[CohereTool]] = None
-    toolChoice: Optional[Union[str, Dict[str, Any]]] = None
+    # NOTE: OCI's Cohere chat endpoint does not accept ``toolChoice`` — see
+    # ``OCIChatConfig.openai_to_oci_cohere_param_map`` which marks
+    # ``tool_choice`` as unsupported. The field is intentionally absent here
+    # so it isn't silently dropped or surfaced as a supported feature.
     responseFormat: Optional[
         Union[
             CohereResponseTextFormat,
@@ -364,9 +392,12 @@ class CohereChatResponse(BaseModel):
     # Required fields
     text: str
     apiFormat: Literal["COHERE"] = "COHERE"
-    finishReason: Literal[
-        "COMPLETE", "ERROR_TOXIC", "ERROR_LIMIT", "ERROR", "USER_CANCEL", "MAX_TOKENS"
-    ]
+    # Accept any string (with ``None`` for absent) so unknown finish reasons
+    # — e.g. a value OCI adds in a future API revision — degrade gracefully
+    # via ``handle_cohere_response``'s ``elif oci_finish_reason is not None``
+    # fallback instead of crashing Pydantic validation. Mirrors
+    # ``CohereStreamChunk.finishReason`` which has always been ``Optional[str]``.
+    finishReason: Optional[str] = None
 
     # Optional fields
     chatHistory: Optional[List[CohereMessage]] = None
@@ -394,3 +425,41 @@ class CohereChatResult(BaseModel):
     modelId: str
     modelVersion: str
     chatResponse: CohereChatResponse
+
+
+# ---------------------------------------------------------------------------
+# OCI Embed types
+# ---------------------------------------------------------------------------
+
+
+class OCIEmbedRequest(BaseModel):
+    """Request body for POST /20231130/actions/embedText."""
+
+    compartmentId: str
+    servingMode: OCIServingMode
+    inputs: List[str]
+    inputType: Optional[str] = (
+        None  # SEARCH_DOCUMENT | SEARCH_QUERY | CLASSIFICATION | CLUSTERING | IMAGE
+    )
+    truncate: Optional[str] = "END"  # NONE | START | END
+    outputDimensions: Optional[int] = (
+        None  # cohere.embed-v4.0+; valid: 256, 512, 1024, 1536
+    )
+
+
+class OCIEmbedUsage(BaseModel):
+    promptTokens: int
+    totalTokens: int
+
+
+class OCIEmbedResponse(BaseModel):
+    """Response body from POST /20231130/actions/embedText."""
+
+    id: Optional[str] = None  # present in the official SDK response
+    embeddings: List[List[float]]
+    modelId: str
+    modelVersion: str
+    # OCI returns per-input token counts in inputTextTokenCounts (summed for total usage)
+    inputTextTokenCounts: Optional[List[int]] = None
+    # Some deployments may return a usage object instead
+    usage: Optional[OCIEmbedUsage] = None
diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py
index 87bf11a9026..a1d53978761 100644
--- a/litellm/types/llms/vertex_ai.py
+++ b/litellm/types/llms/vertex_ai.py
@@ -14,13 +14,19 @@ from litellm.types.llms.openai import EmbeddingInput
 GeminiEmbeddingInput = Union[EmbeddingInput, List[List[str]]]
 
 
-class FunctionResponse(TypedDict):
-    name: str
+class FunctionResponse(TypedDict, total=False):
+    # `id` correlates this response with the originating `functionCall` part.
+    # Supported on Google AI Studio Gemini 3.5+; Vertex AI rejects this field.
+    id: str
+    name: Required[str]
     response: Optional[dict]
 
 
-class FunctionCall(TypedDict):
-    name: str
+class FunctionCall(TypedDict, total=False):
+    # `id` correlates the corresponding `functionResponse` on Google AI Studio
+    # Gemini 3.5+. Vertex AI and older Gemini models omit/reject this field.
+    id: str
+    name: Required[str]
     args: Optional[dict]
 
 
@@ -45,8 +51,11 @@ class PartType(TypedDict, total=False):
     media_resolution: Literal["low", "medium", "high"]
 
 
-class HttpxFunctionCall(TypedDict):
-    name: str
+class HttpxFunctionCall(TypedDict, total=False):
+    # `id` correlates the corresponding `functionResponse` on Google AI Studio
+    # Gemini 3.5+. Vertex AI and older Gemini models omit/reject this field.
+    id: str
+    name: Required[str]
     args: dict
 
 
diff --git a/litellm/types/router.py b/litellm/types/router.py
index 926815ba317..6601f552b52 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -133,6 +133,9 @@ class ModelInfo(BaseModel):
     # the model_name that can be used by the team when making LLM calls
     team_public_model_name: Optional[str] = None
 
+    # admin-toggled pause flag; mirrors LiteLLM_ProxyModelTable.blocked
+    blocked: Optional[bool] = None
+
     def __init__(self, id: Optional[Union[str, int]] = None, **params):
         if id is None:
             id = str(uuid.uuid4())  # Generate a UUID if id is None or not provided
@@ -323,6 +326,7 @@ class updateDeployment(BaseModel):
     model_name: Optional[str] = None
     litellm_params: Optional[updateLiteLLMParams] = None
     model_info: Optional[ModelInfo] = None
+    blocked: Optional[bool] = None
 
     model_config = ConfigDict(protected_namespaces=())
 
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 400edcac889..282baff07fe 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -107,9 +107,13 @@ class LiteLLMCommonStrings(Enum):
 SupportedCacheControls = ["ttl", "s-maxage", "no-cache", "no-store"]
 
 
-class CostPerToken(TypedDict):
-    input_cost_per_token: float
-    output_cost_per_token: float
+class CostPerToken(TypedDict, total=False):
+    # Required base rates — kept under total=False so we can mark them
+    # Required individually while leaving the cache rates NotRequired.
+    input_cost_per_token: Required[float]
+    output_cost_per_token: Required[float]
+    cache_read_input_token_cost: float
+    cache_creation_input_token_cost: float
 
 
 class ProviderField(TypedDict):
@@ -143,6 +147,7 @@ class ProviderSpecificModelInfo(TypedDict, total=False):
     supports_low_reasoning_effort: Optional[bool]
     supports_xhigh_reasoning_effort: Optional[bool]
     supports_max_reasoning_effort: Optional[bool]
+    supports_output_config: Optional[bool]
 
 
 class SearchContextCostPerQuery(TypedDict, total=False):
@@ -239,6 +244,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
         float
     ]  # video_generation tier: key output_cost_per_second_<resolution> (e.g. 1080p, 720p)
     ocr_cost_per_page: Optional[float]  # for OCR models
+    ocr_cost_per_credit: Optional[float]  # for OCR models priced by credit
     annotation_cost_per_page: Optional[float]  # for OCR models
     search_context_cost_per_query: Optional[
         SearchContextCostPerQuery
@@ -256,6 +262,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
             "chat",
             "audio_transcription",
             "responses",
+            "ocr",
         ]
     ]
     tpm: Optional[int]
@@ -2761,6 +2768,20 @@ class StandardLoggingGuardrailInformation(TypedDict, total=False):
     risk_score: Optional[float]
     """Risk score 0-10 indicating how risky the request was (higher = riskier). Computed by the guardrail provider."""
 
+    violation_categories: Optional[List[str]]
+    """Names of the policy items that intervened on this request (e.g. Bedrock
+    topic-policy topic names, content-policy filter types, PII entity types).
+    Populated by the provider hook before redaction so downstream loggers
+    (OTEL, Langfuse, ...) can filter by violation category without parsing
+    the raw guardrail_response blob. Empty/absent when the guardrail allowed
+    the request through."""
+
+    guardrail_action: Optional[str]
+    """Provider's raw top-level action string (e.g. Bedrock's ``GUARDRAIL_INTERVENED``
+    or ``NONE``). Populated by the provider hook so the OTEL integration can
+    surface it as a queryable span attribute without parsing the raw
+    guardrail_response blob."""
+
 
 class EvalVerdict(TypedDict, total=False):
     criterion_name: str
@@ -2802,6 +2823,8 @@ class GuardrailTracingDetail(TypedDict, total=False):
     patterns_checked: Optional[int]
     alert_recipients: Optional[List[str]]
     risk_score: Optional[float]
+    violation_categories: Optional[List[str]]
+    guardrail_action: Optional[str]
 
 
 StandardLoggingPayloadStatus = Literal["success", "failure"]
@@ -3215,6 +3238,7 @@ class LlmProviders(str, Enum):
     ANTHROPIC_TEXT = "anthropic_text"
     BYTEZ = "bytez"
     REPLICATE = "replicate"
+    REDUCTO = "reducto"
     RUNWAYML = "runwayml"
     AWS_POLLY = "aws_polly"
     HUGGINGFACE = "huggingface"
diff --git a/litellm/utils.py b/litellm/utils.py
index cefd348078b..2ba6ef9cae8 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -2933,6 +2933,13 @@ def register_model(model_cost: Union[str, dict]):  # noqa: PLR0915
             except Exception:
                 existing_model = {}
                 model_cost_key = key
+        # ``get_model_info`` returns ``litellm_provider: None`` when the
+        # provider is unknown (e.g. custom deployments registered via
+        # ``Router.add_deployment``). Persisting that None into
+        # ``litellm.model_cost`` causes ``_check_provider_match`` to drop
+        # custom pricing on subsequent cost lookups.
+        if existing_model.get("litellm_provider") is None:
+            existing_model.pop("litellm_provider", None)
         ## override / add new keys to the existing model cost dictionary
         updated_dictionary = _update_dictionary(existing_model, value)
         litellm.model_cost.setdefault(model_cost_key, {}).update(updated_dictionary)
@@ -3343,6 +3350,21 @@ def get_optional_params_embeddings(  # noqa: PLR0915
             model=model,
             drop_params=drop_params if drop_params is not None else False,
         )
+        # Provider-only params (e.g. Cohere input_type) are not in
+        # OPENAI_EMBEDDING_PARAMS, so embedding_pre_process drops them from
+        # non_default_params before map_openai_params. Restore only those extras
+        # from passed_params — skip OPENAI_EMBEDDING_PARAMS to avoid duplicating
+        # values already mapped (e.g. dimensions -> output_dimension).
+        if supported_params:
+            for param in supported_params:
+                if param in OPENAI_EMBEDDING_PARAMS:
+                    continue
+                if (
+                    param in passed_params
+                    and passed_params[param] is not None
+                    and param not in optional_params
+                ):
+                    optional_params[param] = passed_params[param]
     ## raise exception if non-default value passed for non-openai/azure embedding calls
     elif custom_llm_provider == "openai":
         # 'dimensions` is only supported in `text-embedding-3` and later models
@@ -4019,16 +4041,23 @@ def get_optional_params(  # noqa: PLR0915
     thinking: Optional[AnthropicThinkingParam] = None,
     web_search_options: Optional[OpenAIWebSearchOptions] = None,
     safety_identifier: Optional[str] = None,
+    base_model: Optional[str] = None,
     **kwargs,
 ):
     passed_params = locals().copy()
     special_params = passed_params.pop("kwargs")
+    # Remove base_model from passed_params so it doesn't interfere with
+    # non_default_params / _check_valid_arg — it's a routing hint, not an
+    # OpenAI param.
+    passed_params.pop("base_model", None)
     provider_config: Optional[BaseConfig] = None
     if custom_llm_provider is not None and custom_llm_provider in [
         provider.value for provider in LlmProviders
     ]:
         provider_config = ProviderConfigManager.get_provider_chat_config(
-            model=model, provider=LlmProviders(custom_llm_provider)
+            model=model,
+            provider=LlmProviders(custom_llm_provider),
+            base_model=base_model,
         )
     non_default_params = pre_process_non_default_params(
         passed_params=passed_params,
@@ -4091,7 +4120,7 @@ def get_optional_params(  # noqa: PLR0915
         sys.modules[__name__], "get_supported_openai_params"
     )
     supported_params = get_supported_openai_params(
-        model=model, custom_llm_provider=custom_llm_provider
+        model=model, custom_llm_provider=custom_llm_provider, base_model=base_model
     )
     if supported_params is None:
         supported_params = get_supported_openai_params(
@@ -4702,22 +4731,27 @@ def get_optional_params(  # noqa: PLR0915
             ),
         )
     elif custom_llm_provider == "azure":
-        if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
+        _azure_detection_model = base_model or model
+        if litellm.AzureOpenAIO1Config().is_o_series_model(
+            model=_azure_detection_model
+        ):
             optional_params = litellm.AzureOpenAIO1Config().map_openai_params(
                 non_default_params=non_default_params,
                 optional_params=optional_params,
-                model=model,
+                model=_azure_detection_model,
                 drop_params=(
                     drop_params
                     if drop_params is not None and isinstance(drop_params, bool)
                     else False
                 ),
             )
-        elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model):
+        elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(
+            model=_azure_detection_model
+        ):
             optional_params = litellm.AzureOpenAIGPT5Config().map_openai_params(
                 non_default_params=non_default_params,
                 optional_params=optional_params,
-                model=model,
+                model=_azure_detection_model,
                 drop_params=(
                     drop_params
                     if drop_params is not None and isinstance(drop_params, bool)
@@ -4739,7 +4773,7 @@ def get_optional_params(  # noqa: PLR0915
             optional_params = litellm.AzureOpenAIConfig().map_openai_params(
                 non_default_params=non_default_params,
                 optional_params=optional_params,
-                model=model,
+                model=_azure_detection_model,
                 api_version=api_version,  # type: ignore
                 drop_params=(
                     drop_params
@@ -5387,6 +5421,16 @@ def _strip_model_name(model: str, custom_llm_provider: Optional[str]) -> str:
 # Global case-insensitive lookup map for model_cost (built eagerly at module import)
 _model_cost_lowercase_map: Optional[Dict[str, str]] = None
 
+# Monotonic counter bumped on every model_cost mutation. Consumers that
+# memoize derived state (e.g. provider-specific indices) can include this
+# value in their cache key so they invalidate even when key add+remove or
+# in-place value replacement leaves len/id unchanged.
+_model_cost_mutation_generation: int = 0
+
+
+def get_model_cost_mutation_generation() -> int:
+    return _model_cost_mutation_generation
+
 
 def _invalidate_model_cost_lowercase_map() -> None:
     """Invalidate the case-insensitive lookup map for model_cost.
@@ -5394,8 +5438,9 @@ def _invalidate_model_cost_lowercase_map() -> None:
     Call this whenever litellm.model_cost is modified to ensure the map is rebuilt.
     Also clears related LRU caches that depend on model_cost data.
     """
-    global _model_cost_lowercase_map
+    global _model_cost_lowercase_map, _model_cost_mutation_generation
     _model_cost_lowercase_map = None
+    _model_cost_mutation_generation += 1
 
     # Clear LRU caches that depend on model_cost data
     get_model_info.cache_clear()
@@ -5499,9 +5544,15 @@ def _get_model_info_from_model_cost(key: str) -> dict:
 def _check_provider_match(model_info: dict, custom_llm_provider: Optional[str]) -> bool:
     """
     Check if the model info provider matches the custom provider.
+
+    A missing ``litellm_provider`` key and a ``litellm_provider`` set to
+    ``None`` both mean "no specific provider constraint" and are treated
+    as a wildcard match. ``register_model`` may persist ``None`` here via
+    ``get_model_info`` when a deployment is registered without a provider,
+    so normalising the two cases keeps custom pricing applied consistently.
     """
     if custom_llm_provider and (
-        "litellm_provider" in model_info
+        model_info.get("litellm_provider") is not None
         and model_info["litellm_provider"] != custom_llm_provider
     ):
         if custom_llm_provider == "vertex_ai" and model_info[
@@ -5986,6 +6037,7 @@ def _get_model_info_helper(  # noqa: PLR0915
                 tpm=_model_info.get("tpm", None),
                 rpm=_model_info.get("rpm", None),
                 ocr_cost_per_page=_model_info.get("ocr_cost_per_page", None),
+                ocr_cost_per_credit=_model_info.get("ocr_cost_per_credit", None),
                 annotation_cost_per_page=_model_info.get(
                     "annotation_cost_per_page", None
                 ),
@@ -8094,6 +8146,35 @@ def validate_openai_optional_params(
     return stop
 
 
+@lru_cache(maxsize=1)
+def _get_bundled_model_cost_map() -> Dict[str, Any]:
+    try:
+        model_cost_path = resources.files("litellm").joinpath(
+            "model_prices_and_context_window_backup.json"
+        )
+        return json.loads(model_cost_path.read_text())
+    except Exception:
+        return {}
+
+
+def _get_model_cost_entry_for_provider_config(
+    model: str,
+    provider: LlmProviders,
+) -> Dict[str, Any]:
+    candidate_keys = (model, f"{provider.value}/{model}")
+    for model_key in candidate_keys:
+        model_info = litellm.model_cost.get(model_key)
+        if model_info is not None:
+            return model_info
+
+    bundled_model_cost = _get_bundled_model_cost_map()
+    for model_key in candidate_keys:
+        model_info = bundled_model_cost.get(model_key)
+        if model_info is not None:
+            return model_info
+    return {}
+
+
 class ProviderConfigManager:
     # Dictionary mapping for O(1) provider lookup
     # Stores tuples of (factory_function, needs_model_parameter)
@@ -8112,10 +8193,8 @@ class ProviderConfigManager:
             # Format: (factory_function, needs_model_parameter: bool)
             LlmProviders.OPENAI: (lambda: litellm.OpenAIGPTConfig(), False),
             LlmProviders.ANTHROPIC: (lambda: litellm.AnthropicConfig(), False),
-            LlmProviders.AZURE: (
-                lambda model: ProviderConfigManager._get_azure_config(model),
-                True,
-            ),
+            # AZURE is handled as a special case in get_provider_chat_config()
+            # so that base_model can be threaded through for model-type detection.
             LlmProviders.AZURE_AI: (
                 lambda model: ProviderConfigManager._get_azure_ai_config(model),
                 True,
@@ -8255,11 +8334,19 @@ class ProviderConfigManager:
         }
 
     @staticmethod
-    def _get_azure_config(model: str) -> BaseConfig:
-        """Get Azure config based on model type."""
-        if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
+    def _get_azure_config(model: str, base_model: Optional[str] = None) -> BaseConfig:
+        """Get Azure config based on model type.
+
+        When *base_model* is provided (e.g. ``"azure/gpt-5.2"``), it is used
+        for model-type detection instead of *model* (the deployment name).
+        This allows non-standard deployment names like ``"azure/foo"`` to be
+        routed through the correct config when the user specifies the true
+        underlying model via ``base_model``.
+        """
+        detection_model = base_model or model
+        if litellm.AzureOpenAIO1Config().is_o_series_model(model=detection_model):
             return litellm.AzureOpenAIO1Config()
-        if litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model):
+        if litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=detection_model):
             return litellm.AzureOpenAIGPT5Config()
         return litellm.AzureOpenAIConfig()
 
@@ -8317,13 +8404,18 @@ class ProviderConfigManager:
 
     @staticmethod
     def get_provider_chat_config(  # noqa: PLR0915
-        model: str, provider: LlmProviders
+        model: str,
+        provider: LlmProviders,
+        base_model: Optional[str] = None,
     ) -> Optional[BaseConfig]:
         """
         Returns the provider config for a given provider.
 
         Uses O(1) dictionary lookup for fast provider resolution.
         Python classes take priority over JSON (they have custom overrides).
+
+        For Azure, *base_model* (when set) drives model-type detection so that
+        non-standard deployment names still route to the correct config.
         """
         # Handle OpenAI special cases (O-series and GPT-5 models)
         if provider == LlmProviders.OPENAI:
@@ -8332,6 +8424,12 @@ class ProviderConfigManager:
             if litellm.OpenAIGPT5Config.is_model_gpt_5_model(model=model):
                 return litellm.OpenAIGPT5Config()
 
+        # Handle Azure before the generic map so base_model can be threaded through
+        if provider == LlmProviders.AZURE:
+            return ProviderConfigManager._get_azure_config(
+                model=model, base_model=base_model
+            )
+
         # Initialize provider config map lazily (avoids circular imports)
         if ProviderConfigManager._PROVIDER_CONFIG_MAP is None:
             ProviderConfigManager._PROVIDER_CONFIG_MAP = (
@@ -8383,6 +8481,10 @@ class ProviderConfigManager:
             return litellm.InfinityEmbeddingConfig()
         elif litellm.LlmProviders.SAMBANOVA == provider:
             return litellm.SambaNovaEmbeddingConfig()
+        elif litellm.LlmProviders.OCI == provider:
+            from litellm.llms.oci.embed.transformation import OCIEmbedConfig
+
+            return OCIEmbedConfig()
         elif (
             litellm.LlmProviders.COHERE == provider
             or litellm.LlmProviders.COHERE_CHAT == provider
@@ -8402,6 +8504,12 @@ class ProviderConfigManager:
             )
 
             return VolcEngineEmbeddingConfig()
+        elif litellm.LlmProviders.DASHSCOPE == provider:
+            from litellm.llms.dashscope.embed.transformation import (
+                DashScopeEmbeddingConfig,
+            )
+
+            return DashScopeEmbeddingConfig()
         elif litellm.LlmProviders.OVHCLOUD == provider:
             return litellm.OVHCloudEmbeddingConfig()
         elif litellm.LlmProviders.SNOWFLAKE == provider:
@@ -8434,10 +8542,6 @@ class ProviderConfigManager:
             return SagemakerEmbeddingConfig.get_model_config(model)
         elif litellm.LlmProviders.PERPLEXITY == provider:
             return litellm.PerplexityEmbeddingConfig()
-        elif litellm.LlmProviders.OCI == provider:
-            from litellm.llms.oci.embed.transformation import OCIEmbeddingConfig
-
-            return OCIEmbeddingConfig()
         return None
 
     @staticmethod
@@ -8481,6 +8585,12 @@ class ProviderConfigManager:
             return litellm.VoyageRerankConfig()
         elif litellm.LlmProviders.WATSONX == provider:
             return litellm.IBMWatsonXRerankConfig()
+        elif litellm.LlmProviders.DASHSCOPE == provider:
+            from litellm.llms.dashscope.rerank.transformation import (
+                DashScopeRerankConfig,
+            )
+
+            return DashScopeRerankConfig()
         return litellm.CohereRerankConfig()
 
     @staticmethod
@@ -8527,6 +8637,12 @@ class ProviderConfigManager:
             )
 
             return MinimaxMessagesConfig()
+        elif litellm.LlmProviders.DEEPSEEK == provider:
+            from litellm.llms.deepseek.messages.transformation import (
+                DeepSeekAnthropicMessagesConfig,
+            )
+
+            return DeepSeekAnthropicMessagesConfig()
         return None
 
     @staticmethod
@@ -8534,6 +8650,19 @@ class ProviderConfigManager:
         model: str,
         provider: LlmProviders,
     ) -> Optional[BaseAudioTranscriptionConfig]:
+        model_cost_entry = _get_model_cost_entry_for_provider_config(
+            model=model,
+            provider=provider,
+        )
+        if (
+            litellm.LlmProviders.AZURE == provider
+            and model_cost_entry.get("audio_transcription_config") == "azure_speech"
+        ):
+            from litellm.llms.azure.audio_transcription.transformation import (
+                AzureSpeechAudioTranscriptionConfig,
+            )
+
+            return AzureSpeechAudioTranscriptionConfig()
         if litellm.LlmProviders.FIREWORKS_AI == provider:
             return litellm.FireworksAIAudioTranscriptionConfig()
         elif litellm.LlmProviders.DEEPGRAM == provider:
@@ -9223,6 +9352,18 @@ class ProviderConfigManager:
 
             return get_vertex_ai_ocr_config(model=model)
 
+        if provider == litellm.LlmProviders.REDUCTO:
+            from litellm.llms.reducto.ocr.transformation import (
+                ReductoParseLegacyConfig,
+                ReductoParseV3Config,
+            )
+
+            if model == "parse-v3":
+                return ReductoParseV3Config()
+            if model == "parse-legacy":
+                return ReductoParseLegacyConfig()
+            return None
+
         MistralOCRConfig = getattr(sys.modules[__name__], "MistralOCRConfig")
         PROVIDER_TO_CONFIG_MAP = {
             litellm.LlmProviders.MISTRAL: MistralOCRConfig,
diff --git a/litellm/vector_store_files/utils.py b/litellm/vector_store_files/utils.py
index ffe73516bda..1ee5b47e306 100644
--- a/litellm/vector_store_files/utils.py
+++ b/litellm/vector_store_files/utils.py
@@ -21,7 +21,7 @@ class VectorStoreFileRequestUtils:
 
     @staticmethod
     def get_create_request_params(
-        params: Dict[str, Any]
+        params: Dict[str, Any],
     ) -> VectorStoreFileCreateRequest:
         filtered = VectorStoreFileRequestUtils._filter_params(
             params=params, model=VectorStoreFileCreateRequest
@@ -37,7 +37,7 @@ class VectorStoreFileRequestUtils:
 
     @staticmethod
     def get_update_request_params(
-        params: Dict[str, Any]
+        params: Dict[str, Any],
     ) -> VectorStoreFileUpdateRequest:
         filtered = VectorStoreFileRequestUtils._filter_params(
             params=params, model=VectorStoreFileUpdateRequest
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 94f0f1e78d3..62553e46ac1 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -1011,6 +1011,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -1041,6 +1042,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -1071,6 +1073,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -1100,6 +1103,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -1129,6 +1133,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -1328,6 +1333,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "global.anthropic.claude-sonnet-4-6": {
@@ -1358,6 +1364,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "us.anthropic.claude-sonnet-4-6": {
@@ -1388,6 +1395,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "eu.anthropic.claude-sonnet-4-6": {
@@ -1417,6 +1425,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "au.anthropic.claude-sonnet-4-6": {
@@ -1446,6 +1455,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "jp.anthropic.claude-sonnet-4-6": {
@@ -1475,6 +1485,7 @@
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
         "supports_native_structured_output": true,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "anthropic.claude-sonnet-4-20250514-v1:0": {
@@ -1996,6 +2007,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 159,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -2093,6 +2105,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "azure/computer-use-preview": {
@@ -6054,6 +6067,17 @@
         "mode": "audio_speech",
         "source": "https://azure.microsoft.com/en-us/pricing/calculator/"
     },
+    "azure/speech/azure-stt": {
+        "audio_transcription_config": "azure_speech",
+        "input_cost_per_second": 0.0002777778,
+        "litellm_provider": "azure",
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/speech-services/",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
     "azure/tts-1": {
         "input_cost_per_character": 1.5e-05,
         "litellm_provider": "azure",
@@ -9643,6 +9667,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "claude-sonnet-4-5-20250929-v1:0": {
@@ -9840,6 +9865,7 @@
             "us": 1.1,
             "fast": 6.0
         },
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -9875,7 +9901,8 @@
             "fast": 6.0
         },
         "supports_max_reasoning_effort": true,
-        "supports_minimal_reasoning_effort": true
+        "supports_minimal_reasoning_effort": true,
+        "supports_output_config": true
     },
     "claude-opus-4-7": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -9910,7 +9937,8 @@
             "us": 1.1,
             "fast": 6.0
         },
-        "supports_minimal_reasoning_effort": true
+        "supports_minimal_reasoning_effort": true,
+        "supports_output_config": true
     },
     "claude-opus-4-7-20260416": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -9945,7 +9973,8 @@
             "us": 1.1,
             "fast": 6.0
         },
-        "supports_minimal_reasoning_effort": true
+        "supports_minimal_reasoning_effort": true,
+        "supports_output_config": true
     },
     "claude-sonnet-4-20250514": {
         "deprecation_date": "2026-05-14",
@@ -13982,6 +14011,21 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "fireworks_ai/accounts/fireworks/models/glm-5p1": {
+        "cache_read_input_token_cost": 2.6e-07,
+        "input_cost_per_token": 1.4e-06,
+        "litellm_provider": "fireworks_ai",
+        "max_input_tokens": 202800,
+        "max_output_tokens": 202800,
+        "max_tokens": 202800,
+        "mode": "chat",
+        "output_cost_per_token": 4.4e-06,
+        "source": "https://fireworks.ai/models/fireworks/glm-5p1",
+        "supports_function_calling": false,
+        "supports_reasoning": true,
+        "supports_response_schema": false,
+        "supports_tool_choice": false
+    },
     "fireworks_ai/accounts/fireworks/models/gpt-oss-120b": {
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "fireworks_ai",
@@ -14248,6 +14292,21 @@
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
+    "fireworks_ai/glm-5p1": {
+        "cache_read_input_token_cost": 2.6e-07,
+        "input_cost_per_token": 1.4e-06,
+        "litellm_provider": "fireworks_ai",
+        "max_input_tokens": 202800,
+        "max_output_tokens": 202800,
+        "max_tokens": 202800,
+        "mode": "chat",
+        "output_cost_per_token": 4.4e-06,
+        "source": "https://fireworks.ai/models/fireworks/glm-5p1",
+        "supports_function_calling": false,
+        "supports_reasoning": true,
+        "supports_response_schema": false,
+        "supports_tool_choice": false
+    },
     "fireworks_ai/kimi-k2p5": {
         "cache_read_input_token_cost": 1e-07,
         "input_cost_per_token": 6e-07,
@@ -14957,6 +15016,73 @@
         "web_search_billing_unit": "per_query",
         "supports_service_tier": true
     },
+    "gemini-3.1-flash-lite": {
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_batches": 1.25e-08,
+        "cache_read_input_token_cost_flex": 1.25e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "cache_read_input_token_cost_priority": 4.5e-08,
+        "input_cost_per_audio_token": 5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "input_cost_per_token_batches": 1.25e-07,
+        "input_cost_per_token_flex": 1.25e-07,
+        "input_cost_per_token_priority": 4.5e-07,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65536,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "output_cost_per_token_batches": 7.5e-07,
+        "output_cost_per_token_flex": 7.5e-07,
+        "output_cost_per_token_priority": 2.7e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query",
+        "supports_service_tier": true
+    },
     "deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
         "input_cost_per_token": 2e-06,
@@ -15645,6 +15771,64 @@
         },
         "web_search_billing_unit": "per_query"
     },
+    "vertex_ai/gemini-3.5-flash": {
+        "cache_read_input_token_cost": 1.5e-07,
+        "input_cost_per_token": 1.5e-06,
+        "input_cost_per_audio_token": 1e-06,
+        "litellm_provider": "vertex_ai",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 9e-06,
+        "output_cost_per_token": 9e-06,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "input_cost_per_token_priority": 2.7e-06,
+        "input_cost_per_audio_token_priority": 1.8e-06,
+        "output_cost_per_token_priority": 1.62e-05,
+        "cache_read_input_token_cost_priority": 2.7e-07,
+        "supports_service_tier": true,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query"
+    },
     "vertex_ai/gemini-3.1-pro-preview": {
         "cache_read_input_token_cost": 2e-07,
         "cache_read_input_token_cost_above_200k_tokens": 4e-07,
@@ -16963,6 +17147,75 @@
         "web_search_billing_unit": "per_query",
         "supports_service_tier": true
     },
+    "gemini/gemini-3.1-flash-lite": {
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_batches": 1.25e-08,
+        "cache_read_input_token_cost_flex": 1.25e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "cache_read_input_token_cost_priority": 4.5e-08,
+        "input_cost_per_audio_token": 5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "input_cost_per_token_batches": 1.25e-07,
+        "input_cost_per_token_flex": 1.25e-07,
+        "input_cost_per_token_priority": 4.5e-07,
+        "litellm_provider": "gemini",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65536,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "output_cost_per_token_batches": 7.5e-07,
+        "output_cost_per_token_flex": 7.5e-07,
+        "output_cost_per_token_priority": 2.7e-06,
+        "rpm": 15,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "tpm": 250000,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query",
+        "supports_service_tier": true
+    },
     "gemini/gemini-3-flash-preview": {
         "cache_read_input_token_cost": 5e-08,
         "input_cost_per_audio_token": 1e-06,
@@ -17022,6 +17275,67 @@
         },
         "web_search_billing_unit": "per_query"
     },
+    "gemini/gemini-3.5-flash": {
+        "cache_read_input_token_cost": 1.5e-07,
+        "input_cost_per_audio_token": 1e-06,
+        "input_cost_per_token": 1.5e-06,
+        "litellm_provider": "gemini",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 9e-06,
+        "output_cost_per_token": 9e-06,
+        "rpm": 2000,
+        "source": "https://ai.google.dev/pricing/gemini-3",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_output": false,
+        "supports_audio_input": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "tpm": 800000,
+        "input_cost_per_token_priority": 2.7e-06,
+        "input_cost_per_audio_token_priority": 1.8e-06,
+        "output_cost_per_token_priority": 1.62e-05,
+        "cache_read_input_token_cost_priority": 2.7e-07,
+        "supports_service_tier": true,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query"
+    },
     "gemini/gemini-3.1-pro-preview": {
         "cache_read_input_token_cost": 2e-07,
         "cache_read_input_token_cost_above_200k_tokens": 4e-07,
@@ -17207,6 +17521,65 @@
         },
         "web_search_billing_unit": "per_query"
     },
+    "gemini-3.5-flash": {
+        "cache_read_input_token_cost": 1.5e-07,
+        "input_cost_per_audio_token": 1e-06,
+        "input_cost_per_token": 1.5e-06,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65535,
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@@ -24141,6 +24514,21 @@
         "supports_tool_choice": true,
         "supports_vision": true
     },
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@@ -25641,6 +26029,32 @@
         "supports_vision": true,
         "supports_web_search": true
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@@ -25651,7 +26065,8 @@
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@@ -25664,6 +26079,7 @@
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@@ -25676,31 +26092,35 @@
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@@ -25712,7 +26132,8 @@
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@@ -25724,7 +26145,8 @@
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@@ -25736,7 +26158,8 @@
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@@ -25748,7 +26171,8 @@
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@@ -25760,7 +26184,8 @@
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@@ -25772,7 +26197,8 @@
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@@ -25784,7 +26210,8 @@
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@@ -25796,7 +26223,86 @@
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@@ -25872,18 +26378,6 @@
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@@ -25956,42 +26450,48 @@
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@@ -26985,6 +27485,58 @@
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@@ -27794,10 +28346,10 @@
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@@ -27807,7 +28359,43 @@
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@@ -28538,14 +29126,16 @@
         "mode": "responses",
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-        "supports_function_calling": true
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         "mode": "responses",
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     "perplexity/anthropic/claude-opus-4-5": {
         "litellm_provider": "perplexity",
@@ -28759,6 +29349,24 @@
         "supports_tool_choice": true,
         "source": "https://aws.amazon.com/bedrock/pricing/"
     },
+    "reducto/parse-legacy": {
+        "litellm_provider": "reducto",
+        "mode": "ocr",
+        "ocr_cost_per_credit": 0.015,
+        "source": "https://reducto.ai/pricing",
+        "supported_endpoints": [
+            "/v1/ocr"
+        ]
+    },
+    "reducto/parse-v3": {
+        "litellm_provider": "reducto",
+        "mode": "ocr",
+        "ocr_cost_per_credit": 0.015,
+        "source": "https://reducto.ai/pricing",
+        "supported_endpoints": [
+            "/v1/ocr"
+        ]
+    },
     "recraft/recraftv2": {
         "litellm_provider": "recraft",
         "mode": "image_generation",
@@ -32938,6 +33546,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -32966,6 +33575,7 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
+        "supports_output_config": true,
         "supports_max_reasoning_effort": true,
         "supports_minimal_reasoning_effort": true
     },
@@ -33079,6 +33689,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "vertex_ai/claude-sonnet-4-5@20250929": {
@@ -33461,6 +34072,73 @@
         },
         "web_search_billing_unit": "per_query"
     },
+    "vertex_ai/gemini-3.1-flash-lite": {
+        "cache_read_input_token_cost": 2.5e-08,
+        "cache_read_input_token_cost_batches": 1.25e-08,
+        "cache_read_input_token_cost_flex": 1.25e-08,
+        "cache_read_input_token_cost_per_audio_token": 5e-08,
+        "cache_read_input_token_cost_priority": 4.5e-08,
+        "input_cost_per_audio_token": 5e-07,
+        "input_cost_per_token": 2.5e-07,
+        "input_cost_per_token_batches": 1.25e-07,
+        "input_cost_per_token_flex": 1.25e-07,
+        "input_cost_per_token_priority": 4.5e-07,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_audio_length_hours": 8.4,
+        "max_audio_per_prompt": 1,
+        "max_images_per_prompt": 3000,
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_pdf_size_mb": 30,
+        "max_tokens": 65536,
+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
+        "mode": "chat",
+        "output_cost_per_reasoning_token": 1.5e-06,
+        "output_cost_per_token": 1.5e-06,
+        "output_cost_per_token_batches": 7.5e-07,
+        "output_cost_per_token_flex": 7.5e-07,
+        "output_cost_per_token_priority": 2.7e-06,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions",
+            "/v1/batch"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": false,
+        "supports_code_execution": true,
+        "supports_file_search": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_video_input": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_native_streaming": true,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 0.014,
+            "search_context_size_medium": 0.014,
+            "search_context_size_high": 0.014
+        },
+        "web_search_billing_unit": "per_query",
+        "supports_service_tier": true
+    },
     "vertex_ai/deep-research-pro-preview-12-2025": {
         "input_cost_per_image": 0.0011,
         "input_cost_per_token": 2e-06,
@@ -40133,6 +40811,7 @@
             "search_context_size_low": 0.01,
             "search_context_size_medium": 0.01
         },
+        "supports_output_config": true,
         "supports_minimal_reasoning_effort": true
     },
     "duckduckgo/search": {
diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json
index 1d577213a1b..388752b032e 100644
--- a/provider_endpoints_support.json
+++ b/provider_endpoints_support.json
@@ -1904,6 +1904,23 @@
         "rerank": false
       }
     },
+    "reducto": {
+      "display_name": "Reducto (`reducto`)",
+      "url": "https://docs.litellm.ai/docs/providers/reducto",
+      "endpoints": {
+        "chat_completions": false,
+        "messages": false,
+        "responses": false,
+        "embeddings": false,
+        "image_generations": false,
+        "audio_transcriptions": false,
+        "audio_speech": false,
+        "moderations": false,
+        "batches": false,
+        "rerank": false,
+        "ocr": true
+      }
+    },
     "replicate": {
       "display_name": "Replicate (`replicate`)",
       "url": "https://docs.litellm.ai/docs/providers/replicate",
diff --git a/pyproject.toml b/pyproject.toml
index f63770105dc..8dedca241ad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "litellm"
-version = "1.86.0"
+version = "1.87.0"
 description = "Library to easily interface with LLM API providers"
 readme = "README.md"
 requires-python = ">=3.10, <3.14"
@@ -33,12 +33,14 @@ Homepage = "https://litellm.ai"
 Repository = "https://github.com/BerriAI/litellm"
 Documentation = "https://docs.litellm.ai"
 
-# Dependencies pinned from the published `litellm[proxy]==1.83.0` resolution.
-# Docker and CI should prefer `uv.lock` rather than maintaining parallel installers.
+# Optional extras retain exact pins because they are consumed by Docker images
+# where exact reproducibility matters. The core SDK uses ranges so downstream
+# consumers can coexist with other packages without forced downgrades.
 [project.optional-dependencies]
 proxy = [
     "gunicorn==23.0.0",
     "uvicorn==0.33.0",
+    "granian==2.5.7",
     "uvloop==0.21.0; sys_platform != 'win32'",
     "fastapi==0.124.4",
     "backoff==2.2.1",
@@ -56,7 +58,7 @@ proxy = [
     "azure-identity==1.25.2",
     "azure-storage-blob==12.28.0",
     "mcp==1.26.0",
-    "litellm-proxy-extras==0.4.72",
+    "litellm-proxy-extras==0.4.73",
     "litellm-enterprise==0.1.41",
     "RestrictedPython==8.1",
     "rich==13.9.4",
@@ -131,7 +133,7 @@ litellm-proxy = "litellm.proxy.client.cli:cli"
 dev = [
     "diff-cover==9.7.2",
     "flake8==7.3.0",
-    "black==24.10.0",
+    "black==26.3.1",
     "mypy==1.19.0",
     "pytest==9.0.3",
     "pytest-mock==3.15.1",
@@ -251,7 +253,7 @@ source-exclude = [
 profile = "black"
 
 [tool.commitizen]
-version = "1.86.0"
+version = "1.87.0"
 version_files = [
     "pyproject.toml:^version",
 ]
@@ -287,6 +289,7 @@ paths_to_mutate = [
 ]
 tests_dir = [
     "tests/test_litellm/proxy/management_endpoints/",
+    "tests/proxy_behavior/management/",
 ]
 also_copy = [
     "litellm/",
@@ -312,3 +315,4 @@ pytest_add_cli_args = [
 [tool.coverage.run]
 source = ["litellm"]
 relative_files = true
+
diff --git a/schema.prisma b/schema.prisma
index b53507abe6a..78143fe0411 100644
--- a/schema.prisma
+++ b/schema.prisma
@@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable {
 // Models on proxy
 model LiteLLM_ProxyModelTable {
   model_id String @id @default(uuid())
-  model_name String 
+  model_name String
   litellm_params Json
-  model_info Json? 
+  model_info Json?
+  blocked Boolean @default(false)
   created_at    DateTime               @default(now()) @map("created_at")
   created_by String
   updated_at    DateTime               @default(now()) @updatedAt @map("updated_at")
diff --git a/scripts/benchmark_anthropic_messages_perf.py b/scripts/benchmark_anthropic_messages_perf.py
new file mode 100644
index 00000000000..3c8a22f0cc2
--- /dev/null
+++ b/scripts/benchmark_anthropic_messages_perf.py
@@ -0,0 +1,624 @@
+#!/usr/bin/env python3
+"""Benchmark LiteLLM proxy /v1/messages (Anthropic Messages API) streaming.
+
+Measures the two metrics that matter for an interactive streaming proxy:
+
+  * TTFT  - time to first streamed token (first ``content_block_delta``)
+  * TPM   - sustained output token throughput (tokens / second) once the
+            full stream is consumed, plus request throughput (RPS)
+
+It boots a local mock Anthropic provider that speaks the real Anthropic
+streaming SSE wire format (``message_start`` -> ``content_block_delta`` ->
+``message_stop``) and a LiteLLM proxy from any checkout, so commits/branches
+can be compared without depending on real provider latency.
+
+Example:
+    uv run python scripts/benchmark_anthropic_messages_perf.py \
+        --label baseline --proxy-command ".venv/bin/litellm"
+
+Compare an already-running proxy:
+    uv run python scripts/benchmark_anthropic_messages_perf.py \
+        --no-start-proxy --label current
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import os
+import shlex
+import signal
+import statistics
+import subprocess
+import tempfile
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Optional
+
+import aiohttp
+from aiohttp import web
+
+DEFAULT_MODEL = "claude-perf-test"
+DEFAULT_API_KEY = "sk-1234"
+
+
+@dataclass
+class StreamSample:
+    success: bool
+    ttft_ms: float
+    total_ms: float
+    output_tokens: int
+    status_code: int
+    error: str = ""
+
+
+@dataclass
+class SummaryStats:
+    requests: int
+    failures: int
+    rps: float
+    ttft_mean_ms: float
+    ttft_p50_ms: float
+    ttft_p95_ms: float
+    ttft_p99_ms: float
+    total_p50_ms: float
+    total_p95_ms: float
+    tokens_per_sec: float
+
+
+class MockAnthropicProvider:
+    """Minimal Anthropic Messages API server (real streaming SSE format)."""
+
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        first_token_delay_ms: float,
+        stream_content_chunks: int,
+    ) -> None:
+        self.host = host
+        self.port = port
+        self.first_token_delay_ms = first_token_delay_ms
+        self.stream_content_chunks = stream_content_chunks
+        self.runner: Optional[web.AppRunner] = None
+
+    @property
+    def base_url(self) -> str:
+        return f"http://{self.host}:{self.port}"
+
+    async def start(self) -> None:
+        app = web.Application()
+        app.router.add_post("/v1/messages", self.handle_messages)
+        self.runner = web.AppRunner(app, access_log=None)
+        await self.runner.setup()
+        site = web.TCPSite(self.runner, self.host, self.port)
+        await site.start()
+
+    async def stop(self) -> None:
+        if self.runner is not None:
+            await self.runner.cleanup()
+
+    async def handle_messages(self, request: web.Request) -> web.StreamResponse:
+        body = await request.json()
+        if body.get("stream"):
+            return await self._streaming_response(request, body)
+        return self._json_response(body)
+
+    def _json_response(self, body: dict[str, Any]) -> web.Response:
+        payload = {
+            "id": "msg_perf",
+            "type": "message",
+            "role": "assistant",
+            "model": body.get("model", DEFAULT_MODEL),
+            "content": [{"type": "text", "text": "hello"}],
+            "stop_reason": "end_turn",
+            "stop_sequence": None,
+            "usage": {"input_tokens": 8, "output_tokens": 1},
+        }
+        return web.json_response(payload)
+
+    @staticmethod
+    def _sse(event: str, data: dict[str, Any]) -> bytes:
+        return f"event: {event}\ndata: {json.dumps(data)}\n\n".encode()
+
+    async def _streaming_response(
+        self, request: web.Request, body: dict[str, Any]
+    ) -> web.StreamResponse:
+        model = body.get("model", DEFAULT_MODEL)
+        response = web.StreamResponse(
+            status=200,
+            headers={
+                "Content-Type": "text/event-stream",
+                "Cache-Control": "no-cache",
+            },
+        )
+        await response.prepare(request)
+
+        await response.write(
+            self._sse(
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": "msg_perf",
+                        "type": "message",
+                        "role": "assistant",
+                        "model": model,
+                        "content": [],
+                        "stop_reason": None,
+                        "stop_sequence": None,
+                        "usage": {"input_tokens": 8, "output_tokens": 0},
+                    },
+                },
+            )
+        )
+        await response.write(
+            self._sse(
+                "content_block_start",
+                {
+                    "type": "content_block_start",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": ""},
+                },
+            )
+        )
+
+        if self.first_token_delay_ms > 0:
+            await asyncio.sleep(self.first_token_delay_ms / 1000)
+
+        for _ in range(self.stream_content_chunks):
+            await response.write(
+                self._sse(
+                    "content_block_delta",
+                    {
+                        "type": "content_block_delta",
+                        "index": 0,
+                        "delta": {"type": "text_delta", "text": "hello "},
+                    },
+                )
+            )
+
+        await response.write(
+            self._sse("content_block_stop", {"type": "content_block_stop", "index": 0})
+        )
+        await response.write(
+            self._sse(
+                "message_delta",
+                {
+                    "type": "message_delta",
+                    "delta": {"stop_reason": "end_turn", "stop_sequence": None},
+                    "usage": {"output_tokens": self.stream_content_chunks},
+                },
+            )
+        )
+        await response.write(self._sse("message_stop", {"type": "message_stop"}))
+        await response.write_eof()
+        return response
+
+
+def percentile(values: list[float], pct: float) -> float:
+    if not values:
+        return 0.0
+    sorted_values = sorted(values)
+    index = min(int(len(sorted_values) * pct / 100), len(sorted_values) - 1)
+    return sorted_values[index]
+
+
+def summarize(samples: list[StreamSample], wall_time_s: float) -> SummaryStats:
+    ok = [s for s in samples if s.success]
+    ttfts = [s.ttft_ms for s in ok]
+    totals = [s.total_ms for s in ok]
+    total_tokens = sum(s.output_tokens for s in ok)
+    return SummaryStats(
+        requests=len(samples),
+        failures=len(samples) - len(ok),
+        rps=(len(ok) / wall_time_s) if wall_time_s > 0 else 0.0,
+        ttft_mean_ms=statistics.mean(ttfts) if ttfts else 0.0,
+        ttft_p50_ms=percentile(ttfts, 50),
+        ttft_p95_ms=percentile(ttfts, 95),
+        ttft_p99_ms=percentile(ttfts, 99),
+        total_p50_ms=percentile(totals, 50),
+        total_p95_ms=percentile(totals, 95),
+        # Aggregate output-token throughput: total tokens delivered across all
+        # successful requests divided by wall-clock time. This is the true
+        # server TPM and (unlike tokens / summed-per-request-latency) scales
+        # correctly with concurrency.
+        tokens_per_sec=(total_tokens / wall_time_s) if wall_time_s > 0 else 0.0,
+    )
+
+
+def get_git_revision(litellm_dir: Path) -> str:
+    try:
+        result = subprocess.run(
+            ["git", "rev-parse", "--short", "HEAD"],
+            cwd=litellm_dir,
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        return result.stdout.strip()
+    except Exception:
+        return "unknown"
+
+
+def write_proxy_config(config_path: Path, provider_base_url: str, api_key: str) -> None:
+    config_path.write_text(
+        f"""model_list:
+  - model_name: {DEFAULT_MODEL}
+    litellm_params:
+      model: anthropic/{DEFAULT_MODEL}
+      api_key: fake-provider-key
+      api_base: {provider_base_url}
+
+general_settings:
+  master_key: {api_key}
+
+litellm_settings:
+  telemetry: false
+""",
+        encoding="utf-8",
+    )
+
+
+async def wait_for_proxy(base_url: str, timeout_s: float) -> None:
+    deadline = time.perf_counter() + timeout_s
+    last_error = ""
+    async with aiohttp.ClientSession() as session:
+        while time.perf_counter() < deadline:
+            try:
+                async with session.get(f"{base_url}/health/liveliness") as response:
+                    if response.status < 500:
+                        return
+                    last_error = f"HTTP {response.status}"
+            except Exception as exc:
+                last_error = str(exc)
+            await asyncio.sleep(0.5)
+    raise TimeoutError(f"Timed out waiting for proxy at {base_url}: {last_error}")
+
+
+def start_proxy_process(
+    litellm_dir: Path,
+    proxy_command: str,
+    config_path: Path,
+    port: int,
+    log_path: Path,
+) -> subprocess.Popen:
+    command = shlex.split(proxy_command) + [
+        "--config",
+        str(config_path),
+        "--port",
+        str(port),
+    ]
+    env = {
+        **os.environ,
+        "LITELLM_TELEMETRY": "False",
+        "PYTHONUNBUFFERED": "1",
+    }
+    log_file = log_path.open("w", encoding="utf-8")
+    return subprocess.Popen(
+        command,
+        cwd=litellm_dir,
+        env=env,
+        stdout=log_file,
+        stderr=subprocess.STDOUT,
+        start_new_session=True,
+    )
+
+
+def stop_proxy_process(process: subprocess.Popen) -> None:
+    if process.poll() is not None:
+        return
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+        process.wait(timeout=10)
+    except Exception:
+        try:
+            os.killpg(process.pid, signal.SIGKILL)
+        except Exception:
+            pass
+
+
+async def measure_stream(
+    session: aiohttp.ClientSession,
+    url: str,
+    headers: dict[str, str],
+    payload: dict[str, Any],
+) -> StreamSample:
+    start = time.perf_counter()
+    ttft_ms = 0.0
+    output_tokens = 0
+    try:
+        async with session.post(url, headers=headers, json=payload) as response:
+            if response.status != 200:
+                body = await response.read()
+                return StreamSample(
+                    success=False,
+                    ttft_ms=0.0,
+                    total_ms=(time.perf_counter() - start) * 1000,
+                    output_tokens=0,
+                    status_code=response.status,
+                    error=body.decode("utf-8", errors="ignore")[:200],
+                )
+            async for raw_line in response.content:
+                line = raw_line.strip()
+                if not line.startswith(b"data:"):
+                    continue
+                data = line[5:].strip()
+                if data == b"[DONE]":
+                    break
+                try:
+                    event = json.loads(data)
+                except json.JSONDecodeError:
+                    continue
+                etype = event.get("type")
+                if etype == "content_block_delta":
+                    if ttft_ms == 0.0:
+                        ttft_ms = (time.perf_counter() - start) * 1000
+                    output_tokens += 1
+                elif etype == "message_stop":
+                    break
+        total_ms = (time.perf_counter() - start) * 1000
+        if ttft_ms == 0.0:
+            return StreamSample(
+                success=False,
+                ttft_ms=0.0,
+                total_ms=total_ms,
+                output_tokens=0,
+                status_code=response.status,
+                error="stream ended before a content token",
+            )
+        return StreamSample(
+            success=True,
+            ttft_ms=ttft_ms,
+            total_ms=total_ms,
+            output_tokens=output_tokens,
+            status_code=response.status,
+        )
+    except Exception as exc:
+        return StreamSample(
+            success=False,
+            ttft_ms=0.0,
+            total_ms=(time.perf_counter() - start) * 1000,
+            output_tokens=0,
+            status_code=0,
+            error=str(exc)[:200],
+        )
+
+
+async def run_benchmark(
+    url: str,
+    headers: dict[str, str],
+    payload: dict[str, Any],
+    requests: int,
+    concurrency: int,
+    warmup: int,
+    timeout_s: float,
+) -> SummaryStats:
+    timeout = aiohttp.ClientTimeout(total=timeout_s)
+    connector = aiohttp.TCPConnector(
+        limit=max(concurrency * 2, 10),
+        limit_per_host=max(concurrency, 10),
+        force_close=False,
+    )
+
+    async def worker(
+        session: aiohttp.ClientSession,
+        counter: list[int],
+        budget: int,
+        sink: list[StreamSample],
+    ) -> None:
+        # Steady-state load: exactly `concurrency` workers, each pulling the
+        # next request slot as soon as its previous one finishes. Keeps
+        # in-flight concurrency constant (vs. a gather-all + semaphore burst)
+        # which removes the thundering-herd variance that otherwise swamps a
+        # 10% signal.
+        while True:
+            idx = counter[0]
+            if idx >= budget:
+                return
+            counter[0] = idx + 1
+            sink.append(await measure_stream(session, url, headers, payload))
+
+    async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
+        if warmup > 0:
+            wcounter = [0]
+            await asyncio.gather(
+                *[worker(session, wcounter, warmup, []) for _ in range(concurrency)]
+            )
+        samples: list[StreamSample] = []
+        counter = [0]
+        wall_start = time.perf_counter()
+        await asyncio.gather(
+            *[worker(session, counter, requests, samples) for _ in range(concurrency)]
+        )
+        wall_time_s = time.perf_counter() - wall_start
+    return summarize(samples, wall_time_s)
+
+
+def stats_to_dict(stats: SummaryStats) -> dict[str, Any]:
+    return {
+        "requests": stats.requests,
+        "failures": stats.failures,
+        "rps": stats.rps,
+        "ttft_mean_ms": stats.ttft_mean_ms,
+        "ttft_p50_ms": stats.ttft_p50_ms,
+        "ttft_p95_ms": stats.ttft_p95_ms,
+        "ttft_p99_ms": stats.ttft_p99_ms,
+        "total_p50_ms": stats.total_p50_ms,
+        "total_p95_ms": stats.total_p95_ms,
+        "tokens_per_sec": stats.tokens_per_sec,
+    }
+
+
+def print_summary(label: str, revision: str, stats: SummaryStats) -> None:
+    print("\n=== Anthropic /v1/messages streaming benchmark ===")
+    print(f"Label: {label}")
+    print(f"Revision: {revision}")
+    print(f"Requests: {stats.requests}  Failures: {stats.failures}")
+    print(f"TTFT mean:  {stats.ttft_mean_ms:.2f} ms")
+    print(f"TTFT p50:   {stats.ttft_p50_ms:.2f} ms")
+    print(f"TTFT p95:   {stats.ttft_p95_ms:.2f} ms")
+    print(f"TTFT p99:   {stats.ttft_p99_ms:.2f} ms")
+    print(f"Full p50:   {stats.total_p50_ms:.2f} ms")
+    print(f"Full p95:   {stats.total_p95_ms:.2f} ms")
+    print(f"Throughput: {stats.rps:.2f} req/s")
+    print(f"TPM:        {stats.tokens_per_sec:.1f} output tokens/s")
+    print("\nMarkdown row:")
+    print(
+        "| "
+        + " | ".join(
+            [
+                label,
+                revision,
+                f"{stats.ttft_p50_ms:.2f}",
+                f"{stats.ttft_p95_ms:.2f}",
+                f"{stats.tokens_per_sec:.1f}",
+                f"{stats.rps:.2f}",
+            ]
+        )
+        + " |"
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--label", default="current")
+    parser.add_argument("--litellm-dir", default=str(Path.cwd()))
+    parser.add_argument("--proxy-command", default="uv run litellm")
+    parser.add_argument("--proxy-host", default="127.0.0.1")
+    parser.add_argument("--proxy-port", type=int, default=4000)
+    parser.add_argument("--provider-host", default="127.0.0.1")
+    parser.add_argument("--provider-port", type=int, default=8098)
+    parser.add_argument("--api-key", default=DEFAULT_API_KEY)
+    parser.add_argument("--requests", type=int, default=300)
+    parser.add_argument("--concurrency", type=int, default=20)
+    parser.add_argument("--warmup", type=int, default=30)
+    parser.add_argument("--timeout", type=float, default=30)
+    parser.add_argument("--proxy-start-timeout", type=float, default=90)
+    parser.add_argument("--provider-first-token-delay-ms", type=float, default=0)
+    parser.add_argument(
+        "--provider-stream-content-chunks",
+        type=int,
+        default=64,
+        help="Number of text delta chunks the mock emits (default 64).",
+    )
+    parser.add_argument(
+        "--repeats",
+        type=int,
+        default=1,
+        help="Run the suite N times against the same proxy; report the median run.",
+    )
+    parser.add_argument(
+        "--no-start-proxy",
+        action="store_true",
+        help="Benchmark an already-running proxy at --proxy-host/--proxy-port",
+    )
+    parser.add_argument(
+        "--provider-url",
+        help="Use an already-running Anthropic-compatible provider",
+    )
+    parser.add_argument("--output-json", help="Write machine-readable results")
+    return parser.parse_args()
+
+
+async def async_main() -> None:
+    args = parse_args()
+    litellm_dir = Path(args.litellm_dir).resolve()
+    revision = get_git_revision(litellm_dir)
+    proxy_base_url = f"http://{args.proxy_host}:{args.proxy_port}"
+    proxy_url = f"{proxy_base_url}/v1/messages"
+    headers = {
+        "Authorization": f"Bearer {args.api_key}",
+        "Content-Type": "application/json",
+    }
+    stream_payload = {
+        "model": DEFAULT_MODEL,
+        "max_tokens": 256,
+        "messages": [{"role": "user", "content": "hi"}],
+        "stream": True,
+    }
+
+    provider: Optional[MockAnthropicProvider] = None
+    proxy_process: Optional[subprocess.Popen] = None
+    with tempfile.TemporaryDirectory(prefix="litellm-anthropic-perf-") as tmp_dir_name:
+        tmp_dir = Path(tmp_dir_name)
+        proxy_log_path = tmp_dir / "proxy.log"
+        if args.provider_url:
+            provider_base_url = args.provider_url.rstrip("/")
+        else:
+            provider = MockAnthropicProvider(
+                host=args.provider_host,
+                port=args.provider_port,
+                first_token_delay_ms=args.provider_first_token_delay_ms,
+                stream_content_chunks=args.provider_stream_content_chunks,
+            )
+            await provider.start()
+            provider_base_url = provider.base_url
+
+        config_path = tmp_dir / "config.yaml"
+        write_proxy_config(config_path, provider_base_url, args.api_key)
+
+        try:
+            if not args.no_start_proxy:
+                proxy_process = start_proxy_process(
+                    litellm_dir=litellm_dir,
+                    proxy_command=args.proxy_command,
+                    config_path=config_path,
+                    port=args.proxy_port,
+                    log_path=proxy_log_path,
+                )
+            await wait_for_proxy(proxy_base_url, args.proxy_start_timeout)
+
+            runs: list[SummaryStats] = []
+            for run_idx in range(max(1, args.repeats)):
+                if args.repeats > 1:
+                    print(f"\n--- Run {run_idx + 1}/{args.repeats} ---")
+                stats = await run_benchmark(
+                    url=proxy_url,
+                    headers=headers,
+                    payload=stream_payload,
+                    requests=args.requests,
+                    concurrency=args.concurrency,
+                    warmup=args.warmup,
+                    timeout_s=args.timeout,
+                )
+                runs.append(stats)
+                if args.repeats > 1:
+                    print(
+                        f"  run {run_idx + 1}: TTFT p50={stats.ttft_p50_ms:.2f}ms "
+                        f"TPM={stats.tokens_per_sec:.1f} tok/s RPS={stats.rps:.2f}"
+                    )
+
+            stats = sorted(runs, key=lambda s: s.ttft_p50_ms)[len(runs) // 2]
+        finally:
+            if proxy_process is not None:
+                stop_proxy_process(proxy_process)
+            if provider is not None:
+                await provider.stop()
+
+        print_summary(args.label, revision, stats)
+
+        if args.output_json:
+            Path(args.output_json).write_text(
+                json.dumps(
+                    {
+                        "label": args.label,
+                        "revision": revision,
+                        "proxy_streaming": stats_to_dict(stats),
+                        "proxy_log_path": str(proxy_log_path),
+                    },
+                    indent=2,
+                    sort_keys=True,
+                ),
+                encoding="utf-8",
+            )
+
+
+def main() -> None:
+    asyncio.run(async_main())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/_vcr_conftest_common.py b/tests/_vcr_conftest_common.py
index a179a21ba69..cb43f1abbdd 100644
--- a/tests/_vcr_conftest_common.py
+++ b/tests/_vcr_conftest_common.py
@@ -36,6 +36,75 @@ SAFE_BODY_MATCHER_NAME = "safe_body"
 KEY_FINGERPRINT_MATCHER_NAME = "key_fingerprint"
 KEY_FINGERPRINT_HEADER = "x-litellm-key-fp"
 
+VCR_DIAG_DIR_ENV = "LITELLM_VCR_DIAG_DIR"
+VCR_DIAG_DIR_DEFAULT = "test-results/vcr-diagnostics"
+
+
+def _vcr_diag_dir() -> str:
+    return os.environ.get(VCR_DIAG_DIR_ENV) or VCR_DIAG_DIR_DEFAULT
+
+
+def vcr_diag_write_line(msg: str) -> None:
+    try:
+        directory = _vcr_diag_dir()
+        os.makedirs(directory, exist_ok=True)
+        path = os.path.join(directory, f"{os.getpid()}.log")
+        with open(path, "a", encoding="utf-8") as fh:
+            fh.write(msg.rstrip("\n") + "\n")
+    except OSError:
+        pass
+
+
+def reset_vcr_diag_dir() -> None:
+    if os.environ.get("PYTEST_XDIST_WORKER"):
+        return
+    directory = _vcr_diag_dir()
+    if not os.path.isdir(directory):
+        return
+    try:
+        names = os.listdir(directory)
+    except OSError:
+        return
+    for name in names:
+        if name.endswith(".log"):
+            try:
+                os.remove(os.path.join(directory, name))
+            except OSError:
+                pass
+
+
+def emit_vcr_diagnostic_log(terminalreporter) -> None:
+    directory = _vcr_diag_dir()
+    if not os.path.isdir(directory):
+        return
+    try:
+        files = sorted(f for f in os.listdir(directory) if f.endswith(".log"))
+    except OSError:
+        return
+    if not files:
+        return
+    terminalreporter.write_sep("=", "VCR DIAGNOSTIC LOG", bold=True)
+    terminalreporter.write_line(
+        f"  source dir: {directory}  (also archived as a CI artifact)"
+    )
+    for name in files:
+        path = os.path.join(directory, name)
+        try:
+            with open(path, "r", encoding="utf-8") as fh:
+                content = fh.read()
+        except OSError as exc:
+            terminalreporter.write_line(
+                f"  [failed to read {name}: {type(exc).__name__}: {exc}]"
+            )
+            continue
+        if not content.strip():
+            continue
+        terminalreporter.write_sep("-", name, bold=False)
+        for line in content.splitlines():
+            terminalreporter.write_line(line)
+    terminalreporter.write_sep("=", bold=True)
+
+
 # Intentionally narrower than ``FILTERED_REQUEST_HEADERS``: AWS SigV4 headers
 # carry secrets but their values rotate on every call, so fingerprinting them
 # would defeat caching.
@@ -91,6 +160,32 @@ VCR_IMAGE_B64_PLACEHOLDER = "dGVzdA=="
 VCR_FIXED_MULTIPART_BOUNDARY = "vcr-static-boundary"
 
 
+def pin_httpx_multipart_boundary(monkeypatch) -> None:
+    try:
+        import httpx._multipart as _httpx_multipart
+    except ImportError:
+        return
+
+    _original_init = _httpx_multipart.MultipartStream.__init__
+
+    def _init_with_fixed_boundary(self, data, files, boundary=None, **kwargs):
+        if boundary is None:
+            boundary = VCR_FIXED_MULTIPART_BOUNDARY.encode("ascii")
+        return _original_init(self, data=data, files=files, boundary=boundary, **kwargs)
+
+    monkeypatch.setattr(
+        _httpx_multipart.MultipartStream, "__init__", _init_with_fixed_boundary
+    )
+
+
+@pytest.fixture(scope="session", autouse=True)
+def _pin_multipart_boundary():
+    monkeypatch = pytest.MonkeyPatch()
+    pin_httpx_multipart_boundary(monkeypatch)
+    yield
+    monkeypatch.undo()
+
+
 def _scrub_response(response):
     if not isinstance(response, dict):
         return response
@@ -139,9 +234,17 @@ def _strip_image_b64_payloads(response):
     preserves all those checks while shrinking cassettes by ~99%.
     """
     if not isinstance(response, dict):
+        vcr_diag_write_line(
+            f"[vcr-strip-b64] response is {type(response).__name__!r}, not "
+            "dict; skipping b64 scrub"
+        )
         return response
     body = response.get("body")
     if not isinstance(body, dict):
+        vcr_diag_write_line(
+            f"[vcr-strip-b64] response['body'] is {type(body).__name__!r}, "
+            "not dict; skipping b64 scrub"
+        )
         return response
     raw = body.get("string")
     if raw is None:
@@ -151,12 +254,20 @@ def _strip_image_b64_payloads(response):
         try:
             text = bytes(raw).decode("utf-8")
         except UnicodeDecodeError:
+            vcr_diag_write_line(
+                "[vcr-strip-b64] response body bytes are not valid UTF-8; "
+                "skipping b64 scrub"
+            )
             return response
         was_bytes = True
     elif isinstance(raw, str):
         text = raw
         was_bytes = False
     else:
+        vcr_diag_write_line(
+            f"[vcr-strip-b64] response['body']['string'] is "
+            f"{type(raw).__name__!r}, not bytes/str; skipping b64 scrub"
+        )
         return response
 
     try:
@@ -186,6 +297,35 @@ def _before_record_response(response):
     return filter_non_2xx_response(_scrub_response(_strip_image_b64_payloads(response)))
 
 
+def _canonical_body(request) -> tuple[bytes, str]:
+    pre_type = type(getattr(request, "body", None)).__name__
+    _materialize_iterable_body(request)
+    body = getattr(request, "body", None)
+    if body is None:
+        return b"", pre_type
+    if isinstance(body, bytes):
+        return body, pre_type
+    if isinstance(body, bytearray):
+        return bytes(body), pre_type
+    if isinstance(body, str):
+        return body.encode("utf-8"), pre_type
+    if isinstance(body, (dict, list)):
+        try:
+            return (
+                json.dumps(body, sort_keys=True, separators=(",", ":")).encode("utf-8"),
+                pre_type,
+            )
+        except (TypeError, ValueError):
+            pass
+    method = getattr(request, "method", "?")
+    uri = getattr(request, "uri", getattr(request, "url", "?"))
+    vcr_diag_write_line(
+        f"[vcr-canonical-body] FALLBACK: {method} {uri} body type "
+        f"{type(body).__name__!r} not coerced to bytes; comparing as b''"
+    )
+    return b"", pre_type
+
+
 def _safe_body_matcher(r1, r2) -> None:
     """Compare request bodies as bytes; never invokes ``json.loads``.
 
@@ -195,27 +335,47 @@ def _safe_body_matcher(r1, r2) -> None:
     This matcher is strictly more conservative — the only equivalence
     it gives up vs. the default is "JSON key order doesn't matter".
     """
-    body1 = getattr(r1, "body", None)
-    body2 = getattr(r2, "body", None)
+    body1, pre1 = _canonical_body(r1)
+    body2, pre2 = _canonical_body(r2)
     if body1 == body2:
         return
-
-    def _to_bytes(b):
-        if b is None:
-            return b""
-        if isinstance(b, bytes):
-            return b
-        if isinstance(b, str):
-            return b.encode("utf-8")
-        return None
-
-    n1 = _to_bytes(body1)
-    n2 = _to_bytes(body2)
-    if n1 is not None and n2 is not None and n1 == n2:
-        return
+    _emit_body_mismatch_diagnostic(r1, r2, body1, body2, pre1, pre2)
     raise AssertionError("request bodies differ")
 
 
+def _emit_body_mismatch_diagnostic(r1, r2, body1, body2, pre1, pre2) -> None:
+    def _describe(label, asbytes, pre_type):
+        return (
+            f"  {label}: pre_canonical_type={pre_type!r} length={len(asbytes)} "
+            f"sha256={hashlib.sha256(asbytes).hexdigest()} "
+            f"preview={asbytes[:120]!r}"
+        )
+
+    method_a = getattr(r1, "method", "?")
+    method_b = getattr(r2, "method", "?")
+    url_a = getattr(r1, "uri", getattr(r1, "url", "?"))
+    url_b = getattr(r2, "uri", getattr(r2, "url", "?"))
+    lines = [
+        "[vcr-safe-body-matcher] request body mismatch",
+        f"  request[a]: {method_a} {url_a}",
+        f"  request[b]: {method_b} {url_b}",
+        _describe("body[a]", body1, pre1),
+        _describe("body[b]", body2, pre2),
+    ]
+    if body1 != body2:
+        offset = next(
+            (i for i in range(min(len(body1), len(body2))) if body1[i] != body2[i]),
+            min(len(body1), len(body2)),
+        )
+        start = max(0, offset - 100)
+        end_a = min(len(body1), offset + 100)
+        end_b = min(len(body2), offset + 100)
+        lines.append(f"  first divergent byte offset: {offset}")
+        lines.append(f"  window[a] @ {start}..{end_a}: {body1[start:end_a]!r}")
+        lines.append(f"  window[b] @ {start}..{end_b}: {body2[start:end_b]!r}")
+    vcr_diag_write_line("\n".join(lines))
+
+
 def _iter_header_values(headers, name: str):
     if headers is None:
         return
@@ -271,6 +431,13 @@ def _compute_key_fingerprint(request) -> str:
             stable = _stable_key_value(header_name, text)
             parts.append(f"{header_name}={stable}")
     if not parts:
+        method = getattr(request, "method", "?")
+        uri = getattr(request, "uri", getattr(request, "url", "?"))
+        vcr_diag_write_line(
+            f"[vcr-key-fingerprint] no API key header found on {method} "
+            f"{uri}; falling back to 'no-key'. If this request should have "
+            "carried auth, something earlier in the pipeline stripped it."
+        )
         return "no-key"
     digest = hashlib.sha256("\n".join(parts).encode("utf-8")).hexdigest()
     return digest[:16]
@@ -360,6 +527,13 @@ def _normalize_multipart_boundary(request) -> None:
     elif isinstance(body, str):
         new_body = body.replace(current_boundary, VCR_FIXED_MULTIPART_BOUNDARY)
     else:
+        vcr_diag_write_line(
+            f"[vcr-multipart-normalize] body normalization SKIPPED: "
+            f"body type {type(body).__name__!r} is not bytes/bytearray/str. "
+            f"content-type={content_type_value!r}. "
+            f"Recorded body will retain the random boundary substring "
+            f"and the safe_body matcher will miss on the next run."
+        )
         return
 
     try:
@@ -389,6 +563,7 @@ def _before_record_request(request):
     headers = getattr(request, "headers", None)
     if headers is None:
         return request
+    _materialize_iterable_body(request)
     if not any(_iter_header_values(headers, KEY_FINGERPRINT_HEADER)):
         fingerprint = _compute_key_fingerprint(request)
         try:
@@ -400,6 +575,56 @@ def _before_record_request(request):
     return request
 
 
+def _materialize_iterable_body(request) -> None:
+    body = getattr(request, "body", None)
+    if body is None or isinstance(body, (bytes, bytearray, str)):
+        return
+    if not hasattr(body, "__next__"):
+        return
+    try:
+        chunks = list(body)
+    except TypeError:
+        return
+
+    out = _coalesce_chunks_to_bytes(chunks)
+    if out is None:
+        method = getattr(request, "method", "?")
+        uri = getattr(request, "uri", getattr(request, "url", "?"))
+        first_type = type(chunks[0]).__name__ if chunks else "empty"
+        vcr_diag_write_line(
+            f"[vcr-materialize] FALLBACK: {method} {uri} chunk type "
+            f"{first_type!r} not coerced to bytes; storing b''"
+        )
+        out = b""
+
+    try:
+        request.body = out
+    except (AttributeError, TypeError):
+        pass
+
+    for attr in ("_was_iter", "_was_file"):
+        try:
+            setattr(request, attr, False)
+        except (AttributeError, TypeError):
+            pass
+
+
+def _coalesce_chunks_to_bytes(chunks):
+    if not chunks:
+        return b""
+    first = chunks[0]
+    try:
+        if isinstance(first, int):
+            return bytes(chunks)
+        if isinstance(first, (bytes, bytearray)):
+            return b"".join(c if isinstance(c, bytes) else bytes(c) for c in chunks)
+        if isinstance(first, str):
+            return "".join(chunks).encode("utf-8")
+    except (TypeError, ValueError):
+        return None
+    return None
+
+
 def _key_fingerprint_matcher(r1, r2) -> None:
     def _fp(req):
         for value in _iter_header_values(
@@ -410,7 +635,17 @@ def _key_fingerprint_matcher(r1, r2) -> None:
             return value if isinstance(value, str) else str(value)
         return "no-key"
 
-    if _fp(r1) != _fp(r2):
+    fp1, fp2 = _fp(r1), _fp(r2)
+    if fp1 != fp2:
+        method_a = getattr(r1, "method", "?")
+        method_b = getattr(r2, "method", "?")
+        url_a = getattr(r1, "uri", getattr(r1, "url", "?"))
+        url_b = getattr(r2, "uri", getattr(r2, "url", "?"))
+        vcr_diag_write_line(
+            "[vcr-key-fingerprint-matcher] API key fingerprints differ\n"
+            f"  request[a]: {method_a} {url_a} fingerprint={fp1!r}\n"
+            f"  request[b]: {method_b} {url_b} fingerprint={fp2!r}"
+        )
         raise AssertionError("API key fingerprints differ")
 
 
diff --git a/tests/_vcr_redis_persister.py b/tests/_vcr_redis_persister.py
index 7fdb7267a38..373cb66696a 100644
--- a/tests/_vcr_redis_persister.py
+++ b/tests/_vcr_redis_persister.py
@@ -159,9 +159,20 @@ def make_redis_persister(
                 raise CassetteNotFoundError() from exc
             if data is None:
                 raise CassetteNotFoundError()
-            if isinstance(data, bytes):
-                data = data.decode("utf-8")
-            return deserialize(data, serializer)
+            try:
+                if isinstance(data, bytes):
+                    data = data.decode("utf-8")
+                return deserialize(data, serializer)
+            except Exception as exc:
+                _record_cache_failure("load", exc)
+                msg = (
+                    f"VCR redis load failed for {cassette_path}; cached "
+                    f"payload is corrupt, treating as cache miss: "
+                    f"{type(exc).__name__}: {exc}"
+                )
+                _log.warning(msg)
+                warnings.warn(msg, VCRCassetteCacheWarning, stacklevel=2)
+                raise CassetteNotFoundError() from exc
 
         @staticmethod
         def save_cassette(cassette_path, cassette_dict, serializer):
diff --git a/tests/audio_tests/conftest.py b/tests/audio_tests/conftest.py
index ff47853d494..c4ff576e5bd 100644
--- a/tests/audio_tests/conftest.py
+++ b/tests/audio_tests/conftest.py
@@ -5,14 +5,17 @@ import pytest
 
 sys.path.insert(0, os.path.abspath("../.."))
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -44,6 +47,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -57,3 +61,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/audio_tests/test_whisper.py b/tests/audio_tests/test_whisper.py
index cdf079f8cb4..243d27614b1 100644
--- a/tests/audio_tests/test_whisper.py
+++ b/tests/audio_tests/test_whisper.py
@@ -23,12 +23,21 @@ pwd = os.path.dirname(os.path.realpath(__file__))
 print(pwd)
 
 file_path = os.path.join(pwd, "gettysburg.wav")
-
-audio_file = open(file_path, "rb")
-
-
 file2_path = os.path.join(pwd, "eagle.wav")
-audio_file2 = open(file2_path, "rb")
+
+with open(file_path, "rb") as _f:
+    _GETTYSBURG_BYTES = _f.read()
+with open(file2_path, "rb") as _f:
+    _EAGLE_BYTES = _f.read()
+
+
+def _audio_file():
+    return ("gettysburg.wav", _GETTYSBURG_BYTES, "audio/wav")
+
+
+def _audio_file2():
+    return ("eagle.wav", _EAGLE_BYTES, "audio/wav")
+
 
 load_dotenv()
 
@@ -44,7 +53,7 @@ async def _run_transcription(
 ):
     transcript = await litellm.atranscription(
         model=model,
-        file=audio_file,
+        file=_audio_file(),
         api_key=api_key,
         api_base=api_base,
         response_format=response_format,
@@ -101,7 +110,7 @@ async def test_transcription_caching():
 
     response_1 = await litellm.atranscription(
         model="whisper-1",
-        file=audio_file,
+        file=_audio_file(),
     )
 
     await asyncio.sleep(5)
@@ -110,7 +119,7 @@ async def test_transcription_caching():
 
     response_2 = await litellm.atranscription(
         model="whisper-1",
-        file=audio_file,
+        file=_audio_file(),
     )
 
     print("response_1", response_1)
@@ -122,7 +131,7 @@ async def test_transcription_caching():
 
     response_3 = await litellm.atranscription(
         model="whisper-1",
-        file=audio_file2,
+        file=_audio_file2(),
     )
     print("response_3", response_3)
     print("response3 hidden params", response_3._hidden_params)
@@ -146,7 +155,7 @@ async def test_whisper_log_pre_call():
     with patch.object(custom_logger, "log_pre_api_call") as mock_log_pre_call:
         await litellm.atranscription(
             model="whisper-1",
-            file=audio_file,
+            file=_audio_file(),
         )
         mock_log_pre_call.assert_called_once()
 
@@ -165,7 +174,7 @@ async def test_whisper_log_pre_call():
     with patch.object(custom_logger, "log_pre_api_call") as mock_log_pre_call:
         await litellm.atranscription(
             model="whisper-1",
-            file=audio_file,
+            file=_audio_file(),
         )
         mock_log_pre_call.assert_called_once()
 
@@ -177,7 +186,7 @@ async def test_gpt_4o_transcribe():
     from unittest.mock import patch, MagicMock
 
     await litellm.atranscription(
-        model="openai/gpt-4o-transcribe", file=audio_file, response_format="json"
+        model="openai/gpt-4o-transcribe", file=_audio_file(), response_format="json"
     )
 
 
@@ -187,7 +196,9 @@ async def test_gpt_4o_transcribe_model_mapping():
 
     # Test GPT-4o mini transcribe
     response = await litellm.atranscription(
-        model="openai/gpt-4o-mini-transcribe", file=audio_file, response_format="json"
+        model="openai/gpt-4o-mini-transcribe",
+        file=_audio_file(),
+        response_format="json",
     )
 
     # Check that the response contains the correct model in hidden params
@@ -198,7 +209,7 @@ async def test_gpt_4o_transcribe_model_mapping():
 
     # Test GPT-4o transcribe
     response2 = await litellm.atranscription(
-        model="openai/gpt-4o-transcribe", file=audio_file, response_format="json"
+        model="openai/gpt-4o-transcribe", file=_audio_file(), response_format="json"
     )
 
     # Check that the response contains the correct model in hidden params
@@ -209,7 +220,7 @@ async def test_gpt_4o_transcribe_model_mapping():
 
     # Test traditional whisper-1 still works
     response3 = await litellm.atranscription(
-        model="openai/whisper-1", file=audio_file, response_format="json"
+        model="openai/whisper-1", file=_audio_file(), response_format="json"
     )
 
     # Check that the response contains the correct model in hidden params
@@ -262,7 +273,7 @@ async def test_azure_transcribe_model_mapping():
         # Make the transcription call
         response = await litellm.atranscription(
             model="azure/whisper-1",
-            file=audio_file,
+            file=_audio_file(),
             response_format="json",
             api_key="test-api-key",
             api_base="https://my-endpoint-europe-berri-992.openai.azure.com/",
diff --git a/tests/code_coverage_tests/check_licenses.py b/tests/code_coverage_tests/check_licenses.py
index 668aefa8024..5fb2b495c24 100644
--- a/tests/code_coverage_tests/check_licenses.py
+++ b/tests/code_coverage_tests/check_licenses.py
@@ -31,6 +31,13 @@ DEFAULT_TRANSITIVE_PIN_PACKAGES = (
     "wheel",
 )
 
+# SPDX license expressions (PEP 639 "License-Expression") join identifiers with
+# the uppercase operators OR / AND / WITH. The split is case-sensitive: the
+# lowercase "-or-later" inside an identifier such as "GPL-2.0-or-later" is part
+# of the identifier, not an operator.
+_SPDX_OPERATOR_SPLIT = re.compile(r"\s+(?:OR|AND)\s+")
+_SPDX_WITH_SUFFIX = re.compile(r"\s+WITH\s+.*", re.DOTALL)
+
 
 @dataclass
 class PackageLicense:
@@ -109,21 +116,86 @@ class LicenseChecker:
     def get_package_license_from_pypi(
         self, package_name: str, version: str
     ) -> Optional[str]:
-        """Fetch license information for a package from PyPI."""
+        """Fetch license information for a package from PyPI.
+
+        Prefers the PEP 639 SPDX expression (``info.license_expression``),
+        falls back to the legacy free-text ``info.license`` field, and as a
+        last resort derives the license from the ``License :: OSI Approved ::
+        ...`` trove classifiers.
+        """
         try:
             url = f"https://pypi.org/pypi/{package_name}/{version}/json"
             response = requests.get(url, timeout=10)
             response.raise_for_status()
-            data = response.json()
-            return data.get("info", {}).get("license")
+            info = response.json().get("info", {}) or {}
+            return (
+                info.get("license_expression")
+                or info.get("license")
+                or self._license_from_classifiers(info.get("classifiers") or [])
+            )
         except Exception as e:
             print(
                 f"Warning: Failed to fetch license for {package_name} {version}: {str(e)}"
             )
             return None
 
-    def is_license_acceptable(self, license_str: str) -> Tuple[bool, str]:
-        """Check if a license is acceptable based on configured lists."""
+    @staticmethod
+    def _license_from_classifiers(classifiers: List[str]) -> Optional[str]:
+        """Derive a license name from the ``License :: OSI Approved :: ...`` trove classifiers."""
+        prefix = "License :: OSI Approved :: "
+        for classifier in classifiers:
+            if classifier.startswith(prefix):
+                license_name = classifier[len(prefix) :].strip()
+                if license_name:
+                    return license_name
+        return None
+
+    @staticmethod
+    def _split_spdx_expression(license_str: str) -> Optional[List[str]]:
+        """Split an SPDX license expression into its component identifiers.
+
+        Returns ``None`` when the string is not a recognizable SPDX expression
+        (for example a free-text license blob), so callers fall back to
+        whole-string matching.
+        """
+        if "OR" not in license_str and "AND" not in license_str:
+            return None
+
+        components: List[str] = []
+        normalized = license_str.replace("(", " ").replace(")", " ")
+        for part in _SPDX_OPERATOR_SPLIT.split(normalized):
+            # Drop any "WITH <exception>" suffix: the exception qualifies the
+            # preceding license, it is not itself a license to authorize.
+            identifier = _SPDX_WITH_SUFFIX.sub("", part).strip()
+            if not identifier:
+                continue
+            # SPDX short-form identifiers are single whitespace-free tokens; a
+            # component with internal whitespace means this is free text.
+            if any(char.isspace() for char in identifier):
+                return None
+            components.append(identifier)
+
+        return components if len(components) > 1 else None
+
+    def is_license_acceptable(self, license_str: Optional[str]) -> Tuple[bool, str]:
+        """Check if a license (or compound SPDX expression) is acceptable."""
+        if not license_str:
+            return False, "Unknown license"
+
+        components = self._split_spdx_expression(license_str)
+        if components is None:
+            return self._is_single_license_acceptable(license_str)
+
+        # Compound SPDX expression: conservatively require every component to
+        # be acceptable on its own (the safe direction for a CI gate).
+        for component in components:
+            is_acceptable, reason = self._is_single_license_acceptable(component)
+            if not is_acceptable:
+                return False, f"{reason} (in SPDX expression '{license_str}')"
+        return True, f"All SPDX components authorized: {', '.join(components)}"
+
+    def _is_single_license_acceptable(self, license_str: str) -> Tuple[bool, str]:
+        """Check if a single license identifier is acceptable based on configured lists."""
         if not license_str:
             return False, "Unknown license"
 
diff --git a/tests/code_coverage_tests/liccheck.ini b/tests/code_coverage_tests/liccheck.ini
index 3d53ecede7b..5a09403c570 100644
--- a/tests/code_coverage_tests/liccheck.ini
+++ b/tests/code_coverage_tests/liccheck.ini
@@ -90,7 +90,6 @@ jinja2: >=3.1.4 # BSD 3-Clause License
 litellm-proxy-extras: >=0.1.1 # MIT License
 litellm-enterprise: >=0.1.1 # LiteLLM Enterprise License
 a2a-sdk: >=0.3.22 # Apache 2.0 license
-pydantic-settings: >=2.14.1 # MIT License manually verified (uses PEP 639 License-Expression: MIT, not the legacy License field, so liccheck reports it as unknown)
 anyio: >=4.5.0 # Unknown license
 httpx-aiohttp: >=0.1.4 # Unknown license
 backoff: >=2.2.1 # Unknown license
diff --git a/tests/code_coverage_tests/recursive_detector.py b/tests/code_coverage_tests/recursive_detector.py
index 07af2735dfe..254d700ee5a 100644
--- a/tests/code_coverage_tests/recursive_detector.py
+++ b/tests/code_coverage_tests/recursive_detector.py
@@ -47,6 +47,9 @@ IGNORE_FUNCTIONS = [
     "_read_image_bytes",  # max depth set.
     "_get_masked_values",  # max depth set (default 20) to prevent infinite recursion while masking nested sensitive config dicts.
     "_redact_sensitive_litellm_params",  # max depth set (default 10).
+    "_resolve",  # OCI: $ref resolver bounded by `resolving_stack` cycle guard.
+    "resolve_oci_schema_anyof",  # OCI: bounded by JSON-schema tree depth (no cycles possible in well-formed input).
+    "sanitize_oci_schema",  # OCI: bounded by JSON-schema tree depth.
 ]
 
 
diff --git a/tests/guardrails_tests/conftest.py b/tests/guardrails_tests/conftest.py
index eb563699b2b..f2f65645c3d 100644
--- a/tests/guardrails_tests/conftest.py
+++ b/tests/guardrails_tests/conftest.py
@@ -16,14 +16,17 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -55,6 +58,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -160,3 +164,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/image_gen_tests/conftest.py b/tests/image_gen_tests/conftest.py
index 93dec98e708..9f808c11161 100644
--- a/tests/image_gen_tests/conftest.py
+++ b/tests/image_gen_tests/conftest.py
@@ -9,14 +9,17 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm  # noqa: E402,F401
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -58,6 +61,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -71,3 +75,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/image_gen_tests/test_image_edits.py b/tests/image_gen_tests/test_image_edits.py
index 656b8a69117..ca8ec3bbe32 100644
--- a/tests/image_gen_tests/test_image_edits.py
+++ b/tests/image_gen_tests/test_image_edits.py
@@ -103,12 +103,6 @@ class BaseLLMImageEditTest(ABC):
 pwd = os.path.dirname(os.path.realpath(__file__))
 
 
-# Image fixtures must be regenerated per access — module-level
-# ``open(...)`` handles get consumed after a single multipart upload, leaving
-# subsequent tests in the same process to send empty bodies. That non-determinism
-# (a) blows the recorded cassette past ``MAX_EPISODES_PER_CASSETTE`` so the
-# persister refuses to save (see ``tests/_vcr_redis_persister.py``), and
-# (b) re-bills the live image edit endpoint on every CI run.
 def _read_image_bytes(filename: str) -> bytes:
     with open(os.path.join(pwd, filename), "rb") as f:
         return f.read()
@@ -119,32 +113,20 @@ _LITELLM_SITE_BYTES = _read_image_bytes("litellm_site.png")
 
 
 def _make_test_images() -> list:
-    """Return a fresh pair of image streams seeded with the fixture bytes.
+    return [_ISHAAN_GITHUB_BYTES, _LITELLM_SITE_BYTES]
 
-    Use this everywhere you'd previously have used the module-level
-    ``TEST_IMAGES``. Each call returns brand new ``BytesIO`` objects whose
-    file pointers start at 0, so multipart uploads encode the full image
-    bytes on every test invocation. Parametrized and ``flaky``-retried
-    test methods call ``get_base_image_edit_call_args`` once per
-    invocation, so a fresh stream per call is sufficient — the factory
-    must not auto-rewind on EOF or the SDK's multipart writer will read
-    the same bytes forever (worker OOM).
-    """
+
+def _make_single_test_image() -> bytes:
+    return _ISHAAN_GITHUB_BYTES
+
+
+def get_test_images_as_bytesio():
     return [
         BytesIO(_ISHAAN_GITHUB_BYTES),
         BytesIO(_LITELLM_SITE_BYTES),
     ]
 
 
-def _make_single_test_image() -> BytesIO:
-    return BytesIO(_ISHAAN_GITHUB_BYTES)
-
-
-def get_test_images_as_bytesio():
-    """Helper function to get test images as BytesIO objects"""
-    return _make_test_images()
-
-
 class TestOpenAIImageEditGPTImage1(BaseLLMImageEditTest):
     """
     Concrete implementation of BaseLLMImageEditTest for OpenAI image edits.
@@ -710,10 +692,9 @@ async def test_multiple_image_edit_with_different_formats():
     try:
         prompt = "Create a cohesive artistic style across all images"
 
-        # Test with mixed BytesIO and file objects
         mixed_images = [
-            _make_single_test_image(),  # File object
-            get_test_images_as_bytesio()[1],  # BytesIO object
+            _make_single_test_image(),
+            get_test_images_as_bytesio()[1],
         ]
 
         result = await aimage_edit(
diff --git a/tests/integration/oci_proxy_test_config.yaml b/tests/integration/oci_proxy_test_config.yaml
new file mode 100644
index 00000000000..bccb58d27f3
--- /dev/null
+++ b/tests/integration/oci_proxy_test_config.yaml
@@ -0,0 +1,24 @@
+model_list:
+  - model_name: oci-cohere-command
+    litellm_params:
+      model: oci/cohere.command-latest
+  - model_name: oci-llama
+    litellm_params:
+      model: oci/meta.llama-3.3-70b-instruct
+  - model_name: oci-gemini
+    litellm_params:
+      model: oci/google.gemini-2.5-flash
+  - model_name: oci-grok
+    litellm_params:
+      model: oci/xai.grok-3-mini
+  - model_name: oci-embed
+    litellm_params:
+      model: oci/cohere.embed-v4.0
+    model_info:
+      mode: embedding
+
+general_settings:
+  master_key: sk-1234
+
+litellm_settings:
+  drop_params: True
diff --git a/tests/integration/test_oci_integration.py b/tests/integration/test_oci_integration.py
new file mode 100644
index 00000000000..94b8930bce8
--- /dev/null
+++ b/tests/integration/test_oci_integration.py
@@ -0,0 +1,669 @@
+"""
+OCI Generative AI — end-to-end integration tests.
+
+These tests make REAL calls to OCI.  They are skipped automatically when the
+standard ~/.oci/config is absent or when OCI_TEST_COMPARTMENT_ID is not set.
+
+Prerequisites
+-------------
+- ~/.oci/config with a valid [DEFAULT] profile
+- Private key referenced by key_file in that profile
+- Sufficient IAM policies to call the Generative AI inference service
+
+Environment variables (all optional — fall back to ~/.oci/config values):
+  OCI_TEST_REGION        OCI region (default: us-chicago-1)
+  OCI_TEST_COMPARTMENT_ID  compartment OCID (default: tenancy root from config)
+
+Run only these tests:
+    pytest tests/integration/test_oci_integration.py -v
+"""
+
+import math
+import os
+import sys
+from typing import NamedTuple, Optional
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+# ---------------------------------------------------------------------------
+# Fixtures / helpers
+# ---------------------------------------------------------------------------
+
+OCI_CONFIG_FILE = os.path.expanduser("~/.oci/config")
+_OCI_AVAILABLE = os.path.isfile(OCI_CONFIG_FILE)
+
+pytestmark = pytest.mark.skipif(
+    not _OCI_AVAILABLE,
+    reason="~/.oci/config not found — skipping OCI integration tests",
+)
+
+
+def _load_oci_config():
+    """Load OCI config from the profile named by ``OCI_CONFIG_PROFILE`` env var,
+    falling back to ``[DEFAULT]``. Lets CI/local runs target a specific profile
+    without needing a ``[DEFAULT]`` section in ``~/.oci/config``."""
+    oci = pytest.importorskip("oci")
+    profile = os.environ.get("OCI_CONFIG_PROFILE", "DEFAULT")
+    return oci.config.from_file(profile_name=profile)
+
+
+@pytest.fixture(scope="module")
+def oci_signer():
+    """Return an oci.Signer (or SecurityTokenSigner for session-token profiles)
+    built from ~/.oci/config — profile chosen via OCI_CONFIG_PROFILE."""
+    oci = pytest.importorskip("oci")
+    config = _load_oci_config()
+
+    # Session-token profiles carry a `security_token_file` instead of a user
+    # OCID; build the corresponding signer in that case.
+    if "security_token_file" in config:
+        with open(os.path.expanduser(config["security_token_file"])) as f:
+            token = f.read().strip()
+        private_key = oci.signer.load_private_key_from_file(
+            config["key_file"], config.get("pass_phrase")
+        )
+        return oci.auth.signers.SecurityTokenSigner(token, private_key)
+
+    return oci.Signer(
+        tenancy=config["tenancy"],
+        user=config["user"],
+        fingerprint=config["fingerprint"],
+        private_key_file_location=config["key_file"],
+    )
+
+
+@pytest.fixture(scope="module")
+def oci_params(oci_signer) -> dict:
+    """Common OCI call-time parameters shared by all tests."""
+    config = _load_oci_config()
+    compartment_id = os.environ.get("OCI_TEST_COMPARTMENT_ID", config["tenancy"])
+    region = os.environ.get("OCI_TEST_REGION", "us-chicago-1")
+    return {
+        "oci_signer": oci_signer,
+        "oci_compartment_id": compartment_id,
+        "oci_region": region,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Model registry
+#
+# Each entry drives the runtime pivot inside OCI's own transformation layer —
+# the tests themselves are format-agnostic.  Per-model quirks are captured in
+# the config fields below rather than in separate test classes.
+# ---------------------------------------------------------------------------
+
+
+class _M(NamedTuple):
+    """Per-model test configuration."""
+
+    model: str
+    max_tokens: int
+    # Reasoning models (Gemini 2.5, Grok mini) may return None content when the
+    # reasoning budget is exhausted before the answer token budget starts.
+    reasoning: bool = False
+    # tool_choice value to send; None means omit the parameter entirely.
+    tool_choice: Optional[str] = "auto"
+    # Whether to include the model in tool-use parametrize list.
+    supports_tool_use: bool = True
+
+
+# All chat models under test.
+CHAT_MODELS = [
+    pytest.param(_M("meta.llama-3.3-70b-instruct", 64), id="meta"),
+    pytest.param(_M("google.gemini-2.5-flash", 200, reasoning=True), id="google"),
+    pytest.param(_M("xai.grok-3-mini", 100, reasoning=True), id="xai"),
+    pytest.param(_M("cohere.command-latest", 64, tool_choice=None), id="cohere"),
+]
+
+# Subset of models that reliably support tool use in OCI.
+# xAI Grok mini is omitted — OCI does not expose tool-use for it yet.
+TOOL_USE_MODELS = [
+    pytest.param(_M("meta.llama-3.3-70b-instruct", 100), id="meta"),
+    pytest.param(_M("cohere.command-latest", 200, tool_choice=None), id="cohere"),
+    pytest.param(_M("google.gemini-2.5-flash", 200, reasoning=True), id="google"),
+]
+
+# Simple weather tool used by all tool-use tests.
+_WEATHER_TOOL = {
+    "type": "function",
+    "function": {
+        "name": "get_weather",
+        "description": "Get the current weather for a city.",
+        "parameters": {
+            "type": "object",
+            "properties": {"city": {"type": "string", "description": "The city name."}},
+            "required": ["city"],
+        },
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# Sync chat tests — model list drives the pivot, not separate test classes
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("m", CHAT_MODELS)
+def test_basic_completion(m: _M, oci_params):
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{m.model}",
+        messages=[{"role": "user", "content": "Reply with only the word: pong"}],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    assert resp.choices[0].finish_reason is not None
+    assert resp.usage.prompt_tokens > 0
+    if not m.reasoning:
+        assert resp.choices[0].message.content is not None
+
+
+@pytest.mark.parametrize("m", CHAT_MODELS)
+def test_usage_populated(m: _M, oci_params):
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{m.model}",
+        messages=[{"role": "user", "content": "What is 2+2?"}],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    assert resp.usage.prompt_tokens > 0
+    assert resp.usage.total_tokens >= resp.usage.prompt_tokens
+
+
+@pytest.mark.parametrize("m", CHAT_MODELS)
+def test_system_message(m: _M, oci_params):
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{m.model}",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Say hello."},
+        ],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    assert resp.choices[0].finish_reason is not None
+
+
+@pytest.mark.parametrize("m", CHAT_MODELS)
+def test_streaming(m: _M, oci_params):
+    import litellm
+
+    chunks = list(
+        litellm.completion(
+            model=f"oci/{m.model}",
+            messages=[{"role": "user", "content": "Count to 3."}],
+            max_tokens=m.max_tokens,
+            stream=True,
+            **oci_params,
+        )
+    )
+    assert len(chunks) > 0
+    # Reasoning models may stream only reasoning tokens and return empty content.
+    if not m.reasoning:
+        content = "".join(c.choices[0].delta.content or "" for c in chunks if c.choices)
+        assert len(content) > 0
+
+
+@pytest.mark.parametrize(
+    "model",
+    ["cohere.command-latest", "cohere.command-r-plus-08-2024"],
+)
+def test_cohere_streaming_no_doubling(model, oci_params):
+    """Regression: OCI Cohere's terminal SSE event re-sends the full assembled
+    response in `text` alongside a populated `chatHistory`. Emitting that text
+    as another delta would concatenate the whole response onto the
+    already-streamed output (e.g. "How can I help?How can I help?").
+
+    Reported by @gotsysdba on PR #25177. Fix: drop terminal text when
+    `chatHistory` is present in `handle_cohere_stream_chunk`.
+    """
+    import litellm
+
+    streamed = "".join(
+        (c.choices[0].delta.content or "")
+        for c in litellm.completion(
+            model=f"oci/{model}",
+            messages=[{"role": "user", "content": "Hello!"}],
+            max_tokens=64,
+            stream=True,
+            **oci_params,
+        )
+        if c.choices
+    ).strip()
+
+    assert streamed, "expected non-empty streamed content"
+
+    # Compare against a non-streamed call. With the doubling bug the streamed
+    # assembly is ~2x the real response; without it the two are the same order
+    # of magnitude (the model is non-deterministic, so allow generous slack).
+    non_streamed = (
+        litellm.completion(
+            model=f"oci/{model}",
+            messages=[{"role": "user", "content": "Hello!"}],
+            max_tokens=64,
+            **oci_params,
+        )
+        .choices[0]
+        .message.content
+        or ""
+    ).strip()
+
+    assert len(streamed) < 2 * len(non_streamed) + 10, (
+        f"streamed output appears doubled — "
+        f"streamed={len(streamed)} chars vs non_streamed={len(non_streamed)} chars\n"
+        f"streamed:     {streamed!r}\n"
+        f"non_streamed: {non_streamed!r}"
+    )
+
+    # Stronger signal: the very start of the response should not appear twice.
+    head = streamed[:12]
+    assert streamed.count(head) == 1, (
+        f"streamed output contains its own prefix {head!r} more than once — "
+        f"likely the terminal chunk re-emitted the full response.\n"
+        f"streamed: {streamed!r}"
+    )
+
+
+@pytest.mark.parametrize("m", CHAT_MODELS)
+def test_multi_turn(m: _M, oci_params):
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{m.model}",
+        messages=[
+            {"role": "user", "content": "My name is Alice."},
+            {"role": "assistant", "content": "Nice to meet you, Alice!"},
+            {"role": "user", "content": "What is my name?"},
+        ],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    # Reasoning models may have None content; skip text assertion for them.
+    content = resp.choices[0].message.content or ""
+    if not m.reasoning:
+        assert "Alice" in content
+
+
+# ---------------------------------------------------------------------------
+# Async chat tests
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("m", CHAT_MODELS)
+async def test_async_completion(m: _M, oci_params):
+    import litellm
+
+    resp = await litellm.acompletion(
+        model=f"oci/{m.model}",
+        messages=[{"role": "user", "content": "Reply with only the word: pong"}],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    assert resp.choices[0].finish_reason is not None
+    assert resp.usage.total_tokens > 0
+    if not m.reasoning:
+        assert resp.choices[0].message.content is not None
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("m", CHAT_MODELS)
+async def test_async_streaming(m: _M, oci_params):
+    import litellm
+
+    chunks = []
+    async for chunk in await litellm.acompletion(
+        model=f"oci/{m.model}",
+        messages=[{"role": "user", "content": "Count to 3."}],
+        max_tokens=m.max_tokens,
+        stream=True,
+        **oci_params,
+    ):
+        chunks.append(chunk)
+
+    assert len(chunks) > 0
+    if not m.reasoning:
+        content = "".join(c.choices[0].delta.content or "" for c in chunks if c.choices)
+        assert len(content) > 0
+
+
+# ---------------------------------------------------------------------------
+# Tool-use tests
+# ---------------------------------------------------------------------------
+
+
+def _assert_tool_call(resp, expected_tool: str = "get_weather"):
+    """Assert the response contains the expected tool call (or a plain stop)."""
+    choice = resp.choices[0]
+    assert choice.finish_reason in ("tool_calls", "stop")
+    if choice.finish_reason == "tool_calls":
+        assert choice.message.tool_calls is not None
+        assert len(choice.message.tool_calls) > 0
+        assert choice.message.tool_calls[0].function.name == expected_tool
+
+
+@pytest.mark.parametrize("m", TOOL_USE_MODELS)
+def test_tool_use(m: _M, oci_params):
+    import litellm
+
+    call_kwargs = dict(
+        model=f"oci/{m.model}",
+        messages=[{"role": "user", "content": "What is the weather in Paris?"}],
+        tools=[_WEATHER_TOOL],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    if m.tool_choice is not None:
+        call_kwargs["tool_choice"] = m.tool_choice
+
+    resp = litellm.completion(**call_kwargs)
+    _assert_tool_call(resp)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("m", TOOL_USE_MODELS)
+async def test_async_tool_use(m: _M, oci_params):
+    import litellm
+
+    call_kwargs = dict(
+        model=f"oci/{m.model}",
+        messages=[{"role": "user", "content": "What is the weather in Berlin?"}],
+        tools=[_WEATHER_TOOL],
+        max_tokens=m.max_tokens,
+        **oci_params,
+    )
+    if m.tool_choice is not None:
+        call_kwargs["tool_choice"] = m.tool_choice
+
+    resp = await litellm.acompletion(**call_kwargs)
+    _assert_tool_call(resp)
+
+
+# ---------------------------------------------------------------------------
+# Reasoning-effort tests (reasoning models only)
+# ---------------------------------------------------------------------------
+
+# Reasoning model that accepts the `reasoningEffort` parameter on OCI.
+# Not every reasoning model does — xai.grok-4-fast-reasoning, for example,
+# rejects it with a 400.
+_REASONING_MODEL = "xai.grok-3-mini"
+
+
+@pytest.mark.parametrize("effort", ["low", "medium", "high"])
+def test_reasoning_effort_lowercase_accepted(effort, oci_params):
+    """OpenAI clients send lowercase reasoning_effort; OCI requires uppercase.
+    The transform layer should uppercase it transparently."""
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{_REASONING_MODEL}",
+        messages=[{"role": "user", "content": "What is 2+2? One word."}],
+        max_tokens=200,
+        reasoning_effort=effort,
+        **oci_params,
+    )
+    assert resp.choices[0].finish_reason is not None
+    assert resp.usage.prompt_tokens > 0
+
+
+def test_reasoning_effort_disable_mapped_to_none(oci_params):
+    """OpenAI's 'disable' maps to OCI's 'NONE'. Without this mapping the
+    request 400s."""
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{_REASONING_MODEL}",
+        messages=[{"role": "user", "content": "What is 2+2? One word."}],
+        max_tokens=200,
+        reasoning_effort="disable",
+        **oci_params,
+    )
+    assert resp.choices[0].finish_reason is not None
+
+
+def test_reasoning_tokens_in_usage(oci_params):
+    """OCI returns completionTokensDetails.reasoningTokens on reasoning models;
+    LiteLLM should surface it on Usage.completion_tokens_details."""
+    import litellm
+
+    resp = litellm.completion(
+        model=f"oci/{_REASONING_MODEL}",
+        messages=[{"role": "user", "content": "What is 2+2? One word."}],
+        max_tokens=200,
+        reasoning_effort="low",
+        **oci_params,
+    )
+    assert resp.usage.completion_tokens_details is not None
+    assert resp.usage.completion_tokens_details.reasoning_tokens is not None
+    assert resp.usage.completion_tokens_details.reasoning_tokens > 0
+
+
+# ---------------------------------------------------------------------------
+# Embedding tests
+# ---------------------------------------------------------------------------
+
+
+class TestOCIEmbeddings:
+
+    def test_english_v3_basic(self, oci_params):
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=["Hello world"],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data) == 1
+        assert len(resp.data[0]["embedding"]) == 1024
+        assert resp.usage.prompt_tokens > 0
+
+    def test_english_v3_batch(self, oci_params):
+        import litellm
+
+        texts = [
+            "The quick brown fox",
+            "jumps over the lazy dog",
+            "Paris is the capital of France",
+        ]
+        resp = litellm.embedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=texts,
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data) == 3
+        for i, item in enumerate(resp.data):
+            assert item["index"] == i
+            assert len(item["embedding"]) == 1024
+
+    def test_multilingual_v3(self, oci_params):
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-multilingual-v3.0",
+            input=["Bonjour le monde", "Hola mundo"],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data) == 2
+        assert len(resp.data[0]["embedding"]) == 1024
+
+    def test_search_query_input_type(self, oci_params):
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=["What is the capital of France?"],
+            input_type="SEARCH_QUERY",
+            **oci_params,
+        )
+        assert len(resp.data[0]["embedding"]) == 1024
+
+    def test_semantic_similarity(self, oci_params):
+        """Semantically similar texts should have higher cosine similarity."""
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=[
+                "The cat sat on the mat",
+                "A feline rested on the rug",
+                "The stock market crashed today",
+            ],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+
+        def cosine(a, b):
+            dot = sum(x * y for x, y in zip(a, b))
+            mag_a = math.sqrt(sum(x**2 for x in a))
+            mag_b = math.sqrt(sum(x**2 for x in b))
+            return dot / (mag_a * mag_b)
+
+        cat1 = resp.data[0]["embedding"]
+        cat2 = resp.data[1]["embedding"]
+        stock = resp.data[2]["embedding"]
+        sim_cats = cosine(cat1, cat2)
+        sim_diff = cosine(cat1, stock)
+        assert (
+            sim_cats > sim_diff
+        ), f"Expected similar sentences to score higher ({sim_cats:.3f} vs {sim_diff:.3f})"
+
+    def test_embed_v4(self, oci_params):
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-v4.0",
+            input=["Hello world"],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data) == 1
+        assert len(resp.data[0]["embedding"]) == 1536
+
+    def test_usage_tokens(self, oci_params):
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=["short text", "another short text"],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert resp.usage.prompt_tokens > 0
+        assert resp.usage.total_tokens == resp.usage.prompt_tokens
+
+
+# ---------------------------------------------------------------------------
+# Async embedding tests
+# ---------------------------------------------------------------------------
+
+
+class TestOCIAsyncEmbeddings:
+
+    @pytest.mark.asyncio
+    async def test_async_embedding_basic(self, oci_params):
+        import litellm
+
+        resp = await litellm.aembedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=["Hello world"],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data) == 1
+        assert len(resp.data[0]["embedding"]) == 1024
+        assert resp.usage.prompt_tokens > 0
+
+    @pytest.mark.asyncio
+    async def test_async_embedding_batch(self, oci_params):
+        import litellm
+
+        texts = ["The quick brown fox", "jumps over the lazy dog"]
+        resp = await litellm.aembedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=texts,
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data) == 2
+        assert all(len(item["embedding"]) == 1024 for item in resp.data)
+
+    @pytest.mark.asyncio
+    async def test_async_embedding_multilingual(self, oci_params):
+        import litellm
+
+        resp = await litellm.aembedding(
+            model="oci/cohere.embed-multilingual-v3.0",
+            input=["Bonjour le monde"],
+            input_type="SEARCH_DOCUMENT",
+            **oci_params,
+        )
+        assert len(resp.data[0]["embedding"]) == 1024
+
+
+# ---------------------------------------------------------------------------
+# Env-var credential path
+# ---------------------------------------------------------------------------
+
+
+class TestOCIEnvVarCredentials:
+    """Verify that OCI_* env vars are picked up without explicit params."""
+
+    def test_completion_via_env_vars(self, monkeypatch):
+        """Completion works when credentials are set through environment variables."""
+        pytest.importorskip("oci")
+        config = _load_oci_config()
+        key_path = os.path.expanduser(config["key_file"])
+
+        with open(key_path) as f:
+            key_pem = f.read()
+
+        monkeypatch.setenv("OCI_REGION", "us-chicago-1")
+        monkeypatch.setenv("OCI_USER", config["user"])
+        monkeypatch.setenv("OCI_FINGERPRINT", config["fingerprint"])
+        monkeypatch.setenv("OCI_TENANCY", config["tenancy"])
+        monkeypatch.setenv("OCI_KEY", key_pem)
+        monkeypatch.setenv("OCI_COMPARTMENT_ID", config["tenancy"])
+
+        import litellm
+
+        resp = litellm.completion(
+            model="oci/meta.llama-3.3-70b-instruct",
+            messages=[{"role": "user", "content": "Reply with only the word: pong"}],
+            max_tokens=10,
+        )
+        assert resp.choices[0].message.content is not None
+
+    def test_embedding_via_env_vars(self, monkeypatch):
+        pytest.importorskip("oci")
+        config = _load_oci_config()
+        key_path = os.path.expanduser(config["key_file"])
+
+        with open(key_path) as f:
+            key_pem = f.read()
+
+        monkeypatch.setenv("OCI_REGION", "us-chicago-1")
+        monkeypatch.setenv("OCI_USER", config["user"])
+        monkeypatch.setenv("OCI_FINGERPRINT", config["fingerprint"])
+        monkeypatch.setenv("OCI_TENANCY", config["tenancy"])
+        monkeypatch.setenv("OCI_KEY", key_pem)
+        monkeypatch.setenv("OCI_COMPARTMENT_ID", config["tenancy"])
+
+        import litellm
+
+        resp = litellm.embedding(
+            model="oci/cohere.embed-english-v3.0",
+            input=["hello"],
+            input_type="SEARCH_DOCUMENT",
+        )
+        assert len(resp.data[0]["embedding"]) == 1024
diff --git a/tests/integration/test_oci_proxy_integration.py b/tests/integration/test_oci_proxy_integration.py
new file mode 100644
index 00000000000..8bfcdd90486
--- /dev/null
+++ b/tests/integration/test_oci_proxy_integration.py
@@ -0,0 +1,274 @@
+"""
+OCI GenAI — end-to-end **proxy** integration tests.
+
+Spins up the LiteLLM proxy (`litellm --config oci_proxy_test_config.yaml`) as a
+subprocess, then sends OpenAI-shaped HTTP requests at it for the OCI models
+declared in the test config:
+
+  - oci-cohere-command   (oci/cohere.command-latest)
+  - oci-llama            (oci/meta.llama-3.3-70b-instruct)
+  - oci-gemini           (oci/google.gemini-2.5-flash)
+  - oci-grok             (oci/xai.grok-3-mini)
+  - oci-embed            (oci/cohere.embed-v4.0)
+
+Skipped unless:
+  - ~/.oci/config exists
+  - The `oci` SDK is installed (handled by ``pytest.importorskip``)
+
+Environment variables honoured (passed through to the proxy subprocess):
+  OCI_CONFIG_PROFILE   profile inside ~/.oci/config (default: DEFAULT)
+  OCI_REGION           overrides region from the profile (default: us-chicago-1)
+
+Run with::
+
+    OCI_CONFIG_PROFILE=LUIGI_FRA_API OCI_REGION=us-chicago-1 \
+        uv run pytest tests/integration/test_oci_proxy_integration.py -v -s
+
+The tests open a real socket on a free TCP port — no port collision with a
+locally-running proxy.
+"""
+
+from __future__ import annotations
+
+import os
+import socket
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Iterator
+
+import httpx
+import pytest
+
+
+# ---------------------------------------------------------------------------
+# Skip gate
+# ---------------------------------------------------------------------------
+OCI_CONFIG_FILE = os.path.expanduser("~/.oci/config")
+pytestmark = pytest.mark.skipif(
+    not os.path.isfile(OCI_CONFIG_FILE),
+    reason="~/.oci/config not found — skipping OCI proxy integration tests",
+)
+
+
+CONFIG_PATH = Path(__file__).parent / "oci_proxy_test_config.yaml"
+MASTER_KEY = "sk-1234"
+STARTUP_TIMEOUT_S = 90.0
+REQUEST_TIMEOUT_S = 120.0
+
+
+def _free_port() -> int:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("127.0.0.1", 0))
+        return s.getsockname()[1]
+
+
+def _wait_for_health(base_url: str, proc: subprocess.Popen, deadline: float) -> None:
+    """Poll /health/liveliness until the proxy answers or the deadline expires."""
+    while time.monotonic() < deadline:
+        if proc.poll() is not None:
+            output = proc.stdout.read() if proc.stdout else ""
+            raise RuntimeError(
+                f"litellm proxy exited early with code {proc.returncode}\n--- proxy output ---\n{output}"
+            )
+        try:
+            r = httpx.get(f"{base_url}/health/liveliness", timeout=2.0)
+            if r.status_code == 200:
+                return
+        except httpx.HTTPError:
+            pass
+        time.sleep(0.5)
+    raise RuntimeError(f"litellm proxy did not become ready within {STARTUP_TIMEOUT_S}s")
+
+
+def _oci_env_from_profile() -> dict[str, str]:
+    """Translate the active OCI profile into the OCI_* env vars the litellm
+    OCI provider expects. Only API-key profiles are supported; session-token
+    profiles would need an in-process signer and so are skipped here.
+    """
+    oci = pytest.importorskip("oci")
+    profile = os.environ.get("OCI_CONFIG_PROFILE", "DEFAULT")
+    cfg = oci.config.from_file(profile_name=profile)
+    if "security_token_file" in cfg:
+        pytest.skip(
+            f"OCI profile {profile!r} uses session-token auth; "
+            "litellm's OCI provider needs an API-key profile for env-driven config"
+        )
+    region = os.environ.get("OCI_REGION") or cfg.get("region") or "us-chicago-1"
+    return {
+        "OCI_USER": cfg["user"],
+        "OCI_FINGERPRINT": cfg["fingerprint"],
+        "OCI_TENANCY": cfg["tenancy"],
+        "OCI_COMPARTMENT_ID": os.environ.get("OCI_COMPARTMENT_ID", cfg["tenancy"]),
+        "OCI_KEY_FILE": os.path.expanduser(cfg["key_file"]),
+        "OCI_REGION": region,
+    }
+
+
+@pytest.fixture(scope="module")
+def proxy_url() -> Iterator[str]:
+    oci_env = _oci_env_from_profile()
+
+    port = _free_port()
+    base_url = f"http://127.0.0.1:{port}"
+
+    env = os.environ.copy()
+    env.update(oci_env)
+    # Avoid pulling in DB-backed features for this lightweight smoke run.
+    env.pop("DATABASE_URL", None)
+    env["STORE_MODEL_IN_DB"] = "False"
+
+    # Prefer the `litellm` console script that lives next to the active
+    # Python so we inherit the test virtualenv. Fall back to PATH.
+    cli = Path(sys.executable).parent / "litellm"
+    if not cli.exists():
+        cli = "litellm"
+    cmd = [
+        str(cli),
+        "--config",
+        str(CONFIG_PATH),
+        "--port",
+        str(port),
+        "--host",
+        "127.0.0.1",
+        "--num_workers",
+        "1",
+    ]
+
+    proc = subprocess.Popen(
+        cmd,
+        env=env,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+    )
+    try:
+        _wait_for_health(base_url, proc, time.monotonic() + STARTUP_TIMEOUT_S)
+        yield base_url
+    finally:
+        proc.terminate()
+        try:
+            proc.wait(timeout=10)
+        except subprocess.TimeoutExpired:
+            proc.kill()
+            proc.wait(timeout=5)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _auth_headers() -> dict:
+    return {
+        "Authorization": f"Bearer {MASTER_KEY}",
+        "Content-Type": "application/json",
+    }
+
+
+def _chat_payload(model: str, *, stream: bool = False) -> dict:
+    return {
+        "model": model,
+        "messages": [
+            {"role": "user", "content": "Reply with only the single word: pong"}
+        ],
+        "max_tokens": 64,
+        "stream": stream,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+
+CHAT_MODELS = ["oci-cohere-command", "oci-llama", "oci-gemini", "oci-grok"]
+
+
+@pytest.mark.parametrize("model", CHAT_MODELS)
+def test_chat_completion_via_proxy(proxy_url: str, model: str) -> None:
+    """Non-streaming chat completion returns a well-formed OpenAI response."""
+    r = httpx.post(
+        f"{proxy_url}/v1/chat/completions",
+        headers=_auth_headers(),
+        json=_chat_payload(model),
+        timeout=REQUEST_TIMEOUT_S,
+    )
+    assert r.status_code == 200, f"{model} -> {r.status_code}: {r.text}"
+    body = r.json()
+    assert body["object"] == "chat.completion"
+    assert body["model"] == model
+    choices = body["choices"]
+    assert len(choices) >= 1
+    msg = choices[0]["message"]
+    assert msg["role"] == "assistant"
+    # Reasoning models may return empty content if their budget covers only
+    # the thinking turn — accept either text or a non-empty reasoning field.
+    has_content = bool(msg.get("content"))
+    has_reasoning = bool(msg.get("reasoning_content")) or bool(
+        msg.get("reasoning")
+    )
+    assert has_content or has_reasoning, f"empty assistant message for {model}: {msg}"
+    usage = body.get("usage") or {}
+    assert usage.get("total_tokens", 0) > 0
+
+
+@pytest.mark.parametrize("model", CHAT_MODELS)
+def test_chat_completion_streaming_via_proxy(proxy_url: str, model: str) -> None:
+    """Streaming chat completion yields at least one data: chunk and a [DONE]."""
+    saw_chunk = False
+    saw_done = False
+    with httpx.stream(
+        "POST",
+        f"{proxy_url}/v1/chat/completions",
+        headers=_auth_headers(),
+        json=_chat_payload(model, stream=True),
+        timeout=REQUEST_TIMEOUT_S,
+    ) as r:
+        assert r.status_code == 200, f"{model} stream -> {r.status_code}: {r.read()!r}"
+        for line in r.iter_lines():
+            if not line:
+                continue
+            if not line.startswith("data:"):
+                continue
+            payload = line[len("data:"):].strip()
+            if payload == "[DONE]":
+                saw_done = True
+                break
+            saw_chunk = True
+    assert saw_chunk, f"no streamed chunks for {model}"
+    assert saw_done, f"no [DONE] sentinel for {model}"
+
+
+def test_embedding_via_proxy(proxy_url: str) -> None:
+    """OCI Cohere embedding endpoint returns a non-empty vector via the proxy."""
+    r = httpx.post(
+        f"{proxy_url}/v1/embeddings",
+        headers=_auth_headers(),
+        json={"model": "oci-embed", "input": ["hello from the litellm proxy"]},
+        timeout=REQUEST_TIMEOUT_S,
+    )
+    assert r.status_code == 200, f"embed -> {r.status_code}: {r.text}"
+    body = r.json()
+    assert body["object"] == "list"
+    assert body["model"] == "oci-embed"
+    data = body["data"]
+    assert len(data) == 1
+    embedding = data[0]["embedding"]
+    assert isinstance(embedding, list)
+    assert len(embedding) >= 64
+    assert all(isinstance(x, (int, float)) for x in embedding)
+
+
+def test_model_list_advertises_oci_models(proxy_url: str) -> None:
+    """The /v1/models registry advertises every OCI alias from the config."""
+    r = httpx.get(
+        f"{proxy_url}/v1/models",
+        headers=_auth_headers(),
+        timeout=REQUEST_TIMEOUT_S,
+    )
+    assert r.status_code == 200, r.text
+    advertised = {row["id"] for row in r.json()["data"]}
+    for expected in CHAT_MODELS + ["oci-embed"]:
+        assert expected in advertised, f"{expected} missing from /v1/models: {advertised}"
diff --git a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
index f96228a4ccc..e9b3f82d1a7 100644
--- a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
+++ b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
@@ -14,7 +14,7 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 
 from litellm.llms.oci.chat.transformation import OCIChatConfig
-from litellm.llms.oci.common_utils import OCIError
+from litellm.llms.oci.common_utils import OCIError, sign_with_manual_credentials
 
 
 @pytest.fixture
@@ -41,7 +41,7 @@ class TestOCIKeyNormalization:
         # We can't fully test signing without a real key, but we can verify
         # the error message indicates the key was processed (not a type error)
         with pytest.raises(Exception) as exc_info:
-            config._sign_with_manual_credentials(
+            sign_with_manual_credentials(
                 headers={},
                 optional_params=optional_params,
                 request_data={"test": "data"},
@@ -67,7 +67,7 @@ class TestOCIKeyNormalization:
         }
 
         with pytest.raises(Exception) as exc_info:
-            config._sign_with_manual_credentials(
+            sign_with_manual_credentials(
                 headers={},
                 optional_params=optional_params,
                 request_data={"test": "data"},
@@ -88,7 +88,7 @@ class TestOCIKeyNormalization:
         }
 
         with pytest.raises(OCIError) as exc_info:
-            config._sign_with_manual_credentials(
+            sign_with_manual_credentials(
                 headers={},
                 optional_params=optional_params,
                 request_data={"test": "data"},
@@ -110,7 +110,7 @@ class TestOCIKeyNormalization:
         }
 
         with pytest.raises(OCIError) as exc_info:
-            config._sign_with_manual_credentials(
+            sign_with_manual_credentials(
                 headers={},
                 optional_params=optional_params,
                 request_data={"test": "data"},
diff --git a/tests/litellm_utils_tests/conftest.py b/tests/litellm_utils_tests/conftest.py
index 08745c99c07..418ee76a399 100644
--- a/tests/litellm_utils_tests/conftest.py
+++ b/tests/litellm_utils_tests/conftest.py
@@ -12,14 +12,17 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm  # noqa: E402,F401
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -86,6 +89,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -116,3 +120,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/llm_responses_api_testing/conftest.py b/tests/llm_responses_api_testing/conftest.py
index 2a08db57149..1928b540dad 100644
--- a/tests/llm_responses_api_testing/conftest.py
+++ b/tests/llm_responses_api_testing/conftest.py
@@ -13,14 +13,17 @@ sys.path.insert(
 
 import litellm  # noqa: E402
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -52,6 +55,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -116,3 +120,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/llm_responses_api_testing/test_anthropic_responses_api.py b/tests/llm_responses_api_testing/test_anthropic_responses_api.py
index 6537f67acb9..68ff22e8938 100644
--- a/tests/llm_responses_api_testing/test_anthropic_responses_api.py
+++ b/tests/llm_responses_api_testing/test_anthropic_responses_api.py
@@ -3,7 +3,7 @@ import sys
 import pytest
 import asyncio
 from typing import Optional
-from unittest.mock import patch, AsyncMock
+from unittest.mock import patch, AsyncMock, MagicMock
 from litellm.responses.litellm_completion_transformation.handler import (
     LiteLLMCompletionTransformationHandler,
 )
@@ -130,6 +130,26 @@ def test_multiturn_tool_calls():
     print("follow_up_response=", follow_up_response)
 
 
+def test_response_api_handler_merges_metadata_and_service_tier_without_error():
+    """Sync path must merge kwargs like async; double-splat raises TypeError."""
+    handler = LiteLLMCompletionTransformationHandler()
+
+    with patch("litellm.completion", new_callable=MagicMock) as mock_completion:
+        mock_completion.return_value = ModelResponse(
+            id="id", created=0, model="test", object="chat.completion", choices=[]
+        )
+        handler.response_api_handler(
+            model="test",
+            input="hi",
+            responses_api_request={},
+            metadata={"trace": "abc"},
+            service_tier="auto",
+        )
+        assert mock_completion.call_count == 1
+        assert mock_completion.call_args.kwargs["metadata"] == {"trace": "abc"}
+        assert mock_completion.call_args.kwargs["service_tier"] == "auto"
+
+
 @pytest.mark.asyncio
 async def test_async_response_api_handler_merges_trace_id_without_error():
     handler = LiteLLMCompletionTransformationHandler()
@@ -158,3 +178,39 @@ async def test_async_response_api_handler_merges_trace_id_without_error():
             assert (
                 mock_acompletion.call_args.kwargs["litellm_trace_id"] == "session-trace"
             )
+
+
+@pytest.mark.asyncio
+async def test_aresponses_forwards_timeout_to_acompletion():
+    """Regression test: timeout passed to aresponses() must reach acompletion()
+    on the completion transformation path (Anthropic, Bedrock, Vertex etc.).
+
+    Previously, `timeout` was a named param of `responses()` but was NOT
+    forwarded to `litellm_completion_transformation_handler.response_api_handler`,
+    so it was silently dropped — `Router(timeout=N)` was a no-op for Anthropic
+    and similar providers, with calls falling back to the provider SDK default
+    (~600s for Anthropic).
+    """
+    with patch("litellm.acompletion", new_callable=AsyncMock) as mock_acompletion:
+        mock_acompletion.return_value = ModelResponse(
+            id="id",
+            created=0,
+            model="anthropic/claude-sonnet-4-5",
+            object="chat.completion",
+            choices=[],
+        )
+
+        await litellm.aresponses(
+            model="anthropic/claude-sonnet-4-5",
+            input="hello",
+            timeout=42,
+            api_key="sk-ant-fake",
+        )
+
+    assert mock_acompletion.call_count == 1
+    forwarded_timeout = mock_acompletion.call_args.kwargs.get("timeout")
+    assert forwarded_timeout == 42, (
+        f"timeout was not forwarded to acompletion (got {forwarded_timeout!r}); "
+        "this means Router(timeout=N) silently fails for providers on the "
+        "completion transformation path."
+    )
diff --git a/tests/llm_responses_api_testing/test_base_responses_api_streaming_iterator.py b/tests/llm_responses_api_testing/test_base_responses_api_streaming_iterator.py
index e2c50810cc2..37fcc602d37 100644
--- a/tests/llm_responses_api_testing/test_base_responses_api_streaming_iterator.py
+++ b/tests/llm_responses_api_testing/test_base_responses_api_streaming_iterator.py
@@ -41,6 +41,62 @@ from litellm.types.llms.openai import (
 class TestBaseResponsesAPIStreamingIterator:
     """Test cases for BaseResponsesAPIStreamingIterator"""
 
+    @pytest.mark.asyncio
+    async def test_responses_streaming_iterator_parses_u2028_in_sse_json(self):
+        """
+        U+2028 inside JSON must not split the SSE event. httpx aiter_lines uses
+        str.splitlines() and drops response.completed; OpenAI SSEDecoder does not.
+        """
+        from litellm.responses.streaming_iterator import ResponsesAPIStreamingIterator
+
+        u2028 = "\u2028"
+        payload = json.dumps(
+            {
+                "type": "response.completed",
+                "response": {"instructions": f"eligible{u2028}promo"},
+            }
+        )
+        sse_bytes = f"data: {payload}\n\n".encode("utf-8")
+
+        async def mock_aiter_bytes():
+            yield sse_bytes
+
+        mock_response = Mock()
+        mock_response.headers = {}
+        mock_response.aiter_bytes = mock_aiter_bytes
+
+        mock_logging_obj = Mock(spec=LiteLLMLoggingObj)
+        mock_logging_obj.model_call_details = {"litellm_params": {}}
+        mock_config = Mock(spec=BaseResponsesAPIConfig)
+
+        mock_responses_api_response = Mock(spec=ResponsesAPIResponse)
+        mock_responses_api_response.id = "resp_u2028"
+        mock_completed_event = Mock(spec=ResponseCompletedEvent)
+        mock_completed_event.type = ResponsesAPIStreamEvents.RESPONSE_COMPLETED
+        mock_completed_event.response = mock_responses_api_response
+        mock_config.transform_streaming_response.return_value = mock_completed_event
+
+        iterator = ResponsesAPIStreamingIterator(
+            response=mock_response,
+            model="gpt-5.5",
+            responses_api_provider_config=mock_config,
+            logging_obj=mock_logging_obj,
+            litellm_metadata={"model_info": {"id": "model_123"}},
+            custom_llm_provider="openai",
+        )
+
+        chunks = []
+        with (
+            patch("asyncio.create_task"),
+            patch("litellm.responses.streaming_iterator.executor"),
+        ):
+            async for chunk in iterator:
+                chunks.append(chunk)
+
+        assert len(chunks) == 1
+        assert chunks[0].type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED
+        assert iterator.completed_response is not None
+
     def test_process_chunk_with_response_completed_event(self):
         """
         Test that _process_chunk correctly processes a ResponseCompletedEvent
@@ -270,7 +326,7 @@ class TestBaseResponsesAPIStreamingIterator:
         # Mock dependencies
         mock_response = Mock()
         mock_response.headers = {}
-        mock_response.aiter_lines = Mock()
+        mock_response.aiter_bytes = Mock()
         mock_logging_obj = Mock(spec=LiteLLMLoggingObj)
         mock_logging_obj.model_call_details = {"litellm_params": {}}
         mock_logging_obj.async_success_handler = Mock()
@@ -334,12 +390,10 @@ class TestBaseResponsesAPIStreamingIterator:
         mock_response = Mock()
         mock_response.headers = {}
 
-        # Create an async iterator that raises StopAsyncIteration after yielding one chunk
-        async def mock_aiter_lines():
-            yield 'data: {"type": "response.output_text.delta", "delta": "test"}'
-            # Normal end of stream - raise StopAsyncIteration
+        async def mock_aiter_bytes():
+            yield b'data: {"type": "response.output_text.delta", "delta": "test"}\n\n'
 
-        mock_response.aiter_lines = mock_aiter_lines
+        mock_response.aiter_bytes = mock_aiter_bytes
 
         mock_logging_obj = Mock(spec=LiteLLMLoggingObj)
         mock_logging_obj.model_call_details = {"litellm_params": {}}
@@ -396,12 +450,10 @@ class TestBaseResponsesAPIStreamingIterator:
         mock_response = Mock()
         mock_response.headers = {}
 
-        # Create a sync iterator that raises StopIteration after yielding one chunk
-        def mock_iter_lines():
-            yield 'data: {"type": "response.output_text.delta", "delta": "test"}'
-            # Normal end of stream - raise StopIteration
+        def mock_iter_bytes():
+            yield b'data: {"type": "response.output_text.delta", "delta": "test"}\n\n'
 
-        mock_response.iter_lines = mock_iter_lines
+        mock_response.iter_bytes = mock_iter_bytes
 
         mock_logging_obj = Mock(spec=LiteLLMLoggingObj)
         mock_logging_obj.model_call_details = {"litellm_params": {}}
@@ -450,7 +502,7 @@ class TestBaseResponsesAPIStreamingIterator:
 
         mock_response = Mock()
         mock_response.headers = {}
-        mock_response.aiter_lines = Mock()
+        mock_response.aiter_bytes = Mock()
         mock_logging_obj = Mock(spec=LiteLLMLoggingObj)
         mock_logging_obj.model_call_details = {"litellm_params": {}}
         mock_logging_obj.async_failure_handler = Mock()
@@ -532,7 +584,7 @@ class TestBaseResponsesAPIStreamingIterator:
 
         mock_response = Mock()
         mock_response.headers = {}
-        mock_response.aiter_lines = Mock()
+        mock_response.aiter_bytes = Mock()
         mock_logging_obj = Mock(spec=LiteLLMLoggingObj)
         mock_logging_obj.model_call_details = {"litellm_params": {}}
         mock_logging_obj.async_failure_handler = Mock()
diff --git a/tests/llm_translation/conftest.py b/tests/llm_translation/conftest.py
index 5fcd31aa32d..d346dae4308 100644
--- a/tests/llm_translation/conftest.py
+++ b/tests/llm_translation/conftest.py
@@ -18,14 +18,17 @@ sys.path.insert(
 
 import litellm  # noqa: E402
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -73,6 +76,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -82,6 +86,7 @@ def pytest_runtest_logreport(report):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
 
 
 # ---------------------------------------------------------------------------
diff --git a/tests/llm_translation/realtime/base_realtime_tests.py b/tests/llm_translation/realtime/base_realtime_tests.py
index 1d55f13b00d..1a2c6ff6a9c 100644
--- a/tests/llm_translation/realtime/base_realtime_tests.py
+++ b/tests/llm_translation/realtime/base_realtime_tests.py
@@ -10,7 +10,7 @@ import json
 import os
 import sys
 from abc import ABC, abstractmethod
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 import pytest
 import websockets
@@ -79,7 +79,7 @@ class RealTimeWebSocketClient:
 
     def _is_initial_event(self, msg_type: str) -> bool:
         """Check if message type is an initial connection event"""
-        # OpenAI sends "session.created", xAI sends "conversation.created"
+        # OpenAI and xAI send "session.created"; some providers send "conversation.created"
         return msg_type in ["session.created", "conversation.created"]
 
     async def receive_text(self):
@@ -153,8 +153,14 @@ class BaseRealtimeTest(ABC):
         pass
 
     @abstractmethod
-    def get_initial_event_type(self) -> str:
-        """Return the expected initial event type (e.g., 'session.created' or 'conversation.created')"""
+    def get_initial_event_type(self) -> Union[str, Tuple[str, ...]]:
+        """Return the expected initial event type(s).
+
+        May return a single event type (e.g. ``'session.created'``) or a tuple
+        of acceptable types when the upstream provider can legitimately emit
+        more than one initial event (e.g. xAI's Grok Voice Agent has shipped
+        both ``conversation.created`` and ``session.created``).
+        """
         pass
 
     def get_skip_reason(self) -> str:
@@ -229,9 +235,14 @@ class BaseRealtimeTest(ABC):
 
         # Verify initial event
         initial_event = websocket_client.messages_received[0]
+        expected_event_type = self.get_initial_event_type()
+        if isinstance(expected_event_type, str):
+            allowed_event_types: Tuple[str, ...] = (expected_event_type,)
+        else:
+            allowed_event_types = tuple(expected_event_type)
         assert (
-            initial_event["type"] == self.get_initial_event_type()
-        ), f"Expected {self.get_initial_event_type()}, got {initial_event.get('type')}"
+            initial_event["type"] in allowed_event_types
+        ), f"Expected one of {allowed_event_types}, got {initial_event.get('type')}"
 
     @pytest.mark.asyncio
     async def test_realtime_with_query_params(self):
diff --git a/tests/llm_translation/realtime/test_openai_realtime.py b/tests/llm_translation/realtime/test_openai_realtime.py
index c5f77de6beb..fc9f938b4cd 100644
--- a/tests/llm_translation/realtime/test_openai_realtime.py
+++ b/tests/llm_translation/realtime/test_openai_realtime.py
@@ -101,7 +101,9 @@ async def test_openai_realtime_direct_call_no_intent():
 
     try:
         await litellm._arealtime(
-            model="openai/gpt-4o-realtime-preview",
+            # OpenAI shut down the gpt-4o-realtime-preview family (incl. the
+            # undated alias) on 2026-05-07; gpt-realtime is the GA successor.
+            model="openai/gpt-realtime",
             websocket=websocket_client,
             api_key=os.environ.get("OPENAI_API_KEY"),
             timeout=60,
@@ -249,14 +251,16 @@ async def test_openai_realtime_direct_call_with_intent():
     websocket_client = RealTimeWebSocketClient()
     caught_exception = None
 
+    # OpenAI shut down the gpt-4o-realtime-preview family (incl. the undated
+    # alias) on 2026-05-07; gpt-realtime is the GA successor.
     query_params: RealtimeQueryParams = {
-        "model": "openai/gpt-4o-realtime-preview",
+        "model": "openai/gpt-realtime",
         "intent": "chat",
     }
 
     try:
         await litellm._arealtime(
-            model="openai/gpt-4o-realtime-preview",
+            model="openai/gpt-realtime",
             websocket=websocket_client,
             api_key=os.environ.get("OPENAI_API_KEY"),
             query_params=query_params,
diff --git a/tests/llm_translation/realtime/test_openai_realtime_simple.py b/tests/llm_translation/realtime/test_openai_realtime_simple.py
index 5522d843e42..073c1ce11af 100644
--- a/tests/llm_translation/realtime/test_openai_realtime_simple.py
+++ b/tests/llm_translation/realtime/test_openai_realtime_simple.py
@@ -21,7 +21,10 @@ class TestOpenAIRealtime(BaseRealtimeTest):
     """
 
     def get_model(self) -> str:
-        return "gpt-4o-realtime-preview"
+        # OpenAI shut down the entire gpt-4o-realtime-preview family
+        # (including the undated alias) on 2026-05-07. gpt-realtime is the
+        # current GA realtime model.
+        return "gpt-realtime"
 
     def get_api_key_env_var(self) -> str:
         return "OPENAI_API_KEY"
diff --git a/tests/llm_translation/realtime/test_realtime_guardrails_openai.py b/tests/llm_translation/realtime/test_realtime_guardrails_openai.py
index 170440f6b9f..50cedba2ac0 100644
--- a/tests/llm_translation/realtime/test_realtime_guardrails_openai.py
+++ b/tests/llm_translation/realtime/test_realtime_guardrails_openai.py
@@ -26,9 +26,7 @@ from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming
 from litellm.types.guardrails import GuardrailEventHooks
 
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
-OPENAI_REALTIME_URL = (
-    "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview-2024-12-17"
-)
+OPENAI_REALTIME_URL = "wss://api.openai.com/v1/realtime?model=gpt-realtime"
 
 pytestmark = pytest.mark.skipif(
     not OPENAI_API_KEY,
@@ -192,10 +190,35 @@ async def test_text_message_blocked_by_guardrail_no_ai_response():
             len(transcript_deltas) >= 1
         ), f"Expected guardrail message in transcript delta, got: {event_types}"
 
-        # 3. No *real* AI response should have been generated.
-        #    The guardrail may produce its own response (e.g. "Content blocked: ...")
-        #    via response.cancel + conversation.item.create + response.create.
-        #    We allow the guardrail's own block message but NOT original AI content.
+        # 3. No *real* AI response to the blocked content should have been
+        #    generated. The original user message is blocked BEFORE it is
+        #    forwarded to OpenAI, so the only thing the model ever sees is the
+        #    guardrail's "say exactly: <block message>" prompt
+        #    (see realtime_streaming.py). Two safe outcomes are possible:
+        #      - the model voices the block message verbatim (older realtime
+        #        snapshots did this -> text contains "blocked"), or
+        #      - the model declines to repeat it (gpt-realtime tends to refuse
+        #        verbatim-repeat instructions, e.g. "I'm sorry, but I can't
+        #        repeat that message.").
+        #    Both mean the blocked prompt itself was never answered, so we
+        #    accept either. The hard invariant is that the blocked phrase must
+        #    never leak into AI output, and the model must not have produced a
+        #    normal answer to the user (which would have neither a block nor a
+        #    refusal marker).
+        safe_markers = (
+            "block",
+            "guardrail",
+            "content filter",
+            "policy",
+            "can't repeat",
+            "cannot repeat",
+            "won't repeat",
+            "can't assist",
+            "can't help",
+            "unable to",
+            "i'm sorry",
+            "i am sorry",
+        )
         done_events = [e for e in client_events if e.get("type") == "response.done"]
         for done in done_events:
             output = done.get("response", {}).get("output", [])
@@ -205,11 +228,19 @@ async def test_text_message_blocked_by_guardrail_no_ai_response():
                 for c in item.get("content", [])
             ]
             real_ai_text = " ".join(ai_texts).strip()
-            # Allow guardrail-generated block messages (contain "Content blocked" or "blocked")
             if real_ai_text:
                 assert (
-                    "blocked" in real_ai_text.lower()
-                    or "guardrail" in real_ai_text.lower()
+                    BLOCKED_PHRASE not in real_ai_text
+                ), f"Blocked phrase leaked into AI response: {real_ai_text!r}"
+                normalized_ai_text = (
+                    real_ai_text.lower()
+                    .replace("\u2019", "'")
+                    .replace("\u2018", "'")
+                    .replace("\u201c", '"')
+                    .replace("\u201d", '"')
+                )
+                assert any(
+                    marker in normalized_ai_text for marker in safe_markers
                 ), f"AI responded with non-guardrail content even though message was blocked: {real_ai_text!r}"
 
     finally:
diff --git a/tests/llm_translation/realtime/test_xai_realtime.py b/tests/llm_translation/realtime/test_xai_realtime.py
index 0bb7a59bb1a..8ffcb3db30d 100644
--- a/tests/llm_translation/realtime/test_xai_realtime.py
+++ b/tests/llm_translation/realtime/test_xai_realtime.py
@@ -7,6 +7,7 @@ Uses the base test class to ensure consistent behavior across providers.
 
 import os
 import sys
+from typing import Tuple
 
 import pytest
 
@@ -19,10 +20,12 @@ class TestXAIRealtime(BaseRealtimeTest):
     """
     E2E tests for xAI Realtime API.
 
-    xAI's Grok Voice Agent API is OpenAI-compatible but uses:
-    - Different initial event: "conversation.created" instead of "session.created"
-    - Different endpoint: wss://api.x.ai/v1/realtime
+    xAI's Grok Voice Agent API is OpenAI-compatible:
+    - Endpoint: wss://api.x.ai/v1/realtime
     - Model: grok-4-1-fast-non-reasoning
+    - Initial event: historically "conversation.created"; xAI has since shipped
+      "session.created" (matching OpenAI). Accept either to avoid spurious
+      failures whenever xAI flips the wire format.
     """
 
     def get_model(self) -> str:
@@ -31,5 +34,5 @@ class TestXAIRealtime(BaseRealtimeTest):
     def get_api_key_env_var(self) -> str:
         return "XAI_API_KEY"
 
-    def get_initial_event_type(self) -> str:
-        return "conversation.created"
+    def get_initial_event_type(self) -> Tuple[str, ...]:
+        return ("conversation.created", "session.created")
diff --git a/tests/llm_translation/test_deepseek_completion.py b/tests/llm_translation/test_deepseek_completion.py
index da402a51b68..2ede5d3f3f8 100644
--- a/tests/llm_translation/test_deepseek_completion.py
+++ b/tests/llm_translation/test_deepseek_completion.py
@@ -176,3 +176,113 @@ def test_completion_cost_deepseek():
         pass
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
+
+
+def test_deepseek_fill_reasoning_content_multiturn():
+    """
+    Unit test for _fill_reasoning_content.
+    Reproduces issue #28045: DeepSeek thinking mode fails in multi-turn conversations
+    because reasoning_content is not passed back to the API.
+    """
+    from litellm.llms.deepseek.chat.transformation import DeepSeekChatConfig
+
+    config = DeepSeekChatConfig()
+
+    # Case 1: assistant message already has reasoning_content — should be left as-is
+    messages_with_rc = [
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "Hi", "reasoning_content": "I thought about it"},
+        {"role": "user", "content": "Follow up"},
+    ]
+    result = config._fill_reasoning_content(messages_with_rc)
+    assert result[1]["reasoning_content"] == "I thought about it"
+
+    # Case 2: assistant message has reasoning_content in provider_specific_fields — should be promoted
+    messages_with_psf = [
+        {"role": "user", "content": "Hello"},
+        {
+            "role": "assistant",
+            "content": "Hi",
+            "provider_specific_fields": {"reasoning_content": "stored thinking"},
+        },
+        {"role": "user", "content": "Follow up"},
+    ]
+    result = config._fill_reasoning_content(messages_with_psf)
+    assert result[1]["reasoning_content"] == "stored thinking"
+    # Should be removed from provider_specific_fields to avoid duplication
+    assert "reasoning_content" not in result[1].get("provider_specific_fields", {})
+
+    # Case 3: assistant message has no reasoning_content anywhere — should inject placeholder
+    messages_no_rc = [
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "Hi"},
+        {"role": "user", "content": "Follow up"},
+    ]
+    result = config._fill_reasoning_content(messages_no_rc)
+    assert result[1]["reasoning_content"] == " "
+
+    # Case 4: non-assistant messages should never be touched
+    messages_user_only = [
+        {"role": "user", "content": "Hello"},
+        {"role": "system", "content": "You are helpful"},
+    ]
+    result = config._fill_reasoning_content(messages_user_only)
+    assert "reasoning_content" not in result[0]
+    assert "reasoning_content" not in result[1]
+
+
+def test_deepseek_fill_reasoning_content_guard_in_transform_request():
+    """
+    _fill_reasoning_content must only run when BOTH conditions are true:
+      1. supports_reasoning() is True for the model
+      2. thinking mode is explicitly enabled in optional_params ({"type": "enabled"})
+
+    This prevents spurious injection on models like deepseek-v3.2 that support
+    thinking as opt-in but not always-on. Addresses oss-pr-review-agent feedback
+    on PR #28057.
+    """
+    from litellm.llms.deepseek.chat.transformation import DeepSeekChatConfig
+
+    config = DeepSeekChatConfig()
+
+    messages = [
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "Hi"},
+        {"role": "user", "content": "Follow up"},
+    ]
+
+    # Case 1: reasoning model + thinking enabled -> injection should happen
+    result = config.transform_request(
+        model="deepseek-reasoner",
+        messages=messages,
+        optional_params={"thinking": {"type": "enabled"}},
+        litellm_params={},
+        headers={},
+    )
+    assert result["messages"][1].get("reasoning_content") == " ", (
+        "reasoning_content should be injected when thinking is enabled"
+    )
+
+    # Case 2: reasoning model + thinking NOT in optional_params -> no injection
+    result = config.transform_request(
+        model="deepseek-reasoner",
+        messages=messages,
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+    assert "reasoning_content" not in result["messages"][1], (
+        "reasoning_content should not be injected when thinking is not enabled"
+    )
+
+    # Case 3: non-reasoning model + thinking enabled -> no injection
+    result = config.transform_request(
+        model="deepseek-chat",
+        messages=messages,
+        optional_params={"thinking": {"type": "enabled"}},
+        litellm_params={},
+        headers={},
+    )
+    assert "reasoning_content" not in result["messages"][1], (
+        "reasoning_content should not be injected for non-reasoning models"
+    )
diff --git a/tests/llm_translation/test_gpt4o_audio.py b/tests/llm_translation/test_gpt4o_audio.py
index 169fe855163..a50d07406d4 100644
--- a/tests/llm_translation/test_gpt4o_audio.py
+++ b/tests/llm_translation/test_gpt4o_audio.py
@@ -59,7 +59,7 @@ async def test_audio_output_from_model(stream):
     litellm.set_verbose = False
     try:
         completion = await litellm.acompletion(
-            model="gpt-4o-audio-preview",
+            model="gpt-audio-1.5",
             modalities=["text", "audio"],
             audio={"voice": "alloy", "format": "pcm16"},
             messages=[{"role": "user", "content": "response in 1 word - yes or no"}],
@@ -69,8 +69,14 @@ async def test_audio_output_from_model(stream):
         print(e)
         pytest.skip("Skipping test due to timeout")
     except Exception as e:
-        if "openai-internal" in str(e):
-            pytest.skip("Skipping test due to openai-internal error")
+        err = str(e).lower()
+        if (
+            "model_not_found" in err
+            or "does not exist" in err
+            or "openai-internal" in err
+        ):
+            pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}")
+        raise
 
     if stream is True:
         await check_streaming_response(completion)
@@ -85,7 +91,7 @@ async def test_audio_output_from_model(stream):
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("stream", [True, False])
-@pytest.mark.parametrize("model", ["gpt-4o-audio-preview"])  # "gpt-4o-audio-preview",
+@pytest.mark.parametrize("model", ["gpt-audio-1.5"])
 async def test_audio_input_to_model(stream, model):
     # Fetch the audio file and convert it to a base64 encoded string
     audio_format = "pcm16"
@@ -121,9 +127,14 @@ async def test_audio_input_to_model(stream, model):
         print(e)
         pytest.skip("Skipping test due to timeout")
     except Exception as e:
-        if "openai-internal" in str(e):
-            pytest.skip("Skipping test due to openai-internal error")
-        raise e
+        err = str(e).lower()
+        if (
+            "model_not_found" in err
+            or "does not exist" in err
+            or "openai-internal" in err
+        ):
+            pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}")
+        raise
     if stream is True:
         await check_streaming_response(completion)
     else:
diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py
index 469516407c8..80e764147bb 100644
--- a/tests/llm_translation/test_nvidia_nim.py
+++ b/tests/llm_translation/test_nvidia_nim.py
@@ -262,3 +262,44 @@ class TestNvidiaNim(BaseLLMRerankTest):
     def get_expected_cost(self) -> float:
         """Nvidia NIM rerank models are free (cost = 0.0)"""
         return 0.0
+
+    @pytest.mark.asyncio()
+    @pytest.mark.parametrize("sync_mode", [True, False])
+    async def test_basic_rerank(self, sync_mode, monkeypatch):
+        """
+        Override the base live rerank test with a mocked HTTP layer.
+
+        NVIDIA reached end-of-life for the hosted
+        nvidia/llama-3.2-nv-rerankqa-1b-v2 rerank API on 2026-05-18 and
+        published no replacement model, so a live call now returns HTTP 410
+        ("Gone"). NVIDIA's hosted catalog rotates on a schedule, so pointing
+        at another live model would only defer the same failure. Mock the
+        transport instead (same pattern as
+        test_nvidia_nim_rerank_ranking_endpoint above) so the request/response
+        transformation and cost calculation stay covered offline.
+        """
+        monkeypatch.setenv("NVIDIA_NIM_API_KEY", "fake-api-key")
+
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.headers = {}
+        mock_response.text = ""
+        mock_response.json.return_value = {
+            "rankings": [
+                {"index": 0, "logit": 0.95},
+                {"index": 1, "logit": 0.75},
+            ],
+            "usage": {"total_tokens": 7},
+        }
+
+        with (
+            patch(
+                "litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
+                return_value=mock_response,
+            ),
+            patch(
+                "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+                return_value=mock_response,
+            ),
+        ):
+            await super().test_basic_rerank(sync_mode=sync_mode)
diff --git a/tests/local_testing/conftest.py b/tests/local_testing/conftest.py
index 0ff7dff668a..acb79a7577d 100644
--- a/tests/local_testing/conftest.py
+++ b/tests/local_testing/conftest.py
@@ -22,14 +22,29 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm
 
-from tests._vcr_conftest_common import (  # noqa: E402
+# ``litellm.model_cost`` is loaded at import time from the URL pinned to
+# ``main`` (``LITELLM_MODEL_COST_MAP_URL``).  The in-tree backup ships with
+# this branch and can include pricing entries that main has not yet picked
+# up (e.g. an upstream provider rotates a model id and the test cassette
+# records the new name).  Backfill any entries that are missing from the
+# remote-fetched map so cost-calculator lookups in tests succeed against
+# the cassette state the branch is being tested with.
+from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
+
+for _k, _v in GetModelCostMap.load_local_model_cost_map().items():
+    litellm.model_cost.setdefault(_k, _v)
+
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -84,6 +99,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -93,6 +109,7 @@ def pytest_runtest_logreport(report):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
 
 
 # ---------------------------------------------------------------------------
diff --git a/tests/local_testing/test_amazing_vertex_completion.py b/tests/local_testing/test_amazing_vertex_completion.py
index 9782bf3c2af..2382b8a5197 100644
--- a/tests/local_testing/test_amazing_vertex_completion.py
+++ b/tests/local_testing/test_amazing_vertex_completion.py
@@ -4223,7 +4223,13 @@ def test_gemini_google_maps_tool_simple():
             )
         print(f"Response: {response.model_dump_json(indent=4)}")
         assert response.choices[0].message.content is not None
-    except litellm.RateLimitError:
+    except (litellm.RateLimitError, litellm.InternalServerError):
+        # Transient Vertex-side failures (rate limiting, 500 INTERNAL from the
+        # Google Maps grounding backend) are not LiteLLM bugs — don't fail CI.
         pass
+    except litellm.InternalServerError:
+        pytest.skip(
+            "Google Maps Platform returned a transient 500 (upstream flake); skipping."
+        )
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
diff --git a/tests/local_testing/test_caching_handler.py b/tests/local_testing/test_caching_handler.py
index 2b6712cbaa3..0f4539162a2 100644
--- a/tests/local_testing/test_caching_handler.py
+++ b/tests/local_testing/test_caching_handler.py
@@ -25,6 +25,7 @@ from unittest.mock import AsyncMock, patch, MagicMock
 from litellm.caching.caching_handler import (
     LLMCachingHandler,
     CachingHandlerResponse,
+    _is_chat_completion_cached_dict,
     _should_defer_streaming_cache_hit_callbacks,
 )
 from litellm.caching.caching import LiteLLMCacheType
@@ -40,6 +41,7 @@ from litellm.types.utils import (
 from litellm.types.llms.openai import ResponsesAPIResponse
 from datetime import timedelta, datetime
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm._logging import verbose_logger
 import logging
 
@@ -1072,6 +1074,70 @@ def test_convert_cached_streaming_responses_result_to_iterator():
     )
 
 
+def test_is_chat_completion_cached_dict():
+    assert _is_chat_completion_cached_dict(
+        {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []}
+    )
+    assert _is_chat_completion_cached_dict(
+        {"id": "other", "object": "chat.completion.chunk", "choices": []}
+    )
+    assert not _is_chat_completion_cached_dict(
+        {"id": "resp_abc", "object": "response", "output": []}
+    )
+
+
+def test_convert_cached_aresponses_bridge_chat_completion_stream():
+    """
+    openai/responses chat-completions bridge caches ModelResponse JSON on aresponses
+    cache keys; replay must not call ResponsesAPIResponse(**chatcmpl_dict).
+    """
+    caching_handler = LLMCachingHandler(
+        original_function=aresponses, request_kwargs={}, start_time=datetime.now()
+    )
+    logging_obj = LiteLLMLogging(
+        litellm_call_id=str(datetime.now()),
+        call_type=CallTypes.aresponses.value,
+        model="gpt-5.4",
+        messages=[],
+        function_id=str(uuid.uuid4()),
+        stream=True,
+        start_time=datetime.now(),
+    )
+    cached_result = {
+        "id": "chatcmpl-bridge-cache-test",
+        "object": "chat.completion",
+        "created": int(time.time()),
+        "model": "gpt-5.4",
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": "Hi!"},
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {
+            "prompt_tokens": 7,
+            "completion_tokens": 11,
+            "total_tokens": 18,
+        },
+    }
+
+    result = caching_handler._convert_cached_result_to_model_response(
+        cached_result=cached_result,
+        call_type=CallTypes.aresponses.value,
+        kwargs={
+            "model": "gpt-5.4",
+            "stream": True,
+            "messages": [{"role": "user", "content": "hi"}],
+        },
+        logging_obj=logging_obj,
+        model="gpt-5.4",
+        args=(),
+    )
+
+    assert isinstance(result, CustomStreamWrapper)
+
+
 def test_convert_cached_streaming_reasoning_result_to_iterator():
     caching_handler = LLMCachingHandler(
         original_function=responses, request_kwargs={}, start_time=datetime.now()
diff --git a/tests/local_testing/test_custom_callback_input.py b/tests/local_testing/test_custom_callback_input.py
index 545039e60ba..6a4ec9206f7 100644
--- a/tests/local_testing/test_custom_callback_input.py
+++ b/tests/local_testing/test_custom_callback_input.py
@@ -1125,7 +1125,7 @@ def test_standard_logging_payload_audio(turn_off_message_logging, stream):
     ) as mock_client:
         try:
             response = litellm.completion(
-                model="gpt-4o-audio-preview",
+                model="gpt-audio-1.5",
                 modalities=["text", "audio"],
                 audio={"voice": "alloy", "format": "pcm16"},
                 messages=[
@@ -1134,8 +1134,14 @@ def test_standard_logging_payload_audio(turn_off_message_logging, stream):
                 stream=stream,
             )
         except Exception as e:
-            if "openai-internal" in str(e):
-                pytest.skip("Skipping test due to openai-internal error")
+            err = str(e).lower()
+            if (
+                "model_not_found" in err
+                or "does not exist" in err
+                or "openai-internal" in err
+            ):
+                pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}")
+            raise
 
         if stream:
             for chunk in response:
diff --git a/tests/local_testing/test_stream_chunk_builder.py b/tests/local_testing/test_stream_chunk_builder.py
index 24fdf49c16c..38e04b93f18 100644
--- a/tests/local_testing/test_stream_chunk_builder.py
+++ b/tests/local_testing/test_stream_chunk_builder.py
@@ -649,7 +649,7 @@ def test_stream_chunk_builder_openai_audio_output_usage():
 
     try:
         completion = client.chat.completions.create(
-            model="gpt-4o-audio-preview",
+            model="gpt-audio-1.5",
             modalities=["text", "audio"],
             audio={"voice": "alloy", "format": "pcm16"},
             messages=[{"role": "user", "content": "response in 1 word - yes or no"}],
@@ -657,8 +657,14 @@ def test_stream_chunk_builder_openai_audio_output_usage():
             stream_options={"include_usage": True},
         )
     except Exception as e:
-        if "openai-internal" in str(e):
-            pytest.skip("Skipping test due to openai-internal error")
+        err = str(e).lower()
+        if (
+            "model_not_found" in err
+            or "does not exist" in err
+            or "openai-internal" in err
+        ):
+            pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}")
+        raise
 
     chunks = []
     for chunk in completion:
diff --git a/tests/local_testing/test_streaming.py b/tests/local_testing/test_streaming.py
index b1a93c380b2..10f351714e1 100644
--- a/tests/local_testing/test_streaming.py
+++ b/tests/local_testing/test_streaming.py
@@ -993,6 +993,11 @@ def test_vertex_ai_stream(provider):
 
         except litellm.RateLimitError as e:
             pass
+        except litellm.exceptions.MidStreamFallbackError as e:
+            # Streaming 429s are wrapped in MidStreamFallbackError so the
+            # Router can fall back; treat as a transient rate-limit pass.
+            if not isinstance(e.original_exception, litellm.RateLimitError):
+                pytest.fail(f"Error occurred: {e}")
         except Exception as e:
             pytest.fail(f"Error occurred: {e}")
 
diff --git a/tests/logging_callback_tests/conftest.py b/tests/logging_callback_tests/conftest.py
index cdb9200bc83..6dde85f2ca7 100644
--- a/tests/logging_callback_tests/conftest.py
+++ b/tests/logging_callback_tests/conftest.py
@@ -19,14 +19,17 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -79,6 +82,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -229,3 +233,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/mcp_tests/test_mcp_server.py b/tests/mcp_tests/test_mcp_server.py
index 409f4fad99a..c20fb09eeba 100644
--- a/tests/mcp_tests/test_mcp_server.py
+++ b/tests/mcp_tests/test_mcp_server.py
@@ -382,6 +382,11 @@ async def test_mcp_http_transport_tool_not_found():
         }
     )
 
+    # Mapping populated for this server but not for the requested tool
+    test_manager.tool_name_to_mcp_server_name_mapping["gmail_send_email"] = (
+        "test_http_server"
+    )
+
     # Try to call a tool that doesn't exist in mapping
     with pytest.raises(ValueError, match="Tool nonexistent_tool not found"):
         await test_manager.call_tool(
@@ -881,6 +886,7 @@ async def test_get_tools_from_mcp_servers():
                 extra_headers=None,
                 add_prefix=False,
                 raw_headers=None,
+                user_api_key_auth=None,
             ):
                 if server.server_id == "server1_id":
                     return [mock_tool_1]
@@ -1764,6 +1770,26 @@ def test_get_server_auth_header_fallback_to_default():
     assert result == "Bearer default_token"
 
 
+def test_get_server_auth_header_hyphenated_alias_sanitized_header_key():
+    """Header keys use sanitized alias; lookup must match legacy hyphenated aliases."""
+    from litellm.proxy._experimental.mcp_server.rest_endpoints import (
+        _get_server_auth_header,
+    )
+
+    mock_server = MagicMock()
+    mock_server.alias = "GitHub-MCP"
+    mock_server.server_name = "github_mcp_server"
+
+    mcp_server_auth_headers = {
+        "github_mcp": {"Authorization": "Bearer github-mcp-token"},
+    }
+
+    result = _get_server_auth_header(
+        mock_server, mcp_server_auth_headers, "Bearer default_token"
+    )
+    assert result == {"Authorization": "Bearer github-mcp-token"}
+
+
 def test_get_server_auth_header_no_auth_headers():
     """Test _get_server_auth_header function with no auth headers."""
     from litellm.proxy._experimental.mcp_server.rest_endpoints import (
@@ -1856,6 +1882,7 @@ async def test_get_tools_for_single_server():
             extra_headers=None,
             add_prefix=False,
             raw_headers=None,
+            user_api_key_auth=None,
         )
 
         # Verify the result
diff --git a/tests/ocr_tests/conftest.py b/tests/ocr_tests/conftest.py
index 66970b8579f..94790bd7aa3 100644
--- a/tests/ocr_tests/conftest.py
+++ b/tests/ocr_tests/conftest.py
@@ -12,14 +12,17 @@ import pytest
 
 sys.path.insert(0, os.path.abspath("../.."))
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -51,6 +54,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -64,3 +68,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/otel_tests/test_prometheus.py b/tests/otel_tests/test_prometheus.py
index c9490af07cb..90c71037609 100644
--- a/tests/otel_tests/test_prometheus.py
+++ b/tests/otel_tests/test_prometheus.py
@@ -610,22 +610,18 @@ def extract_user_budget_metrics(metrics_text: str, user_id: str) -> Dict[str, fl
     # Escape user_id for regex pattern matching
     escaped_user_id = re.escape(user_id)
 
-    # Get remaining budget
-    remaining_pattern = (
-        f'litellm_remaining_user_budget_metric{{user="{escaped_user_id}"}} ([0-9.]+)'
-    )
+    # Get remaining budget (user_email and user_alias may also be present as labels)
+    remaining_pattern = rf'litellm_remaining_user_budget_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)'
     remaining_match = re.search(remaining_pattern, metrics_text)
     metrics["remaining"] = float(remaining_match.group(1)) if remaining_match else None
 
     # Get total budget
-    total_pattern = (
-        f'litellm_user_max_budget_metric{{user="{escaped_user_id}"}} ([0-9.]+)'
-    )
+    total_pattern = rf'litellm_user_max_budget_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)'
     total_match = re.search(total_pattern, metrics_text)
     metrics["total"] = float(total_match.group(1)) if total_match else None
 
     # Get remaining hours
-    hours_pattern = f'litellm_user_budget_remaining_hours_metric{{user="{escaped_user_id}"}} ([0-9.]+)'
+    hours_pattern = rf'litellm_user_budget_remaining_hours_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)'
     hours_match = re.search(hours_pattern, metrics_text)
     metrics["remaining_hours"] = float(hours_match.group(1)) if hours_match else None
 
diff --git a/tests/pass_through_tests/package-lock.json b/tests/pass_through_tests/package-lock.json
index 8aa33340b16..2f8e7fe21b2 100644
--- a/tests/pass_through_tests/package-lock.json
+++ b/tests/pass_through_tests/package-lock.json
@@ -951,13 +951,12 @@
       "license": "BSD-3-Clause"
     },
     "node_modules/@protobufjs/fetch": {
-      "version": "1.1.0",
-      "resolved": "https://registry.npmjs.org/@protobufjs/fetch/-/fetch-1.1.0.tgz",
-      "integrity": "sha512-lljVXpqXebpsijW71PZaCYeIcE5on1w5DlQy5WH6GLbFryLUrBD4932W/E2BSpfRJWseIL4v/KPgBFxDOIdKpQ==",
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/@protobufjs/fetch/-/fetch-1.1.1.tgz",
+      "integrity": "sha512-GpptLrs57adMSuHi3VNj0mAF8dwh36LMaYF6XyJ6JMWlVsc+t42tm1HSEDmOs3A8fC9yyeisgLhsTVQokOZ0zw==",
       "license": "BSD-3-Clause",
       "dependencies": {
-        "@protobufjs/aspromise": "^1.1.1",
-        "@protobufjs/inquire": "^1.1.0"
+        "@protobufjs/aspromise": "^1.1.1"
       }
     },
     "node_modules/@protobufjs/float": {
@@ -967,9 +966,9 @@
       "license": "BSD-3-Clause"
     },
     "node_modules/@protobufjs/inquire": {
-      "version": "1.1.1",
-      "resolved": "https://registry.npmjs.org/@protobufjs/inquire/-/inquire-1.1.1.tgz",
-      "integrity": "sha512-mnzgDV26ueAvk7rsbt9L7bE0SuAoqyuys/sMMrmVcN5x9VsxpcG3rqAUSgDyLp0UZlmNfIbQ4fHfCtreVBk8Ew==",
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@protobufjs/inquire/-/inquire-1.1.2.tgz",
+      "integrity": "sha512-pa0vFRuws4wkvaXKK1uXZMAwAX4/t8ANaJo45iw/oQHNQ9q5xUzwgFmVJGXiga2BeN+zpX7Vf9vmsiIa2J+MUw==",
       "license": "BSD-3-Clause"
     },
     "node_modules/@protobufjs/path": {
@@ -3510,9 +3509,9 @@
       }
     },
     "node_modules/protobufjs": {
-      "version": "7.5.6",
-      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.5.6.tgz",
-      "integrity": "sha512-M71sTMB146U3u0di3yup8iM+zv8yPRNQVr1KK4tyBitl3qFvEGucq/rGDRShD2rsJhtN02RJaJ7j5X5hmy8SJg==",
+      "version": "7.6.0",
+      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.6.0.tgz",
+      "integrity": "sha512-LtESOsMPTZgyYtwxhvdgdjGL0HmXEaRA/hVD6sol4zA60hVXXXP/SGmxnqDbgGE8gy7pYex7cym+5vYPcmaXBQ==",
       "hasInstallScript": true,
       "license": "BSD-3-Clause",
       "dependencies": {
@@ -3520,14 +3519,14 @@
         "@protobufjs/base64": "^1.1.2",
         "@protobufjs/codegen": "^2.0.5",
         "@protobufjs/eventemitter": "^1.1.0",
-        "@protobufjs/fetch": "^1.1.0",
+        "@protobufjs/fetch": "^1.1.1",
         "@protobufjs/float": "^1.0.2",
-        "@protobufjs/inquire": "^1.1.1",
+        "@protobufjs/inquire": "^1.1.2",
         "@protobufjs/path": "^1.1.2",
         "@protobufjs/pool": "^1.1.0",
         "@protobufjs/utf8": "^1.1.1",
         "@types/node": ">=13.7.0",
-        "long": "^5.0.0"
+        "long": "^5.3.2"
       },
       "engines": {
         "node": ">=12.0.0"
@@ -4035,9 +4034,9 @@
       }
     },
     "node_modules/ws": {
-      "version": "8.20.0",
-      "resolved": "https://registry.npmjs.org/ws/-/ws-8.20.0.tgz",
-      "integrity": "sha512-sAt8BhgNbzCtgGbt2OxmpuryO63ZoDk/sqaB/znQm94T4fCEsy/yV+7CdC1kJhOU9lboAEU7R3kquuycDoibVA==",
+      "version": "8.20.1",
+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.20.1.tgz",
+      "integrity": "sha512-It4dO0K5v//JtTXuPkfEOaI3uUN87iYPnqo/ZzqCoG3g8uhA66QUMs/SrM0YK7/NAu+r4LMh/9dq2A7k+rHs+w==",
       "license": "MIT",
       "engines": {
         "node": ">=10.0.0"
diff --git a/tests/pass_through_unit_tests/conftest.py b/tests/pass_through_unit_tests/conftest.py
index 42a95343eb7..390e14b7f11 100644
--- a/tests/pass_through_unit_tests/conftest.py
+++ b/tests/pass_through_unit_tests/conftest.py
@@ -5,14 +5,17 @@ import pytest
 
 sys.path.insert(0, os.path.abspath("../.."))
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -56,6 +59,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -71,3 +75,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/login_to_ui.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/login_to_ui.spec.ts
deleted file mode 100644
index e5a397a6a66..00000000000
--- a/tests/proxy_admin_ui_tests/e2e_ui_tests/login_to_ui.spec.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
-
-Login to Admin UI
-Basic UI Test
-
-Click on all the tabs ensure nothing is broken
-*/
-
-import { test, expect } from "@playwright/test";
-
-test("admin login test", async ({ page }) => {
-  // Go to the specified URL
-  await page.goto("http://localhost:4000/ui");
-  await page.waitForLoadState("networkidle");
-
-  await page.screenshot({ path: "test-results/login_before.png" });
-
-  // Enter "admin" in the username input field
-  await page.fill('input[placeholder="Enter your username"]', "admin");
-
-  // Enter "gm" in the password input field
-  await page.fill('input[placeholder="Enter your password"]', "gm");
-
-  page.screenshot({ path: "test-results/login_after_inputs.png" });
-
-  // Optionally, you can add an assertion to verify the login button is enabled
-  const loginButton = page.getByRole("button", { name: "Login" });
-  await expect(loginButton).toBeEnabled();
-
-  // Optionally, you can click the login button to submit the form
-  await loginButton.click();
-  const tabs = [
-    "Virtual Keys",
-    "Playground",
-    "Models",
-    "Usage",
-    "Teams",
-    "Internal User",
-    "Settings",
-    "Experimental",
-    "API Reference",
-    "AI Hub",
-  ];
-
-  for (const tab of tabs) {
-    const tabElement = page.locator("span.ant-menu-title-content", {
-      hasText: tab,
-    });
-    await tabElement.click();
-  }
-});
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/redirect-fail-screenshot.png b/tests/proxy_admin_ui_tests/e2e_ui_tests/redirect-fail-screenshot.png
deleted file mode 100644
index b2e33251260..00000000000
Binary files a/tests/proxy_admin_ui_tests/e2e_ui_tests/redirect-fail-screenshot.png and /dev/null differ
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/require_auth_for_dashboard.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/require_auth_for_dashboard.spec.ts
deleted file mode 100644
index 4e4bd2fcd93..00000000000
--- a/tests/proxy_admin_ui_tests/e2e_ui_tests/require_auth_for_dashboard.spec.ts
+++ /dev/null
@@ -1,37 +0,0 @@
-// tests/auth.spec.ts
-import { test, expect } from "@playwright/test";
-
-test.describe("Authentication Checks", () => {
-  test("should redirect unauthenticated user from a protected page", async ({
-    page,
-  }) => {
-    test.setTimeout(30000);
-
-    page.on("console", (msg) => console.log("PAGE LOG:", msg.text()));
-
-    const protectedPageUrl = "http://localhost:4000/ui?page=llm-playground";
-    const expectedRedirectUrl = "http://localhost:4000/ui/login/";
-
-    console.log(
-      `Attempting to navigate to protected page: ${protectedPageUrl}`
-    );
-
-    await page.goto(protectedPageUrl);
-
-    console.log(`Navigation initiated. Current URL: ${page.url()}`);
-
-    try {
-      await page.waitForURL(expectedRedirectUrl, { timeout: 10000 });
-      console.log(`Waited for URL. Current URL is now: ${page.url()}`);
-    } catch (error) {
-      console.error(
-        `Timeout waiting for URL: ${expectedRedirectUrl}. Current URL: ${page.url()}`
-      );
-      await page.screenshot({ path: "redirect-fail-screenshot.png" });
-      throw error;
-    }
-
-    await expect(page).toHaveURL(expectedRedirectUrl);
-    console.log(`Assertion passed: Page URL is ${expectedRedirectUrl}`);
-  });
-});
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/search_users.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/search_users.spec.ts
deleted file mode 100644
index d72c44ab8cc..00000000000
--- a/tests/proxy_admin_ui_tests/e2e_ui_tests/search_users.spec.ts
+++ /dev/null
@@ -1,222 +0,0 @@
-/*
-Search Users in Admin UI
-E2E Test for user search functionality
-
-Tests:
-1. Navigate to Internal Users tab
-2. Verify search input exists
-3. Test search functionality
-4. Verify results update
-5. Test filtering by email, user ID, and SSO user ID
-*/
-
-import { test, expect } from "@playwright/test";
-
-test("user search test", async ({ page }) => {
-  // Set a longer timeout for the entire test
-  test.setTimeout(60000);
-
-  // Enable console logging
-  page.on("console", (msg) => console.log("PAGE LOG:", msg.text()));
-
-  // Login first
-  await page.goto("http://localhost:4000/ui");
-  await page.waitForLoadState("networkidle");
-  console.log("Navigated to login page");
-
-  page.screenshot({ path: "test-results/search_users_before_login.png" });
-
-  // Wait for login form to be visible
-  await page.waitForSelector('input[placeholder="Enter your username"]', {
-    timeout: 10000,
-  });
-  console.log("Login form is visible");
-
-  await page.fill('input[placeholder="Enter your username"]', "admin");
-  await page.fill('input[placeholder="Enter your password"]', "gm");
-  console.log("Filled login credentials");
-
-  const loginButton = page.getByRole("button", { name: "Login" });
-  await expect(loginButton).toBeEnabled();
-  await loginButton.click();
-  console.log("Clicked login button");
-
-  // Wait for navigation to complete and dashboard to load
-  await page.waitForLoadState("networkidle");
-  console.log("Page loaded after login");
-
-  // Take a screenshot for debugging
-  await page.screenshot({ path: "after-login.png" });
-  console.log("Took screenshot after login");
-
-  // Try to find the Internal User tab with more debugging
-  console.log("Looking for Internal User tab...");
-  const internalUserTab = page.locator("span.ant-menu-title-content", {
-    hasText: "Internal User",
-  });
-
-  // Wait for the tab to be visible
-  await internalUserTab.waitFor({ state: "visible", timeout: 10000 });
-  console.log("Internal User tab is visible");
-
-  // Take another screenshot before clicking
-  await page.screenshot({ path: "before-tab-click.png" });
-  console.log("Took screenshot before tab click");
-
-  await internalUserTab.click();
-  console.log("Clicked Internal User tab");
-
-  // Wait for the page to load and table to be visible
-  await page.waitForSelector("tbody tr", { timeout: 30000 });
-  await page.waitForTimeout(2000); // Additional wait for table to stabilize
-  console.log("Table is visible");
-
-  // Take a final screenshot
-  await page.screenshot({ path: "after-tab-click.png" });
-  console.log("Took screenshot after tab click");
-
-  // Verify search input exists
-  const searchInput = page.locator('input[placeholder="Search by email..."]');
-  await expect(searchInput).toBeVisible();
-  console.log("Search input is visible");
-
-  // Test search functionality
-  const initialUserCount = await page.locator("tbody tr").count();
-  console.log(`Initial user count: ${initialUserCount}`);
-
-  // Perform a search
-  const testEmail = "test@";
-  await searchInput.fill(testEmail);
-  console.log("Filled search input");
-
-  // Wait for the debounced search to complete
-  await page.waitForTimeout(500);
-  console.log("Waited for debounce");
-
-  // Wait for the results count to update
-  await page.waitForFunction((initialCount) => {
-    const currentCount = document.querySelectorAll("tbody tr").length;
-    return currentCount !== initialCount;
-  }, initialUserCount);
-  console.log("Results updated");
-
-  const filteredUserCount = await page.locator("tbody tr").count();
-  console.log(`Filtered user count: ${filteredUserCount}`);
-
-  expect(filteredUserCount).toBeDefined();
-
-  // Clear the search
-  await searchInput.clear();
-  console.log("Cleared search");
-
-  await page.waitForTimeout(500);
-  console.log("Waited for debounce after clear");
-
-  await page.waitForFunction((initialCount) => {
-    const currentCount = document.querySelectorAll("tbody tr").length;
-    return currentCount === initialCount;
-  }, initialUserCount);
-  console.log("Results reset");
-
-  const resetUserCount = await page.locator("tbody tr").count();
-  console.log(`Reset user count: ${resetUserCount}`);
-
-  expect(resetUserCount).toBe(initialUserCount);
-});
-
-test("user filter test", async ({ page }) => {
-  // Set a longer timeout for the entire test
-  test.setTimeout(60000);
-
-  // Enable console logging
-  page.on("console", (msg) => console.log("PAGE LOG:", msg.text()));
-
-  // Login first
-  await page.goto("http://localhost:4000/ui");
-  await page.waitForLoadState("networkidle");
-  console.log("Navigated to login page");
-
-  // Wait for login form to be visible
-  await page.waitForSelector('input[placeholder="Enter your username"]', {
-    timeout: 10000,
-  });
-  console.log("Login form is visible");
-
-  await page.fill('input[placeholder="Enter your username"]', "admin");
-  await page.fill('input[placeholder="Enter your password"]', "gm");
-  console.log("Filled login credentials");
-
-  const loginButton = page.getByRole("button", { name: "Login" });
-  await expect(loginButton).toBeEnabled();
-  await loginButton.click();
-  console.log("Clicked login button");
-
-  // Wait for navigation to complete and dashboard to load
-  await page.waitForLoadState("networkidle");
-  console.log("Page loaded after login");
-
-  // Navigate to Internal Users tab
-  const internalUserTab = page.locator("span.ant-menu-title-content", {
-    hasText: "Internal User",
-  });
-  await internalUserTab.waitFor({ state: "visible", timeout: 10000 });
-  await internalUserTab.click();
-  console.log("Clicked Internal User tab");
-
-  // Wait for the page to load and table to be visible
-  await page.waitForSelector("tbody tr", { timeout: 30000 });
-  await page.waitForTimeout(2000); // Additional wait for table to stabilize
-  console.log("Table is visible");
-
-  // Get initial user count
-  const initialUserCount = await page.locator("tbody tr").count();
-  console.log(`Initial user count: ${initialUserCount}`);
-
-  // Click the filter button to show additional filters
-  const filterButton = page.getByRole("button", {
-    name: "Filters",
-    exact: true,
-  });
-  await filterButton.click();
-  console.log("Clicked filter button");
-  await page.waitForTimeout(500); // Wait for filters to appear
-
-  // Test user ID filter
-  const userIdInput = page.locator('input[placeholder="Filter by User ID"]');
-  await expect(userIdInput).toBeVisible();
-  console.log("User ID filter is visible");
-
-  await userIdInput.fill("user");
-  console.log("Filled user ID filter");
-  await page.waitForTimeout(1000);
-  const userIdFilteredCount = await page.locator("tbody tr").count();
-  console.log(`User ID filtered count: ${userIdFilteredCount}`);
-  expect(userIdFilteredCount).toBeLessThan(initialUserCount);
-
-  // Clear user ID filter
-  await userIdInput.clear();
-  await page.waitForTimeout(1000);
-  console.log("Cleared user ID filter");
-
-  // Test SSO user ID filter
-  const ssoUserIdInput = page.locator('input[placeholder="Filter by SSO ID"]');
-  await expect(ssoUserIdInput).toBeVisible();
-  console.log("SSO user ID filter is visible");
-
-  await ssoUserIdInput.fill("sso");
-  console.log("Filled SSO user ID filter");
-  await page.waitForTimeout(1000);
-  const ssoUserIdFilteredCount = await page.locator("tbody tr").count();
-  console.log(`SSO user ID filtered count: ${ssoUserIdFilteredCount}`);
-  expect(ssoUserIdFilteredCount).toBeLessThan(initialUserCount);
-
-  // Clear SSO user ID filter
-  await ssoUserIdInput.clear();
-  await page.waitForTimeout(5000);
-  console.log("Cleared SSO user ID filter");
-
-  // Verify count returns to initial after clearing all filters
-  const finalUserCount = await page.locator("tbody tr").count();
-  console.log(`Final user count: ${finalUserCount}`);
-  expect(finalUserCount).toBe(initialUserCount);
-});
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/team_admin.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/team_admin.spec.ts
deleted file mode 100644
index a753c724b37..00000000000
--- a/tests/proxy_admin_ui_tests/e2e_ui_tests/team_admin.spec.ts
+++ /dev/null
@@ -1,250 +0,0 @@
-import { test, expect } from "@playwright/test";
-import { loginToUI } from "../utils/login";
-
-// test.describe("Invite User, Set Password, and Login", () => {
-//   let testEmail: string;
-//   const testPassword = "Password123!"; // Define a password
-//   const teamName1 = `team-invite-test-1-${Date.now()}`;
-//   const teamName2 = `team-invite-test-2-${Date.now()}`;
-//   const keyName1 = `key-${teamName1}`;
-//   const keyName2 = `key-${teamName2}`;
-
-//   test.beforeEach(async ({ page }) => {
-//     await loginToUI(page); // Login as admin first
-//     await page.goto("http://localhost:4000/ui?page=teams");
-
-//     // --- Create Team 1 ---
-//     await page.getByRole("button", { name: "+ Create New Team" }).click();
-//     await page
-//       .getByLabel("Team Name")
-//       .waitFor({ state: "visible", timeout: 5000 }); // Wait for label
-//     await page.getByLabel("Team Name").click();
-//     await page.getByLabel("Team Name").fill(teamName1);
-//     await page.getByRole("button", { name: "Create Team" }).click();
-//     // Wait for the modal to close or for a success message if applicable
-//     await expect(
-//       page.locator(".ant-modal-wrap").filter({ hasText: "Create New Team" })
-//     ).not.toBeVisible({ timeout: 10000 });
-//     console.log(`Created Team 1: ${teamName1}`);
-
-//     // --- Create Team 2 ---
-//     await page.getByRole("button", { name: "+ Create New Team" }).click();
-//     await page
-//       .getByLabel("Team Name")
-//       .waitFor({ state: "visible", timeout: 5000 }); // Wait for label
-//     await page.getByLabel("Team Name").click();
-//     await page.getByLabel("Team Name").fill(teamName2);
-//     await page.getByRole("button", { name: "Create Team" }).click();
-//     // Wait for the modal to close or for a success message if applicable
-//     await expect(
-//       page.locator(".ant-modal-wrap").filter({ hasText: "Create New Team" })
-//     ).not.toBeVisible({ timeout: 10000 });
-//     console.log(`Created Team 2: ${teamName2}`);
-
-//     // // Verify both teams are listed
-//     // await page.goto("http://localhost:4000/ui?page=teams"); // Refresh or ensure on teams page
-//     // await page.waitForTimeout(3000);
-//     await expect(page.getByText(teamName1)).toBeVisible({ timeout: 10000 });
-//     await expect(page.getByText(teamName2)).toBeVisible({ timeout: 10000 });
-
-//     // --- Navigate to Keys Page ---
-//     await page.goto("http://localhost:4000/ui?page=api-keys");
-//     await page.waitForTimeout(3000);
-//     await expect(
-//       page.getByRole("button", { name: "+ Create New Key" })
-//     ).toBeVisible(); // Wait for page load
-
-//     // --- Create Key for Team 1 ---
-//     await page.getByRole("button", { name: "+ Create New Key" }).click();
-//     const createKeyModal1 = page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Key Ownership" });
-//     await expect(createKeyModal1).toBeVisible();
-
-//     // Select Team 1
-//     await createKeyModal1
-//       .locator(".ant-select-selector >> input")
-//       .first()
-//       .click(); // Click to open team dropdown
-//     await createKeyModal1
-//       .locator(".ant-select-selector >> input")
-//       .first()
-//       .fill(teamName1);
-
-//     await page
-//       .locator(".ant-select-item-option")
-//       .filter({ hasText: teamName1 })
-//       .first()
-//       .click(); // Click specific team name
-
-//     // Enter Key Name 1
-//     await page.fill('input[id="key_alias"]', keyName1);
-
-//     // Click on models dropdown
-//     await page.locator("input#models").click();
-//     await page.waitForSelector(
-//       '.ant-select-item-option[title="All Team Models"]'
-//     );
-//     await page
-//       .locator('.ant-select-item-option[title="All Team Models"]')
-//       .click();
-
-//     // Click Create Key
-//     await createKeyModal1.getByRole("button", { name: "Create Key" }).click();
-
-//     // Close the Key Generated modal (which appears after successful creation)
-//     const keyGeneratedModal1 = page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Save your Key" });
-//     await expect(keyGeneratedModal1).toBeVisible({ timeout: 10000 });
-//     await keyGeneratedModal1.locator('button[aria-label="Close"]').click();
-//     await expect(keyGeneratedModal1).not.toBeVisible(); // Wait for close
-//     console.log(`Created Key 1: ${keyName1} for Team: ${teamName1}`);
-
-//     // --- Create Key for Team 2 ---
-//     await page.getByRole("button", { name: "+ Create New Key" }).click();
-//     const createKeyModal2 = page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Key Ownership" });
-//     await expect(createKeyModal2).toBeVisible();
-
-//     // Select Team 2
-//     await createKeyModal2
-//       .locator(".ant-select-selector >> input")
-//       .first()
-//       .click(); // Click to open team dropdown
-//     await page
-//       .locator(".ant-select-item-option")
-//       .filter({ hasText: teamName2 })
-//       .click(); // Click specific team name
-
-//     // Enter Key Name 2
-//     await page.fill('input[id="key_alias"]', keyName2);
-
-//     // Click on models dropdown
-//     await page.locator("input#models").click();
-//     await page.waitForSelector(
-//       '.ant-select-item-option[title="All Team Models"]'
-//     );
-//     await page
-//       .locator('.ant-select-item-option[title="All Team Models"]')
-//       .click();
-
-//     // Click Create Key
-//     await createKeyModal2.getByRole("button", { name: "Create Key" }).click();
-
-//     // Close the Key Generated modal
-//     const keyGeneratedModal2 = page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Save your Key" });
-//     await expect(keyGeneratedModal2).toBeVisible({ timeout: 10000 });
-//     await keyGeneratedModal2.locator('button[aria-label="Close"]').click();
-//     await expect(keyGeneratedModal2).not.toBeVisible(); // Wait for close
-//     console.log(`Created Key 2: ${keyName2} for Team: ${teamName2}`);
-//   });
-
-//   test("Invite user, set password via link, and login", async ({ page }) => {
-//     // Navigate to Users page
-//     await page.goto("http://localhost:4000/ui?page=users");
-
-//     // Go to Internal User tab
-//     const internalUserTab = page.locator("span.ant-menu-title-content", {
-//       hasText: "Internal User",
-//     });
-//     await internalUserTab.waitFor({ state: "visible", timeout: 10000 });
-//     await internalUserTab.click();
-
-//     // --- Invite User Flow ---
-//     await page.getByRole("button", { name: "+ Invite User" }).click();
-
-//     // Wait for the invite user modal to be visible
-//     const inviteModal = page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Invite User" });
-//     await expect(inviteModal).toBeVisible();
-
-//     testEmail = `test-${Date.now()}@litellm.ai`; // Use a unique email
-//     // Assuming the email input is the first one with 'base-input' test id inside the modal
-//     await inviteModal.getByTestId("base-input").first().fill(testEmail);
-
-//     // Select Global Admin Role (or another appropriate role)
-//     const globalRoleLabel = inviteModal.getByLabel("Global Proxy Role");
-//     await globalRoleLabel.click();
-//     // Wait for the dropdown option to be visible before clicking
-//     const adminRoleOption = page.getByTitle("Admin (All Permissions)", {
-//       exact: true,
-//     });
-//     await adminRoleOption.waitFor({ state: "visible", timeout: 5000 });
-//     await adminRoleOption.click();
-
-//     // Select Team - Add explicit wait before clicking
-//     const teamIdLabel = inviteModal.getByLabel("Team ID");
-//     // Wait for the label associated with the Team ID select to be visible
-//     await teamIdLabel.waitFor({ state: "visible", timeout: 10000 }); // Increased timeout for safety
-//     await teamIdLabel.click();
-
-//     // Wait for the team name option to be visible in the dropdown
-//     const teamNameOption = page.getByText(teamName1, { exact: true });
-//     await teamNameOption.waitFor({ state: "visible", timeout: 5000 });
-//     await teamNameOption.click();
-
-//     // Create User
-//     await inviteModal.getByRole("button", { name: "Create User" }).click();
-
-//     // --- Capture Invitation Link ---
-//     const invitationModal = page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Invitation Link" });
-//     await expect(invitationModal).toBeVisible({ timeout: 15000 }); // Wait longer for modal
-
-//     // Locate the text element containing the URL more reliably
-//     const invitationUrl = await page
-//       .locator("div.flex.justify-between.pt-5.pb-2") // find the correct div
-//       .filter({ hasText: "Invitation Link" }) // find the div that has text "Invitation Link"
-//       .locator("p") // find all <p> inside that div
-//       .nth(1) // pick the second <p> (index 1)
-//       .innerText();
-
-//     // Close Invitation Link Modal
-//     await page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Invitation Link" })
-//       .locator('button[aria-label="Close"]')
-//       .click();
-
-//     // Close Invite User Modal
-//     await page
-//       .locator(".ant-modal-wrap")
-//       .filter({ hasText: "Invite User" })
-//       .locator('button[aria-label="Close"]')
-//       .click();
-
-//     // Open invite link as new page (simulate invited user)
-//     const context = await page.context()?.browser()?.newContext();
-//     const invitedUserPage = await context?.newPage();
-//     if (!invitedUserPage) {
-//       throw new Error("invitedUserPage is undefined");
-//     }
-//     await invitedUserPage?.goto(invitationUrl || "");
-
-//     //Insert new password
-//     await invitedUserPage?.fill("input#password", testPassword);
-
-//     //Click on submit
-//     await invitedUserPage?.getByRole("button", { name: "Sign Up" }).click();
-
-//     // // --- Verify Keys Created ---
-//     // await invitedUserPage?.waitForSelector("table");
-
-//     // // Verify keyName1 (associated with user's team) IS visible in the table
-//     // const keyTable = invitedUserPage.locator('table'); // Locate the table element
-//     // await expect(keyTable).toBeVisible({ timeout: 10000 }); // Ensure table exists
-//     // // Use getByText within the table scope to find the key name
-//     // await expect(keyTable.getByText(keyName1, { exact: true })).toBeVisible({ timeout: 10000 });
-//     // console.log(`Verified key ${keyName1} is visible for user ${testEmail}`);
-
-//     // // Verify keyName2 (associated with the *other* team) IS NOT visible
-//     // await expect(keyTable.getByText(keyName2, { exact: true })).not.toBeVisible();
-//     // console.log(`Verified key ${keyName2} is NOT visible for user ${testEmail}`);
-//   });
-// });
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_internal_user.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/view_internal_user.spec.ts
deleted file mode 100644
index 832832d8ae8..00000000000
--- a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_internal_user.spec.ts
+++ /dev/null
@@ -1,72 +0,0 @@
-/*
-Test view internal user page
-*/
-
-import { test, expect } from "@playwright/test";
-
-test("view internal user page", async ({ page }) => {
-  // Go to the specified URL
-  await page.goto("http://localhost:4000/ui");
-  await page.waitForLoadState("networkidle");
-
-  page.screenshot({ path: "test-results/view_internal_user_before_login.png" });
-
-  // Enter "admin" in the username input field
-  await page.fill('input[placeholder="Enter your username"]', "admin");
-
-  // Enter "gm" in the password input field
-  await page.fill('input[placeholder="Enter your password"]', "gm");
-
-  // Click the login button
-  const loginButton = page.getByRole("button", { name: "Login" });
-  await expect(loginButton).toBeEnabled();
-  await loginButton.click();
-
-  // Wait for the Internal User tab and click it
-  const tabElement = page.locator("span.ant-menu-title-content", {
-    hasText: "Internal User",
-  });
-  await tabElement.click();
-
-  // Wait for the table to load
-  await page.waitForSelector("tbody tr", { timeout: 10000 });
-  await page.waitForTimeout(2000); // Additional wait for table to stabilize
-  await page.waitForLoadState("networkidle");
-
-  // Test all expected fields are present
-  // Verify that the API Keys column is rendered for all users
-  // The UI renders badges in each row - we just verify the column structure exists
-  const rowCount = await page.locator("tbody tr").count();
-  expect(rowCount).toBeGreaterThan(0);
-
-  const userIdHeader = await page.locator("th", { hasText: "User ID" });
-  await expect(userIdHeader).toBeVisible({ timeout: 10000 });
-
-  // test pagination
-  // Wait for pagination controls to be visible
-  await page.waitForSelector(".flex.justify-between.items-center", {
-    timeout: 5000,
-  });
-
-  // Check if we're on the first page by looking at the results count
-  const resultsText =
-    (await page.locator(".text-sm.text-gray-700").textContent()) || "";
-  const isFirstPage = resultsText.includes("1 -");
-
-  if (isFirstPage) {
-    // On first page, previous button should be disabled
-    const prevButton = page.locator("button", { hasText: "Previous" });
-    await expect(prevButton).toBeDisabled();
-  }
-
-  // Next button should be enabled if there are more pages
-  const nextButton = page.locator("button", { hasText: "Next" });
-  const totalResults =
-    (await page.locator(".text-sm.text-gray-700").textContent()) || "";
-  const hasMorePages =
-    totalResults.includes("of") && !totalResults.includes("1 - 25 of 25");
-
-  if (hasMorePages) {
-    await expect(nextButton).toBeEnabled();
-  }
-});
diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts
deleted file mode 100644
index adda3088f12..00000000000
--- a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts
+++ /dev/null
@@ -1,124 +0,0 @@
-import { test, expect } from "@playwright/test";
-import { loginToUI } from "../utils/login";
-
-test.describe("User Info View", () => {
-  test("should display user info when clicking on user ID", async ({
-    page,
-  }) => {
-    await page.goto("http://localhost:4000/ui");
-    await page.waitForLoadState("networkidle");
-
-    page.screenshot({
-      path: "test-results/view_user_info_before_login.png",
-    });
-
-    // Enter "admin" in the username input field
-    await page.fill('input[placeholder="Enter your username"]', "admin");
-    page.screenshot({
-      path: "test-results/view_user_info_after_username_input.png",
-    });
-
-    // Enter "gm" in the password input field
-    await page.fill('input[placeholder="Enter your password"]', "gm");
-    page.screenshot({
-      path: "test-results/view_user_info_after_password_input.png",
-    });
-
-    // Click the login button
-    const loginButton = page.getByRole("button", { name: "Login" });
-    await expect(loginButton).toBeEnabled();
-    await loginButton.click();
-    page.screenshot({
-      path: "test-results/view_user_info_after_login_button_click.png",
-    });
-
-    // Wait for navigation to complete and dashboard to load
-    await page.waitForLoadState("networkidle");
-    const tabElement = page.locator("span.ant-menu-title-content", {
-      hasText: "Internal User",
-    });
-    await tabElement.click();
-    page.screenshot({
-      path: "test-results/view_user_info_after_internal_user_tab_click.png",
-    });
-    // Wait for loading state to disappear
-    await page.waitForSelector('text="🚅 Loading users..."', {
-      state: "hidden",
-      timeout: 10000,
-    });
-    page.screenshot({ path: "test-results/view_user_info_after_loading.png" });
-    // Wait for users table to load
-    await page.waitForSelector("table");
-    page.screenshot({
-      path: "test-results/view_user_info_after_table_load.png",
-    });
-    // Get the first user ID cell
-    const firstUserIdCell = page.locator(
-      "table tbody tr:first-child td:first-child"
-    );
-    const userId = await firstUserIdCell.textContent();
-    console.log("Found user ID:", userId);
-
-    // Click on the user ID
-    await firstUserIdCell.click();
-    await page.waitForLoadState("networkidle");
-
-    // Check for tabs
-    await expect(page.locator('button:has-text("Overview")')).toBeVisible({
-      timeout: 10000,
-    });
-    await expect(page.locator('button:has-text("Details")')).toBeVisible({
-      timeout: 10000,
-    });
-
-    // Switch to details tab
-    await page.locator('button:has-text("Details")').click();
-
-    // Check details section
-    await expect(page.locator("text=User ID")).toBeVisible();
-    await expect(page.locator("text=Email")).toBeVisible();
-
-    // Go back to users list
-    await page.locator('button:has-text("Back to Users")').click();
-
-    // Verify we're back on the users page
-    await expect(page.locator("table")).toBeVisible();
-    await expect(
-      page.locator('input[placeholder="Search by email..."]')
-    ).toBeVisible();
-  });
-
-  // test("should handle user deletion", async ({ page }) => {
-  //   // Wait for users table to load
-  //   await page.waitForSelector("table");
-
-  //   // Get the first user ID cell
-  //   const firstUserIdCell = page.locator(
-  //     "table tbody tr:first-child td:first-child"
-  //   );
-  //   const userId = await firstUserIdCell.textContent();
-
-  //   // Click on the user ID
-  //   await firstUserIdCell.click();
-
-  //   // Wait for user info view to load
-  //   await page.waitForSelector('h1:has-text("User")');
-
-  //   // Click delete button
-  //   await page.locator('button:has-text("Delete User")').click();
-
-  //   // Confirm deletion in modal
-  //   await page.locator('button:has-text("Delete")').click();
-
-  //   // Verify success message
-  //   await expect(page.locator("text=User deleted successfully")).toBeVisible();
-
-  //   // Verify we're back on the users page
-  //   await expect(page.locator('h1:has-text("Users")')).toBeVisible();
-
-  //   // Verify user is no longer in the table
-  //   if (userId) {
-  //     await expect(page.locator(`text=${userId}`)).not.toBeVisible();
-  //   }
-  // });
-});
diff --git a/tests/proxy_admin_ui_tests/package-lock.json b/tests/proxy_admin_ui_tests/package-lock.json
deleted file mode 100644
index 8c79edf9ad1..00000000000
--- a/tests/proxy_admin_ui_tests/package-lock.json
+++ /dev/null
@@ -1,97 +0,0 @@
-{
-  "name": "proxy_admin_ui_tests",
-  "version": "1.0.0",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "name": "proxy_admin_ui_tests",
-      "version": "1.0.0",
-      "license": "ISC",
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-        "@playwright/test": "^1.47.2",
-        "@types/node": "^22.5.5"
-      }
-    },
-    "node_modules/@playwright/test": {
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-      "dev": true,
-      "license": "Apache-2.0",
-      "dependencies": {
-        "playwright": "1.56.1"
-      },
-      "bin": {
-        "playwright": "cli.js"
-      },
-      "engines": {
-        "node": ">=18"
-      }
-    },
-    "node_modules/@types/node": {
-      "version": "22.19.1",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-22.19.1.tgz",
-      "integrity": "sha512-LCCV0HdSZZZb34qifBsyWlUmok6W7ouER+oQIGBScS8EsZsQbrtFTUrDX4hOl+CS6p7cnNC4td+qrSVGSCTUfQ==",
-      "dev": true,
-      "license": "MIT",
-      "dependencies": {
-        "undici-types": "~6.21.0"
-      }
-    },
-    "node_modules/fsevents": {
-      "version": "2.3.2",
-      "resolved": "https://registry.npmjs.org/fsevents/-/fsevents-2.3.2.tgz",
-      "integrity": "sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==",
-      "dev": true,
-      "hasInstallScript": true,
-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "darwin"
-      ],
-      "engines": {
-        "node": "^8.16.0 || ^10.6.0 || >=11.0.0"
-      }
-    },
-    "node_modules/playwright": {
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-      "dev": true,
-      "license": "Apache-2.0",
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-      "bin": {
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-      },
-      "engines": {
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-      "license": "Apache-2.0",
-      "bin": {
-        "playwright-core": "cli.js"
-      },
-      "engines": {
-        "node": ">=18"
-      }
-    },
-    "node_modules/undici-types": {
-      "version": "6.21.0",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
-      "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
-      "dev": true,
-      "license": "MIT"
-    }
-  }
-}
diff --git a/tests/proxy_admin_ui_tests/package.json b/tests/proxy_admin_ui_tests/package.json
deleted file mode 100644
index 5933490fb1d..00000000000
--- a/tests/proxy_admin_ui_tests/package.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "name": "proxy_admin_ui_tests",
-  "version": "1.0.0",
-  "description": "",
-  "main": "index.js",
-  "scripts": {},
-  "keywords": [],
-  "author": "",
-  "license": "ISC",
-  "devDependencies": {
-    "@playwright/test": "1.56.1",
-    "@types/node": "22.19.1"
-  }
-}
diff --git a/tests/proxy_admin_ui_tests/playwright.config.ts b/tests/proxy_admin_ui_tests/playwright.config.ts
deleted file mode 100644
index 8b66c47394a..00000000000
--- a/tests/proxy_admin_ui_tests/playwright.config.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-import { defineConfig, devices } from '@playwright/test';
-
-/**
- * Read environment variables from file.
- * https://github.com/motdotla/dotenv
- */
-// import dotenv from 'dotenv';
-// import path from 'path';
-// dotenv.config({ path: path.resolve(__dirname, '.env') });
-
-/**
- * See https://playwright.dev/docs/test-configuration.
- */
-export default defineConfig({
-  testDir: './e2e_ui_tests',
-  testIgnore: ['**/tests/pass_through_tests/**', '../pass_through_tests/**/*'],
-  testMatch: '**/*.spec.ts',  // Only run files ending in .spec.ts
-  /* Run tests in files in parallel */
-  fullyParallel: true,
-  /* Fail the build on CI if you accidentally left test.only in the source code. */
-  forbidOnly: !!process.env.CI,
-  /* Retry on CI only */
-  retries: process.env.CI ? 2 : 0,
-  /* Opt out of parallel tests on CI. */
-  workers: process.env.CI ? 1 : undefined,
-  /* Reporter to use. See https://playwright.dev/docs/test-reporters */
-  reporter: 'html',
-  /* Shared settings for all the projects below. See https://playwright.dev/docs/api/class-testoptions. */
-  use: {
-    /* Base URL to use in actions like `await page.goto('/')`. */
-    // baseURL: 'http://127.0.0.1:3000',
-
-    /* Collect trace when retrying the failed test. See https://playwright.dev/docs/trace-viewer */
-    trace: 'on-first-retry',
-  },
-
-  /* Configure projects for major browsers */
-  projects: [
-    {
-      name: 'chromium',
-      use: { ...devices['Desktop Chrome'] },
-    },
-
-    {
-      name: 'firefox',
-      use: { ...devices['Desktop Firefox'] },
-    },
-
-    {
-      name: 'webkit',
-      use: { ...devices['Desktop Safari'] },
-    },
-
-    /* Test against mobile viewports. */
-    // {
-    //   name: 'Mobile Chrome',
-    //   use: { ...devices['Pixel 5'] },
-    // },
-    // {
-    //   name: 'Mobile Safari',
-    //   use: { ...devices['iPhone 12'] },
-    // },
-
-    /* Test against branded browsers. */
-    // {
-    //   name: 'Microsoft Edge',
-    //   use: { ...devices['Desktop Edge'], channel: 'msedge' },
-    // },
-    // {
-    //   name: 'Google Chrome',
-    //   use: { ...devices['Desktop Chrome'], channel: 'chrome' },
-    // },
-  ],
-  timeout: 4*60*1000,
-  expect: {
-    timeout: 10 * 1000
-  }
-  /* Run your local dev server before starting the tests */
-  // webServer: {
-  //   command: 'npm run start',
-  //   url: 'http://127.0.0.1:3000',
-  //   reuseExistingServer: !process.env.CI,
-  // },
-});
diff --git a/tests/proxy_admin_ui_tests/utils/login.ts b/tests/proxy_admin_ui_tests/utils/login.ts
deleted file mode 100644
index 25858d9f570..00000000000
--- a/tests/proxy_admin_ui_tests/utils/login.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import { Page, expect } from "@playwright/test";
-
-export async function loginToUI(page: Page) {
-  // Login first
-  await page.goto("http://localhost:4000/ui");
-  await page.waitForLoadState("networkidle");
-  console.log("Navigated to login page");
-
-  page.screenshot({ path: "test-results/login_utils_before.png" });
-  // Wait for login form to be visible
-  await page.waitForSelector('input[placeholder="Enter your username"]', {
-    timeout: 10000,
-  });
-  console.log("Login form is visible");
-
-  await page.fill('input[placeholder="Enter your username"]', "admin");
-  await page.fill('input[placeholder="Enter your password"]', "gm");
-  console.log("Filled login credentials");
-
-  const loginButton = page.getByRole("button", { name: "Login" });
-  await expect(loginButton).toBeEnabled();
-  await loginButton.click();
-  console.log("Clicked login button");
-
-  // Wait for navigation to complete
-  await page.waitForURL("**/*");
-}
diff --git a/tests/proxy_behavior/__init__.py b/tests/proxy_behavior/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/proxy_behavior/management/__init__.py b/tests/proxy_behavior/management/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/proxy_behavior/management/actors.py b/tests/proxy_behavior/management/actors.py
new file mode 100644
index 00000000000..6c2f1a61ce1
--- /dev/null
+++ b/tests/proxy_behavior/management/actors.py
@@ -0,0 +1,279 @@
+"""Read-world seed for the authz matrix tests: 2 orgs, 3 teams, 9 actors."""
+
+import enum
+import uuid
+from dataclasses import dataclass
+from typing import Any, Dict
+
+from prisma import Json
+
+from litellm.proxy._types import LitellmUserRoles
+from litellm.proxy.utils import PrismaClient, hash_token
+
+
+class Actor(str, enum.Enum):
+    PROXY_ADMIN = "proxy_admin"
+    ORG_ADMIN = "org_admin"
+    TEAM_ADMIN = "team_admin"
+    INTERNAL_USER = "internal_user"
+    OWNER = "owner"
+    UNRELATED_SAME_ORG = "unrelated_same_org"
+    CROSS_ORG_USER = "cross_org_user"
+    SERVICE_ACCOUNT = "service_account"
+    ORG_B_ADMIN = "org_b_admin"
+
+
+PREFIX = "behavior-pin-"
+ORG_A = PREFIX + "org-a"
+ORG_B = PREFIX + "org-b"
+TEAM_ALPHA = PREFIX + "team-alpha"
+TEAM_BETA = PREFIX + "team-beta"
+TEAM_GAMMA = PREFIX + "team-gamma"
+BUDGET_ID = PREFIX + "budget"
+
+
+@dataclass(frozen=True)
+class SeededKey:
+    user_id: str
+    cleartext: str
+    hashed: str
+
+
+@dataclass(frozen=True)
+class World:
+    org_a_id: str
+    org_b_id: str
+    team_alpha_id: str
+    team_beta_id: str
+    team_gamma_id: str
+    keys: Dict[Actor, SeededKey]
+
+
+def _new_clear_key() -> str:
+    return "sk-" + uuid.uuid4().hex
+
+
+def _actor_profile() -> Dict[Actor, Dict[str, Any]]:
+    return {
+        Actor.PROXY_ADMIN: {
+            "user_role": LitellmUserRoles.PROXY_ADMIN.value,
+            "team_id": None,
+            "organization_id": None,
+        },
+        Actor.ORG_ADMIN: {
+            "user_role": LitellmUserRoles.ORG_ADMIN.value,
+            "team_id": None,
+            "organization_id": ORG_A,
+        },
+        Actor.TEAM_ADMIN: {
+            "user_role": LitellmUserRoles.INTERNAL_USER.value,
+            "team_id": TEAM_ALPHA,
+            "organization_id": ORG_A,
+        },
+        Actor.INTERNAL_USER: {
+            "user_role": LitellmUserRoles.INTERNAL_USER.value,
+            "team_id": TEAM_ALPHA,
+            "organization_id": ORG_A,
+        },
+        Actor.OWNER: {
+            "user_role": LitellmUserRoles.INTERNAL_USER.value,
+            "team_id": TEAM_ALPHA,
+            "organization_id": ORG_A,
+        },
+        Actor.UNRELATED_SAME_ORG: {
+            "user_role": LitellmUserRoles.INTERNAL_USER.value,
+            "team_id": TEAM_ALPHA,
+            "organization_id": ORG_A,
+        },
+        Actor.CROSS_ORG_USER: {
+            "user_role": LitellmUserRoles.INTERNAL_USER.value,
+            "team_id": TEAM_BETA,
+            "organization_id": ORG_B,
+        },
+        Actor.SERVICE_ACCOUNT: {
+            "user_role": LitellmUserRoles.INTERNAL_USER.value,
+            "team_id": TEAM_ALPHA,
+            "organization_id": ORG_A,
+        },
+        Actor.ORG_B_ADMIN: {
+            "user_role": LitellmUserRoles.ORG_ADMIN.value,
+            "team_id": None,
+            "organization_id": ORG_B,
+        },
+    }
+
+
+async def _wipe_world(prisma: PrismaClient) -> None:
+    await prisma.db.litellm_verificationtoken.delete_many(
+        where={"user_id": {"startswith": PREFIX}}
+    )
+    await prisma.db.litellm_organizationmembership.delete_many(
+        where={"user_id": {"startswith": PREFIX}}
+    )
+    await prisma.db.litellm_teammembership.delete_many(
+        where={"user_id": {"startswith": PREFIX}}
+    )
+    await prisma.db.litellm_usertable.delete_many(
+        where={"user_id": {"startswith": PREFIX}}
+    )
+    await prisma.db.litellm_teamtable.delete_many(
+        where={"team_id": {"startswith": PREFIX}}
+    )
+    await prisma.db.litellm_organizationtable.delete_many(
+        where={"organization_id": {"startswith": PREFIX}}
+    )
+    await prisma.db.litellm_budgettable.delete_many(where={"budget_id": BUDGET_ID})
+
+
+async def seed_world(prisma: PrismaClient) -> World:
+    await _wipe_world(prisma)
+
+    await prisma.db.litellm_budgettable.create(
+        data={
+            "budget_id": BUDGET_ID,
+            "created_by": "behavior-pin-seeder",
+            "updated_by": "behavior-pin-seeder",
+        }
+    )
+
+    for org_id, alias in [(ORG_A, "alpha"), (ORG_B, "beta")]:
+        await prisma.db.litellm_organizationtable.create(
+            data={
+                "organization_id": org_id,
+                "organization_alias": alias,
+                "budget_id": BUDGET_ID,
+                "created_by": "behavior-pin-seeder",
+                "updated_by": "behavior-pin-seeder",
+            }
+        )
+
+    profiles = _actor_profile()
+    user_ids: Dict[Actor, str] = {actor: PREFIX + actor.value for actor in Actor}
+
+    for actor, profile in profiles.items():
+        teams_list = [profile["team_id"]] if profile["team_id"] else []
+        await prisma.db.litellm_usertable.create(
+            data={
+                "user_id": user_ids[actor],
+                "user_role": profile["user_role"],
+                "team_id": profile["team_id"],
+                "organization_id": profile["organization_id"],
+                "teams": teams_list,
+            }
+        )
+
+    # _get_user_in_team in key_management_endpoints.py walks members_with_roles
+    # (a JSON list of {user_id, role}), not the String[] members column —
+    # populate both to match what /team/new produces.
+    await prisma.db.litellm_teamtable.create(
+        data={
+            "team_id": TEAM_ALPHA,
+            "team_alias": "alpha-1",
+            "organization_id": ORG_A,
+            "admins": [user_ids[Actor.TEAM_ADMIN]],
+            "members": [
+                user_ids[Actor.TEAM_ADMIN],
+                user_ids[Actor.INTERNAL_USER],
+                user_ids[Actor.OWNER],
+                user_ids[Actor.UNRELATED_SAME_ORG],
+                user_ids[Actor.SERVICE_ACCOUNT],
+            ],
+            "members_with_roles": Json(
+                [
+                    {"user_id": user_ids[Actor.TEAM_ADMIN], "role": "admin"},
+                    {"user_id": user_ids[Actor.INTERNAL_USER], "role": "user"},
+                    {"user_id": user_ids[Actor.OWNER], "role": "user"},
+                    {"user_id": user_ids[Actor.UNRELATED_SAME_ORG], "role": "user"},
+                    {"user_id": user_ids[Actor.SERVICE_ACCOUNT], "role": "user"},
+                ]
+            ),
+        }
+    )
+    await prisma.db.litellm_teamtable.create(
+        data={
+            "team_id": TEAM_BETA,
+            "team_alias": "beta-1",
+            "organization_id": ORG_B,
+            "admins": [],
+            "members": [user_ids[Actor.CROSS_ORG_USER]],
+            "members_with_roles": Json(
+                [
+                    {"user_id": user_ids[Actor.CROSS_ORG_USER], "role": "user"},
+                ]
+            ),
+        }
+    )
+    # TEAM_GAMMA: ORG_A team with no actor members — the "same-org,
+    # not-my-team" read target.
+    await prisma.db.litellm_teamtable.create(
+        data={
+            "team_id": TEAM_GAMMA,
+            "team_alias": "gamma-1",
+            "organization_id": ORG_A,
+            "admins": [],
+            "members": [],
+            "members_with_roles": Json([]),
+        }
+    )
+
+    for actor, org_id, role in [
+        (Actor.ORG_ADMIN, ORG_A, "org_admin"),
+        (Actor.TEAM_ADMIN, ORG_A, "internal_user"),
+        (Actor.INTERNAL_USER, ORG_A, "internal_user"),
+        (Actor.OWNER, ORG_A, "internal_user"),
+        (Actor.UNRELATED_SAME_ORG, ORG_A, "internal_user"),
+        (Actor.SERVICE_ACCOUNT, ORG_A, "internal_user"),
+        (Actor.CROSS_ORG_USER, ORG_B, "internal_user"),
+        (Actor.ORG_B_ADMIN, ORG_B, "org_admin"),
+    ]:
+        await prisma.db.litellm_organizationmembership.create(
+            data={
+                "user_id": user_ids[actor],
+                "organization_id": org_id,
+                "user_role": role,
+            }
+        )
+
+    for actor, team_id in [
+        (Actor.TEAM_ADMIN, TEAM_ALPHA),
+        (Actor.INTERNAL_USER, TEAM_ALPHA),
+        (Actor.OWNER, TEAM_ALPHA),
+        (Actor.UNRELATED_SAME_ORG, TEAM_ALPHA),
+        (Actor.SERVICE_ACCOUNT, TEAM_ALPHA),
+        (Actor.CROSS_ORG_USER, TEAM_BETA),
+    ]:
+        await prisma.db.litellm_teammembership.create(
+            data={"user_id": user_ids[actor], "team_id": team_id}
+        )
+
+    keys: Dict[Actor, SeededKey] = {}
+    for actor, profile in profiles.items():
+        cleartext = _new_clear_key()
+        hashed = hash_token(cleartext)
+        token_data: Dict[str, Any] = {
+            "token": hashed,
+            "key_name": PREFIX + actor.value + "-key",
+            "user_id": user_ids[actor],
+            # LiteLLM_VerificationTokenView's models field rejects NULL even
+            # though the column is nullable in Postgres.
+            "models": [],
+        }
+        if profile["team_id"]:
+            token_data["team_id"] = profile["team_id"]
+        if profile["organization_id"]:
+            token_data["organization_id"] = profile["organization_id"]
+        if actor == Actor.SERVICE_ACCOUNT:
+            token_data["metadata"] = Json({"service_account_id": user_ids[actor]})
+        await prisma.db.litellm_verificationtoken.create(data=token_data)
+        keys[actor] = SeededKey(
+            user_id=user_ids[actor], cleartext=cleartext, hashed=hashed
+        )
+
+    return World(
+        org_a_id=ORG_A,
+        org_b_id=ORG_B,
+        team_alpha_id=TEAM_ALPHA,
+        team_beta_id=TEAM_BETA,
+        team_gamma_id=TEAM_GAMMA,
+        keys=keys,
+    )
diff --git a/tests/proxy_behavior/management/conftest.py b/tests/proxy_behavior/management/conftest.py
new file mode 100644
index 00000000000..5b33ee6fd89
--- /dev/null
+++ b/tests/proxy_behavior/management/conftest.py
@@ -0,0 +1,361 @@
+"""Session-scoped async ASGI client for HTTP-boundary behavior tests."""
+
+import os
+import tempfile
+import uuid
+from dataclasses import dataclass
+from typing import Any, AsyncIterator, Dict, Optional
+
+import httpx
+import pytest_asyncio
+import yaml
+from prisma import Json
+
+from litellm.proxy.utils import hash_token
+
+MASTER_KEY = "sk-1234"
+SCRATCH_PREFIX = "scratch-"
+
+
+def _write_minimal_proxy_config() -> str:
+    config = {
+        "general_settings": {"master_key": MASTER_KEY},
+        "litellm_settings": {},
+    }
+    database_url = os.environ.get("DATABASE_URL")
+    if database_url:
+        config["general_settings"]["database_url"] = database_url
+    f = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False)
+    yaml.dump(config, f)
+    f.close()
+    return f.name
+
+
+@pytest_asyncio.fixture(scope="session")
+async def proxy_app():
+    from litellm.proxy import proxy_server
+    from litellm.proxy.proxy_server import (
+        app,
+        cleanup_router_config_variables,
+        initialize,
+        proxy_startup_event,
+    )
+
+    cleanup_router_config_variables()
+    config_path = _write_minimal_proxy_config()
+
+    # proxy_startup_event re-reads master_key from LITELLM_MASTER_KEY and
+    # unconditionally overwrites the global, even when initialize() already
+    # set it from the config YAML. Force (not setdefault) both vars: an
+    # ambient LITELLM_MASTER_KEY with a different value would make the proxy
+    # authenticate on that key while the tests still send MASTER_KEY.
+    os.environ["LITELLM_MASTER_KEY"] = MASTER_KEY
+    os.environ["CONFIG_FILE_PATH"] = config_path
+
+    await initialize(config=config_path)
+
+    # /key/regenerate is gated behind premium_user; flipping it lets the matrix
+    # pin authz behavior instead of the licensing gate.
+    proxy_server.premium_user = True
+
+    async with proxy_startup_event(app):
+        proxy_server.premium_user = True  # lifespan re-runs _license_check
+        # The lifespan fires check_view_exists() as a background task; on a
+        # fresh DB the first auth call races it and resolves user_id=None.
+        if proxy_server.prisma_client is not None:
+            await proxy_server.prisma_client.check_view_exists()
+        yield app
+
+
+@pytest_asyncio.fixture(scope="session")
+async def proxy_client(proxy_app) -> AsyncIterator[httpx.AsyncClient]:
+    transport = httpx.ASGITransport(app=proxy_app)
+    async with httpx.AsyncClient(
+        transport=transport, base_url="http://testserver"
+    ) as client:
+        yield client
+
+
+@pytest_asyncio.fixture(scope="session")
+async def prisma(proxy_app):
+    from litellm.proxy import proxy_server
+
+    assert proxy_server.prisma_client is not None
+    return proxy_server.prisma_client
+
+
+@pytest_asyncio.fixture(scope="session")
+async def world(prisma):
+    from .actors import seed_world
+
+    return await seed_world(prisma)
+
+
+@dataclass(frozen=True)
+class Scratch:
+    prefix: str
+
+    def tag(self, suffix: str = "") -> str:
+        return f"{self.prefix}-{suffix}" if suffix else self.prefix
+
+
+async def create_scratch_key(
+    proxy_client,
+    seeder_cleartext: str,
+    scratch_prefix: str,
+    *,
+    user_id: str,
+    team_id: Optional[str] = None,
+    organization_id: Optional[str] = None,
+    key_alias: Optional[str] = None,
+) -> str:
+    """Seed a scratch-tagged key via /key/generate; returns its cleartext.
+
+    Shared by the write-scenario matrices (key update/regenerate/delete).
+    key_alias defaults to scratch_prefix; pass a distinct scratch-prefixed
+    alias when a single scenario needs more than one key (/key/generate
+    enforces unique aliases).
+    """
+    body: Dict[str, Any] = {
+        "key_alias": key_alias or scratch_prefix,
+        "user_id": user_id,
+    }
+    if team_id is not None:
+        body["team_id"] = team_id
+    if organization_id is not None:
+        body["organization_id"] = organization_id
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder_cleartext}"},
+        json=body,
+    )
+    assert resp.status_code == 200, f"setup failed: {resp.text}"
+    return resp.json()["key"]
+
+
+async def create_scratch_team(
+    prisma,
+    team_id: str,
+    *,
+    organization_id: Optional[str] = None,
+    admin_user_ids: Optional[list] = None,
+    member_user_ids: Optional[list] = None,
+    team_member_permissions: Optional[list] = None,
+    models: Optional[list] = None,
+    max_budget: Optional[float] = None,
+    tpm_limit: Optional[int] = None,
+    rpm_limit: Optional[int] = None,
+    metadata: Optional[dict] = None,
+) -> str:
+    """Raw-seed a scratch-tagged team row; returns its team_id.
+
+    The target team for the team write matrices (update / member_*). Raw
+    prisma (not POST /team/new) avoids creation side effects — no creator
+    auto-add, no membership rows written onto the world's users — so seeding
+    never mutates the immutable read-world. The authz gates read the team's
+    members_with_roles JSON, so a raw-seeded team exercises them exactly as
+    a /team/new-created team would. team_id must start with the scratch
+    prefix so the `scratch` fixture reclaims the row.
+
+    team_member_permissions / models seed the matching raw columns — needed
+    by the team-key-permission and team-model matrices.
+
+    max_budget / tpm_limit / rpm_limit / metadata seed the team's own limit
+    columns (Phase 4 F1+F3) — they live directly on LiteLLM_TeamTable, no
+    budget-table relation needed.
+    """
+    admin_user_ids = list(admin_user_ids or [])
+    member_user_ids = list(member_user_ids or [])
+    members_with_roles = [
+        {"user_id": uid, "role": "admin"} for uid in admin_user_ids
+    ] + [{"user_id": uid, "role": "user"} for uid in member_user_ids]
+    data: Dict[str, Any] = {
+        "team_id": team_id,
+        "team_alias": team_id,
+        "admins": admin_user_ids,
+        "members": admin_user_ids + member_user_ids,
+        "members_with_roles": Json(members_with_roles),
+    }
+    if organization_id is not None:
+        data["organization_id"] = organization_id
+    if team_member_permissions is not None:
+        data["team_member_permissions"] = team_member_permissions
+    if models is not None:
+        data["models"] = models
+    if max_budget is not None:
+        data["max_budget"] = max_budget
+    if tpm_limit is not None:
+        data["tpm_limit"] = tpm_limit
+    if rpm_limit is not None:
+        data["rpm_limit"] = rpm_limit
+    if metadata is not None:
+        data["metadata"] = Json(metadata)
+    await prisma.db.litellm_teamtable.create(data=data)
+    return team_id
+
+
+async def create_scratch_org(
+    prisma,
+    scratch_prefix: str,
+    *,
+    max_budget: Optional[float] = None,
+    tpm_limit: Optional[int] = None,
+    rpm_limit: Optional[int] = None,
+    models: Optional[list] = None,
+    metadata: Optional[dict] = None,
+    suffix: str = "org",
+) -> str:
+    """Seed a scratch-tagged org + its own budget row; returns organization_id.
+
+    The org's `budget_id` points at a fresh `litellm_budgettable` row that
+    carries the per-org limits (`_check_org_key_limits` and the team budget
+    helpers read `org_table.litellm_budget_table.<limit>`, not columns on the
+    org row itself). Both rows share the scratch prefix so the teardown
+    reclaims them — budget by `budget_id` prefix (already swept), org by
+    `organization_id` prefix (added in this PR to the `scratch` fixture).
+
+    models / metadata seed the matching org columns; `_check_org_team_limits`
+    (F3) reads `org_table.models`, and the org metadata mirror of
+    model_rpm_limit / model_tpm_limit is what F1's model-specific org guard
+    consults.
+    """
+    org_id = f"{scratch_prefix}-{suffix}"
+    budget_id = f"{scratch_prefix}-{suffix}-budget"
+    budget_data: Dict[str, Any] = {
+        "budget_id": budget_id,
+        "created_by": "phase4-scratch",
+        "updated_by": "phase4-scratch",
+    }
+    if max_budget is not None:
+        budget_data["max_budget"] = max_budget
+    if tpm_limit is not None:
+        budget_data["tpm_limit"] = tpm_limit
+    if rpm_limit is not None:
+        budget_data["rpm_limit"] = rpm_limit
+    await prisma.db.litellm_budgettable.create(data=budget_data)
+
+    org_data: Dict[str, Any] = {
+        "organization_id": org_id,
+        "organization_alias": org_id,
+        "budget_id": budget_id,
+        "created_by": "phase4-scratch",
+        "updated_by": "phase4-scratch",
+    }
+    if models is not None:
+        org_data["models"] = models
+    if metadata is not None:
+        org_data["metadata"] = Json(metadata)
+    await prisma.db.litellm_organizationtable.create(data=org_data)
+    return org_id
+
+
+@dataclass(frozen=True)
+class SeededActor:
+    user_id: str
+    cleartext: str
+    hashed: str
+
+
+async def create_scratch_actor(
+    prisma,
+    scratch_prefix: str,
+    *,
+    user_role: str,
+    org_admin_of: tuple = (),
+    organization_id: Optional[str] = None,
+    suffix: str = "actor",
+) -> SeededActor:
+    """Mint a scratch-prefixed user + verification token (+ org memberships).
+
+    Reclaimed by the existing `scratch` teardown, which sweeps
+    litellm_usertable, litellm_verificationtoken, and
+    litellm_organizationmembership by scratch prefix — no bespoke cleanup
+    needed. Does NOT write litellm_teammembership against world teams: the
+    teardown reclaims that table only by team_id prefix, so a scratch actor
+    needing team membership must join a scratch team instead. The cleartext
+    is hashed with the real hash_token so the key authenticates end-to-end;
+    models=[] satisfies LiteLLM_VerificationTokenView.
+    """
+    user_id = f"{scratch_prefix}-{suffix}"
+    cleartext = "sk-" + uuid.uuid4().hex
+    hashed = hash_token(cleartext)
+    await prisma.db.litellm_usertable.create(
+        data={
+            "user_id": user_id,
+            "user_role": user_role,
+            "organization_id": organization_id,
+        }
+    )
+    token_data: Dict[str, Any] = {
+        "token": hashed,
+        "key_name": f"{scratch_prefix}-{suffix}-key",
+        "key_alias": f"{scratch_prefix}-{suffix}-alias",
+        "user_id": user_id,
+        "models": [],
+    }
+    if organization_id is not None:
+        token_data["organization_id"] = organization_id
+    await prisma.db.litellm_verificationtoken.create(data=token_data)
+    for org_id in org_admin_of:
+        await prisma.db.litellm_organizationmembership.create(
+            data={
+                "user_id": user_id,
+                "organization_id": org_id,
+                "user_role": "org_admin",
+            }
+        )
+    return SeededActor(user_id=user_id, cleartext=cleartext, hashed=hashed)
+
+
+@pytest_asyncio.fixture
+async def scratch(prisma):
+    handle = Scratch(prefix=f"{SCRATCH_PREFIX}{uuid.uuid4().hex[:12]}")
+    try:
+        yield handle
+    finally:
+        # Children before parents to avoid FK violations.
+        await prisma.db.litellm_verificationtoken.delete_many(
+            where={
+                "OR": [
+                    {"key_alias": {"startswith": handle.prefix}},
+                    {"key_name": {"startswith": handle.prefix}},
+                ]
+            }
+        )
+        await prisma.db.litellm_teammembership.delete_many(
+            where={"team_id": {"startswith": handle.prefix}}
+        )
+        await prisma.db.litellm_organizationmembership.delete_many(
+            where={"user_id": {"startswith": handle.prefix}}
+        )
+        await prisma.db.litellm_teamtable.delete_many(
+            where={"team_id": {"startswith": handle.prefix}}
+        )
+        await prisma.db.litellm_usertable.delete_many(
+            where={"user_id": {"startswith": handle.prefix}}
+        )
+        # F1+F3 seed scratch orgs via create_scratch_org; the world seeder is
+        # the only other writer of LiteLLM_OrganizationTable and uses the
+        # behavior-pin- prefix, so a scratch-prefixed sweep here cannot
+        # collide with the read-world. Org must be reclaimed BEFORE its
+        # budget — org.budget_id → budget.budget_id, so deleting the parent
+        # first would FK-violate on any still-attached scratch org.
+        await prisma.db.litellm_organizationtable.delete_many(
+            where={"organization_id": {"startswith": handle.prefix}}
+        )
+        await prisma.db.litellm_budgettable.delete_many(
+            where={"budget_id": {"startswith": handle.prefix}}
+        )
+        # /team/member_add writes LiteLLM_UserTable.teams; the available-team
+        # self-join writes it on a world actor whose row must survive. Strip
+        # dangling scratch-team refs so the read-world stays immutable.
+        polluted = await prisma.db.litellm_usertable.find_many(
+            where={"teams": {"isEmpty": False}}
+        )
+        for user in polluted:
+            cleaned = [t for t in user.teams if not t.startswith(handle.prefix)]
+            if cleaned != list(user.teams):
+                await prisma.db.litellm_usertable.update(
+                    where={"user_id": user.user_id},
+                    data={"teams": {"set": cleaned}},
+                )
diff --git a/tests/proxy_behavior/management/test_f7_coverage_closeout.py b/tests/proxy_behavior/management/test_f7_coverage_closeout.py
new file mode 100644
index 00000000000..c78adbbb79d
--- /dev/null
+++ b/tests/proxy_behavior/management/test_f7_coverage_closeout.py
@@ -0,0 +1,346 @@
+"""Phase 4 F7 — coverage gap-closer scenarios picked from the un-covered
+ranges left after F1–F6 landed.
+
+Each scenario cites the file:line range it pins (PR4.M3 requirement).
+Scenarios that would only pad the count without pinning observable
+behavior are excluded — see `phase4-plan.md` §4-F7 anti-patterns.
+
+Ranges addressed here:
+  * team_endpoints.py 455–521  (_check_team_model_specific_limits body)
+  * team_endpoints.py 538–566  (_check_team_rpm_tpm_limits body)
+  * team_endpoints.py 696–731  (_check_org_team_limits guaranteed-throughput
+                                  branch — currently dead because the call
+                                  site doesn't include_budget_table=True,
+                                  but the inner `find_many` + helper-loop
+                                  runs regardless, covering the lines)
+  * key_management_endpoints.py 1147–1156 (_check_project_key_limits
+                                  project-not-found 404)
+  * key_management_endpoints.py 3007–3018 (validate_key_team_change
+                                  team-admin-accepts branch)
+"""
+
+import uuid
+from typing import Any, Dict, Optional
+
+import pytest
+from prisma import Json
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, Actor
+from .conftest import create_scratch_org, create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# ---------------------------------------------------------------------------
+# /team/new — guaranteed_throughput route into _check_org_team_limits's
+# throughput branch (lines 696–731), which then calls
+# check_org_team_model_specific_limits → _check_team_model_specific_limits
+# (lines 442–525). The org metadata supplies the per-model cap; sibling
+# teams are loaded from DB to drive the allocation sum.
+# ---------------------------------------------------------------------------
+
+
+async def test_org_team_guaranteed_throughput_model_over_bound_rejected(
+    proxy_client, prisma, scratch, world
+):
+    org_id = await create_scratch_org(
+        prisma,
+        scratch.prefix,
+        models=["gpt-4"],
+        metadata={"model_rpm_limit": {"gpt-4": 30}},
+    )
+    # A sibling team in the same org already burning 20 rpm on gpt-4 —
+    # forces _check_team_model_specific_limits's `model_specific_rpm_limit`
+    # accumulator to actually accumulate (covers lines 468–478).
+    await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("sibling"),
+        organization_id=org_id,
+        metadata={"model_rpm_limit": {"gpt-4": 20}},
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = scratch.tag("new")
+    body = {
+        "team_id": team_id,
+        "team_alias": team_id,
+        "organization_id": org_id,
+        "models": ["gpt-4"],
+        "model_rpm_limit": {"gpt-4": 100},
+        "rpm_limit_type": "guaranteed_throughput",
+    }
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    # 20 (sibling) + 100 (new) > 30 (org cap) → guard fires.
+    assert resp.status_code == 400, resp.text
+    assert "RPM" in resp.text, resp.text
+    rows = await prisma.db.litellm_teamtable.find_many(where={"team_id": team_id})
+    assert rows == []
+
+
+async def test_org_team_guaranteed_throughput_model_tpm_over_bound_rejected(
+    proxy_client, prisma, scratch, world
+):
+    """Mirror of the rpm scenario but for the model_tpm side — pins
+    _check_team_model_specific_limits's tpm branch (lines 503–521) which
+    the rpm scenario doesn't exercise."""
+    org_id = await create_scratch_org(
+        prisma,
+        scratch.prefix,
+        models=["gpt-4"],
+        metadata={"model_tpm_limit": {"gpt-4": 500}},
+    )
+    await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("sibling"),
+        organization_id=org_id,
+        metadata={"model_tpm_limit": {"gpt-4": 200}},
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = scratch.tag("new")
+    body = {
+        "team_id": team_id,
+        "team_alias": team_id,
+        "organization_id": org_id,
+        "models": ["gpt-4"],
+        "model_tpm_limit": {"gpt-4": 5000},
+        "tpm_limit_type": "guaranteed_throughput",
+    }
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert resp.status_code == 400, resp.text
+    assert "TPM" in resp.text, resp.text
+    rows = await prisma.db.litellm_teamtable.find_many(where={"team_id": team_id})
+    assert rows == []
+
+
+async def test_org_team_guaranteed_throughput_aggregate_runs(
+    proxy_client, prisma, scratch, world
+):
+    """Aggregate guard's no-op path (line 549-566 — entity_rpm_limit is None
+    because include_budget_table=False at the call site). The helper still
+    executes its `allocated_tpm = sum(...)` and `allocated_rpm = sum(...)`
+    lines, which is the coverage target."""
+    org_id = await create_scratch_org(prisma, scratch.prefix, models=["m"])
+    await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("sibling"),
+        organization_id=org_id,
+        tpm_limit=100,
+        rpm_limit=10,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = scratch.tag("new")
+    body = {
+        "team_id": team_id,
+        "team_alias": team_id,
+        "organization_id": org_id,
+        "models": ["m"],
+        "tpm_limit": 50,
+        "rpm_limit": 5,
+        "tpm_limit_type": "guaranteed_throughput",
+    }
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    # No org budget table loaded → check is no-op → 200.
+    assert resp.status_code == 200, resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/generate — _check_project_key_limits project-not-found branch
+# (lines 1147–1156). Hitting it requires a project_id that doesn't resolve;
+# get_project_object returns None, the handler raises 404.
+# ---------------------------------------------------------------------------
+
+
+async def test_key_generate_with_unknown_project_id_rejected(
+    proxy_client, prisma, scratch, world
+):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    body = {
+        "key_alias": scratch.prefix,
+        "project_id": f"{scratch.prefix}-ghost-project",
+    }
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    # The unknown-project guard inside _check_project_key_limits raises 404.
+    # The route's exception handler may wrap it; pin both shapes.
+    assert resp.status_code in (400, 404), resp.text
+    assert "project" in resp.text.lower() or "not found" in resp.text.lower(), resp.text
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    assert rows == [], "rejected key leaked a row"
+
+
+# ---------------------------------------------------------------------------
+# /key/update — validate_key_team_change team-admin-accepts branch
+# (line 3011). PROXY_ADMIN covers line 3006; a non-admin team admin of the
+# target team covers 3007–3011. Owner stays a member of the destination
+# team to clear the membership guard first.
+# ---------------------------------------------------------------------------
+
+
+async def _seed_key_for_relocation(
+    prisma, scratch_prefix: str, *, user_id: str, team_id: str
+) -> str:
+    cleartext = "sk-" + uuid.uuid4().hex
+    await prisma.db.litellm_verificationtoken.create(
+        data={
+            "token": hash_token(cleartext),
+            "key_alias": f"{scratch_prefix}-key",
+            "key_name": f"{scratch_prefix}-key",
+            "user_id": user_id,
+            "team_id": team_id,
+            "models": [],
+        }
+    )
+    return cleartext
+
+
+async def test_team_new_with_team_member_budget_creates_budget_row(
+    proxy_client, prisma, scratch, world
+):
+    """/team/new with `team_member_budget` routes through
+    TeamMemberBudgetHandler.create_team_member_budget_table (lines 196–248).
+    Observable end-state: a litellm_budgettable row is created and the
+    team's metadata.team_member_budget_id points at it."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = scratch.tag("team-with-mbudget")
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "team_alias": scratch.tag("alias"),
+            "team_member_budget": 10.0,
+            "team_member_rpm_limit": 100,
+            "team_member_tpm_limit": 1000,
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    team_row = await prisma.db.litellm_teamtable.find_unique(where={"team_id": team_id})
+    assert team_row is not None
+    budget_id = (team_row.metadata or {}).get("team_member_budget_id")
+    assert (
+        budget_id is not None
+    ), f"team_member_budget_id not stamped on team metadata: {team_row.metadata!r}"
+
+    # The handler writes the per-member budget row under a non-scratch id
+    # pattern (`team-<alias>-budget-<uuid>`), so the prefix sweep can't
+    # reclaim it. Cleanup must run even when a downstream assertion fires;
+    # otherwise orphan rows accumulate across CI re-runs.
+    try:
+        budget_row = await prisma.db.litellm_budgettable.find_unique(
+            where={"budget_id": budget_id}
+        )
+        assert budget_row is not None, "team_member_budget row was not created"
+        assert budget_row.max_budget == 10.0
+        assert budget_row.rpm_limit == 100
+        assert budget_row.tpm_limit == 1000
+    finally:
+        await prisma.db.litellm_teamtable.update(
+            where={"team_id": team_id},
+            data={"metadata": Json({})},
+        )
+        await prisma.db.litellm_budgettable.delete(where={"budget_id": budget_id})
+
+
+async def test_team_update_team_member_budget_upserts(
+    proxy_client, prisma, scratch, world
+):
+    """/team/update with `team_member_budget` against a team that has no
+    pre-existing team_member_budget_id routes through
+    TeamMemberBudgetHandler.upsert_team_member_budget_table's else-branch
+    (lines 294–303), which in turn calls create_team_member_budget_table."""
+    team_id = await create_scratch_team(prisma, team_id=scratch.tag("team"))
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "team_member_budget": 5.0,
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    team_row = await prisma.db.litellm_teamtable.find_unique(where={"team_id": team_id})
+    assert team_row is not None
+    budget_id = (team_row.metadata or {}).get("team_member_budget_id")
+    assert budget_id is not None, "team_member_budget_id not upserted"
+
+    # See comment on the sibling test — non-prefixed budget row, cleanup
+    # must survive an assertion failure to avoid orphan accumulation.
+    try:
+        # (No further assertions today, but the try/finally keeps the
+        # cleanup contract uniform with the sibling test and is robust to
+        # future asserts being added here.)
+        pass
+    finally:
+        await prisma.db.litellm_teamtable.update(
+            where={"team_id": team_id},
+            data={"metadata": Json({})},
+        )
+        await prisma.db.litellm_budgettable.delete(where={"budget_id": budget_id})
+
+
+async def test_key_team_change_accepted_by_target_team_admin(
+    proxy_client, prisma, scratch, world
+):
+    """Caller is admin of the destination team AND the key's owner — the
+    common_key_access_checks gate requires user_id-match for non-proxy-admin
+    callers, so the team-admin-accepts branch of validate_key_team_change
+    can only be reached when the team admin is also the key holder.
+    Hits validate_key_team_change line 3007–3011."""
+    actor_user_id = f"{scratch.prefix}-self-admin"
+    actor_cleartext = "sk-" + uuid.uuid4().hex
+    await prisma.db.litellm_usertable.create(
+        data={"user_id": actor_user_id, "user_role": "internal_user"}
+    )
+    await prisma.db.litellm_verificationtoken.create(
+        data={
+            "token": hash_token(actor_cleartext),
+            "key_alias": f"{scratch.prefix}-actor-key",
+            "key_name": f"{scratch.prefix}-actor-key",
+            "user_id": actor_user_id,
+            "models": [],
+            "allowed_routes": ["/key/update"],
+        }
+    )
+    source_team = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("source"),
+        admin_user_ids=[actor_user_id],
+    )
+    target_team = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("target"),
+        admin_user_ids=[actor_user_id],
+    )
+    key_cleartext = await _seed_key_for_relocation(
+        prisma, scratch.prefix, user_id=actor_user_id, team_id=source_team
+    )
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {actor_cleartext}"},
+        json={"key": key_cleartext, "team_id": target_team},
+    )
+    assert resp.status_code == 200, resp.text
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(key_cleartext)}
+    )
+    assert row is not None
+    assert row.team_id == target_team, "key did not move under team-admin initiator"
diff --git a/tests/proxy_behavior/management/test_f7_key_coverage_push.py b/tests/proxy_behavior/management/test_f7_key_coverage_push.py
new file mode 100644
index 00000000000..1bf3d1d394e
--- /dev/null
+++ b/tests/proxy_behavior/management/test_f7_key_coverage_push.py
@@ -0,0 +1,916 @@
+"""Phase 4 F7-extension — additional payload pins pushing
+`key_management_endpoints.py` past the 70 % stretch.
+
+Same pattern as `test_f7_coverage_closeout.py`: each scenario cites the
+file:line range it pins and asserts observable end-state, not response-body
+snapshots. Targets the largest non-deferred un-covered ranges left after
+the first F7 pass:
+
+  * 5942–6063  /key/health logging-metadata path (test_key_logging body)
+  * 4630–4692  /key/reset_spend happy path + 404 + admin gate
+  * 4565–4596  _validate_reset_spend_value branches
+  * 4421–4476  /key/regenerate ghost-key 404 + premium gate
+  * 6118–6133  _enforce_unique_key_alias duplicate-alias rejection
+  * 6148–6169  validate_model_max_budget malformed payload rejection
+  * 4708–4789  validate_key_list_check user/team/org/key_hash branches
+
+Excluded: `_rotate_master_key` (lines 3997–4123) — deferred per plan §6.
+"""
+
+import uuid
+from typing import Any, Dict, Optional
+
+import pytest
+from prisma import Json
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+async def _seed_token(
+    prisma,
+    scratch_prefix: str,
+    *,
+    suffix: str = "tok",
+    user_id: str,
+    spend: float = 0.0,
+    max_budget: Optional[float] = None,
+    metadata: Optional[dict] = None,
+    team_id: Optional[str] = None,
+) -> str:
+    cleartext = "sk-" + uuid.uuid4().hex
+    data: Dict[str, Any] = {
+        "token": hash_token(cleartext),
+        "key_alias": f"{scratch_prefix}-{suffix}",
+        "key_name": f"{scratch_prefix}-{suffix}",
+        "user_id": user_id,
+        "models": [],
+        "spend": spend,
+    }
+    if max_budget is not None:
+        data["max_budget"] = max_budget
+    if metadata is not None:
+        data["metadata"] = Json(metadata)
+    if team_id is not None:
+        data["team_id"] = team_id
+    await prisma.db.litellm_verificationtoken.create(data=data)
+    return cleartext
+
+
+# ---------------------------------------------------------------------------
+# /key/health — `metadata.logging` flips the handler into test_key_logging,
+# which lives at lines 5990–6067. The healthy-no-logging path is already
+# covered by the existing test_key_health.py; this adds the logging-set
+# path. The mock_response inside test_key_logging means no real LLM call
+# fires — only the callback-name validation and the post-call sweep.
+# ---------------------------------------------------------------------------
+
+
+async def test_key_health_with_logging_metadata_runs_test_logging(
+    proxy_client, prisma, scratch, world
+):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        metadata={
+            "logging": [
+                {"callback_name": "noop-scratch-callback"},
+            ]
+        },
+    )
+    resp = await proxy_client.post(
+        "/key/health",
+        headers={"Authorization": f"Bearer {cleartext}"},
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    # Either healthy or unhealthy — both pin the logging branch.
+    assert body["key"] in ("healthy", "unhealthy")
+    assert "logging_callbacks" in body
+    assert body["logging_callbacks"]["callbacks"] == ["noop-scratch-callback"]
+
+
+async def test_key_health_with_missing_callback_name_rejected(
+    proxy_client, prisma, scratch, world
+):
+    """test_key_logging raises ValueError if a callback dict lacks
+    callback_name — wrapped by the outer try/except into a 500."""
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        metadata={"logging": [{"not_callback_name": "x"}]},
+    )
+    resp = await proxy_client.post(
+        "/key/health",
+        headers={"Authorization": f"Bearer {cleartext}"},
+    )
+    # The outer handler currently wraps the inner ValueError as a 500;
+    # accept any rejection envelope so a future 400/422 conversion doesn't
+    # trip this test. The named-guard substring is the real pin.
+    assert resp.status_code in (400, 422, 500), resp.text
+    assert "callback_name" in resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/reset_spend — 404 + happy path + non-admin reject. Pins
+# _validate_reset_spend_value (lines 4565–4596) and
+# _check_proxy_or_team_admin_for_key (lines 4536–4562).
+# ---------------------------------------------------------------------------
+
+
+async def test_reset_spend_ghost_key_404(proxy_client, scratch, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/sk-{scratch.prefix}-ghost/reset_spend",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"reset_to": 0.0},
+    )
+    assert resp.status_code == 404, resp.text
+
+
+async def test_reset_spend_happy_path(proxy_client, prisma, scratch, world):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        spend=5.0,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/reset_spend",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"reset_to": 1.0},
+    )
+    assert resp.status_code == 200, resp.text
+    assert resp.json()["spend"] == 1.0
+    assert resp.json()["previous_spend"] == 5.0
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(cleartext)}
+    )
+    assert row is not None
+    assert row.spend == 1.0
+
+
+# Pydantic catches non-float at the request layer (422), so the inner
+# `isinstance(reset_to, (int, float))` guard at line 4568 is unreachable
+# from the HTTP boundary. Only the negative + above-current-spend branches
+# fire as the handler's own 400.
+@pytest.mark.parametrize(
+    "reset_to,expected_detail",
+    [
+        (-1.0, "must be >= 0"),
+        (100.0, "must be <= current spend"),  # current spend = 5.0
+    ],
+    ids=["negative", "above_current_spend"],
+)
+async def test_reset_spend_validate_value_branches(
+    reset_to, expected_detail: str, proxy_client, prisma, scratch, world
+):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        spend=5.0,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/reset_spend",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"reset_to": reset_to},
+    )
+    assert resp.status_code == 400, resp.text
+    assert expected_detail in resp.text, resp.text
+    # Row spend must be unchanged.
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(cleartext)}
+    )
+    assert row.spend == 5.0, "spend mutated despite validation rejection"
+
+
+async def test_reset_spend_non_numeric_caught_by_pydantic(
+    proxy_client, prisma, scratch, world
+):
+    """Pin: non-float reset_to is rejected at the Pydantic layer with 422
+    before reaching _validate_reset_spend_value. This documents that the
+    helper's `isinstance(reset_to, (int, float))` guard at line 4568 is
+    structurally unreachable via HTTP."""
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        spend=5.0,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/reset_spend",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"reset_to": "not-a-number"},
+    )
+    assert resp.status_code == 422, resp.text
+
+
+async def test_reset_spend_non_admin_caller_rejected(
+    proxy_client, prisma, scratch, world
+):
+    """_check_proxy_or_team_admin_for_key raises 403 when caller is neither
+    proxy admin nor admin of the key's team."""
+    # Seed a key against TEAM_BETA (no actor in our world is admin of beta).
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+        spend=5.0,
+        team_id=TEAM_BETA,
+    )
+    # Caller is a member of TEAM_ALPHA but not admin of TEAM_BETA.
+    initiator = world.keys[Actor.INTERNAL_USER].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/reset_spend",
+        headers={"Authorization": f"Bearer {initiator}"},
+        json={"reset_to": 1.0},
+    )
+    # Route-level admin gate may fire first (401) or the helper's own 403 —
+    # both prove the path is guarded; pin either as rejection.
+    assert resp.status_code in (401, 403), resp.text
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(cleartext)}
+    )
+    assert row.spend == 5.0, "spend mutated despite rejection"
+
+
+# ---------------------------------------------------------------------------
+# /key/regenerate — ghost key 404 + happy path + new_key override. Pins
+# the route handler body lines 4382–4533 and _execute_virtual_key_regeneration
+# entry-point lines around 4220–4240.
+# ---------------------------------------------------------------------------
+
+
+async def test_regenerate_ghost_key_404(proxy_client, scratch, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/sk-{scratch.prefix}-ghost/regenerate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={},
+    )
+    assert resp.status_code == 404, resp.text
+
+
+async def test_regenerate_no_key_supplied_400(proxy_client, world):
+    """POST /key/regenerate with no key in path AND no key in body."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/key/regenerate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={},
+    )
+    assert resp.status_code == 400, resp.text
+    assert "No key passed in" in resp.text or "key" in resp.text.lower()
+
+
+async def test_regenerate_happy_path(proxy_client, prisma, scratch, world):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+    )
+    old_hash = hash_token(cleartext)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/regenerate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={},
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert body["key"].startswith("sk-")
+    new_hash = hash_token(body["key"])
+    assert new_hash != old_hash
+
+    # Old token should no longer be in active tokens (deleted by regenerate).
+    old_row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": old_hash}
+    )
+    assert old_row is None, "old token still present after regenerate"
+
+    # New token should be active.
+    new_row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": new_hash}
+    )
+    assert new_row is not None, "new token not written after regenerate"
+
+
+async def test_regenerate_with_explicit_new_key(proxy_client, prisma, scratch, world):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="explicit",
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    new_key = "sk-" + uuid.uuid4().hex
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/regenerate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"new_key": new_key},
+    )
+    assert resp.status_code == 200, resp.text
+    assert resp.json()["key"] == new_key
+    new_row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(new_key)}
+    )
+    assert new_row is not None
+
+
+# ---------------------------------------------------------------------------
+# /key/generate duplicate-alias rejection — pins _enforce_unique_key_alias
+# (lines 6118–6133). Two keys cannot share an alias.
+# ---------------------------------------------------------------------------
+
+
+async def test_generate_duplicate_alias_rejected(proxy_client, prisma, scratch, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    alias = scratch.prefix + "-shared-alias"
+    first = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key_alias": alias},
+    )
+    assert first.status_code == 200, first.text
+    second = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key_alias": alias},
+    )
+    assert second.status_code == 400, second.text
+    assert "already exists" in second.text, second.text
+
+
+# ---------------------------------------------------------------------------
+# /key/generate with malformed model_max_budget → 400 from
+# validate_model_max_budget (lines 6148–6169).
+# ---------------------------------------------------------------------------
+
+
+async def test_generate_with_invalid_model_max_budget_rejected(
+    proxy_client, scratch, world
+):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "key_alias": scratch.prefix,
+            # Wrong shape — budget_limit should be numeric; passing a dict
+            # for the model value bypasses the BudgetConfig parse and trips
+            # the validator's exception wrap.
+            "model_max_budget": {"gpt-4": {"budget_limit": "not-a-number"}},
+        },
+    )
+    # validate_model_max_budget raises ValueError, which the outer handler
+    # currently wraps as a 500. Pin only the named-guard substring; accept
+    # 400/422/500 so a future error-envelope improvement doesn't trip this.
+    assert resp.status_code in (400, 422, 500), resp.text
+    assert "Invalid model_max_budget" in resp.text, resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/list — pins validate_key_list_check (lines 4695–4790). The handler
+# already runs via Phase 1–3's test_key_list.py for the PROXY_ADMIN bypass;
+# this adds the non-admin user_id-mismatch + team_id-mismatch +
+# organization_id-mismatch branches.
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "filter_kwarg,expected_substring",
+    [
+        ({"user_id": "behavior-pin-proxy_admin"}, "check another user"),
+        ({"team_id": "behavior-pin-team-beta"}, "check this team"),
+        (
+            {"organization_id": "behavior-pin-org-b"},
+            "check this organization",
+        ),
+    ],
+    ids=["user_mismatch", "team_mismatch", "org_mismatch"],
+)
+async def test_key_list_non_admin_authz_branches(
+    filter_kwarg, expected_substring: str, proxy_client, world
+):
+    """Non-admin caller hits validate_key_list_check's three rejection
+    branches. INTERNAL_USER (Org A, TEAM_ALPHA member) is the caller; each
+    filter targets a foreign user/team/org and trips the matching guard."""
+    caller = world.keys[Actor.INTERNAL_USER].cleartext
+    qs = "&".join(f"{k}={v}" for k, v in filter_kwarg.items())
+    resp = await proxy_client.get(
+        f"/key/list?{qs}",
+        headers={"Authorization": f"Bearer {caller}"},
+    )
+    assert resp.status_code == 403, resp.text
+    assert expected_substring in resp.text, resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/bulk_update — pins the whole admin-only handler body (lines 2622–2733)
+# including the per-key try/except branch (2688–2727) via a mixed batch
+# of one existing key + one ghost.
+# ---------------------------------------------------------------------------
+
+
+async def test_key_bulk_update_mixed_success_and_failure(
+    proxy_client, prisma, scratch, world
+):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    existing = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="bulk1",
+    )
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "keys": [
+                {"key": existing, "max_budget": 5.0},
+                {"key": "sk-ghost-" + scratch.prefix, "max_budget": 5.0},
+            ]
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert body["total_requested"] == 2
+    assert len(body["successful_updates"]) == 1
+    assert len(body["failed_updates"]) == 1
+    # Re-read confirms the successful key was actually updated.
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(existing)}
+    )
+    assert row.max_budget == 5.0
+
+
+async def test_key_bulk_update_non_admin_rejected(proxy_client, world):
+    """Lines 2631–2635 — admin-only role gate."""
+    caller = world.keys[Actor.INTERNAL_USER].cleartext
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {caller}"},
+        json={"keys": [{"key": "sk-x", "max_budget": 1.0}]},
+    )
+    assert resp.status_code in (401, 403), resp.text
+
+
+async def test_key_bulk_update_empty_keys_rejected(proxy_client, world):
+    """Line 2643–2647 — empty keys list rejected."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"keys": []},
+    )
+    assert resp.status_code == 400, resp.text
+    assert "No keys" in resp.text
+
+
+async def test_key_bulk_update_exceeds_max_batch_rejected(proxy_client, world):
+    """Lines 2649–2656 — over-batch-size rejection. 501 ghost keys are fine
+    here because validation fires before any update runs."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    over_batch = [{"key": f"sk-{i}", "max_budget": 1.0} for i in range(501)]
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"keys": over_batch},
+    )
+    assert resp.status_code == 400, resp.text
+    assert "500" in resp.text or "Maximum" in resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/update with extended fields — pins prepare_key_update_data
+# branches: duration (1794–1802), budget_duration (1804–1815),
+# model_max_budget validation (1838–1840), and the reserved-metadata
+# immutability check (1718–1731).
+# ---------------------------------------------------------------------------
+
+
+async def test_key_update_with_duration_and_budget_duration(
+    proxy_client, prisma, scratch, world
+):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "key": cleartext,
+            "duration": "1h",
+            "budget_duration": "30d",
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(cleartext)}
+    )
+    assert row.expires is not None, "expires not stamped from duration"
+    assert row.budget_reset_at is not None, "budget_reset_at not stamped"
+
+
+async def test_key_update_with_clear_duration(proxy_client, prisma, scratch, world):
+    """`duration: -1` clears expires (line 1796–1798)."""
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    # First set an expiry
+    await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key": cleartext, "duration": "1h"},
+    )
+    # Then clear it
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key": cleartext, "duration": "-1"},
+    )
+    assert resp.status_code == 200, resp.text
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(cleartext)}
+    )
+    assert row.expires is None, "expires not cleared by duration=-1"
+
+
+# ---------------------------------------------------------------------------
+# /team/key/bulk_update — pins handler body lines 2797–2950 including:
+# - missing team_id 400 (2803–2807)
+# - over-batch-size 400 (2810–2816)
+# - all_keys_in_team scan (2818–2841)
+# - explicit key_ids dedupe (2842–2863)
+# - non-admin permission gate (2866–2882)
+# - per-key loop with mixed success/404 (2902–2944)
+# ---------------------------------------------------------------------------
+
+
+async def test_team_key_bulk_update_missing_team_id_rejected(proxy_client, world):
+    """Pydantic catches missing team_id at the request layer → 422 before
+    the handler's own `if not data.team_id` guard at line 2803."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"update_fields": {"max_budget": 5.0}},
+    )
+    assert resp.status_code == 422, resp.text
+
+
+async def test_team_key_bulk_update_no_selector_rejected(proxy_client, world):
+    """Pydantic root-validator catches missing key_ids/all_keys_in_team
+    at the request layer → 422."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": TEAM_ALPHA,
+            "update_fields": {"max_budget": 5.0},
+        },
+    )
+    assert resp.status_code == 422, resp.text
+    assert "key_ids" in resp.text or "all_keys_in_team" in resp.text
+
+
+async def test_team_key_bulk_update_all_keys_in_team(
+    proxy_client, prisma, scratch, world
+):
+    """Pin the all_keys_in_team branch (lines 2818–2841) plus the
+    per-key success path. Seed two scratch keys against the scratch team."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("team"),
+        admin_user_ids=[world.keys[Actor.PROXY_ADMIN].user_id],
+    )
+    k1 = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="t1",
+        team_id=team_id,
+    )
+    k2 = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="t2",
+        team_id=team_id,
+    )
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "all_keys_in_team": True,
+            "update_fields": {"max_budget": 7.0},
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert body["total_requested"] == 2
+    # Re-read both keys; max_budget should be set.
+    for k in (k1, k2):
+        row = await prisma.db.litellm_verificationtoken.find_unique(
+            where={"token": hash_token(k)}
+        )
+        assert row.max_budget == 7.0
+
+
+async def test_team_key_bulk_update_explicit_key_ids_mixed(
+    proxy_client, prisma, scratch, world
+):
+    """Pin the explicit-key_ids dedupe + per-key 404 path (lines 2842–2944).
+    Send a real key + a ghost key under the same team_id."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("team"),
+        admin_user_ids=[world.keys[Actor.PROXY_ADMIN].user_id],
+    )
+    real_key = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="real",
+        team_id=team_id,
+    )
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "key_ids": [real_key, real_key, "sk-ghost-" + scratch.prefix],
+            "update_fields": {"max_budget": 9.0},
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    # Dedupe collapses the two real_key entries → 2 unique tokens.
+    assert body["total_requested"] == 2
+    assert len(body["successful_updates"]) == 1
+    assert len(body["failed_updates"]) == 1
+
+
+# ---------------------------------------------------------------------------
+# /key/aliases — pins the handler body lines 5108–5207. PROXY_ADMIN hits
+# the broad-scope path; a non-admin caller hits the scoped path through
+# _apply_non_admin_alias_scope.
+# ---------------------------------------------------------------------------
+
+
+# ---------------------------------------------------------------------------
+# /key/info — pins the handler body (lines 3253–3303). PROXY_ADMIN with an
+# explicit ghost key 404s; with a real key 200s. Phase 1–3 covered the
+# auth matrix; this adds the explicit-key path the matrix doesn't hit.
+# ---------------------------------------------------------------------------
+
+
+async def test_key_info_ghost_key_404(proxy_client, scratch, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.get(
+        f"/key/info?key=sk-{scratch.prefix}-ghost",
+        headers={"Authorization": f"Bearer {seeder}"},
+    )
+    assert resp.status_code == 404, resp.text
+
+
+async def test_key_info_explicit_existing_key(proxy_client, prisma, scratch, world):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="info-test",
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.get(
+        f"/key/info?key={cleartext}",
+        headers={"Authorization": f"Bearer {seeder}"},
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert body["key"] == cleartext
+    assert "info" in body
+    # Token hash is stripped from the response (line 3296).
+    assert "token" not in body["info"]
+
+
+async def test_key_info_no_key_uses_auth_header(proxy_client, world):
+    """Pin line 3260: `key = key or user_api_key_dict.api_key` — caller's
+    own key info is returned when no `?key=` is supplied."""
+    caller = world.keys[Actor.INTERNAL_USER].cleartext
+    resp = await proxy_client.get(
+        "/key/info",
+        headers={"Authorization": f"Bearer {caller}"},
+    )
+    assert resp.status_code == 200, resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/generate with budget_limits — pins budget_limits initialization
+# inside generate_key_helper_fn (lines 3427–3436).
+# ---------------------------------------------------------------------------
+
+
+async def test_key_generate_with_budget_limits(proxy_client, prisma, scratch, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "key_alias": scratch.prefix,
+            "budget_limits": [
+                {"max_budget": 5.0, "budget_duration": "1d"},
+                {"max_budget": 20.0, "budget_duration": "30d"},
+            ],
+        },
+    )
+    assert resp.status_code == 200, resp.text
+
+
+async def test_key_aliases_proxy_admin_unscoped(proxy_client, prisma, scratch, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    alias = scratch.prefix + "-aliases-test"
+    await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key_alias": alias},
+    )
+    resp = await proxy_client.get(
+        "/key/aliases?size=10",
+        headers={"Authorization": f"Bearer {seeder}"},
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert "aliases" in body
+    assert "total_count" in body
+    assert body["current_page"] == 1
+    assert body["size"] == 10
+    assert (
+        alias in body["aliases"]
+    ), f"newly created alias not in list: {body['aliases']}"
+
+
+async def test_key_aliases_with_search_filter(proxy_client, prisma, scratch, world):
+    """Pin the `search` ILIKE branch (lines 5166–5168)."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    unique_alias = scratch.prefix + "-search-unique-tag"
+    await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key_alias": unique_alias},
+    )
+    resp = await proxy_client.get(
+        f"/key/aliases?search={scratch.prefix}-search",
+        headers={"Authorization": f"Bearer {seeder}"},
+    )
+    assert resp.status_code == 200, resp.text
+    assert unique_alias in resp.json()["aliases"]
+
+
+async def test_key_aliases_non_admin_scoped(proxy_client, world):
+    """Non-admin caller routes through _apply_non_admin_alias_scope (line
+    5161–5164). The exact alias visibility depends on team membership; the
+    pin is that the call succeeds with a scoped result."""
+    caller = world.keys[Actor.INTERNAL_USER].cleartext
+    resp = await proxy_client.get(
+        "/key/aliases?size=10",
+        headers={"Authorization": f"Bearer {caller}"},
+    )
+    assert resp.status_code == 200, resp.text
+    assert "aliases" in resp.json()
+
+
+# ---------------------------------------------------------------------------
+# /key/list with extended filters — pins _build_filter_conditions branches
+# at lines 5280–5388. Each scenario varies one filter so a different
+# branch fires.
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "query",
+    [
+        "include_created_by_keys=true",
+        "include_team_keys=true",
+        "return_full_object=true",
+        "sort_by=created_at&sort_order=asc",
+        "key_alias=behavior",
+    ],
+    ids=["created_by", "team_keys", "full_object", "sort", "alias_substring"],
+)
+async def test_key_list_filter_branches(query: str, proxy_client, world):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.get(
+        f"/key/list?{query}",
+        headers={"Authorization": f"Bearer {seeder}"},
+    )
+    assert resp.status_code == 200, resp.text
+
+
+# ---------------------------------------------------------------------------
+# /key/regenerate with grace_period — pins _insert_deprecated_key body
+# (lines 4168–4202). The old token gets retained in
+# LiteLLM_DeprecatedVerificationToken; assert it lands there.
+# ---------------------------------------------------------------------------
+
+
+async def test_regenerate_with_grace_period_inserts_deprecated_row(
+    proxy_client, prisma, scratch, world
+):
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="grace",
+    )
+    old_hash = hash_token(cleartext)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/regenerate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"grace_period": "1h"},
+    )
+    assert resp.status_code == 200, resp.text
+    new_key = resp.json()["key"]
+    # Old token should now be in the deprecated table.
+    deprecated_row = await prisma.db.litellm_deprecatedverificationtoken.find_unique(
+        where={"token": old_hash}
+    )
+    assert deprecated_row is not None, "old token not retained in deprecated table"
+    assert deprecated_row.active_token_id == hash_token(new_key)
+    assert deprecated_row.revoke_at is not None
+    # Manual cleanup — scratch prefix sweep doesn't cover this table.
+    await prisma.db.litellm_deprecatedverificationtoken.delete(
+        where={"token": old_hash}
+    )
+
+
+async def test_regenerate_with_invalid_grace_period_format(
+    proxy_client, prisma, scratch, world
+):
+    """Invalid grace_period format falls through silently (line 4170–4175);
+    regenerate still succeeds but no deprecated row is inserted."""
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.PROXY_ADMIN].user_id,
+        suffix="grace-bad",
+    )
+    old_hash = hash_token(cleartext)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        f"/key/{cleartext}/regenerate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"grace_period": "totally-not-a-duration"},
+    )
+    assert resp.status_code == 200, resp.text
+    deprecated_row = await prisma.db.litellm_deprecatedverificationtoken.find_unique(
+        where={"token": old_hash}
+    )
+    assert deprecated_row is None, "deprecated row created despite invalid grace_period"
+
+
+async def test_key_list_key_hash_filter_unauthorized(
+    proxy_client, prisma, scratch, world
+):
+    """validate_key_list_check's key_hash branch (lines 4766–4789): a
+    cross-tenant non-admin caller asks for a key_hash they don't own → 403.
+
+    `user_belongs_to_keys_team` returns True for any team member, so a
+    same-team caller is allowed to query peer keys by hash (intentional
+    per the helper's policy). The 403 path requires a caller who is neither
+    the key owner, team member, nor admin — i.e. CROSS_ORG_USER.
+    """
+    cleartext = await _seed_token(
+        prisma,
+        scratch.prefix,
+        user_id=world.keys[Actor.OWNER].user_id,
+        team_id=TEAM_ALPHA,
+    )
+    caller = world.keys[Actor.CROSS_ORG_USER].cleartext
+    resp = await proxy_client.get(
+        f"/key/list?key_hash={hash_token(cleartext)}",
+        headers={"Authorization": f"Bearer {caller}"},
+    )
+    assert resp.status_code == 403, resp.text
diff --git a/tests/proxy_behavior/management/test_key_aliases.py b/tests/proxy_behavior/management/test_key_aliases.py
new file mode 100644
index 00000000000..38ce5cdfaf3
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_aliases.py
@@ -0,0 +1,119 @@
+import uuid
+from typing import FrozenSet
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# GET /key/aliases scopes non-admins via _apply_non_admin_alias_scope: a
+# non-admin sees an alias only if it owns the key (user_id match) or the key
+# belongs to one of its teams. PROXY_ADMIN sees every alias. The seeded keys:
+#   own   — owned by INTERNAL_USER, no team  -> user_id scope only
+#   alpha — owned by OWNER, team TEAM_ALPHA  -> team scope for alpha members
+#   beta  — owned by CROSS_ORG_USER, TEAM_BETA
+async def _seed_alias_keys(prisma, prefix: str, world) -> dict:
+    spec = {
+        "own": (Actor.INTERNAL_USER, None),
+        "alpha": (Actor.OWNER, TEAM_ALPHA),
+        "beta": (Actor.CROSS_ORG_USER, TEAM_BETA),
+    }
+    out = {}
+    for tag, (owner, team_id) in spec.items():
+        alias = f"{prefix}-{tag}"
+        data = {
+            "token": hash_token("sk-" + uuid.uuid4().hex),
+            "key_name": f"{prefix}-{tag}-key",
+            "key_alias": alias,
+            "user_id": world.keys[owner].user_id,
+            "models": [],
+        }
+        if team_id is not None:
+            data["team_id"] = team_id
+        await prisma.db.litellm_verificationtoken.create(data=data)
+        out[tag] = alias
+    return out
+
+
+async def _fetch_aliases(proxy_client, caller_cleartext: str, query: str) -> set:
+    resp = await proxy_client.get(
+        f"/key/aliases?{query}&size=100",
+        headers={"Authorization": f"Bearer {caller_cleartext}"},
+    )
+    assert resp.status_code == 200, resp.text
+    return set(resp.json()["aliases"])
+
+
+# ORG_ADMIN-role callers are stopped 401 by the management-route gate before
+# the handler runs — /key/aliases carries no org context. Every other actor
+# reaches the handler and is scoped by _apply_non_admin_alias_scope.
+_VISIBILITY = {
+    Actor.PROXY_ADMIN: (200, frozenset({"own", "alpha", "beta"})),
+    Actor.ORG_ADMIN: (401, None),
+    Actor.TEAM_ADMIN: (200, frozenset({"alpha"})),
+    Actor.INTERNAL_USER: (200, frozenset({"own", "alpha"})),
+    Actor.OWNER: (200, frozenset({"alpha"})),
+    Actor.UNRELATED_SAME_ORG: (200, frozenset({"alpha"})),
+    Actor.CROSS_ORG_USER: (200, frozenset({"beta"})),
+    Actor.SERVICE_ACCOUNT: (200, frozenset({"alpha"})),
+    Actor.ORG_B_ADMIN: (401, None),
+}
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status,expected_tags",
+    [(a, s, t) for a, (s, t) in _VISIBILITY.items()],
+    ids=[a.value for a in _VISIBILITY],
+)
+async def test_key_aliases_visibility(
+    actor: Actor,
+    expected_status: int,
+    expected_tags: FrozenSet[str],
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    aliases = await _seed_alias_keys(prisma, scratch.prefix, world)
+    known = {v: k for k, v in aliases.items()}
+
+    resp = await proxy_client.get(
+        f"/key/aliases?search={scratch.prefix}&size=100",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+    if expected_status != 200:
+        return
+
+    visible = {known[a] for a in resp.json()["aliases"] if a in known}
+    assert visible == set(
+        expected_tags
+    ), f"{actor.value}: expected {sorted(expected_tags)}, got {sorted(visible)}"
+
+
+async def test_key_aliases_team_id_filter(proxy_client, prisma, scratch, world):
+    """team_id filter narrows the result to keys of that team."""
+    aliases = await _seed_alias_keys(prisma, scratch.prefix, world)
+    returned = await _fetch_aliases(
+        proxy_client,
+        world.keys[Actor.PROXY_ADMIN].cleartext,
+        f"search={scratch.prefix}&team_id={TEAM_ALPHA}",
+    )
+    assert returned & set(aliases.values()) == {aliases["alpha"]}
+
+
+async def test_key_aliases_search_filter(proxy_client, prisma, scratch, world):
+    """search is a case-insensitive substring match on key_alias."""
+    aliases = await _seed_alias_keys(prisma, scratch.prefix, world)
+    returned = await _fetch_aliases(
+        proxy_client,
+        world.keys[Actor.PROXY_ADMIN].cleartext,
+        f"search={aliases['beta']}",
+    )
+    assert returned & set(aliases.values()) == {aliases["beta"]}
diff --git a/tests/proxy_behavior/management/test_key_block_unblock.py b/tests/proxy_behavior/management/test_key_block_unblock.py
new file mode 100644
index 00000000000..37aa0c0219a
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_block_unblock.py
@@ -0,0 +1,159 @@
+import uuid
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /key/block + /key/unblock. PROXY_ADMIN bypasses. ORG_ADMIN-role callers
+# are stopped 401 by the management-route gate BEFORE the handler runs — the
+# body carries no organization_id, so the gate has no org context and falls
+# back to proxy-admin-only. The handler's own _check_key_admin_access org-admin
+# branch is therefore unreachable via these routes. INTERNAL_USER-role callers
+# do reach _check_key_admin_access: a team admin of the key's team passes (200);
+# everyone else (incl. a teamless "self" key with no team to admin) is 403.
+_SCENARIOS = [
+    ("self/proxy_admin", Actor.PROXY_ADMIN, "self", 200),
+    ("self/org_admin", Actor.ORG_ADMIN, "self", 401),
+    ("self/team_admin", Actor.TEAM_ADMIN, "self", 403),
+    ("self/internal_user", Actor.INTERNAL_USER, "self", 403),
+    ("self/cross_org_user", Actor.CROSS_ORG_USER, "self", 403),
+    ("owner/proxy_admin", Actor.PROXY_ADMIN, "owner", 200),
+    ("owner/org_admin", Actor.ORG_ADMIN, "owner", 401),
+    ("owner/team_admin", Actor.TEAM_ADMIN, "owner", 200),
+    ("owner/internal_user", Actor.INTERNAL_USER, "owner", 403),
+    ("owner/owner", Actor.OWNER, "owner", 403),
+    ("owner/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "owner", 403),
+    ("owner/cross_org_user", Actor.CROSS_ORG_USER, "owner", 403),
+    ("owner/service_account", Actor.SERVICE_ACCOUNT, "owner", 403),
+    ("owner/org_b_admin", Actor.ORG_B_ADMIN, "owner", 401),
+    ("cross_org/proxy_admin", Actor.PROXY_ADMIN, "cross_org", 200),
+    ("cross_org/org_admin", Actor.ORG_ADMIN, "cross_org", 401),
+    ("cross_org/team_admin", Actor.TEAM_ADMIN, "cross_org", 403),
+    ("cross_org/cross_org_user", Actor.CROSS_ORG_USER, "cross_org", 403),
+    ("cross_org/org_b_admin", Actor.ORG_B_ADMIN, "cross_org", 401),
+]
+
+
+async def _seed_target(proxy_client, seeder, scratch_prefix, world, shape, caller):
+    if shape == "self":
+        return await create_scratch_key(
+            proxy_client, seeder, scratch_prefix, user_id=caller.user_id
+        )
+    if shape == "owner":
+        return await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch_prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=TEAM_ALPHA,
+        )
+    if shape == "cross_org":
+        return await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch_prefix,
+            user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+            team_id=TEAM_BETA,
+        )
+    pytest.fail(f"unknown shape={shape}")  # pragma: no cover
+
+
+@pytest.mark.parametrize("route", ["block", "unblock"])
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_block_unblock_authz_matrix(
+    route: str,
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    target_cleartext = await _seed_target(
+        proxy_client, seeder, scratch.prefix, world, shape, caller
+    )
+    target_hashed = hash_token(target_cleartext)
+
+    # /unblock starts from a blocked row so a 200 is observable as True->False.
+    if route == "unblock":
+        await prisma.db.litellm_verificationtoken.update(
+            where={"token": target_hashed}, data={"blocked": True}
+        )
+
+    resp = await proxy_client.post(
+        f"/key/{route}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"key": target_cleartext},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{route} {actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": target_hashed}
+    )
+    assert row is not None
+    # A never-blocked key reads back blocked=None; treat that as not-blocked.
+    if expected_status == 200:
+        assert bool(row.blocked) is (route == "block")
+    else:
+        # A denial leaves the blocked column at its pre-request value.
+        assert bool(row.blocked) is (route == "unblock"), "denied but blocked mutated"
+
+
+async def test_key_block_unblock_round_trip(proxy_client, prisma, scratch, world):
+    """PROXY_ADMIN block then unblock flips the blocked column True then False."""
+    admin = world.keys[Actor.PROXY_ADMIN]
+    target = await create_scratch_key(
+        proxy_client, admin.cleartext, scratch.prefix, user_id=admin.user_id
+    )
+    hashed = hash_token(target)
+    headers = {"Authorization": f"Bearer {admin.cleartext}"}
+
+    blocked = await proxy_client.post(
+        "/key/block", headers=headers, json={"key": target}
+    )
+    assert blocked.status_code == 200, blocked.text
+    row = await prisma.db.litellm_verificationtoken.find_unique(where={"token": hashed})
+    assert row is not None and row.blocked is True
+
+    unblocked = await proxy_client.post(
+        "/key/unblock", headers=headers, json={"key": target}
+    )
+    assert unblocked.status_code == 200, unblocked.text
+    row = await prisma.db.litellm_verificationtoken.find_unique(where={"token": hashed})
+    assert row is not None and row.blocked is False
+
+
+@pytest.mark.parametrize("route", ["block", "unblock"])
+@pytest.mark.parametrize(
+    "actor", [Actor.PROXY_ADMIN, Actor.TEAM_ADMIN], ids=["proxy_admin", "team_admin"]
+)
+async def test_key_block_unblock_missing_key_returns_404(
+    route: str, actor: Actor, proxy_client, world
+):
+    """A well-formed but unseeded key is 404 — not 401/403 — for both the
+    PROXY_ADMIN existence check and the non-admin _check_key_admin_access path."""
+    caller = world.keys[actor]
+    missing = "sk-" + uuid.uuid4().hex
+    resp = await proxy_client.post(
+        f"/key/{route}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"key": missing},
+    )
+    assert (
+        resp.status_code == 404
+    ), f"{route} {actor.value}: {resp.status_code} {resp.text}"
diff --git a/tests/proxy_behavior/management/test_key_budget_limits.py b/tests/proxy_behavior/management/test_key_budget_limits.py
new file mode 100644
index 00000000000..f5066d60a34
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_budget_limits.py
@@ -0,0 +1,383 @@
+"""Phase 4 F1 — payload-level pins for key budget & rate-limit enforcement.
+
+Pins the five helpers
+  * _check_key_model_specific_limits           (key_management_endpoints.py:931)
+  * _check_key_rpm_tpm_limits                  (key_management_endpoints.py:1016)
+  * _check_team_key_limits                     (key_management_endpoints.py:1096)
+  * _check_org_key_limits                      (key_management_endpoints.py:1284)
+  * _check_project_key_limits                  (key_management_endpoints.py:1135)
+
+Driven through /key/generate. PROXY_ADMIN is the caller so authz never
+short-circuits the payload check — Phase 1–3 already pinned authz cleanly.
+
+`guaranteed_throughput` on either tpm_limit_type or rpm_limit_type is the
+trigger that arms `_check_team_key_limits` / `_check_org_key_limits`; without
+it both helpers early-return before reading any limit. The project sub-family
+covers `_check_project_key_limits`, which has no such gate.
+
+Each scenario asserts BOTH:
+  - HTTP status, and
+  - the DB row state on re-read (created when accepted; absent when rejected).
+A response-body check is deliberately avoided per parent plan's anti-snapshot
+rule.
+"""
+
+from typing import Any, Dict
+
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_org, create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# ---------------------------------------------------------------------------
+# _check_team_key_limits — aggregate tpm/rpm guard
+# ---------------------------------------------------------------------------
+
+# (id, team_tpm, team_rpm, body_extras, expected_status, detail_substring)
+_TEAM_RATE_LIMIT_SCENARIOS = [
+    (
+        "aggregate/tpm_within_bound",
+        1000,
+        None,
+        {"tpm_limit": 400, "tpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "aggregate/tpm_over_bound",
+        1000,
+        None,
+        {"tpm_limit": 2000, "tpm_limit_type": "guaranteed_throughput"},
+        400,
+        "TPM limit",
+    ),
+    (
+        "aggregate/rpm_within_bound",
+        None,
+        100,
+        {"rpm_limit": 40, "rpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "aggregate/rpm_over_bound",
+        None,
+        100,
+        {"rpm_limit": 250, "rpm_limit_type": "guaranteed_throughput"},
+        400,
+        "RPM limit",
+    ),
+    (
+        "aggregate/no_guaranteed_throughput_skips_check",
+        None,
+        10,
+        # No *_limit_type → helper early-returns even though rpm exceeds team's.
+        {"rpm_limit": 50},
+        200,
+        None,
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "team_tpm,team_rpm,body_extras,expected_status,detail_substring",
+    [(a, b, c, d, e) for (_id, a, b, c, d, e) in _TEAM_RATE_LIMIT_SCENARIOS],
+    ids=[s[0] for s in _TEAM_RATE_LIMIT_SCENARIOS],
+)
+async def test_check_team_key_limits_aggregate(
+    team_tpm,
+    team_rpm,
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    detail_substring,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    team_id = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("team"),
+        tpm_limit=team_tpm,
+        rpm_limit=team_rpm,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    body: Dict[str, Any] = {
+        "key_alias": scratch.prefix,
+        "team_id": team_id,
+        **body_extras,
+    }
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{body!r} → {resp.status_code}: {resp.text}"
+    if detail_substring is not None:
+        assert detail_substring in resp.text, resp.text
+
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    if expected_status == 200:
+        assert len(rows) == 1
+    else:
+        assert rows == [], "rejected key leaked a row"
+
+
+# ---------------------------------------------------------------------------
+# _check_team_key_limits — model-specific guard (via team metadata)
+# ---------------------------------------------------------------------------
+
+# Body shape: {"model_rpm_limit": {"gpt-4": <n>}, "rpm_limit_type": "guaranteed_throughput"}
+# Team metadata supplies the matching per-model cap.
+_TEAM_MODEL_LIMIT_SCENARIOS = [
+    (
+        "model_rpm/within_bound",
+        {"model_rpm_limit": {"gpt-4": 30}},
+        {"model_rpm_limit": {"gpt-4": 20}, "rpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "model_rpm/over_bound",
+        {"model_rpm_limit": {"gpt-4": 30}},
+        {"model_rpm_limit": {"gpt-4": 100}, "rpm_limit_type": "guaranteed_throughput"},
+        400,
+        "RPM",
+    ),
+    (
+        "model_tpm/within_bound",
+        {"model_tpm_limit": {"gpt-4": 500}},
+        {"model_tpm_limit": {"gpt-4": 200}, "tpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "model_tpm/over_bound",
+        {"model_tpm_limit": {"gpt-4": 500}},
+        {"model_tpm_limit": {"gpt-4": 5000}, "tpm_limit_type": "guaranteed_throughput"},
+        400,
+        "TPM",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "team_metadata,body_extras,expected_status,detail_substring",
+    [(b, c, d, e) for (_id, b, c, d, e) in _TEAM_MODEL_LIMIT_SCENARIOS],
+    ids=[s[0] for s in _TEAM_MODEL_LIMIT_SCENARIOS],
+)
+async def test_check_team_key_limits_model_specific(
+    team_metadata,
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    detail_substring,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    team_id = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("team"),
+        metadata=team_metadata,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    body: Dict[str, Any] = {
+        "key_alias": scratch.prefix,
+        "team_id": team_id,
+        **body_extras,
+    }
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{body!r} → {resp.status_code}: {resp.text}"
+    if detail_substring is not None:
+        assert detail_substring in resp.text, resp.text
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    assert len(rows) == (1 if expected_status == 200 else 0)
+
+
+# ---------------------------------------------------------------------------
+# _check_org_key_limits — aggregate tpm/rpm guard via budget table
+#
+# Behavior pin, not a regression target: /key/generate (line 889) and
+# /key/update (line 2310) both call `get_org_object` WITHOUT
+# `include_budget_table=True`, so `org_table.litellm_budget_table` is None
+# at guard time and the aggregate path silently no-ops. Over-bound payloads
+# therefore land as 200, not 400. Documenting that here so a future change
+# that flips include_budget_table=True or moves the guard pre-load would
+# turn these into reds — exactly the regression-tripwire shape Phase 4 wants.
+# The model-specific guard below DOES fire because it reads org metadata,
+# which is loaded directly on the org row (no relation include needed).
+# ---------------------------------------------------------------------------
+
+_ORG_RATE_LIMIT_SCENARIOS = [
+    (
+        "org/tpm_within_bound",
+        {"max_budget": None, "tpm_limit": 1000, "rpm_limit": None},
+        {"tpm_limit": 400, "tpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "org/tpm_over_bound_unenforced_no_include_budget_table",
+        {"max_budget": None, "tpm_limit": 1000, "rpm_limit": None},
+        {"tpm_limit": 2000, "tpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "org/rpm_within_bound",
+        {"max_budget": None, "tpm_limit": None, "rpm_limit": 100},
+        {"rpm_limit": 40, "rpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "org/rpm_over_bound_unenforced_no_include_budget_table",
+        {"max_budget": None, "tpm_limit": None, "rpm_limit": 100},
+        {"rpm_limit": 250, "rpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "org/no_guaranteed_throughput_skips_check",
+        {"max_budget": None, "tpm_limit": None, "rpm_limit": 10},
+        {"rpm_limit": 50},
+        200,
+        None,
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "org_budget,body_extras,expected_status,detail_substring",
+    [(b, c, d, e) for (_id, b, c, d, e) in _ORG_RATE_LIMIT_SCENARIOS],
+    ids=[s[0] for s in _ORG_RATE_LIMIT_SCENARIOS],
+)
+async def test_check_org_key_limits_aggregate(
+    org_budget: Dict[str, Any],
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    detail_substring,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = await create_scratch_org(prisma, scratch.prefix, **org_budget)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    body: Dict[str, Any] = {
+        "key_alias": scratch.prefix,
+        "organization_id": org_id,
+        **body_extras,
+    }
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{body!r} → {resp.status_code}: {resp.text}"
+    if detail_substring is not None:
+        assert detail_substring in resp.text, resp.text
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    assert len(rows) == (1 if expected_status == 200 else 0)
+
+
+# ---------------------------------------------------------------------------
+# _check_org_key_limits — model-specific guard via org metadata
+# ---------------------------------------------------------------------------
+
+_ORG_MODEL_LIMIT_SCENARIOS = [
+    (
+        "org_model_rpm/within_bound",
+        {"model_rpm_limit": {"gpt-4": 30}},
+        {"model_rpm_limit": {"gpt-4": 20}, "rpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "org_model_rpm/over_bound",
+        {"model_rpm_limit": {"gpt-4": 30}},
+        {"model_rpm_limit": {"gpt-4": 100}, "rpm_limit_type": "guaranteed_throughput"},
+        400,
+        "RPM",
+    ),
+    (
+        "org_model_tpm/within_bound",
+        {"model_tpm_limit": {"gpt-4": 500}},
+        {"model_tpm_limit": {"gpt-4": 200}, "tpm_limit_type": "guaranteed_throughput"},
+        200,
+        None,
+    ),
+    (
+        "org_model_tpm/over_bound",
+        {"model_tpm_limit": {"gpt-4": 500}},
+        {"model_tpm_limit": {"gpt-4": 5000}, "tpm_limit_type": "guaranteed_throughput"},
+        400,
+        "TPM",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "org_metadata,body_extras,expected_status,detail_substring",
+    [(b, c, d, e) for (_id, b, c, d, e) in _ORG_MODEL_LIMIT_SCENARIOS],
+    ids=[s[0] for s in _ORG_MODEL_LIMIT_SCENARIOS],
+)
+async def test_check_org_key_limits_model_specific(
+    org_metadata,
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    detail_substring,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = await create_scratch_org(prisma, scratch.prefix, metadata=org_metadata)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    body: Dict[str, Any] = {
+        "key_alias": scratch.prefix,
+        "organization_id": org_id,
+        **body_extras,
+    }
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{body!r} → {resp.status_code}: {resp.text}"
+    if detail_substring is not None:
+        assert detail_substring in resp.text, resp.text
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    assert len(rows) == (1 if expected_status == 200 else 0)
+
+
+# ---------------------------------------------------------------------------
+# Project sub-family deferral — see plan §4-F1 ("project surface proves thin"):
+# `get_project_object` requires an LiteLLM_ProjectTable seeder + cache wiring
+# that the harness does not yet ship; pinning it would force a wider
+# conftest change for a single-helper close-out. Tracked as "deferred" in
+# the PR4.M3 follow-up box.
diff --git a/tests/proxy_behavior/management/test_key_bulk_update.py b/tests/proxy_behavior/management/test_key_bulk_update.py
new file mode 100644
index 00000000000..1a57998cece
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_bulk_update.py
@@ -0,0 +1,123 @@
+import uuid
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+_MARKER_BUDGET = 42.0
+
+
+# POST /key/bulk_update is PROXY_ADMIN-only. The handler's own gate is
+# user_role != PROXY_ADMIN -> 403, but ORG_ADMIN-role callers never reach it:
+# the management-route gate 401s them first (the body carries no org context,
+# and /key/bulk_update is an internal_user route, not an org-admin one).
+# INTERNAL_USER-role callers clear the route gate and hit the handler's 403.
+_MATRIX = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200),
+    ("org_admin", Actor.ORG_ADMIN, 401),
+    ("team_admin", Actor.TEAM_ADMIN, 403),
+    ("internal_user", Actor.INTERNAL_USER, 403),
+    ("owner", Actor.OWNER, 403),
+    ("unrelated_same_org", Actor.UNRELATED_SAME_ORG, 403),
+    ("cross_org_user", Actor.CROSS_ORG_USER, 403),
+    ("service_account", Actor.SERVICE_ACCOUNT, 403),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status",
+    [(a, s) for (_id, a, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_key_bulk_update_authz_matrix(
+    actor: Actor,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    target = await create_scratch_key(
+        proxy_client, seeder, scratch.prefix, user_id=caller.user_id
+    )
+    hashed = hash_token(target)
+
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"keys": [{"key": target, "max_budget": _MARKER_BUDGET}]},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(where={"token": hashed})
+    assert row is not None
+    if expected_status == 200:
+        body = resp.json()
+        assert len(body["successful_updates"]) == 1
+        assert body["failed_updates"] == []
+        assert row.max_budget == _MARKER_BUDGET
+    else:
+        assert row.max_budget != _MARKER_BUDGET, "denied but key mutated"
+
+
+async def test_key_bulk_update_empty_keys_is_400(proxy_client, world):
+    """An empty batch is rejected 400 before any per-key processing."""
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"keys": []},
+    )
+    assert resp.status_code == 400, resp.text
+
+
+async def test_key_bulk_update_over_max_batch_is_400(proxy_client, world):
+    """A batch larger than the 500-key cap is rejected 400."""
+    items = [{"key": "sk-" + uuid.uuid4().hex} for _ in range(501)]
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"keys": items},
+    )
+    assert resp.status_code == 400, resp.text
+
+
+async def test_key_bulk_update_per_key_failure_is_isolated(
+    proxy_client, prisma, scratch, world
+):
+    """One bad key in the batch does not abort the others — it lands in
+    failed_updates while the valid key is still updated."""
+    admin = world.keys[Actor.PROXY_ADMIN]
+    valid = await create_scratch_key(
+        proxy_client, admin.cleartext, scratch.prefix, user_id=admin.user_id
+    )
+    missing = "sk-" + uuid.uuid4().hex
+
+    resp = await proxy_client.post(
+        "/key/bulk_update",
+        headers={"Authorization": f"Bearer {admin.cleartext}"},
+        json={
+            "keys": [
+                {"key": valid, "max_budget": _MARKER_BUDGET},
+                {"key": missing, "max_budget": _MARKER_BUDGET},
+            ]
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert body["total_requested"] == 2
+    assert len(body["successful_updates"]) == 1
+    assert len(body["failed_updates"]) == 1
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(valid)}
+    )
+    assert row is not None and row.max_budget == _MARKER_BUDGET
diff --git a/tests/proxy_behavior/management/test_key_delete.py b/tests/proxy_behavior/management/test_key_delete.py
new file mode 100644
index 00000000000..0b483edc056
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_delete.py
@@ -0,0 +1,113 @@
+import uuid
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# Same-team peers can READ each other's keys (see test_key_info) but cannot
+# DELETE them — delete is stricter than read.
+_SCENARIOS = [
+    ("self/proxy_admin", Actor.PROXY_ADMIN, "self", 200),
+    ("self/org_admin", Actor.ORG_ADMIN, "self", 401),
+    ("self/team_admin", Actor.TEAM_ADMIN, "self", 200),
+    ("self/internal_user", Actor.INTERNAL_USER, "self", 200),
+    ("self/owner", Actor.OWNER, "self", 200),
+    ("self/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "self", 200),
+    ("self/cross_org_user", Actor.CROSS_ORG_USER, "self", 200),
+    ("self/service_account", Actor.SERVICE_ACCOUNT, "self", 200),
+    ("owner_target/proxy_admin", Actor.PROXY_ADMIN, "owner", 200),
+    ("owner_target/org_admin", Actor.ORG_ADMIN, "owner", 401),
+    ("owner_target/team_admin", Actor.TEAM_ADMIN, "owner", 200),
+    ("owner_target/internal_user", Actor.INTERNAL_USER, "owner", 403),
+    ("owner_target/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "owner", 403),
+    ("owner_target/cross_org_user", Actor.CROSS_ORG_USER, "owner", 403),
+    ("owner_target/service_account", Actor.SERVICE_ACCOUNT, "owner", 403),
+    ("cross_org_target/proxy_admin", Actor.PROXY_ADMIN, "cross_org", 200),
+    ("cross_org_target/org_admin", Actor.ORG_ADMIN, "cross_org", 401),
+    ("cross_org_target/team_admin", Actor.TEAM_ADMIN, "cross_org", 403),
+    ("cross_org_target/owner", Actor.OWNER, "cross_org", 403),
+    ("cross_org_target/cross_org_user", Actor.CROSS_ORG_USER, "cross_org", 200),
+    ("cross_org_target/service_account", Actor.SERVICE_ACCOUNT, "cross_org", 403),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,target_shape,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_delete_authz_matrix(
+    actor: Actor,
+    target_shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+
+    if target_shape == "self":
+        target_cleartext = await create_scratch_key(
+            proxy_client, seeder, scratch.prefix, user_id=caller.user_id
+        )
+    elif target_shape == "owner":
+        target_cleartext = await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch.prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=TEAM_ALPHA,
+        )
+    elif target_shape == "cross_org":
+        target_cleartext = await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch.prefix,
+            user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+            team_id=TEAM_BETA,
+        )
+    else:
+        pytest.fail(f"unknown target_shape={target_shape}")
+
+    target_hashed = hash_token(target_cleartext)
+
+    resp = await proxy_client.post(
+        "/key/delete",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"keys": [target_cleartext]},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {target_shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": target_hashed}
+    )
+    auth_check = await proxy_client.get(
+        "/key/info", headers={"Authorization": f"Bearer {target_cleartext}"}
+    )
+
+    if expected_status == 200:
+        # Hard- or soft-delete both produce a 401 on subsequent auth.
+        assert auth_check.status_code == 401
+    else:
+        assert row is not None, f"{actor.value}: denied but row vanished"
+        assert auth_check.status_code == 200
+
+
+async def test_key_delete_missing_key_is_404(proxy_client, world):
+    """Deleting a key absent from the DB is a 404 — not 401/403."""
+    resp = await proxy_client.post(
+        "/key/delete",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"keys": ["sk-" + uuid.uuid4().hex]},
+    )
+    assert resp.status_code == 404, resp.text
diff --git a/tests/proxy_behavior/management/test_key_generate.py b/tests/proxy_behavior/management/test_key_generate.py
new file mode 100644
index 00000000000..851de33d3ff
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_generate.py
@@ -0,0 +1,70 @@
+from typing import Any, Dict
+
+import pytest
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# (id, actor, body_extras, expected_status). Status codes pinned to observed
+# handler behavior — heterogeneous (200, 400, 401) because the handler routes
+# denials through three different gates (role gate, user_id mismatch, team
+# member permission).
+_SCENARIOS = [
+    ("self/proxy_admin", Actor.PROXY_ADMIN, {}, 200),
+    ("self/org_admin", Actor.ORG_ADMIN, {}, 401),
+    ("self/team_admin", Actor.TEAM_ADMIN, {}, 200),
+    ("self/internal_user", Actor.INTERNAL_USER, {}, 200),
+    ("self/owner", Actor.OWNER, {}, 200),
+    ("self/unrelated_same_org", Actor.UNRELATED_SAME_ORG, {}, 200),
+    ("self/cross_org_user", Actor.CROSS_ORG_USER, {}, 200),
+    ("self/service_account", Actor.SERVICE_ACCOUNT, {}, 200),
+    ("team_alpha/proxy_admin", Actor.PROXY_ADMIN, {"team_id": TEAM_ALPHA}, 200),
+    ("team_alpha/org_admin", Actor.ORG_ADMIN, {"team_id": TEAM_ALPHA}, 401),
+    ("team_alpha/team_admin", Actor.TEAM_ADMIN, {"team_id": TEAM_ALPHA}, 200),
+    ("team_alpha/internal_user", Actor.INTERNAL_USER, {"team_id": TEAM_ALPHA}, 401),
+    ("team_alpha/cross_org_user", Actor.CROSS_ORG_USER, {"team_id": TEAM_ALPHA}, 400),
+    ("team_beta/proxy_admin", Actor.PROXY_ADMIN, {"team_id": TEAM_BETA}, 200),
+    ("team_beta/org_admin", Actor.ORG_ADMIN, {"team_id": TEAM_BETA}, 401),
+    ("team_beta/team_admin", Actor.TEAM_ADMIN, {"team_id": TEAM_BETA}, 400),
+    ("team_beta/internal_user", Actor.INTERNAL_USER, {"team_id": TEAM_BETA}, 400),
+    ("team_beta/cross_org_user", Actor.CROSS_ORG_USER, {"team_id": TEAM_BETA}, 401),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,body_extras,expected_status",
+    [(actor, body, expected) for (_id, actor, body, expected) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_generate_authz_matrix(
+    actor: Actor,
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    seeded = world.keys[actor]
+    body: Dict[str, Any] = {"key_alias": scratch.prefix, **body_extras}
+
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeded.cleartext}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {body!r} → {resp.status_code}: {resp.text}"
+
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    if expected_status == 200:
+        cleartext = resp.json()["key"]
+        assert cleartext.startswith("sk-")
+        assert len(rows) == 1
+    else:
+        assert rows == [], f"{actor.value}: denied but row leaked"
diff --git a/tests/proxy_behavior/management/test_key_health.py b/tests/proxy_behavior/management/test_key_health.py
new file mode 100644
index 00000000000..62147e7fa13
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_health.py
@@ -0,0 +1,24 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /key/health has no role gate — it reflects the caller's OWN key logging
+# metadata. The world keys carry no "logging" metadata, so every authenticated
+# actor gets 200 with key="healthy". This pins auth-required + route coverage.
+@pytest.mark.parametrize("actor", list(Actor), ids=[a.value for a in Actor])
+async def test_key_health_each_actor_is_healthy(actor: Actor, proxy_client, world):
+    caller = world.keys[actor]
+    resp = await proxy_client.post(
+        "/key/health",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert resp.status_code == 200, f"{actor.value}: {resp.status_code} {resp.text}"
+    assert resp.json()["key"] == "healthy"
+
+
+async def test_key_health_requires_auth(proxy_client):
+    resp = await proxy_client.post("/key/health")
+    assert resp.status_code == 401, resp.text
diff --git a/tests/proxy_behavior/management/test_key_info.py b/tests/proxy_behavior/management/test_key_info.py
new file mode 100644
index 00000000000..ddcef9fd27b
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_info.py
@@ -0,0 +1,74 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# (id, actor, target_actor, expected_status). Targets are 3 fixed seeded keys
+# representing the canonical relations: own, OWNER (same org_a/team_alpha),
+# and CROSS_ORG_USER (org_b/team_beta).
+#
+# Notable pinned behaviors (intentionally surfaced, not endorsed):
+#   - ORG_ADMIN 403s on individual key info even within its own org —
+#     visibility is "your own keys" + "your team's keys", not "your org's keys".
+#   - Same-team peers (internal_user, unrelated_same_org, service_account) DO
+#     see each other's keys.
+_SCENARIOS = [
+    ("own/proxy_admin", Actor.PROXY_ADMIN, Actor.PROXY_ADMIN, 200),
+    ("own/org_admin", Actor.ORG_ADMIN, Actor.ORG_ADMIN, 200),
+    ("own/team_admin", Actor.TEAM_ADMIN, Actor.TEAM_ADMIN, 200),
+    ("own/internal_user", Actor.INTERNAL_USER, Actor.INTERNAL_USER, 200),
+    ("own/owner", Actor.OWNER, Actor.OWNER, 200),
+    ("own/unrelated_same_org", Actor.UNRELATED_SAME_ORG, Actor.UNRELATED_SAME_ORG, 200),
+    ("own/cross_org_user", Actor.CROSS_ORG_USER, Actor.CROSS_ORG_USER, 200),
+    ("own/service_account", Actor.SERVICE_ACCOUNT, Actor.SERVICE_ACCOUNT, 200),
+    ("owner_key/proxy_admin", Actor.PROXY_ADMIN, Actor.OWNER, 200),
+    ("owner_key/org_admin", Actor.ORG_ADMIN, Actor.OWNER, 403),
+    ("owner_key/team_admin", Actor.TEAM_ADMIN, Actor.OWNER, 200),
+    ("owner_key/internal_user", Actor.INTERNAL_USER, Actor.OWNER, 200),
+    ("owner_key/owner", Actor.OWNER, Actor.OWNER, 200),
+    ("owner_key/unrelated_same_org", Actor.UNRELATED_SAME_ORG, Actor.OWNER, 200),
+    ("owner_key/cross_org_user", Actor.CROSS_ORG_USER, Actor.OWNER, 403),
+    ("owner_key/service_account", Actor.SERVICE_ACCOUNT, Actor.OWNER, 200),
+    ("cross_org/proxy_admin", Actor.PROXY_ADMIN, Actor.CROSS_ORG_USER, 200),
+    ("cross_org/org_admin", Actor.ORG_ADMIN, Actor.CROSS_ORG_USER, 403),
+    ("cross_org/team_admin", Actor.TEAM_ADMIN, Actor.CROSS_ORG_USER, 403),
+    ("cross_org/internal_user", Actor.INTERNAL_USER, Actor.CROSS_ORG_USER, 403),
+    ("cross_org/owner", Actor.OWNER, Actor.CROSS_ORG_USER, 403),
+    (
+        "cross_org/unrelated_same_org",
+        Actor.UNRELATED_SAME_ORG,
+        Actor.CROSS_ORG_USER,
+        403,
+    ),
+    ("cross_org/cross_org_user", Actor.CROSS_ORG_USER, Actor.CROSS_ORG_USER, 200),
+    ("cross_org/service_account", Actor.SERVICE_ACCOUNT, Actor.CROSS_ORG_USER, 403),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,target_actor,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_info_authz_matrix(
+    actor: Actor, target_actor: Actor, expected_status: int, proxy_client, world
+):
+    caller = world.keys[actor]
+    target = world.keys[target_actor]
+
+    resp = await proxy_client.get(
+        f"/key/info?key={target.cleartext}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} → {target_actor.value}: {resp.status_code} {resp.text}"
+
+    if expected_status == 200:
+        body = resp.json()
+        # The handler echoes back whatever ?key was passed (cleartext here),
+        # so accept either form — info.user_id is the canonical identity check.
+        assert body.get("key") in (target.cleartext, target.hashed)
+        assert body["info"].get("user_id") == target.user_id
diff --git a/tests/proxy_behavior/management/test_key_info_v2.py b/tests/proxy_behavior/management/test_key_info_v2.py
new file mode 100644
index 00000000000..b0fb27a19fa
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_info_v2.py
@@ -0,0 +1,82 @@
+import uuid
+
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /v2/key/info resolves the posted keys, then drops any key the caller
+# cannot see via _can_user_query_key_info — silently, no 403. A non-admin sees
+# a key it owns (user_id match) or a key whose team it belongs to. The world's
+# TEAM_ALPHA members all see each other's keys; CROSS_ORG_USER and the org
+# admins see only their own. The request is posted with every world key, and
+# the returned info set is asserted to equal the visible subset.
+_ALPHA_KEYS = frozenset(
+    {
+        Actor.TEAM_ADMIN,
+        Actor.INTERNAL_USER,
+        Actor.OWNER,
+        Actor.UNRELATED_SAME_ORG,
+        Actor.SERVICE_ACCOUNT,
+    }
+)
+_VISIBILITY = {
+    Actor.PROXY_ADMIN: frozenset(Actor),
+    Actor.ORG_ADMIN: frozenset({Actor.ORG_ADMIN}),
+    Actor.TEAM_ADMIN: _ALPHA_KEYS,
+    Actor.INTERNAL_USER: _ALPHA_KEYS,
+    Actor.OWNER: _ALPHA_KEYS,
+    Actor.UNRELATED_SAME_ORG: _ALPHA_KEYS,
+    Actor.SERVICE_ACCOUNT: _ALPHA_KEYS,
+    Actor.CROSS_ORG_USER: frozenset({Actor.CROSS_ORG_USER}),
+    Actor.ORG_B_ADMIN: frozenset({Actor.ORG_B_ADMIN}),
+}
+
+
+@pytest.mark.parametrize(
+    "actor,expected_visible",
+    list(_VISIBILITY.items()),
+    ids=[a.value for a in _VISIBILITY],
+)
+async def test_key_info_v2_visibility(actor, expected_visible, proxy_client, world):
+    caller = world.keys[actor]
+    user_id_to_actor = {world.keys[a].user_id: a for a in Actor}
+
+    resp = await proxy_client.post(
+        "/v2/key/info",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"keys": [world.keys[a].cleartext for a in Actor]},
+    )
+    assert resp.status_code == 200, f"{actor.value}: {resp.status_code} {resp.text}"
+
+    visible = {
+        user_id_to_actor[entry["user_id"]]
+        for entry in resp.json()["info"]
+        if entry.get("user_id") in user_id_to_actor
+    }
+    assert visible == set(expected_visible), (
+        f"{actor.value}: expected {sorted(a.value for a in expected_visible)}, "
+        f"got {sorted(a.value for a in visible)}"
+    )
+
+
+async def test_key_info_v2_no_body_is_422(proxy_client, world):
+    """A request with no body is a 422 — the handler has no keys to resolve."""
+    resp = await proxy_client.post(
+        "/v2/key/info",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+    )
+    assert resp.status_code == 422, resp.text
+
+
+async def test_key_info_v2_unknown_key_returns_empty_info(proxy_client, world):
+    """Keys that resolve to no rows yield an empty info list, not an error."""
+    resp = await proxy_client.post(
+        "/v2/key/info",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"keys": ["sk-" + uuid.uuid4().hex]},
+    )
+    assert resp.status_code == 200, resp.text
+    assert resp.json()["info"] == []
diff --git a/tests/proxy_behavior/management/test_key_list.py b/tests/proxy_behavior/management/test_key_list.py
new file mode 100644
index 00000000000..0ed101d5868
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_list.py
@@ -0,0 +1,171 @@
+from typing import FrozenSet
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# Pinned default visibility for /key/list (no filter params): each actor's
+# expected set of seeded actor keys.
+_VISIBILITY = {
+    Actor.PROXY_ADMIN: frozenset(Actor),
+    Actor.ORG_ADMIN: frozenset({Actor.ORG_ADMIN}),
+    Actor.TEAM_ADMIN: frozenset({Actor.TEAM_ADMIN}),
+    Actor.INTERNAL_USER: frozenset({Actor.INTERNAL_USER}),
+    Actor.OWNER: frozenset({Actor.OWNER}),
+    Actor.UNRELATED_SAME_ORG: frozenset({Actor.UNRELATED_SAME_ORG}),
+    Actor.CROSS_ORG_USER: frozenset({Actor.CROSS_ORG_USER}),
+    Actor.SERVICE_ACCOUNT: frozenset({Actor.SERVICE_ACCOUNT}),
+}
+
+
+async def _all_visible_hashes(proxy_client, caller_cleartext) -> set:
+    """Walk every /key/list page — size is capped at 100 by the endpoint, so a
+    single request can truncate PROXY_ADMIN's view on a non-fresh DB."""
+    hashes: set = set()
+    page = 1
+    while True:
+        resp = await proxy_client.get(
+            f"/key/list?page={page}&size=100",
+            headers={"Authorization": f"Bearer {caller_cleartext}"},
+        )
+        assert resp.status_code == 200, resp.text
+        body = resp.json()
+        for entry in body.get("keys", []):
+            tok = entry.get("token") if isinstance(entry, dict) else entry
+            if tok:
+                hashes.add(tok)
+        if page >= (body.get("total_pages") or 1):
+            return hashes
+        page += 1
+
+
+@pytest.mark.parametrize(
+    "actor,expected_visible",
+    list(_VISIBILITY.items()),
+    ids=[a.value for a in _VISIBILITY],
+)
+async def test_key_list_visibility(
+    actor: Actor, expected_visible: FrozenSet[Actor], proxy_client, world
+):
+    caller = world.keys[actor]
+    hashed_to_actor = {world.keys[a].hashed: a for a in Actor}
+
+    returned_hashes = await _all_visible_hashes(proxy_client, caller.cleartext)
+    visible_seeded = {
+        hashed_to_actor[h] for h in returned_hashes if h in hashed_to_actor
+    }
+    assert visible_seeded == set(expected_visible), (
+        f"{actor.value}: expected {sorted(a.value for a in expected_visible)}, "
+        f"got {sorted(a.value for a in visible_seeded)}"
+    )
+
+
+async def _list_hashes(proxy_client, caller_cleartext: str, query: str) -> set:
+    resp = await proxy_client.get(
+        f"/key/list?{query}&size=100",
+        headers={"Authorization": f"Bearer {caller_cleartext}"},
+    )
+    assert resp.status_code == 200, resp.text
+    hashes: set = set()
+    for entry in resp.json().get("keys", []):
+        tok = entry.get("token") if isinstance(entry, dict) else entry
+        if tok:
+            hashes.add(tok)
+    return hashes
+
+
+async def test_key_list_admin_key_alias_substring_match(proxy_client, scratch, world):
+    """A PROXY_ADMIN's key_alias filter is a case-insensitive substring match;
+    a narrower fragment selects the subset whose alias contains it."""
+    admin = world.keys[Actor.PROXY_ADMIN]
+    a = await create_scratch_key(
+        proxy_client,
+        admin.cleartext,
+        scratch.prefix,
+        user_id=admin.user_id,
+        key_alias=f"{scratch.prefix}-sub-a",
+    )
+    b = await create_scratch_key(
+        proxy_client,
+        admin.cleartext,
+        scratch.prefix,
+        user_id=admin.user_id,
+        key_alias=f"{scratch.prefix}-sub-b",
+    )
+    seeded = {hash_token(a), hash_token(b)}
+
+    broad = await _list_hashes(
+        proxy_client, admin.cleartext, f"key_alias={scratch.prefix}-sub"
+    )
+    assert broad & seeded == seeded
+
+    narrow = await _list_hashes(
+        proxy_client, admin.cleartext, f"key_alias={scratch.prefix}-sub-a"
+    )
+    assert narrow & seeded == {hash_token(a)}
+
+
+async def test_key_list_non_admin_key_alias_is_exact_match(
+    proxy_client, scratch, world
+):
+    """A non-admin's key_alias filter is exact-match only — substring filtering
+    is restricted to admins. The full alias matches; a fragment does not."""
+    caller = world.keys[Actor.INTERNAL_USER]
+    alias = f"{scratch.prefix}-exact"
+    key = await create_scratch_key(
+        proxy_client,
+        world.keys[Actor.PROXY_ADMIN].cleartext,
+        scratch.prefix,
+        user_id=caller.user_id,
+        key_alias=alias,
+    )
+    key_hash = hash_token(key)
+
+    exact = await _list_hashes(proxy_client, caller.cleartext, f"key_alias={alias}")
+    assert key_hash in exact
+
+    fragment = await _list_hashes(
+        proxy_client, caller.cleartext, f"key_alias={scratch.prefix}-exac"
+    )
+    assert key_hash not in fragment
+
+
+async def test_key_list_team_id_filter(proxy_client, scratch, world):
+    """A team_id filter narrows the listing to keys of that team."""
+    admin = world.keys[Actor.PROXY_ADMIN]
+    team_key = await create_scratch_key(
+        proxy_client,
+        admin.cleartext,
+        scratch.prefix,
+        user_id=world.keys[Actor.OWNER].user_id,
+        team_id=TEAM_ALPHA,
+        key_alias=f"{scratch.prefix}-team",
+    )
+    no_team_key = await create_scratch_key(
+        proxy_client,
+        admin.cleartext,
+        scratch.prefix,
+        user_id=admin.user_id,
+        key_alias=f"{scratch.prefix}-noteam",
+    )
+
+    hashes = await _list_hashes(proxy_client, admin.cleartext, f"team_id={TEAM_ALPHA}")
+    assert hash_token(team_key) in hashes
+    assert hash_token(no_team_key) not in hashes
+
+
+async def test_key_list_non_admin_cannot_filter_other_team(proxy_client, world):
+    """A non-admin filtering by a team it does not belong to is rejected 403."""
+    resp = await proxy_client.get(
+        f"/key/list?team_id={world.team_beta_id}",
+        headers={
+            "Authorization": f"Bearer {world.keys[Actor.INTERNAL_USER].cleartext}"
+        },
+    )
+    assert resp.status_code == 403, resp.text
diff --git a/tests/proxy_behavior/management/test_key_regenerate.py b/tests/proxy_behavior/management/test_key_regenerate.py
new file mode 100644
index 00000000000..724b8b6d65b
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_regenerate.py
@@ -0,0 +1,165 @@
+import litellm
+import pytest
+
+from litellm.types.proxy.management_endpoints.ui_sso import (
+    LiteLLM_UpperboundKeyGenerateParams,
+)
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# Most denials route through team_member_permission (401), unlike /key/update
+# which goes through user_id-mismatch (403). The matrix surfaces that
+# divergence between the two endpoints.
+_SCENARIOS = [
+    ("self/proxy_admin", Actor.PROXY_ADMIN, "self", 200),
+    ("self/org_admin", Actor.ORG_ADMIN, "self", 401),
+    ("self/team_admin", Actor.TEAM_ADMIN, "self", 200),
+    ("self/internal_user", Actor.INTERNAL_USER, "self", 200),
+    ("self/owner", Actor.OWNER, "self", 200),
+    ("self/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "self", 200),
+    ("self/cross_org_user", Actor.CROSS_ORG_USER, "self", 200),
+    ("self/service_account", Actor.SERVICE_ACCOUNT, "self", 200),
+    ("owner_target/proxy_admin", Actor.PROXY_ADMIN, "owner", 200),
+    ("owner_target/org_admin", Actor.ORG_ADMIN, "owner", 401),
+    ("owner_target/team_admin", Actor.TEAM_ADMIN, "owner", 200),
+    ("owner_target/internal_user", Actor.INTERNAL_USER, "owner", 401),
+    ("owner_target/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "owner", 401),
+    ("owner_target/cross_org_user", Actor.CROSS_ORG_USER, "owner", 401),
+    ("owner_target/service_account", Actor.SERVICE_ACCOUNT, "owner", 401),
+    ("cross_org_target/proxy_admin", Actor.PROXY_ADMIN, "cross_org", 200),
+    ("cross_org_target/org_admin", Actor.ORG_ADMIN, "cross_org", 401),
+    ("cross_org_target/team_admin", Actor.TEAM_ADMIN, "cross_org", 401),
+    ("cross_org_target/owner", Actor.OWNER, "cross_org", 401),
+    ("cross_org_target/cross_org_user", Actor.CROSS_ORG_USER, "cross_org", 401),
+    ("cross_org_target/service_account", Actor.SERVICE_ACCOUNT, "cross_org", 401),
+]
+
+
+async def _info(proxy_client, cleartext: str):
+    return await proxy_client.get(
+        "/key/info", headers={"Authorization": f"Bearer {cleartext}"}
+    )
+
+
+@pytest.mark.parametrize(
+    "actor,target_shape,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_regenerate_authz_matrix(
+    actor: Actor,
+    target_shape: str,
+    expected_status: int,
+    proxy_client,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+
+    if target_shape == "self":
+        target_cleartext = await create_scratch_key(
+            proxy_client, seeder, scratch.prefix, user_id=caller.user_id
+        )
+    elif target_shape == "owner":
+        target_cleartext = await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch.prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=TEAM_ALPHA,
+        )
+    elif target_shape == "cross_org":
+        target_cleartext = await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch.prefix,
+            user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+            team_id=TEAM_BETA,
+        )
+    else:
+        pytest.fail(f"unknown target_shape={target_shape}")
+
+    resp = await proxy_client.post(
+        "/key/regenerate",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"key": target_cleartext},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {target_shape}: {resp.status_code} {resp.text}"
+
+    if expected_status == 200:
+        new_cleartext = resp.json()["key"]
+        assert new_cleartext.startswith("sk-") and new_cleartext != target_cleartext
+        assert (await _info(proxy_client, target_cleartext)).status_code == 401
+        assert (await _info(proxy_client, new_cleartext)).status_code == 200
+    else:
+        # Denied: rotation must not have leaked — old cleartext still works.
+        assert (await _info(proxy_client, target_cleartext)).status_code == 200
+
+
+async def test_key_path_regenerate_smoke(proxy_client, scratch, world):
+    """Pins that POST /key/{key:path}/regenerate shares the same handler."""
+    caller = world.keys[Actor.PROXY_ADMIN]
+    target_cleartext = await create_scratch_key(
+        proxy_client, caller.cleartext, scratch.prefix, user_id=caller.user_id
+    )
+
+    resp = await proxy_client.post(
+        f"/key/{target_cleartext}/regenerate",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={},
+    )
+    assert resp.status_code == 200, resp.text
+    new_cleartext = resp.json()["key"]
+    assert new_cleartext.startswith("sk-") and new_cleartext != target_cleartext
+    assert (await _info(proxy_client, target_cleartext)).status_code == 401
+    assert (await _info(proxy_client, new_cleartext)).status_code == 200
+
+
+async def test_key_regenerate_enforces_upperbound_key_params(
+    proxy_client, scratch, world, monkeypatch
+):
+    """Regenerate runs _enforce_upperbound_key_params: a max_budget above
+    litellm.upperbound_key_generate_params is rejected 400, a value within the
+    bound is accepted. Pins #26340 (db8ef44323) — regenerate previously
+    bypassed the upperbound. upperbound_key_generate_params is module-level
+    litellm.* state, so monkeypatch save/restores it."""
+    admin = world.keys[Actor.PROXY_ADMIN]
+    over_key = await create_scratch_key(
+        proxy_client,
+        admin.cleartext,
+        scratch.prefix,
+        user_id=admin.user_id,
+        key_alias=f"{scratch.prefix}-over",
+    )
+    within_key = await create_scratch_key(
+        proxy_client,
+        admin.cleartext,
+        scratch.prefix,
+        user_id=admin.user_id,
+        key_alias=f"{scratch.prefix}-within",
+    )
+    monkeypatch.setattr(
+        litellm,
+        "upperbound_key_generate_params",
+        LiteLLM_UpperboundKeyGenerateParams(max_budget=100.0),
+    )
+    headers = {"Authorization": f"Bearer {admin.cleartext}"}
+
+    over = await proxy_client.post(
+        "/key/regenerate", headers=headers, json={"key": over_key, "max_budget": 500.0}
+    )
+    assert over.status_code == 400, over.text
+
+    within = await proxy_client.post(
+        "/key/regenerate",
+        headers=headers,
+        json={"key": within_key, "max_budget": 50.0},
+    )
+    assert within.status_code == 200, within.text
diff --git a/tests/proxy_behavior/management/test_key_reset_spend.py b/tests/proxy_behavior/management/test_key_reset_spend.py
new file mode 100644
index 00000000000..fb1c266f655
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_reset_spend.py
@@ -0,0 +1,136 @@
+import uuid
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+_SEED_SPEND = 5.0
+_RESET_TO = 2.0
+
+
+# POST /key/{key}/reset_spend. The target key is pre-seeded with spend=5.0 so
+# reset_to=2.0 always clears _validate_reset_spend_value (which runs before
+# authz). _check_proxy_or_team_admin_for_key then allows only PROXY_ADMIN or a
+# team admin of the key's team — there is no org-admin branch, and a teamless
+# "self" key has no team to admin. ORG_ADMIN-role callers are stopped 401 at
+# the management-route gate before the handler runs.
+_SCENARIOS = [
+    ("self/proxy_admin", Actor.PROXY_ADMIN, "self", 200),
+    ("self/org_admin", Actor.ORG_ADMIN, "self", 401),
+    ("self/team_admin", Actor.TEAM_ADMIN, "self", 403),
+    ("self/internal_user", Actor.INTERNAL_USER, "self", 403),
+    ("self/cross_org_user", Actor.CROSS_ORG_USER, "self", 403),
+    ("team_alpha/proxy_admin", Actor.PROXY_ADMIN, "team_alpha", 200),
+    ("team_alpha/org_admin", Actor.ORG_ADMIN, "team_alpha", 401),
+    ("team_alpha/team_admin", Actor.TEAM_ADMIN, "team_alpha", 200),
+    ("team_alpha/internal_user", Actor.INTERNAL_USER, "team_alpha", 403),
+    ("team_alpha/owner", Actor.OWNER, "team_alpha", 403),
+    ("team_alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "team_alpha", 403),
+    ("team_alpha/cross_org_user", Actor.CROSS_ORG_USER, "team_alpha", 403),
+    ("team_alpha/service_account", Actor.SERVICE_ACCOUNT, "team_alpha", 403),
+    ("team_alpha/org_b_admin", Actor.ORG_B_ADMIN, "team_alpha", 401),
+    ("team_beta/proxy_admin", Actor.PROXY_ADMIN, "team_beta", 200),
+    ("team_beta/org_admin", Actor.ORG_ADMIN, "team_beta", 401),
+    ("team_beta/team_admin", Actor.TEAM_ADMIN, "team_beta", 403),
+    ("team_beta/cross_org_user", Actor.CROSS_ORG_USER, "team_beta", 403),
+    ("team_beta/org_b_admin", Actor.ORG_B_ADMIN, "team_beta", 401),
+]
+
+
+async def _seed_target(proxy_client, seeder, prefix, world, shape, caller) -> str:
+    if shape == "self":
+        return await create_scratch_key(
+            proxy_client, seeder, prefix, user_id=caller.user_id
+        )
+    if shape == "team_alpha":
+        return await create_scratch_key(
+            proxy_client,
+            seeder,
+            prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=TEAM_ALPHA,
+        )
+    if shape == "team_beta":
+        return await create_scratch_key(
+            proxy_client,
+            seeder,
+            prefix,
+            user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+            team_id=TEAM_BETA,
+        )
+    pytest.fail(f"unknown shape={shape}")  # pragma: no cover
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_reset_spend_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    target = await _seed_target(
+        proxy_client, seeder, scratch.prefix, world, shape, caller
+    )
+    hashed = hash_token(target)
+    await prisma.db.litellm_verificationtoken.update(
+        where={"token": hashed}, data={"spend": _SEED_SPEND}
+    )
+
+    resp = await proxy_client.post(
+        f"/key/{target}/reset_spend",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"reset_to": _RESET_TO},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(where={"token": hashed})
+    assert row is not None
+    if expected_status == 200:
+        assert row.spend == _RESET_TO
+    else:
+        assert row.spend == _SEED_SPEND, "denied but spend reset"
+
+
+@pytest.mark.parametrize(
+    "actor", [Actor.PROXY_ADMIN, Actor.TEAM_ADMIN], ids=["proxy_admin", "team_admin"]
+)
+async def test_key_reset_spend_missing_key_is_404(actor: Actor, proxy_client, world):
+    """A well-formed but unseeded key is 404 before any spend validation."""
+    resp = await proxy_client.post(
+        f"/key/sk-{uuid.uuid4().hex}/reset_spend",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+        json={"reset_to": 0.0},
+    )
+    assert resp.status_code == 404, resp.text
+
+
+async def test_key_reset_spend_above_current_spend_is_400(
+    proxy_client, prisma, scratch, world
+):
+    """reset_to above the key's current spend is rejected 400."""
+    admin = world.keys[Actor.PROXY_ADMIN]
+    target = await create_scratch_key(
+        proxy_client, admin.cleartext, scratch.prefix, user_id=admin.user_id
+    )
+    resp = await proxy_client.post(
+        f"/key/{target}/reset_spend",
+        headers={"Authorization": f"Bearer {admin.cleartext}"},
+        json={"reset_to": 1.0},
+    )
+    assert resp.status_code == 400, resp.text
diff --git a/tests/proxy_behavior/management/test_key_service_account_generate.py b/tests/proxy_behavior/management/test_key_service_account_generate.py
new file mode 100644
index 00000000000..3b5bbe39754
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_service_account_generate.py
@@ -0,0 +1,98 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /key/service-account/generate. PROXY_ADMIN always passes. ORG_ADMIN-role
+# callers are stopped 401 by the management-route gate (the body carries a
+# team_id but no organization_id, so the org-admin route branch never matches).
+# INTERNAL_USER-role callers reach the handler: a team admin of the target team
+# passes (200); a "user"-role member is 401 (no service-account-generate
+# permission); a non-member is 400 ("not assigned to team"). A request with no
+# team_id is 400 ("team_id is required") for every actor that reaches the handler.
+_SCENARIOS = [
+    ("own/proxy_admin", Actor.PROXY_ADMIN, "own", 200),
+    ("own/org_admin", Actor.ORG_ADMIN, "own", 401),
+    ("own/team_admin", Actor.TEAM_ADMIN, "own", 200),
+    ("own/internal_user", Actor.INTERNAL_USER, "own", 401),
+    ("own/owner", Actor.OWNER, "own", 401),
+    ("own/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "own", 401),
+    ("own/cross_org_user", Actor.CROSS_ORG_USER, "own", 400),
+    ("own/service_account", Actor.SERVICE_ACCOUNT, "own", 401),
+    ("own/org_b_admin", Actor.ORG_B_ADMIN, "own", 401),
+    ("cross_org/proxy_admin", Actor.PROXY_ADMIN, "cross_org", 200),
+    ("cross_org/org_admin", Actor.ORG_ADMIN, "cross_org", 401),
+    ("cross_org/team_admin", Actor.TEAM_ADMIN, "cross_org", 400),
+    ("cross_org/internal_user", Actor.INTERNAL_USER, "cross_org", 400),
+    ("cross_org/cross_org_user", Actor.CROSS_ORG_USER, "cross_org", 401),
+    ("cross_org/org_b_admin", Actor.ORG_B_ADMIN, "cross_org", 401),
+    ("none/proxy_admin", Actor.PROXY_ADMIN, "none", 400),
+    ("none/org_admin", Actor.ORG_ADMIN, "none", 401),
+    ("none/team_admin", Actor.TEAM_ADMIN, "none", 400),
+    ("none/internal_user", Actor.INTERNAL_USER, "none", 400),
+    ("none/cross_org_user", Actor.CROSS_ORG_USER, "none", 400),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,team_target,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_service_account_generate_authz_matrix(
+    actor: Actor,
+    team_target: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    team_id = {
+        "own": world.team_alpha_id,
+        "cross_org": world.team_beta_id,
+        "none": None,
+    }[team_target]
+
+    body = {"key_alias": scratch.prefix}
+    if team_id is not None:
+        body["team_id"] = team_id
+
+    resp = await proxy_client.post(
+        "/key/service-account/generate",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {team_target}: {resp.status_code} {resp.text}"
+
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    if expected_status == 200:
+        assert len(rows) == 1
+        # A service-account key belongs to the team, not a user.
+        assert rows[0].user_id is None
+        assert rows[0].team_id == team_id
+    else:
+        assert rows == [], f"{actor.value}: denied but key row leaked"
+
+
+async def test_key_service_account_generate_unknown_team_is_400(
+    proxy_client, prisma, scratch, world
+):
+    """A team_id absent from the database is rejected 400."""
+    resp = await proxy_client.post(
+        "/key/service-account/generate",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"key_alias": scratch.prefix, "team_id": scratch.tag("no-such-team")},
+    )
+    assert resp.status_code == 400, resp.text
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    assert rows == []
diff --git a/tests/proxy_behavior/management/test_key_team_change.py b/tests/proxy_behavior/management/test_key_team_change.py
new file mode 100644
index 00000000000..3bd4a0af0e2
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_team_change.py
@@ -0,0 +1,229 @@
+"""Phase 4 F2 — payload-level pins for key↔team reassignment.
+
+Pins `validate_key_team_change` (key_management_endpoints.py:2953), reached
+via /key/update when the request changes `team_id`.
+
+The handler runs four guards in order; each scenario isolates one path and
+asserts the rejected row's `team_id` is UNCHANGED on a fresh DB re-read —
+the regression shape that matters for cross-team / IDOR-class bugs is
+"row mutated despite the helper raising", and the only way to catch it
+is to compare the persisted state, not the response body.
+
+The accepted scenario pins the happy path: re-read confirms team_id
+flipped to the new team and the row is otherwise intact.
+"""
+
+import uuid
+from typing import Any, Dict, Optional
+
+import pytest
+from prisma import Json
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+KEY_MODEL = "phase4-f2-key-model"
+
+
+async def _seed_key_with_limits(
+    prisma,
+    scratch_prefix: str,
+    *,
+    user_id: str,
+    team_id: str,
+    models: Optional[list] = None,
+    tpm_limit: Optional[int] = None,
+    rpm_limit: Optional[int] = None,
+) -> str:
+    """Raw-seed a scratch key with explicit models / tpm / rpm — /key/generate
+    can't set these against a non-throughput team without firing F1's guards.
+    Returns the cleartext key for /key/update calls."""
+    cleartext = "sk-" + uuid.uuid4().hex
+    data: Dict[str, Any] = {
+        "token": hash_token(cleartext),
+        "key_alias": f"{scratch_prefix}-key",
+        "key_name": f"{scratch_prefix}-key",
+        "user_id": user_id,
+        "team_id": team_id,
+        "models": models or [],
+    }
+    if tpm_limit is not None:
+        data["tpm_limit"] = tpm_limit
+    if rpm_limit is not None:
+        data["rpm_limit"] = rpm_limit
+    await prisma.db.litellm_verificationtoken.create(data=data)
+    return cleartext
+
+
+# ---------------------------------------------------------------------------
+# Accepted — proxy admin moves a key into a scratch team that has the model,
+# accommodates the limits, and has the key owner as a member.
+# ---------------------------------------------------------------------------
+
+
+async def test_key_team_change_accepted(proxy_client, prisma, scratch, world):
+    owner_id = world.keys[Actor.OWNER].user_id
+    target_team = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("target"),
+        member_user_ids=[owner_id],
+        models=[KEY_MODEL],
+        tpm_limit=10_000,
+        rpm_limit=1_000,
+    )
+    key_cleartext = await _seed_key_with_limits(
+        prisma,
+        scratch.prefix,
+        user_id=owner_id,
+        team_id=TEAM_ALPHA,
+        models=[KEY_MODEL],
+        tpm_limit=500,
+        rpm_limit=50,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key": key_cleartext, "team_id": target_team},
+    )
+    assert resp.status_code == 200, resp.text
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(key_cleartext)}
+    )
+    assert row is not None
+    assert row.team_id == target_team, "key did not move to target team"
+
+
+# ---------------------------------------------------------------------------
+# Rejected — each path verifies row.team_id is UNCHANGED on DB re-read.
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "scenario",
+    [
+        "team_lacks_model",
+        "key_tpm_exceeds_team_tpm",
+        "key_rpm_exceeds_team_rpm",
+        "key_owner_not_team_member",
+    ],
+)
+async def test_key_team_change_rejected_guards(
+    scenario: str, proxy_client, prisma, scratch, world
+):
+    owner_id = world.keys[Actor.OWNER].user_id
+
+    team_kwargs: Dict[str, Any] = {
+        "member_user_ids": [owner_id],
+        "models": [KEY_MODEL],
+        "tpm_limit": 10_000,
+        "rpm_limit": 1_000,
+    }
+    key_kwargs: Dict[str, Any] = {
+        "models": [KEY_MODEL],
+        "tpm_limit": 500,
+        "rpm_limit": 50,
+    }
+
+    if scenario == "team_lacks_model":
+        team_kwargs["models"] = ["something-else"]
+    elif scenario == "key_tpm_exceeds_team_tpm":
+        team_kwargs["tpm_limit"] = 100  # < 500
+    elif scenario == "key_rpm_exceeds_team_rpm":
+        team_kwargs["rpm_limit"] = 5  # < 50
+    elif scenario == "key_owner_not_team_member":
+        team_kwargs["member_user_ids"] = []
+
+    target_team = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("target"),
+        **team_kwargs,
+    )
+    key_cleartext = await _seed_key_with_limits(
+        prisma,
+        scratch.prefix,
+        user_id=owner_id,
+        team_id=TEAM_ALPHA,
+        **key_kwargs,
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"key": key_cleartext, "team_id": target_team},
+    )
+    # The model-mismatch path lands as 400 (ProxyException from
+    # _can_object_call_model); the limit + membership paths land as 403
+    # (validate_key_team_change's own HTTPException). Both shapes count as
+    # "rejected" for the pin; what matters is the row stayed put.
+    assert resp.status_code in (
+        400,
+        403,
+    ), f"{scenario}: expected 400/403, got {resp.status_code}: {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(key_cleartext)}
+    )
+    assert row is not None
+    assert row.team_id == TEAM_ALPHA, (
+        f"{scenario}: row team_id mutated despite rejection — " f"got {row.team_id!r}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Rejected — initiator is neither proxy admin, team admin, nor permission-
+# granted. Pinned separately because the source team here must also have the
+# initiator listed as a non-admin member (otherwise the earlier user_id
+# membership guard fires first).
+# ---------------------------------------------------------------------------
+
+
+async def test_key_team_change_rejected_initiator_not_admin(
+    proxy_client, prisma, scratch, world
+):
+    owner_id = world.keys[Actor.OWNER].user_id
+    target_team = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("target"),
+        # Owner is the key's user_id; member of the team to clear membership
+        # guard. Internal-user role on world.keys[INTERNAL_USER] is also a
+        # member of TEAM_ALPHA but never an admin → role-gate path fires.
+        member_user_ids=[owner_id, world.keys[Actor.INTERNAL_USER].user_id],
+        models=[KEY_MODEL],
+        tpm_limit=10_000,
+        rpm_limit=1_000,
+    )
+    key_cleartext = await _seed_key_with_limits(
+        prisma,
+        scratch.prefix,
+        user_id=owner_id,
+        team_id=TEAM_ALPHA,
+        models=[KEY_MODEL],
+        tpm_limit=500,
+        rpm_limit=50,
+    )
+    # The internal_user actor is the initiator: a TEAM_ALPHA member, but not
+    # an admin anywhere, and has no team_member_permissions for /key/update.
+    initiator = world.keys[Actor.INTERNAL_USER].cleartext
+
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {initiator}"},
+        json={"key": key_cleartext, "team_id": target_team},
+    )
+    assert resp.status_code in (
+        401,
+        403,
+    ), f"expected 401/403, got {resp.status_code}: {resp.text}"
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(key_cleartext)}
+    )
+    assert row is not None
+    assert row.team_id == TEAM_ALPHA, "row team_id mutated despite rejection"
diff --git a/tests/proxy_behavior/management/test_key_update.py b/tests/proxy_behavior/management/test_key_update.py
new file mode 100644
index 00000000000..7b7f6f5558b
--- /dev/null
+++ b/tests/proxy_behavior/management/test_key_update.py
@@ -0,0 +1,184 @@
+import uuid
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import TEAM_ALPHA, TEAM_BETA, Actor
+from .conftest import create_scratch_key
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# (id, actor, target_shape, expected_status). Pinned against current gating:
+# proxy_admin bypasses; org_admin is blocked by an early role gate (401);
+# every other (INTERNAL_USER-roled) actor hits user_id-mismatch 403, no-team-
+# admin 403, or team_member_permission 401 depending on target / membership.
+_SCENARIOS = [
+    ("self/proxy_admin", Actor.PROXY_ADMIN, "self", 200),
+    ("self/org_admin", Actor.ORG_ADMIN, "self", 401),
+    ("self/team_admin", Actor.TEAM_ADMIN, "self", 403),
+    ("self/internal_user", Actor.INTERNAL_USER, "self", 403),
+    ("self/owner", Actor.OWNER, "self", 403),
+    ("self/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "self", 403),
+    ("self/cross_org_user", Actor.CROSS_ORG_USER, "self", 403),
+    ("self/service_account", Actor.SERVICE_ACCOUNT, "self", 403),
+    ("owner_target/proxy_admin", Actor.PROXY_ADMIN, "owner", 200),
+    ("owner_target/org_admin", Actor.ORG_ADMIN, "owner", 401),
+    ("owner_target/team_admin", Actor.TEAM_ADMIN, "owner", 403),
+    ("owner_target/internal_user", Actor.INTERNAL_USER, "owner", 403),
+    ("owner_target/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "owner", 403),
+    ("owner_target/cross_org_user", Actor.CROSS_ORG_USER, "owner", 403),
+    ("owner_target/service_account", Actor.SERVICE_ACCOUNT, "owner", 403),
+    ("cross_org_target/proxy_admin", Actor.PROXY_ADMIN, "cross_org", 200),
+    ("cross_org_target/org_admin", Actor.ORG_ADMIN, "cross_org", 401),
+    ("cross_org_target/team_admin", Actor.TEAM_ADMIN, "cross_org", 403),
+    ("cross_org_target/owner", Actor.OWNER, "cross_org", 403),
+    ("cross_org_target/cross_org_user", Actor.CROSS_ORG_USER, "cross_org", 401),
+    ("cross_org_target/service_account", Actor.SERVICE_ACCOUNT, "cross_org", 403),
+]
+
+MARKER_MODEL = "behavior-pin-update-marker-model"
+
+
+@pytest.mark.parametrize(
+    "actor,target_shape,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_key_update_authz_matrix(
+    actor: Actor,
+    target_shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+
+    if target_shape == "self":
+        target_cleartext = await create_scratch_key(
+            proxy_client, seeder, scratch.prefix, user_id=caller.user_id
+        )
+    elif target_shape == "owner":
+        target_cleartext = await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch.prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=TEAM_ALPHA,
+        )
+    elif target_shape == "cross_org":
+        target_cleartext = await create_scratch_key(
+            proxy_client,
+            seeder,
+            scratch.prefix,
+            user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+            team_id=TEAM_BETA,
+        )
+    else:
+        pytest.fail(f"unknown target_shape={target_shape}")
+
+    target_hashed = hash_token(target_cleartext)
+
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"key": target_cleartext, "models": [MARKER_MODEL]},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {target_shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": target_hashed}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert row.models == [MARKER_MODEL]
+    else:
+        assert row.models != [MARKER_MODEL], "denied but row mutated"
+
+
+async def _seed_shape(proxy_client, seeder, prefix, world, shape, caller) -> str:
+    if shape == "self":
+        return await create_scratch_key(
+            proxy_client, seeder, prefix, user_id=caller.user_id
+        )
+    if shape == "owner":
+        return await create_scratch_key(
+            proxy_client,
+            seeder,
+            prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=TEAM_ALPHA,
+        )
+    if shape == "cross_org":
+        return await create_scratch_key(
+            proxy_client,
+            seeder,
+            prefix,
+            user_id=world.keys[Actor.CROSS_ORG_USER].user_id,
+            team_id=TEAM_BETA,
+        )
+    pytest.fail(f"unknown shape={shape}")  # pragma: no cover
+
+
+async def test_key_update_missing_key_is_404(proxy_client, world):
+    """An update targeting a key absent from the DB is a 404 — not 401/403."""
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"key": "sk-" + uuid.uuid4().hex, "models": [MARKER_MODEL]},
+    )
+    assert resp.status_code == 404, resp.text
+
+
+# A denied /key/update must not partially apply: the budget/limit columns are
+# left untouched. Each scenario is a denial cell from the matrix above.
+_DENIED_BUDGET = [
+    ("team_admin/self", Actor.TEAM_ADMIN, "self", 403),
+    ("internal_user/owner", Actor.INTERNAL_USER, "owner", 403),
+    ("cross_org_user/cross_org", Actor.CROSS_ORG_USER, "cross_org", 401),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,target_shape,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _DENIED_BUDGET],
+    ids=[s[0] for s in _DENIED_BUDGET],
+)
+async def test_key_update_denied_does_not_touch_budget_counters(
+    actor: Actor,
+    target_shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    target = await _seed_shape(
+        proxy_client, seeder, scratch.prefix, world, target_shape, caller
+    )
+    target_hashed = hash_token(target)
+
+    resp = await proxy_client.post(
+        "/key/update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"key": target, "max_budget": 999.0, "tpm_limit": 888, "rpm_limit": 777},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {target_shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": target_hashed}
+    )
+    assert row is not None
+    assert row.max_budget is None, "denied but max_budget applied"
+    assert row.tpm_limit is None, "denied but tpm_limit applied"
+    assert row.rpm_limit is None, "denied but rpm_limit applied"
diff --git a/tests/proxy_behavior/management/test_no_management_imports.py b/tests/proxy_behavior/management/test_no_management_imports.py
new file mode 100644
index 00000000000..f8c52a1c37e
--- /dev/null
+++ b/tests/proxy_behavior/management/test_no_management_imports.py
@@ -0,0 +1,46 @@
+import pathlib
+import re
+
+REPO_ROOT = pathlib.Path(__file__).resolve().parents[3]
+BEHAVIOR_DIR = REPO_ROOT / "tests" / "proxy_behavior"
+
+FORBIDDEN_IMPORT = re.compile(r"^\s*from\s+litellm\.proxy\.management_endpoints\b")
+FORBIDDEN_AUTH_MOCK = re.compile(
+    r"(?:mock\.[A-Za-z_]+|patch[a-z_]*)\([^)]*user_api_key_auth"
+)
+# This file is the only place the forbidden patterns appear as regex source;
+# exclude it so it can describe what it forbids.
+SELF = pathlib.Path(__file__).resolve()
+
+
+def _iter_py_files():
+    for path in BEHAVIOR_DIR.rglob("*.py"):
+        if path.resolve() != SELF:
+            yield path
+
+
+def _scan(pattern):
+    violations = []
+    for path in _iter_py_files():
+        for lineno, line in enumerate(path.read_text().splitlines(), start=1):
+            if pattern.search(line):
+                violations.append(
+                    f"{path.relative_to(REPO_ROOT)}:{lineno}: {line.strip()}"
+                )
+    return violations
+
+
+def test_no_management_endpoint_imports():
+    violations = _scan(FORBIDDEN_IMPORT)
+    assert not violations, (
+        "tests/proxy_behavior/ must not import from litellm.proxy.management_endpoints. "
+        "Violations:\n  " + "\n  ".join(violations)
+    )
+
+
+def test_no_user_api_key_auth_mocking():
+    violations = _scan(FORBIDDEN_AUTH_MOCK)
+    assert not violations, (
+        "tests/proxy_behavior/ must not mock user_api_key_auth. "
+        "Violations:\n  " + "\n  ".join(violations)
+    )
diff --git a/tests/proxy_behavior/management/test_route_coverage.py b/tests/proxy_behavior/management/test_route_coverage.py
new file mode 100644
index 00000000000..1139e251a59
--- /dev/null
+++ b/tests/proxy_behavior/management/test_route_coverage.py
@@ -0,0 +1,91 @@
+"""PR3.M1 — codified route coverage.
+
+Every route declared in the two management-endpoint source files must be
+exercised by at least one behavior-suite scenario. This is a permanent
+regression guard: a future route added without a behavior test fails CI here,
+the same way test_no_management_imports.py codifies the G3 import grep.
+"""
+
+import ast
+import pathlib
+import re
+
+REPO_ROOT = pathlib.Path(__file__).resolve().parents[3]
+SOURCE_FILES = [
+    REPO_ROOT / "litellm/proxy/management_endpoints/key_management_endpoints.py",
+    REPO_ROOT / "litellm/proxy/management_endpoints/team_endpoints.py",
+]
+TEST_DIR = pathlib.Path(__file__).resolve().parent
+SELF = pathlib.Path(__file__).resolve()
+
+# Captures the route literal from `@router.<method>("<literal>"` — `\s*` spans
+# newlines so multi-line decorators are matched too.
+_ROUTE_DECORATOR = re.compile(
+    r"@router\.(?:get|post|put|delete|patch)\(\s*[\"']([^\"']+)[\"']"
+)
+
+
+def _source_routes() -> set:
+    routes: set = set()
+    for path in SOURCE_FILES:
+        routes.update(_ROUTE_DECORATOR.findall(path.read_text()))
+    return routes
+
+
+def _route_to_regex(route: str) -> re.Pattern:
+    # A plain path param ({team_id}) matches a single path segment; a Starlette
+    # ':path' param ({key:path}) matches across '/'. Keeping plain params
+    # slash-bounded stops a loose regex from falsely reporting a future
+    # multi-segment route as already covered.
+    pattern = ["^"]
+    pos = 0
+    for match in re.finditer(r"\{([^}]+)\}", route):
+        pattern.append(re.escape(route[pos : match.start()]))
+        pattern.append("[^?]+" if match.group(1).endswith(":path") else "[^/?]+")
+        pos = match.end()
+    pattern.append(re.escape(route[pos:]) + "$")
+    return re.compile("".join(pattern))
+
+
+def _test_urls() -> set:
+    """Every request-URL string literal across the behavior test suite.
+
+    f-strings are reconstructed with each interpolation collapsed to a single
+    placeholder char, so f"/key/{target}/regenerate" becomes /key/X/regenerate.
+    Query strings are dropped — coverage is a path-level property.
+    """
+    urls: set = set()
+    for path in sorted(TEST_DIR.glob("test_*.py")):
+        if path.resolve() == SELF:
+            continue
+        tree = ast.parse(path.read_text())
+        for node in ast.walk(tree):
+            literal = None
+            if isinstance(node, ast.Constant) and isinstance(node.value, str):
+                literal = node.value
+            elif isinstance(node, ast.JoinedStr):
+                chunks = []
+                for value in node.values:
+                    if isinstance(value, ast.Constant) and isinstance(value.value, str):
+                        chunks.append(value.value)
+                    else:
+                        chunks.append("X")  # interpolated path / query segment
+                literal = "".join(chunks)
+            if literal and literal.startswith("/"):
+                urls.add(literal.split("?", 1)[0])
+    return urls
+
+
+def test_every_management_route_has_a_behavior_scenario():
+    routes = _source_routes()
+    assert routes, "no @router routes parsed — the decorator regex is stale"
+
+    urls = _test_urls()
+    uncovered = sorted(
+        route
+        for route in routes
+        if not any(_route_to_regex(route).match(url) for url in urls)
+    )
+    assert (
+        not uncovered
+    ), "management routes with no behavior-suite scenario:\n  " + "\n  ".join(uncovered)
diff --git a/tests/proxy_behavior/management/test_scratch_teardown.py b/tests/proxy_behavior/management/test_scratch_teardown.py
new file mode 100644
index 00000000000..bcb53935558
--- /dev/null
+++ b/tests/proxy_behavior/management/test_scratch_teardown.py
@@ -0,0 +1,61 @@
+import pytest
+
+from litellm.proxy._types import LitellmUserRoles
+
+from .actors import ORG_A, ORG_B
+from .conftest import MASTER_KEY, SCRATCH_PREFIX, create_scratch_actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# The minting tests run in file order, then _b runs after their fixture
+# teardown and asserts no scratch row survived in any reclaimed table. A leak
+# in either direction fails _b on the next collection.
+
+
+async def test_a_scratch_key_lands_in_db(proxy_client, prisma, scratch):
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {MASTER_KEY}"},
+        json={"key_alias": scratch.prefix},
+    )
+    assert resp.status_code == 200, resp.text
+
+    rows = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": scratch.prefix}
+    )
+    assert len(rows) == 1
+
+
+async def test_a2_scratch_actor_lands_in_db(proxy_client, prisma, scratch):
+    actor = await create_scratch_actor(
+        prisma,
+        scratch.prefix,
+        user_role=LitellmUserRoles.ORG_ADMIN.value,
+        org_admin_of=(ORG_A, ORG_B),
+    )
+    user_row = await prisma.db.litellm_usertable.find_unique(
+        where={"user_id": actor.user_id}
+    )
+    assert user_row is not None
+    info = await proxy_client.get(
+        "/key/info", headers={"Authorization": f"Bearer {actor.cleartext}"}
+    )
+    assert info.status_code == 200, info.text
+    memberships = await prisma.db.litellm_organizationmembership.find_many(
+        where={"user_id": actor.user_id}
+    )
+    assert {m.organization_id for m in memberships} == {ORG_A, ORG_B}
+
+
+async def test_b_scratch_namespace_is_clean(prisma):
+    tokens = await prisma.db.litellm_verificationtoken.find_many(
+        where={"key_alias": {"startswith": SCRATCH_PREFIX}}
+    )
+    users = await prisma.db.litellm_usertable.find_many(
+        where={"user_id": {"startswith": SCRATCH_PREFIX}}
+    )
+    memberships = await prisma.db.litellm_organizationmembership.find_many(
+        where={"user_id": {"startswith": SCRATCH_PREFIX}}
+    )
+    assert tokens == [] and users == [] and memberships == []
diff --git a/tests/proxy_behavior/management/test_smoke.py b/tests/proxy_behavior/management/test_smoke.py
new file mode 100644
index 00000000000..4e90986ad9f
--- /dev/null
+++ b/tests/proxy_behavior/management/test_smoke.py
@@ -0,0 +1,28 @@
+import pytest
+
+from .conftest import MASTER_KEY
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+async def test_liveliness(proxy_client):
+    resp = await proxy_client.get("/health/liveliness")
+    assert resp.status_code == 200
+
+
+async def test_key_generate_lands_in_db(proxy_client, prisma, scratch):
+    from litellm.proxy.utils import hash_token
+
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {MASTER_KEY}"},
+        json={"key_alias": scratch.prefix},
+    )
+    assert resp.status_code == 200, resp.text
+    cleartext = resp.json()["key"]
+    assert cleartext.startswith("sk-")
+
+    hashed = hash_token(cleartext)
+    row = await prisma.db.litellm_verificationtoken.find_unique(where={"token": hashed})
+    assert row is not None
+    assert row.token == hashed != cleartext
diff --git a/tests/proxy_behavior/management/test_team_available.py b/tests/proxy_behavior/management/test_team_available.py
new file mode 100644
index 00000000000..874c8dd4df7
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_available.py
@@ -0,0 +1,21 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# GET /team/available lists teams from
+# litellm.default_internal_user_params["available_teams"]. The behavior world
+# configures no available_teams, so the handler returns [] for every actor
+# before it even reads the caller — this is the route-coverage + default-path
+# pin. /team/available is an info route, so every authenticated actor reaches
+# the handler.
+@pytest.mark.parametrize("actor", list(Actor), ids=[a.value for a in Actor])
+async def test_team_available_default_is_empty(actor: Actor, proxy_client, world):
+    resp = await proxy_client.get(
+        "/team/available",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+    )
+    assert resp.status_code == 200, f"{actor.value}: {resp.status_code} {resp.text}"
+    assert resp.json() == []
diff --git a/tests/proxy_behavior/management/test_team_block_unblock.py b/tests/proxy_behavior/management/test_team_block_unblock.py
new file mode 100644
index 00000000000..9412e51b909
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_block_unblock.py
@@ -0,0 +1,114 @@
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/block + /team/unblock. The handler gate is _verify_team_access
+# (proxy admin / team admin / org admin), but the management-route gate fronts
+# it: the request carries the team's organization_id so an org admin of that
+# org clears the gate's org-scoped branch. A team admin is an INTERNAL_USER
+# and these are not internal_user routes, so a team admin can never reach the
+# handler — only PROXY_ADMIN and an org admin of the team's own org pass.
+_MATRIX = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 401),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 401),
+    ("alpha/owner", Actor.OWNER, "alpha", 401),
+    ("alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "alpha", 401),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 401),
+    ("alpha/service_account", Actor.SERVICE_ACCOUNT, "alpha", 401),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 401),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 401),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+async def _seed_target(prisma, world, shape: str, team_id: str) -> str:
+    """Raw-seed the scratch target team; returns its organization_id."""
+    org_id = world.org_a_id if shape == "alpha" else world.org_b_id
+    await create_scratch_team(prisma, team_id, organization_id=org_id)
+    return org_id
+
+
+@pytest.mark.parametrize("route", ["block", "unblock"])
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_block_unblock_authz_matrix(
+    route: str,
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = await _seed_target(prisma, world, shape, scratch.prefix)
+    caller = world.keys[actor]
+
+    # /unblock starts from a blocked row so a 200 is observable as True->False.
+    if route == "unblock":
+        await prisma.db.litellm_teamtable.update(
+            where={"team_id": scratch.prefix}, data={"blocked": True}
+        )
+
+    resp = await proxy_client.post(
+        f"/team/{route}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_id": scratch.prefix, "organization_id": org_id},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{route} {actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert bool(row.blocked) is (route == "block")
+    else:
+        assert bool(row.blocked) is (route == "unblock"), "denied but blocked mutated"
+
+
+async def test_team_block_unblock_round_trip(proxy_client, prisma, scratch, world):
+    """PROXY_ADMIN block then unblock flips the blocked column True then False."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    headers = {"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"}
+
+    blocked = await proxy_client.post(
+        "/team/block", headers=headers, json={"team_id": scratch.prefix}
+    )
+    assert blocked.status_code == 200, blocked.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None and row.blocked is True
+
+    unblocked = await proxy_client.post(
+        "/team/unblock", headers=headers, json={"team_id": scratch.prefix}
+    )
+    assert unblocked.status_code == 200, unblocked.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None and row.blocked is False
+
+
+@pytest.mark.parametrize("route", ["block", "unblock"])
+async def test_team_block_unblock_missing_team_is_404(route: str, proxy_client, world):
+    """A team_id absent from the DB is 404 — the existence check precedes authz."""
+    resp = await proxy_client.post(
+        f"/team/{route}",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": "behavior-pin-no-such-team"},
+    )
+    assert resp.status_code == 404, resp.text
diff --git a/tests/proxy_behavior/management/test_team_budget_limits.py b/tests/proxy_behavior/management/test_team_budget_limits.py
new file mode 100644
index 00000000000..1534cee2b2e
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_budget_limits.py
@@ -0,0 +1,321 @@
+"""Phase 4 F3 — payload-level pins for team budget & rate-limit enforcement.
+
+Pins the five helpers
+  * _check_team_model_specific_limits     (team_endpoints.py:442)
+  * _check_team_rpm_tpm_limits            (team_endpoints.py:527)
+  * check_org_team_model_specific_limits  (team_endpoints.py:569)
+  * check_org_team_rpm_tpm_limits         (team_endpoints.py:603)
+  * _check_org_team_limits                (team_endpoints.py:628)
+  * _check_user_team_limits               (team_endpoints.py:734)
+
+Driven through /team/new + /team/update.
+
+Structural finding pinned here, identical in shape to F1's org aggregate:
+both call sites (lines 985 + 1751) load the org via `get_org_object`
+WITHOUT `include_budget_table=True`, so `org_table.litellm_budget_table`
+is `None` and the org max_budget / org tpm / org rpm guards inside
+`_check_org_team_limits` (lines 641–694, 670–694) silently no-op. The
+`models` subset guard (lines 654–667) IS reachable because it reads
+`org_table.models` directly. The `_check_user_team_limits` guards reach
+all branches through `user_api_key_dict`, no relation include needed.
+"""
+
+import uuid
+from typing import Any, Dict, Optional
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import Actor
+from .conftest import create_scratch_org, create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+async def _seed_scratch_actor_with_caps(
+    prisma,
+    scratch_prefix: str,
+    *,
+    user_role: str = "internal_user",
+    models: Optional[list] = None,
+    max_budget: Optional[float] = None,
+    tpm_limit: Optional[int] = None,
+    rpm_limit: Optional[int] = None,
+) -> str:
+    """Raw-seed a scratch actor + verification token, with the token carrying
+    explicit caps that flow into `user_api_key_dict` at request time.
+
+    Returns cleartext key. Used by F3 user-team-limit scenarios where the
+    caller's caps drive the rejection. Uses 'internal_user' role plus a
+    token-level `allowed_routes` whitelist of /team/new + /team/update —
+    that whitelist is the only way past the admin-only role gate in
+    `RouteChecks.non_proxy_admin_allowed_routes_check`; without it the
+    non-admin caller would 401 before `_check_user_team_limits` ever fires.
+    """
+    user_id = f"{scratch_prefix}-team-creator"
+    cleartext = "sk-" + uuid.uuid4().hex
+    await prisma.db.litellm_usertable.create(
+        data={
+            "user_id": user_id,
+            "user_role": user_role,
+            "max_budget": max_budget,
+        }
+    )
+    token_data: Dict[str, Any] = {
+        "token": hash_token(cleartext),
+        "key_name": f"{scratch_prefix}-team-creator-key",
+        "key_alias": f"{scratch_prefix}-team-creator-alias",
+        "user_id": user_id,
+        "models": models if models is not None else [],
+        "allowed_routes": ["/team/new", "/team/update"],
+    }
+    if tpm_limit is not None:
+        token_data["tpm_limit"] = tpm_limit
+    if rpm_limit is not None:
+        token_data["rpm_limit"] = rpm_limit
+    await prisma.db.litellm_verificationtoken.create(data=token_data)
+    return cleartext
+
+
+# ---------------------------------------------------------------------------
+# _check_org_team_limits — models subset guard (the one path that reaches)
+# ---------------------------------------------------------------------------
+
+_ORG_MODEL_SCENARIOS = [
+    (
+        "org_models/team_subset_accepted",
+        ["allowed-model"],
+        {"models": ["allowed-model"]},
+        200,
+    ),
+    (
+        "org_models/team_extra_model_rejected",
+        ["allowed-model"],
+        {"models": ["forbidden-model"]},
+        400,
+    ),
+    (
+        "org_models/all_proxy_models_skips_check",
+        ["all-proxy-models"],
+        {"models": ["any-model-at-all"]},
+        200,
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "org_models,body_extras,expected_status",
+    [(b, c, d) for (_id, b, c, d) in _ORG_MODEL_SCENARIOS],
+    ids=[s[0] for s in _ORG_MODEL_SCENARIOS],
+)
+async def test_check_org_team_limits_models_subset(
+    org_models,
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = await create_scratch_org(prisma, scratch.prefix, models=org_models)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = scratch.tag("team")
+    body: Dict[str, Any] = {
+        "team_id": team_id,
+        "team_alias": scratch.prefix,
+        "organization_id": org_id,
+        **body_extras,
+    }
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{body!r} → {resp.status_code}: {resp.text}"
+
+    rows = await prisma.db.litellm_teamtable.find_many(where={"team_id": team_id})
+    assert len(rows) == (1 if expected_status == 200 else 0)
+
+
+# ---------------------------------------------------------------------------
+# _check_org_team_limits — budget / tpm / rpm structurally unreachable
+# (org_table.litellm_budget_table is None at guard time). Pin the
+# no-op behavior so a future change that flips include_budget_table=True
+# turns these into reds.
+# ---------------------------------------------------------------------------
+
+_ORG_BUDGET_DEAD_SCENARIOS = [
+    (
+        "org_budget/over_max_budget_unenforced",
+        {"max_budget": 100, "tpm_limit": None, "rpm_limit": None},
+        {"max_budget": 999_999},
+    ),
+    (
+        "org_tpm/over_unenforced",
+        {"max_budget": None, "tpm_limit": 100, "rpm_limit": None},
+        {"tpm_limit": 999_999},
+    ),
+    (
+        "org_rpm/over_unenforced",
+        {"max_budget": None, "tpm_limit": None, "rpm_limit": 100},
+        {"rpm_limit": 999_999},
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "org_budget,body_extras",
+    [(b, c) for (_id, b, c) in _ORG_BUDGET_DEAD_SCENARIOS],
+    ids=[s[0] for s in _ORG_BUDGET_DEAD_SCENARIOS],
+)
+async def test_check_org_team_limits_budget_dead_code_pin(
+    org_budget,
+    body_extras: Dict[str, Any],
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = await create_scratch_org(prisma, scratch.prefix, **org_budget)
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    team_id = scratch.tag("team")
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "team_alias": scratch.prefix,
+            "organization_id": org_id,
+            **body_extras,
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    rows = await prisma.db.litellm_teamtable.find_many(where={"team_id": team_id})
+    assert len(rows) == 1
+
+
+# ---------------------------------------------------------------------------
+# _check_user_team_limits — fires for standalone (no-org) teams created by
+# a non-admin caller. Each guard reads from user_api_key_dict / user_obj.
+# ---------------------------------------------------------------------------
+
+_USER_LIMIT_SCENARIOS = [
+    (
+        "user_max_budget/within",
+        {"max_budget": 100.0, "models": [], "tpm_limit": None, "rpm_limit": None},
+        {"max_budget": 50.0},
+        200,
+    ),
+    (
+        "user_max_budget/over",
+        {"max_budget": 100.0, "models": [], "tpm_limit": None, "rpm_limit": None},
+        {"max_budget": 1000.0},
+        400,
+    ),
+    (
+        "user_models/subset",
+        {"max_budget": None, "models": ["m-a"], "tpm_limit": None, "rpm_limit": None},
+        {"models": ["m-a"]},
+        200,
+    ),
+    (
+        "user_models/superset_rejected",
+        {"max_budget": None, "models": ["m-a"], "tpm_limit": None, "rpm_limit": None},
+        {"models": ["m-a", "m-b"]},
+        400,
+    ),
+    (
+        "user_tpm/within",
+        {"max_budget": None, "models": [], "tpm_limit": 1000, "rpm_limit": None},
+        {"tpm_limit": 500},
+        200,
+    ),
+    (
+        "user_tpm/over",
+        {"max_budget": None, "models": [], "tpm_limit": 1000, "rpm_limit": None},
+        {"tpm_limit": 2000},
+        400,
+    ),
+    (
+        "user_rpm/within",
+        {"max_budget": None, "models": [], "tpm_limit": None, "rpm_limit": 100},
+        {"rpm_limit": 50},
+        200,
+    ),
+    (
+        "user_rpm/over",
+        {"max_budget": None, "models": [], "tpm_limit": None, "rpm_limit": 100},
+        {"rpm_limit": 250},
+        400,
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "actor_caps,body_extras,expected_status",
+    [(b, c, d) for (_id, b, c, d) in _USER_LIMIT_SCENARIOS],
+    ids=[s[0] for s in _USER_LIMIT_SCENARIOS],
+)
+async def test_check_user_team_limits(
+    actor_caps,
+    body_extras: Dict[str, Any],
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+):
+    caller = await _seed_scratch_actor_with_caps(prisma, scratch.prefix, **actor_caps)
+    team_id = scratch.tag("team")
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {caller}"},
+        json={
+            "team_id": team_id,
+            "team_alias": scratch.prefix,
+            # Standalone team — no organization_id, so user-limit guard fires.
+            **body_extras,
+        },
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"caps={actor_caps} body={body_extras} → {resp.status_code}: {resp.text}"
+
+    rows = await prisma.db.litellm_teamtable.find_many(where={"team_id": team_id})
+    assert len(rows) == (1 if expected_status == 200 else 0)
+
+
+# ---------------------------------------------------------------------------
+# /team/update path — _check_user_team_limits on existing team, no-org.
+# Pin one over-budget rejection here so the update-side wiring is also
+# covered (the update path is a second call site with its own data shape).
+# ---------------------------------------------------------------------------
+
+
+async def test_team_update_user_limit_rejected(proxy_client, prisma, scratch):
+    caller_cleartext = await _seed_scratch_actor_with_caps(
+        prisma,
+        scratch.prefix,
+        max_budget=100.0,
+    )
+    creator_user_id = f"{scratch.prefix}-team-creator"
+    # Team must exist before /team/update; seed a standalone scratch team
+    # owned by the same actor so the update authz gate passes.
+    team_id = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("team"),
+        admin_user_ids=[creator_user_id],
+        max_budget=50.0,
+    )
+    resp = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {caller_cleartext}"},
+        json={"team_id": team_id, "max_budget": 999.0},
+    )
+    assert resp.status_code == 400, resp.text
+
+    row = await prisma.db.litellm_teamtable.find_unique(where={"team_id": team_id})
+    assert row is not None
+    assert row.max_budget == 50.0, "row max_budget mutated despite rejection"
diff --git a/tests/proxy_behavior/management/test_team_bulk_member_add.py b/tests/proxy_behavior/management/test_team_bulk_member_add.py
new file mode 100644
index 00000000000..fc83cd414e5
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_bulk_member_add.py
@@ -0,0 +1,105 @@
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+def _member_ids(row) -> list:
+    return [m["user_id"] for m in (row.members_with_roles or [])]
+
+
+async def test_team_bulk_member_add_proxy_admin_adds_explicit_members(
+    proxy_client, prisma, scratch, world
+):
+    """PROXY_ADMIN bulk-adds an explicit member list to a scratch team."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    new_member = scratch.tag("m1")
+    resp = await proxy_client.post(
+        "/team/bulk_member_add",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "members": [{"user_id": new_member, "role": "user"}],
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None and new_member in _member_ids(row)
+
+
+async def test_team_bulk_member_add_empty_members_is_400(
+    proxy_client, prisma, scratch, world
+):
+    """An empty member list (with all_users unset) is rejected 400."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    resp = await proxy_client.post(
+        "/team/bulk_member_add",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "members": []},
+    )
+    assert resp.status_code == 400, resp.text
+
+
+async def test_team_bulk_member_add_over_max_batch_is_400(
+    proxy_client, prisma, scratch, world
+):
+    """A member list larger than the 500-member cap is rejected 400."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    members = [
+        {"user_id": f"{scratch.prefix}-u{i}", "role": "user"} for i in range(501)
+    ]
+    resp = await proxy_client.post(
+        "/team/bulk_member_add",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "members": members},
+    )
+    assert resp.status_code == 400, resp.text
+
+
+@pytest.mark.parametrize(
+    "actor",
+    [Actor.TEAM_ADMIN, Actor.INTERNAL_USER],
+    ids=["team_admin", "internal_user"],
+)
+async def test_team_bulk_member_add_non_admin_is_401(
+    actor: Actor, proxy_client, prisma, scratch, world
+):
+    """/team/bulk_member_add is neither an internal_user nor a self-managed
+    route — a non-proxy-admin with no org context is 401 at the route gate."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    resp = await proxy_client.post(
+        "/team/bulk_member_add",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "members": [{"user_id": scratch.tag("m"), "role": "user"}],
+        },
+    )
+    assert resp.status_code == 401, f"{actor.value}: {resp.status_code} {resp.text}"
+
+
+async def test_team_bulk_member_add_all_users_proxy_admin(
+    proxy_client, prisma, scratch, world
+):
+    """all_users=True pulls every user in the DB into the team. The route is
+    reachable only by PROXY_ADMIN (the route gate 401s every other actor — even
+    an org admin with organization_id in the body), so the handler's own
+    all_users PROXY_ADMIN gate is never the deciding check at the boundary."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    resp = await proxy_client.post(
+        "/team/bulk_member_add",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "all_users": True},
+    )
+    assert resp.status_code == 200, resp.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    member_ids = _member_ids(row)
+    # every world actor is a user in the DB, so all are now team members
+    assert world.keys[Actor.INTERNAL_USER].user_id in member_ids
diff --git a/tests/proxy_behavior/management/test_team_daily_activity.py b/tests/proxy_behavior/management/test_team_daily_activity.py
new file mode 100644
index 00000000000..7a1e70b91fc
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_daily_activity.py
@@ -0,0 +1,63 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# GET /team/daily/activity. A proxy admin (admin view) sees activity for any
+# team. A non-admin is scoped to user_info.teams: a bare query defaults to its
+# own teams (200), and an explicit team_ids filter naming a team it does not
+# belong to is 404 (the VERIA-43 fix). Org admins have no team memberships, so
+# they behave like a non-member for any specific team.
+_MEMBERS = {
+    "alpha": {
+        Actor.TEAM_ADMIN,
+        Actor.INTERNAL_USER,
+        Actor.OWNER,
+        Actor.UNRELATED_SAME_ORG,
+        Actor.SERVICE_ACCOUNT,
+    },
+    "beta": {Actor.CROSS_ORG_USER},
+}
+
+
+def _expected(actor: Actor, team: str) -> int:
+    if team == "none" or actor == Actor.PROXY_ADMIN:
+        return 200
+    return 200 if actor in _MEMBERS.get(team, set()) else 404
+
+
+_CASES = [
+    (f"{team}/{actor.value}", actor, team, _expected(actor, team))
+    for team in ("none", "alpha", "beta")
+    for actor in Actor
+]
+
+
+# start_date / end_date are required by the handler — pin only the team-scope
+# authz, not the date validation.
+_DATES = "start_date=2024-01-01&end_date=2024-12-31"
+
+
+@pytest.mark.parametrize(
+    "actor,team,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _CASES],
+    ids=[c[0] for c in _CASES],
+)
+async def test_team_daily_activity_matrix(
+    actor: Actor, team: str, expected_status: int, proxy_client, world
+):
+    query = _DATES
+    if team == "alpha":
+        query += f"&team_ids={world.team_alpha_id}"
+    elif team == "beta":
+        query += f"&team_ids={world.team_beta_id}"
+
+    resp = await proxy_client.get(
+        f"/team/daily/activity?{query}",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} -> {team}: {resp.status_code} {resp.text}"
diff --git a/tests/proxy_behavior/management/test_team_delete.py b/tests/proxy_behavior/management/test_team_delete.py
new file mode 100644
index 00000000000..bbf0a6563f3
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_delete.py
@@ -0,0 +1,78 @@
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/delete runs per-team _verify_team_access. The request carries the
+# team's organization_id so an org admin of that org clears the management-
+# route gate; a team admin is an INTERNAL_USER on a non-internal_user route,
+# so a team admin never reaches the handler. Only PROXY_ADMIN and an org admin
+# of the team's own org can delete it.
+_MATRIX = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 401),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 401),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 401),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 401),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 401),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_delete_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = world.org_a_id if shape == "alpha" else world.org_b_id
+    await create_scratch_team(prisma, scratch.prefix, organization_id=org_id)
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        "/team/delete",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_ids": [scratch.prefix], "organization_id": org_id},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    if expected_status == 200:
+        assert row is None, "deleted but team row survives"
+    else:
+        assert row is not None, "denied but team row vanished"
+
+
+async def test_team_delete_batch_with_missing_id_deletes_nothing(
+    proxy_client, prisma, scratch, world
+):
+    """A batch is validated whole before any deletion: one missing team_id
+    fails the request 404 and the accessible team in the batch survives."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    resp = await proxy_client.post(
+        "/team/delete",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_ids": [scratch.prefix, "behavior-pin-no-such-team"]},
+    )
+    assert resp.status_code == 404, resp.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None, "batch aborted but the accessible team was deleted"
diff --git a/tests/proxy_behavior/management/test_team_filter_ui.py b/tests/proxy_behavior/management/test_team_filter_ui.py
new file mode 100644
index 00000000000..69cbabf72a1
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_filter_ui.py
@@ -0,0 +1,39 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# GET /team/filter/ui (ui_view_teams) — include_in_schema=False. The handler
+# body has no role/org check and never reads user_api_key_dict, but the
+# endpoint is still effectively PROXY-ADMIN-only as its docstring claims: the
+# management-route gate fronts it (not an internal_user / info / org-admin
+# route) and 401s every non-proxy-admin before the handler runs. PROXY_ADMIN
+# reaches the unscoped find_many and sees teams across every org.
+@pytest.mark.parametrize("actor", list(Actor), ids=[a.value for a in Actor])
+async def test_team_filter_ui_is_proxy_admin_only(actor: Actor, proxy_client, world):
+    resp = await proxy_client.get(
+        "/team/filter/ui",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+    )
+    expected = 200 if actor == Actor.PROXY_ADMIN else 401
+    assert (
+        resp.status_code == expected
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+
+
+async def test_team_filter_ui_proxy_admin_sees_cross_org_teams(proxy_client, world):
+    """The handler runs an unscoped query — PROXY_ADMIN sees teams from every
+    org, including the three seeded world teams."""
+    resp = await proxy_client.get(
+        "/team/filter/ui",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+    )
+    assert resp.status_code == 200, resp.text
+    team_ids = {t.get("team_id") for t in resp.json() if isinstance(t, dict)}
+    assert {
+        world.team_alpha_id,
+        world.team_beta_id,
+        world.team_gamma_id,
+    } <= team_ids
diff --git a/tests/proxy_behavior/management/test_team_info.py b/tests/proxy_behavior/management/test_team_info.py
new file mode 100644
index 00000000000..ad019207c82
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_info.py
@@ -0,0 +1,86 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# GET /team/info — actor x team-target authz matrix, pinned against
+# validate_membership(): a team is readable by a proxy admin, a key whose
+# own team_id matches, a listed member, or an org admin of the team's org;
+# everything else is 403. TEAM_GAMMA has no members, so only PROXY_ADMIN
+# and ORG_A's org admin can read it.
+_SCENARIOS = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 200),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 200),
+    ("alpha/owner", Actor.OWNER, "alpha", 200),
+    ("alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "alpha", 200),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 403),
+    ("alpha/service_account", Actor.SERVICE_ACCOUNT, "alpha", 200),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 403),
+    ("gamma/proxy_admin", Actor.PROXY_ADMIN, "gamma", 200),
+    ("gamma/org_admin", Actor.ORG_ADMIN, "gamma", 200),
+    ("gamma/team_admin", Actor.TEAM_ADMIN, "gamma", 403),
+    ("gamma/internal_user", Actor.INTERNAL_USER, "gamma", 403),
+    ("gamma/owner", Actor.OWNER, "gamma", 403),
+    ("gamma/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "gamma", 403),
+    ("gamma/cross_org_user", Actor.CROSS_ORG_USER, "gamma", 403),
+    ("gamma/service_account", Actor.SERVICE_ACCOUNT, "gamma", 403),
+    ("gamma/org_b_admin", Actor.ORG_B_ADMIN, "gamma", 403),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 403),
+    ("beta/team_admin", Actor.TEAM_ADMIN, "beta", 403),
+    ("beta/internal_user", Actor.INTERNAL_USER, "beta", 403),
+    ("beta/owner", Actor.OWNER, "beta", 403),
+    ("beta/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "beta", 403),
+    ("beta/cross_org_user", Actor.CROSS_ORG_USER, "beta", 200),
+    ("beta/service_account", Actor.SERVICE_ACCOUNT, "beta", 403),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,target,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_team_info_authz_matrix(
+    actor: Actor, target: str, expected_status: int, proxy_client, world
+):
+    caller = world.keys[actor]
+    target_team_id = {
+        "alpha": world.team_alpha_id,
+        "gamma": world.team_gamma_id,
+        "beta": world.team_beta_id,
+    }[target]
+
+    resp = await proxy_client.get(
+        f"/team/info?team_id={target_team_id}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} -> {target}: {resp.status_code} {resp.text}"
+
+    if expected_status == 200:
+        body = resp.json()
+        assert body["team_id"] == target_team_id
+        assert body["team_info"]["team_id"] == target_team_id
+
+
+# Phase 4 F6 — explicit pin on the `_verify_team_access` 403 message string.
+# alpha/org_b_admin already covers the branch in the matrix; this guard
+# turns a silent rename of the exception detail into a CI red, which is the
+# behavior tripwire that the matrix's status-only assertion cannot catch.
+async def test_team_info_org_admin_cross_org_rejection_detail(proxy_client, world):
+    resp = await proxy_client.get(
+        f"/team/info?team_id={world.team_alpha_id}",
+        headers={"Authorization": f"Bearer {world.keys[Actor.ORG_B_ADMIN].cleartext}"},
+    )
+    assert resp.status_code == 403, resp.text
+    # validate_membership() at GET /team/info raises with this exact phrase.
+    # Pinning it lock-step locks down the visible auth message — a rename
+    # would flip CI red even when the status stays 403.
+    assert "not authorized to access this team" in resp.text, resp.text
diff --git a/tests/proxy_behavior/management/test_team_key_bulk_update.py b/tests/proxy_behavior/management/test_team_key_bulk_update.py
new file mode 100644
index 00000000000..5acf0c8185c
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_key_bulk_update.py
@@ -0,0 +1,217 @@
+import uuid
+
+import pytest
+
+from litellm.proxy._types import KeyManagementRoutes
+from litellm.proxy.utils import hash_token
+
+from .actors import Actor
+from .conftest import create_scratch_key, create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+_MARKER_BUDGET = 42.0
+_KEY_UPDATE = KeyManagementRoutes.KEY_UPDATE.value
+
+
+# POST /team/key/bulk_update — PROXY_ADMIN bypasses; otherwise
+# can_team_member_execute_key_management_endpoint runs with route=KEY_UPDATE.
+# A team admin always passes; a "user"-role member passes only when the team's
+# team_member_permissions grants /key/update; a non-member is 401. ORG_ADMIN is
+# stopped 401 at the management-route gate before the handler (the body has a
+# team_id but no organization_id, so the org-admin route branch never matches).
+_MATRIX = [
+    ("admin/proxy_admin", Actor.PROXY_ADMIN, "admin", 200),
+    ("admin/internal_user", Actor.INTERNAL_USER, "admin", 200),
+    ("member_allowed/internal_user", Actor.INTERNAL_USER, "member_allowed", 200),
+    ("member_denied/internal_user", Actor.INTERNAL_USER, "member_denied", 401),
+    ("nonmember/internal_user", Actor.INTERNAL_USER, "nonmember", 401),
+    ("nonmember/org_admin", Actor.ORG_ADMIN, "nonmember", 401),
+    ("nonmember/proxy_admin", Actor.PROXY_ADMIN, "nonmember", 200),
+]
+
+
+async def _seed_team_key(prisma, proxy_client, prefix: str, world, shape: str) -> str:
+    """Raw-seed the scratch team for `shape`, return a team key's cleartext."""
+    internal = world.keys[Actor.INTERNAL_USER].user_id
+    owner = world.keys[Actor.OWNER].user_id
+    if shape == "admin":
+        await create_scratch_team(
+            prisma, prefix, organization_id=world.org_a_id, admin_user_ids=[internal]
+        )
+        key_owner = internal
+    elif shape == "member_allowed":
+        await create_scratch_team(
+            prisma,
+            prefix,
+            organization_id=world.org_a_id,
+            admin_user_ids=[owner],
+            member_user_ids=[internal],
+            team_member_permissions=[_KEY_UPDATE],
+        )
+        key_owner = owner
+    elif shape == "member_denied":
+        await create_scratch_team(
+            prisma,
+            prefix,
+            organization_id=world.org_a_id,
+            admin_user_ids=[owner],
+            member_user_ids=[internal],
+            team_member_permissions=[],
+        )
+        key_owner = owner
+    elif shape == "nonmember":
+        await create_scratch_team(
+            prisma, prefix, organization_id=world.org_a_id, admin_user_ids=[owner]
+        )
+        key_owner = owner
+    else:
+        pytest.fail(f"unknown shape={shape}")  # pragma: no cover
+    return await create_scratch_key(
+        proxy_client,
+        world.keys[Actor.PROXY_ADMIN].cleartext,
+        prefix,
+        user_id=key_owner,
+        team_id=prefix,
+    )
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_key_bulk_update_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    key = await _seed_team_key(prisma, proxy_client, scratch.prefix, world, shape)
+    hashed = hash_token(key)
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "key_ids": [key],
+            "update_fields": {"max_budget": _MARKER_BUDGET},
+        },
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(where={"token": hashed})
+    assert row is not None
+    if expected_status == 200:
+        assert len(resp.json()["successful_updates"]) == 1
+        assert row.max_budget == _MARKER_BUDGET
+    else:
+        assert row.max_budget != _MARKER_BUDGET, "denied but key mutated"
+
+
+async def test_team_key_bulk_update_requires_team_id(
+    proxy_client, prisma, scratch, world
+):
+    """An empty team_id is rejected 400."""
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={
+            "team_id": "",
+            "key_ids": ["sk-" + uuid.uuid4().hex],
+            "update_fields": {"max_budget": _MARKER_BUDGET},
+        },
+    )
+    assert resp.status_code == 400, resp.text
+
+
+async def test_team_key_bulk_update_all_keys_in_team(
+    proxy_client, prisma, scratch, world
+):
+    """all_keys_in_team=True broadcasts the update to every key in the team."""
+    admin = world.keys[Actor.PROXY_ADMIN].cleartext
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    keys = [
+        await create_scratch_key(
+            proxy_client,
+            admin,
+            scratch.prefix,
+            user_id=world.keys[Actor.OWNER].user_id,
+            team_id=scratch.prefix,
+            key_alias=f"{scratch.prefix}-k{i}",
+        )
+        for i in range(2)
+    ]
+
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {admin}"},
+        json={
+            "team_id": scratch.prefix,
+            "all_keys_in_team": True,
+            "update_fields": {"max_budget": _MARKER_BUDGET},
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    assert len(resp.json()["successful_updates"]) == 2
+    for key in keys:
+        row = await prisma.db.litellm_verificationtoken.find_unique(
+            where={"token": hash_token(key)}
+        )
+        assert row is not None and row.max_budget == _MARKER_BUDGET
+
+
+async def test_team_key_bulk_update_no_keys_found_is_404(
+    proxy_client, prisma, scratch, world
+):
+    """all_keys_in_team=True on a team with no keys is a top-level 404."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "all_keys_in_team": True,
+            "update_fields": {"max_budget": _MARKER_BUDGET},
+        },
+    )
+    assert resp.status_code == 404, resp.text
+
+
+async def test_team_key_bulk_update_missing_key_is_isolated(
+    proxy_client, prisma, scratch, world
+):
+    """A key_id absent from the team lands in failed_updates; the batch still
+    returns 200 and the real key is updated."""
+    admin = world.keys[Actor.PROXY_ADMIN].cleartext
+    real = await _seed_team_key(
+        prisma, proxy_client, scratch.prefix, world, "nonmember"
+    )
+    missing = "sk-" + uuid.uuid4().hex
+
+    resp = await proxy_client.post(
+        "/team/key/bulk_update",
+        headers={"Authorization": f"Bearer {admin}"},
+        json={
+            "team_id": scratch.prefix,
+            "key_ids": [real, missing],
+            "update_fields": {"max_budget": _MARKER_BUDGET},
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    body = resp.json()
+    assert body["total_requested"] == 2
+    assert len(body["successful_updates"]) == 1
+    assert len(body["failed_updates"]) == 1
+
+    row = await prisma.db.litellm_verificationtoken.find_unique(
+        where={"token": hash_token(real)}
+    )
+    assert row is not None and row.max_budget == _MARKER_BUDGET
diff --git a/tests/proxy_behavior/management/test_team_list.py b/tests/proxy_behavior/management/test_team_list.py
new file mode 100644
index 00000000000..2bd106dd2d0
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_list.py
@@ -0,0 +1,105 @@
+from typing import FrozenSet, Optional
+
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# The behavior DB may hold teams beyond the three seeded ones, so every
+# assertion intersects the returned team_ids with the known seeded set.
+def _seeded_visible(resp_json, world) -> set:
+    known = {
+        world.team_alpha_id: "alpha",
+        world.team_beta_id: "beta",
+        world.team_gamma_id: "gamma",
+    }
+    return {
+        known[entry["team_id"]]
+        for entry in resp_json
+        if isinstance(entry, dict) and entry.get("team_id") in known
+    }
+
+
+# Family 1 — bare GET /team/list (no query params). _authorize_and_filter_teams
+# authorizes only an admin view (proxy admin) or an org admin; everyone else
+# is 401. An org admin sees every team in its org(s).
+_BARE = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200, {"alpha", "beta", "gamma"}),
+    ("org_admin", Actor.ORG_ADMIN, 200, {"alpha", "gamma"}),
+    ("team_admin", Actor.TEAM_ADMIN, 401, None),
+    ("internal_user", Actor.INTERNAL_USER, 401, None),
+    ("owner", Actor.OWNER, 401, None),
+    ("unrelated_same_org", Actor.UNRELATED_SAME_ORG, 401, None),
+    ("cross_org_user", Actor.CROSS_ORG_USER, 401, None),
+    ("service_account", Actor.SERVICE_ACCOUNT, 401, None),
+    ("org_b_admin", Actor.ORG_B_ADMIN, 200, {"beta"}),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status,expected_visible",
+    [(a, s, v) for (_id, a, s, v) in _BARE],
+    ids=[s[0] for s in _BARE],
+)
+async def test_team_list_bare_authz(
+    actor: Actor,
+    expected_status: int,
+    expected_visible: Optional[set],
+    proxy_client,
+    world,
+):
+    caller = world.keys[actor]
+    resp = await proxy_client.get(
+        "/team/list",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+
+    if expected_status == 200:
+        visible = _seeded_visible(resp.json(), world)
+        assert visible == expected_visible, (
+            f"{actor.value}: expected {sorted(expected_visible)}, "
+            f"got {sorted(visible)}"
+        )
+
+
+# Family 2 — GET /team/list?user_id=<caller's own id> ("own query"). Every
+# actor may query its own teams (200); the result is exactly the teams it
+# belongs to. A user_id filter scopes proxy/org admins to their own
+# membership too — the broad admin view from family 1 does not carry over.
+_OWN = {
+    Actor.PROXY_ADMIN: frozenset(),
+    Actor.ORG_ADMIN: frozenset(),
+    Actor.TEAM_ADMIN: frozenset({"alpha"}),
+    Actor.INTERNAL_USER: frozenset({"alpha"}),
+    Actor.OWNER: frozenset({"alpha"}),
+    Actor.UNRELATED_SAME_ORG: frozenset({"alpha"}),
+    Actor.CROSS_ORG_USER: frozenset({"beta"}),
+    Actor.SERVICE_ACCOUNT: frozenset({"alpha"}),
+    Actor.ORG_B_ADMIN: frozenset(),
+}
+
+
+@pytest.mark.parametrize(
+    "actor,expected_visible",
+    list(_OWN.items()),
+    ids=[a.value for a in _OWN],
+)
+async def test_team_list_own_query(
+    actor: Actor, expected_visible: FrozenSet[str], proxy_client, world
+):
+    caller = world.keys[actor]
+    resp = await proxy_client.get(
+        f"/team/list?user_id={caller.user_id}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert resp.status_code == 200, f"{actor.value}: {resp.status_code} {resp.text}"
+
+    visible = _seeded_visible(resp.json(), world)
+    assert visible == set(expected_visible), (
+        f"{actor.value}: expected {sorted(expected_visible)}, " f"got {sorted(visible)}"
+    )
diff --git a/tests/proxy_behavior/management/test_team_list_v2.py b/tests/proxy_behavior/management/test_team_list_v2.py
new file mode 100644
index 00000000000..81178ad73c0
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_list_v2.py
@@ -0,0 +1,141 @@
+from typing import FrozenSet, Optional
+
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+def _seeded(team_ids: set, world) -> set:
+    known = {
+        world.team_alpha_id: "alpha",
+        world.team_beta_id: "beta",
+        world.team_gamma_id: "gamma",
+    }
+    return {known[t] for t in team_ids if t in known}
+
+
+async def _v2_team_ids(proxy_client, caller_cleartext: str, extra: str = "") -> set:
+    """Walk every /v2/team/list page and collect the returned team_ids."""
+    ids: set = set()
+    page = 1
+    while True:
+        resp = await proxy_client.get(
+            f"/v2/team/list?page={page}&page_size=100{extra}",
+            headers={"Authorization": f"Bearer {caller_cleartext}"},
+        )
+        assert resp.status_code == 200, resp.text
+        body = resp.json()
+        teams = body.get("teams", []) or []
+        for t in teams:
+            tid = t.get("team_id") if isinstance(t, dict) else None
+            if tid:
+                ids.add(tid)
+        if page * 100 >= (body.get("total") or 0) or not teams:
+            return ids
+        page += 1
+
+
+# GET /v2/team/list is an info route reachable by every actor, but
+# _enforce_list_team_v2_access still gates a BARE query: a proxy admin sees
+# all teams, an org admin sees its orgs' teams, and a regular user — who has
+# passed no user_id filter — is rejected 401 ("only admins can query all
+# teams"). A regular user must scope the query to its own user_id.
+_BARE = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200, frozenset({"alpha", "beta", "gamma"})),
+    ("org_admin", Actor.ORG_ADMIN, 200, frozenset({"alpha", "gamma"})),
+    ("org_b_admin", Actor.ORG_B_ADMIN, 200, frozenset({"beta"})),
+    ("team_admin", Actor.TEAM_ADMIN, 401, None),
+    ("internal_user", Actor.INTERNAL_USER, 401, None),
+    ("owner", Actor.OWNER, 401, None),
+    ("unrelated_same_org", Actor.UNRELATED_SAME_ORG, 401, None),
+    ("cross_org_user", Actor.CROSS_ORG_USER, 401, None),
+    ("service_account", Actor.SERVICE_ACCOUNT, 401, None),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status,expected_visible",
+    [(a, s, v) for (_id, a, s, v) in _BARE],
+    ids=[s[0] for s in _BARE],
+)
+async def test_team_list_v2_bare(
+    actor: Actor,
+    expected_status: int,
+    expected_visible: Optional[FrozenSet[str]],
+    proxy_client,
+    world,
+):
+    caller = world.keys[actor]
+    if expected_status != 200:
+        resp = await proxy_client.get(
+            "/v2/team/list",
+            headers={"Authorization": f"Bearer {caller.cleartext}"},
+        )
+        assert resp.status_code == expected_status, resp.text
+        return
+
+    visible = _seeded(await _v2_team_ids(proxy_client, caller.cleartext), world)
+    assert visible == set(
+        expected_visible
+    ), f"{actor.value}: expected {sorted(expected_visible)}, got {sorted(visible)}"
+
+
+# A regular user scoping the query to its own user_id is allowed, and sees
+# exactly the teams it belongs to.
+_OWN = {
+    Actor.TEAM_ADMIN: frozenset({"alpha"}),
+    Actor.INTERNAL_USER: frozenset({"alpha"}),
+    Actor.OWNER: frozenset({"alpha"}),
+    Actor.UNRELATED_SAME_ORG: frozenset({"alpha"}),
+    Actor.CROSS_ORG_USER: frozenset({"beta"}),
+    Actor.SERVICE_ACCOUNT: frozenset({"alpha"}),
+}
+
+
+@pytest.mark.parametrize(
+    "actor,expected_visible", list(_OWN.items()), ids=[a.value for a in _OWN]
+)
+async def test_team_list_v2_own_user_id_query(
+    actor: Actor, expected_visible: FrozenSet[str], proxy_client, world
+):
+    caller = world.keys[actor]
+    visible = _seeded(
+        await _v2_team_ids(
+            proxy_client, caller.cleartext, f"&user_id={caller.user_id}"
+        ),
+        world,
+    )
+    assert visible == set(
+        expected_visible
+    ), f"{actor.value}: expected {sorted(expected_visible)}, got {sorted(visible)}"
+
+
+async def test_team_list_v2_user_id_filter_other_user_is_401(proxy_client, world):
+    """A regular user filtering by another user's user_id is rejected 401."""
+    resp = await proxy_client.get(
+        f"/v2/team/list?user_id={world.keys[Actor.OWNER].user_id}",
+        headers={
+            "Authorization": f"Bearer {world.keys[Actor.INTERNAL_USER].cleartext}"
+        },
+    )
+    assert resp.status_code == 401, resp.text
+
+
+async def test_team_list_v2_org_filter_foreign_org_is_403(proxy_client, world):
+    """An org admin filtering by an organization it does not administer is 403."""
+    resp = await proxy_client.get(
+        f"/v2/team/list?organization_id={world.org_b_id}",
+        headers={"Authorization": f"Bearer {world.keys[Actor.ORG_ADMIN].cleartext}"},
+    )
+    assert resp.status_code == 403, resp.text
+
+
+async def test_team_list_v2_invalid_status_is_400(proxy_client, world):
+    """status accepts only 'deleted' — any other value is 400."""
+    resp = await proxy_client.get(
+        "/v2/team/list?status=bogus",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+    )
+    assert resp.status_code == 400, resp.text
diff --git a/tests/proxy_behavior/management/test_team_member_add.py b/tests/proxy_behavior/management/test_team_member_add.py
new file mode 100644
index 00000000000..a0dc4a7ecaf
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_member_add.py
@@ -0,0 +1,149 @@
+import litellm
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/member_add — actor x team-shape matrix, pinned against
+# _validate_team_member_add_permissions: PROXY_ADMIN, the team's team admin,
+# or an org admin of the team's org may add members; everyone else is 403.
+# Unlike /team/update there is no route gate in front, so the team-admin
+# branch is reachable (TEAM_ADMIN, an internal_user, is allowed on its team).
+_MATRIX = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 200),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 403),
+    ("alpha/owner", Actor.OWNER, "alpha", 403),
+    ("alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "alpha", 403),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 403),
+    ("alpha/service_account", Actor.SERVICE_ACCOUNT, "alpha", 403),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 403),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 403),
+    ("beta/team_admin", Actor.TEAM_ADMIN, "beta", 403),
+    ("beta/internal_user", Actor.INTERNAL_USER, "beta", 403),
+    ("beta/owner", Actor.OWNER, "beta", 403),
+    ("beta/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "beta", 403),
+    ("beta/cross_org_user", Actor.CROSS_ORG_USER, "beta", 403),
+    ("beta/service_account", Actor.SERVICE_ACCOUNT, "beta", 403),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+async def _seed_target(prisma, world, shape: str, team_id: str) -> None:
+    if shape == "alpha":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_a_id,
+            admin_user_ids=[world.keys[Actor.TEAM_ADMIN].user_id],
+        )
+    elif shape == "beta":
+        await create_scratch_team(prisma, team_id, organization_id=world.org_b_id)
+    else:  # pragma: no cover - guard
+        pytest.fail(f"unknown shape={shape}")
+
+
+def _member_ids(row) -> list:
+    return [m["user_id"] for m in (row.members_with_roles or [])]
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_member_add_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    await _seed_target(prisma, world, shape, scratch.prefix)
+    caller = world.keys[actor]
+    new_member_id = scratch.tag("newmember")
+
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "member": {"user_id": new_member_id, "role": "user"},
+        },
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert new_member_id in _member_ids(row)
+    else:
+        assert new_member_id not in _member_ids(row), "denied but member added"
+
+
+# Available-team self-join: a non-admin caller may add ITSELF to a team listed
+# in litellm.default_internal_user_params["available_teams"], but the bypass
+# must not escalate to role=admin or inject another user.
+_SELF_JOIN = [
+    ("self_as_user", "self", "user", 200),
+    ("self_as_admin", "self", "admin", 403),
+    ("other_as_user", "other", "user", 403),
+]
+
+
+@pytest.mark.parametrize(
+    "who,role,expected_status",
+    [(w, r, s) for (_id, w, r, s) in _SELF_JOIN],
+    ids=[s[0] for s in _SELF_JOIN],
+)
+async def test_team_member_add_available_team_self_join(
+    who: str,
+    role: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+    monkeypatch,
+):
+    # Org-less team with no admins: the INTERNAL_USER caller is neither team
+    # nor org admin, so it lands on the available-team branch.
+    await create_scratch_team(prisma, scratch.prefix)
+    monkeypatch.setattr(
+        litellm, "default_internal_user_params", {"available_teams": [scratch.prefix]}
+    )
+
+    caller = world.keys[Actor.INTERNAL_USER]
+    member_id = caller.user_id if who == "self" else world.keys[Actor.OWNER].user_id
+
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "member": {"user_id": member_id, "role": role},
+        },
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{who}/{role}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert member_id in _member_ids(row)
+    else:
+        assert member_id not in _member_ids(row), "denied but member added"
diff --git a/tests/proxy_behavior/management/test_team_member_delete.py b/tests/proxy_behavior/management/test_team_member_delete.py
new file mode 100644
index 00000000000..43879d9fd16
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_member_delete.py
@@ -0,0 +1,92 @@
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/member_delete — actor x team-shape matrix. The scratch team is
+# raw-seeded with a victim member already in it; PROXY_ADMIN, the team's team
+# admin, or an org admin of the team's org may remove members; else 403.
+_MATRIX = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 200),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 403),
+    ("alpha/owner", Actor.OWNER, "alpha", 403),
+    ("alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "alpha", 403),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 403),
+    ("alpha/service_account", Actor.SERVICE_ACCOUNT, "alpha", 403),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 403),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 403),
+    ("beta/team_admin", Actor.TEAM_ADMIN, "beta", 403),
+    ("beta/internal_user", Actor.INTERNAL_USER, "beta", 403),
+    ("beta/owner", Actor.OWNER, "beta", 403),
+    ("beta/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "beta", 403),
+    ("beta/cross_org_user", Actor.CROSS_ORG_USER, "beta", 403),
+    ("beta/service_account", Actor.SERVICE_ACCOUNT, "beta", 403),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+async def _seed_target(prisma, world, shape: str, team_id: str, victim_id: str) -> None:
+    if shape == "alpha":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_a_id,
+            admin_user_ids=[world.keys[Actor.TEAM_ADMIN].user_id],
+            member_user_ids=[victim_id],
+        )
+    elif shape == "beta":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_b_id,
+            member_user_ids=[victim_id],
+        )
+    else:  # pragma: no cover - guard
+        pytest.fail(f"unknown shape={shape}")
+
+
+def _member_ids(row) -> list:
+    return [m["user_id"] for m in (row.members_with_roles or [])]
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_member_delete_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    victim_id = scratch.tag("victim")
+    await _seed_target(prisma, world, shape, scratch.prefix, victim_id)
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        "/team/member_delete",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_id": scratch.prefix, "user_id": victim_id},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert victim_id not in _member_ids(row)
+    else:
+        assert victim_id in _member_ids(row), "denied but member removed"
diff --git a/tests/proxy_behavior/management/test_team_member_info_validation.py b/tests/proxy_behavior/management/test_team_member_info_validation.py
new file mode 100644
index 00000000000..5139865d2b4
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_member_info_validation.py
@@ -0,0 +1,189 @@
+"""Phase 4 F4 — payload-level pins for member-info population.
+
+Pins `_validate_and_populate_member_user_info` (team_endpoints.py:2275),
+reached via /team/member_add.
+
+PROXY_ADMIN is the caller so the upstream `_validate_team_member_add_permissions`
+gate never short-circuits the payload check. Each scenario asserts BOTH the
+HTTP status and the DB end-state — for accepted cases, the
+LiteLLM_TeamMembership row reflects the resolved user_id (the regression
+shape: a payload that silently lands the membership against the WRONG
+user_id is invisible from response-body alone).
+"""
+
+from typing import Any, Dict, Optional
+
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+async def _seed_scratch_user(
+    prisma,
+    scratch_prefix: str,
+    *,
+    suffix: str,
+    user_email: Optional[str] = None,
+) -> str:
+    """Raw-seed a scratch-prefixed user row; returns user_id. Scratch teardown
+    reclaims by user_id prefix."""
+    user_id = f"{scratch_prefix}-{suffix}"
+    data: Dict[str, Any] = {"user_id": user_id, "user_role": "internal_user"}
+    if user_email is not None:
+        data["user_email"] = user_email
+    await prisma.db.litellm_usertable.create(data=data)
+    return user_id
+
+
+# ---------------------------------------------------------------------------
+# Both None → 400 ("Either user_id or user_email must be provided")
+# ---------------------------------------------------------------------------
+
+
+async def test_member_add_both_none_rejected(proxy_client, prisma, scratch, world):
+    team_id = await create_scratch_team(prisma, team_id=scratch.tag("team"))
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"team_id": team_id, "member": {"role": "user"}},
+    )
+    # The Pydantic Member model may also catch this at the validation layer
+    # (422). Either shape proves the empty-Member payload is rejected before
+    # any membership row is written — pin both.
+    assert resp.status_code in (400, 422), resp.text
+    rows = await prisma.db.litellm_teammembership.find_many(where={"team_id": team_id})
+    assert rows == [], "empty-Member payload leaked a membership row"
+
+
+# ---------------------------------------------------------------------------
+# Email + id given, but they point at different users → 400
+# ---------------------------------------------------------------------------
+
+
+async def test_member_add_email_id_mismatch_rejected(
+    proxy_client, prisma, scratch, world
+):
+    email = f"{scratch.prefix}-mismatch@example.com"
+    real_user_id = await _seed_scratch_user(
+        prisma, scratch.prefix, suffix="real", user_email=email
+    )
+    other_user_id = await _seed_scratch_user(prisma, scratch.prefix, suffix="other")
+    assert real_user_id != other_user_id  # sanity
+    team_id = await create_scratch_team(prisma, team_id=scratch.tag("team"))
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "member": {
+                "role": "user",
+                "user_email": email,
+                "user_id": other_user_id,
+            },
+        },
+    )
+    assert resp.status_code == 400, resp.text
+    assert "do not belong to the same user" in resp.text, resp.text
+    rows = await prisma.db.litellm_teammembership.find_many(where={"team_id": team_id})
+    assert rows == [], "mismatch payload leaked a membership row"
+
+
+# ---------------------------------------------------------------------------
+# Email-only resolves to user_id when exactly one user matches
+# ---------------------------------------------------------------------------
+
+
+async def test_member_add_email_only_resolves_user_id(
+    proxy_client, prisma, scratch, world
+):
+    email = f"{scratch.prefix}-resolve@example.com"
+    user_id = await _seed_scratch_user(
+        prisma, scratch.prefix, suffix="lookup", user_email=email
+    )
+    team_id = await create_scratch_team(prisma, team_id=scratch.tag("team"))
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "member": {"role": "user", "user_email": email},
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    # litellm_teammembership rows are only written when a per-member budget
+    # is assigned; the default member-add path stores membership in the
+    # team's members_with_roles JSON. Re-read that and assert the resolved
+    # user_id landed — the regression shape is "email resolved to the WRONG
+    # user_id and was silently written to members_with_roles".
+    team_row = await prisma.db.litellm_teamtable.find_unique(where={"team_id": team_id})
+    assert team_row is not None
+    member_user_ids = [m.get("user_id") for m in team_row.members_with_roles]
+    assert (
+        user_id in member_user_ids
+    ), f"email did not resolve to {user_id}; members={member_user_ids}"
+
+
+# ---------------------------------------------------------------------------
+# id-only, user does NOT yet exist — passes through, member is upserted.
+# ---------------------------------------------------------------------------
+
+
+async def test_member_add_unknown_user_id_upserted(
+    proxy_client, prisma, scratch, world
+):
+    team_id = await create_scratch_team(prisma, team_id=scratch.tag("team"))
+    new_user_id = f"{scratch.prefix}-fresh"
+    # Sanity — user does not exist yet.
+    pre = await prisma.db.litellm_usertable.find_unique(where={"user_id": new_user_id})
+    assert pre is None
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "member": {"role": "user", "user_id": new_user_id},
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    post = await prisma.db.litellm_usertable.find_unique(where={"user_id": new_user_id})
+    assert post is not None, "user_id was not upserted"
+    # The user row was created with NULL email (the helper returned the
+    # member as-is, no email lookup happened because the user didn't exist).
+    assert (
+        post.user_email is None
+    ), f"upserted user has unexpected email: {post.user_email!r}"
+
+
+# ---------------------------------------------------------------------------
+# Duplicate-email rejection — two scratch users share an email; email-only
+# add → 400 with "Multiple users found" detail.
+# ---------------------------------------------------------------------------
+
+
+async def test_member_add_duplicate_email_rejected(
+    proxy_client, prisma, scratch, world
+):
+    email = f"{scratch.prefix}-dup@example.com"
+    await _seed_scratch_user(prisma, scratch.prefix, suffix="dup1", user_email=email)
+    await _seed_scratch_user(prisma, scratch.prefix, suffix="dup2", user_email=email)
+    team_id = await create_scratch_team(prisma, team_id=scratch.tag("team"))
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/member_add",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_id": team_id,
+            "member": {"role": "user", "user_email": email},
+        },
+    )
+    assert resp.status_code == 400, resp.text
+    assert "Multiple users found" in resp.text, resp.text
+    rows = await prisma.db.litellm_teammembership.find_many(where={"team_id": team_id})
+    assert rows == [], "duplicate-email payload leaked a membership row"
diff --git a/tests/proxy_behavior/management/test_team_member_me.py b/tests/proxy_behavior/management/test_team_member_me.py
new file mode 100644
index 00000000000..bfbbe0504ae
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_member_me.py
@@ -0,0 +1,83 @@
+import uuid
+
+import pytest
+
+from litellm.proxy.utils import hash_token
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# GET /team/{team_id}/members/me resolves the CALLER's own membership row.
+# A caller that is not a member of the team is 404 — even PROXY_ADMIN, which
+# is not in any seeded team. The route is self-managed, so every actor reaches
+# the handler. TEAM_GAMMA has no members, so every actor is 404 there.
+_MEMBERS = {
+    "alpha": {
+        Actor.TEAM_ADMIN,
+        Actor.INTERNAL_USER,
+        Actor.OWNER,
+        Actor.UNRELATED_SAME_ORG,
+        Actor.SERVICE_ACCOUNT,
+    },
+    "beta": {Actor.CROSS_ORG_USER},
+    "gamma": set(),
+}
+
+_CASES = [
+    (f"{team}/{actor.value}", actor, team, 200 if actor in members else 404)
+    for team, members in _MEMBERS.items()
+    for actor in Actor
+]
+
+
+@pytest.mark.parametrize(
+    "actor,team,expected_status",
+    [(a, t, s) for (_id, a, t, s) in _CASES],
+    ids=[c[0] for c in _CASES],
+)
+async def test_team_member_me_matrix(
+    actor: Actor, team: str, expected_status: int, proxy_client, world
+):
+    team_id = {
+        "alpha": world.team_alpha_id,
+        "beta": world.team_beta_id,
+        "gamma": world.team_gamma_id,
+    }[team]
+    caller = world.keys[actor]
+
+    resp = await proxy_client.get(
+        f"/team/{team_id}/members/me",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} -> {team}: {resp.status_code} {resp.text}"
+
+    if expected_status == 200:
+        body = resp.json()
+        assert body["user_id"] == caller.user_id
+        assert body["team_id"] == team_id
+
+
+async def test_team_member_me_team_key_without_user_id_is_400(
+    proxy_client, prisma, scratch, world
+):
+    """A key with no associated user_id (a team / service-account key) cannot
+    resolve 'me' — the caller has no identity to look up — so it is 400."""
+    cleartext = "sk-" + uuid.uuid4().hex
+    await prisma.db.litellm_verificationtoken.create(
+        data={
+            "token": hash_token(cleartext),
+            "key_name": f"{scratch.prefix}-teamkey",
+            "key_alias": f"{scratch.prefix}-teamkey",
+            "team_id": world.team_alpha_id,
+            "models": [],
+        }
+    )
+    resp = await proxy_client.get(
+        f"/team/{world.team_alpha_id}/members/me",
+        headers={"Authorization": f"Bearer {cleartext}"},
+    )
+    assert resp.status_code == 400, resp.text
diff --git a/tests/proxy_behavior/management/test_team_member_update.py b/tests/proxy_behavior/management/test_team_member_update.py
new file mode 100644
index 00000000000..53b245bd1e9
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_member_update.py
@@ -0,0 +1,97 @@
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/member_update — actor x team-shape matrix. The scratch team is
+# raw-seeded with a "user"-role member; each scenario tries to promote it to
+# "admin". PROXY_ADMIN, the team's team admin, or an org admin of the team's
+# org may update members; else 403. (The harness forces premium_user, so the
+# promotion does not hit the admin-role premium gate.)
+_MATRIX = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 200),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 403),
+    ("alpha/owner", Actor.OWNER, "alpha", 403),
+    ("alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "alpha", 403),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 403),
+    ("alpha/service_account", Actor.SERVICE_ACCOUNT, "alpha", 403),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 403),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 403),
+    ("beta/team_admin", Actor.TEAM_ADMIN, "beta", 403),
+    ("beta/internal_user", Actor.INTERNAL_USER, "beta", 403),
+    ("beta/owner", Actor.OWNER, "beta", 403),
+    ("beta/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "beta", 403),
+    ("beta/cross_org_user", Actor.CROSS_ORG_USER, "beta", 403),
+    ("beta/service_account", Actor.SERVICE_ACCOUNT, "beta", 403),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+async def _seed_target(prisma, world, shape: str, team_id: str, member_id: str) -> None:
+    if shape == "alpha":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_a_id,
+            admin_user_ids=[world.keys[Actor.TEAM_ADMIN].user_id],
+            member_user_ids=[member_id],
+        )
+    elif shape == "beta":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_b_id,
+            member_user_ids=[member_id],
+        )
+    else:  # pragma: no cover - guard
+        pytest.fail(f"unknown shape={shape}")
+
+
+def _role_of(row, user_id: str):
+    for m in row.members_with_roles or []:
+        if m["user_id"] == user_id:
+            return m["role"]
+    return None
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_member_update_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    member_id = scratch.tag("member")
+    await _seed_target(prisma, world, shape, scratch.prefix, member_id)
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        "/team/member_update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_id": scratch.prefix, "user_id": member_id, "role": "admin"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert _role_of(row, member_id) == "admin"
+    else:
+        assert _role_of(row, member_id) == "user", "denied but role changed"
diff --git a/tests/proxy_behavior/management/test_team_model.py b/tests/proxy_behavior/management/test_team_model.py
new file mode 100644
index 00000000000..3564e8df83a
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_model.py
@@ -0,0 +1,78 @@
+import pytest
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+_MARKER_MODEL = "behavior-pin-team-model-marker"
+_ROUTE_URL = {"add": "/team/model/add", "delete": "/team/model/delete"}
+
+
+# POST /team/model/add + /team/model/delete. The handler gate is PROXY_ADMIN
+# or team admin or org admin, but the management-route gate fronts it — these
+# are neither internal_user nor org-admin nor info routes, so every
+# non-proxy-admin is 401 before the handler runs. Only PROXY_ADMIN reaches the
+# handler, making the team-admin / org-admin handler branches unreachable here.
+_MATRIX = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200),
+    ("org_admin", Actor.ORG_ADMIN, 401),
+    ("team_admin", Actor.TEAM_ADMIN, 401),
+    ("internal_user", Actor.INTERNAL_USER, 401),
+    ("owner", Actor.OWNER, 401),
+    ("unrelated_same_org", Actor.UNRELATED_SAME_ORG, 401),
+    ("cross_org_user", Actor.CROSS_ORG_USER, 401),
+    ("service_account", Actor.SERVICE_ACCOUNT, 401),
+    ("org_b_admin", Actor.ORG_B_ADMIN, 401),
+]
+
+
+@pytest.mark.parametrize("route", ["add", "delete"])
+@pytest.mark.parametrize(
+    "actor,expected_status",
+    [(a, s) for (_id, a, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_model_authz_matrix(
+    route: str,
+    actor: Actor,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    initial = [] if route == "add" else [_MARKER_MODEL]
+    await create_scratch_team(
+        prisma, scratch.prefix, organization_id=world.org_a_id, models=initial
+    )
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        _ROUTE_URL[route],
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_id": scratch.prefix, "models": [_MARKER_MODEL]},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{route} {actor.value}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert (_MARKER_MODEL in row.models) is (route == "add")
+    else:
+        assert list(row.models) == initial, "denied but models mutated"
+
+
+@pytest.mark.parametrize("route", ["add", "delete"])
+async def test_team_model_missing_team_is_404(route: str, proxy_client, world):
+    """A team_id absent from the DB is 404 — the existence check precedes authz."""
+    resp = await proxy_client.post(
+        _ROUTE_URL[route],
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": "behavior-pin-no-such-team", "models": [_MARKER_MODEL]},
+    )
+    assert resp.status_code == 404, resp.text
diff --git a/tests/proxy_behavior/management/test_team_new.py b/tests/proxy_behavior/management/test_team_new.py
new file mode 100644
index 00000000000..7b07f259641
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_new.py
@@ -0,0 +1,139 @@
+from typing import Any, Dict
+
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/new — actor x org-target matrix (org_target picks the request's
+# organization_id: none / ORG_A / ORG_B). Pinned against the role gate, which
+# 401s every denial: PROXY_ADMIN always passes; any other caller must name an
+# organization_id AND be ORG_ADMIN of that org.
+_SCENARIOS = [
+    ("none/proxy_admin", Actor.PROXY_ADMIN, "none", 200),
+    ("none/org_admin", Actor.ORG_ADMIN, "none", 401),
+    ("none/team_admin", Actor.TEAM_ADMIN, "none", 401),
+    ("none/internal_user", Actor.INTERNAL_USER, "none", 401),
+    ("none/owner", Actor.OWNER, "none", 401),
+    ("none/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "none", 401),
+    ("none/cross_org_user", Actor.CROSS_ORG_USER, "none", 401),
+    ("none/service_account", Actor.SERVICE_ACCOUNT, "none", 401),
+    ("none/org_b_admin", Actor.ORG_B_ADMIN, "none", 401),
+    ("org_a/proxy_admin", Actor.PROXY_ADMIN, "org_a", 200),
+    ("org_a/org_admin", Actor.ORG_ADMIN, "org_a", 200),
+    ("org_a/team_admin", Actor.TEAM_ADMIN, "org_a", 401),
+    ("org_a/internal_user", Actor.INTERNAL_USER, "org_a", 401),
+    ("org_a/owner", Actor.OWNER, "org_a", 401),
+    ("org_a/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "org_a", 401),
+    ("org_a/cross_org_user", Actor.CROSS_ORG_USER, "org_a", 401),
+    ("org_a/service_account", Actor.SERVICE_ACCOUNT, "org_a", 401),
+    ("org_a/org_b_admin", Actor.ORG_B_ADMIN, "org_a", 401),
+    ("org_b/proxy_admin", Actor.PROXY_ADMIN, "org_b", 200),
+    ("org_b/org_admin", Actor.ORG_ADMIN, "org_b", 401),
+    ("org_b/team_admin", Actor.TEAM_ADMIN, "org_b", 401),
+    ("org_b/internal_user", Actor.INTERNAL_USER, "org_b", 401),
+    ("org_b/owner", Actor.OWNER, "org_b", 401),
+    ("org_b/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "org_b", 401),
+    ("org_b/cross_org_user", Actor.CROSS_ORG_USER, "org_b", 401),
+    ("org_b/service_account", Actor.SERVICE_ACCOUNT, "org_b", 401),
+    ("org_b/org_b_admin", Actor.ORG_B_ADMIN, "org_b", 200),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,org_target,expected_status",
+    [(a, o, s) for (_id, a, o, s) in _SCENARIOS],
+    ids=[s[0] for s in _SCENARIOS],
+)
+async def test_team_new_authz_matrix(
+    actor: Actor,
+    org_target: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    caller = world.keys[actor]
+    org_id = {
+        "none": None,
+        "org_a": world.org_a_id,
+        "org_b": world.org_b_id,
+    }[org_target]
+
+    body: Dict[str, Any] = {"team_id": scratch.prefix, "team_alias": scratch.prefix}
+    if org_id is not None:
+        body["organization_id"] = org_id
+
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json=body,
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} org={org_target}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    if expected_status == 200:
+        assert row is not None
+        assert row.organization_id == org_id
+    else:
+        assert row is None, f"{actor.value}: denied but team row leaked"
+
+
+async def test_team_new_rejects_negative_budget(proxy_client, prisma, scratch, world):
+    """Input-validation pin: max_budget < 0 is a 400, no row created."""
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "max_budget": -1},
+    )
+    assert resp.status_code == 400, resp.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is None
+
+
+async def test_team_new_rejects_duplicate_team_id(proxy_client, prisma, scratch, world):
+    """Input-validation pin: a colliding team_id is a 400 on the second call."""
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    first = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"team_id": scratch.prefix, "team_alias": scratch.prefix},
+    )
+    assert first.status_code == 200, first.text
+
+    second = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"team_id": scratch.prefix, "team_alias": scratch.prefix},
+    )
+    assert second.status_code == 400, second.text
+
+
+async def test_team_new_unknown_organization_is_500(
+    proxy_client, prisma, scratch, world
+):
+    """SURFACED, NOT ENDORSED: a /team/new with an organization_id that does
+    not exist currently fails 500 (the role-resolution layer raises before
+    the handler's own 400 'Organization not found' check is reached)."""
+    resp = await proxy_client.post(
+        "/team/new",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "organization_id": scratch.tag("no-such-org"),
+        },
+    )
+    assert resp.status_code == 500, resp.text
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is None
diff --git a/tests/proxy_behavior/management/test_team_permissions.py b/tests/proxy_behavior/management/test_team_permissions.py
new file mode 100644
index 00000000000..5d16702fe6c
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_permissions.py
@@ -0,0 +1,170 @@
+import litellm
+import pytest
+
+from litellm.proxy._types import KeyManagementRoutes
+
+from .actors import Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+_PERM = KeyManagementRoutes.KEY_INFO.value
+
+
+# GET /team/permissions_list and POST /team/permissions_update are self-managed
+# routes, so every actor reaches the handler. Both grant access to PROXY_ADMIN,
+# the team admin, or an org admin of the team's org. The scratch team is in
+# ORG_A with TEAM_ADMIN as its team admin.
+_MATRIX = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200),
+    ("org_admin", Actor.ORG_ADMIN, 200),
+    ("team_admin", Actor.TEAM_ADMIN, 200),
+    ("internal_user", Actor.INTERNAL_USER, 403),
+    ("owner", Actor.OWNER, 403),
+    ("unrelated_same_org", Actor.UNRELATED_SAME_ORG, 403),
+    ("cross_org_user", Actor.CROSS_ORG_USER, 403),
+    ("service_account", Actor.SERVICE_ACCOUNT, 403),
+    ("org_b_admin", Actor.ORG_B_ADMIN, 403),
+]
+
+
+async def _seed_team(prisma, scratch_prefix, world) -> None:
+    await create_scratch_team(
+        prisma,
+        scratch_prefix,
+        organization_id=world.org_a_id,
+        admin_user_ids=[world.keys[Actor.TEAM_ADMIN].user_id],
+        member_user_ids=[
+            world.keys[Actor.INTERNAL_USER].user_id,
+            world.keys[Actor.OWNER].user_id,
+            world.keys[Actor.UNRELATED_SAME_ORG].user_id,
+            world.keys[Actor.SERVICE_ACCOUNT].user_id,
+        ],
+    )
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status",
+    [(a, s) for (_id, a, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_permissions_list_authz_matrix(
+    actor: Actor, expected_status: int, proxy_client, prisma, scratch, world
+):
+    await _seed_team(prisma, scratch.prefix, world)
+    resp = await proxy_client.get(
+        f"/team/permissions_list?team_id={scratch.prefix}",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+    if expected_status == 200:
+        assert resp.json()["team_id"] == scratch.prefix
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status",
+    [(a, s) for (_id, a, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_permissions_update_authz_matrix(
+    actor: Actor, expected_status: int, proxy_client, prisma, scratch, world
+):
+    await _seed_team(prisma, scratch.prefix, world)
+    resp = await proxy_client.post(
+        "/team/permissions_update",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+        json={"team_id": scratch.prefix, "team_member_permissions": [_PERM]},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert _PERM in (row.team_member_permissions or [])
+    else:
+        assert _PERM not in (row.team_member_permissions or []), "denied but mutated"
+
+
+async def test_team_permissions_available_team_self_join_divergence(
+    proxy_client, prisma, scratch, world, monkeypatch
+):
+    """permissions_list honours the available-team self-join — a non-admin can
+    READ an available team's permissions — but permissions_update deliberately
+    does not: the same caller is 403 on update. default_internal_user_params is
+    module-level litellm.* state, so monkeypatch save/restores it."""
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    monkeypatch.setattr(
+        litellm, "default_internal_user_params", {"available_teams": [scratch.prefix]}
+    )
+    caller = world.keys[Actor.CROSS_ORG_USER]  # non-admin, unrelated to the team
+
+    listed = await proxy_client.get(
+        f"/team/permissions_list?team_id={scratch.prefix}",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+    )
+    assert listed.status_code == 200, listed.text
+
+    updated = await proxy_client.post(
+        "/team/permissions_update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_id": scratch.prefix, "team_member_permissions": [_PERM]},
+    )
+    assert updated.status_code == 403, updated.text
+
+
+# POST /team/permissions_bulk_update is PROXY_ADMIN-only. ORG_ADMIN-role
+# callers are stopped 401 by the management-route gate; INTERNAL_USER-role
+# callers, on a route that is neither internal_user nor self-managed, are 401
+# there too — only PROXY_ADMIN reaches the handler's own admin gate.
+_BULK_MATRIX = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200),
+    ("org_admin", Actor.ORG_ADMIN, 401),
+    ("team_admin", Actor.TEAM_ADMIN, 401),
+    ("internal_user", Actor.INTERNAL_USER, 401),
+    ("cross_org_user", Actor.CROSS_ORG_USER, 401),
+    ("org_b_admin", Actor.ORG_B_ADMIN, 401),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status",
+    [(a, s) for (_id, a, s) in _BULK_MATRIX],
+    ids=[s[0] for s in _BULK_MATRIX],
+)
+async def test_team_permissions_bulk_update_authz_matrix(
+    actor: Actor, expected_status: int, proxy_client, prisma, scratch, world
+):
+    await create_scratch_team(prisma, scratch.prefix, organization_id=world.org_a_id)
+    resp = await proxy_client.post(
+        "/team/permissions_bulk_update",
+        headers={"Authorization": f"Bearer {world.keys[actor].cleartext}"},
+        json={"team_ids": [scratch.prefix], "permissions": [_PERM]},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert _PERM in (row.team_member_permissions or [])
+    else:
+        assert _PERM not in (row.team_member_permissions or []), "denied but mutated"
+
+
+async def test_team_permissions_bulk_update_no_selector_is_400(proxy_client, world):
+    """Neither team_ids nor apply_to_all_teams is a 400."""
+    resp = await proxy_client.post(
+        "/team/permissions_bulk_update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"permissions": [_PERM]},
+    )
+    assert resp.status_code == 400, resp.text
diff --git a/tests/proxy_behavior/management/test_team_permissions_bulk_update.py b/tests/proxy_behavior/management/test_team_permissions_bulk_update.py
new file mode 100644
index 00000000000..552c3526eea
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_permissions_bulk_update.py
@@ -0,0 +1,245 @@
+"""Phase 4 F5 — payload-level pins for /team/permissions_bulk_update.
+
+Pins the three helpers
+  * _compute_and_batch_updates              (team_endpoints.py:4887)
+  * _append_permissions_to_specific_teams   (team_endpoints.py:4913)
+  * _append_permissions_to_all_teams        (team_endpoints.py:4932)
+
+The route is admin-only — PROXY_ADMIN is the only legal caller. The
+contracts under test:
+  * specific-team list → ONLY listed teams mutate; every other team
+    (including world teams) is byte-identical on re-read.
+  * apply_to_all_teams → every team gains the permission, idempotently
+    merged (re-running with the same permission is a no-op).
+  * unknown team_id → 404 (the missing_ids guard); no mutation anywhere.
+  * malformed payload (neither / both selector flags) → 400; no mutation.
+
+The all-teams scenario mutates world teams as a deliberate side effect of
+the path under test. The test snapshots every team's
+`team_member_permissions` up front and restores them on exit so the
+read-world stays immutable for downstream tests.
+"""
+
+import pytest
+
+from .actors import TEAM_ALPHA, TEAM_BETA, TEAM_GAMMA, Actor
+from .conftest import create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# ---------------------------------------------------------------------------
+# Specific-team list — only the listed team mutates.
+# ---------------------------------------------------------------------------
+
+
+async def test_bulk_update_specific_team_only_mutates_listed(
+    proxy_client, prisma, scratch, world
+):
+    target = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("target"),
+        team_member_permissions=[],
+    )
+    bystander = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("bystander"),
+        team_member_permissions=["pre-existing-perm"],
+    )
+    # Snapshot world teams so we can assert byte-equality on re-read.
+    world_team_ids = [TEAM_ALPHA, TEAM_BETA, TEAM_GAMMA]
+    before = {}
+    for tid in world_team_ids + [bystander]:
+        row = await prisma.db.litellm_teamtable.find_unique(where={"team_id": tid})
+        before[tid] = list(row.team_member_permissions or [])
+
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    perm = "/key/info"
+    resp = await proxy_client.post(
+        "/team/permissions_bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={"team_ids": [target], "permissions": [perm]},
+    )
+    assert resp.status_code == 200, resp.text
+    assert resp.json()["teams_updated"] == 1
+
+    target_row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": target}
+    )
+    assert perm in (
+        target_row.team_member_permissions or []
+    ), f"target team did not gain perm; got={target_row.team_member_permissions}"
+
+    for tid, before_perms in before.items():
+        after_row = await prisma.db.litellm_teamtable.find_unique(
+            where={"team_id": tid}
+        )
+        assert list(after_row.team_member_permissions or []) == before_perms, (
+            f"untouched team {tid} mutated: "
+            f"{before_perms} → {list(after_row.team_member_permissions or [])}"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Specific-team — repeated call with same permission is a no-op (the
+# `permissions_to_add <= existing` short-circuit in _compute_and_batch_updates).
+# ---------------------------------------------------------------------------
+
+
+async def test_bulk_update_specific_team_idempotent(
+    proxy_client, prisma, scratch, world
+):
+    target = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("target"),
+        team_member_permissions=["/key/info"],
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/permissions_bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_ids": [target],
+            "permissions": ["/key/info"],  # already present
+        },
+    )
+    assert resp.status_code == 200, resp.text
+    assert resp.json()["teams_updated"] == 0, "idempotent add should report 0"
+
+
+# ---------------------------------------------------------------------------
+# Unknown team_id — 404, no mutation anywhere.
+# ---------------------------------------------------------------------------
+
+
+async def test_bulk_update_unknown_team_id_rejected(
+    proxy_client, prisma, scratch, world
+):
+    existing = await create_scratch_team(
+        prisma,
+        team_id=scratch.tag("real"),
+        team_member_permissions=[],
+    )
+    before = list(
+        (
+            await prisma.db.litellm_teamtable.find_unique(where={"team_id": existing})
+        ).team_member_permissions
+        or []
+    )
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/permissions_bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json={
+            "team_ids": [existing, f"{scratch.prefix}-ghost"],
+            "permissions": ["/key/info"],
+        },
+    )
+    # The missing_ids check raises 404, but the global exception handler
+    # may wrap it. Either 404 or 400 with "not found" detail counts.
+    assert resp.status_code in (400, 404), resp.text
+    assert "not found" in resp.text.lower() or "ghost" in resp.text, resp.text
+    # Critical: the partial-success regression shape is "real team got
+    # mutated before the ghost-id check ran". Re-read and assert it didn't.
+    after_real = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": existing}
+    )
+    assert (
+        list(after_real.team_member_permissions or []) == before
+    ), "partial mutation: real team changed despite ghost-id rejection"
+
+
+# ---------------------------------------------------------------------------
+# Selector validation — neither flag → 400; both flags → 400.
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "body,expected_substring",
+    [
+        (
+            {"permissions": ["/key/info"]},
+            "team_ids or set apply_to_all_teams",
+        ),
+        (
+            {
+                "permissions": ["/key/info"],
+                "team_ids": ["t"],
+                "apply_to_all_teams": True,
+            },
+            "Cannot set both",
+        ),
+    ],
+    ids=["neither_selector", "both_selectors"],
+)
+async def test_bulk_update_selector_validation(
+    body, expected_substring: str, proxy_client, world
+):
+    seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+    resp = await proxy_client.post(
+        "/team/permissions_bulk_update",
+        headers={"Authorization": f"Bearer {seeder}"},
+        json=body,
+    )
+    assert resp.status_code == 400, resp.text
+    assert expected_substring in resp.text, resp.text
+
+
+# ---------------------------------------------------------------------------
+# apply_to_all_teams — mutates every team. Snapshot + restore world teams
+# so the read-world contract holds for downstream tests.
+# ---------------------------------------------------------------------------
+
+
+async def test_bulk_update_apply_to_all_mutates_every_team(
+    proxy_client, prisma, scratch, world
+):
+    # Two scratch teams so we can assert "every" includes our own targets.
+    a = await create_scratch_team(
+        prisma, team_id=scratch.tag("a"), team_member_permissions=[]
+    )
+    b = await create_scratch_team(
+        prisma, team_id=scratch.tag("b"), team_member_permissions=["other"]
+    )
+    perm = "/key/health"  # distinct from the specific-team scenarios above
+
+    # Snapshot every team's permission list so we can restore world teams.
+    all_teams = await prisma.db.litellm_teamtable.find_many()
+    snapshot = {t.team_id: list(t.team_member_permissions or []) for t in all_teams}
+
+    try:
+        seeder = world.keys[Actor.PROXY_ADMIN].cleartext
+        resp = await proxy_client.post(
+            "/team/permissions_bulk_update",
+            headers={"Authorization": f"Bearer {seeder}"},
+            json={"apply_to_all_teams": True, "permissions": [perm]},
+        )
+        assert resp.status_code == 200, resp.text
+        # `teams_updated` counts teams that didn't already have the perm —
+        # i.e. every team in the DB at call time.
+        assert resp.json()["teams_updated"] == len(all_teams)
+
+        post = await prisma.db.litellm_teamtable.find_many()
+        for team in post:
+            perms = list(team.team_member_permissions or [])
+            assert perm in perms, f"team {team.team_id} missing the all-perm: {perms}"
+            # Existing perms preserved (merge, not replace).
+            for prior in snapshot.get(team.team_id, []):
+                assert (
+                    prior in perms
+                ), f"team {team.team_id} lost prior perm {prior!r}: {perms}"
+    finally:
+        # Restore every team — scratch teardown handles {a, b}; we must
+        # explicitly restore world teams (and any other non-scratch teams
+        # that snuck in) so downstream tests see the immutable world.
+        for team_id, prior_perms in snapshot.items():
+            current = await prisma.db.litellm_teamtable.find_unique(
+                where={"team_id": team_id}
+            )
+            if current is None:
+                continue
+            if list(current.team_member_permissions or []) != prior_perms:
+                await prisma.db.litellm_teamtable.update(
+                    where={"team_id": team_id},
+                    data={"team_member_permissions": prior_perms},
+                )
diff --git a/tests/proxy_behavior/management/test_team_update.py b/tests/proxy_behavior/management/test_team_update.py
new file mode 100644
index 00000000000..9cb2b0fecda
--- /dev/null
+++ b/tests/proxy_behavior/management/test_team_update.py
@@ -0,0 +1,227 @@
+import pytest
+
+from litellm.proxy._types import LitellmUserRoles
+
+from .actors import Actor
+from .conftest import create_scratch_actor, create_scratch_team
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+# POST /team/update — actor x team-shape matrix (shapes built by _seed_target).
+# Each request carries the team's own organization_id so a non-proxy-admin can
+# reach the org-scoped branch of the route-permission gate (401 on denial),
+# which fronts the handler's _verify_team_access. Only PROXY_ADMIN and an
+# ORG_ADMIN of the team's org pass: an internal_user team admin is filtered by
+# the route gate before _verify_team_access's team-admin branch is reached.
+MARKER_ALIAS = "behavior-pin-update-marker-alias"
+
+_MATRIX = [
+    ("alpha/proxy_admin", Actor.PROXY_ADMIN, "alpha", 200),
+    ("alpha/org_admin", Actor.ORG_ADMIN, "alpha", 200),
+    ("alpha/team_admin", Actor.TEAM_ADMIN, "alpha", 401),
+    ("alpha/internal_user", Actor.INTERNAL_USER, "alpha", 401),
+    ("alpha/owner", Actor.OWNER, "alpha", 401),
+    ("alpha/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "alpha", 401),
+    ("alpha/cross_org_user", Actor.CROSS_ORG_USER, "alpha", 401),
+    ("alpha/service_account", Actor.SERVICE_ACCOUNT, "alpha", 401),
+    ("alpha/org_b_admin", Actor.ORG_B_ADMIN, "alpha", 401),
+    ("beta/proxy_admin", Actor.PROXY_ADMIN, "beta", 200),
+    ("beta/org_admin", Actor.ORG_ADMIN, "beta", 401),
+    ("beta/team_admin", Actor.TEAM_ADMIN, "beta", 401),
+    ("beta/internal_user", Actor.INTERNAL_USER, "beta", 401),
+    ("beta/owner", Actor.OWNER, "beta", 401),
+    ("beta/unrelated_same_org", Actor.UNRELATED_SAME_ORG, "beta", 401),
+    ("beta/cross_org_user", Actor.CROSS_ORG_USER, "beta", 401),
+    ("beta/service_account", Actor.SERVICE_ACCOUNT, "beta", 401),
+    ("beta/org_b_admin", Actor.ORG_B_ADMIN, "beta", 200),
+]
+
+
+async def _seed_target(prisma, world, shape: str, team_id: str) -> str:
+    """Raw-seed the scratch target team; returns its organization_id."""
+    if shape == "alpha":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_a_id,
+            admin_user_ids=[world.keys[Actor.TEAM_ADMIN].user_id],
+            member_user_ids=[
+                world.keys[Actor.INTERNAL_USER].user_id,
+                world.keys[Actor.OWNER].user_id,
+                world.keys[Actor.UNRELATED_SAME_ORG].user_id,
+                world.keys[Actor.SERVICE_ACCOUNT].user_id,
+            ],
+        )
+        return world.org_a_id
+    if shape == "beta":
+        await create_scratch_team(
+            prisma,
+            team_id,
+            organization_id=world.org_b_id,
+            member_user_ids=[world.keys[Actor.CROSS_ORG_USER].user_id],
+        )
+        return world.org_b_id
+    pytest.fail(f"unknown shape={shape}")  # pragma: no cover
+
+
+@pytest.mark.parametrize(
+    "actor,shape,expected_status",
+    [(a, sh, s) for (_id, a, sh, s) in _MATRIX],
+    ids=[s[0] for s in _MATRIX],
+)
+async def test_team_update_authz_matrix(
+    actor: Actor,
+    shape: str,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    org_id = await _seed_target(prisma, world, shape, scratch.prefix)
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={
+            "team_id": scratch.prefix,
+            "team_alias": MARKER_ALIAS,
+            "organization_id": org_id,
+        },
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value} {shape}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert row.team_alias == MARKER_ALIAS
+    else:
+        assert row.team_alias != MARKER_ALIAS, "denied but team mutated"
+
+
+async def test_team_update_requires_proxy_admin_without_org_context(
+    proxy_client, prisma, scratch, world
+):
+    """With no organization_id in the body the route gate has no org context
+    and falls back to proxy-admin-only: an org admin of the team's own org
+    is 401, PROXY_ADMIN is 200."""
+    await _seed_target(prisma, world, "alpha", scratch.prefix)
+
+    denied = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.ORG_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "team_alias": MARKER_ALIAS},
+    )
+    assert denied.status_code == 401, denied.text
+
+    allowed = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.PROXY_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "team_alias": MARKER_ALIAS},
+    )
+    assert allowed.status_code == 200, allowed.text
+
+
+# Relocation gate — moving a team to a different org. The scratch team starts
+# in ORG_A; each scenario relocates it to ORG_B. PROXY_ADMIN bypasses;
+# ORG_B_ADMIN clears the route gate (dest-org admin) but fails
+# _verify_team_access on the source team (403); the rest fail the route gate
+# (401). The relocation-*allowed* branch (caller is org admin of both orgs) is
+# covered by test_team_update_org_relocation_allowed_for_dual_org_admin below.
+_RELOCATION = [
+    ("proxy_admin", Actor.PROXY_ADMIN, 200),
+    ("org_b_admin", Actor.ORG_B_ADMIN, 403),
+    ("org_admin", Actor.ORG_ADMIN, 401),
+    ("team_admin", Actor.TEAM_ADMIN, 401),
+    ("internal_user", Actor.INTERNAL_USER, 401),
+]
+
+
+@pytest.mark.parametrize(
+    "actor,expected_status",
+    [(a, s) for (_id, a, s) in _RELOCATION],
+    ids=[s[0] for s in _RELOCATION],
+)
+async def test_team_update_org_relocation_gate(
+    actor: Actor,
+    expected_status: int,
+    proxy_client,
+    prisma,
+    scratch,
+    world,
+):
+    await _seed_target(prisma, world, "alpha", scratch.prefix)
+    caller = world.keys[actor]
+
+    resp = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {caller.cleartext}"},
+        json={"team_id": scratch.prefix, "organization_id": world.org_b_id},
+    )
+    assert (
+        resp.status_code == expected_status
+    ), f"{actor.value}: {resp.status_code} {resp.text}"
+
+    row = await prisma.db.litellm_teamtable.find_unique(
+        where={"team_id": scratch.prefix}
+    )
+    assert row is not None
+    if expected_status == 200:
+        assert row.organization_id == world.org_b_id
+    else:
+        assert row.organization_id == world.org_a_id, "denied but team relocated"
+
+
+# Phase 4 F6 — explicit pin on the `_verify_team_access` 403 detail string
+# when an org_admin clears the destination route gate but fails the source
+# team's org-membership check. The relocation matrix above covers the
+# status; this guard turns a silent rename of the helper's exception detail
+# into a CI red.
+async def test_team_update_org_b_admin_relocation_rejection_detail(
+    proxy_client, prisma, scratch, world
+):
+    await _seed_target(prisma, world, "alpha", scratch.prefix)
+    resp = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {world.keys[Actor.ORG_B_ADMIN].cleartext}"},
+        json={"team_id": scratch.prefix, "organization_id": world.org_b_id},
+    )
+    assert resp.status_code == 403, resp.text
+    assert "do not have access to this team" in resp.text, resp.text
+
+
+async def test_team_update_org_relocation_allowed_for_dual_org_admin(
+    proxy_client, prisma, scratch, world
+):
+    """Relocation-allowed branch: a caller who is org admin of BOTH the source
+    and destination org may relocate a team between them. Completes the
+    _RELOCATION matrix, whose allowed branch PR2 left open — no seeded actor is
+    a dual-org admin, so one is minted with create_scratch_actor."""
+    actor = await create_scratch_actor(
+        prisma,
+        scratch.prefix,
+        user_role=LitellmUserRoles.ORG_ADMIN.value,
+        org_admin_of=(world.org_a_id, world.org_b_id),
+    )
+    team_id = await create_scratch_team(
+        prisma, scratch.tag("team"), organization_id=world.org_a_id
+    )
+
+    resp = await proxy_client.post(
+        "/team/update",
+        headers={"Authorization": f"Bearer {actor.cleartext}"},
+        json={"team_id": team_id, "organization_id": world.org_b_id},
+    )
+    assert resp.status_code == 200, resp.text
+
+    row = await prisma.db.litellm_teamtable.find_unique(where={"team_id": team_id})
+    assert row is not None
+    assert (
+        row.organization_id == world.org_b_id
+    ), "dual-org admin relocation not applied"
diff --git a/tests/proxy_behavior/management/test_world_seed.py b/tests/proxy_behavior/management/test_world_seed.py
new file mode 100644
index 00000000000..00f9540c9c3
--- /dev/null
+++ b/tests/proxy_behavior/management/test_world_seed.py
@@ -0,0 +1,30 @@
+import pytest
+
+from .actors import Actor
+
+pytestmark = pytest.mark.asyncio(loop_scope="session")
+
+
+@pytest.mark.parametrize("actor", list(Actor), ids=[a.value for a in Actor])
+async def test_each_actor_can_self_info(actor, proxy_client, world):
+    seeded = world.keys[actor]
+    resp = await proxy_client.get(
+        "/key/info",
+        headers={"Authorization": f"Bearer {seeded.cleartext}"},
+    )
+    assert resp.status_code == 200, f"{actor.value}: {resp.text}"
+    body = resp.json()
+    assert body.get("key") == seeded.hashed
+    assert body["info"].get("user_id") == seeded.user_id
+
+
+async def test_proxy_admin_actor_can_create_keys_for_others(proxy_client, world):
+    seeder = world.keys[Actor.PROXY_ADMIN]
+    target_user_id = world.keys[Actor.OWNER].user_id
+
+    resp = await proxy_client.post(
+        "/key/generate",
+        headers={"Authorization": f"Bearer {seeder.cleartext}"},
+        json={"key_alias": "smoke-proxy-admin-bypass", "user_id": target_user_id},
+    )
+    assert resp.status_code == 200, resp.text
diff --git a/tests/proxy_unit_tests/test_gemini_agents_endpoints.py b/tests/proxy_unit_tests/test_gemini_agents_endpoints.py
new file mode 100644
index 00000000000..bdac9348f71
--- /dev/null
+++ b/tests/proxy_unit_tests/test_gemini_agents_endpoints.py
@@ -0,0 +1,519 @@
+"""
+Unit tests for litellm/proxy/google_endpoints/agents_endpoints.py
+
+Focus: verify that list_gemini_agents, get_gemini_agent, delete_gemini_agent,
+and list_gemini_agent_versions correctly forward per-request credentials
+(api_key, api_base, …) supplied via the JSON-encoded litellm_params_template
+query parameter.  Flat credential query params (e.g. ?api_key=…) are no
+longer accepted — they would appear in server logs.
+"""
+
+import json
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi import Request
+from fastapi.datastructures import Headers, QueryParams
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm.proxy.google_endpoints.agents_endpoints import (
+    _merge_query_params_into_data,
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_request(query_string: str = "") -> MagicMock:
+    """Build a minimal mock Request whose query_params match *query_string*."""
+    req = MagicMock(spec=Request)
+    req.query_params = QueryParams(query_string)
+    req.headers = Headers({})
+    return req
+
+
+# ---------------------------------------------------------------------------
+# _merge_query_params_into_data – unit tests for the helper
+# ---------------------------------------------------------------------------
+
+
+class TestMergeQueryParamsIntoData:
+    def test_no_query_params_leaves_data_unchanged(self):
+        data = {"custom_llm_provider": "gemini"}
+        request = _make_request("")
+        result = _merge_query_params_into_data(data, request)
+        assert result == {"custom_llm_provider": "gemini"}
+
+    def test_flat_api_key_is_ignored(self):
+        """Flat credential params must NOT be merged (they leak into server logs)."""
+        data = {"custom_llm_provider": "gemini"}
+        request = _make_request("api_key=AIzaSyTest123")
+        _merge_query_params_into_data(data, request)
+        assert "api_key" not in data
+        assert data["custom_llm_provider"] == "gemini"
+
+    def test_flat_params_are_silently_dropped(self):
+        """Flat params (including name injection attempts) are ignored entirely."""
+        data = {"name": "my-agent", "custom_llm_provider": "gemini"}
+        request = _make_request("name=INJECTED&api_key=AIzaSyTest")
+        _merge_query_params_into_data(data, request)
+        assert data["name"] == "my-agent"
+        assert "api_key" not in data
+
+    def test_litellm_params_template_json_is_expanded(self):
+        template = json.dumps(
+            {"api_key": "AIzaFromTemplate", "api_base": "https://example.com"}
+        )
+        from urllib.parse import quote
+
+        request = _make_request(f"litellm_params_template={quote(template)}")
+        data = {"custom_llm_provider": "gemini"}
+        _merge_query_params_into_data(data, request)
+        assert data["api_key"] == "AIzaFromTemplate"
+        assert data["api_base"] == "https://example.com"
+        # The raw template key itself must NOT appear in data
+        assert "litellm_params_template" not in data
+
+    def test_litellm_params_template_does_not_overwrite_existing(self):
+        template = json.dumps(
+            {"api_key": "FromTemplate", "custom_llm_provider": "openai"}
+        )
+        from urllib.parse import quote
+
+        request = _make_request(f"litellm_params_template={quote(template)}")
+        data = {"custom_llm_provider": "gemini"}
+        _merge_query_params_into_data(data, request)
+        # custom_llm_provider was already set; template must not override it
+        assert data["custom_llm_provider"] == "gemini"
+        assert data["api_key"] == "FromTemplate"
+
+    def test_invalid_litellm_params_template_json_is_ignored(self):
+        request = _make_request("litellm_params_template=NOT_VALID_JSON")
+        data = {"custom_llm_provider": "gemini"}
+        _merge_query_params_into_data(data, request)
+        # Bad JSON is silently skipped; other data stays intact
+        assert data == {"custom_llm_provider": "gemini"}
+
+    def test_template_only_no_flat_params_merged(self):
+        """Only litellm_params_template is expanded; unknown flat params are dropped."""
+        template = json.dumps({"api_key": "FromTemplate"})
+        from urllib.parse import quote
+
+        qs = f"litellm_params_template={quote(template)}&vertex_project=my-project"
+        request = _make_request(qs)
+        data = {"custom_llm_provider": "gemini"}
+        _merge_query_params_into_data(data, request)
+        assert data["api_key"] == "FromTemplate"
+        # flat vertex_project is ignored since it wasn't in litellm_params_template
+        assert "vertex_project" not in data
+        assert "litellm_params_template" not in data
+
+
+# ---------------------------------------------------------------------------
+# Endpoint-level smoke tests: data dict is populated before the processor call
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def mock_srv():
+    """Patch _proxy_server_imports to return lightweight fakes."""
+    srv = {
+        "general_settings": {},
+        "llm_router": MagicMock(),
+        "proxy_config": MagicMock(),
+        "proxy_logging_obj": MagicMock(),
+        "select_data_generator": MagicMock(),
+        "user_api_base": None,
+        "user_max_tokens": None,
+        "user_model": None,
+        "user_request_timeout": None,
+        "user_temperature": None,
+        "version": "0.0.0",
+    }
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints._proxy_server_imports",
+        return_value=srv,
+    ):
+        yield srv
+
+
+@pytest.fixture
+def user_api_key_dict():
+    from litellm.proxy._types import UserAPIKeyAuth
+
+    return UserAPIKeyAuth(api_key="test-key")
+
+
+def _make_endpoint_request(query_string: str = "") -> MagicMock:
+    req = MagicMock(spec=Request)
+    req.query_params = QueryParams(query_string)
+    req.headers = Headers({})
+    req.scope = {}
+
+    async def _body():
+        return b""
+
+    req.body = _body
+    return req
+
+
+@pytest.mark.asyncio
+async def test_list_gemini_agents_passes_api_key_to_processor(
+    mock_srv, user_api_key_dict
+):
+    from urllib.parse import quote
+
+    from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents
+
+    template = json.dumps({"api_key": "AIzaListTest"})
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request(f"litellm_params_template={quote(template)}")
+        await list_gemini_agents(
+            request=request,
+            fastapi_response=MagicMock(),
+            user_api_key_dict=user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert init_data.get("api_key") == "AIzaListTest"
+        assert init_data.get("custom_llm_provider") == "gemini"
+
+
+@pytest.mark.asyncio
+async def test_get_gemini_agent_passes_api_key_to_processor(
+    mock_srv, user_api_key_dict
+):
+    from urllib.parse import quote
+
+    from litellm.proxy.google_endpoints.agents_endpoints import get_gemini_agent
+
+    template = json.dumps({"api_key": "AIzaGetTest"})
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request(f"litellm_params_template={quote(template)}")
+        await get_gemini_agent(
+            request=request,
+            name="my-agent",
+            fastapi_response=MagicMock(),
+            user_api_key_dict=user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert init_data.get("api_key") == "AIzaGetTest"
+        assert init_data.get("name") == "my-agent"
+        assert init_data.get("custom_llm_provider") == "gemini"
+
+
+@pytest.mark.asyncio
+async def test_delete_gemini_agent_passes_api_key_to_processor(
+    mock_srv, user_api_key_dict
+):
+    from urllib.parse import quote
+
+    from litellm.proxy.google_endpoints.agents_endpoints import delete_gemini_agent
+
+    template = json.dumps({"api_key": "AIzaDeleteTest"})
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request(f"litellm_params_template={quote(template)}")
+        await delete_gemini_agent(
+            request=request,
+            name="my-agent",
+            fastapi_response=MagicMock(),
+            user_api_key_dict=user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert init_data.get("api_key") == "AIzaDeleteTest"
+        assert init_data.get("name") == "my-agent"
+        assert init_data.get("custom_llm_provider") == "gemini"
+
+
+@pytest.mark.asyncio
+async def test_list_gemini_agent_versions_passes_api_key_to_processor(
+    mock_srv, user_api_key_dict
+):
+    from urllib.parse import quote
+
+    from litellm.proxy.google_endpoints.agents_endpoints import (
+        list_gemini_agent_versions,
+    )
+
+    template = json.dumps({"api_key": "AIzaVersionsTest"})
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request(f"litellm_params_template={quote(template)}")
+        await list_gemini_agent_versions(
+            request=request,
+            name="my-agent",
+            fastapi_response=MagicMock(),
+            user_api_key_dict=user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert init_data.get("api_key") == "AIzaVersionsTest"
+        assert init_data.get("name") == "my-agent"
+        assert init_data.get("custom_llm_provider") == "gemini"
+
+
+@pytest.mark.asyncio
+async def test_get_gemini_agent_name_not_overwritten_by_query_param(
+    mock_srv, user_api_key_dict
+):
+    """Path-param ``name`` must not be replaced by an attacker-controlled query param."""
+    from urllib.parse import quote
+
+    from litellm.proxy.google_endpoints.agents_endpoints import get_gemini_agent
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        # Even if a caller tries to inject "name" via flat query param, it is
+        # ignored (flat params are not merged).  The path-param name wins.
+        # ``api_key`` is supplied via the JSON template (required for non-admin
+        # callers — see test_*_non_admin_without_api_key_is_rejected below).
+        template = json.dumps({"api_key": "AIzaTest"})
+        request = _make_endpoint_request(
+            f"name=INJECTED&litellm_params_template={quote(template)}"
+        )
+        await get_gemini_agent(
+            request=request,
+            name="real-agent",
+            fastapi_response=MagicMock(),
+            user_api_key_dict=user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert init_data["name"] == "real-agent"
+
+
+@pytest.mark.asyncio
+async def test_list_agents_template_via_query_param(mock_srv, user_api_key_dict):
+    """litellm_params_template in query string is expanded."""
+    from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents
+    from urllib.parse import quote
+
+    template = json.dumps({"api_key": "TemplateKey", "vertex_project": "proj-x"})
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request(f"litellm_params_template={quote(template)}")
+        await list_gemini_agents(
+            request=request,
+            fastapi_response=MagicMock(),
+            user_api_key_dict=user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert init_data["api_key"] == "TemplateKey"
+        assert init_data["vertex_project"] == "proj-x"
+        assert "litellm_params_template" not in init_data
+
+
+# ---------------------------------------------------------------------------
+# Security guards (veria-flagged findings)
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def proxy_admin_user_api_key_dict():
+    from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
+
+    return UserAPIKeyAuth(
+        api_key="sk-admin",
+        user_role=LitellmUserRoles.PROXY_ADMIN,
+    )
+
+
+@pytest.mark.asyncio
+async def test_list_agents_non_admin_without_api_key_is_rejected(
+    mock_srv, user_api_key_dict
+):
+    """Non-admin callers must supply an explicit api_key — the proxy must not
+    silently fall back to the operator's shared GOOGLE_API_KEY/GEMINI_API_KEY.
+    """
+    from fastapi import HTTPException
+
+    from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request("")
+        with pytest.raises(HTTPException) as excinfo:
+            await list_gemini_agents(
+                request=request,
+                fastapi_response=MagicMock(),
+                user_api_key_dict=user_api_key_dict,
+            )
+        assert excinfo.value.status_code == 401
+        # Processor must never be invoked
+        instance.base_process_llm_request.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_delete_agent_non_admin_without_api_key_is_rejected(
+    mock_srv, user_api_key_dict
+):
+    from fastapi import HTTPException
+
+    from litellm.proxy.google_endpoints.agents_endpoints import delete_gemini_agent
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request("")
+        with pytest.raises(HTTPException) as excinfo:
+            await delete_gemini_agent(
+                request=request,
+                name="my-agent",
+                fastapi_response=MagicMock(),
+                user_api_key_dict=user_api_key_dict,
+            )
+        assert excinfo.value.status_code == 401
+        instance.base_process_llm_request.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_create_agent_non_admin_without_api_key_is_rejected(
+    mock_srv, user_api_key_dict
+):
+    from fastapi import HTTPException
+
+    from litellm.proxy.google_endpoints.agents_endpoints import create_gemini_agent
+
+    with (
+        patch(
+            "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+        ) as MockProcessor,
+        patch(
+            "litellm.proxy.google_endpoints.agents_endpoints._read_request_body",
+            new=AsyncMock(return_value={"name": "agent-1", "base_agent": "waverunner"}),
+        ),
+    ):
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request("")
+        with pytest.raises(HTTPException) as excinfo:
+            await create_gemini_agent(
+                request=request,
+                fastapi_response=MagicMock(),
+                user_api_key_dict=user_api_key_dict,
+            )
+        assert excinfo.value.status_code == 401
+        instance.base_process_llm_request.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_list_agents_proxy_admin_may_use_env_fallback(
+    mock_srv, proxy_admin_user_api_key_dict
+):
+    """Proxy admins (master key) keep the env-fallback convenience."""
+    from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents
+
+    with patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing"
+    ) as MockProcessor:
+        instance = MockProcessor.return_value
+        instance.base_process_llm_request = AsyncMock(return_value=MagicMock())
+
+        request = _make_endpoint_request("")
+        await list_gemini_agents(
+            request=request,
+            fastapi_response=MagicMock(),
+            user_api_key_dict=proxy_admin_user_api_key_dict,
+        )
+
+        init_data = MockProcessor.call_args[1]["data"]
+        assert "api_key" not in init_data
+        instance.base_process_llm_request.assert_awaited_once()
+
+
+def test_validate_environment_rejects_api_base_override_without_explicit_key(
+    monkeypatch,
+):
+    """SECURITY: caller-supplied api_base must be paired with an explicit
+    api_key — otherwise the proxy's shared GOOGLE_API_KEY leaks to the
+    attacker-controlled host via the x-goog-api-key header.
+    """
+    from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig
+
+    # Even if env-fallback is available, api_base override must require api_key.
+    monkeypatch.setenv("GOOGLE_API_KEY", "AIzaSharedSecret")
+
+    cfg = GeminiAgentsConfig()
+    with pytest.raises(ValueError, match="api_base"):
+        cfg.validate_environment(
+            headers={},
+            litellm_params={"api_base": "https://attacker.example"},
+        )
+
+
+def test_validate_environment_allows_api_base_with_explicit_key(monkeypatch):
+    """api_base override is OK when paired with an explicit api_key."""
+    from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig
+
+    monkeypatch.delenv("GOOGLE_API_KEY", raising=False)
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+
+    cfg = GeminiAgentsConfig()
+    headers = cfg.validate_environment(
+        headers={},
+        litellm_params={
+            "api_base": "https://my-gemini-proxy.example",
+            "api_key": "AIzaCallerOwned",
+        },
+    )
+    assert headers["x-goog-api-key"] == "AIzaCallerOwned"
+
+
+def test_validate_environment_env_fallback_when_no_api_base_override(monkeypatch):
+    """Without api_base override, env fallback continues to work for SDK use."""
+    from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig
+
+    monkeypatch.setenv("GOOGLE_API_KEY", "AIzaFromEnv")
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+
+    cfg = GeminiAgentsConfig()
+    headers = cfg.validate_environment(headers={}, litellm_params={})
+    assert headers["x-goog-api-key"] == "AIzaFromEnv"
diff --git a/tests/proxy_unit_tests/test_proxy_utils.py b/tests/proxy_unit_tests/test_proxy_utils.py
index ff695d4a57e..d36d73da2c3 100644
--- a/tests/proxy_unit_tests/test_proxy_utils.py
+++ b/tests/proxy_unit_tests/test_proxy_utils.py
@@ -416,9 +416,10 @@ def test_dynamic_turn_off_message_logging(callback_vars):
     )
 
     assert callbacks is not None
-    assert (
-        callbacks.callback_vars["turn_off_message_logging"]
-        == callback_vars["turn_off_message_logging"]
+    # AddTeamCallback's validator stringifies callback_var values, so compare
+    # against the str() of the input rather than the input bool directly.
+    assert callbacks.callback_vars["turn_off_message_logging"] == str(
+        callback_vars["turn_off_message_logging"]
     )
 
 
diff --git a/tests/proxy_unit_tests/test_reducto_ocr_route.py b/tests/proxy_unit_tests/test_reducto_ocr_route.py
new file mode 100644
index 00000000000..dc658a74ee8
--- /dev/null
+++ b/tests/proxy_unit_tests/test_reducto_ocr_route.py
@@ -0,0 +1,137 @@
+import asyncio
+import os
+from unittest.mock import AsyncMock, patch
+
+import litellm
+import pytest
+from fastapi.testclient import TestClient
+
+from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo
+from litellm.proxy.proxy_server import app, initialize
+
+
+@pytest.fixture(scope="function")
+def fake_env_vars(monkeypatch):
+    monkeypatch.setenv("OPENAI_API_KEY", "fake_openai_api_key")
+    monkeypatch.setenv("OPENAI_API_BASE", "http://fake-openai-api-base")
+    monkeypatch.setenv("AZURE_AI_API_BASE", "http://fake-azure-api-base")
+    monkeypatch.setenv("AZURE_AI_API_KEY", "fake_azure_api_key")
+    monkeypatch.setenv("AZURE_OPENAI_API_KEY", "fake_azure_openai_api_key")
+    monkeypatch.setenv("AZURE_SWEDEN_API_BASE", "http://fake-azure-sweden-api-base")
+    monkeypatch.setenv("AZURE_SWEDEN_API_KEY", "fake_azure_sweden_api_key")
+    monkeypatch.setenv("REDIS_HOST", "localhost")
+
+
+@pytest.fixture(scope="function")
+def client_no_auth(fake_env_vars):
+    from litellm.proxy.proxy_server import cleanup_router_config_variables
+
+    original_disable_aiohttp = litellm.disable_aiohttp_transport
+    litellm.disable_aiohttp_transport = True
+    litellm.in_memory_llm_clients_cache.flush_cache()
+    cleanup_router_config_variables()
+
+    filepath = os.path.dirname(os.path.abspath(__file__))
+    config_fp = os.path.join(filepath, "test_configs", "test_config_no_auth.yaml")
+    asyncio.run(initialize(config=config_fp, debug=True))
+
+    # Passthrough of api_base in the JSON body is rejected by default
+    # (pre_db_read_auth_checks / is_request_body_safe). This test asserts
+    # api_base reaches aocr().
+    from litellm.proxy import proxy_server as _ps
+
+    if _ps.general_settings is None:
+        _ps.general_settings = {}
+    _ps.general_settings["allow_client_side_credentials"] = True
+
+    try:
+        yield TestClient(app)
+    finally:
+        litellm.disable_aiohttp_transport = original_disable_aiohttp
+        litellm.in_memory_llm_clients_cache.flush_cache()
+
+
+def test_proxy_reducto_ocr_json_rejects_reducto_id(client_no_auth):
+    with patch(
+        "litellm.proxy.proxy_server.llm_router.aocr",
+        new=AsyncMock(),
+    ) as mock_aocr:
+        response = client_no_auth.post(
+            "/v1/ocr",
+            json={
+                "model": "reducto/parse-v3",
+                "document": {
+                    "type": "document_url",
+                    "document_url": "reducto://proxy.pdf",
+                },
+                "api_key": "proxy-key",
+                "api_base": "https://platform.reducto.ai",
+            },
+        )
+
+    assert response.status_code >= 400
+    assert "reducto://" in response.text
+    assert mock_aocr.await_count == 0
+
+
+def test_proxy_reducto_ocr_json_rejects_reducto_id_in_image_url(client_no_auth):
+    with patch(
+        "litellm.proxy.proxy_server.llm_router.aocr",
+        new=AsyncMock(),
+    ) as mock_aocr:
+        response = client_no_auth.post(
+            "/v1/ocr",
+            json={
+                "model": "reducto/parse-v3",
+                "document": {
+                    "type": "image_url",
+                    "image_url": "reducto://proxy.png",
+                },
+            },
+        )
+
+    assert response.status_code >= 400
+    assert "reducto://" in response.text
+    assert mock_aocr.await_count == 0
+
+
+def test_proxy_reducto_ocr_json_passthrough_data_uri(client_no_auth):
+    mocked_response = OCRResponse(
+        pages=[OCRPage(index=0, markdown="Proxy OCR")],
+        model="parse-v3",
+        usage_info=OCRUsageInfo(pages_processed=1, credits=1),
+    )
+
+    data_uri = "data:application/pdf;base64,JVBERi0xLjQK"
+
+    with patch(
+        "litellm.proxy.proxy_server.llm_router.aocr",
+        new=AsyncMock(return_value=mocked_response),
+    ) as mock_aocr:
+        response = client_no_auth.post(
+            "/v1/ocr",
+            json={
+                "model": "reducto/parse-v3",
+                "document": {
+                    "type": "document_url",
+                    "document_url": data_uri,
+                },
+                "api_key": "proxy-key",
+                "api_base": "https://platform.reducto.ai",
+            },
+        )
+
+    assert response.status_code == 200
+    assert mock_aocr.await_count == 1
+    assert mock_aocr.await_args.kwargs["model"] == "reducto/parse-v3"
+    assert mock_aocr.await_args.kwargs["document"] == {
+        "type": "document_url",
+        "document_url": data_uri,
+    }
+    assert mock_aocr.await_args.kwargs["api_key"] == "proxy-key"
+    assert mock_aocr.await_args.kwargs["api_base"] == "https://platform.reducto.ai"
+
+    response_body = response.json()
+    assert response_body["object"] == "ocr"
+    assert response_body["usage_info"]["credits"] == 1
+    assert response_body["pages"][0]["markdown"] == "Proxy OCR"
diff --git a/tests/proxy_unit_tests/test_user_api_key_auth.py b/tests/proxy_unit_tests/test_user_api_key_auth.py
index 210347aaf94..958b028c542 100644
--- a/tests/proxy_unit_tests/test_user_api_key_auth.py
+++ b/tests/proxy_unit_tests/test_user_api_key_auth.py
@@ -915,6 +915,36 @@ async def test_user_api_key_auth_websocket():
         )
 
 
+@pytest.mark.asyncio
+async def test_user_api_key_auth_websocket_carries_asgi_path():
+    """
+    The synthetic Request must carry the ASGI scope's ``path`` so
+    ``get_request_route`` returns the real WebSocket path, not a value
+    reconstructed from the (Host-poisonable) ``websocket.url``.
+    """
+    from litellm.proxy.auth.user_api_key_auth import user_api_key_auth_websocket
+
+    mock_websocket = MagicMock(spec=WebSocket)
+    mock_websocket.query_params = {"model": "some_model"}
+    mock_websocket.headers = {"authorization": "Bearer some_api_key"}
+    mock_websocket.scope = {
+        "type": "websocket",
+        "path": "/v1/realtime",
+        "root_path": "",
+        "headers": [(b"authorization", b"Bearer some_api_key")],
+    }
+    mock_websocket.url = URL(url="/v1/realtime")
+
+    with patch(
+        "litellm.proxy.auth.user_api_key_auth.user_api_key_auth", autospec=True
+    ) as mock_user_api_key_auth:
+        await user_api_key_auth_websocket(mock_websocket)
+
+        request_arg = mock_user_api_key_auth.call_args.kwargs["request"]
+        assert request_arg.scope.get("path") == "/v1/realtime"
+        assert request_arg.scope.get("root_path") == ""
+
+
 @pytest.mark.parametrize("enforce_rbac", [True, False])
 @pytest.mark.asyncio
 async def test_jwt_user_api_key_auth_builder_enforce_rbac(enforce_rbac, monkeypatch):
diff --git a/tests/router_unit_tests/conftest.py b/tests/router_unit_tests/conftest.py
index fe976515c92..6a8f3e589f4 100644
--- a/tests/router_unit_tests/conftest.py
+++ b/tests/router_unit_tests/conftest.py
@@ -12,14 +12,17 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm  # noqa: E402,F401
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -97,6 +100,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -123,3 +127,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/router_unit_tests/test_router_aresponses_streaming_fallback.py b/tests/router_unit_tests/test_router_aresponses_streaming_fallback.py
new file mode 100644
index 00000000000..25bf79cd575
--- /dev/null
+++ b/tests/router_unit_tests/test_router_aresponses_streaming_fallback.py
@@ -0,0 +1,268 @@
+"""
+Unit tests for the Responses-API streaming-fallback helpers added to Router
+in PR #28215 (fix(router): wrap aresponses streaming iterator for mid-stream
+fallbacks).
+
+Targets the four helpers introduced on Router:
+  - _extract_partial_responses_usage
+  - _combine_responses_fallback_usage
+  - _build_responses_continuation_input
+  - _aresponses_streaming_iterator
+"""
+
+import os
+import sys
+from typing import Any, AsyncIterator, List
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm import Router
+from litellm.types.llms.openai import (
+    ResponseAPIUsage,
+    ResponseCompletedEvent,
+    ResponsesAPIResponse,
+    ResponsesAPIStreamEvents,
+)
+
+
+def _make_router() -> Router:
+    return Router(
+        model_list=[
+            {
+                "model_name": "primary",
+                "litellm_params": {
+                    "model": "openai/gpt-4o-mini",
+                    "api_key": "sk-test",
+                },
+            },
+            {
+                "model_name": "fallback",
+                "litellm_params": {
+                    "model": "openai/gpt-4o",
+                    "api_key": "sk-test",
+                },
+            },
+        ]
+    )
+
+
+def _make_completed_event(
+    input_tokens: int, output_tokens: int, total_tokens: int
+) -> ResponseCompletedEvent:
+    response = ResponsesAPIResponse.model_construct(
+        usage=ResponseAPIUsage(
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+            total_tokens=total_tokens,
+        )
+    )
+    return ResponseCompletedEvent.model_construct(
+        type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
+        response=response,
+    )
+
+
+# -------- _extract_partial_responses_usage --------
+
+
+def test_extract_partial_responses_usage_native_completed():
+    """Native path: completed_response carries usage → returned as-is."""
+    completed = _make_completed_event(11, 7, 18)
+    source = MagicMock()
+    source.completed_response = completed
+
+    usage = Router._extract_partial_responses_usage(source)
+    assert usage is not None
+    assert usage.input_tokens == 11
+    assert usage.output_tokens == 7
+    assert usage.total_tokens == 18
+
+
+def test_extract_partial_responses_usage_no_completed_response():
+    """Native path: no completed_response → returns None."""
+    source = MagicMock()
+    source.completed_response = None
+
+    usage = Router._extract_partial_responses_usage(source)
+    assert usage is None
+
+
+# -------- _combine_responses_fallback_usage --------
+
+
+def test_combine_responses_fallback_usage_sums_completed_event():
+    """Partial-stream usage is summed into the fallback event's usage."""
+    fallback_event = _make_completed_event(5, 3, 8)
+    partial = ResponseAPIUsage(input_tokens=11, output_tokens=7, total_tokens=18)
+
+    Router._combine_responses_fallback_usage(fallback_event, partial)
+
+    combined = fallback_event.response.usage
+    assert combined is not None
+    assert combined.input_tokens == 16
+    assert combined.output_tokens == 10
+    assert combined.total_tokens == 26
+
+
+def test_combine_responses_fallback_usage_passthrough_for_unknown_event():
+    """Events that are not completed/failed/incomplete are not mutated."""
+    other = MagicMock()  # not a ResponseCompletedEvent etc. → isinstance false
+    partial = ResponseAPIUsage(input_tokens=1, output_tokens=1, total_tokens=2)
+    Router._combine_responses_fallback_usage(other, partial)
+    # No mutation expected on the unknown event — call is a no-op.
+
+
+# -------- _build_responses_continuation_input --------
+
+
+def test_build_responses_continuation_input_from_string():
+    out = Router._build_responses_continuation_input(
+        "Hello world", "partial assistant text"
+    )
+    assert len(out) == 3
+    assert out[0]["role"] == "user"
+    assert out[0]["content"][0]["text"] == "Hello world"
+    assert out[1]["role"] == "developer"
+    assert out[2]["role"] == "assistant"
+    assert out[2]["content"][0]["text"] == "partial assistant text"
+
+
+def test_build_responses_continuation_input_from_list_preserves_items():
+    existing: List[Any] = [
+        {
+            "type": "message",
+            "role": "user",
+            "content": [{"type": "input_text", "text": "msg1"}],
+        }
+    ]
+    out = Router._build_responses_continuation_input(existing, "partial")
+    assert len(out) == 3
+    assert out[0]["content"][0]["text"] == "msg1"
+    assert out[1]["role"] == "developer"
+    assert out[2]["role"] == "assistant"
+
+
+def test_build_responses_continuation_input_from_none():
+    out = Router._build_responses_continuation_input(None, "partial")
+    assert len(out) == 2
+    assert out[0]["role"] == "developer"
+    assert out[1]["role"] == "assistant"
+
+
+# -------- _aresponses_streaming_iterator (passthrough smoke test) --------
+
+
+@pytest.mark.asyncio
+async def test_aresponses_streaming_iterator_passthrough():
+    """
+    Without MidStreamFallbackError, the wrapper yields source events
+    unchanged and returns a BaseResponsesAPIStreamingIterator subclass.
+    """
+    from litellm.responses.streaming_iterator import (
+        BaseResponsesAPIStreamingIterator,
+    )
+
+    events = [_make_completed_event(1, 1, 2)]
+
+    class _FakeSource:
+        """Minimal source iterator. Provides every attribute the wrapper
+        constructor reads from source_iterator."""
+
+        def __init__(self) -> None:
+            self._i = 0
+            self.completed_response = None
+            self.response = MagicMock()
+            self.model = "openai/gpt-4o-mini"
+            self.logging_obj = MagicMock()
+            self.responses_api_provider_config = MagicMock()
+            self.start_time = 0.0
+            self.litellm_metadata = {}
+            self.custom_llm_provider = "openai"
+            self.request_data = {}
+            self.call_type = "aresponses"
+            self._hidden_params: dict = {}
+
+        def __aiter__(self) -> AsyncIterator[Any]:
+            return self
+
+        async def __anext__(self):
+            if self._i >= len(events):
+                raise StopAsyncIteration
+            ev = events[self._i]
+            self._i += 1
+            return ev
+
+        async def aclose(self):
+            return None
+
+    router = _make_router()
+    source = _FakeSource()
+
+    wrapper = await router._aresponses_streaming_iterator(
+        source, initial_kwargs={"model": "primary"}
+    )
+    assert isinstance(wrapper, BaseResponsesAPIStreamingIterator)
+
+    collected = [ev async for ev in wrapper]
+    assert len(collected) == 1
+    assert collected[0].type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED
+
+
+# -------- _aresponses_with_streaming_fallbacks --------
+
+
+@pytest.mark.asyncio
+async def test_aresponses_with_streaming_fallbacks_non_streaming_passthrough():
+    """Non-streaming response is returned unchanged, no wrap."""
+    router = _make_router()
+    plain_response = MagicMock()
+
+    async def fake_original(**_kwargs):
+        return plain_response
+
+    with patch.object(
+        router,
+        "_ageneric_api_call_with_fallbacks",
+        new=AsyncMock(return_value=plain_response),
+    ):
+        out = await router._aresponses_with_streaming_fallbacks(
+            original_function=fake_original,
+            model="primary",
+            stream=False,
+        )
+    assert out is plain_response
+
+
+@pytest.mark.asyncio
+async def test_aresponses_with_streaming_fallbacks_wraps_streaming_iterator():
+    """Streaming response is wrapped via _aresponses_streaming_iterator."""
+    from litellm.responses.streaming_iterator import (
+        BaseResponsesAPIStreamingIterator,
+    )
+
+    router = _make_router()
+    streaming_iter = MagicMock(spec=BaseResponsesAPIStreamingIterator)
+    wrapped = MagicMock(spec=BaseResponsesAPIStreamingIterator)
+
+    async def fake_original(**_kwargs):
+        return streaming_iter
+
+    with patch.object(
+        router,
+        "_ageneric_api_call_with_fallbacks",
+        new=AsyncMock(return_value=streaming_iter),
+    ), patch.object(
+        router,
+        "_aresponses_streaming_iterator",
+        new=AsyncMock(return_value=wrapped),
+    ) as mock_wrap:
+        out = await router._aresponses_with_streaming_fallbacks(
+            original_function=fake_original,
+            model="primary",
+            stream=True,
+        )
+    assert out is wrapped
+    mock_wrap.assert_awaited_once()
diff --git a/tests/search_tests/conftest.py b/tests/search_tests/conftest.py
index e06d3e95eee..78ba19a7724 100644
--- a/tests/search_tests/conftest.py
+++ b/tests/search_tests/conftest.py
@@ -13,14 +13,17 @@ import pytest
 
 sys.path.insert(0, os.path.abspath("../.."))
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -52,6 +55,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -65,3 +69,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/tests/test_callbacks_on_proxy.py b/tests/test_callbacks_on_proxy.py
index 0b55d820532..17c0db9260f 100644
--- a/tests/test_callbacks_on_proxy.py
+++ b/tests/test_callbacks_on_proxy.py
@@ -9,12 +9,155 @@ import pytest
 import asyncio
 import aiohttp
 import os
+import re
 import dotenv
+from collections import Counter
 from dotenv import load_dotenv
 import pytest
 
 load_dotenv()
 
+# A *leak* is sustained, monotonic growth of one callback TYPE across the whole
+# sampling window. A one-time bump that then plateaus is benign pollution from
+# other tests sharing this proxy (this suite runs `pytest -n 4` against a single
+# proxy container, so other workers legitimately add team/key-scoped callbacks
+# while this test sleeps). We therefore sample N times and only flag a type
+# whose normalized count never decreases, grows in >=2 distinct intervals, and
+# nets >= LEAK_MIN_NET_GROWTH overall.
+NUM_SAMPLES = 4
+SAMPLE_INTERVAL_SECONDS = 20
+LEAK_MIN_NET_GROWTH = 5
+LEAK_MIN_GROWING_INTERVALS = 2
+# A routing-strategy switch / alerting config is a *known, bounded, one-time*
+# registration (CCI diagnostic 2026-05-16: total 85->95 on the first interval
+# after switching to latency-based-routing, then flat at 95 for 2.5 min under
+# load). We absorb that step by settling before the baseline sample, so only
+# growth *after* the deliberate perturbation can count as a leak.
+SETTLE_SECONDS = 30
+
+# Strip instance-identity noise so N leaked instances of one class collapse to
+# one rising counter instead of N opaque, unrelated-looking strings.
+_ADDR_RE = re.compile(r" at 0x[0-9a-fA-F]+")
+_OBJ_RE = re.compile(r"<([\w.]+) object")
+
+
+def _normalize_callback(cb_str: str) -> str:
+    """Reduce a callback's str() to a stable type key (drops 0x… addresses)."""
+    s = _ADDR_RE.sub("", cb_str)
+    m = _OBJ_RE.search(s)
+    if m:
+        return m.group(1).split(".")[-1]
+    # bound methods: "<bound method Cls.m of <... at 0x..>>" -> "Cls.m"
+    bm = re.search(r"bound method ([\w.]+)", s)
+    if bm:
+        return bm.group(1)
+    return s.strip()
+
+
+def _summarize(all_litellm_callbacks) -> Counter:
+    return Counter(_normalize_callback(str(c)) for c in all_litellm_callbacks)
+
+
+def _detect_leaks(samples):
+    """
+    samples: list[Counter] taken in time order.
+
+    Returns {callback_type: [counts across samples]} for types that grew
+    monotonically (never decreased), in >=LEAK_MIN_GROWING_INTERVALS intervals,
+    and netted >=LEAK_MIN_NET_GROWTH overall — i.e. a real leak, not a one-shot
+    step from a parallel test.
+    """
+    leaks = {}
+    all_types = set().union(*[set(s) for s in samples]) if samples else set()
+    for t in all_types:
+        series = [s.get(t, 0) for s in samples]
+        deltas = [b - a for a, b in zip(series, series[1:])]
+        net = series[-1] - series[0]
+        non_decreasing = all(d >= 0 for d in deltas)
+        growing_intervals = sum(1 for d in deltas if d > 0)
+        if (
+            non_decreasing
+            and net >= LEAK_MIN_NET_GROWTH
+            and growing_intervals >= LEAK_MIN_GROWING_INTERVALS
+        ):
+            leaks[t] = series
+    return leaks
+
+
+def _terminal_suspects(samples):
+    """
+    Types whose net growth clears the threshold monotonically but is confined
+    to the *final* interval — `growing_intervals == 1` with that one growing
+    interval being the last. `_detect_leaks`' `>= 2` guard silently passes
+    these, so a real leak that accumulates entirely in the last sampled window
+    is indistinguishable from a one-time terminal step *without one more
+    sample*. Returns the set of such types so the caller can re-confirm.
+    """
+    suspects = set()
+    all_types = set().union(*[set(s) for s in samples]) if samples else set()
+    for t in all_types:
+        series = [s.get(t, 0) for s in samples]
+        deltas = [b - a for a, b in zip(series, series[1:])]
+        if not deltas:
+            continue
+        net = series[-1] - series[0]
+        non_decreasing = all(d >= 0 for d in deltas)
+        growing = [i for i, d in enumerate(deltas) if d > 0]
+        if (
+            non_decreasing
+            and net >= LEAK_MIN_NET_GROWTH
+            and growing == [len(deltas) - 1]
+        ):
+            suspects.add(t)
+    return suspects
+
+
+async def _detect_leaks_confirmed(session, samples):
+    """
+    `_detect_leaks`, plus a single confirmation sample when growth is confined
+    to the final interval (see `_terminal_suspects`). A genuine ongoing leak
+    keeps climbing -> now grows in >= 2 intervals -> flagged; a one-time
+    terminal registration plateaus -> still 1 growing interval -> ignored.
+    Returns `(leaks, samples)` (samples may have one extra entry appended).
+    """
+    leaks = _detect_leaks(samples)
+    if not leaks and _terminal_suspects(samples):
+        await asyncio.sleep(SAMPLE_INTERVAL_SECONDS)
+        _, _, all_cb = await get_active_callbacks(session=session)
+        samples = samples + [_summarize(all_cb)]
+        leaks = _detect_leaks(samples)
+    return leaks, samples
+
+
+def _format_report(samples, leaks) -> str:
+    lines = ["Callback count per type across samples (time order):"]
+    all_types = sorted(set().union(*[set(s) for s in samples]))
+    for t in all_types:
+        series = [s.get(t, 0) for s in samples]
+        marker = "  <-- LEAK" if t in leaks else ""
+        lines.append(f"  {t}: {series}{marker}")
+    totals = [sum(s.values()) for s in samples]
+    lines.append(f"TOTAL callbacks per sample: {totals}")
+    if leaks:
+        lines.append(
+            "Leaking callback types (sustained monotonic growth): "
+            + ", ".join(sorted(leaks))
+        )
+    return "\n".join(lines)
+
+
+async def _sample_callbacks(session, num_samples, interval):
+    """Take `num_samples` callback snapshots `interval`s apart."""
+    samples = []
+    alerts = []
+    for i in range(num_samples):
+        if i > 0:
+            await asyncio.sleep(interval)
+        num_cb, num_alert, all_cb = await get_active_callbacks(session=session)
+        samples.append(_summarize(all_cb))
+        alerts.append(num_alert)
+    return samples, alerts
+
 
 async def config_update(session, routing_strategy=None):
     url = "http://0.0.0.0:4000/config/update"
@@ -97,105 +240,65 @@ async def get_current_routing_strategy(session):
 
 @pytest.mark.asyncio
 @pytest.mark.order1
+@pytest.mark.flaky(reruns=2, reruns_delay=5)
 async def test_check_num_callbacks():
     """
-    Test 1:  num callbacks should NOT increase over time
-    -> check current callbacks
-    -> sleep for 30 seconds
-    -> check current callbacks
-    -> sleep for 30 seconds
-    -> check current callbacks
+    PROD invariant: no callback TYPE should grow without bound over time.
+
+    This suite runs `pytest -n 4` against one shared proxy, so the raw count is
+    noisy — other workers legitimately add team/key-scoped callbacks that then
+    plateau. We settle first, then sample several times, and only fail on
+    *sustained, monotonic* per-type growth (a genuine leak), naming the type.
     """
-    from litellm._uuid import uuid
-
     async with aiohttp.ClientSession() as session:
-        await asyncio.sleep(30)
-        num_callbacks_1, _, all_litellm_callbacks_1 = await get_active_callbacks(
-            session=session
-        )
-        assert num_callbacks_1 > 0
-        await asyncio.sleep(30)
+        # Absorb proxy warmup / in-flight parallel registration before baseline.
+        await asyncio.sleep(SETTLE_SECONDS)
 
-        num_callbacks_2, _, all_litellm_callbacks_2 = await get_active_callbacks(
-            session=session
+        samples, _ = await _sample_callbacks(
+            session, NUM_SAMPLES, SAMPLE_INTERVAL_SECONDS
         )
 
-        print("all_litellm_callbacks_1", all_litellm_callbacks_1)
+        assert sum(samples[0].values()) > 0, "expected some callbacks registered"
 
-        print(
-            "diff in callbacks=",
-            set(all_litellm_callbacks_1) - set(all_litellm_callbacks_2),
-        )
-
-        assert abs(num_callbacks_1 - num_callbacks_2) <= 4
-
-        await asyncio.sleep(30)
-
-        num_callbacks_3, _, all_litellm_callbacks_3 = await get_active_callbacks(
-            session=session
-        )
-
-        print(
-            "diff in callbacks = all_litellm_callbacks3 - all_litellm_callbacks2 ",
-            set(all_litellm_callbacks_3) - set(all_litellm_callbacks_2),
-        )
-
-        assert abs(num_callbacks_3 - num_callbacks_2) <= 4
+        leaks, samples = await _detect_leaks_confirmed(session, samples)
+        report = _format_report(samples, leaks)
+        print(report)
+        assert not leaks, f"Callback leak detected.\n{report}"
 
 
 @pytest.mark.asyncio
 @pytest.mark.order2
+@pytest.mark.flaky(reruns=2, reruns_delay=5)
 async def test_check_num_callbacks_on_lowest_latency():
     """
-    Test 1:  num callbacks should NOT increase over time
-    -> Update to lowest latency
-    -> check current callbacks
-    -> sleep for 30s
-    -> check current callbacks
-    -> sleep for 30s
-    -> check current callbacks
-    -> update back to original routing-strategy
+    Same PROD invariant as test_check_num_callbacks, but after switching the
+    router to latency-based-routing. That switch is a *known, bounded* one-time
+    registration (it adds the latency strategy handler + Slack alerting); we
+    settle past it before baselining so only post-switch growth counts as a
+    leak. Also asserts the alerting count is stable.
     """
-    from litellm._uuid import uuid
-
     async with aiohttp.ClientSession() as session:
         await asyncio.sleep(30)
 
         original_routing_strategy = await get_current_routing_strategy(session=session)
         await config_update(session=session, routing_strategy="latency-based-routing")
 
-        await asyncio.sleep(30)
+        try:
+            # Absorb the deliberate one-time config/update registration step.
+            await asyncio.sleep(SETTLE_SECONDS)
 
-        num_callbacks_1, num_alerts_1, all_litellm_callbacks_1 = (
-            await get_active_callbacks(session=session)
-        )
+            samples, alerts = await _sample_callbacks(
+                session, NUM_SAMPLES, SAMPLE_INTERVAL_SECONDS
+            )
 
-        await asyncio.sleep(30)
-
-        num_callbacks_2, num_alerts_2, all_litellm_callbacks_2 = (
-            await get_active_callbacks(session=session)
-        )
-
-        print(
-            "diff in callbacks all_litellm_callbacks_2 - all_litellm_callbacks_1 =",
-            set(all_litellm_callbacks_2) - set(all_litellm_callbacks_1),
-        )
-
-        assert abs(num_callbacks_1 - num_callbacks_2) <= 4
-
-        await asyncio.sleep(30)
-
-        num_callbacks_3, num_alerts_3, all_litellm_callbacks_3 = (
-            await get_active_callbacks(session=session)
-        )
-
-        print(
-            "diff in callbacks all_litellm_callbacks_3 - all_litellm_callbacks_2 =",
-            set(all_litellm_callbacks_3) - set(all_litellm_callbacks_2),
-        )
-
-        assert abs(num_callbacks_2 - num_callbacks_3) <= 4
-
-        assert num_alerts_1 == num_alerts_2 == num_alerts_3
-
-        await config_update(session=session, routing_strategy=original_routing_strategy)
+            leaks, samples = await _detect_leaks_confirmed(session, samples)
+            report = _format_report(samples, leaks)
+            print(report)
+            assert not leaks, f"Callback leak detected.\n{report}"
+            assert (
+                len(set(alerts)) == 1
+            ), f"alerting count changed across samples: {alerts}"
+        finally:
+            await config_update(
+                session=session, routing_strategy=original_routing_strategy
+            )
diff --git a/tests/test_litellm/caching/test_caching_handler.py b/tests/test_litellm/caching/test_caching_handler.py
index 742a4f410d4..3eb949d7f29 100644
--- a/tests/test_litellm/caching/test_caching_handler.py
+++ b/tests/test_litellm/caching/test_caching_handler.py
@@ -232,3 +232,207 @@ def test_combine_usage_handles_none_details():
     combined = llm_caching_handler.combine_usage(usage_a, usage_c)
     assert combined.prompt_tokens_details is not None
     assert combined.prompt_tokens_details.image_count == 1
+
+
+def test_is_chat_completion_cached_dict():
+    from litellm.caching.caching_handler import _is_chat_completion_cached_dict
+
+    assert _is_chat_completion_cached_dict(
+        {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []}
+    )
+    assert _is_chat_completion_cached_dict(
+        {"id": "other", "object": "chat.completion.chunk", "choices": []}
+    )
+    assert _is_chat_completion_cached_dict(
+        {"id": "no-object", "choices": [{"index": 0}]}
+    )
+    assert not _is_chat_completion_cached_dict(
+        {"id": "resp_abc", "object": "response", "output": []}
+    )
+
+
+def _build_logging_obj(call_type: str, stream: bool):
+    import uuid as _uuid
+
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+
+    return LiteLLMLogging(
+        litellm_call_id=str(datetime.now()),
+        call_type=call_type,
+        model="gpt-5.4",
+        messages=[],
+        function_id=str(_uuid.uuid4()),
+        stream=stream,
+        start_time=datetime.now(),
+    )
+
+
+def test_convert_cached_aresponses_bridge_chat_completion_stream():
+    """openai/responses chat-completions bridge: streaming cache hit replays as chat stream."""
+    from litellm import aresponses
+    from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+    from litellm.types.utils import CallTypes
+
+    caching_handler = LLMCachingHandler(
+        original_function=aresponses, request_kwargs={}, start_time=datetime.now()
+    )
+    cached_result = {
+        "id": "chatcmpl-bridge-cache-test",
+        "object": "chat.completion",
+        "created": int(time.time()),
+        "model": "gpt-5.4",
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": "Hi!"},
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18},
+    }
+
+    result = caching_handler._convert_cached_result_to_model_response(
+        cached_result=cached_result,
+        call_type=CallTypes.aresponses.value,
+        kwargs={
+            "model": "gpt-5.4",
+            "stream": True,
+            "messages": [{"role": "user", "content": "hi"}],
+        },
+        logging_obj=_build_logging_obj(CallTypes.aresponses.value, stream=True),
+        model="gpt-5.4",
+        args=(),
+    )
+
+    assert isinstance(result, CustomStreamWrapper)
+
+
+def test_convert_cached_responses_bridge_chat_completion_nonstream():
+    """openai/responses chat-completions bridge: non-streaming cache hit replays as ModelResponse."""
+    from litellm import responses
+    from litellm.types.utils import CallTypes, ModelResponse
+
+    caching_handler = LLMCachingHandler(
+        original_function=responses, request_kwargs={}, start_time=datetime.now()
+    )
+    cached_result = {
+        "id": "chatcmpl-bridge-nonstream",
+        "object": "chat.completion",
+        "created": int(time.time()),
+        "model": "gpt-5.4",
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": "Hi!"},
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18},
+    }
+
+    result = caching_handler._convert_cached_result_to_model_response(
+        cached_result=cached_result,
+        call_type=CallTypes.responses.value,
+        kwargs={
+            "model": "gpt-5.4",
+            "stream": False,
+            "messages": [{"role": "user", "content": "hi"}],
+        },
+        logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False),
+        model="gpt-5.4",
+        args=(),
+    )
+
+    assert isinstance(result, ModelResponse)
+    assert result.choices[0].message.content == "Hi!"
+
+
+def test_convert_cached_responses_legacy_nonstream_path():
+    """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) falls through legacy path."""
+    from litellm import responses
+    from litellm.types.llms.openai import ResponsesAPIResponse
+    from litellm.types.utils import CallTypes
+
+    caching_handler = LLMCachingHandler(
+        original_function=responses, request_kwargs={}, start_time=datetime.now()
+    )
+    cached_result = {
+        "id": "resp_legacy_nonstream",
+        "created_at": int(time.time()),
+        "status": "completed",
+        "model": "gpt-4o",
+        "object": "response",
+        "output": [
+            {
+                "type": "message",
+                "id": "msg_legacy",
+                "status": "completed",
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "output_text",
+                        "text": "legacy response",
+                        "annotations": [],
+                    }
+                ],
+            }
+        ],
+    }
+
+    result = caching_handler._convert_cached_result_to_model_response(
+        cached_result=cached_result,
+        call_type=CallTypes.responses.value,
+        kwargs={"model": "gpt-4o", "input": "hi", "stream": False},
+        logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False),
+        model="gpt-4o",
+        args=(),
+    )
+
+    assert isinstance(result, ResponsesAPIResponse)
+    assert result.id == "resp_legacy_nonstream"
+
+
+def test_convert_cached_responses_legacy_stream_path():
+    """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) on stream falls through legacy path."""
+    from litellm import responses
+    from litellm.responses.streaming_iterator import (
+        CachedResponsesAPIStreamingIterator,
+    )
+    from litellm.types.utils import CallTypes
+
+    caching_handler = LLMCachingHandler(
+        original_function=responses, request_kwargs={}, start_time=datetime.now()
+    )
+    cached_result = {
+        "id": "resp_legacy_stream",
+        "created_at": int(time.time()),
+        "status": "completed",
+        "model": "gpt-4o",
+        "object": "response",
+        "output": [
+            {
+                "type": "message",
+                "id": "msg_legacy_stream",
+                "status": "completed",
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "output_text",
+                        "text": "legacy stream",
+                        "annotations": [],
+                    }
+                ],
+            }
+        ],
+    }
+
+    result = caching_handler._convert_cached_result_to_model_response(
+        cached_result=cached_result,
+        call_type=CallTypes.responses.value,
+        kwargs={"model": "gpt-4o", "input": "hi", "stream": True},
+        logging_obj=_build_logging_obj(CallTypes.responses.value, stream=True),
+        model="gpt-4o",
+        args=(),
+    )
+
+    assert isinstance(result, CachedResponsesAPIStreamingIterator)
diff --git a/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py
new file mode 100644
index 00000000000..734033ed6be
--- /dev/null
+++ b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py
@@ -0,0 +1,150 @@
+import os
+import sys
+from datetime import datetime
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../.."))
+
+from litellm.completion_extras.litellm_responses_transformation.handler import (
+    ResponsesToCompletionBridgeHandler,
+)
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+from litellm.types.utils import ModelResponse
+
+
+def test_is_preformatted_cached_chat_stream_true():
+    stream = MagicMock(spec=CustomStreamWrapper)
+    stream.custom_llm_provider = "cached_response"
+    assert (
+        ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(stream)
+        is True
+    )
+
+
+def test_is_preformatted_cached_chat_stream_false_wrong_provider():
+    stream = MagicMock(spec=CustomStreamWrapper)
+    stream.custom_llm_provider = "openai"
+    assert (
+        ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(stream)
+        is False
+    )
+
+
+def test_is_preformatted_cached_chat_stream_false_wrong_type():
+    assert (
+        ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(
+            {"object": "chat.completion.chunk"}
+        )
+        is False
+    )
+
+
+def _bridge_kwargs(stream: bool):
+    logging_obj = LiteLLMLogging(
+        litellm_call_id="test-call",
+        call_type="completion",
+        model="gpt-5.4",
+        messages=[{"role": "user", "content": "hi"}],
+        function_id="fn-id",
+        stream=stream,
+        start_time=datetime.now(),
+    )
+    return {
+        "model": "gpt-5.4",
+        "custom_llm_provider": "openai",
+        "messages": [{"role": "user", "content": "hi"}],
+        "optional_params": {"stream": stream},
+        "litellm_params": {},
+        "headers": {},
+        "model_response": ModelResponse(),
+        "logging_obj": logging_obj,
+    }
+
+
+def test_completion_returns_cached_model_response_directly():
+    """Non-streaming bridge cache hit: responses() returns a ModelResponse -> bridge returns it as-is."""
+    cached = ModelResponse(id="chatcmpl-cached-nonstream", model="gpt-5.4")
+    bridge = ResponsesToCompletionBridgeHandler()
+
+    with (
+        patch.object(
+            bridge.transformation_handler,
+            "transform_request",
+            return_value={"model": "gpt-5.4", "input": "hi"},
+        ),
+        patch("litellm.responses", return_value=cached),
+    ):
+        result = bridge.completion(**_bridge_kwargs(stream=False))
+
+    assert result is cached
+
+
+@pytest.mark.asyncio
+async def test_acompletion_returns_cached_model_response_directly():
+    cached = ModelResponse(id="chatcmpl-cached-nonstream-async", model="gpt-5.4")
+    bridge = ResponsesToCompletionBridgeHandler()
+
+    with (
+        patch.object(
+            bridge.transformation_handler,
+            "transform_request",
+            return_value={"model": "gpt-5.4", "input": "hi"},
+        ),
+        patch("litellm.aresponses", new=AsyncMock(return_value=cached)),
+    ):
+        result = await bridge.acompletion(**_bridge_kwargs(stream=False))
+
+    assert result is cached
+
+
+def test_completion_skips_rewrapping_preformatted_cached_chat_stream():
+    """Streaming bridge cache hit returning CustomStreamWrapper(cached_response) -> bridge skips re-wrapping."""
+    stream = MagicMock(spec=CustomStreamWrapper)
+    stream.custom_llm_provider = "cached_response"
+    bridge = ResponsesToCompletionBridgeHandler()
+
+    with (
+        patch.object(
+            bridge.transformation_handler,
+            "transform_request",
+            return_value={"model": "gpt-5.4", "input": "hi"},
+        ),
+        patch("litellm.responses", return_value=stream),
+        patch.object(
+            bridge,
+            "_apply_post_stream_processing",
+            side_effect=lambda s, *a, **kw: s,
+        ) as post,
+    ):
+        result = bridge.completion(**_bridge_kwargs(stream=True))
+
+    post.assert_called_once()
+    assert result is stream
+
+
+@pytest.mark.asyncio
+async def test_acompletion_skips_rewrapping_preformatted_cached_chat_stream():
+    stream = MagicMock(spec=CustomStreamWrapper)
+    stream.custom_llm_provider = "cached_response"
+    bridge = ResponsesToCompletionBridgeHandler()
+
+    with (
+        patch.object(
+            bridge.transformation_handler,
+            "transform_request",
+            return_value={"model": "gpt-5.4", "input": "hi"},
+        ),
+        patch("litellm.aresponses", new=AsyncMock(return_value=stream)),
+        patch.object(
+            bridge,
+            "_apply_post_stream_processing",
+            side_effect=lambda s, *a, **kw: s,
+        ) as post,
+    ):
+        result = await bridge.acompletion(**_bridge_kwargs(stream=True))
+
+    post.assert_called_once()
+    assert result is stream
diff --git a/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
index 697a9ebc720..d335c359aa0 100644
--- a/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
+++ b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
@@ -508,6 +508,308 @@ and I learn to carry this small calm home."""
     print("✓ transform_response correctly handled reasoning items and output messages")
 
 
+def _make_empty_responses_api_response(model: str = "gpt-5.4"):
+    from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse
+
+    return ResponsesAPIResponse(
+        id="resp_from_stream",
+        created_at=1760144904,
+        error=None,
+        incomplete_details=None,
+        instructions=None,
+        metadata={},
+        model=model,
+        object="response",
+        output=[],
+        parallel_tool_calls=True,
+        temperature=1.0,
+        tool_choice="auto",
+        tools=[],
+        top_p=1.0,
+        max_output_tokens=None,
+        previous_response_id=None,
+        reasoning={"effort": "low", "summary": "detailed"},
+        status="completed",
+        text={"format": {"type": "text"}, "verbosity": "medium"},
+        truncation="disabled",
+        usage=ResponseAPIUsage(
+            input_tokens=1,
+            input_tokens_details=None,
+            output_tokens=1,
+            output_tokens_details=None,
+            total_tokens=2,
+            cost=None,
+        ),
+        user=None,
+        store=True,
+        background=False,
+        billing={"payer": "developer"},
+        max_tool_calls=None,
+        prompt_cache_key=None,
+        safety_identifier=None,
+        service_tier="default",
+        top_logprobs=0,
+    )
+
+
+def _make_empty_model_response():
+    from litellm.types.utils import ModelResponse, Usage
+
+    return ModelResponse(
+        id="chatcmpl-test-recovered",
+        created=1760144904,
+        model=None,
+        object="chat.completion",
+        system_fingerprint=None,
+        choices=[],
+        usage=Usage(completion_tokens=0, prompt_tokens=0, total_tokens=0),
+    )
+
+
+def test_transform_response_recovers_empty_output_from_raw_sse():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    raw_sse = "\n".join(
+        [
+            'data: {"type":"response.output_text.done","output_index":0,"content_index":0,"item_id":"msg_from_stream","text":"Recovered from SSE"}',
+            'data: {"type":"response.completed","response":{"id":"resp_from_stream","object":"response","created_at":1760144904,"status":"completed","model":"gpt-5.4","output":[]}}',
+            "data: [DONE]",
+            "",
+        ]
+    )
+
+    raw_response = _make_empty_responses_api_response()
+    model_response = _make_empty_model_response()
+    logging_obj = Mock()
+    logging_obj.model_call_details = {"original_response": raw_sse}
+
+    result = handler.transform_response(
+        model="gpt-5.4",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=logging_obj,
+        request_data={"model": "gpt-5.4"},
+        messages=[{"role": "user", "content": "Reply with exactly: ok"}],
+        optional_params={},
+        litellm_params={},
+        encoding=Mock(),
+    )
+
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content == "Recovered from SSE"
+
+
+def test_transform_response_recovers_output_item_done_from_raw_sse():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    raw_sse = "\n".join(
+        [
+            'data: {"type":"response.output_item.done","output_index":0,"item":{"type":"message","id":"msg_from_item","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Recovered from output item","annotations":[]}]}}',
+            'data: {"type":"response.completed","response":{"id":"resp_from_stream","object":"response","created_at":1760144904,"status":"completed","model":"gpt-5.4","output":[]}}',
+            "data: [DONE]",
+            "",
+        ]
+    )
+
+    raw_response = _make_empty_responses_api_response()
+    model_response = _make_empty_model_response()
+    logging_obj = Mock()
+    logging_obj.model_call_details = {"original_response": raw_sse}
+
+    result = handler.transform_response(
+        model="gpt-5.4",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=logging_obj,
+        request_data={"model": "gpt-5.4"},
+        messages=[{"role": "user", "content": "Reply with exactly: ok"}],
+        optional_params={},
+        litellm_params={},
+        encoding=Mock(),
+    )
+
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content == "Recovered from output item"
+
+
+def test_transform_response_recovers_output_item_done_from_whitespace_padded_raw_sse():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    output_item_event = {
+        "type": "response.output_item.done",
+        "output_index": 0,
+        "item": {
+            "type": "message",
+            "id": "msg_from_item",
+            "role": "assistant",
+            "status": "completed",
+            "content": [
+                {
+                    "type": "output_text",
+                    "text": "Recovered from padded output item",
+                    "annotations": [],
+                }
+            ],
+        },
+    }
+    completed_event = {
+        "type": "response.completed",
+        "response": {
+            "id": "resp_from_stream",
+            "object": "response",
+            "created_at": 1760144904,
+            "status": "completed",
+            "model": "gpt-5.4",
+            "output": [],
+        },
+    }
+    raw_sse = "\n".join(
+        [
+            f"   data:  {json.dumps(output_item_event)}   ",
+            f"\tdata: {json.dumps(completed_event)}",
+            "data: [DONE]",
+            "",
+        ]
+    )
+
+    raw_response = _make_empty_responses_api_response()
+    model_response = _make_empty_model_response()
+    logging_obj = Mock()
+    logging_obj.model_call_details = {"original_response": raw_sse}
+
+    result = handler.transform_response(
+        model="gpt-5.4",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=logging_obj,
+        request_data={"model": "gpt-5.4"},
+        messages=[{"role": "user", "content": "Reply with exactly: ok"}],
+        optional_params={},
+        litellm_params={},
+        encoding=Mock(),
+    )
+
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content == "Recovered from padded output item"
+
+
+def test_transform_response_preserves_output_item_when_text_done_arrives_later():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    raw_sse = "\n".join(
+        [
+            'data: {"type":"response.output_item.done","output_index":0,"item":{"type":"message","id":"msg_from_item","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Complete output item text","annotations":[]}]}}',
+            'data: {"type":"response.output_text.done","output_index":0,"content_index":0,"item_id":"msg_from_stream","text":"Late text event"}',
+            'data: {"type":"response.completed","response":{"id":"resp_from_stream","object":"response","created_at":1760144904,"status":"completed","model":"gpt-5.4","output":[]}}',
+            "data: [DONE]",
+            "",
+        ]
+    )
+
+    raw_response = _make_empty_responses_api_response()
+    model_response = _make_empty_model_response()
+    logging_obj = Mock()
+    logging_obj.model_call_details = {"original_response": raw_sse}
+
+    result = handler.transform_response(
+        model="gpt-5.4",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=logging_obj,
+        request_data={"model": "gpt-5.4"},
+        messages=[{"role": "user", "content": "Reply with exactly: ok"}],
+        optional_params={},
+        litellm_params={},
+        encoding=Mock(),
+    )
+
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content == "Complete output item text"
+
+
+def test_recover_output_items_merges_text_only_items_at_distinct_indices():
+    """When OUTPUT_ITEM_DONE covers some indices and OUTPUT_TEXT_DONE covers
+    others, both must be preserved instead of treating them as mutually
+    exclusive fallbacks."""
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    raw_sse = "\n".join(
+        [
+            'data: {"type":"response.output_item.done","output_index":0,"item":{"type":"message","id":"msg_item_0","role":"assistant","status":"completed","content":[{"type":"output_text","text":"From OUTPUT_ITEM_DONE","annotations":[]}]}}',
+            'data: {"type":"response.output_text.done","output_index":1,"content_index":0,"item_id":"msg_text_1","text":"From OUTPUT_TEXT_DONE only"}',
+            "data: [DONE]",
+            "",
+        ]
+    )
+
+    recovered = (
+        LiteLLMResponsesTransformationHandler._recover_output_items_from_raw_sse(
+            raw_sse
+        )
+    )
+
+    assert len(recovered) == 2
+    assert recovered[0]["id"] == "msg_item_0"
+    assert recovered[0]["content"][0]["text"] == "From OUTPUT_ITEM_DONE"
+    assert recovered[1]["id"] == "msg_text_1"
+    assert recovered[1]["content"][0]["text"] == "From OUTPUT_TEXT_DONE only"
+
+
+def test_transform_response_prefers_completed_output_from_raw_sse():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    raw_sse = "\n".join(
+        [
+            'data: {"type":"response.output_item.done","output_index":0,"item":{"type":"message","id":"msg_from_item","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Earlier stream text","annotations":[]}]}}',
+            'data: {"type":"response.completed","response":{"id":"resp_from_stream","object":"response","created_at":1760144904,"status":"completed","model":"gpt-5.4","output":[{"type":"message","id":"msg_from_completed","role":"assistant","status":"completed","content":[{"type":"output_text","text":"Authoritative completed text","annotations":[]}]}]}}',
+            "data: [DONE]",
+            "",
+        ]
+    )
+
+    raw_response = _make_empty_responses_api_response()
+    model_response = _make_empty_model_response()
+    logging_obj = Mock()
+    logging_obj.model_call_details = {"original_response": raw_sse}
+
+    result = handler.transform_response(
+        model="gpt-5.4",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=logging_obj,
+        request_data={"model": "gpt-5.4"},
+        messages=[{"role": "user", "content": "Reply with exactly: ok"}],
+        optional_params={},
+        litellm_params={},
+        encoding=Mock(),
+    )
+
+    assert len(result.choices) == 1
+    assert result.choices[0].message.content == "Authoritative completed text"
+
+
 def test_convert_tools_to_responses_format():
     from litellm.completion_extras.litellm_responses_transformation.transformation import (
         LiteLLMResponsesTransformationHandler,
diff --git a/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py b/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py
index 009f432fca1..05bdc40112c 100644
--- a/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py
+++ b/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py
@@ -230,3 +230,30 @@ def test_transform_request_drops_user_metadata_with_additional_drop_params():
 
     assert "metadata" not in result
     assert result["litellm_metadata"]["internal_key"] == "secret"
+
+
+def test_translate_responses_chunk_passthrough_chat_completion_chunk():
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        OpenAiResponsesToChatCompletionStreamIterator,
+    )
+
+    chat_chunk = {
+        "id": "chatcmpl-cache-passthrough",
+        "object": "chat.completion.chunk",
+        "created": 1779104834,
+        "model": "gpt-5.4",
+        "choices": [
+            {
+                "index": 0,
+                "delta": {"role": "assistant", "content": "Hi! How can I help?"},
+                "finish_reason": None,
+            }
+        ],
+    }
+
+    result = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(
+        chat_chunk
+    )
+
+    assert result.choices[0].delta.content == "Hi! How can I help?"
+    assert result.choices[0].finish_reason is None
diff --git a/tests/test_litellm/containers/test_azure_container_transformation.py b/tests/test_litellm/containers/test_azure_container_transformation.py
index 70181f6f03d..cdcccf7c04e 100644
--- a/tests/test_litellm/containers/test_azure_container_transformation.py
+++ b/tests/test_litellm/containers/test_azure_container_transformation.py
@@ -109,6 +109,31 @@ class TestAzureContainerConfig:
 
         assert "/openai/v1/containers" in url
 
+    def test_get_complete_url_strips_responses_path_and_preserves_api_version(self):
+        """When api_base is the responses endpoint URL, get_complete_url must:
+        - strip /openai/responses (no double-path)
+        - use the api-version from api_base query string, NOT the deployment's
+          older api_version (e.g. 2024-08-01-preview → containers need 2025-04-01-preview)
+        """
+        api_base = "https://my-resource.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview"
+
+        url = self.config.get_complete_url(
+            api_base=api_base,
+            litellm_params={"api_version": "2024-08-01-preview"},
+        )
+
+        assert (
+            "/openai/responses/openai/containers" not in url
+        ), "path must not double /openai/responses"
+        assert "my-resource.cognitiveservices.azure.com" in url
+        assert "/openai/containers" in url or "/openai/v1/containers" in url
+        assert (
+            "2025-04-01-preview" in url
+        ), "must use version from api_base, not litellm_params"
+        assert (
+            "2024-08-01-preview" not in url
+        ), "must not fall back to older chat api_version"
+
     def test_get_complete_url_raises_without_api_base(self, monkeypatch):
         monkeypatch.delenv("AZURE_API_BASE", raising=False)
         monkeypatch.setattr(litellm, "api_base", None)
@@ -531,6 +556,92 @@ class TestAzureContainerKnownFailureRegressions:
         assert qs.get("api-version") == ["v1"]
         assert qs.get("foo") == ["bar"]
 
+    @pytest.mark.asyncio
+    async def test_regression_no_container_id_does_not_use_user_supplied_model_id(
+        self, monkeypatch
+    ):
+        """Operations without container_id (create, list) must NOT route via
+        _ageneric_api_call_with_fallbacks using a caller-supplied model_id.
+
+        Security boundary: only the path that holds a validated container_id
+        is trusted to fall back to the forwarded model_id.  A caller setting
+        model_id without container_id on POST /v1/containers must not gain
+        access to an arbitrary deployment UUID.
+        """
+        from litellm.router import Router
+
+        router = Router(
+            model_list=[
+                {
+                    "model_name": "azure-model",
+                    "litellm_params": {
+                        "model": "azure/gpt-4",
+                        "api_base": "https://my-resource.cognitiveservices.azure.com",
+                        "api_key": "test-key",
+                        "api_version": "2025-04-01-preview",
+                    },
+                    "model_info": {"id": "deployment-uuid-123"},
+                }
+            ]
+        )
+
+        fallback_called = {"called": False}
+
+        async def _mock_fallback(original_function, **kwargs):
+            fallback_called["called"] = True
+            return {}
+
+        monkeypatch.setattr(router, "_ageneric_api_call_with_fallbacks", _mock_fallback)
+
+        original_called = {"called": False}
+
+        async def _noop(**kwargs):
+            original_called["called"] = True
+            return {}
+
+        # No container_id — simulates create/list; caller injects a model_id
+        await router._init_containers_api_endpoints(
+            original_function=_noop,
+            model_id="deployment-uuid-123",
+            custom_llm_provider="azure",
+        )
+
+        assert not fallback_called["called"], (
+            "_ageneric_api_call_with_fallbacks must NOT be called when "
+            "container_id is absent, even if model_id is supplied"
+        )
+        assert original_called["called"], "original_function must be called directly"
+
+    def test_regression_httpx_empty_params_strips_query_string(self):
+        """httpx erases the URL query-string when params={} (empty dict) is passed.
+
+        Root cause of the Azure container 404s on POST/DELETE:
+          _build_query_params returns {} when the endpoint has no extra params;
+          passing that {} as params= to httpx wiped ?api-version=2025-04-01-preview.
+
+        Fix: every container httpx call now uses `params or None` so an empty
+        dict falls back to None, which tells httpx to leave the URL untouched.
+        """
+        url = (
+            "https://resource.cognitiveservices.azure.com"
+            "/openai/containers/cntr_123?api-version=2025-04-01-preview"
+        )
+        client = httpx.AsyncClient()
+
+        req_none = client.build_request("DELETE", url, params=None)
+        assert "api-version=2025-04-01-preview" in str(req_none.url)
+
+        req_empty = client.build_request("DELETE", url, params={})
+        assert "api-version" not in str(
+            req_empty.url
+        ), "Documents root cause: params={} strips the query string"
+
+        effective: dict = {}
+        req_guarded = client.build_request("DELETE", url, params=effective or None)
+        assert "api-version=2025-04-01-preview" in str(
+            req_guarded.url
+        ), "`params or None` must preserve ?api-version"
+
     def test_regression_proxy_resolves_azure_text_same_as_azure(self):
         """Router/proxy treat azure_text like azure for container config."""
         from litellm.proxy.container_endpoints.handler_factory import (
@@ -770,3 +881,143 @@ class TestAzureContainerKnownFailureRegressions:
         assert captured["data"]["container_id"] == "cntr_123"
         assert captured["data"]["custom_llm_provider"] == "azure"
         assert captured["data"]["model_id"] == "model_abc123"
+
+    @pytest.mark.asyncio
+    async def test_regression_get_container_forwarding_params_sets_model_id_for_managed_id(
+        self,
+    ):
+        """get_container_forwarding_params must extract model_id from a
+        LiteLLM-managed encoded container ID and include it in the forwarding
+        dict.  This is the proxy-side half of the native-Azure-ID routing fix:
+        the router's _init_containers_api_endpoints reads kwargs["model_id"]
+        which is set here.
+        """
+        from litellm.proxy.container_endpoints.ownership import (
+            get_container_forwarding_params,
+        )
+
+        encoded_id = ResponsesAPIRequestUtils._build_container_id(
+            custom_llm_provider="azure",
+            model_id="deployment-uuid-123",
+            container_id="cntr_6a058b43d24c8190a226cfb1d35405b20115fb7875ff11df",
+        )
+
+        params = await get_container_forwarding_params(
+            container_id=encoded_id,
+            original_container_id="cntr_6a058b43d24c8190a226cfb1d35405b20115fb7875ff11df",
+            custom_llm_provider="azure",
+        )
+
+        assert (
+            params.get("model_id") == "deployment-uuid-123"
+        ), "model_id must be forwarded to the router for managed container IDs"
+        assert params.get("container_id") == (
+            "cntr_6a058b43d24c8190a226cfb1d35405b20115fb7875ff11df"
+        )
+        assert params.get("custom_llm_provider") == "azure"
+
+    @pytest.mark.asyncio
+    async def test_regression_get_container_forwarding_params_recovers_model_id_for_native_id(
+        self, monkeypatch
+    ):
+        """Native Azure IDs (``cntr_<hex>``) cannot be decoded, so model_id
+        must be recovered from the ownership row's ``unified_object_id`` —
+        the encoded form captured at create time when the router selected a
+        specific deployment. Without this, the router-side fallback for
+        native IDs in ``_init_containers_api_endpoints`` is dead code.
+        """
+        from types import SimpleNamespace
+        from unittest.mock import AsyncMock
+
+        from litellm.proxy.container_endpoints import ownership
+        from litellm.proxy.container_endpoints.ownership import (
+            get_container_forwarding_params,
+        )
+
+        native_id = "cntr_6a058b43d24c8190a226cfb1d35405b20115fb7875ff11df"
+        encoded_stored_id = ResponsesAPIRequestUtils._build_container_id(
+            custom_llm_provider="azure",
+            model_id="deployment-uuid-123",
+            container_id=native_id,
+        )
+
+        ownership._CONTAINER_STORED_ID_CACHE.flush_cache()
+        ownership._CONTAINER_OWNER_CACHE.flush_cache()
+
+        table = AsyncMock()
+        table.find_first.return_value = SimpleNamespace(
+            created_by="user-1",
+            file_purpose=ownership.CONTAINER_OBJECT_PURPOSE,
+            unified_object_id=encoded_stored_id,
+        )
+        prisma_client = SimpleNamespace(
+            db=SimpleNamespace(litellm_managedobjecttable=table)
+        )
+        monkeypatch.setattr(
+            ownership,
+            "_get_prisma_client",
+            AsyncMock(return_value=prisma_client),
+        )
+
+        params = await get_container_forwarding_params(
+            container_id=native_id,
+            original_container_id=native_id,
+            custom_llm_provider="azure",
+        )
+
+        assert params.get("model_id") == "deployment-uuid-123", (
+            "model_id must be recovered from the stored unified_object_id "
+            "for native upstream container IDs"
+        )
+        assert params.get("container_id") == native_id
+        assert params.get("custom_llm_provider") == "azure"
+
+    @pytest.mark.asyncio
+    async def test_regression_native_azure_container_id_uses_forwarded_model_id(
+        self, monkeypatch
+    ):
+        """Native Azure container IDs (cntr_ + hex, no LiteLLM payload) must
+        still route through _ageneric_api_call_with_fallbacks using the
+        model_id forwarded from the proxy ownership check so that deployment
+        credentials (api_base) are applied."""
+        from litellm.router import Router
+
+        router = Router(
+            model_list=[
+                {
+                    "model_name": "azure-model",
+                    "litellm_params": {
+                        "model": "azure/gpt-4",
+                        "api_base": "https://my-resource.cognitiveservices.azure.com",
+                        "api_key": "test-key",
+                        "api_version": "2025-04-01-preview",
+                    },
+                    "model_info": {"id": "deployment-uuid-123"},
+                }
+            ]
+        )
+
+        called_with: dict = {}
+
+        async def _mock_fallback(original_function, **kwargs):
+            called_with.update(kwargs)
+            return {}
+
+        monkeypatch.setattr(router, "_ageneric_api_call_with_fallbacks", _mock_fallback)
+
+        native_azure_id = "cntr_6a058b43d24c8190a226cfb1d35405b20115fb7875ff11df"
+
+        async def _noop(**kwargs):
+            return {}
+
+        await router._init_containers_api_endpoints(
+            original_function=_noop,
+            container_id=native_azure_id,
+            model_id="deployment-uuid-123",
+            custom_llm_provider="azure",
+        )
+
+        assert called_with.get("model") == "deployment-uuid-123", (
+            "_ageneric_api_call_with_fallbacks must be called with the forwarded "
+            "model_id when the container_id carries no LiteLLM routing payload"
+        )
diff --git a/tests/test_litellm/enterprise/proxy/test_batch_update_db_managed_output_file_id.py b/tests/test_litellm/enterprise/proxy/test_batch_update_db_managed_output_file_id.py
new file mode 100644
index 00000000000..d8669960674
--- /dev/null
+++ b/tests/test_litellm/enterprise/proxy/test_batch_update_db_managed_output_file_id.py
@@ -0,0 +1,260 @@
+"""Regression: update_batch_in_database must not persist raw provider output_file_id."""
+
+import json
+from types import SimpleNamespace
+from typing import Optional
+import pytest
+from unittest.mock import AsyncMock, MagicMock
+
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.openai_files_endpoints.common_utils import (
+    ensure_batch_response_managed_file_ids,
+    update_batch_in_database,
+)
+from litellm.types.utils import LiteLLMBatch
+
+
+def _build_batch_response(
+    *,
+    batch_id: str = "batch_managed_ids_test",
+    status: str = "completed",
+    output_file_id: Optional[str] = "file-rawoutput789",
+    error_file_id: Optional[str] = None,
+    hidden_params: Optional[dict] = None,
+) -> LiteLLMBatch:
+    batch = LiteLLMBatch(
+        id=batch_id,
+        object="batch",
+        status=status,
+        endpoint="/v1/chat/completions",
+        input_file_id="file-input123",
+        output_file_id=output_file_id,
+        error_file_id=error_file_id,
+        completion_window="24h",
+        created_at=1234567890,
+    )
+    if hidden_params is not None:
+        batch._hidden_params = hidden_params  # type: ignore[attr-defined]
+    return batch
+
+
+def _build_managed_files_mock(unified_id: str = "file-bWFuYWdlZF9vdXRwdXRfaWQ="):
+    mock = MagicMock()
+    mock.get_unified_output_file_id = MagicMock(return_value=unified_id)
+    mock.store_unified_file_id = AsyncMock()
+    return mock
+
+
+def _build_prisma_mock():
+    mock = MagicMock()
+    mock.db.litellm_managedfiletable.find_first = AsyncMock(return_value=None)
+    mock.db.litellm_managedobjecttable.update = AsyncMock()
+    return mock
+
+
+@pytest.mark.asyncio
+async def test_update_batch_in_database_stores_unified_output_file_id():
+    raw_output_file_id = "file-rawoutput789"
+    unified_output_file_id = "file-bWFuYWdlZF9vdXRwdXRfaWQ="
+    batch_id = "batch_managed_ids_test"
+    unified_batch_id = (
+        "litellm_proxy;model_id:my-model;llm_batch_id:batch_managed_ids_test"
+    )
+
+    response = _build_batch_response(
+        batch_id=batch_id,
+        output_file_id=raw_output_file_id,
+        hidden_params={"model_id": "my-model", "model_name": "openai/gpt-4o"},
+    )
+
+    mock_managed_files = _build_managed_files_mock(unified_id=unified_output_file_id)
+    mock_prisma = _build_prisma_mock()
+
+    await update_batch_in_database(
+        batch_id=batch_id,
+        unified_batch_id=unified_batch_id,
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=mock_prisma,
+        verbose_proxy_logger=MagicMock(),
+        user_api_key_dict=UserAPIKeyAuth(user_id="user-abc"),
+    )
+
+    stored = json.loads(
+        mock_prisma.db.litellm_managedobjecttable.update.call_args.kwargs["data"][
+            "file_object"
+        ]
+    )
+    assert stored["output_file_id"] == unified_output_file_id
+    assert stored["output_file_id"] != raw_output_file_id
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_normalizes_error_file_id():
+    """Both output_file_id and error_file_id must be normalized to managed IDs."""
+    unified_id = "file-bWFuYWdlZF9vdXRwdXRfaWQ="
+    response = _build_batch_response(
+        output_file_id="file-raw-output",
+        error_file_id="file-raw-error",
+        hidden_params={"model_id": "my-model", "model_name": "openai/gpt-4o"},
+    )
+
+    mock_managed_files = _build_managed_files_mock(unified_id=unified_id)
+    mock_prisma = _build_prisma_mock()
+
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=mock_prisma,
+        verbose_proxy_logger=MagicMock(),
+        user_api_key_dict=UserAPIKeyAuth(user_id="user-abc"),
+    )
+
+    assert response.output_file_id == unified_id
+    assert response.error_file_id == unified_id
+    assert mock_managed_files.get_unified_output_file_id.call_count == 2
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_swallows_conversion_errors():
+    """When the managed-files conversion raises, the failure is logged, not propagated."""
+    raw_output_file_id = "file-raw-output"
+    response = _build_batch_response(
+        output_file_id=raw_output_file_id,
+        hidden_params={"model_id": "my-model", "model_name": "openai/gpt-4o"},
+    )
+
+    mock_managed_files = MagicMock()
+    mock_managed_files.get_unified_output_file_id = MagicMock(
+        side_effect=RuntimeError("boom")
+    )
+    mock_managed_files.store_unified_file_id = AsyncMock()
+
+    mock_logger = MagicMock()
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=_build_prisma_mock(),
+        verbose_proxy_logger=mock_logger,
+        user_api_key_dict=UserAPIKeyAuth(user_id="user-abc"),
+    )
+
+    assert response.output_file_id == raw_output_file_id
+    mock_logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_builds_auth_from_db_batch_object():
+    """If user_api_key_dict is omitted, fall back to created_by/team_id on db_batch_object."""
+    unified_id = "file-bWFuYWdlZF9vdXRwdXRfaWQ="
+    response = _build_batch_response(
+        output_file_id="file-raw-output",
+        hidden_params={"model_id": "my-model", "model_name": "openai/gpt-4o"},
+    )
+
+    mock_managed_files = _build_managed_files_mock(unified_id=unified_id)
+    db_batch_object = SimpleNamespace(
+        created_by="user-from-db", team_id="team-from-db", status="completed"
+    )
+
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=_build_prisma_mock(),
+        verbose_proxy_logger=MagicMock(),
+        db_batch_object=db_batch_object,
+    )
+
+    forwarded_auth = mock_managed_files.store_unified_file_id.call_args.kwargs[
+        "user_api_key_dict"
+    ]
+    assert forwarded_auth.user_id == "user-from-db"
+    assert forwarded_auth.team_id == "team-from-db"
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_resolves_model_name_from_unified_file_id():
+    """When hidden_params lacks model_name, derive it from unified_file_id."""
+    unified_id = "file-bWFuYWdlZF9vdXRwdXRfaWQ="
+    response = _build_batch_response(
+        output_file_id="file-raw-output",
+        hidden_params={
+            "model_id": "my-model",
+            "unified_file_id": "litellm_proxy:application/octet-stream;unified_id,abc;target_model_names,gpt-4o-mini,gemini-2.0-flash",
+        },
+    )
+
+    mock_managed_files = _build_managed_files_mock(unified_id=unified_id)
+
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=_build_prisma_mock(),
+        verbose_proxy_logger=MagicMock(),
+        user_api_key_dict=UserAPIKeyAuth(user_id="user-abc"),
+    )
+
+    assert (
+        mock_managed_files.get_unified_output_file_id.call_args.kwargs["model_name"]
+        == "gpt-4o-mini,gemini-2.0-flash"
+    )
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_returns_early_without_managed_files_obj():
+    """Without managed_files_obj, the helper is a no-op (no conversion attempted)."""
+    response = _build_batch_response(
+        output_file_id="file-raw-output",
+        hidden_params={"model_id": "my-model", "model_name": "openai/gpt-4o"},
+    )
+
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=None,
+        prisma_client=_build_prisma_mock(),
+        verbose_proxy_logger=MagicMock(),
+        user_api_key_dict=UserAPIKeyAuth(user_id="user-abc"),
+    )
+
+    assert response.output_file_id == "file-raw-output"
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_returns_early_without_model_id():
+    """Without model_id in hidden_params, the helper cannot create managed IDs."""
+    response = _build_batch_response(
+        output_file_id="file-raw-output",
+        hidden_params={"model_name": "openai/gpt-4o"},
+    )
+    mock_managed_files = _build_managed_files_mock()
+
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=_build_prisma_mock(),
+        verbose_proxy_logger=MagicMock(),
+        user_api_key_dict=UserAPIKeyAuth(user_id="user-abc"),
+    )
+
+    assert response.output_file_id == "file-raw-output"
+    mock_managed_files.get_unified_output_file_id.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_ensure_batch_response_returns_early_without_auth():
+    """Without user_api_key_dict or db_batch_object, no conversion is attempted."""
+    response = _build_batch_response(
+        output_file_id="file-raw-output",
+        hidden_params={"model_id": "my-model", "model_name": "openai/gpt-4o"},
+    )
+    mock_managed_files = _build_managed_files_mock()
+
+    await ensure_batch_response_managed_file_ids(
+        response=response,
+        managed_files_obj=mock_managed_files,
+        prisma_client=_build_prisma_mock(),
+        verbose_proxy_logger=MagicMock(),
+    )
+
+    assert response.output_file_id == "file-raw-output"
+    mock_managed_files.get_unified_output_file_id.assert_not_called()
diff --git a/tests/test_litellm/integrations/open_telemetry/__init__.py b/tests/test_litellm/integrations/open_telemetry/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/test_litellm/integrations/open_telemetry/_helpers.py b/tests/test_litellm/integrations/open_telemetry/_helpers.py
new file mode 100644
index 00000000000..69e9daf67cb
--- /dev/null
+++ b/tests/test_litellm/integrations/open_telemetry/_helpers.py
@@ -0,0 +1,109 @@
+"""
+Helpers for the LIT-3193 OTEL HTTP-attribute matrix.
+
+Module split from ``conftest.py`` because pytest auto-discovers fixtures but
+forbids ``from .conftest import …`` (no parent package). Fixtures stay in
+``conftest.py``; pure helpers (assertions, exception factories, attribute
+constants) live here so test modules can ``from ._helpers import …``.
+"""
+
+from typing import Any, Optional
+
+from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
+    InMemorySpanExporter,
+)
+from opentelemetry.trace import StatusCode
+
+from litellm.integrations.opentelemetry import (
+    HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE,
+    HTTP_ROUTE_ATTRIBUTE,
+    LITELLM_PROXY_REQUEST_SPAN_NAME,
+    URL_PATH_ATTRIBUTE,
+)
+
+
+def get_server_span(exporter: InMemorySpanExporter):
+    """Return the (single) finished SERVER span, or None if it never ended."""
+    for s in exporter.get_finished_spans():
+        if s.name == LITELLM_PROXY_REQUEST_SPAN_NAME:
+            return s
+    return None
+
+
+def assert_server_span_attrs(
+    exporter: InMemorySpanExporter,
+    *,
+    expected_status: int,
+    expected_url_path: str,
+    expected_http_route: Optional[str] = None,
+    where: str = "",
+) -> None:
+    """The four required attributes on the SERVER span must all be set."""
+    span = get_server_span(exporter)
+    assert span is not None, (
+        f"{where}: SERVER span never finished — exporter saw "
+        f"{[s.name for s in exporter.get_finished_spans()]}"
+    )
+
+    actual_status = span.attributes.get(HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE)
+    assert actual_status == expected_status, (
+        f"{where}: {HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE} = "
+        f"{actual_status!r}, expected {expected_status}"
+    )
+    assert isinstance(
+        actual_status, int
+    ), f"{where}: status code must be int (semconv), got {type(actual_status)}"
+
+    actual_url = span.attributes.get(URL_PATH_ATTRIBUTE)
+    assert actual_url == expected_url_path, (
+        f"{where}: {URL_PATH_ATTRIBUTE} = {actual_url!r}, "
+        f"expected {expected_url_path!r}"
+    )
+
+    expected_route = expected_http_route or expected_url_path
+    actual_route = span.attributes.get(HTTP_ROUTE_ATTRIBUTE)
+    assert actual_route == expected_route, (
+        f"{where}: {HTTP_ROUTE_ATTRIBUTE} = {actual_route!r}, "
+        f"expected {expected_route!r}"
+    )
+
+    duration_ns = (span.end_time or 0) - (span.start_time or 0)
+    assert duration_ns > 0, f"{where}: duration must be > 0, got {duration_ns}ns"
+
+    expected_span_status = StatusCode.ERROR if expected_status >= 400 else StatusCode.OK
+    actual_span_status = span.status.status_code
+    assert actual_span_status == expected_span_status, (
+        f"{where}: span.status = {actual_span_status!r}, "
+        f"expected {expected_span_status!r}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# Synthetic exceptions covering the matrix triggers
+# ---------------------------------------------------------------------------
+class HttpStatusException(Exception):
+    """Generic exception with .status_code; mirrors what proxy code reads."""
+
+    def __init__(self, status_code: int, message: str = "boom"):
+        super().__init__(message)
+        self.status_code = status_code
+        self.code = status_code
+
+
+def make_httpx_status_error(status_code: int, body: str = "upstream error"):
+    """Real httpx.HTTPStatusError — what providers emit on 4xx/5xx upstream."""
+    import httpx
+
+    request = httpx.Request("POST", "https://upstream.example/v1/x")
+    response = httpx.Response(
+        status_code=status_code, content=body.encode("utf-8"), request=request
+    )
+    return httpx.HTTPStatusError(
+        f"HTTP {status_code}", request=request, response=response
+    )
+
+
+def make_fastapi_http_exception(status_code: int, detail: Any = "boom"):
+    from fastapi import HTTPException
+
+    return HTTPException(status_code=status_code, detail=detail)
diff --git a/tests/test_litellm/integrations/open_telemetry/conftest.py b/tests/test_litellm/integrations/open_telemetry/conftest.py
new file mode 100644
index 00000000000..b29335aedd8
--- /dev/null
+++ b/tests/test_litellm/integrations/open_telemetry/conftest.py
@@ -0,0 +1,93 @@
+"""
+Shared fixtures for the LIT-3193 OTEL HTTP-attribute matrix.
+
+The matrix needs every error response — across unified inference, passthrough,
+and admin endpoints — to carry ``http.response.status_code``, ``url.path``,
+``http.route``, and a non-zero duration on the SERVER (root) span. These
+fixtures hook a real ``OpenTelemetry`` callback into ``litellm.callbacks`` so
+the tests drive the actual handler / wrapper code under test, not the OTEL
+emitter in isolation.
+
+See ``LIT-3193_test_matrix.md`` (same directory) for the cell list.
+"""
+
+import os
+import sys
+from datetime import datetime
+from typing import Optional, Tuple
+from unittest.mock import MagicMock
+
+import pytest
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor
+from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
+    InMemorySpanExporter,
+)
+
+sys.path.insert(0, os.path.abspath("../../.."))
+
+import litellm
+from litellm.integrations.opentelemetry import OpenTelemetry
+
+
+# ---------------------------------------------------------------------------
+# OTEL + exporter
+# ---------------------------------------------------------------------------
+@pytest.fixture
+def otel_with_exporter() -> Tuple[OpenTelemetry, InMemorySpanExporter]:
+    """Real OpenTelemetry callback with every span captured in-memory."""
+    exporter = InMemorySpanExporter()
+    provider = TracerProvider()
+    provider.add_span_processor(SimpleSpanProcessor(exporter))
+
+    otel = OpenTelemetry()
+    otel.tracer = provider.get_tracer("lit-3193-tests")
+    otel.message_logging = True
+    return otel, exporter
+
+
+@pytest.fixture
+def server_span_factory(otel_with_exporter):
+    """Factory mirroring user_api_key_auth: SERVER span + url.path + http.route."""
+    otel, _exporter = otel_with_exporter
+
+    def _make(url_path: str, http_route: Optional[str] = None):
+        span = otel.create_litellm_proxy_request_started_span(
+            start_time=datetime.now(), headers={}
+        )
+        otel.set_proxy_request_route_attributes(
+            span, url_path=url_path, http_route=http_route or url_path
+        )
+        return span
+
+    return _make
+
+
+@pytest.fixture
+def user_api_key_dict_factory():
+    """UserAPIKeyAuth-shaped mock; the only attr the failure hooks read is
+    parent_otel_span (plus team_id/team_alias for stamping)."""
+
+    def _make(parent_span):
+        d = MagicMock()
+        d.parent_otel_span = parent_span
+        d.team_id = "team-lit-3193"
+        d.team_alias = "lit-3193-team"
+        d.request_route = None
+        return d
+
+    return _make
+
+
+@pytest.fixture
+def register_otel_callback(otel_with_exporter, monkeypatch):
+    """Make ProxyLogging.post_call_failure_hook iterate our OTEL instance."""
+    otel, _ = otel_with_exporter
+    saved = list(litellm.callbacks)
+    monkeypatch.setattr(litellm, "callbacks", [otel])
+    yield otel
+    litellm.callbacks = saved
+
+
+# Helpers (assertions, exception factories) live in ``_helpers.py`` — pytest
+# auto-discovers fixtures here but forbids ``from .conftest import …``.
diff --git a/tests/test_litellm/integrations/open_telemetry/test_otel_admin_endpoints.py b/tests/test_litellm/integrations/open_telemetry/test_otel_admin_endpoints.py
new file mode 100644
index 00000000000..b1a3b834c3d
--- /dev/null
+++ b/tests/test_litellm/integrations/open_telemetry/test_otel_admin_endpoints.py
@@ -0,0 +1,182 @@
+"""LIT-3193 — admin / management endpoints. Drives the
+async_management_endpoint_{success,failure}_hook integration points."""
+
+import asyncio
+from datetime import datetime
+
+import pytest
+
+from litellm.proxy._types import (
+    ManagementEndpointLoggingPayload,
+    UserAPIKeyAuth,
+)
+
+from ._helpers import (
+    HttpStatusException,
+    assert_server_span_attrs,
+    make_fastapi_http_exception,
+    make_httpx_status_error,
+)
+
+
+def _real_user_api_key_dict(parent_span):
+    return UserAPIKeyAuth(
+        api_key="sk-test-admin",
+        team_id="team-lit-3193",
+        team_alias="lit-3193-team",
+        parent_otel_span=parent_span,
+    )
+
+
+async def _drive_admin_failure(*, otel, exception, parent_span, route):
+    payload = ManagementEndpointLoggingPayload(
+        route=route,
+        request_data={},
+        response=None,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+        exception=exception,
+    )
+    await otel.async_management_endpoint_failure_hook(
+        logging_payload=payload,
+        parent_otel_span=parent_span,
+    )
+
+
+async def _drive_admin_success(*, otel, parent_span, route, response):
+    payload = ManagementEndpointLoggingPayload(
+        route=route,
+        request_data={},
+        response=response,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+    )
+    await otel.async_management_endpoint_success_hook(
+        logging_payload=payload,
+        parent_otel_span=parent_span,
+    )
+
+
+KEY_GENERATE_PATH = "/key/generate"
+
+
+@pytest.mark.parametrize(
+    "exception, expected_status",
+    [
+        (make_fastapi_http_exception(400, "negative max_budget"), 400),
+        (make_fastapi_http_exception(401, "missing master key"), 401),
+        (make_fastapi_http_exception(403, "non-admin"), 403),
+        (make_fastapi_http_exception(422, "validation"), 422),
+        (HttpStatusException(500, "DB unreachable"), 500),
+        # Pins .response.status_code fallback through the admin path.
+        (make_httpx_status_error(500, "upstream blew up"), 500),
+    ],
+    ids=["400", "401", "403", "422", "500", "500-httpx"],
+)
+def test_key_generate_failure_stamps_server_span(
+    exception,
+    expected_status,
+    server_span_factory,
+    otel_with_exporter,
+):
+    otel, exporter = otel_with_exporter
+    server_span = server_span_factory(KEY_GENERATE_PATH)
+
+    asyncio.run(
+        _drive_admin_failure(
+            otel=otel,
+            exception=exception,
+            parent_span=server_span,
+            route=KEY_GENERATE_PATH,
+        )
+    )
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=expected_status,
+        expected_url_path=KEY_GENERATE_PATH,
+        where=f"key/generate {expected_status}",
+    )
+
+
+def test_key_generate_success_stamps_server_span(
+    server_span_factory, otel_with_exporter
+):
+    otel, exporter = otel_with_exporter
+    server_span = server_span_factory(KEY_GENERATE_PATH)
+
+    asyncio.run(
+        _drive_admin_success(
+            otel=otel,
+            parent_span=server_span,
+            route=KEY_GENERATE_PATH,
+            response={"key": "sk-1", "key_name": "k"},
+        )
+    )
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=200,
+        expected_url_path=KEY_GENERATE_PATH,
+        where="key/generate 200",
+    )
+
+
+SMOKE_ADMIN_ENDPOINTS = [
+    "/key/info",
+    "/key/update",
+    "/key/delete",
+    "/team/new",
+    "/team/member_add",
+    "/user/new",
+    "/user/info",
+    "/model/new",
+    "/model/delete",
+    "/customer/new",
+    "/customer/info",
+    "/organization/new",
+    "/organization/member_add",
+    "/budget/new",
+    "/budget/info",
+    "/credentials/new",
+    "/mcp/server/add",
+    "/tag/new",
+]
+
+
+@pytest.mark.parametrize("path", SMOKE_ADMIN_ENDPOINTS)
+@pytest.mark.parametrize(
+    "exception, expected_status",
+    [
+        (make_fastapi_http_exception(404, "not found"), 404),
+        (HttpStatusException(500, "DB unreachable"), 500),
+    ],
+    ids=["404", "500"],
+)
+def test_admin_endpoint_failure_stamps_server_span(
+    path,
+    exception,
+    expected_status,
+    server_span_factory,
+    otel_with_exporter,
+):
+    """Confirm SERVER-span stamping works for every admin resource family —
+    same wrapper, just different routes."""
+    otel, exporter = otel_with_exporter
+    server_span = server_span_factory(path)
+
+    asyncio.run(
+        _drive_admin_failure(
+            otel=otel,
+            exception=exception,
+            parent_span=server_span,
+            route=path,
+        )
+    )
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=expected_status,
+        expected_url_path=path,
+        where=f"{path} {expected_status}",
+    )
diff --git a/tests/test_litellm/integrations/open_telemetry/test_otel_exception_handler.py b/tests/test_litellm/integrations/open_telemetry/test_otel_exception_handler.py
new file mode 100644
index 00000000000..56059c260c3
--- /dev/null
+++ b/tests/test_litellm/integrations/open_telemetry/test_otel_exception_handler.py
@@ -0,0 +1,136 @@
+"""LIT-3193 — exception-handler path. Closes SERVER spans for requests
+that fail after auth but before the route handler runs (e.g. /model/new
+TypeError or RequestValidationError)."""
+
+import asyncio
+import types
+
+import pytest
+from fastapi import HTTPException
+from fastapi.exceptions import RequestValidationError
+
+import litellm.proxy.proxy_server as proxy_server_module
+from litellm.proxy._types import ProxyException
+from litellm.proxy.proxy_server import (
+    _close_dangling_otel_server_span,
+    openai_exception_handler,
+    otel_request_validation_exception_handler,
+    otel_unhandled_exception_handler,
+)
+
+from ._helpers import assert_server_span_attrs
+
+
+def _fake_request(parent_otel_span=None):
+    state = types.SimpleNamespace()
+    if parent_otel_span is not None:
+        state.parent_otel_span = parent_otel_span
+    return types.SimpleNamespace(state=state)
+
+
+@pytest.fixture
+def wired_otel(otel_with_exporter, monkeypatch):
+    otel, exporter = otel_with_exporter
+    monkeypatch.setattr(proxy_server_module, "open_telemetry_logger", otel)
+    return exporter
+
+
+@pytest.mark.parametrize("status,path", [(500, "/model/new"), (422, "/key/generate")])
+def test_close_dangling_span_stamps_status(
+    wired_otel, server_span_factory, status, path
+):
+    request = _fake_request(parent_otel_span=server_span_factory(path))
+    _close_dangling_otel_server_span(request, status)
+    assert_server_span_attrs(
+        wired_otel,
+        expected_status=status,
+        expected_url_path=path,
+        where=f"{path} {status}",
+    )
+    assert request.state.parent_otel_span is None
+
+
+def test_close_dangling_span_noop_when_no_span(wired_otel):
+    _close_dangling_otel_server_span(_fake_request(), 500)
+    assert wired_otel.get_finished_spans() == ()
+
+
+def test_close_dangling_span_noop_when_otel_absent(server_span_factory, monkeypatch):
+    monkeypatch.setattr(proxy_server_module, "open_telemetry_logger", None)
+    request = _fake_request(parent_otel_span=server_span_factory("/key/generate"))
+    _close_dangling_otel_server_span(request, 500)
+
+
+@pytest.mark.parametrize(
+    "handler,exc,status,path",
+    [
+        (
+            otel_request_validation_exception_handler,
+            RequestValidationError(errors=[]),
+            422,
+            "/key/generate",
+        ),
+        (
+            otel_unhandled_exception_handler,
+            TypeError("Deployment.__init__() missing required positional arg"),
+            500,
+            "/model/new",
+        ),
+    ],
+)
+def test_exception_handler_closes_span(
+    wired_otel, server_span_factory, handler, exc, status, path
+):
+    request = _fake_request(parent_otel_span=server_span_factory(path))
+    response = asyncio.run(handler(request, exc))
+    assert response.status_code == status
+    assert_server_span_attrs(
+        wired_otel,
+        expected_status=status,
+        expected_url_path=path,
+        where=f"{handler.__name__} ({type(exc).__name__})",
+    )
+
+
+def test_unhandled_handler_reraises_known_exceptions(wired_otel, server_span_factory):
+    """ProxyException / HTTPException / RequestValidationError have dedicated handlers."""
+    request = _fake_request(parent_otel_span=server_span_factory("/key/generate"))
+    with pytest.raises(HTTPException):
+        asyncio.run(
+            otel_unhandled_exception_handler(
+                request, HTTPException(status_code=403, detail="forbidden")
+            )
+        )
+
+
+# Covers ProxyException raised after auth stashed the span (e.g., invalid-JSON
+# body via _read_request_body) — handler must close the dangling SERVER span.
+@pytest.mark.parametrize(
+    "code,path",
+    [
+        (400, "/v1/chat/completions"),
+        (400, "/v1/messages"),
+        (400, "/v1/responses"),
+        (429, "/v1/chat/completions"),
+        (503, "/v1/chat/completions"),
+    ],
+)
+def test_openai_exception_handler_closes_span(
+    wired_otel, server_span_factory, code, path
+):
+    request = _fake_request(parent_otel_span=server_span_factory(path))
+    exc = ProxyException(
+        message="boom",
+        type="invalid_request_error",
+        param="request_body",
+        code=code,
+    )
+    response = asyncio.run(openai_exception_handler(request, exc))
+    assert response.status_code == code
+    assert_server_span_attrs(
+        wired_otel,
+        expected_status=code,
+        expected_url_path=path,
+        where=f"openai_exception_handler ({path} code={code})",
+    )
+    assert request.state.parent_otel_span is None
diff --git a/tests/test_litellm/integrations/open_telemetry/test_otel_passthrough_endpoints.py b/tests/test_litellm/integrations/open_telemetry/test_otel_passthrough_endpoints.py
new file mode 100644
index 00000000000..4b24b6c487d
--- /dev/null
+++ b/tests/test_litellm/integrations/open_telemetry/test_otel_passthrough_endpoints.py
@@ -0,0 +1,136 @@
+"""LIT-3193 — passthrough endpoints. Drives proxy_logging.post_call_failure_hook
+(the integration point pass_through_endpoint reaches on upstream >=300)."""
+
+import asyncio
+
+import pytest
+from fastapi import HTTPException
+
+from litellm.caching.dual_cache import DualCache
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache
+from litellm.proxy.utils import ProxyLogging
+
+from ._helpers import (
+    assert_server_span_attrs,
+    make_fastapi_http_exception,
+    make_httpx_status_error,
+)
+
+
+def _real_user_api_key_dict(parent_span):
+    return UserAPIKeyAuth(
+        api_key="sk-test",
+        team_id="team-lit-3193",
+        team_alias="lit-3193-team",
+        parent_otel_span=parent_span,
+    )
+
+
+def _proxy_logging():
+    return ProxyLogging(user_api_key_cache=UserApiKeyCache(DualCache()))
+
+
+def _drive_passthrough_failure(*, exception, user_api_key_dict):
+    asyncio.run(
+        _proxy_logging().post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict,
+            original_exception=exception,
+            request_data={},
+        )
+    )
+
+
+VERTEX_PATH = "/vertex_ai/v1/projects/p/locations/us-central1/publishers/google/models/gemini-1.5-pro:generateContent"
+
+
+@pytest.mark.parametrize(
+    "exception, expected_status",
+    [
+        (make_fastapi_http_exception(401, "no proxy key"), 401),
+        (make_fastapi_http_exception(400, "bad request"), 400),
+        (make_fastapi_http_exception(403, "upstream forbidden"), 403),
+        (make_fastapi_http_exception(404, "upstream not found"), 404),
+        (make_fastapi_http_exception(429, "upstream rate limit"), 429),
+        (make_httpx_status_error(500, "upstream blew up"), 500),
+        (make_httpx_status_error(502, "bad gateway"), 502),
+        (make_httpx_status_error(503, "service unavailable"), 503),
+        (make_fastapi_http_exception(502, "wrapped 502"), 502),
+    ],
+    ids=[
+        "401-litellm-auth",
+        "400-upstream",
+        "403-upstream",
+        "404-upstream",
+        "429-upstream",
+        "500-upstream-httpx",
+        "502-upstream-httpx",
+        "503-upstream-httpx",
+        "502-wrapped",
+    ],
+)
+def test_vertex_passthrough_failure_stamps_server_span(
+    exception,
+    expected_status,
+    server_span_factory,
+    otel_with_exporter,
+    register_otel_callback,
+):
+    _otel, exporter = otel_with_exporter
+    server_span = server_span_factory(
+        VERTEX_PATH, http_route="/vertex_ai/{endpoint:path}"
+    )
+    uakd = _real_user_api_key_dict(server_span)
+
+    _drive_passthrough_failure(exception=exception, user_api_key_dict=uakd)
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=expected_status,
+        expected_url_path=VERTEX_PATH,
+        expected_http_route="/vertex_ai/{endpoint:path}",
+        where=f"vertex passthrough {expected_status}",
+    )
+
+
+SMOKE_PASSTHROUGHS = [
+    ("/bedrock/model/anthropic.claude-v2/invoke", "/bedrock/{endpoint:path}"),
+    ("/anthropic/v1/messages", "/anthropic/{endpoint:path}"),
+    ("/openai/v1/chat/completions", "/openai/{endpoint:path}"),
+    ("/gemini/v1beta/models/gemini-pro:generateContent", "/gemini/{endpoint:path}"),
+    ("/cohere/v1/chat", "/cohere/{endpoint:path}"),
+    ("/azure/openai/deployments/gpt4/chat/completions", "/azure/{endpoint:path}"),
+]
+
+
+@pytest.mark.parametrize("path,http_route", SMOKE_PASSTHROUGHS)
+@pytest.mark.parametrize(
+    "exception, expected_status",
+    [
+        (make_fastapi_http_exception(400, "upstream bad request"), 400),
+        (make_httpx_status_error(502, "upstream"), 502),
+    ],
+    ids=["400", "502"],
+)
+def test_passthrough_failure_stamps_server_span(
+    path,
+    http_route,
+    exception,
+    expected_status,
+    server_span_factory,
+    otel_with_exporter,
+    register_otel_callback,
+):
+    _otel, exporter = otel_with_exporter
+    server_span = server_span_factory(path, http_route=http_route)
+    uakd = _real_user_api_key_dict(server_span)
+
+    _drive_passthrough_failure(exception=exception, user_api_key_dict=uakd)
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=expected_status,
+        expected_url_path=path,
+        expected_http_route=http_route,
+        where=f"{path} {expected_status}",
+    )
diff --git a/tests/test_litellm/integrations/open_telemetry/test_otel_unified_endpoints.py b/tests/test_litellm/integrations/open_telemetry/test_otel_unified_endpoints.py
new file mode 100644
index 00000000000..0279c22154e
--- /dev/null
+++ b/tests/test_litellm/integrations/open_telemetry/test_otel_unified_endpoints.py
@@ -0,0 +1,223 @@
+"""LIT-3193 — unified inference endpoints. Drives _handle_llm_api_exception
+to assert SERVER-span attrs (status, url.path, http.route, duration)."""
+
+import asyncio
+
+import pytest
+from fastapi import HTTPException
+
+from opentelemetry.trace import Status, StatusCode
+
+import litellm
+from litellm.caching.dual_cache import DualCache
+from litellm.proxy._types import ProxyException, UserAPIKeyAuth
+from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
+from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache
+from litellm.proxy.utils import ProxyLogging
+
+from ._helpers import (
+    HttpStatusException,
+    assert_server_span_attrs,
+    make_fastapi_http_exception,
+    make_httpx_status_error,
+)
+
+
+def _real_user_api_key_dict(parent_span):
+    return UserAPIKeyAuth(
+        api_key="sk-test",
+        team_id="team-lit-3193",
+        team_alias="lit-3193-team",
+        parent_otel_span=parent_span,
+    )
+
+
+def _proxy_logging():
+    return ProxyLogging(user_api_key_cache=UserApiKeyCache(DualCache()))
+
+
+def _drive_unified_failure(
+    *,
+    exception,
+    server_span,
+    user_api_key_dict,
+):
+    proc = ProxyBaseLLMRequestProcessing(data={})
+    try:
+        asyncio.run(
+            proc._handle_llm_api_exception(
+                e=exception,
+                user_api_key_dict=user_api_key_dict,
+                proxy_logging_obj=_proxy_logging(),
+            )
+        )
+    except (ProxyException, HTTPException):
+        pass
+
+
+CHAT_PATH = "/v1/chat/completions"
+
+
+@pytest.mark.parametrize(
+    "exception, expected_status",
+    [
+        (make_fastapi_http_exception(400, "bad request"), 400),
+        (make_fastapi_http_exception(401, "no key"), 401),
+        (make_fastapi_http_exception(403, "no model access"), 403),
+        (make_fastapi_http_exception(404, "model not in router"), 404),
+        (make_fastapi_http_exception(422, "validation"), 422),
+        (make_fastapi_http_exception(429, "rate limit"), 429),
+        (HttpStatusException(500, "uncaught"), 500),
+        (make_httpx_status_error(502, "upstream blew up"), 502),
+        (make_httpx_status_error(503, "upstream down"), 503),
+        (make_httpx_status_error(504, "upstream timeout"), 504),
+    ],
+    ids=[
+        "400-bad-request",
+        "401-no-key",
+        "403-no-model-access",
+        "404-model-not-found",
+        "422-validation",
+        "429-rate-limit",
+        "500-uncaught",
+        "502-upstream",
+        "503-upstream",
+        "504-upstream-timeout",
+    ],
+)
+def test_chat_completions_failure_stamps_server_span(
+    exception,
+    expected_status,
+    server_span_factory,
+    user_api_key_dict_factory,
+    otel_with_exporter,
+    register_otel_callback,
+):
+    _otel, exporter = otel_with_exporter
+    server_span = server_span_factory(CHAT_PATH)
+    uakd = _real_user_api_key_dict(server_span)
+
+    _drive_unified_failure(
+        exception=exception, server_span=server_span, user_api_key_dict=uakd
+    )
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=expected_status,
+        expected_url_path=CHAT_PATH,
+        where=f"chat/completions {expected_status}",
+    )
+
+
+def test_chat_completions_success_path_stamps_200(
+    otel_with_exporter, server_span_factory
+):
+    otel, exporter = otel_with_exporter
+    server_span = server_span_factory(CHAT_PATH)
+    _real_user_api_key_dict(server_span)
+
+    otel.set_response_status_code_attribute(server_span, 200)
+    otel.set_preprocessing_duration_attribute(server_span, {})
+    server_span.set_status(Status(StatusCode.OK))
+    server_span.end()
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=200,
+        expected_url_path=CHAT_PATH,
+        where="chat/completions 200",
+    )
+
+
+# /v1/responses ends the proxy span before async_post_call_success_hook fires,
+# so the 200 stamp must happen at span close (here), not in the hook.
+@pytest.mark.parametrize(
+    "path", ["/v1/chat/completions", "/v1/messages", "/v1/responses"]
+)
+def test_end_proxy_span_from_kwargs_stamps_200(
+    path, otel_with_exporter, server_span_factory
+):
+    from datetime import datetime
+
+    otel, exporter = otel_with_exporter
+    server_span = server_span_factory(path)
+    kwargs = {"litellm_params": {"metadata": {"litellm_parent_otel_span": server_span}}}
+    otel._end_proxy_span_from_kwargs(kwargs, datetime.now())
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=200,
+        expected_url_path=path,
+        where=f"{path} _end_proxy_span_from_kwargs",
+    )
+
+
+# Bare TypeError has no .code/.status_code, so error_information.error_code is
+# empty and _record_exception_on_span skips the stamp — must default to 500.
+def test_async_post_call_failure_hook_defaults_to_500(
+    otel_with_exporter, server_span_factory
+):
+    otel, exporter = otel_with_exporter
+    server_span = server_span_factory("/v1/responses")
+    uakd = _real_user_api_key_dict(server_span)
+
+    asyncio.run(
+        otel.async_post_call_failure_hook(
+            request_data={},
+            original_exception=TypeError("missing required argument"),
+            user_api_key_dict=uakd,
+        )
+    )
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=500,
+        expected_url_path="/v1/responses",
+        where="async_post_call_failure_hook (TypeError) defaults to 500",
+    )
+
+
+SMOKE_ENDPOINTS = [
+    "/v1/embeddings",
+    "/v1/completions",
+    "/v1/images/generations",
+    "/v1/audio/speech",
+    "/v1/audio/transcriptions",
+    "/v1/moderations",
+    "/v1/rerank",
+    "/v1/responses",
+    "/v1/messages",
+]
+
+
+@pytest.mark.parametrize("path", SMOKE_ENDPOINTS)
+@pytest.mark.parametrize(
+    "exception, expected_status",
+    [
+        (make_fastapi_http_exception(401, "no key"), 401),
+        (make_httpx_status_error(502, "upstream"), 502),
+    ],
+    ids=["401", "502"],
+)
+def test_unified_endpoint_failure_stamps_server_span(
+    path,
+    exception,
+    expected_status,
+    server_span_factory,
+    otel_with_exporter,
+    register_otel_callback,
+):
+    _otel, exporter = otel_with_exporter
+    server_span = server_span_factory(path)
+    uakd = _real_user_api_key_dict(server_span)
+
+    _drive_unified_failure(
+        exception=exception, server_span=server_span, user_api_key_dict=uakd
+    )
+
+    assert_server_span_attrs(
+        exporter,
+        expected_status=expected_status,
+        expected_url_path=path,
+        where=f"{path} {expected_status}",
+    )
diff --git a/tests/test_litellm/integrations/rubrik_test_helpers.py b/tests/test_litellm/integrations/rubrik_test_helpers.py
new file mode 100644
index 00000000000..1bdb8cb247b
--- /dev/null
+++ b/tests/test_litellm/integrations/rubrik_test_helpers.py
@@ -0,0 +1,23 @@
+"""Shared helpers for Rubrik plugin tests."""
+
+from typing import Any, Dict
+
+from litellm.types.utils import GenericGuardrailAPIInputs
+
+
+def make_tool_call_dict(
+    tc_id: str, name: str, arguments: str = "{}"
+) -> Dict[str, Any]:
+    """Create a tool call dict matching the ChatCompletionMessageToolCall schema."""
+    return {
+        "id": tc_id,
+        "type": "function",
+        "function": {"name": name, "arguments": arguments},
+    }
+
+
+def make_inputs_with_tools(
+    tool_calls: list, texts: list | None = None
+) -> GenericGuardrailAPIInputs:
+    """Create GenericGuardrailAPIInputs with tool_calls."""
+    return GenericGuardrailAPIInputs(texts=texts or [], tool_calls=tool_calls)
diff --git a/tests/test_litellm/integrations/test_opentelemetry.py b/tests/test_litellm/integrations/test_opentelemetry.py
index c754ea5251c..a6dac545ab3 100644
--- a/tests/test_litellm/integrations/test_opentelemetry.py
+++ b/tests/test_litellm/integrations/test_opentelemetry.py
@@ -66,7 +66,7 @@ class TestOpenTelemetryGuardrails(unittest.TestCase):
         mock_span.set_attribute.assert_any_call("guardrail_name", "test_guardrail")
         mock_span.set_attribute.assert_any_call("guardrail_mode", "input")
         mock_span.set_attribute.assert_any_call(
-            "guardrail_response", "filtered_content"
+            "guardrail_response", safe_dumps("filtered_content")
         )
         mock_span.set_attribute.assert_any_call(
             "masked_entity_count", safe_dumps({"CREDIT_CARD": 2})
@@ -87,6 +87,208 @@ class TestOpenTelemetryGuardrails(unittest.TestCase):
         # Verify that start_span was never called
         otel.tracer.start_span.assert_not_called()
 
+    @patch("litellm.integrations.opentelemetry.datetime")
+    def test_guardrail_response_dict_is_json_serialized(self, mock_datetime):
+        """Dict guardrail_response (e.g. OpenAI moderation result) must reach
+        the span as a JSON string so downstream pipelines can parse it for
+        metric extraction — this is the bug the PR fixes."""
+        otel = OpenTelemetry()
+        otel.tracer = MagicMock()
+        mock_span = MagicMock()
+        otel.tracer.start_span.return_value = mock_span
+
+        moderation_payload = {
+            "id": "modr-7740",
+            "model": "omni-moderation-latest",
+            "results": [{"categories": {"harassment": False}}],
+        }
+        guardrail_info = {
+            "guardrail_name": "test_guardrail",
+            "guardrail_mode": "input",
+            "guardrail_response": moderation_payload,
+            "start_time": 1609459200.0,
+            "end_time": 1609459201.0,
+        }
+        kwargs = {
+            "standard_logging_object": {"guardrail_information": [guardrail_info]}
+        }
+
+        otel._create_guardrail_span(kwargs=kwargs, context=None)
+
+        mock_span.set_attribute.assert_any_call(
+            "guardrail_response", safe_dumps(moderation_payload)
+        )
+
+    @patch("litellm.integrations.opentelemetry.datetime")
+    def test_guardrail_response_none_is_skipped(self, mock_datetime):
+        """When guardrail_response is None, the attribute must not be set —
+        guards against round-tripping ``"null"`` into traces."""
+        otel = OpenTelemetry()
+        otel.tracer = MagicMock()
+        mock_span = MagicMock()
+        otel.tracer.start_span.return_value = mock_span
+
+        guardrail_info = {
+            "guardrail_name": "test_guardrail",
+            "guardrail_mode": "input",
+            "guardrail_response": None,
+            "start_time": 1609459200.0,
+            "end_time": 1609459201.0,
+        }
+        kwargs = {
+            "standard_logging_object": {"guardrail_information": [guardrail_info]}
+        }
+
+        otel._create_guardrail_span(kwargs=kwargs, context=None)
+
+        attribute_keys = [
+            call.args[0] for call in mock_span.set_attribute.call_args_list
+        ]
+        self.assertNotIn("guardrail_response", attribute_keys)
+
+
+class TestOpenTelemetryTeamAttributesOnChildSpans(unittest.TestCase):
+    """team_id / team_alias must land on every child span of a
+    litellm_request trace, not only the root litellm_request span."""
+
+    def _slo_metadata(self):
+        return {
+            "user_api_key_team_id": "team-123",
+            "user_api_key_team_alias": "my-team",
+        }
+
+    @patch("litellm.integrations.opentelemetry.datetime")
+    def test_guardrail_span_has_team_attributes(self, mock_datetime):
+        otel = OpenTelemetry()
+        otel.tracer = MagicMock()
+        mock_span = MagicMock()
+        otel.tracer.start_span.return_value = mock_span
+
+        guardrail_info = {
+            "guardrail_name": "test_guardrail",
+            "guardrail_mode": "input",
+            "guardrail_response": "filtered_content",
+            "start_time": 1609459200.0,
+            "end_time": 1609459201.0,
+        }
+        kwargs = {
+            "standard_logging_object": {
+                "guardrail_information": [guardrail_info],
+                "metadata": self._slo_metadata(),
+            }
+        }
+
+        otel._create_guardrail_span(kwargs=kwargs, context=None)
+
+        mock_span.set_attribute.assert_any_call(
+            "metadata.user_api_key_team_id", "team-123"
+        )
+        mock_span.set_attribute.assert_any_call(
+            "metadata.user_api_key_team_alias", "my-team"
+        )
+
+    @patch.dict(os.environ, {"OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT": ""})
+    @patch("litellm.turn_off_message_logging", False)
+    def test_raw_request_span_has_team_attributes(self):
+        otel = OpenTelemetry()
+        otel.message_logging = True
+
+        mock_tracer = MagicMock()
+        mock_span = MagicMock()
+        mock_tracer.start_span.return_value = mock_span
+        otel.get_tracer_to_use_for_request = MagicMock(return_value=mock_tracer)
+        otel.set_raw_request_attributes = MagicMock()
+        otel._to_ns = MagicMock(return_value=1234567890)
+
+        kwargs = {
+            "litellm_params": {"metadata": {}},
+            "standard_logging_object": {"metadata": self._slo_metadata()},
+        }
+        otel._maybe_log_raw_request(
+            kwargs, {}, datetime.now(), datetime.now(), MagicMock()
+        )
+
+        mock_span.set_attribute.assert_any_call(
+            "metadata.user_api_key_team_id", "team-123"
+        )
+        mock_span.set_attribute.assert_any_call(
+            "metadata.user_api_key_team_alias", "my-team"
+        )
+
+    def test_helper_skips_when_team_values_missing(self):
+        otel = OpenTelemetry()
+        mock_span = MagicMock()
+
+        otel._set_team_attributes_on_span(span=mock_span, team_id=None, team_alias=None)
+
+        mock_span.set_attribute.assert_not_called()
+
+    def test_helper_skips_when_team_values_are_empty_strings(self):
+        """A master-key / team-less request carries user_api_key_team_id=''
+        in metadata. Propagating '' to every span is noise that makes
+        traces look mis-instrumented; treat empty as absent."""
+        otel = OpenTelemetry()
+        mock_span = MagicMock()
+
+        otel._set_team_attributes_on_span(span=mock_span, team_id="", team_alias="")
+
+        mock_span.set_attribute.assert_not_called()
+
+    def test_helper_reads_metadata_from_kwargs(self):
+        otel = OpenTelemetry()
+        mock_span = MagicMock()
+
+        otel._set_team_attributes_from_kwargs(
+            mock_span,
+            {"standard_logging_object": {"metadata": self._slo_metadata()}},
+        )
+
+        mock_span.set_attribute.assert_any_call(
+            "metadata.user_api_key_team_id", "team-123"
+        )
+        mock_span.set_attribute.assert_any_call(
+            "metadata.user_api_key_team_alias", "my-team"
+        )
+
+    def test_helper_handles_missing_standard_logging_object(self):
+        otel = OpenTelemetry()
+        mock_span = MagicMock()
+
+        otel._set_team_attributes_from_kwargs(mock_span, {})
+
+        mock_span.set_attribute.assert_not_called()
+
+    def test_failure_hook_exception_span_has_team_attributes(self):
+        exporter = InMemorySpanExporter()
+        provider = TracerProvider()
+        provider.add_span_processor(SimpleSpanProcessor(exporter))
+        tracer = provider.get_tracer(__name__)
+
+        otel = OpenTelemetry()
+        otel.tracer = tracer
+        server_span = tracer.start_span("Received Proxy Server Request")
+
+        user_api_key_dict = MagicMock()
+        user_api_key_dict.parent_otel_span = server_span
+        user_api_key_dict.team_id = "team-123"
+        user_api_key_dict.team_alias = "my-team"
+
+        asyncio.run(
+            otel.async_post_call_failure_hook(
+                request_data={},
+                original_exception=ValueError("boom"),
+                user_api_key_dict=user_api_key_dict,
+                traceback_str="trace",
+            )
+        )
+
+        finished = {s.name: s for s in exporter.get_finished_spans()}
+        exception_span = finished["Failed Proxy Server Request"]
+        assert exception_span.attributes["metadata.user_api_key_team_id"] == "team-123"
+        assert (
+            exception_span.attributes["metadata.user_api_key_team_alias"] == "my-team"
+        )
+
 
 class TestOpenTelemetryCostBreakdown(unittest.TestCase):
     def test_cost_breakdown_emitted_to_otel_span(self):
@@ -1026,7 +1228,7 @@ class TestOpenTelemetry(unittest.TestCase):
         mock_span.set_attribute.assert_any_call("guardrail_name", "test_guardrail")
         mock_span.set_attribute.assert_any_call("guardrail_mode", "input")
         mock_span.set_attribute.assert_any_call(
-            "guardrail_response", "filtered_content"
+            "guardrail_response", safe_dumps("filtered_content")
         )
         mock_span.set_attribute.assert_any_call(
             "masked_entity_count", safe_dumps({"CREDIT_CARD": 2})
diff --git a/tests/test_litellm/integrations/test_otel_guardrail_violation_spans.py b/tests/test_litellm/integrations/test_otel_guardrail_violation_spans.py
new file mode 100644
index 00000000000..ace9399cf53
--- /dev/null
+++ b/tests/test_litellm/integrations/test_otel_guardrail_violation_spans.py
@@ -0,0 +1,641 @@
+"""
+Tests for guardrail OTEL spans on violation.
+
+Two distinct gaps surface together when a pre-call guardrail blocks the
+request before it reaches the LLM provider:
+
+  1. ``async_post_call_failure_hook`` (the OTEL hook that actually runs on
+     the proxy failure path) only stamps attributes on the proxy parent
+     span. It never creates the child ``guardrail`` span, even though
+     ``request_data["metadata"]["standard_logging_guardrail_information"]``
+     is populated by the time the hook runs.
+
+  2. ``_create_guardrail_span`` records ``guardrail_name`` / ``guardrail_mode``
+     / ``guardrail_response`` but does not surface ``guardrail_status``
+     (success / guardrail_intervened / guardrail_failed_to_respond /
+     not_run) or the violation categories (Bedrock topic policy names,
+     content filter types, etc.) as queryable span attributes — the data
+     is buried inside the serialised ``guardrail_response`` blob and cannot
+     be filtered on in the trace backend.
+
+The tests below use real OTEL SDK objects (TracerProvider +
+InMemorySpanExporter + a real BatchSpanProcessor-equivalent) and the
+real ``OpenTelemetry`` integration. No monkey patching of the integration
+under test — only the OTEL exporter is in-memory.
+"""
+
+import os
+import sys
+import time
+import unittest
+from datetime import datetime, timedelta, timezone
+
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor
+from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
+from opentelemetry.trace import StatusCode
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm.integrations.opentelemetry import (
+    LITELLM_REQUEST_SPAN_NAME,
+    OpenTelemetry,
+)
+from litellm.proxy._types import UserAPIKeyAuth
+
+
+GUARDRAIL_SPAN_NAME = "guardrail"
+PROXY_SPAN_NAME = "Received Proxy Server Request"
+
+
+def _bedrock_block_response():
+    """Realistic Bedrock ApplyGuardrail response when a topic policy fires.
+
+    Mirrors the shape in ``litellm/types/proxy/guardrails/guardrail_hooks/
+    bedrock_guardrails.py`` so the violation-category extraction can be
+    tested against the exact payload Bedrock returns.
+    """
+    return {
+        "action": "GUARDRAIL_INTERVENED",
+        "assessments": [
+            {
+                "topicPolicy": {
+                    "topics": [
+                        {
+                            "name": "Fiduciary Advice",
+                            "type": "DENY",
+                            "action": "BLOCKED",
+                        }
+                    ]
+                },
+                "contentPolicy": {
+                    "filters": [
+                        {
+                            "type": "VIOLENCE",
+                            "confidence": "HIGH",
+                            "action": "BLOCKED",
+                        }
+                    ]
+                },
+                "wordPolicy": {
+                    "customWords": [{"match": "secret-codeword", "action": "BLOCKED"}],
+                    "managedWordLists": [
+                        {"match": "fuck", "type": "PROFANITY", "action": "BLOCKED"}
+                    ],
+                },
+            }
+        ],
+        "outputs": [{"text": "Sorry, the model cannot respond to this request."}],
+    }
+
+
+def _slg_entry(
+    guardrail_status,
+    guardrail_response,
+    *,
+    name="bedrock-test",
+    mode="pre_call",
+    provider="bedrock",
+    start=1.0,
+    end=2.0,
+    violation_categories=None,
+    guardrail_action=None,
+):
+    """Build a StandardLoggingGuardrailInformation entry the way
+    ``add_standard_logging_guardrail_information_to_request_data`` does."""
+    entry = {
+        "guardrail_name": name,
+        "guardrail_provider": provider,
+        "guardrail_mode": mode,
+        "guardrail_response": guardrail_response,
+        "guardrail_status": guardrail_status,
+        "start_time": start,
+        "end_time": end,
+        "duration": end - start,
+    }
+    if violation_categories is not None:
+        entry["violation_categories"] = violation_categories
+    if guardrail_action is not None:
+        entry["guardrail_action"] = guardrail_action
+    return entry
+
+
+def _kwargs_with_guardrail(
+    *,
+    entries,
+    parent_span=None,
+    include_exception=False,
+):
+    """Build the kwargs / model_call_details shape that the OTEL integration
+    consumes. ``litellm_params.metadata`` is the SAME dict that the proxy's
+    ``request_data["metadata"]`` becomes after ``update_environment_variables``,
+    so ``_otel_internal`` dedupe state lives there too."""
+    metadata = {"standard_logging_guardrail_information": list(entries)}
+    if parent_span is not None:
+        metadata["litellm_parent_otel_span"] = parent_span
+    kwargs = {
+        "model": "gpt-4",
+        "messages": [{"role": "user", "content": "Hello"}],
+        "optional_params": {},
+        "litellm_params": {
+            "custom_llm_provider": "openai",
+            "metadata": metadata,
+        },
+        "standard_logging_object": {
+            "id": "test-call-id",
+            "call_type": "completion",
+            "metadata": metadata,
+            "hidden_params": {},
+            "guardrail_information": list(entries),
+        },
+    }
+    if include_exception:
+        kwargs["exception"] = Exception("guardrail blocked the request")
+    return kwargs
+
+
+def _make_otel():
+    """Spin up a real OTEL pipeline backed by an in-memory exporter."""
+    exporter = InMemorySpanExporter()
+    provider = TracerProvider()
+    provider.add_span_processor(SimpleSpanProcessor(exporter))
+    otel = OpenTelemetry(tracer_provider=provider)
+    otel.tracer = provider.get_tracer(__name__)
+    return otel, provider, exporter
+
+
+def _run(coro):
+    """Run a coroutine on a fresh event loop and close it — prevents the
+    "unclosed event loop" / ResourceWarning that you get from
+    asyncio.new_event_loop().run_until_complete() with no cleanup."""
+    import asyncio
+
+    loop = asyncio.new_event_loop()
+    try:
+        return loop.run_until_complete(coro)
+    finally:
+        loop.close()
+
+
+def _attr(span, key):
+    return (span.attributes or {}).get(key)
+
+
+class TestGuardrailSpanOnViolation(unittest.TestCase):
+    """Bug 1: when a pre-call guardrail blocks, the guardrail span and the
+    litellm_request span must both appear with the correct status."""
+
+    def test_handle_failure_creates_litellm_request_and_guardrail_spans(self):
+        """Driving ``_handle_failure`` with a populated
+        ``standard_logging_object['guardrail_information']`` entry must
+        emit both spans, parented correctly, with ERROR on the parent."""
+        otel, _, exporter = _make_otel()
+
+        kwargs = _kwargs_with_guardrail(
+            entries=[
+                _slg_entry("guardrail_intervened", _bedrock_block_response()),
+            ],
+            include_exception=True,
+        )
+
+        start = datetime.now(timezone.utc)
+        end = start + timedelta(milliseconds=20)
+        otel._handle_failure(kwargs, response_obj=None, start_time=start, end_time=end)
+
+        spans = exporter.get_finished_spans()
+        litellm_spans = [s for s in spans if s.name == LITELLM_REQUEST_SPAN_NAME]
+        guardrail_spans = [s for s in spans if s.name == GUARDRAIL_SPAN_NAME]
+
+        self.assertEqual(
+            len(litellm_spans),
+            1,
+            "Expected exactly one litellm_request span on guardrail block",
+        )
+        self.assertEqual(litellm_spans[0].status.status_code, StatusCode.ERROR)
+
+        self.assertEqual(
+            len(guardrail_spans),
+            1,
+            "Expected exactly one guardrail span on guardrail block",
+        )
+
+        # Guardrail span must be a child of the litellm_request span
+        self.assertIsNotNone(
+            guardrail_spans[0].parent,
+            "Guardrail span must be parented (not a root span)",
+        )
+        self.assertEqual(
+            guardrail_spans[0].parent.span_id,
+            litellm_spans[0].context.span_id,
+        )
+
+    def test_async_post_call_failure_hook_emits_guardrail_span(self):
+        """The production failure path on the proxy calls
+        ``async_post_call_failure_hook`` with the (still-populated)
+        ``request_data``. The hook currently only stamps attrs on the proxy
+        span; it must also emit the guardrail span so the violation is
+        visible in the trace."""
+        otel, provider, exporter = _make_otel()
+        parent_span = provider.get_tracer(__name__).start_span(PROXY_SPAN_NAME)
+
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-test",
+            parent_otel_span=parent_span,
+            request_route="/chat/completions",
+        )
+
+        request_data = {
+            "model": "gpt-4",
+            "messages": [{"role": "user", "content": "Hello"}],
+            "metadata": {
+                "standard_logging_guardrail_information": [
+                    _slg_entry("guardrail_intervened", _bedrock_block_response())
+                ],
+            },
+        }
+
+        _run(
+            otel.async_post_call_failure_hook(
+                request_data=request_data,
+                original_exception=Exception("guardrail blocked"),
+                user_api_key_dict=user_api_key_dict,
+            )
+        )
+
+        spans = exporter.get_finished_spans()
+        guardrail_spans = [s for s in spans if s.name == GUARDRAIL_SPAN_NAME]
+        self.assertEqual(
+            len(guardrail_spans),
+            1,
+            "async_post_call_failure_hook must emit the guardrail span when "
+            "request_data['metadata'] carries standard_logging_guardrail_information",
+        )
+
+        # The guardrail span must be parented to the proxy request span so
+        # backends correlate it with the rest of the trace.
+        self.assertIsNotNone(guardrail_spans[0].parent)
+        self.assertEqual(
+            guardrail_spans[0].parent.span_id,
+            parent_span.context.span_id,
+        )
+
+    def test_handle_failure_and_post_call_failure_hook_dedupe(self):
+        """When _handle_failure and async_post_call_failure_hook BOTH fire
+        for the same request (the production flow on a guardrail block),
+        exactly one guardrail span must be emitted. The dedupe relies on
+        request_data['metadata'] and kwargs['litellm_params']['metadata']
+        referencing the SAME dict so _emit_once sees its earlier marker."""
+        otel, provider, exporter = _make_otel()
+        parent_span = provider.get_tracer(__name__).start_span(PROXY_SPAN_NAME)
+
+        # Shared metadata dict — same identity, mirroring how
+        # update_environment_variables wires them in the proxy.
+        shared_metadata = {
+            "standard_logging_guardrail_information": [
+                _slg_entry(
+                    "guardrail_intervened",
+                    _bedrock_block_response(),
+                    violation_categories=["Fiduciary Advice"],
+                )
+            ],
+        }
+
+        kwargs = {
+            "model": "gpt-4",
+            "messages": [{"role": "user", "content": "Hello"}],
+            "optional_params": {},
+            "litellm_params": {
+                "custom_llm_provider": "openai",
+                "metadata": shared_metadata,
+            },
+            "standard_logging_object": {
+                "id": "test-call-id",
+                "call_type": "completion",
+                "metadata": shared_metadata,
+                "hidden_params": {},
+                "guardrail_information": shared_metadata[
+                    "standard_logging_guardrail_information"
+                ],
+            },
+            "exception": Exception("guardrail blocked"),
+        }
+        request_data = {
+            "model": "gpt-4",
+            "messages": [{"role": "user", "content": "Hello"}],
+            "metadata": shared_metadata,
+        }
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-test",
+            parent_otel_span=parent_span,
+            request_route="/chat/completions",
+        )
+
+        start = datetime.now(timezone.utc)
+        end = start + timedelta(milliseconds=20)
+        otel._handle_failure(kwargs, response_obj=None, start_time=start, end_time=end)
+        _run(
+            otel.async_post_call_failure_hook(
+                request_data=request_data,
+                original_exception=Exception("guardrail blocked"),
+                user_api_key_dict=user_api_key_dict,
+            )
+        )
+
+        guardrail_spans = [
+            s for s in exporter.get_finished_spans() if s.name == GUARDRAIL_SPAN_NAME
+        ]
+        self.assertEqual(
+            len(guardrail_spans),
+            1,
+            "Dedupe must collapse the two emit calls into one span when the "
+            "metadata dict identity is shared between kwargs and request_data",
+        )
+
+
+class TestGuardrailSpanAttributesOnViolation(unittest.TestCase):
+    """Bug 2: the guardrail span must surface the violation status and
+    violation categories as queryable span attributes, not bury them inside
+    ``guardrail_response`` (which is logged as a single serialised blob)."""
+
+    def _emit_and_get_guardrail_span(self, entry):
+        otel, _, exporter = _make_otel()
+        kwargs = _kwargs_with_guardrail(entries=[entry])
+        otel._create_guardrail_span(kwargs=kwargs, context=None)
+
+        guardrail_spans = [
+            s for s in exporter.get_finished_spans() if s.name == GUARDRAIL_SPAN_NAME
+        ]
+        self.assertEqual(len(guardrail_spans), 1)
+        return guardrail_spans[0]
+
+    def test_status_attribute_present_for_intervened(self):
+        entry = _slg_entry("guardrail_intervened", _bedrock_block_response())
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertEqual(
+            _attr(span, "guardrail_status"),
+            "guardrail_intervened",
+            "guardrail_status must be exposed as a top-level span attribute",
+        )
+
+    def test_status_attribute_present_for_success(self):
+        entry = _slg_entry(
+            "success",
+            {"action": "NONE", "assessments": []},
+        )
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertEqual(_attr(span, "guardrail_status"), "success")
+
+    def test_status_attribute_present_for_failed_to_respond(self):
+        entry = _slg_entry(
+            "guardrail_failed_to_respond",
+            {"error": "endpoint unreachable"},
+        )
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertEqual(_attr(span, "guardrail_status"), "guardrail_failed_to_respond")
+
+    def test_violation_categories_surfaced_when_provider_populates_them(self):
+        """The provider hook (e.g. Bedrock) extracts violation categories
+        from the raw response BEFORE redaction and stamps them onto the
+        StandardLoggingGuardrailInformation entry. OTEL must surface that
+        list as a queryable span attribute so dashboards can group by
+        violation type without parsing the redacted guardrail_response."""
+        entry = _slg_entry(
+            "guardrail_intervened",
+            _bedrock_block_response(),
+            violation_categories=["Fiduciary Advice", "VIOLENCE", "PROFANITY"],
+        )
+        span = self._emit_and_get_guardrail_span(entry)
+
+        categories = _attr(span, "guardrail_violation_categories")
+        self.assertIsNotNone(
+            categories,
+            "guardrail_violation_categories must be set when the entry "
+            "carries violation_categories",
+        )
+        # Serialised as JSON to keep set_attribute typing simple.
+        as_str = categories if isinstance(categories, str) else repr(list(categories))
+        self.assertIn("Fiduciary Advice", as_str)
+        self.assertIn("VIOLENCE", as_str)
+        self.assertIn("PROFANITY", as_str)
+
+    def test_no_violation_categories_when_field_absent(self):
+        """When the provider didn't populate violation_categories (success
+        path, or provider didn't extract them), don't pollute the trace
+        with an empty attribute."""
+        entry = _slg_entry("success", {"action": "NONE", "assessments": []})
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertIsNone(_attr(span, "guardrail_violation_categories"))
+
+    def test_no_violation_categories_when_field_is_empty(self):
+        """Empty list must not produce a span attribute either."""
+        entry = _slg_entry(
+            "guardrail_intervened",
+            _bedrock_block_response(),
+            violation_categories=[],
+        )
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertIsNone(_attr(span, "guardrail_violation_categories"))
+
+    def test_guardrail_action_surfaced_when_provider_populates_it(self):
+        """The provider hook (e.g. Bedrock) writes its raw top-level
+        ``action`` string onto StandardLoggingGuardrailInformation as
+        ``guardrail_action``. OTEL must expose it as a queryable span
+        attribute so dashboards can pivot on the raw provider verdict
+        (Bedrock ``GUARDRAIL_INTERVENED`` / ``NONE``) without parsing
+        the redacted guardrail_response blob."""
+        entry = _slg_entry(
+            "guardrail_intervened",
+            _bedrock_block_response(),
+            guardrail_action="GUARDRAIL_INTERVENED",
+        )
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertEqual(
+            _attr(span, "guardrail_action"),
+            "GUARDRAIL_INTERVENED",
+            "guardrail_action must be exposed as a top-level span attribute",
+        )
+
+    def test_guardrail_action_surfaced_for_allowed_request(self):
+        """Even on the success path, the provider's raw action (e.g.
+        Bedrock ``NONE``) should be queryable so dashboards can group
+        allowed-vs-blocked counts off the same attribute."""
+        entry = _slg_entry(
+            "success",
+            {"action": "NONE", "assessments": []},
+            guardrail_action="NONE",
+        )
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertEqual(_attr(span, "guardrail_action"), "NONE")
+
+    def test_no_guardrail_action_when_field_absent(self):
+        """If the provider didn't populate the field (older payloads,
+        non-Bedrock providers without a top-level action), don't emit
+        an empty attribute."""
+        entry = _slg_entry("success", {"action": "NONE", "assessments": []})
+        span = self._emit_and_get_guardrail_span(entry)
+        self.assertIsNone(_attr(span, "guardrail_action"))
+
+
+class TestMultipleGuardrailsOneBlocks(unittest.TestCase):
+    """When several guardrails run sequentially and only the last one
+    intervenes, every guardrail span must appear with its own status —
+    losing the early "allowed" spans would mask which checks ran."""
+
+    def test_all_guardrail_spans_emitted_with_per_entry_status(self):
+        otel, _, exporter = _make_otel()
+
+        entries = [
+            _slg_entry(
+                "success",
+                {"action": "NONE", "assessments": []},
+                name="pii-mask",
+                start=1.0,
+                end=1.5,
+            ),
+            _slg_entry(
+                "success",
+                {"action": "NONE", "assessments": []},
+                name="prompt-injection",
+                start=2.0,
+                end=2.2,
+            ),
+            _slg_entry(
+                "guardrail_intervened",
+                _bedrock_block_response(),
+                name="bedrock-policy",
+                start=3.0,
+                end=3.4,
+            ),
+        ]
+        kwargs = _kwargs_with_guardrail(
+            entries=entries,
+            include_exception=True,
+        )
+
+        start = datetime.now(timezone.utc)
+        end = start + timedelta(milliseconds=50)
+        otel._handle_failure(kwargs, response_obj=None, start_time=start, end_time=end)
+
+        spans = exporter.get_finished_spans()
+        guardrail_spans = sorted(
+            (s for s in spans if s.name == GUARDRAIL_SPAN_NAME),
+            key=lambda s: (s.attributes or {}).get("guardrail_name", ""),
+        )
+        self.assertEqual(
+            len(guardrail_spans),
+            3,
+            "Every guardrail invocation must emit a span — even the ones "
+            "that allowed the request through before the blocker fired",
+        )
+
+        statuses = {
+            _attr(s, "guardrail_name"): _attr(s, "guardrail_status")
+            for s in guardrail_spans
+        }
+        self.assertEqual(statuses["pii-mask"], "success")
+        self.assertEqual(statuses["prompt-injection"], "success")
+        self.assertEqual(statuses["bedrock-policy"], "guardrail_intervened")
+
+
+class TestCustomGuardrailEndToEnd(unittest.TestCase):
+    """End-to-end: a real ``CustomGuardrail`` subclass calls
+    ``add_standard_logging_guardrail_information_to_request_data`` and then
+    raises. We then drive ``_handle_failure`` with the resulting kwargs
+    (matching the shape ``async_failure_handler`` would build) and verify
+    the guardrail span carries the recorded information."""
+
+    def test_real_custom_guardrail_violation_path(self):
+        # Deliberately not importing fastapi here — the real Bedrock guardrail
+        # raises HTTPException, but the OTEL span flow is exception-type
+        # agnostic. Using a plain Exception keeps this test runnable in
+        # SDK-only installs that don't ship fastapi.
+        from litellm.integrations.custom_guardrail import CustomGuardrail
+        from litellm.types.guardrails import GuardrailEventHooks
+
+        class BlockingViolation(Exception):
+            pass
+
+        class BlockingGuardrail(CustomGuardrail):
+            async def async_pre_call_hook(
+                self,
+                user_api_key_dict,
+                cache,
+                data,
+                call_type,
+            ):
+                start_ts = time.time()
+                self.add_standard_logging_guardrail_information_to_request_data(
+                    guardrail_provider="bedrock",
+                    guardrail_json_response=_bedrock_block_response(),
+                    request_data=data,
+                    guardrail_status="guardrail_intervened",
+                    start_time=start_ts,
+                    end_time=start_ts + 0.01,
+                    duration=0.01,
+                    event_type=GuardrailEventHooks.pre_call,
+                    tracing_detail={
+                        "violation_categories": ["Fiduciary Advice", "VIOLENCE"]
+                    },
+                )
+                raise BlockingViolation("violation")
+
+        request_data = {
+            "model": "gpt-4",
+            "messages": [{"role": "user", "content": "Hi"}],
+            "metadata": {},
+        }
+        guardrail = BlockingGuardrail(
+            guardrail_name="blocking-test",
+            event_hook=GuardrailEventHooks.pre_call,
+        )
+
+        with self.assertRaises(BlockingViolation):
+            _run(
+                guardrail.async_pre_call_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"),
+                    cache=None,
+                    data=request_data,
+                    call_type="completion",
+                )
+            )
+
+        slg_info = request_data["metadata"].get(
+            "standard_logging_guardrail_information"
+        )
+        self.assertTrue(
+            slg_info,
+            "Guardrail must have recorded its information to request_data "
+            "BEFORE raising — otherwise the OTEL hook sees nothing",
+        )
+
+        # Now simulate the OTEL failure handler picking up this metadata
+        otel, _, exporter = _make_otel()
+        kwargs = _kwargs_with_guardrail(
+            entries=slg_info,
+            include_exception=True,
+        )
+        start = datetime.now(timezone.utc)
+        end = start + timedelta(milliseconds=15)
+        otel._handle_failure(kwargs, response_obj=None, start_time=start, end_time=end)
+
+        spans = exporter.get_finished_spans()
+        guardrail_spans = [s for s in spans if s.name == GUARDRAIL_SPAN_NAME]
+        self.assertEqual(len(guardrail_spans), 1)
+        self.assertEqual(
+            _attr(guardrail_spans[0], "guardrail_status"),
+            "guardrail_intervened",
+        )
+        self.assertEqual(
+            _attr(guardrail_spans[0], "guardrail_name"),
+            "blocking-test",
+        )
+        # End-to-end: the violation_categories the guardrail passed through
+        # tracing_detail must arrive as a queryable span attribute.
+        categories = _attr(guardrail_spans[0], "guardrail_violation_categories")
+        self.assertIsNotNone(categories)
+        self.assertIn("Fiduciary Advice", str(categories))
+        self.assertIn("VIOLENCE", str(categories))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_litellm/integrations/test_otel_team_attributes_matrix.py b/tests/test_litellm/integrations/test_otel_team_attributes_matrix.py
new file mode 100644
index 00000000000..1ce55fa7a58
--- /dev/null
+++ b/tests/test_litellm/integrations/test_otel_team_attributes_matrix.py
@@ -0,0 +1,285 @@
+"""
+Matrix test: team_id / team_alias must land on EVERY span of a proxy
+request trace, for a representative set of endpoints x HTTP outcomes.
+
+Endpoints
+  - /v1/chat/completions      (OpenAI-format LLM path)
+  - /v1/messages              (Anthropic-format LLM path)
+  - /team/info                (management/admin path)
+
+Outcomes
+  - 2xx  success
+  - 3xx  redirect              (LLM endpoints never 3xx -> N/A; admin too)
+  - 4xx  client error          (auth / validation failure)
+  - 5xx  server error          (upstream / DB failure)
+
+Strategy
+  These assertions exercise the real OpenTelemetry callback the proxy
+  invokes for each path, with a SERVER parent span (as
+  ``user_api_key_auth`` creates) and an in-memory exporter. Each cell
+  drives the path, then asserts team attributes on every span that path
+  actually emits.
+
+  - success path  -> ``log_success_event`` -> litellm_request +
+    raw_gen_ai_request + guardrail child spans.
+  - failure path  -> ``async_post_call_failure_hook`` -> Failed Proxy
+    Server Request exception child span.
+
+  Admin endpoints do not run the LLM success callback, so their only
+  trace surface is the SERVER span (success) or the exception child span
+  (failure) -- the cells below assert exactly that.
+"""
+
+import asyncio
+import os
+import sys
+import unittest
+from datetime import datetime
+from unittest.mock import MagicMock
+
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor
+from opentelemetry.sdk.trace.export.in_memory_span_exporter import (
+    InMemorySpanExporter,
+)
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm.integrations.opentelemetry import (
+    LITELLM_PROXY_REQUEST_SPAN_NAME,
+    OpenTelemetry,
+)
+
+TEAM_ID = "team-123"
+TEAM_ALIAS = "my-team"
+TEAM_ID_ATTR = "metadata.user_api_key_team_id"
+TEAM_ALIAS_ATTR = "metadata.user_api_key_team_alias"
+
+
+def _make_otel():
+    """OTel callback whose every span lands in an in-memory exporter."""
+    exporter = InMemorySpanExporter()
+    provider = TracerProvider()
+    provider.add_span_processor(SimpleSpanProcessor(exporter))
+
+    otel = OpenTelemetry()
+    otel.tracer = provider.get_tracer(__name__)
+    # raw_gen_ai_request sub-span is gated on message logging.
+    otel.message_logging = True
+    return otel, exporter
+
+
+def _server_span(otel):
+    """Mirror the SERVER span user_api_key_auth opens per request."""
+    return otel.create_litellm_proxy_request_started_span(
+        start_time=datetime.now(), headers={}
+    )
+
+
+def _slo(call_type, with_guardrail=False):
+    """standard_logging_object the proxy attaches, carrying team metadata."""
+    md = {
+        "user_api_key_team_id": TEAM_ID,
+        "user_api_key_team_alias": TEAM_ALIAS,
+    }
+    slo = {"metadata": md, "call_type": call_type}
+    if with_guardrail:
+        slo["guardrail_information"] = [
+            {
+                "guardrail_name": "test_guardrail",
+                "guardrail_mode": "input",
+                "guardrail_response": "ok",
+                "start_time": 1609459200.0,
+                "end_time": 1609459201.0,
+            }
+        ]
+    return slo
+
+
+def _success_kwargs(call_type, server_span, with_guardrail=True):
+    """kwargs the success callback receives for an LLM proxy request."""
+    return {
+        "model": "gpt-4.1-mini",
+        "litellm_call_id": "call-abc",
+        "call_type": call_type,
+        "litellm_params": {
+            "metadata": {
+                "litellm_parent_otel_span": server_span,
+                "user_api_key_team_id": TEAM_ID,
+                "user_api_key_team_alias": TEAM_ALIAS,
+            }
+        },
+        "standard_logging_object": _slo(call_type, with_guardrail=with_guardrail),
+        "messages": [{"role": "user", "content": "hi"}],
+    }
+
+
+def _team_user_api_key_dict(server_span):
+    d = MagicMock()
+    d.parent_otel_span = server_span
+    d.team_id = TEAM_ID
+    d.team_alias = TEAM_ALIAS
+    return d
+
+
+def _spans_by_name(exporter):
+    return {s.name: s for s in exporter.get_finished_spans()}
+
+
+def _assert_team_attrs(span, where):
+    assert span.attributes.get(TEAM_ID_ATTR) == TEAM_ID, (
+        f"{where}: missing/blank {TEAM_ID_ATTR} "
+        f"(got {span.attributes.get(TEAM_ID_ATTR)!r})"
+    )
+    assert span.attributes.get(TEAM_ALIAS_ATTR) == TEAM_ALIAS, (
+        f"{where}: missing/blank {TEAM_ALIAS_ATTR} "
+        f"(got {span.attributes.get(TEAM_ALIAS_ATTR)!r})"
+    )
+
+
+class _Boom(Exception):
+    """Upstream/DB style 5xx."""
+
+    status_code = 500
+
+
+class _ClientErr(Exception):
+    """Auth/validation style 4xx."""
+
+    status_code = 401
+
+
+# ---------------------------------------------------------------------------
+# LLM success cells: litellm_request + raw_gen_ai_request + guardrail spans
+# ---------------------------------------------------------------------------
+class TestLLMSuccessCells(unittest.TestCase):
+    def _run_success(self, call_type):
+        otel, exporter = _make_otel()
+        server_span = _server_span(otel)
+        kwargs = _success_kwargs(call_type, server_span)
+        now = datetime.now()
+        otel.log_success_event(kwargs, {"id": "resp-1"}, now, now)
+        return _spans_by_name(exporter)
+
+    def test_chat_completions_2xx(self):
+        spans = self._run_success("completion")
+        for name in (
+            LITELLM_PROXY_REQUEST_SPAN_NAME,
+            "litellm_request",
+            "raw_gen_ai_request",
+            "guardrail",
+        ):
+            assert name in spans, f"chat/completions 2xx: missing span {name}"
+            _assert_team_attrs(spans[name], f"chat/completions 2xx [{name}]")
+
+    def test_v1_messages_2xx(self):
+        spans = self._run_success("anthropic_messages")
+        for name in (
+            LITELLM_PROXY_REQUEST_SPAN_NAME,
+            "litellm_request",
+            "raw_gen_ai_request",
+            "guardrail",
+        ):
+            assert name in spans, f"v1/messages 2xx: missing span {name}"
+            _assert_team_attrs(spans[name], f"v1/messages 2xx [{name}]")
+
+
+# ---------------------------------------------------------------------------
+# LLM failure cells: Failed Proxy Server Request exception child span
+# ---------------------------------------------------------------------------
+class TestLLMFailureCells(unittest.TestCase):
+    def _run_failure(self, exc):
+        """Drive the failure hook, then close the SERVER span (the proxy
+        closes it after the hook in real flow) so both the exception child
+        span and the SERVER root span are asserted."""
+        otel, exporter = _make_otel()
+        server_span = _server_span(otel)
+        asyncio.run(
+            otel.async_post_call_failure_hook(
+                request_data={},
+                original_exception=exc,
+                user_api_key_dict=_team_user_api_key_dict(server_span),
+                traceback_str="tb",
+            )
+        )
+        server_span.end()
+        return _spans_by_name(exporter)
+
+    def _assert_all(self, spans, where):
+        for name in ("Failed Proxy Server Request", LITELLM_PROXY_REQUEST_SPAN_NAME):
+            assert name in spans, f"{where}: missing span {name}"
+            _assert_team_attrs(spans[name], f"{where} [{name}]")
+
+    def test_chat_completions_4xx(self):
+        self._assert_all(
+            self._run_failure(_ClientErr("bad key")), "chat/completions 4xx"
+        )
+
+    def test_chat_completions_5xx(self):
+        self._assert_all(
+            self._run_failure(_Boom("upstream blew up")), "chat/completions 5xx"
+        )
+
+    def test_v1_messages_4xx(self):
+        self._assert_all(
+            self._run_failure(_ClientErr("bad anthropic key")), "v1/messages 4xx"
+        )
+
+    def test_v1_messages_5xx(self):
+        self._assert_all(
+            self._run_failure(_Boom("anthropic upstream timeout")), "v1/messages 5xx"
+        )
+
+
+# ---------------------------------------------------------------------------
+# Admin /team/info cells.
+#   2xx: admin path never runs the LLM success callback -> its only trace
+#        surface is the SERVER span; no child spans are emitted.
+#   3xx: management endpoints do not redirect -> N/A (documented, no run).
+#   4xx/5xx: proxy_logging post_call_failure_hook -> exception child span.
+# ---------------------------------------------------------------------------
+class TestAdminTeamInfoCells(unittest.TestCase):
+    def _run_admin_failure(self, exc):
+        otel, exporter = _make_otel()
+        server_span = _server_span(otel)
+        asyncio.run(
+            otel.async_post_call_failure_hook(
+                request_data={},
+                original_exception=exc,
+                user_api_key_dict=_team_user_api_key_dict(server_span),
+                traceback_str="tb",
+            )
+        )
+        server_span.end()
+        return _spans_by_name(exporter)
+
+    def test_team_info_4xx(self):
+        spans = self._run_admin_failure(_ClientErr("team not found"))
+        for name in ("Failed Proxy Server Request", LITELLM_PROXY_REQUEST_SPAN_NAME):
+            _assert_team_attrs(spans[name], f"/team/info 4xx [{name}]")
+
+    def test_team_info_5xx(self):
+        spans = self._run_admin_failure(_Boom("db connection lost"))
+        for name in ("Failed Proxy Server Request", LITELLM_PROXY_REQUEST_SPAN_NAME):
+            _assert_team_attrs(spans[name], f"/team/info 5xx [{name}]")
+
+    def test_team_info_2xx_only_server_span_no_orphan_children(self):
+        """Admin success path emits no LLM child spans; nothing to stamp
+        beyond the SERVER span. This pins that contract so a future
+        regression that starts emitting child spans here without team
+        attrs is caught."""
+        otel, exporter = _make_otel()
+        server_span = _server_span(otel)
+        server_span.end()
+        spans = _spans_by_name(exporter)
+        assert set(spans) == {
+            LITELLM_PROXY_REQUEST_SPAN_NAME
+        }, f"/team/info 2xx: unexpected child spans {set(spans)}"
+
+    def test_team_info_3xx_not_applicable(self):
+        """Management endpoints return JSON, never a 3xx redirect."""
+        self.skipTest("/team/info has no 3xx redirect path (N/A)")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_litellm/integrations/test_prometheus_token_detail_metrics.py b/tests/test_litellm/integrations/test_prometheus_token_detail_metrics.py
new file mode 100644
index 00000000000..72a4e80717b
--- /dev/null
+++ b/tests/test_litellm/integrations/test_prometheus_token_detail_metrics.py
@@ -0,0 +1,260 @@
+"""
+Unit tests for the per-token-type Prometheus detail metrics added for LIT-3220.
+
+These metrics break out cached, cache-creation, audio and reasoning tokens
+from the Usage object that providers report. They are sparse — only
+incremented when the underlying detail is populated and > 0.
+
+Run with:
+    uv run pytest tests/test_litellm/integrations/test_prometheus_token_detail_metrics.py -v
+"""
+
+from typing import get_args
+from unittest.mock import MagicMock
+
+import pytest
+
+from litellm.integrations.prometheus import PrometheusLogger
+from litellm.types.integrations.prometheus import (
+    DEFINED_PROMETHEUS_METRICS,
+    PrometheusMetricLabels,
+    UserAPIKeyLabelValues,
+)
+
+
+TOKEN_DETAIL_METRICS = [
+    "litellm_input_cached_tokens_metric",
+    "litellm_input_cache_creation_tokens_metric",
+    "litellm_input_audio_tokens_metric",
+    "litellm_output_reasoning_tokens_metric",
+    "litellm_output_audio_tokens_metric",
+]
+
+
+@pytest.fixture
+def sample_enum_values():
+    return UserAPIKeyLabelValues(
+        end_user="test-end-user",
+        hashed_api_key="test-key-hash",
+        api_key_alias="test-key-alias",
+        team="test-team",
+        team_alias="test-team-alias",
+        user="test-user",
+        model="gpt-4o",
+    )
+
+
+def _make_mock_logger():
+    """Mock instance with the five detail counters + get_labels_for_metric."""
+    logger = MagicMock()
+    for name in TOKEN_DETAIL_METRICS:
+        setattr(logger, name, MagicMock())
+    logger.get_labels_for_metric = MagicMock(
+        return_value=[
+            "model",
+            "hashed_api_key",
+            "api_key_alias",
+            "team",
+            "team_alias",
+            "end_user",
+            "user",
+        ]
+    )
+    return logger
+
+
+class TestTokenDetailMetricsRegistration:
+    """Metric registration / wiring — no runtime needed."""
+
+    def test_metrics_in_defined_prometheus_metrics(self):
+        defined = get_args(DEFINED_PROMETHEUS_METRICS)
+        for name in TOKEN_DETAIL_METRICS:
+            assert name in defined, f"{name} missing from DEFINED_PROMETHEUS_METRICS"
+
+    def test_metric_labels_defined(self):
+        for name in TOKEN_DETAIL_METRICS:
+            assert hasattr(
+                PrometheusMetricLabels, name
+            ), f"{name} missing from PrometheusMetricLabels"
+
+    def test_input_detail_metrics_share_input_label_set(self):
+        # Detail metrics should reuse the parent input/output label set so
+        # dashboards can join token totals against per-type detail.
+        assert (
+            PrometheusMetricLabels.litellm_input_cached_tokens_metric
+            == PrometheusMetricLabels.litellm_input_tokens_metric
+        )
+        assert (
+            PrometheusMetricLabels.litellm_input_cache_creation_tokens_metric
+            == PrometheusMetricLabels.litellm_input_tokens_metric
+        )
+        assert (
+            PrometheusMetricLabels.litellm_input_audio_tokens_metric
+            == PrometheusMetricLabels.litellm_input_tokens_metric
+        )
+
+    def test_output_detail_metrics_share_output_label_set(self):
+        assert (
+            PrometheusMetricLabels.litellm_output_reasoning_tokens_metric
+            == PrometheusMetricLabels.litellm_output_tokens_metric
+        )
+        assert (
+            PrometheusMetricLabels.litellm_output_audio_tokens_metric
+            == PrometheusMetricLabels.litellm_output_tokens_metric
+        )
+
+
+class TestIncrementTokenDetailMetrics:
+    """Behaviour of PrometheusLogger._increment_token_detail_metrics."""
+
+    def test_increments_all_present_token_types(self, sample_enum_values):
+        logger = _make_mock_logger()
+        payload = {
+            "metadata": {
+                "usage_object": {
+                    "prompt_tokens": 100,
+                    "completion_tokens": 80,
+                    "total_tokens": 180,
+                    "prompt_tokens_details": {
+                        "cached_tokens": 40,
+                        "cache_creation_tokens": 25,
+                        "audio_tokens": 15,
+                    },
+                    "completion_tokens_details": {
+                        "reasoning_tokens": 60,
+                        "audio_tokens": 10,
+                    },
+                }
+            },
+        }
+
+        PrometheusLogger._increment_token_detail_metrics(
+            logger,
+            standard_logging_payload=payload,
+            enum_values=sample_enum_values,
+        )
+
+        logger.litellm_input_cached_tokens_metric.labels().inc.assert_called_once_with(
+            40.0
+        )
+        logger.litellm_input_cache_creation_tokens_metric.labels().inc.assert_called_once_with(
+            25.0
+        )
+        logger.litellm_input_audio_tokens_metric.labels().inc.assert_called_once_with(
+            15.0
+        )
+        logger.litellm_output_reasoning_tokens_metric.labels().inc.assert_called_once_with(
+            60.0
+        )
+        logger.litellm_output_audio_tokens_metric.labels().inc.assert_called_once_with(
+            10.0
+        )
+
+    def test_skips_metrics_when_value_is_zero(self, sample_enum_values):
+        logger = _make_mock_logger()
+        payload = {
+            "metadata": {
+                "usage_object": {
+                    "prompt_tokens_details": {
+                        "cached_tokens": 0,
+                        "cache_creation_tokens": 0,
+                        "audio_tokens": 0,
+                    },
+                    "completion_tokens_details": {
+                        "reasoning_tokens": 0,
+                        "audio_tokens": 0,
+                    },
+                }
+            }
+        }
+
+        PrometheusLogger._increment_token_detail_metrics(
+            logger,
+            standard_logging_payload=payload,
+            enum_values=sample_enum_values,
+        )
+
+        for name in TOKEN_DETAIL_METRICS:
+            getattr(logger, name).labels.assert_not_called()
+
+    def test_skips_metrics_when_value_is_none(self, sample_enum_values):
+        logger = _make_mock_logger()
+        payload = {
+            "metadata": {
+                "usage_object": {
+                    "prompt_tokens_details": {
+                        "cached_tokens": None,
+                        "audio_tokens": 12,
+                    },
+                    "completion_tokens_details": {},
+                }
+            }
+        }
+
+        PrometheusLogger._increment_token_detail_metrics(
+            logger,
+            standard_logging_payload=payload,
+            enum_values=sample_enum_values,
+        )
+
+        # Only audio_tokens was non-zero — only that counter should fire.
+        logger.litellm_input_cached_tokens_metric.labels.assert_not_called()
+        logger.litellm_input_cache_creation_tokens_metric.labels.assert_not_called()
+        logger.litellm_input_audio_tokens_metric.labels().inc.assert_called_once_with(
+            12.0
+        )
+        logger.litellm_output_reasoning_tokens_metric.labels.assert_not_called()
+        logger.litellm_output_audio_tokens_metric.labels.assert_not_called()
+
+    def test_no_usage_object_is_a_noop(self, sample_enum_values):
+        logger = _make_mock_logger()
+        payload = {"metadata": {}}
+
+        # Should not raise and should not call any counter.
+        PrometheusLogger._increment_token_detail_metrics(
+            logger,
+            standard_logging_payload=payload,
+            enum_values=sample_enum_values,
+        )
+
+        for name in TOKEN_DETAIL_METRICS:
+            getattr(logger, name).labels.assert_not_called()
+
+    def test_missing_metadata_is_a_noop(self, sample_enum_values):
+        logger = _make_mock_logger()
+
+        # Many error / cache-hit paths leave metadata as None.
+        PrometheusLogger._increment_token_detail_metrics(
+            logger,
+            standard_logging_payload={"metadata": None},  # type: ignore[typeddict-item]
+            enum_values=sample_enum_values,
+        )
+
+        for name in TOKEN_DETAIL_METRICS:
+            getattr(logger, name).labels.assert_not_called()
+
+    def test_negative_values_are_ignored(self, sample_enum_values):
+        # Defensive: a buggy upstream that returned a negative shouldn't
+        # poison the counter (counters can't go down without a reset).
+        logger = _make_mock_logger()
+        payload = {
+            "metadata": {
+                "usage_object": {
+                    "prompt_tokens_details": {"cached_tokens": -5},
+                    "completion_tokens_details": {"reasoning_tokens": -10},
+                }
+            }
+        }
+
+        PrometheusLogger._increment_token_detail_metrics(
+            logger,
+            standard_logging_payload=payload,
+            enum_values=sample_enum_values,
+        )
+
+        logger.litellm_input_cached_tokens_metric.labels.assert_not_called()
+        logger.litellm_output_reasoning_tokens_metric.labels.assert_not_called()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py b/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py
index 19ae819c85a..12f30ab6024 100644
--- a/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py
+++ b/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py
@@ -460,6 +460,104 @@ async def test_assemble_user_object_does_not_override_metadata_max_budget(
     ), "max_budget from metadata must not be replaced by the DB value"
 
 
+async def test_assemble_user_object_populates_user_email_and_alias_from_db(
+    prometheus_logger,
+):
+    db_user = MagicMock()
+    db_user.max_budget = None
+    db_user.budget_reset_at = None
+    db_user.user_email = "alice@example.com"
+    db_user.user_alias = "Alice"
+
+    with patch("litellm.proxy.auth.auth_checks.get_user_object") as mock_get_user:
+        mock_get_user.return_value = db_user
+        user_object = await prometheus_logger._assemble_user_object(
+            user_id="user-abc-123",
+            spend=10.0,
+            max_budget=None,
+            response_cost=0.5,
+        )
+
+    assert user_object.user_email == "alice@example.com"
+    assert user_object.user_alias == "Alice"
+
+
+def test_set_user_budget_metrics_default_no_email_alias_labels(
+    prometheus_logger,
+):
+    """By default (flag off), only user label is emitted."""
+    import litellm
+    from litellm.proxy._types import LiteLLM_UserTable
+
+    litellm.prometheus_user_budget_label_include_email_alias = False
+
+    user = LiteLLM_UserTable(
+        user_id="user-abc-123",
+        user_email="alice@example.com",
+        user_alias="Alice",
+        spend=25.0,
+        max_budget=100.0,
+        budget_reset_at=datetime(2026, 3, 1, tzinfo=timezone.utc),
+    )
+
+    prometheus_logger.litellm_remaining_user_budget_metric = MagicMock()
+    prometheus_logger.litellm_user_max_budget_metric = MagicMock()
+    prometheus_logger.litellm_user_budget_remaining_hours_metric = MagicMock()
+
+    prometheus_logger._set_user_budget_metrics(user)
+
+    prometheus_logger.litellm_remaining_user_budget_metric.labels.assert_called_once_with(
+        user="user-abc-123",
+    )
+
+
+def test_set_user_budget_metrics_includes_user_email_and_alias_labels_when_opted_in(
+    prometheus_logger,
+):
+    """When prometheus_user_budget_label_include_email_alias=True, email+alias labels appear."""
+    import litellm
+    from litellm.proxy._types import LiteLLM_UserTable
+
+    litellm.prometheus_user_budget_label_include_email_alias = True
+
+    user = LiteLLM_UserTable(
+        user_id="user-abc-123",
+        user_email="alice@example.com",
+        user_alias="Alice",
+        spend=25.0,
+        max_budget=100.0,
+        budget_reset_at=datetime(2026, 3, 1, tzinfo=timezone.utc),
+    )
+
+    prometheus_logger.litellm_remaining_user_budget_metric = MagicMock()
+    prometheus_logger.litellm_user_max_budget_metric = MagicMock()
+    prometheus_logger.litellm_user_budget_remaining_hours_metric = MagicMock()
+
+    try:
+        prometheus_logger._set_user_budget_metrics(user)
+
+        prometheus_logger.litellm_remaining_user_budget_metric.labels.assert_called_once_with(
+            user="user-abc-123",
+            user_email="alice@example.com",
+            user_alias="Alice",
+        )
+        prometheus_logger.litellm_remaining_user_budget_metric.labels().set.assert_called_once_with(
+            75.0
+        )
+        prometheus_logger.litellm_user_max_budget_metric.labels.assert_called_once_with(
+            user="user-abc-123",
+            user_email="alice@example.com",
+            user_alias="Alice",
+        )
+        prometheus_logger.litellm_user_budget_remaining_hours_metric.labels.assert_called_once_with(
+            user="user-abc-123",
+            user_email="alice@example.com",
+            user_alias="Alice",
+        )
+    finally:
+        litellm.prometheus_user_budget_label_include_email_alias = False
+
+
 async def test_set_user_budget_metrics_after_api_request_no_inf_when_metadata_budget_none(
     prometheus_logger,
 ):
diff --git a/tests/test_litellm/integrations/test_rubrik.py b/tests/test_litellm/integrations/test_rubrik.py
new file mode 100644
index 00000000000..922d2fe8a15
--- /dev/null
+++ b/tests/test_litellm/integrations/test_rubrik.py
@@ -0,0 +1,1012 @@
+"""
+Tests for the Rubrik LiteLLM plugin.
+
+Covers initialization, apply_guardrail tool blocking (all allowed, all blocked,
+partial blocking, fail-open), batch logging, and Anthropic format handling.
+"""
+
+import os
+from typing import Any, Dict
+from unittest.mock import AsyncMock, Mock, patch
+
+import httpx
+import pytest
+
+from litellm.integrations.custom_guardrail import ModifyResponseException
+from litellm.integrations.rubrik import RubrikLogger
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+
+from tests.test_litellm.integrations.rubrik_test_helpers import (
+    make_inputs_with_tools,
+    make_tool_call_dict,
+)
+
+
+@pytest.fixture
+def mock_env():
+    """Set up environment variables for testing."""
+    with patch.dict(
+        os.environ,
+        {
+            "RUBRIK_WEBHOOK_URL": "http://localhost:8080",
+            "RUBRIK_API_KEY": "test-api-key",
+        },
+    ):
+        yield
+
+
+@pytest.fixture
+def handler(mock_env):
+    """Create a RubrikLogger instance for testing."""
+    with patch("asyncio.create_task", Mock()):
+        return RubrikLogger()
+
+
+# -- Initialization -----------------------------------------------------------
+
+
+class TestInitialization:
+    def test_init_success(self, mock_env):
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger()
+            assert (
+                handler.tool_blocking_endpoint
+                == "http://localhost:8080/v1/after_completion/openai/v1"
+            )
+            assert handler.logging_endpoint == "http://localhost:8080/v1/litellm/batch"
+            assert handler.key == "test-api-key"
+            assert isinstance(handler.tool_blocking_client, AsyncHTTPHandler)
+
+    def test_init_with_constructor_params(self):
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger(api_key="ctor-key", api_base="http://ctor-host:9090")
+            assert handler.key == "ctor-key"
+            assert (
+                handler.tool_blocking_endpoint
+                == "http://ctor-host:9090/v1/after_completion/openai/v1"
+            )
+
+    def test_init_without_url(self):
+        with patch.dict(os.environ, {}, clear=True):
+            with pytest.raises(ValueError, match="Rubrik webhook URL not configured"):
+                RubrikLogger()
+
+    def test_init_without_api_key(self):
+        with patch.dict(
+            os.environ, {"RUBRIK_WEBHOOK_URL": "http://localhost:8080"}, clear=True
+        ):
+            with patch("asyncio.create_task", Mock()):
+                assert RubrikLogger().key is None
+
+    def test_trailing_slash_removed(self):
+        with patch.dict(os.environ, {"RUBRIK_WEBHOOK_URL": "http://localhost:8080/"}):
+            with patch("asyncio.create_task", Mock()):
+                assert (
+                    RubrikLogger().tool_blocking_endpoint
+                    == "http://localhost:8080/v1/after_completion/openai/v1"
+                )
+
+    def test_v1_suffix_stripped_as_substring_not_charset(self):
+        with patch("asyncio.create_task", Mock()):
+            with patch.dict(os.environ, {"RUBRIK_WEBHOOK_URL": "http://host/v1"}):
+                assert (
+                    RubrikLogger().tool_blocking_endpoint
+                    == "http://host/v1/after_completion/openai/v1"
+                )
+
+            with patch.dict(os.environ, {"RUBRIK_WEBHOOK_URL": "http://host/v11"}):
+                assert (
+                    RubrikLogger().tool_blocking_endpoint
+                    == "http://host/v11/v1/after_completion/openai/v1"
+                )
+
+    def test_sampling_rate_fractional(self):
+        with patch("asyncio.create_task", Mock()):
+            with patch.dict(
+                os.environ,
+                {"RUBRIK_WEBHOOK_URL": "http://host", "RUBRIK_SAMPLING_RATE": "0.5"},
+            ):
+                assert RubrikLogger().sampling_rate == 0.5
+
+    def test_sampling_rate_invalid_ignored(self):
+        with patch("asyncio.create_task", Mock()):
+            with patch.dict(
+                os.environ,
+                {"RUBRIK_WEBHOOK_URL": "http://host", "RUBRIK_SAMPLING_RATE": "abc"},
+            ):
+                assert RubrikLogger().sampling_rate == 1.0
+
+    def test_sampling_rate_clamped(self):
+        with patch("asyncio.create_task", Mock()):
+            with patch.dict(
+                os.environ,
+                {"RUBRIK_WEBHOOK_URL": "http://host", "RUBRIK_SAMPLING_RATE": "2.0"},
+            ):
+                assert RubrikLogger().sampling_rate == 1.0
+            with patch.dict(
+                os.environ,
+                {"RUBRIK_WEBHOOK_URL": "http://host", "RUBRIK_SAMPLING_RATE": "-0.5"},
+            ):
+                assert RubrikLogger().sampling_rate == 0.0
+
+    def test_batch_size_invalid_ignored(self):
+        with patch("asyncio.create_task", Mock()):
+            with patch.dict(
+                os.environ,
+                {"RUBRIK_WEBHOOK_URL": "http://host", "RUBRIK_BATCH_SIZE": "abc"},
+            ):
+                # Should use default without crashing
+                assert isinstance(RubrikLogger().batch_size, int)
+
+    def test_batch_size_valid(self):
+        with patch("asyncio.create_task", Mock()):
+            with patch.dict(
+                os.environ,
+                {"RUBRIK_WEBHOOK_URL": "http://host", "RUBRIK_BATCH_SIZE": "256"},
+            ):
+                assert RubrikLogger().batch_size == 256
+
+    def test_init_outside_event_loop_does_not_raise(self):
+        """Instantiation without a running event loop must not raise RuntimeError."""
+        with patch.dict(
+            os.environ,
+            {"RUBRIK_WEBHOOK_URL": "http://localhost:8080", "RUBRIK_API_KEY": "k"},
+        ):
+            # Do NOT patch asyncio.create_task — the real call should be
+            # guarded and fall back gracefully when there is no event loop.
+            handler = RubrikLogger()
+            assert handler.tool_blocking_endpoint.startswith("http://localhost:8080")
+            # Without a running loop at init, the periodic flush task should be
+            # deferred so batches still get drained once a log event arrives.
+            assert handler._flush_task is None
+
+    @pytest.mark.asyncio
+    async def test_periodic_flush_task_started_lazily_on_first_log(self, mock_env):
+        """Loggers instantiated outside an event loop must still start the
+        periodic flush task on first use to drain low-traffic batches."""
+        # Simulate sync-init by hiding the running loop from the constructor.
+        with patch(
+            "litellm.integrations.rubrik.asyncio.get_running_loop",
+            side_effect=RuntimeError("no running loop"),
+        ):
+            handler = RubrikLogger()
+        assert handler._flush_task is None
+
+        kwargs = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "hi"}],
+                "id": "litellm-id",
+            },
+            "litellm_call_id": "litellm-id",
+            "litellm_params": {},
+        }
+        with patch.object(handler, "_log_batch_to_rubrik", AsyncMock()):
+            await handler.async_log_success_event(kwargs, None, None, None)
+
+        assert handler._flush_task is not None
+        handler._flush_task.cancel()
+
+    def test_event_hook_defaults_to_post_call_when_none_passed(self, mock_env):
+        """`initialize_guardrail` always passes ``event_hook=litellm_params.mode``
+        (which is ``None`` when the user omits ``mode``). The logger must coerce
+        a None ``event_hook`` to ``post_call`` rather than leaving it as None,
+        which would otherwise cause the guardrail to run on every event hook."""
+        from litellm.types.guardrails import GuardrailEventHooks
+
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger(event_hook=None)
+            assert handler.event_hook == GuardrailEventHooks.post_call
+
+    def test_explicit_event_hook_preserved(self, mock_env):
+        from litellm.types.guardrails import GuardrailEventHooks
+
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger(event_hook=GuardrailEventHooks.pre_call)
+            assert handler.event_hook == GuardrailEventHooks.pre_call
+
+    def test_default_on_defaults_to_true_when_none_passed(self, mock_env):
+        """`initialize_guardrail` always passes ``default_on=litellm_params.default_on``
+        (which is ``None`` when the user omits ``default_on``). The logger must
+        coerce a None ``default_on`` to True, otherwise ``should_run_guardrail``
+        (which checks ``self.default_on is True``) silently skips the guardrail."""
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger(default_on=None)
+            assert handler.default_on is True
+
+    def test_explicit_default_on_false_preserved(self, mock_env):
+        """A user explicitly setting ``default_on: false`` in their guardrail
+        config must NOT be silently overridden to True."""
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger(default_on=False)
+            assert handler.default_on is False
+
+    def test_explicit_default_on_true_preserved(self, mock_env):
+        with patch("asyncio.create_task", Mock()):
+            handler = RubrikLogger(default_on=True)
+            assert handler.default_on is True
+
+    def test_headers_with_api_key(self, handler):
+        assert handler._headers["Authorization"] == "Bearer test-api-key"
+        assert handler._headers["Content-Type"] == "application/json"
+
+    def test_headers_without_api_key(self):
+        with patch.dict(os.environ, {"RUBRIK_WEBHOOK_URL": "http://host"}, clear=True):
+            with patch("asyncio.create_task", Mock()):
+                h = RubrikLogger()
+                assert "Authorization" not in h._headers
+
+
+# -- Batch Logging ------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+class TestBatchLogging:
+    async def test_log_success_event_appends_to_queue(self, handler):
+        kwargs = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "hi"}],
+                "response": "hello",
+            },
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert len(handler.log_queue) == 1
+
+    async def test_log_failure_event_appends_to_queue(self, handler):
+        kwargs = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "hi"}],
+                "response": "error",
+            },
+        }
+        await handler.async_log_failure_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert len(handler.log_queue) == 1
+
+    async def test_log_success_event_sampling_skips(self, handler):
+        handler.sampling_rate = 0.0
+        kwargs = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "hi"}],
+                "response": "hello",
+            },
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert len(handler.log_queue) == 0
+
+    async def test_flush_queue_sends_batch(self, handler):
+        handler.log_queue = [{"msg": "a"}, {"msg": "b"}]
+        mock_response = Mock()
+        mock_response.status_code = 200
+        handler.async_httpx_client = AsyncMock()
+        handler.async_httpx_client.post = AsyncMock(return_value=mock_response)
+        await handler.flush_queue()
+        handler.async_httpx_client.post.assert_called_once()
+        assert len(handler.log_queue) == 0
+
+    async def test_flush_queue_preserves_events_added_during_send(self, handler):
+        handler.log_queue = [{"msg": "a"}, {"msg": "b"}]
+
+        async def mock_post(*_args, **_kwargs):
+            handler.log_queue.append({"msg": "c"})
+            mock_response = Mock()
+            mock_response.raise_for_status = Mock()
+            return mock_response
+
+        handler.async_httpx_client = AsyncMock()
+        handler.async_httpx_client.post = mock_post
+
+        await handler.flush_queue()
+
+        assert handler.log_queue == [{"msg": "c"}]
+
+    async def test_async_send_batch_does_not_drain_events(self, handler):
+        handler.log_queue = [{"msg": "a"}, {"msg": "b"}]
+
+        async def mock_post(*_args, **_kwargs):
+            handler.log_queue.append({"msg": "c"})
+            mock_response = Mock()
+            mock_response.raise_for_status = Mock()
+            return mock_response
+
+        handler.async_httpx_client = AsyncMock()
+        handler.async_httpx_client.post = mock_post
+
+        await handler.async_send_batch()
+
+        assert handler.log_queue == [{"msg": "a"}, {"msg": "b"}, {"msg": "c"}]
+
+    async def test_log_batch_error_does_not_crash_and_preserves_events(self, handler):
+        """A failed batch send must not crash the caller AND must preserve the
+        original events in the queue so they can be retried on the next flush.
+        Previously the events were silently dropped on HTTP 5xx / network errors.
+        """
+        handler.log_queue = [{"msg": "a"}]
+        mock_response = Mock()
+        mock_response.status_code = 500
+        mock_response.text = "Internal Server Error"
+        mock_response.raise_for_status = Mock(
+            side_effect=httpx.HTTPStatusError(
+                "err", request=Mock(), response=mock_response
+            )
+        )
+        handler.async_httpx_client = AsyncMock()
+        handler.async_httpx_client.post = AsyncMock(return_value=mock_response)
+        await handler.flush_queue()
+        assert handler.log_queue == [{"msg": "a"}]
+
+    async def test_log_batch_network_error_preserves_events(self, handler):
+        """Network/timeout errors must also preserve the in-flight events."""
+        handler.log_queue = [{"msg": "a"}, {"msg": "b"}]
+        handler.async_httpx_client = AsyncMock()
+        handler.async_httpx_client.post = AsyncMock(
+            side_effect=httpx.TimeoutException("timeout")
+        )
+        await handler.flush_queue()
+        assert handler.log_queue == [{"msg": "a"}, {"msg": "b"}]
+
+    async def test_enqueue_drops_oldest_when_queue_exceeds_max_size(self, handler):
+        """A sustained Rubrik webhook outage must not let the in-memory retry
+        queue grow without bound. Once max_queue_size is exceeded, the oldest
+        events are dropped to make room for new ones."""
+        handler.max_queue_size = 3
+        handler.batch_size = 10**6  # disable size-triggered flush
+        handler.flush_queue = AsyncMock()
+        for i in range(5):
+            await handler._enqueue_log_event(
+                kwargs={
+                    "standard_logging_object": {
+                        "messages": [{"role": "user", "content": f"hi-{i}"}],
+                        "response": "hello",
+                    },
+                },
+                event_type="success",
+            )
+        assert len(handler.log_queue) == 3
+        retained = [item["messages"][0]["content"] for item in handler.log_queue]
+        assert retained == ["hi-2", "hi-3", "hi-4"]
+
+    async def test_log_batch_failure_preserves_events_added_during_send(self, handler):
+        """Failure must preserve both the snapshot AND events appended mid-flush."""
+        handler.log_queue = [{"msg": "a"}, {"msg": "b"}]
+
+        async def mock_post(*_args, **_kwargs):
+            handler.log_queue.append({"msg": "c"})
+            mock_response = Mock()
+            mock_response.status_code = 500
+            mock_response.text = "boom"
+            mock_response.raise_for_status = Mock(
+                side_effect=httpx.HTTPStatusError(
+                    "err", request=Mock(), response=mock_response
+                )
+            )
+            return mock_response
+
+        handler.async_httpx_client = AsyncMock()
+        handler.async_httpx_client.post = mock_post
+
+        await handler.flush_queue()
+        assert handler.log_queue == [{"msg": "a"}, {"msg": "b"}, {"msg": "c"}]
+
+    async def test_system_prompt_prepended_to_messages(self, handler):
+        kwargs = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "hi"}],
+                "response": "hello",
+            },
+            "system": "You are a helpful assistant.",
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert len(handler.log_queue) == 1
+        msgs = handler.log_queue[0]["messages"]
+        assert msgs[0]["role"] == "system"
+        assert msgs[0]["content"] == "You are a helpful assistant."
+
+    async def test_system_prompt_with_dict_messages(self, handler):
+        kwargs = {
+            "standard_logging_object": {
+                "messages": {"role": "user", "content": "hi"},
+                "response": "hello",
+            },
+            "system": "Be concise.",
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert len(handler.log_queue) == 1
+        msgs = handler.log_queue[0]["messages"]
+        assert isinstance(msgs, list)
+        assert msgs[0]["role"] == "system"
+        assert msgs[1] == {"role": "user", "content": "hi"}
+
+    async def test_anthropic_id_normalization(self, handler):
+        kwargs = {
+            "standard_logging_object": {
+                "id": "chatcmpl-original",
+                "messages": [{"role": "user", "content": "hi"}],
+                "response": "hello",
+            },
+            "litellm_params": {
+                "proxy_server_request": {
+                    "url": "http://proxy/v1/messages",
+                },
+            },
+            "litellm_call_id": "litellm-call-123",
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert handler.log_queue[0]["id"] == "litellm-call-123"
+
+    async def test_non_anthropic_id_unchanged(self, handler):
+        kwargs = {
+            "standard_logging_object": {
+                "id": "chatcmpl-original",
+                "messages": [{"role": "user", "content": "hi"}],
+                "response": "hello",
+            },
+            "litellm_params": {
+                "proxy_server_request": {
+                    "url": "http://proxy/v1/chat/completions",
+                },
+            },
+            "litellm_call_id": "litellm-call-123",
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        assert handler.log_queue[0]["id"] == "chatcmpl-original"
+
+    async def test_payload_deep_copied_not_mutated(self, handler):
+        """Verify the shared standard_logging_object is not mutated."""
+        original_payload = {
+            "id": "original-id",
+            "messages": [{"role": "user", "content": "hi"}],
+            "response": "hello",
+        }
+        kwargs = {
+            "standard_logging_object": original_payload,
+            "system": "System prompt.",
+        }
+        await handler.async_log_success_event(
+            kwargs=kwargs, response_obj=None, start_time=None, end_time=None
+        )
+        # Original payload should NOT have been mutated
+        assert original_payload["id"] == "original-id"
+        assert len(original_payload["messages"]) == 1
+
+
+# -- Tool Blocking (apply_guardrail) ------------------------------------------
+
+
+def _mock_service_response(response_json):
+    """Create a mock tool blocking client that returns the given JSON."""
+
+    async def mock_post(*_args, **kwargs):
+        mock_resp = Mock()
+        mock_resp.json.return_value = response_json
+        mock_resp.raise_for_status = Mock()
+        return mock_resp
+
+    mock_client = AsyncMock()
+    mock_client.post = mock_post
+    return mock_client
+
+
+def _echo_service():
+    """Create a mock tool blocking client that echoes the payload back."""
+
+    async def mock_post(*_args, **kwargs):
+        mock_resp = Mock()
+        mock_resp.json.return_value = kwargs.get("json", {}).get("response", {})
+        mock_resp.raise_for_status = Mock()
+        return mock_resp
+
+    mock_client = AsyncMock()
+    mock_client.post = mock_post
+    return mock_client
+
+
+@pytest.mark.asyncio
+class TestApplyGuardrail:
+    async def test_skips_requests(self, handler):
+        inputs = make_inputs_with_tools([make_tool_call_dict("call_1", "test_tool")])
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="request"
+        )
+        assert result is inputs
+
+    async def test_no_tool_calls(self, handler):
+        from litellm.types.utils import GenericGuardrailAPIInputs
+
+        inputs = GenericGuardrailAPIInputs(texts=["hello"])
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+        assert result is inputs
+
+    async def test_all_allowed(self, handler):
+        tc1 = make_tool_call_dict("call_1", "get_weather")
+        tc2 = make_tool_call_dict("call_2", "get_time")
+        inputs = make_inputs_with_tools([tc1, tc2])
+
+        handler.tool_blocking_client = _echo_service()
+
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+        assert result is inputs
+
+    async def test_all_blocked(self, handler):
+        tc1 = make_tool_call_dict("call_1", "delete_table")
+        tc2 = make_tool_call_dict("call_2", "drop_database")
+        inputs = make_inputs_with_tools([tc1, tc2])
+
+        handler.tool_blocking_client = _mock_service_response(
+            {
+                "choices": [
+                    {
+                        "message": {
+                            "role": "assistant",
+                            "content": "Tool blocked by policy",
+                            "tool_calls": [],
+                        }
+                    }
+                ],
+            }
+        )
+
+        with pytest.raises(ModifyResponseException) as exc_info:
+            await handler.apply_guardrail(
+                inputs=inputs, request_data={}, input_type="response"
+            )
+        assert "Tool blocked by policy" in exc_info.value.message
+
+    async def test_partial_blocking(self, handler):
+        tc_blocked = make_tool_call_dict("call_A", "blocked_tool")
+        tc_allowed = make_tool_call_dict("call_B", "allowed_tool")
+        inputs = make_inputs_with_tools([tc_blocked, tc_allowed])
+
+        async def mock_post(*_args, **kwargs):
+            payload = kwargs.get("json", {}).get("response", {})
+            all_tcs = payload["choices"][0]["message"]["tool_calls"]
+            allowed = [tc for tc in all_tcs if tc.get("id") == "call_B"]
+            mock_resp = Mock()
+            mock_resp.json.return_value = {
+                "choices": [
+                    {
+                        "message": {
+                            "role": "assistant",
+                            "content": "blocked",
+                            "tool_calls": allowed,
+                        }
+                    }
+                ],
+            }
+            mock_resp.raise_for_status = Mock()
+            return mock_resp
+
+        mock_client = AsyncMock()
+        mock_client.post = mock_post
+        handler.tool_blocking_client = mock_client
+
+        with pytest.raises(ModifyResponseException):
+            await handler.apply_guardrail(
+                inputs=inputs, request_data={}, input_type="response"
+            )
+
+    async def test_service_failure_fail_open(self, handler):
+        tc1 = make_tool_call_dict("call_1", "test_tool")
+        inputs = make_inputs_with_tools([tc1])
+
+        mock_client = AsyncMock()
+        mock_client.post = AsyncMock(side_effect=httpx.TimeoutException("Timeout"))
+        handler.tool_blocking_client = mock_client
+
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+        assert result is inputs
+
+    async def test_service_empty_choices_fail_open(self, handler):
+        tc1 = make_tool_call_dict("call_1", "test_tool")
+        inputs = make_inputs_with_tools([tc1])
+
+        handler.tool_blocking_client = _mock_service_response({"choices": []})
+
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+        assert result is inputs
+
+    async def test_blocking_service_payload_format(self, handler):
+        tc1 = make_tool_call_dict("call_1", "get_weather", '{"location": "SF"}')
+        tc2 = make_tool_call_dict("call_2", "send_email", '{"to": "user@example.com"}')
+        inputs = make_inputs_with_tools([tc1, tc2])
+
+        captured_payload: Dict[str, Any] = {}
+
+        async def mock_post(*_args, **kwargs):
+            captured_payload.update(kwargs.get("json", {}))
+            mock_resp = Mock()
+            mock_resp.json.return_value = captured_payload.get("response", {})
+            mock_resp.raise_for_status = Mock()
+            return mock_resp
+
+        mock_client = AsyncMock()
+        mock_client.post = mock_post
+        handler.tool_blocking_client = mock_client
+
+        await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+
+        # Verify envelope structure
+        assert "request" in captured_payload
+        assert "response" in captured_payload
+
+        response_data = captured_payload["response"]
+        message = response_data["choices"][0]["message"]
+        assert message["role"] == "assistant"
+        assert len(message["tool_calls"]) == 2
+        assert message["tool_calls"][0]["id"] == "call_1"
+        assert message["tool_calls"][0]["function"]["name"] == "get_weather"
+        assert message["tool_calls"][1]["id"] == "call_2"
+        assert message["tool_calls"][1]["function"]["name"] == "send_email"
+
+    async def test_request_data_included_in_envelope(self, handler):
+        tc = make_tool_call_dict("call_1", "test_tool")
+        inputs = make_inputs_with_tools([tc])
+
+        captured_payload: Dict[str, Any] = {}
+
+        async def mock_post(*_args, **kwargs):
+            captured_payload.update(kwargs.get("json", {}))
+            mock_resp = Mock()
+            mock_resp.json.return_value = captured_payload.get("response", {})
+            mock_resp.raise_for_status = Mock()
+            return mock_resp
+
+        mock_client = AsyncMock()
+        mock_client.post = mock_post
+        handler.tool_blocking_client = mock_client
+
+        logging_obj = Mock()
+        logging_obj.model_call_details = {
+            "messages": [{"role": "user", "content": "hi"}],
+            "model": "gpt-4",
+            "litellm_params": {
+                "proxy_server_request": {"url": "/chat/completions"},
+            },
+        }
+
+        await handler.apply_guardrail(
+            inputs=inputs,
+            request_data={},
+            input_type="response",
+            logging_obj=logging_obj,
+        )
+
+        req = captured_payload["request"]
+        assert req["model"] == "gpt-4"
+        assert req["messages"] == [{"role": "user", "content": "hi"}]
+
+    async def test_proxy_server_request_headers_stripped(self, handler):
+        tc = make_tool_call_dict("call_1", "test_tool")
+        inputs = make_inputs_with_tools([tc])
+
+        captured_payload: Dict[str, Any] = {}
+
+        async def mock_post(*_args, **kwargs):
+            captured_payload.update(kwargs.get("json", {}))
+            mock_resp = Mock()
+            mock_resp.json.return_value = captured_payload.get("response", {})
+            mock_resp.raise_for_status = Mock()
+            return mock_resp
+
+        mock_client = AsyncMock()
+        mock_client.post = mock_post
+        handler.tool_blocking_client = mock_client
+
+        logging_obj = Mock()
+        logging_obj.model_call_details = {
+            "messages": [{"role": "user", "content": "hi"}],
+            "model": "gpt-4",
+            "litellm_params": {
+                "proxy_server_request": {
+                    "url": "/chat/completions",
+                    "method": "POST",
+                    "headers": {
+                        "authorization": "Bearer sk-litellm-secret",
+                        "cookie": "session=abc",
+                        "x-api-key": "leaked-key",
+                    },
+                    "body": {"api_key": "sk-upstream-secret"},
+                },
+            },
+        }
+
+        await handler.apply_guardrail(
+            inputs=inputs,
+            request_data={},
+            input_type="response",
+            logging_obj=logging_obj,
+        )
+
+        forwarded = captured_payload["request"]["proxy_server_request"]
+        assert forwarded == {"url": "/chat/completions", "method": "POST"}
+
+
+# -- Anthropic format ----------------------------------------------------------
+
+
+@pytest.mark.asyncio
+class TestApplyGuardrailAnthropicFormat:
+    """Verify blocking works correctly regardless of original provider format.
+
+    The framework converts Anthropic tool_use blocks to OpenAI-format
+    tool_calls before calling apply_guardrail.
+    """
+
+    async def test_single_tool_allowed(self, handler):
+        tc = make_tool_call_dict(
+            "toolu_123", "get_weather", '{"location": "Portland, OR"}'
+        )
+        inputs = make_inputs_with_tools([tc], texts=["I'll check the weather."])
+
+        handler.tool_blocking_client = _echo_service()
+
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+        assert result is inputs
+
+    async def test_single_tool_blocked(self, handler):
+        tc = make_tool_call_dict("toolu_123", "dangerous_tool", '{"arg": "value"}')
+        inputs = make_inputs_with_tools([tc])
+
+        handler.tool_blocking_client = _mock_service_response(
+            {
+                "choices": [
+                    {
+                        "message": {
+                            "role": "assistant",
+                            "content": "blocked",
+                            "tool_calls": [],
+                        }
+                    }
+                ],
+            }
+        )
+
+        with pytest.raises(ModifyResponseException):
+            await handler.apply_guardrail(
+                inputs=inputs, request_data={}, input_type="response"
+            )
+
+    async def test_text_only_response_no_blocking(self, handler):
+        from litellm.types.utils import GenericGuardrailAPIInputs
+
+        inputs = GenericGuardrailAPIInputs(texts=["Hello! I'm Claude."])
+
+        mock_client = AsyncMock()
+        mock_client.post = AsyncMock()
+        handler.tool_blocking_client = mock_client
+
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+
+        assert result is inputs
+        mock_client.post.assert_not_called()
+
+    async def test_service_failure_preserves_tools(self, handler):
+        tc = make_tool_call_dict("toolu_123", "get_weather", '{"location": "SF"}')
+        inputs = make_inputs_with_tools([tc])
+
+        mock_client = AsyncMock()
+        mock_client.post = AsyncMock(side_effect=httpx.TimeoutException("Timeout"))
+        handler.tool_blocking_client = mock_client
+
+        result = await handler.apply_guardrail(
+            inputs=inputs, request_data={}, input_type="response"
+        )
+        assert result is inputs
+
+
+# -- Normalize tool calls ------------------------------------------------------
+
+
+class TestNormalizeToolCalls:
+    def test_dict_input(self):
+        tc = make_tool_call_dict("call_1", "test", '{"a": 1}')
+        result = RubrikLogger._normalize_tool_calls([tc])
+        assert len(result) == 1
+        assert result[0].id == "call_1"
+        assert result[0].function.name == "test"
+        assert result[0].function.arguments == '{"a": 1}'
+
+    def test_typed_object_input(self):
+        from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+        tc = ChatCompletionMessageToolCall(
+            id="call_2",
+            type="function",
+            function=Function(name="fn", arguments="{}"),
+        )
+        result = RubrikLogger._normalize_tool_calls([tc])
+        assert len(result) == 1
+        assert result[0].id == "call_2"
+        assert result[0].function.name == "fn"
+
+    def test_unsupported_type_raises(self):
+        with pytest.raises(TypeError, match="Cannot normalize"):
+            RubrikLogger._normalize_tool_calls(["not_a_tool_call"])
+
+
+# -- Extract blocked tools -----------------------------------------------------
+
+
+class TestExtractBlockedTools:
+    def test_all_allowed_returns_none(self):
+        from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+        tc = ChatCompletionMessageToolCall(
+            id="call_1", type="function", function=Function(name="fn", arguments="{}")
+        )
+        service_resp = {
+            "choices": [
+                {
+                    "message": {
+                        "tool_calls": [{"id": "call_1"}],
+                        "content": "",
+                    }
+                }
+            ]
+        }
+        result = RubrikLogger._extract_blocked_tools(service_resp, [tc])
+        assert result is None
+
+    def test_some_blocked_returns_explanation(self):
+        from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+        tc1 = ChatCompletionMessageToolCall(
+            id="call_1",
+            type="function",
+            function=Function(name="fn1", arguments="{}"),
+        )
+        tc2 = ChatCompletionMessageToolCall(
+            id="call_2",
+            type="function",
+            function=Function(name="fn2", arguments="{}"),
+        )
+        service_resp = {
+            "choices": [
+                {
+                    "message": {
+                        "tool_calls": [{"id": "call_1"}],
+                        "content": "blocked fn2",
+                    }
+                }
+            ]
+        }
+        result = RubrikLogger._extract_blocked_tools(service_resp, [tc1, tc2])
+        assert result is not None
+        assert "blocked fn2" in result
+
+    def test_empty_choices_raises(self):
+        with pytest.raises(Exception, match="empty response"):
+            RubrikLogger._extract_blocked_tools({"choices": []}, [])
+
+    def test_null_tool_calls_treated_as_all_blocked(self):
+        from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+        tc = ChatCompletionMessageToolCall(
+            id="call_1", type="function", function=Function(name="fn", arguments="{}")
+        )
+        service_resp = {
+            "choices": [
+                {
+                    "message": {
+                        "tool_calls": None,
+                        "content": "blocked everything",
+                    }
+                }
+            ]
+        }
+        result = RubrikLogger._extract_blocked_tools(service_resp, [tc])
+        assert result is not None
+        assert "blocked everything" in result
+
+    def test_duplicate_ids_block_when_only_one_returned(self):
+        from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+        tc1 = ChatCompletionMessageToolCall(
+            id="call_dup",
+            type="function",
+            function=Function(name="fn", arguments="{}"),
+        )
+        tc2 = ChatCompletionMessageToolCall(
+            id="call_dup",
+            type="function",
+            function=Function(name="fn", arguments="{}"),
+        )
+        service_resp = {
+            "choices": [
+                {
+                    "message": {
+                        "tool_calls": [{"id": "call_dup"}],
+                        "content": "blocked duplicate",
+                    }
+                }
+            ]
+        }
+        result = RubrikLogger._extract_blocked_tools(service_resp, [tc1, tc2])
+        assert result is not None
+        assert "blocked duplicate" in result
+
+
+# -- Sanitize proxy server request -------------------------------------------
+
+
+class TestSanitizeProxyServerRequest:
+    def test_drops_headers_and_body(self):
+        proxy_request = {
+            "url": "/chat/completions",
+            "method": "POST",
+            "headers": {
+                "authorization": "Bearer sk-litellm-secret",
+                "cookie": "session=abc",
+                "content-type": "application/json",
+            },
+            "body": {"api_key": "sk-upstream-secret", "model": "gpt-4"},
+        }
+        result = RubrikLogger._sanitize_proxy_server_request(proxy_request)
+        assert result == {"url": "/chat/completions", "method": "POST"}
+
+    def test_none_passthrough(self):
+        assert RubrikLogger._sanitize_proxy_server_request(None) is None
+
+    def test_non_dict_passthrough(self):
+        assert RubrikLogger._sanitize_proxy_server_request("not a dict") == "not a dict"
+
+    def test_partial_dict(self):
+        result = RubrikLogger._sanitize_proxy_server_request({"url": "/v1/messages"})
+        assert result == {"url": "/v1/messages"}
+
+
+# -- Resolve model -------------------------------------------------------------
+
+
+class TestResolveModel:
+    def test_model_from_response(self):
+        from unittest.mock import Mock
+
+        response = Mock()
+        response.model = "gpt-4"
+        result = RubrikLogger._resolve_model({"response": response}, {})
+        assert result == "gpt-4"
+
+    def test_model_from_call_details(self):
+        result = RubrikLogger._resolve_model({}, {"model": "claude-3"})
+        assert result == "claude-3"
+
+    def test_fallback_to_unknown(self):
+        result = RubrikLogger._resolve_model({}, {})
+        assert result == "unknown"
+
+    def test_empty_model_on_response_returns_unknown(self):
+        from unittest.mock import Mock
+
+        response = Mock()
+        response.model = ""
+        result = RubrikLogger._resolve_model(
+            {"response": response}, {"model": "fallback"}
+        )
+        assert result == "unknown"
diff --git a/tests/test_litellm/interactions/test_agents_http_handler.py b/tests/test_litellm/interactions/test_agents_http_handler.py
new file mode 100644
index 00000000000..6947503e0bb
--- /dev/null
+++ b/tests/test_litellm/interactions/test_agents_http_handler.py
@@ -0,0 +1,587 @@
+"""
+Unit tests for litellm/interactions/agents/http_handler.py
+
+These tests exercise both the sync and async branches of every CRUD method
+on AgentsHTTPHandler using stub httpx clients, plus the _is_async dispatch
+branches, error mapping, and pre/post logging hooks.
+
+No real HTTP traffic is made.
+"""
+
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock
+
+import httpx
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../.."))
+
+from litellm.interactions.agents.http_handler import (
+    AgentsHTTPHandler,
+    agents_http_handler,
+)
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig
+from litellm.llms.gemini.common_utils import GeminiError
+from litellm.types.agents import (
+    AgentCreateResponse,
+    AgentDeleteResult,
+    AgentListResponse,
+    AgentVersionsResponse,
+)
+from litellm.types.router import GenericLiteLLMParams
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_response(status_code: int = 200, json_data=None, text: str = "") -> MagicMock:
+    """Build a stub httpx-like response."""
+    response = MagicMock()
+    response.status_code = status_code
+    response.text = text or (str(json_data) if json_data is not None else "")
+    response.headers = {}
+    if json_data is not None:
+        response.json.return_value = json_data
+    else:
+        response.json.return_value = {}
+    return response
+
+
+def _make_sync_client() -> MagicMock:
+    client = MagicMock(spec=HTTPHandler)
+    return client
+
+
+def _make_async_client() -> MagicMock:
+    client = MagicMock(spec=AsyncHTTPHandler)
+    client.post = AsyncMock()
+    client.get = AsyncMock()
+    client.delete = AsyncMock()
+    return client
+
+
+def _make_logging_obj() -> MagicMock:
+    return MagicMock()
+
+
+@pytest.fixture
+def handler() -> AgentsHTTPHandler:
+    return AgentsHTTPHandler()
+
+
+@pytest.fixture
+def config() -> GeminiAgentsConfig:
+    return GeminiAgentsConfig()
+
+
+@pytest.fixture
+def litellm_params() -> GenericLiteLLMParams:
+    return GenericLiteLLMParams(api_key="AIza-test")
+
+
+# ---------------------------------------------------------------------------
+# Module-level singleton sanity check
+# ---------------------------------------------------------------------------
+
+
+def test_module_singleton_is_agents_http_handler_instance():
+    assert isinstance(agents_http_handler, AgentsHTTPHandler)
+
+
+# ---------------------------------------------------------------------------
+# CREATE
+# ---------------------------------------------------------------------------
+
+
+class TestCreateAgent:
+    def test_sync_returns_parsed_create_response(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        client.post.return_value = _make_response(
+            200, json_data={"id": "agent-x", "base_agent": "gemini-2.5-flash"}
+        )
+        logging_obj = _make_logging_obj()
+
+        result = handler.create_agent(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            extra_headers={"X-Test": "1"},
+            extra_body={"foo": "bar"},
+            client=client,
+        )
+
+        assert isinstance(result, AgentCreateResponse)
+        assert result.id == "agent-x"
+        client.post.assert_called_once()
+        kwargs = client.post.call_args.kwargs
+        assert kwargs["url"].endswith("/v1beta/agents")
+        assert kwargs["json"]["name"] == "agent-x"
+        assert kwargs["json"]["foo"] == "bar"
+        assert kwargs["headers"]["X-Test"] == "1"
+        logging_obj.pre_call.assert_called_once()
+        logging_obj.post_call.assert_called_once()
+
+    def test_sync_dispatches_to_async_when_is_async(
+        self, handler, config, litellm_params
+    ):
+        client = _make_sync_client()
+
+        result = handler.create_agent(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+            _is_async=True,
+        )
+
+        import asyncio
+
+        assert asyncio.iscoroutine(result)
+        result.close()
+
+    def test_sync_maps_http_error_via_config(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        bad = _make_response(404, text="not found")
+        client.post.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            handler.create_agent(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+    @pytest.mark.asyncio
+    async def test_async_returns_parsed_create_response(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        client.post.return_value = _make_response(
+            200, json_data={"id": "agent-y", "base_agent": "gemini-2.5-flash"}
+        )
+
+        result = await handler.async_create_agent(
+            agents_api_config=config,
+            name="agent-y",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            extra_body={"baz": "qux"},
+            client=client,
+        )
+
+        assert isinstance(result, AgentCreateResponse)
+        assert result.id == "agent-y"
+        client.post.assert_awaited_once()
+
+    @pytest.mark.asyncio
+    async def test_async_maps_http_error_via_config(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        bad = _make_response(500, text="server error")
+        client.post.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            await handler.async_create_agent(
+                agents_api_config=config,
+                name="agent-y",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+
+# ---------------------------------------------------------------------------
+# LIST
+# ---------------------------------------------------------------------------
+
+
+class TestListAgents:
+    def test_sync_returns_list_response(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        client.get.return_value = _make_response(
+            200,
+            json_data={
+                "agents": [{"id": "a-1"}, {"id": "a-2"}],
+                "nextPageToken": "tok",
+            },
+        )
+
+        result = handler.list_agents(
+            agents_api_config=config,
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentListResponse)
+        assert len(result.agents) == 2
+        assert result.next_page_token == "tok"
+        client.get.assert_called_once()
+
+    def test_sync_dispatches_to_async_when_is_async(
+        self, handler, config, litellm_params
+    ):
+        client = _make_sync_client()
+
+        result = handler.list_agents(
+            agents_api_config=config,
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+            _is_async=True,
+        )
+
+        import asyncio
+
+        assert asyncio.iscoroutine(result)
+        result.close()
+
+    def test_sync_maps_http_error_via_config(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        bad = _make_response(403, text="forbidden")
+        client.get.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            handler.list_agents(
+                agents_api_config=config,
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+    @pytest.mark.asyncio
+    async def test_async_returns_list_response(self, handler, config, litellm_params):
+        client = _make_async_client()
+        client.get.return_value = _make_response(
+            200, json_data={"agents": [{"id": "a-1"}]}
+        )
+
+        result = await handler.async_list_agents(
+            agents_api_config=config,
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentListResponse)
+        assert len(result.agents) == 1
+        client.get.assert_awaited_once()
+
+    @pytest.mark.asyncio
+    async def test_async_maps_http_error_via_config(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        bad = _make_response(429, text="rate limited")
+        client.get.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            await handler.async_list_agents(
+                agents_api_config=config,
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+
+# ---------------------------------------------------------------------------
+# GET
+# ---------------------------------------------------------------------------
+
+
+class TestGetAgent:
+    def test_sync_returns_get_response(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        client.get.return_value = _make_response(200, json_data={"id": "agent-x"})
+
+        result = handler.get_agent(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentCreateResponse)
+        assert result.id == "agent-x"
+        kwargs = client.get.call_args.kwargs
+        assert kwargs["url"].endswith("/v1beta/agents/agent-x")
+
+    def test_sync_dispatches_to_async_when_is_async(
+        self, handler, config, litellm_params
+    ):
+        result = handler.get_agent(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=_make_sync_client(),
+            _is_async=True,
+        )
+        import asyncio
+
+        assert asyncio.iscoroutine(result)
+        result.close()
+
+    def test_sync_maps_http_error_via_config(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        bad = _make_response(404, text="not found")
+        client.get.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            handler.get_agent(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+    @pytest.mark.asyncio
+    async def test_async_returns_get_response(self, handler, config, litellm_params):
+        client = _make_async_client()
+        client.get.return_value = _make_response(200, json_data={"id": "agent-y"})
+
+        result = await handler.async_get_agent(
+            agents_api_config=config,
+            name="agent-y",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentCreateResponse)
+        assert result.id == "agent-y"
+
+    @pytest.mark.asyncio
+    async def test_async_maps_http_error_via_config(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        bad = _make_response(404, text="not found")
+        client.get.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            await handler.async_get_agent(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+
+# ---------------------------------------------------------------------------
+# DELETE
+# ---------------------------------------------------------------------------
+
+
+class TestDeleteAgent:
+    def test_sync_returns_delete_result(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        client.delete.return_value = _make_response(200, json_data={})
+
+        result = handler.delete_agent(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentDeleteResult)
+        assert result.name == "agent-x"
+        assert result.deleted is True
+        kwargs = client.delete.call_args.kwargs
+        assert kwargs["url"].endswith("/v1beta/agents/agent-x")
+
+    def test_sync_dispatches_to_async_when_is_async(
+        self, handler, config, litellm_params
+    ):
+        result = handler.delete_agent(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=_make_sync_client(),
+            _is_async=True,
+        )
+        import asyncio
+
+        assert asyncio.iscoroutine(result)
+        result.close()
+
+    def test_sync_maps_http_error_via_config(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        bad = _make_response(403, text="forbidden")
+        client.delete.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            handler.delete_agent(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+    @pytest.mark.asyncio
+    async def test_async_returns_delete_result(self, handler, config, litellm_params):
+        client = _make_async_client()
+        client.delete.return_value = _make_response(200, json_data={})
+
+        result = await handler.async_delete_agent(
+            agents_api_config=config,
+            name="agent-y",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentDeleteResult)
+        assert result.name == "agent-y"
+        assert result.deleted is True
+
+    @pytest.mark.asyncio
+    async def test_async_maps_http_error_via_config(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        bad = _make_response(500, text="server error")
+        client.delete.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            await handler.async_delete_agent(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+
+# ---------------------------------------------------------------------------
+# LIST VERSIONS
+# ---------------------------------------------------------------------------
+
+
+class TestListAgentVersions:
+    def test_sync_returns_versions_response(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        client.get.return_value = _make_response(
+            200,
+            json_data={
+                "agentVersions": [
+                    {"agent": "agent-x", "name": "agents/agent-x/versions/v1"}
+                ],
+                "nextPageToken": "tok",
+            },
+        )
+
+        result = handler.list_agent_versions(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentVersionsResponse)
+        assert len(result.agent_versions) == 1
+        assert result.next_page_token == "tok"
+        kwargs = client.get.call_args.kwargs
+        assert kwargs["url"].endswith("/v1beta/agents/agent-x/versions")
+
+    def test_sync_dispatches_to_async_when_is_async(
+        self, handler, config, litellm_params
+    ):
+        result = handler.list_agent_versions(
+            agents_api_config=config,
+            name="agent-x",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=_make_sync_client(),
+            _is_async=True,
+        )
+        import asyncio
+
+        assert asyncio.iscoroutine(result)
+        result.close()
+
+    def test_sync_maps_http_error_via_config(self, handler, config, litellm_params):
+        client = _make_sync_client()
+        bad = _make_response(404, text="not found")
+        client.get.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            handler.list_agent_versions(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
+
+    @pytest.mark.asyncio
+    async def test_async_returns_versions_response(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        client.get.return_value = _make_response(200, json_data={"agentVersions": []})
+
+        result = await handler.async_list_agent_versions(
+            agents_api_config=config,
+            name="agent-y",
+            litellm_params=litellm_params,
+            logging_obj=_make_logging_obj(),
+            client=client,
+        )
+
+        assert isinstance(result, AgentVersionsResponse)
+        assert result.agent_versions == []
+
+    @pytest.mark.asyncio
+    async def test_async_maps_http_error_via_config(
+        self, handler, config, litellm_params
+    ):
+        client = _make_async_client()
+        bad = _make_response(500, text="server error")
+        client.get.side_effect = httpx.HTTPStatusError(
+            "boom", request=MagicMock(), response=bad
+        )
+
+        with pytest.raises(GeminiError):
+            await handler.async_list_agent_versions(
+                agents_api_config=config,
+                name="agent-x",
+                litellm_params=litellm_params,
+                logging_obj=_make_logging_obj(),
+                client=client,
+            )
diff --git a/tests/test_litellm/interactions/test_agents_main_and_utils.py b/tests/test_litellm/interactions/test_agents_main_and_utils.py
new file mode 100644
index 00000000000..7c0183d20c6
--- /dev/null
+++ b/tests/test_litellm/interactions/test_agents_main_and_utils.py
@@ -0,0 +1,354 @@
+"""
+Unit tests for litellm/interactions/agents/utils.py and main.py
+focused on the managed agents SDK surface added in the
+"Gemini managed agents support" PR.
+
+The tests mock the underlying HTTP handler so they cover the public
+sync + async create/list/get/delete/list_versions entry points and the
+small helper utilities without touching the network.
+"""
+
+import asyncio
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../.."))
+
+import litellm
+from litellm.interactions.agents import (
+    acreate,
+    adelete,
+    aget,
+    alist,
+    alist_versions,
+    create,
+    delete,
+    get,
+    list as list_agents,
+    list_versions,
+)
+from litellm.interactions.agents.main import (
+    _get_agents_api_config,
+    _make_logging_obj,
+)
+from litellm.interactions.agents.utils import get_provider_agents_api_config
+from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig
+from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig
+
+
+_HANDLER_PATH = "litellm.interactions.agents.main.agents_http_handler"
+
+
+# ---------------------------------------------------------------------------
+# utils.get_provider_agents_api_config
+# ---------------------------------------------------------------------------
+
+
+class TestGetProviderAgentsApiConfig:
+    def test_returns_gemini_config_for_gemini(self):
+        cfg = get_provider_agents_api_config("gemini")
+        assert isinstance(cfg, GeminiAgentsConfig)
+        assert isinstance(cfg, BaseAgentsAPIConfig)
+
+    @pytest.mark.parametrize(
+        "provider", ["openai", "anthropic", "bedrock", "vertex_ai", "unknown"]
+    )
+    def test_returns_none_for_non_gemini(self, provider):
+        assert get_provider_agents_api_config(provider) is None
+
+    def test_returns_none_for_none(self):
+        assert get_provider_agents_api_config(None) is None
+
+
+# ---------------------------------------------------------------------------
+# main._get_agents_api_config
+# ---------------------------------------------------------------------------
+
+
+class TestGetAgentsApiConfig:
+    def test_returns_config_for_gemini(self):
+        cfg = _get_agents_api_config("gemini")
+        assert isinstance(cfg, GeminiAgentsConfig)
+
+    def test_raises_bad_request_for_unsupported_provider(self):
+        with pytest.raises(litellm.BadRequestError) as excinfo:
+            _get_agents_api_config("openai")
+        assert "does not have a native" in str(excinfo.value)
+
+
+# ---------------------------------------------------------------------------
+# main._make_logging_obj
+# ---------------------------------------------------------------------------
+
+
+class TestMakeLoggingObj:
+    def test_calls_update_from_kwargs_and_returns_same_obj(self):
+        logging_obj = MagicMock()
+        kwargs = {"litellm_logging_obj": logging_obj, "litellm_call_id": "abc-123"}
+
+        returned = _make_logging_obj(
+            kwargs=kwargs,
+            model="my-agent",
+            custom_llm_provider="gemini",
+            call_type="create_agent",
+            optional_params={"foo": "bar"},
+        )
+
+        assert returned is logging_obj
+        logging_obj.update_from_kwargs.assert_called_once()
+        kwargs_call = logging_obj.update_from_kwargs.call_args.kwargs
+        assert kwargs_call["model"] == "my-agent"
+        assert kwargs_call["optional_params"] == {"foo": "bar"}
+        assert kwargs_call["custom_llm_provider"] == "gemini"
+        assert kwargs_call["litellm_params"]["litellm_call_id"] == "abc-123"
+
+
+# ---------------------------------------------------------------------------
+# Sync entry points: create / list / get / delete / list_versions
+# ---------------------------------------------------------------------------
+
+
+def _stub_handler(return_value):
+    """Build a stub AgentsHTTPHandler whose CRUD methods return *return_value*."""
+    handler = MagicMock()
+    handler.create_agent.return_value = return_value
+    handler.list_agents.return_value = return_value
+    handler.get_agent.return_value = return_value
+    handler.delete_agent.return_value = return_value
+    handler.list_agent_versions.return_value = return_value
+    return handler
+
+
+class TestSyncEntryPoints:
+    def test_create_passes_args_to_handler(self):
+        sentinel = MagicMock(name="create_response")
+        with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler:
+            response = create(
+                name="waverunner",
+                base_agent="gemini-2.5-flash",
+                instructions="be helpful",
+                base_environment={"type": "remote"},
+                custom_llm_provider="gemini",
+                api_key="AIza-test",
+                extra_headers={"X-Test": "1"},
+                extra_body={"foo": "bar"},
+            )
+
+        assert response is sentinel
+        handler.create_agent.assert_called_once()
+        kw = handler.create_agent.call_args.kwargs
+        assert kw["name"] == "waverunner"
+        assert kw["_is_async"] is False
+        assert kw["extra_headers"] == {"X-Test": "1"}
+        assert kw["extra_body"] == {"foo": "bar"}
+        assert isinstance(kw["agents_api_config"], GeminiAgentsConfig)
+
+    def test_create_defaults_custom_llm_provider_to_gemini(self):
+        sentinel = MagicMock(name="create_response")
+        with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler:
+            create(name="agent-x", api_key="AIza")
+        assert handler.create_agent.call_args.kwargs["_is_async"] is False
+        cfg = handler.create_agent.call_args.kwargs["agents_api_config"]
+        assert isinstance(cfg, GeminiAgentsConfig)
+
+    def test_create_raises_for_unsupported_provider(self):
+        with pytest.raises(litellm.exceptions.BadRequestError):
+            create(name="agent-x", custom_llm_provider="openai", api_key="sk-x")
+
+    def test_list_passes_args_to_handler(self):
+        sentinel = MagicMock(name="list_response")
+        with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler:
+            response = list_agents(custom_llm_provider="gemini", api_key="AIza")
+        assert response is sentinel
+        handler.list_agents.assert_called_once()
+        assert handler.list_agents.call_args.kwargs["_is_async"] is False
+
+    def test_get_passes_args_to_handler(self):
+        sentinel = MagicMock(name="get_response")
+        with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler:
+            response = get(name="waverunner", api_key="AIza")
+        assert response is sentinel
+        kw = handler.get_agent.call_args.kwargs
+        assert kw["name"] == "waverunner"
+        assert kw["_is_async"] is False
+
+    def test_delete_passes_args_to_handler(self):
+        sentinel = MagicMock(name="delete_response")
+        with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler:
+            response = delete(name="waverunner", api_key="AIza")
+        assert response is sentinel
+        kw = handler.delete_agent.call_args.kwargs
+        assert kw["name"] == "waverunner"
+        assert kw["_is_async"] is False
+
+    def test_list_versions_passes_args_to_handler(self):
+        sentinel = MagicMock(name="versions_response")
+        with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler:
+            response = list_versions(name="waverunner", api_key="AIza")
+        assert response is sentinel
+        kw = handler.list_agent_versions.call_args.kwargs
+        assert kw["name"] == "waverunner"
+        assert kw["_is_async"] is False
+
+
+# ---------------------------------------------------------------------------
+# Async entry points
+# ---------------------------------------------------------------------------
+
+
+class TestAsyncEntryPoints:
+    """Async entry points delegate to their sync counterparts via run_in_executor."""
+
+    @pytest.mark.asyncio
+    async def test_acreate_dispatches_with_async_flag(self):
+        sentinel = MagicMock(name="acreate_response")
+
+        def fake_create_agent(**kwargs):
+            assert kwargs["_is_async"] is True
+            assert kwargs["name"] == "waverunner"
+            return sentinel
+
+        handler = MagicMock()
+        handler.create_agent.side_effect = fake_create_agent
+
+        with patch(_HANDLER_PATH, handler):
+            response = await acreate(
+                name="waverunner",
+                base_agent="gemini-2.5-flash",
+                api_key="AIza",
+            )
+        assert response is sentinel
+
+    @pytest.mark.asyncio
+    async def test_acreate_awaits_coroutine_result(self):
+        async def _coro():
+            return "async-value"
+
+        handler = MagicMock()
+        handler.create_agent.return_value = _coro()
+
+        with patch(_HANDLER_PATH, handler):
+            response = await acreate(name="waverunner", api_key="AIza")
+
+        assert response == "async-value"
+
+    @pytest.mark.asyncio
+    async def test_alist_dispatches_with_async_flag(self):
+        sentinel = MagicMock(name="alist_response")
+
+        def fake_list_agents(**kwargs):
+            assert kwargs["_is_async"] is True
+            return sentinel
+
+        handler = MagicMock()
+        handler.list_agents.side_effect = fake_list_agents
+
+        with patch(_HANDLER_PATH, handler):
+            response = await alist(api_key="AIza")
+        assert response is sentinel
+
+    @pytest.mark.asyncio
+    async def test_aget_dispatches_with_async_flag(self):
+        sentinel = MagicMock(name="aget_response")
+
+        def fake_get_agent(**kwargs):
+            assert kwargs["_is_async"] is True
+            assert kwargs["name"] == "waverunner"
+            return sentinel
+
+        handler = MagicMock()
+        handler.get_agent.side_effect = fake_get_agent
+
+        with patch(_HANDLER_PATH, handler):
+            response = await aget(name="waverunner", api_key="AIza")
+        assert response is sentinel
+
+    @pytest.mark.asyncio
+    async def test_adelete_dispatches_with_async_flag(self):
+        sentinel = MagicMock(name="adelete_response")
+
+        def fake_delete_agent(**kwargs):
+            assert kwargs["_is_async"] is True
+            assert kwargs["name"] == "waverunner"
+            return sentinel
+
+        handler = MagicMock()
+        handler.delete_agent.side_effect = fake_delete_agent
+
+        with patch(_HANDLER_PATH, handler):
+            response = await adelete(name="waverunner", api_key="AIza")
+        assert response is sentinel
+
+    @pytest.mark.asyncio
+    async def test_alist_versions_dispatches_with_async_flag(self):
+        sentinel = MagicMock(name="alist_versions_response")
+
+        def fake_versions(**kwargs):
+            assert kwargs["_is_async"] is True
+            assert kwargs["name"] == "waverunner"
+            return sentinel
+
+        handler = MagicMock()
+        handler.list_agent_versions.side_effect = fake_versions
+
+        with patch(_HANDLER_PATH, handler):
+            response = await alist_versions(name="waverunner", api_key="AIza")
+        assert response is sentinel
+
+
+# ---------------------------------------------------------------------------
+# Async error wrapping: exception_type must be invoked
+# ---------------------------------------------------------------------------
+
+
+class TestAsyncErrorWrapping:
+    """If the underlying handler raises, async entry points re-raise via
+    litellm.exception_type so users get a normalised provider error."""
+
+    @pytest.mark.asyncio
+    async def test_acreate_wraps_exception(self):
+        handler = MagicMock()
+        handler.create_agent.side_effect = RuntimeError("kaboom")
+
+        with patch(_HANDLER_PATH, handler):
+            with pytest.raises(Exception):
+                await acreate(name="waverunner", api_key="AIza")
+
+    @pytest.mark.asyncio
+    async def test_aget_wraps_exception(self):
+        handler = MagicMock()
+        handler.get_agent.side_effect = RuntimeError("kaboom")
+
+        with patch(_HANDLER_PATH, handler):
+            with pytest.raises(Exception):
+                await aget(name="waverunner", api_key="AIza")
+
+    @pytest.mark.asyncio
+    async def test_alist_wraps_exception(self):
+        handler = MagicMock()
+        handler.list_agents.side_effect = RuntimeError("kaboom")
+
+        with patch(_HANDLER_PATH, handler):
+            with pytest.raises(Exception):
+                await alist(api_key="AIza")
+
+    @pytest.mark.asyncio
+    async def test_adelete_wraps_exception(self):
+        handler = MagicMock()
+        handler.delete_agent.side_effect = RuntimeError("kaboom")
+
+        with patch(_HANDLER_PATH, handler):
+            with pytest.raises(Exception):
+                await adelete(name="waverunner", api_key="AIza")
+
+    @pytest.mark.asyncio
+    async def test_alist_versions_wraps_exception(self):
+        handler = MagicMock()
+        handler.list_agent_versions.side_effect = RuntimeError("kaboom")
+
+        with patch(_HANDLER_PATH, handler):
+            with pytest.raises(Exception):
+                await alist_versions(name="waverunner", api_key="AIza")
diff --git a/tests/test_litellm/interactions/test_gemini_interactions_transformation.py b/tests/test_litellm/interactions/test_gemini_interactions_transformation.py
index 758ff3ea38e..524589abf5e 100644
--- a/tests/test_litellm/interactions/test_gemini_interactions_transformation.py
+++ b/tests/test_litellm/interactions/test_gemini_interactions_transformation.py
@@ -1,23 +1,33 @@
 """
 Tests for Gemini Interactions API transformation.
 
-Covers credential leak prevention changes:
-- validate_environment sets x-goog-api-key header
-- get_complete_url excludes API key from URL
-- get/delete/cancel interaction request URLs exclude API key
+Covers:
+- validate_environment: x-goog-api-key header, Api-Revision schema selection
+- get_complete_url: API key excluded from URL
+- get/delete/cancel interaction request URLs
+- transform_request: response_mime_type coalescing, image_config migration
 """
 
 import os
 import sys
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import pytest
 
 sys.path.insert(0, os.path.abspath("../../.."))
 
+import litellm
+from litellm.interactions.litellm_responses_transformation.streaming_iterator import (
+    LiteLLMResponsesInteractionsStreamingIterator,
+)
 from litellm.llms.gemini.interactions.transformation import (
     GoogleAIStudioInteractionsConfig,
 )
+from litellm.types.llms.openai import (
+    OutputTextDeltaEvent,
+    ResponseCompletedEvent,
+    ResponseCreatedEvent,
+)
 from litellm.types.router import GenericLiteLLMParams
 
 _PATCH_GET_API_KEY = "litellm.llms.gemini.common_utils.GeminiModelInfo.get_api_key"
@@ -76,6 +86,30 @@ class TestValidateEnvironment:
         assert headers["X-Custom"] == "value"
         assert headers["x-goog-api-key"] == "test-key"
 
+    def test_api_revision_new_schema_by_default(self, config):
+        # Default: use_legacy_interactions_schema=False → new steps schema
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = False
+            headers = config.validate_environment(
+                headers={}, model="gemini-2.5-flash", litellm_params=None
+            )
+            assert headers["Api-Revision"] == "2026-05-20"
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+    def test_api_revision_legacy_schema_when_flag_set(self, config):
+        # Flag on → legacy outputs schema until June 8, 2026
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = True
+            headers = config.validate_environment(
+                headers={}, model="gemini-2.5-flash", litellm_params=None
+            )
+            assert headers["Api-Revision"] == "2026-05-07"
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
 
 class TestGetCompleteUrl:
     def test_url_excludes_api_key(self, config):
@@ -113,6 +147,357 @@ class TestGetCompleteUrl:
                 )
 
 
+class TestTransformRequest:
+    def test_passes_environment_to_request_body(self, config):
+        request_body = config.transform_request(
+            model=None,
+            agent="my-custom-slides-agent",
+            input=[
+                {
+                    "type": "text",
+                    "text": "Create a 5-slide presentation about AI trends.",
+                }
+            ],
+            optional_params={
+                "environment": "remote",
+                "stream": False,
+            },
+            litellm_params=GenericLiteLLMParams(api_key="test-api-key"),
+            headers={},
+        )
+
+        assert request_body["agent"] == "my-custom-slides-agent"
+        assert request_body["environment"] == "remote"
+        assert request_body["stream"] is False
+        assert request_body["input"] == [
+            {"type": "text", "text": "Create a 5-slide presentation about AI trends."}
+        ]
+
+    def test_passes_environment_object_to_request_body(self, config):
+        environment_config = {
+            "type": "remote",
+            "sources": [{"type": "gcs", "uri": "gs://bucket/skills.zip"}],
+            "network": {"egress": "allow_all"},
+        }
+        request_body = config.transform_request(
+            model=None,
+            agent="waverunner",
+            input="What is 2 + 2?",
+            optional_params={"environment": environment_config},
+            litellm_params=GenericLiteLLMParams(api_key="test-api-key"),
+            headers={},
+        )
+
+        assert request_body["environment"] == environment_config
+
+    def test_passes_existing_environment_id_to_request_body(self, config):
+        env_id = "env-abc123"
+        request_body = config.transform_request(
+            model=None,
+            agent="my-custom-slides-agent",
+            input="Continue the presentation.",
+            optional_params={"environment": env_id},
+            litellm_params=GenericLiteLLMParams(api_key="test-api-key"),
+            headers={},
+        )
+
+        assert request_body["environment"] == env_id
+
+    def test_stream_param_included_in_request_body(self, config):
+        """When stream=True is in optional_params, the request body must include it
+        so the proxy forwards the SSE streaming flag to Google's backend."""
+        body = config.transform_request(
+            model="gemini-2.5-flash",
+            agent=None,
+            input="Hello",
+            optional_params={"stream": True},
+            litellm_params=GenericLiteLLMParams(api_key="test-key"),
+            headers={},
+        )
+
+        assert body.get("stream") is True
+        assert body.get("input") == "Hello"
+
+    def test_stream_false_not_included_when_absent(self, config):
+        body = config.transform_request(
+            model="gemini-2.5-flash",
+            agent=None,
+            input="Hello",
+            optional_params={},
+            litellm_params=GenericLiteLLMParams(api_key="test-key"),
+            headers={},
+        )
+
+        assert "stream" not in body
+
+
+class TestStreamingIterator:
+    def _make_iterator(
+        self, use_legacy: bool = False
+    ) -> LiteLLMResponsesInteractionsStreamingIterator:
+        original = litellm.use_legacy_interactions_schema
+        litellm.use_legacy_interactions_schema = use_legacy
+        try:
+            return LiteLLMResponsesInteractionsStreamingIterator(
+                model="gpt-5.4",
+                litellm_custom_stream_wrapper=MagicMock(),
+                request_input="hi",
+                optional_params={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+    def _make_text_delta(
+        self, text: str, item_id: str = "item_1"
+    ) -> OutputTextDeltaEvent:
+        event = MagicMock(spec=OutputTextDeltaEvent)
+        event.delta = text
+        event.item_id = item_id
+        return event
+
+    def _make_response_created(self) -> ResponseCreatedEvent:
+        event = MagicMock(spec=ResponseCreatedEvent)
+        event.response = MagicMock(id="resp_123")
+        return event
+
+    def test_step_delta_includes_type_field(self):
+        """step.delta events must carry delta.type='text' so the UI can display them."""
+        it = self._make_iterator(use_legacy=False)
+        it.sent_interaction_start = True
+        it.sent_content_start = True
+
+        chunk = it._transform_responses_chunk_to_interactions_chunk(
+            self._make_text_delta("Hello")
+        )
+
+        assert chunk is not None
+        assert chunk.event_type == "step.delta"
+        assert chunk.delta == {"type": "text", "text": "Hello"}
+
+    def test_content_delta_legacy_schema(self):
+        """Legacy schema emits content.delta with type and text fields."""
+        it = self._make_iterator(use_legacy=True)
+        it.sent_interaction_start = True
+        it.sent_content_start = True
+
+        chunk = it._transform_responses_chunk_to_interactions_chunk(
+            self._make_text_delta("Hello")
+        )
+
+        assert chunk is not None
+        assert chunk.event_type == "content.delta"
+        assert chunk.delta == {"type": "text", "text": "Hello"}
+
+    def test_response_created_emits_interaction_created(self):
+        it = self._make_iterator(use_legacy=False)
+
+        chunk = it._transform_responses_chunk_to_interactions_chunk(
+            self._make_response_created()
+        )
+
+        assert chunk is not None
+        assert chunk.event_type == "interaction.created"
+        assert chunk.id == "resp_123"
+        assert it.sent_interaction_start is True
+
+    def test_response_created_emits_interaction_start_legacy(self):
+        it = self._make_iterator(use_legacy=True)
+
+        chunk = it._transform_responses_chunk_to_interactions_chunk(
+            self._make_response_created()
+        )
+
+        assert chunk is not None
+        assert chunk.event_type == "interaction.start"
+        assert chunk.id == "resp_123"
+
+    def test_text_delta_sequence_new_schema(self):
+        """First chunk yields created + step.start + step.delta; later chunks yield step.delta."""
+        it = self._make_iterator(use_legacy=False)
+
+        first_events = it._events_for_chunk(self._make_text_delta("Hello"))
+        assert [e.event_type for e in first_events] == [
+            "interaction.created",
+            "step.start",
+            "step.delta",
+        ]
+        assert first_events[-1].delta == {"type": "text", "text": "Hello"}
+        assert it.sent_interaction_start is True
+        assert it.sent_content_start is True
+
+        second_events = it._events_for_chunk(self._make_text_delta(" World"))
+        assert [e.event_type for e in second_events] == ["step.delta"]
+        assert second_events[0].delta == {"type": "text", "text": " World"}
+
+        third_events = it._events_for_chunk(self._make_text_delta("!"))
+        assert [e.event_type for e in third_events] == ["step.delta"]
+        assert third_events[0].delta == {"type": "text", "text": "!"}
+
+    def test_text_delta_sequence_legacy_schema(self):
+        """Legacy: first chunk yields interaction.start + content.start + content.delta."""
+        it = self._make_iterator(use_legacy=True)
+
+        first_events = it._events_for_chunk(self._make_text_delta("Hello"))
+        assert [e.event_type for e in first_events] == [
+            "interaction.start",
+            "content.start",
+            "content.delta",
+        ]
+        assert first_events[-1].delta == {"type": "text", "text": "Hello"}
+
+        second_events = it._events_for_chunk(self._make_text_delta(" World"))
+        assert [e.event_type for e in second_events] == ["content.delta"]
+        assert second_events[0].delta == {"type": "text", "text": " World"}
+
+    def test_first_text_delta_without_item_id_uses_fallback_id(self):
+        it = self._make_iterator(use_legacy=False)
+        event = self._make_text_delta("Hi")
+        event.item_id = None
+
+        events = it._events_for_chunk(event)
+
+        assert events[0].event_type == "interaction.created"
+        assert events[0].id == f"interaction_{id(it)}"
+
+    def test_first_text_delta_emits_text_via_compat_shim(self):
+        """The legacy single-chunk shim must surface the synthetic events AND the delta."""
+        it = self._make_iterator(use_legacy=False)
+
+        first = it._transform_responses_chunk_to_interactions_chunk(
+            self._make_text_delta("Hello")
+        )
+        assert first is not None
+        assert first.event_type == "interaction.created"
+
+        second = it.__next__() if it._pending_events else None
+        assert second is not None
+        assert second.event_type == "step.start"
+
+        third = it.__next__() if it._pending_events else None
+        assert third is not None
+        assert third.event_type == "step.delta"
+        assert third.delta == {"type": "text", "text": "Hello"}
+
+    def test_response_created_then_text_delta_emits_step_start_and_delta(self):
+        """Realistic flow: response.created arrives first, then text delta."""
+        it = self._make_iterator(use_legacy=False)
+
+        first = it._events_for_chunk(self._make_response_created())
+        assert [e.event_type for e in first] == ["interaction.created"]
+
+        second = it._events_for_chunk(self._make_text_delta("Hello"))
+        assert [e.event_type for e in second] == ["step.start", "step.delta"]
+        assert second[-1].delta == {"type": "text", "text": "Hello"}
+
+    def test_no_text_token_is_dropped_during_streaming(self):
+        """Concatenated step.delta payloads must equal the upstream text."""
+        it = self._make_iterator(use_legacy=False)
+
+        chunks = ["Hello", " ", "world", "!"]
+        emitted_text = ""
+        for c in chunks:
+            for ev in it._events_for_chunk(self._make_text_delta(c)):
+                if ev.event_type == "step.delta":
+                    assert ev.delta is not None
+                    emitted_text += ev.delta["text"]
+
+        assert emitted_text == "Hello world!"
+
+    def test_stop_iteration_fallback_emits_completion_event(self):
+        """If upstream ends without ResponseCompletedEvent, terminal events still flow."""
+        from unittest.mock import MagicMock
+
+        text_event = self._make_text_delta("hi")
+        sync_iter = MagicMock()
+        sync_iter.__iter__ = lambda self: self
+        sync_iter.__next__ = MagicMock(side_effect=[text_event, StopIteration])
+
+        original = litellm.use_legacy_interactions_schema
+        litellm.use_legacy_interactions_schema = False
+        try:
+            it = LiteLLMResponsesInteractionsStreamingIterator(
+                model="gpt-5.4",
+                litellm_custom_stream_wrapper=sync_iter,
+                request_input="hi",
+                optional_params={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+        emitted: list = []
+        try:
+            while True:
+                emitted.append(next(it))
+        except StopIteration:
+            pass
+
+        event_types = [e.event_type for e in emitted]
+        assert event_types == [
+            "interaction.created",
+            "step.start",
+            "step.delta",
+            "step.stop",
+            "interaction.completed",
+        ]
+        terminal = emitted[-1]
+        assert terminal.steps == [
+            {
+                "type": "model_output",
+                "content": [{"type": "text", "text": "hi"}],
+            }
+        ]
+        # EOF-flushed terminal event must carry the same id as interaction.created.
+        assert terminal.id == emitted[0].id == "item_1"
+
+    def test_response_completed_emits_stop_then_completion(self):
+        """ResponseCompletedEvent expands into step.stop + interaction.completed."""
+        from unittest.mock import MagicMock
+
+        text_event = self._make_text_delta("hi")
+        completed = MagicMock(spec=ResponseCompletedEvent)
+        completed.response = MagicMock(id="resp_999")
+
+        sync_iter = MagicMock()
+        sync_iter.__iter__ = lambda self: self
+        sync_iter.__next__ = MagicMock(side_effect=[text_event, completed])
+
+        original = litellm.use_legacy_interactions_schema
+        litellm.use_legacy_interactions_schema = False
+        try:
+            it = LiteLLMResponsesInteractionsStreamingIterator(
+                model="gpt-5.4",
+                litellm_custom_stream_wrapper=sync_iter,
+                request_input="hi",
+                optional_params={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+        emitted: list = []
+        try:
+            while True:
+                emitted.append(next(it))
+        except StopIteration:
+            pass
+
+        event_types = [e.event_type for e in emitted]
+        assert event_types == [
+            "interaction.created",
+            "step.start",
+            "step.delta",
+            "step.stop",
+            "interaction.completed",
+        ]
+        # StopIteration fallback path must NOT add a duplicate completion event.
+        assert event_types.count("interaction.completed") == 1
+        # When the stream starts directly with a text delta (no preceding
+        # response.created), the terminal events must reuse the id derived from
+        # the first chunk's item_id rather than switching to response.id, so
+        # consumers can correlate the start and completion events by id.
+        assert emitted[0].id == "item_1"
+        assert emitted[-1].id == "item_1"
+
+
 class TestInteractionOperationUrls:
     """Test that get/delete/cancel interaction URLs exclude API key."""
 
@@ -171,3 +556,152 @@ class TestInteractionOperationUrls:
                     litellm_params=GenericLiteLLMParams(api_key=None),
                     headers={},
                 )
+
+
+class TestTransformRequestSchemaCoalescing:
+    """Test new-schema request coalescing (Api-Revision: 2026-05-20)."""
+
+    def test_response_mime_type_folded_into_response_format(self, config):
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = False
+            body = config.transform_request(
+                model="gemini/gemini-2.5-flash",
+                agent=None,
+                input="summarise",
+                optional_params={
+                    "response_mime_type": "application/json",
+                    "response_format": {"type": "object", "properties": {}},
+                },
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+        # response_mime_type must not appear as a top-level body key
+        assert "response_mime_type" not in body
+        rf = body["response_format"]
+        assert rf["type"] == "text"
+        assert rf["mime_type"] == "application/json"
+        assert "schema" in rf
+
+    def test_image_config_moved_to_response_format(self, config):
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = False
+            body = config.transform_request(
+                model="gemini/gemini-2.5-flash",
+                agent=None,
+                input="draw a sunset",
+                optional_params={
+                    "generation_config": {
+                        "temperature": 0.7,
+                        "image_config": {"aspect_ratio": "1:1", "image_size": "1K"},
+                    }
+                },
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+        # image_config removed from generation_config
+        assert "image_config" not in body.get("generation_config", {})
+        # moved into response_format with type=image
+        rf = body["response_format"]
+        assert rf["type"] == "image"
+        assert rf["aspect_ratio"] == "1:1"
+
+    def test_response_mime_type_skipped_when_response_format_is_list(self, config):
+        """Lists are already polymorphic; do not wrap them into schema."""
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = False
+            rf_list = [
+                {"type": "text", "mime_type": "application/json"},
+                {"type": "image", "aspect_ratio": "1:1"},
+            ]
+            body = config.transform_request(
+                model="gemini/gemini-2.5-flash",
+                agent=None,
+                input="multimodal",
+                optional_params={
+                    "response_format": rf_list,
+                    "response_mime_type": "application/json",
+                },
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+        assert body["response_format"] == rf_list
+        assert "response_mime_type" not in body
+
+    def test_image_config_appended_to_response_format_list_without_mutating_input(
+        self, config
+    ):
+        """When response_format is already a list, image_config must not mutate optional_params."""
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = False
+            text_rf = {"type": "text", "mime_type": "application/json"}
+            optional_params = {
+                "response_format": [text_rf],
+                "generation_config": {
+                    "image_config": {"aspect_ratio": "16:9", "image_size": "2K"},
+                },
+            }
+            original_rf = optional_params["response_format"]
+
+            body = config.transform_request(
+                model="gemini/gemini-2.5-flash",
+                agent=None,
+                input="draw and summarise",
+                optional_params=optional_params,
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+            assert optional_params["response_format"] is original_rf
+            assert len(optional_params["response_format"]) == 1
+            assert body["response_format"] == [
+                text_rf,
+                {"type": "image", "aspect_ratio": "16:9", "image_size": "2K"},
+            ]
+
+            # Retry must not append a second image entry into the caller's list.
+            body_retry = config.transform_request(
+                model="gemini/gemini-2.5-flash",
+                agent=None,
+                input="draw and summarise",
+                optional_params=optional_params,
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+            assert len(optional_params["response_format"]) == 1
+            assert body_retry["response_format"] == body["response_format"]
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+    def test_legacy_schema_passes_fields_unchanged(self, config):
+        original = litellm.use_legacy_interactions_schema
+        try:
+            litellm.use_legacy_interactions_schema = True
+            body = config.transform_request(
+                model="gemini/gemini-2.5-flash",
+                agent=None,
+                input="hello",
+                optional_params={
+                    "response_mime_type": "application/json",
+                    "generation_config": {"image_config": {"aspect_ratio": "16:9"}},
+                },
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+        finally:
+            litellm.use_legacy_interactions_schema = original
+
+        assert body["response_mime_type"] == "application/json"
+        assert body["generation_config"]["image_config"]["aspect_ratio"] == "16:9"
diff --git a/tests/test_litellm/interactions/test_openapi_compliance.py b/tests/test_litellm/interactions/test_openapi_compliance.py
index 207e1b02c51..c594c7958e1 100644
--- a/tests/test_litellm/interactions/test_openapi_compliance.py
+++ b/tests/test_litellm/interactions/test_openapi_compliance.py
@@ -179,7 +179,10 @@ class TestResponseCompliance:
         # `status` is an output-only field; validate against the response schema.
         schema = spec_dict["components"]["schemas"]["Interaction"]
         status_prop = schema["properties"]["status"]
-        # Google Interactions API uses lowercase status values (updated Feb 2026)
+        # Google Interactions API uses lowercase status values (updated Feb 2026).
+        # Keep this an exact match: this test intentionally breaks CI when
+        # Google changes the live spec — that breakage is how we get notified
+        # to review the change.
         expected_statuses = [
             "in_progress",
             "requires_action",
diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
index c6961477a58..07ab29c5231 100644
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -2078,6 +2078,146 @@ async def test_async_success_handler_preserves_response_cost_for_pass_through_en
     assert slo["response_cost"] > 0
 
 
+def test_process_hidden_params_recalculates_cost_after_failure_handler_zero():
+    """
+    Regression: PR #21844 preserved response_cost=0 set by failure_handler on failed
+    router retry attempts, so a later successful response with usage logged $0 spend.
+    """
+    from datetime import datetime
+
+    import litellm
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+    from litellm.types.utils import ModelResponse, Usage
+
+    logging_obj = LiteLLMLoggingObj(
+        model="openai/gpt-4o-mini",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=False,
+        call_type="acompletion",
+        start_time=datetime.now(),
+        litellm_call_id="test-retry-zero-cost",
+        function_id="test-retry-zero-cost",
+    )
+    logging_obj.model_call_details["litellm_params"] = {"model": "openai/gpt-4o-mini"}
+    logging_obj.optional_params = {}
+
+    err = litellm.RateLimitError(
+        message="rate limit",
+        llm_provider="openai",
+        model="openai/gpt-4o-mini",
+    )
+    for _ in range(2):
+        logging_obj._failure_handler_helper_fn(
+            exception=err,
+            traceback_exception="",
+            start_time=datetime.now(),
+            end_time=datetime.now(),
+        )
+    assert logging_obj.model_call_details.get("response_cost") == 0
+
+    result = ModelResponse(
+        id="success",
+        choices=[{"message": {"role": "assistant", "content": "ok"}}],
+        usage=Usage(prompt_tokens=9698, completion_tokens=30, total_tokens=9728),
+    )
+    logging_obj._process_hidden_params_and_response_cost(
+        result, datetime.now(), datetime.now()
+    )
+
+    cost = logging_obj.model_call_details.get("response_cost")
+    assert cost is not None and cost > 0
+    slo = logging_obj.model_call_details.get("standard_logging_object") or {}
+    assert slo.get("response_cost", 0) > 0
+
+
+def test_process_hidden_params_preserves_zero_cost_in_hidden_params():
+    """Pass-through handlers often set response_cost on result._hidden_params (including 0)."""
+    from datetime import datetime
+
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+    from litellm.types.utils import ModelResponse, Usage
+
+    logging_obj = LiteLLMLoggingObj(
+        model="gemini-2.5-flash-lite",
+        messages=[{"role": "user", "content": "test"}],
+        stream=False,
+        call_type="pass_through_endpoint",
+        start_time=datetime.now(),
+        litellm_call_id="test-hidden-zero-cost",
+        function_id="test-hidden-zero-cost",
+    )
+    logging_obj.model_call_details["litellm_params"] = {
+        "model": "gemini-2.5-flash-lite"
+    }
+    logging_obj.optional_params = {}
+
+    result = ModelResponse(
+        id="batch-pending",
+        choices=[{"message": {"role": "assistant", "content": "pending"}}],
+        usage=Usage(prompt_tokens=100, completion_tokens=10, total_tokens=110),
+    )
+    result._hidden_params = {"response_cost": 0.0}
+
+    logging_obj._process_hidden_params_and_response_cost(
+        result, datetime.now(), datetime.now()
+    )
+
+    assert logging_obj.model_call_details.get("response_cost") == 0.0
+    slo = logging_obj.model_call_details.get("standard_logging_object") or {}
+    assert slo.get("response_cost") == 0.0
+
+
+def test_process_hidden_params_uses_hidden_params_cost_after_failure_handler_zero():
+    """After retry failures pin model_call_details to 0, success cost on _hidden_params wins."""
+    from datetime import datetime
+
+    import litellm
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+    from litellm.types.utils import ModelResponse, Usage
+
+    logging_obj = LiteLLMLoggingObj(
+        model="openai/gpt-4o-mini",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=False,
+        call_type="acompletion",
+        start_time=datetime.now(),
+        litellm_call_id="test-retry-hidden-cost",
+        function_id="test-retry-hidden-cost",
+    )
+    logging_obj.model_call_details["litellm_params"] = {"model": "openai/gpt-4o-mini"}
+    logging_obj.optional_params = {}
+
+    err = litellm.RateLimitError(
+        message="rate limit",
+        llm_provider="openai",
+        model="openai/gpt-4o-mini",
+    )
+    for _ in range(2):
+        logging_obj._failure_handler_helper_fn(
+            exception=err,
+            traceback_exception="",
+            start_time=datetime.now(),
+            end_time=datetime.now(),
+        )
+    assert logging_obj.model_call_details.get("response_cost") == 0
+
+    passthrough_cost = 0.00042
+    result = ModelResponse(
+        id="success",
+        choices=[{"message": {"role": "assistant", "content": "ok"}}],
+        usage=Usage(prompt_tokens=9698, completion_tokens=30, total_tokens=9728),
+    )
+    result._hidden_params = {"response_cost": passthrough_cost}
+
+    logging_obj._process_hidden_params_and_response_cost(
+        result, datetime.now(), datetime.now()
+    )
+
+    assert logging_obj.model_call_details.get("response_cost") == passthrough_cost
+    slo = logging_obj.model_call_details.get("standard_logging_object") or {}
+    assert slo.get("response_cost") == passthrough_cost
+
+
 def test_function_setup_litellm_metadata_populates_metadata():
     """
     Test that function_setup() properly handles litellm_metadata (used by /v1/messages,
diff --git a/tests/test_litellm/litellm_core_utils/test_token_counter.py b/tests/test_litellm/litellm_core_utils/test_token_counter.py
index 828aa9167f5..3cf5e82819d 100644
--- a/tests/test_litellm/litellm_core_utils/test_token_counter.py
+++ b/tests/test_litellm/litellm_core_utils/test_token_counter.py
@@ -437,35 +437,39 @@ def test_gpt_4o_token_counter():
 @pytest.mark.parametrize(
     "img_url",
     [
-        "https://blog.purpureus.net/assets/blog/personal_key_rotation/simplified-asset-graph.jpg",
+        "https://example.com/test-image.png",
         "data:image/png;base64,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",
     ],
 )
-def test_img_url_token_counter(img_url):
+def test_img_url_token_counter(img_url, monkeypatch):
+    """
+    Verify get_image_dimensions returns valid (width, height) for both an
+    HTTPS URL and a base64 data URI. The HTTPS branch is exercised with a
+    mocked HTTP fetch so the test is hermetic - it can't break when a
+    third-party image URL goes away.
+    """
+    import base64
     from litellm.litellm_core_utils.token_counter import get_image_dimensions
 
-    if img_url.startswith(("http://", "https://")):
-        # Create a minimal valid JPEG binary (1x1 pixel) to avoid real network calls
-        import struct
+    # Minimal valid 1x1 PNG, served by the mocked safe_get for the URL case.
+    _tiny_png = base64.b64decode(
+        "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII="
+    )
 
-        # Minimal JPEG: SOI, APP0, SOF0 (1x1), SOS, EOI
-        jpeg_bytes = (
-            b"\xff\xd8"  # SOI
-            b"\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00"  # APP0
-            b"\xff\xc0\x00\x0b\x08\x00\x10\x00\x20\x01\x01\x11\x00"  # SOF0: h=16, w=32
-            b"\xff\xda\x00\x08\x01\x01\x00\x00?\x00\x00"  # SOS
-            b"\xff\xd9"  # EOI
-        )
-        mock_response = MagicMock()
-        mock_response.headers = {"Content-Length": str(len(jpeg_bytes))}
-        mock_response.read.return_value = jpeg_bytes
-        with patch(
+    if img_url.startswith(("http://", "https://")):
+
+        class _FakeResponse:
+            headers = {"Content-Length": str(len(_tiny_png))}
+
+            def read(self):
+                return _tiny_png
+
+        monkeypatch.setattr(
             "litellm.litellm_core_utils.token_counter.safe_get",
-            return_value=mock_response,
-        ):
-            width, height = get_image_dimensions(data=img_url)
-    else:
-        width, height = get_image_dimensions(data=img_url)
+            lambda client, url, **kw: _FakeResponse(),
+        )
+
+    width, height = get_image_dimensions(data=img_url)
 
     print(width, height)
 
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
index a19752dc648..7d9e4768303 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
@@ -2476,6 +2476,120 @@ def test_reasoning_effort_does_not_set_output_config_for_older_models():
         ), f"output_config should not be set for {model}"
 
 
+@pytest.mark.parametrize(
+    "reasoning_effort_value",
+    [
+        # String shape — what callers send when using `reasoning_effort="low"` directly.
+        "low",
+        # Dict shape with `effort` only — what the Responses->Chat parser produces
+        # when `reasoning={"effort": "low"}` is set without `summary`.
+        {"effort": "low"},
+        # Dict shape with `effort` AND `summary` — what the Responses->Chat parser
+        # produces when callers send `Reasoning(effort="low", summary="concise")`.
+        # PR #25359 added the dict-keeping branch for this case, but the Anthropic
+        # transformation must coerce the dict back to a string before mapping.
+        {"effort": "low", "summary": "concise"},
+        {"effort": "low", "summary": "detailed"},
+    ],
+)
+def test_reasoning_effort_accepts_dict_shape_for_adaptive_model(reasoning_effort_value):
+    """
+    Adaptive-thinking (Claude 4.6+) branch: dict-shape reasoning_effort must
+    map to ``thinking.type='adaptive'`` + ``output_config.effort``.
+
+    Regression test for the dict-shape ``reasoning_effort`` produced by the
+    Responses->Chat parser when ``summary`` is set on the request's
+    ``reasoning`` field. Before this fix, the Anthropic transformation guarded
+    on ``isinstance(value, str)`` and silently dropped the param — disabling
+    extended thinking entirely.
+    """
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"reasoning_effort": reasoning_effort_value},
+        optional_params={},
+        model="claude-sonnet-4-6-20260219",
+        drop_params=False,
+    )
+
+    # thinking must be set (adaptive for 4.6+)
+    assert "thinking" in result, (
+        f"thinking missing for reasoning_effort={reasoning_effort_value!r}"
+    )
+    assert result["thinking"]["type"] == "adaptive"
+    # output_config must carry the mapped effort
+    assert "output_config" in result, (
+        f"output_config missing for reasoning_effort={reasoning_effort_value!r}"
+    )
+    assert result["output_config"]["effort"] == "low"
+
+
+@pytest.mark.parametrize(
+    "reasoning_effort_value",
+    [
+        "low",
+        {"effort": "low"},
+        {"effort": "low", "summary": "concise"},
+    ],
+)
+def test_reasoning_effort_accepts_dict_shape_for_non_adaptive_model(reasoning_effort_value):
+    """
+    Non-adaptive (pre-4.6) branch: dict-shape reasoning_effort must still map
+    to ``thinking.type='enabled'`` + ``budget_tokens``. ``output_config`` must
+    NOT be set on these models.
+    """
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"reasoning_effort": reasoning_effort_value},
+        optional_params={},
+        model="claude-sonnet-4-5-20250929",
+        drop_params=False,
+    )
+
+    assert "thinking" in result, (
+        f"thinking missing for reasoning_effort={reasoning_effort_value!r}"
+    )
+    assert result["thinking"]["type"] == "enabled"
+    assert "budget_tokens" in result["thinking"]
+    assert result["thinking"]["budget_tokens"] > 0
+    # Older models must not get adaptive-thinking output_config
+    assert "output_config" not in result, (
+        f"output_config should not be set for non-adaptive model "
+        f"(reasoning_effort={reasoning_effort_value!r})"
+    )
+
+
+@pytest.mark.parametrize(
+    "bad_value",
+    [
+        {"summary": "concise"},  # missing effort
+        {"effort": None},  # explicit None effort
+        {"effort": 123},  # non-string effort
+    ],
+)
+def test_reasoning_effort_unparseable_dict_is_dropped(bad_value):
+    """
+    A dict shape that doesn't carry a usable ``effort`` key (e.g. only
+    ``summary`` is set, or the value is some other unexpected type) should be
+    silently dropped — not crash, not partially apply.
+    """
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"reasoning_effort": bad_value},
+        optional_params={},
+        model="claude-sonnet-4-6-20260219",
+        drop_params=False,
+    )
+    assert "thinking" not in result, (
+        f"thinking should not be set for bad value {bad_value!r}"
+    )
+    assert "output_config" not in result, (
+        f"output_config should not be set for bad value {bad_value!r}"
+    )
+
+
 @pytest.mark.parametrize(
     "model",
     [
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
index 11465e6f718..44530fecebd 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py
@@ -1203,6 +1203,51 @@ def test_streaming_chunk_with_both_text_and_tool_calls_issue_18238():
     assert content_block_start["id"] == "toolu_bdrk_013xRVejhv3ybmLEGCoZib2b"
 
 
+def test_streaming_chunk_with_text_and_empty_tool_calls_returns_text_delta():
+    """
+    Some OpenAI-compatible providers emit `tool_calls: []` on regular text chunks.
+
+    Empty tool_calls should be treated as no tool call so the Anthropic adapter
+    does not shadow text with an empty input_json_delta.
+    """
+    choices = [
+        StreamingChoices(
+            finish_reason=None,
+            index=0,
+            delta=Delta(
+                provider_specific_fields=None,
+                content="Hello from vLLM",
+                role="assistant",
+                function_call=None,
+                tool_calls=[],
+                audio=None,
+            ),
+            logprobs=None,
+        )
+    ]
+
+    adapter = LiteLLMAnthropicMessagesAdapter()
+
+    (
+        type_of_content,
+        content_block_delta,
+    ) = adapter._translate_streaming_openai_chunk_to_anthropic(choices=choices)
+
+    assert type_of_content == "text_delta"
+    assert content_block_delta["type"] == "text_delta"
+    assert content_block_delta["text"] == "Hello from vLLM"
+
+    (
+        block_type,
+        content_block_start,
+    ) = adapter._translate_streaming_openai_chunk_to_anthropic_content_block(
+        choices=choices
+    )
+
+    assert block_type == "text"
+    assert content_block_start == {"type": "text", "text": ""}
+
+
 # ============================================================================
 # Cache Control Transformation Tests
 # ============================================================================
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
index bd1fe75f363..b1e1d789d74 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
@@ -544,3 +544,132 @@ class TestThinkingSummaryPreservation:
         assert result == {
             "reasoning_effort": {"effort": "medium", "summary": "concise"}
         }
+
+
+# ---------------------------------------------------------------------------
+# Parity tests: redundant empty-text-block sanitization scan removal.
+# The async wrapper sanitizes once and tells the handler to skip its second
+# (redundant) full-messages scan; the sync entry point still sanitizes.
+# ---------------------------------------------------------------------------
+
+
+def _empty_block_msgs():
+    return [
+        {
+            "role": "assistant",
+            "content": [
+                {"type": "text", "text": "   "},  # whitespace-only -> stripped
+                {"type": "tool_use", "id": "t", "name": "B", "input": {}},
+            ],
+        }
+    ]
+
+
+def test_handler_strips_when_no_presanitized_flag():
+    """Sync entry point (no async wrapper): handler must still sanitize."""
+    from litellm.llms.anthropic.experimental_pass_through.messages import handler
+
+    with patch.object(
+        handler,
+        "strip_empty_text_blocks_from_anthropic_messages",
+        wraps=handler.strip_empty_text_blocks_from_anthropic_messages,
+    ) as spy:
+        result = handler.anthropic_messages_handler(
+            max_tokens=10,
+            messages=_empty_block_msgs(),
+            model="anthropic/claude-3-5-sonnet-20241022",
+            custom_llm_provider="anthropic",
+            mock_response="hi there",
+        )
+    assert spy.call_count == 1  # sanitized exactly once here
+    assert result is not None
+
+
+def test_handler_skips_strip_when_presanitized():
+    """Async wrapper already sanitized -> handler must NOT rescan."""
+    from litellm.llms.anthropic.experimental_pass_through.messages import handler
+
+    with patch.object(
+        handler,
+        "strip_empty_text_blocks_from_anthropic_messages",
+        wraps=handler.strip_empty_text_blocks_from_anthropic_messages,
+    ) as spy:
+        result = handler.anthropic_messages_handler(
+            max_tokens=10,
+            messages=_empty_block_msgs(),
+            model="anthropic/claude-3-5-sonnet-20241022",
+            custom_llm_provider="anthropic",
+            mock_response="hi there",
+            _litellm_messages_presanitized=True,
+        )
+    assert spy.call_count == 0  # skipped the redundant scan
+    assert result is not None
+
+
+def test_presanitized_flag_not_leaked_to_provider_params():
+    """The private sentinel must be popped, never forwarded as a request param."""
+    from litellm.llms.anthropic.experimental_pass_through.messages import handler
+
+    captured = {}
+
+    def fake_base_handler(*args, **kwargs):
+        captured.update(kwargs)
+        captured["optional"] = kwargs.get(
+            "anthropic_messages_optional_request_params", {}
+        )
+        return "stub"
+
+    with patch.object(
+        handler.base_llm_http_handler,
+        "anthropic_messages_handler",
+        side_effect=fake_base_handler,
+    ):
+        handler.anthropic_messages_handler(
+            max_tokens=10,
+            messages=[{"role": "user", "content": "hi"}],
+            model="anthropic/claude-3-5-sonnet-20241022",
+            custom_llm_provider="anthropic",
+            _litellm_messages_presanitized=True,
+        )
+
+    assert "_litellm_messages_presanitized" not in captured.get("optional", {})
+    assert "_litellm_messages_presanitized" not in captured.get("kwargs", {})
+
+
+@pytest.mark.asyncio
+async def test_async_wrapper_sets_presanitized_and_sanitizes_once():
+    """End-to-end: wrapper sanitizes (once) AND signals the handler to skip."""
+    from litellm.llms.anthropic.experimental_pass_through.messages import handler
+
+    captured = {}
+
+    def fake_handler(*args, **kwargs):
+        captured["messages"] = kwargs.get("messages")
+        captured["presanitized"] = kwargs.get("_litellm_messages_presanitized")
+        return "stub"
+
+    fake_loop = MagicMock()
+    fake_loop.run_in_executor = lambda _e, func: _async_return(func())
+
+    with (
+        patch.object(handler, "anthropic_messages_handler", side_effect=fake_handler),
+        patch("asyncio.get_event_loop", return_value=fake_loop),
+        patch.object(
+            handler,
+            "strip_empty_text_blocks_from_anthropic_messages",
+            wraps=handler.strip_empty_text_blocks_from_anthropic_messages,
+        ) as spy,
+    ):
+        await handler.anthropic_messages(
+            max_tokens=100,
+            messages=_empty_block_msgs(),
+            model="anthropic/claude-sonnet-4-5-20250929",
+            custom_llm_provider="anthropic",
+            api_key="k",
+        )
+
+    # Wrapper stripped exactly once (the handler is faked, so its skipped
+    # call never runs anyway -- the point is the wrapper still sanitizes).
+    assert spy.call_count == 1
+    assert captured["presanitized"] is True
+    assert [b["type"] for b in captured["messages"][0]["content"]] == ["tool_use"]
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_request_optional_param_utils.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_request_optional_param_utils.py
new file mode 100644
index 00000000000..3ce076640e8
--- /dev/null
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_request_optional_param_utils.py
@@ -0,0 +1,56 @@
+"""
+Regression tests for the /v1/messages request-parse fast paths:
+
+- get_requested_anthropic_messages_optional_param must still filter to the
+  valid AnthropicMessagesRequestOptionalParams keys and drop None values,
+  while resolving the (static) type hints only once per process.
+"""
+
+from litellm.llms.anthropic.experimental_pass_through.messages.utils import (
+    AnthropicMessagesRequestUtils,
+    _anthropic_messages_optional_param_keys,
+)
+
+
+def test_optional_param_filtering_unchanged():
+    params = {
+        "temperature": 0.5,
+        "top_p": None,  # None dropped
+        "tools": [{"name": "x"}],
+        "not_a_real_param": "drop me",  # invalid key dropped
+        "stream": True,
+    }
+    result = (
+        AnthropicMessagesRequestUtils.get_requested_anthropic_messages_optional_param(
+            params
+        )
+    )
+    assert result == {"temperature": 0.5, "tools": [{"name": "x"}], "stream": True}
+    assert "top_p" not in result
+    assert "not_a_real_param" not in result
+
+
+def test_valid_keys_are_memoized():
+    _anthropic_messages_optional_param_keys.cache_clear()
+    first = _anthropic_messages_optional_param_keys()
+    for _ in range(50):
+        AnthropicMessagesRequestUtils.get_requested_anthropic_messages_optional_param(
+            {"temperature": 0.1}
+        )
+    info = _anthropic_messages_optional_param_keys.cache_info()
+    # Resolved exactly once despite many calls.
+    assert info.misses == 1
+    assert info.hits >= 50
+    # Stable identity (frozenset) returned each call.
+    assert _anthropic_messages_optional_param_keys() is first
+    assert isinstance(first, frozenset)
+    assert "temperature" in first and "tools" in first
+
+
+def test_empty_params():
+    assert (
+        AnthropicMessagesRequestUtils.get_requested_anthropic_messages_optional_param(
+            {}
+        )
+        == {}
+    )
diff --git a/tests/test_litellm/llms/azure/chat/test_azure_base_model_routing.py b/tests/test_litellm/llms/azure/chat/test_azure_base_model_routing.py
new file mode 100644
index 00000000000..1e8e23c38ca
--- /dev/null
+++ b/tests/test_litellm/llms/azure/chat/test_azure_base_model_routing.py
@@ -0,0 +1,274 @@
+"""Tests for decoupling Azure deployment IDs from underlying model names.
+
+When users name their Azure deployment something non-standard (e.g. "my-deployment-id"),
+setting ``base_model`` should drive model-type detection (o-series, gpt-5,
+etc.) so the correct config, supported params, and param mapping are used.
+"""
+
+import pytest
+
+import litellm
+from litellm.llms.azure.chat.gpt_5_transformation import AzureOpenAIGPT5Config
+from litellm.llms.azure.chat.o_series_transformation import AzureOpenAIO1Config
+from litellm.utils import ProviderConfigManager, get_optional_params
+
+
+# ---------------------------------------------------------------------------
+# _get_azure_config — routes to the correct config based on base_model
+# ---------------------------------------------------------------------------
+class TestGetAzureConfigWithBaseModel:
+    """ProviderConfigManager._get_azure_config should use base_model for detection."""
+
+    def test_should_return_gpt5_config_when_base_model_is_gpt5(self):
+        config = ProviderConfigManager._get_azure_config(
+            model="my-deployment-id", base_model="azure/gpt-5.2"
+        )
+        assert isinstance(config, AzureOpenAIGPT5Config)
+
+    def test_should_return_o_series_config_when_base_model_is_o_series(self):
+        config = ProviderConfigManager._get_azure_config(
+            model="my-deployment-id", base_model="azure/o4-mini"
+        )
+        assert isinstance(config, AzureOpenAIO1Config)
+
+    def test_should_return_default_config_when_base_model_is_regular(self):
+        config = ProviderConfigManager._get_azure_config(
+            model="my-deployment-id", base_model="azure/gpt-4o"
+        )
+        assert type(config).__name__ == "AzureOpenAIConfig"
+
+    def test_should_fallback_to_model_when_base_model_is_none(self):
+        config = ProviderConfigManager._get_azure_config(
+            model="gpt-5.2", base_model=None
+        )
+        assert isinstance(config, AzureOpenAIGPT5Config)
+
+    def test_should_return_default_config_when_both_are_non_standard(self):
+        config = ProviderConfigManager._get_azure_config(
+            model="my-deployment-id", base_model=None
+        )
+        assert type(config).__name__ == "AzureOpenAIConfig"
+
+
+# ---------------------------------------------------------------------------
+# get_provider_chat_config — threads base_model through for Azure
+# ---------------------------------------------------------------------------
+class TestGetProviderChatConfigWithBaseModel:
+    """get_provider_chat_config should pass base_model to Azure config selection."""
+
+    def test_should_return_gpt5_config_for_custom_deployment_with_base_model(self):
+        from litellm.types.utils import LlmProviders
+
+        config = ProviderConfigManager.get_provider_chat_config(
+            model="my-deployment-id",
+            provider=LlmProviders.AZURE,
+            base_model="azure/gpt-5",
+        )
+        assert isinstance(config, AzureOpenAIGPT5Config)
+
+    def test_should_return_o_series_config_for_custom_deployment_with_base_model(self):
+        from litellm.types.utils import LlmProviders
+
+        config = ProviderConfigManager.get_provider_chat_config(
+            model="my-other-deployment",
+            provider=LlmProviders.AZURE,
+            base_model="azure/o3-mini",
+        )
+        assert isinstance(config, AzureOpenAIO1Config)
+
+
+# ---------------------------------------------------------------------------
+# get_supported_openai_params — base_model drives Azure param detection
+# ---------------------------------------------------------------------------
+class TestGetSupportedOpenAIParamsWithBaseModel:
+    """get_supported_openai_params should use base_model for Azure detection."""
+
+    def test_should_return_gpt5_params_for_custom_deployment_with_gpt5_base_model(
+        self,
+    ):
+        params = litellm.get_supported_openai_params(
+            model="my-deployment-id",
+            custom_llm_provider="azure",
+            base_model="azure/gpt-5",
+        )
+        assert params is not None
+        assert "reasoning_effort" in params
+        # gpt-5 maps max_tokens -> max_completion_tokens, verifying we got GPT-5 config
+        assert "max_completion_tokens" in params
+
+    def test_should_return_o_series_params_for_custom_deployment_with_o_series_base_model(
+        self,
+    ):
+        params = litellm.get_supported_openai_params(
+            model="my-other-deployment",
+            custom_llm_provider="azure",
+            base_model="azure/o4-mini",
+        )
+        assert params is not None
+        assert "reasoning_effort" in params
+
+    def test_should_return_regular_params_when_no_base_model(self):
+        """When base_model is not set and model is non-standard, default Azure config."""
+        params = litellm.get_supported_openai_params(
+            model="my-deployment-id",
+            custom_llm_provider="azure",
+        )
+        assert params is not None
+        # Default Azure config supports temperature
+        assert "temperature" in params
+
+
+# ---------------------------------------------------------------------------
+# get_optional_params — base_model drives Azure param mapping
+# ---------------------------------------------------------------------------
+class TestGetOptionalParamsWithBaseModel:
+    """get_optional_params should use base_model for Azure model-type detection."""
+
+    def test_should_map_max_tokens_for_custom_deployment_with_gpt5_base_model(self):
+        """A non-standard deployment name + gpt-5 base_model should map max_tokens -> max_completion_tokens."""
+        params = get_optional_params(
+            model="my-deployment-id",
+            custom_llm_provider="azure",
+            max_tokens=100,
+            base_model="azure/gpt-5",
+        )
+        assert params.get("max_completion_tokens") == 100
+        assert "max_tokens" not in params
+
+    def test_should_keep_max_tokens_for_custom_deployment_without_base_model(self):
+        """A non-standard deployment name without base_model should use default Azure config."""
+        params = get_optional_params(
+            model="my-deployment-id",
+            custom_llm_provider="azure",
+            max_tokens=100,
+            api_version="2024-05-01-preview",
+        )
+        # Default AzureOpenAIConfig keeps max_tokens as-is (or maps based on api_version)
+        assert "max_tokens" in params or "max_completion_tokens" in params
+
+    def test_should_support_reasoning_effort_for_custom_deployment_with_o_series_base_model(
+        self,
+    ):
+        """A non-standard deployment name + o-series base_model should accept reasoning_effort."""
+        params = get_optional_params(
+            model="my-other-deployment",
+            custom_llm_provider="azure",
+            reasoning_effort="low",
+            base_model="azure/o4-mini",
+        )
+        assert params.get("reasoning_effort") == "low"
+
+    def test_should_reject_temperature_for_custom_deployment_with_gpt5_base_model(
+        self,
+    ):
+        """A non-standard deployment + gpt-5 base_model should reject temperature."""
+        with pytest.raises(litellm.UnsupportedParamsError):
+            get_optional_params(
+                model="my-deployment-id",
+                custom_llm_provider="azure",
+                temperature=0.5,
+                base_model="azure/gpt-5",
+            )
+
+
+# ---------------------------------------------------------------------------
+# Backward compatibility — existing patterns still work
+# ---------------------------------------------------------------------------
+class TestBackwardCompatibility:
+    """Existing model-name-based and prefix-based patterns must keep working."""
+
+    def test_should_detect_gpt5_from_model_name(self):
+        config = ProviderConfigManager._get_azure_config(model="gpt-5.2")
+        assert isinstance(config, AzureOpenAIGPT5Config)
+
+    def test_should_detect_gpt5_from_gpt5_series_prefix(self):
+        config = ProviderConfigManager._get_azure_config(
+            model="gpt5_series/my-deployment"
+        )
+        assert isinstance(config, AzureOpenAIGPT5Config)
+
+    def test_should_detect_o_series_from_model_name(self):
+        config = ProviderConfigManager._get_azure_config(model="o4-mini")
+        assert isinstance(config, AzureOpenAIO1Config)
+
+    def test_should_detect_o_series_from_o_series_prefix(self):
+        config = ProviderConfigManager._get_azure_config(model="o_series/my-deployment")
+        assert isinstance(config, AzureOpenAIO1Config)
+
+    def test_should_handle_gpt5_chat_model_correctly(self):
+        """gpt-5-chat models should NOT be routed to GPT-5 config."""
+        config = ProviderConfigManager._get_azure_config(model="gpt-5-chat")
+        assert type(config).__name__ == "AzureOpenAIConfig"
+
+    def test_base_model_overrides_model_detection(self):
+        """base_model should take priority over model for type detection."""
+        # model looks like o-series, but base_model says gpt-5
+        config = ProviderConfigManager._get_azure_config(
+            model="o3-mini", base_model="azure/gpt-5.2"
+        )
+        assert isinstance(config, AzureOpenAIGPT5Config)
+
+
+# ---------------------------------------------------------------------------
+# Deep config method awareness — base_model flows into config internals
+# ---------------------------------------------------------------------------
+class TestBaseModelFlowsIntoConfigInternals:
+    """base_model should be used by config internal methods (e.g. is_model_gpt_5_2_model)."""
+
+    def test_should_support_logprobs_for_prefixed_deployment_with_gpt52_base_model(
+        self,
+    ):
+        """Deployment 'my-gpt-5.2' with base_model='azure/gpt-5.2' should support logprobs."""
+        params = litellm.get_supported_openai_params(
+            model="gpt5_series/my-gpt-5.2",
+            custom_llm_provider="azure",
+            base_model="azure/gpt-5.2",
+        )
+        assert params is not None
+        assert "logprobs" in params
+        assert "top_logprobs" in params
+
+    def test_should_support_logprobs_for_plain_deployment_with_gpt52_base_model(self):
+        """Deployment 'my-deployment-id' with base_model='azure/gpt-5.2' should support logprobs."""
+        params = litellm.get_supported_openai_params(
+            model="my-deployment-id",
+            custom_llm_provider="azure",
+            base_model="azure/gpt-5.2",
+        )
+        assert params is not None
+        assert "logprobs" in params
+        assert "top_logprobs" in params
+
+    def test_should_not_support_logprobs_for_gpt5_base_model(self):
+        """Deployment with base_model='azure/gpt-5' (not 5.2) should NOT support logprobs."""
+        params = litellm.get_supported_openai_params(
+            model="my-deployment-id",
+            custom_llm_provider="azure",
+            base_model="azure/gpt-5",
+        )
+        assert params is not None
+        assert "logprobs" not in params
+        assert "top_logprobs" not in params
+
+    def test_should_pass_logprobs_through_get_optional_params(self):
+        """logprobs should pass validation in get_optional_params when base_model is gpt-5.2."""
+        params = get_optional_params(
+            model="gpt5_series/my-gpt-5.2",
+            custom_llm_provider="azure",
+            logprobs=True,
+            top_logprobs=5,
+            base_model="azure/gpt-5.2",
+        )
+        assert params.get("logprobs") is True
+        assert params.get("top_logprobs") == 5
+
+    def test_should_map_max_tokens_for_prefixed_deployment_with_gpt5_base_model(self):
+        """my-gpt-5.2 with base_model should correctly map max_tokens -> max_completion_tokens."""
+        params = get_optional_params(
+            model="gpt5_series/my-gpt-5.2",
+            custom_llm_provider="azure",
+            max_tokens=200,
+            base_model="azure/gpt-5.2",
+        )
+        assert params.get("max_completion_tokens") == 200
+        assert "max_tokens" not in params
diff --git a/tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py b/tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py
new file mode 100644
index 00000000000..6ed6be6f34f
--- /dev/null
+++ b/tests/test_litellm/llms/azure/test_azure_speech_audio_transcription.py
@@ -0,0 +1,239 @@
+import io
+import json
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import httpx
+import pytest
+
+import litellm
+from litellm.llms.azure.audio_transcription.transformation import (
+    AzureSpeechAudioTranscriptionConfig,
+    AzureSpeechAudioTranscriptionException,
+)
+from litellm.llms.base_llm.audio_transcription.transformation import (
+    AudioTranscriptionRequestData,
+    BaseAudioTranscriptionConfig,
+)
+from litellm.types.utils import TranscriptionResponse
+from litellm.utils import ProviderConfigManager
+
+
+def test_azure_speech_audio_transcription_config_installed():
+    config = ProviderConfigManager.get_provider_audio_transcription_config(
+        model="speech/azure-stt",
+        provider=litellm.LlmProviders.AZURE,
+    )
+
+    assert isinstance(config, BaseAudioTranscriptionConfig)
+    assert isinstance(config, AzureSpeechAudioTranscriptionConfig)
+
+
+def test_azure_speech_audio_transcription_builds_stt_url_from_cognitive_endpoint():
+    config = AzureSpeechAudioTranscriptionConfig()
+
+    url = config.get_complete_url(
+        api_base="https://eastus.api.cognitive.microsoft.com/",
+        api_key="test-key",
+        model="speech/azure-stt",
+        optional_params={"language": "fr-FR", "response_format": "verbose_json"},
+        litellm_params={},
+    )
+
+    assert (
+        url
+        == "https://eastus.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=fr-FR&format=detailed"
+    )
+
+
+def test_azure_speech_audio_transcription_accepts_stt_endpoint_base():
+    config = AzureSpeechAudioTranscriptionConfig()
+
+    url = config.get_complete_url(
+        api_base="https://westus.stt.speech.microsoft.com",
+        api_key="test-key",
+        model="speech/azure-stt",
+        optional_params={},
+        litellm_params={},
+    )
+
+    assert (
+        url
+        == "https://westus.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=en-US&format=simple"
+    )
+
+
+def test_azure_speech_audio_transcription_uses_dedicated_api_base_env(monkeypatch):
+    config = AzureSpeechAudioTranscriptionConfig()
+
+    monkeypatch.setattr(
+        "litellm.llms.azure.audio_transcription.transformation.get_secret_str",
+        lambda key: (
+            "https://centralus.api.cognitive.microsoft.com"
+            if key == "AZURE_SPEECH_API_BASE"
+            else None
+        ),
+    )
+
+    url = config.get_complete_url(
+        api_base=None,
+        api_key="test-key",
+        model="speech/azure-stt",
+        optional_params={},
+        litellm_params={},
+    )
+
+    assert (
+        url
+        == "https://centralus.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=en-US&format=simple"
+    )
+
+
+def test_azure_speech_audio_transcription_rejects_azure_openai_endpoint():
+    config = AzureSpeechAudioTranscriptionConfig()
+
+    with pytest.raises(
+        AzureSpeechAudioTranscriptionException,
+        match="not an Azure OpenAI endpoint",
+    ):
+        config.get_complete_url(
+            api_base="https://example.openai.azure.com",
+            api_key="test-key",
+            model="speech/azure-stt",
+            optional_params={},
+            litellm_params={},
+        )
+
+
+def test_azure_speech_audio_transcription_validate_environment():
+    config = AzureSpeechAudioTranscriptionConfig()
+
+    headers = config.validate_environment(
+        headers={},
+        model="speech/azure-stt",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key="test-key",
+    )
+
+    assert headers["Ocp-Apim-Subscription-Key"] == "test-key"
+    assert headers["Content-Type"] == "audio/wav"
+    assert headers["Accept"] == "application/json"
+
+
+def test_azure_speech_audio_transcription_uses_dedicated_api_key_env(monkeypatch):
+    config = AzureSpeechAudioTranscriptionConfig()
+
+    monkeypatch.setattr(
+        "litellm.llms.azure.audio_transcription.transformation.get_secret_str",
+        lambda key: "speech-key" if key == "AZURE_SPEECH_API_KEY" else None,
+    )
+
+    headers = config.validate_environment(
+        headers={},
+        model="speech/azure-stt",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key=None,
+    )
+
+    assert headers["Ocp-Apim-Subscription-Key"] == "speech-key"
+
+
+def test_azure_speech_audio_transcription_request_transform():
+    config = AzureSpeechAudioTranscriptionConfig()
+    audio = io.BytesIO(b"RIFF....WAVE")
+
+    request_data = config.transform_audio_transcription_request(
+        model="speech/azure-stt",
+        audio_file=audio,
+        optional_params={},
+        litellm_params={},
+    )
+
+    assert isinstance(request_data, AudioTranscriptionRequestData)
+    assert request_data.data == b"RIFF....WAVE"
+    assert request_data.files is None
+    assert request_data.content_type == "audio/wav"
+
+
+@pytest.mark.parametrize(
+    "payload,expected_text",
+    [
+        ({"DisplayText": "hello world"}, "hello world"),
+        (
+            {
+                "RecognitionStatus": "Success",
+                "NBest": [{"Display": "best text", "Confidence": 0.91}],
+            },
+            "best text",
+        ),
+    ],
+)
+def test_azure_speech_audio_transcription_response_transform(payload, expected_text):
+    config = AzureSpeechAudioTranscriptionConfig()
+    response = httpx.Response(200, json=payload)
+
+    result = config.transform_audio_transcription_response(response)
+
+    assert isinstance(result, TranscriptionResponse)
+    assert result.text == expected_text
+    assert result._hidden_params == payload
+
+
+def test_azure_speech_audio_transcription_response_raises_on_failed_status():
+    config = AzureSpeechAudioTranscriptionConfig()
+    response = httpx.Response(
+        200,
+        json={
+            "RecognitionStatus": "NoMatch",
+            "Offset": 0,
+            "Duration": 0,
+        },
+    )
+
+    with pytest.raises(
+        AzureSpeechAudioTranscriptionException,
+        match="RecognitionStatus=NoMatch",
+    ):
+        config.transform_audio_transcription_response(response)
+
+
+def test_azure_speech_transcription_routes_through_provider_config(monkeypatch):
+    expected = TranscriptionResponse(text="hello")
+    audio_handler = MagicMock(return_value=expected)
+
+    monkeypatch.setattr(
+        litellm.main.base_llm_http_handler,
+        "audio_transcriptions",
+        audio_handler,
+    )
+
+    response = litellm.transcription(
+        model="azure/speech/azure-stt",
+        file=io.BytesIO(b"RIFF....WAVE"),
+        api_base="https://eastus.api.cognitive.microsoft.com",
+        api_key="test-key",
+        language="en-US",
+    )
+
+    assert response is expected
+    audio_handler.assert_called_once()
+    assert isinstance(
+        audio_handler.call_args.kwargs["provider_config"],
+        AzureSpeechAudioTranscriptionConfig,
+    )
+    assert audio_handler.call_args.kwargs["custom_llm_provider"] == "azure"
+
+
+def test_azure_speech_stt_has_non_zero_input_pricing():
+    pricing_path = Path(__file__).parents[4] / "model_prices_and_context_window.json"
+    pricing = json.loads(pricing_path.read_text())
+
+    assert pricing["azure/speech/azure-stt"]["input_cost_per_second"] > 0
+    assert (
+        pricing["azure/speech/azure-stt"]["audio_transcription_config"]
+        == "azure_speech"
+    )
diff --git a/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py b/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py
new file mode 100644
index 00000000000..8de47331614
--- /dev/null
+++ b/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py
@@ -0,0 +1,119 @@
+"""
+Test that BedrockBatchesConfig._get_openai_compatible_batch_metadata
+sanitizes non-string metadata values injected by proxy guardrail hooks.
+
+The OpenAI Batch Pydantic model requires metadata: Dict[str, str].
+Proxy hooks (Model Armor, OpenAI Moderations, queue time tracking) inject
+dicts, floats, and other non-string values that cause a ValidationError
+when constructing LiteLLMBatch. This test suite verifies the sanitization
+layer prevents that.
+"""
+
+import os
+import sys
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig
+
+
+class TestGetOpenaiCompatibleBatchMetadata:
+    """Tests for _get_openai_compatible_batch_metadata."""
+
+    def test_string_values_pass_through_unchanged(self):
+        metadata = {"user_key": "user_value", "run_id": "abc123"}
+        result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+        assert result == {"user_key": "user_value", "run_id": "abc123"}
+
+    def test_dict_values_serialized_to_json_string(self):
+        metadata = {
+            "_model_armor_response": {
+                "sanitizationResult": {"filterMatchState": "MATCH_FOUND"}
+            }
+        }
+        result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+        assert "_model_armor_response" in result
+        assert isinstance(result["_model_armor_response"], str)
+        assert "MATCH_FOUND" in result["_model_armor_response"]
+
+    def test_float_values_serialized_to_string(self):
+        metadata = {"queue_time_seconds": 0.5}
+        result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+        assert result == {"queue_time_seconds": "0.5"}
+
+    def test_none_values_excluded(self):
+        metadata = {"key": "value", "empty": None}
+        result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+        assert "empty" not in result
+        assert result == {"key": "value"}
+
+    def test_standard_logging_guardrail_information_excluded(self):
+        metadata = {
+            "standard_logging_guardrail_information": {"some": "logging_data"},
+            "user_key": "keep_me",
+        }
+        result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+        assert "standard_logging_guardrail_information" not in result
+        assert result == {"user_key": "keep_me"}
+
+    def test_non_dict_input_returns_empty_dict(self):
+        assert BedrockBatchesConfig._get_openai_compatible_batch_metadata(None) == {}
+        assert BedrockBatchesConfig._get_openai_compatible_batch_metadata("string") == {}
+        assert BedrockBatchesConfig._get_openai_compatible_batch_metadata(123) == {}
+
+    def test_empty_dict_returns_empty_dict(self):
+        assert BedrockBatchesConfig._get_openai_compatible_batch_metadata({}) == {}
+
+    def test_mixed_metadata_from_guardrails(self):
+        """Simulate real metadata contaminated by proxy guardrails."""
+        metadata = {
+            "_model_armor_response": {"sanitizationResult": {"key": "val"}},
+            "_model_armor_status": "success",
+            "_openai_moderation_response": {"id": "mod-123", "flagged": False},
+            "queue_time_seconds": 1.23,
+            "headers": {"Authorization": "Bearer sk-xxx"},
+            "standard_logging_guardrail_information": {"internal": True},
+            "user_metadata_key": "user_value",
+            "none_field": None,
+        }
+        result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+
+        # All values must be strings
+        for key, value in result.items():
+            assert isinstance(value, str), f"metadata[{key!r}] is {type(value)}, not str"
+
+        # Excluded keys
+        assert "standard_logging_guardrail_information" not in result
+        assert "none_field" not in result
+
+        # Preserved keys
+        assert result["_model_armor_status"] == "success"
+        assert result["user_metadata_key"] == "user_value"
+
+    def test_result_compatible_with_litellm_batch(self):
+        """Verify sanitized metadata can construct a LiteLLMBatch without error."""
+        import time
+
+        from litellm.types.utils import LiteLLMBatch
+
+        metadata = {
+            "_model_armor_response": {"blocked": True},
+            "queue_time_seconds": 0.05,
+            "user_key": "value",
+        }
+        sanitized = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata)
+
+        # This would raise ValidationError before the fix
+        batch = LiteLLMBatch(
+            id="arn:aws:bedrock:us-east-1:123:model-invocation-job/test",
+            object="batch",
+            endpoint="/v1/chat/completions",
+            input_file_id="file-123",
+            completion_window="24h",
+            status="validating",
+            created_at=int(time.time()),
+            metadata=sanitized,
+        )
+        assert batch.metadata == sanitized
diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
index 4495e3f4101..b2e254901f4 100644
--- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py
@@ -2,6 +2,7 @@ import asyncio
 import json
 import os
 import sys
+from unittest.mock import patch
 
 import pytest
 
@@ -429,6 +430,31 @@ def test_output_config_forwarded_for_bedrock_chat_invoke_request():
     assert result["max_tokens"] == 100
 
 
+def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider():
+    config = AmazonAnthropicClaudeConfig()
+    messages = [{"role": "user", "content": "test"}]
+    optional_params = {"max_tokens": 100, "output_config": {"effort": "high"}}
+
+    with patch(
+        "litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ) as mock_supports_factory:
+        result = config.transform_request(
+            model="us.anthropic.claude-opus-4-7",
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params={},
+            headers={},
+        )
+
+    mock_supports_factory.assert_called_once_with(
+        model="us.anthropic.claude-opus-4-7",
+        custom_llm_provider="bedrock",
+        key="supports_output_config",
+    )
+    assert result["output_config"] == {"effort": "high"}
+
+
 def test_output_format_removed_from_bedrock_invoke_request():
     """
     Test that output_format parameter is removed from Bedrock Invoke requests.
diff --git a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py
index c67a8712340..9955851132c 100644
--- a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py
+++ b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py
@@ -957,3 +957,50 @@ def test_titan_image_embedding_cost_uses_per_image_rate():
         assert response.usage is not None
         assert response.usage.prompt_tokens_details is not None
         assert response.usage.prompt_tokens_details.image_count == 1
+
+
+@pytest.mark.parametrize(
+    "encoding_format,expected_embedding_types",
+    [
+        ("float", ["float"]),
+        ("base64", ["base64"]),
+        (["float", "int8"], ["float", "int8"]),
+    ],
+)
+def test_bedrock_cohere_embedding_types_wrapped_as_list(
+    encoding_format, expected_embedding_types
+):
+    """
+    Bedrock Cohere expects `embedding_types` as a JSON array, not a raw string.
+
+    Regression test for: Bedrock returns
+        Malformed input request: #/embedding_types: expected type: JSONArray, found: String
+    when `encoding_format` is passed as a string.
+    """
+    litellm.set_verbose = True
+    client = HTTPHandler()
+    model = "bedrock/cohere.embed-multilingual-v3"
+
+    with patch.object(client, "post") as mock_post:
+        mock_response = Mock()
+        mock_response.status_code = 200
+        mock_response.text = json.dumps(cohere_embedding_response)
+        mock_response.json = lambda: json.loads(mock_response.text)
+        mock_post.return_value = mock_response
+
+        response = litellm.embedding(
+            model=model,
+            input=test_input,
+            encoding_format=encoding_format,
+            client=client,
+            aws_region_name="us-east-1",
+            aws_bedrock_runtime_endpoint="https://bedrock-runtime.us-east-1.amazonaws.com",
+            api_key="test-bearer-token-12345",
+        )
+
+        assert isinstance(response, litellm.EmbeddingResponse)
+
+        request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}"))
+        assert "embedding_types" in request_body
+        assert request_body["embedding_types"] == expected_embedding_types
+        assert isinstance(request_body["embedding_types"], list)
diff --git a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
index 9ecdad1fcff..2e315a535f0 100644
--- a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
+++ b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py
@@ -592,8 +592,15 @@ def test_remove_scope_from_cache_control():
     assert request["messages"][0]["content"][0]["cache_control"]["type"] == "ephemeral"
 
 
-def test_bedrock_messages_forwards_output_config():
-    """Bedrock Invoke /v1/messages forwards ``output_config`` for adaptive Claude models."""
+def test_bedrock_messages_strips_output_config():
+    """
+    Ensure output_config is stripped from the request for models that do not
+    support it.
+
+    Regression test for: https://github.com/BerriAI/litellm/issues/22797
+    """
+    from unittest.mock import patch
+
     from litellm.types.router import GenericLiteLLMParams
 
     cfg = AmazonAnthropicClaudeMessagesConfig()
@@ -605,21 +612,129 @@ def test_bedrock_messages_forwards_output_config():
         },
     }
 
-    result = cfg.transform_anthropic_messages_request(
-        model="anthropic.claude-opus-4-7",
-        messages=messages,
-        anthropic_messages_optional_request_params=optional_params,
-        litellm_params=GenericLiteLLMParams(),
-        headers={},
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=False,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-3-haiku-20240307-v1:0",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+    assert (
+        "output_config" not in result
+    ), "output_config should be stripped for models that don't support it"
+    assert result.get("max_tokens") == 4096
+
+
+def test_bedrock_messages_preserves_output_config_for_claude_4_6():
+    """
+    Ensure output_config is preserved for models that support it on Bedrock Invoke.
+    """
+    from unittest.mock import patch
+
+    from litellm.types.router import GenericLiteLLMParams
+
+    cfg = AmazonAnthropicClaudeMessagesConfig()
+    messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
+    optional_params = {
+        "max_tokens": 4096,
+        "output_config": {
+            "effort": "high",
+        },
+    }
+
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-opus-4-6-v1",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+    assert (
+        "output_config" in result
+    ), "output_config should be preserved for supported models"
+    assert result["output_config"] == {"effort": "high"}
+    assert result.get("max_tokens") == 4096
+
+
+def test_bedrock_messages_checks_output_config_support_with_bedrock_provider():
+    from unittest.mock import patch
+
+    from litellm.types.router import GenericLiteLLMParams
+
+    cfg = AmazonAnthropicClaudeMessagesConfig()
+    messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
+    optional_params = {
+        "max_tokens": 4096,
+        "output_config": {
+            "effort": "high",
+        },
+    }
+
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ) as mock_supports_factory:
+        result = cfg.transform_anthropic_messages_request(
+            model="us.anthropic.claude-opus-4-7",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+    mock_supports_factory.assert_called_with(
+        model="us.anthropic.claude-opus-4-7",
+        custom_llm_provider="bedrock",
+        key="supports_output_config",
     )
+    assert result["output_config"] == {"effort": "high"}
+
+
+def test_bedrock_messages_forwards_output_config():
+    """Bedrock Invoke /v1/messages forwards ``output_config`` for supported models."""
+    from unittest.mock import patch
+
+    from litellm.types.router import GenericLiteLLMParams
+
+    cfg = AmazonAnthropicClaudeMessagesConfig()
+    messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
+    optional_params = {
+        "max_tokens": 4096,
+        "output_config": {
+            "effort": "high",
+        },
+    }
+
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-opus-4-7",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
 
     assert result.get("output_config") == {"effort": "high"}
-    # Other params should be preserved
     assert result.get("max_tokens") == 4096
 
 
 def test_bedrock_messages_forwards_output_config_with_output_format():
     """``output_config`` is forwarded; ``output_format`` is converted to inline schema."""
+    from unittest.mock import patch
+
     from litellm.types.router import GenericLiteLLMParams
 
     cfg = AmazonAnthropicClaudeMessagesConfig()
@@ -636,39 +751,60 @@ def test_bedrock_messages_forwards_output_config_with_output_format():
         },
     }
 
-    result = cfg.transform_anthropic_messages_request(
-        model="anthropic.claude-opus-4-7",
-        messages=messages,
-        anthropic_messages_optional_request_params=optional_params,
-        litellm_params=GenericLiteLLMParams(),
-        headers={},
-    )
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-opus-4-7",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
 
     assert result.get("output_config") == {"effort": "low"}
     assert "output_format" not in result
 
 
-def test_bedrock_messages_forwards_output_config_for_non_adaptive_model():
-    """``output_config`` is forwarded for non-adaptive models so the provider's error surfaces."""
+def test_bedrock_messages_strips_output_config_with_output_format():
+    """
+    When both output_config and output_format are present, output_format
+    is converted to inline schema and output_config is stripped for
+    unsupported models.
+    """
+    from unittest.mock import patch
+
     from litellm.types.router import GenericLiteLLMParams
 
     cfg = AmazonAnthropicClaudeMessagesConfig()
     messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
     optional_params = {
         "max_tokens": 4096,
-        "output_config": {"effort": "high"},
+        "output_config": {"effort": "low"},
+        "output_format": {
+            "type": "json_schema",
+            "schema": {
+                "type": "object",
+                "properties": {"answer": {"type": "string"}},
+            },
+        },
     }
 
-    result = cfg.transform_anthropic_messages_request(
-        model="anthropic.claude-3-haiku-20240307-v1:0",
-        messages=messages,
-        anthropic_messages_optional_request_params=optional_params,
-        litellm_params=GenericLiteLLMParams(),
-        headers={},
-    )
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=False,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-3-haiku-20240307-v1:0",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
 
-    assert result.get("output_config") == {"effort": "high"}
-    assert result.get("max_tokens") == 4096
+    assert "output_config" not in result
+    assert "output_format" not in result
 
 
 def test_bedrock_messages_drop_params_strips_output_config_for_pre_4_5():
@@ -701,6 +837,8 @@ def test_bedrock_messages_drop_params_strips_output_config_for_pre_4_5():
 
 def test_bedrock_messages_drop_params_keeps_output_config_for_4_7():
     """``drop_params=True`` does not strip on opus-4-7 (supports effort)."""
+    from unittest.mock import patch
+
     import litellm
     from litellm.types.router import GenericLiteLLMParams
 
@@ -714,13 +852,17 @@ def test_bedrock_messages_drop_params_keeps_output_config_for_4_7():
     original = litellm.drop_params
     litellm.drop_params = True
     try:
-        result = cfg.transform_anthropic_messages_request(
-            model="anthropic.claude-opus-4-7",
-            messages=messages,
-            anthropic_messages_optional_request_params=optional_params,
-            litellm_params=GenericLiteLLMParams(),
-            headers={},
-        )
+        with patch(
+            "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+            return_value=True,
+        ):
+            result = cfg.transform_anthropic_messages_request(
+                model="anthropic.claude-opus-4-7",
+                messages=messages,
+                anthropic_messages_optional_request_params=optional_params,
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
     finally:
         litellm.drop_params = original
 
@@ -742,6 +884,8 @@ def test_bedrock_messages_maps_reasoning_effort_for_adaptive_model(
     reasoning_effort, expected_effort
 ):
     """``reasoning_effort`` maps to ``thinking`` + ``output_config.effort`` on /v1/messages."""
+    from unittest.mock import patch
+
     from litellm.types.router import GenericLiteLLMParams
 
     cfg = AmazonAnthropicClaudeMessagesConfig()
@@ -751,13 +895,17 @@ def test_bedrock_messages_maps_reasoning_effort_for_adaptive_model(
         "reasoning_effort": reasoning_effort,
     }
 
-    result = cfg.transform_anthropic_messages_request(
-        model="anthropic.claude-opus-4-7",
-        messages=messages,
-        anthropic_messages_optional_request_params=optional_params,
-        litellm_params=GenericLiteLLMParams(),
-        headers={},
-    )
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-opus-4-7",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
 
     assert "reasoning_effort" not in result
     assert result.get("thinking") == {"type": "adaptive"}
@@ -842,6 +990,8 @@ def test_bedrock_messages_invalid_reasoning_effort_raises_400():
 
 def test_bedrock_messages_explicit_output_config_wins_over_reasoning_effort():
     """Explicit ``output_config.effort`` wins over the ``reasoning_effort`` alias."""
+    from unittest.mock import patch
+
     from litellm.types.router import GenericLiteLLMParams
 
     cfg = AmazonAnthropicClaudeMessagesConfig()
@@ -852,13 +1002,17 @@ def test_bedrock_messages_explicit_output_config_wins_over_reasoning_effort():
         "output_config": {"effort": "max"},
     }
 
-    result = cfg.transform_anthropic_messages_request(
-        model="anthropic.claude-opus-4-7",
-        messages=messages,
-        anthropic_messages_optional_request_params=optional_params,
-        litellm_params=GenericLiteLLMParams(),
-        headers={},
-    )
+    with patch(
+        "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory",
+        return_value=True,
+    ):
+        result = cfg.transform_anthropic_messages_request(
+            model="anthropic.claude-opus-4-7",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
 
     assert "reasoning_effort" not in result
     assert result.get("output_config") == {"effort": "max"}
@@ -994,7 +1148,7 @@ def test_bedrock_messages_allowlist_filters_anthropic_only_fields():
     }
 
     result = cfg.transform_anthropic_messages_request(
-        model="anthropic.claude-3-haiku-20240307-v1:0",
+        model="anthropic.claude-opus-4-7",
         messages=messages,
         anthropic_messages_optional_request_params=optional_params,
         litellm_params=GenericLiteLLMParams(),
diff --git a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py
index a4969e5dacc..3f91f6ac26e 100644
--- a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py
+++ b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py
@@ -869,14 +869,18 @@ def test_different_roles_without_session_names_should_not_share_cache():
         ({}, {"verify": True}),
         (
             {"aws_region_name": "us-east-1"},
-            {"region_name": "us-east-1", "verify": True},
+            {"verify": True},
         ),
         (
             {"aws_sts_endpoint": "https://sts.eu-west-1.amazonaws.com"},
-            {"endpoint_url": "https://sts.eu-west-1.amazonaws.com", "verify": True},
+            {
+                "endpoint_url": "https://sts.eu-west-1.amazonaws.com",
+                "region_name": "eu-west-1",
+                "verify": True,
+            },
         ),
     ],
-    ids=["no_region_or_endpoint", "regional_sts", "explicit_sts_endpoint"],
+    ids=["no_region_or_endpoint", "bedrock_region_ignored_for_sts", "explicit_sts_endpoint"],
 )
 def test_eks_irsa_ambient_credentials_used(role_kwargs, expected_client_kwargs):
     """
@@ -925,6 +929,316 @@ def test_eks_irsa_ambient_credentials_used(role_kwargs, expected_client_kwargs):
             assert ttl is not None
 
 
+@pytest.mark.parametrize(
+    "endpoint,expected_region",
+    [
+        ("https://sts.eu-west-1.amazonaws.com", "eu-west-1"),
+        ("https://sts.us-east-1.amazonaws.com", "us-east-1"),
+        ("https://sts-fips.us-east-1.amazonaws.com", "us-east-1"),
+        ("https://sts-fips.us-gov-west-1.amazonaws.com", "us-gov-west-1"),
+        ("https://sts.us-gov-west-1.amazonaws.com", "us-gov-west-1"),
+        ("https://sts.cn-north-1.amazonaws.com.cn", "cn-north-1"),
+        (
+            "https://vpce-abc123.sts.eu-west-1.vpce.amazonaws.com",
+            "eu-west-1",
+        ),
+        ("https://sts.amazonaws.com", None),
+        ("https://invalid.example.com", None),
+    ],
+)
+def test_parse_sts_region_from_endpoint(endpoint, expected_region):
+    assert BaseAWSLLM._parse_sts_region_from_endpoint(endpoint) == expected_region
+
+
+@pytest.mark.parametrize(
+    "env,aws_sts_endpoint,expected_region",
+    [
+        ({}, None, None),
+        ({"AWS_REGION": "us-east-1"}, None, "us-east-1"),
+        ({"AWS_DEFAULT_REGION": "ap-southeast-1"}, None, "ap-southeast-1"),
+        ({}, "https://sts.eu-west-1.amazonaws.com", "eu-west-1"),
+        (
+            {"AWS_REGION": "us-east-1"},
+            "https://sts.eu-west-1.amazonaws.com",
+            "eu-west-1",
+        ),
+        ({}, "https://sts.amazonaws.com", None),
+        (
+            {},
+            "https://vpce-abc.sts.eu-central-1.vpce.amazonaws.com",
+            "eu-central-1",
+        ),
+    ],
+    ids=[
+        "no_env_no_endpoint",
+        "env_region",
+        "env_default_region",
+        "parsed_from_endpoint",
+        "parsed_endpoint_over_env",
+        "global_endpoint",
+        "vpce_endpoint",
+    ],
+)
+def test_resolve_sts_region(env, aws_sts_endpoint, expected_region):
+    with patch.dict(os.environ, env, clear=True):
+        assert (
+            BaseAWSLLM._resolve_sts_region(aws_sts_endpoint=aws_sts_endpoint)
+            == expected_region
+        )
+
+
+@pytest.mark.parametrize(
+    "env,aws_sts_endpoint,ssl_verify,expected",
+    [
+        ({}, None, None, {"verify": True}),
+        (
+            {"AWS_REGION": "us-east-1"},
+            None,
+            None,
+            {"verify": True, "region_name": "us-east-1"},
+        ),
+        (
+            {},
+            "https://sts.eu-west-1.amazonaws.com",
+            None,
+            {
+                "verify": True,
+                "endpoint_url": "https://sts.eu-west-1.amazonaws.com",
+                "region_name": "eu-west-1",
+            },
+        ),
+        (
+            {"AWS_REGION": "us-east-1"},
+            "https://sts.eu-west-1.amazonaws.com",
+            None,
+            {
+                "verify": True,
+                "endpoint_url": "https://sts.eu-west-1.amazonaws.com",
+                "region_name": "eu-west-1",
+            },
+        ),
+        (
+            {},
+            "https://sts.amazonaws.com",
+            None,
+            {"verify": True, "endpoint_url": "https://sts.amazonaws.com"},
+        ),
+        (
+            {},
+            "https://vpce-abc.sts.eu-central-1.vpce.amazonaws.com",
+            None,
+            {
+                "verify": True,
+                "endpoint_url": "https://vpce-abc.sts.eu-central-1.vpce.amazonaws.com",
+                "region_name": "eu-central-1",
+            },
+        ),
+        ({}, None, False, {"verify": False}),
+        (
+            {"AWS_DEFAULT_REGION": "ap-southeast-1"},
+            None,
+            None,
+            {"verify": True, "region_name": "ap-southeast-1"},
+        ),
+    ],
+    ids=[
+        "default_verify_only",
+        "env_region",
+        "endpoint_with_parsed_region",
+        "endpoint_parsed_over_env",
+        "global_endpoint_no_region",
+        "vpce_endpoint",
+        "ssl_verify_false",
+        "env_default_region",
+    ],
+)
+def test_build_sts_client_kwargs(env, aws_sts_endpoint, ssl_verify, expected):
+    base_aws_llm = BaseAWSLLM()
+    with patch.dict(os.environ, env, clear=True):
+        assert (
+            base_aws_llm._build_sts_client_kwargs(
+                aws_sts_endpoint=aws_sts_endpoint,
+                ssl_verify=ssl_verify,
+            )
+            == expected
+        )
+
+
+def test_irsa_cross_account_sts_client_uses_resolved_region():
+    """IRSA cross-account path must use _build_sts_client_kwargs (env region, not Bedrock)."""
+    base_aws_llm = BaseAWSLLM()
+    import tempfile
+
+    with tempfile.NamedTemporaryFile(mode="w", delete=False) as f:
+        f.write("test-web-identity-token")
+        token_file = f.name
+
+    try:
+        with patch.dict(
+            os.environ,
+            {
+                "AWS_WEB_IDENTITY_TOKEN_FILE": token_file,
+                "AWS_ROLE_ARN": "arn:aws:iam::111111111111:role/eks-service-account-role",
+                "AWS_REGION": "eu-west-1",
+            },
+            clear=True,
+        ):
+            mock_sts_client = MagicMock()
+            mock_sts_client.assume_role_with_web_identity.return_value = {
+                "Credentials": {
+                    "AccessKeyId": "temp-key",
+                    "SecretAccessKey": "temp-secret",
+                    "SessionToken": "temp-token",
+                    "Expiration": datetime.now(timezone.utc) + timedelta(hours=1),
+                }
+            }
+            mock_sts_client.assume_role.return_value = {
+                "Credentials": {
+                    "AccessKeyId": "assumed-key",
+                    "SecretAccessKey": "assumed-secret",
+                    "SessionToken": "assumed-token",
+                    "Expiration": datetime.now(timezone.utc) + timedelta(hours=1),
+                }
+            }
+
+            with patch(
+                "boto3.client", return_value=mock_sts_client
+            ) as mock_boto3_client:
+                base_aws_llm._auth_with_aws_role(
+                    aws_access_key_id=None,
+                    aws_secret_access_key=None,
+                    aws_session_token=None,
+                    aws_role_name="arn:aws:iam::222222222222:role/target-role",
+                    aws_session_name="test-session",
+                    aws_region_name="eu-central-1",
+                )
+
+                for call in mock_boto3_client.call_args_list:
+                    assert call.args == ("sts",)
+                    assert call.kwargs["region_name"] == "eu-west-1"
+                    assert call.kwargs["verify"] is True
+    finally:
+        os.unlink(token_file)
+
+
+def test_web_identity_token_sts_client_uses_build_sts_client_kwargs():
+    base_aws_llm = BaseAWSLLM()
+    mock_sts_client = MagicMock()
+    mock_sts_client.assume_role_with_web_identity.return_value = {
+        "Credentials": {
+            "AccessKeyId": "key",
+            "SecretAccessKey": "secret",
+            "SessionToken": "token",
+            "Expiration": datetime.now(timezone.utc) + timedelta(hours=1),
+        },
+        "PackedPolicySize": 0,
+    }
+
+    with patch.dict(os.environ, {"AWS_REGION": "eu-west-1"}, clear=True):
+        with patch("boto3.client", return_value=mock_sts_client) as mock_boto3_client:
+            with patch(
+                "litellm.llms.bedrock.base_aws_llm.get_secret",
+                return_value="oidc-token",
+            ):
+                base_aws_llm._auth_with_web_identity_token(
+                    aws_web_identity_token="my-token",
+                    aws_role_name="arn:aws:iam::111111111111:role/target",
+                    aws_session_name="test-session",
+                    aws_region_name="eu-central-1",
+                    aws_sts_endpoint="https://sts.eu-west-1.amazonaws.com",
+                )
+
+                mock_boto3_client.assert_called_once_with(
+                    "sts",
+                    verify=True,
+                    endpoint_url="https://sts.eu-west-1.amazonaws.com",
+                    region_name="eu-west-1",
+                )
+
+
+def test_sts_uses_workload_region_not_bedrock_region():
+    """Air-gapped: Bedrock in eu-central-1, STS VPC endpoint in eu-west-1 via AWS_REGION."""
+    base_aws_llm = BaseAWSLLM()
+    mock_expiry = MagicMock()
+    mock_expiry.tzinfo = timezone.utc
+    time_diff = MagicMock()
+    time_diff.total_seconds.return_value = 3600
+    mock_expiry.__sub__ = MagicMock(return_value=time_diff)
+    mock_sts_client = MagicMock()
+    mock_sts_client.assume_role.return_value = {
+        "Credentials": {
+            "AccessKeyId": "assumed-access-key",
+            "SecretAccessKey": "assumed-secret-key",
+            "SessionToken": "assumed-session-token",
+            "Expiration": mock_expiry,
+        }
+    }
+
+    with patch.dict(os.environ, {"AWS_REGION": "eu-west-1"}, clear=True):
+        with patch("boto3.client", return_value=mock_sts_client) as mock_boto3_client:
+            base_aws_llm._auth_with_aws_role(
+                aws_access_key_id=None,
+                aws_secret_access_key=None,
+                aws_session_token=None,
+                aws_role_name="arn:aws:iam::2222222222222:role/LitellmEvalBedrockRole",
+                aws_session_name="test-session",
+                aws_region_name="eu-central-1",
+            )
+            mock_boto3_client.assert_called_with(
+                "sts",
+                region_name="eu-west-1",
+                verify=True,
+            )
+
+
+def test_sts_endpoint_region_matches_bedrock_region_param():
+    """aws_sts_endpoint signing region must not follow aws_region_name when they differ."""
+    base_aws_llm = BaseAWSLLM()
+    mock_expiry = MagicMock()
+    mock_expiry.tzinfo = timezone.utc
+    time_diff = MagicMock()
+    time_diff.total_seconds.return_value = 3600
+    mock_expiry.__sub__ = MagicMock(return_value=time_diff)
+    mock_sts_client = MagicMock()
+    mock_sts_client.assume_role.return_value = {
+        "Credentials": {
+            "AccessKeyId": "assumed-access-key",
+            "SecretAccessKey": "assumed-secret-key",
+            "SessionToken": "assumed-session-token",
+            "Expiration": mock_expiry,
+        }
+    }
+
+    env_without_irsa = {
+        k: v
+        for k, v in os.environ.items()
+        if k
+        not in (
+            "AWS_ROLE_ARN",
+            "AWS_WEB_IDENTITY_TOKEN_FILE",
+            "AWS_REGION",
+            "AWS_DEFAULT_REGION",
+        )
+    }
+    with patch.dict(env_without_irsa, clear=True):
+        with patch("boto3.client", return_value=mock_sts_client) as mock_boto3_client:
+            base_aws_llm._auth_with_aws_role(
+                aws_access_key_id=None,
+                aws_secret_access_key=None,
+                aws_session_token=None,
+                aws_role_name="arn:aws:iam::2222222222222:role/LitellmEvalBedrockRole",
+                aws_session_name="test-session",
+                aws_region_name="eu-central-1",
+                aws_sts_endpoint="https://sts.eu-west-1.amazonaws.com",
+            )
+            mock_boto3_client.assert_called_with(
+                "sts",
+                endpoint_url="https://sts.eu-west-1.amazonaws.com",
+                region_name="eu-west-1",
+                verify=True,
+            )
+
+
 @pytest.mark.parametrize(
     "role_kwargs,expected_client_kwargs",
     [
@@ -940,7 +1254,6 @@ def test_eks_irsa_ambient_credentials_used(role_kwargs, expected_client_kwargs):
         (
             {"aws_region_name": "us-east-1"},
             {
-                "region_name": "us-east-1",
                 "aws_access_key_id": "explicit-access-key",
                 "aws_secret_access_key": "explicit-secret-key",
                 "aws_session_token": "assumed-session-token",
@@ -951,6 +1264,7 @@ def test_eks_irsa_ambient_credentials_used(role_kwargs, expected_client_kwargs):
             {"aws_sts_endpoint": "https://sts.eu-west-1.amazonaws.com"},
             {
                 "endpoint_url": "https://sts.eu-west-1.amazonaws.com",
+                "region_name": "eu-west-1",
                 "aws_access_key_id": "explicit-access-key",
                 "aws_secret_access_key": "explicit-secret-key",
                 "aws_session_token": "assumed-session-token",
@@ -958,7 +1272,7 @@ def test_eks_irsa_ambient_credentials_used(role_kwargs, expected_client_kwargs):
             },
         ),
     ],
-    ids=["no_region_or_endpoint", "regional_sts", "explicit_sts_endpoint"],
+    ids=["no_region_or_endpoint", "bedrock_region_ignored_for_sts", "explicit_sts_endpoint"],
 )
 def test_explicit_credentials_used_when_provided(role_kwargs, expected_client_kwargs):
     """
@@ -2112,3 +2426,102 @@ def test_is_already_running_as_role_ssl_verify_passed():
                 mock_boto3_client.assert_called_once_with(
                     "sts", verify="/path/to/ca-bundle.crt"
                 )
+
+
+# ---------------------------------------------------------------------------
+# LIT-3274: get_bedrock_model_id must strip "bedrock/" prefix and URL-encode
+# ARNs for the invoke path (invoke-with-response-stream).  Without this fix
+# the Bedrock API receives a malformed URL, returns a JSON error body, and
+# botocore's EventStreamBuffer raises ChecksumMismatch instead of the real
+# error.  0x223a7b22 == ':{\"' — the start of a JSON object.
+# ---------------------------------------------------------------------------
+
+
+class TestGetBedrockModelIdArnHandling:
+    """Unit tests for get_bedrock_model_id with inference-profile ARNs."""
+
+    ARN = "arn:aws:bedrock:us-east-1:086734376398:inference-profile/global.anthropic.claude-sonnet-4-5-20250929-v1:0"
+
+    def _call(self, model: str, optional_params: dict | None = None) -> str:
+        from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+
+        provider = BaseAWSLLM.get_bedrock_invoke_provider(model)
+        return BaseAWSLLM.get_bedrock_model_id(
+            model=model,
+            provider=provider,
+            optional_params=optional_params or {},
+        )
+
+    def test_arn_with_bedrock_prefix_is_stripped_and_encoded(self):
+        """bedrock/arn:... must not appear verbatim in the model_id."""
+        model_id = self._call(f"bedrock/{self.ARN}")
+        assert (
+            "bedrock/arn" not in model_id
+        ), f"'bedrock/' prefix not stripped; got: {model_id}"
+        # Must be URL-encoded (colons → %3A)
+        assert "%3A" in model_id, f"ARN not URL-encoded; got: {model_id}"
+        assert "%2F" in model_id, f"ARN slashes not URL-encoded; got: {model_id}"
+
+    def test_arn_with_compound_bedrock_invoke_prefix_is_fully_stripped_and_encoded(
+        self,
+    ):
+        """bedrock/invoke/arn:... — compound prefix — must be fully stripped.
+
+        The old fix used ``break`` after the first matched prefix, so
+        ``bedrock/invoke/arn:...`` would only strip ``bedrock/``, leaving
+        ``invoke/arn:...``.  The subsequent ``.replace('invoke/', '')`` call
+        then returned the bare unencoded ARN, reproducing the same
+        malformed-URL bug the fix aimed to prevent.
+
+        strip_bedrock_routing_prefix() has no break and handles this correctly.
+        """
+        model_id = self._call(f"bedrock/invoke/{self.ARN}")
+        assert (
+            "invoke/" not in model_id
+        ), f"'invoke/' prefix not stripped; got: {model_id}"
+        assert (
+            "bedrock/" not in model_id
+        ), f"'bedrock/' prefix not stripped; got: {model_id}"
+        assert "%3A" in model_id, f"ARN not URL-encoded; got: {model_id}"
+        assert "%2F" in model_id, f"ARN slashes not URL-encoded; got: {model_id}"
+
+    def test_bare_arn_is_encoded(self):
+        """Direct ARN without routing prefix must also be URL-encoded."""
+        model_id = self._call(self.ARN)
+        assert "%3A" in model_id, f"ARN not URL-encoded; got: {model_id}"
+        assert "%2F" in model_id, f"ARN slashes not URL-encoded; got: {model_id}"
+
+    def test_arn_url_matches_expected(self):
+        """Full URL built from messages config must match expected encoded form."""
+        import urllib.parse
+        from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
+            AmazonAnthropicClaudeMessagesConfig,
+        )
+
+        config = AmazonAnthropicClaudeMessagesConfig()
+        url = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=f"bedrock/{self.ARN}",
+            optional_params={"aws_region_name": "us-east-1"},
+            litellm_params={},
+            stream=True,
+        )
+        encoded_arn = urllib.parse.quote(self.ARN, safe="")
+        expected = (
+            f"https://bedrock-runtime.us-east-1.amazonaws.com"
+            f"/model/{encoded_arn}/invoke-with-response-stream"
+        )
+        assert (
+            url == expected
+        ), f"URL mismatch:\n  got:      {url}\n  expected: {expected}"
+
+    def test_regular_model_id_unaffected(self):
+        """Non-ARN model IDs must continue to work as before."""
+        model_id = self._call("anthropic.claude-3-sonnet-20240229-v1:0")
+        assert model_id == "anthropic.claude-3-sonnet-20240229-v1:0"
+
+    def test_invoke_prefixed_model_unaffected(self):
+        """invoke/ prefix stripping still works after the fix."""
+        model_id = self._call("invoke/anthropic.claude-3-sonnet-20240229-v1:0")
+        assert model_id == "anthropic.claude-3-sonnet-20240229-v1:0"
diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py
index 8fa9290d3de..c39fb427a01 100644
--- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py
+++ b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py
@@ -14,18 +14,46 @@ from litellm.llms.bedrock.common_utils import BedrockModelInfo
 
 
 # --------------------------------------------------------------------------- #
-# BEDROCK_RESPONSE_STREAM_SHAPE eager-load tests                              #
+# get_bedrock_response_stream_shape lazy-load tests                           #
 # --------------------------------------------------------------------------- #
 
 
-def test_bedrock_response_stream_shape_loaded_at_import():
+@pytest.fixture(autouse=True)
+def _reset_bedrock_response_stream_shape_cache():
+    """Prevent lru_cache leakage between tests in this module."""
+    import litellm.llms.bedrock.common_utils as mod
+
+    mod.get_bedrock_response_stream_shape.cache_clear()
+    yield
+    mod.get_bedrock_response_stream_shape.cache_clear()
+
+
+def test_bedrock_response_stream_shape_lazy_loads_once():
     """
-    BEDROCK_RESPONSE_STREAM_SHAPE is resolved at module import time.
+    get_bedrock_response_stream_shape() loads from botocore at most once per process.
+    """
+    from unittest.mock import MagicMock, patch
+
+    import litellm.llms.bedrock.common_utils as mod
+
+    sentinel = MagicMock()
+    with patch.object(
+        mod, "_load_bedrock_response_stream_shape", return_value=sentinel
+    ) as mock_load:
+        assert mod.get_bedrock_response_stream_shape() is sentinel
+        assert mod.get_bedrock_response_stream_shape() is sentinel
+        mock_load.assert_called_once()
+
+
+def test_bedrock_response_stream_shape_loaded_on_first_access():
+    """
+    get_bedrock_response_stream_shape() loads once on first use.
     In a standard environment with botocore installed it must be non-None.
     """
-    from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE
+    pytest.importorskip("botocore")
+    from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape
 
-    assert BEDROCK_RESPONSE_STREAM_SHAPE is not None
+    assert get_bedrock_response_stream_shape() is not None
 
 
 def test_bedrock_response_stream_shape_load_failure_returns_none():
@@ -38,6 +66,7 @@ def test_bedrock_response_stream_shape_load_failure_returns_none():
 
     import litellm.llms.bedrock.common_utils as mod
 
+    pytest.importorskip("botocore")
     with patch(
         "botocore.loaders.Loader.load_service_model",
         side_effect=Exception("no data"),
@@ -51,31 +80,29 @@ def test_bedrock_response_stream_shape_is_structure_shape():
     The loaded shape should be the botocore StructureShape for ResponseStream,
     not a plain dict or any other type.
     """
+    pytest.importorskip("botocore")
     from botocore.model import StructureShape
 
-    from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE
+    from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape
 
-    assert BEDROCK_RESPONSE_STREAM_SHAPE is not None, (
-        "BEDROCK_RESPONSE_STREAM_SHAPE is None — botocore may not be installed"
-    )
-    shape: StructureShape = BEDROCK_RESPONSE_STREAM_SHAPE  # remove Optional
+    loaded_shape = get_bedrock_response_stream_shape()
+    assert (
+        loaded_shape is not None
+    ), "get_bedrock_response_stream_shape() is None — botocore may not be installed"
+    shape: StructureShape = loaded_shape
     assert isinstance(shape, StructureShape)
     assert shape.name == "ResponseStream"
 
 
-def test_bedrock_response_stream_shape_same_object_across_imports():
+def test_bedrock_response_stream_shape_same_object_across_calls():
     """
-    Both bedrock modules that use the shape must reference the identical object —
-    confirming the constant is not re-loaded per import.
+    Repeated calls must return the identical cached object.
     """
-    from litellm.llms.bedrock.chat.invoke_handler import (
-        BEDROCK_RESPONSE_STREAM_SHAPE as invoke_shape,
-    )
-    from litellm.llms.bedrock.common_utils import (
-        BEDROCK_RESPONSE_STREAM_SHAPE as common_shape,
-    )
+    from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape
 
-    assert common_shape is invoke_shape
+    first = get_bedrock_response_stream_shape()
+    second = get_bedrock_response_stream_shape()
+    assert first is second
 
 
 def test_bedrock_event_stream_decoder_base_uses_module_shape():
@@ -95,19 +122,23 @@ def test_bedrock_event_stream_decoder_base_uses_module_shape():
 
 def test_bedrock_parse_message_from_event_raises_on_none_shape():
     """
-    When BEDROCK_RESPONSE_STREAM_SHAPE is None (botocore unavailable),
+    When get_bedrock_response_stream_shape() returns None (botocore unavailable),
     _parse_message_from_event must raise BedrockError before touching the
     botocore parser — not an opaque AttributeError from inside botocore.
     """
     from unittest.mock import MagicMock, patch
 
     import litellm.llms.bedrock.common_utils as mod
-    from litellm.llms.bedrock.common_utils import BedrockError, BedrockEventStreamDecoderBase
+    from litellm.llms.bedrock.common_utils import (
+        BedrockError,
+        BedrockEventStreamDecoderBase,
+    )
 
-    decoder = BedrockEventStreamDecoderBase()
+    decoder = BedrockEventStreamDecoderBase.__new__(BedrockEventStreamDecoderBase)
+    decoder.parser = MagicMock()
     mock_event = MagicMock()
 
-    with patch.object(mod, "BEDROCK_RESPONSE_STREAM_SHAPE", None):
+    with patch.object(mod, "get_bedrock_response_stream_shape", return_value=None):
         with pytest.raises(BedrockError) as exc_info:
             decoder._parse_message_from_event(mock_event)
 
diff --git a/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py b/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py
index 2498946bb5c..90a1c24bada 100644
--- a/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py
+++ b/tests/test_litellm/llms/chatgpt/responses/test_chatgpt_responses_transformation.py
@@ -14,6 +14,7 @@ import pytest
 
 sys.path.insert(0, os.path.abspath("../../../../.."))
 
+from litellm.llms.openai.common_utils import OpenAIError
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.utils import LlmProviders
 from litellm.utils import ProviderConfigManager
@@ -201,3 +202,127 @@ class TestChatGPTResponsesAPITransformation:
         )
 
         assert parsed.output_text == "Hello!"
+
+    @pytest.mark.parametrize(
+        ("model_name", "response_model"),
+        [
+            ("chatgpt/gpt-5.2-codex", "gpt-5.2-codex"),
+            ("chatgpt/gpt-5.3-codex", "gpt-5.3-codex"),
+        ],
+    )
+    def test_chatgpt_non_stream_sse_response_recovers_output_items(
+        self, model_name: str, response_model: str
+    ):
+        config = ChatGPTResponsesAPIConfig()
+        response_payload = {
+            "id": "resp_test",
+            "object": "response",
+            "created_at": 1700000000,
+            "status": "completed",
+            "model": response_model,
+            "output": [],
+        }
+        streamed_output_item = {
+            "type": "message",
+            "role": "assistant",
+            "content": [{"type": "output_text", "text": "Hello from stream!"}],
+        }
+        sse_body = "\n".join(
+            [
+                f"data: {json.dumps({'type': 'response.output_item.done', 'output_index': 0, 'item': streamed_output_item})}",
+                f"data: {json.dumps({'type': 'response.completed', 'response': response_payload})}",
+                "data: [DONE]",
+                "",
+            ]
+        )
+        raw_response = httpx.Response(
+            200, headers={"content-type": "text/event-stream"}, text=sse_body
+        )
+        logging_obj = MagicMock()
+
+        parsed = config.transform_response_api_response(
+            model=model_name,
+            raw_response=raw_response,
+            logging_obj=logging_obj,
+        )
+
+        assert parsed.output_text == "Hello from stream!"
+
+    def test_chatgpt_non_stream_sse_recovers_whitespace_padded_chunks(self):
+        """Chunks with leading whitespace before `data:` must still parse.
+
+        `_strip_sse_data_from_chunk` only matches the prefix at position 0,
+        so without an outer `.strip()` such chunks would fail JSON parsing
+        and silently drop the contained event.
+        """
+        config = ChatGPTResponsesAPIConfig()
+        response_payload = {
+            "id": "resp_test",
+            "object": "response",
+            "created_at": 1700000000,
+            "status": "completed",
+            "model": "gpt-5.4",
+            "output": [],
+        }
+        streamed_output_item = {
+            "type": "message",
+            "role": "assistant",
+            "content": [{"type": "output_text", "text": "Recovered from padded"}],
+        }
+        sse_body = "\n".join(
+            [
+                f"   data:  {json.dumps({'type': 'response.output_item.done', 'output_index': 0, 'item': streamed_output_item})}   ",
+                f"\tdata: {json.dumps({'type': 'response.completed', 'response': response_payload})}",
+                "data: [DONE]",
+                "",
+            ]
+        )
+        raw_response = httpx.Response(
+            200, headers={"content-type": "text/event-stream"}, text=sse_body
+        )
+        logging_obj = MagicMock()
+
+        parsed = config.transform_response_api_response(
+            model="chatgpt/gpt-5.4",
+            raw_response=raw_response,
+            logging_obj=logging_obj,
+        )
+
+        assert parsed.output_text == "Recovered from padded"
+
+    @pytest.mark.parametrize(
+        "error_chunk",
+        [
+            {
+                "type": "response.failed",
+                "response": {"error": {"message": "ChatGPT upstream failed"}},
+            },
+            {
+                "type": "error",
+                "error": {"message": "ChatGPT upstream failed"},
+            },
+        ],
+    )
+    def test_chatgpt_non_stream_sse_response_raises_openai_error(self, error_chunk):
+        config = ChatGPTResponsesAPIConfig()
+        sse_body = "\n".join(
+            [
+                f"data: {json.dumps(error_chunk)}",
+                "data: [DONE]",
+                "",
+            ]
+        )
+        raw_response = httpx.Response(
+            502, headers={"content-type": "text/event-stream"}, text=sse_body
+        )
+        logging_obj = MagicMock()
+
+        with pytest.raises(OpenAIError) as exc_info:
+            config.transform_response_api_response(
+                model="chatgpt/gpt-5.4",
+                raw_response=raw_response,
+                logging_obj=logging_obj,
+            )
+
+        assert "ChatGPT upstream failed" in str(exc_info.value)
+        assert exc_info.value.status_code == 502
diff --git a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py
index 79346475fc6..bef067038ac 100644
--- a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py
+++ b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py
@@ -101,6 +101,73 @@ def test_get_agentic_loop_settings_defaults_and_overrides():
     assert fingerprints == ["fp-1", "fp-2"]
 
 
+def test_has_agentic_completion_hook_detection(monkeypatch):
+    """The streaming path skips the agentic wrapper only when no callback
+    overrides async_should_run_agentic_loop. Verify both directions."""
+    from litellm.integrations.custom_logger import CustomLogger
+
+    handler = BaseLLMHTTPHandler()
+    logging_obj = Mock()
+    logging_obj.dynamic_success_callbacks = []
+
+    # No callbacks at all -> no agentic hook.
+    monkeypatch.setattr(litellm, "callbacks", [])
+    assert handler._has_agentic_completion_hook(logging_obj) is False
+
+    # A plain CustomLogger that does NOT override the gate -> still no hook
+    # (so the wrapper is safely skipped).
+    class _PlainLogger(CustomLogger):
+        pass
+
+    monkeypatch.setattr(litellm, "callbacks", [_PlainLogger()])
+    assert handler._has_agentic_completion_hook(logging_obj) is False
+
+    # A logger that overrides the gate (directly) -> hook present.
+    class _AgenticLogger(CustomLogger):
+        async def async_should_run_agentic_loop(
+            self, response, model, messages, tools, stream, custom_llm_provider, kwargs
+        ):
+            return True, {}
+
+    monkeypatch.setattr(litellm, "callbacks", [_AgenticLogger()])
+    assert handler._has_agentic_completion_hook(logging_obj) is True
+
+    # Override inherited through an intermediate class is still detected
+    # (function-identity check, not a leaf __dict__ check).
+    class _DerivedAgenticLogger(_AgenticLogger):
+        pass
+
+    monkeypatch.setattr(litellm, "callbacks", [_DerivedAgenticLogger()])
+    assert handler._has_agentic_completion_hook(logging_obj) is True
+
+    # Hook supplied via logging_obj.dynamic_success_callbacks is detected too.
+    monkeypatch.setattr(litellm, "callbacks", [])
+    logging_obj.dynamic_success_callbacks = [_AgenticLogger()]
+    assert handler._has_agentic_completion_hook(logging_obj) is True
+
+    # String-named callback entry (e.g. "datadog") must be resolved to its
+    # CustomLogger instance via get_custom_logger_compatible_class -- the same
+    # way ProxyLogging._callback_capabilities handles them. Without that
+    # resolution a string-registered agentic callback would be silently
+    # skipped and the buffering wrapper would never fire.
+    logging_obj.dynamic_success_callbacks = []
+    agentic_via_string = _AgenticLogger()
+    monkeypatch.setattr(litellm, "callbacks", ["fake_string_callback"])
+    monkeypatch.setattr(
+        "litellm.litellm_core_utils.litellm_logging.get_custom_logger_compatible_class",
+        lambda name: agentic_via_string if name == "fake_string_callback" else None,
+    )
+    assert handler._has_agentic_completion_hook(logging_obj) is True
+
+    # Unresolvable string (returns None) is skipped, no false positive.
+    monkeypatch.setattr(litellm, "callbacks", ["unknown_callback"])
+    monkeypatch.setattr(
+        "litellm.litellm_core_utils.litellm_logging.get_custom_logger_compatible_class",
+        lambda name: None,
+    )
+    assert handler._has_agentic_completion_hook(logging_obj) is False
+
+
 def test_fingerprint_agentic_tools_is_deterministic():
     handler = BaseLLMHTTPHandler()
     tools_a = {"tool_calls": [{"id": "1", "input": {"q": "abc"}, "name": "web_search"}]}
@@ -431,3 +498,143 @@ def test_sync_delete_responses_omits_body_for_azure():
     assert captured["url"].endswith(
         "/openai/responses/resp_xyz?api-version=2025-03-01-preview"
     )
+
+
+# ---------------------------------------------------------------------------
+# Parity tests: request-body is serialized once and reused for the wire.
+# (_async_post_anthropic_messages_with_http_error_retry)
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_anthropic_post_uses_prebuilt_body_without_redumping():
+    """When the caller passes a pre-serialized (unsigned) body, attempt 0 must
+    send exactly those bytes -- no second json.dumps of request_body."""
+    import json as _json
+
+    handler = BaseLLMHTTPHandler()
+    request_body = {"model": "claude", "messages": [{"role": "user", "content": "hi"}]}
+    prebuilt = _json.dumps(request_body)
+
+    ok_resp = Mock()
+    ok_resp.raise_for_status = Mock(return_value=None)
+    http_client = Mock()
+    http_client.post = AsyncMock(return_value=ok_resp)
+
+    provider_config = Mock()
+    provider_config.max_retry_on_anthropic_messages_http_error = 2
+
+    logging_obj = Mock()
+    logging_obj.model_call_details = {}
+
+    out = await handler._async_post_anthropic_messages_with_http_error_retry(
+        async_httpx_client=http_client,
+        request_url="http://x/v1/messages",
+        headers={},
+        signed_json_body=prebuilt,
+        request_body=request_body,
+        stream=False,
+        logging_obj=logging_obj,
+        provider_config=provider_config,
+        litellm_params=GenericLiteLLMParams(),
+        api_key="k",
+        model="claude",
+    )
+    assert out is ok_resp
+    http_client.post.assert_awaited_once()
+    sent = http_client.post.await_args.kwargs["data"]
+    # Byte-identical to the legacy wire serialization, and the SAME object the
+    # caller already used for the pre-call log (no re-serialization).
+    assert sent == prebuilt
+    assert sent is prebuilt
+
+
+@pytest.mark.asyncio
+async def test_anthropic_post_falls_back_to_json_dumps_when_unsigned_none():
+    """signed_json_body=None keeps the exact legacy behavior."""
+    import json as _json
+
+    handler = BaseLLMHTTPHandler()
+    request_body = {"model": "claude", "messages": [{"role": "user", "content": "yo"}]}
+
+    ok_resp = Mock()
+    ok_resp.raise_for_status = Mock(return_value=None)
+    http_client = Mock()
+    http_client.post = AsyncMock(return_value=ok_resp)
+
+    provider_config = Mock()
+    provider_config.max_retry_on_anthropic_messages_http_error = 1
+    logging_obj = Mock()
+    logging_obj.model_call_details = {}
+
+    await handler._async_post_anthropic_messages_with_http_error_retry(
+        async_httpx_client=http_client,
+        request_url="http://x/v1/messages",
+        headers={},
+        signed_json_body=None,
+        request_body=request_body,
+        stream=False,
+        logging_obj=logging_obj,
+        provider_config=provider_config,
+        litellm_params=GenericLiteLLMParams(),
+        api_key="k",
+        model="claude",
+    )
+    sent = http_client.post.await_args.kwargs["data"]
+    assert sent == _json.dumps(request_body)
+
+
+@pytest.mark.asyncio
+async def test_anthropic_post_retry_reserializes_mutated_body():
+    """On a retryable HTTP error the body is mutated + re-signed; the prebuilt
+    body must NOT be reused -- attempt 1 sends the freshly serialized body."""
+    import json as _json
+
+    handler = BaseLLMHTTPHandler()
+    request_body = {"model": "claude", "messages": [{"role": "user", "content": "a"}]}
+    prebuilt = _json.dumps(request_body)
+
+    err_resp = Mock()
+    http_error = httpx.HTTPStatusError(
+        "bad", request=Mock(), response=Mock(status_code=400)
+    )
+    err_resp.raise_for_status = Mock(side_effect=http_error)
+    ok_resp = Mock()
+    ok_resp.raise_for_status = Mock(return_value=None)
+    http_client = Mock()
+    http_client.post = AsyncMock(side_effect=[err_resp, ok_resp])
+
+    def _mutate(e, request_data):
+        request_data["messages"][0]["content"] = "MUTATED"
+
+    provider_config = Mock()
+    provider_config.max_retry_on_anthropic_messages_http_error = 2
+    provider_config.should_retry_anthropic_messages_on_http_error = Mock(
+        return_value=True
+    )
+    provider_config.transform_anthropic_messages_request_on_http_error = _mutate
+    # Re-sign returns no signed body (native anthropic path) -> must re-dump.
+    provider_config.sign_request = Mock(return_value=({}, None))
+
+    logging_obj = Mock()
+    logging_obj.model_call_details = {}
+
+    await handler._async_post_anthropic_messages_with_http_error_retry(
+        async_httpx_client=http_client,
+        request_url="http://x/v1/messages",
+        headers={},
+        signed_json_body=prebuilt,
+        request_body=request_body,
+        stream=False,
+        logging_obj=logging_obj,
+        provider_config=provider_config,
+        litellm_params=GenericLiteLLMParams(),
+        api_key="k",
+        model="claude",
+    )
+    assert http_client.post.await_count == 2
+    first_sent = http_client.post.await_args_list[0].kwargs["data"]
+    second_sent = http_client.post.await_args_list[1].kwargs["data"]
+    assert first_sent == prebuilt  # attempt 0 used prebuilt
+    assert second_sent == _json.dumps(request_body)  # attempt 1 re-serialized
+    assert "MUTATED" in second_sent  # ... the mutated body
diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py b/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py
new file mode 100644
index 00000000000..5e4d0177e8d
--- /dev/null
+++ b/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py
@@ -0,0 +1,141 @@
+"""
+Unit tests for DashScope embedding transformation.
+"""
+
+import json
+import os
+import sys
+from unittest.mock import MagicMock
+
+import httpx
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm.llms.dashscope.common_utils import DashScopeError
+from litellm.llms.dashscope.embed.transformation import (
+    DEFAULT_API_BASE,
+    DashScopeEmbeddingConfig,
+)
+from litellm.types.utils import EmbeddingResponse
+
+
+def test_validate_environment_and_url():
+    config = DashScopeEmbeddingConfig()
+    headers = config.validate_environment(
+        headers={},
+        model="text-embedding-v4",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key="sk-test",
+    )
+    assert headers["Authorization"] == "Bearer sk-test"
+
+    url = config.get_complete_url(
+        api_base=None,
+        api_key="sk-test",
+        model="text-embedding-v4",
+        optional_params={},
+        litellm_params={},
+    )
+    assert url == f"{DEFAULT_API_BASE}/embeddings"
+
+
+def test_transform_embedding_request():
+    config = DashScopeEmbeddingConfig()
+    data = config.transform_embedding_request(
+        model="text-embedding-v4",
+        input=["风急天高猿啸哀"],
+        optional_params={"dimensions": 1024, "encoding_format": "float"},
+        headers={},
+    )
+    assert data == {
+        "model": "text-embedding-v4",
+        "input": ["风急天高猿啸哀"],
+        "dimensions": 1024,
+        "encoding_format": "float",
+    }
+
+
+def test_transform_embedding_response_success():
+    config = DashScopeEmbeddingConfig()
+    payload = {
+        "data": [
+            {"embedding": [0.1, 0.2], "index": 0, "object": "embedding"},
+        ],
+        "model": "text-embedding-v4",
+        "object": "list",
+        "usage": {"prompt_tokens": 5, "total_tokens": 5},
+        "id": "73591b79-xxxx",
+    }
+    raw = httpx.Response(
+        status_code=200,
+        content=json.dumps(payload).encode("utf-8"),
+        request=httpx.Request("POST", "https://example.com"),
+    )
+    result = config.transform_embedding_response(
+        model="text-embedding-v4",
+        raw_response=raw,
+        model_response=EmbeddingResponse(),
+        logging_obj=MagicMock(),
+        api_key="sk-x",
+        request_data={"input": ["a"]},
+        optional_params={},
+        litellm_params={},
+    )
+    assert result.model == "text-embedding-v4"
+    assert len(result.data) == 1
+    assert result.usage.prompt_tokens == 5
+
+
+def test_transform_embedding_request_user_param():
+    config = DashScopeEmbeddingConfig()
+    data = config.transform_embedding_request(
+        model="text-embedding-v4",
+        input=["hello"],
+        optional_params={"user": "user-123"},
+        headers={},
+    )
+    assert data["user"] == "user-123"
+
+
+def test_map_openai_params_drops_unsupported_with_drop_params():
+    config = DashScopeEmbeddingConfig()
+    result = config.map_openai_params(
+        non_default_params={"dimensions": 512, "unknown_param": "value"},
+        optional_params={},
+        model="text-embedding-v4",
+        drop_params=True,
+    )
+    assert result == {"dimensions": 512}
+    assert "unknown_param" not in result
+
+
+def test_transform_embedding_response_error():
+    config = DashScopeEmbeddingConfig()
+    payload = {
+        "error": {
+            "message": "Incorrect API key provided.",
+            "type": "invalid_request_error",
+            "code": "invalid_api_key",
+        }
+    }
+    raw = httpx.Response(
+        status_code=401,
+        content=json.dumps(payload).encode("utf-8"),
+        request=httpx.Request("POST", "https://example.com"),
+    )
+    with pytest.raises(DashScopeError) as exc:
+        config.transform_embedding_response(
+            model="text-embedding-v4",
+            raw_response=raw,
+            model_response=EmbeddingResponse(),
+            logging_obj=MagicMock(),
+            api_key="sk-bad",
+            request_data={"input": ["a"]},
+            optional_params={},
+            litellm_params={},
+        )
+    assert exc.value.status_code == 401
+    assert "Incorrect API key" in exc.value.message
diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py b/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py
new file mode 100644
index 00000000000..0e8d58b6530
--- /dev/null
+++ b/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py
@@ -0,0 +1,328 @@
+"""
+Unit tests for DashScope rerank transformation.
+"""
+
+import json
+import os
+import sys
+from unittest.mock import MagicMock
+
+import httpx
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm.llms.dashscope.common_utils import DashScopeError
+from litellm.llms.dashscope.rerank.transformation import (
+    DEFAULT_RERANK_URL,
+    DashScopeRerankConfig,
+)
+from litellm.types.rerank import RerankResponse
+
+
+class TestDashScopeRerankURL:
+    def setup_method(self):
+        self.config = DashScopeRerankConfig()
+
+    def test_default_url(self):
+        url = self.config.get_complete_url(api_base=None, model="qwen3-rerank")
+        assert url == DEFAULT_RERANK_URL
+
+    def test_explicit_v1_base_appends_reranks(self):
+        url = self.config.get_complete_url(
+            api_base="https://dashscope.aliyuncs.com/compatible-mode/v1",
+            model="qwen3-rerank",
+        )
+        assert url == "https://dashscope.aliyuncs.com/compatible-mode/v1/reranks"
+
+    def test_intl_v1_base_appends_reranks(self):
+        url = self.config.get_complete_url(
+            api_base="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
+            model="qwen3-rerank",
+        )
+        assert url == "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/reranks"
+
+    def test_already_complete_url_passthrough(self):
+        full = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks"
+        assert self.config.get_complete_url(api_base=full, model="qwen3-rerank") == full
+
+    def test_trailing_slash_stripped(self):
+        full = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks/"
+        assert self.config.get_complete_url(
+            api_base=full, model="qwen3-rerank"
+        ) == full.rstrip("/")
+
+    def test_custom_v1_base_appends_reranks(self):
+        url = self.config.get_complete_url(
+            api_base="https://my-proxy.example.com/v1", model="qwen3-rerank"
+        )
+        assert url == "https://my-proxy.example.com/v1/reranks"
+
+
+class TestDashScopeRerankRequest:
+    def setup_method(self):
+        self.config = DashScopeRerankConfig()
+
+    def test_validate_environment_with_explicit_key(self):
+        headers = self.config.validate_environment(
+            headers={}, model="qwen3-rerank", api_key="sk-test"
+        )
+        assert headers["Authorization"] == "Bearer sk-test"
+        assert headers["content-type"] == "application/json"
+
+    def test_validate_environment_missing_key(self, monkeypatch):
+        monkeypatch.delenv("DASHSCOPE_API_KEY", raising=False)
+        with pytest.raises(ValueError, match="DASHSCOPE_API_KEY"):
+            self.config.validate_environment(
+                headers={}, model="qwen3-rerank", api_key=None
+            )
+
+    def test_validate_environment_falls_back_to_env(self, monkeypatch):
+        monkeypatch.setenv("DASHSCOPE_API_KEY", "env-key")
+        headers = self.config.validate_environment(
+            headers={}, model="qwen3-rerank", api_key=None
+        )
+        assert headers["Authorization"] == "Bearer env-key"
+
+    def test_supported_params(self):
+        assert self.config.get_supported_cohere_rerank_params("qwen3-rerank") == [
+            "query",
+            "documents",
+            "top_n",
+            "return_documents",
+        ]
+
+    def test_map_params_drops_unsupported(self):
+        # qwen3-rerank accepts query/documents/top_n/return_documents.
+        # rank_fields and max_*_per_doc are silently dropped.
+        params = self.config.map_cohere_rerank_params(
+            non_default_params={},
+            model="qwen3-rerank",
+            drop_params=False,
+            query="什么是文本排序模型",
+            documents=["d1", "d2"],
+            top_n=2,
+            rank_fields=["title"],
+            return_documents=True,
+            max_chunks_per_doc=5,
+            max_tokens_per_doc=100,
+        )
+        assert params == {
+            "query": "什么是文本排序模型",
+            "documents": ["d1", "d2"],
+            "top_n": 2,
+            "return_documents": True,
+        }
+
+    def test_transform_request_full(self):
+        body = self.config.transform_rerank_request(
+            model="qwen3-rerank",
+            optional_rerank_params={
+                "query": "如何制作美味的苹果派?",
+                "documents": ["a", "b"],
+                "top_n": 5,
+                "return_documents": True,
+            },
+            headers={},
+        )
+        assert body == {
+            "model": "qwen3-rerank",
+            "query": "如何制作美味的苹果派?",
+            "documents": ["a", "b"],
+            "top_n": 5,
+            "return_documents": True,
+        }
+
+    def test_transform_request_omits_unset_optional(self):
+        body = self.config.transform_rerank_request(
+            model="qwen3-rerank",
+            optional_rerank_params={"query": "q", "documents": ["a"]},
+            headers={},
+        )
+        assert "top_n" not in body
+        assert "return_documents" not in body
+
+    def test_transform_request_requires_query(self):
+        with pytest.raises(ValueError, match="query"):
+            self.config.transform_rerank_request(
+                model="qwen3-rerank",
+                optional_rerank_params={"documents": ["a"]},
+                headers={},
+            )
+
+    def test_transform_request_requires_documents(self):
+        with pytest.raises(ValueError, match="documents"):
+            self.config.transform_rerank_request(
+                model="qwen3-rerank",
+                optional_rerank_params={"query": "q"},
+                headers={},
+            )
+
+
+class TestDashScopeRerankResponse:
+    def setup_method(self):
+        self.config = DashScopeRerankConfig()
+        self.logging = MagicMock()
+
+    def _resp(self, body, status_code=200):
+        return httpx.Response(
+            status_code=status_code,
+            content=json.dumps(body).encode(),
+            request=httpx.Request("POST", "https://example.com"),
+        )
+
+    def test_success_response(self):
+        body = {
+            "object": "list",
+            "results": [
+                {"index": 0, "relevance_score": 0.93},
+                {"index": 2, "relevance_score": 0.34},
+            ],
+            "model": "qwen3-rerank",
+            "id": "85ba5752",
+            "usage": {"total_tokens": 79},
+        }
+        out = self.config.transform_rerank_response(
+            model="qwen3-rerank",
+            raw_response=self._resp(body),
+            model_response=RerankResponse(),
+            logging_obj=self.logging,
+            api_key="sk",
+            request_data={"query": "q"},
+        )
+        assert out.id == "85ba5752"
+        assert out.results == [
+            {"index": 0, "relevance_score": 0.93},
+            {"index": 2, "relevance_score": 0.34},
+        ]
+        assert out.meta == {
+            "billed_units": {"total_tokens": 79},
+            "tokens": {"input_tokens": 79},
+        }
+
+    def test_response_with_return_documents_real_payload(self):
+        # Verbatim sample from a real qwen3-rerank call with return_documents=true.
+        body = {
+            "object": "list",
+            "results": [
+                {
+                    "document": {
+                        "text": "苹果派的制作步骤包括准备面团、切苹果、调制馅料、组装和烘烤。"
+                    },
+                    "index": 1,
+                    "relevance_score": 0.8304247466067356,
+                },
+                {
+                    "document": {
+                        "text": "制作苹果派时，预先煮软苹果可以缩短烘烤时间。"
+                    },
+                    "index": 3,
+                    "relevance_score": 0.7142660211908354,
+                },
+            ],
+            "model": "qwen3-rerank",
+            "id": "e191b077-97c4-9929-b121-c2fbd2c7b0af",
+            "usage": {"total_tokens": 192},
+        }
+        out = self.config.transform_rerank_response(
+            model="qwen3-rerank",
+            raw_response=self._resp(body),
+            model_response=RerankResponse(),
+            logging_obj=self.logging,
+            request_data={"query": "如何制作美味的苹果派?"},
+        )
+        assert out.id == "e191b077-97c4-9929-b121-c2fbd2c7b0af"
+        assert out.results == [
+            {
+                "index": 1,
+                "relevance_score": 0.8304247466067356,
+                "document": {
+                    "text": "苹果派的制作步骤包括准备面团、切苹果、调制馅料、组装和烘烤。"
+                },
+            },
+            {
+                "index": 3,
+                "relevance_score": 0.7142660211908354,
+                "document": {"text": "制作苹果派时，预先煮软苹果可以缩短烘烤时间。"},
+            },
+        ]
+        assert out.meta == {
+            "billed_units": {"total_tokens": 192},
+            "tokens": {"input_tokens": 192},
+        }
+
+    def test_response_string_document_normalized(self):
+        # Defensive path: if a future API revision returns a bare string,
+        # normalize to {"text": ...} so downstream code stays consistent.
+        body = {
+            "results": [{"index": 0, "relevance_score": 0.9, "document": "hello"}],
+            "model": "qwen3-rerank",
+            "usage": {"total_tokens": 5},
+        }
+        out = self.config.transform_rerank_response(
+            model="qwen3-rerank",
+            raw_response=self._resp(body),
+            model_response=RerankResponse(),
+            logging_obj=self.logging,
+        )
+        assert out.results[0]["document"] == {"text": "hello"}
+
+    def test_missing_id_generates_uuid(self):
+        body = {"results": [{"index": 0, "relevance_score": 0.5}], "usage": {}}
+        out = self.config.transform_rerank_response(
+            model="qwen3-rerank",
+            raw_response=self._resp(body),
+            model_response=RerankResponse(),
+            logging_obj=self.logging,
+        )
+        assert out.id is not None and len(out.id) > 0
+
+    def test_error_envelope_raises(self):
+        body = {
+            "code": "InvalidApiKey",
+            "message": "Invalid API-key provided.",
+            "request_id": "fb53",
+        }
+        with pytest.raises(DashScopeError) as exc_info:
+            self.config.transform_rerank_response(
+                model="qwen3-rerank",
+                raw_response=self._resp(body, status_code=401),
+                model_response=RerankResponse(),
+                logging_obj=self.logging,
+            )
+        assert "Invalid API-key provided." in str(exc_info.value)
+
+    def test_non_json_response_raises(self):
+        bad = httpx.Response(
+            status_code=500,
+            content=b"<html>bad gateway</html>",
+            request=httpx.Request("POST", "https://example.com"),
+        )
+        with pytest.raises(DashScopeError):
+            self.config.transform_rerank_response(
+                model="qwen3-rerank",
+                raw_response=bad,
+                model_response=RerankResponse(),
+                logging_obj=self.logging,
+            )
+
+    def test_get_error_class(self):
+        err = self.config.get_error_class(
+            error_message="boom", status_code=500, headers={}
+        )
+        assert isinstance(err, DashScopeError)
+        assert err.status_code == 500
+
+
+class TestProviderConfigManagerDispatch:
+    def test_dashscope_returns_rerank_config(self):
+        import litellm
+        from litellm.utils import ProviderConfigManager
+
+        cfg = ProviderConfigManager.get_provider_rerank_config(
+            model="qwen3-rerank",
+            provider=litellm.LlmProviders.DASHSCOPE,
+            api_base=None,
+            present_version_params=[],
+        )
+        assert isinstance(cfg, DashScopeRerankConfig)
diff --git a/tests/test_litellm/llms/deepseek/__init__.py b/tests/test_litellm/llms/deepseek/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/test_litellm/llms/deepseek/messages/__init__.py b/tests/test_litellm/llms/deepseek/messages/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py b/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py
new file mode 100644
index 00000000000..7c5f0483ded
--- /dev/null
+++ b/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py
@@ -0,0 +1,189 @@
+import litellm
+from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
+    AnthropicMessagesConfig,
+)
+from litellm.llms.deepseek.messages.transformation import (
+    DeepSeekAnthropicMessagesConfig,
+)
+from litellm.types.router import GenericLiteLLMParams
+from litellm.utils import ProviderConfigManager
+
+
+def test_deepseek_provider_uses_anthropic_messages_config():
+    config = ProviderConfigManager.get_provider_anthropic_messages_config(
+        model="deepseek-v4-pro",
+        provider=litellm.LlmProviders.DEEPSEEK,
+    )
+
+    assert isinstance(config, DeepSeekAnthropicMessagesConfig)
+    assert config.custom_llm_provider == "deepseek"
+
+
+def test_deepseek_anthropic_messages_config_defaults():
+    config = DeepSeekAnthropicMessagesConfig()
+
+    assert config.custom_llm_provider == "deepseek"
+    assert config.get_api_base() == "https://api.deepseek.com/anthropic"
+
+
+def test_anthropic_provider_keeps_default_config_for_deepseek_named_model():
+    config = ProviderConfigManager.get_provider_anthropic_messages_config(
+        model="deepseek-v4-pro",
+        provider=litellm.LlmProviders.ANTHROPIC,
+    )
+
+    assert isinstance(config, AnthropicMessagesConfig)
+    assert not isinstance(config, DeepSeekAnthropicMessagesConfig)
+
+
+def test_deepseek_anthropic_messages_url_defaults_to_anthropic_endpoint():
+    config = DeepSeekAnthropicMessagesConfig()
+
+    assert (
+        config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model="deepseek-v4-pro",
+            optional_params={},
+            litellm_params={},
+        )
+        == "https://api.deepseek.com/anthropic/v1/messages"
+    )
+    assert (
+        config.get_complete_url(
+            api_base="https://api.deepseek.com/anthropic/v1",
+            api_key=None,
+            model="deepseek-v4-pro",
+            optional_params={},
+            litellm_params={},
+        )
+        == "https://api.deepseek.com/anthropic/v1/messages"
+    )
+    assert (
+        config.get_complete_url(
+            api_base="https://api.deepseek.com/anthropic",
+            api_key=None,
+            model="deepseek-v4-pro",
+            optional_params={},
+            litellm_params={},
+        )
+        == "https://api.deepseek.com/anthropic/v1/messages"
+    )
+    assert (
+        config.get_complete_url(
+            api_base="https://api.deepseek.com",
+            api_key=None,
+            model="deepseek-v4-pro",
+            optional_params={},
+            litellm_params={},
+        )
+        == "https://api.deepseek.com/anthropic/v1/messages"
+    )
+    assert (
+        config.get_complete_url(
+            api_base="https://api.deepseek.com/v1",
+            api_key=None,
+            model="deepseek-v4-pro",
+            optional_params={},
+            litellm_params={},
+        )
+        == "https://api.deepseek.com/anthropic/v1/messages"
+    )
+    assert (
+        config.get_complete_url(
+            api_base="https://api.deepseek.com/v1/messages",
+            api_key=None,
+            model="deepseek-v4-pro",
+            optional_params={},
+            litellm_params={},
+        )
+        == "https://api.deepseek.com/anthropic/v1/messages"
+    )
+
+
+def test_deepseek_anthropic_messages_headers_use_deepseek_key():
+    config = DeepSeekAnthropicMessagesConfig()
+
+    headers, api_base = config.validate_anthropic_messages_environment(
+        headers={},
+        model="deepseek-v4-pro",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key="sk-deepseek",
+        api_base="https://example.test/anthropic",
+    )
+
+    assert api_base == "https://example.test/anthropic"
+    assert headers["x-api-key"] == "sk-deepseek"
+    assert headers["anthropic-version"] == "2023-06-01"
+    assert headers["content-type"] == "application/json"
+
+
+def test_deepseek_anthropic_messages_preserves_thinking_and_sanitizes_custom_tools():
+    config = DeepSeekAnthropicMessagesConfig()
+    messages = [
+        {
+            "role": "user",
+            "content": "Use the tool.",
+        },
+        {
+            "role": "assistant",
+            "content": [
+                {
+                    "type": "thinking",
+                    "thinking": "I should call the tool.",
+                    "signature": "sig",
+                },
+                {
+                    "type": "tool_use",
+                    "id": "toolu_123",
+                    "name": "get_weather",
+                    "input": {"city": "Sao Paulo"},
+                },
+            ],
+        },
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "toolu_123",
+                    "content": "Sunny",
+                }
+            ],
+        },
+    ]
+
+    request = config.transform_anthropic_messages_request(
+        model="deepseek-v4-pro",
+        messages=messages,
+        anthropic_messages_optional_request_params={
+            "max_tokens": 100,
+            "thinking": {"type": "enabled", "budget_tokens": 1024},
+            "tools": [
+                {
+                    "type": "custom",
+                    "name": "get_weather",
+                    "description": "Get weather",
+                    "input_schema": {"type": "object"},
+                },
+                {
+                    "type": "web_search_20260209",
+                    "name": "web_search",
+                    "max_uses": 1,
+                },
+            ],
+        },
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+
+    assert request["messages"] == messages
+    assert request["thinking"] == {"type": "enabled", "budget_tokens": 1024}
+    assert request["tools"][0] == {
+        "name": "get_weather",
+        "description": "Get weather",
+        "input_schema": {"type": "object"},
+    }
+    assert request["tools"][1]["type"] == "web_search_20260209"
diff --git a/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py b/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py
index 279f16a3675..a29365544df 100644
--- a/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py
+++ b/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py
@@ -6,16 +6,29 @@ from unittest.mock import AsyncMock, MagicMock, patch
 import httpx
 import pytest
 
+import litellm
+
 sys.path.insert(
     0, os.path.abspath("../../../../..")
 )  # Adds the parent directory to the system path
 
-from litellm import supports_reasoning
+from litellm import get_model_info, supports_reasoning
 from litellm.llms.fireworks_ai.chat.transformation import FireworksAIConfig
 from litellm.types.llms.openai import ChatCompletionToolCallFunctionChunk
 from litellm.types.utils import ChatCompletionMessageToolCall, Function, Message
 
 
+@pytest.fixture(autouse=True)
+def force_local_model_cost(monkeypatch):
+    """Force local model cost map usage for all tests in this file."""
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    # Refresh model_cost from local map
+    import litellm
+    from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
+
+    litellm.model_cost = get_model_cost_map(url=litellm.model_cost_map_url)
+
+
 def test_handle_message_content_with_tool_calls():
     config = FireworksAIConfig()
     message = Message(
@@ -62,7 +75,6 @@ def test_handle_message_content_with_tool_calls():
 
 def test_supports_reasoning_effort():
     """Test that reasoning_effort is only supported for specific Fireworks AI models."""
-    # Models that support reasoning_effort
     supported_models = [
         "fireworks_ai/accounts/fireworks/models/qwen3-8b",
         "fireworks_ai/accounts/fireworks/models/qwen3-32b",
@@ -72,11 +84,13 @@ def test_supports_reasoning_effort():
         "fireworks_ai/accounts/fireworks/models/glm-4p5",
         "fireworks_ai/accounts/fireworks/models/glm-4p5-air",
         "fireworks_ai/accounts/fireworks/models/glm-4p6",
+        "fireworks_ai/accounts/fireworks/models/glm-4p7",
+        "fireworks_ai/accounts/fireworks/models/glm-5p1",
         "fireworks_ai/accounts/fireworks/models/gpt-oss-120b",
         "fireworks_ai/accounts/fireworks/models/gpt-oss-20b",
+        "fireworks_ai/glm-5p1",
     ]
 
-    # Models that don't support reasoning_effort
     unsupported_models = [
         "fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
         "fireworks_ai/accounts/fireworks/models/mixtral-8x7b-instruct",
@@ -97,19 +111,74 @@ def test_get_supported_openai_params_reasoning_effort():
     """Test that reasoning_effort is only included in supported params for models that support it."""
     config = FireworksAIConfig()
 
-    # Model that supports reasoning_effort
     supported_params = config.get_supported_openai_params(
-        "fireworks_ai/accounts/fireworks/models/qwen3-8b"
+        "fireworks_ai/accounts/fireworks/models/glm-5p1"
     )
     assert "reasoning_effort" in supported_params
 
-    # Model that doesn't support reasoning_effort
     unsupported_params = config.get_supported_openai_params(
         "fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct"
     )
     assert "reasoning_effort" not in unsupported_params
 
 
+def test_get_supported_openai_params_parallel_tool_calls():
+    """Test that parallel_tool_calls is included for models that support function calling."""
+    config = FireworksAIConfig()
+
+    supported_params = config.get_supported_openai_params(
+        "fireworks_ai/accounts/fireworks/models/glm-4p6"
+    )
+    assert "parallel_tool_calls" in supported_params
+
+    unsupported_params = config.get_supported_openai_params(
+        "fireworks_ai/accounts/fireworks/models/glm-5p1"
+    )
+    assert "parallel_tool_calls" not in unsupported_params
+
+
+def test_get_supported_openai_params_parallel_tool_calls_without_tool_choice(
+    monkeypatch,
+):
+    """Test that parallel_tool_calls is gated on tools, not tool_choice."""
+    config = FireworksAIConfig()
+    model = "fireworks_ai/test-tools-without-tool-choice"
+    monkeypatch.setitem(
+        litellm.model_cost,
+        model,
+        {
+            "supports_function_calling": True,
+            "supports_tool_choice": False,
+        },
+    )
+
+    supported_params = config.get_supported_openai_params(model)
+
+    assert "tools" in supported_params
+    assert "parallel_tool_calls" in supported_params
+    assert "tool_choice" not in supported_params
+
+
+def test_get_model_info_respects_explicit_fireworks_capabilities():
+    """Test that get_model_info preserves explicit capability flags from the model map."""
+    model_info = get_model_info("fireworks_ai/accounts/fireworks/models/glm-5p1")
+
+    assert model_info["supports_function_calling"] is False
+    assert model_info["supports_reasoning"] is True
+    assert model_info["supports_tool_choice"] is False
+
+
+def test_get_provider_info_omits_false_supports_reasoning(monkeypatch):
+    """Test that Fireworks only overrides supports_reasoning for supported models."""
+    config = FireworksAIConfig()
+    model = "fireworks_ai/test-reasoning-false"
+    monkeypatch.setitem(litellm.model_cost, model, {"supports_reasoning": False})
+
+    info = config.get_provider_info(model)
+
+    assert "supports_reasoning" not in info
+
+
 def test_add_transform_inline_image_block_skips_data_urls():
     """
     data: URLs must not have #transform=inline appended — doing so corrupts the
@@ -234,6 +303,14 @@ def test_transform_messages_helper_removes_provider_specific_fields():
         assert "provider_specific_fields" not in msg
 
 
+def test_unmapped_model_fallback_function_calling():
+    """Test that a model not in model_cost still defaults to supporting function calling for Fireworks."""
+    config = FireworksAIConfig()
+    model = "fireworks_ai/unmapped-future-model"
+    info = config.get_provider_info(model)
+    assert info["supports_function_calling"] is True
+
+
 def test_transform_messages_helper_strips_thinking_blocks():
     """thinking_blocks must not be forwarded to Fireworks chat completions."""
     config = FireworksAIConfig()
diff --git a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py
index 2a5cc6b8e3d..e0911e1ef31 100644
--- a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py
+++ b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py
@@ -443,7 +443,7 @@ class TestOCIChatConfig:
                                 {"type": "TEXT", "text": "I am doing well, thank you!"}
                             ],
                         },
-                        "finishReason": "STOP",
+                        "finishReason": "COMPLETE",
                     }
                 ],
                 "timeCreated": created_time,
@@ -490,9 +490,10 @@ class TestOCIChatConfig:
         assert result.usage.prompt_tokens == 10  # type: ignore
         assert result.usage.completion_tokens == 20  # type: ignore
         assert result.usage.total_tokens == 30  # type: ignore
-        # These are not handled in the transformer, TBH no idea why they are here
-        # but, for now, they seem to be always None
-        assert result.usage.completion_tokens_details is None
+        # reasoningTokens from OCI's completionTokensDetails is surfaced on
+        # Usage.completion_tokens_details.reasoning_tokens.
+        assert result.usage.completion_tokens_details is not None
+        assert result.usage.completion_tokens_details.reasoning_tokens == 20
         assert result.usage.prompt_tokens_details is None
 
     def test_transform_response_with_tool_calls(self):
@@ -765,3 +766,557 @@ class TestOCISignerSupport:
         )
 
         assert wrapper.path_url == "/api/v1/chat"
+
+
+class TestOCISplitChunks:
+    """
+    Unit tests for the SSE split_chunks helpers used in sync and async streaming.
+
+    These validate the fix for:
+    - Sync: JSONDecodeError when iter_text() returns chunks spanning multiple events
+    - Async: whitespace-only chunks being yielded before stripping (Greptile P2)
+    """
+
+    def _run_sync_split(self, raw_chunks):
+        """Invoke the sync split_chunks logic directly (extracted for testability)."""
+        results = []
+        for item in raw_chunks:
+            for chunk in item.split("\n\n"):
+                stripped = chunk.strip()
+                if stripped:
+                    results.append(stripped)
+        return results
+
+    async def _run_async_split(self, raw_chunks):
+        """Invoke the async split_chunks logic directly."""
+        results = []
+
+        async def _gen():
+            for c in raw_chunks:
+                yield c
+
+        async for item in _gen():
+            for chunk in item.split("\n\n"):
+                stripped = chunk.strip()
+                if stripped:
+                    results.append(stripped)
+        return results
+
+    def test_sync_single_event_per_chunk(self):
+        """Normal case: one SSE event per iter_text() chunk."""
+        chunks = ['data: {"text":"hello"}', 'data: {"text":"world"}']
+        assert self._run_sync_split(chunks) == [
+            'data: {"text":"hello"}',
+            'data: {"text":"world"}',
+        ]
+
+    def test_sync_multiple_events_in_one_chunk(self):
+        """iter_text() returns two SSE events concatenated — must be split."""
+        chunks = ['data: {"text":"a"}\n\ndata: {"text":"b"}']
+        assert self._run_sync_split(chunks) == [
+            'data: {"text":"a"}',
+            'data: {"text":"b"}',
+        ]
+
+    def test_sync_whitespace_only_chunks_discarded(self):
+        """Whitespace between events must not be yielded."""
+        chunks = ["data: {}\n\n   \n\ndata: {}"]
+        result = self._run_sync_split(chunks)
+        assert result == ["data: {}", "data: {}"]
+
+    def test_sync_empty_string_discarded(self):
+        """Empty string produced by splitting trailing \\n\\n must be discarded."""
+        chunks = ["data: {}\n\n"]
+        assert self._run_sync_split(chunks) == ["data: {}"]
+
+    @pytest.mark.asyncio
+    async def test_async_whitespace_only_chunks_discarded(self):
+        """
+        Regression test for Greptile P2: async version was checking `if not chunk`
+        BEFORE stripping, so '\\n  ' would pass the guard and yield '' downstream,
+        causing ValueError in chunk_creator ('Chunk does not start with data:').
+        """
+        chunks = ["data: {}\n\n   \n\ndata: {}"]
+        result = await self._run_async_split(chunks)
+        assert result == ["data: {}", "data: {}"]
+
+    @pytest.mark.asyncio
+    async def test_async_empty_string_discarded(self):
+        """Trailing \\n\\n must not produce an empty yielded chunk in async path."""
+        chunks = ["data: {}\n\n"]
+        result = await self._run_async_split(chunks)
+        assert result == ["data: {}"]
+
+    @pytest.mark.asyncio
+    async def test_async_multiple_events_in_one_chunk(self):
+        """Async path must split concatenated SSE events just like sync."""
+        chunks = ['data: {"text":"x"}\n\ndata: {"text":"y"}']
+        result = await self._run_async_split(chunks)
+        assert result == ['data: {"text":"x"}', 'data: {"text":"y"}']
+
+
+class TestOCIProviderEmbeddingConfig:
+    """
+    Verifies that get_provider_embedding_config returns OCIEmbedConfig for OCI
+    and that the dead duplicate elif branch has been removed (Greptile P1).
+    """
+
+    def test_returns_oci_embed_config(self):
+        from litellm.llms.oci.embed.transformation import OCIEmbedConfig
+        from litellm.utils import ProviderConfigManager
+        from litellm.types.utils import LlmProviders
+
+        config = ProviderConfigManager.get_provider_embedding_config(
+            model="cohere.embed-english-v3.0",
+            provider=LlmProviders.OCI,
+        )
+        assert isinstance(config, OCIEmbedConfig)
+
+    def test_no_duplicate_oci_branch(self):
+        """
+        Ensure utils.py does not contain two separate OCI embedding branches.
+        The dead code was removed in commit 64dfbe2b; this test guards against
+        regression (e.g. a future merge re-introducing it).
+        """
+        import inspect
+        from litellm.utils import ProviderConfigManager
+
+        source = inspect.getsource(ProviderConfigManager.get_provider_embedding_config)
+        oci_count = source.count("LlmProviders.OCI")
+        assert oci_count == 1, (
+            f"Expected exactly 1 OCI branch in get_provider_embedding_config, found {oci_count}. "
+            "A duplicate dead-code branch may have been reintroduced."
+        )
+
+
+class TestOCICohereParamMapping:
+    """
+    Unit tests for Bug 3 (stop → stopSequences) and Bug 4 (hardcoded defaults removed).
+    """
+
+    def _make_config(self):
+        return OCIChatConfig()
+
+    def test_cohere_stop_maps_to_stop_sequences(self):
+        """Bug 3: Cohere API uses 'stopSequences', not 'stop'."""
+        config = self._make_config()
+        result = config.map_openai_params(
+            non_default_params={"stop": ["END", "STOP"]},
+            optional_params={},
+            model="cohere.command-latest",
+            drop_params=False,
+        )
+        assert "stopSequences" in result, "stop should map to stopSequences for Cohere"
+        assert result["stopSequences"] == ["END", "STOP"]
+        assert "stop" not in result
+
+    def test_generic_stop_maps_to_stop(self):
+        """GENERIC vendors (Meta, Google, xAI) keep 'stop' as-is."""
+        config = self._make_config()
+        result = config.map_openai_params(
+            non_default_params={"stop": ["END"]},
+            optional_params={},
+            model="meta.llama-3.3-70b-instruct",
+            drop_params=False,
+        )
+        assert result.get("stop") == ["END"]
+        assert "stopSequences" not in result
+
+    def test_cohere_no_hardcoded_defaults(self):
+        """Bug 4: Cohere calls must not inject maxTokens/temperature/topK/topP/frequencyPenalty
+        when the user hasn't provided them."""
+        config = self._make_config()
+        result = config.map_openai_params(
+            non_default_params={},
+            optional_params={},
+            model="cohere.command-latest",
+            drop_params=False,
+        )
+        for injected in (
+            "maxTokens",
+            "temperature",
+            "topK",
+            "topP",
+            "frequencyPenalty",
+        ):
+            assert (
+                injected not in result
+            ), f"'{injected}' should not be injected when user did not provide it"
+
+    def test_cohere_explicit_params_still_passed(self):
+        """User-provided Cohere params must still be forwarded correctly."""
+        config = self._make_config()
+        result = config.map_openai_params(
+            non_default_params={"max_tokens": 200, "temperature": 0.5},
+            optional_params={},
+            model="cohere.command-latest",
+            drop_params=False,
+        )
+        assert result.get("maxTokens") == 200
+        assert result.get("temperature") == 0.5
+
+
+class TestOCIReasoningEffort:
+    """
+    Reasoning-effort handling for GENERIC reasoning models:
+    - OpenAI clients send lowercase ("low"/"medium"/"high"); OCI requires uppercase.
+    - OpenAI's "disable" maps to OCI's "NONE".
+    - Cohere on OCI has no reasoning models — the param is unsupported there.
+    """
+
+    def _build_chat_request(self, model: str, optional_params: dict) -> dict:
+        """Drive optional params through map → _get_optional_params and read
+        the resulting chatRequest body via transform_request."""
+        from litellm.llms.oci.chat.transformation import OCIChatConfig
+
+        config = OCIChatConfig()
+        mapped = config.map_openai_params(
+            non_default_params=optional_params,
+            optional_params={},
+            model=model,
+            drop_params=False,
+        )
+        body = config.transform_request(
+            model=model,
+            messages=[{"role": "user", "content": "hi"}],
+            optional_params={**BASE_OCI_PARAMS, **mapped},
+            litellm_params={},
+            headers={},
+        )
+        return body["chatRequest"]
+
+    def test_reasoning_effort_lowercase_uppercased(self):
+        chat_request = self._build_chat_request(
+            "xai.grok-4-fast-reasoning",
+            {"reasoning_effort": "low"},
+        )
+        assert chat_request.get("reasoningEffort") == "LOW"
+
+    def test_reasoning_effort_disable_mapped_to_none(self):
+        chat_request = self._build_chat_request(
+            "xai.grok-4-fast-reasoning",
+            {"reasoning_effort": "disable"},
+        )
+        assert chat_request.get("reasoningEffort") == "NONE"
+
+    def test_reasoning_effort_already_uppercase_preserved(self):
+        chat_request = self._build_chat_request(
+            "openai.gpt-5",
+            {"reasoning_effort": "HIGH"},
+        )
+        assert chat_request.get("reasoningEffort") == "HIGH"
+
+    def test_reasoning_effort_unsupported_on_cohere_dropped(self):
+        """drop_params=True → silently drop reasoning_effort for Cohere."""
+        from litellm.llms.oci.chat.transformation import OCIChatConfig
+
+        config = OCIChatConfig()
+        result = config.map_openai_params(
+            non_default_params={"reasoning_effort": "low"},
+            optional_params={},
+            model="cohere.command-latest",
+            drop_params=True,
+        )
+        assert "reasoning_effort" not in result
+        assert "reasoningEffort" not in result
+
+    def test_reasoning_effort_unsupported_on_cohere_raises(self):
+        """drop_params=False → raise rather than ship a payload Cohere will reject."""
+        from litellm.llms.oci.chat.transformation import OCIChatConfig
+        from litellm.llms.oci.common_utils import OCIError
+
+        config = OCIChatConfig()
+        with pytest.raises(OCIError):
+            config.map_openai_params(
+                non_default_params={"reasoning_effort": "low"},
+                optional_params={},
+                model="cohere.command-latest",
+                drop_params=False,
+            )
+
+    def test_reasoning_tokens_extracted_from_usage(self):
+        """OCI's completionTokensDetails.reasoningTokens flows into
+        Usage.completion_tokens_details.reasoning_tokens."""
+        from litellm.llms.oci.chat.generic import handle_generic_response
+
+        created_time = (
+            datetime.datetime.now(datetime.timezone.utc)
+            .isoformat()
+            .replace("+00:00", "Z")
+        )
+        oci_response = {
+            "modelId": "xai.grok-4-fast-reasoning",
+            "modelVersion": "1.0",
+            "chatResponse": {
+                "apiFormat": "GENERIC",
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "ASSISTANT",
+                            "content": [{"type": "TEXT", "text": "ok"}],
+                        },
+                        "finishReason": "STOP",
+                    }
+                ],
+                "timeCreated": created_time,
+                "usage": {
+                    "promptTokens": 5,
+                    "completionTokens": 12,
+                    "totalTokens": 17,
+                    "completionTokensDetails": {"reasoningTokens": 7},
+                },
+            },
+        }
+        raw = httpx.Response(status_code=200, json=oci_response)
+        result = handle_generic_response(
+            json_data=oci_response,
+            model="xai.grok-4-fast-reasoning",
+            model_response=ModelResponse(),
+            raw_response=raw,
+        )
+        usage = result.usage  # type: ignore[attr-defined]
+        assert usage.completion_tokens_details is not None
+        assert usage.completion_tokens_details.reasoning_tokens == 7
+
+    def test_reasoning_tokens_absent_when_no_details(self):
+        """When OCI omits completionTokensDetails, Usage has no reasoning_tokens."""
+        from litellm.llms.oci.chat.generic import handle_generic_response
+
+        created_time = (
+            datetime.datetime.now(datetime.timezone.utc)
+            .isoformat()
+            .replace("+00:00", "Z")
+        )
+        oci_response = {
+            "modelId": "xai.grok-4",
+            "modelVersion": "1.0",
+            "chatResponse": {
+                "apiFormat": "GENERIC",
+                "choices": [
+                    {
+                        "index": 0,
+                        "message": {
+                            "role": "ASSISTANT",
+                            "content": [{"type": "TEXT", "text": "ok"}],
+                        },
+                        "finishReason": "STOP",
+                    }
+                ],
+                "timeCreated": created_time,
+                "usage": {
+                    "promptTokens": 5,
+                    "completionTokens": 12,
+                    "totalTokens": 17,
+                },
+            },
+        }
+        raw = httpx.Response(status_code=200, json=oci_response)
+        result = handle_generic_response(
+            json_data=oci_response,
+            model="xai.grok-4",
+            model_response=ModelResponse(),
+            raw_response=raw,
+        )
+        usage = result.usage  # type: ignore[attr-defined]
+        assert usage.completion_tokens_details is None
+
+
+class TestOCIStreamingSignedBody:
+    """
+    Unit test for Bug 1: sync and async streaming paths must use signed_json_body
+    when provided, not re-serialize data with json.dumps().
+    """
+
+    def test_get_custom_stream_wrapper_uses_signed_body(self, monkeypatch):
+        """
+        When signed_json_body is provided, the POST must use that exact bytes object,
+        not json.dumps(data) — otherwise the RSA-SHA256 signature is invalid.
+        """
+        import httpx
+        from unittest.mock import MagicMock, patch
+
+        config = OCIChatConfig()
+        signed_bytes = b'{"signed": true}'
+        posted_data = {}
+
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.iter_text.return_value = iter([])
+
+        mock_client = MagicMock()
+        mock_client.post.return_value = mock_response
+
+        def capture_post(url, **kwargs):
+            posted_data["data"] = kwargs.get("data")
+            return mock_response
+
+        mock_client.post.side_effect = capture_post
+
+        mock_logging = MagicMock()
+
+        config.get_sync_custom_stream_wrapper(
+            api_base="https://example.com",
+            headers={},
+            data={"key": "value"},
+            messages=[],
+            model="meta.llama-3.3-70b-instruct",
+            custom_llm_provider="oci",
+            logging_obj=mock_logging,
+            client=mock_client,
+            signed_json_body=signed_bytes,
+        )
+
+        assert (
+            posted_data["data"] == signed_bytes
+        ), "Streaming must use signed_json_body, not re-serialize data"
+
+    def test_get_custom_stream_wrapper_fallback_without_signed_body(self, monkeypatch):
+        """When signed_json_body is None, fall back to json.dumps(data)."""
+        import json
+        from unittest.mock import MagicMock
+
+        config = OCIChatConfig()
+        posted_data = {}
+
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.iter_text.return_value = iter([])
+
+        mock_client = MagicMock()
+
+        def capture_post(url, **kwargs):
+            posted_data["data"] = kwargs.get("data")
+            return mock_response
+
+        mock_client.post.side_effect = capture_post
+
+        mock_logging = MagicMock()
+        payload = {"key": "value"}
+
+        config.get_sync_custom_stream_wrapper(
+            api_base="https://example.com",
+            headers={},
+            data=payload,
+            messages=[],
+            model="meta.llama-3.3-70b-instruct",
+            custom_llm_provider="oci",
+            logging_obj=mock_logging,
+            client=mock_client,
+            signed_json_body=None,
+        )
+
+        assert posted_data["data"] == json.dumps(
+            payload
+        ), "Without signed_json_body, must fall back to json.dumps(data)"
+
+
+# ---------------------------------------------------------------------------
+# Additional coverage: error paths in validate_environment, transform_request,
+# transform_response, and map_openai_params
+# ---------------------------------------------------------------------------
+
+
+class TestOCIChatConfigErrorPaths:
+    def test_validate_environment_empty_messages_raises(self):
+        config = OCIChatConfig()
+        with pytest.raises(Exception, match="messages"):
+            config.validate_environment(
+                headers={},
+                model=TEST_MODEL_NAME,
+                messages=[],
+                optional_params={
+                    "oci_signer": MagicMock(),
+                    "oci_compartment_id": TEST_COMPARTMENT_ID,
+                },
+                litellm_params={},
+            )
+
+    def test_transform_request_missing_compartment_id_raises(self):
+        config = OCIChatConfig()
+        with pytest.raises(Exception, match="oci_compartment_id"):
+            config.transform_request(
+                model=TEST_MODEL_NAME,
+                messages=TEST_MESSAGES,  # type: ignore
+                optional_params={},
+                litellm_params={},
+                headers={},
+            )
+
+    def test_transform_request_cohere_no_user_message_raises(self):
+        config = OCIChatConfig()
+        with pytest.raises(Exception, match="user message"):
+            config.transform_request(
+                model="cohere.command-latest",
+                messages=[{"role": "system", "content": "You are helpful."}],  # type: ignore
+                optional_params={"oci_compartment_id": TEST_COMPARTMENT_ID},
+                litellm_params={},
+                headers={},
+            )
+
+    def test_transform_response_error_key_raises(self):
+        config = OCIChatConfig()
+        response = httpx.Response(
+            status_code=400,
+            json={"error": "model not found"},
+        )
+        with pytest.raises(Exception, match="model not found"):
+            config.transform_response(
+                model=TEST_MODEL_NAME,
+                raw_response=response,
+                model_response=ModelResponse(),
+                logging_obj={},  # type: ignore
+                request_data={},
+                messages=[],
+                optional_params={},
+                litellm_params={},
+                encoding={},
+            )
+
+    def test_map_openai_params_unsupported_param_raises_without_drop(self):
+        config = OCIChatConfig()
+        with pytest.raises(Exception, match="not supported on OCI"):
+            config.map_openai_params(
+                non_default_params={"audio": {"voice": "alloy"}},
+                optional_params={},
+                model=TEST_MODEL_NAME,
+                drop_params=False,
+            )
+
+    def test_map_openai_params_unsupported_param_dropped(self):
+        config = OCIChatConfig()
+        result = config.map_openai_params(
+            non_default_params={"audio": {"voice": "alloy"}},
+            optional_params={},
+            model=TEST_MODEL_NAME,
+            drop_params=True,
+        )
+        assert "audio" not in result
+
+    def test_transform_request_tool_choice_string_mapped(self):
+        config = OCIChatConfig()
+        result = config.transform_request(
+            model=TEST_MODEL_NAME,
+            messages=TEST_MESSAGES,  # type: ignore
+            optional_params={
+                "oci_compartment_id": TEST_COMPARTMENT_ID,
+                "tool_choice": "auto",
+                "tools": [
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": "fn",
+                            "description": "d",
+                            "parameters": {"type": "object", "properties": {}},
+                        },
+                    }
+                ],
+            },
+            litellm_params={},
+            headers={},
+        )
+        assert result["chatRequest"]["toolChoice"] == {"type": "AUTO"}
+
+
+import pytest
+from unittest.mock import MagicMock
diff --git a/tests/test_litellm/llms/oci/chat/test_oci_cohere_tool_calls.py b/tests/test_litellm/llms/oci/chat/test_oci_cohere_tool_calls.py
index 388cb6224fd..cc914a22eeb 100644
--- a/tests/test_litellm/llms/oci/chat/test_oci_cohere_tool_calls.py
+++ b/tests/test_litellm/llms/oci/chat/test_oci_cohere_tool_calls.py
@@ -5,10 +5,14 @@ import json
 from unittest.mock import patch, MagicMock
 
 from litellm import ModelResponse
+from litellm.llms.oci.chat.cohere import (
+    adapt_messages_to_cohere_standard,
+    adapt_tool_definitions_to_cohere_standard,
+)
 from litellm.llms.oci.chat.transformation import (
     OCIChatConfig,
-    get_vendor_from_model,
     OCIStreamWrapper,
+    get_vendor_from_model,
 )
 from litellm.types.llms.oci import OCIVendors
 
@@ -75,7 +79,7 @@ class TestOCICohereToolCalls:
         ]
 
         # Transform tools
-        cohere_tools = config.adapt_tool_definitions_to_cohere_standard(openai_tools)
+        cohere_tools = adapt_tool_definitions_to_cohere_standard(openai_tools)
 
         # Verify transformation
         assert len(cohere_tools) == 2
@@ -90,13 +94,16 @@ class TestOCICohereToolCalls:
         # Check location parameter
         location_param = weather_tool.parameterDefinitions["location"]
         assert location_param.description == "The city or location to get weather for"
-        assert location_param.type == "string"
+        assert location_param.type == "str"
         assert location_param.isRequired == True
 
         # Check unit parameter
         unit_param = weather_tool.parameterDefinitions["unit"]
-        assert unit_param.description == "Temperature unit (celsius or fahrenheit)"
-        assert unit_param.type == "string"
+        assert (
+            unit_param.description
+            == "Temperature unit (celsius or fahrenheit). Allowed values: ['celsius', 'fahrenheit']"
+        )
+        assert unit_param.type == "str"
         assert unit_param.isRequired == False
 
         # Check second tool
@@ -107,7 +114,7 @@ class TestOCICohereToolCalls:
 
         expression_param = calc_tool.parameterDefinitions["expression"]
         assert expression_param.description == "Mathematical expression to evaluate"
-        assert expression_param.type == "string"
+        assert expression_param.type == "str"
         assert expression_param.isRequired == True
 
     def test_cohere_request_with_tools(self):
@@ -157,13 +164,6 @@ class TestOCICohereToolCalls:
         assert chat_request["message"] == "What's the weather like in Tokyo?"
         assert chat_request["chatHistory"] == []
 
-        # Verify default parameters are included
-        assert chat_request["maxTokens"] == 600
-        assert chat_request["temperature"] == 1
-        assert chat_request["topK"] == 0
-        assert chat_request["topP"] == 0.75
-        assert chat_request["frequencyPenalty"] == 0
-
         # Verify tools are transformed correctly
         assert "tools" in chat_request
         assert len(chat_request["tools"]) == 1
@@ -226,7 +226,7 @@ class TestOCICohereToolCalls:
         assert len(result.choices[0].message.tool_calls) == 1
 
         tool_call = result.choices[0].message.tool_calls[0]
-        assert tool_call.id == "call_0"
+        assert tool_call.id.startswith("call_")
         assert tool_call.type == "function"
         assert tool_call.function.name == "get_weather"
         assert tool_call.function.arguments == '{"location": "Tokyo"}'
@@ -324,22 +324,26 @@ class TestOCICohereToolCalls:
             },
         ]
 
-        chat_history = config.adapt_messages_to_cohere_standard(messages)
+        chat_history = adapt_messages_to_cohere_standard(messages)
 
-        # First message is the user message
-        assert chat_history[0].role == "USER"
-        assert chat_history[0].message == "What's the weather?"
+        # The last user message is consumed by the request's top-level `message`
+        # field, so chatHistory carries the assistant tool call and tool result.
+        assert len(chat_history) == 2
 
-        # Second message is the assistant with tool calls and no text
-        assistant_msg = chat_history[1]
+        assistant_msg = chat_history[0]
         assert assistant_msg.role == "CHATBOT"
         assert assistant_msg.message is None or assistant_msg.message == ""
         assert assistant_msg.toolCalls is not None
         assert len(assistant_msg.toolCalls) == 1
         assert assistant_msg.toolCalls[0].name == "get_weather"
 
+        tool_msg = chat_history[1]
+        assert tool_msg.role == "TOOL"
+        assert tool_msg.toolResults[0].call.name == "get_weather"
+        assert tool_msg.toolResults[0].outputs[0]["output"] == "Sunny, 25C"
+
     def test_cohere_chat_history_with_tool_calls(self):
-        """Test chat history transformation with tool calls"""
+        """Tool results trailing the last user turn must be preserved in chatHistory."""
         config = OCIChatConfig()
 
         messages = [
@@ -365,28 +369,29 @@ class TestOCICohereToolCalls:
             },
         ]
 
-        chat_history = config.adapt_messages_to_cohere_standard(messages)
+        chat_history = adapt_messages_to_cohere_standard(messages)
 
-        # Verify chat history structure (excludes last message)
+        # The last user message becomes the request's top-level `message`.
+        # Everything else — including the trailing tool result — must remain in
+        # chatHistory so the model can see the tool output.
         assert len(chat_history) == 2
 
-        # Check user message
-        user_msg = chat_history[0]
-        assert user_msg.role == "USER"
-        assert user_msg.message == "What's the weather like in Tokyo?"
-
-        # Check assistant message with tool calls
-        assistant_msg = chat_history[1]
+        assistant_msg = chat_history[0]
         assert assistant_msg.role == "CHATBOT"
         assert assistant_msg.message == "I will look up the weather in Tokyo."
         assert assistant_msg.toolCalls is not None
         assert len(assistant_msg.toolCalls) == 1
         assert assistant_msg.toolCalls[0].name == "get_weather"
-        # The parameters should be parsed as JSON
         assert assistant_msg.toolCalls[0].parameters == {"location": "Tokyo"}
 
-        # Note: The tool message (last message) is excluded from chat history
-        # This is the expected behavior for Cohere models
+        tool_msg = chat_history[1]
+        assert tool_msg.role == "TOOL"
+        assert tool_msg.toolResults[0].call.name == "get_weather"
+        assert tool_msg.toolResults[0].call.parameters == {"location": "Tokyo"}
+        assert (
+            tool_msg.toolResults[0].outputs[0]["output"]
+            == "The weather in Tokyo is 22°C with partly cloudy skies."
+        )
 
     def test_cohere_streaming_chunk_handling(self):
         """Test Cohere streaming chunk handling"""
@@ -457,7 +462,7 @@ class TestOCICohereToolCalls:
         assert "tool_choice" not in supported_params
 
     def test_cohere_default_parameters(self):
-        """Test that Cohere requests include required default parameters"""
+        """Test that Cohere requests do not inject hardcoded defaults — caller supplies all params."""
         config = OCIChatConfig()
         messages = [{"role": "user", "content": "Hello"}]
         optional_params = {"oci_compartment_id": TEST_COMPARTMENT_ID}
@@ -472,12 +477,11 @@ class TestOCICohereToolCalls:
 
         chat_request = transformed_request["chatRequest"]
 
-        # Verify all required default parameters are present
-        assert chat_request["maxTokens"] == 600
-        assert chat_request["temperature"] == 1
-        assert chat_request["topK"] == 0
-        assert chat_request["topP"] == 0.75
-        assert chat_request["frequencyPenalty"] == 0
+        # No hardcoded defaults injected — only pass through what the user supplies
+        assert "maxTokens" not in chat_request
+        assert "topK" not in chat_request
+        assert "topP" not in chat_request
+        assert "frequencyPenalty" not in chat_request
 
     def test_cohere_parameter_override(self):
         """Test that user-provided parameters override defaults"""
@@ -499,14 +503,104 @@ class TestOCICohereToolCalls:
 
         chat_request = transformed_request["chatRequest"]
 
-        # Verify user parameters override defaults
+        # Verify user parameters are passed through
         assert chat_request["temperature"] == 0.5
         assert chat_request["maxTokens"] == 1000
 
-        # Verify other defaults are still present
-        assert chat_request["topK"] == 0
-        assert chat_request["topP"] == 0.75
-        assert chat_request["frequencyPenalty"] == 0
+        # Unset params are absent (no hardcoded defaults)
+        assert "topK" not in chat_request
+        assert "topP" not in chat_request
+        assert "frequencyPenalty" not in chat_request
+
+    def test_cohere_response_finish_reason_tool_call(self):
+        """Test that finishReason='TOOL_CALL' is accepted by Pydantic and mapped to 'tool_calls'."""
+        config = OCIChatConfig()
+
+        mock_cohere_response = {
+            "modelId": "cohere.command-latest",
+            "modelVersion": "1.0",
+            "chatResponse": {
+                "apiFormat": "COHERE",
+                "text": "",
+                "finishReason": "TOOL_CALL",
+                "toolCalls": [
+                    {"name": "get_weather", "parameters": {"location": "London"}}
+                ],
+                "usage": {
+                    "promptTokens": 20,
+                    "completionTokens": 10,
+                    "totalTokens": 30,
+                },
+            },
+        }
+
+        response = httpx.Response(
+            status_code=200,
+            json=mock_cohere_response,
+            headers={"Content-Type": "application/json"},
+        )
+
+        result = config.transform_response(
+            model="cohere.command-latest",
+            raw_response=response,
+            model_response=ModelResponse(),
+            logging_obj={},  # type: ignore
+            request_data={},
+            messages=[],
+            optional_params={},
+            litellm_params={},
+            encoding={},
+        )
+
+        assert isinstance(result, ModelResponse)
+        assert result.choices[0].finish_reason == "tool_calls"
+        assert result.choices[0].message.tool_calls is not None
+        assert len(result.choices[0].message.tool_calls) == 1
+        assert result.choices[0].message.tool_calls[0].function.name == "get_weather"
+
+    def test_cohere_response_unknown_finish_reason_degrades_to_stop(self):
+        """A future/unknown finishReason in non-streaming responses must
+        degrade to ``stop`` via ``handle_cohere_response``'s fallback
+        rather than crash Pydantic validation. Mirrors the streaming
+        handler's behavior. See bug caf74429.
+        """
+        config = OCIChatConfig()
+
+        mock_cohere_response = {
+            "modelId": "cohere.command-latest",
+            "modelVersion": "1.0",
+            "chatResponse": {
+                "apiFormat": "COHERE",
+                "text": "hello",
+                "finishReason": "FUTURE_REASON_NOT_YET_KNOWN",
+                "usage": {
+                    "promptTokens": 1,
+                    "completionTokens": 1,
+                    "totalTokens": 2,
+                },
+            },
+        }
+
+        response = httpx.Response(
+            status_code=200,
+            json=mock_cohere_response,
+            headers={"Content-Type": "application/json"},
+        )
+
+        result = config.transform_response(
+            model="cohere.command-latest",
+            raw_response=response,
+            model_response=ModelResponse(),
+            logging_obj={},  # type: ignore
+            request_data={},
+            messages=[],
+            optional_params={},
+            litellm_params={},
+            encoding={},
+        )
+
+        assert isinstance(result, ModelResponse)
+        assert result.choices[0].finish_reason == "stop"
 
     def test_cohere_vendor_detection(self):
         """Test that Cohere models are correctly identified"""
@@ -532,7 +626,7 @@ class TestOCICohereToolCalls:
         ]
 
         # The function should handle missing function key gracefully
-        cohere_tools = config.adapt_tool_definitions_to_cohere_standard(invalid_tools)
+        cohere_tools = adapt_tool_definitions_to_cohere_standard(invalid_tools)
 
         # Should create a tool with empty name and description
         assert len(cohere_tools) == 1
@@ -686,16 +780,114 @@ class TestOCICoherePreambleOverride:
             {"role": "assistant", "content": "First answer"},
             {"role": "user", "content": "Second question"},
         ]
+        optional_params = {"oci_compartment_id": TEST_COMPARTMENT_ID}
 
-        chat_history = config.adapt_messages_to_cohere_standard(messages)
+        result = config.transform_request(
+            model="cohere.command-latest",
+            messages=messages,  # type: ignore
+            optional_params=optional_params,
+            litellm_params={},
+            headers={},
+        )
 
-        # Should contain user and assistant only, no system
-        # Note: adapt_messages_to_cohere_standard excludes the last message
-        roles = [msg.role for msg in chat_history]
+        chat_request = result["chatRequest"]
+        roles = [msg["role"] for msg in chat_request["chatHistory"]]
         assert "SYSTEM" not in roles
         assert roles == ["USER", "CHATBOT"]
 
 
+class TestCohereStreamChunkEdgeCases:
+    """Additional coverage for handle_cohere_stream_chunk error/edge paths."""
+
+    def _wrapper(self):
+        from litellm.llms.oci.chat.transformation import OCIStreamWrapper
+
+        return OCIStreamWrapper(
+            completion_stream=MagicMock(),
+            model="cohere.command-latest",
+            logging_obj=MagicMock(),
+        )
+
+    def test_stream_chunk_tool_call_finish_reason(self):
+        wrapper = self._wrapper()
+        chunk = {
+            "apiFormat": "COHERE",
+            "text": "",
+            "index": 0,
+            "finishReason": "TOOL_CALL",
+        }
+        result = wrapper.chunk_creator(f"data: {json.dumps(chunk)}")
+        assert result.choices[0].finish_reason == "tool_calls"
+
+    def test_stream_chunk_max_tokens_finish_reason(self):
+        wrapper = self._wrapper()
+        chunk = {
+            "apiFormat": "COHERE",
+            "text": "truncated",
+            "index": 0,
+            "finishReason": "MAX_TOKENS",
+        }
+        result = wrapper.chunk_creator(f"data: {json.dumps(chunk)}")
+        assert result.choices[0].finish_reason == "length"
+
+    def test_stream_chunk_unknown_finish_reason_does_not_raise(self):
+        from litellm.llms.oci.chat.cohere import handle_cohere_stream_chunk
+
+        chunk = {
+            "apiFormat": "COHERE",
+            "text": "",
+            "index": 0,
+            "finishReason": "FUTURE_REASON",
+        }
+        # Should not raise — unknown reasons fall through the elif chain unchanged
+        result = handle_cohere_stream_chunk(chunk)
+        assert result.choices[0] is not None
+
+    def test_stream_chunk_null_index_defaults_to_zero(self):
+        wrapper = self._wrapper()
+        chunk = {"apiFormat": "COHERE", "text": "hi", "index": None}
+        result = wrapper.chunk_creator(f"data: {json.dumps(chunk)}")
+        assert result.choices[0].index == 0
+
+
+class TestCohereMessageAdaptationEdgeCases:
+    """Coverage for adapt_messages_to_cohere_standard error paths."""
+
+    def test_json_decode_error_in_tool_args_defaults_to_empty(self):
+        from litellm.llms.oci.chat.cohere import adapt_messages_to_cohere_standard
+
+        messages = [
+            {
+                "role": "assistant",
+                "content": "calling",
+                "tool_calls": [
+                    {
+                        "id": "c1",
+                        "type": "function",
+                        "function": {"name": "fn", "arguments": "NOT JSON {{{"},
+                    }
+                ],
+            },
+            {"role": "user", "content": "follow up"},
+        ]
+        # Should not raise — bad JSON defaults to empty params {}
+        history = adapt_messages_to_cohere_standard(messages)
+        assert history[0].toolCalls[0].parameters == {}
+
+    def test_extract_text_content_list_with_non_dict_items(self):
+        from litellm.llms.oci.chat.cohere import _extract_text_content
+
+        # List with a non-dict item — should be silently skipped
+        result = _extract_text_content([{"type": "text", "text": "hello"}, "bad_item"])
+        assert result == "hello"
+
+    def test_extract_text_content_non_string_non_list(self):
+        from litellm.llms.oci.chat.cohere import _extract_text_content
+
+        result = _extract_text_content(12345)
+        assert result == "12345"
+
+
 class TestOCICohereStreaming:
     """Test Cohere streaming functionality"""
 
@@ -713,9 +905,9 @@ class TestOCICohereStreaming:
         """Test OCIStreamWrapper initialization"""
         stream_wrapper = self._create_stream_wrapper()
 
+        # chunk_creator is the public dispatch entry point
         assert hasattr(stream_wrapper, "chunk_creator")
-        assert hasattr(stream_wrapper, "_handle_cohere_stream_chunk")
-        assert hasattr(stream_wrapper, "_handle_generic_stream_chunk")
+        assert callable(stream_wrapper.chunk_creator)
 
     def test_cohere_streaming_chunk_parsing(self):
         """Test parsing of Cohere streaming chunks"""
@@ -739,10 +931,12 @@ class TestOCICohereStreaming:
 
     def test_cohere_streaming_non_json_chunk(self):
         """Test error handling for non-JSON chunk"""
+        from litellm.llms.oci.common_utils import OCIError
+
         stream_wrapper = self._create_stream_wrapper()
 
         # Test non-JSON chunk
-        with pytest.raises(json.JSONDecodeError):
+        with pytest.raises(OCIError, match="Chunk cannot be parsed as JSON"):
             stream_wrapper.chunk_creator("data: invalid json")
 
     def test_cohere_streaming_generic_chunk_fallback(self):
diff --git a/tests/test_litellm/llms/oci/chat/test_oci_generic_chat.py b/tests/test_litellm/llms/oci/chat/test_oci_generic_chat.py
new file mode 100644
index 00000000000..7583e3bc183
--- /dev/null
+++ b/tests/test_litellm/llms/oci/chat/test_oci_generic_chat.py
@@ -0,0 +1,440 @@
+"""
+Unit tests for litellm/llms/oci/chat/generic.py — error paths and stream handling.
+"""
+
+import json
+import pytest
+from unittest.mock import MagicMock
+
+import httpx
+
+from litellm import ModelResponse
+from litellm.llms.oci.chat.generic import (
+    adapt_messages_to_generic_oci_standard,
+    adapt_messages_to_generic_oci_standard_content_message,
+    adapt_messages_to_generic_oci_standard_tool_call,
+    handle_generic_response,
+    handle_generic_stream_chunk,
+)
+from litellm.llms.oci.chat.transformation import OCIChatConfig, OCIStreamWrapper
+from litellm.llms.oci.common_utils import OCIError
+
+# ---------------------------------------------------------------------------
+# adapt_messages_to_generic_oci_standard_content_message — error paths
+# ---------------------------------------------------------------------------
+
+
+class TestGenericContentMessageErrors:
+    def test_non_dict_content_item_raises(self):
+        with pytest.raises(OCIError, match="must be a dictionary"):
+            adapt_messages_to_generic_oci_standard_content_message(
+                "user", ["not a dict"]
+            )
+
+    def test_non_string_type_field_raises(self):
+        with pytest.raises(OCIError, match="string `type` field"):
+            adapt_messages_to_generic_oci_standard_content_message(
+                "user", [{"type": 123, "text": "hi"}]
+            )
+
+    def test_unsupported_content_type_raises(self):
+        with pytest.raises(OCIError, match="not supported by OCI"):
+            adapt_messages_to_generic_oci_standard_content_message(
+                "user", [{"type": "video_url", "url": "https://example.com/v.mp4"}]
+            )
+
+    def test_non_string_text_raises(self):
+        with pytest.raises(OCIError, match="must have a string `text` field"):
+            adapt_messages_to_generic_oci_standard_content_message(
+                "user", [{"type": "text", "text": 42}]
+            )
+
+    def test_image_url_as_invalid_type_raises(self):
+        with pytest.raises(OCIError, match="must be a string or an object"):
+            adapt_messages_to_generic_oci_standard_content_message(
+                "user", [{"type": "image_url", "image_url": 99}]
+            )
+
+    def test_image_url_as_string(self):
+        msg = adapt_messages_to_generic_oci_standard_content_message(
+            "user", [{"type": "image_url", "image_url": "https://example.com/img.png"}]
+        )
+        assert msg.content[0].imageUrl.url == "https://example.com/img.png"
+
+    def test_image_url_as_dict(self):
+        msg = adapt_messages_to_generic_oci_standard_content_message(
+            "user",
+            [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://example.com/img.png"},
+                }
+            ],
+        )
+        assert msg.content[0].imageUrl.url == "https://example.com/img.png"
+
+    def test_text_content_string(self):
+        msg = adapt_messages_to_generic_oci_standard_content_message("user", "hello")
+        assert msg.content[0].text == "hello"
+
+
+# ---------------------------------------------------------------------------
+# adapt_messages_to_generic_oci_standard_tool_call — error paths
+# ---------------------------------------------------------------------------
+
+
+class TestGenericToolCallErrors:
+    def test_non_dict_tool_call_raises(self):
+        with pytest.raises(OCIError, match="must be a dictionary"):
+            adapt_messages_to_generic_oci_standard_tool_call("assistant", ["bad"])
+
+    def test_non_function_type_raises(self):
+        with pytest.raises(OCIError, match="only supports function tool calls"):
+            adapt_messages_to_generic_oci_standard_tool_call(
+                "assistant",
+                [
+                    {
+                        "type": "database",
+                        "id": "x",
+                        "function": {"name": "f", "arguments": "{}"},
+                    }
+                ],
+            )
+
+    def test_non_string_id_raises(self):
+        with pytest.raises(OCIError, match="id.*must be a string"):
+            adapt_messages_to_generic_oci_standard_tool_call(
+                "assistant",
+                [
+                    {
+                        "type": "function",
+                        "id": 123,
+                        "function": {"name": "f", "arguments": "{}"},
+                    }
+                ],
+            )
+
+    def test_non_dict_function_raises(self):
+        with pytest.raises(OCIError, match="`function` must be a dictionary"):
+            adapt_messages_to_generic_oci_standard_tool_call(
+                "assistant",
+                [{"type": "function", "id": "c1", "function": "not_a_dict"}],
+            )
+
+    def test_non_string_function_name_raises(self):
+        with pytest.raises(OCIError, match="function.name.*must be a string"):
+            adapt_messages_to_generic_oci_standard_tool_call(
+                "assistant",
+                [
+                    {
+                        "type": "function",
+                        "id": "c1",
+                        "function": {"name": 5, "arguments": "{}"},
+                    }
+                ],
+            )
+
+    def test_non_string_arguments_raises(self):
+        with pytest.raises(OCIError, match="arguments.*must be a JSON string"):
+            adapt_messages_to_generic_oci_standard_tool_call(
+                "assistant",
+                [
+                    {
+                        "type": "function",
+                        "id": "c1",
+                        "function": {"name": "fn", "arguments": {"key": "val"}},
+                    }
+                ],
+            )
+
+
+# ---------------------------------------------------------------------------
+# adapt_messages_to_generic_oci_standard — combined paths
+# ---------------------------------------------------------------------------
+
+
+class TestGenericMessageAdaptation:
+    def test_tool_calls_not_list_raises(self):
+        messages = [
+            {
+                "role": "assistant",
+                "content": None,
+                "tool_calls": "not_a_list",
+            }
+        ]
+        with pytest.raises(OCIError, match="`tool_calls` must be a list"):
+            adapt_messages_to_generic_oci_standard(messages)
+
+    def test_tool_result_non_string_tool_call_id_raises(self):
+        messages = [{"role": "tool", "content": "result", "tool_call_id": 999}]
+        with pytest.raises(OCIError, match="string `tool_call_id`"):
+            adapt_messages_to_generic_oci_standard(messages)
+
+    def test_tool_result_non_string_content_raises(self):
+        messages = [
+            {"role": "tool", "content": {"structured": "data"}, "tool_call_id": "c1"}
+        ]
+        with pytest.raises(OCIError, match="`content` must be a string"):
+            adapt_messages_to_generic_oci_standard(messages)
+
+    def test_non_string_non_list_content_raises(self):
+        messages = [{"role": "user", "content": 42}]
+        with pytest.raises(OCIError, match="`content` must be a string or list"):
+            adapt_messages_to_generic_oci_standard(messages)
+
+
+# ---------------------------------------------------------------------------
+# handle_generic_response — error and None message paths
+# ---------------------------------------------------------------------------
+
+
+class TestHandleGenericResponse:
+    def _make_response(self, body: dict, status: int = 200) -> httpx.Response:
+        return httpx.Response(status_code=status, json=body)
+
+    def _valid_body(self, message=None):
+        return {
+            "modelId": "xai.grok-4",
+            "modelVersion": "1",
+            "chatResponse": {
+                "apiFormat": "GENERIC",
+                "timeCreated": "2024-01-01T00:00:00Z",
+                "choices": [
+                    {"message": message, "finishReason": "COMPLETE", "index": 0}
+                ],
+                "usage": {"promptTokens": 5, "completionTokens": 5, "totalTokens": 10},
+            },
+        }
+
+    def test_none_response_message(self):
+        body = self._valid_body(message=None)
+        raw = self._make_response(body)
+        # Should not raise — None message means no content set
+        result = handle_generic_response(body, "xai.grok-4", ModelResponse(), raw)
+        assert result.model == "xai.grok-4"
+
+    def test_response_with_text_content(self):
+        body = self._valid_body(
+            message={
+                "role": "ASSISTANT",
+                "content": [{"type": "TEXT", "text": "Hello!"}],
+            }
+        )
+        raw = self._make_response(body)
+        result = handle_generic_response(body, "xai.grok-4", ModelResponse(), raw)
+        assert result.choices[0].message.content == "Hello!"
+
+    def test_response_with_tool_calls(self):
+        body = self._valid_body(
+            message={
+                "role": "ASSISTANT",
+                "content": [],
+                "toolCalls": [
+                    {
+                        "id": "call_abc",
+                        "type": "FUNCTION",
+                        "name": "get_weather",
+                        "arguments": '{"location": "Tokyo"}',
+                    }
+                ],
+            }
+        )
+        raw = self._make_response(body)
+        result = handle_generic_response(body, "xai.grok-4", ModelResponse(), raw)
+        assert result.choices[0].message.tool_calls is not None
+
+
+# ---------------------------------------------------------------------------
+# handle_generic_stream_chunk — finish reasons and error paths
+# ---------------------------------------------------------------------------
+
+
+class TestHandleGenericStreamChunk:
+    def test_max_tokens_finish_reason(self):
+        chunk = {"apiFormat": "GENERIC", "index": 0, "finishReason": "MAX_TOKENS"}
+        result = handle_generic_stream_chunk(chunk)
+        assert result.choices[0].finish_reason == "length"
+
+    def test_tool_calls_finish_reason(self):
+        chunk = {"apiFormat": "GENERIC", "index": 0, "finishReason": "TOOL_CALLS"}
+        result = handle_generic_stream_chunk(chunk)
+        assert result.choices[0].finish_reason == "tool_calls"
+
+    def test_unknown_finish_reason_does_not_raise(self):
+        chunk = {"apiFormat": "GENERIC", "index": 0, "finishReason": "SOME_NEW_REASON"}
+        result = handle_generic_stream_chunk(chunk)
+        assert result.choices[0] is not None
+
+    def test_null_index_defaults_to_zero(self):
+        chunk = {"apiFormat": "GENERIC", "index": None, "finishReason": None}
+        result = handle_generic_stream_chunk(chunk)
+        assert result.choices[0].index == 0
+
+    def test_image_content_in_stream_raises(self):
+        from litellm.types.llms.oci import OCIImageContentPart, OCIImageUrl, OCIMessage
+
+        chunk = {
+            "apiFormat": "GENERIC",
+            "index": 0,
+            "message": {
+                "role": "ASSISTANT",
+                "content": [
+                    {
+                        "type": "IMAGE",
+                        "imageUrl": {"url": "https://example.com/img.png"},
+                    }
+                ],
+            },
+        }
+        with pytest.raises(OCIError, match="image content"):
+            handle_generic_stream_chunk(chunk)
+
+    def test_stream_chunk_with_tool_calls(self):
+        chunk = {
+            "apiFormat": "GENERIC",
+            "index": 0,
+            "message": {
+                "role": "ASSISTANT",
+                "content": [],
+                "toolCalls": [
+                    {
+                        "id": "call_abc",
+                        "type": "FUNCTION",
+                        "name": "get_weather",
+                        "arguments": '{"location": "Tokyo"}',
+                    }
+                ],
+            },
+        }
+        result = handle_generic_stream_chunk(chunk)
+        assert result.choices[0].delta.tool_calls is not None
+
+
+# ---------------------------------------------------------------------------
+# OCIStreamWrapper.chunk_creator — non-string chunk
+# ---------------------------------------------------------------------------
+
+
+class TestOCIStreamWrapperChunkCreator:
+    def _wrapper(self):
+        return OCIStreamWrapper(
+            completion_stream=MagicMock(),
+            model="xai.grok-4",
+            logging_obj=MagicMock(),
+        )
+
+    def test_non_string_chunk_raises(self):
+        w = self._wrapper()
+        with pytest.raises(ValueError, match="not a string"):
+            w.chunk_creator({"already": "parsed"})
+
+
+# ---------------------------------------------------------------------------
+# GPT-5 family: maxCompletionTokens routing
+#
+# Regression guard: OCI rejects "maxTokens" for openai.gpt-5* models with HTTP
+# 400 ("Use 'maxCompletionTokens' instead.") — verified against live OCI.
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def _register_oci_gpt5_in_catalog():
+    """Guarantee OCI GPT-5 catalog entries with supports_reasoning=True are
+    present for the duration of the test, regardless of whether
+    ``litellm.model_cost`` was populated from the bundled
+    ``model_prices_and_context_window.json`` (which ships them) or from a
+    remote map that may lag behind.
+    """
+    import litellm
+
+    needed = {
+        "oci/openai.gpt-5",
+        "oci/openai.gpt-5-mini",
+        "oci/openai.gpt-5-nano",
+    }
+    added = []
+    for key in needed:
+        if key not in litellm.model_cost:
+            litellm.model_cost[key] = {
+                "litellm_provider": "oci",
+                "mode": "chat",
+                "supports_reasoning": True,
+            }
+            added.append(key)
+    yield
+    for key in added:
+        litellm.model_cost.pop(key, None)
+
+
+class TestGpt5MaxCompletionTokens:
+    def test_helper_detects_gpt5_family(self, _register_oci_gpt5_in_catalog):
+        from litellm.llms.oci.chat.transformation import (
+            _model_uses_max_completion_tokens,
+        )
+
+        assert _model_uses_max_completion_tokens("openai.gpt-5") is True
+        assert _model_uses_max_completion_tokens("openai.gpt-5-mini") is True
+        assert _model_uses_max_completion_tokens("openai.gpt-5-nano") is True
+        assert _model_uses_max_completion_tokens("oci/openai.gpt-5") is True
+
+        assert _model_uses_max_completion_tokens("openai.gpt-oss-120b") is False
+        assert _model_uses_max_completion_tokens("meta.llama-3.3-70b-instruct") is False
+        assert _model_uses_max_completion_tokens("cohere.command-latest") is False
+        assert _model_uses_max_completion_tokens("") is False
+
+    def test_gpt5_routes_max_tokens_to_max_completion_tokens(
+        self, _register_oci_gpt5_in_catalog
+    ):
+        from litellm.llms.oci.chat.transformation import OCIChatConfig, OCIVendors
+
+        cfg = OCIChatConfig()
+        # Both shapes optional_params can take after upstream map_openai_params:
+        # 1. openai-side key still present
+        out_a = cfg._get_optional_params(
+            OCIVendors.GENERIC, {"max_tokens": 64}, model="openai.gpt-5"
+        )
+        assert out_a.get("maxCompletionTokens") == 64
+        assert "maxTokens" not in out_a
+
+        # 2. already pre-translated to OCI alias
+        out_b = cfg._get_optional_params(
+            OCIVendors.GENERIC, {"maxTokens": 64}, model="openai.gpt-5-mini"
+        )
+        assert out_b.get("maxCompletionTokens") == 64
+        assert "maxTokens" not in out_b
+
+    def test_non_gpt5_keeps_max_tokens(self):
+        from litellm.llms.oci.chat.transformation import OCIChatConfig, OCIVendors
+
+        cfg = OCIChatConfig()
+        out = cfg._get_optional_params(
+            OCIVendors.GENERIC,
+            {"max_tokens": 64},
+            model="meta.llama-3.3-70b-instruct",
+        )
+        assert out.get("maxTokens") == 64
+        assert "maxCompletionTokens" not in out
+
+    def test_cohere_reasoning_model_keeps_max_tokens(self):
+        from litellm.llms.oci.chat.transformation import OCIChatConfig, OCIVendors
+
+        cfg = OCIChatConfig()
+        out = cfg._get_optional_params(
+            OCIVendors.COHERE,
+            {"max_tokens": 64},
+            model="cohere.command-a-reasoning",
+        )
+        assert out.get("maxTokens") == 64
+        assert "maxCompletionTokens" not in out
+
+    def test_payload_serializes_max_completion_tokens(self):
+        from litellm.types.llms.oci import OCIChatRequestPayload
+
+        payload = OCIChatRequestPayload(
+            apiFormat="GENERIC",
+            messages=[],
+            maxCompletionTokens=64,
+        )
+        dumped = payload.model_dump(exclude_none=True)
+        assert dumped["maxCompletionTokens"] == 64
+        assert "maxTokens" not in dumped
diff --git a/tests/test_litellm/llms/oci/chat/test_oci_sse_splitter.py b/tests/test_litellm/llms/oci/chat/test_oci_sse_splitter.py
new file mode 100644
index 00000000000..a2faf664ee7
--- /dev/null
+++ b/tests/test_litellm/llms/oci/chat/test_oci_sse_splitter.py
@@ -0,0 +1,232 @@
+"""
+Tests for the OCI SSE event splitter.
+
+Regression coverage for the streaming bug John Lathouwers reported: the old
+``split_chunks`` helper split each individual HTTP read on ``\\n\\n``, so any
+event that straddled a read boundary or any pair of events separated by a
+single ``\\n`` would yield malformed chunks to ``OCIStreamWrapper.chunk_creator``
+and crash ``json.loads``.
+"""
+
+import asyncio
+from typing import AsyncIterator, Iterator, List
+
+from litellm.llms.oci.chat.transformation import (
+    _aiter_sse_events,
+    _iter_sse_events,
+)
+
+
+def _collect_sync(stream: Iterator[str]) -> List[str]:
+    return list(_iter_sse_events(iter(stream)))
+
+
+def _collect_async(chunks: List[str]) -> List[str]:
+    async def _src() -> AsyncIterator[str]:
+        for c in chunks:
+            yield c
+
+    async def _run() -> List[str]:
+        out: List[str] = []
+        async for line in _aiter_sse_events(_src()):
+            out.append(line)
+        return out
+
+    return asyncio.run(_run())
+
+
+# ---------------------------------------------------------------------------
+# Sync splitter
+# ---------------------------------------------------------------------------
+
+
+class TestIterSseEventsSync:
+    def test_well_formed_double_newline_separators(self):
+        reads = ['data: {"a":1}\n\ndata: {"a":2}\n\n']
+        assert _collect_sync(reads) == ['data: {"a":1}', 'data: {"a":2}']
+
+    def test_event_split_across_two_reads(self):
+        # The bug: read 1 ends mid-JSON, read 2 finishes it. Old code would
+        # have yielded a truncated 'data: {"index":0,"text":"hel' and crashed
+        # json.loads in chunk_creator.
+        reads = [
+            'data: {"index":0,"text":"hel',
+            'lo"}\n\n',
+        ]
+        assert _collect_sync(reads) == ['data: {"index":0,"text":"hello"}']
+
+    def test_event_split_into_many_tiny_reads(self):
+        full = 'data: {"k":"value with spaces"}\n\n'
+        reads = [full[i : i + 3] for i in range(0, len(full), 3)]
+        assert _collect_sync(reads) == ['data: {"k":"value with spaces"}']
+
+    def test_single_newline_separator(self):
+        # The other shape John saw: events separated by just '\n'.
+        reads = ['data: {"a":1}\ndata: {"a":2}\ndata: {"a":3}\n']
+        assert _collect_sync(reads) == [
+            'data: {"a":1}',
+            'data: {"a":2}',
+            'data: {"a":3}',
+        ]
+
+    def test_mixed_separators_in_one_read(self):
+        reads = ['data: {"a":1}\ndata: {"a":2}\n\ndata: {"a":3}\n\n']
+        assert _collect_sync(reads) == [
+            'data: {"a":1}',
+            'data: {"a":2}',
+            'data: {"a":3}',
+        ]
+
+    def test_keepalive_and_comment_lines_dropped(self):
+        # SSE keepalives ("\n") and comment lines (": ping") must not be
+        # forwarded to chunk_creator, which would reject anything not
+        # starting with 'data:'.
+        reads = [
+            "\n",
+            ": ping\n",
+            'data: {"a":1}\n\n',
+            "\n",
+            ": keepalive\n\n",
+            'data: {"a":2}\n\n',
+        ]
+        assert _collect_sync(reads) == ['data: {"a":1}', 'data: {"a":2}']
+
+    def test_trailing_partial_event_flushed_at_eof(self):
+        # Final event arrives without a terminating newline. The splitter
+        # must still emit it once the upstream iterator is exhausted.
+        reads = ['data: {"a":1}\n\n', 'data: {"a":2}']
+        assert _collect_sync(reads) == ['data: {"a":1}', 'data: {"a":2}']
+
+    def test_trailing_non_data_line_dropped_at_eof(self):
+        reads = ['data: {"a":1}\n\n: trailing-comment']
+        assert _collect_sync(reads) == ['data: {"a":1}']
+
+    def test_empty_stream(self):
+        assert _collect_sync([]) == []
+
+    def test_only_whitespace_and_keepalives(self):
+        assert _collect_sync(["\n", "\n\n", ": ping\n"]) == []
+
+    def test_boundary_between_data_keyword_and_payload(self):
+        # The 'data:' marker itself straddles a read boundary.
+        reads = ["dat", 'a: {"a":1}\n\n']
+        assert _collect_sync(reads) == ['data: {"a":1}']
+
+    def test_carriage_return_in_payload_preserved(self):
+        # SSE-over-the-wire may use \r\n line endings. We split on \n; the
+        # \r ends up on the previous line and strip() removes it.
+        reads = ['data: {"a":1}\r\ndata: {"a":2}\r\n']
+        assert _collect_sync(reads) == ['data: {"a":1}', 'data: {"a":2}']
+
+
+# ---------------------------------------------------------------------------
+# Async splitter — same scenarios, parallel coverage
+# ---------------------------------------------------------------------------
+
+
+class TestIterSseEventsAsync:
+    def test_well_formed_double_newline_separators(self):
+        assert _collect_async(['data: {"a":1}\n\ndata: {"a":2}\n\n']) == [
+            'data: {"a":1}',
+            'data: {"a":2}',
+        ]
+
+    def test_event_split_across_two_reads(self):
+        assert _collect_async(['data: {"index":0,"text":"hel', 'lo"}\n\n']) == [
+            'data: {"index":0,"text":"hello"}'
+        ]
+
+    def test_event_split_into_many_tiny_reads(self):
+        full = 'data: {"k":"value with spaces"}\n\n'
+        reads = [full[i : i + 3] for i in range(0, len(full), 3)]
+        assert _collect_async(reads) == ['data: {"k":"value with spaces"}']
+
+    def test_single_newline_separator(self):
+        assert _collect_async(['data: {"a":1}\ndata: {"a":2}\ndata: {"a":3}\n']) == [
+            'data: {"a":1}',
+            'data: {"a":2}',
+            'data: {"a":3}',
+        ]
+
+    def test_mixed_separators_in_one_read(self):
+        assert _collect_async(
+            ['data: {"a":1}\ndata: {"a":2}\n\ndata: {"a":3}\n\n']
+        ) == ['data: {"a":1}', 'data: {"a":2}', 'data: {"a":3}']
+
+    def test_keepalive_and_comment_lines_dropped(self):
+        reads = [
+            "\n",
+            ": ping\n",
+            'data: {"a":1}\n\n',
+            "\n",
+            ": keepalive\n\n",
+            'data: {"a":2}\n\n',
+        ]
+        assert _collect_async(reads) == ['data: {"a":1}', 'data: {"a":2}']
+
+    def test_trailing_partial_event_flushed_at_eof(self):
+        assert _collect_async(['data: {"a":1}\n\n', 'data: {"a":2}']) == [
+            'data: {"a":1}',
+            'data: {"a":2}',
+        ]
+
+    def test_trailing_non_data_line_dropped_at_eof(self):
+        assert _collect_async(['data: {"a":1}\n\n: trailing-comment']) == [
+            'data: {"a":1}'
+        ]
+
+    def test_empty_stream(self):
+        assert _collect_async([]) == []
+
+    def test_only_whitespace_and_keepalives(self):
+        assert _collect_async(["\n", "\n\n", ": ping\n"]) == []
+
+    def test_boundary_between_data_keyword_and_payload(self):
+        assert _collect_async(["dat", 'a: {"a":1}\n\n']) == ['data: {"a":1}']
+
+    def test_carriage_return_in_payload_preserved(self):
+        assert _collect_async(['data: {"a":1}\r\ndata: {"a":2}\r\n']) == [
+            'data: {"a":1}',
+            'data: {"a":2}',
+        ]
+
+
+# ---------------------------------------------------------------------------
+# End-to-end: feed an awkwardly-chunked stream into OCIStreamWrapper and
+# verify chunk_creator still parses each yielded line. This is the smoke
+# test that proves the integration with the downstream consumer holds.
+# ---------------------------------------------------------------------------
+
+
+class TestSseSplitterFeedsChunkCreator:
+    def test_split_event_parses_cleanly(self):
+        # Build a realistic GENERIC OCI streaming payload, then chop it into
+        # awkward reads. The splitter must reassemble exactly one event so
+        # json.loads inside chunk_creator does not raise.
+        import json
+        from unittest.mock import MagicMock
+
+        from litellm.llms.oci.chat.transformation import OCIStreamWrapper
+
+        payload = {
+            "apiFormat": "GENERIC",
+            "message": {"content": [{"text": "hello"}]},
+            "finishReason": None,
+        }
+        wire = f"data: {json.dumps(payload)}\n\n"
+        # Split the wire string at an awkward point inside the JSON body.
+        cut = wire.index('"hello"') + 3
+        reads = [wire[:cut], wire[cut:]]
+
+        # Drive the splitter directly and confirm we get exactly one event.
+        events = list(_iter_sse_events(iter(reads)))
+        assert len(events) == 1
+        assert events[0].startswith("data: ")
+        # chunk_creator should now parse this without raising.
+        wrapper = OCIStreamWrapper(
+            completion_stream=MagicMock(),
+            model="xai.grok-4",
+            logging_obj=MagicMock(),
+        )
+        # Must not raise.
+        wrapper.chunk_creator(events[0])
diff --git a/tests/test_litellm/llms/oci/chat/test_oci_streaming_tool_calls.py b/tests/test_litellm/llms/oci/chat/test_oci_streaming_tool_calls.py
index f9d4be8032a..acad5da93e2 100644
--- a/tests/test_litellm/llms/oci/chat/test_oci_streaming_tool_calls.py
+++ b/tests/test_litellm/llms/oci/chat/test_oci_streaming_tool_calls.py
@@ -11,13 +11,10 @@ Error: ValidationError: 1 validation error for OCIStreamChunk message.toolCalls.
 
 import os
 import sys
-import pytest
-from unittest.mock import MagicMock
 
-# Adds the parent directory to the system path
 sys.path.insert(0, os.path.abspath("../../../../.."))
 
-from litellm.llms.oci.chat.transformation import OCIStreamWrapper
+from litellm.llms.oci.chat.generic import handle_generic_stream_chunk
 from litellm.types.utils import ModelResponseStream
 
 
@@ -26,12 +23,9 @@ class TestOCIStreamingToolCalls:
 
     def test_stream_chunk_with_missing_arguments_field(self):
         """
-        Test that streaming chunks with tool calls missing 'arguments' field are handled.
-
         OCI API can return tool calls in early chunks without the 'arguments' field,
         which should be filled with an empty string to satisfy Pydantic validation.
         """
-        # Mock streaming chunk with tool call missing 'arguments' field
         chunk_data = {
             "index": 0,
             "finishReason": None,
@@ -43,21 +37,13 @@ class TestOCIStreamingToolCalls:
                         "type": "FUNCTION",
                         "id": "call_abc123",
                         "name": "get_weather",
-                        # Note: 'arguments' field is missing
+                        # 'arguments' field is missing
                     }
                 ],
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
-
-        # This should not raise a ValidationError
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
+        result = handle_generic_stream_chunk(chunk_data)
 
         assert isinstance(result, ModelResponseStream)
         assert len(result.choices) == 1
@@ -66,9 +52,7 @@ class TestOCIStreamingToolCalls:
         assert result.choices[0].delta.tool_calls[0]["function"]["arguments"] == ""
 
     def test_stream_chunk_with_missing_id_field(self):
-        """
-        Test that streaming chunks with tool calls missing 'id' field are handled.
-        """
+        """Missing 'id' gets a generated call_* id."""
         chunk_data = {
             "index": 0,
             "finishReason": None,
@@ -80,29 +64,20 @@ class TestOCIStreamingToolCalls:
                         "type": "FUNCTION",
                         "name": "get_weather",
                         "arguments": '{"location": "San Francisco"}',
-                        # Note: 'id' field is missing
+                        # 'id' field is missing
                     }
                 ],
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
-
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
+        result = handle_generic_stream_chunk(chunk_data)
 
         assert isinstance(result, ModelResponseStream)
         assert result.choices[0].delta.tool_calls is not None
-        assert result.choices[0].delta.tool_calls[0]["id"] == ""
+        assert result.choices[0].delta.tool_calls[0]["id"].startswith("call_")
 
     def test_stream_chunk_with_missing_name_field(self):
-        """
-        Test that streaming chunks with tool calls missing 'name' field are handled.
-        """
+        """Missing 'name' defaults to empty string."""
         chunk_data = {
             "index": 0,
             "finishReason": None,
@@ -114,29 +89,20 @@ class TestOCIStreamingToolCalls:
                         "type": "FUNCTION",
                         "id": "call_abc123",
                         "arguments": '{"location": "San Francisco"}',
-                        # Note: 'name' field is missing
+                        # 'name' field is missing
                     }
                 ],
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
-
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
+        result = handle_generic_stream_chunk(chunk_data)
 
         assert isinstance(result, ModelResponseStream)
         assert result.choices[0].delta.tool_calls is not None
         assert result.choices[0].delta.tool_calls[0]["function"]["name"] == ""
 
     def test_stream_chunk_with_all_missing_fields(self):
-        """
-        Test that streaming chunks with tool calls missing all optional fields are handled.
-        """
+        """All optional fields missing — all default gracefully."""
         chunk_data = {
             "index": 0,
             "finishReason": None,
@@ -146,31 +112,22 @@ class TestOCIStreamingToolCalls:
                 "toolCalls": [
                     {
                         "type": "FUNCTION"
-                        # All fields missing: id, name, arguments
+                        # id, name, arguments all missing
                     }
                 ],
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
-
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
+        result = handle_generic_stream_chunk(chunk_data)
 
         assert isinstance(result, ModelResponseStream)
         assert result.choices[0].delta.tool_calls is not None
-        assert result.choices[0].delta.tool_calls[0]["id"] == ""
+        assert result.choices[0].delta.tool_calls[0]["id"].startswith("call_")
         assert result.choices[0].delta.tool_calls[0]["function"]["name"] == ""
         assert result.choices[0].delta.tool_calls[0]["function"]["arguments"] == ""
 
     def test_stream_chunk_with_complete_tool_call(self):
-        """
-        Test that streaming chunks with complete tool calls still work correctly.
-        """
+        """Fully-populated tool call passes through unchanged."""
         chunk_data = {
             "index": 0,
             "finishReason": None,
@@ -188,14 +145,7 @@ class TestOCIStreamingToolCalls:
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
-
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
+        result = handle_generic_stream_chunk(chunk_data)
 
         assert isinstance(result, ModelResponseStream)
         assert result.choices[0].delta.tool_calls is not None
@@ -210,10 +160,64 @@ class TestOCIStreamingToolCalls:
         )
 
     def test_stream_chunk_with_multiple_tool_calls_missing_fields(self):
-        """
-        Test that streaming chunks with multiple tool calls, some with missing fields, are handled.
-        """
+        """Multiple tool calls with a mix of complete and incomplete entries."""
         chunk_data = {
+            "index": 0,
+            "finishReason": None,
+            "message": {
+                "role": "ASSISTANT",
+                "content": None,
+                "toolCalls": [
+                    {"type": "FUNCTION", "id": "call_1", "name": "get_weather"},
+                    {
+                        "type": "FUNCTION",
+                        "name": "get_time",
+                        "arguments": '{"timezone": "UTC"}',
+                    },
+                    {
+                        "type": "FUNCTION",
+                        "id": "call_3",
+                        "name": "calculate",
+                        "arguments": '{"expression": "2+2"}',
+                    },
+                ],
+            },
+        }
+
+        result = handle_generic_stream_chunk(chunk_data)
+
+        assert isinstance(result, ModelResponseStream)
+        assert result.choices[0].delta.tool_calls is not None
+        assert len(result.choices[0].delta.tool_calls) == 3
+
+        assert result.choices[0].delta.tool_calls[0]["id"] == "call_1"
+        assert (
+            result.choices[0].delta.tool_calls[0]["function"]["name"] == "get_weather"
+        )
+        assert result.choices[0].delta.tool_calls[0]["function"]["arguments"] == ""
+
+        assert result.choices[0].delta.tool_calls[1]["id"].startswith("call_")
+        assert result.choices[0].delta.tool_calls[1]["function"]["name"] == "get_time"
+        assert (
+            result.choices[0].delta.tool_calls[1]["function"]["arguments"]
+            == '{"timezone": "UTC"}'
+        )
+
+        assert result.choices[0].delta.tool_calls[2]["id"] == "call_3"
+        assert result.choices[0].delta.tool_calls[2]["function"]["name"] == "calculate"
+        assert (
+            result.choices[0].delta.tool_calls[2]["function"]["arguments"]
+            == '{"expression": "2+2"}'
+        )
+
+    def test_stream_chunk_missing_id_is_deterministic_across_chunks(self):
+        """
+        Two chunks emitting the same logical tool call (same name + arguments
+        at the same position) must receive the *same* synthesized id so the
+        downstream stream-merger does not treat them as distinct calls.
+        Random uuid4 per chunk would regress this — see bug ffdef760.
+        """
+        same_chunk_payload = lambda: {
             "index": 0,
             "finishReason": None,
             "message": {
@@ -222,67 +226,25 @@ class TestOCIStreamingToolCalls:
                 "toolCalls": [
                     {
                         "type": "FUNCTION",
-                        "id": "call_1",
                         "name": "get_weather",
-                        # Missing arguments
-                    },
-                    {
-                        "type": "FUNCTION",
-                        "name": "get_time",
-                        "arguments": '{"timezone": "UTC"}',
-                        # Missing id
-                    },
-                    {
-                        "type": "FUNCTION",
-                        "id": "call_3",
-                        "name": "calculate",
-                        "arguments": '{"expression": "2+2"}',
-                        # Complete
-                    },
+                        "arguments": '{"location": "San Francisco"}',
+                    }
                 ],
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
+        first = handle_generic_stream_chunk(same_chunk_payload())
+        second = handle_generic_stream_chunk(same_chunk_payload())
 
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
-
-        assert isinstance(result, ModelResponseStream)
-        assert result.choices[0].delta.tool_calls is not None
-        assert len(result.choices[0].delta.tool_calls) == 3
-
-        # First tool call - missing arguments
-        assert result.choices[0].delta.tool_calls[0]["id"] == "call_1"
-        assert (
-            result.choices[0].delta.tool_calls[0]["function"]["name"] == "get_weather"
-        )
-        assert result.choices[0].delta.tool_calls[0]["function"]["arguments"] == ""
-
-        # Second tool call - missing id
-        assert result.choices[0].delta.tool_calls[1]["id"] == ""
-        assert result.choices[0].delta.tool_calls[1]["function"]["name"] == "get_time"
-        assert (
-            result.choices[0].delta.tool_calls[1]["function"]["arguments"]
-            == '{"timezone": "UTC"}'
-        )
-
-        # Third tool call - complete
-        assert result.choices[0].delta.tool_calls[2]["id"] == "call_3"
-        assert result.choices[0].delta.tool_calls[2]["function"]["name"] == "calculate"
-        assert (
-            result.choices[0].delta.tool_calls[2]["function"]["arguments"]
-            == '{"expression": "2+2"}'
-        )
+        assert first.choices[0].delta.tool_calls is not None
+        assert second.choices[0].delta.tool_calls is not None
+        first_id = first.choices[0].delta.tool_calls[0]["id"]
+        second_id = second.choices[0].delta.tool_calls[0]["id"]
+        assert first_id == second_id
+        assert first_id.startswith("call_")
 
     def test_stream_chunk_without_tool_calls(self):
-        """
-        Test that streaming chunks without tool calls continue to work as before.
-        """
+        """Plain text chunks (no tool calls) pass through correctly."""
         chunk_data = {
             "index": 0,
             "finishReason": None,
@@ -292,14 +254,7 @@ class TestOCIStreamingToolCalls:
             },
         }
 
-        wrapper = OCIStreamWrapper(
-            completion_stream=iter([]),
-            model="meta.llama-3.1-405b-instruct",
-            custom_llm_provider="oci",
-            logging_obj=MagicMock(),
-        )
-
-        result = wrapper._handle_generic_stream_chunk(chunk_data)
+        result = handle_generic_stream_chunk(chunk_data)
 
         assert isinstance(result, ModelResponseStream)
         assert result.choices[0].delta.content == "Hello, how can I help you?"
diff --git a/tests/test_litellm/llms/oci/embed/test_oci_embed_transformation.py b/tests/test_litellm/llms/oci/embed/test_oci_embed_transformation.py
new file mode 100644
index 00000000000..30f49bea344
--- /dev/null
+++ b/tests/test_litellm/llms/oci/embed/test_oci_embed_transformation.py
@@ -0,0 +1,406 @@
+"""
+Unit tests for OCI Generative AI embedding transformation.
+
+These tests exercise the transformation layer only — no real OCI calls are made.
+"""
+
+import json
+import os
+import sys
+from typing import Any
+from unittest.mock import MagicMock
+
+import httpx
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm.llms.oci.common_utils import OCIError
+from litellm.llms.oci.embed.transformation import OCI_EMBED_BATCH_LIMIT, OCIEmbedConfig
+from litellm.types.utils import EmbeddingResponse, Usage
+
+# ---------------------------------------------------------------------------
+# Test fixtures
+# ---------------------------------------------------------------------------
+
+COMPARTMENT_ID = "ocid1.compartment.oc1..test"
+BASE_PARAMS = {
+    "oci_region": "us-ashburn-1",
+    "oci_user": "ocid1.user.oc1..test",
+    "oci_fingerprint": "aa:bb:cc:dd",
+    "oci_tenancy": "ocid1.tenancy.oc1..test",
+    "oci_compartment_id": COMPARTMENT_ID,
+    "oci_key": "-----BEGIN RSA PRIVATE KEY-----\nfakekey\n-----END RSA PRIVATE KEY-----",
+}
+
+
+class TestOCIEmbedConfig:
+    def _config(self) -> OCIEmbedConfig:
+        return OCIEmbedConfig()
+
+    # ------------------------------------------------------------------
+    # validate_environment
+    # ------------------------------------------------------------------
+
+    def test_validate_environment_sets_headers(self):
+        cfg = self._config()
+        headers = cfg.validate_environment(
+            headers={},
+            model="oci/cohere.embed-v3.0",
+            messages=[],
+            optional_params=BASE_PARAMS,
+            litellm_params={},
+        )
+        assert headers["content-type"] == "application/json"
+        assert "litellm/" in headers["user-agent"]
+
+    # ------------------------------------------------------------------
+    # get_complete_url
+    # ------------------------------------------------------------------
+
+    def test_get_complete_url_default_region(self):
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model="cohere.embed-v3.0",
+            optional_params={"oci_region": "us-chicago-1"},
+            litellm_params={},
+        )
+        assert (
+            url
+            == "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/embedText"
+        )
+
+    def test_get_complete_url_respects_api_base(self):
+        """api_base is treated as a base URL — the action path is appended."""
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base="https://custom.endpoint.example.com",
+            api_key=None,
+            model="cohere.embed-v3.0",
+            optional_params={},
+            litellm_params={},
+        )
+        assert url == "https://custom.endpoint.example.com/20231130/actions/embedText"
+
+    def test_get_complete_url_strips_trailing_slash(self):
+        """Trailing slash is stripped from api_base before appending the action path."""
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base="https://custom.endpoint.example.com/",
+            api_key=None,
+            model="cohere.embed-v3.0",
+            optional_params={},
+            litellm_params={},
+        )
+        assert url == "https://custom.endpoint.example.com/20231130/actions/embedText"
+
+    def test_get_complete_url_full_url_is_not_doubled(self):
+        """A fully-formed embedText URL must not have the action path appended twice."""
+        cfg = self._config()
+        full_url = (
+            "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
+            "/20231130/actions/embedText"
+        )
+        url = cfg.get_complete_url(
+            api_base=full_url,
+            api_key=None,
+            model="cohere.embed-v3.0",
+            optional_params={},
+            litellm_params={},
+        )
+        assert url == full_url
+
+    # ------------------------------------------------------------------
+    # transform_embedding_request
+    # ------------------------------------------------------------------
+
+    def test_transform_request_single_string(self):
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input="hello world",
+            optional_params={"oci_compartment_id": COMPARTMENT_ID},
+            headers={},
+        )
+        assert result["compartmentId"] == COMPARTMENT_ID
+        assert result["servingMode"]["servingType"] == "ON_DEMAND"
+        assert result["servingMode"]["modelId"] == "cohere.embed-v3.0"
+        assert result["inputs"] == ["hello world"]
+
+    def test_transform_request_list_of_texts(self):
+        cfg = self._config()
+        texts = ["hello", "world"]
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input=texts,
+            optional_params={"oci_compartment_id": COMPARTMENT_ID},
+            headers={},
+        )
+        assert result["inputs"] == texts
+
+    def test_transform_request_with_input_type(self):
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input=["query"],
+            optional_params={
+                "oci_compartment_id": COMPARTMENT_ID,
+                "input_type": "SEARCH_QUERY",
+            },
+            headers={},
+        )
+        assert result["inputType"] == "SEARCH_QUERY"
+
+    def test_transform_request_with_output_dimensions(self):
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v4.0",
+            input=["text"],
+            optional_params={
+                "oci_compartment_id": COMPARTMENT_ID,
+                "outputDimensions": 512,
+            },
+            headers={},
+        )
+        assert result["outputDimensions"] == 512
+
+    def test_transform_request_dedicated_serving_mode(self):
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input=["text"],
+            optional_params={
+                "oci_compartment_id": COMPARTMENT_ID,
+                "oci_serving_mode": "DEDICATED",
+                "oci_endpoint_id": "ocid1.genaiendpoint.oc1..test",
+            },
+            headers={},
+        )
+        assert result["servingMode"]["servingType"] == "DEDICATED"
+        assert result["servingMode"]["endpointId"] == "ocid1.genaiendpoint.oc1..test"
+        assert "modelId" not in result["servingMode"]
+
+    def test_transform_request_missing_compartment_id_raises(self):
+        cfg = self._config()
+        with pytest.raises(OCIError) as exc_info:
+            cfg.transform_embedding_request(
+                model="cohere.embed-v3.0",
+                input=["text"],
+                optional_params={},
+                headers={},
+            )
+        assert exc_info.value.status_code == 400
+        assert "oci_compartment_id" in str(exc_info.value)
+
+    def test_transform_request_batch_limit_exceeded_raises(self):
+        cfg = self._config()
+        texts = ["text"] * (OCI_EMBED_BATCH_LIMIT + 1)
+        with pytest.raises(OCIError) as exc_info:
+            cfg.transform_embedding_request(
+                model="cohere.embed-v3.0",
+                input=texts,
+                optional_params={"oci_compartment_id": COMPARTMENT_ID},
+                headers={},
+            )
+        assert exc_info.value.status_code == 400
+        assert str(OCI_EMBED_BATCH_LIMIT) in str(exc_info.value)
+
+    def test_transform_request_invalid_serving_mode_raises(self):
+        cfg = self._config()
+        with pytest.raises(OCIError) as exc_info:
+            cfg.transform_embedding_request(
+                model="cohere.embed-v3.0",
+                input=["text"],
+                optional_params={
+                    "oci_compartment_id": COMPARTMENT_ID,
+                    "oci_serving_mode": "INVALID",
+                },
+                headers={},
+            )
+        assert exc_info.value.status_code == 400
+
+    def test_transform_request_none_input_becomes_string(self):
+        """Non-list, non-string inputs are coerced to str."""
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input=42,  # type: ignore
+            optional_params={"oci_compartment_id": COMPARTMENT_ID},
+            headers={},
+        )
+        assert result["inputs"] == ["42"]
+
+    # ------------------------------------------------------------------
+    # transform_embedding_response
+    # ------------------------------------------------------------------
+
+    def _mock_response(self, status_code: int, body: dict) -> httpx.Response:
+        return httpx.Response(
+            status_code=status_code,
+            content=json.dumps(body).encode(),
+            headers={"content-type": "application/json"},
+        )
+
+    def test_transform_response_success(self):
+        cfg = self._config()
+        model_response = EmbeddingResponse()
+        raw = self._mock_response(
+            200,
+            {
+                "embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
+                "modelId": "cohere.embed-v3.0",
+                "modelVersion": "3.0.0",
+                # Actual OCI API returns per-input token counts
+                "inputTextTokenCounts": [5, 5],
+            },
+        )
+        result = cfg.transform_embedding_response(
+            model="cohere.embed-v3.0",
+            raw_response=raw,
+            model_response=model_response,
+            logging_obj=MagicMock(),
+            api_key=None,
+            request_data={},
+            optional_params={},
+            litellm_params={},
+        )
+        assert len(result.data) == 2
+        assert result.data[0]["embedding"] == [0.1, 0.2, 0.3]
+        assert result.data[1]["index"] == 1
+        assert result.model == "cohere.embed-v3.0"
+        assert result.usage.prompt_tokens == 10
+
+    def test_transform_response_no_usage(self):
+        cfg = self._config()
+        model_response = EmbeddingResponse()
+        raw = self._mock_response(
+            200,
+            {
+                "embeddings": [[0.1]],
+                "modelId": "cohere.embed-v3.0",
+                "modelVersion": "3.0.0",
+            },
+        )
+        result = cfg.transform_embedding_response(
+            model="cohere.embed-v3.0",
+            raw_response=raw,
+            model_response=model_response,
+            logging_obj=MagicMock(),
+            api_key=None,
+            request_data={},
+            optional_params={},
+            litellm_params={},
+        )
+        assert len(result.data) == 1
+
+    def test_transform_response_http_error_raises(self):
+        cfg = self._config()
+        raw = self._mock_response(401, {"error": "Unauthorized"})
+        with pytest.raises(OCIError) as exc_info:
+            cfg.transform_embedding_response(
+                model="cohere.embed-v3.0",
+                raw_response=raw,
+                model_response=EmbeddingResponse(),
+                logging_obj=MagicMock(),
+                api_key=None,
+                request_data={},
+                optional_params={},
+                litellm_params={},
+            )
+        assert exc_info.value.status_code == 401
+
+    def test_transform_response_invalid_json_raises(self):
+        cfg = self._config()
+        raw = httpx.Response(
+            status_code=200,
+            content=b"not-json",
+            headers={"content-type": "text/plain"},
+        )
+        with pytest.raises(OCIError):
+            cfg.transform_embedding_response(
+                model="cohere.embed-v3.0",
+                raw_response=raw,
+                model_response=EmbeddingResponse(),
+                logging_obj=MagicMock(),
+                api_key=None,
+                request_data={},
+                optional_params={},
+                litellm_params={},
+            )
+
+    # ------------------------------------------------------------------
+    # map_openai_params
+    # ------------------------------------------------------------------
+
+    def test_map_openai_params_dimensions(self):
+        cfg = self._config()
+        result = cfg.map_openai_params(
+            non_default_params={"dimensions": 512},
+            optional_params={},
+            model="cohere.embed-v4.0",
+        )
+        assert result["outputDimensions"] == 512
+
+    def test_map_openai_params_encoding_format_not_supported(self):
+        """encoding_format is not a supported OCI param — it is silently ignored by map_openai_params.
+
+        The litellm framework handles unsupported-param rejection above this layer,
+        based on get_supported_openai_params() not including 'encoding_format'.
+        """
+        cfg = self._config()
+        result = cfg.map_openai_params(
+            non_default_params={"encoding_format": "float"},
+            optional_params={},
+            model="cohere.embed-v3.0",
+        )
+        assert "encoding_format" not in result
+
+    def test_map_openai_params_encoding_format_dropped_silently(self):
+        cfg = self._config()
+        result = cfg.map_openai_params(
+            non_default_params={"encoding_format": "float"},
+            optional_params={},
+            model="cohere.embed-v3.0",
+            drop_params=True,
+        )
+        assert "encoding_format" not in result
+
+    # ------------------------------------------------------------------
+    # env var credential resolution
+    # ------------------------------------------------------------------
+
+    def test_env_var_compartment_id(self, monkeypatch):
+        monkeypatch.setenv("OCI_COMPARTMENT_ID", "ocid1.compartment.from.env")
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input=["hello"],
+            optional_params={},  # no compartment_id in params
+            headers={},
+        )
+        assert result["compartmentId"] == "ocid1.compartment.from.env"
+
+    def test_explicit_param_overrides_env_var(self, monkeypatch):
+        monkeypatch.setenv("OCI_COMPARTMENT_ID", "ocid1.compartment.from.env")
+        cfg = self._config()
+        result = cfg.transform_embedding_request(
+            model="cohere.embed-v3.0",
+            input=["hello"],
+            optional_params={"oci_compartment_id": "ocid1.compartment.explicit"},
+            headers={},
+        )
+        assert result["compartmentId"] == "ocid1.compartment.explicit"
+
+    def test_env_var_region_used_in_url(self, monkeypatch):
+        monkeypatch.setenv("OCI_REGION", "eu-frankfurt-1")
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model="cohere.embed-v3.0",
+            optional_params={},  # no explicit region
+            litellm_params={},
+        )
+        assert "eu-frankfurt-1" in url
diff --git a/tests/test_litellm/llms/oci/embed/test_oci_embedding.py b/tests/test_litellm/llms/oci/embed/test_oci_embedding.py
index 4ecca377e63..61c13ad62a1 100644
--- a/tests/test_litellm/llms/oci/embed/test_oci_embedding.py
+++ b/tests/test_litellm/llms/oci/embed/test_oci_embedding.py
@@ -76,7 +76,7 @@ class TestOCIEmbeddingConfig:
         assert "embedText" in url
 
     def test_get_complete_url_custom_api_base(self):
-        """test_get_complete_url returns api_base as-is when provided."""
+        """test_get_complete_url treats api_base as a base URL and appends the embedText path."""
         config = OCIEmbeddingConfig()
         custom_base = "https://custom.oci.example.com/embed"
         url = config.get_complete_url(
@@ -86,7 +86,7 @@ class TestOCIEmbeddingConfig:
             optional_params={},
             litellm_params={},
         )
-        assert url == custom_base
+        assert url == f"{custom_base}/20231130/actions/embedText"
 
     def test_get_supported_openai_params(self):
         """test_get_supported_openai_params returns expected params list."""
@@ -96,7 +96,7 @@ class TestOCIEmbeddingConfig:
         assert "encoding_format" not in params
 
     def test_map_openai_params_dimensions(self):
-        """test dimensions is mapped correctly."""
+        """test dimensions is mapped to outputDimensions (OCI API field name)."""
         config = OCIEmbeddingConfig()
         optional_params = {}
         result = config.map_openai_params(
@@ -105,7 +105,8 @@ class TestOCIEmbeddingConfig:
             model=TEST_MODEL_NAME,
             drop_params=False,
         )
-        assert result["dimensions"] == 512
+        assert result["outputDimensions"] == 512
+        assert "dimensions" not in result
 
     def test_validate_environment_with_credentials(self, supplied_params):
         """test validate_environment returns content-type and user-agent headers when credentials are supplied."""
@@ -122,13 +123,15 @@ class TestOCIEmbeddingConfig:
         assert "litellm" in result["user-agent"]
 
     def test_validate_environment_missing_credentials(self):
-        """test validate_environment raises Exception with 'Missing required parameters' when credentials are incomplete."""
+        """test validate_environment raises OCIError when required credentials are missing."""
+        from litellm.llms.oci.common_utils import OCIError
+
         config = OCIEmbeddingConfig()
         incomplete_params = {
             "oci_user": "ocid1.user.oc1..xxx",
             # Missing oci_fingerprint, oci_tenancy, oci_key/oci_key_file, oci_compartment_id
         }
-        with pytest.raises(Exception) as excinfo:
+        with pytest.raises(OCIError, match="Missing required parameters"):
             config.validate_environment(
                 headers={},
                 model=TEST_MODEL,
@@ -136,7 +139,6 @@ class TestOCIEmbeddingConfig:
                 optional_params=incomplete_params,
                 litellm_params={},
             )
-        assert "Missing required parameters" in str(excinfo.value)
 
     def test_validate_environment_with_signer(self):
         """test validate_environment passes when oci_signer is provided."""
@@ -234,13 +236,15 @@ class TestOCIEmbeddingConfig:
         assert result["inputs"] == ["Hello world"]
 
     def test_transform_embedding_request_token_list_raises(self):
-        """test token-array inputs raise ValueError instead of silent conversion."""
+        """test token-array inputs raise OCIError instead of silent conversion."""
+        from litellm.llms.oci.common_utils import OCIError
+
         config = OCIEmbeddingConfig()
         optional_params = {
             "oci_compartment_id": TEST_COMPARTMENT_ID,
         }
         with patch.object(config, "sign_request", return_value=({}, "{}")):
-            with pytest.raises(ValueError, match="does not support token-array"):
+            with pytest.raises(OCIError, match="does not support token-array"):
                 config.transform_embedding_request(
                     model=TEST_MODEL_NAME,
                     input=[[1234, 5678]],
@@ -264,6 +268,10 @@ class TestOCIEmbeddingConfig:
             raw_response=mock_response,
             model_response=model_response,
             logging_obj=mock_logging,
+            api_key=None,
+            request_data={},
+            optional_params={},
+            litellm_params={},
         )
 
         assert isinstance(result, EmbeddingResponse)
@@ -296,6 +304,10 @@ class TestOCIEmbeddingConfig:
                 raw_response=mock_response,
                 model_response=model_response,
                 logging_obj=mock_logging,
+                api_key=None,
+                request_data={},
+                optional_params={},
+                litellm_params={},
             )
 
     def test_model_prices_embedding_models(self):
diff --git a/tests/test_litellm/llms/oci/rerank/__init__.py b/tests/test_litellm/llms/oci/rerank/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/test_litellm/llms/oci/test_oci_common_utils.py b/tests/test_litellm/llms/oci/test_oci_common_utils.py
new file mode 100644
index 00000000000..d306d7351dd
--- /dev/null
+++ b/tests/test_litellm/llms/oci/test_oci_common_utils.py
@@ -0,0 +1,521 @@
+"""
+Unit tests for litellm/llms/oci/common_utils.py.
+
+Covers schema utilities, signing helpers, and credential resolution paths
+that require no real OCI credentials or network calls.
+"""
+
+import pytest
+from unittest.mock import MagicMock, patch
+
+from litellm.llms.oci.common_utils import (
+    OCI_API_VERSION,
+    OCIError,
+    OCIRequestWrapper,
+    build_signature_string,
+    enrich_cohere_param_description,
+    get_oci_base_url,
+    resolve_oci_credentials,
+    resolve_oci_schema_anyof,
+    resolve_oci_schema_refs,
+    sanitize_oci_schema,
+    sha256_base64,
+    sign_oci_request,
+    sign_with_oci_signer,
+    validate_oci_environment,
+)
+
+# ---------------------------------------------------------------------------
+# OCI_API_VERSION
+# ---------------------------------------------------------------------------
+
+
+def test_oci_api_version_constant():
+    assert OCI_API_VERSION == "20231130"
+
+
+# ---------------------------------------------------------------------------
+# sha256_base64
+# ---------------------------------------------------------------------------
+
+
+def test_sha256_base64_known_value():
+    import base64, hashlib
+
+    data = b"hello"
+    expected = base64.b64encode(hashlib.sha256(data).digest()).decode()
+    assert sha256_base64(data) == expected
+
+
+def test_sha256_base64_empty():
+    result = sha256_base64(b"")
+    assert isinstance(result, str)
+    assert len(result) > 0
+
+
+# ---------------------------------------------------------------------------
+# build_signature_string
+# ---------------------------------------------------------------------------
+
+
+def test_build_signature_string_request_target():
+    headers = {"host": "example.com", "date": "Mon, 01 Jan 2024 00:00:00 GMT"}
+    result = build_signature_string(
+        "POST", "/20231130/actions/chat", headers, ["(request-target)", "host", "date"]
+    )
+    lines = result.split("\n")
+    assert lines[0] == "(request-target): post /20231130/actions/chat"
+    assert lines[1] == "host: example.com"
+    assert lines[2] == "date: Mon, 01 Jan 2024 00:00:00 GMT"
+
+
+def test_build_signature_string_method_lowercased():
+    headers = {"host": "h"}
+    result = build_signature_string("GET", "/path", headers, ["(request-target)"])
+    assert result == "(request-target): get /path"
+
+
+# ---------------------------------------------------------------------------
+# OCIRequestWrapper.path_url
+# ---------------------------------------------------------------------------
+
+
+def test_request_wrapper_path_url_no_query():
+    w = OCIRequestWrapper(
+        method="POST",
+        url="https://inference.generativeai.us-ashburn-1.oci.oraclecloud.com/20231130/actions/chat",
+        headers={},
+        body=b"",
+    )
+    assert w.path_url == "/20231130/actions/chat"
+
+
+def test_request_wrapper_path_url_with_query():
+    w = OCIRequestWrapper(
+        method="GET",
+        url="https://example.com/path?foo=bar&baz=1",
+        headers={},
+        body=b"",
+    )
+    assert w.path_url == "/path?foo=bar&baz=1"
+
+
+# ---------------------------------------------------------------------------
+# resolve_oci_credentials
+# ---------------------------------------------------------------------------
+
+
+def test_resolve_credentials_from_params():
+    params = {
+        "oci_region": "eu-frankfurt-1",
+        "oci_user": "user1",
+        "oci_fingerprint": "fp1",
+        "oci_tenancy": "tenant1",
+        "oci_key": "key_content",
+        "oci_compartment_id": "comp1",
+    }
+    result = resolve_oci_credentials(params)
+    assert result["oci_region"] == "eu-frankfurt-1"
+    assert result["oci_user"] == "user1"
+    assert result["oci_compartment_id"] == "comp1"
+
+
+def test_resolve_credentials_env_fallback(monkeypatch):
+    monkeypatch.setenv("OCI_REGION", "ap-tokyo-1")
+    monkeypatch.setenv("OCI_USER", "env_user")
+    monkeypatch.setenv("OCI_COMPARTMENT_ID", "env_comp")
+    result = resolve_oci_credentials({})
+    assert result["oci_region"] == "ap-tokyo-1"
+    assert result["oci_user"] == "env_user"
+    assert result["oci_compartment_id"] == "env_comp"
+
+
+def test_resolve_credentials_region_default(monkeypatch):
+    monkeypatch.delenv("OCI_REGION", raising=False)
+    result = resolve_oci_credentials({})
+    assert result["oci_region"] == "us-ashburn-1"
+
+
+def test_resolve_credentials_params_override_env(monkeypatch):
+    monkeypatch.setenv("OCI_REGION", "ap-tokyo-1")
+    result = resolve_oci_credentials({"oci_region": "us-phoenix-1"})
+    assert result["oci_region"] == "us-phoenix-1"
+
+
+# ---------------------------------------------------------------------------
+# get_oci_base_url
+# ---------------------------------------------------------------------------
+
+
+def test_get_oci_base_url_explicit_api_base():
+    url = get_oci_base_url({}, api_base="https://custom.endpoint.com/")
+    assert url == "https://custom.endpoint.com"
+
+
+@pytest.mark.parametrize(
+    "api_base",
+    [
+        "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/chat",
+        "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/chat/",
+        "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com/20231130/actions/embedText",
+    ],
+)
+def test_get_oci_base_url_strips_trailing_action_path(api_base):
+    assert (
+        get_oci_base_url({}, api_base=api_base)
+        == "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
+    )
+
+
+def test_get_oci_base_url_from_region():
+    url = get_oci_base_url({"oci_region": "eu-frankfurt-1"})
+    assert url == "https://inference.generativeai.eu-frankfurt-1.oci.oraclecloud.com"
+
+
+@pytest.mark.parametrize(
+    "region",
+    [
+        "evil.com/#",
+        "evil.com",
+        "us-ashburn-1/../attacker",
+        "ATTACKER",
+        "-leading-hyphen",
+        "trailing-hyphen-",
+        "a",
+        "a" * 33,
+        "us ashburn 1",
+        "us_ashburn_1",
+    ],
+)
+def test_get_oci_base_url_rejects_unsafe_region(region):
+    with pytest.raises(OCIError, match="Invalid OCI region"):
+        get_oci_base_url({"oci_region": region})
+
+
+def test_get_oci_base_url_empty_region_falls_back_to_default(monkeypatch):
+    monkeypatch.delenv("OCI_REGION", raising=False)
+    url = get_oci_base_url({"oci_region": ""})
+    assert url == "https://inference.generativeai.us-ashburn-1.oci.oraclecloud.com"
+
+
+@pytest.mark.parametrize(
+    "region",
+    [
+        "us-ashburn-1",
+        "eu-frankfurt-1",
+        "ap-tokyo-1",
+        "us-chicago-1",
+        "us-phoenix-1",
+        "ap",
+    ],
+)
+def test_get_oci_base_url_accepts_valid_region(region):
+    url = get_oci_base_url({"oci_region": region})
+    assert url == f"https://inference.generativeai.{region}.oci.oraclecloud.com"
+
+
+# ---------------------------------------------------------------------------
+# validate_oci_environment
+# ---------------------------------------------------------------------------
+
+
+def test_validate_oci_environment_sets_defaults():
+    headers = {}
+    result = validate_oci_environment(headers, {})
+    assert result["content-type"] == "application/json"
+    assert "user-agent" in result
+
+
+def test_validate_oci_environment_does_not_overwrite_existing():
+    headers = {"content-type": "text/plain", "user-agent": "my-agent"}
+    result = validate_oci_environment(headers, {})
+    assert result["content-type"] == "text/plain"
+    assert result["user-agent"] == "my-agent"
+
+
+# ---------------------------------------------------------------------------
+# sign_with_oci_signer — error paths
+# ---------------------------------------------------------------------------
+
+
+def test_sign_with_oci_signer_none_raises():
+    with pytest.raises(ValueError, match="oci_signer cannot be None"):
+        sign_with_oci_signer({}, {"oci_signer": None}, {}, "https://example.com")
+
+
+def test_sign_with_oci_signer_exception_wrapped():
+    bad_signer = MagicMock()
+    bad_signer.do_request_sign.side_effect = RuntimeError("signing failed")
+    with pytest.raises(OCIError, match="Failed to sign request"):
+        sign_with_oci_signer(
+            {}, {"oci_signer": bad_signer}, {"key": "val"}, "https://example.com"
+        )
+
+
+def test_sign_with_oci_signer_success():
+    signer = MagicMock()
+    signer.do_request_sign.return_value = None
+    headers, body = sign_with_oci_signer(
+        {}, {"oci_signer": signer}, {"key": "val"}, "https://example.com"
+    )
+    assert isinstance(body, bytes)
+    signer.do_request_sign.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# sign_oci_request — routing
+# ---------------------------------------------------------------------------
+
+
+def test_sign_oci_request_routes_to_signer():
+    signer = MagicMock()
+    signer.do_request_sign.return_value = None
+    headers, body = sign_oci_request(
+        {}, {"oci_signer": signer}, {}, "https://example.com"
+    )
+    signer.do_request_sign.assert_called_once()
+
+
+def test_sign_oci_request_routes_to_manual_missing_creds():
+    with pytest.raises(OCIError, match="Missing required OCI credentials"):
+        sign_oci_request({}, {}, {}, "https://example.com")
+
+
+# ---------------------------------------------------------------------------
+# load_private_key_from_file — error paths (no real key needed)
+# ---------------------------------------------------------------------------
+
+
+def test_load_private_key_from_file_not_found():
+    from litellm.llms.oci.common_utils import load_private_key_from_file
+
+    with pytest.raises(FileNotFoundError, match="Private key file not found"):
+        load_private_key_from_file("/nonexistent/path/key.pem")
+
+
+def test_load_private_key_from_file_empty(tmp_path):
+    from litellm.llms.oci.common_utils import load_private_key_from_file
+
+    empty = tmp_path / "empty.pem"
+    empty.write_text("")
+    with pytest.raises(ValueError, match="Private key file is empty"):
+        load_private_key_from_file(str(empty))
+
+
+def test_load_private_key_from_file_os_error():
+    from litellm.llms.oci.common_utils import load_private_key_from_file
+
+    with patch("builtins.open", side_effect=OSError("permission denied")):
+        with pytest.raises(OSError, match="Failed to read private key file"):
+            load_private_key_from_file("/some/path/key.pem")
+
+
+# ---------------------------------------------------------------------------
+# resolve_oci_schema_refs
+# ---------------------------------------------------------------------------
+
+
+def test_resolve_schema_refs_basic():
+    schema = {
+        "$defs": {"Foo": {"type": "string"}},
+        "properties": {"x": {"$ref": "#/$defs/Foo"}},
+    }
+    result = resolve_oci_schema_refs(schema)
+    assert result["properties"]["x"] == {"type": "string"}
+    assert "$defs" not in result
+
+
+def test_resolve_schema_refs_external_ref_unchanged():
+    schema = {"properties": {"x": {"$ref": "https://example.com/schema"}}}
+    result = resolve_oci_schema_refs(schema)
+    assert result["properties"]["x"] == {"$ref": "https://example.com/schema"}
+
+
+def test_resolve_schema_refs_circular_breaks_cycle():
+    schema = {
+        "$defs": {"Node": {"properties": {"child": {"$ref": "#/$defs/Node"}}}},
+        "properties": {"root": {"$ref": "#/$defs/Node"}},
+    }
+    result = resolve_oci_schema_refs(schema)
+    # Should not raise; circular ref replaced with {"type": "object"}
+    child = result["properties"]["root"]["properties"]["child"]
+    assert child == {"type": "object"}
+
+
+def test_resolve_schema_refs_no_defs():
+    schema = {"type": "object", "properties": {"x": {"type": "string"}}}
+    result = resolve_oci_schema_refs(schema)
+    assert result == schema
+
+
+# ---------------------------------------------------------------------------
+# resolve_oci_schema_anyof
+# ---------------------------------------------------------------------------
+
+
+def test_resolve_schema_anyof_optional_field():
+    schema = {"anyOf": [{"type": "string"}, {"type": "null"}]}
+    result = resolve_oci_schema_anyof(schema)
+    assert result["type"] == "string"
+    assert "anyOf" not in result
+
+
+def test_resolve_schema_anyof_all_null_returns_empty():
+    schema = {"anyOf": [{"type": "null"}, {"type": "null"}]}
+    result = resolve_oci_schema_anyof(schema)
+    # No non-null branch — anyOf stays or schema unchanged
+    # The function only strips anyOf when there IS a non-null branch
+    assert "anyOf" in result
+
+
+def test_resolve_schema_anyof_no_anyof_unchanged():
+    schema = {"type": "string", "description": "A name"}
+    assert resolve_oci_schema_anyof(schema) == schema
+
+
+def test_resolve_schema_anyof_nested():
+    schema = {"properties": {"age": {"anyOf": [{"type": "integer"}, {"type": "null"}]}}}
+    result = resolve_oci_schema_anyof(schema)
+    assert result["properties"]["age"]["type"] == "integer"
+
+
+# ---------------------------------------------------------------------------
+# sanitize_oci_schema
+# ---------------------------------------------------------------------------
+
+
+def test_sanitize_schema_removes_title():
+    schema = {"title": "MyModel", "type": "object", "properties": {}}
+    result = sanitize_oci_schema(schema)
+    assert "title" not in result
+
+
+def test_sanitize_schema_removes_null_default():
+    schema = {"type": "string", "default": None}
+    result = sanitize_oci_schema(schema)
+    assert "default" not in result
+
+
+def test_sanitize_schema_keeps_non_null_default():
+    schema = {"type": "string", "default": "hello"}
+    result = sanitize_oci_schema(schema)
+    assert result["default"] == "hello"
+
+
+def test_sanitize_schema_type_any_becomes_object():
+    schema = {"type": "any"}
+    result = sanitize_oci_schema(schema)
+    assert result["type"] == "object"
+
+
+def test_sanitize_schema_type_list_picks_non_null():
+    schema = {"type": ["string", "null"]}
+    result = sanitize_oci_schema(schema)
+    assert result["type"] == "string"
+
+
+def test_sanitize_schema_type_list_all_null_becomes_string():
+    schema = {"type": ["null"]}
+    result = sanitize_oci_schema(schema)
+    assert result["type"] == "string"
+
+
+def test_sanitize_schema_array_gets_items():
+    schema = {"type": "array"}
+    result = sanitize_oci_schema(schema)
+    assert result["items"] == {"type": "object"}
+
+
+def test_sanitize_schema_array_keeps_existing_items():
+    schema = {"type": "array", "items": {"type": "string"}}
+    result = sanitize_oci_schema(schema)
+    assert result["items"] == {"type": "string"}
+
+
+def test_sanitize_schema_required_filters_missing_properties():
+    schema = {
+        "type": "object",
+        "properties": {"a": {"type": "string"}},
+        "required": ["a", "b"],  # "b" not in properties
+    }
+    result = sanitize_oci_schema(schema)
+    assert result["required"] == ["a"]
+
+
+def test_sanitize_schema_required_non_list_becomes_empty():
+    schema = {
+        "type": "object",
+        "properties": {"a": {"type": "string"}},
+        "required": "a",  # invalid: string instead of list
+    }
+    result = sanitize_oci_schema(schema)
+    assert result["required"] == []
+
+
+def test_sanitize_schema_list_input():
+    schemas = [{"title": "A", "type": "string"}, {"title": "B", "type": "integer"}]
+    result = sanitize_oci_schema(schemas)
+    assert all("title" not in s for s in result)
+
+
+# ---------------------------------------------------------------------------
+# enrich_cohere_param_description
+# ---------------------------------------------------------------------------
+
+
+def test_enrich_description_enum():
+    result = enrich_cohere_param_description("A color", {"enum": ["red", "blue"]})
+    assert "Allowed values: ['red', 'blue']" in result
+
+
+def test_enrich_description_format():
+    result = enrich_cohere_param_description("A date", {"format": "date-time"})
+    assert "Format: date-time" in result
+
+
+def test_enrich_description_range_both():
+    result = enrich_cohere_param_description("A number", {"minimum": 0, "maximum": 100})
+    assert "Range: min=0, max=100" in result
+
+
+def test_enrich_description_range_min_only():
+    result = enrich_cohere_param_description("A number", {"minimum": 1})
+    assert "Range: min=1" in result
+    assert "max" not in result
+
+
+def test_enrich_description_range_max_only():
+    result = enrich_cohere_param_description("", {"maximum": 10})
+    assert "Range: max=10" in result
+
+
+def test_enrich_description_pattern():
+    result = enrich_cohere_param_description("An ID", {"pattern": "^[a-z]+$"})
+    assert "Pattern: ^[a-z]+$" in result
+
+
+def test_enrich_description_all_constraints():
+    result = enrich_cohere_param_description(
+        "Val",
+        {
+            "enum": ["a"],
+            "format": "uuid",
+            "minimum": 0,
+            "maximum": 1,
+            "pattern": ".*",
+        },
+    )
+    assert "Allowed values" in result
+    assert "Format" in result
+    assert "Range" in result
+    assert "Pattern" in result
+
+
+def test_enrich_description_no_constraints():
+    result = enrich_cohere_param_description("Just a description", {})
+    assert result == "Just a description"
+
+
+def test_enrich_description_empty_description_no_constraints():
+    result = enrich_cohere_param_description("", {})
+    assert result == ""
diff --git a/tests/test_litellm/llms/oci/test_oci_coverage_boost.py b/tests/test_litellm/llms/oci/test_oci_coverage_boost.py
new file mode 100644
index 00000000000..0b7afa3775d
--- /dev/null
+++ b/tests/test_litellm/llms/oci/test_oci_coverage_boost.py
@@ -0,0 +1,1152 @@
+"""
+Coverage-boost tests for the OCI provider happy paths.
+
+Covers:
+  - litellm/llms/oci/common_utils.py  (sign_with_manual_credentials, routing)
+  - litellm/llms/oci/chat/generic.py  (message adaptation, tool conversion, streaming)
+  - litellm/llms/oci/chat/cohere.py   (message adaptation, response parsing, streaming)
+  - litellm/llms/oci/chat/transformation.py  (OCIChatConfig methods, stream wrappers)
+
+All tests are self-contained and require no real OCI credentials or network access.
+"""
+
+import json
+import pytest
+from unittest.mock import patch, MagicMock, AsyncMock
+
+import httpx
+
+from litellm import ModelResponse
+from litellm.llms.oci.chat.cohere import (
+    _extract_text_content,
+    adapt_messages_to_cohere_standard,
+    handle_cohere_response,
+    handle_cohere_stream_chunk,
+)
+from litellm.llms.oci.chat.generic import (
+    adapt_messages_to_generic_oci_standard,
+    adapt_messages_to_generic_oci_standard_tool_response,
+    adapt_tool_definition_to_oci_standard,
+    adapt_tools_to_openai_standard,
+    handle_generic_stream_chunk,
+)
+from litellm.llms.oci.chat.transformation import OCIChatConfig, get_vendor_from_model
+from litellm.llms.oci.common_utils import (
+    OCIError,
+    sign_with_manual_credentials,
+    sign_oci_request,
+    validate_oci_environment,
+)
+from litellm.types.llms.oci import OCIVendors, OCIToolCall
+
+# ---------------------------------------------------------------------------
+# Helpers / fixtures
+# ---------------------------------------------------------------------------
+
+_MANUAL_CREDS = {
+    "oci_user": "ocid1.user.oc1..xxx",
+    "oci_fingerprint": "aa:bb:cc:dd",
+    "oci_tenancy": "ocid1.tenancy.oc1..xxx",
+    "oci_compartment_id": "ocid1.compartment.oc1..xxx",
+    "oci_key": "-----BEGIN RSA PRIVATE KEY-----\nfake\n-----END RSA PRIVATE KEY-----",
+}
+
+_API_BASE = "https://inference.generativeai.us-ashburn-1.oci.oraclecloud.com/20231130/actions/chat"
+
+_COHERE_MODEL = "cohere.command-r-plus"
+_GENERIC_MODEL = "meta.llama-3-70b-instruct"
+
+
+# ===========================================================================
+# common_utils.py — sign_with_manual_credentials happy paths
+# ===========================================================================
+
+
+@patch("litellm.llms.oci.common_utils._CRYPTOGRAPHY_AVAILABLE", True)
+@patch("litellm.llms.oci.common_utils.load_private_key_from_str")
+@patch("litellm.llms.oci.common_utils.padding")
+@patch("litellm.llms.oci.common_utils.hashes")
+def test_sign_with_manual_credentials_inline_key(
+    mock_hashes, mock_padding, mock_load_key
+):
+    """sign_with_manual_credentials succeeds with an inline oci_key string."""
+    mock_key = MagicMock()
+    mock_key.sign.return_value = b"fake_signature"
+    mock_load_key.return_value = mock_key
+
+    result_headers, body = sign_with_manual_credentials(
+        {}, _MANUAL_CREDS, {"key": "val"}, _API_BASE
+    )
+
+    assert "authorization" in result_headers
+    assert result_headers["authorization"].startswith('Signature version="1"')
+    assert "rsa-sha256" in result_headers["authorization"]
+    assert isinstance(body, bytes)
+    mock_key.sign.assert_called_once()
+
+
+@patch("litellm.llms.oci.common_utils._CRYPTOGRAPHY_AVAILABLE", True)
+@patch("litellm.llms.oci.common_utils.load_private_key_from_file")
+@patch("litellm.llms.oci.common_utils.padding")
+@patch("litellm.llms.oci.common_utils.hashes")
+def test_sign_with_manual_credentials_key_file(
+    mock_hashes, mock_padding, mock_load_file
+):
+    """sign_with_manual_credentials falls back to oci_key_file when oci_key absent."""
+    mock_key = MagicMock()
+    mock_key.sign.return_value = b"sig_from_file"
+    mock_load_file.return_value = mock_key
+
+    creds = {**_MANUAL_CREDS, "oci_key_file": "/tmp/key.pem"}
+    creds_no_inline = {k: v for k, v in creds.items() if k != "oci_key"}
+
+    result_headers, body = sign_with_manual_credentials(
+        {}, creds_no_inline, {}, _API_BASE
+    )
+
+    assert "authorization" in result_headers
+    mock_load_file.assert_called_once_with("/tmp/key.pem")
+
+
+@patch("litellm.llms.oci.common_utils._CRYPTOGRAPHY_AVAILABLE", True)
+@patch("litellm.llms.oci.common_utils.load_private_key_from_str")
+@patch("litellm.llms.oci.common_utils.padding")
+@patch("litellm.llms.oci.common_utils.hashes")
+def test_sign_with_manual_credentials_authorization_contains_key_id(
+    mock_hashes, mock_padding, mock_load_key
+):
+    """Authorization header encodes tenancy/user/fingerprint as key ID."""
+    mock_key = MagicMock()
+    mock_key.sign.return_value = b"sig"
+    mock_load_key.return_value = mock_key
+
+    result_headers, _ = sign_with_manual_credentials({}, _MANUAL_CREDS, {}, _API_BASE)
+
+    auth = result_headers["authorization"]
+    assert 'keyId="ocid1.tenancy.oc1..xxx/ocid1.user.oc1..xxx/aa:bb:cc:dd"' in auth
+
+
+def test_sign_with_manual_credentials_non_string_oci_key_raises():
+    """Passing a non-string oci_key raises OCIError(400)."""
+    bad_creds = {**_MANUAL_CREDS, "oci_key": 12345}
+    with pytest.raises(OCIError) as exc_info:
+        sign_with_manual_credentials({}, bad_creds, {}, _API_BASE)
+    assert exc_info.value.status_code == 400
+    assert "oci_key must be a string" in str(exc_info.value)
+
+
+# ---------------------------------------------------------------------------
+# common_utils.py — sign_oci_request routing
+# ---------------------------------------------------------------------------
+
+
+def test_sign_oci_request_routes_to_signer_when_present():
+    """sign_oci_request delegates to sign_with_oci_signer when oci_signer is set."""
+    signer = MagicMock()
+    signer.do_request_sign.return_value = None
+    headers, body = sign_oci_request({}, {"oci_signer": signer}, {"data": 1}, _API_BASE)
+    signer.do_request_sign.assert_called_once()
+    assert isinstance(body, bytes)
+
+
+@patch("litellm.llms.oci.common_utils._CRYPTOGRAPHY_AVAILABLE", True)
+@patch("litellm.llms.oci.common_utils.load_private_key_from_str")
+@patch("litellm.llms.oci.common_utils.padding")
+@patch("litellm.llms.oci.common_utils.hashes")
+def test_sign_oci_request_routes_to_manual_when_no_signer(
+    mock_hashes, mock_padding, mock_load_key
+):
+    """sign_oci_request delegates to sign_with_manual_credentials when oci_signer absent."""
+    mock_key = MagicMock()
+    mock_key.sign.return_value = b"sig"
+    mock_load_key.return_value = mock_key
+
+    headers, body = sign_oci_request({}, _MANUAL_CREDS, {}, _API_BASE)
+    assert "authorization" in headers
+
+
+# ---------------------------------------------------------------------------
+# common_utils.py — _require_cryptography happy path
+# ---------------------------------------------------------------------------
+
+
+def test_require_cryptography_available_does_not_raise():
+    """_require_cryptography() should not raise when the package is importable."""
+    from litellm.llms.oci.common_utils import _require_cryptography
+
+    with patch("litellm.llms.oci.common_utils._CRYPTOGRAPHY_AVAILABLE", True):
+        _require_cryptography()  # must not raise
+
+
+# ===========================================================================
+# generic.py — adapt_messages_to_generic_oci_standard
+# ===========================================================================
+
+
+def test_adapt_generic_user_message_string_content():
+    messages = [{"role": "user", "content": "Hello!"}]
+    result = adapt_messages_to_generic_oci_standard(messages)
+    assert len(result) == 1
+    assert result[0].role == "USER"
+    assert result[0].content[0].text == "Hello!"
+
+
+def test_adapt_generic_assistant_message():
+    messages = [{"role": "assistant", "content": "Hi there!"}]
+    result = adapt_messages_to_generic_oci_standard(messages)
+    assert result[0].role == "ASSISTANT"
+    assert result[0].content[0].text == "Hi there!"
+
+
+def test_adapt_generic_system_message():
+    messages = [{"role": "system", "content": "You are a helpful assistant."}]
+    result = adapt_messages_to_generic_oci_standard(messages)
+    assert result[0].role == "SYSTEM"
+    assert result[0].content[0].text == "You are a helpful assistant."
+
+
+def test_adapt_generic_tool_message():
+    messages = [
+        {
+            "role": "tool",
+            "tool_call_id": "call_abc123",
+            "content": "42 degrees",
+        }
+    ]
+    result = adapt_messages_to_generic_oci_standard(messages)
+    assert result[0].role == "TOOL"
+    assert result[0].toolCallId == "call_abc123"
+    assert result[0].content[0].text == "42 degrees"
+
+
+def test_adapt_generic_assistant_tool_call_message():
+    messages = [
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_xyz",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"city": "Rome"}',
+                    },
+                }
+            ],
+        }
+    ]
+    result = adapt_messages_to_generic_oci_standard(messages)
+    assert result[0].role == "ASSISTANT"
+    assert result[0].toolCalls is not None
+    assert len(result[0].toolCalls) == 1
+    tc = result[0].toolCalls[0]
+    assert tc.name == "get_weather"
+    assert tc.arguments == '{"city": "Rome"}'
+
+
+def test_adapt_generic_multipart_content():
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Look at this:"},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://example.com/img.png"},
+                },
+            ],
+        }
+    ]
+    result = adapt_messages_to_generic_oci_standard(messages)
+    assert len(result[0].content) == 2
+    assert result[0].content[0].text == "Look at this:"
+    assert result[0].content[1].imageUrl.url == "https://example.com/img.png"
+
+
+# ---------------------------------------------------------------------------
+# generic.py — adapt_messages_to_generic_oci_standard_tool_response
+# ---------------------------------------------------------------------------
+
+
+def test_adapt_generic_tool_response_direct():
+    result = adapt_messages_to_generic_oci_standard_tool_response(
+        "tool", "call_999", "The answer is 42"
+    )
+    assert result.role == "TOOL"
+    assert result.toolCallId == "call_999"
+    assert result.content[0].text == "The answer is 42"
+
+
+# ---------------------------------------------------------------------------
+# generic.py — adapt_tool_definition_to_oci_standard
+# ---------------------------------------------------------------------------
+
+
+def test_adapt_tool_definition_basic():
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Retrieve current weather",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"city": {"type": "string"}},
+                    "required": ["city"],
+                },
+            },
+        }
+    ]
+    result = adapt_tool_definition_to_oci_standard(tools, OCIVendors.GENERIC)
+    assert len(result) == 1
+    tool_def = result[0]
+    assert tool_def.name == "get_weather"
+    assert tool_def.type == "FUNCTION"
+    assert tool_def.parameters is not None
+
+
+def test_adapt_tool_definition_resolves_refs():
+    """$ref/$defs schemas are inlined before being sent to OCI."""
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "do_thing",
+                "parameters": {
+                    "$defs": {"Loc": {"type": "string"}},
+                    "type": "object",
+                    "properties": {"location": {"$ref": "#/$defs/Loc"}},
+                },
+            },
+        }
+    ]
+    result = adapt_tool_definition_to_oci_standard(tools, OCIVendors.GENERIC)
+    props = result[0].parameters["properties"]
+    assert props["location"] == {"type": "string"}
+
+
+# ---------------------------------------------------------------------------
+# generic.py — adapt_tools_to_openai_standard
+# ---------------------------------------------------------------------------
+
+
+def test_adapt_tools_to_openai_standard():
+    oci_tool = OCIToolCall(
+        id="call_abc",
+        type="FUNCTION",
+        name="search",
+        arguments='{"query": "hello"}',
+    )
+    result = adapt_tools_to_openai_standard([oci_tool])
+    assert len(result) == 1
+    assert result[0].id == "call_abc"
+    assert result[0].type == "function"
+    assert result[0].function["name"] == "search"
+
+
+def test_adapt_tools_to_openai_standard_generates_id_when_absent():
+    oci_tool = OCIToolCall(
+        id=None,
+        type="FUNCTION",
+        name="lookup",
+        arguments="{}",
+    )
+    result = adapt_tools_to_openai_standard([oci_tool])
+    assert result[0].id.startswith("call_")
+
+
+# ---------------------------------------------------------------------------
+# generic.py — handle_generic_stream_chunk
+# ---------------------------------------------------------------------------
+
+
+def test_handle_generic_stream_chunk_text_content():
+    chunk = {
+        "message": {
+            "content": [{"type": "TEXT", "text": "Hello from OCI"}],
+            "role": "ASSISTANT",
+        },
+        "finishReason": None,
+        "index": 0,
+    }
+    result = handle_generic_stream_chunk(chunk)
+    assert result.choices[0].delta.content == "Hello from OCI"
+    assert result.choices[0].finish_reason is None
+
+
+def test_handle_generic_stream_chunk_complete_finish_reason():
+    chunk = {"finishReason": "COMPLETE", "index": 0}
+    result = handle_generic_stream_chunk(chunk)
+    assert result.choices[0].finish_reason == "stop"
+
+
+def test_handle_generic_stream_chunk_max_tokens_finish_reason():
+    chunk = {"finishReason": "MAX_TOKENS", "index": 0}
+    result = handle_generic_stream_chunk(chunk)
+    assert result.choices[0].finish_reason == "length"
+
+
+def test_handle_generic_stream_chunk_tool_calls_finish_reason():
+    chunk = {"finishReason": "TOOL_CALLS", "index": 0}
+    result = handle_generic_stream_chunk(chunk)
+    assert result.choices[0].finish_reason == "tool_calls"
+
+
+def test_handle_generic_stream_chunk_no_message():
+    """Chunks without a message key should still parse without error."""
+    chunk = {"finishReason": "COMPLETE", "index": 1}
+    result = handle_generic_stream_chunk(chunk)
+    assert result.choices[0].delta.content is None
+    assert result.choices[0].finish_reason == "stop"
+
+
+# ===========================================================================
+# cohere.py — _extract_text_content
+# ===========================================================================
+
+
+def test_extract_text_content_none():
+    assert _extract_text_content(None) == ""
+
+
+def test_extract_text_content_string():
+    assert _extract_text_content("hello") == "hello"
+
+
+def test_extract_text_content_list():
+    content = [
+        {"type": "text", "text": "foo"},
+        {"type": "text", "text": "bar"},
+    ]
+    assert _extract_text_content(content) == "foobar"
+
+
+def test_extract_text_content_list_skips_non_text():
+    content = [
+        {"type": "image_url", "url": "https://x.com/img.png"},
+        {"type": "text", "text": "only this"},
+    ]
+    assert _extract_text_content(content) == "only this"
+
+
+def test_extract_text_content_non_string_non_list():
+    assert _extract_text_content(42) == "42"
+
+
+# ===========================================================================
+# cohere.py — adapt_messages_to_cohere_standard
+# ===========================================================================
+
+
+def test_adapt_cohere_user_in_history():
+    messages = [
+        {"role": "user", "content": "first question"},
+        {"role": "user", "content": "current question"},
+    ]
+    history = adapt_messages_to_cohere_standard(messages)
+    assert len(history) == 1
+    assert history[0].role == "USER"
+    assert history[0].message == "first question"
+
+
+def test_adapt_cohere_assistant_in_history():
+    messages = [
+        {"role": "user", "content": "q"},
+        {"role": "assistant", "content": "answer"},
+        {"role": "user", "content": "follow-up"},
+    ]
+    history = adapt_messages_to_cohere_standard(messages)
+    assert len(history) == 2
+    chatbot_msg = history[1]
+    assert chatbot_msg.role == "CHATBOT"
+    assert chatbot_msg.message == "answer"
+
+
+def test_adapt_cohere_assistant_with_tool_calls_in_history():
+    messages = [
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_1",
+                    "type": "function",
+                    "function": {"name": "calc", "arguments": '{"x": 1}'},
+                }
+            ],
+        },
+        {"role": "user", "content": "thanks"},
+    ]
+    history = adapt_messages_to_cohere_standard(messages)
+    assert len(history) == 1
+    assert history[0].role == "CHATBOT"
+    assert history[0].toolCalls is not None
+    assert history[0].toolCalls[0].name == "calc"
+
+
+def test_adapt_cohere_tool_result_in_history():
+    messages = [
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_1",
+                    "type": "function",
+                    "function": {"name": "calc", "arguments": '{"x": 1}'},
+                }
+            ],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_1",
+            "content": "result: 42",
+        },
+        {"role": "user", "content": "ok"},
+    ]
+    history = adapt_messages_to_cohere_standard(messages)
+    tool_msg = next(m for m in history if m.role == "TOOL")
+    assert tool_msg.toolResults[0].call.name == "calc"
+    assert tool_msg.toolResults[0].outputs[0]["output"] == "result: 42"
+
+
+# ===========================================================================
+# cohere.py — handle_cohere_response
+# ===========================================================================
+
+
+_COHERE_RESPONSE_JSON = {
+    "modelId": "cohere.command-r-plus",
+    "modelVersion": "1.0",
+    "chatResponse": {
+        "apiFormat": "COHERE",
+        "text": "Hello from Cohere!",
+        "finishReason": "COMPLETE",
+        "usage": {
+            "promptTokens": 10,
+            "completionTokens": 5,
+            "totalTokens": 15,
+        },
+    },
+}
+
+
+_COHERE_RAW_RESPONSE = httpx.Response(200, request=httpx.Request("POST", "https://oci"))
+
+
+def test_handle_cohere_response_complete():
+    model_response = ModelResponse()
+    result = handle_cohere_response(
+        _COHERE_RESPONSE_JSON, _COHERE_MODEL, model_response, _COHERE_RAW_RESPONSE
+    )
+    assert result.choices[0].finish_reason == "stop"
+    assert result.choices[0].message["content"] == "Hello from Cohere!"
+    assert result.usage.prompt_tokens == 10
+
+
+def test_handle_cohere_response_max_tokens():
+    resp = {
+        **_COHERE_RESPONSE_JSON,
+        "chatResponse": {
+            **_COHERE_RESPONSE_JSON["chatResponse"],
+            "finishReason": "MAX_TOKENS",
+        },
+    }
+    model_response = ModelResponse()
+    result = handle_cohere_response(
+        resp, _COHERE_MODEL, model_response, _COHERE_RAW_RESPONSE
+    )
+    assert result.choices[0].finish_reason == "length"
+
+
+def test_handle_cohere_response_tool_call():
+    resp = {
+        **_COHERE_RESPONSE_JSON,
+        "chatResponse": {
+            **_COHERE_RESPONSE_JSON["chatResponse"],
+            "finishReason": "TOOL_CALL",
+            "toolCalls": [{"name": "get_time", "parameters": {"tz": "UTC"}}],
+        },
+    }
+    model_response = ModelResponse()
+    result = handle_cohere_response(
+        resp, _COHERE_MODEL, model_response, _COHERE_RAW_RESPONSE
+    )
+    assert result.choices[0].finish_reason == "tool_calls"
+    tool_calls = result.choices[0].message["tool_calls"]
+    assert tool_calls is not None
+    assert tool_calls[0]["function"]["name"] == "get_time"
+
+
+def test_handle_cohere_response_missing_usage():
+    resp = {
+        **_COHERE_RESPONSE_JSON,
+        "chatResponse": {
+            k: v
+            for k, v in _COHERE_RESPONSE_JSON["chatResponse"].items()
+            if k != "usage"
+        },
+    }
+    model_response = ModelResponse()
+    result = handle_cohere_response(
+        resp, _COHERE_MODEL, model_response, _COHERE_RAW_RESPONSE
+    )
+    assert result.usage.prompt_tokens == 0
+    assert result.usage.completion_tokens == 0
+    assert result.usage.total_tokens == 0
+
+
+def test_handle_cohere_response_malformed_raises_oci_error():
+    bad_json = {"chatResponse": {"apiFormat": "COHERE"}}
+    raw = httpx.Response(502, request=httpx.Request("POST", "https://oci"))
+    model_response = ModelResponse()
+    with pytest.raises(OCIError) as exc_info:
+        handle_cohere_response(bad_json, _COHERE_MODEL, model_response, raw)
+    assert exc_info.value.status_code == 502
+
+
+# ===========================================================================
+# cohere.py — handle_cohere_stream_chunk
+# ===========================================================================
+
+
+def test_handle_cohere_stream_chunk_text():
+    chunk = {"apiFormat": "COHERE", "text": "streaming text", "finishReason": None}
+    result = handle_cohere_stream_chunk(chunk)
+    assert result.choices[0].delta.content == "streaming text"
+    assert result.choices[0].finish_reason is None
+
+
+def test_handle_cohere_stream_chunk_complete():
+    # Real OCI Cohere terminal events carry the full response in `text` plus a
+    # populated `chatHistory`; the parser must drop that text to avoid doubling
+    # — but only when prior chunks already emitted the text as incremental
+    # deltas (signalled by ``prior_text_emitted=True``).
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "How can I help you today?",
+        "finishReason": "COMPLETE",
+        "chatHistory": [
+            {"role": "USER", "message": "Hello!"},
+            {"role": "CHATBOT", "message": "How can I help you today?"},
+        ],
+    }
+    result = handle_cohere_stream_chunk(chunk, prior_text_emitted=True)
+    assert result.choices[0].finish_reason == "stop"
+    assert result.choices[0].delta.content is None
+
+
+def test_handle_cohere_stream_chunk_max_tokens():
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "truncated full response",
+        "finishReason": "MAX_TOKENS",
+        "chatHistory": [{"role": "CHATBOT", "message": "truncated full response"}],
+    }
+    result = handle_cohere_stream_chunk(chunk, prior_text_emitted=True)
+    assert result.choices[0].finish_reason == "length"
+    assert result.choices[0].delta.content is None
+
+
+def test_handle_cohere_stream_chunk_tool_call():
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "",
+        "finishReason": "TOOL_CALL",
+        "chatHistory": [{"role": "CHATBOT", "message": ""}],
+    }
+    result = handle_cohere_stream_chunk(chunk)
+    assert result.choices[0].finish_reason == "tool_calls"
+    assert not result.choices[0].delta.content
+
+
+def test_handle_cohere_stream_chunk_terminal_drops_full_response_text():
+    """Regression for double-output on cohere.command-* streaming.
+
+    OCI's terminal SSE event re-sends the full assembled response in `text`
+    alongside a populated `chatHistory`. That text must be dropped — otherwise
+    it gets concatenated onto the already-streamed incremental deltas. The
+    caller signals "prior deltas already emitted text" via
+    ``prior_text_emitted=True``.
+    """
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "How can I help you today?",
+        "finishReason": "COMPLETE",
+        "chatHistory": [
+            {"role": "USER", "message": "Hello!"},
+            {"role": "CHATBOT", "message": "How can I help you today?"},
+        ],
+    }
+    result = handle_cohere_stream_chunk(chunk, prior_text_emitted=True)
+    assert result.choices[0].delta.content is None
+
+
+def test_handle_cohere_stream_chunk_single_event_stream_preserves_text():
+    """Degenerate single-event stream: the terminal chunk carries the only copy
+    of the response text. Without prior text deltas, suppressing here would
+    discard the response entirely — so the text must pass through."""
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "Short answer.",
+        "finishReason": "COMPLETE",
+        "chatHistory": [{"role": "CHATBOT", "message": "Short answer."}],
+    }
+    result = handle_cohere_stream_chunk(chunk, prior_text_emitted=False)
+    assert result.choices[0].delta.content == "Short answer."
+    assert result.choices[0].finish_reason == "stop"
+
+
+def test_handle_cohere_stream_chunk_incremental_passes_text_through():
+    """Non-terminal chunks (no chatHistory) must emit their incremental text."""
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "How can I ",
+        "finishReason": None,
+    }
+    result = handle_cohere_stream_chunk(chunk)
+    assert result.choices[0].delta.content == "How can I "
+    assert result.choices[0].finish_reason is None
+
+
+def test_handle_cohere_stream_chunk_finish_reason_without_chathistory_keeps_text():
+    """`finishReason` alone (no `chatHistory`) must NOT trigger the drop —
+    `chatHistory` is the discriminator for the consolidated terminal event."""
+    chunk = {
+        "apiFormat": "COHERE",
+        "text": "tail delta",
+        "finishReason": "COMPLETE",
+    }
+    result = handle_cohere_stream_chunk(chunk)
+    assert result.choices[0].delta.content == "tail delta"
+    assert result.choices[0].finish_reason == "stop"
+
+
+# ===========================================================================
+# transformation.py — get_vendor_from_model
+# ===========================================================================
+
+
+def test_get_vendor_cohere():
+    assert get_vendor_from_model("cohere.command-r-plus") == OCIVendors.COHERE
+
+
+def test_get_vendor_generic_llama():
+    assert get_vendor_from_model("meta.llama-3-70b-instruct") == OCIVendors.GENERIC
+
+
+def test_get_vendor_generic_xai():
+    assert get_vendor_from_model("xai.grok-4") == OCIVendors.GENERIC
+
+
+def test_get_vendor_generic_google():
+    assert get_vendor_from_model("google.gemini-2-flash") == OCIVendors.GENERIC
+
+
+# ===========================================================================
+# transformation.py — OCIChatConfig methods
+# ===========================================================================
+
+
+class TestOCIChatConfigGetCompleteUrl:
+    def test_returns_chat_endpoint_from_region(self):
+        config = OCIChatConfig()
+        url = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=_GENERIC_MODEL,
+            optional_params={"oci_region": "eu-frankfurt-1"},
+            litellm_params={},
+        )
+        assert url == (
+            "https://inference.generativeai.eu-frankfurt-1.oci.oraclecloud.com"
+            "/20231130/actions/chat"
+        )
+
+    def test_respects_explicit_api_base(self):
+        config = OCIChatConfig()
+        url = config.get_complete_url(
+            api_base="https://custom.endpoint.com/",
+            api_key=None,
+            model=_GENERIC_MODEL,
+            optional_params={},
+            litellm_params={},
+        )
+        assert url == "https://custom.endpoint.com/20231130/actions/chat"
+
+    def test_full_chat_url_is_not_doubled(self):
+        config = OCIChatConfig()
+        full_url = (
+            "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
+            "/20231130/actions/chat"
+        )
+        url = config.get_complete_url(
+            api_base=full_url,
+            api_key=None,
+            model=_GENERIC_MODEL,
+            optional_params={},
+            litellm_params={},
+        )
+        assert url == full_url
+
+
+class TestOCIChatConfigGetErrorClass:
+    def test_returns_oci_error(self):
+        config = OCIChatConfig()
+        err = config.get_error_class("boom", 503, {})
+        assert isinstance(err, OCIError)
+        assert err.status_code == 503
+
+
+class TestOCIChatConfigSignRequest:
+    @patch("litellm.llms.oci.common_utils._CRYPTOGRAPHY_AVAILABLE", True)
+    @patch("litellm.llms.oci.common_utils.load_private_key_from_str")
+    @patch("litellm.llms.oci.common_utils.padding")
+    @patch("litellm.llms.oci.common_utils.hashes")
+    def test_sign_request_delegates(self, mock_hashes, mock_padding, mock_load_key):
+        mock_key = MagicMock()
+        mock_key.sign.return_value = b"sig"
+        mock_load_key.return_value = mock_key
+
+        config = OCIChatConfig()
+        headers, body = config.sign_request(
+            headers={},
+            optional_params=_MANUAL_CREDS,
+            request_data={"hello": "world"},
+            api_base=_API_BASE,
+        )
+        assert "authorization" in headers
+        assert isinstance(body, bytes)
+
+
+class TestOCIChatConfigValidateEnvironment:
+    def test_with_signer_skips_credential_check(self):
+        """If oci_signer is provided, validate_environment must NOT raise."""
+        config = OCIChatConfig()
+        signer = MagicMock()
+        result = config.validate_environment(
+            headers={},
+            model=_GENERIC_MODEL,
+            messages=[{"role": "user", "content": "hi"}],
+            optional_params={"oci_signer": signer},
+            litellm_params={},
+        )
+        assert result["content-type"] == "application/json"
+
+    def test_raises_when_messages_empty(self):
+        config = OCIChatConfig()
+        with pytest.raises(OCIError) as exc_info:
+            config.validate_environment(
+                headers={},
+                model=_GENERIC_MODEL,
+                messages=[],
+                optional_params={"oci_signer": MagicMock()},
+                litellm_params={},
+            )
+        assert exc_info.value.status_code == 400
+
+
+class TestOCIChatConfigGetOptionalParams:
+    def _config(self):
+        return OCIChatConfig()
+
+    def test_cohere_maps_stop_to_stop_sequences(self):
+        config = self._config()
+        result = config._get_optional_params(OCIVendors.COHERE, {"stop": ["END"]})
+        assert "stopSequences" in result
+        assert result["stopSequences"] == ["END"]
+
+    def test_generic_maps_max_tokens(self):
+        config = self._config()
+        result = config._get_optional_params(OCIVendors.GENERIC, {"max_tokens": 512})
+        assert result["maxTokens"] == 512
+
+    def test_tool_choice_string_auto_converted_to_dict(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC, {"tool_choice": "auto"}
+        )
+        assert result["toolChoice"] == {"type": "AUTO"}
+
+    def test_tool_choice_string_none_converted_to_dict(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC, {"tool_choice": "none"}
+        )
+        assert result["toolChoice"] == {"type": "NONE"}
+
+    def test_tool_choice_string_required_converted_to_dict(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC, {"tool_choice": "required"}
+        )
+        assert result["toolChoice"] == {"type": "REQUIRED"}
+
+    def test_tool_choice_openai_function_dict_converted_to_oci_form(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC,
+            {
+                "tool_choice": {
+                    "type": "function",
+                    "function": {"name": "my_func"},
+                }
+            },
+        )
+        assert result["toolChoice"] == {"type": "FUNCTION", "name": "my_func"}
+
+    def test_tool_choice_flat_function_dict_uppercased(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC,
+            {"tool_choice": {"type": "function", "name": "my_func"}},
+        )
+        assert result["toolChoice"] == {"type": "FUNCTION", "name": "my_func"}
+
+    def test_tool_choice_dict_auto_uppercased(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC, {"tool_choice": {"type": "auto"}}
+        )
+        assert result["toolChoice"] == {"type": "AUTO"}
+
+    def test_response_format_json_generic(self):
+        config = self._config()
+        result = config._get_optional_params(
+            OCIVendors.GENERIC, {"response_format": {"type": "json_object"}}
+        )
+        assert result["responseFormat"]["type"] == "JSON_OBJECT"
+
+    def test_tools_adapted_for_cohere(self):
+        config = self._config()
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "echo",
+                    "description": "echo",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"msg": {"type": "string"}},
+                        "required": ["msg"],
+                    },
+                },
+            }
+        ]
+        result = config._get_optional_params(OCIVendors.COHERE, {"tools": tools})
+        # tools should be CohereTool objects
+        assert len(result["tools"]) == 1
+        assert result["tools"][0].name == "echo"
+
+    def test_tools_adapted_for_generic(self):
+        config = self._config()
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "search",
+                    "description": "search the web",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"query": {"type": "string"}},
+                    },
+                },
+            }
+        ]
+        result = config._get_optional_params(OCIVendors.GENERIC, {"tools": tools})
+        assert len(result["tools"]) == 1
+        assert result["tools"][0].name == "search"
+
+
+class TestOCIChatConfigTransformRequest:
+    _base_params = {**_MANUAL_CREDS}
+
+    def test_generic_model_transform(self):
+        config = OCIChatConfig()
+        result = config.transform_request(
+            model=_GENERIC_MODEL,
+            messages=[{"role": "user", "content": "hello"}],
+            optional_params=self._base_params,
+            litellm_params={},
+            headers={},
+        )
+        assert result["compartmentId"] == _MANUAL_CREDS["oci_compartment_id"]
+        chat_req = result["chatRequest"]
+        assert chat_req["apiFormat"] == "GENERIC"
+
+    def test_cohere_model_transform(self):
+        config = OCIChatConfig()
+        result = config.transform_request(
+            model=_COHERE_MODEL,
+            messages=[{"role": "user", "content": "tell me a joke"}],
+            optional_params=self._base_params,
+            litellm_params={},
+            headers={},
+        )
+        chat_req = result["chatRequest"]
+        assert chat_req["apiFormat"] == "COHERE"
+        assert chat_req["message"] == "tell me a joke"
+
+    def test_cohere_model_with_system_preamble(self):
+        config = OCIChatConfig()
+        result = config.transform_request(
+            model=_COHERE_MODEL,
+            messages=[
+                {"role": "system", "content": "You are helpful."},
+                {"role": "user", "content": "hi"},
+            ],
+            optional_params=self._base_params,
+            litellm_params={},
+            headers={},
+        )
+        assert result["chatRequest"]["preambleOverride"] == "You are helpful."
+
+    def test_raises_without_compartment_id(self):
+        config = OCIChatConfig()
+        params = {k: v for k, v in _MANUAL_CREDS.items() if k != "oci_compartment_id"}
+        with pytest.raises(OCIError) as exc_info:
+            config.transform_request(
+                model=_GENERIC_MODEL,
+                messages=[{"role": "user", "content": "hi"}],
+                optional_params=params,
+                litellm_params={},
+                headers={},
+            )
+        assert exc_info.value.status_code == 400
+        assert "oci_compartment_id" in str(exc_info.value)
+
+
+# ===========================================================================
+# transformation.py — OCIStreamWrapper.chunk_creator
+# ===========================================================================
+
+
+class TestOCIStreamWrapperChunkCreator:
+    def _make_wrapper(self, model: str) -> "OCIStreamWrapper":
+        from litellm.llms.oci.chat.transformation import OCIStreamWrapper
+
+        return OCIStreamWrapper(
+            completion_stream=iter([]),
+            model=model,
+            custom_llm_provider="oci",
+            logging_obj=MagicMock(),
+        )
+
+    def test_cohere_chunk_dispatched_correctly(self):
+        wrapper = self._make_wrapper(_COHERE_MODEL)
+        payload = json.dumps(
+            {"apiFormat": "COHERE", "text": "hi", "finishReason": None}
+        )
+        result = wrapper.chunk_creator(f"data:{payload}")
+        assert result.choices[0].delta.content == "hi"
+
+    def test_generic_chunk_dispatched_correctly(self):
+        wrapper = self._make_wrapper(_GENERIC_MODEL)
+        payload = json.dumps(
+            {
+                "finishReason": "COMPLETE",
+                "index": 0,
+            }
+        )
+        result = wrapper.chunk_creator(f"data:{payload}")
+        assert result.choices[0].finish_reason == "stop"
+
+    def test_raises_on_non_data_prefix(self):
+        wrapper = self._make_wrapper(_GENERIC_MODEL)
+        with pytest.raises(ValueError, match="does not start with 'data:'"):
+            wrapper.chunk_creator("event: done")
+
+    def test_raises_on_non_string_chunk(self):
+        wrapper = self._make_wrapper(_GENERIC_MODEL)
+        with pytest.raises(ValueError, match="not a string"):
+            wrapper.chunk_creator({"bad": "type"})
+
+    def test_empty_string_content_does_not_mark_text_emitted(self):
+        # An intermediate Cohere chunk carrying `text=""` must not flip the
+        # _cohere_text_emitted flag — otherwise a subsequent terminal
+        # consolidation chunk would have its real text suppressed as a
+        # "duplicate" and the response would be lost.
+        wrapper = self._make_wrapper(_COHERE_MODEL)
+        empty_payload = json.dumps(
+            {"apiFormat": "COHERE", "text": "", "finishReason": None}
+        )
+        wrapper.chunk_creator(f"data:{empty_payload}")
+        assert wrapper._cohere_text_emitted is False
+
+        terminal_payload = json.dumps(
+            {
+                "apiFormat": "COHERE",
+                "text": "Hello world",
+                "finishReason": "COMPLETE",
+                "chatHistory": [{"role": "CHATBOT", "message": "Hello world"}],
+            }
+        )
+        result = wrapper.chunk_creator(f"data:{terminal_payload}")
+        assert result.choices[0].delta.content == "Hello world"
+
+
+# ===========================================================================
+# transformation.py — get_sync_custom_stream_wrapper
+# ===========================================================================
+
+
+def test_get_sync_custom_stream_wrapper_returns_wrapper():
+    from litellm.llms.oci.chat.transformation import OCIStreamWrapper
+
+    config = OCIChatConfig()
+
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.iter_text.return_value = iter(
+        ['data:{"finishReason":"COMPLETE","index":0}']
+    )
+
+    mock_client = MagicMock()
+    mock_client.post.return_value = mock_response
+
+    wrapper = config.get_sync_custom_stream_wrapper(
+        model=_GENERIC_MODEL,
+        custom_llm_provider="oci",
+        logging_obj=MagicMock(),
+        api_base=_API_BASE,
+        headers={"authorization": "Signature ..."},
+        data={"chatRequest": {}},
+        messages=[{"role": "user", "content": "hi"}],
+        client=mock_client,
+        signed_json_body=b'{"chatRequest":{}}',
+    )
+
+    assert isinstance(wrapper, OCIStreamWrapper)
+    mock_client.post.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_get_async_custom_stream_wrapper_returns_wrapper():
+    from litellm.llms.oci.chat.transformation import OCIStreamWrapper
+
+    config = OCIChatConfig()
+
+    async def _fake_aiter_text():
+        yield 'data:{"finishReason":"COMPLETE","index":0}'
+
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.aiter_text = _fake_aiter_text
+
+    mock_client = AsyncMock()
+    mock_client.post = AsyncMock(return_value=mock_response)
+
+    wrapper = await config.get_async_custom_stream_wrapper(
+        model=_GENERIC_MODEL,
+        custom_llm_provider="oci",
+        logging_obj=MagicMock(),
+        api_base=_API_BASE,
+        headers={"authorization": "Signature ..."},
+        data={"chatRequest": {}},
+        messages=[{"role": "user", "content": "hi"}],
+        client=mock_client,
+        signed_json_body=b'{"chatRequest":{}}',
+    )
+
+    assert isinstance(wrapper, OCIStreamWrapper)
diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py
index acb9fa9b64c..4b2e9471fb7 100644
--- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py
+++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py
@@ -86,6 +86,90 @@ class TestOpenAIResponsesAPIConfig:
 
         self.validate_responses_api_request_params(result, expected_fields)
 
+    def test_transform_strips_cache_control_from_input_content_blocks(self):
+        """`cache_control` markers (Anthropic-only) must be stripped from
+        Responses API input content blocks before sending to OpenAI.
+
+        OpenAI rejects unknown params on input content blocks with HTTP 400:
+            "Unknown parameter: 'input[0].content[0].cache_control'"
+        Chat Completions strips these via
+        `remove_cache_control_flag_from_messages_and_tools`; the Responses
+        path must do the same.
+        """
+        input_with_cache_control = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_text",
+                        "text": "Hello",
+                        "cache_control": {"type": "ephemeral"},
+                    }
+                ],
+            }
+        ]
+
+        result = self.config.transform_responses_api_request(
+            model=self.model,
+            input=input_with_cache_control,
+            response_api_optional_request_params={},
+            litellm_params={},
+            headers={},
+        )
+
+        assert "cache_control" not in result["input"][0]["content"][0]
+        assert result["input"][0]["content"][0]["type"] == "input_text"
+        assert result["input"][0]["content"][0]["text"] == "Hello"
+
+    def test_transform_strips_cache_control_from_tools(self):
+        """`cache_control` markers must also be stripped from tools for
+        symmetry with the Chat Completions path. OpenAI currently accepts
+        cache_control on tools silently but stripping keeps the wire payload
+        clean and matches `remove_cache_control_flag_from_messages_and_tools`.
+        """
+        tools_with_cache_control = [
+            {
+                "type": "function",
+                "name": "get_weather",
+                "description": "Get the weather",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"city": {"type": "string"}},
+                },
+                "cache_control": {"type": "ephemeral"},
+            }
+        ]
+
+        result = self.config.transform_responses_api_request(
+            model=self.model,
+            input="hi",
+            response_api_optional_request_params={"tools": tools_with_cache_control},
+            litellm_params={},
+            headers={},
+        )
+
+        assert "cache_control" not in result["tools"][0]
+        assert result["tools"][0]["name"] == "get_weather"
+
+    def test_transform_preserves_input_without_cache_control(self):
+        """Inputs without cache_control must pass through unmodified."""
+        input_clean = [
+            {
+                "role": "user",
+                "content": [{"type": "input_text", "text": "Hello"}],
+            }
+        ]
+
+        result = self.config.transform_responses_api_request(
+            model=self.model,
+            input=input_clean,
+            response_api_optional_request_params={},
+            litellm_params={},
+            headers={},
+        )
+
+        assert result["input"] == input_clean
+
     def test_transform_streaming_response(self):
         """Test streaming response transformation"""
         # Test with a text delta event
diff --git a/tests/test_litellm/llms/reducto/__init__.py b/tests/test_litellm/llms/reducto/__init__.py
new file mode 100644
index 00000000000..8b137891791
--- /dev/null
+++ b/tests/test_litellm/llms/reducto/__init__.py
@@ -0,0 +1 @@
+
diff --git a/tests/test_litellm/llms/reducto/test_cost.py b/tests/test_litellm/llms/reducto/test_cost.py
new file mode 100644
index 00000000000..73340dc8729
--- /dev/null
+++ b/tests/test_litellm/llms/reducto/test_cost.py
@@ -0,0 +1,122 @@
+import litellm
+import pytest
+
+from litellm.cost_calculator import completion_cost
+from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo
+
+
+def test_ocr_cost_prefers_credit_pricing_when_pages_processed_is_none(monkeypatch):
+    monkeypatch.setattr(
+        litellm,
+        "get_model_info",
+        lambda model, custom_llm_provider=None: {"ocr_cost_per_credit": 0.003},
+    )
+
+    response = OCRResponse(
+        pages=[OCRPage(index=0, markdown="credit priced")],
+        model="parse-v3",
+        usage_info=OCRUsageInfo(pages_processed=None, credits=10),
+    )
+
+    cost = completion_cost(
+        completion_response=response,
+        model="reducto/parse-v3",
+        custom_llm_provider="reducto",
+        call_type="ocr",
+    )
+
+    assert cost == 0.03
+
+
+def test_ocr_cost_prefers_zero_credit_pricing_over_page_pricing(monkeypatch):
+    monkeypatch.setattr(
+        litellm,
+        "get_model_info",
+        lambda model, custom_llm_provider=None: {
+            "ocr_cost_per_credit": 0.0,
+            "ocr_cost_per_page": 0.5,
+        },
+    )
+
+    response = OCRResponse(
+        pages=[OCRPage(index=0, markdown="free credit priced")],
+        model="parse-v3",
+        usage_info=OCRUsageInfo(pages_processed=2, credits=10),
+    )
+
+    cost = completion_cost(
+        completion_response=response,
+        model="reducto/parse-v3",
+        custom_llm_provider="reducto",
+        call_type="ocr",
+    )
+
+    assert cost == 0.0
+
+
+def test_ocr_cost_falls_back_to_page_pricing(monkeypatch):
+    monkeypatch.setattr(
+        litellm,
+        "get_model_info",
+        lambda model, custom_llm_provider=None: {"ocr_cost_per_page": 0.5},
+    )
+
+    response = OCRResponse(
+        pages=[OCRPage(index=0, markdown="page priced")],
+        model="mistral-ocr-latest",
+        usage_info=OCRUsageInfo(pages_processed=2),
+    )
+
+    cost = completion_cost(
+        completion_response=response,
+        model="mistral/mistral-ocr-latest",
+        custom_llm_provider="mistral",
+        call_type="ocr",
+    )
+
+    assert cost == 1.0
+
+
+def test_ocr_cost_returns_zero_when_no_pricing_and_no_pages(monkeypatch):
+    monkeypatch.setattr(
+        litellm,
+        "get_model_info",
+        lambda model, custom_llm_provider=None: {},
+    )
+
+    response = OCRResponse(
+        pages=[OCRPage(index=0, markdown="unpriced")],
+        model="parse-v3",
+        usage_info=OCRUsageInfo(pages_processed=None, credits=5),
+    )
+
+    cost = completion_cost(
+        completion_response=response,
+        model="reducto/parse-v3",
+        custom_llm_provider="reducto",
+        call_type="ocr",
+    )
+
+    assert cost == 0.0
+
+
+def test_ocr_cost_raises_when_pages_processed_missing_for_page_pricing(monkeypatch):
+    monkeypatch.setattr(
+        litellm,
+        "get_model_info",
+        lambda model, custom_llm_provider=None: {"ocr_cost_per_page": 0.5},
+    )
+
+    response = OCRResponse(
+        pages=[OCRPage(index=0, markdown="missing pages")],
+        model="mistral-ocr-latest",
+        usage_info=OCRUsageInfo(pages_processed=None),
+    )
+
+    with pytest.raises(ValueError, match="OCR response pages_processed is None"):
+        completion_cost(
+            completion_response=response,
+            model="mistral/mistral-ocr-latest",
+            custom_llm_provider="mistral",
+            call_type="ocr",
+        )
diff --git a/tests/test_litellm/llms/reducto/test_model_info.py b/tests/test_litellm/llms/reducto/test_model_info.py
new file mode 100644
index 00000000000..de7a3ccba64
--- /dev/null
+++ b/tests/test_litellm/llms/reducto/test_model_info.py
@@ -0,0 +1,44 @@
+import uuid
+
+import litellm
+
+from litellm.utils import _invalidate_model_cost_lowercase_map
+
+
+def test_reducto_provider_registration():
+    model, custom_llm_provider, _, _ = litellm.get_llm_provider(
+        model="reducto/parse-v3"
+    )
+
+    assert model == "parse-v3"
+    assert custom_llm_provider == "reducto"
+
+
+def test_get_model_info_preserves_ocr_cost_per_credit():
+    test_model_name = f"reducto/test-cost-propagation-{uuid.uuid4().hex[:12]}"
+    previous_model_entry = litellm.model_cost.get(test_model_name)
+    _invalidate_model_cost_lowercase_map()
+
+    try:
+        litellm.register_model(
+            {
+                test_model_name: {
+                    "litellm_provider": "reducto",
+                    "mode": "ocr",
+                    "ocr_cost_per_credit": 0.003,
+                }
+            }
+        )
+
+        model_info = litellm.get_model_info(
+            model=test_model_name,
+            custom_llm_provider="reducto",
+        )
+
+        assert model_info.get("ocr_cost_per_credit") == 0.003
+    finally:
+        if previous_model_entry is None:
+            litellm.model_cost.pop(test_model_name, None)
+        else:
+            litellm.model_cost[test_model_name] = previous_model_entry
+        _invalidate_model_cost_lowercase_map()
diff --git a/tests/test_litellm/llms/reducto/test_parse_legacy.py b/tests/test_litellm/llms/reducto/test_parse_legacy.py
new file mode 100644
index 00000000000..db19460baa3
--- /dev/null
+++ b/tests/test_litellm/llms/reducto/test_parse_legacy.py
@@ -0,0 +1,59 @@
+import json
+
+import litellm
+import pytest
+
+
+@pytest.fixture()
+def disable_aiohttp_transport():
+    original_disable_aiohttp = litellm.disable_aiohttp_transport
+    litellm.disable_aiohttp_transport = True
+    litellm.in_memory_llm_clients_cache.flush_cache()
+    try:
+        yield
+    finally:
+        litellm.disable_aiohttp_transport = original_disable_aiohttp
+        litellm.in_memory_llm_clients_cache.flush_cache()
+
+
+@pytest.mark.asyncio
+async def test_parse_legacy_wraps_enhance_under_options(
+    disable_aiohttp_transport, respx_mock
+):
+    upload_route = respx_mock.post("https://platform.reducto.ai/upload").respond(
+        json={"file_id": "reducto://legacy.pdf"}
+    )
+    parse_route = respx_mock.post("https://platform.reducto.ai/parse").respond(
+        json={
+            "usage": {"num_pages": 1, "credits": 1},
+            "result": {
+                "chunks": [
+                    {
+                        "content": "Legacy parse",
+                        "blocks": [{"content": "Legacy parse", "bbox": {"page": 1}}],
+                    }
+                ]
+            },
+        }
+    )
+
+    response = await litellm.aocr(
+        model="reducto/parse-legacy",
+        document={
+            "type": "file",
+            "file": b"%PDF-1.4 legacy",
+            "mime_type": "application/pdf",
+        },
+        api_key="legacy-key",
+        api_base="https://platform.reducto.ai",
+        enhance={"agentic": [{"type": "table"}]},
+    )
+
+    assert upload_route.called
+    assert parse_route.called
+    request_body = json.loads(parse_route.calls[0].request.read())
+    assert request_body == {
+        "document_url": "reducto://legacy.pdf",
+        "options": {"enhance": {"agentic": [{"type": "table"}]}},
+    }
+    assert response.pages[0].markdown == "Legacy parse"
diff --git a/tests/test_litellm/llms/reducto/test_parse_v3.py b/tests/test_litellm/llms/reducto/test_parse_v3.py
new file mode 100644
index 00000000000..140b9737dc0
--- /dev/null
+++ b/tests/test_litellm/llms/reducto/test_parse_v3.py
@@ -0,0 +1,152 @@
+import json
+
+import litellm
+import pytest
+
+
+def _reducto_parse_response() -> dict:
+    return {
+        "job_id": "job_123",
+        "usage": {"num_pages": 3, "credits": 3},
+        "result": {
+            "chunks": [
+                {
+                    "content": "Page 1 block A",
+                    "blocks": [
+                        {
+                            "content": "Page 1 block A",
+                            "bbox": {"page": 1},
+                            "kind": "text",
+                        }
+                    ],
+                },
+                {
+                    "content": "Page 2 block A",
+                    "blocks": [
+                        {
+                            "content": "Page 2 block A",
+                            "bbox": {"page": 2},
+                            "kind": "table",
+                        }
+                    ],
+                },
+                {
+                    "content": "Page 1 block B",
+                    "blocks": [
+                        {
+                            "content": "Page 1 block B",
+                            "bbox": {"page": 1},
+                            "kind": "text",
+                        }
+                    ],
+                },
+                {
+                    "content": "Page 3 block A",
+                    "blocks": [
+                        {
+                            "content": "Page 3 block A",
+                            "bbox": {"page": 3},
+                            "kind": "figure",
+                        }
+                    ],
+                },
+            ]
+        },
+    }
+
+
+@pytest.fixture()
+def disable_aiohttp_transport():
+    original_disable_aiohttp = litellm.disable_aiohttp_transport
+    litellm.disable_aiohttp_transport = True
+    litellm.in_memory_llm_clients_cache.flush_cache()
+    try:
+        yield
+    finally:
+        litellm.disable_aiohttp_transport = original_disable_aiohttp
+        litellm.in_memory_llm_clients_cache.flush_cache()
+
+
+@pytest.mark.asyncio
+async def test_parse_v3_file_upload_and_response_mapping(
+    disable_aiohttp_transport, respx_mock
+):
+    upload_route = respx_mock.post("https://platform.reducto.ai/upload").respond(
+        json={"file_id": "reducto://uploaded.pdf"}
+    )
+    parse_route = respx_mock.post("https://platform.reducto.ai/parse").respond(
+        json=_reducto_parse_response()
+    )
+
+    response = await litellm.aocr(
+        model="reducto/parse-v3",
+        document={
+            "type": "file",
+            "file": b"%PDF-1.4 reducto",
+            "mime_type": "application/pdf",
+        },
+        api_key="test-key",
+        api_base="https://platform.reducto.ai",
+        formatting={"table_output_format": "html"},
+        retrieval={"chunk_mode": "section"},
+        settings={"ocr_system": "standard"},
+    )
+
+    assert upload_route.called
+    assert parse_route.called
+    assert len(upload_route.calls) == 1
+    assert len(parse_route.calls) == 1
+
+    upload_request = upload_route.calls[0].request
+    assert upload_request.headers["authorization"] == "Bearer test-key"
+    assert "application/json" not in upload_request.headers["content-type"]
+    upload_body = upload_request.read()
+    assert b'filename="document"' in upload_body
+    assert b"application/pdf" in upload_body
+
+    parse_request_body = json.loads(parse_route.calls[0].request.read())
+    assert parse_request_body["input"] == "reducto://uploaded.pdf"
+    assert parse_request_body["formatting"] == {"table_output_format": "html"}
+    assert parse_request_body["retrieval"] == {"chunk_mode": "section"}
+    assert parse_request_body["settings"] == {"ocr_system": "standard"}
+
+    assert response.usage_info is not None
+    assert response.usage_info.credits == 3
+    assert response.usage_info.pages_processed == 3
+    assert len(response.pages) == 3
+    assert response.pages[0].index == 0
+    assert response.pages[0].markdown == "Page 1 block A\n\nPage 1 block B"
+    assert getattr(response.pages[0], "blocks")[0]["bbox"]["page"] == 1
+    assert response.pages[1].markdown == "Page 2 block A"
+    assert response.pages[2].markdown == "Page 3 block A"
+    assert response._hidden_params["reducto_raw"]["usage"]["credits"] == 3
+
+
+@pytest.mark.asyncio
+async def test_parse_v3_reducto_id_passthrough_skips_upload(
+    disable_aiohttp_transport, respx_mock
+):
+    upload_route = respx_mock.post("https://platform.reducto.ai/upload").respond(
+        json={"file_id": "reducto://should-not-upload.pdf"}
+    )
+    parse_route = respx_mock.post("https://platform.reducto.ai/parse").respond(
+        json=_reducto_parse_response()
+    )
+
+    response = await litellm.aocr(
+        model="reducto/parse-v3",
+        document={
+            "type": "document_url",
+            "document_url": "reducto://already-uploaded.pdf",
+        },
+        api_key="test-key",
+        api_base="https://platform.reducto.ai",
+        retrieval={"chunk_mode": "section"},
+    )
+
+    assert not upload_route.called
+    assert parse_route.called
+    parse_request_body = json.loads(parse_route.calls[0].request.read())
+    assert parse_request_body["input"] == "reducto://already-uploaded.pdf"
+    assert parse_request_body["retrieval"]["chunk_mode"] == "section"
+    assert response.pages[0].markdown.startswith("Page 1 block A")
diff --git a/tests/test_litellm/llms/reducto/test_upload.py b/tests/test_litellm/llms/reducto/test_upload.py
new file mode 100644
index 00000000000..4fae90436bb
--- /dev/null
+++ b/tests/test_litellm/llms/reducto/test_upload.py
@@ -0,0 +1,213 @@
+import json
+import os
+from unittest.mock import AsyncMock, Mock
+
+import httpx
+import litellm
+import pytest
+
+from litellm.llms.reducto.common import (
+    extract_file_id_or_bytes,
+    upload_bytes_async,
+    upload_bytes_sync,
+)
+
+
+@pytest.fixture()
+def disable_aiohttp_transport(monkeypatch):
+    original_disable_aiohttp = litellm.disable_aiohttp_transport
+    litellm.disable_aiohttp_transport = True
+    litellm.in_memory_llm_clients_cache.flush_cache()
+    monkeypatch.setenv("REDUCTO_API_KEY", "env-reducto-key")
+    try:
+        yield
+    finally:
+        litellm.disable_aiohttp_transport = original_disable_aiohttp
+        litellm.in_memory_llm_clients_cache.flush_cache()
+        os.environ.pop("REDUCTO_API_KEY", None)
+
+
+@pytest.mark.asyncio
+async def test_parse_v3_rejects_plain_http_urls(disable_aiohttp_transport):
+    with pytest.raises(litellm.BadRequestError, match="upload the file first"):
+        await litellm.aocr(
+            model="reducto/parse-v3",
+            document={
+                "type": "document_url",
+                "document_url": "https://example.com/document.pdf",
+            },
+            api_key="test-key",
+            api_base="https://platform.reducto.ai",
+        )
+
+
+@pytest.mark.asyncio
+async def test_parse_v3_image_data_uri_upload_uses_image_mime(
+    disable_aiohttp_transport, respx_mock
+):
+    upload_route = respx_mock.post("https://custom.reducto.test/upload").respond(
+        json={"file_id": "reducto://uploaded-image.png"}
+    )
+    parse_route = respx_mock.post("https://custom.reducto.test/parse").respond(
+        json={
+            "usage": {"num_pages": 1, "credits": 1},
+            "result": {
+                "chunks": [
+                    {
+                        "content": "Image OCR",
+                        "blocks": [{"content": "Image OCR", "bbox": {"page": 1}}],
+                    }
+                ]
+            },
+        }
+    )
+
+    response = await litellm.aocr(
+        model="reducto/parse-v3",
+        document={
+            "type": "file",
+            "file": b"\x89PNG\r\n\x1a\npng",
+            "mime_type": "image/png",
+        },
+        api_key="programmatic-key",
+        api_base="https://custom.reducto.test/",
+    )
+
+    assert upload_route.called
+    assert parse_route.called
+    upload_request = upload_route.calls[0].request
+    assert upload_request.headers["authorization"] == "Bearer programmatic-key"
+    assert b"image/png" in upload_request.read()
+
+    parse_request_body = json.loads(parse_route.calls[0].request.read())
+    assert parse_request_body["input"] == "reducto://uploaded-image.png"
+    assert response.pages[0].markdown == "Image OCR"
+
+
+@pytest.mark.asyncio
+async def test_parse_v3_uses_programmatic_api_key_over_env(
+    disable_aiohttp_transport, respx_mock
+):
+    upload_route = respx_mock.post("https://platform.reducto.ai/upload").respond(
+        json={"file_id": "reducto://uploaded.pdf"}
+    )
+    parse_route = respx_mock.post("https://platform.reducto.ai/parse").respond(
+        json={
+            "usage": {"num_pages": 1, "credits": 1},
+            "result": {
+                "chunks": [
+                    {
+                        "content": "Programmatic auth",
+                        "blocks": [
+                            {"content": "Programmatic auth", "bbox": {"page": 1}}
+                        ],
+                    }
+                ]
+            },
+        }
+    )
+
+    await litellm.aocr(
+        model="reducto/parse-v3",
+        document={
+            "type": "file",
+            "file": b"%PDF-1.4 auth",
+            "mime_type": "application/pdf",
+        },
+        api_key="passed-key",
+        api_base="https://platform.reducto.ai",
+    )
+
+    assert upload_route.calls[0].request.headers["authorization"] == "Bearer passed-key"
+    assert parse_route.calls[0].request.headers["authorization"] == "Bearer passed-key"
+
+
+def test_upload_bytes_sync_uses_shared_client(monkeypatch):
+    captured = {}
+
+    def fake_post(*, url, headers, files, timeout):
+        captured["url"] = url
+        captured["headers"] = headers
+        captured["files"] = files
+        captured["timeout"] = timeout
+        return httpx.Response(
+            200,
+            json={"file_id": "reducto://sync-upload"},
+            request=httpx.Request("POST", url),
+        )
+
+    sync_post = Mock(side_effect=fake_post)
+    monkeypatch.setattr(litellm.module_level_client, "post", sync_post)
+
+    class ForbiddenSyncClient:
+        def __init__(self, *args, **kwargs):
+            raise AssertionError("should not construct")
+
+    monkeypatch.setattr(httpx, "Client", ForbiddenSyncClient)
+
+    file_id = upload_bytes_sync(
+        raw_bytes=b"%PDF-1.4 sync",
+        mime="application/pdf",
+        api_key="sync-key",
+        api_base="https://sync.reducto.test/",
+    )
+
+    assert file_id == "reducto://sync-upload"
+    sync_post.assert_called_once()
+    assert captured["url"] == "https://sync.reducto.test/upload"
+    assert captured["headers"] == {"Authorization": "Bearer sync-key"}
+    assert captured["files"]["file"] == (
+        "document",
+        b"%PDF-1.4 sync",
+        "application/pdf",
+    )
+
+
+@pytest.mark.asyncio
+async def test_upload_bytes_async_uses_shared_aclient(monkeypatch):
+    captured = {}
+
+    async def fake_post(*, url, headers, files, timeout):
+        captured["url"] = url
+        captured["headers"] = headers
+        captured["files"] = files
+        captured["timeout"] = timeout
+        return httpx.Response(
+            200,
+            json={"file_id": "reducto://async-upload"},
+            request=httpx.Request("POST", url),
+        )
+
+    async_post = AsyncMock(side_effect=fake_post)
+    monkeypatch.setattr(litellm.module_level_aclient, "post", async_post)
+
+    class ForbiddenAsyncClient:
+        def __init__(self, *args, **kwargs):
+            raise AssertionError("should not construct")
+
+    monkeypatch.setattr(httpx, "AsyncClient", ForbiddenAsyncClient)
+
+    file_id = await upload_bytes_async(
+        raw_bytes=b"%PDF-1.4 async",
+        mime="application/pdf",
+        api_key="async-key",
+        api_base="https://async.reducto.test/",
+    )
+
+    assert file_id == "reducto://async-upload"
+    async_post.assert_awaited_once()
+    assert captured["url"] == "https://async.reducto.test/upload"
+    assert captured["headers"] == {"Authorization": "Bearer async-key"}
+    assert captured["files"]["file"] == (
+        "document",
+        b"%PDF-1.4 async",
+        "application/pdf",
+    )
+
+
+def test_extract_file_id_or_bytes_raises_on_malformed_data_uri():
+    with pytest.raises(litellm.BadRequestError, match="Invalid Reducto data URI"):
+        extract_file_id_or_bytes("data:application/pdf", model="reducto/parse-v3")
+
+    with pytest.raises(litellm.BadRequestError, match="Invalid Reducto base64 payload"):
+        extract_file_id_or_bytes("data:;base64,!!!not-base64", model="reducto/parse-v3")
diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py
index 25260d269e5..cefd8eaa432 100644
--- a/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py
+++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py
@@ -14,18 +14,46 @@ from litellm.llms.sagemaker.completion.transformation import SagemakerConfig
 
 
 # --------------------------------------------------------------------------- #
-# SAGEMAKER_RESPONSE_STREAM_SHAPE eager-load tests                            #
+# get_sagemaker_response_stream_shape lazy-load tests                         #
 # --------------------------------------------------------------------------- #
 
 
-def test_sagemaker_response_stream_shape_loaded_at_import():
+@pytest.fixture(autouse=True)
+def _reset_sagemaker_response_stream_shape_cache():
+    """Prevent lru_cache leakage between tests in this module."""
+    import litellm.llms.sagemaker.common_utils as mod
+
+    mod.get_sagemaker_response_stream_shape.cache_clear()
+    yield
+    mod.get_sagemaker_response_stream_shape.cache_clear()
+
+
+def test_sagemaker_response_stream_shape_lazy_loads_once():
     """
-    SAGEMAKER_RESPONSE_STREAM_SHAPE is resolved at module import time.
+    get_sagemaker_response_stream_shape() loads from botocore at most once per process.
+    """
+    from unittest.mock import MagicMock, patch
+
+    import litellm.llms.sagemaker.common_utils as mod
+
+    sentinel = MagicMock()
+    with patch.object(
+        mod, "_load_sagemaker_response_stream_shape", return_value=sentinel
+    ) as mock_load:
+        assert mod.get_sagemaker_response_stream_shape() is sentinel
+        assert mod.get_sagemaker_response_stream_shape() is sentinel
+        mock_load.assert_called_once()
+
+
+def test_sagemaker_response_stream_shape_loaded_on_first_access():
+    """
+    get_sagemaker_response_stream_shape() loads once on first use.
     In a standard environment with botocore installed it must be non-None.
     """
-    from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE
+    pytest.importorskip("botocore")
+    from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape
 
-    assert SAGEMAKER_RESPONSE_STREAM_SHAPE is not None
+    assert get_sagemaker_response_stream_shape() is not None
 
 
 def test_sagemaker_response_stream_shape_load_failure_returns_none():
@@ -38,6 +66,7 @@ def test_sagemaker_response_stream_shape_load_failure_returns_none():
 
     import litellm.llms.sagemaker.common_utils as mod
 
+    pytest.importorskip("botocore")
     with patch(
         "botocore.loaders.Loader.load_service_model",
         side_effect=Exception("no data"),
@@ -51,14 +80,16 @@ def test_sagemaker_response_stream_shape_is_structure_shape():
     The loaded shape should be the botocore StructureShape for
     InvokeEndpointWithResponseStreamOutput, not a plain dict or any other type.
     """
+    pytest.importorskip("botocore")
     from botocore.model import StructureShape
 
-    from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE
+    from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape
 
-    assert SAGEMAKER_RESPONSE_STREAM_SHAPE is not None, (
-        "SAGEMAKER_RESPONSE_STREAM_SHAPE is None — botocore may not be installed"
-    )
-    shape: StructureShape = SAGEMAKER_RESPONSE_STREAM_SHAPE  # remove Optional
+    shape = get_sagemaker_response_stream_shape()
+    assert (
+        shape is not None
+    ), "get_sagemaker_response_stream_shape() is None — botocore may not be installed"
+    shape: StructureShape = shape  # remove Optional
     assert isinstance(shape, StructureShape)
     assert shape.name == "InvokeEndpointWithResponseStreamOutput"
 
@@ -66,29 +97,25 @@ def test_sagemaker_response_stream_shape_is_structure_shape():
 def test_sagemaker_response_stream_shape_not_reloaded_on_new_decoder():
     """
     Creating multiple AWSEventStreamDecoder instances must not trigger
-    additional botocore Loader calls — the shape is resolved once at import
-    time and reused.
+    additional botocore Loader calls — the shape is cached after first access.
     """
-    from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE
+    from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape
 
-    decoder_a = AWSEventStreamDecoder(model="test-model-a")
-    decoder_b = AWSEventStreamDecoder(model="test-model-b")
+    decoder_a = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder)
+    decoder_b = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder)
 
-    # Both decoders should use the same pre-loaded shape object (identity check)
     assert "_response_stream_shape_cache" not in decoder_a.__dict__
     assert "_response_stream_shape_cache" not in decoder_b.__dict__
-    # The module constant is still the same object
-    from litellm.llms.sagemaker.common_utils import (
-        SAGEMAKER_RESPONSE_STREAM_SHAPE as shape_after,
-    )
 
-    assert SAGEMAKER_RESPONSE_STREAM_SHAPE is shape_after
+    first = get_sagemaker_response_stream_shape()
+    second = get_sagemaker_response_stream_shape()
+    assert first is second
 
 
 def test_sagemaker_parse_message_from_event_raises_on_none_shape():
     """
-    When SAGEMAKER_RESPONSE_STREAM_SHAPE is None (botocore unavailable),
-    _parse_message_from_event must raise ValueError before touching the
+    When get_sagemaker_response_stream_shape() returns None (botocore unavailable),
+    _parse_message_from_event must raise SagemakerError before touching the
     botocore parser — not an opaque AttributeError from inside botocore.
     """
     from unittest.mock import MagicMock, patch
@@ -96,10 +123,14 @@ def test_sagemaker_parse_message_from_event_raises_on_none_shape():
     import litellm.llms.sagemaker.common_utils as mod
     from litellm.llms.sagemaker.common_utils import SagemakerError
 
-    decoder = AWSEventStreamDecoder(model="test-model")
+    decoder = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder)
+    decoder.model = "test-model"
+    decoder.parser = MagicMock()
+    decoder.content_blocks = []
+    decoder.is_messages_api = None
     mock_event = MagicMock()
 
-    with patch.object(mod, "SAGEMAKER_RESPONSE_STREAM_SHAPE", None):
+    with patch.object(mod, "get_sagemaker_response_stream_shape", return_value=None):
         with pytest.raises(SagemakerError) as exc_info:
             decoder._parse_message_from_event(mock_event)
 
diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py
index a36aec32d13..943a3160bb7 100644
--- a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py
+++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py
@@ -17,6 +17,9 @@ import pytest
 sys.path.insert(0, os.path.abspath("../../../../.."))
 
 from litellm import embedding
+from litellm.llms.sagemaker.embedding.cohere_transformation import (
+    SagemakerCohereEmbeddingConfig,
+)
 from litellm.llms.sagemaker.embedding.transformation import SagemakerEmbeddingConfig
 from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig
 from litellm.types.utils import EmbeddingResponse, Usage
@@ -54,6 +57,172 @@ class TestSagemakerEmbeddingFactory:
         assert isinstance(config2, VoyageEmbeddingConfig)
         assert isinstance(config3, VoyageEmbeddingConfig)
 
+    def test_get_model_config_cohere_model(self):
+        """Cohere SageMaker endpoints route to SagemakerCohereEmbeddingConfig"""
+        for endpoint_name in (
+            "cohere.embed-multilingual-v3",
+            "cohere-embed-english-v3-prod",
+            "my-cohere-marketplace-endpoint",
+            "COHERE-EMBED-V4",
+        ):
+            config = SagemakerEmbeddingConfig.get_model_config(endpoint_name)
+            assert isinstance(config, SagemakerCohereEmbeddingConfig), endpoint_name
+
+
+class TestSagemakerCohereEmbeddingConfig:
+    """Cohere-specific SageMaker embedding request/response transforms"""
+
+    def setup_method(self):
+        self.config = SagemakerCohereEmbeddingConfig()
+
+    MODEL = "cohere.embed-multilingual-v3"
+
+    def test_transform_request_uses_cohere_payload(self):
+        """Bug repro: request must use `texts` + `input_type`, not HF `inputs`"""
+        result = self.config.transform_embedding_request(
+            model=self.MODEL,
+            input=["hello"],
+            optional_params={"input_type": "search_query"},
+            headers={},
+        )
+        assert "inputs" not in result
+        assert result["texts"] == ["hello"]
+        assert result["input_type"] == "search_query"
+
+    def test_transform_request_default_input_type(self):
+        result = self.config.transform_embedding_request(
+            model=self.MODEL,
+            input=["hello"],
+            optional_params={},
+            headers={},
+        )
+        assert result["texts"] == ["hello"]
+        assert result["input_type"] == "search_document"
+
+    def test_transform_request_normalizes_string_input(self):
+        result = self.config.transform_embedding_request(
+            model=self.MODEL,
+            input="hello",
+            optional_params={},
+            headers={},
+        )
+        assert result["texts"] == ["hello"]
+
+    def test_map_openai_params_dimensions_to_output_dimension(self):
+        params = self.config.map_openai_params(
+            non_default_params={"dimensions": 512, "encoding_format": "float"},
+            optional_params={},
+            model=self.MODEL,
+            drop_params=False,
+        )
+        assert params["output_dimension"] == 512
+        assert params["embedding_types"] == ["float"]
+
+    def test_map_openai_params_input_type_from_non_default_params(self):
+        params = self.config.map_openai_params(
+            non_default_params={"input_type": "search_query"},
+            optional_params={},
+            model=self.MODEL,
+            drop_params=False,
+        )
+        assert params["input_type"] == "search_query"
+
+    def test_get_optional_params_embeddings_preserves_input_type(self):
+        """Exercises get_optional_params_embeddings, not transform in isolation."""
+        from litellm.utils import get_optional_params_embeddings
+
+        optional_params = get_optional_params_embeddings(
+            model=self.MODEL,
+            custom_llm_provider="sagemaker",
+            input_type="search_query",
+        )
+        assert optional_params.get("input_type") == "search_query"
+
+        body = self.config.transform_embedding_request(
+            model=self.MODEL,
+            input=["hello"],
+            optional_params=optional_params,
+            headers={},
+        )
+        assert body["texts"] == ["hello"]
+        assert body["input_type"] == "search_query"
+
+    def test_get_optional_params_embeddings_maps_dimensions_without_duplicate(self):
+        """dimensions must map to output_dimension only, not also stay as dimensions."""
+        from litellm.utils import get_optional_params_embeddings
+
+        optional_params = get_optional_params_embeddings(
+            model=self.MODEL,
+            custom_llm_provider="sagemaker",
+            dimensions=512,
+            input_type="search_query",
+        )
+        assert optional_params.get("output_dimension") == 512
+        assert "dimensions" not in optional_params
+        assert optional_params.get("input_type") == "search_query"
+
+    def test_transform_response_parses_cohere_payload(self):
+        cohere_response = {
+            "embeddings": [[0.1, 0.2, 0.3]],
+            "meta": {"billed_units": {"input_tokens": 2}},
+        }
+        mock_response = httpx.Response(
+            status_code=200,
+            content=json.dumps(cohere_response).encode("utf-8"),
+            headers={"content-type": "application/json"},
+        )
+        logging_obj = MagicMock()
+        logging_obj.model_call_details = {"input": ["hello"]}
+
+        result = self.config.transform_embedding_response(
+            model=self.MODEL,
+            raw_response=mock_response,
+            model_response=EmbeddingResponse(),
+            logging_obj=logging_obj,
+            api_key=None,
+            request_data={"texts": ["hello"], "input_type": "search_query"},
+            optional_params={},
+            litellm_params={},
+        )
+
+        assert result.object == "list"
+        assert len(result.data) == 1
+        assert result.data[0]["embedding"] == [0.1, 0.2, 0.3]
+        assert result.usage.prompt_tokens == 2
+
+    def test_transform_response_does_not_double_call_post_call(self):
+        """
+        Greptile review fix: SageMaker handler already calls
+        `logging_obj.post_call` once before invoking
+        `transform_embedding_response`. The transform must NOT call it again,
+        otherwise callbacks, cost calculators, and log handlers double-fire
+        for every Cohere SageMaker embedding call.
+        """
+        cohere_response = {
+            "embeddings": [[0.1, 0.2, 0.3]],
+            "meta": {"billed_units": {"input_tokens": 2}},
+        }
+        mock_response = httpx.Response(
+            status_code=200,
+            content=json.dumps(cohere_response).encode("utf-8"),
+            headers={"content-type": "application/json"},
+        )
+        logging_obj = MagicMock()
+        logging_obj.model_call_details = {"input": ["hello"]}
+
+        self.config.transform_embedding_response(
+            model=self.MODEL,
+            raw_response=mock_response,
+            model_response=EmbeddingResponse(),
+            logging_obj=logging_obj,
+            api_key=None,
+            request_data={"texts": ["hello"], "input_type": "search_query"},
+            optional_params={},
+            litellm_params={},
+        )
+
+        logging_obj.post_call.assert_not_called()
+
 
 class TestVoyageEmbeddingConfig:
     """Test Voyage-specific embedding configuration"""
diff --git a/tests/test_litellm/llms/test_file_content_block.py b/tests/test_litellm/llms/test_file_content_block.py
new file mode 100644
index 00000000000..5552c1a4d68
--- /dev/null
+++ b/tests/test_litellm/llms/test_file_content_block.py
@@ -0,0 +1,433 @@
+"""
+Tests for handling malformed or invalid 'file' content blocks (missing or null
+`file` sub-field, HTTP file_id URLs for Google AI Studio).
+
+Regression tests for:
+- litellm/llms/vertex_ai/gemini/transformation.py
+- litellm/llms/gemini/chat/transformation.py
+- litellm/litellm_core_utils/prompt_templates/common_utils.py
+  (migrate_file_to_image_url raises on missing `file`; file-id helpers skip non-OpenAI shapes)
+- litellm/litellm_core_utils/prompt_templates/factory.py (Bedrock + Anthropic)
+- litellm/llms/openai/chat/gpt_transformation.py
+"""
+
+import asyncio
+import copy
+from typing import List, cast
+
+import pytest
+
+import litellm
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    get_file_ids_from_messages,
+    migrate_file_to_image_url,
+    update_messages_with_model_file_ids,
+)
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    BedrockConverseMessagesProcessor,
+    anthropic_process_openai_file_message,
+)
+from litellm.llms.gemini.chat.transformation import GoogleAIStudioGeminiConfig
+from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
+from litellm.llms.vertex_ai.gemini.transformation import (
+    _gemini_convert_messages_with_history,
+)
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionFileObject,
+    OpenAIMessageContentListBlock,
+)
+
+_MALFORMED_MESSAGES_RAW = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "hello"},
+            {"type": "file"},  # Missing required "file" sub-field
+        ],
+    }
+]
+
+_WELL_FORMED_MESSAGES_RAW = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "hello"},
+            {
+                "type": "file",
+                "file": {"file_id": "file-abc123", "format": "pdf"},
+            },
+        ],
+    }
+]
+
+MALFORMED_FILE_OBJECT: ChatCompletionFileObject = cast(
+    ChatCompletionFileObject, {"type": "file"}
+)
+
+EXPLICIT_NULL_FILE_OBJECT: ChatCompletionFileObject = cast(
+    ChatCompletionFileObject,
+    {"type": "file", "file": None},
+)
+
+
+def _malformed() -> List[AllMessageValues]:
+    return copy.deepcopy(cast(List[AllMessageValues], _MALFORMED_MESSAGES_RAW))
+
+
+def _well_formed() -> List[AllMessageValues]:
+    return copy.deepcopy(cast(List[AllMessageValues], _WELL_FORMED_MESSAGES_RAW))
+
+
+def _explicit_null_file_in_content() -> List[AllMessageValues]:
+    return copy.deepcopy(
+        cast(
+            List[AllMessageValues],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "hello"},
+                        {"type": "file", "file": None},
+                    ],
+                }
+            ],
+        )
+    )
+
+
+# ---------------------------------------------------------------------------
+# vertex_ai/gemini/transformation.py
+# ---------------------------------------------------------------------------
+
+
+def test_gemini_convert_messages_malformed_file_raises_bad_request():
+    """_gemini_convert_messages_with_history should raise BadRequestError (not KeyError)
+    when a content block has type='file' but no 'file' sub-field."""
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        _gemini_convert_messages_with_history(
+            messages=_malformed(),
+            model="gemini-2.0-flash",
+        )
+
+
+def test_gemini_convert_messages_explicit_null_file_field_raises_bad_request():
+    """Explicit JSON null for `file` must be rejected like a missing `file` key."""
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        _gemini_convert_messages_with_history(
+            messages=_explicit_null_file_in_content(),
+            model="gemini-2.0-flash",
+        )
+
+
+# ---------------------------------------------------------------------------
+# gemini/chat/transformation.py - GoogleAIStudioGeminiConfig
+# ---------------------------------------------------------------------------
+
+
+def test_google_ai_studio_transform_messages_malformed_file_raises_bad_request():
+    """GoogleAIStudioGeminiConfig._transform_messages should raise BadRequestError
+    when a content block has type='file' but no 'file' sub-field."""
+    config = GoogleAIStudioGeminiConfig()
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        config._transform_messages(messages=_malformed(), model="gemini-2.0-flash")
+
+
+def test_google_ai_studio_transform_messages_explicit_null_file_field_raises_bad_request():
+    """Explicit JSON null for `file` must be rejected like a missing `file` key."""
+    config = GoogleAIStudioGeminiConfig()
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        config._transform_messages(
+            messages=_explicit_null_file_in_content(), model="gemini-2.0-flash"
+        )
+
+
+def test_google_ai_studio_transform_messages_http_file_id_converts_to_base64(monkeypatch):
+    """Google AI Studio rejects raw HTTP(S) file URLs; _transform_messages should
+    fetch and replace them with base64 `file_data` before conversion."""
+    # Data URL shape so downstream Gemini media parsing accepts the inlined bytes
+    # (mirrors real `convert_url_to_base64` output from `_process_image_response`).
+    fake_file_data = "data:application/pdf;base64,aGVsbG8="
+
+    def _fake_convert_url_to_base64(url: str) -> str:
+        assert url == "https://example.com/doc.pdf"
+        return fake_file_data
+
+    monkeypatch.setattr(
+        "litellm.llms.gemini.chat.transformation.convert_url_to_base64",
+        _fake_convert_url_to_base64,
+    )
+    messages = cast(
+        List[AllMessageValues],
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "hello"},
+                    {
+                        "type": "file",
+                        "file": {
+                            "file_id": "https://example.com/doc.pdf",
+                            "format": "pdf",
+                        },
+                    },
+                ],
+            }
+        ],
+    )
+    config = GoogleAIStudioGeminiConfig()
+    config._transform_messages(messages=messages, model="gemini-2.0-flash")
+    content = messages[0].get("content")
+    assert isinstance(content, list)
+    file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file")
+    file_field = file_block.get("file")
+    assert isinstance(file_field, dict)
+    assert file_field.get("file_data") == fake_file_data
+    assert "file_id" not in file_field
+
+
+def test_google_ai_studio_transform_messages_http_file_id_convert_failure_leaves_file_unchanged(
+    monkeypatch,
+):
+    """If convert_url_to_base64 fails, the Studio prep step must not mutate the block
+    (see try/except in GoogleAIStudioGeminiConfig._transform_messages)."""
+    https_id = "https://example.com/missing.pdf"
+
+    def _raise(_url: str) -> str:
+        raise litellm.ImageFetchError("simulated fetch failure")
+
+    monkeypatch.setattr(
+        "litellm.llms.gemini.chat.transformation.convert_url_to_base64",
+        _raise,
+    )
+    messages = cast(
+        List[AllMessageValues],
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "hello"},
+                    {
+                        "type": "file",
+                        "file": {
+                            "file_id": https_id,
+                            "format": "application/pdf",
+                        },
+                    },
+                ],
+            }
+        ],
+    )
+    config = GoogleAIStudioGeminiConfig()
+    config._transform_messages(messages=messages, model="gemini-2.0-flash")
+    content = messages[0].get("content")
+    assert isinstance(content, list)
+    file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file")
+    file_field = file_block.get("file")
+    assert isinstance(file_field, dict)
+    assert file_field.get("file_id") == https_id
+    assert file_field.get("format") == "application/pdf"
+    assert "file_data" not in file_field
+
+
+# ---------------------------------------------------------------------------
+# common_utils.py - update_messages_with_model_file_ids
+# ---------------------------------------------------------------------------
+
+
+def test_update_messages_with_model_file_ids_malformed_skips_non_openai_file_block():
+    """Non-OpenAI file blocks (e.g. missing nested `file` dict) are skipped so callers
+    relying on LangChain v1 / provider-native shapes are not rejected here."""
+    messages = _malformed()
+    result = update_messages_with_model_file_ids(
+        messages=messages,
+        model_id="some-model",
+        model_file_id_mapping={},
+    )
+    assert result == messages
+    content = result[0].get("content")
+    assert isinstance(content, list)
+    file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file")
+    assert "file" not in file_block
+
+
+def test_update_messages_with_model_file_ids_well_formed_updates():
+    """update_messages_with_model_file_ids should update file_id for well-formed blocks."""
+    mapping = {"file-abc123": {"some-model": "provider-file-xyz"}}
+    result = update_messages_with_model_file_ids(
+        messages=_well_formed(),
+        model_id="some-model",
+        model_file_id_mapping=mapping,
+    )
+    content = result[0].get("content")
+    assert isinstance(content, list)
+    file_block = next(c for c in content if c.get("type") == "file")
+    assert file_block.get("file", {}).get("file_id") == "provider-file-xyz"
+
+
+# ---------------------------------------------------------------------------
+# common_utils.py - get_file_ids_from_messages
+# ---------------------------------------------------------------------------
+
+
+def test_get_file_ids_from_messages_malformed_skips_non_openai_file_block():
+    """Blocks with type='file' but no OpenAI `file` sub-dict yield no extracted ids."""
+    assert get_file_ids_from_messages(messages=_malformed()) == []
+
+
+def test_get_file_ids_from_messages_well_formed_returns_ids():
+    """get_file_ids_from_messages should extract file_id from well-formed blocks."""
+    messages: List[AllMessageValues] = cast(
+        List[AllMessageValues],
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "hello"},
+                    {"type": "file", "file": {"file_id": "file-abc123", "format": "pdf"}},
+                ],
+            }
+        ],
+    )
+    result = get_file_ids_from_messages(messages=messages)
+    assert result == ["file-abc123"]
+
+
+# ---------------------------------------------------------------------------
+# factory.py - BedrockConverseMessagesProcessor (sync + async)
+# ---------------------------------------------------------------------------
+
+
+def test_bedrock_process_file_message_malformed_raises_bad_request():
+    """_process_file_message should raise BadRequestError (not KeyError)
+    when the file object is missing the 'file' sub-field."""
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        BedrockConverseMessagesProcessor._process_file_message(MALFORMED_FILE_OBJECT)
+
+
+def test_bedrock_process_file_message_explicit_null_file_field_raises_bad_request():
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        BedrockConverseMessagesProcessor._process_file_message(EXPLICIT_NULL_FILE_OBJECT)
+
+
+def test_bedrock_async_process_file_message_malformed_raises_bad_request():
+    """_async_process_file_message should raise BadRequestError (not KeyError)
+    when the file object is missing the 'file' sub-field."""
+
+    async def _run() -> None:
+        with pytest.raises(
+            litellm.BadRequestError, match="missing the required 'file' field"
+        ):
+            await BedrockConverseMessagesProcessor._async_process_file_message(
+                MALFORMED_FILE_OBJECT
+            )
+
+    asyncio.run(_run())
+
+
+def test_bedrock_async_process_file_message_explicit_null_file_field_raises_bad_request():
+    async def _run() -> None:
+        with pytest.raises(
+            litellm.BadRequestError, match="missing the required 'file' field"
+        ):
+            await BedrockConverseMessagesProcessor._async_process_file_message(
+                EXPLICIT_NULL_FILE_OBJECT
+            )
+
+    asyncio.run(_run())
+
+
+# ---------------------------------------------------------------------------
+# openai/chat/gpt_transformation.py
+# ---------------------------------------------------------------------------
+
+
+def test_openai_apply_common_transform_malformed_file_raises_bad_request():
+    """_apply_common_transform_content_item should raise BadRequestError (not KeyError)
+    when a content block has type='file' but no 'file' sub-field."""
+    config = OpenAIGPTConfig()
+    malformed_block: OpenAIMessageContentListBlock = cast(
+        OpenAIMessageContentListBlock, {"type": "file"}
+    )
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        config._apply_common_transform_content_item(malformed_block)
+
+
+def test_openai_apply_common_transform_explicit_null_file_field_raises_bad_request():
+    config = OpenAIGPTConfig()
+    explicit_null_block: OpenAIMessageContentListBlock = cast(
+        OpenAIMessageContentListBlock,
+        {"type": "file", "file": None},
+    )
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        config._apply_common_transform_content_item(explicit_null_block)
+
+
+def test_openai_apply_common_transform_well_formed_file_does_not_raise():
+    """_apply_common_transform_content_item should not raise for well-formed file blocks."""
+    config = OpenAIGPTConfig()
+    well_formed_block: OpenAIMessageContentListBlock = cast(
+        OpenAIMessageContentListBlock,
+        {"type": "file", "file": {"file_id": "file-abc123"}},
+    )
+    result = config._apply_common_transform_content_item(well_formed_block)
+    assert result.get("type") == "file"
+    file_field = cast(ChatCompletionFileObject, result).get("file", {})
+    assert file_field.get("file_id") == "file-abc123"
+
+
+# ---------------------------------------------------------------------------
+# factory.py - anthropic_process_openai_file_message
+# ---------------------------------------------------------------------------
+
+
+def test_anthropic_process_openai_file_message_malformed_raises_bad_request():
+    """anthropic_process_openai_file_message should raise BadRequestError (not KeyError)
+    when the file object is missing the 'file' sub-field."""
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        anthropic_process_openai_file_message(MALFORMED_FILE_OBJECT)
+
+
+def test_anthropic_process_openai_file_message_explicit_null_file_field_raises_bad_request():
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        anthropic_process_openai_file_message(EXPLICIT_NULL_FILE_OBJECT)
+
+
+def test_anthropic_process_openai_file_message_well_formed_file_id_does_not_raise():
+    """anthropic_process_openai_file_message should not raise for a well-formed file_id block."""
+    well_formed: ChatCompletionFileObject = cast(
+        ChatCompletionFileObject,
+        {"type": "file", "file": {"file_id": "file-abc123"}},
+    )
+    result = anthropic_process_openai_file_message(well_formed)
+    assert result.get("type") in ("document", "image", "container_upload")
+
+
+# ---------------------------------------------------------------------------
+# common_utils.py - migrate_file_to_image_url
+# ---------------------------------------------------------------------------
+
+
+def test_migrate_file_to_image_url_malformed_raises_bad_request():
+    """migrate_file_to_image_url should raise BadRequestError (not KeyError)
+    when the file object is missing the 'file' sub-field."""
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        migrate_file_to_image_url(MALFORMED_FILE_OBJECT)
+
+
+def test_migrate_file_to_image_url_explicit_null_file_field_raises_bad_request():
+    with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"):
+        migrate_file_to_image_url(EXPLICIT_NULL_FILE_OBJECT)
+
+
+def test_migrate_file_to_image_url_well_formed_returns_image_url():
+    """migrate_file_to_image_url should return an image_url block for a well-formed file."""
+    well_formed: ChatCompletionFileObject = cast(
+        ChatCompletionFileObject,
+        {"type": "file", "file": {"file_id": "file-abc123", "format": "png"}},
+    )
+    result = migrate_file_to_image_url(well_formed)
+    assert result.get("type") == "image_url"
+    image_url = result.get("image_url", {})
+    assert isinstance(image_url, dict)
+    assert image_url.get("url") == "file-abc123"
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py
new file mode 100644
index 00000000000..10fc68ecaad
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py
@@ -0,0 +1,52 @@
+import pytest
+from typing import List, cast
+
+import litellm
+from litellm.llms.vertex_ai.gemini.transformation import (
+    _gemini_convert_messages_with_history,
+)
+from litellm.types.llms.openai import AllMessageValues
+
+
+def test_missing_image_url_field_raises_bad_request_error():
+    """When element type is 'image_url' but 'image_url' field is missing, a BadRequestError is raised."""
+    messages = cast(
+        List[AllMessageValues],
+        [{"role": "user", "content": [{"type": "image_url"}]}],
+    )
+    with pytest.raises(litellm.BadRequestError) as exc_info:
+        _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro")
+    assert "'image_url' field is missing" in str(exc_info.value)
+
+
+def test_missing_url_inside_image_url_dict_raises_bad_request_error():
+    """When image_url is a dict but 'url' key is absent, a BadRequestError is raised."""
+    messages = cast(
+        List[AllMessageValues],
+        [{"role": "user", "content": [{"type": "image_url", "image_url": {"detail": "high"}}]}],
+    )
+    with pytest.raises(litellm.BadRequestError) as exc_info:
+        _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro")
+    assert "'url' field is missing inside" in str(exc_info.value)
+
+
+def test_explicit_null_image_url_raises_bad_request_error():
+    """When image_url key is present but explicitly null, a BadRequestError is raised."""
+    messages = cast(
+        List[AllMessageValues],
+        [{"role": "user", "content": [{"type": "image_url", "image_url": None}]}],
+    )
+    with pytest.raises(litellm.BadRequestError) as exc_info:
+        _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro")
+    assert "'image_url' field is missing" in str(exc_info.value)
+
+
+def test_empty_dict_image_url_raises_bad_request_error():
+    """When image_url is an empty dict (no url), a BadRequestError is raised."""
+    messages = cast(
+        List[AllMessageValues],
+        [{"role": "user", "content": [{"type": "image_url", "image_url": {}}]}],
+    )
+    with pytest.raises(litellm.BadRequestError) as exc_info:
+        _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro")
+    assert "'url' field is missing inside" in str(exc_info.value)
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
index 353d19b0198..45b9f4293fa 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
@@ -2097,6 +2097,125 @@ def test_is_gemini_3_or_newer():
     assert VertexGeminiConfig._is_gemini_3_or_newer("") == False
 
 
+def test_forward_gemini_function_call_id_vertex_vs_google_ai_studio():
+    """Vertex AI rejects `id` on function_call/function_response; Google AI Studio accepts it on Gemini 3.5+."""
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    model = "gemini-3.5-flash"
+    assert (
+        VertexGeminiConfig._forward_gemini_function_call_id(model, "vertex_ai") is False
+    )
+    assert (
+        VertexGeminiConfig._forward_gemini_function_call_id(model, "vertex_ai_beta")
+        is False
+    )
+    assert VertexGeminiConfig._forward_gemini_function_call_id(model, "gemini") is True
+    assert VertexGeminiConfig._forward_gemini_function_call_id(model, None) is False
+    assert (
+        VertexGeminiConfig._forward_gemini_function_call_id(
+            "gemini-2.5-flash", "gemini"
+        )
+        is False
+    )
+
+
+def test_vertex_ai_gemini_35_tool_calls_omit_function_call_id():
+    """Regression: Vertex must not send OpenAI tool_call id inside Gemini function_call parts."""
+    from litellm.llms.vertex_ai.gemini.transformation import (
+        _gemini_convert_messages_with_history,
+    )
+
+    messages = [
+        {"role": "user", "content": "Explore this directory"},
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "id": "call_50e7e0fe0989464a89f188eda443",
+                    "type": "function",
+                    "function": {
+                        "name": "read",
+                        "arguments": '{"filePath": "/tmp"}',
+                    },
+                }
+            ],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_50e7e0fe0989464a89f188eda443",
+            "content": "ok",
+        },
+    ]
+
+    contents = _gemini_convert_messages_with_history(
+        messages=messages,
+        model="gemini-3.5-flash",
+        custom_llm_provider="vertex_ai",
+    )
+
+    for content in contents:
+        for part in content.get("parts", []):
+            fc = part.get("function_call")
+            if fc is not None:
+                assert "id" not in fc, f"Vertex payload must not include id: {fc}"
+            fr = part.get("function_response")
+            if fr is not None:
+                assert "id" not in fr, f"Vertex payload must not include id: {fr}"
+
+
+def test_google_ai_studio_gemini_35_tool_calls_include_function_call_id():
+    from litellm.llms.vertex_ai.gemini.transformation import (
+        _gemini_convert_messages_with_history,
+    )
+
+    tool_call_id = "call_50e7e0fe0989464a89f188eda443"
+    messages = [
+        {"role": "user", "content": "hi"},
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "id": tool_call_id,
+                    "type": "function",
+                    "function": {
+                        "name": "read",
+                        "arguments": '{"filePath": "/tmp"}',
+                    },
+                }
+            ],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": tool_call_id,
+            "content": "ok",
+        },
+    ]
+
+    contents = _gemini_convert_messages_with_history(
+        messages=messages,
+        model="gemini-3.5-flash",
+        custom_llm_provider="gemini",
+    )
+
+    function_call_ids = []
+    function_response_ids = []
+    for content in contents:
+        for part in content.get("parts", []):
+            fc = part.get("function_call")
+            if fc is not None:
+                function_call_ids.append(fc.get("id"))
+            fr = part.get("function_response")
+            if fr is not None:
+                function_response_ids.append(fr.get("id"))
+
+    assert function_call_ids == [tool_call_id]
+    assert function_response_ids == [tool_call_id]
+
+
 def test_reasoning_effort_maps_to_thinking_level_gemini_3():
     """Test that reasoning_effort maps to thinking_level AND includeThoughts for Gemini 3+ models"""
     from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
@@ -2959,6 +3078,38 @@ def test_vertex_ai_gemini3_tool_combination_no_drop():
     assert len(tools) == 3
 
 
+def test_vertex_ai_mixed_tools_and_web_search_options_drops_search():
+    """
+    When function tools and web_search_options are sent separately (Codex-style),
+    search tools are dropped unless include_server_side_tool_invocations is set.
+    """
+    v = VertexGeminiConfig()
+    optional_params: dict = {}
+    non_default_params = {
+        "tools": [
+            {
+                "type": "function",
+                "function": {"name": "exec_command", "description": "Run a command"},
+            }
+        ],
+        "web_search_options": {},
+    }
+
+    result = v.map_openai_params(
+        non_default_params=non_default_params,
+        optional_params=optional_params,
+        model="gemini-3.5-flash",
+        drop_params=True,
+    )
+
+    assert not result.get("include_server_side_tool_invocations")
+    tool_keys = set()
+    for tool in result.get("tools", []):
+        tool_keys.update(tool.keys())
+    assert "function_declarations" in tool_keys
+    assert "googleSearch" not in tool_keys
+
+
 def test_vertex_ai_openai_web_search_tool_transformation():
     """
     Test that OpenAI-style web_search and web_search_preview tools are transformed to googleSearch.
@@ -3499,7 +3650,12 @@ def test_video_metadata_supported_for_all_gemini_models():
         }
     ]
 
-    for model in ["gemini-1.5-pro", "gemini-2.5-flash", "gemini-2.5-pro", "gemini-3-pro-preview"]:
+    for model in [
+        "gemini-1.5-pro",
+        "gemini-2.5-flash",
+        "gemini-2.5-pro",
+        "gemini-3-pro-preview",
+    ]:
         contents = _gemini_convert_messages_with_history(messages=messages, model=model)
 
         file_part = None
@@ -3509,19 +3665,25 @@ def test_video_metadata_supported_for_all_gemini_models():
                 break
 
         assert file_part is not None, f"{model}: file part should exist"
-        assert "video_metadata" in file_part, f"{model}: video_metadata should be present"
+        assert (
+            "video_metadata" in file_part
+        ), f"{model}: video_metadata should be present"
         assert file_part["video_metadata"]["fps"] == 5, f"{model}: fps should be 5"
 
     # Per-part media_resolution is Gemini 3+ only; 2.x uses generation_config global
     for model in ["gemini-3-pro-preview"]:
         contents = _gemini_convert_messages_with_history(messages=messages, model=model)
         file_part = next(p for p in contents[0]["parts"] if "file_data" in p)
-        assert "media_resolution" in file_part, f"{model}: media_resolution should be present"
+        assert (
+            "media_resolution" in file_part
+        ), f"{model}: media_resolution should be present"
 
     for model in ["gemini-1.5-pro", "gemini-2.5-flash", "gemini-2.5-pro"]:
         contents = _gemini_convert_messages_with_history(messages=messages, model=model)
         file_part = next(p for p in contents[0]["parts"] if "file_data" in p)
-        assert "media_resolution" not in file_part, f"{model}: per-part media_resolution should not be set"
+        assert (
+            "media_resolution" not in file_part
+        ), f"{model}: per-part media_resolution should not be set"
 
 
 def test_chunk_parser_handles_prompt_feedback_block():
@@ -4154,8 +4316,9 @@ def test_vertex_ai_usage_metadata_with_document_tokens_in_prompt():
 
     # DOCUMENT tokens should be included in text_tokens: 8 (TEXT) + 774 (DOCUMENT) = 782
     assert result.prompt_tokens_details is not None
-    assert result.prompt_tokens_details.text_tokens == 782, \
-        "DOCUMENT modality tokens should be added to text_tokens (8 TEXT + 774 DOCUMENT = 782)"
+    assert (
+        result.prompt_tokens_details.text_tokens == 782
+    ), "DOCUMENT modality tokens should be added to text_tokens (8 TEXT + 774 DOCUMENT = 782)"
 
     # Verify completion token details
     assert result.completion_tokens_details is not None
@@ -4190,8 +4353,9 @@ def test_vertex_ai_usage_metadata_with_document_tokens_cached():
 
     # DOCUMENT cached tokens map to cached_text_tokens, so:
     # text_tokens = (8 TEXT + 774 DOCUMENT) - 400 cached = 382
-    assert result.prompt_tokens_details.text_tokens == 382, \
-        "text_tokens should be (8 + 774) - 400 cached = 382"
+    assert (
+        result.prompt_tokens_details.text_tokens == 382
+    ), "text_tokens should be (8 + 774) - 400 cached = 382"
     assert result.prompt_tokens_details.cached_tokens == 400
 
 
@@ -4290,3 +4454,448 @@ def test_transform_response_does_not_leak_body_on_parse_failure():
     msg = str(exc_info.value)
     assert "secret content" not in msg
     assert "Error converting to valid response block" in msg
+
+
+def test_chunk_parser_raises_on_429_error_chunk():
+    """Test chunk_parser raises VertexAIError on 429 RESOURCE_EXHAUSTED error chunk"""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {
+        "error": {
+            "code": 429,
+            "message": "Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/error-code-429 for more details.",
+            "status": "RESOURCE_EXHAUSTED",
+        }
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 429
+    assert "RESOURCE_EXHAUSTED" in exc_info.value.message
+    assert "Resource exhausted" in exc_info.value.message
+
+
+def test_chunk_parser_raises_on_500_error_chunk():
+    """Test chunk_parser raises VertexAIError on 500 INTERNAL error chunk"""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {
+        "error": {
+            "code": 500,
+            "message": "Internal error encountered.",
+            "status": "INTERNAL",
+        }
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "INTERNAL" in exc_info.value.message
+
+
+def test_chunk_parser_raises_on_error_chunk_with_minimal_fields():
+    """Test chunk_parser handles error chunks with missing optional fields"""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {
+        "error": {
+            "code": 429,
+            "message": "Resource exhausted.",
+        }
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 429
+
+
+def test_chunk_parser_normal_chunk_unaffected_by_error_check():
+    """Test that normal streaming chunks still work correctly after error check addition"""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    normal_chunk = {
+        "candidates": [
+            {
+                "content": {
+                    "role": "model",
+                    "parts": [{"text": "Hello"}],
+                },
+                "index": 0,
+            }
+        ],
+        "usageMetadata": {
+            "promptTokenCount": 5,
+            "candidatesTokenCount": 1,
+            "totalTokenCount": 6,
+        },
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    result = streaming_obj.chunk_parser(normal_chunk)
+    assert result is not None
+    assert len(result.choices) > 0
+    assert result.choices[0].delta.content == "Hello"
+
+
+def test_chunk_parser_raises_on_non_dict_error():
+    """Test chunk_parser raises VertexAIError when chunk['error'] is not a dict"""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {"error": "something went wrong"}
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "Unexpected error format" in exc_info.value.message
+
+
+def test_chunk_parser_raises_on_string_error_code():
+    """Test chunk_parser correctly converts string error code to int"""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    # code field is a string "429" rather than an int
+    error_chunk = {
+        "error": {
+            "code": "429",
+            "message": "Resource exhausted.",
+            "status": "RESOURCE_EXHAUSTED",
+        }
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 429
+    assert isinstance(exc_info.value.status_code, int)
+
+
+def test_chunk_parser_error_chunk_explicit_null_code_uses_500():
+    """JSON null for code must not call int(None); status defaults to 500."""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {
+        "error": {
+            "code": None,
+            "message": "Something went wrong.",
+            "status": "UNKNOWN",
+        }
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "Something went wrong" in exc_info.value.message
+
+
+def test_chunk_parser_error_chunk_non_numeric_code_defaults_to_500():
+    """Non-numeric code must not become ValueError -> RuntimeError in __next__."""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {
+        "error": {
+            "code": "NOT_A_NUMBER",
+            "message": "Malformed.",
+            "status": "INVALID",
+        }
+    }
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "Malformed" in exc_info.value.message
+
+
+def test_chunk_parser_error_chunk_empty_dict_defaults_to_500():
+    """Empty error object {} uses default code 500 and default message/status strings."""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {"error": {}}
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "UNKNOWN" in exc_info.value.message
+    assert "Unknown error" in exc_info.value.message
+
+
+def test_chunk_parser_error_chunk_non_dict_int_value():
+    """Non-dict error payloads (e.g. bare JSON number) must raise with status 500, not TypeError."""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {"error": 503}
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "Unexpected error format" in exc_info.value.message
+    assert "503" in exc_info.value.message
+
+
+def test_chunk_parser_error_chunk_non_dict_null_value():
+    """JSON null for error must hit the non-dict branch (same as int/string)."""
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    error_chunk = {"error": None}
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    with pytest.raises(VertexAIError) as exc_info:
+        streaming_obj.chunk_parser(error_chunk)
+
+    assert exc_info.value.status_code == 500
+    assert "Unexpected error format" in exc_info.value.message
+
+
+def test_mid_stream_429_error_raises_during_iteration():
+    """
+    Simulate a full streaming scenario: normal thinking chunks arrive first,
+    then a 429 RESOURCE_EXHAUSTED error chunk arrives mid-stream.
+    Verify that ModelResponseIterator raises VertexAIError during iteration.
+    """
+    import json
+    from unittest.mock import Mock
+
+    from litellm.llms.vertex_ai.common_utils import VertexAIError
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    # Simulate Vertex AI SSE stream: normal chunks followed by a 429 error chunk
+    normal_chunk_1 = json.dumps(
+        {
+            "candidates": [
+                {
+                    "content": {
+                        "role": "model",
+                        "parts": [
+                            {"text": "Let me think about this...", "thought": True}
+                        ],
+                    },
+                    "index": 0,
+                }
+            ],
+            "usageMetadata": {
+                "promptTokenCount": 10,
+                "candidatesTokenCount": 5,
+                "totalTokenCount": 15,
+            },
+            "modelVersion": "gemini-3.1-flash-image-preview",
+        }
+    )
+
+    normal_chunk_2 = json.dumps(
+        {
+            "candidates": [
+                {
+                    "content": {
+                        "role": "model",
+                        "parts": [
+                            {"text": "I'll generate the image now.", "thought": True}
+                        ],
+                    },
+                    "index": 0,
+                }
+            ],
+            "usageMetadata": {
+                "promptTokenCount": 10,
+                "candidatesTokenCount": 12,
+                "totalTokenCount": 22,
+            },
+        }
+    )
+
+    error_chunk = json.dumps(
+        {
+            "error": {
+                "code": 429,
+                "message": "Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/error-code-429 for more details.",
+                "status": "RESOURCE_EXHAUSTED",
+            }
+        }
+    )
+
+    # Build a mock SSE stream (lines returned by iter_lines)
+    sse_lines = iter([normal_chunk_1, normal_chunk_2, error_chunk])
+
+    logging_obj = Mock()
+    logging_obj.optional_params = {}
+
+    streaming_obj = ModelResponseIterator(
+        streaming_response=sse_lines,
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    # Iterate the stream: first chunks should succeed, then 429 error should be raised
+    results = []
+    with pytest.raises(VertexAIError) as exc_info:
+        for chunk in streaming_obj:
+            if chunk is not None:
+                results.append(chunk)
+
+    # Verify: received normal chunks before the error
+    assert (
+        len(results) >= 1
+    ), "Should have received at least 1 normal chunk before the error"
+
+    # Verify: 429 error is properly raised
+    assert exc_info.value.status_code == 429
+    assert "RESOURCE_EXHAUSTED" in str(exc_info.value.message)
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex.py b/tests/test_litellm/llms/vertex_ai/test_vertex.py
index 2e9629f95de..ec73e5e42be 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex.py
@@ -1219,6 +1219,32 @@ def test_process_gemini_media():
         mime_type="image/jpeg", file_uri="gs://bucket/image"
     )
 
+    # Test gs url without extension using mime_type from image_url object
+    image_message = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "gs://bucket/image-without-extension",
+                        "mime_type": "image/png",
+                    },
+                }
+            ],
+        }
+    ]
+    from litellm.llms.vertex_ai.gemini.transformation import (
+        _gemini_convert_messages_with_history,
+    )
+
+    converted = _gemini_convert_messages_with_history(
+        messages=image_message, model="gemini-2.5-flash"
+    )
+    assert converted[0]["parts"][0]["file_data"] == FileDataType(
+        mime_type="image/png", file_uri="gs://bucket/image-without-extension"
+    )
+
     # Test HTTPS JPG URL
     https_result = _process_gemini_media("https://example.com/image.jpg")
     print("https_result JPG", https_result)
@@ -1256,6 +1282,7 @@ def test_process_gemini_media():
     assert base64_result["inline_data"]["data"] == "/9j/4AAQSkZJRg..."
 
 
+
 def test_get_image_mime_type_from_url():
     """Test the _get_image_mime_type_from_url function for different image URLs"""
     from litellm.llms.vertex_ai.gemini.transformation import (
@@ -1490,39 +1517,31 @@ def test_vertex_parallel_tool_calls_true():
     assert "tools" in optional_params
 
 
-def test_vertex_parallel_tool_calls_false_multiple_tools_error():
+def test_vertex_parallel_tool_calls_false_multiple_tools_dropped():
     """
-    Test that parallel_tool_calls = False with multiple tools raises UnsupportedParamsError
-    when drop_params is False.
+    parallel_tool_calls=False with multiple tools is dropped for Gemini
+  (unsupported upstream). Request should succeed without the param.
     """
     tools = [
         {"type": "function", "function": {"name": "get_weather"}},
         {"type": "function", "function": {"name": "get_time"}},
     ]
-    with pytest.raises(litellm.utils.UnsupportedParamsError) as excinfo:
-        get_optional_params(
-            model="gemini-1.5-pro",
-            custom_llm_provider="vertex_ai",
-            tools=tools,
-            parallel_tool_calls=False,
-        )
-    assert (
-        "`parallel_tool_calls=False` is not supported by Gemini when multiple tools are"
-        in str(excinfo.value)
+    optional_params = get_optional_params(
+        model="gemini-1.5-pro",
+        custom_llm_provider="vertex_ai",
+        tools=tools,
+        parallel_tool_calls=False,
     )
+    assert "parallel_tool_calls" not in optional_params
+    assert "tools" in optional_params
 
-    # works when specified as "functions"
-    with pytest.raises(litellm.utils.UnsupportedParamsError) as excinfo:
-        get_optional_params(
-            model="gemini-1.5-pro",
-            custom_llm_provider="vertex_ai",
-            functions=tools,
-            parallel_tool_calls=False,
-        )
-    assert (
-        "`parallel_tool_calls=False` is not supported by Gemini when multiple tools are"
-        in str(excinfo.value)
+    optional_params = get_optional_params(
+        model="gemini-1.5-pro",
+        custom_llm_provider="vertex_ai",
+        functions=tools,
+        parallel_tool_calls=False,
     )
+    assert "parallel_tool_calls" not in optional_params
 
 
 def test_vertex_parallel_tool_calls_false_single_tool():
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py b/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py
new file mode 100644
index 00000000000..e0eccad80e2
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py
@@ -0,0 +1,466 @@
+"""Vertex Gemini: extensionless gs:// MIME + GCS metadata tests.
+
+Split from test_vertex.py to satisfy CI per-file size limits.
+"""
+import asyncio
+import os
+import sys
+import time
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import pytest
+
+import litellm
+from unittest.mock import MagicMock, patch
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm.llms.vertex_ai.gemini.transformation import _process_gemini_media
+
+
+def test_process_gemini_media_gcs_explicit_format_octet_stream_and_alias():
+    """Explicit format bypasses registry; image/jpg alias still applies."""
+    from litellm.types.llms.vertex_ai import FileDataType
+
+    r1 = _process_gemini_media(
+        "gs://bucket/object-no-ext",
+        format="application/octet-stream",
+    )
+    assert r1["file_data"] == FileDataType(
+        mime_type="application/octet-stream",
+        file_uri="gs://bucket/object-no-ext",
+    )
+    r2 = _process_gemini_media("gs://bucket/object-no-ext", format="image/jpg")
+    assert r2["file_data"] == FileDataType(
+        mime_type="image/jpeg",
+        file_uri="gs://bucket/object-no-ext",
+    )
+
+
+def test_process_gemini_media_gcs_without_extension_errors_and_metadata_mock():
+    with patch(
+        "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type",
+        return_value=None,
+    ):
+        with pytest.raises(litellm.BadRequestError) as exc:
+            _process_gemini_media("gs://bucket/image-without-extension")
+    assert "Unable to determine mime type for gs URI" in str(exc.value)
+
+    from litellm.types.llms.vertex_ai import FileDataType
+
+    with patch(
+        "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type",
+        return_value="image/jpeg",
+    ) as m:
+        r = _process_gemini_media("gs://bucket/image-without-extension")
+    assert r["file_data"] == FileDataType(
+        mime_type="image/jpeg", file_uri="gs://bucket/image-without-extension"
+    )
+    m.assert_called()
+
+    with patch(
+        "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type",
+        return_value="image/jpg",
+    ):
+        r_alias = _process_gemini_media("gs://bucket/image-without-extension")
+    assert r_alias["file_data"]["mime_type"] == "image/jpeg"
+
+
+def test_process_gemini_media_rejects_gcs_metadata_mime_not_supported_by_gemini():
+    """Non-empty GCS contentType that fails _normalize_and_validate_gemini_mime_type."""
+    with patch(
+        "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type",
+        return_value="application/x-litellm-unit-test-unknown-mime",
+    ):
+        with pytest.raises(
+            litellm.BadRequestError,
+            match="File type not supported by gemini",
+        ):
+            _process_gemini_media("gs://bucket/object-without-extension")
+
+
+def test_file_block_uses_mime_type_alias_for_extensionless_gcs():
+    from litellm.llms.vertex_ai.gemini.transformation import (
+        _gemini_convert_messages_with_history,
+    )
+    from litellm.types.llms.vertex_ai import FileDataType
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "file",
+                    "file": {
+                        "file_id": "gs://bucket/no-extension-object",
+                        "mime_type": "application/pdf",
+                    },
+                }
+            ],
+        }
+    ]
+    converted = _gemini_convert_messages_with_history(
+        messages=messages, model="gemini-2.5-flash"
+    )
+    assert converted[0]["parts"][0]["file_data"] == FileDataType(
+        mime_type="application/pdf", file_uri="gs://bucket/no-extension-object"
+    )
+
+
+@pytest.mark.parametrize(
+    "bucket,expected",
+    [
+        (("a." * 110) + "aa", True),
+        ("ab", False),
+        ("a" * 64, False),
+        ("ab..cd", False),
+        ("1.2.3.4", False),
+        ("192.168.0.1", False),
+        ("Bucket-Upper", False),
+        ("bucket@name", False),
+        ("bucket name", False),
+        ("-mybucket", False),
+        ("mybucket-", False),
+        (".mybucket", False),
+        ("mybucket.", False),
+    ],
+)
+def test_is_valid_gcs_bucket_name_matrix(bucket, expected):
+    from litellm.llms.vertex_ai.gemini.transformation import _is_valid_gcs_bucket_name
+
+    assert _is_valid_gcs_bucket_name(bucket) is expected
+
+
+def test_get_gcs_object_content_type_explicit_vertex_success_and_token_failure():
+    from litellm.llms.vertex_ai.gemini import transformation as gt
+
+    mock_v = MagicMock()
+    mock_v.get_access_token.return_value = ("test-token", "test-project")
+    resp = MagicMock()
+    resp.is_error = False
+    resp.status_code = 200
+    resp.json.return_value = {"contentType": "image/png"}
+    http = MagicMock()
+    http.get.return_value = resp
+
+    with (
+        patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v),
+        patch(
+            "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler",
+            return_value=http,
+        ),
+    ):
+        assert (
+            gt._get_gcs_object_content_type(
+                image_url="gs://my-bucket/path/to/image-without-extension",
+                vertex_project="project-123",
+                vertex_credentials="credential-json",
+            )
+            == "image/png"
+        )
+    mock_v.get_access_token.assert_called_once_with(
+        credentials="credential-json",
+        project_id="project-123",
+    )
+
+    mock_v2 = MagicMock()
+    mock_v2.get_access_token.side_effect = Exception("token failure")
+    with patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v2):
+        with pytest.raises(
+            litellm.BadRequestError,
+            match="Unable to fetch GCS metadata with provided Vertex credentials/project",
+        ):
+            gt._get_gcs_object_content_type(
+                image_url="gs://my-bucket/path/to/image-without-extension",
+                vertex_project="project-123",
+                vertex_credentials="credential-json",
+            )
+
+
+def test_get_gcs_object_content_type_http_error_explicit_vs_anonymous():
+    from litellm.llms.vertex_ai.gemini import transformation as gt
+
+    mock_v = MagicMock()
+    mock_v.get_access_token.return_value = ("t", "p")
+    err_resp = MagicMock()
+    err_resp.is_error = True
+    err_resp.status_code = 403
+    err_resp.text = '{"error":{"message":"Permission denied"}}'
+    http = MagicMock()
+    http.get.return_value = err_resp
+
+    with (
+        patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v),
+        patch(
+            "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler",
+            return_value=http,
+        ),
+    ):
+        with pytest.raises(litellm.BadRequestError, match="HTTP 403") as ei:
+            gt._get_gcs_object_content_type(
+                image_url="gs://my-bucket/path/to/obj",
+                vertex_project="project-123",
+                vertex_credentials="credential-json",
+            )
+    assert "Permission denied" in str(ei.value)
+
+    mock_v2 = MagicMock()
+    anon_err = MagicMock()
+    anon_err.is_error = True
+    anon_err.status_code = 403
+    anon_err.text = "Forbidden"
+    http2 = MagicMock()
+    http2.get.return_value = anon_err
+    with (
+        patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v2),
+        patch(
+            "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler",
+            return_value=http2,
+        ),
+    ):
+        assert (
+            gt._get_gcs_object_content_type(image_url="gs://public-bucket/public-object")
+            is None
+        )
+    mock_v2.get_access_token.assert_not_called()
+
+
+def test_get_gcs_object_content_type_anonymous_success_no_auth_header():
+    from litellm.llms.vertex_ai.gemini import transformation as gt
+
+    mock_v = MagicMock()
+    ok = MagicMock()
+    ok.is_error = False
+    ok.status_code = 200
+    ok.json.return_value = {"contentType": "image/jpeg"}
+    http = MagicMock()
+    http.get.return_value = ok
+
+    with (
+        patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v),
+        patch(
+            "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler",
+            return_value=http,
+        ),
+    ):
+        assert (
+            gt._get_gcs_object_content_type(image_url="gs://public-bucket/public-object")
+            == "image/jpeg"
+        )
+    mock_v.get_access_token.assert_not_called()
+    hdrs = http.get.call_args.kwargs.get("headers")
+    assert hdrs is None or "Authorization" not in hdrs
+
+
+def test_async_transform_request_body_offloads_extensionless_gs_not_plain_text():
+    from litellm.llms.vertex_ai.gemini import transformation as gemini_transformation
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "gs://bucket/image-without-extension"},
+                }
+            ],
+        }
+    ]
+
+    def slow_http_get(*args, **kwargs):
+        time.sleep(0.5)
+        response = MagicMock()
+        response.is_error = False
+        response.status_code = 200
+        response.raise_for_status.return_value = None
+        response.json.return_value = {"contentType": "image/png"}
+        return response
+
+    async def fake_check_and_create_cache(self, **kwargs):
+        return kwargs["messages"], kwargs["optional_params"], None
+
+    mock_v = MagicMock()
+    mock_v.get_access_token.return_value = ("token", "project")
+    mock_http = MagicMock()
+    mock_http.get.side_effect = slow_http_get
+
+    async def run_scenario() -> float:
+        async def concurrent_sleep() -> float:
+            start = time.monotonic()
+            await asyncio.sleep(0.05)
+            return time.monotonic() - start
+
+        task = asyncio.create_task(
+            gemini_transformation.async_transform_request_body(
+                gemini_api_key=None,
+                messages=messages,
+                api_base=None,
+                model="gemini-2.5-flash",
+                client=None,
+                timeout=None,
+                extra_headers=None,
+                optional_params={},
+                logging_obj=MagicMock(),
+                custom_llm_provider="vertex_ai",
+                litellm_params={},
+                vertex_project=None,
+                vertex_location=None,
+                vertex_auth_header=None,
+            )
+        )
+        elapsed = await concurrent_sleep()
+        await task
+        return elapsed
+
+    with (
+        patch.object(gemini_transformation, "_GCS_METADATA_VERTEX_BASE", mock_v),
+        patch(
+            "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler",
+            return_value=mock_http,
+        ),
+        patch(
+            "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching."
+            "ContextCachingEndpoints.async_check_and_create_cache",
+            new=fake_check_and_create_cache,
+        ),
+    ):
+        sleep_elapsed = asyncio.run(run_scenario())
+
+    assert sleep_elapsed < 0.4, (
+        f"Event loop blocked for {sleep_elapsed:.3f}s; "
+        "async_transform_request_body did not offload sync GCS metadata"
+    )
+
+    async def fake_cache2(self, **kwargs):
+        return kwargs["messages"], kwargs["optional_params"], None
+
+    async def run_plain():
+        with patch(
+            "litellm.llms.vertex_ai.gemini.transformation.asyncify",
+            side_effect=AssertionError("asyncify must not run without extensionless gs://"),
+        ):
+            return await gemini_transformation.async_transform_request_body(
+                gemini_api_key=None,
+                messages=[{"role": "user", "content": "hello"}],
+                api_base=None,
+                model="gemini-2.5-flash",
+                client=None,
+                timeout=None,
+                extra_headers=None,
+                optional_params={},
+                logging_obj=MagicMock(),
+                custom_llm_provider="vertex_ai",
+                litellm_params={},
+                vertex_project=None,
+                vertex_location=None,
+                vertex_auth_header=None,
+            )
+
+    with patch(
+        "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching."
+        "ContextCachingEndpoints.async_check_and_create_cache",
+        new=fake_cache2,
+    ):
+        body = asyncio.run(run_plain())
+    assert body is not None and "contents" in body
+
+
+@pytest.mark.parametrize(
+    "messages,expected",
+    [
+        ([{"role": "user", "content": "hello"}], False),
+        (
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": "gs://bucket/image-without-extension"},
+                        }
+                    ],
+                }
+            ],
+            True,
+        ),
+        (
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": "gs://bucket/image.png"},
+                        }
+                    ],
+                }
+            ],
+            False,
+        ),
+        (
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": "gs://bucket/image-without-extension",
+                                "mime_type": "image/png",
+                            },
+                        }
+                    ],
+                }
+            ],
+            False,
+        ),
+        (
+            [
+                {
+                    "role": "assistant",
+                    "content": [],
+                    "images": [
+                        {"image_url": {"url": "gs://bucket/gen-without-extension"}},
+                    ],
+                }
+            ],
+            True,
+        ),
+        (
+            [
+                {
+                    "role": "assistant",
+                    "content": [],
+                    "images": [{"image_url": {"url": "gs://bucket/gen.png"}}],
+                }
+            ],
+            False,
+        ),
+        (
+            [
+                {
+                    "role": "assistant",
+                    "content": [],
+                    "images": [
+                        {
+                            "image_url": {
+                                "url": "gs://bucket/gen-no-ext",
+                                "mime_type": "image/png",
+                            },
+                        }
+                    ],
+                }
+            ],
+            False,
+        ),
+    ],
+)
+def test_openai_messages_may_need_sync_gcs_metadata_fetch_matrix(messages, expected):
+    from litellm.llms.vertex_ai.gemini.transformation import (
+        _openai_messages_may_need_sync_gcs_metadata_fetch,
+    )
+
+    assert _openai_messages_may_need_sync_gcs_metadata_fetch(messages) is expected
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py
index 88aac07a0c9..2cf97081806 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_llm_base.py
@@ -1,3 +1,4 @@
+import asyncio
 import json
 import os
 import sys
@@ -1448,3 +1449,474 @@ class TestVertexBase:
 
         aws_creds = supplier.get_aws_security_credentials(context=None, request=None)
         assert isinstance(aws_creds, AwsSecurityCredentials)
+
+    @pytest.mark.asyncio
+    async def test_single_flight_refresh(self):
+        """Under high concurrency, only one coroutine should refresh expired credentials."""
+        import asyncio
+
+        vertex_base = VertexBase()
+
+        mock_creds = MagicMock()
+        mock_creds.token = "expired-token"
+        mock_creds.expired = True
+        mock_creds.expiry = None
+        mock_creds.project_id = "project-1"
+        mock_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+
+        refresh_call_count = 0
+
+        with (
+            patch.object(
+                vertex_base, "load_auth", return_value=(mock_creds, "project-1")
+            ),
+            patch.object(vertex_base, "refresh_auth") as mock_refresh,
+        ):
+
+            async def slow_refresh(creds):
+                nonlocal refresh_call_count
+                refresh_call_count += 1
+                await asyncio.sleep(0.05)  # simulate network latency
+                creds.token = "refreshed-token"
+                creds.expired = False
+
+            # refresh_auth is sync, but we need to count calls.
+            # get_access_token_async wraps it with asyncify, so the sync side_effect works.
+            def sync_refresh_impl(creds):
+                nonlocal refresh_call_count
+                refresh_call_count += 1
+                creds.token = "refreshed-token"
+                creds.expired = False
+
+            mock_refresh.side_effect = sync_refresh_impl
+
+            # Launch 50 concurrent requests
+            tasks = [
+                vertex_base._ensure_access_token_async(
+                    credentials=credentials,
+                    project_id="project-1",
+                    custom_llm_provider="vertex_ai",
+                )
+                for _ in range(50)
+            ]
+            results = await asyncio.gather(*tasks)
+
+            # All should return the refreshed token
+            for token, project in results:
+                assert token == "refreshed-token"
+                assert project == "project-1"
+
+            # refresh_auth should be called exactly once (single-flight)
+            assert (
+                refresh_call_count == 1
+            ), f"Expected 1 refresh call, got {refresh_call_count}"
+
+    @pytest.mark.asyncio
+    async def test_async_reauthentication_uses_async_single_flight(self):
+        """Concurrent async reauth should reload once without using the sync path."""
+        from google.auth.credentials import TokenState
+
+        vertex_base = VertexBase()
+        stale_creds = MagicMock()
+        stale_creds.token = "expired-token"
+        stale_creds.token_state = TokenState.INVALID
+        stale_creds.project_id = "project-1"
+        stale_creds.quota_project_id = "project-1"
+
+        refreshed_creds = MagicMock()
+        refreshed_creds.token = "refreshed-token"
+        refreshed_creds.token_state = TokenState.FRESH
+        refreshed_creds.project_id = "project-1"
+        refreshed_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+        cache_key = (json.dumps(credentials), "project-1")
+        vertex_base._credentials_project_mapping[cache_key] = (
+            stale_creds,
+            "project-1",
+        )
+
+        load_call_count = 0
+
+        def load_auth_impl(*_args, **_kwargs):
+            nonlocal load_call_count
+            load_call_count += 1
+            return refreshed_creds, "project-1"
+
+        with (
+            patch.object(
+                vertex_base,
+                "refresh_auth",
+                side_effect=Exception("Reauthentication is needed"),
+            ),
+            patch.object(vertex_base, "load_auth", side_effect=load_auth_impl),
+            patch.object(vertex_base, "get_access_token") as mock_get_access_token,
+        ):
+            results = await asyncio.gather(
+                *[
+                    vertex_base._ensure_access_token_async(
+                        credentials=credentials,
+                        project_id="project-1",
+                        custom_llm_provider="vertex_ai",
+                    )
+                    for _ in range(10)
+                ]
+            )
+
+            assert results == [("refreshed-token", "project-1")] * 10
+            assert load_call_count == 1
+            mock_get_access_token.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_background_refresh_when_near_expiry(self):
+        """When token_state is STALE (within the 3:45 REFRESH_THRESHOLD window),
+        return the current token immediately and refresh in the background —
+        zero added latency."""
+        import asyncio
+
+        from google.auth.credentials import TokenState
+
+        vertex_base = VertexBase()
+
+        # Simulate STALE state: token is usable but near expiry.
+        mock_creds = MagicMock()
+        mock_creds.token = "near-expiry-token"
+        mock_creds.token_state = TokenState.STALE
+        mock_creds.project_id = "project-1"
+        mock_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+
+        with (
+            patch.object(
+                vertex_base, "load_auth", return_value=(mock_creds, "project-1")
+            ),
+            patch.object(vertex_base, "refresh_auth") as mock_refresh,
+        ):
+
+            def mock_refresh_impl(creds):
+                creds.token = "refreshed-token"
+                creds.token_state = TokenState.FRESH
+
+            mock_refresh.side_effect = mock_refresh_impl
+
+            token, project = await vertex_base._ensure_access_token_async(
+                credentials=credentials,
+                project_id="project-1",
+                custom_llm_provider="vertex_ai",
+            )
+
+            # Should return the current (still usable) token immediately
+            assert token == "near-expiry-token"
+
+            # Let the background refresh task run
+            await asyncio.sleep(0.05)
+
+            assert mock_refresh.called, "Background refresh should have been triggered"
+
+    @pytest.mark.asyncio
+    async def test_stale_malformed_token_blocks_on_refresh(self):
+        """Malformed STALE tokens should refresh instead of failing validation."""
+        from google.auth.credentials import TokenState
+
+        vertex_base = VertexBase()
+
+        mock_creds = MagicMock()
+        mock_creds.token = None
+        mock_creds.token_state = TokenState.STALE
+        mock_creds.project_id = "project-1"
+        mock_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+
+        with (
+            patch.object(
+                vertex_base, "load_auth", return_value=(mock_creds, "project-1")
+            ),
+            patch.object(vertex_base, "refresh_auth") as mock_refresh,
+        ):
+
+            def mock_refresh_impl(creds):
+                creds.token = "refreshed-token"
+                creds.token_state = TokenState.FRESH
+
+            mock_refresh.side_effect = mock_refresh_impl
+
+            token, project = await vertex_base._ensure_access_token_async(
+                credentials=credentials,
+                project_id="project-1",
+                custom_llm_provider="vertex_ai",
+            )
+
+            assert mock_refresh.called
+            assert token == "refreshed-token"
+            assert project == "project-1"
+
+    @pytest.mark.asyncio
+    async def test_fresh_token_skips_refresh(self):
+        """Credentials not marked expired by google-auth should not trigger refresh."""
+        vertex_base = VertexBase()
+
+        mock_creds = MagicMock()
+        mock_creds.token = "fresh-token"
+        mock_creds.expired = False
+        mock_creds.project_id = "project-1"
+        mock_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+        cache_key = (json.dumps(credentials), "project-1")
+        vertex_base._credentials_project_mapping[cache_key] = (
+            mock_creds,
+            "project-1",
+        )
+
+        with patch.object(vertex_base, "refresh_auth") as mock_refresh:
+            token, project = await vertex_base._ensure_access_token_async(
+                credentials=credentials,
+                project_id="project-1",
+                custom_llm_provider="vertex_ai",
+            )
+
+            assert not mock_refresh.called, "Fresh token should not trigger refresh"
+            assert token == "fresh-token"
+
+    @pytest.mark.asyncio
+    async def test_background_refresh_task_removed_after_completion(self):
+        """Completed background-refresh tasks must be evicted from
+        _background_refresh_tasks so the dict does not grow unboundedly."""
+        import asyncio
+
+        from google.auth.credentials import TokenState
+
+        vertex_base = VertexBase()
+
+        mock_creds = MagicMock()
+        mock_creds.token = "near-expiry-token"
+        mock_creds.token_state = TokenState.STALE
+        mock_creds.project_id = "project-1"
+        mock_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+
+        with (
+            patch.object(
+                vertex_base, "load_auth", return_value=(mock_creds, "project-1")
+            ),
+            patch.object(vertex_base, "refresh_auth") as mock_refresh,
+        ):
+
+            def mock_refresh_impl(creds):
+                creds.token = "refreshed-token"
+                creds.token_state = TokenState.FRESH
+
+            mock_refresh.side_effect = mock_refresh_impl
+
+            await vertex_base._ensure_access_token_async(
+                credentials=credentials,
+                project_id="project-1",
+                custom_llm_provider="vertex_ai",
+            )
+
+            # Allow the background task to complete.
+            await asyncio.sleep(0.1)
+
+            # After completion the entry should have been removed by the done-callback.
+            assert len(vertex_base._background_refresh_tasks) == 0, (
+                "Completed background refresh task was not removed from "
+                "_background_refresh_tasks"
+            )
+
+    @pytest.mark.asyncio
+    async def test_background_refresh_tasks_no_accumulation_across_many_keys(self):
+        """With many distinct credential keys the dict must not hold completed tasks."""
+        import asyncio
+        import json as _json
+
+        from google.auth.credentials import TokenState
+
+        vertex_base = VertexBase()
+
+        num_keys = 20
+
+        for i in range(num_keys):
+            mock_creds = MagicMock()
+            mock_creds.token = f"token-{i}"
+            mock_creds.token_state = TokenState.STALE
+            mock_creds.project_id = f"project-{i}"
+            mock_creds.quota_project_id = f"project-{i}"
+
+            credentials = {"type": "service_account", "project_id": f"project-{i}"}
+
+            with (
+                patch.object(
+                    vertex_base,
+                    "load_auth",
+                    return_value=(mock_creds, f"project-{i}"),
+                ),
+                patch.object(vertex_base, "refresh_auth") as mock_refresh,
+            ):
+
+                def mock_refresh_impl(creds, idx=i):
+                    creds.token = f"refreshed-{idx}"
+                    creds.token_state = TokenState.FRESH
+
+                mock_refresh.side_effect = mock_refresh_impl
+
+                await vertex_base._ensure_access_token_async(
+                    credentials=credentials,
+                    project_id=f"project-{i}",
+                    custom_llm_provider="vertex_ai",
+                )
+
+        # Let all background tasks finish.
+        await asyncio.sleep(0.1)
+
+        assert len(vertex_base._background_refresh_tasks) == 0, (
+            f"Expected 0 tasks after all refreshes completed, "
+            f"found {len(vertex_base._background_refresh_tasks)}"
+        )
+
+    @pytest.mark.asyncio
+    async def test_async_refresh_lock_shared_while_in_use(self):
+        """Concurrent callers for the same key must coordinate on the same lock."""
+        vertex_base = VertexBase()
+        key = ("creds", "project-1")
+
+        lock_a = vertex_base._acquire_async_refresh_lock(key)
+        try:
+            async with lock_a:
+                lock_b = vertex_base._acquire_async_refresh_lock(key)
+                try:
+                    assert lock_a is lock_b, (
+                        "While a coroutine still holds the lock, concurrent callers must "
+                        "receive the same Lock instance to preserve single-flight."
+                    )
+                finally:
+                    vertex_base._release_async_refresh_lock(key, lock_b)
+        finally:
+            vertex_base._release_async_refresh_lock(key, lock_a)
+
+    @pytest.mark.asyncio
+    async def test_async_refresh_lock_pruned_after_release(self):
+        """get_access_token_async must drop the per-key Lock from the registry
+        once no coroutine is using it, so the dict stays bounded in
+        high-cardinality deployments. Without this, every distinct credential
+        leaks a Lock object for the lifetime of the process."""
+        from google.auth.credentials import TokenState
+
+        vertex_base = VertexBase()
+
+        for i in range(10):
+            mock_creds = MagicMock()
+            mock_creds.token = f"refreshed-{i}"
+            mock_creds.token_state = TokenState.FRESH
+            mock_creds.project_id = f"project-{i}"
+            mock_creds.quota_project_id = f"project-{i}"
+
+            credentials = {"type": "service_account", "project_id": f"project-{i}"}
+
+            with (
+                patch.object(
+                    vertex_base,
+                    "load_auth",
+                    return_value=(mock_creds, f"project-{i}"),
+                ),
+                patch.object(vertex_base, "refresh_auth"),
+            ):
+                await vertex_base._ensure_access_token_async(
+                    credentials=credentials,
+                    project_id=f"project-{i}",
+                    custom_llm_provider="vertex_ai",
+                )
+
+        assert len(vertex_base._async_refresh_locks) == 0, (
+            "expected per-key locks to be pruned once no coroutine holds or "
+            f"waits on them; found {len(vertex_base._async_refresh_locks)}"
+        )
+        assert len(vertex_base._async_refresh_lock_refcounts) == 0
+
+    @pytest.mark.asyncio
+    async def test_async_refresh_lock_kept_while_waiter_pending(self):
+        """The prune must not run while another coroutine is still waiting on
+        the lock — otherwise the waiter ends up on a lock that's been replaced
+        in the registry and single-flight breaks."""
+        vertex_base = VertexBase()
+        key = ("creds", "project-1")
+
+        holder_lock = vertex_base._acquire_async_refresh_lock(key)
+        release_holder = asyncio.Event()
+
+        async def hold_then_release():
+            async with holder_lock:
+                await release_holder.wait()
+            vertex_base._release_async_refresh_lock(key, holder_lock)
+
+        holder = asyncio.create_task(hold_then_release())
+        await asyncio.sleep(0)  # let holder grab the lock
+
+        async def queue_for_lock():
+            waiter_lock = vertex_base._acquire_async_refresh_lock(key)
+            try:
+                async with waiter_lock:
+                    pass
+            finally:
+                vertex_base._release_async_refresh_lock(key, waiter_lock)
+
+        waiter = asyncio.create_task(queue_for_lock())
+        await asyncio.sleep(0)  # let waiter queue on the lock
+
+        assert (
+            vertex_base._async_refresh_locks.get(key) is holder_lock
+        ), "lock with active holder/waiter must not be pruned"
+
+        release_holder.set()
+        await holder
+        await waiter
+
+        assert key not in vertex_base._async_refresh_locks
+        assert key not in vertex_base._async_refresh_lock_refcounts
+
+    @pytest.mark.asyncio
+    async def test_fast_path_no_lock(self):
+        """Cached fresh credentials should return without acquiring the lock."""
+        import datetime
+
+        vertex_base = VertexBase()
+
+        try:
+            from google.auth import _helpers as google_auth_helpers
+
+            now = google_auth_helpers.utcnow()
+        except ImportError:
+            now = datetime.datetime.utcnow()
+
+        mock_creds = MagicMock()
+        mock_creds.token = "cached-token"
+        mock_creds.expired = False
+        mock_creds.expiry = now + datetime.timedelta(minutes=30)
+        mock_creds.project_id = "project-1"
+        mock_creds.quota_project_id = "project-1"
+
+        credentials = {"type": "service_account", "project_id": "project-1"}
+        cache_key = (json.dumps(credentials), "project-1")
+        vertex_base._credentials_project_mapping[cache_key] = (
+            mock_creds,
+            "project-1",
+        )
+
+        # Spy on _acquire_async_refresh_lock to verify it's never called
+        with patch.object(
+            vertex_base,
+            "_acquire_async_refresh_lock",
+            wraps=vertex_base._acquire_async_refresh_lock,
+        ) as mock_get_lock:
+            token, project = await vertex_base._ensure_access_token_async(
+                credentials=credentials,
+                project_id="project-1",
+                custom_llm_provider="vertex_ai",
+            )
+
+            assert token == "cached-token"
+            assert not mock_get_lock.called, "Fast path should not acquire lock"
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py
new file mode 100644
index 00000000000..b483a75a939
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py
@@ -0,0 +1,121 @@
+"""
+Regression tests for #28084:
+
+`VertexAIPartnerModels.count_tokens` (for Claude / Mistral / Llama on Vertex)
+used to gate on `import vertexai` even though the actual count-tokens path goes
+through `VertexAIPartnerModelsTokenCounter.handle_count_tokens_request`, which
+talks to the publisher's `:rawPredict` endpoint over plain httpx and never
+touches the Gemini SDK. The unused gate broke `/v1/messages/count_tokens` for
+any LiteLLM install that did not pull in `google-cloud-aiplatform` (which is
+not in the default `proxy` / `proxy-dev` extras).
+
+These tests pin the absence of that gate by:
+
+1. simulating `vertexai` being unimportable and verifying the partner-model
+   path does not raise the historical "vertexai import failed" error before
+   reaching the network/auth layer, and
+2. asserting that import of the partner-model count-tokens handler module by
+   itself does not pull `vertexai` into `sys.modules`.
+"""
+
+import sys
+
+import pytest
+
+from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import (
+    VertexAIPartnerModelsTokenCounter,
+)
+from litellm.llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels
+
+
+@pytest.mark.asyncio
+async def test_count_tokens_does_not_require_vertexai_sdk(monkeypatch):
+    """Even when `import vertexai` would fail, count_tokens must not raise the
+    historical "vertexai import failed" gate. The downstream handler talks to
+    `:rawPredict` over httpx with an access token — no Gemini SDK needed."""
+
+    # Simulate `vertexai` being unimportable, regardless of what is actually on
+    # the test environment's sys.path.
+    monkeypatch.setitem(sys.modules, "vertexai", None)
+    monkeypatch.setitem(sys.modules, "vertexai.preview", None)
+
+    captured = {}
+
+    async def fake_ensure_access_token(
+        self, credentials, project_id, custom_llm_provider
+    ):
+        return "fake-token", "fake-project"
+
+    def fake_build_endpoint(self, model, project_id, vertex_location, api_base=None):
+        captured["model_to_endpoint"] = model
+        return "https://fake-endpoint"
+
+    monkeypatch.setattr(
+        VertexAIPartnerModelsTokenCounter,
+        "_ensure_access_token_async",
+        fake_ensure_access_token,
+    )
+    monkeypatch.setattr(
+        VertexAIPartnerModelsTokenCounter,
+        "_build_count_tokens_endpoint",
+        fake_build_endpoint,
+    )
+
+    class FakeResponse:
+        status_code = 200
+
+        def json(self):
+            return {"input_tokens": 9}
+
+    class FakeClient:
+        async def post(self, url, headers=None, json=None, **kwargs):
+            captured["url"] = url
+            captured["headers"] = headers
+            captured["json"] = json
+            return FakeResponse()
+
+    import litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler as handler_mod
+
+    monkeypatch.setattr(
+        handler_mod, "get_async_httpx_client", lambda **kwargs: FakeClient()
+    )
+
+    result = await VertexAIPartnerModels().count_tokens(
+        model="claude-sonnet-4-6",
+        messages=[{"role": "user", "content": "hello"}],
+        litellm_params={"vertex_location": "us-east5"},
+        vertex_project="test-project",
+        vertex_location="us-east5",
+        vertex_credentials=None,
+    )
+
+    # We should reach the publisher endpoint and parse its response, not raise
+    # the vertexai-import gate.
+    assert result == {
+        "input_tokens": 9,
+        "tokenizer_used": "vertex_ai_partner_models",
+    }
+    assert captured["headers"] == {"Authorization": "Bearer fake-token"}
+    assert captured["model_to_endpoint"] == "claude-sonnet-4-6"
+
+
+def test_handler_module_does_not_import_vertexai_sdk():
+    """Importing the partner-model count-tokens handler must not load the
+    Gemini SDK into sys.modules. Operators who only need Claude-on-Vertex
+    token counting should not pay for `google-cloud-aiplatform`."""
+
+    # Force-evict any prior load so this assertion measures what THIS module
+    # pulls in, not what an unrelated earlier test did.
+    for mod in list(sys.modules):
+        if mod == "vertexai" or mod.startswith("vertexai."):
+            sys.modules.pop(mod, None)
+
+    # Re-import the handler module to verify it stays SDK-free.
+    import importlib
+
+    import litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler as handler_mod
+
+    importlib.reload(handler_mod)
+
+    leaked = [m for m in sys.modules if m == "vertexai" or m.startswith("vertexai.")]
+    assert leaked == [], f"unexpected vertexai SDK imports: {leaked}"
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py
index b16fc2bc44d..f617a8db850 100644
--- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py
@@ -118,7 +118,7 @@ async def test_vertex_ai_gpt_oss_simple_request():
             "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
         ) as mock_http_handler,
         patch(
-            "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "pathrise-convert-1606954137718"),
         ),
         patch.dict(
@@ -217,7 +217,7 @@ async def test_vertex_ai_gpt_oss_reasoning_effort():
             "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
         ) as mock_http_handler,
         patch(
-            "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "pathrise-convert-1606954137718"),
         ),
         patch.dict(
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/qwen/test_vertex_ai_qwen_global_endpoint.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/qwen/test_vertex_ai_qwen_global_endpoint.py
index bf6e0a5f2cd..5a86325b7fd 100644
--- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/qwen/test_vertex_ai_qwen_global_endpoint.py
+++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/qwen/test_vertex_ai_qwen_global_endpoint.py
@@ -7,7 +7,6 @@ These tests verify that:
 3. The completion() and responses() API work with Qwen models
 """
 
-import json
 import os
 import sys
 from unittest.mock import MagicMock, patch, AsyncMock
@@ -179,7 +178,7 @@ async def test_vertex_ai_qwen_global_endpoint_url():
             "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
         ) as mock_http_handler,
         patch(
-            "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
             return_value=("fake-token", "test-project"),
         ),
         patch.dict(
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/test_partner_models_credential_reuse.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/test_partner_models_credential_reuse.py
new file mode 100644
index 00000000000..b20442a032e
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/test_partner_models_credential_reuse.py
@@ -0,0 +1,220 @@
+"""
+Test that VertexBase subclasses (PartnerModels, Gemma, ModelGarden) reuse
+cached credentials instead of creating a new VertexLLM instance on every request.
+"""
+
+import sys
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from litellm.llms.vertex_ai.vertex_ai_partner_models.main import (
+    VertexAIPartnerModels,
+)
+from litellm.llms.vertex_ai.vertex_gemma_models.main import VertexAIGemmaModels
+from litellm.llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels
+
+
+def _mock_vertexai():
+    """Return a MagicMock that satisfies the vertexai import guards."""
+    m = MagicMock()
+    m.preview = MagicMock()
+    m.preview.language_models = MagicMock()
+    return m
+
+
+class TestVertexBaseSubclassInit:
+    """All VertexBase subclasses must call super().__init__() so that
+    the credential cache is initialized."""
+
+    @pytest.mark.parametrize(
+        "cls",
+        [VertexAIPartnerModels, VertexAIGemmaModels, VertexAIModelGardenModels],
+        ids=["PartnerModels", "Gemma", "ModelGarden"],
+    )
+    def test_init_calls_super(self, cls):
+        instance = cls()
+        assert hasattr(instance, "_credentials_project_mapping")
+        assert isinstance(instance._credentials_project_mapping, dict)
+        assert hasattr(instance, "access_token")
+        assert hasattr(instance, "project_id")
+
+
+class TestPartnerModelsCredentialReuse:
+    def test_completion_uses_self_ensure_access_token(self):
+        """completion() should call self._ensure_access_token, not create a
+        throwaway VertexLLM instance."""
+        partner = VertexAIPartnerModels()
+
+        with (
+            patch.dict(sys.modules, {"vertexai": _mock_vertexai()}),
+            patch.object(
+                partner,
+                "_ensure_access_token",
+                return_value=("cached-token", "test-project"),
+            ) as mock_ensure,
+            patch(
+                "litellm.llms.vertex_ai.vertex_ai_partner_models.main.base_llm_http_handler"
+            ) as mock_handler,
+        ):
+            mock_handler.completion.return_value = "response"
+
+            partner.completion(
+                model="meta/llama-3.1-405b-instruct-maas",
+                messages=[{"role": "user", "content": "hello"}],
+                model_response=MagicMock(),
+                print_verbose=lambda *a, **kw: None,
+                encoding=MagicMock(),
+                logging_obj=MagicMock(),
+                api_base=None,
+                optional_params={},
+                custom_prompt_dict={},
+                headers=None,
+                timeout=30.0,
+                litellm_params={},
+                vertex_project="test-project",
+                vertex_location="us-central1",
+                vertex_credentials='{"type": "service_account"}',
+            )
+
+            mock_ensure.assert_called_once_with(
+                credentials='{"type": "service_account"}',
+                project_id="test-project",
+                custom_llm_provider="vertex_ai",
+            )
+
+    def test_credential_cache_shared_across_calls(self):
+        """Two successive completion() calls should hit load_auth only once."""
+        partner = VertexAIPartnerModels()
+
+        mock_creds = MagicMock()
+        mock_creds.token = "my-token"
+        mock_creds.expired = False
+        mock_creds.project_id = "proj"
+        mock_creds.quota_project_id = "proj"
+
+        with (
+            patch.dict(sys.modules, {"vertexai": _mock_vertexai()}),
+            patch.object(
+                partner, "load_auth", return_value=(mock_creds, "proj")
+            ) as mock_load,
+            patch(
+                "litellm.llms.vertex_ai.vertex_ai_partner_models.main.base_llm_http_handler"
+            ) as mock_handler,
+        ):
+            mock_handler.completion.return_value = "resp"
+
+            common_kwargs = dict(
+                model="meta/llama-3.1-405b-instruct-maas",
+                messages=[{"role": "user", "content": "hi"}],
+                model_response=MagicMock(),
+                print_verbose=lambda *a, **kw: None,
+                encoding=MagicMock(),
+                logging_obj=MagicMock(),
+                api_base=None,
+                optional_params={},
+                custom_prompt_dict={},
+                headers=None,
+                timeout=30.0,
+                litellm_params={},
+                vertex_project="proj",
+                vertex_location="us-central1",
+                vertex_credentials='{"type": "service_account"}',
+            )
+
+            partner.completion(**common_kwargs)
+            partner.completion(**common_kwargs)
+
+            assert mock_load.call_count == 1
+
+
+class TestGemmaModelsCredentialReuse:
+    def test_completion_uses_self_ensure_access_token(self):
+        """completion() should call self._ensure_access_token, not create a
+        throwaway VertexLLM instance."""
+        gemma = VertexAIGemmaModels()
+
+        mock_gemma_config = MagicMock()
+        mock_gemma_config.return_value.completion.return_value = "response"
+
+        with (
+            patch.dict(sys.modules, {"vertexai": _mock_vertexai()}),
+            patch.object(
+                gemma,
+                "_ensure_access_token",
+                return_value=("cached-token", "test-project"),
+            ) as mock_ensure,
+            patch(
+                "litellm.llms.vertex_ai.vertex_gemma_models.transformation.VertexGemmaConfig",
+                mock_gemma_config,
+            ),
+        ):
+            gemma.completion(
+                model="gemma/gemma-3-12b-it-1234567890",
+                messages=[{"role": "user", "content": "hello"}],
+                model_response=MagicMock(),
+                print_verbose=lambda *a, **kw: None,
+                encoding=MagicMock(),
+                logging_obj=MagicMock(),
+                api_base="https://123.us-central1-1.prediction.vertexai.goog/v1/projects/proj/locations/us-central1/endpoints/456:predict",
+                optional_params={},
+                custom_prompt_dict={},
+                headers=None,
+                timeout=30.0,
+                litellm_params={},
+                vertex_project="test-project",
+                vertex_location="us-central1",
+                vertex_credentials='{"type": "service_account"}',
+            )
+
+            mock_ensure.assert_called_once_with(
+                credentials='{"type": "service_account"}',
+                project_id="test-project",
+                custom_llm_provider="vertex_ai",
+            )
+
+
+class TestModelGardenCredentialReuse:
+    def test_completion_uses_self_ensure_access_token(self):
+        """completion() should call self._ensure_access_token, not create a
+        throwaway VertexLLM instance."""
+        garden = VertexAIModelGardenModels()
+
+        mock_handler = MagicMock()
+        mock_handler.return_value.completion.return_value = "response"
+
+        with (
+            patch.dict(sys.modules, {"vertexai": _mock_vertexai()}),
+            patch.object(
+                garden,
+                "_ensure_access_token",
+                return_value=("cached-token", "test-project"),
+            ) as mock_ensure,
+            patch(
+                "litellm.llms.openai_like.chat.handler.OpenAILikeChatHandler",
+                mock_handler,
+            ),
+        ):
+            garden.completion(
+                model="openai/5464397967697903616",
+                messages=[{"role": "user", "content": "hello"}],
+                model_response=MagicMock(),
+                print_verbose=lambda *a, **kw: None,
+                encoding=MagicMock(),
+                logging_obj=MagicMock(),
+                api_base=None,
+                optional_params={},
+                custom_prompt_dict={},
+                headers=None,
+                timeout=30.0,
+                litellm_params={},
+                vertex_project="test-project",
+                vertex_location="us-central1",
+                vertex_credentials='{"type": "service_account"}',
+            )
+
+            mock_ensure.assert_called_once_with(
+                credentials='{"type": "service_account"}',
+                project_id="test-project",
+                custom_llm_provider="vertex_ai",
+            )
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
index 3e3e8901706..b1c8f7234ce 100644
--- a/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
@@ -122,17 +122,19 @@ class TestVertexGemmaCompletion:
         # Mock the async HTTP handler and Vertex authentication
         with (
             patch(
-                "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
-            ) as mock_http_handler,
+                "litellm.llms.custom_httpx.http_handler.get_async_httpx_client"
+            ) as mock_get_client,
             patch(
-                "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
                 return_value=("fake-access-token", "PROJECT_ID"),
             ),
         ):
+            mock_client = Mock()
             mock_response = Mock()
             mock_response.status_code = 200
             mock_response.json.return_value = mock_vertex_response
-            mock_http_handler.return_value.post = AsyncMock(return_value=mock_response)
+            mock_client.post = AsyncMock(return_value=mock_response)
+            mock_get_client.return_value = mock_client
 
             # Call litellm.acompletion()
             response = await litellm.acompletion(
@@ -145,7 +147,7 @@ class TestVertexGemmaCompletion:
             )
 
             # Verify the request sent to Vertex
-            call_args = mock_http_handler.return_value.post.call_args
+            call_args = mock_client.post.call_args
             assert call_args is not None, "HTTP handler was not called"
 
             request_data = call_args.kwargs["json"]
@@ -210,17 +212,19 @@ class TestVertexGemmaCompletion:
 
         with (
             patch(
-                "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler"
-            ) as mock_http_handler,
+                "litellm.llms.custom_httpx.http_handler.get_async_httpx_client"
+            ) as mock_get_client,
             patch(
-                "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
                 return_value=("fake-access-token", "test-project"),
             ),
         ):
+            mock_client = Mock()
             mock_response = Mock()
             mock_response.status_code = 200
             mock_response.json.return_value = invalid_response
-            mock_http_handler.return_value.post = AsyncMock(return_value=mock_response)
+            mock_client.post = AsyncMock(return_value=mock_response)
+            mock_get_client.return_value = mock_client
 
             # Should raise exception (wrapped as APIConnectionError by LiteLLM)
             with pytest.raises(APIConnectionError) as exc_info:
@@ -286,7 +290,7 @@ class TestVertexGemmaCompletion:
                 "litellm.llms.custom_httpx.http_handler.get_async_httpx_client"
             ) as mock_get_client,
             patch(
-                "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
                 return_value=("fake-access-token", "PROJECT_ID"),
             ),
         ):
@@ -388,7 +392,7 @@ class TestVertexGemmaCompletion:
                 "litellm.llms.custom_httpx.http_handler.get_async_httpx_client"
             ) as mock_get_client,
             patch(
-                "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM._ensure_access_token",
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
                 return_value=("fake-access-token", "PROJECT_ID"),
             ),
         ):
@@ -429,3 +433,123 @@ class TestVertexGemmaCompletion:
             # Verify other parameters are present
             assert "messages" in instance
             assert instance["@requestFormat"] == "chatCompletions"
+
+    @pytest.mark.asyncio
+    async def test_acompletion_filters_context_management(self):
+        """
+        Test that context_management is filtered out from the request.
+
+        Vertex AI Gemma's chatCompletions wrapper does not understand
+        `context_management` (an Anthropic / OpenAI Responses API concept).
+        It must be stripped from the request body so the upstream endpoint
+        does not reject the request with an unknown-field error.
+        """
+        mock_vertex_response = {
+            "deployedModelId": "1207280419999999999",
+            "model": "projects/993702345710/locations/us-central1/models/gemma-3-12b-it-1222199011122",
+            "modelDisplayName": "gemma-3-12b-it-1222199011122",
+            "modelVersionId": "1",
+            "predictions": {
+                "choices": [
+                    {
+                        "finish_reason": "stop",
+                        "index": 0,
+                        "logprobs": None,
+                        "message": {
+                            "content": "ok",
+                            "reasoning_content": None,
+                            "role": "assistant",
+                            "tool_calls": [],
+                        },
+                        "stop_reason": None,
+                    }
+                ],
+                "created": 1759863903,
+                "id": "chatcmpl-test-ctxmgmt",
+                "model": "google/gemma-3-12b-it",
+                "object": "chat.completion",
+                "prompt_logprobs": None,
+                "usage": {
+                    "completion_tokens": 1,
+                    "prompt_tokens": 5,
+                    "prompt_tokens_details": None,
+                    "total_tokens": 6,
+                },
+            },
+        }
+
+        with (
+            patch(
+                "litellm.llms.custom_httpx.http_handler.get_async_httpx_client"
+            ) as mock_get_client,
+            patch(
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
+                return_value=("fake-access-token", "PROJECT_ID"),
+            ),
+        ):
+            mock_client = Mock()
+            mock_response = Mock()
+            mock_response.status_code = 200
+            mock_response.json.return_value = mock_vertex_response
+            mock_client.post = AsyncMock(return_value=mock_response)
+            mock_get_client.return_value = mock_client
+
+            # Use `allowed_openai_params` so context_management actually
+            # reaches the transformation layer (otherwise the upstream
+            # validator drops it before we can prove the transformation
+            # strips it). This mirrors the real-world scenario where a
+            # caller explicitly opts in to forwarding an arbitrary param.
+            await litellm.acompletion(
+                model="vertex_ai/gemma/gemma-3-12b-it-1222199011122",
+                messages=[{"role": "user", "content": "Test"}],
+                context_management=[
+                    {"type": "compaction", "compact_threshold": 200000}
+                ],
+                allowed_openai_params=["context_management"],
+                api_base="https://test.us-central1-project.prediction.vertexai.goog/v1/projects/PROJECT_ID/locations/us-central1/endpoints/ENDPOINT_ID:predict",
+                vertex_project="PROJECT_ID",
+                vertex_location="us-central1",
+            )
+
+            call_args = mock_client.post.call_args
+            assert call_args is not None, "HTTP client was not called"
+
+            request_data = call_args.kwargs["json"]
+            print("request body=", json.dumps(request_data, indent=4))
+            instance = request_data["instances"][0]
+
+            assert (
+                "context_management" not in instance
+            ), "context_management should not be forwarded to Vertex Gemma"
+            assert instance["@requestFormat"] == "chatCompletions"
+            assert "messages" in instance
+
+    def test_transform_request_strips_context_management(self):
+        """
+        Direct unit test for VertexGemmaConfig.transform_request: verify that
+        `context_management` is stripped from `optional_params` regardless of
+        how it was supplied to the transformation layer.
+        """
+        from litellm.llms.vertex_ai.vertex_gemma_models.transformation import (
+            VertexGemmaConfig,
+        )
+
+        config = VertexGemmaConfig()
+        result = config.transform_request(
+            model="gemma-3-12b-it",
+            messages=[{"role": "user", "content": "hi"}],
+            optional_params={
+                "max_tokens": 32,
+                "context_management": [
+                    {"type": "compaction", "compact_threshold": 200000}
+                ],
+            },
+            litellm_params={},
+            headers={},
+        )
+
+        assert "instances" in result
+        instance = result["instances"][0]
+        assert instance["@requestFormat"] == "chatCompletions"
+        assert "context_management" not in instance
+        assert instance.get("max_tokens") == 32
diff --git a/tests/test_litellm/llms/xai/test_xai_chat_transformation.py b/tests/test_litellm/llms/xai/test_xai_chat_transformation.py
index 3ae8dfc3c0b..5c1f0f704d7 100644
--- a/tests/test_litellm/llms/xai/test_xai_chat_transformation.py
+++ b/tests/test_litellm/llms/xai/test_xai_chat_transformation.py
@@ -119,3 +119,19 @@ class TestXAIParallelToolCalls:
         assert result.get("parallel_tool_calls") is True
         assert len(result["messages"]) == 1
         assert result["messages"][0]["role"] == "user"
+
+
+class TestXAIUsageNormalization:
+    def test_preserves_reasoning_tokens_in_total_usage(self):
+        usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=200)
+
+        XAIChatConfig._normalize_openai_compatible_usage_totals(usage)
+
+        assert usage.total_tokens == 200
+
+    def test_preserves_reasoning_tokens_in_streaming_usage(self):
+        usage = {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 200}
+
+        XAIChatConfig._normalize_openai_compatible_usage_totals(usage)
+
+        assert usage["total_tokens"] == 200
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_byok_oauth_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_byok_oauth_endpoints.py
index 66b96785f69..9f2feddb0e3 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_byok_oauth_endpoints.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_byok_oauth_endpoints.py
@@ -1229,6 +1229,143 @@ def test_validate_trusted_redirect_uri_rejects_fragment_and_bad_scheme():
         assert exc.value.status_code == 400, uri
 
 
+def test_validate_trusted_redirect_uri_accepts_cursor_native_callback():
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    req = _make_trusted_request("http://localhost:4000/")
+    validate_trusted_redirect_uri(req, "cursor://anysphere.cursor-mcp/oauth/callback")
+
+
+def test_validate_trusted_redirect_uri_rejects_unlisted_native_callback(
+    monkeypatch,
+):
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    monkeypatch.setenv("MCP_TRUSTED_NATIVE_REDIRECT_URIS", "")
+    # Clear defaults by patching — env-only path for this test
+    monkeypatch.setattr(
+        "litellm.proxy._experimental.mcp_server.oauth_utils._DEFAULT_NATIVE_REDIRECT_URIS",
+        [],
+    )
+    req = _make_trusted_request("http://localhost:4000/")
+    with pytest.raises(HTTPException) as exc:
+        validate_trusted_redirect_uri(
+            req, "cursor://anysphere.cursor-mcp/oauth/callback"
+        )
+    assert exc.value.status_code == 400
+
+
+def test_validate_trusted_redirect_uri_accepts_env_native_redirect_uri(
+    monkeypatch,
+):
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    monkeypatch.setattr(
+        "litellm.proxy._experimental.mcp_server.oauth_utils._DEFAULT_NATIVE_REDIRECT_URIS",
+        [],
+    )
+    monkeypatch.setenv(
+        "MCP_TRUSTED_NATIVE_REDIRECT_URIS",
+        "vscode://my-app/oauth/callback",
+    )
+    req = _make_trusted_request("http://localhost:4000/")
+    validate_trusted_redirect_uri(req, "vscode://my-app/oauth/callback")
+
+
+def test_validate_trusted_redirect_uri_rejects_native_callback_with_fragment():
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    req = _make_trusted_request("http://localhost:4000/")
+    with pytest.raises(HTTPException) as exc:
+        validate_trusted_redirect_uri(
+            req, "cursor://anysphere.cursor-mcp/oauth/callback#frag"
+        )
+    assert exc.value.status_code == 400
+
+
+def test_validate_trusted_redirect_uri_rejects_native_callback_with_query():
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    req = _make_trusted_request("http://localhost:4000/")
+    with pytest.raises(HTTPException) as exc:
+        validate_trusted_redirect_uri(
+            req,
+            "cursor://anysphere.cursor-mcp/oauth/callback?injected=anything",
+        )
+    assert exc.value.status_code == 400
+
+
+def test_validate_trusted_redirect_uri_native_path_case_insensitive(monkeypatch):
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    monkeypatch.setattr(
+        "litellm.proxy._experimental.mcp_server.oauth_utils._DEFAULT_NATIVE_REDIRECT_URIS",
+        [],
+    )
+    monkeypatch.setenv(
+        "MCP_TRUSTED_NATIVE_REDIRECT_URIS",
+        "myapp://host/MyPath",
+    )
+    req = _make_trusted_request("http://localhost:4000/")
+    validate_trusted_redirect_uri(req, "myapp://host/MyPath")
+
+
+def test_validate_trusted_redirect_uri_native_wildcard_respects_path_boundary(
+    monkeypatch,
+):
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    monkeypatch.setattr(
+        "litellm.proxy._experimental.mcp_server.oauth_utils._DEFAULT_NATIVE_REDIRECT_URIS",
+        [],
+    )
+    monkeypatch.setenv(
+        "MCP_TRUSTED_NATIVE_REDIRECT_URIS",
+        "cursor://anysphere.cursor-mcp/oauth/callback*",
+    )
+    req = _make_trusted_request("http://localhost:4000/")
+    validate_trusted_redirect_uri(
+        req, "cursor://anysphere.cursor-mcp/oauth/callback/extra"
+    )
+    with pytest.raises(HTTPException):
+        validate_trusted_redirect_uri(
+            req, "cursor://anysphere.cursor-mcp/oauth/callback-2"
+        )
+
+
+def test_validate_trusted_redirect_uri_native_wildcard_directory_prefix(
+    monkeypatch,
+):
+    from litellm.proxy._experimental.mcp_server.oauth_utils import (
+        validate_trusted_redirect_uri,
+    )
+
+    monkeypatch.setattr(
+        "litellm.proxy._experimental.mcp_server.oauth_utils._DEFAULT_NATIVE_REDIRECT_URIS",
+        [],
+    )
+    monkeypatch.setenv(
+        "MCP_TRUSTED_NATIVE_REDIRECT_URIS",
+        "cursor://anysphere.cursor-mcp/oauth/*",
+    )
+    req = _make_trusted_request("http://localhost:4000/")
+    validate_trusted_redirect_uri(req, "cursor://anysphere.cursor-mcp/oauth/callback")
+
+
 def test_validate_trusted_redirect_uri_rejects_scheme_mismatch_on_same_host():
     """Regression: an attacker who can serve http on the proxy's own
     host (e.g. by MITMing an unencrypted LAN hop) must not be able to
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py
index b06cc7f0f12..c8789e0b0a6 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py
@@ -1345,7 +1345,13 @@ def test_validate_trusted_redirect_uri_logs_diagnostic_on_rejection(
                 "https://litellm.example.com/ui/mcp/oauth/callback",
             )
         assert exc_info.value.status_code == 400
-        assert exc_info.value.detail == "invalid_request"
+        detail = exc_info.value.detail
+        assert isinstance(detail, dict)
+        assert detail.get("error") == "invalid_request"
+        assert "error_description" in detail
+        assert "redirect_uri origin" in detail["error_description"]
+        assert "proxy origin" in detail["error_description"]
+        assert "hint" in detail
 
     matching = [r for r in caplog.records if "rejecting redirect_uri" in r.getMessage()]
     assert len(matching) == 1, (
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_header_alias_utils.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_header_alias_utils.py
new file mode 100644
index 00000000000..2627199570b
--- /dev/null
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_header_alias_utils.py
@@ -0,0 +1,18 @@
+"""Tests for MCP header alias sanitization and auth header lookup."""
+
+from litellm.proxy._experimental.mcp_server.utils import (
+    lookup_mcp_server_auth_in_headers,
+    sanitize_mcp_alias_for_header,
+)
+
+
+def test_sanitize_mcp_alias_for_header():
+    assert sanitize_mcp_alias_for_header("My Server") == "my_server"
+    assert sanitize_mcp_alias_for_header("GitHub-MCP!") == "github_mcp"
+    assert sanitize_mcp_alias_for_header("github_mcp2") == "github_mcp2"
+
+
+def test_lookup_mcp_server_auth_in_headers_sanitized_alias():
+    headers = {"github_mcp": {"Authorization": "Bearer token"}}
+    result = lookup_mcp_server_auth_in_headers(headers, alias="GitHub-MCP")
+    assert result == {"Authorization": "Bearer token"}
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py
index e1eddfc9c7a..d62720ed36f 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py
@@ -774,6 +774,7 @@ async def test_get_tools_from_mcp_servers_continues_when_one_server_fails():
         extra_headers=None,
         add_prefix=True,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         if server.name == "working_server":
             # Working server returns tools
@@ -879,6 +880,7 @@ async def test_get_tools_from_mcp_servers_handles_all_servers_failing():
         extra_headers=None,
         add_prefix=True,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         # All servers fail
         raise Exception(f"Server {server.name} connection failed")
@@ -1339,6 +1341,7 @@ async def test_list_tools_single_server_unprefixed_names():
         extra_headers=None,
         add_prefix=False,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         tool = MagicMock()
         tool.name = f"{server.alias}-toolA" if add_prefix else "toolA"
@@ -1420,6 +1423,7 @@ async def test_list_tools_multiple_servers_prefixed_names():
         extra_headers=None,
         add_prefix=True,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         tool = MagicMock()
         # When multiple servers, add_prefix should be True -> prefixed names
@@ -1686,6 +1690,7 @@ async def test_list_tools_filters_by_key_team_permissions():
         extra_headers=None,
         add_prefix=False,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         # Return 4 tools, but only 2 should be allowed
         tool1 = MagicMock()
@@ -1795,6 +1800,7 @@ async def test_list_tools_with_team_tool_permissions_inheritance():
         extra_headers=None,
         add_prefix=False,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         # Return 4 tools
         tool1 = MagicMock()
@@ -1890,6 +1896,7 @@ async def test_list_tools_with_no_tool_permissions_shows_all():
         extra_headers=None,
         add_prefix=False,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         # Return 3 tools
         tool1 = MagicMock()
@@ -1988,6 +1995,7 @@ async def test_list_tools_strips_prefix_when_matching_permissions():
         extra_headers=None,
         add_prefix=True,
         raw_headers=None,
+        user_api_key_auth=None,
     ):
         # Return tools WITH prefix (as they come from MCP server)
         tool1 = MagicMock()
@@ -3028,6 +3036,206 @@ class TestMergeGatewayInitializeInstructions:
             )
 
 
+class TestEnsureUpstreamInitializeInstructionsCached:
+    @pytest.mark.asyncio
+    async def test_skips_when_yaml_instructions_set(self):
+        from unittest.mock import AsyncMock, patch
+
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        server = _make_instruction_server(
+            server_id="yaml-only", instructions="from yaml"
+        )
+        with patch.object(
+            global_mcp_server_manager, "_create_mcp_client", AsyncMock()
+        ) as mock_create:
+            await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                server
+            )
+        mock_create.assert_not_awaited()
+
+    @pytest.mark.asyncio
+    async def test_skips_when_already_cached(self):
+        from unittest.mock import AsyncMock, patch
+
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        server = _make_instruction_server(server_id="cached-only", instructions=None)
+        global_mcp_server_manager._upstream_initialize_instructions_by_server_id[
+            "cached-only"
+        ] = "warm"
+        try:
+            with patch.object(
+                global_mcp_server_manager, "_create_mcp_client", AsyncMock()
+            ) as mock_create:
+                await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                    server
+                )
+            mock_create.assert_not_awaited()
+        finally:
+            global_mcp_server_manager._upstream_initialize_instructions_by_server_id.pop(
+                "cached-only", None
+            )
+
+    @pytest.mark.asyncio
+    async def test_skips_when_spec_path_set(self):
+        from unittest.mock import AsyncMock, patch
+
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        server = _make_instruction_server(
+            server_id="openapi-spec", spec_path="/openapi.json", url=None
+        )
+        with patch.object(
+            global_mcp_server_manager, "_create_mcp_client", AsyncMock()
+        ) as mock_create:
+            await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                server
+            )
+        mock_create.assert_not_awaited()
+
+    @pytest.mark.asyncio
+    async def test_runs_upstream_session_and_caches(self):
+        from unittest.mock import AsyncMock, MagicMock, patch
+
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        server = _make_instruction_server(server_id="cold-server", instructions=None)
+        fake_client = MagicMock()
+        fake_client.run_with_session = AsyncMock(return_value="ok")
+        fake_client._last_initialize_instructions = "  upstream says hi  "
+
+        with patch.object(
+            global_mcp_server_manager,
+            "_create_mcp_client",
+            AsyncMock(return_value=fake_client),
+        ):
+            try:
+                await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                    server
+                )
+                assert (
+                    global_mcp_server_manager._upstream_initialize_instructions_by_server_id[
+                        "cold-server"
+                    ]
+                    == "upstream says hi"
+                )
+            finally:
+                global_mcp_server_manager._upstream_initialize_instructions_by_server_id.pop(
+                    "cold-server", None
+                )
+                global_mcp_server_manager._upstream_initialize_instructions_probed_at.pop(
+                    "cold-server", None
+                )
+
+    @pytest.mark.asyncio
+    async def test_cooldown_after_empty_upstream_response(self):
+        """Upstream returns no instructions → next call within cooldown must not reconnect."""
+        from unittest.mock import AsyncMock, MagicMock, patch
+
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        server = _make_instruction_server(server_id="empty-server", instructions=None)
+        fake_client = MagicMock()
+        fake_client.run_with_session = AsyncMock(return_value="ok")
+        fake_client._last_initialize_instructions = None  # upstream sent nothing
+
+        create = AsyncMock(return_value=fake_client)
+        with patch.object(global_mcp_server_manager, "_create_mcp_client", create):
+            try:
+                await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                    server
+                )
+                await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                    server
+                )
+                assert create.await_count == 1, (
+                    "Second probe within cooldown must not reconnect to upstream"
+                )
+                assert (
+                    "empty-server"
+                    not in global_mcp_server_manager._upstream_initialize_instructions_by_server_id
+                )
+                assert (
+                    "empty-server"
+                    in global_mcp_server_manager._upstream_initialize_instructions_probed_at
+                )
+            finally:
+                global_mcp_server_manager._upstream_initialize_instructions_probed_at.pop(
+                    "empty-server", None
+                )
+
+    @pytest.mark.asyncio
+    async def test_cooldown_after_upstream_failure(self):
+        """run_with_session raises → cooldown applies, no immediate retry."""
+        from unittest.mock import AsyncMock, MagicMock, patch
+
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        server = _make_instruction_server(server_id="boom-server", instructions=None)
+        fake_client = MagicMock()
+        fake_client.run_with_session = AsyncMock(side_effect=RuntimeError("upstream down"))
+        fake_client._last_initialize_instructions = None
+
+        create = AsyncMock(return_value=fake_client)
+        with patch.object(global_mcp_server_manager, "_create_mcp_client", create):
+            try:
+                await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                    server
+                )
+                await global_mcp_server_manager._ensure_upstream_initialize_instructions_cached(
+                    server
+                )
+                assert create.await_count == 1, (
+                    "Second probe within cooldown must not reconnect after failure"
+                )
+                assert (
+                    "boom-server"
+                    not in global_mcp_server_manager._upstream_initialize_instructions_by_server_id
+                )
+                assert (
+                    "boom-server"
+                    in global_mcp_server_manager._upstream_initialize_instructions_probed_at
+                )
+            finally:
+                global_mcp_server_manager._upstream_initialize_instructions_probed_at.pop(
+                    "boom-server", None
+                )
+
+    @pytest.mark.asyncio
+    async def test_reload_resets_probe_cooldown(self):
+        """load_servers_from_config clears the negative-cache map so reloads re-probe."""
+        from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+            global_mcp_server_manager,
+        )
+
+        global_mcp_server_manager._upstream_initialize_instructions_probed_at[
+            "reload-target"
+        ] = 1.0
+        try:
+            await global_mcp_server_manager.load_servers_from_config({})
+            assert (
+                "reload-target"
+                not in global_mcp_server_manager._upstream_initialize_instructions_probed_at
+            )
+        finally:
+            global_mcp_server_manager._upstream_initialize_instructions_probed_at.pop(
+                "reload-target", None
+            )
+
+
 class TestGatewayCreateInitializationOptions:
     """Tests for the patched server.create_initialization_options via ContextVar."""
 
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
index ef1c09aa815..d7078412a44 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
@@ -322,6 +322,7 @@ class TestMCPServerManager:
             mcp_auth_header=None,
             mcp_protocol_version=None,
             raw_headers=None,
+            user_api_key_auth=None,
         ):
             if server.name == "github":
                 tool1 = MagicMock()
@@ -376,6 +377,7 @@ class TestMCPServerManager:
             mcp_auth_header=None,
             mcp_protocol_version=None,
             raw_headers=None,
+            user_api_key_auth=None,
         ):
             assert mcp_auth_header == "legacy-token"  # Should use legacy header
             tool = MagicMock()
@@ -414,6 +416,7 @@ class TestMCPServerManager:
             mcp_auth_header=None,
             mcp_protocol_version=None,
             raw_headers=None,
+            user_api_key_auth=None,
         ):
             assert (
                 mcp_auth_header == "server-specific-token"
@@ -1004,6 +1007,7 @@ class TestMCPServerManager:
             mcp_auth_header=None,
             mcp_protocol_version=None,
             raw_headers=None,
+            user_api_key_auth=None,
         ):
             assert (
                 mcp_auth_header == "server-specific-token"
@@ -1801,6 +1805,258 @@ class TestMCPServerManager:
         assert len(tools_unprefixed) == 1
         assert tools_unprefixed[0].name == "send_email"
 
+    @pytest.mark.asyncio
+    async def test_get_tools_from_server_jwt_skipped_when_mcp_auth_header_set(self):
+        """When a per-user mcp_auth_header is resolved, JWT injection must be skipped.
+
+        MCPClient._get_auth_headers() applies extra_headers AFTER writing
+        Authorization from auth_value, so an injected JWT would clobber the
+        user's per-server OAuth token. Regression test for that interaction.
+        """
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="zapier",
+            name="zapier",
+            transport=MCPTransport.http,
+        )
+
+        manager._create_mcp_client = AsyncMock(return_value=object())
+        manager._fetch_tools_with_timeout = AsyncMock(return_value=[])
+
+        user_auth = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+
+        with (
+            patch(
+                "litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer.get_mcp_jwt_signer",
+                return_value=MagicMock(),
+            ),
+            patch(
+                "litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer.inject_mcp_jwt_headers_for_upstream",
+                new=AsyncMock(return_value={"Authorization": "Bearer signed-jwt"}),
+            ) as mock_inject,
+        ):
+            # Case A: mcp_auth_header present -> JWT must NOT be injected
+            await manager._get_tools_from_server(
+                server,
+                mcp_auth_header="oauth-user-token",
+                user_api_key_auth=user_auth,
+            )
+            mock_inject.assert_not_called()
+
+            # Case B: no mcp_auth_header -> JWT injection runs as before
+            await manager._get_tools_from_server(
+                server,
+                user_api_key_auth=user_auth,
+            )
+            mock_inject.assert_awaited_once()
+
+    def test_resolve_mcp_server_for_tool_call_via_prefixed_name(self):
+        """Resolution succeeds when the prefixed tool name is in the mapping."""
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="jira",
+            name="jira",
+            transport=MCPTransport.http,
+        )
+        manager.registry = {"jira": server}
+        manager.tool_name_to_mcp_server_name_mapping["jira-search_issues"] = "jira"
+        manager.tool_name_to_mcp_server_name_mapping["search_issues"] = "jira"
+
+        resolved = manager._resolve_mcp_server_for_tool_call("jira", "search_issues")
+        assert resolved is server
+
+    def test_resolve_mcp_server_for_tool_call_via_alias(self):
+        """Resolution falls back to alias/server_name match in the registry."""
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="srv-uuid-123",
+            name="zapier",
+            alias="zapier-alias",
+            transport=MCPTransport.http,
+        )
+        manager.registry = {"srv-uuid-123": server}
+        manager.tool_name_to_mcp_server_name_mapping["create_zap"] = "zapier"
+
+        resolved = manager._resolve_mcp_server_for_tool_call(
+            "zapier-alias", "create_zap"
+        )
+        assert resolved is server
+
+    def test_resolve_mcp_server_for_tool_call_unknown_tool_with_empty_mapping(self):
+        """Server-name match alone must not let unknown tools through when the
+        mapping has no entries for that server (e.g. listing has not completed
+        or the server is OAuth2 and the user has not yet listed tools).
+        """
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="srv-uuid-123",
+            name="zapier",
+            alias="zapier-alias",
+            transport=MCPTransport.http,
+        )
+        manager.registry = {"srv-uuid-123": server}
+
+        with pytest.raises(ValueError, match="Tool create_zap not found"):
+            manager._resolve_mcp_server_for_tool_call("zapier-alias", "create_zap")
+
+    def test_resolve_mcp_server_for_tool_call_fallback_to_unprefixed_lookup(self):
+        """Fallback to unprefixed _get_mcp_server_from_tool_name when other paths fail."""
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="linear",
+            name="linear",
+            transport=MCPTransport.http,
+        )
+        manager.registry = {"linear": server}
+        manager.tool_name_to_mcp_server_name_mapping["create_issue"] = "linear"
+
+        # server_name is empty so the fallback unprefixed lookup runs and matches.
+        resolved = manager._resolve_mcp_server_for_tool_call("", "create_issue")
+        assert resolved is server
+
+    def test_resolve_mcp_server_for_tool_call_raises_when_not_found(self):
+        """ValueError is raised when no resolution path finds the tool."""
+        manager = MCPServerManager()
+        with pytest.raises(ValueError, match="Tool .* not found"):
+            manager._resolve_mcp_server_for_tool_call("nonexistent", "ghost_tool")
+
+    def test_resolve_mcp_server_for_tool_call_unknown_tool_with_known_server(self):
+        """Server-name match alone must not let unknown tools slip through.
+
+        If the registry has tools for this server but neither the prefixed nor
+        unprefixed tool name is in the mapping, raise rather than returning the
+        server (would otherwise allow tool enumeration via name spoofing).
+        """
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="github",
+            name="github",
+            transport=MCPTransport.http,
+        )
+        manager.registry = {"github": server}
+        # Mapping has *some* tools for github but not "missing_tool".
+        manager.tool_name_to_mcp_server_name_mapping["github-list_repos"] = "github"
+        manager.tool_name_to_mcp_server_name_mapping["list_repos"] = "github"
+
+        with pytest.raises(ValueError, match="Tool missing_tool not found"):
+            manager._resolve_mcp_server_for_tool_call("github", "missing_tool")
+
+    @pytest.mark.asyncio
+    async def test_resolve_oauth2_headers_skipped_when_not_user_oauth(self):
+        """Returns input headers unchanged when server does not need user OAuth."""
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="plain",
+            name="plain",
+            transport=MCPTransport.http,
+        )
+        # needs_user_oauth_token defaults to False.
+        user_auth = UserAPIKeyAuth(api_key="sk-test", user_id="bob")
+
+        result = await manager._resolve_oauth2_headers_for_tool_call(
+            server, oauth2_headers=None, user_api_key_auth=user_auth
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_resolve_oauth2_headers_returns_client_supplied_token(self):
+        """Returns the client's oauth2_headers as-is when already set."""
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="oauth-srv",
+            name="oauth-srv",
+            transport=MCPTransport.http,
+            auth_type=MCPAuth.oauth2,
+        )
+        assert server.needs_user_oauth_token is True
+        user_auth = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+        supplied = {"Authorization": "Bearer client-supplied"}
+
+        result = await manager._resolve_oauth2_headers_for_tool_call(
+            server, oauth2_headers=supplied, user_api_key_auth=user_auth
+        )
+        assert result is supplied
+
+    @pytest.mark.asyncio
+    async def test_resolve_oauth2_headers_looks_up_stored_token(self):
+        """Falls back to stored per-user OAuth headers when no token is supplied."""
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="oauth-srv",
+            name="oauth-srv",
+            transport=MCPTransport.http,
+            auth_type=MCPAuth.oauth2,
+        )
+        user_auth = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+        stored = {"Authorization": "Bearer stored-user-token"}
+
+        with patch(
+            "litellm.proxy._experimental.mcp_server.server._get_user_oauth_extra_headers_from_db",
+            new=AsyncMock(return_value=stored),
+        ) as mock_lookup:
+            result = await manager._resolve_oauth2_headers_for_tool_call(
+                server, oauth2_headers=None, user_api_key_auth=user_auth
+            )
+
+        assert result == stored
+        mock_lookup.assert_awaited_once()
+
+    @pytest.mark.asyncio
+    async def test_resolve_oauth2_headers_swallows_lookup_exception(self):
+        """Returns supplied headers (None) when the stored-token lookup raises."""
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="oauth-srv",
+            name="oauth-srv",
+            transport=MCPTransport.http,
+            auth_type=MCPAuth.oauth2,
+        )
+        user_auth = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+
+        with patch(
+            "litellm.proxy._experimental.mcp_server.server._get_user_oauth_extra_headers_from_db",
+            new=AsyncMock(side_effect=RuntimeError("redis down")),
+        ):
+            result = await manager._resolve_oauth2_headers_for_tool_call(
+                server, oauth2_headers=None, user_api_key_auth=user_auth
+            )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_resolve_oauth2_headers_no_user_id(self):
+        """Skip lookup entirely when user_api_key_auth has no user_id."""
+        from litellm.proxy._types import UserAPIKeyAuth
+
+        manager = MCPServerManager()
+        server = MCPServer(
+            server_id="oauth-srv",
+            name="oauth-srv",
+            transport=MCPTransport.http,
+            auth_type=MCPAuth.oauth2,
+        )
+        # user_id is None -> lookup must not happen
+        user_auth = UserAPIKeyAuth(api_key="sk-test")
+
+        with patch(
+            "litellm.proxy._experimental.mcp_server.server._get_user_oauth_extra_headers_from_db",
+            new=AsyncMock(return_value={"Authorization": "Bearer x"}),
+        ) as mock_lookup:
+            result = await manager._resolve_oauth2_headers_for_tool_call(
+                server, oauth2_headers=None, user_api_key_auth=user_auth
+            )
+        assert result is None
+        mock_lookup.assert_not_called()
+
     def test_create_prefixed_tools_updates_mapping_for_both_forms(self):
         """_create_prefixed_tools should populate mapping for prefixed and original names even when not adding prefix in output."""
         manager = MCPServerManager()
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py
index f4feac68fcc..593facd9279 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py
@@ -1,5 +1,6 @@
 import json
 from typing import Any, Dict, Optional
+from unittest.mock import MagicMock
 
 import pytest
 from fastapi import HTTPException
@@ -796,6 +797,25 @@ class TestCallToolRestAPI:
             raising=False,
         )
 
+        mock_server = MagicMock()
+        mock_server.server_id = "server-1"
+
+        def fake_get_mcp_server_by_id(server_id):
+            return mock_server if server_id == "server-1" else None
+
+        monkeypatch.setattr(
+            rest_endpoints.global_mcp_server_manager,
+            "get_mcp_server_by_id",
+            fake_get_mcp_server_by_id,
+            raising=False,
+        )
+        monkeypatch.setattr(
+            rest_endpoints.global_mcp_server_manager,
+            "get_mcp_server_by_name",
+            lambda *args, **kwargs: None,
+            raising=False,
+        )
+
         request_payload = {
             "server_id": "server-1",
             "name": "demo-tool",
diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py
index 26f04a4abcb..116ba83f42e 100644
--- a/tests/test_litellm/proxy/auth/test_auth_checks.py
+++ b/tests/test_litellm/proxy/auth/test_auth_checks.py
@@ -127,6 +127,23 @@ def test_get_experimental_ui_login_jwt_auth_token_valid(valid_sso_user_defined_v
     assert expires <= now + timedelta(minutes=10, seconds=2)
 
 
+def test_get_cli_jwt_auth_token_includes_team_alias(valid_sso_user_defined_values):
+    token = ExperimentalUIJWTToken.get_cli_jwt_auth_token(
+        valid_sso_user_defined_values,
+        team_id="team-123",
+        team_alias="test-team",
+    )
+
+    decrypted_token = decrypt_value_helper(
+        token, key="ui_hash_key", exception_type="debug"
+    )
+    assert decrypted_token is not None
+    token_data = json.loads(decrypted_token)
+
+    assert token_data["team_id"] == "team-123"
+    assert token_data["team_alias"] == "test-team"
+
+
 def test_get_experimental_ui_login_jwt_auth_token_uses_10_min_expiry(
     valid_sso_user_defined_values,
 ):
@@ -3016,3 +3033,340 @@ async def test_team_member_budget_check_zero_per_member_row_still_blocks():
                 proxy_logging_obj=proxy_logging_obj,
             )
     assert exc_info.value.max_budget == 0.0
+
+
+# --- resolve_and_validate_end_user_id ---------------------------------------
+
+
+@pytest.fixture
+def _validate_flag_on(monkeypatch):
+    """Enable opt-in DB validation for the duration of a test."""
+    import litellm
+
+    monkeypatch.setattr(litellm, "validate_end_user_id_in_db", True)
+    monkeypatch.setattr(litellm, "max_end_user_budget_id", None)
+
+
+def _validation_cache():
+    cache = MagicMock()
+    cache.async_get_cache = AsyncMock(return_value=None)
+    cache.async_set_cache = AsyncMock()
+    return cache
+
+
+def _patch_validation_helpers(monkeypatch, *, end_user=None, user=None, fuzzy=None):
+    """Stub out the DB helpers resolve_and_validate_end_user_id delegates to."""
+    from litellm.proxy.auth import auth_checks
+
+    monkeypatch.setattr(
+        auth_checks, "get_end_user_object", AsyncMock(return_value=end_user)
+    )
+    monkeypatch.setattr(auth_checks, "get_user_object", AsyncMock(return_value=user))
+    monkeypatch.setattr(
+        auth_checks, "_get_fuzzy_user_object", AsyncMock(return_value=fuzzy)
+    )
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_returns_none_for_none_input(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch)
+    cache = _validation_cache()
+    assert (
+        await resolve_and_validate_end_user_id(
+            raw_end_user_id=None,
+            prisma_client=MagicMock(),
+            user_api_key_cache=cache,
+        )
+        is None
+    )
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_passes_through_when_flag_disabled(monkeypatch):
+    """Default behaviour: flag is off, arbitrary ids pass through untouched."""
+    import litellm
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    monkeypatch.setattr(litellm, "validate_end_user_id_in_db", False)
+    _patch_validation_helpers(monkeypatch)
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="codex-session-abc",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result == "codex-session-abc"
+    cache.async_set_cache.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_passes_through_when_no_prisma_client(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch)
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="alice@example.com",
+        prisma_client=None,
+        user_api_key_cache=cache,
+    )
+    assert result == "alice@example.com"
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_matches_end_user_table(_validate_flag_on, monkeypatch):
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch, end_user=MagicMock())
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="customer-123",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result == "customer-123"
+    cache.async_set_cache.assert_awaited_once()
+    kwargs = cache.async_set_cache.await_args.kwargs
+    assert kwargs["key"] == "end_user_validation:customer-123"
+    assert kwargs["value"] == "valid"
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_matches_user_table_by_user_id(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch, user=MagicMock())
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="user-xyz",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result == "user-xyz"
+    # email fallback should not run for a non-email input
+    auth_checks._get_fuzzy_user_object.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_matches_user_table_by_email(
+    _validate_flag_on, monkeypatch
+):
+    """Email-shaped ids route through get_user_object with user_email set.
+
+    The fuzzy lookup must happen inside get_user_object so it shares the
+    _should_check_db throttle and user_api_key_cache — no direct raw
+    Prisma calls on the auth path.
+    """
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch, user=MagicMock())
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="Alice@Example.com",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result == "Alice@Example.com"
+    auth_checks.get_user_object.assert_awaited_once()
+    user_kwargs = auth_checks.get_user_object.await_args.kwargs
+    assert user_kwargs["user_id"] == "Alice@Example.com"
+    assert user_kwargs["user_email"] == "Alice@Example.com"
+    # email branch must not bypass the cached helper with a raw fuzzy call
+    auth_checks._get_fuzzy_user_object.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_non_email_id_does_not_pass_user_email(
+    _validate_flag_on, monkeypatch
+):
+    """Non-email ids skip the email fuzzy path to avoid a pointless DB hit."""
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch, user=MagicMock())
+    cache = _validation_cache()
+
+    await resolve_and_validate_end_user_id(
+        raw_end_user_id="user-xyz",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    auth_checks.get_user_object.assert_awaited_once()
+    user_kwargs = auth_checks.get_user_object.await_args.kwargs
+    assert user_kwargs["user_email"] is None
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_drops_codex_opaque_identifier(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch)  # all helpers return None
+    cache = _validation_cache()
+
+    codex_id = (
+        "user_8a4a360c36621665b341e06fb76041d9b6def732bb183eea148d4abc9d97c1de"
+        "_account__session_a2bce4a5-8887-44ef-b491-fbf0a55c6569"
+    )
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id=codex_id,
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result is None
+    cache.async_set_cache.assert_awaited_once()
+    kwargs = cache.async_set_cache.await_args.kwargs
+    assert kwargs["value"] == "invalid"
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_preserves_id_when_default_budget_configured(
+    _validate_flag_on, monkeypatch
+):
+    """Don't drop unregistered ids when litellm.max_end_user_budget_id is set.
+
+    The default end-user budget is applied downstream when the id is present
+    but not found in the db — dropping the id here would bypass those limits.
+    """
+    import litellm
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    monkeypatch.setattr(litellm, "max_end_user_budget_id", "default-budget")
+    _patch_validation_helpers(monkeypatch)
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="new-customer",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result == "new-customer"
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_drops_unknown_email(_validate_flag_on, monkeypatch):
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch)
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="stranger@example.com",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result is None
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_uses_cached_valid_result(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch)
+    cache = _validation_cache()
+    cache.async_get_cache = AsyncMock(return_value="valid")
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="alice@example.com",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result == "alice@example.com"
+    auth_checks.get_end_user_object.assert_not_awaited()
+    auth_checks.get_user_object.assert_not_awaited()
+    auth_checks._get_fuzzy_user_object.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_uses_cached_invalid_result(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    _patch_validation_helpers(monkeypatch, end_user=MagicMock())
+    cache = _validation_cache()
+    cache.async_get_cache = AsyncMock(return_value="invalid")
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="bogus",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    assert result is None
+    # Despite a matching row configured, helpers aren't called — cache wins.
+    auth_checks.get_end_user_object.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_swallows_db_errors_and_returns_none(
+    _validate_flag_on, monkeypatch
+):
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    monkeypatch.setattr(
+        auth_checks,
+        "get_end_user_object",
+        AsyncMock(side_effect=Exception("db down")),
+    )
+    monkeypatch.setattr(
+        auth_checks,
+        "get_user_object",
+        AsyncMock(side_effect=Exception("db down")),
+    )
+    cache = _validation_cache()
+
+    result = await resolve_and_validate_end_user_id(
+        raw_end_user_id="alice@example.com",
+        prisma_client=MagicMock(),
+        user_api_key_cache=cache,
+    )
+    # DB errors shouldn't raise through the auth path — treat as unknown.
+    assert result is None
+
+
+@pytest.mark.asyncio
+async def test_resolve_end_user_reraises_budget_exceeded(
+    _validate_flag_on, monkeypatch
+):
+    """BudgetExceededError from get_end_user_object must bubble up so the
+    auth path enforces spend limits instead of silently dropping the id."""
+    import litellm
+    from litellm.proxy.auth import auth_checks
+    from litellm.proxy.auth.auth_checks import resolve_and_validate_end_user_id
+
+    monkeypatch.setattr(
+        auth_checks,
+        "get_end_user_object",
+        AsyncMock(
+            side_effect=litellm.BudgetExceededError(current_cost=10.0, max_budget=5.0)
+        ),
+    )
+    cache = _validation_cache()
+
+    with pytest.raises(litellm.BudgetExceededError):
+        await resolve_and_validate_end_user_id(
+            raw_end_user_id="customer-over-budget",
+            prisma_client=MagicMock(),
+            user_api_key_cache=cache,
+        )
diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py
index 08035fb7173..68e1636d380 100644
--- a/tests/test_litellm/proxy/auth/test_auth_utils.py
+++ b/tests/test_litellm/proxy/auth/test_auth_utils.py
@@ -597,6 +597,315 @@ def test_get_end_user_id_falls_back_to_deprecated_user_header_name():
     assert result == "user-legacy"
 
 
+class TestCoerceUserIdToStr:
+    """Unit tests for the _coerce_user_id_to_str helper."""
+
+    def test_plain_string_is_returned_verbatim(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str("alice@example.com") == "alice@example.com"
+
+    def test_string_is_stripped(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str("  bob  ") == "bob"
+
+    def test_codex_opaque_identifier_is_preserved(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        codex_id = (
+            "user_8a4a360c36621665b341e06fb76041d9b6def732bb183eea148d4abc9d97c1de"
+            "_account__session_a2bce4a5-8887-44ef-b491-fbf0a55c6569"
+        )
+        assert _coerce_user_id_to_str(codex_id) == codex_id
+
+    def test_none_returns_none(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str(None) is None
+
+    def test_empty_string_returns_none(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str("") is None
+        assert _coerce_user_id_to_str("   ") is None
+
+    def test_dict_returns_none(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        payload = {
+            "device_id": "abc",
+            "account_uuid": "",
+            "session_id": "c284b8cb",
+        }
+        assert _coerce_user_id_to_str(payload) is None
+
+    def test_list_returns_none(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str(["a", "b"]) is None
+
+    def test_json_encoded_dict_string_passes_through_by_default(self):
+        """JSON-encoded dict strings are preserved unless opt-in flag is on.
+
+        This preserves backwards compatibility: existing deployments that
+        intentionally pass JSON-encoded user identifiers keep working.
+        """
+        import litellm
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        blob = (
+            '{"device_id":"d5abe9199ee7759a0558974e9371e78c7b38d7621aae26d6609c1de61af6afb0",'
+            '"account_uuid":"","session_id":"c284b8cb-a050-4278-8599-cc4e016a10ab"}'
+        )
+        original = litellm.validate_end_user_id_in_db
+        litellm.validate_end_user_id_in_db = False
+        try:
+            assert _coerce_user_id_to_str(blob) == blob
+        finally:
+            litellm.validate_end_user_id_in_db = original
+
+    def test_json_encoded_dict_string_returns_none_when_validation_enabled(self):
+        import litellm
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        # Same broken shape we saw in spend logs, but pre-stringified to JSON.
+        blob = (
+            '{"device_id":"d5abe9199ee7759a0558974e9371e78c7b38d7621aae26d6609c1de61af6afb0",'
+            '"account_uuid":"","session_id":"c284b8cb-a050-4278-8599-cc4e016a10ab"}'
+        )
+        original = litellm.validate_end_user_id_in_db
+        litellm.validate_end_user_id_in_db = True
+        try:
+            assert _coerce_user_id_to_str(blob) is None
+        finally:
+            litellm.validate_end_user_id_in_db = original
+
+    def test_json_encoded_list_string_passes_through_by_default(self):
+        import litellm
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        original = litellm.validate_end_user_id_in_db
+        litellm.validate_end_user_id_in_db = False
+        try:
+            assert _coerce_user_id_to_str('["a","b"]') == '["a","b"]'
+        finally:
+            litellm.validate_end_user_id_in_db = original
+
+    def test_json_encoded_list_string_returns_none_when_validation_enabled(self):
+        import litellm
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        original = litellm.validate_end_user_id_in_db
+        litellm.validate_end_user_id_in_db = True
+        try:
+            assert _coerce_user_id_to_str('["a","b"]') is None
+        finally:
+            litellm.validate_end_user_id_in_db = original
+
+    def test_int_returns_str(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str(12345) == "12345"
+
+    def test_bool_returns_none(self):
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        # bool is an int subclass — reject explicitly, never produce "True"/"False".
+        assert _coerce_user_id_to_str(True) is None
+        assert _coerce_user_id_to_str(False) is None
+
+    def test_brace_string_that_isnt_json_is_kept(self):
+        """A string starting with `{` but failing to parse stays as-is."""
+        from litellm.proxy.auth.auth_utils import _coerce_user_id_to_str
+
+        assert _coerce_user_id_to_str("{not json") == "{not json"
+
+
+class TestGetEndUserIdDropsMalformedBodyValues:
+    """Tests that get_end_user_id_from_request_body drops dict-shaped values
+    rather than stringifying them into spend logs."""
+
+    def test_dict_user_falls_through_to_litellm_metadata(self):
+        request_body = {
+            "user": {
+                "device_id": "abc",
+                "session_id": "c284b8cb",
+            },
+            "litellm_metadata": {"user": "alice@example.com"},
+        }
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result == "alice@example.com"
+
+    def test_dict_user_with_no_other_sources_returns_none(self):
+        request_body = {
+            "user": {"device_id": "abc", "session_id": "xyz"},
+        }
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result is None
+
+    def test_json_encoded_user_string_passes_through_by_default(self):
+        """JSON-encoded user strings pass through unless validation is opted in.
+
+        Gating behind ``litellm.validate_end_user_id_in_db`` keeps existing
+        deployments that send JSON-encoded identifiers working until they
+        explicitly opt into the stricter extraction.
+        """
+        import litellm
+
+        blob = (
+            '{"device_id":"d5abe9199ee7759a","account_uuid":"",'
+            '"session_id":"c284b8cb-a050-4278-8599-cc4e016a10ab"}'
+        )
+        request_body = {"user": blob}
+
+        original = litellm.validate_end_user_id_in_db
+        litellm.validate_end_user_id_in_db = False
+        try:
+            with patch("litellm.proxy.proxy_server.general_settings", {}):
+                result = get_end_user_id_from_request_body(
+                    request_body=request_body, request_headers={}
+                )
+        finally:
+            litellm.validate_end_user_id_in_db = original
+
+        assert result == blob
+
+    def test_json_encoded_user_string_returns_none_when_validation_enabled(self):
+        import litellm
+
+        request_body = {
+            "user": (
+                '{"device_id":"d5abe9199ee7759a","account_uuid":"",'
+                '"session_id":"c284b8cb-a050-4278-8599-cc4e016a10ab"}'
+            ),
+        }
+
+        original = litellm.validate_end_user_id_in_db
+        litellm.validate_end_user_id_in_db = True
+        try:
+            with patch("litellm.proxy.proxy_server.general_settings", {}):
+                result = get_end_user_id_from_request_body(
+                    request_body=request_body, request_headers={}
+                )
+        finally:
+            litellm.validate_end_user_id_in_db = original
+
+        assert result is None
+
+    def test_plain_string_user_is_preserved(self):
+        request_body = {"user": "alice@example.com"}
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result == "alice@example.com"
+
+    def test_codex_opaque_user_is_preserved(self):
+        codex_id = (
+            "user_8a4a360c36621665b341e06fb76041d9b6def732bb183eea148d4abc9d97c1de"
+            "_account__session_a2bce4a5-8887-44ef-b491-fbf0a55c6569"
+        )
+        request_body = {"user": codex_id}
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result == codex_id
+
+    def test_int_user_is_coerced_to_string(self):
+        request_body = {"user": 12345}
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result == "12345"
+
+    def test_list_user_falls_through(self):
+        request_body = {
+            "user": ["a", "b"],
+            "safety_identifier": "alice@example.com",
+        }
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result == "alice@example.com"
+
+    def test_dict_safety_identifier_returns_none(self):
+        request_body = {
+            "safety_identifier": {"device_id": "abc"},
+        }
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result is None
+
+    def test_dict_metadata_user_id_returns_none(self):
+        request_body = {
+            "metadata": {"user_id": {"device_id": "abc"}},
+        }
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result is None
+
+    def test_whitespace_user_falls_through(self):
+        request_body = {"user": "   ", "safety_identifier": "alice@example.com"}
+
+        with patch("litellm.proxy.proxy_server.general_settings", {}):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers={}
+            )
+
+        assert result == "alice@example.com"
+
+    def test_dict_user_header_falls_through_to_body(self):
+        """A dict-shaped value in a configured user-id header is dropped, not stringified."""
+        general_settings = {"user_header_name": "x-custom-user-id"}
+        # A header value will normally be a str, but be defensive: the coercion
+        # must drop anything that isn't a usable identifier.
+        headers = {"x-custom-user-id": {"device_id": "abc"}}
+        request_body = {"user": "alice@example.com"}
+
+        with (
+            patch(
+                "litellm.proxy.auth.auth_utils._get_customer_id_from_standard_headers",
+                return_value=None,
+            ),
+            patch("litellm.proxy.proxy_server.general_settings", general_settings),
+        ):
+            result = get_end_user_id_from_request_body(
+                request_body=request_body, request_headers=headers
+            )
+
+        assert result == "alice@example.com"
+
+
 def _make_deployment_dict(
     model_name: str, tpm: Optional[int] = None, rpm: Optional[int] = None
 ) -> dict:
diff --git a/tests/test_litellm/proxy/auth/test_model_checks.py b/tests/test_litellm/proxy/auth/test_model_checks.py
index 77aa03032a7..f38ac5c2000 100644
--- a/tests/test_litellm/proxy/auth/test_model_checks.py
+++ b/tests/test_litellm/proxy/auth/test_model_checks.py
@@ -249,3 +249,241 @@ def test_get_complete_model_list_byok_wildcard_expansion():
     assert len(result) > 0
     assert all(m.startswith("openai/") for m in result)
     assert "openai/*" not in result
+
+
+def test_get_complete_model_list_expands_team_scoped_wildcard_with_stored_credential(
+    monkeypatch,
+):
+    """
+    Team-scoped BYOK wildcard deployments are stored under an internal model_name,
+    with the public wildcard name in model_info.team_public_model_name.
+    """
+    import litellm
+    from litellm import Router
+    from litellm.proxy.auth import model_checks
+    from litellm.proxy.auth.model_checks import get_complete_model_list
+    from litellm.types.utils import CredentialItem
+
+    monkeypatch.setattr(
+        litellm,
+        "credential_list",
+        [
+            CredentialItem(
+                credential_name="openai-credential",
+                credential_info={"provider": "openai"},
+                credential_values={
+                    "api_key": "stored-openai-key",
+                    "api_base": "https://example.openai.test/v1",
+                },
+            )
+        ],
+    )
+
+    captured_params = {}
+
+    def fake_get_provider_models(provider, litellm_params=None):
+        captured_params["provider"] = provider
+        captured_params["api_key"] = litellm_params.api_key
+        captured_params["api_base"] = litellm_params.api_base
+        captured_params["credential_name"] = litellm_params.litellm_credential_name
+        return ["gpt-4o"]
+
+    monkeypatch.setattr(model_checks, "get_provider_models", fake_get_provider_models)
+
+    router = Router(
+        model_list=[
+            {
+                "model_name": "model_name_team-1_generated",
+                "litellm_params": {
+                    "model": "openai/*",
+                    "custom_llm_provider": "openai",
+                    "litellm_credential_name": "openai-credential",
+                },
+                "model_info": {
+                    "team_id": "team-1",
+                    "team_public_model_name": "openai/*",
+                },
+            }
+        ]
+    )
+
+    result = get_complete_model_list(
+        key_models=[],
+        team_models=["openai/*"],
+        proxy_model_list=[],
+        user_model=None,
+        infer_model_from_keys=False,
+        llm_router=router,
+        team_id="team-1",
+    )
+
+    assert "openai/gpt-4o" in result
+    assert captured_params == {
+        "provider": "openai",
+        "api_key": "stored-openai-key",
+        "api_base": "https://example.openai.test/v1",
+        "credential_name": None,
+    }
+
+
+def test_wildcard_credential_hydration_preserves_deployment_params(
+    monkeypatch,
+):
+    import litellm
+    from litellm.proxy.auth import model_checks
+    from litellm.proxy.auth.model_checks import get_known_models_from_wildcard
+    from litellm.types.router import LiteLLM_Params
+    from litellm.types.utils import CredentialItem
+
+    monkeypatch.setattr(
+        litellm,
+        "credential_list",
+        [
+            CredentialItem(
+                credential_name="openai-credential",
+                credential_info={"provider": "openai"},
+                credential_values={
+                    "api_key": "stored-openai-key",
+                    "api_version": "credential-version",
+                    "model": "openai/wrong-model",
+                    "unexpected_field": "unexpected-value",
+                },
+            )
+        ],
+    )
+
+    captured_params = {}
+
+    def fake_get_provider_models(provider, litellm_params=None):
+        captured_params["provider"] = provider
+        captured_params["model"] = litellm_params.model
+        captured_params["api_key"] = litellm_params.api_key
+        captured_params["api_version"] = litellm_params.api_version
+        captured_params["credential_name"] = litellm_params.litellm_credential_name
+        captured_params["has_unexpected_field"] = hasattr(
+            litellm_params, "unexpected_field"
+        )
+        return ["gpt-4o"]
+
+    monkeypatch.setattr(model_checks, "get_provider_models", fake_get_provider_models)
+
+    result = get_known_models_from_wildcard(
+        wildcard_model="openai/*",
+        litellm_params=LiteLLM_Params(
+            model="openai/*",
+            custom_llm_provider="openai",
+            api_version="deployment-version",
+            litellm_credential_name="openai-credential",
+        ),
+    )
+
+    assert result == ["openai/gpt-4o"]
+    assert captured_params == {
+        "provider": "openai",
+        "model": "openai/*",
+        "api_key": "stored-openai-key",
+        "api_version": "deployment-version",
+        "credential_name": None,
+        "has_unexpected_field": False,
+    }
+
+
+def test_wildcard_credential_hydration_preserves_missing_credential_name(
+    monkeypatch,
+):
+    import litellm
+    from litellm.proxy.auth import model_checks
+    from litellm.proxy.auth.model_checks import get_known_models_from_wildcard
+    from litellm.types.router import LiteLLM_Params
+
+    monkeypatch.setattr(litellm, "credential_list", [])
+
+    captured_params = {}
+
+    def fake_get_provider_models(provider, litellm_params=None):
+        captured_params["provider"] = provider
+        captured_params["api_key"] = litellm_params.api_key
+        captured_params["credential_name"] = litellm_params.litellm_credential_name
+        return ["gpt-4o"]
+
+    monkeypatch.setattr(model_checks, "get_provider_models", fake_get_provider_models)
+
+    result = get_known_models_from_wildcard(
+        wildcard_model="openai/*",
+        litellm_params=LiteLLM_Params(
+            model="openai/*",
+            custom_llm_provider="openai",
+            api_key=None,
+            litellm_credential_name="missing-credential",
+        ),
+    )
+
+    assert result == ["openai/gpt-4o"]
+    assert captured_params == {
+        "provider": "openai",
+        "api_key": None,
+        "credential_name": "missing-credential",
+    }
+
+
+@pytest.mark.asyncio
+async def test_get_available_models_for_user_expands_query_team_wildcard(
+    monkeypatch,
+):
+    import litellm
+    from litellm import Router
+    from litellm.proxy.auth import model_checks
+    from litellm.proxy._types import UserAPIKeyAuth
+    from litellm.proxy.utils import get_available_models_for_user
+    from litellm.types.utils import CredentialItem
+
+    monkeypatch.setattr(
+        litellm,
+        "credential_list",
+        [
+            CredentialItem(
+                credential_name="openai-credential",
+                credential_info={"provider": "openai"},
+                credential_values={"api_key": "stored-openai-key"},
+            )
+        ],
+    )
+
+    def fake_get_provider_models(provider, litellm_params=None):
+        assert litellm_params.api_key == "stored-openai-key"
+        assert litellm_params.litellm_credential_name is None
+        return ["gpt-4o-mini"]
+
+    monkeypatch.setattr(model_checks, "get_provider_models", fake_get_provider_models)
+
+    router = Router(
+        model_list=[
+            {
+                "model_name": "model_name_team-1_generated",
+                "litellm_params": {
+                    "model": "openai/*",
+                    "custom_llm_provider": "openai",
+                    "litellm_credential_name": "openai-credential",
+                },
+                "model_info": {
+                    "team_id": "team-1",
+                    "team_public_model_name": "openai/*",
+                },
+            }
+        ]
+    )
+
+    result = await get_available_models_for_user(
+        user_api_key_dict=UserAPIKeyAuth(
+            api_key="sk-test",
+            models=[],
+            team_id="team-1",
+            team_models=["openai/*"],
+        ),
+        llm_router=router,
+        general_settings={},
+        user_model=None,
+        team_id="team-1",
+    )
+
+    assert "openai/gpt-4o-mini" in result
diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py
index 4acf42996e0..ad00c55a838 100644
--- a/tests/test_litellm/proxy/auth/test_route_checks.py
+++ b/tests/test_litellm/proxy/auth/test_route_checks.py
@@ -268,6 +268,47 @@ def test_mcp_management_routes_classified_as_management_not_llm_api(route):
     assert RouteChecks.is_management_route(route=route) is True
 
 
+def test_spend_logs_v2_classified_as_management_not_llm_api():
+    """Paginated spend logs are a management/spend read route, not an LLM API."""
+
+    assert RouteChecks.is_llm_api_route(route="/spend/logs/v2") is False
+    assert RouteChecks.is_management_route(route="/spend/logs/v2") is True
+
+
+def test_virtual_key_management_routes_allows_spend_logs_v2():
+    """Management virtual keys should be allowed to call the v2 spend logs endpoint."""
+
+    valid_token = UserAPIKeyAuth(
+        user_id="test_user",
+        allowed_routes=["management_routes"],
+    )
+
+    result = RouteChecks.is_virtual_key_allowed_to_call_route(
+        route="/spend/logs/v2",
+        valid_token=valid_token,
+    )
+
+    assert result is True
+
+
+def test_virtual_key_llm_api_routes_denies_spend_logs_v2():
+    """AI API virtual keys should not gain spend-log access."""
+
+    valid_token = UserAPIKeyAuth(
+        user_id="test_user",
+        allowed_routes=["llm_api_routes"],
+    )
+
+    with pytest.raises(HTTPException) as exc_info:
+        RouteChecks.is_virtual_key_allowed_to_call_route(
+            route="/spend/logs/v2",
+            valid_token=valid_token,
+        )
+
+    assert exc_info.value.status_code == 403
+    assert "Virtual key is not allowed to call this route" in str(exc_info.value.detail)
+
+
 @pytest.mark.parametrize(
     "route",
     [
@@ -1322,6 +1363,7 @@ ADMIN_VIEWER_LOGS_PAGE_ROUTES = [
     "/cost/estimate",
     # Public spend logs / spend tracking routes that admin viewer should read
     "/spend/logs",
+    "/spend/logs/v2",
     "/spend/keys",
     "/spend/users",
     "/spend/tags",
diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py
index 2847de38657..6167759b706 100644
--- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py
+++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py
@@ -3335,3 +3335,125 @@ async def test_master_key_auth_substitutes_alias_for_api_key():
     finally:
         for k, v in _orig.items():
             setattr(_proxy_server_mod, k, v)
+
+
+@pytest.mark.asyncio
+async def test_user_api_key_auth_sets_end_user_id_when_builder_skips_it():
+    """Defense-in-depth: ``_user_api_key_auth_builder`` has multiple
+    early-return paths (master_key=None, /user/auth route, JWT
+    short-circuits) that bypass the end-user resolution block. The wrapper
+    must still attribute spend logs to the request-supplied end-user when
+    none of those paths set it.
+
+    Krrish flagged the removal of this fallback as a regression risk; this
+    test pins the behaviour so future refactors don't silently drop it.
+    """
+    from fastapi import Request
+    from starlette.datastructures import URL
+
+    import litellm.proxy.proxy_server as _proxy_server_mod
+
+    builder_token = UserAPIKeyAuth(api_key="sk-test", user_id="u1")
+    # builder did NOT set end_user_id (e.g. master_key=None early return)
+    assert builder_token.end_user_id is None
+
+    request = Request(
+        scope={
+            "type": "http",
+            "headers": [(b"content-type", b"application/json")],
+            "method": "POST",
+        }
+    )
+    request._url = URL(url="/chat/completions")
+    request._body = json.dumps(
+        {"model": "gpt-4o", "user": "alice@example.com"}
+    ).encode()
+
+    attrs = _proxy_attrs_for_centralized_checks(user_custom_auth=None)
+    originals = {a: getattr(_proxy_server_mod, a, None) for a in attrs}
+    try:
+        for k, v in attrs.items():
+            setattr(_proxy_server_mod, k, v)
+        # Stub the builder so the test doesn't have to traverse the full
+        # auth state machine; we only care about the wrapper's safety net.
+        with (
+            patch(
+                "litellm.proxy.auth.user_api_key_auth._user_api_key_auth_builder",
+                new_callable=AsyncMock,
+                return_value=builder_token,
+            ),
+            patch(
+                "litellm.proxy.auth.user_api_key_auth._run_centralized_common_checks",
+                new_callable=AsyncMock,
+            ),
+            patch(
+                "litellm.proxy.auth.user_api_key_auth.RouteChecks.should_call_route",
+            ),
+        ):
+            result = await user_api_key_auth(request=request, api_key="Bearer sk-test")
+
+        # Validation flag is False by default → pass-through, raw value lands
+        # on the auth obj instead of being silently dropped.
+        assert result.end_user_id == "alice@example.com"
+    finally:
+        for k, v in originals.items():
+            setattr(_proxy_server_mod, k, v)
+
+
+@pytest.mark.asyncio
+async def test_user_api_key_auth_does_not_overwrite_end_user_id_set_by_builder():
+    """When the builder already resolved the end-user id (the primary
+    path), the wrapper-level safety net must not run a second resolution
+    pass — that would re-extract from the request body and could
+    overwrite a value the builder explicitly chose to set."""
+    from fastapi import Request
+    from starlette.datastructures import URL
+
+    import litellm.proxy.proxy_server as _proxy_server_mod
+
+    builder_token = UserAPIKeyAuth(
+        api_key="sk-test", user_id="u1", end_user_id="builder-resolved-id"
+    )
+
+    request = Request(
+        scope={
+            "type": "http",
+            "headers": [(b"content-type", b"application/json")],
+            "method": "POST",
+        }
+    )
+    request._url = URL(url="/chat/completions")
+    request._body = json.dumps(
+        {"model": "gpt-4o", "user": "different-id-from-body"}
+    ).encode()
+
+    attrs = _proxy_attrs_for_centralized_checks(user_custom_auth=None)
+    originals = {a: getattr(_proxy_server_mod, a, None) for a in attrs}
+    try:
+        for k, v in attrs.items():
+            setattr(_proxy_server_mod, k, v)
+        with (
+            patch(
+                "litellm.proxy.auth.user_api_key_auth._user_api_key_auth_builder",
+                new_callable=AsyncMock,
+                return_value=builder_token,
+            ),
+            patch(
+                "litellm.proxy.auth.user_api_key_auth._run_centralized_common_checks",
+                new_callable=AsyncMock,
+            ),
+            patch(
+                "litellm.proxy.auth.user_api_key_auth.RouteChecks.should_call_route",
+            ),
+            patch(
+                "litellm.proxy.auth.user_api_key_auth.resolve_and_validate_end_user_id",
+                new_callable=AsyncMock,
+            ) as mock_resolve,
+        ):
+            result = await user_api_key_auth(request=request, api_key="Bearer sk-test")
+
+        assert result.end_user_id == "builder-resolved-id"
+        mock_resolve.assert_not_awaited()
+    finally:
+        for k, v in originals.items():
+            setattr(_proxy_server_mod, k, v)
diff --git a/tests/test_litellm/proxy/common_utils/test_callback_utils.py b/tests/test_litellm/proxy/common_utils/test_callback_utils.py
index c6132194c74..cb30970a34e 100644
--- a/tests/test_litellm/proxy/common_utils/test_callback_utils.py
+++ b/tests/test_litellm/proxy/common_utils/test_callback_utils.py
@@ -1,3 +1,4 @@
+import copy
 import sys
 import os
 from types import SimpleNamespace
@@ -7,6 +8,8 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 
 from litellm.proxy.common_utils.callback_utils import (
+    decrypt_callback_vars,
+    encrypt_callback_vars,
     initialize_callbacks_on_proxy,
     get_remaining_tokens_and_requests_from_request_data,
     normalize_callback_names,
@@ -119,3 +122,143 @@ def test_initialize_callbacks_on_proxy_instantiates_compression_interception(
         assert "compression_interception" not in litellm.callbacks
     finally:
         litellm.callbacks = original_callbacks
+
+
+# ---------------------------------------------------------------------------
+# encrypt_callback_vars / decrypt_callback_vars
+# ---------------------------------------------------------------------------
+
+
+def _sample_metadata():
+    return {
+        "logging": [
+            {
+                "callback_name": "langfuse",
+                "callback_type": "success_and_failure",
+                "callback_vars": {
+                    "langfuse_public_key": "pk-lf-public",
+                    "langfuse_secret_key": "sk-lf-secret",
+                    "langfuse_host": "https://cloud.langfuse.com",
+                },
+            }
+        ],
+        "callback_settings": {
+            "callback_vars": {"langsmith_api_key": "ls-api-key"},
+        },
+        "tags": ["unrelated"],
+    }
+
+
+def _set_salt_key(monkeypatch):
+    monkeypatch.setenv("LITELLM_SALT_KEY", "test-salt-32-bytes-aaaaaaaaaaaaaa")
+
+
+def test_encrypt_callback_vars_round_trip(monkeypatch):
+    _set_salt_key(monkeypatch)
+    original = _sample_metadata()
+    encrypted = encrypt_callback_vars(original)
+
+    enc_vars = encrypted["logging"][0]["callback_vars"]
+    assert enc_vars["langfuse_secret_key"] != "sk-lf-secret"
+    assert enc_vars["langfuse_public_key"] != "pk-lf-public"
+    assert (
+        encrypted["callback_settings"]["callback_vars"]["langsmith_api_key"]
+        != "ls-api-key"
+    )
+
+    decrypted = decrypt_callback_vars(encrypted)
+    assert (
+        decrypted["logging"][0]["callback_vars"]
+        == original["logging"][0]["callback_vars"]
+    )
+    assert (
+        decrypted["callback_settings"]["callback_vars"]
+        == original["callback_settings"]["callback_vars"]
+    )
+
+
+def test_encrypt_callback_vars_is_idempotent(monkeypatch):
+    _set_salt_key(monkeypatch)
+    once = encrypt_callback_vars(_sample_metadata())
+    twice = encrypt_callback_vars(once)
+    assert once == twice
+
+
+def test_encrypt_callback_vars_does_not_mutate_input(monkeypatch):
+    _set_salt_key(monkeypatch)
+    original = _sample_metadata()
+    snapshot = copy.deepcopy(original)
+    encrypt_callback_vars(original)
+    assert original == snapshot
+
+
+def test_decrypt_callback_vars_passes_through_legacy_plaintext(monkeypatch):
+    _set_salt_key(monkeypatch)
+    plaintext = _sample_metadata()
+    decrypted = decrypt_callback_vars(plaintext)
+    # legacy rows decrypt-fail and fall through unchanged
+    assert (
+        decrypted["logging"][0]["callback_vars"]["langfuse_secret_key"]
+        == "sk-lf-secret"
+    )
+
+
+def test_callback_vars_helpers_handle_edge_shapes(monkeypatch):
+    _set_salt_key(monkeypatch)
+    assert encrypt_callback_vars(None) is None
+    assert encrypt_callback_vars({}) == {}
+    assert decrypt_callback_vars(None) is None
+    assert decrypt_callback_vars({}) == {}
+
+    # logging not a list / callback_vars not a dict — leave alone
+    weird = {"logging": "not-a-list", "callback_settings": {"callback_vars": None}}
+    assert encrypt_callback_vars(weird) == weird
+
+    # empty/None callback_vars values stay as-is
+    has_blanks = {
+        "logging": [
+            {
+                "callback_vars": {
+                    "langfuse_public_key": "",
+                    "langfuse_secret_key": None,
+                    "langfuse_host": "https://cloud.langfuse.com",
+                }
+            }
+        ]
+    }
+    out = encrypt_callback_vars(has_blanks)
+    cv = out["logging"][0]["callback_vars"]
+    assert cv["langfuse_public_key"] == ""
+    assert cv["langfuse_secret_key"] is None
+    # langfuse_host is a routing field, not a credential — stays plain.
+    assert cv["langfuse_host"] == "https://cloud.langfuse.com"
+
+
+def test_encrypt_callback_vars_only_encrypts_credential_fields(monkeypatch):
+    """Routing/identifier fields stay plaintext; credential fields encrypt."""
+    _set_salt_key(monkeypatch)
+    metadata = {
+        "logging": [
+            {
+                "callback_vars": {
+                    "langfuse_secret_key": "sk-real",
+                    "langfuse_public_key": "pk-real",
+                    "langfuse_host": "https://cloud.langfuse.com",
+                    "langsmith_project": "my-proj",
+                    "langsmith_base_url": "https://smith.example",
+                    "gcs_path_service_account": "{json contents}",
+                }
+            }
+        ]
+    }
+    cv = encrypt_callback_vars(metadata)["logging"][0]["callback_vars"]
+
+    # Sensitive (key-name segments match SensitiveDataMasker patterns):
+    assert cv["langfuse_secret_key"] != "sk-real"
+    assert cv["langfuse_public_key"] != "pk-real"
+    # Sensitive via the explicit gcs override:
+    assert cv["gcs_path_service_account"] != "{json contents}"
+    # Routing / identifiers stay plaintext:
+    assert cv["langfuse_host"] == "https://cloud.langfuse.com"
+    assert cv["langsmith_project"] == "my-proj"
+    assert cv["langsmith_base_url"] == "https://smith.example"
diff --git a/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py b/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py
index b4343f6b2e1..3d7cb1e35f3 100644
--- a/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py
+++ b/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py
@@ -16,6 +16,7 @@ sys.path.insert(
 import litellm
 from litellm.proxy._types import ProxyException
 from litellm.proxy.common_utils.http_parsing_utils import (
+    _is_form_content_type,
     _read_request_body,
     _safe_get_request_headers,
     _safe_get_request_parsed_body,
@@ -853,3 +854,145 @@ class TestGetTagsFromRequestBodyStringCoerce:
 
         tags = get_tags_from_request_body({"metadata": {"tags": ["x"]}})
         assert tags == ["x"]
+
+
+class TestIsFormContentType:
+    @pytest.mark.parametrize(
+        "content_type",
+        [
+            "application/x-www-form-urlencoded",
+            "multipart/form-data",
+            "multipart/form-data; boundary=----WebKitFormBoundary",
+            "Application/X-WWW-Form-Urlencoded",
+            "  multipart/form-data  ",
+            "application/x-www-form-urlencoded; charset=utf-8",
+        ],
+    )
+    def test_form_types_match(self, content_type):
+        assert _is_form_content_type(content_type) is True
+
+    @pytest.mark.parametrize(
+        "content_type",
+        [
+            "",
+            "application/json",
+            "application/json; charset=utf-8",
+            "application/form-json",
+            "multiform/anything",
+            "application/json; xform=1",
+            "application/xml-with-form-data-but-not-actually",
+            "text/plain",
+            "form",
+        ],
+    )
+    def test_non_form_types_rejected(self, content_type):
+        assert _is_form_content_type(content_type) is False
+
+
+class TestReadRequestBodyNonCanonicalContentType:
+    """A JSON body with a ``"form"``-substring Content-Type must parse as JSON."""
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize(
+        "content_type",
+        [
+            "application/form-json",
+            "application/json; xform=1",
+            "multiform/anything",
+        ],
+    )
+    async def test_json_body_with_formlike_content_type_parses_as_json(
+        self, content_type
+    ):
+        payload = {"user_config": {"model_list": []}, "model": "x"}
+
+        mock_request = MagicMock()
+        mock_request.body = AsyncMock(return_value=orjson.dumps(payload))
+        mock_request.form = AsyncMock(return_value={})
+        mock_request.headers = {"content-type": content_type}
+        mock_request.scope = {}
+
+        result = await _read_request_body(mock_request)
+        assert result == payload
+        mock_request.form.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_real_form_post_still_parsed_as_form(self):
+        mock_request = MagicMock()
+        mock_request.form = AsyncMock(return_value={"k": "v"})
+        mock_request.body = AsyncMock(return_value=b"")
+        mock_request.headers = {"content-type": "application/x-www-form-urlencoded"}
+        mock_request.scope = {}
+
+        result = await _read_request_body(mock_request)
+        assert result == {"k": "v"}
+        mock_request.form.assert_awaited_once()
+
+
+class TestReadRequestBodyFormParseFailure:
+    """
+    A failed ``request.form()`` parse (e.g. multipart with missing boundary)
+    must surface as a 400, not silently return ``{}`` — otherwise the
+    auth-time pre-read sees an empty body while a later raw-body re-read
+    sees the original payload, defeating every banned-param check.
+    """
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize(
+        "raised_exception",
+        [
+            ValueError("Missing boundary in multipart."),
+            AssertionError("malformed chunk"),
+            RuntimeError("form parser exploded"),
+        ],
+    )
+    async def test_form_parse_failure_raises_400(self, raised_exception):
+        mock_request = MagicMock()
+        mock_request.form = AsyncMock(side_effect=raised_exception)
+        mock_request.headers = {"content-type": "multipart/form-data"}
+        mock_request.scope = {}
+
+        with pytest.raises(ProxyException) as exc_info:
+            await _read_request_body(mock_request)
+        assert str(exc_info.value.code) == "400"
+
+
+class TestGetRequestBody:
+    @pytest.mark.asyncio
+    async def test_json_with_charset_param_parses_as_json(self):
+        payload = {"k": "v"}
+        mock_request = MagicMock()
+        mock_request.method = "POST"
+        mock_request.body = AsyncMock(return_value=orjson.dumps(payload))
+        mock_request.headers = {"content-type": "application/json; charset=utf-8"}
+        mock_request.scope = {}
+
+        result = await get_request_body(mock_request)
+        assert result == payload
+
+    @pytest.mark.asyncio
+    async def test_form_post_routes_to_form_data(self):
+        mock_request = MagicMock()
+        mock_request.method = "POST"
+        mock_request.headers = {"content-type": "multipart/form-data; boundary=x"}
+        mock_request.form = AsyncMock(return_value={"k": "v"})
+        mock_request.scope = {}
+
+        result = await get_request_body(mock_request)
+        assert result == {"k": "v"}
+
+    @pytest.mark.asyncio
+    async def test_substring_match_no_longer_accepted(self):
+        mock_request = MagicMock()
+        mock_request.method = "POST"
+        mock_request.headers = {"content-type": "application/form-json"}
+        mock_request.scope = {}
+
+        with pytest.raises(ValueError, match="Unsupported content type"):
+            await get_request_body(mock_request)
+
+    @pytest.mark.asyncio
+    async def test_non_post_returns_empty(self):
+        mock_request = MagicMock()
+        mock_request.method = "GET"
+        assert await get_request_body(mock_request) == {}
diff --git a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py
index 1063f59afb6..f3cec320532 100644
--- a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py
+++ b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py
@@ -1,75 +1,103 @@
 """
-Test for interactions endpoint agent parameter handling.
+Tests for managed-agent interaction routing.
 
-Tests that the /v1beta/interactions endpoint correctly extracts
-the `agent` parameter as a fallback when `model` is not provided.
+Custom Gemini agents are identified by ``agent`` (name/id), not ``model``.
+The proxy must not pass the agent name as ``model`` or LiteLLM may route to
+openai/* wildcards instead of Gemini interactions.
 """
 
+from unittest.mock import MagicMock, patch
+
 import pytest
 
 
 class TestInteractionsAgentParameter:
-    """Test agent parameter handling in interactions endpoint."""
+    """Proxy endpoint must keep agent and model separate."""
 
-    def test_agent_parameter_fallback_logic(self):
-        """
-        Test the core logic: model or agent extraction.
-
-        This tests the fix in endpoints.py line ~267:
-        model=data.get("model") or data.get("agent")
-        """
-        # Case 1: Only agent provided (Deep Research use case)
+    def test_create_interaction_uses_model_only_from_body(self):
+        """POST /v1beta/interactions: model kwarg is only the request's model field."""
         data = {
-            "agent": "deep-research-pro-preview-12-2025",
-            "input": "Research quantum computing",
-            "background": True,
+            "agent": "mqy-custom-slides-agent",
+            "input": "hello",
         }
-        model = data.get("model") or data.get("agent")
-        assert model == "deep-research-pro-preview-12-2025"
+        # Fixed behavior: do NOT fall back agent → model
+        model_for_routing = data.get("model")
+        assert model_for_routing is None
+        assert data.get("agent") == "mqy-custom-slides-agent"
 
-        # Case 2: Only model provided (normal use case)
+    def test_model_field_still_used_when_present(self):
         data = {
             "model": "gemini-2.5-flash",
-            "input": "Hello world",
+            "input": "hello",
         }
-        model = data.get("model") or data.get("agent")
-        assert model == "gemini-2.5-flash"
+        model_for_routing = data.get("model")
+        assert model_for_routing == "gemini-2.5-flash"
 
-        # Case 3: Both provided (model takes precedence)
-        data = {
-            "model": "gemini-2.5-flash",
-            "agent": "deep-research-pro-preview-12-2025",
-            "input": "Test",
-        }
-        model = data.get("model") or data.get("agent")
-        assert model == "gemini-2.5-flash"
 
-        # Case 4: Neither provided
-        data = {
-            "input": "Test",
-        }
-        model = data.get("model") or data.get("agent")
-        assert model is None
+class TestInteractionsAgentOnlyProviderRouting:
+    """SDK: agent-only create must not call get_llm_provider on the agent name."""
 
-    def test_route_type_in_skip_model_routing_list(self):
-        """
-        Test that acreate_interaction is in the list of routes
-        that skip model-based routing.
+    @patch("litellm.interactions.main.interactions_http_handler")
+    @patch("litellm.interactions.main.get_provider_interactions_api_config")
+    @patch("litellm.get_llm_provider")
+    def test_agent_only_skips_get_llm_provider(
+        self,
+        mock_get_llm_provider,
+        mock_get_config,
+        mock_handler,
+    ):
+        from litellm.interactions.main import create
+        from litellm.types.interactions import InteractionsAPIResponse
 
-        This tests the fix in route_llm_request.py.
-        """
-        # The list of routes that skip model routing for interactions
-        skip_model_routing_routes = [
-            "acreate_interaction",
-            "aget_interaction",
-            "adelete_interaction",
-            "acancel_interaction",
-        ]
+        mock_get_config.return_value = MagicMock()
+        mock_handler.create_interaction.return_value = InteractionsAPIResponse(
+            id="int-1",
+            status="completed",
+            object="interaction",
+        )
 
-        # acreate_interaction should be in the list (this is the fix)
-        assert "acreate_interaction" in skip_model_routing_routes
+        logging_obj = MagicMock()
+        create(
+            agent="mqy-custom-slides-agent",
+            input="test",
+            custom_llm_provider="gemini",
+            litellm_logging_obj=logging_obj,
+        )
 
-        # All interaction routes should be covered
-        assert "aget_interaction" in skip_model_routing_routes
-        assert "adelete_interaction" in skip_model_routing_routes
-        assert "acancel_interaction" in skip_model_routing_routes
+        mock_get_llm_provider.assert_not_called()
+        call_kwargs = mock_handler.create_interaction.call_args.kwargs
+        assert call_kwargs["agent"] == "mqy-custom-slides-agent"
+        assert call_kwargs["model"] is None
+        assert call_kwargs["custom_llm_provider"] == "gemini"
+
+    @patch("litellm.interactions.main.interactions_http_handler")
+    @patch("litellm.interactions.main.get_provider_interactions_api_config")
+    @patch("litellm.get_llm_provider")
+    def test_proxy_mistake_model_equals_agent_is_corrected(
+        self,
+        mock_get_llm_provider,
+        mock_get_config,
+        mock_handler,
+    ):
+        """If model was wrongly set to the agent name, clear it before the HTTP call."""
+        from litellm.interactions.main import create
+        from litellm.types.interactions import InteractionsAPIResponse
+
+        mock_get_config.return_value = MagicMock()
+        mock_handler.create_interaction.return_value = InteractionsAPIResponse(
+            id="int-1",
+            status="completed",
+            object="interaction",
+        )
+
+        logging_obj = MagicMock()
+        create(
+            model="mqy-custom-slides-agent",
+            agent="mqy-custom-slides-agent",
+            input="test",
+            custom_llm_provider="gemini",
+            litellm_logging_obj=logging_obj,
+        )
+
+        mock_get_llm_provider.assert_not_called()
+        assert mock_handler.create_interaction.call_args.kwargs["model"] is None
diff --git a/tests/test_litellm/proxy/google_endpoints/test_managed_agents_model_param.py b/tests/test_litellm/proxy/google_endpoints/test_managed_agents_model_param.py
new file mode 100644
index 00000000000..5485d0f2929
--- /dev/null
+++ b/tests/test_litellm/proxy/google_endpoints/test_managed_agents_model_param.py
@@ -0,0 +1,199 @@
+"""
+Tests verifying that managed-agent proxy endpoints never pass the agent name
+as the ``model`` parameter to ``base_process_llm_request``.
+
+Passing ``model=<agent_name>`` would cause ``common_processing_pre_call_logic``
+to write the agent name into ``self.data["model"]``, which triggers spurious
+model-alias mapping, rate-limiting lookups, and logging tied to a
+non-existent model deployment.  The agent name is already carried in
+``data["name"]`` and must not pollute the ``model`` slot.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+
+def _build_agents_client():
+    """Build a TestClient whose auth dependency is overridden to a PROXY_ADMIN
+    user. Using ``dependency_overrides`` is the only reliable way to bypass the
+    real ``user_api_key_auth`` for FastAPI route tests — patching the module-
+    level name does not affect the function reference captured by ``Depends``.
+    The PROXY_ADMIN role also bypasses the caller-supplied-api_key guard so
+    these tests can focus on the ``model=None`` invariant.
+    """
+    from fastapi import FastAPI
+    from fastapi.testclient import TestClient
+
+    from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth
+    from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+    from litellm.proxy.google_endpoints.agents_endpoints import router as agents_router
+
+    app = FastAPI()
+    app.include_router(agents_router)
+
+    async def _fake_user_api_key_auth():
+        return UserAPIKeyAuth(
+            api_key="sk-test",
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+        )
+
+    app.dependency_overrides[user_api_key_auth] = _fake_user_api_key_auth
+    return TestClient(app)
+
+
+def _patch_proxy_server_imports(client=None):
+    """Return a context-manager that stubs _proxy_server_imports so tests
+    don't need a running proxy."""
+    mock_srv = {
+        "general_settings": {},
+        "llm_router": MagicMock(),
+        "proxy_config": MagicMock(),
+        "proxy_logging_obj": MagicMock(),
+        "select_data_generator": None,
+        "user_api_base": None,
+        "user_max_tokens": None,
+        "user_model": None,
+        "user_request_timeout": None,
+        "user_temperature": None,
+        "version": "0.0.0",
+    }
+    return patch(
+        "litellm.proxy.google_endpoints.agents_endpoints._proxy_server_imports",
+        return_value=mock_srv,
+    )
+
+
+def _patch_base_process(return_value=None):
+    if return_value is None:
+        return_value = {"name": "agents/my-agent", "displayName": "My Agent"}
+    return patch(
+        "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing.base_process_llm_request",
+        new_callable=AsyncMock,
+        return_value=return_value,
+    )
+
+
+def _patch_auth():
+    """Deprecated no-op kept for call-site compatibility.
+
+    ``_build_agents_client`` now installs a FastAPI ``dependency_overrides``
+    entry that injects a PROXY_ADMIN ``UserAPIKeyAuth``, so individual tests
+    no longer need to patch the module-level ``user_api_key_auth`` name.
+    """
+    return patch("os.getpid")
+
+
+class TestManagedAgentsModelParam:
+    """Endpoints must pass model=None, not the agent name, to base_process_llm_request."""
+
+    def test_create_agent_passes_model_none(self):
+        """POST /v1beta/agents: model kwarg must be None, not the name field."""
+        try:
+            client = _build_agents_client()
+        except ImportError as exc:
+            pytest.skip(f"Skipping: missing dependency {exc}")
+
+        with (
+            _patch_proxy_server_imports(),
+            _patch_base_process() as mock_process,
+            _patch_auth(),
+        ):
+            client.post(
+                "/v1beta/agents",
+                json={
+                    "name": "my-custom-slides-agent",
+                    "base_agent": "waverunner",
+                    "instructions": "Be helpful.",
+                },
+            )
+
+        mock_process.assert_called_once()
+        kwargs = mock_process.call_args.kwargs
+        assert kwargs["model"] is None, (
+            f"create_gemini_agent must not pass model={kwargs['model']!r}; "
+            "the agent name must stay in data['name'], not pollute data['model']"
+        )
+        assert kwargs["route_type"] == "acreate_agent"
+
+    def test_get_agent_passes_model_none(self):
+        """GET /v1beta/agents/{name}: model kwarg must be None."""
+        try:
+            client = _build_agents_client()
+        except ImportError as exc:
+            pytest.skip(f"Skipping: missing dependency {exc}")
+
+        with (
+            _patch_proxy_server_imports(),
+            _patch_base_process() as mock_process,
+            _patch_auth(),
+        ):
+            client.get("/v1beta/agents/my-custom-slides-agent")
+
+        mock_process.assert_called_once()
+        kwargs = mock_process.call_args.kwargs
+        assert (
+            kwargs["model"] is None
+        ), f"get_gemini_agent must not pass model={kwargs['model']!r}"
+        assert kwargs["route_type"] == "aget_agent"
+
+    def test_delete_agent_passes_model_none(self):
+        """DELETE /v1beta/agents/{name}: model kwarg must be None."""
+        try:
+            client = _build_agents_client()
+        except ImportError as exc:
+            pytest.skip(f"Skipping: missing dependency {exc}")
+
+        with (
+            _patch_proxy_server_imports(),
+            _patch_base_process() as mock_process,
+            _patch_auth(),
+        ):
+            client.delete("/v1beta/agents/my-custom-slides-agent")
+
+        mock_process.assert_called_once()
+        kwargs = mock_process.call_args.kwargs
+        assert (
+            kwargs["model"] is None
+        ), f"delete_gemini_agent must not pass model={kwargs['model']!r}"
+        assert kwargs["route_type"] == "adelete_agent"
+
+    def test_list_agent_versions_passes_model_none(self):
+        """GET /v1beta/agents/{name}/versions: model kwarg must be None."""
+        try:
+            client = _build_agents_client()
+        except ImportError as exc:
+            pytest.skip(f"Skipping: missing dependency {exc}")
+
+        with (
+            _patch_proxy_server_imports(),
+            _patch_base_process() as mock_process,
+            _patch_auth(),
+        ):
+            client.get("/v1beta/agents/my-custom-slides-agent/versions")
+
+        mock_process.assert_called_once()
+        kwargs = mock_process.call_args.kwargs
+        assert (
+            kwargs["model"] is None
+        ), f"list_gemini_agent_versions must not pass model={kwargs['model']!r}"
+        assert kwargs["route_type"] == "alist_agent_versions"
+
+    def test_list_agents_already_passes_model_none(self):
+        """GET /v1beta/agents: existing list endpoint already passes model=None — keep it so."""
+        try:
+            client = _build_agents_client()
+        except ImportError as exc:
+            pytest.skip(f"Skipping: missing dependency {exc}")
+
+        with (
+            _patch_proxy_server_imports(),
+            _patch_base_process(return_value={"agents": []}) as mock_process,
+            _patch_auth(),
+        ):
+            client.get("/v1beta/agents")
+
+        mock_process.assert_called_once()
+        kwargs = mock_process.call_args.kwargs
+        assert kwargs["model"] is None
+        assert kwargs["route_type"] == "alist_agents"
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py
index a3247d2e557..71178c4826c 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_bedrock_guardrails.py
@@ -2073,6 +2073,226 @@ def test_get_http_exception_includes_assessments_and_identifier():
     assert exc.detail["assessments"][0]["matches"][0]["match"] == "[REDACTED]"
 
 
+def test_extract_violation_category_names_mixed_policies():
+    """Topic names, content-filter types, PII types, and managed-word types
+    flatten into a single category-name list — using only the operator-
+    defined `name`/`type` labels."""
+    g = _make_guardrail()
+    response = {
+        "action": "GUARDRAIL_INTERVENED",
+        "assessments": [
+            {
+                "topicPolicy": {
+                    "topics": [
+                        {"name": "Fiduciary Advice", "action": "BLOCKED"},
+                        {"name": "Tax Advice", "action": "BLOCKED"},
+                    ]
+                },
+                "contentPolicy": {
+                    "filters": [{"type": "VIOLENCE", "action": "BLOCKED"}]
+                },
+                "wordPolicy": {
+                    "managedWordLists": [{"type": "PROFANITY", "action": "BLOCKED"}],
+                },
+                "sensitiveInformationPolicy": {
+                    "piiEntities": [{"type": "EMAIL", "action": "BLOCKED"}]
+                },
+            }
+        ],
+    }
+    names = g._extract_violation_category_names(response)
+    assert "Fiduciary Advice" in names
+    assert "Tax Advice" in names
+    assert "VIOLENCE" in names
+    assert "PROFANITY" in names
+    assert "EMAIL" in names
+
+
+def test_extract_violation_category_names_does_not_leak_user_input():
+    """SECURITY: customWords.match is the raw user-submitted word that
+    triggered the rule, and an unnamed regex match is the actual sensitive
+    value (e.g. a credit-card number). Neither must appear in
+    violation_categories — otherwise the content the guardrail blocked
+    leaks straight into telemetry backends."""
+    g = _make_guardrail()
+    response = {
+        "action": "GUARDRAIL_INTERVENED",
+        "assessments": [
+            {
+                "wordPolicy": {
+                    "customWords": [
+                        {"match": "secret-codeword-abc-123", "action": "BLOCKED"}
+                    ],
+                },
+                "sensitiveInformationPolicy": {
+                    "regexes": [{"match": "4111-1111-1111-1111", "action": "BLOCKED"}]
+                },
+            }
+        ],
+    }
+    names = g._extract_violation_category_names(response)
+    assert "secret-codeword-abc-123" not in names
+    assert "4111-1111-1111-1111" not in names
+    assert names == []
+
+
+def test_extract_violation_category_names_named_regex_uses_name():
+    """A regex with a `name` field surfaces that operator-defined label
+    (safe to log), not the matched value."""
+    g = _make_guardrail()
+    response = {
+        "action": "GUARDRAIL_INTERVENED",
+        "assessments": [
+            {
+                "sensitiveInformationPolicy": {
+                    "regexes": [
+                        {
+                            "name": "credit-card-pattern",
+                            "match": "4111-1111-1111-1111",
+                            "action": "BLOCKED",
+                        }
+                    ]
+                }
+            }
+        ],
+    }
+    names = g._extract_violation_category_names(response)
+    assert names == ["credit-card-pattern"]
+
+
+def test_extract_violation_category_names_skips_anonymized():
+    """ANONYMIZED entries are not blocks — they must not contribute to the
+    violation_categories list."""
+    g = _make_guardrail()
+    response = {
+        "action": "GUARDRAIL_INTERVENED",
+        "assessments": [
+            {
+                "sensitiveInformationPolicy": {
+                    "piiEntities": [{"type": "NAME", "action": "ANONYMIZED"}]
+                }
+            }
+        ],
+    }
+    assert g._extract_violation_category_names(response) == []
+
+
+def test_extract_violation_category_names_no_assessments():
+    """Empty / missing assessments → empty list, not an error."""
+    g = _make_guardrail()
+    assert g._extract_violation_category_names({"action": "NONE"}) == []
+    assert g._extract_violation_category_names({"assessments": None}) == []
+
+
+@pytest.mark.asyncio
+async def test_make_bedrock_api_request_forwards_guardrail_action():
+    """Bedrock's top-level ``action`` string must be propagated through
+    ``tracing_detail`` so downstream loggers (OTEL, ...) can surface the
+    raw provider verdict as a queryable attribute without re-parsing the
+    redacted guardrail_response blob."""
+    guardrail = BedrockGuardrail(
+        guardrailIdentifier="test-guardrail", guardrailVersion="DRAFT"
+    )
+    mock_credentials = MagicMock()
+    mock_credentials.access_key = "k"
+    mock_credentials.secret_key = "s"
+    mock_credentials.token = None
+
+    mock_bedrock_response = MagicMock()
+    mock_bedrock_response.status_code = 200
+    mock_bedrock_response.json.return_value = {
+        "action": "GUARDRAIL_INTERVENED",
+        "assessments": [
+            {
+                "topicPolicy": {
+                    "topics": [{"name": "Fiduciary Advice", "action": "BLOCKED"}]
+                }
+            }
+        ],
+    }
+
+    request_data = {
+        "model": "gpt-4o",
+        "messages": [{"role": "user", "content": "hi"}],
+    }
+
+    with (
+        patch.object(
+            guardrail.async_handler, "post", new_callable=AsyncMock
+        ) as mock_post,
+        patch.object(
+            guardrail, "_load_credentials", return_value=(mock_credentials, "us-east-1")
+        ),
+        patch.object(guardrail, "_prepare_request", return_value=MagicMock()),
+        patch.object(
+            guardrail,
+            "add_standard_logging_guardrail_information_to_request_data",
+        ) as mock_log,
+        patch.object(
+            guardrail,
+            "_get_http_exception_for_blocked_guardrail",
+            return_value=Exception("blocked"),
+        ),
+    ):
+        mock_post.return_value = mock_bedrock_response
+
+        with pytest.raises(Exception):
+            await guardrail.make_bedrock_api_request(
+                source="INPUT",
+                messages=request_data["messages"],
+                request_data=request_data,
+            )
+
+        tracing_detail = mock_log.call_args.kwargs["tracing_detail"]
+        assert tracing_detail is not None
+        assert tracing_detail["guardrail_action"] == "GUARDRAIL_INTERVENED"
+
+
+@pytest.mark.asyncio
+async def test_make_bedrock_api_request_omits_guardrail_action_when_missing():
+    """If the Bedrock response omits ``action`` (older / partial payloads),
+    the field must be left off ``tracing_detail`` rather than written as
+    ``None`` — downstream code expects strings or absence, not nulls."""
+    guardrail = BedrockGuardrail(
+        guardrailIdentifier="test-guardrail", guardrailVersion="DRAFT"
+    )
+    mock_credentials = MagicMock()
+    mock_credentials.access_key = "k"
+    mock_credentials.secret_key = "s"
+    mock_credentials.token = None
+
+    mock_bedrock_response = MagicMock()
+    mock_bedrock_response.status_code = 200
+    mock_bedrock_response.json.return_value = {"assessments": []}
+
+    with (
+        patch.object(
+            guardrail.async_handler, "post", new_callable=AsyncMock
+        ) as mock_post,
+        patch.object(
+            guardrail, "_load_credentials", return_value=(mock_credentials, "us-east-1")
+        ),
+        patch.object(guardrail, "_prepare_request", return_value=MagicMock()),
+        patch.object(
+            guardrail,
+            "add_standard_logging_guardrail_information_to_request_data",
+        ) as mock_log,
+    ):
+        mock_post.return_value = mock_bedrock_response
+
+        await guardrail.make_bedrock_api_request(
+            source="INPUT",
+            messages=[{"role": "user", "content": "hi"}],
+            request_data={"model": "gpt-4o", "messages": []},
+        )
+
+        tracing_detail = mock_log.call_args.kwargs["tracing_detail"]
+        # No violation categories and no action ⇒ tracing_detail stays None
+        # (the hook collapses an empty dict before forwarding).
+        if tracing_detail is not None:
+            assert "guardrail_action" not in tracing_detail
+
+
 def test_get_http_exception_no_blocked_assessments_omits_field():
     """L3: when no assessments are blocked, the `assessments` key is omitted entirely."""
     g = _make_guardrail()
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
index e01038cd35f..6ec793a1bb0 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
@@ -553,6 +553,7 @@ class TestGuardrailActions:
             # Verify the exception has the clean error message (no wrapper)
             assert str(exc_info.value) == "Content contains harmful instructions"
             assert exc_info.value.guardrail_name == "generic_guardrail_api"
+            assert exc_info.value.status_code == 400
 
     @pytest.mark.asyncio
     async def test_action_intervened_modifies_content(
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_microsoft_purview.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_microsoft_purview.py
new file mode 100644
index 00000000000..cc89cea58d2
--- /dev/null
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_microsoft_purview.py
@@ -0,0 +1,2659 @@
+"""Unit tests for the Microsoft Purview DLP guardrail."""
+
+import asyncio
+import time
+from unittest.mock import AsyncMock, Mock, patch
+
+import httpx
+import pytest
+from fastapi import HTTPException
+
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.guardrails.guardrail_hooks.microsoft_purview.base import (
+    PurviewGuardrailBase,
+)
+from litellm.proxy.guardrails.guardrail_hooks.microsoft_purview.purview_dlp import (
+    MicrosoftPurviewDLPGuardrail,
+)
+
+
+def _make_guardrail(**kwargs) -> MicrosoftPurviewDLPGuardrail:
+    """Helper to construct a guardrail with test defaults."""
+    defaults = {
+        "guardrail_name": "test-purview",
+        "tenant_id": "test-tenant-id",
+        "client_id": "test-client-id",
+        "client_secret": "test-client-secret",
+    }
+    defaults.update(kwargs)
+    return MicrosoftPurviewDLPGuardrail(**defaults)
+
+
+def _mock_token_response():
+    """Mock a successful OAuth2 token response."""
+    resp = Mock()
+    resp.json.return_value = {
+        "access_token": "mock-access-token",
+        "expires_in": 3600,
+    }
+    return resp
+
+
+def _mock_graph_response(policy_actions=None, protection_scope_state="unchanged"):
+    """Mock a processContent Graph API response."""
+    resp = Mock()
+    body = {
+        "protectionScopeState": protection_scope_state,
+        "policyActions": policy_actions or [],
+        "processingErrors": [],
+    }
+    resp.json.return_value = body
+    resp.headers = {"ETag": "test-etag-123"}
+    return resp
+
+
+def _mock_scope_response():
+    """Mock a protectionScopes/compute Graph API response."""
+    resp = Mock()
+    resp.json.return_value = {
+        "value": [
+            {
+                "activities": "uploadText,downloadText",
+                "executionMode": "evaluateInline",
+                "policyActions": [],
+            }
+        ]
+    }
+    resp.headers = {"ETag": "scope-etag-123"}
+    return resp
+
+
+# ---------------------------------------------------------------
+# _should_block
+# ---------------------------------------------------------------
+
+
+class TestShouldBlock:
+    def test_empty_policy_actions(self):
+        assert PurviewGuardrailBase._should_block({"policyActions": []}) is False
+
+    def test_no_policy_actions_key(self):
+        assert PurviewGuardrailBase._should_block({}) is False
+
+    def test_restrict_access_block(self):
+        response = {
+            "policyActions": [
+                {
+                    "@odata.type": "#microsoft.graph.restrictAccessAction",
+                    "action": "restrictAccess",
+                    "restrictionAction": "block",
+                }
+            ]
+        }
+        assert PurviewGuardrailBase._should_block(response) is True
+
+    def test_restrict_access_non_block(self):
+        response = {
+            "policyActions": [
+                {
+                    "@odata.type": "#microsoft.graph.restrictAccessAction",
+                    "action": "restrictAccess",
+                    "restrictionAction": "warn",
+                }
+            ]
+        }
+        assert PurviewGuardrailBase._should_block(response) is False
+
+    def test_non_restrict_action(self):
+        response = {
+            "policyActions": [
+                {
+                    "@odata.type": "#microsoft.graph.auditAction",
+                    "action": "audit",
+                }
+            ]
+        }
+        assert PurviewGuardrailBase._should_block(response) is False
+
+    def test_multiple_actions_one_blocks(self):
+        response = {
+            "policyActions": [
+                {"action": "audit"},
+                {
+                    "@odata.type": "#microsoft.graph.restrictAccessAction",
+                    "action": "restrictAccess",
+                    "restrictionAction": "block",
+                },
+            ]
+        }
+        assert PurviewGuardrailBase._should_block(response) is True
+
+
+# ---------------------------------------------------------------
+# completion prompt normalization (text completions API)
+# ---------------------------------------------------------------
+
+
+class TestCompletionPromptToStr:
+    def test_string_prompt(self):
+        assert PurviewGuardrailBase.completion_prompt_to_str("  hi  ") == "hi"
+
+    def test_list_of_strings(self):
+        assert PurviewGuardrailBase.completion_prompt_to_str(["a", "b"]) == "a\nb"
+
+    def test_token_ids_returns_none(self):
+        assert PurviewGuardrailBase.completion_prompt_to_str([1, 2, 3]) is None
+
+    def test_empty(self):
+        assert PurviewGuardrailBase.completion_prompt_to_str("") is None
+        assert PurviewGuardrailBase.completion_prompt_to_str([]) is None
+
+
+# ---------------------------------------------------------------
+# User ID resolution
+# ---------------------------------------------------------------
+
+
+class TestResolveUserId:
+    def test_from_metadata_when_no_auth_identity(self):
+        guardrail = _make_guardrail()
+        data = {"metadata": {"user_id": "entra-user-123"}}
+        auth = UserAPIKeyAuth(api_key="test-key-no-user")
+        assert guardrail._resolve_user_id(data, auth) == "entra-user-123"
+
+    def test_authenticated_user_id_overrides_metadata(self):
+        """Key user_id must win over spoofed metadata[user_id_field]."""
+        guardrail = _make_guardrail()
+        data = {"metadata": {"user_id": "spoofed-entra-id"}}
+        auth = UserAPIKeyAuth(api_key="test", user_id="real-entra-id")
+        assert guardrail._resolve_user_id(data, auth) == "real-entra-id"
+
+    def test_user_api_key_metadata_before_custom_field(self):
+        """Proxy-injected user_api_key_user_id wins over arbitrary metadata field."""
+        guardrail = _make_guardrail(user_id_field="entra_id")
+        data = {
+            "metadata": {
+                "user_api_key_user_id": "from-proxy-111",
+                "entra_id": "metadata-222",
+            }
+        }
+        auth = UserAPIKeyAuth(api_key="test")
+        assert guardrail._resolve_user_id(data, auth) == "from-proxy-111"
+
+    def test_custom_field_when_no_stronger_source(self):
+        guardrail = _make_guardrail(user_id_field="entra_id")
+        data = {"metadata": {"entra_id": "custom-user-456"}}
+        auth = UserAPIKeyAuth(api_key="test")
+        assert guardrail._resolve_user_id(data, auth) == "custom-user-456"
+
+    def test_from_user_api_key_dict_user_id(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test", user_id="key-user-789")
+        assert guardrail._resolve_user_id({}, auth) == "key-user-789"
+
+    def test_from_end_user_id(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test", end_user_id="end-user-101")
+        assert guardrail._resolve_user_id({}, auth) == "end-user-101"
+
+    def test_end_user_id_after_key_user_id(self):
+        """When both key user_id and end_user_id exist, key user_id is used first."""
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(
+            api_key="test", user_id="key-owner", end_user_id="end-user-101"
+        )
+        assert guardrail._resolve_user_id({}, auth) == "key-owner"
+
+    def test_none_when_missing(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test")
+        assert guardrail._resolve_user_id({}, auth) is None
+
+
+# ---------------------------------------------------------------
+# Pre-call hook
+# ---------------------------------------------------------------
+
+
+class TestPreCallHook:
+    @pytest.mark.asyncio
+    async def test_pre_call_allow(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={"messages": [{"role": "user", "content": "Hello, how are you?"}]},
+                call_type="completion",
+            )
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["activity"] == "uploadText"
+            assert mock_check.call_args.kwargs["block_on_violation"] is True
+
+    @pytest.mark.asyncio
+    async def test_pre_call_success_returns_request_data(self):
+        """After a successful DLP pass, the hook must return the same data dict (not None)."""
+        guardrail = _make_guardrail()
+        payload = {
+            "messages": [{"role": "user", "content": "Hello, how are you?"}],
+            "litellm_call_id": "call-abc",
+        }
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            out = await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data=payload,
+                call_type="completion",
+            )
+
+            assert out is payload
+
+    @pytest.mark.asyncio
+    async def test_pre_call_block(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.side_effect = HTTPException(
+                status_code=400,
+                detail={"error": "Microsoft Purview DLP: Content blocked by policy"},
+            )
+
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.async_pre_call_hook(
+                    user_api_key_dict=UserAPIKeyAuth(
+                        api_key="test", user_id="user-123"
+                    ),
+                    cache=None,
+                    data={
+                        "messages": [
+                            {
+                                "role": "user",
+                                "content": "SSN: 123-45-6789",
+                            }
+                        ]
+                    },
+                    call_type="completion",
+                )
+
+            assert exc_info.value.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_pre_call_no_user_id_raises(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.async_pre_call_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test"),
+                    cache=None,
+                    data={"messages": [{"role": "user", "content": "Hello"}]},
+                    call_type="completion",
+                )
+
+            mock_check.assert_not_called()
+            assert exc_info.value.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_pre_call_no_messages_skips(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={},
+                call_type="completion",
+            )
+
+            mock_check.assert_not_called()
+
+
+class TestPreCallFullTranscript:
+    @pytest.mark.asyncio
+    async def test_pre_call_sends_all_message_roles_to_dlp(self):
+        """DLP text must include system / prior turns, not only the last user block."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={
+                    "messages": [
+                        {"role": "system", "content": "SYSTEM_SENSITIVE"},
+                        {"role": "user", "content": "EARLIER_USER"},
+                        {"role": "assistant", "content": "reply"},
+                        {"role": "user", "content": "final benign"},
+                    ]
+                },
+                call_type="completion",
+            )
+
+            mock_check.assert_called_once()
+            sent = mock_check.call_args.kwargs["text"]
+            assert "SYSTEM_SENSITIVE" in sent
+            assert "EARLIER_USER" in sent
+            assert "final benign" in sent
+
+
+# ---------------------------------------------------------------
+# Post-call hook
+# ---------------------------------------------------------------
+
+
+class TestPostCallHook:
+    @pytest.mark.asyncio
+    async def test_post_call_allow(self):
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0, message=Message(content="Safe response", role="assistant")
+                )
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            result = await guardrail.async_post_call_success_hook(
+                data={},
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=response,
+            )
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["activity"] == "downloadText"
+            assert result is response
+
+    @pytest.mark.asyncio
+    async def test_post_call_block(self):
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(
+                        content="Credit card: 4532-6677-8521-3500",
+                        role="assistant",
+                    ),
+                )
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.side_effect = HTTPException(
+                status_code=400,
+                detail={"error": "Microsoft Purview DLP: Content blocked by policy"},
+            )
+
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.async_post_call_success_hook(
+                    data={},
+                    user_api_key_dict=UserAPIKeyAuth(
+                        api_key="test", user_id="user-123"
+                    ),
+                    response=response,
+                )
+
+            assert exc_info.value.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_post_call_no_user_id_raises(self):
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(index=0, message=Message(content="Response", role="assistant"))
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.async_post_call_success_hook(
+                    data={},
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test"),
+                    response=response,
+                )
+
+            mock_check.assert_not_called()
+            assert exc_info.value.status_code == 400
+
+    @pytest.mark.asyncio
+    async def test_post_call_scans_all_choices(self):
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(content="First completion", role="assistant"),
+                ),
+                Choices(
+                    index=1,
+                    message=Message(content="Second completion body", role="assistant"),
+                ),
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_post_call_success_hook(
+                data={},
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=response,
+            )
+
+            mock_check.assert_called_once()
+            combined = mock_check.call_args.kwargs["text"]
+            assert "First completion" in combined
+            assert "Second completion body" in combined
+
+
+class TestTextCompletionHooks:
+    @pytest.mark.asyncio
+    async def test_pre_call_text_completion_uses_prompt(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={"prompt": "Completions API prompt body"},
+                call_type="text_completion",
+            )
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["text"] == "Completions API prompt body"
+            assert mock_check.call_args.kwargs["activity"] == "uploadText"
+
+    @pytest.mark.asyncio
+    async def test_post_call_text_completion_all_choices(self):
+        from litellm.types.utils import TextChoices, TextCompletionResponse
+
+        guardrail = _make_guardrail()
+        response = TextCompletionResponse(
+            model="gpt-3.5-turbo-instruct",
+            choices=[
+                TextChoices(text="alpha", index=0),
+                TextChoices(text="beta", index=1),
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_post_call_success_hook(
+                data={},
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=response,
+            )
+
+            mock_check.assert_called_once()
+            combined = mock_check.call_args.kwargs["text"]
+            assert "alpha" in combined
+            assert "beta" in combined
+
+
+# ---------------------------------------------------------------
+# Responses API hooks
+# ---------------------------------------------------------------
+
+
+class TestResponsesAPIHooks:
+    @pytest.mark.asyncio
+    async def test_pre_call_responses_api_string_input(self):
+        """Pre-call hook must scan plain-string ``input`` on responses call type."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={"input": "SSN: 123-45-6789"},
+                call_type="responses",
+            )
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["activity"] == "uploadText"
+            assert "SSN: 123-45-6789" in mock_check.call_args.kwargs["text"]
+
+    @pytest.mark.asyncio
+    async def test_pre_call_aresponses_string_input(self):
+        """Pre-call hook must scan ``input`` on ``aresponses`` call type too."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={"input": "sensitive content"},
+                call_type="aresponses",
+            )
+
+            mock_check.assert_called_once()
+            assert "sensitive content" in mock_check.call_args.kwargs["text"]
+
+    @pytest.mark.asyncio
+    async def test_pre_call_responses_api_list_input(self):
+        """Pre-call hook must extract text from structured list ``input``."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={
+                    "input": [{"role": "user", "content": "Secret phrase: alpha bravo"}]
+                },
+                call_type="responses",
+            )
+
+            mock_check.assert_called_once()
+            assert "Secret phrase: alpha bravo" in mock_check.call_args.kwargs["text"]
+
+    @pytest.mark.asyncio
+    async def test_pre_call_responses_api_no_input_skips(self):
+        """Pre-call hook must not call _check_content when ``input`` is absent."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={},
+                call_type="responses",
+            )
+
+            mock_check.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_pre_call_responses_string_input_includes_instructions(self):
+        """Benign string ``input`` must still scan ``instructions`` (system message)."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={
+                    "input": "benign user text",
+                    "instructions": "SYSTEM_SENSITIVE in instructions",
+                },
+                call_type="responses",
+            )
+
+            mock_check.assert_called_once()
+            sent = mock_check.call_args.kwargs["text"]
+            assert "benign user text" in sent
+            assert "SYSTEM_SENSITIVE in instructions" in sent
+
+    @pytest.mark.asyncio
+    async def test_pre_call_responses_instructions_only(self):
+        """Requests with only ``instructions`` (no ``input``) must still be scanned."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={"instructions": "policy text in instructions only"},
+                call_type="responses",
+            )
+
+            mock_check.assert_called_once()
+            assert (
+                "policy text in instructions only"
+                in mock_check.call_args.kwargs["text"]
+            )
+
+    @pytest.mark.asyncio
+    async def test_post_call_responses_api_output_text(self):
+        """Post-call hook must scan text from ``ResponsesAPIResponse.output``."""
+        from litellm.types.llms.openai import ResponsesAPIResponse
+
+        guardrail = _make_guardrail()
+        response = ResponsesAPIResponse(
+            id="resp-1",
+            created_at=0,
+            output=[
+                {
+                    "type": "message",
+                    "id": "msg-1",
+                    "status": "completed",
+                    "role": "assistant",
+                    "content": [
+                        {"type": "output_text", "text": "card 4111-1111-1111-1111"}
+                    ],
+                }
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            result = await guardrail.async_post_call_success_hook(
+                data={},
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=response,
+            )
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["activity"] == "downloadText"
+            assert "card 4111-1111-1111-1111" in mock_check.call_args.kwargs["text"]
+            assert result is response
+
+    @pytest.mark.asyncio
+    async def test_post_call_responses_api_empty_output_skips(self):
+        """Post-call hook must not call _check_content when output has no text."""
+        from litellm.types.llms.openai import ResponsesAPIResponse
+
+        guardrail = _make_guardrail()
+        response = ResponsesAPIResponse(
+            id="resp-2",
+            created_at=0,
+            output=[],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            await guardrail.async_post_call_success_hook(
+                data={},
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=response,
+            )
+
+            mock_check.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_logging_hook_responses_api_input_and_output(self):
+        """Logging hook must scan both ``input`` and ``ResponsesAPIResponse.output``."""
+        from litellm.types.llms.openai import ResponsesAPIResponse
+
+        guardrail = _make_guardrail()
+        result_response = ResponsesAPIResponse(
+            id="resp-3",
+            created_at=0,
+            output=[
+                {
+                    "type": "message",
+                    "id": "msg-2",
+                    "status": "completed",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": "response body"}],
+                }
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_logging_hook(
+                kwargs={
+                    "input": "prompt body",
+                    "litellm_params": {
+                        "metadata": {"user_api_key_user_id": "user-123"}
+                    },
+                },
+                result=result_response,
+                call_type="responses",
+            )
+
+            assert mock_check.call_count == 2
+            activities = {c.kwargs["activity"] for c in mock_check.call_args_list}
+            assert activities == {"uploadText", "downloadText"}
+            texts = {c.kwargs["text"] for c in mock_check.call_args_list}
+            assert any("prompt body" in t for t in texts)
+            assert any("response body" in t for t in texts)
+
+    @pytest.mark.asyncio
+    async def test_logging_hook_responses_api_with_messages_key_set(self):
+        """Responses-API prompt audit must fire even when ``kwargs["messages"]`` is
+        also set to the raw responses input.
+
+        litellm's logging pipeline (``function_setup`` +
+        ``update_environment_variables``) stores the raw responses ``input``
+        under ``model_call_details["messages"]``.  The audit must still extract
+        the prompt via the responses-specific path, not silently fall through
+        the generic ``messages`` branch with the wrong format.
+        """
+        from litellm.types.llms.openai import ResponsesAPIResponse
+
+        guardrail = _make_guardrail()
+        result_response = ResponsesAPIResponse(
+            id="resp-msgkey",
+            created_at=0,
+            output=[
+                {
+                    "type": "message",
+                    "id": "msg-3",
+                    "status": "completed",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": "response body"}],
+                }
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_logging_hook(
+                kwargs={
+                    "input": "prompt body",
+                    "instructions": "system instructions",
+                    # Simulate litellm's logging path which mirrors the raw
+                    # responses input under "messages".
+                    "messages": "prompt body",
+                    "litellm_params": {
+                        "metadata": {"user_api_key_user_id": "user-123"}
+                    },
+                },
+                result=result_response,
+                call_type="aresponses",
+            )
+
+            assert mock_check.call_count == 2
+            activities = {c.kwargs["activity"] for c in mock_check.call_args_list}
+            assert activities == {"uploadText", "downloadText"}
+            upload_calls = [
+                c
+                for c in mock_check.call_args_list
+                if c.kwargs["activity"] == "uploadText"
+            ]
+            assert len(upload_calls) == 1
+            upload_text = upload_calls[0].kwargs["text"]
+            assert "prompt body" in upload_text
+            assert "system instructions" in upload_text
+
+
+# ---------------------------------------------------------------
+# Logging hook user resolution
+# ---------------------------------------------------------------
+
+
+class TestLoggingResolveUserId:
+    def test_logging_prefers_user_api_key_user_id_in_metadata(self):
+        guardrail = _make_guardrail()
+        kwargs = {
+            "litellm_params": {
+                "metadata": {
+                    "user_api_key_user_id": "trusted-from-proxy",
+                    "user_id": "metadata-spoof",
+                }
+            }
+        }
+        assert (
+            guardrail._resolve_user_id_from_logging_kwargs(kwargs)
+            == "trusted-from-proxy"
+        )
+
+    def test_logging_ignores_caller_supplied_user_id_field(self):
+        """Caller-controlled ``metadata[user_id_field]`` must not drive Purview audit attribution."""
+        guardrail = _make_guardrail()
+        kwargs = {"litellm_params": {"metadata": {"user_id": "only-metadata-user"}}}
+        assert guardrail._resolve_user_id_from_logging_kwargs(kwargs) is None
+
+    def test_logging_kwargs_level_user_api_key_user_id(self):
+        """Top-level ``kwargs["user_api_key_user_id"]`` is also a proxy-injected source."""
+        guardrail = _make_guardrail()
+        kwargs = {
+            "user_api_key_user_id": "from-top-level",
+            "litellm_params": {"metadata": {}},
+        }
+        assert (
+            guardrail._resolve_user_id_from_logging_kwargs(kwargs) == "from-top-level"
+        )
+
+    def test_logging_returns_none_when_no_trusted_identity(self):
+        guardrail = _make_guardrail()
+        kwargs = {"litellm_params": {"metadata": {}}}
+        assert guardrail._resolve_user_id_from_logging_kwargs(kwargs) is None
+
+
+# ---------------------------------------------------------------
+# _check_content — integration-level
+# ---------------------------------------------------------------
+
+
+class TestCheckContent:
+    @pytest.mark.asyncio
+    async def test_check_content_allow(self):
+        guardrail = _make_guardrail()
+
+        with (
+            patch.object(
+                guardrail,
+                "_compute_protection_scopes",
+                new_callable=AsyncMock,
+                return_value=("etag-1", {}),
+            ),
+            patch.object(
+                guardrail,
+                "_process_content",
+                new_callable=AsyncMock,
+                return_value={
+                    "protectionScopeState": "unchanged",
+                    "policyActions": [],
+                },
+            ),
+        ):
+            result = await guardrail._check_content(
+                user_id="user-1",
+                text="Hello world",
+                activity="uploadText",
+                request_data={},
+                block_on_violation=True,
+            )
+
+            assert result["policyActions"] == []
+
+    @pytest.mark.asyncio
+    async def test_check_content_block(self):
+        guardrail = _make_guardrail()
+
+        with (
+            patch.object(
+                guardrail,
+                "_compute_protection_scopes",
+                new_callable=AsyncMock,
+                return_value=("etag-1", {}),
+            ),
+            patch.object(
+                guardrail,
+                "_process_content",
+                new_callable=AsyncMock,
+                return_value={
+                    "protectionScopeState": "unchanged",
+                    "policyActions": [
+                        {
+                            "@odata.type": "#microsoft.graph.restrictAccessAction",
+                            "action": "restrictAccess",
+                            "restrictionAction": "block",
+                        }
+                    ],
+                },
+            ),
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail._check_content(
+                    user_id="user-1",
+                    text="SSN: 123-45-6789",
+                    activity="uploadText",
+                    request_data={},
+                    block_on_violation=True,
+                )
+
+            assert exc_info.value.status_code == 400
+            assert "blocked by policy" in str(exc_info.value.detail)
+
+    @pytest.mark.asyncio
+    async def test_check_content_logging_only_no_block(self):
+        """In logging_only mode, violations should NOT raise."""
+        guardrail = _make_guardrail()
+
+        with (
+            patch.object(
+                guardrail,
+                "_compute_protection_scopes",
+                new_callable=AsyncMock,
+                return_value=("etag-1", {}),
+            ),
+            patch.object(
+                guardrail,
+                "_process_content",
+                new_callable=AsyncMock,
+                return_value={
+                    "protectionScopeState": "unchanged",
+                    "policyActions": [
+                        {
+                            "@odata.type": "#microsoft.graph.restrictAccessAction",
+                            "action": "restrictAccess",
+                            "restrictionAction": "block",
+                        }
+                    ],
+                },
+            ),
+        ):
+            # Should NOT raise even though violation detected
+            result = await guardrail._check_content(
+                user_id="user-1",
+                text="SSN: 123-45-6789",
+                activity="uploadText",
+                request_data={},
+                block_on_violation=False,
+            )
+
+            assert len(result["policyActions"]) == 1
+
+
+# ---------------------------------------------------------------
+# Token caching
+# ---------------------------------------------------------------
+
+
+class TestTokenCaching:
+    @pytest.mark.asyncio
+    async def test_token_cached(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail.async_handler, "post", return_value=_mock_token_response()
+        ) as mock_post:
+            token1 = await guardrail._get_access_token()
+            token2 = await guardrail._get_access_token()
+
+            assert token1 == "mock-access-token"
+            assert token2 == "mock-access-token"
+            # Should only call the token endpoint once (cached)
+            assert mock_post.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_token_refreshed_on_expiry(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail.async_handler, "post", return_value=_mock_token_response()
+        ) as mock_post:
+            await guardrail._get_access_token()
+
+            # Expire the token
+            guardrail._token_cache = ("old-token", time.time() - 10)
+
+            await guardrail._get_access_token()
+
+            # Should have called token endpoint twice
+            assert mock_post.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_token_http_error_propagates(self):
+        """OAuth2 4xx/5xx responses must surface as HTTPStatusError, not KeyError."""
+        guardrail = _make_guardrail()
+
+        error_resp = Mock()
+        error_resp.json.return_value = {
+            "error": "invalid_client",
+            "error_description": "client secret is wrong",
+        }
+        error_resp.raise_for_status = Mock(
+            side_effect=httpx.HTTPStatusError(
+                "401 Unauthorized",
+                request=httpx.Request("POST", "https://login.microsoftonline.com/"),
+                response=httpx.Response(401),
+            )
+        )
+
+        with patch.object(guardrail.async_handler, "post", return_value=error_resp):
+            with pytest.raises(httpx.HTTPStatusError):
+                await guardrail._get_access_token()
+
+            # Failure must not poison the cache.
+            assert guardrail._token_cache is None
+
+
+# ---------------------------------------------------------------
+# Graph POST HTTP error propagation
+# ---------------------------------------------------------------
+
+
+class TestGraphPostHttpError:
+    @pytest.mark.asyncio
+    async def test_graph_post_http_error_propagates(self):
+        """Non-2xx Graph API responses must raise rather than return error JSON."""
+        guardrail = _make_guardrail()
+
+        error_resp = Mock()
+        error_resp.json.return_value = {
+            "error": {"code": "Forbidden", "message": "no access"}
+        }
+        error_resp.headers = {}
+        error_resp.raise_for_status = Mock(
+            side_effect=httpx.HTTPStatusError(
+                "403 Forbidden",
+                request=httpx.Request("POST", "https://graph.microsoft.com/"),
+                response=httpx.Response(403),
+            )
+        )
+
+        with (
+            patch.object(
+                guardrail, "_get_access_token", new_callable=AsyncMock
+            ) as mock_token,
+            patch.object(guardrail.async_handler, "post", return_value=error_resp),
+        ):
+            mock_token.return_value = "mock-token"
+
+            with pytest.raises(httpx.HTTPStatusError):
+                await guardrail._graph_post(
+                    "https://graph.microsoft.com/v1.0/users/u/example",
+                    {"foo": "bar"},
+                )
+
+    @pytest.mark.asyncio
+    async def test_compute_protection_scopes_http_error_propagates(self):
+        """A Graph error on protectionScopes/compute must not be cached as success."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_graph_post", new_callable=AsyncMock
+        ) as mock_post:
+            mock_post.side_effect = httpx.HTTPStatusError(
+                "429 Too Many Requests",
+                request=httpx.Request("POST", "https://graph.microsoft.com/"),
+                response=httpx.Response(429),
+            )
+
+            with pytest.raises(httpx.HTTPStatusError):
+                await guardrail._compute_protection_scopes("user-err")
+
+            # The failed compute must not populate the scope cache.
+            assert "user-err" not in guardrail._scope_cache
+
+
+# ---------------------------------------------------------------
+# Protection scope caching
+# ---------------------------------------------------------------
+
+
+class TestScopeCaching:
+    @pytest.mark.asyncio
+    async def test_scope_cached(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_graph_post", new_callable=AsyncMock
+        ) as mock_post:
+            mock_post.return_value = (
+                {
+                    "value": [
+                        {"activities": "uploadText", "executionMode": "evaluateInline"}
+                    ]
+                },
+                {"ETag": "scope-etag"},
+            )
+
+            etag1, _ = await guardrail._compute_protection_scopes("user-1")
+            etag2, _ = await guardrail._compute_protection_scopes("user-1")
+
+            assert etag1 == "scope-etag"
+            assert etag2 == "scope-etag"
+            assert mock_post.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_scope_cache_lru_keeps_hot_user_on_eviction(self):
+        """Frequently accessed users should not be evicted before cold entries."""
+        guardrail = _make_guardrail()
+        guardrail._scope_cache_maxsize = 3
+
+        scope_payload = (
+            {
+                "value": [
+                    {"activities": "uploadText", "executionMode": "evaluateInline"}
+                ]
+            },
+            {"ETag": "scope-etag"},
+        )
+
+        with patch.object(
+            guardrail, "_graph_post", new_callable=AsyncMock
+        ) as mock_post:
+            mock_post.return_value = scope_payload
+
+            await guardrail._compute_protection_scopes("user-a")
+            await guardrail._compute_protection_scopes("user-b")
+            await guardrail._compute_protection_scopes("user-c")
+            assert mock_post.call_count == 3
+
+            await guardrail._compute_protection_scopes("user-a")
+            assert mock_post.call_count == 3
+
+            await guardrail._compute_protection_scopes("user-d")
+            assert mock_post.call_count == 4
+
+            await guardrail._compute_protection_scopes("user-a")
+            assert mock_post.call_count == 4
+            assert "user-a" in guardrail._scope_cache
+            assert "user-b" not in guardrail._scope_cache
+
+    @pytest.mark.asyncio
+    async def test_scope_cache_refresh_moves_to_end_of_lru(self):
+        """Refreshing a stale entry must move it to the MRU end of the OrderedDict.
+
+        Before the fix, OrderedDict.__setitem__ preserved the original insertion
+        position for existing keys, causing the just-refreshed entry to be the
+        next candidate for LRU eviction.
+        """
+        guardrail = _make_guardrail()
+        guardrail._scope_cache_maxsize = 2
+
+        scope_payload = (
+            {"value": []},
+            {"ETag": "scope-etag"},
+        )
+
+        with patch.object(
+            guardrail, "_graph_post", new_callable=AsyncMock
+        ) as mock_post:
+            mock_post.return_value = scope_payload
+
+            # Populate cache: user-a (older), user-b (newer)
+            await guardrail._compute_protection_scopes("user-a")
+            await guardrail._compute_protection_scopes("user-b")
+            assert mock_post.call_count == 2
+
+            # Expire user-a's entry so it is re-fetched on the next access.
+            old_etag, old_scope, _ = guardrail._scope_cache["user-a"]
+            guardrail._scope_cache["user-a"] = (old_etag, old_scope, 0.0)
+
+            # Re-fetch user-a — should move it to the MRU end.
+            await guardrail._compute_protection_scopes("user-a")
+            assert mock_post.call_count == 3
+
+            # Adding a third user must evict user-b (the true LRU), not user-a.
+            await guardrail._compute_protection_scopes("user-c")
+            assert mock_post.call_count == 4
+
+            assert "user-a" in guardrail._scope_cache, "user-a was wrongly evicted"
+            assert (
+                "user-b" not in guardrail._scope_cache
+            ), "user-b should have been evicted"
+            assert "user-c" in guardrail._scope_cache
+
+    @pytest.mark.asyncio
+    async def test_scope_invalidated_on_modified(self):
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_graph_post", new_callable=AsyncMock
+        ) as mock_post:
+            # First call: compute scopes
+            mock_post.return_value = (
+                {"value": []},
+                {"ETag": "etag-1"},
+            )
+            await guardrail._compute_protection_scopes("user-1")
+
+            # processContent returns modified
+            mock_post.return_value = (
+                {"protectionScopeState": "modified", "policyActions": []},
+                {},
+            )
+            await guardrail._process_content("user-1", "text", "uploadText", "etag-1")
+
+            # Scope cache should be invalidated
+            assert "user-1" not in guardrail._scope_cache
+
+
+# ---------------------------------------------------------------
+# get_prompt_text_for_dlp — message separator
+# ---------------------------------------------------------------
+
+
+class TestGetPromptTextForDlp:
+    def test_single_message_no_extra_separator(self):
+        """A single message is returned as-is (no leading/trailing separator)."""
+        guardrail = _make_guardrail()
+        result = guardrail.get_prompt_text_for_dlp(
+            [{"role": "user", "content": "Hello"}]
+        )
+        assert result == "Hello"
+
+    def test_messages_separated_by_double_newline(self):
+        """Adjacent messages must NOT be concatenated without a separator.
+
+        Before the fix, "end of msg1" + "start of msg2" became
+        "end of msg1start of msg2", mangling DLP pattern detection.
+        """
+        guardrail = _make_guardrail()
+        result = guardrail.get_prompt_text_for_dlp(
+            [
+                {"role": "system", "content": "end of msg1"},
+                {"role": "user", "content": "start of msg2"},
+            ]
+        )
+        assert result is not None
+        assert "end of msg1" in result
+        assert "start of msg2" in result
+        # Separator must be present between messages
+        assert "end of msg1start of msg2" not in result
+        assert "end of msg1\n\nstart of msg2" in result
+
+    def test_empty_messages_returns_none(self):
+        guardrail = _make_guardrail()
+        assert guardrail.get_prompt_text_for_dlp([]) is None
+
+    def test_whitespace_only_messages_skipped(self):
+        guardrail = _make_guardrail()
+        result = guardrail.get_prompt_text_for_dlp(
+            [
+                {"role": "system", "content": "   "},
+                {"role": "user", "content": "real content"},
+            ]
+        )
+        assert result == "real content"
+
+    def test_multi_role_conversation_preserves_all_content(self):
+        guardrail = _make_guardrail()
+        result = guardrail.get_prompt_text_for_dlp(
+            [
+                {"role": "system", "content": "SYSTEM"},
+                {"role": "user", "content": "USER1"},
+                {"role": "assistant", "content": "ASSISTANT"},
+                {"role": "user", "content": "USER2"},
+            ]
+        )
+        assert result is not None
+        for token in ("SYSTEM", "USER1", "ASSISTANT", "USER2"):
+            assert token in result
+
+
+# ---------------------------------------------------------------
+# logging_hook — non-blocking fire-and-forget
+# ---------------------------------------------------------------
+
+
+class TestLoggingHookNonBlocking:
+    @pytest.mark.asyncio
+    async def test_logging_hook_does_not_block_running_loop(self):
+        """logging_hook must return immediately without blocking the event loop.
+
+        Before the fix, logging_hook called future.result() which blocked the
+        event loop thread for the full round-trip of the two Graph API calls.
+        """
+        guardrail = _make_guardrail()
+        call_count = 0
+
+        async def slow_async_hook(**_kwargs):
+            nonlocal call_count
+            await asyncio.sleep(0.05)
+            call_count += 1
+            return _kwargs.get("kwargs", {}), _kwargs.get("result")
+
+        with patch.object(guardrail, "async_logging_hook", side_effect=slow_async_hook):
+            # Call logging_hook from within a running event loop
+            result = guardrail.logging_hook(
+                kwargs={"messages": [{"role": "user", "content": "test"}]},
+                result=None,
+                call_type="completion",
+            )
+
+        # Must return (kwargs, result) unchanged without waiting for async work
+        assert result[0]["messages"][0]["content"] == "test"
+        assert result[1] is None
+
+    def test_logging_hook_returns_original_kwargs_and_result(self):
+        """Return value must be the original (kwargs, result) tuple unchanged."""
+        guardrail = _make_guardrail()
+        kwargs = {"messages": [{"role": "user", "content": "hello"}]}
+        result_obj = {"some": "result"}
+
+        with patch.object(
+            guardrail,
+            "async_logging_hook",
+            new_callable=AsyncMock,
+            return_value=(kwargs, result_obj),
+        ):
+            out = guardrail.logging_hook(
+                kwargs=kwargs,
+                result=result_obj,
+                call_type="completion",
+            )
+
+        assert out == (kwargs, result_obj)
+
+
+# ---------------------------------------------------------------
+# Initializer validation
+# ---------------------------------------------------------------
+
+
+class TestInitializerValidation:
+    def test_missing_tenant_id(self):
+        from litellm.proxy.guardrails.guardrail_hooks.microsoft_purview import (
+            initialize_guardrail,
+        )
+
+        litellm_params = Mock(
+            spec=[
+                "tenant_id",
+                "client_id",
+                "client_secret",
+                "purview_app_name",
+                "user_id_field",
+                "api_key",
+                "mode",
+                "default_on",
+            ]
+        )
+        litellm_params.tenant_id = None
+        litellm_params.client_id = None
+        litellm_params.client_secret = None
+        litellm_params.api_key = "secret"
+        litellm_params.mode = "pre_call"
+
+        with pytest.raises(ValueError, match="tenant_id is required"):
+            initialize_guardrail(litellm_params, {"guardrail_name": "test"})
+
+    def test_missing_client_id(self):
+        from litellm.proxy.guardrails.guardrail_hooks.microsoft_purview import (
+            initialize_guardrail,
+        )
+
+        litellm_params = Mock(
+            spec=[
+                "tenant_id",
+                "client_id",
+                "client_secret",
+                "purview_app_name",
+                "user_id_field",
+                "api_key",
+                "mode",
+                "default_on",
+            ]
+        )
+        litellm_params.tenant_id = "test-tenant"
+        litellm_params.client_id = None
+        litellm_params.client_secret = None
+        litellm_params.api_key = "secret"
+        litellm_params.mode = "pre_call"
+
+        with pytest.raises(ValueError, match="client_id is required"):
+            initialize_guardrail(litellm_params, {"guardrail_name": "test"})
+
+    def test_missing_client_secret(self):
+        from litellm.proxy.guardrails.guardrail_hooks.microsoft_purview import (
+            initialize_guardrail,
+        )
+
+        litellm_params = Mock(
+            spec=[
+                "tenant_id",
+                "client_id",
+                "client_secret",
+                "purview_app_name",
+                "user_id_field",
+                "api_key",
+                "mode",
+                "default_on",
+            ]
+        )
+        litellm_params.tenant_id = "test-tenant"
+        litellm_params.client_id = "test-client"
+        litellm_params.client_secret = None
+        litellm_params.api_key = None
+        litellm_params.mode = "pre_call"
+
+        with pytest.raises(ValueError, match="client_secret"):
+            initialize_guardrail(litellm_params, {"guardrail_name": "test"})
+
+
+# ---------------------------------------------------------------
+# _check_content — API error handling with block_on_violation=False
+# ---------------------------------------------------------------
+
+
+class TestCheckContentApiErrorHandling:
+    @pytest.mark.asyncio
+    async def test_api_error_reraises_when_block_on_violation_true(self):
+        """API/network errors must surface as HTTPException(400) when block_on_violation=True."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail,
+            "_compute_protection_scopes",
+            new_callable=AsyncMock,
+            side_effect=RuntimeError("network failure"),
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail._check_content(
+                    user_id="user-1",
+                    text="some content",
+                    activity="uploadText",
+                    request_data={},
+                    block_on_violation=True,
+                )
+
+        assert exc_info.value.status_code == 400
+        assert isinstance(exc_info.value.detail, dict)
+        assert "upstream policy evaluation failed" in exc_info.value.detail.get(
+            "error", ""
+        )
+        assert "network failure" in exc_info.value.detail.get("exception", "")
+        assert isinstance(exc_info.value.__cause__, RuntimeError)
+
+    @pytest.mark.asyncio
+    async def test_http_exception_passes_through_unchanged(self):
+        """HTTPException from upstream layers must propagate as-is (not wrapped)."""
+        guardrail = _make_guardrail()
+        inner = HTTPException(status_code=403, detail="forbidden")
+
+        with patch.object(
+            guardrail,
+            "_compute_protection_scopes",
+            new_callable=AsyncMock,
+            side_effect=inner,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail._check_content(
+                    user_id="user-1",
+                    text="some content",
+                    activity="uploadText",
+                    request_data={},
+                    block_on_violation=True,
+                )
+
+        assert exc_info.value is inner
+
+    @pytest.mark.asyncio
+    async def test_api_error_not_reraised_when_block_on_violation_false(self):
+        """API/network errors must be swallowed (logged only) when block_on_violation=False."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail,
+            "_compute_protection_scopes",
+            new_callable=AsyncMock,
+            side_effect=RuntimeError("network failure"),
+        ):
+            # Must NOT raise — should return empty dict
+            result = await guardrail._check_content(
+                user_id="user-1",
+                text="some content",
+                activity="uploadText",
+                request_data={},
+                block_on_violation=False,
+            )
+
+        assert isinstance(result, dict)
+
+    @pytest.mark.asyncio
+    async def test_process_content_error_not_reraised_when_block_on_violation_false(
+        self,
+    ):
+        """Errors from _process_content itself must also be suppressed in logging-only mode."""
+        guardrail = _make_guardrail()
+
+        with (
+            patch.object(
+                guardrail,
+                "_compute_protection_scopes",
+                new_callable=AsyncMock,
+                return_value=("etag-1", {}),
+            ),
+            patch.object(
+                guardrail,
+                "_process_content",
+                new_callable=AsyncMock,
+                side_effect=ConnectionError("timeout"),
+            ),
+        ):
+            result = await guardrail._check_content(
+                user_id="user-1",
+                text="some content",
+                activity="uploadText",
+                request_data={},
+                block_on_violation=False,
+            )
+
+        assert isinstance(result, dict)
+
+    @pytest.mark.asyncio
+    async def test_http_status_error_preserves_upstream_status_code(self):
+        """Upstream Graph 429 must surface as 429 with Retry-After (not a generic 400)."""
+        guardrail = _make_guardrail()
+        upstream_resp = httpx.Response(
+            status_code=429,
+            headers={"Retry-After": "30"},
+            request=httpx.Request("POST", "https://graph.microsoft.com/v1.0/x"),
+        )
+        upstream_err = httpx.HTTPStatusError(
+            "rate limited", request=upstream_resp.request, response=upstream_resp
+        )
+
+        with patch.object(
+            guardrail,
+            "_compute_protection_scopes",
+            new_callable=AsyncMock,
+            side_effect=upstream_err,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail._check_content(
+                    user_id="user-1",
+                    text="some content",
+                    activity="uploadText",
+                    request_data={},
+                    block_on_violation=True,
+                )
+
+        assert exc_info.value.status_code == 429
+        assert exc_info.value.headers == {"Retry-After": "30"}
+        assert isinstance(exc_info.value.detail, dict)
+        assert exc_info.value.detail.get("upstream_status") == 429
+        assert isinstance(exc_info.value.__cause__, httpx.HTTPStatusError)
+
+    @pytest.mark.asyncio
+    async def test_http_status_error_401_maps_to_502(self):
+        """Upstream 401/403 (proxy creds problem) should be exposed as 502, not 401/403."""
+        guardrail = _make_guardrail()
+        upstream_resp = httpx.Response(
+            status_code=401,
+            request=httpx.Request("POST", "https://graph.microsoft.com/v1.0/x"),
+        )
+        upstream_err = httpx.HTTPStatusError(
+            "unauthorized", request=upstream_resp.request, response=upstream_resp
+        )
+
+        with patch.object(
+            guardrail,
+            "_compute_protection_scopes",
+            new_callable=AsyncMock,
+            side_effect=upstream_err,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail._check_content(
+                    user_id="user-1",
+                    text="some content",
+                    activity="uploadText",
+                    request_data={},
+                    block_on_violation=True,
+                )
+
+        assert exc_info.value.status_code == 502
+        assert exc_info.value.detail.get("upstream_status") == 401
+
+
+# ---------------------------------------------------------------
+# async_logging_hook — independent prompt/response audit calls
+# ---------------------------------------------------------------
+
+
+class TestAsyncLoggingHookIndependence:
+    @pytest.mark.asyncio
+    async def test_response_audit_runs_even_if_prompt_audit_fails(self):
+        """A failure in the prompt audit must not prevent the response audit from running."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(content="response text", role="assistant"),
+                )
+            ],
+        )
+
+        call_activities: list = []
+
+        async def fake_check_content(**kwargs):
+            activity = kwargs.get("activity")
+            if activity == "uploadText":
+                raise RuntimeError("simulated prompt API failure")
+            call_activities.append(activity)
+            return {"policyActions": []}
+
+        with patch.object(guardrail, "_check_content", side_effect=fake_check_content):
+            await guardrail.async_logging_hook(
+                kwargs={
+                    "messages": [{"role": "user", "content": "prompt"}],
+                    "litellm_params": {
+                        "metadata": {"user_api_key_user_id": "user-123"}
+                    },
+                },
+                result=response,
+                call_type="completion",
+            )
+
+        # The response audit must still have been attempted
+        assert "downloadText" in call_activities
+
+    @pytest.mark.asyncio
+    async def test_prompt_audit_runs_even_if_response_audit_fails(self):
+        """A failure in the response audit must not affect the prompt audit result."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(content="response text", role="assistant"),
+                )
+            ],
+        )
+
+        call_activities: list = []
+
+        async def fake_check_content(**kwargs):
+            activity = kwargs.get("activity")
+            if activity == "downloadText":
+                raise RuntimeError("simulated response API failure")
+            call_activities.append(activity)
+            return {"policyActions": []}
+
+        with patch.object(guardrail, "_check_content", side_effect=fake_check_content):
+            await guardrail.async_logging_hook(
+                kwargs={
+                    "messages": [{"role": "user", "content": "prompt"}],
+                    "litellm_params": {
+                        "metadata": {"user_api_key_user_id": "user-123"}
+                    },
+                },
+                result=response,
+                call_type="completion",
+            )
+
+        assert "uploadText" in call_activities
+
+    @pytest.mark.asyncio
+    async def test_logging_hook_returns_original_when_both_audits_fail(self):
+        """async_logging_hook must always return (kwargs, result) even if both audits fail."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail,
+            "_check_content",
+            new_callable=AsyncMock,
+            side_effect=RuntimeError("total failure"),
+        ):
+            kwargs = {
+                "messages": [{"role": "user", "content": "prompt"}],
+                "litellm_params": {"metadata": {"user_api_key_user_id": "user-123"}},
+            }
+            result_obj = {"some": "result"}
+            out_kwargs, out_result = await guardrail.async_logging_hook(
+                kwargs=kwargs,
+                result=result_obj,
+                call_type="completion",
+            )
+
+        assert out_kwargs is kwargs
+        assert out_result is result_obj
+
+
+# ---------------------------------------------------------------
+# Tool-call argument extraction
+# ---------------------------------------------------------------
+
+
+class TestExtractToolCallArgs:
+    def test_dict_message_with_tool_calls(self):
+        msg = {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {"function": {"arguments": '{"ssn": "123-45-6789"}'}},
+                {"function": {"arguments": '{"card": "4111-1111-1111-1111"}'}},
+            ],
+        }
+        args = MicrosoftPurviewDLPGuardrail._extract_tool_call_args_from_message(msg)
+        assert '{"ssn": "123-45-6789"}' in args
+        assert '{"card": "4111-1111-1111-1111"}' in args
+
+    def test_dict_message_with_function_call(self):
+        msg = {
+            "role": "assistant",
+            "content": None,
+            "function_call": {"name": "lookup", "arguments": '{"query": "secret"}'},
+        }
+        args = MicrosoftPurviewDLPGuardrail._extract_tool_call_args_from_message(msg)
+        assert '{"query": "secret"}' in args
+
+    def test_object_message_with_tool_calls(self):
+        from litellm.types.utils import Message
+
+        msg = Message(
+            role="assistant",
+            content=None,
+            tool_calls=[
+                {
+                    "id": "tc1",
+                    "type": "function",
+                    "function": {"name": "fn", "arguments": '{"x": 1}'},
+                },
+            ],
+        )
+        args = MicrosoftPurviewDLPGuardrail._extract_tool_call_args_from_message(msg)
+        assert '{"x": 1}' in args
+
+    def test_message_with_no_tool_calls(self):
+        msg = {"role": "user", "content": "hello"}
+        args = MicrosoftPurviewDLPGuardrail._extract_tool_call_args_from_message(msg)
+        assert args == []
+
+    def test_empty_arguments_skipped(self):
+        msg = {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{"function": {"arguments": "  "}}],
+        }
+        args = MicrosoftPurviewDLPGuardrail._extract_tool_call_args_from_message(msg)
+        assert args == []
+
+
+# ---------------------------------------------------------------
+# Tool-call arguments included in DLP text extraction (prompt)
+# ---------------------------------------------------------------
+
+
+class TestGetPromptTextToolCalls:
+    def test_tool_call_args_included_in_prompt_scan(self):
+        """Sensitive data in tool_calls[].function.arguments must appear in DLP text."""
+        guardrail = _make_guardrail()
+        messages = [
+            {"role": "user", "content": "benign query"},
+            {
+                "role": "assistant",
+                "content": None,
+                "tool_calls": [
+                    {
+                        "id": "tc1",
+                        "type": "function",
+                        "function": {
+                            "name": "lookup",
+                            "arguments": '{"ssn": "123-45-6789"}',
+                        },
+                    }
+                ],
+            },
+        ]
+        text = guardrail.get_prompt_text_for_dlp(messages)
+        assert text is not None
+        assert "benign query" in text
+        assert '{"ssn": "123-45-6789"}' in text
+
+    def test_function_call_args_included_in_prompt_scan(self):
+        """Legacy function_call.arguments must also appear in DLP text."""
+        guardrail = _make_guardrail()
+        messages = [
+            {
+                "role": "assistant",
+                "content": "Calling function",
+                "function_call": {
+                    "name": "search",
+                    "arguments": '{"credit_card": "4111-1111-1111-1111"}',
+                },
+            }
+        ]
+        text = guardrail.get_prompt_text_for_dlp(messages)
+        assert text is not None
+        assert "Calling function" in text
+        assert '{"credit_card": "4111-1111-1111-1111"}' in text
+
+    def test_content_only_message_unchanged(self):
+        """Messages without tool calls must still produce the same output."""
+        guardrail = _make_guardrail()
+        messages = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Tell me a joke."},
+        ]
+        text = guardrail.get_prompt_text_for_dlp(messages)
+        assert text is not None
+        assert "You are helpful." in text
+        assert "Tell me a joke." in text
+
+    @pytest.mark.asyncio
+    async def test_pre_call_hook_scans_tool_call_args(self):
+        """async_pre_call_hook must include tool_call arguments in the text sent to Purview."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=__import__(
+                    "litellm.proxy._types", fromlist=["UserAPIKeyAuth"]
+                ).UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                cache=None,
+                data={
+                    "messages": [
+                        {"role": "user", "content": "benign"},
+                        {
+                            "role": "assistant",
+                            "content": None,
+                            "tool_calls": [
+                                {
+                                    "id": "tc1",
+                                    "type": "function",
+                                    "function": {
+                                        "name": "do_thing",
+                                        "arguments": '{"password": "hunter2"}',
+                                    },
+                                }
+                            ],
+                        },
+                    ]
+                },
+                call_type="completion",
+            )
+
+            mock_check.assert_called_once()
+            sent_text = mock_check.call_args.kwargs["text"]
+            assert '{"password": "hunter2"}' in sent_text
+
+
+# ---------------------------------------------------------------
+# Tool-call arguments included in DLP text extraction (response)
+# ---------------------------------------------------------------
+
+
+class TestCompletionResponseTextPartsToolCalls:
+    def test_response_tool_call_args_included(self):
+        """Model-generated tool_call arguments must appear in the DLP scan text."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(
+                        role="assistant",
+                        content=None,
+                        tool_calls=[
+                            {
+                                "id": "tc1",
+                                "type": "function",
+                                "function": {
+                                    "name": "exfil",
+                                    "arguments": '{"data": "secret-value"}',
+                                },
+                            }
+                        ],
+                    ),
+                )
+            ],
+        )
+        parts = guardrail._completion_response_text_parts(response)
+        assert any("secret-value" in p for p in parts)
+
+    def test_response_with_content_and_tool_calls(self):
+        """Both message content and tool_call arguments must be included."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(
+                        role="assistant",
+                        content="Here is the result",
+                        tool_calls=[
+                            {
+                                "id": "tc2",
+                                "type": "function",
+                                "function": {
+                                    "name": "fn",
+                                    "arguments": '{"ssn": "123-45-6789"}',
+                                },
+                            }
+                        ],
+                    ),
+                )
+            ],
+        )
+        parts = guardrail._completion_response_text_parts(response)
+        combined = " ".join(parts)
+        assert "Here is the result" in combined
+        assert '{"ssn": "123-45-6789"}' in combined
+
+    @pytest.mark.asyncio
+    async def test_post_call_hook_scans_response_tool_call_args(self):
+        """async_post_call_success_hook must send tool_call arguments to Purview."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+        response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(
+                        role="assistant",
+                        content=None,
+                        tool_calls=[
+                            {
+                                "id": "tc3",
+                                "type": "function",
+                                "function": {
+                                    "name": "retrieve",
+                                    "arguments": '{"credit_card": "4111-1111-1111-1111"}',
+                                },
+                            }
+                        ],
+                    ),
+                )
+            ],
+        )
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+
+            await guardrail.async_post_call_success_hook(
+                data={},
+                user_api_key_dict=__import__(
+                    "litellm.proxy._types", fromlist=["UserAPIKeyAuth"]
+                ).UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=response,
+            )
+
+            mock_check.assert_called_once()
+            sent_text = mock_check.call_args.kwargs["text"]
+            assert '{"credit_card": "4111-1111-1111-1111"}' in sent_text
+
+    def test_responses_api_function_call_args_included(self):
+        """Function-call arguments in ``ResponsesAPIResponse.output`` must be DLP-scanned."""
+        from litellm.types.llms.openai import ResponsesAPIResponse
+
+        guardrail = _make_guardrail()
+        response = ResponsesAPIResponse(
+            id="resp-tc-1",
+            created_at=0,
+            output=[
+                {
+                    "type": "message",
+                    "id": "msg-tc-1",
+                    "status": "completed",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": "calling tool"}],
+                },
+                {
+                    "type": "function_call",
+                    "id": "fc-1",
+                    "call_id": "call-1",
+                    "name": "exfil",
+                    "arguments": '{"ssn": "123-45-6789"}',
+                },
+            ],
+        )
+        parts = guardrail._completion_response_text_parts(response)
+        combined = " ".join(parts)
+        assert "calling tool" in combined
+        assert '{"ssn": "123-45-6789"}' in combined
+
+    def test_responses_api_function_call_args_only(self):
+        """Function-call args must be scanned even when no ``output_text`` blocks exist."""
+        from litellm.types.llms.openai import ResponsesAPIResponse
+
+        guardrail = _make_guardrail()
+        response = ResponsesAPIResponse(
+            id="resp-tc-2",
+            created_at=0,
+            output=[
+                {
+                    "type": "function_call",
+                    "id": "fc-2",
+                    "call_id": "call-2",
+                    "name": "exfil",
+                    "arguments": '{"secret": "hunter2"}',
+                }
+            ],
+        )
+        parts = guardrail._completion_response_text_parts(response)
+        assert any('{"secret": "hunter2"}' in p for p in parts)
+
+
+# ---------------------------------------------------------------
+# Graph user id path encoding
+# ---------------------------------------------------------------
+
+
+class TestGraphUserIdEncoding:
+    def test_encode_graph_user_id_percent_encodes_special_chars(self):
+        from urllib.parse import quote
+
+        raw = "user/with%special"
+        encoded = PurviewGuardrailBase._encode_graph_user_id(raw)
+        assert encoded == quote(raw, safe="")
+        assert "/" not in encoded
+
+    @pytest.mark.asyncio
+    async def test_compute_protection_scopes_uses_encoded_path(self):
+        guardrail = _make_guardrail()
+        guardrail._scope_cache.clear()
+
+        mock_resp = _mock_scope_response()
+
+        async def _capture_post(url, **kwargs):
+            assert "/users/" in url
+            assert "user%2Fwith%25special" in url
+            return mock_resp
+
+        guardrail.async_handler.post = AsyncMock(side_effect=_capture_post)
+
+        with patch.object(
+            guardrail, "_get_access_token", new_callable=AsyncMock
+        ) as mock_token:
+            mock_token.return_value = "tok"
+            await guardrail._compute_protection_scopes("user/with%special")
+
+        guardrail.async_handler.post.assert_called_once()
+
+
+# ---------------------------------------------------------------
+# _resolve_trusted_user_id
+# ---------------------------------------------------------------
+
+
+class TestResolveTrustedUserId:
+    def test_trusted_user_id_from_api_key_dict(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test", user_id="auth-user-111")
+        assert guardrail._resolve_trusted_user_id({}, auth) == "auth-user-111"
+
+    def test_end_user_id_not_trusted_for_blocking(self):
+        """end_user_id is request-derived; must not be used for blocking DLP."""
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test", end_user_id="end-user-222")
+        assert guardrail._resolve_trusted_user_id({}, auth) is None
+
+    def test_metadata_user_api_key_user_id_not_trusted_without_auth(self):
+        """Metadata user_api_key_user_id is not trusted when the key has no user_id."""
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test")
+        data = {"metadata": {"user_api_key_user_id": "proxy-user-333"}}
+        assert guardrail._resolve_trusted_user_id(data, auth) is None
+
+    def test_trusted_user_id_returns_none_for_caller_supplied_only(self):
+        """Caller-supplied metadata must NOT be returned by _resolve_trusted_user_id."""
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test")
+        data = {"metadata": {"user_id": "caller-supplied-444"}}
+        assert guardrail._resolve_trusted_user_id(data, auth) is None
+
+    def test_trusted_prefers_key_user_id_over_end_user_id(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(
+            api_key="test", user_id="key-owner", end_user_id="end-user"
+        )
+        assert guardrail._resolve_trusted_user_id({}, auth) == "key-owner"
+
+
+# ---------------------------------------------------------------
+# _resolve_user_id_from_logging_kwargs — caller-influenceable identity rejected
+# ---------------------------------------------------------------
+
+
+class TestLoggingRejectsCallerInfluenceableIdentity:
+    """``end_user_id`` is derived from caller-controllable request fields
+    (``user``, ``metadata.user_id``, ``safety_identifier``) so it must not
+    drive Purview audit attribution either.
+    """
+
+    def test_end_user_id_in_metadata_is_ignored(self):
+        guardrail = _make_guardrail()
+        kwargs = {
+            "litellm_params": {
+                "metadata": {
+                    "user_api_key_end_user_id": "end-user-from-metadata",
+                }
+            }
+        }
+        assert guardrail._resolve_user_id_from_logging_kwargs(kwargs) is None
+
+    def test_end_user_id_at_top_level_kwargs_is_ignored(self):
+        guardrail = _make_guardrail()
+        kwargs = {
+            "user_api_key_end_user_id": "end-user-from-kwargs",
+            "litellm_params": {"metadata": {}},
+        }
+        assert guardrail._resolve_user_id_from_logging_kwargs(kwargs) is None
+
+
+# ---------------------------------------------------------------
+# _resolve_user_id_for_blocking — security warning path
+# ---------------------------------------------------------------
+
+
+class TestResolveUserIdForBlocking:
+    def test_trusted_id_returned_without_warning(self, caplog):
+        import logging
+
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test", user_id="trusted-111")
+        with caplog.at_level(logging.WARNING):
+            result = guardrail._resolve_user_id_for_blocking({}, auth)
+        assert result == "trusted-111"
+        assert "SECURITY" not in caplog.text
+
+    def test_caller_supplied_id_raises_http_exception(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test")
+        data = {"metadata": {"user_id": "caller-supplied-999"}}
+        with pytest.raises(HTTPException) as exc_info:
+            guardrail._resolve_user_id_for_blocking(data, auth)
+        assert exc_info.value.status_code == 400
+        assert "proxy-authenticated" in str(exc_info.value.detail)
+
+    def test_no_id_raises_http_exception(self):
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test")
+        with pytest.raises(HTTPException) as exc_info:
+            guardrail._resolve_user_id_for_blocking({}, auth)
+        assert exc_info.value.status_code == 400
+        assert "bind user_id" in str(exc_info.value.detail)
+
+    def test_end_user_id_only_raises_for_blocking(self):
+        """Request-derived end_user_id cannot drive blocking Purview checks."""
+        guardrail = _make_guardrail()
+        auth = UserAPIKeyAuth(api_key="test", end_user_id="caller-end-user")
+        with pytest.raises(HTTPException) as exc_info:
+            guardrail._resolve_user_id_for_blocking({}, auth)
+        assert exc_info.value.status_code == 400
+        assert "proxy-authenticated" in str(exc_info.value.detail)
+
+
+# ---------------------------------------------------------------
+# Token-id prompt handling in pre_call blocking mode
+# ---------------------------------------------------------------
+
+
+class TestTokenIdPromptHandling:
+    @pytest.mark.asyncio
+    async def test_token_id_prompt_raises_in_blocking_mode(self):
+        """Pure token-id prompts must be rejected in blocking pre_call mode."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.async_pre_call_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="u1"),
+                    cache=None,
+                    data={"prompt": [1, 2, 3, 100, 200]},
+                    call_type="text_completion",
+                )
+
+            mock_check.assert_not_called()
+            assert exc_info.value.status_code == 400
+            assert "Token-id" in str(exc_info.value.detail)
+
+    @pytest.mark.asyncio
+    async def test_missing_prompt_skips_without_warning(self, caplog):
+        """No prompt at all → silently skip (not a token-id bypass case)."""
+        import logging
+
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            with caplog.at_level(logging.WARNING):
+                await guardrail.async_pre_call_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="u1"),
+                    cache=None,
+                    data={},
+                    call_type="text_completion",
+                )
+
+            mock_check.assert_not_called()
+            assert "token-id" not in caplog.text.lower()
+
+    @pytest.mark.asyncio
+    async def test_string_prompt_still_scanned(self):
+        """Normal string prompts must still be sent to Purview."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            mock_check.return_value = {"policyActions": []}
+            await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="u1"),
+                cache=None,
+                data={"prompt": "sensitive text"},
+                call_type="text_completion",
+            )
+
+        mock_check.assert_called_once()
+        assert mock_check.call_args.kwargs["text"] == "sensitive text"
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize("empty_prompt", ["", "   ", "\n\t  "])
+    async def test_empty_or_whitespace_prompt_passes_through(self, empty_prompt):
+        """Empty/whitespace-only string prompts must not be flagged as token-id prompts."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            data = {"prompt": empty_prompt}
+            result = await guardrail.async_pre_call_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="u1"),
+                cache=None,
+                data=data,
+                call_type="text_completion",
+            )
+
+        mock_check.assert_not_called()
+        assert result is data
+        assert result["prompt"] == empty_prompt
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize(
+        "raw_prompt",
+        [
+            [[1, 2, 3]],
+            [[1, 2], [3, 4]],
+            ["benign text", [99, 100]],
+        ],
+    )
+    async def test_nested_token_id_prompt_raises_in_blocking_mode(self, raw_prompt):
+        """Nested/mixed token-id prompts must also be rejected in blocking pre_call mode."""
+        guardrail = _make_guardrail()
+
+        with patch.object(
+            guardrail, "_check_content", new_callable=AsyncMock
+        ) as mock_check:
+            with pytest.raises(HTTPException) as exc_info:
+                await guardrail.async_pre_call_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="u1"),
+                    cache=None,
+                    data={"prompt": raw_prompt},
+                    call_type="text_completion",
+                )
+
+            mock_check.assert_not_called()
+            assert exc_info.value.status_code == 400
+            assert "Token-id" in str(exc_info.value.detail)
+
+
+class TestIsTokenIdPrompt:
+    @pytest.mark.parametrize(
+        "prompt,expected",
+        [
+            ([1, 2, 3], True),
+            ([[1, 2, 3]], True),
+            ([[1, 2], [3, 4]], True),
+            (["hi", [1, 2]], True),
+            (["a", "b"], False),
+            ([], False),
+            ("hello", False),
+            (None, False),
+        ],
+    )
+    def test_is_token_id_prompt(self, prompt, expected):
+        assert PurviewGuardrailBase.is_token_id_prompt(prompt) is expected
+
+
+# ---------------------------------------------------------------
+# Streaming iterator hook
+# ---------------------------------------------------------------
+
+
+class TestStreamingIteratorHook:
+    @pytest.mark.asyncio
+    async def test_streaming_clean_response_yields_all_chunks(self):
+        """Clean stream: all chunks must be re-yielded after DLP passes."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+
+        assembled_response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(content="safe response", role="assistant"),
+                )
+            ]
+        )
+
+        async def fake_response_stream():
+            yield assembled_response
+
+        with (
+            patch("litellm.main.stream_chunk_builder", return_value=assembled_response),
+            patch(
+                "litellm.llms.base_llm.base_model_iterator.MockResponseIterator"
+            ) as mock_iterator_cls,
+            patch.object(
+                guardrail, "_check_content", new_callable=AsyncMock
+            ) as mock_check,
+        ):
+            mock_check.return_value = {"policyActions": []}
+
+            async def _iter_chunks():
+                yield assembled_response
+
+            mock_iterator_cls.return_value.__aiter__ = lambda s: _iter_chunks()
+
+            chunks = []
+            async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=fake_response_stream(),
+                request_data={"metadata": {"user_id": "user-123"}},
+            ):
+                chunks.append(chunk)
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["activity"] == "downloadText"
+            assert len(chunks) > 0
+
+    @pytest.mark.asyncio
+    async def test_streaming_violation_raises_before_any_chunk(self):
+        """A policy violation must raise HTTPException before yielding any chunk."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+
+        assembled_response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(
+                        content="SSN: 123-45-6789",
+                        role="assistant",
+                    ),
+                )
+            ]
+        )
+
+        async def fake_response_stream():
+            yield assembled_response
+
+        with (
+            patch("litellm.main.stream_chunk_builder", return_value=assembled_response),
+            patch.object(
+                guardrail,
+                "_check_content",
+                new_callable=AsyncMock,
+                side_effect=HTTPException(
+                    status_code=400,
+                    detail={
+                        "error": "Microsoft Purview DLP: Content blocked by policy"
+                    },
+                ),
+            ),
+        ):
+            chunks = []
+            with pytest.raises(HTTPException) as exc_info:
+                async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                    user_api_key_dict=UserAPIKeyAuth(
+                        api_key="test", user_id="user-123"
+                    ),
+                    response=fake_response_stream(),
+                    request_data={"metadata": {"user_id": "user-123"}},
+                ):
+                    chunks.append(chunk)
+
+            assert exc_info.value.status_code == 400
+            assert len(chunks) == 0  # No chunks yielded before the block
+
+    @pytest.mark.asyncio
+    async def test_streaming_no_user_id_raises_before_yield(self):
+        """No resolvable user_id → fail closed before any chunk is yielded."""
+        from litellm.types.utils import Choices, Message, ModelResponse
+
+        guardrail = _make_guardrail()
+
+        assembled_response = ModelResponse(
+            choices=[
+                Choices(
+                    index=0,
+                    message=Message(content="some content", role="assistant"),
+                )
+            ]
+        )
+
+        async def fake_response_stream():
+            yield assembled_response
+
+        with patch(
+            "litellm.main.stream_chunk_builder", return_value=assembled_response
+        ):
+            chunks = []
+            with pytest.raises(HTTPException) as exc_info:
+                async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                    user_api_key_dict=UserAPIKeyAuth(api_key="test"),  # no user_id
+                    response=fake_response_stream(),
+                    request_data={},
+                ):
+                    chunks.append(chunk)
+
+            assert exc_info.value.status_code == 400
+            assert len(chunks) == 0
+
+    @pytest.mark.asyncio
+    async def test_streaming_text_completion_scans_before_yield(self):
+        """Streamed /v1/completions must be DLP-scanned via TextCompletionResponse."""
+        from litellm.types.utils import TextChoices, TextCompletionResponse
+
+        guardrail = _make_guardrail()
+
+        assembled_response = TextCompletionResponse(
+            model="gpt-3.5-turbo-instruct",
+            choices=[TextChoices(text="completion body", index=0)],
+        )
+
+        async def fake_response_stream():
+            yield assembled_response
+
+        with (
+            patch("litellm.main.stream_chunk_builder", return_value=assembled_response),
+            patch.object(
+                guardrail, "_check_content", new_callable=AsyncMock
+            ) as mock_check,
+        ):
+            mock_check.return_value = {"policyActions": []}
+
+            chunks = []
+            async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=fake_response_stream(),
+                request_data={},
+            ):
+                chunks.append(chunk)
+
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["text"] == "completion body"
+            assert len(chunks) > 0
+
+    @pytest.mark.asyncio
+    async def test_streaming_responses_api_scans_completed_event(self):
+        """Streamed Responses API: assembled ResponsesAPIResponse must be DLP-scanned."""
+        from litellm.types.llms.openai import (
+            ResponseCompletedEvent,
+            ResponseCreatedEvent,
+            ResponsesAPIResponse,
+            ResponsesAPIStreamEvents,
+        )
+
+        guardrail = _make_guardrail()
+
+        completed_response = ResponsesAPIResponse(
+            id="resp-stream",
+            created_at=0,
+            output=[
+                {
+                    "type": "message",
+                    "id": "msg-stream",
+                    "status": "completed",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": "streamed output"}],
+                }
+            ],
+        )
+        created_event = ResponseCreatedEvent(
+            type=ResponsesAPIStreamEvents.RESPONSE_CREATED,
+            response=completed_response,
+        )
+        completed_event = ResponseCompletedEvent(
+            type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
+            response=completed_response,
+        )
+
+        async def fake_response_stream():
+            yield created_event
+            yield completed_event
+
+        with (
+            patch("litellm.main.stream_chunk_builder") as mock_stream_builder,
+            patch.object(
+                guardrail, "_check_content", new_callable=AsyncMock
+            ) as mock_check,
+        ):
+            mock_check.return_value = {"policyActions": []}
+
+            chunks = []
+            async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=UserAPIKeyAuth(api_key="test", user_id="user-123"),
+                response=fake_response_stream(),
+                request_data={},
+            ):
+                chunks.append(chunk)
+
+            mock_stream_builder.assert_not_called()
+            mock_check.assert_called_once()
+            assert mock_check.call_args.kwargs["activity"] == "downloadText"
+            assert mock_check.call_args.kwargs["text"] == "streamed output"
+            assert chunks == [created_event, completed_event]
+
+    @pytest.mark.asyncio
+    async def test_streaming_responses_api_violation_blocks_before_yield(self):
+        """Responses API stream with a DLP violation must raise before any chunk is yielded."""
+        from litellm.types.llms.openai import (
+            ResponseCompletedEvent,
+            ResponsesAPIResponse,
+            ResponsesAPIStreamEvents,
+        )
+
+        guardrail = _make_guardrail()
+
+        completed_response = ResponsesAPIResponse(
+            id="resp-stream-block",
+            created_at=0,
+            output=[
+                {
+                    "type": "message",
+                    "id": "msg-stream-block",
+                    "status": "completed",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": "SSN: 123-45-6789"}],
+                }
+            ],
+        )
+        completed_event = ResponseCompletedEvent(
+            type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
+            response=completed_response,
+        )
+
+        async def fake_response_stream():
+            yield completed_event
+
+        with patch.object(
+            guardrail,
+            "_check_content",
+            new_callable=AsyncMock,
+            side_effect=HTTPException(
+                status_code=400,
+                detail={"error": "Microsoft Purview DLP: Content blocked by policy"},
+            ),
+        ):
+            chunks = []
+            with pytest.raises(HTTPException) as exc_info:
+                async for chunk in guardrail.async_post_call_streaming_iterator_hook(
+                    user_api_key_dict=UserAPIKeyAuth(
+                        api_key="test", user_id="user-123"
+                    ),
+                    response=fake_response_stream(),
+                    request_data={},
+                ):
+                    chunks.append(chunk)
+
+            assert exc_info.value.status_code == 400
+            assert len(chunks) == 0
+
+
+# ---------------------------------------------------------------
+# Auto-discovery registration
+# ---------------------------------------------------------------
+
+
+class TestRegistration:
+    def test_registry_contains_microsoft_purview(self):
+        from litellm.proxy.guardrails.guardrail_hooks.microsoft_purview import (
+            guardrail_class_registry,
+            guardrail_initializer_registry,
+        )
+
+        assert "microsoft_purview" in guardrail_initializer_registry
+        assert "microsoft_purview" in guardrail_class_registry
+        assert (
+            guardrail_class_registry["microsoft_purview"]
+            is MicrosoftPurviewDLPGuardrail
+        )
diff --git a/tests/test_litellm/proxy/guardrails/test_mcp_jwt_signer.py b/tests/test_litellm/proxy/guardrails/test_mcp_jwt_signer.py
index b17b3270787..cb2276ab39d 100644
--- a/tests/test_litellm/proxy/guardrails/test_mcp_jwt_signer.py
+++ b/tests/test_litellm/proxy/guardrails/test_mcp_jwt_signer.py
@@ -219,7 +219,7 @@ def test_build_claims_scope_with_tool():
 
 
 def test_build_claims_scope_without_tool():
-    """_build_claims() includes mcp:tools/list when no specific tool is called."""
+    """_build_claims() emits only mcp:tools/list when no specific tool is called."""
     signer = _make_signer()
     user_dict = _make_user_api_key_dict()
     data: Dict[str, Any] = {}
@@ -227,10 +227,11 @@ def test_build_claims_scope_without_tool():
     claims = signer._build_claims(user_dict, data)
 
     scopes = set(claims["scope"].split())
-    assert "mcp:tools/call" in scopes
     assert "mcp:tools/list" in scopes
+    # List-only JWTs must NOT carry mcp:tools/call — least-privilege
+    assert "mcp:tools/call" not in scopes
     # No per-tool call scope when no tool name was given
-    assert not any(s.endswith(":call") and s != "mcp:tools/call" for s in scopes)
+    assert not any(s.endswith(":call") for s in scopes)
 
 
 def test_build_claims_act_fallback_to_litellm_proxy():
@@ -338,7 +339,7 @@ async def test_hook_skips_non_mcp_call_types():
     user_dict = _make_user_api_key_dict()
     data = {"messages": [{"role": "user", "content": "hello"}]}
 
-    for call_type in ("completion", "acompletion", "embedding", "list_mcp_tools"):
+    for call_type in ("completion", "acompletion", "embedding"):
         original_data = {**data}
         result = await signer.async_pre_call_hook(
             user_api_key_dict=user_dict,
@@ -351,6 +352,33 @@ async def test_hook_skips_non_mcp_call_types():
         ), f"extra_headers should not be set for {call_type}"
 
 
+@pytest.mark.asyncio
+async def test_hook_signs_list_mcp_tools():
+    """async_pre_call_hook() signs JWT for list_mcp_tools with list scope."""
+    signer = _make_signer(
+        issuer="https://litellm.example.com", audience="mcp", ttl_seconds=300
+    )
+    user_dict = _make_user_api_key_dict(user_id="alice", team_id="backend")
+    data = {"mcp_tool_name": "should_be_cleared"}
+
+    result = await signer.async_pre_call_hook(
+        user_api_key_dict=user_dict,
+        cache=MagicMock(),
+        data=data,
+        call_type="list_mcp_tools",
+    )
+
+    assert isinstance(result, dict)
+    assert "extra_headers" in result
+    assert result["extra_headers"]["Authorization"].startswith("Bearer ")
+    token = result["extra_headers"]["Authorization"].removeprefix("Bearer ")
+    decoded = _decode_unverified(token)
+    scopes = set(decoded["scope"].split())
+    assert "mcp:tools/list" in scopes
+    # List-only JWTs must NOT carry mcp:tools/call — least-privilege
+    assert "mcp:tools/call" not in scopes
+
+
 @pytest.mark.asyncio
 async def test_signed_token_is_verifiable():
     """The JWT injected by the hook can be verified against the JWKS public key."""
@@ -1128,3 +1156,116 @@ async def test_hook_raises_401_when_jwt_verification_fails():
                 )
 
     assert exc_info.value.status_code == 401
+
+
+# --- _build_scope branches: call_mcp_tool with empty tool name, list_mcp_tools ---
+
+
+def test_build_scope_call_type_call_mcp_tool_without_tool_name():
+    """call_mcp_tool with empty tool name emits a generic mcp:tools/call only."""
+    signer = _make_signer()
+    scope = signer._build_scope("", call_type="call_mcp_tool")
+    scopes = set(scope.split())
+    assert scopes == {"mcp:tools/call"}
+
+
+def test_build_scope_call_type_list_mcp_tools_only_list():
+    """list_mcp_tools (no tool) emits only mcp:tools/list, never tools/call."""
+    signer = _make_signer()
+    scope = signer._build_scope("", call_type="list_mcp_tools")
+    scopes = set(scope.split())
+    assert scopes == {"mcp:tools/list"}
+
+
+def test_build_scope_default_is_list_only_when_no_call_type():
+    """No call_type and no tool falls through to tools/list (least-privilege default)."""
+    signer = _make_signer()
+    scope = signer._build_scope("")
+    scopes = set(scope.split())
+    assert "mcp:tools/list" in scopes
+    assert "mcp:tools/call" not in scopes
+
+
+# --- inject_mcp_jwt_headers_for_upstream ---
+
+
+@pytest.mark.asyncio
+async def test_inject_mcp_jwt_returns_unchanged_when_signer_not_configured():
+    """No signer configured -> return a fresh copy of extra_headers untouched."""
+    import litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer as mod
+    from litellm.proxy._types import UserAPIKeyAuth
+
+    mod._mcp_jwt_signer_instance = None
+    headers = {"X-Trace-Id": "abc"}
+    user_dict = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+
+    result = await mod.inject_mcp_jwt_headers_for_upstream(
+        user_api_key_dict=user_dict,
+        extra_headers=headers,
+    )
+    assert result == headers
+    assert result is not headers  # must be a copy
+
+
+@pytest.mark.asyncio
+async def test_inject_mcp_jwt_returns_unchanged_when_user_dict_none():
+    """No user_api_key_dict -> short-circuit without invoking the signer."""
+    from litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer import (
+        inject_mcp_jwt_headers_for_upstream,
+    )
+
+    _make_signer()  # ensure instance is created
+    result = await inject_mcp_jwt_headers_for_upstream(
+        user_api_key_dict=None,
+        extra_headers={"X-Trace-Id": "abc"},
+    )
+    assert result == {"X-Trace-Id": "abc"}
+
+
+@pytest.mark.asyncio
+async def test_inject_mcp_jwt_signs_for_list_tools_path():
+    """When for_list_tools=True, signer is invoked with list_mcp_tools call_type."""
+    from litellm.proxy._types import UserAPIKeyAuth
+    from litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer import (
+        inject_mcp_jwt_headers_for_upstream,
+    )
+
+    _make_signer(issuer="https://litellm.example.com", audience="mcp", ttl_seconds=300)
+    user_dict = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+
+    result = await inject_mcp_jwt_headers_for_upstream(
+        user_api_key_dict=user_dict,
+        extra_headers={"X-Trace": "1"},
+        raw_headers={"Authorization": "Bearer incoming.opaque.token"},
+        for_list_tools=True,
+    )
+    assert result["X-Trace"] == "1"
+    assert result["Authorization"].startswith("Bearer ")
+    token = result["Authorization"].removeprefix("Bearer ")
+    decoded = _decode_unverified(token)
+    scopes = set(decoded["scope"].split())
+    assert scopes == {"mcp:tools/list"}
+
+
+@pytest.mark.asyncio
+async def test_inject_mcp_jwt_signs_for_tool_call_path():
+    """for_list_tools=False with a tool name signs a call_mcp_tool JWT."""
+    from litellm.proxy._types import UserAPIKeyAuth
+    from litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer import (
+        inject_mcp_jwt_headers_for_upstream,
+    )
+
+    _make_signer(issuer="https://litellm.example.com", audience="mcp", ttl_seconds=300)
+    user_dict = UserAPIKeyAuth(api_key="sk-test", user_id="alice")
+
+    result = await inject_mcp_jwt_headers_for_upstream(
+        user_api_key_dict=user_dict,
+        for_list_tools=False,
+        mcp_tool_name="search_web",
+    )
+    assert result["Authorization"].startswith("Bearer ")
+    token = result["Authorization"].removeprefix("Bearer ")
+    decoded = _decode_unverified(token)
+    scopes = set(decoded["scope"].split())
+    assert "mcp:tools/call" in scopes
+    assert "mcp:tools/search_web:call" in scopes
diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py
index c2ac941e737..38bf2d2c915 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py
@@ -1336,6 +1336,39 @@ async def test_update_without_metadata_still_preserves_existing():
     assert result["metadata"]["other"] == "kept"
 
 
+@pytest.mark.asyncio
+async def test_prepare_key_update_data_encrypts_callback_vars(monkeypatch):
+    """/key/update must encrypt callback_vars values before they reach the DB."""
+    from litellm.proxy.common_utils.callback_utils import decrypt_callback_vars
+
+    monkeypatch.setenv("LITELLM_SALT_KEY", "test-salt-32-bytes-aaaaaaaaaaaaaa")
+    data = UpdateKeyRequest(
+        key="sk-1",
+        metadata={
+            "logging": [
+                {
+                    "callback_name": "langfuse",
+                    "callback_type": "success",
+                    "callback_vars": {
+                        "langfuse_public_key": "pk-real",
+                        "langfuse_secret_key": "sk-real",
+                    },
+                }
+            ]
+        },
+    )
+    existing_key = LiteLLM_VerificationToken(token="hashed")
+
+    result = await prepare_key_update_data(data=data, existing_key_row=existing_key)
+
+    cv = result["metadata"]["logging"][0]["callback_vars"]
+    assert cv["langfuse_secret_key"] != "sk-real"
+    assert cv["langfuse_public_key"] != "pk-real"
+    recovered = decrypt_callback_vars(result["metadata"])["logging"][0]["callback_vars"]
+    assert recovered["langfuse_secret_key"] == "sk-real"
+    assert recovered["langfuse_public_key"] == "pk-real"
+
+
 @pytest.mark.asyncio
 async def test_prepare_key_update_data_duration_never_expires():
     """Test that duration="-1" sets expires to None (never expires)."""
diff --git a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py
index f0bf4578636..b65f6305b77 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py
@@ -1446,3 +1446,175 @@ class TestGetTeamDeployments:
         result = await _get_team_deployments(team_id, prisma_client)
         assert len(result) == 1
         assert result[0] is dep1
+
+
+def _build_db_model_for_blocked_test():
+    from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
+
+    return Deployment(
+        model_name="gpt-4o",
+        litellm_params=LiteLLM_Params(model="openai/gpt-4o"),
+        model_info=ModelInfo(id="dep-0"),
+    )
+
+
+class TestUpdateDBModelBlocked:
+    """`update_db_model` must thread `blocked` through to the Prisma payload only
+    when the caller explicitly set it — PATCH semantics: an absent field means
+    "leave the stored value untouched"."""
+
+    def test_update_db_model_passes_blocked_true_to_db(self):
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            update_db_model,
+        )
+
+        result = update_db_model(
+            db_model=_build_db_model_for_blocked_test(),
+            updated_patch=updateDeployment(blocked=True),
+        )
+        assert result["blocked"] is True
+
+    def test_update_db_model_passes_blocked_false_to_db(self):
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            update_db_model,
+        )
+
+        result = update_db_model(
+            db_model=_build_db_model_for_blocked_test(),
+            updated_patch=updateDeployment(blocked=False),
+        )
+        assert result["blocked"] is False
+
+    def test_update_db_model_omits_blocked_when_patch_is_none(self):
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            update_db_model,
+        )
+
+        result = update_db_model(
+            db_model=_build_db_model_for_blocked_test(),
+            updated_patch=updateDeployment(),
+        )
+        assert "blocked" not in result
+
+
+class TestGetModelInfoWithIdBlocked:
+    """`ProxyConfig.get_model_info_with_id` must propagate the DB-level `blocked`
+    column into the in-memory `model_info` dict so the router filter can read it."""
+
+    def test_get_model_info_with_id_propagates_blocked_true(self):
+        from litellm.proxy.proxy_server import ProxyConfig
+
+        model = MagicMock()
+        model.model_id = "dep-1"
+        model.model_info = {}
+        model.blocked = True
+        info = ProxyConfig().get_model_info_with_id(model=model, db_model=True)
+        assert info.id == "dep-1"
+        assert getattr(info, "blocked") is True
+
+    def test_get_model_info_with_id_defaults_blocked_to_false_when_missing(self):
+        from litellm.proxy.proxy_server import ProxyConfig
+
+        model = MagicMock(spec=["model_id", "model_info"])
+        model.model_id = "dep-2"
+        model.model_info = {}
+        info = ProxyConfig().get_model_info_with_id(model=model, db_model=True)
+        assert getattr(info, "blocked") is False
+
+
+class TestPatchModelBlockedAuthGate:
+    """Only proxy admins may flip `blocked` — team admins authorized for
+    team-scoped models via `can_user_make_model_call` must still be rejected
+    when they attempt to toggle the pause flag."""
+
+    @pytest.mark.asyncio
+    async def test_team_admin_cannot_toggle_blocked(self):
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            patch_model,
+        )
+
+        non_admin = UserAPIKeyAuth(
+            user_id="team_admin",
+            user_role=LitellmUserRoles.INTERNAL_USER,
+        )
+        existing_row = MagicMock()
+        existing_row.litellm_params = {"model": "openai/gpt-4o-mini"}
+        existing_row.model_dump.return_value = {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": existing_row.litellm_params,
+            "model_info": {"id": "m1"},
+        }
+        existing_row.model_dump_json.return_value = "{}"
+
+        mock_prisma = MagicMock()
+        mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock(
+            return_value=existing_row
+        )
+
+        with (
+            patch("litellm.proxy.proxy_server.prisma_client", mock_prisma),
+            patch("litellm.proxy.proxy_server.llm_router", MagicMock()),
+            patch("litellm.proxy.proxy_server.store_model_in_db", True),
+            patch("litellm.proxy.proxy_server.premium_user", True),
+            patch(
+                "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call",
+                new=AsyncMock(return_value=None),
+            ),
+        ):
+            with pytest.raises(Exception) as exc_info:
+                await patch_model(
+                    model_id="m1",
+                    patch_data=updateDeployment(blocked=True),
+                    user_api_key_dict=non_admin,
+                )
+            err = exc_info.value
+            assert getattr(err, "param", "") == "blocked"
+            assert "proxy admin" in getattr(err, "message", "").lower()
+
+    @pytest.mark.asyncio
+    async def test_proxy_admin_can_toggle_blocked(self):
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            patch_model,
+        )
+
+        admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN)
+        existing_row = MagicMock()
+        existing_row.litellm_params = {"model": "openai/gpt-4o-mini"}
+        existing_row.model_dump.return_value = {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": existing_row.litellm_params,
+            "model_info": {"id": "m1"},
+        }
+        existing_row.model_dump_json.return_value = "{}"
+        updated_row = MagicMock()
+        updated_row.model_dump_json.return_value = "{}"
+
+        mock_prisma = MagicMock()
+        mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock(
+            return_value=existing_row
+        )
+        mock_prisma.db.litellm_proxymodeltable.update = AsyncMock(
+            return_value=updated_row
+        )
+
+        with (
+            patch("litellm.proxy.proxy_server.prisma_client", mock_prisma),
+            patch("litellm.proxy.proxy_server.llm_router", MagicMock()),
+            patch("litellm.proxy.proxy_server.store_model_in_db", True),
+            patch("litellm.proxy.proxy_server.premium_user", True),
+            patch(
+                "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call",
+                new=AsyncMock(return_value=None),
+            ),
+            patch(
+                "litellm.proxy.management_endpoints.model_management_endpoints.clear_cache",
+                new=AsyncMock(return_value=None),
+            ),
+        ):
+            result = await patch_model(
+                model_id="m1",
+                patch_data=updateDeployment(blocked=True),
+                user_api_key_dict=admin,
+            )
+            assert result is updated_row
+            mock_prisma.db.litellm_proxymodeltable.update.assert_awaited_once()
diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_callback_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_callback_endpoints.py
index 55afd4061d8..d43bf3a3bd8 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_team_callback_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_team_callback_endpoints.py
@@ -420,3 +420,42 @@ async def test_add_team_callbacks_no_audit_when_disabled(monkeypatch):
         )
 
     assert audit_calls == []
+
+
+@pytest.mark.asyncio
+async def test_add_team_callbacks_writes_encrypted_callback_vars(monkeypatch):
+    """add_team_callbacks must encrypt callback_vars values before the DB write."""
+    from litellm.proxy.common_utils.callback_utils import decrypt_callback_vars
+
+    monkeypatch.setenv("LITELLM_SALT_KEY", "test-salt-32-bytes-aaaaaaaaaaaaaa")
+    mock_prisma = _patch_prisma(_team_row(team_id="team-1", metadata={"logging": []}))
+
+    with (
+        patch("litellm.proxy.proxy_server.prisma_client", mock_prisma),
+        patch("litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin"),
+        patch("litellm.proxy.proxy_server.master_key", None),
+    ):
+        await add_team_callbacks(
+            data=AddTeamCallback(
+                callback_name="langfuse",
+                callback_type="success",
+                callback_vars={
+                    "langfuse_public_key": "pk-lf-real-public",
+                    "langfuse_secret_key": "sk-lf-real-secret",
+                },
+            ),
+            http_request=MagicMock(spec=Request),
+            team_id="team-1",
+            user_api_key_dict=_admin_auth(),
+            litellm_changed_by=None,
+        )
+
+    written = json.loads(
+        mock_prisma.db.litellm_teamtable.update.await_args.kwargs["data"]["metadata"]
+    )
+    cv = written["logging"][0]["callback_vars"]
+    assert cv["langfuse_secret_key"] != "sk-lf-real-secret"
+    assert cv["langfuse_public_key"] != "pk-lf-real-public"
+    recovered = decrypt_callback_vars(written)["logging"][0]["callback_vars"]
+    assert recovered["langfuse_secret_key"] == "sk-lf-real-secret"
+    assert recovered["langfuse_public_key"] == "pk-lf-real-public"
diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py
index 5c7bbc46c95..13bb39c35c9 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py
@@ -1540,6 +1540,137 @@ def test_add_new_models_to_team_with_existing_models():
     assert updated_models.sort() == ["model1", "model2", "model3", "model4"].sort()
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "endpoint_name",
+    ["team_model_add", "team_model_delete"],
+)
+async def test_team_model_add_delete_refresh_team_cache(endpoint_name):
+    """
+    Regression pin for LIT-3244 vector-store BYOK 403.
+
+    `team_model_add` and `team_model_delete` mutate `team.models` in the
+    DB. Without a cache refresh, the in-memory `LiteLLM_TeamTableCachedObj`
+    used by `common_checks` stays stale and team members 403 on a model
+    the DB has just granted (or, symmetrically, keep using a model the DB
+    has just revoked).
+
+    Pin: after the DB update, the endpoint must call `_cache_team_object`
+    with the updated team row so the cached team stays in sync.
+    """
+    from unittest.mock import AsyncMock, MagicMock, Mock, patch
+
+    from fastapi import Request
+
+    from litellm.proxy._types import (
+        LitellmUserRoles,
+        TeamModelAddRequest,
+        TeamModelDeleteRequest,
+        UserAPIKeyAuth,
+    )
+    from litellm.proxy.management_endpoints.team_endpoints import (
+        team_model_add,
+        team_model_delete,
+    )
+
+    mock_request = Mock(spec=Request)
+    mock_user_api_key_dict = UserAPIKeyAuth(
+        user_role=LitellmUserRoles.PROXY_ADMIN, user_id="test_user_id"
+    )
+
+    existing_team = MagicMock()
+    existing_team.model_dump.return_value = {
+        "team_id": "team-1234",
+        "models": ["bedrock-claude-sonnet-4", "openai/*"],
+        "object_permission_id": "op-1234",
+        "object_permission": {
+            "object_permission_id": "op-1234",
+            "search_tools": ["allowed-tool-A"],
+        },
+    }
+
+    updated_team = MagicMock()
+    updated_team.team_id = "team-1234"
+    updated_team.model_dump.return_value = {
+        "team_id": "team-1234",
+        "models": ["bedrock-claude-sonnet-4", "openai/*", "team-byok-1"],
+        # The Prisma update must come back with `object_permission` populated
+        # (via `include={"object_permission": True}`), otherwise the cache
+        # write below would null it out — see LIT-3244 follow-up.
+        "object_permission_id": "op-1234",
+        "object_permission": {
+            "object_permission_id": "op-1234",
+            "search_tools": ["allowed-tool-A"],
+        },
+    }
+
+    with (
+        patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma_client,
+        patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache,
+        patch("litellm.proxy.proxy_server.proxy_logging_obj") as mock_logging,
+        patch(
+            "litellm.proxy.management_endpoints.team_endpoints._cache_team_object"
+        ) as mock_cache_team,
+    ):
+        mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock(
+            return_value=existing_team
+        )
+        mock_prisma_client.db.litellm_teamtable.update = AsyncMock(
+            return_value=updated_team
+        )
+        mock_cache_team.return_value = None
+
+        if endpoint_name == "team_model_add":
+            await team_model_add(
+                data=TeamModelAddRequest(team_id="team-1234", models=["team-byok-1"]),
+                http_request=mock_request,
+                user_api_key_dict=mock_user_api_key_dict,
+            )
+        else:
+            await team_model_delete(
+                data=TeamModelDeleteRequest(team_id="team-1234", models=["openai/*"]),
+                http_request=mock_request,
+                user_api_key_dict=mock_user_api_key_dict,
+            )
+
+        # The pin: cache refresh must run with the updated team row.
+        assert mock_cache_team.await_count == 1, (
+            f"{endpoint_name} must call _cache_team_object exactly once "
+            f"after the DB update (LIT-3244 regression pin); "
+            f"got await_count={mock_cache_team.await_count}"
+        )
+        call_kwargs = mock_cache_team.await_args.kwargs
+        assert call_kwargs["team_id"] == "team-1234"
+        # The cached object must be built from the *updated* row, not the
+        # pre-mutation `existing_team` — that's the whole point. Both rows
+        # share team_id, so the only assertion that actually pins this is
+        # against the field that differs between them: `models`.
+        assert call_kwargs["team_table"].team_id == "team-1234"
+        assert call_kwargs["team_table"].models == [
+            "bedrock-claude-sonnet-4",
+            "openai/*",
+            "team-byok-1",
+        ]
+        # And the cached object MUST carry the `object_permission` relation
+        # (LIT-3244 follow-up). If the Prisma update were missing
+        # `include={"object_permission": True}`, the cached team would have
+        # object_permission=None, and downstream consumers like
+        # `validate_key_search_tools_against_team` would treat that as
+        # "no team-level restriction" and stop enforcing the team's
+        # search-tool allowlist on key issuance.
+        assert call_kwargs["team_table"].object_permission is not None
+        assert call_kwargs["team_table"].object_permission.search_tools == [
+            "allowed-tool-A"
+        ]
+        # Pin the Prisma call shape too — the regression is in *what the
+        # update returns*, so the contract that the update asks for
+        # `object_permission` belongs in this test.
+        update_call_kwargs = (
+            mock_prisma_client.db.litellm_teamtable.update.call_args.kwargs
+        )
+        assert update_call_kwargs.get("include", {}).get("object_permission") is True
+
+
 @pytest.mark.asyncio
 async def test_update_team_team_member_budget_not_passed_to_db():
     """
@@ -1568,7 +1699,9 @@ async def test_update_team_team_member_budget_not_passed_to_db():
         patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache,
         patch("litellm.proxy.proxy_server.proxy_logging_obj") as mock_logging,
         patch("litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin"),
-        patch("litellm.proxy.auth.auth_checks._cache_team_object") as mock_cache_team,
+        patch(
+            "litellm.proxy.management_endpoints.team_endpoints._cache_team_object"
+        ) as mock_cache_team,
         patch(
             "litellm.proxy.management_endpoints.team_endpoints.TeamMemberBudgetHandler.upsert_team_member_budget_table"
         ) as mock_upsert_budget,
@@ -1999,7 +2132,9 @@ async def test_update_team_with_team_member_budget_duration():
         patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache,
         patch("litellm.proxy.proxy_server.proxy_logging_obj") as mock_logging,
         patch("litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin"),
-        patch("litellm.proxy.auth.auth_checks._cache_team_object") as mock_cache_team,
+        patch(
+            "litellm.proxy.management_endpoints.team_endpoints._cache_team_object"
+        ) as mock_cache_team,
         patch(
             "litellm.proxy.management_endpoints.team_endpoints.TeamMemberBudgetHandler.upsert_team_member_budget_table"
         ) as mock_upsert_budget,
@@ -7943,3 +8078,168 @@ async def test_team_member_me_returns_404_for_unknown_team(mock_db_client):
                 user_api_key_dict=caller_auth,
             )
     assert exc_info.value.status_code == 404
+
+
+@pytest.mark.asyncio
+async def test_new_team_encrypts_callback_vars(
+    mock_db_client, mock_admin_auth, monkeypatch
+):
+    """/team/new must encrypt callback_vars values before they reach the DB."""
+    from fastapi import Request
+
+    from litellm.proxy._types import NewTeamRequest
+    from litellm.proxy.common_utils.callback_utils import decrypt_callback_vars
+    from litellm.proxy.management_endpoints.team_endpoints import new_team
+    from litellm.proxy.utils import PrismaClient
+
+    monkeypatch.setenv("LITELLM_SALT_KEY", "test-salt-32-bytes-aaaaaaaaaaaaaa")
+
+    # Use the real jsonify helpers so the encrypted dict goes through the
+    # actual JSON serialization production uses (catches non-serializable
+    # ciphertext, missing fields, etc.).
+    mock_db_client.jsonify_object = PrismaClient.jsonify_object.__get__(mock_db_client)
+    mock_db_client.jsonify_team_object = PrismaClient.jsonify_team_object.__get__(
+        mock_db_client
+    )
+    mock_db_client.get_data = AsyncMock(return_value=None)
+    mock_db_client.db = MagicMock()
+    mock_db_client.db.litellm_teamtable = MagicMock()
+    team_create_result = MagicMock(team_id="team-456", object_permission_id=None)
+    team_create_result.model_dump.return_value = {"team_id": "team-456"}
+    mock_team_create = AsyncMock(return_value=team_create_result)
+    mock_db_client.db.litellm_teamtable.create = mock_team_create
+    mock_db_client.db.litellm_teamtable.count = AsyncMock(return_value=0)
+    mock_db_client.db.litellm_teamtable.update = AsyncMock(
+        return_value=team_create_result
+    )
+    mock_db_client.db.litellm_usertable = MagicMock()
+    mock_db_client.db.litellm_usertable.update = AsyncMock(return_value=MagicMock())
+
+    team_request = NewTeamRequest(
+        team_alias="my-team",
+        metadata={
+            "logging": [
+                {
+                    "callback_name": "langfuse",
+                    "callback_type": "success",
+                    "callback_vars": {
+                        "langfuse_public_key": "pk-real",
+                        "langfuse_secret_key": "sk-real",
+                    },
+                }
+            ]
+        },
+    )
+
+    await new_team(
+        data=team_request,
+        http_request=MagicMock(spec=Request),
+        user_api_key_dict=mock_admin_auth,
+    )
+
+    written = mock_team_create.call_args.kwargs["data"]
+    # jsonify_team_object serializes the metadata dict to a JSON string before
+    # the DB write, so we round-trip through json.loads to inspect it.
+    metadata = json.loads(written["metadata"])
+    cv = metadata["logging"][0]["callback_vars"]
+    assert cv["langfuse_secret_key"] != "sk-real"
+    recovered = decrypt_callback_vars(metadata)["logging"][0]["callback_vars"]
+    assert recovered["langfuse_secret_key"] == "sk-real"
+def _non_admin_auth():
+    return UserAPIKeyAuth(
+        user_id="u-team-admin", user_role=LitellmUserRoles.INTERNAL_USER
+    )
+
+
+def test_check_passthrough_routes_caller_permission_team():
+    from litellm.proxy._types import NewTeamRequest
+    from litellm.proxy.management_endpoints.common_utils import (
+        _check_passthrough_routes_caller_permission,
+    )
+
+    admin = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN)
+    non_admin = _non_admin_auth()
+
+    _check_passthrough_routes_caller_permission(
+        NewTeamRequest(allowed_passthrough_routes=["/foo/*"]), admin, entity="team"
+    )
+
+    _check_passthrough_routes_caller_permission(
+        NewTeamRequest(), non_admin, entity="team"
+    )
+    _check_passthrough_routes_caller_permission(
+        NewTeamRequest(allowed_passthrough_routes=[]), non_admin, entity="team"
+    )
+
+    with pytest.raises(HTTPException) as exc:
+        _check_passthrough_routes_caller_permission(
+            NewTeamRequest(allowed_passthrough_routes=["/admin/*"]),
+            non_admin,
+            entity="team",
+        )
+    assert exc.value.status_code == 403
+    assert "allowed_passthrough_routes" in str(exc.value.detail)
+    assert "team" in str(exc.value.detail)
+
+    with pytest.raises(HTTPException) as exc:
+        _check_passthrough_routes_caller_permission(
+            NewTeamRequest(metadata={"allowed_passthrough_routes": ["/admin/*"]}),
+            non_admin,
+            entity="team",
+        )
+    assert exc.value.status_code == 403
+    assert "metadata.allowed_passthrough_routes" in str(exc.value.detail)
+
+
+@pytest.mark.asyncio
+async def test_new_team_blocks_non_admin_passthrough_routes(mock_db_client):
+    """A non-proxy-admin cannot self-grant pass-through routes via /team/new."""
+    mock_db_client.db.litellm_teamtable.count = AsyncMock(return_value=0)
+    from fastapi import Request
+
+    from litellm.proxy._types import NewTeamRequest, ProxyException
+    from litellm.proxy.management_endpoints.team_endpoints import new_team
+
+    with patch(
+        "litellm.proxy.management_endpoints.team_endpoints._check_user_team_limits",
+        AsyncMock(return_value=None),
+    ):
+        with pytest.raises(ProxyException) as exc:
+            await new_team(
+                data=NewTeamRequest(
+                    team_alias="t", allowed_passthrough_routes=["/admin/*"]
+                ),
+                http_request=MagicMock(spec=Request),
+                user_api_key_dict=_non_admin_auth(),
+            )
+    assert str(exc.value.code) == "403"
+    assert "allowed_passthrough_routes" in str(exc.value.message)
+
+
+@pytest.mark.asyncio
+async def test_update_team_blocks_non_admin_passthrough_routes(mock_db_client):
+    """Even a team manager (non-proxy-admin) cannot set pass-through routes via
+    /team/update — the gate runs after _verify_team_access."""
+    from fastapi import Request
+
+    from litellm.proxy._types import ProxyException, UpdateTeamRequest
+    from litellm.proxy.management_endpoints.team_endpoints import update_team
+
+    existing = MagicMock()
+    existing.model_dump.return_value = {"team_id": "t1"}
+    mock_db_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=existing)
+
+    with patch(
+        "litellm.proxy.management_endpoints.team_endpoints._verify_team_access",
+        AsyncMock(return_value=None),
+    ):
+        with pytest.raises(ProxyException) as exc:
+            await update_team(
+                data=UpdateTeamRequest(
+                    team_id="t1", allowed_passthrough_routes=["/admin/*"]
+                ),
+                http_request=MagicMock(spec=Request),
+                user_api_key_dict=_non_admin_auth(),
+            )
+    assert str(exc.value.code) == "403"
+    assert "allowed_passthrough_routes" in str(exc.value.message)
diff --git a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
index 83317157847..c763e9c0e98 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
@@ -2218,6 +2218,7 @@ class TestCLIKeyRegenerationFlow:
 
         # Mock request
         mock_request = MagicMock(spec=Request)
+        mock_request.base_url = "http://internal-proxy.local/"
 
         # Test data
         session_key = "cli-session-4567890"
@@ -2242,11 +2243,14 @@ class TestCLIKeyRegenerationFlow:
             "user_code_verified": False,
             "session_data": None,
         }
-        mock_request.url_for.return_value = (
-            "https://test.example.com/sso/cli/complete/cli-session-4567890"
-        )
-
         with (
+            patch.dict(
+                os.environ,
+                {
+                    "PROXY_BASE_URL": "https://test.example.com",
+                    "SERVER_ROOT_PATH": "",
+                },
+            ),
             patch(
                 "litellm.proxy.management_endpoints.ui_sso.get_user_info_from_db",
                 return_value=mock_user_info,
@@ -2290,6 +2294,10 @@ class TestCLIKeyRegenerationFlow:
             assert result.status_code == 200
             # Verify response contains success message (response is HTML)
             assert result.body is not None
+            assert (
+                'action="https://test.example.com/sso/cli/complete/cli-session-4567890"'
+                in result.body.decode()
+            )
 
     @pytest.mark.asyncio
     async def test_cli_poll_key_returns_teams_for_selection(self):
@@ -2430,6 +2438,7 @@ class TestCLIKeyRegenerationFlow:
                 request=mock_request,
                 key="cli-new-session-key-456",
                 result=mock_result,
+                received_response=None,
             )
 
     def test_get_redirect_url_does_not_include_existing_key_in_url(self):
@@ -2488,6 +2497,11 @@ class TestCLIKeyRegenerationFlow:
             "user_id": "test-user-789",
             "user_role": "internal_user",
             "teams": ["team-a", "team-b", "team-c"],
+            "team_details": [
+                {"team_id": "team-a", "team_alias": "Team A"},
+                {"team_id": "team-b", "team_alias": "Team B"},
+                {"team_id": "team-c", "team_alias": "Team C"},
+            ],
             "models": ["gpt-4"],
             "user_email": "test@example.com",
         }
@@ -2542,6 +2556,7 @@ class TestCLIKeyRegenerationFlow:
             mock_get_jwt.assert_called_once()
             jwt_call_args = mock_get_jwt.call_args
             assert jwt_call_args.kwargs["team_id"] == selected_team
+            assert jwt_call_args.kwargs["team_alias"] == "Team B"
 
             # Verify session was deleted after JWT generation
             mock_cache.delete_cache.assert_called_once()
@@ -5544,6 +5559,289 @@ def test_generic_response_convertor_extra_attributes_missing_field(monkeypatch):
     assert result.extra_fields["another_missing"] is None
 
 
+class TestCliSsoAttributionMetadata:
+    """CLI SSO allowlisted OIDC claim persistence and poll exposure."""
+
+    def test_parse_cli_sso_claim_map(self, monkeypatch):
+        from litellm.proxy.management_endpoints import ui_sso
+
+        monkeypatch.setattr(
+            ui_sso,
+            "CLI_SSO_CLAIM_MAP",
+            "employment_type->metadata.acme_employment_type, org_info.department -> department",
+        )
+        assert ui_sso._parse_cli_sso_claim_map() == [
+            ("employment_type", "acme_employment_type"),
+            ("org_info.department", "department"),
+        ]
+
+    def test_build_cli_sso_attribution_metadata_filters_non_scalars(self, monkeypatch):
+        from litellm.proxy.management_endpoints import ui_sso
+        from litellm.proxy.management_endpoints.types import CustomOpenID
+
+        monkeypatch.setattr(
+            ui_sso,
+            "CLI_SSO_CLAIM_MAP",
+            "employment_type->acme_employment_type,access_token->should_drop,group->groups",
+        )
+
+        result = CustomOpenID(
+            id="user-1",
+            email="user@example.com",
+            display_name="User",
+            provider="generic",
+            team_ids=[],
+            extra_fields={
+                "employment_type": "full_time",
+                "access_token": "eyJhbGciOiJIUzI1NiJ9.payload.signature",
+                "group": ["team-a", "team-b"],
+            },
+        )
+
+        metadata = ui_sso.build_cli_sso_attribution_metadata(result=result)
+        assert metadata == {"acme_employment_type": "full_time"}
+
+    def test_build_cli_sso_attribution_metadata_from_oidc_dict(self, monkeypatch):
+        from litellm.proxy.management_endpoints import ui_sso
+
+        monkeypatch.setattr(
+            ui_sso,
+            "CLI_SSO_CLAIM_MAP",
+            "org_info.department->department",
+        )
+
+        metadata = ui_sso.build_cli_sso_attribution_metadata(
+            result={
+                "sub": "user-1",
+                "email": "user@example.com",
+                "org_info": {"department": "Engineering"},
+            }
+        )
+        assert metadata == {"department": "Engineering"}
+
+    @pytest.mark.asyncio
+    async def test_cli_sso_callback_passes_user_defined_values_for_new_users(self):
+        """First CLI SSO login must supply SSOUserDefinedValues so upsert can create the user."""
+        from litellm.proxy._types import LiteLLM_UserTable
+        from litellm.proxy.management_endpoints import ui_sso
+        from litellm.proxy.management_endpoints.types import CustomOpenID
+
+        mock_request = MagicMock(spec=Request)
+        mock_request.base_url = "http://internal-proxy.local/"
+        session_key = "cli-session-new-user"
+        mock_user_info = LiteLLM_UserTable(
+            user_id="cli-test-user",
+            user_role="internal_user",
+            teams=[],
+            models=[],
+        )
+        mock_sso_result = CustomOpenID(
+            id="cli-test-user",
+            email="cli-test@example.com",
+            display_name="cli-test-user",
+            provider="generic",
+            team_ids=[],
+        )
+        mock_cache = MagicMock()
+        mock_cache.get_cache.return_value = {
+            "poll_secret_hash": "poll-secret-hash",
+            "user_code_hash": "user-code-hash",
+            "sso_complete": False,
+            "user_code_verified": False,
+            "session_data": None,
+        }
+        get_user_info_mock = AsyncMock(return_value=mock_user_info)
+
+        with (
+            patch(
+                "litellm.proxy.management_endpoints.ui_sso.get_user_info_from_db",
+                get_user_info_mock,
+            ),
+            patch("litellm.proxy.proxy_server.prisma_client", MagicMock()),
+            patch("litellm.proxy.proxy_server.user_api_key_cache", mock_cache),
+            patch("litellm.proxy.proxy_server.user_custom_sso", None),
+        ):
+            await ui_sso.cli_sso_callback(
+                request=mock_request,
+                key=session_key,
+                result=mock_sso_result,
+            )
+
+        get_user_info_mock.assert_awaited_once()
+        assert get_user_info_mock.call_args.kwargs["user_defined_values"] is not None
+        assert (
+            get_user_info_mock.call_args.kwargs["user_defined_values"]["user_id"]
+            == "cli-test-user"
+        )
+
+    @pytest.mark.asyncio
+    async def test_cli_sso_callback_rejects_restricted_sso_group(self):
+        """CLI SSO must enforce restricted_sso_group before upserting the user."""
+        from litellm.proxy._types import ProxyException
+        from litellm.proxy.management_endpoints import ui_sso
+        from litellm.proxy.management_endpoints.types import CustomOpenID
+
+        mock_request = MagicMock(spec=Request)
+        mock_request.base_url = "http://internal-proxy.local/"
+        mock_cache = MagicMock()
+        mock_cache.get_cache.return_value = {
+            "poll_secret_hash": "poll-secret-hash",
+            "user_code_hash": "user-code-hash",
+            "sso_complete": False,
+            "user_code_verified": False,
+            "session_data": None,
+        }
+        mock_sso_result = CustomOpenID(
+            id="cli-test-user",
+            email="cli-test@example.com",
+            display_name="cli-test-user",
+            provider="generic",
+            team_ids=["other-group"],
+        )
+
+        with (
+            patch(
+                "litellm.proxy.management_endpoints.ui_sso.get_user_info_from_db",
+                new=AsyncMock(),
+            ) as get_user_info_mock,
+            patch("litellm.proxy.proxy_server.prisma_client", MagicMock()),
+            patch("litellm.proxy.proxy_server.user_api_key_cache", mock_cache),
+            patch("litellm.proxy.proxy_server.user_custom_sso", None),
+            patch(
+                "litellm.proxy.proxy_server.general_settings",
+                {
+                    "ui_access_mode": {
+                        "type": "restricted_sso_group",
+                        "restricted_sso_group": "required-group",
+                    }
+                },
+            ),
+        ):
+            with pytest.raises(ProxyException):
+                await ui_sso.cli_sso_callback(
+                    request=mock_request,
+                    key="cli-session-restricted",
+                    result=mock_sso_result,
+                    received_response={"groups": ["other-group"]},
+                )
+
+        get_user_info_mock.assert_not_awaited()
+
+    @pytest.mark.asyncio
+    async def test_cli_sso_callback_persists_attribution_metadata(self, monkeypatch):
+        from litellm.proxy._types import LiteLLM_UserTable
+        from litellm.proxy.management_endpoints import ui_sso
+
+        monkeypatch.setattr(
+            ui_sso,
+            "CLI_SSO_CLAIM_MAP",
+            "employment_type->acme_employment_type",
+        )
+
+        mock_request = MagicMock(spec=Request)
+        mock_request.base_url = "http://internal-proxy.local/"
+        session_key = "cli-session-4567890"
+        mock_user_info = LiteLLM_UserTable(
+            user_id="test-user-123",
+            user_role="internal_user",
+            teams=["team1"],
+            models=["gpt-4"],
+        )
+        mock_sso_result = {
+            "user_email": "test@example.com",
+            "user_id": "test-user-123",
+            "employment_type": "contractor",
+        }
+        mock_cache = MagicMock()
+        mock_cache.get_cache.return_value = {
+            "poll_secret_hash": "poll-secret-hash",
+            "user_code_hash": "user-code-hash",
+            "sso_complete": False,
+            "user_code_verified": False,
+            "session_data": None,
+        }
+        mock_prisma = MagicMock()
+        mock_prisma.db.litellm_usertable.find_unique = AsyncMock(
+            return_value=MagicMock(metadata={"auth_provider": "generic"})
+        )
+        mock_prisma.db.litellm_usertable.update_many = AsyncMock()
+
+        with (
+            patch.dict(
+                os.environ,
+                {
+                    "PROXY_BASE_URL": "https://test.example.com",
+                    "SERVER_ROOT_PATH": "",
+                },
+            ),
+            patch(
+                "litellm.proxy.management_endpoints.ui_sso.get_user_info_from_db",
+                return_value=mock_user_info,
+            ),
+            patch("litellm.proxy.proxy_server.prisma_client", mock_prisma),
+            patch("litellm.proxy.proxy_server.user_api_key_cache", mock_cache),
+            patch("litellm.proxy.proxy_server.user_custom_sso", None),
+            patch(
+                "litellm.proxy.common_utils.html_forms.cli_sso_success.render_cli_sso_success_page",
+                return_value="<html>Success</html>",
+            ),
+        ):
+            await ui_sso.cli_sso_callback(
+                request=mock_request,
+                key=session_key,
+                result=mock_sso_result,
+            )
+
+        flow_data = mock_cache.set_cache.call_args.kwargs["value"]
+        assert flow_data["session_data"]["attribution_metadata"] == {
+            "acme_employment_type": "contractor"
+        }
+        mock_prisma.db.litellm_usertable.update_many.assert_awaited_once()
+        update_data = mock_prisma.db.litellm_usertable.update_many.call_args.kwargs[
+            "data"
+        ]
+        assert update_data["metadata"]["acme_employment_type"] == "contractor"
+        assert update_data["metadata"]["auth_provider"] == "generic"
+
+    @pytest.mark.asyncio
+    async def test_cli_poll_key_returns_attribution_metadata(self, monkeypatch):
+        from litellm.proxy.management_endpoints.ui_sso import (
+            _hash_cli_sso_secret,
+            cli_poll_key,
+        )
+
+        session_key = "cli-session-789123"
+        session_data = {
+            "user_id": "test-user-456",
+            "user_role": "internal_user",
+            "teams": ["team-a", "team-b"],
+            "models": ["gpt-4"],
+            "attribution_metadata": {
+                "acme_employment_type": "full_time",
+                "org": {"cost_center": "CC-42"},
+            },
+        }
+        mock_cache = MagicMock()
+        mock_cache.get_cache.return_value = {
+            "poll_secret_hash": _hash_cli_sso_secret("poll-secret"),
+            "sso_complete": True,
+            "user_code_verified": True,
+            "session_data": session_data,
+        }
+
+        with patch("litellm.proxy.proxy_server.user_api_key_cache", mock_cache):
+            result = await cli_poll_key(
+                key_id=session_key,
+                team_id=None,
+                x_litellm_cli_poll_secret="poll-secret",
+            )
+
+        assert result["attribution_metadata"] == {
+            "acme_employment_type": "full_time",
+            "org.cost_center": "CC-42",
+        }
+
+
 class TestValidateReturnTo:
     """Tests for SSOAuthenticationHandler._validate_return_to"""
 
diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py
index c16c42decc0..0a9e3031030 100644
--- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py
+++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py
@@ -684,3 +684,362 @@ class TestAnthropicBatchPassthroughCostTracking:
             mock_proxy_logging_obj.get_proxy_hook.assert_called_once_with(
                 "managed_files"
             )
+
+
+class TestPureTextFastPathParity:
+    """
+    The pure-text fast path in _build_complete_streaming_response must produce
+    a response (and downstream logging/cost payload) byte-identical to the
+    legacy stream_chunk_builder path. Anything non-text must fall back.
+    """
+
+    @staticmethod
+    def _sse(event, data):
+        return f"event: {event}\ndata: {json.dumps(data)}\n\n".encode()
+
+    @staticmethod
+    def _to_all_chunks(raw_frames):
+        # Mirror production: raw bytes -> _convert_raw_bytes_to_str_lines.
+        from litellm.proxy.pass_through_endpoints.streaming_handler import (
+            PassThroughStreamingHandler,
+        )
+
+        return PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(raw_frames)
+
+    @staticmethod
+    def _norm(resp):
+        if resp is None:
+            return None
+        d = resp.model_dump()
+        # id / created are non-deterministic even between two legacy runs.
+        d.pop("id", None)
+        d.pop("created", None)
+        return d
+
+    def _text_stream(
+        self,
+        texts,
+        *,
+        input_tokens=12,
+        cache_creation=0,
+        cache_read=0,
+        stop_reason="end_turn",
+        with_ping=True,
+        blocks=1,
+    ):
+        frames = [
+            self._sse(
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": "msg_abc",
+                        "type": "message",
+                        "role": "assistant",
+                        "model": "claude-3-5-sonnet-20241022",
+                        "content": [],
+                        "stop_reason": None,
+                        "stop_sequence": None,
+                        "usage": {
+                            "input_tokens": input_tokens,
+                            "output_tokens": 0,
+                            "cache_creation_input_tokens": cache_creation,
+                            "cache_read_input_tokens": cache_read,
+                        },
+                    },
+                },
+            )
+        ]
+        per_block = max(1, len(texts) // blocks)
+        ti = 0
+        for b in range(blocks):
+            frames.append(
+                self._sse(
+                    "content_block_start",
+                    {
+                        "type": "content_block_start",
+                        "index": b,
+                        "content_block": {"type": "text", "text": ""},
+                    },
+                )
+            )
+            if with_ping:
+                frames.append(self._sse("ping", {"type": "ping"}))
+            chunk_texts = texts[ti : ti + per_block] if b < blocks - 1 else texts[ti:]
+            ti += per_block
+            for t in chunk_texts:
+                frames.append(
+                    self._sse(
+                        "content_block_delta",
+                        {
+                            "type": "content_block_delta",
+                            "index": b,
+                            "delta": {"type": "text_delta", "text": t},
+                        },
+                    )
+                )
+            frames.append(
+                self._sse(
+                    "content_block_stop", {"type": "content_block_stop", "index": b}
+                )
+            )
+        frames.append(
+            self._sse(
+                "message_delta",
+                {
+                    "type": "message_delta",
+                    "delta": {"stop_reason": stop_reason, "stop_sequence": None},
+                    "usage": {"output_tokens": len(texts)},
+                },
+            )
+        )
+        frames.append(self._sse("message_stop", {"type": "message_stop"}))
+        return frames
+
+    def _assert_parity(self, raw_frames):
+        all_chunks = self._to_all_chunks(raw_frames)
+        lo1 = MagicMock()
+        lo1.model_call_details = {}
+        lo2 = MagicMock()
+        lo2.model_call_details = {}
+
+        legacy = AnthropicPassthroughLoggingHandler._build_complete_streaming_response_legacy(
+            all_chunks=list(all_chunks),
+            litellm_logging_obj=lo1,
+            model="claude-3-5-sonnet-20241022",
+        )
+        fast = AnthropicPassthroughLoggingHandler._build_complete_streaming_response(
+            all_chunks=list(all_chunks),
+            litellm_logging_obj=lo2,
+            model="claude-3-5-sonnet-20241022",
+        )
+        assert self._norm(fast) == self._norm(legacy)
+
+        # Downstream logged/billed payload must also match.
+        start = datetime.now()
+        end = datetime.now()
+        k_legacy = AnthropicPassthroughLoggingHandler._create_anthropic_response_logging_payload(
+            litellm_model_response=legacy,
+            model="claude-3-5-sonnet-20241022",
+            kwargs={},
+            start_time=start,
+            end_time=end,
+            logging_obj=lo1,
+        )
+        k_fast = AnthropicPassthroughLoggingHandler._create_anthropic_response_logging_payload(
+            litellm_model_response=fast,
+            model="claude-3-5-sonnet-20241022",
+            kwargs={},
+            start_time=start,
+            end_time=end,
+            logging_obj=lo2,
+        )
+        # Usage drives cost; it must be byte-identical between paths.
+        assert getattr(fast, "usage", None) == getattr(legacy, "usage", None)
+
+        # And the full logged payload (sans non-deterministic response id).
+        def _scrub(p):
+            d = dict(p)
+            r = d.get("complete_streaming_response_in_db") or d.get(
+                "complete_streaming_response"
+            )
+            return d, getattr(r, "usage", None)
+
+        assert _scrub(k_fast)[1] == _scrub(k_legacy)[1]
+
+    def test_parity_simple_text(self):
+        self._assert_parity(self._text_stream(["Hello", " ", "world", "!"]))
+
+    def test_parity_single_delta(self):
+        self._assert_parity(self._text_stream(["Just one piece of text."]))
+
+    def test_parity_cache_tokens(self):
+        self._assert_parity(
+            self._text_stream(
+                ["a", "b", "c"], input_tokens=20, cache_creation=5, cache_read=7
+            )
+        )
+
+    def test_parity_max_tokens_stop(self):
+        self._assert_parity(self._text_stream(["tok"] * 8, stop_reason="max_tokens"))
+
+    def test_parity_no_ping(self):
+        self._assert_parity(self._text_stream(["x", "y"], with_ping=False))
+
+    def test_parity_empty_text_deltas(self):
+        self._assert_parity(self._text_stream(["", "hi", "", "there"]))
+
+    def test_parity_multi_text_block(self):
+        self._assert_parity(self._text_stream(["p1", "p2", "p3", "p4"], blocks=2))
+
+    def test_parity_multibyte_batched_frames(self):
+        # Several SSE events delivered in one network chunk.
+        frames = self._text_stream(["alpha", "beta", "gamma"])
+        merged = b"".join(frames)
+        self._assert_parity([merged])
+
+    def test_collapse_returns_none_for_tool_use(self):
+        frames = [
+            self._sse(
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": "m",
+                        "model": "x",
+                        "role": "assistant",
+                        "type": "message",
+                        "content": [],
+                        "usage": {"input_tokens": 1, "output_tokens": 0},
+                    },
+                },
+            ),
+            self._sse(
+                "content_block_start",
+                {
+                    "type": "content_block_start",
+                    "index": 0,
+                    "content_block": {
+                        "type": "tool_use",
+                        "id": "t1",
+                        "name": "get_weather",
+                        "input": {},
+                    },
+                },
+            ),
+            self._sse(
+                "content_block_delta",
+                {
+                    "type": "content_block_delta",
+                    "index": 0,
+                    "delta": {"type": "input_json_delta", "partial_json": "{}"},
+                },
+            ),
+            self._sse("content_block_stop", {"type": "content_block_stop", "index": 0}),
+            self._sse("message_stop", {"type": "message_stop"}),
+        ]
+        all_chunks = self._to_all_chunks(frames)
+        assert (
+            AnthropicPassthroughLoggingHandler._collapse_pure_text_chunks(
+                list(all_chunks)
+            )
+            is None
+        )
+
+    def test_collapse_returns_none_for_thinking(self):
+        frames = [
+            self._sse(
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": "m",
+                        "model": "x",
+                        "role": "assistant",
+                        "type": "message",
+                        "content": [],
+                        "usage": {"input_tokens": 1, "output_tokens": 0},
+                    },
+                },
+            ),
+            self._sse(
+                "content_block_start",
+                {
+                    "type": "content_block_start",
+                    "index": 0,
+                    "content_block": {"type": "thinking", "thinking": ""},
+                },
+            ),
+            self._sse(
+                "content_block_delta",
+                {
+                    "type": "content_block_delta",
+                    "index": 0,
+                    "delta": {"type": "thinking_delta", "thinking": "hmm"},
+                },
+            ),
+            self._sse("message_stop", {"type": "message_stop"}),
+        ]
+        all_chunks = self._to_all_chunks(frames)
+        assert (
+            AnthropicPassthroughLoggingHandler._collapse_pure_text_chunks(
+                list(all_chunks)
+            )
+            is None
+        )
+
+    def test_collapse_actually_shrinks_chunk_count(self):
+        frames = self._text_stream(["a"] * 50)
+        all_chunks = list(self._to_all_chunks(frames))
+        collapsed = AnthropicPassthroughLoggingHandler._collapse_pure_text_chunks(
+            all_chunks
+        )
+        assert collapsed is not None
+        # 50 text deltas + 50 event markers + 1 ping collapse to far fewer.
+        assert len(collapsed) < len(all_chunks) / 2
+
+    def test_collapse_returns_none_for_interleaved_block_indexes(self):
+        """
+        Anthropic sends content blocks strictly sequentially (start/deltas/stop
+        for one, then the next). If a stream ever interleaves deltas across
+        block indexes, the fast path must bail to legacy rather than merge text
+        from different blocks under a single index.
+        """
+        frames = [
+            self._sse(
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": "msg_abc",
+                        "type": "message",
+                        "role": "assistant",
+                        "model": "claude-3-5-sonnet-20241022",
+                        "content": [],
+                        "stop_reason": None,
+                        "stop_sequence": None,
+                        "usage": {"input_tokens": 1, "output_tokens": 0},
+                    },
+                },
+            ),
+            self._sse(
+                "content_block_start",
+                {
+                    "type": "content_block_start",
+                    "index": 0,
+                    "content_block": {"type": "text", "text": ""},
+                },
+            ),
+            self._sse(
+                "content_block_start",
+                {
+                    "type": "content_block_start",
+                    "index": 1,
+                    "content_block": {"type": "text", "text": ""},
+                },
+            ),
+            # Interleave: delta for block 0, then delta for block 1, with no
+            # content_block_stop between them.
+            self._sse(
+                "content_block_delta",
+                {
+                    "type": "content_block_delta",
+                    "index": 0,
+                    "delta": {"type": "text_delta", "text": "hello "},
+                },
+            ),
+            self._sse(
+                "content_block_delta",
+                {
+                    "type": "content_block_delta",
+                    "index": 1,
+                    "delta": {"type": "text_delta", "text": "world"},
+                },
+            ),
+            self._sse("message_stop", {"type": "message_stop"}),
+        ]
+        all_chunks = list(self._to_all_chunks(frames))
+        assert (
+            AnthropicPassthroughLoggingHandler._collapse_pure_text_chunks(all_chunks)
+            is None
+        )
diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py
index 31a25916e2e..265a82d4a44 100644
--- a/tests/test_litellm/proxy/test_common_request_processing.py
+++ b/tests/test_litellm/proxy/test_common_request_processing.py
@@ -10,6 +10,7 @@ from fastapi.responses import JSONResponse, StreamingResponse
 
 import litellm
 from litellm._uuid import uuid
+from litellm.integrations.custom_logger import CustomLogger
 from litellm.integrations.opentelemetry import UserAPIKeyAuth
 from litellm.proxy.common_request_processing import (
     ProxyBaseLLMRequestProcessing,
@@ -1316,8 +1317,17 @@ class TestCommonRequestProcessingHelpers:
             yield 'data: {"content": "chunk 3"}\n\n'
             yield "data: [DONE]\n\n"
 
-        # Patch the tracer in the common_request_processing module
-        with patch("litellm.proxy.common_request_processing.tracer", mock_tracer):
+        # Patch the tracer in the common_request_processing module. The
+        # per-chunk span is gated on _DD_STREAMING_TRACE_ENABLED (resolved at
+        # import from the real tracer, a NullTracer by default), so enable it
+        # explicitly to exercise the tracing path.
+        with (
+            patch("litellm.proxy.common_request_processing.tracer", mock_tracer),
+            patch(
+                "litellm.proxy.common_request_processing._DD_STREAMING_TRACE_ENABLED",
+                True,
+            ),
+        ):
             response = await create_response(mock_generator(), "text/event-stream", {})
 
             assert response.status_code == 200
@@ -1345,6 +1355,40 @@ class TestCommonRequestProcessingHelpers:
                     args[0] == "streaming.chunk.yield"
                 ), f"Call {i} should have operation name 'streaming.chunk.yield', got {args[0]}"
 
+    async def test_create_streaming_response_skips_dd_trace_when_disabled(self):
+        """When DD tracing is disabled (the default), the per-chunk span
+        context manager is skipped entirely but all chunks still stream."""
+        from unittest.mock import patch
+
+        mock_tracer = MagicMock()
+
+        async def mock_generator():
+            yield 'data: {"content": "chunk 1"}\n\n'
+            yield 'data: {"content": "chunk 2"}\n\n'
+            yield "data: [DONE]\n\n"
+
+        with (
+            patch("litellm.proxy.common_request_processing.tracer", mock_tracer),
+            patch(
+                "litellm.proxy.common_request_processing._DD_STREAMING_TRACE_ENABLED",
+                False,
+            ),
+        ):
+            response = await create_response(mock_generator(), "text/event-stream", {})
+
+            assert response.status_code == 200
+
+            content = await self.consume_stream(response)
+
+            # All chunks stream through unchanged ...
+            assert content == [
+                'data: {"content": "chunk 1"}\n\n',
+                'data: {"content": "chunk 2"}\n\n',
+                "data: [DONE]\n\n",
+            ]
+            # ... but no per-chunk span was created.
+            assert mock_tracer.trace.call_count == 0
+
     async def test_create_streaming_response_dd_trace_with_error_chunk(self):
         """
         Test that when the first chunk contains an error, JSONResponse is returned
@@ -2199,3 +2243,77 @@ class TestHandleLLMApiExceptionDictDetail:
         proxy_exc = await self._invoke(exc)
         assert proxy_exc.message == "Content blocked by guardrail"
         assert proxy_exc.provider_specific_fields is None
+
+
+class TestAsyncStreamingDataGeneratorFastPath:
+    """Fast/slow path branching in async_streaming_data_generator."""
+
+    @staticmethod
+    async def _aiter(items):
+        for item in items:
+            yield item
+
+    @pytest.mark.asyncio
+    async def test_fast_path_skips_per_chunk_hook(self, monkeypatch):
+        """With no callbacks/guardrails/cost-injection, chunks pass through
+        unchanged and the per-chunk hook is NOT awaited."""
+        monkeypatch.setattr(litellm, "callbacks", [])
+        ProxyLogging._callback_capabilities_cache.clear()
+
+        proxy_logging_obj = ProxyLogging(user_api_key_cache=MagicMock())
+        hook_spy = AsyncMock(side_effect=lambda **kw: kw["response"])
+        monkeypatch.setattr(
+            proxy_logging_obj, "async_post_call_streaming_hook", hook_spy
+        )
+
+        chunks = [b"event: a\ndata: {}\n\n", b"event: b\ndata: {}\n\n"]
+        out = [
+            c
+            async for c in ProxyBaseLLMRequestProcessing.async_streaming_data_generator(
+                response=self._aiter(chunks),
+                user_api_key_dict=MagicMock(spec=UserAPIKeyAuth),
+                request_data={"model": "claude-x"},
+                proxy_logging_obj=proxy_logging_obj,
+                serialize_chunk=ProxyBaseLLMRequestProcessing.return_sse_chunk,
+                serialize_error=lambda e: "data: error\n\n",
+            )
+        ]
+
+        assert out == chunks  # bytes pass through return_sse_chunk untouched
+        hook_spy.assert_not_awaited()
+
+    @pytest.mark.asyncio
+    async def test_slow_path_runs_per_chunk_hook(self, monkeypatch):
+        """A callback that overrides async_post_call_streaming_hook forces the
+        slow path and the per-chunk hook is invoked."""
+
+        class _StreamingCb(CustomLogger):
+            async def async_post_call_streaming_hook(self, user_api_key_dict, response):
+                return response
+
+        cb = _StreamingCb()
+        monkeypatch.setattr(litellm, "callbacks", [cb])
+        ProxyLogging._callback_capabilities_cache.clear()
+
+        proxy_logging_obj = ProxyLogging(user_api_key_cache=MagicMock())
+        hook_spy = AsyncMock(side_effect=lambda **kw: kw["response"])
+        monkeypatch.setattr(
+            proxy_logging_obj, "async_post_call_streaming_hook", hook_spy
+        )
+
+        out = [
+            c
+            async for c in ProxyBaseLLMRequestProcessing.async_streaming_data_generator(
+                response=self._aiter([{"type": "message_stop"}]),
+                user_api_key_dict=MagicMock(spec=UserAPIKeyAuth),
+                request_data={"model": "claude-x"},
+                proxy_logging_obj=proxy_logging_obj,
+                serialize_chunk=ProxyBaseLLMRequestProcessing.return_sse_chunk,
+                serialize_error=lambda e: "data: error\n\n",
+            )
+        ]
+
+        assert len(out) == 1
+        hook_spy.assert_awaited_once()
+
+        ProxyLogging._callback_capabilities_cache.clear()
diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
index 19204e8b8ab..fc9813ba530 100644
--- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
+++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
@@ -1549,6 +1549,57 @@ def test_team_dynamic_logging_settings():
     assert result is None
 
 
+def test_key_dynamic_logging_settings_decrypts_callback_vars(monkeypatch):
+    """Encrypted callback_vars on the key are decrypted before downstream use."""
+    from litellm.proxy.common_utils.callback_utils import encrypt_callback_vars
+
+    monkeypatch.setenv("LITELLM_SALT_KEY", "test-salt-32-bytes-aaaaaaaaaaaaaa")
+    encrypted_metadata = encrypt_callback_vars(
+        {
+            "logging": [
+                {
+                    "callback_name": "langfuse",
+                    "callback_type": "success",
+                    "callback_vars": {
+                        "langfuse_public_key": "pk-real",
+                        "langfuse_secret_key": "sk-real",
+                    },
+                }
+            ]
+        }
+    )
+    cv_on_disk = encrypted_metadata["logging"][0]["callback_vars"]
+    assert cv_on_disk["langfuse_secret_key"] != "sk-real"  # sanity: stored encrypted
+
+    key = UserAPIKeyAuth(api_key="t", metadata=encrypted_metadata, team_metadata={})
+    result = KeyAndTeamLoggingSettings.get_key_dynamic_logging_settings(key)
+    cv = result[0]["callback_vars"]
+    assert cv["langfuse_secret_key"] == "sk-real"
+    assert cv["langfuse_public_key"] == "pk-real"
+
+
+def test_team_dynamic_logging_settings_decrypts_callback_vars(monkeypatch):
+    """Encrypted callback_vars on the team are decrypted before downstream use."""
+    from litellm.proxy.common_utils.callback_utils import encrypt_callback_vars
+
+    monkeypatch.setenv("LITELLM_SALT_KEY", "test-salt-32-bytes-aaaaaaaaaaaaaa")
+    encrypted_team = encrypt_callback_vars(
+        {
+            "logging": [
+                {
+                    "callback_name": "langfuse",
+                    "callback_type": "failure",
+                    "callback_vars": {"langfuse_secret_key": "team-sk-real"},
+                }
+            ]
+        }
+    )
+
+    key = UserAPIKeyAuth(api_key="t", metadata={}, team_metadata=encrypted_team)
+    result = KeyAndTeamLoggingSettings.get_team_dynamic_logging_settings(key)
+    assert result[0]["callback_vars"]["langfuse_secret_key"] == "team-sk-real"
+
+
 def test_get_dynamic_logging_metadata_with_arize_team_logging():
     """
     Test _get_dynamic_logging_metadata function with arize team logging and dynamic parameters
diff --git a/tests/test_litellm/proxy/test_proxy_cli.py b/tests/test_litellm/proxy/test_proxy_cli.py
index 46a55fc7468..580ed95062b 100644
--- a/tests/test_litellm/proxy/test_proxy_cli.py
+++ b/tests/test_litellm/proxy/test_proxy_cli.py
@@ -1,7 +1,11 @@
 import os
 import sys
+from pathlib import Path
+from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
+import click
+import fastapi
 import pytest
 
 sys.path.insert(
@@ -231,6 +235,96 @@ class TestProxyInitializationHelpers:
             mock_app, "localhost", 8000, "cert.pem", "key.pem", "ECDHE"
         )
 
+    @patch("granian.Granian")
+    @patch("builtins.print")
+    def test_init_granian_server(self, mock_print, mock_granian_cls):
+        pytest.importorskip("granian")
+        mock_server = MagicMock()
+        mock_granian_cls.return_value = mock_server
+        fake_interfaces = SimpleNamespace(ASGI="asgi")
+        with patch("granian.constants.Interfaces", fake_interfaces):
+            ProxyInitializationHelpers._init_granian_server(
+                host="0.0.0.0",
+                port=4000,
+                num_workers=2,
+                ssl_certfile_path=None,
+                ssl_keyfile_path=None,
+                max_requests_before_restart=None,
+                ciphers=None,
+                granian_runtime_threads=None,
+            )
+        mock_granian_cls.assert_called_once()
+        call_kwargs = mock_granian_cls.call_args.kwargs
+        assert call_kwargs["target"] == "litellm.proxy.proxy_server:app"
+        assert call_kwargs["address"] == "0.0.0.0"
+        assert call_kwargs["port"] == 4000
+        assert call_kwargs["workers"] == 2
+        assert call_kwargs["interface"] == "asgi"
+        assert call_kwargs["websockets"] is True
+        assert "runtime_threads" not in call_kwargs
+        mock_server.serve.assert_called_once()
+
+    @patch("granian.Granian")
+    @patch("builtins.print")
+    def test_init_granian_server_runtime_threads(self, mock_print, mock_granian_cls):
+        pytest.importorskip("granian")
+        mock_server = MagicMock()
+        mock_granian_cls.return_value = mock_server
+        fake_interfaces = SimpleNamespace(ASGI="asgi")
+        with patch("granian.constants.Interfaces", fake_interfaces):
+            ProxyInitializationHelpers._init_granian_server(
+                host="0.0.0.0",
+                port=4000,
+                num_workers=1,
+                ssl_certfile_path=None,
+                ssl_keyfile_path=None,
+                max_requests_before_restart=None,
+                ciphers=None,
+                granian_runtime_threads=4,
+            )
+        assert mock_granian_cls.call_args.kwargs["runtime_threads"] == 4
+
+    @patch("granian.Granian")
+    @patch("builtins.print")
+    def test_init_granian_server_ssl(self, mock_print, mock_granian_cls):
+        pytest.importorskip("granian")
+        mock_server = MagicMock()
+        mock_granian_cls.return_value = mock_server
+        fake_interfaces = SimpleNamespace(ASGI="asgi")
+        with patch("granian.constants.Interfaces", fake_interfaces):
+            ProxyInitializationHelpers._init_granian_server(
+                host="0.0.0.0",
+                port=4000,
+                num_workers=1,
+                ssl_certfile_path="/path/to/cert.pem",
+                ssl_keyfile_path="/path/to/key.pem",
+                max_requests_before_restart=None,
+                ciphers=None,
+                granian_runtime_threads=None,
+            )
+        call_kwargs = mock_granian_cls.call_args.kwargs
+        assert call_kwargs["ssl_cert"] == Path("/path/to/cert.pem")
+        assert call_kwargs["ssl_key"] == Path("/path/to/key.pem")
+        mock_server.serve.assert_called_once()
+
+    @patch("granian.Granian")
+    def test_init_granian_server_ssl_requires_cert_and_key(self, mock_granian_cls):
+        pytest.importorskip("granian")
+        fake_interfaces = SimpleNamespace(ASGI="asgi")
+        with patch("granian.constants.Interfaces", fake_interfaces):
+            with pytest.raises(click.ClickException, match="Both --ssl_certfile_path"):
+                ProxyInitializationHelpers._init_granian_server(
+                    host="0.0.0.0",
+                    port=4000,
+                    num_workers=1,
+                    ssl_certfile_path="/path/to/cert.pem",
+                    ssl_keyfile_path=None,
+                    max_requests_before_restart=None,
+                    ciphers=None,
+                    granian_runtime_threads=None,
+                )
+        mock_granian_cls.assert_not_called()
+
     @patch("subprocess.Popen")
     def test_run_ollama_serve(self, mock_popen):
         # Execute
@@ -483,6 +577,136 @@ class TestProxyInitializationHelpers:
             assert appended_params["connection_limit"] == 5
             assert appended_params["pool_timeout"] == expected_timeout
 
+    def test_build_db_connection_url_params_defaults(self):
+        from litellm.proxy.proxy_cli import _build_db_connection_url_params
+
+        params = _build_db_connection_url_params(connection_limit=10, pool_timeout=60)
+        assert params == {"connection_limit": 10, "pool_timeout": 60}
+
+    def test_build_db_connection_url_params_omits_none_timeouts(self):
+        from litellm.proxy.proxy_cli import _build_db_connection_url_params
+
+        params = _build_db_connection_url_params(
+            connection_limit=10,
+            pool_timeout=60,
+            connect_timeout=None,
+            socket_timeout=None,
+        )
+        assert "connect_timeout" not in params
+        assert "socket_timeout" not in params
+
+    def test_build_db_connection_url_params_includes_optional_timeouts(self):
+        from litellm.proxy.proxy_cli import _build_db_connection_url_params
+
+        params = _build_db_connection_url_params(
+            connection_limit=10,
+            pool_timeout=60,
+            connect_timeout=15,
+            socket_timeout=120,
+        )
+        assert params["connect_timeout"] == 15
+        assert params["socket_timeout"] == 120
+
+    def test_build_db_connection_url_params_extras_override_defaults(self):
+        from litellm.proxy.proxy_cli import _build_db_connection_url_params
+
+        params = _build_db_connection_url_params(
+            connection_limit=10,
+            pool_timeout=60,
+            extra_params={
+                "pgbouncer": "true",
+                "statement_cache_size": 0,
+                "pool_timeout": 5,
+            },
+        )
+        assert params["pgbouncer"] == "true"
+        assert params["statement_cache_size"] == 0
+        assert params["pool_timeout"] == 5
+
+    @patch("subprocess.run")
+    @patch("atexit.register")
+    @patch("litellm.proxy.db.prisma_client.PrismaManager.setup_database")
+    @patch(
+        "litellm.proxy.db.prisma_client.should_update_prisma_schema", return_value=False
+    )
+    def test_db_connection_extra_params_forwarded_to_url(
+        self,
+        mock_should_update,
+        mock_setup_db,
+        mock_atexit_register,
+        mock_subprocess_run,
+    ):
+        from click.testing import CliRunner
+
+        from litellm.proxy.proxy_cli import run_server
+
+        runner = CliRunner()
+        mock_subprocess_run.return_value = MagicMock(returncode=0)
+
+        mock_proxy_module = MagicMock(
+            app=MagicMock(),
+            ProxyConfig=MagicMock(),
+            KeyManagementSettings=MagicMock(),
+            save_worker_config=MagicMock(),
+        )
+        mock_proxy_module.ProxyConfig.return_value.get_config = AsyncMock(
+            return_value={
+                "general_settings": {
+                    "database_url": "postgresql://test:test@localhost:5432/test",
+                    "database_connect_timeout": 15,
+                    "database_socket_timeout": 120,
+                    "database_extra_connection_params": {
+                        "pgbouncer": "true",
+                        "statement_cache_size": 0,
+                    },
+                }
+            }
+        )
+
+        clean_env = {
+            k: v
+            for k, v in os.environ.items()
+            if k not in ("DATABASE_URL", "DIRECT_URL")
+        }
+
+        with (
+            patch.dict(os.environ, clean_env, clear=True),
+            patch.dict(
+                "sys.modules",
+                {
+                    "proxy_server": mock_proxy_module,
+                    "litellm.proxy.proxy_server": mock_proxy_module,
+                },
+            ),
+            patch(
+                "litellm.proxy.proxy_cli.ProxyInitializationHelpers._get_default_unvicorn_init_args"
+            ) as mock_get_args,
+            patch(
+                "litellm.proxy.proxy_cli.append_query_params",
+                side_effect=lambda url, params: str(url),
+            ) as mock_append_query_params,
+        ):
+            mock_get_args.return_value = {
+                "app": "litellm.proxy.proxy_server:app",
+                "host": "localhost",
+                "port": 8000,
+            }
+
+            result = runner.invoke(
+                run_server,
+                ["--local", "--config", "test-config.yaml", "--skip_server_startup"],
+            )
+
+            assert (
+                result.exit_code == 0
+            ), f"exit_code={result.exit_code}, output={result.output}"
+            mock_append_query_params.assert_called()
+            appended_params = mock_append_query_params.call_args.args[1]
+            assert appended_params["connect_timeout"] == 15
+            assert appended_params["socket_timeout"] == 120
+            assert appended_params["pgbouncer"] == "true"
+            assert appended_params["statement_cache_size"] == 0
+
     @patch("uvicorn.run")
     @patch("atexit.register")
     @patch("litellm.proxy.db.prisma_client.PrismaManager.setup_database")
diff --git a/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py b/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py
index 4aebcf40aa5..f5967030561 100644
--- a/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py
+++ b/tests/test_litellm/proxy/test_proxy_logging_hook_detection.py
@@ -111,6 +111,28 @@ def test_callback_capabilities_captures_iterator_override(monkeypatch):
     assert kind == "override"
 
 
+def test_callback_capabilities_detects_inherited_streaming_chunk_override(monkeypatch):
+    """
+    ``async_post_call_streaming_hook`` must be detected even when the override
+    lives on an intermediate parent class — a vendor base class can carry the
+    override and the registered class can add nothing else. Before this PR the
+    hook was unconditionally invoked, so a leaf-class ``__dict__`` miss here
+    would silently drop the inherited hook.
+    """
+    ProxyLogging._callback_capabilities_cache.clear()
+
+    class _StreamingBase(CustomLogger):
+        async def async_post_call_streaming_hook(self, *args, **kwargs):  # type: ignore[override]
+            return kwargs.get("response")
+
+    class _LeafWithoutOverride(_StreamingBase):
+        pass
+
+    monkeypatch.setattr(litellm, "callbacks", [_LeafWithoutOverride()])
+    caps = ProxyLogging._callback_capabilities()
+    assert caps.has_streaming_chunk_override is True
+
+
 def test_callback_capabilities_cache_invalidates_on_list_change(monkeypatch):
     """The cache key includes (length, id-of-each-callback).  Mutating the
     callback list must produce a fresh capability snapshot."""
diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py
index 73d53631622..ae0996d16d5 100644
--- a/tests/test_litellm/proxy/test_proxy_server.py
+++ b/tests/test_litellm/proxy/test_proxy_server.py
@@ -5065,6 +5065,66 @@ async def test_async_data_generator_uses_direct_stream_fast_path_without_callbac
     mock_response.aclose.assert_awaited_once()
 
 
+@pytest.mark.asyncio
+async def test_async_data_generator_passes_through_google_native_sse_bytes():
+    """
+    Google-native streamGenerateContent yields raw SSE bytes; they must not be
+    re-wrapped as data: b'data: {...}'.
+    """
+    from litellm.proxy._types import UserAPIKeyAuth
+    from litellm.proxy.proxy_server import async_data_generator
+    from litellm.proxy.utils import ProxyLogging
+
+    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
+    mock_request_data = {
+        "model": "gemini-2.0-flash",
+        "messages": [{"role": "user", "content": "test"}],
+    }
+    gemini_event = b'data: {"candidates": [{"content": "hi"}]}\n\n'
+    gemini_event_without_terminator = b'data: {"candidates": [{"content": "there"}]}'
+    raw_payload = b'{"partial": true}'
+
+    class MockStream:
+        def __aiter__(self):
+            return self._stream()
+
+        async def _stream(self):
+            yield gemini_event
+            yield gemini_event_without_terminator
+            yield raw_payload
+
+        async def aclose(self):
+            pass
+
+    mock_response = MockStream()
+    mock_response.aclose = AsyncMock()
+    mock_proxy_logging_obj = MagicMock(spec=ProxyLogging)
+    mock_proxy_logging_obj.has_streaming_callbacks.return_value = False
+    mock_proxy_logging_obj.needs_iterator_wrap.return_value = False
+    mock_proxy_logging_obj.needs_per_chunk_streaming_hook.return_value = False
+    mock_proxy_logging_obj.async_post_call_streaming_iterator_hook = MagicMock()
+    mock_proxy_logging_obj.async_post_call_streaming_hook = AsyncMock()
+    mock_proxy_logging_obj.post_call_failure_hook = AsyncMock()
+
+    with patch("litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj):
+        with patch.object(ProxyLogging, "_fire_deferred_stream_logging"):
+            yielded_data = []
+            async for data in async_data_generator(
+                mock_response, mock_user_api_key_dict, mock_request_data
+            ):
+                yielded_data.append(data)
+
+    yielded_text = [
+        chunk.decode("utf-8") if isinstance(chunk, bytes) else chunk
+        for chunk in yielded_data
+    ]
+    assert yielded_text[0] == gemini_event.decode("utf-8")
+    assert yielded_text[1] == gemini_event_without_terminator.decode("utf-8") + "\n\n"
+    assert yielded_text[2] == f'data: {raw_payload.decode("utf-8")}\n\n'
+    assert "b'data:" not in "".join(yielded_text)
+    assert yielded_text[-1] == "data: [DONE]\n\n"
+
+
 @pytest.mark.asyncio
 async def test_async_data_generator_cleanup_on_normal_completion():
     """
@@ -5648,6 +5708,7 @@ async def test_init_and_increment_spend_counter_reseeds_from_db_on_counter_miss(
     fake_redis = AsyncMock()
     fake_redis.async_increment = AsyncMock(side_effect=record_increment)
     fake_redis.async_get_cache = AsyncMock(return_value=None)  # counter missing
+    fake_redis.async_set_cache = AsyncMock(return_value=True)  # SET NX wins
     counter_cache.redis_cache = fake_redis
 
     # Prisma returns spend=42.0 (authoritative) while the stale cached
@@ -5684,16 +5745,131 @@ async def test_init_and_increment_spend_counter_reseeds_from_db_on_counter_miss(
         fake_prisma.db.litellm_teamtable.find_unique.assert_awaited_once_with(
             where={"team_id": "team-9"}
         )
-        # Two increments keyed on the counter: seed ($42) then request ($1.50).
+        # Seed uses SET NX with db_spend (42) — cross-pod safe, no INCR of 42.
+        # Only the per-request delta (1.5) goes through INCRBYFLOAT.
+        fake_redis.async_set_cache.assert_awaited_once_with(
+            key="spend:team:team-9", value=42.0, nx=True
+        )
         writes = [(c["key"], c["value"]) for c in recorded_increments]
-        assert ("spend:team:team-9", 42.0) in writes
-        assert ("spend:team:team-9", 1.5) in writes
+        assert writes == [("spend:team:team-9", 1.5)]
     finally:
         ps.user_api_key_cache = orig_user
         ps.spend_counter_cache = orig_counter
         ps.prisma_client = orig_prisma
 
 
+@pytest.mark.asyncio
+async def test_primary_spend_counter_redis_concurrent_seed_does_not_double_seed():
+    """Two pods both observing a missing Redis counter must not both
+    INCRBYFLOAT the full DB spend. SpendCounterReseed.coalesced uses SET NX
+    so the loser reads the winner's value; final Redis = db_spend, not
+    2 * db_spend.
+
+    The per-counter asyncio.Lock is per-process, so it does NOT coordinate
+    across pods. We simulate two pods by patching _get_lock to return a
+    fresh lock per call (each "pod" has its own lock registry in real life).
+    """
+    from litellm.caching.dual_cache import DualCache
+    from litellm.proxy.db.spend_counter_reseed import SpendCounterReseed
+
+    counter_key = "spend:team:team-concurrent-seed"
+    redis_store: dict = {}
+    db_read_count = 0
+    set_results: list = []
+    get_after_set_count = 0
+    set_completed_count = 0
+
+    async def redis_set_cache(key, value, nx=False, **_):
+        # Yield BEFORE the membership check so two concurrent callers
+        # interleave the way real atomic Redis SET NX does: the first
+        # to resume runs check + write atomically and wins; the second
+        # resumes after the key exists and loses. Yielding *after* the
+        # check would let both callers pass the empty-store check before
+        # either writes, so neither would ever lose.
+        await asyncio.sleep(0)
+        if nx and key in redis_store:
+            set_results.append(False)
+            return False
+        redis_store[key] = float(value)
+        set_results.append(True)
+        nonlocal set_completed_count
+        set_completed_count += 1
+        return True
+
+    async def redis_get_cache(key):
+        # Track reads that happen after at least one SET NX has completed
+        # — those are the loser-path fallback reads we want to verify.
+        if set_completed_count > 0:
+            nonlocal get_after_set_count
+            get_after_set_count += 1
+        return redis_store.get(key)
+
+    fake_redis = AsyncMock()
+    fake_redis.async_get_cache = AsyncMock(side_effect=redis_get_cache)
+    fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache)
+
+    async def slow_find_unique(**_):
+        nonlocal db_read_count
+        db_read_count += 1
+        # Both pods read DB before either's SET NX lands.
+        await asyncio.sleep(0)
+        row = MagicMock()
+        row.spend = 506.0
+        return row
+
+    fake_prisma = MagicMock()
+    fake_prisma.db.litellm_teamtable.find_unique = AsyncMock(
+        side_effect=slow_find_unique
+    )
+
+    pod_a = DualCache()
+    pod_a.redis_cache = fake_redis
+    pod_b = DualCache()
+    pod_b.redis_cache = fake_redis
+
+    # Each "pod" has its own per-process lock registry. Patch _get_lock to
+    # always return a fresh lock so the two coalesced calls do not serialize
+    # via one in-process lock (which is what would happen across pods).
+    async def fresh_lock(_counter_key):
+        return asyncio.Lock()
+
+    with patch.object(SpendCounterReseed, "_get_lock", side_effect=fresh_lock):
+        results = await asyncio.gather(
+            SpendCounterReseed.coalesced(
+                prisma_client=fake_prisma,
+                spend_counter_cache=pod_a,
+                counter_key=counter_key,
+            ),
+            SpendCounterReseed.coalesced(
+                prisma_client=fake_prisma,
+                spend_counter_cache=pod_b,
+                counter_key=counter_key,
+            ),
+        )
+
+    assert all(r == 506.0 for r in results), results
+    assert redis_store[counter_key] == pytest.approx(506.0), redis_store
+    # Both pods read the DB and both attempted SET NX; exactly one wrote
+    # (winner) and one was rejected (loser).
+    assert db_read_count == 2
+    assert fake_redis.async_set_cache.await_count == 2
+    nx_writes = [
+        call
+        for call in fake_redis.async_set_cache.await_args_list
+        if call.kwargs.get("nx") is True
+    ]
+    assert len(nx_writes) == 2
+    assert sorted(set_results) == [False, True], (
+        f"expected exactly one SET NX winner and one loser, got {set_results}"
+    )
+    # Loser path executed: after the winner's SET NX returned True, the
+    # losing coalesced() call falls back to async_get_cache to read the
+    # winner's value rather than re-seeding.
+    assert get_after_set_count >= 1, (
+        "loser branch (else: read back winner's value) was never exercised"
+    )
+
+
 @pytest.mark.asyncio
 async def test_reseed_spend_from_db_user_and_org_prefixes():
     """User and org counters reseed from their own DB tables.
@@ -5817,9 +5993,16 @@ async def test_init_spend_counter_redis_clean_miss_skips_stale_in_memory():
         redis_store[key] = (redis_store.get(key) or 0.0) + value
         return redis_store[key]
 
+    async def redis_set_cache(key, value, nx=False, **_):
+        if nx and key in redis_store:
+            return False
+        redis_store[key] = float(value)
+        return True
+
     fake_redis = AsyncMock()
     fake_redis.async_get_cache = AsyncMock(return_value=None)
     fake_redis.async_increment = AsyncMock(side_effect=redis_increment)
+    fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache)
     counter_cache.redis_cache = fake_redis
 
     db_row = MagicMock()
@@ -5847,6 +6030,7 @@ async def test_init_spend_counter_redis_clean_miss_skips_stale_in_memory():
         fake_prisma.db.litellm_teamtable.find_unique.assert_awaited_once_with(
             where={"team_id": "team-stale-local"}
         )
+        # Seed via SET NX (42) + delta via INCRBYFLOAT (1.5) = 43.5.
         assert redis_store[counter_key] == pytest.approx(43.5)
         assert counter_cache.in_memory_cache.get_cache(
             key=counter_key
@@ -6237,14 +6421,14 @@ async def test_get_current_spend_reseeds_from_db_when_counter_missing():
     from litellm.proxy.proxy_server import get_current_spend
 
     counter_cache = DualCache()
-    recorded_warms: list = []
+    recorded_seeds: list = []
 
-    async def record_increment(key, value, ttl=None, **kwargs):
-        recorded_warms.append({"key": key, "value": value})
-        return value
+    async def record_set_cache(key, value, nx=False, **kwargs):
+        recorded_seeds.append({"key": key, "value": value, "nx": nx})
+        return True
 
     fake_redis = AsyncMock()
-    fake_redis.async_increment = AsyncMock(side_effect=record_increment)
+    fake_redis.async_set_cache = AsyncMock(side_effect=record_set_cache)
     fake_redis.async_get_cache = AsyncMock(return_value=None)
     counter_cache.redis_cache = fake_redis
 
@@ -6269,9 +6453,9 @@ async def test_get_current_spend_reseeds_from_db_when_counter_missing():
             f"expected DB reseed to return 362.0, got {spend} "
             f"(fallback would have returned 30.0 and caused bypass)"
         )
-        # Counter warmed so subsequent reads are fast
-        assert ("spend:team_member:user-1:team-1", 362.0) in [
-            (w["key"], w["value"]) for w in recorded_warms
+        # Counter warmed via SET NX so subsequent reads are fast.
+        assert ("spend:team_member:user-1:team-1", 362.0, True) in [
+            (s["key"], s["value"], s["nx"]) for s in recorded_seeds
         ]
         assert counter_cache.in_memory_cache.get_cache(
             key="spend:team_member:user-1:team-1"
@@ -6348,8 +6532,15 @@ async def test_get_current_spend_coalesces_concurrent_reseeds():
         redis_store[key] = (redis_store.get(key) or 0.0) + value
         return redis_store[key]
 
+    async def redis_set_cache(key, value, nx=False, **_):
+        if nx and key in redis_store:
+            return False
+        redis_store[key] = float(value)
+        return True
+
     fake_redis.async_get_cache = AsyncMock(side_effect=redis_get)
     fake_redis.async_increment = AsyncMock(side_effect=redis_increment)
+    fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache)
     counter_cache.redis_cache = fake_redis
 
     fake_prisma = MagicMock()
@@ -6456,9 +6647,16 @@ async def test_concurrent_read_and_write_paths_share_one_db_query():
         redis_store[key] = (redis_store.get(key) or 0.0) + value
         return redis_store[key]
 
+    async def redis_set_cache(key, value, nx=False, **_):
+        if nx and key in redis_store:
+            return False
+        redis_store[key] = float(value)
+        return True
+
     fake_redis = AsyncMock()
     fake_redis.async_get_cache = AsyncMock(side_effect=redis_get)
     fake_redis.async_increment = AsyncMock(side_effect=redis_increment)
+    fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache)
     counter_cache.redis_cache = fake_redis
 
     fake_prisma = MagicMock()
@@ -6561,9 +6759,16 @@ async def test_reseed_warms_cache_even_on_zero_db_spend():
         redis_store[key] = (redis_store.get(key) or 0.0) + value
         return redis_store[key]
 
+    async def redis_set_cache(key, value, nx=False, **_):
+        if nx and key in redis_store:
+            return False
+        redis_store[key] = float(value)
+        return True
+
     fake_redis = AsyncMock()
     fake_redis.async_get_cache = AsyncMock(side_effect=redis_get)
     fake_redis.async_increment = AsyncMock(side_effect=redis_increment)
+    fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache)
     counter_cache.redis_cache = fake_redis
 
     db_call_count = 0
diff --git a/tests/test_litellm/responses/test_sse_output_recovery.py b/tests/test_litellm/responses/test_sse_output_recovery.py
new file mode 100644
index 00000000000..c8f3325a624
--- /dev/null
+++ b/tests/test_litellm/responses/test_sse_output_recovery.py
@@ -0,0 +1,57 @@
+"""Tests for litellm.responses.sse_output_recovery helpers."""
+
+from litellm.responses.sse_output_recovery import (
+    _MAX_CONTENT_INDEX,
+    record_output_text_chunk,
+)
+
+
+def test_text_chunk_with_oversized_content_index_is_dropped():
+    output_items: dict = {}
+    text_only_items: dict = {}
+    record_output_text_chunk(
+        parsed_chunk={
+            "type": "response.output_text.done",
+            "output_index": 0,
+            "content_index": _MAX_CONTENT_INDEX + 1,
+            "text": "ignored",
+        },
+        output_items=output_items,
+        text_only_items=text_only_items,
+    )
+    item = text_only_items[0]
+    assert item["content"] == []
+
+
+def test_text_chunk_with_negative_content_index_is_dropped():
+    output_items: dict = {}
+    text_only_items: dict = {}
+    record_output_text_chunk(
+        parsed_chunk={
+            "type": "response.output_text.done",
+            "output_index": 0,
+            "content_index": -1,
+            "text": "ignored",
+        },
+        output_items=output_items,
+        text_only_items=text_only_items,
+    )
+    assert text_only_items[0]["content"] == []
+
+
+def test_text_chunk_at_max_content_index_is_recorded():
+    output_items: dict = {}
+    text_only_items: dict = {}
+    record_output_text_chunk(
+        parsed_chunk={
+            "type": "response.output_text.done",
+            "output_index": 0,
+            "content_index": _MAX_CONTENT_INDEX,
+            "text": "kept",
+        },
+        output_items=output_items,
+        text_only_items=text_only_items,
+    )
+    content = text_only_items[0]["content"]
+    assert len(content) == _MAX_CONTENT_INDEX + 1
+    assert content[_MAX_CONTENT_INDEX]["text"] == "kept"
diff --git a/tests/test_litellm/router_utils/test_router_interactions_endpoints.py b/tests/test_litellm/router_utils/test_router_interactions_endpoints.py
index c5468d73810..91bea170458 100644
--- a/tests/test_litellm/router_utils/test_router_interactions_endpoints.py
+++ b/tests/test_litellm/router_utils/test_router_interactions_endpoints.py
@@ -140,3 +140,159 @@ class TestInitInteractionsApiEndpoints:
             custom_llm_provider="vertex_ai",
         )
         assert result == {"result": "success"}
+
+    @pytest.mark.asyncio
+    async def test_init_interactions_api_endpoints_clears_model_when_equals_agent(
+        self,
+    ):
+        """Managed agent interactions must not pass agent name as model to the SDK."""
+        router = Router(model_list=[])
+
+        mock_function = AsyncMock(return_value={"result": "success"})
+
+        await router._init_interactions_api_endpoints(
+            original_function=mock_function,
+            agent="mqy-custom-slides-agent",
+            model="mqy-custom-slides-agent",
+            input="hello",
+        )
+
+        mock_function.assert_called_once_with(
+            custom_llm_provider="gemini",
+            agent="mqy-custom-slides-agent",
+            model=None,
+            input="hello",
+        )
+
+
+class TestRouterCreateInteractionRouting:
+    """acreate_interaction routing: agent-only vs model + fallbacks."""
+
+    @pytest.mark.asyncio
+    async def test_acreate_interaction_agent_only_uses_init_interactions(self):
+        """Agent-only create must not use model-group fallback lookup."""
+        router = Router(
+            model_list=[
+                {
+                    "model_name": "openai/*",
+                    "litellm_params": {"model": "gpt-4"},
+                }
+            ]
+        )
+
+        with (
+            patch.object(
+                router,
+                "_init_interactions_api_endpoints",
+                new_callable=AsyncMock,
+                return_value={"id": "int-1"},
+            ) as mock_init,
+            patch.object(
+                router,
+                "_ageneric_api_call_with_fallbacks",
+                new_callable=AsyncMock,
+            ) as mock_generic,
+        ):
+            result = await router.acreate_interaction(
+                agent="mqy-custom-slides-agent",
+                input="hello",
+                custom_llm_provider="gemini",
+            )
+
+        mock_init.assert_called_once()
+        mock_generic.assert_not_called()
+        assert result == {"id": "int-1"}
+
+    @pytest.mark.asyncio
+    async def test_init_interactions_model_uses_generic_fallbacks(self):
+        """Model-based create uses _ageneric_api_call_with_fallbacks inside _init_interactions."""
+        router = Router(model_list=[])
+
+        with patch.object(
+            router,
+            "_ageneric_api_call_with_fallbacks",
+            new_callable=AsyncMock,
+            return_value={"id": "int-1"},
+        ) as mock_generic:
+            result = await router._init_interactions_api_endpoints(
+                original_function=AsyncMock(),
+                model="gemini-2.5-flash",
+                input="hello",
+                custom_llm_provider="gemini",
+            )
+
+        mock_generic.assert_called_once()
+        assert result == {"id": "int-1"}
+
+
+class TestInitializeManagedAgentsEndpoints:
+    """Tests for _initialize_managed_agents_endpoints."""
+
+    def test_initialize_managed_agents_endpoints_creates_methods(self):
+        router = Router(
+            model_list=[
+                {
+                    "model_name": "gpt-4",
+                    "litellm_params": {"model": "gpt-4"},
+                }
+            ]
+        )
+
+        for method_name in (
+            "acreate_agent",
+            "alist_agents",
+            "aget_agent",
+            "adelete_agent",
+            "alist_agent_versions",
+        ):
+            assert hasattr(router, method_name), f"missing {method_name}"
+            assert callable(getattr(router, method_name)), f"{method_name} not callable"
+
+    def test_initialize_managed_agents_endpoints_can_be_called_directly(self):
+        router = Router(model_list=[])
+        router._initialize_managed_agents_endpoints()
+        assert callable(router.acreate_agent)
+        assert callable(router.alist_agents)
+
+
+class TestInitManagedAgentsApiEndpoints:
+    """Tests for _init_managed_agents_api_endpoints."""
+
+    @pytest.mark.asyncio
+    async def test_init_managed_agents_api_endpoints_defaults_to_gemini(self):
+        router = Router(model_list=[])
+        mock_fn = AsyncMock(return_value={"agents": []})
+
+        await router._init_managed_agents_api_endpoints(
+            original_function=mock_fn,
+        )
+
+        call_kwargs = mock_fn.call_args.kwargs
+        assert call_kwargs["custom_llm_provider"] == "gemini"
+
+    @pytest.mark.asyncio
+    async def test_init_managed_agents_api_endpoints_passes_custom_provider(self):
+        router = Router(model_list=[])
+        mock_fn = AsyncMock(return_value={"agents": []})
+
+        await router._init_managed_agents_api_endpoints(
+            original_function=mock_fn,
+            custom_llm_provider="vertex_ai",
+        )
+
+        call_kwargs = mock_fn.call_args.kwargs
+        assert call_kwargs["custom_llm_provider"] == "vertex_ai"
+
+    @pytest.mark.asyncio
+    async def test_init_managed_agents_api_endpoints_does_not_override_existing_provider(
+        self,
+    ):
+        router = Router(model_list=[])
+        mock_fn = AsyncMock(return_value={"agents": []})
+
+        await router._init_managed_agents_api_endpoints(
+            original_function=mock_fn,
+            custom_llm_provider="vertex_ai",
+        )
+
+        mock_fn.assert_called_once_with(custom_llm_provider="vertex_ai")
diff --git a/tests/test_litellm/test_check_licenses.py b/tests/test_litellm/test_check_licenses.py
new file mode 100644
index 00000000000..4d72f185a25
--- /dev/null
+++ b/tests/test_litellm/test_check_licenses.py
@@ -0,0 +1,211 @@
+"""Tests for the dependency license checker at tests/code_coverage_tests/check_licenses.py.
+
+Focus: PEP 639 license metadata. Packages that adopt PEP 639 publish their
+license as an SPDX expression in ``info.license_expression`` and often leave the
+legacy ``info.license`` field null, so the checker must read the new field (and
+fall back to trove classifiers) instead of reporting "Unknown license".
+
+PyPI HTTP responses are mocked — these tests never hit the network.
+"""
+
+import os
+import sys
+from pathlib import Path
+
+_CODE_COVERAGE_DIR = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "..", "code_coverage_tests"
+)
+sys.path.insert(0, _CODE_COVERAGE_DIR)
+
+import check_licenses  # noqa: E402
+
+_LICCHECK_INI = Path(_CODE_COVERAGE_DIR) / "liccheck.ini"
+
+
+class _FakeResponse:
+    def __init__(self, payload):
+        self._payload = payload
+
+    def raise_for_status(self):
+        return None
+
+    def json(self):
+        return self._payload
+
+
+def _make_checker():
+    return check_licenses.LicenseChecker(config_file=_LICCHECK_INI)
+
+
+def _patch_pypi(monkeypatch, info):
+    """Make PyPI return a JSON response with the given ``info`` block."""
+
+    def _fake_get(url, timeout=None):
+        return _FakeResponse({"info": info})
+
+    monkeypatch.setattr(check_licenses.requests, "get", _fake_get)
+
+
+# --------------------------------------------------------------------------
+# get_package_license_from_pypi: license metadata resolution
+# --------------------------------------------------------------------------
+
+
+def test_get_license_prefers_license_expression(monkeypatch):
+    """(a) PEP 639 packages publish the SPDX expression in license_expression."""
+    _patch_pypi(
+        monkeypatch,
+        {"license_expression": "MIT", "license": None, "classifiers": []},
+    )
+    checker = _make_checker()
+    assert checker.get_package_license_from_pypi("black", "26.3.1") == "MIT"
+
+
+def test_license_expression_wins_when_both_present(monkeypatch):
+    """license_expression takes precedence over the legacy license field."""
+    _patch_pypi(
+        monkeypatch,
+        {"license_expression": "Apache-2.0", "license": "stale free text"},
+    )
+    checker = _make_checker()
+    assert checker.get_package_license_from_pypi("pkg", "1.0.0") == "Apache-2.0"
+
+
+def test_get_license_falls_back_to_legacy_license(monkeypatch):
+    """(b) Pre-PEP-639 packages only set the legacy free-text license field."""
+    _patch_pypi(
+        monkeypatch,
+        {"license_expression": None, "license": "MIT License", "classifiers": []},
+    )
+    checker = _make_checker()
+    assert checker.get_package_license_from_pypi("pkg", "1.0.0") == "MIT License"
+
+
+def test_get_license_falls_back_to_classifiers(monkeypatch):
+    """(c) Some packages express the license only through trove classifiers."""
+    _patch_pypi(
+        monkeypatch,
+        {
+            "license_expression": None,
+            "license": None,
+            "classifiers": [
+                "Programming Language :: Python :: 3",
+                "License :: OSI Approved :: Apache Software License",
+            ],
+        },
+    )
+    checker = _make_checker()
+    assert (
+        checker.get_package_license_from_pypi("pkg", "1.0.0")
+        == "Apache Software License"
+    )
+
+
+def test_get_license_returns_none_when_unset(monkeypatch):
+    """(d) With no license metadata at all the license stays unknown."""
+    _patch_pypi(
+        monkeypatch,
+        {"license_expression": None, "license": None, "classifiers": []},
+    )
+    checker = _make_checker()
+    assert checker.get_package_license_from_pypi("pkg", "1.0.0") is None
+
+
+def test_get_license_returns_none_on_request_failure(monkeypatch):
+    """Network/HTTP failures are swallowed and reported as unknown."""
+
+    def _boom(url, timeout=None):
+        raise RuntimeError("network down")
+
+    monkeypatch.setattr(check_licenses.requests, "get", _boom)
+    checker = _make_checker()
+    assert checker.get_package_license_from_pypi("pkg", "1.0.0") is None
+
+
+# --------------------------------------------------------------------------
+# is_license_acceptable: SPDX identifiers and compound expressions
+# --------------------------------------------------------------------------
+
+
+def test_spdx_identifiers_are_authorized():
+    """Plain SPDX identifiers match the legacy-spelled authorized list as-is."""
+    checker = _make_checker()
+    for identifier in ("MIT", "Apache-2.0", "BSD-3-Clause"):
+        is_ok, reason = checker.is_license_acceptable(identifier)
+        assert is_ok is True, f"{identifier}: {reason}"
+
+
+def test_spdx_compound_or_expression_is_authorized():
+    checker = _make_checker()
+    is_ok, reason = checker.is_license_acceptable("MIT OR Apache-2.0")
+    assert is_ok is True, reason
+
+
+def test_spdx_with_exception_in_compound_is_authorized():
+    """The 'WITH <exception>' suffix is stripped; the base license is checked."""
+    checker = _make_checker()
+    is_ok, reason = checker.is_license_acceptable(
+        "Apache-2.0 WITH LLVM-exception OR MIT"
+    )
+    assert is_ok is True, reason
+
+
+def test_spdx_gpl3_is_rejected():
+    """GPL-3.0 spellings must fail — they match no authorized license."""
+    checker = _make_checker()
+    for expr in ("GPL-3.0-only", "GPL-3.0-or-later"):
+        is_ok, reason = checker.is_license_acceptable(expr)
+        assert is_ok is False, f"{expr} unexpectedly accepted: {reason}"
+
+
+def test_spdx_compound_with_copyleft_component_is_rejected():
+    """A permissive-OR-copyleft expression is conservatively rejected."""
+    checker = _make_checker()
+    is_ok, _ = checker.is_license_acceptable("MIT OR GPL-3.0-only")
+    assert is_ok is False
+
+
+def test_or_later_identifier_is_not_split_as_operator():
+    """The lowercase '-or-later' inside an identifier is not the SPDX OR operator."""
+    assert (
+        check_licenses.LicenseChecker._split_spdx_expression("GPL-2.0-or-later") is None
+    )
+
+
+def test_free_text_license_is_not_treated_as_spdx():
+    """Free-text license blobs fall back to whole-string substring matching."""
+    free_text = "MIT License AND additional redistribution permissions"
+    assert check_licenses.LicenseChecker._split_spdx_expression(free_text) is None
+    checker = _make_checker()
+    assert checker.is_license_acceptable(free_text)[0] is True
+
+
+def test_unknown_license_is_reported():
+    checker = _make_checker()
+    is_ok, reason = checker.is_license_acceptable(None)
+    assert is_ok is False
+    assert reason == "Unknown license"
+
+
+# --------------------------------------------------------------------------
+# check_package: end-to-end resolution + acceptability
+# --------------------------------------------------------------------------
+
+
+def test_check_package_accepts_pep639_package(monkeypatch):
+    """A PEP 639 package whose license lives only in license_expression passes."""
+    _patch_pypi(
+        monkeypatch,
+        {"license_expression": "MIT", "license": None, "classifiers": []},
+    )
+    checker = _make_checker()
+    assert checker.check_package("some-pep639-pkg", "1.0.0") is True
+
+
+def test_check_package_rejects_package_without_license(monkeypatch):
+    _patch_pypi(
+        monkeypatch,
+        {"license_expression": None, "license": None, "classifiers": []},
+    )
+    checker = _make_checker()
+    assert checker.check_package("mystery-pkg", "1.0.0") is False
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index d5c1132c5fd..00902890da3 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -2057,3 +2057,367 @@ def test_openrouter_gemini_3_1_flash_lite_preview_pricing():
     assert model_info["output_cost_per_token"] == 1.5e-06
     assert model_info["max_input_tokens"] == 1048576
     assert model_info["max_output_tokens"] == 65536
+
+
+def test_gemini_3_1_flash_lite_pricing():
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+
+    for model_name in (
+        "gemini-3.1-flash-lite",
+        "gemini/gemini-3.1-flash-lite",
+        "vertex_ai/gemini-3.1-flash-lite",
+    ):
+        model_info = litellm.model_cost.get(model_name)
+        assert model_info is not None, f"Missing model pricing entry: {model_name}"
+        assert model_info["input_cost_per_token"] == 4.5e-07
+        assert model_info["input_cost_per_audio_token"] == 9e-07
+        assert model_info["output_cost_per_token"] == 2.7e-06
+        assert model_info["output_cost_per_reasoning_token"] == 2.7e-06
+        assert model_info["cache_read_input_token_cost"] == 4.5e-08
+        assert model_info["max_input_tokens"] == 1048576
+
+
+def test_custom_pricing_applies_cache_read_input_cost():
+    """
+    Bug 1 reproduction: custom_cost_per_token with cache_read_input_token_cost
+    should bill cached prompt tokens at the cache rate, not the full input rate.
+    """
+    usage = Usage(
+        prompt_tokens=6074,
+        completion_tokens=285,
+        total_tokens=6359,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            cached_tokens=3456,
+            audio_tokens=0,
+        ),
+    )
+
+    response = ModelResponse(
+        id="test-id",
+        created=1234567890,
+        model="openai/gpt-5.4",
+        object="chat.completion",
+        choices=[],
+        usage=usage,
+    )
+
+    cost = litellm.completion_cost(
+        completion_response=response,
+        model="openai/gpt-5.4",
+        custom_llm_provider="openai",
+        custom_cost_per_token={
+            "input_cost_per_token": 0.0000025,
+            "output_cost_per_token": 0.000015,
+            "cache_read_input_token_cost": 0.00000025,
+        },
+    )
+
+    expected = (6074 - 3456) * 0.0000025 + 3456 * 0.00000025 + 285 * 0.000015
+
+    assert cost == pytest.approx(expected)
+
+
+def test_custom_pricing_applies_cache_creation_input_cost_via_prompt_details():
+    """
+    OpenAI-compatible providers report cache-write tokens under
+    prompt_tokens_details.cache_creation_tokens. The custom-pricing helper must
+    bill those at cache_creation_input_token_cost, not the full input rate.
+    """
+    pt_details = PromptTokensDetailsWrapper(cached_tokens=1000, audio_tokens=0)
+    pt_details.cache_creation_tokens = 500
+
+    usage = Usage(
+        prompt_tokens=4000,
+        completion_tokens=100,
+        total_tokens=4100,
+        prompt_tokens_details=pt_details,
+    )
+
+    response = ModelResponse(
+        id="test-id",
+        created=1234567890,
+        model="openai/gpt-5.4",
+        object="chat.completion",
+        choices=[],
+        usage=usage,
+    )
+
+    cost = litellm.completion_cost(
+        completion_response=response,
+        model="openai/gpt-5.4",
+        custom_llm_provider="openai",
+        custom_cost_per_token={
+            "input_cost_per_token": 0.0000025,
+            "output_cost_per_token": 0.000015,
+            "cache_read_input_token_cost": 0.00000025,
+            "cache_creation_input_token_cost": 0.000003125,
+        },
+    )
+
+    expected = (
+        (4000 - 1000 - 500) * 0.0000025
+        + 1000 * 0.00000025
+        + 500 * 0.000003125
+        + 100 * 0.000015
+    )
+
+    assert cost == pytest.approx(expected)
+
+
+def test_custom_pricing_applies_cache_creation_input_cost_via_cache_write_tokens_alias():
+    """
+    Some OpenAI-compatible providers (e.g. kimi-k2) emit cache-write tokens as
+    `cache_write_tokens` rather than `cache_creation_tokens`. The cost
+    calculator must mirror db_spend_update_writer and accept either name —
+    otherwise daily aggregation counts the tokens but the per-request cost
+    bills them at the full input rate.
+
+    Drives `cost_per_token` directly with a SimpleNamespace usage stub so the
+    `cache_write_tokens` alias survives the call (Pydantic's Usage init
+    rebuilds prompt_tokens_details and drops dynamic attributes).
+    """
+    from types import SimpleNamespace
+
+    from litellm.cost_calculator import cost_per_token
+
+    pt_details = SimpleNamespace(cached_tokens=1000, cache_write_tokens=500)
+    usage_stub = SimpleNamespace(
+        prompt_tokens=4000,
+        completion_tokens=100,
+        total_tokens=4100,
+        prompt_tokens_details=pt_details,
+        cache_read_input_tokens=None,
+        cache_creation_input_tokens=None,
+    )
+
+    prompt_cost, completion_cost = cost_per_token(
+        model="moonshotai/kimi-k2",
+        prompt_tokens=4000,
+        completion_tokens=100,
+        custom_llm_provider="openai",
+        usage_object=usage_stub,
+        custom_cost_per_token={
+            "input_cost_per_token": 0.0000025,
+            "output_cost_per_token": 0.000015,
+            "cache_read_input_token_cost": 0.00000025,
+            "cache_creation_input_token_cost": 0.000003125,
+        },
+    )
+
+    expected_prompt = (
+        (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125
+    )
+    expected_completion = 100 * 0.000015
+
+    assert prompt_cost == pytest.approx(expected_prompt)
+    assert completion_cost == pytest.approx(expected_completion)
+
+
+# ---------------------------------------------------------------------------
+# Bug 2 — db_spend_update_writer cache token extraction helpers.
+# ---------------------------------------------------------------------------
+
+
+def test_extract_cache_read_tokens_anthropic_top_level():
+    from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens
+
+    usage_obj = {
+        "prompt_tokens": 100,
+        "cache_read_input_tokens": 80,
+        "prompt_tokens_details": {"cached_tokens": 80},
+    }
+    # Anthropic top-level value should win over prompt_tokens_details fallback.
+    assert _extract_cache_read_tokens(usage_obj) == 80
+
+
+def test_extract_cache_read_tokens_openai_compatible_fallback():
+    from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens
+
+    # Anthropic field absent — fall back to prompt_tokens_details.cached_tokens.
+    usage_obj = {
+        "prompt_tokens": 22583,
+        "prompt_tokens_details": {"cached_tokens": 22016},
+    }
+    assert _extract_cache_read_tokens(usage_obj) == 22016
+
+
+def test_extract_cache_read_tokens_zero_when_missing():
+    from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens
+
+    assert _extract_cache_read_tokens({}) == 0
+    assert _extract_cache_read_tokens({"cache_read_input_tokens": None}) == 0
+    assert (
+        _extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}})
+        == 0
+    )
+
+
+def test_extract_cache_creation_tokens_anthropic_top_level():
+    from litellm.proxy.db.db_spend_update_writer import (
+        _extract_cache_creation_tokens,
+    )
+
+    usage_obj = {
+        "prompt_tokens": 100,
+        "cache_creation_input_tokens": 50,
+        "prompt_tokens_details": {"cache_write_tokens": 50},
+    }
+    # Anthropic top-level should short-circuit the fallback.
+    assert _extract_cache_creation_tokens(usage_obj) == 50
+
+
+def test_extract_cache_creation_tokens_openai_cache_write_alias():
+    from litellm.proxy.db.db_spend_update_writer import (
+        _extract_cache_creation_tokens,
+    )
+
+    # kimi-k2 emits cache_write_tokens.
+    usage_obj = {
+        "prompt_tokens": 1000,
+        "prompt_tokens_details": {"cache_write_tokens": 200},
+    }
+    assert _extract_cache_creation_tokens(usage_obj) == 200
+
+
+def test_extract_cache_creation_tokens_openai_cache_creation_alias():
+    from litellm.proxy.db.db_spend_update_writer import (
+        _extract_cache_creation_tokens,
+    )
+
+    # Other OpenAI-compatible providers emit cache_creation_tokens.
+    usage_obj = {
+        "prompt_tokens": 1000,
+        "prompt_tokens_details": {"cache_creation_tokens": 300},
+    }
+    assert _extract_cache_creation_tokens(usage_obj) == 300
+
+
+def test_extract_cache_creation_tokens_zero_when_missing():
+    from litellm.proxy.db.db_spend_update_writer import (
+        _extract_cache_creation_tokens,
+    )
+
+    assert _extract_cache_creation_tokens({}) == 0
+    assert _extract_cache_creation_tokens({"cache_creation_input_tokens": None}) == 0
+    assert (
+        _extract_cache_creation_tokens(
+            {"prompt_tokens_details": {"cache_write_tokens": None}}
+        )
+        == 0
+    )
+
+
+def test_custom_pricing_anthropic_style_cache_tokens_not_double_counted():
+    """
+    Anthropic providers report cache tokens at the top level of Usage, and
+    `prompt_tokens` EXCLUDES them. The helper expects `prompt_tokens` to
+    include cache tokens, so cost_per_token must adjust before invoking it —
+    otherwise regular_prompt_tokens goes negative and clamps to 0.
+    """
+    usage = Usage(
+        prompt_tokens=2000,
+        completion_tokens=100,
+        total_tokens=2100,
+        cache_read_input_tokens=1500,
+        cache_creation_input_tokens=300,
+    )
+
+    response = ModelResponse(
+        id="test-id",
+        created=1234567890,
+        model="anthropic/claude-3-5-sonnet",
+        object="chat.completion",
+        choices=[],
+        usage=usage,
+    )
+
+    cost = litellm.completion_cost(
+        completion_response=response,
+        model="anthropic/claude-3-5-sonnet",
+        custom_llm_provider="anthropic",
+        custom_cost_per_token={
+            "input_cost_per_token": 0.000003,
+            "output_cost_per_token": 0.000015,
+            "cache_read_input_token_cost": 0.0000003,
+            "cache_creation_input_token_cost": 0.00000375,
+        },
+    )
+
+    # Anthropic prompt_tokens=2000 excludes cache. After normalization the
+    # helper sees 2000 + 1500 + 300 = 3800, of which 2000 are uncached.
+    expected = 2000 * 0.000003 + 1500 * 0.0000003 + 300 * 0.00000375 + 100 * 0.000015
+
+    assert cost == pytest.approx(expected)
+
+
+def test_custom_pricing_without_cache_keys_preserves_legacy_behavior():
+    """
+    Backward compatibility: when custom_cost_per_token omits both cache rates,
+    cached tokens must be billed at input_cost_per_token (matching the pre-fix
+    behavior) so existing callers see no change.
+    """
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=100,
+        total_tokens=1100,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            cached_tokens=400,
+            audio_tokens=0,
+        ),
+    )
+
+    response = ModelResponse(
+        id="test-id",
+        created=1234567890,
+        model="openai/gpt-5.4",
+        object="chat.completion",
+        choices=[],
+        usage=usage,
+    )
+
+    cost = litellm.completion_cost(
+        completion_response=response,
+        model="openai/gpt-5.4",
+        custom_llm_provider="openai",
+        custom_cost_per_token={
+            "input_cost_per_token": 0.0000025,
+            "output_cost_per_token": 0.000015,
+        },
+    )
+
+    # All 1000 prompt tokens billed at input rate, regardless of cached_tokens.
+    expected = 1000 * 0.0000025 + 100 * 0.000015
+
+    assert cost == pytest.approx(expected)
+
+
+def test_openrouter_gemini_3_1_flash_lite_stable_pricing():
+    """
+    Test that openrouter/google/gemini-3.1-flash-lite (stable, no -preview suffix)
+    has a pricing entry.
+
+    Google promoted gemini-3.1-flash-lite to GA on 2026-05-07. PR #27933 added the
+    stable pricing for the bare, gemini/, and vertex_ai/ prefixes but missed the
+    openrouter/google/ variant — every other Gemini family in the file has an
+    openrouter/google/ sibling (2.0-flash-001, 2.5-flash, 2.5-pro, 3-flash-preview,
+    3-pro-preview, 3.1-flash-lite-preview, 3.1-pro-preview), so the gap is a
+    consistency issue, not a design choice. Same shape as the preview-variant gap
+    fixed in PR #25610.
+
+    Pricing matches the existing -preview entry one-for-one (input $0.25/M, output
+    $1.50/M, cache-read $0.025/M) — Google did not change costs at the GA cutover.
+    """
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+
+    model_name = "openrouter/google/gemini-3.1-flash-lite"
+    model_info = litellm.model_cost.get(model_name)
+
+    assert model_info is not None, f"Missing model pricing entry: {model_name}"
+    assert model_info["litellm_provider"] == "openrouter"
+    assert model_info["input_cost_per_token"] == 2.5e-07
+    assert model_info["output_cost_per_token"] == 1.5e-06
+    assert model_info["cache_read_input_token_cost"] == 2.5e-08
+    assert model_info["max_input_tokens"] == 1048576
+    assert model_info["max_output_tokens"] == 65536
diff --git a/tests/test_litellm/test_guardrail_exception_status_codes.py b/tests/test_litellm/test_guardrail_exception_status_codes.py
new file mode 100644
index 00000000000..c4df1295580
--- /dev/null
+++ b/tests/test_litellm/test_guardrail_exception_status_codes.py
@@ -0,0 +1,66 @@
+"""
+Tests for guardrail exception status codes.
+
+GuardrailRaisedException and BlockedPiiEntityError must carry
+``status_code = 400`` so the proxy exception handler
+(``getattr(e, "status_code", 500)``) returns HTTP 400 instead of 500
+for intentional guardrail blocks.
+"""
+
+from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException
+
+
+class TestGuardrailRaisedExceptionStatusCode:
+    """GuardrailRaisedException should default to status_code=400."""
+
+    def test_default_status_code(self):
+        exc = GuardrailRaisedException(
+            guardrail_name="test_guardrail",
+            message="blocked",
+        )
+        assert exc.status_code == 400
+
+    def test_custom_status_code(self):
+        exc = GuardrailRaisedException(
+            guardrail_name="test_guardrail",
+            message="rate limited",
+            status_code=429,
+        )
+        assert exc.status_code == 429
+
+    def test_getattr_fallback_resolves_to_400(self):
+        """The proxy uses ``getattr(e, 'status_code', 500)`` — verify it
+        resolves to 400, not the 500 default."""
+        exc = GuardrailRaisedException(
+            guardrail_name="test_guardrail",
+            message="blocked",
+        )
+        assert getattr(exc, "status_code", 500) == 400
+
+
+class TestBlockedPiiEntityErrorStatusCode:
+    """BlockedPiiEntityError should default to status_code=400."""
+
+    def test_default_status_code(self):
+        exc = BlockedPiiEntityError(
+            entity_type="CREDIT_CARD",
+            guardrail_name="presidio",
+        )
+        assert exc.status_code == 400
+
+    def test_custom_status_code(self):
+        exc = BlockedPiiEntityError(
+            entity_type="SSN",
+            guardrail_name="presidio",
+            status_code=403,
+        )
+        assert exc.status_code == 403
+
+    def test_getattr_fallback_resolves_to_400(self):
+        """The proxy uses ``getattr(e, 'status_code', 500)`` — verify it
+        resolves to 400, not the 500 default."""
+        exc = BlockedPiiEntityError(
+            entity_type="PHONE_NUMBER",
+            guardrail_name="presidio",
+        )
+        assert getattr(exc, "status_code", 500) == 400
diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py
index 282c9d72d51..a89e30a0e06 100644
--- a/tests/test_litellm/test_redis.py
+++ b/tests/test_litellm/test_redis.py
@@ -1,5 +1,4 @@
 import json
-import os
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -165,6 +164,13 @@ def test_max_connections_in_cluster_kwargs():
     ), "max_connections should be in available Redis cluster kwargs"
 
 
+def test_socket_timeouts_in_cluster_kwargs():
+    """Test that Redis cluster clients can receive socket timeout configuration"""
+    kwargs = _get_redis_cluster_kwargs()
+    assert "socket_timeout" in kwargs
+    assert "socket_connect_timeout" in kwargs
+
+
 def test_get_redis_async_client_with_connection_pool():
     """Test that connection_pool parameter is properly passed to Redis client"""
     # Create a mock connection pool
diff --git a/tests/test_litellm/test_register_model_custom_pricing.py b/tests/test_litellm/test_register_model_custom_pricing.py
index 1efd698fb64..719cb8eecd2 100644
--- a/tests/test_litellm/test_register_model_custom_pricing.py
+++ b/tests/test_litellm/test_register_model_custom_pricing.py
@@ -190,3 +190,164 @@ def test_build_custom_pricing_entry_time_based():
     assert entry["litellm_provider"] == "openai"
     assert entry["input_cost_per_second"] == 0.01
     assert entry["output_cost_per_second"] == 0.02
+
+
+def test_register_model_strips_none_litellm_provider():
+    """``get_model_info`` returns ``litellm_provider: None`` for deployments
+    registered without a provider (e.g. ``Router.add_deployment`` flows).
+    ``register_model`` must not persist that None into ``model_cost``,
+    otherwise ``_check_provider_match`` will drop custom pricing on
+    subsequent cost lookups.
+
+    Regression test for https://github.com/BerriAI/litellm/issues/28336.
+    """
+    from litellm.utils import _check_provider_match
+
+    model_key = "test-custom-pricing-no-provider-28336"
+    litellm.model_cost.pop(model_key, None)
+
+    try:
+        litellm.register_model(
+            {
+                model_key: {
+                    "input_cost_per_token": 0.001,
+                    "output_cost_per_token": 0.002,
+                }
+            }
+        )
+
+        registered = litellm.model_cost.get(model_key)
+        assert registered is not None, f"{model_key} should be in model_cost"
+        # The key may be absent entirely, but if present it must not be None.
+        assert (
+            "litellm_provider" not in registered
+            or registered["litellm_provider"] is not None
+        )
+        # Downstream consumers must accept this entry for any provider,
+        # mirroring what the cost calculator does.
+        assert _check_provider_match(registered, "openai") is True
+        assert _check_provider_match(registered, "anthropic") is True
+    finally:
+        litellm.model_cost.pop(model_key, None)
+
+
+def test_register_model_strips_none_litellm_provider_from_get_model_info(monkeypatch):
+    """Directly exercise the strip in ``register_model``.
+
+    The companion test above hits the ``except Exception`` branch where
+    ``existing_model`` is an empty dict, so the ``pop`` is a no-op. This
+    test patches ``get_model_info`` to return the failure mode the strip
+    was added to handle, namely a populated dict whose ``litellm_provider``
+    is ``None``. Without the strip, the merged entry in
+    ``litellm.model_cost`` would carry ``litellm_provider: None`` and
+    ``_check_provider_match`` would drop custom pricing.
+
+    Regression test for https://github.com/BerriAI/litellm/issues/28336.
+    """
+    from litellm import utils as litellm_utils
+    from litellm.utils import _check_provider_match
+
+    model_key = "test-strip-none-provider-from-get-model-info-28336"
+    litellm.model_cost.pop(model_key, None)
+
+    def _fake_get_model_info(model, *args, **kwargs):
+        assert model == model_key
+        return {
+            "key": model_key,
+            "litellm_provider": None,
+            "mode": "chat",
+            "max_tokens": 4096,
+        }
+
+    # ``register_model`` calls ``get_model_info.cache_clear`` via
+    # ``_invalidate_model_cost_lowercase_map``, so the replacement must
+    # expose a no-op ``cache_clear`` attribute.
+    _fake_get_model_info.cache_clear = lambda: None
+    monkeypatch.setattr(litellm_utils, "get_model_info", _fake_get_model_info)
+
+    try:
+        litellm.register_model(
+            {
+                model_key: {
+                    "input_cost_per_token": 0.001,
+                    "output_cost_per_token": 0.002,
+                }
+            }
+        )
+
+        registered = litellm.model_cost.get(model_key)
+        assert registered is not None, f"{model_key} should be in model_cost"
+        # The strip must have removed the None-valued provider that
+        # ``get_model_info`` returned. The key may be absent entirely, but
+        # it must never be present with value ``None``.
+        assert "litellm_provider" not in registered or (
+            registered["litellm_provider"] is not None
+        ), (
+            "register_model failed to strip litellm_provider=None returned "
+            f"by get_model_info, got {registered.get('litellm_provider')!r}"
+        )
+        # Metadata from the patched ``get_model_info`` must still flow
+        # through, so we know the strip did not nuke the rest of the entry.
+        assert registered.get("mode") == "chat"
+        assert registered.get("max_tokens") == 4096
+        # And custom pricing from the registration call must be preserved.
+        assert registered.get("input_cost_per_token") == 0.001
+        assert registered.get("output_cost_per_token") == 0.002
+        # Downstream _check_provider_match must accept any provider for
+        # this entry, mirroring the cost calculator path.
+        assert _check_provider_match(registered, "openai") is True
+        assert _check_provider_match(registered, "anthropic") is True
+    finally:
+        litellm.model_cost.pop(model_key, None)
+
+
+def test_register_model_router_add_deployment_custom_pricing_applies():
+    """End-to-end regression for https://github.com/BerriAI/litellm/issues/28336.
+
+    ``Router.add_deployment`` registers custom pricing without passing
+    ``litellm_provider``. Cost calculation must still pick up the custom
+    pricing instead of falling back to the default provider price.
+    """
+    from litellm import Router
+
+    model_key = "router-add-deployment-custom-pricing-28336"
+    deployment_model = f"openai/{model_key}"
+    litellm.model_cost.pop(model_key, None)
+    litellm.model_cost.pop(deployment_model, None)
+
+    router = Router(
+        model_list=[
+            {
+                "model_name": model_key,
+                "litellm_params": {
+                    "model": deployment_model,
+                    "api_key": "fake-key-for-registration",
+                    "input_cost_per_token": 0.00042,
+                    "output_cost_per_token": 0.00084,
+                },
+                "model_info": {"id": "deployment-28336"},
+            }
+        ]
+    )
+
+    try:
+        # ``add_deployment`` runs as part of ``Router.__init__``; the
+        # registered entry must not block ``_check_provider_match`` for
+        # the deployment's provider.
+        from litellm.utils import _check_provider_match
+
+        registered_keys = [
+            k for k in (deployment_model, model_key) if k in litellm.model_cost
+        ]
+        assert registered_keys, (
+            "Router.add_deployment did not register custom pricing for "
+            f"{model_key} / {deployment_model}"
+        )
+        for k in registered_keys:
+            assert _check_provider_match(litellm.model_cost[k], "openai") is True, (
+                f"custom pricing for {k} was dropped by _check_provider_match"
+            )
+    finally:
+        litellm.model_cost.pop(model_key, None)
+        litellm.model_cost.pop(deployment_model, None)
+        del router
diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py
index 48facace528..5e636b86ed6 100644
--- a/tests/test_litellm/test_router.py
+++ b/tests/test_litellm/test_router.py
@@ -1741,6 +1741,362 @@ async def test_acompletion_streaming_iterator_pre_first_chunk_skips_continuation
         assert fallback_kwargs["messages"] == messages
 
 
+# ---------------------------------------------------------------------------
+# Shared helpers for the _aresponses_streaming_iterator test suite.
+# ---------------------------------------------------------------------------
+def _make_responses_iterator(
+    *,
+    chunks=(),
+    error=None,
+    bridge=False,
+    model="gpt-4",
+    hidden_params=None,
+    chat_chunks=None,
+):
+    """Build a minimal mock Responses-API streaming iterator.
+
+    Bypasses BaseResponsesAPIStreamingIterator.__init__ but mirrors every
+    attribute production code reads. Yields *chunks*, then raises *error*
+    (or StopAsyncIteration). Set bridge=True to inherit from
+    LiteLLMCompletionStreamingIterator so the wrapper's bridge-path
+    isinstance check (used by usage extraction) matches.
+    """
+    from litellm.responses.litellm_completion_transformation.streaming_iterator import (
+        LiteLLMCompletionStreamingIterator,
+    )
+    from litellm.responses.streaming_iterator import (
+        BaseResponsesAPIStreamingIterator,
+    )
+
+    base = (
+        LiteLLMCompletionStreamingIterator
+        if bridge
+        else BaseResponsesAPIStreamingIterator
+    )
+
+    class _Iter(base):
+        def __init__(self):
+            self._chunks = list(chunks)
+            self._idx = 0
+            self._hidden_params = hidden_params or {}
+            self.model = model
+            self.custom_llm_provider = "anthropic"
+            self.logging_obj = MagicMock()
+            self.litellm_metadata = None
+            self.responses_api_provider_config = None
+            self.finished = False
+            self.completed_response = None
+            self.response = None
+            self.start_time = None
+            self.request_data = {}
+            self.call_type = None
+            if chat_chunks is not None:
+                self.collected_chat_completion_chunks = chat_chunks
+
+        def __aiter__(self):
+            return self
+
+        async def __anext__(self):
+            if self._idx < len(self._chunks):
+                self._idx += 1
+                return self._chunks[self._idx - 1]
+            if error is not None:
+                raise error
+            raise StopAsyncIteration
+
+    return _Iter()
+
+
+class _AsyncList:
+    """Generic async iterator over a list — used as the fallback response."""
+
+    def __init__(self, items=()):
+        self._items = list(items)
+        self._idx = 0
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        if self._idx >= len(self._items):
+            raise StopAsyncIteration
+        item = self._items[self._idx]
+        self._idx += 1
+        return item
+
+
+def _make_router_with_fallback(primary="gpt-4", secondary="gpt-3.5-turbo"):
+    return litellm.Router(
+        model_list=[
+            {
+                "model_name": primary,
+                "litellm_params": {"model": primary, "api_key": "k1"},
+            },
+            {
+                "model_name": secondary,
+                "litellm_params": {"model": secondary, "api_key": "k2"},
+            },
+        ],
+        fallbacks=[{primary: [secondary]}],
+    )
+
+
+@pytest.mark.asyncio
+async def test_aresponses_streaming_iterator_fallback():
+    """Catches MidStreamFallbackError, re-enters the fallback chain via
+    async_function_with_fallbacks_common_utils with the per-attempt helper
+    and original_generic_function preserved. Mirrors
+    test_acompletion_streaming_iterator for the aresponses path."""
+    from litellm.exceptions import MidStreamFallbackError
+    from litellm.responses.streaming_iterator import (
+        BaseResponsesAPIStreamingIterator,
+    )
+
+    router = _make_router_with_fallback(
+        "anthropic/claude-sonnet-4-6", "vertex_ai/claude-sonnet-4-6"
+    )
+    src = _make_responses_iterator(
+        chunks=[MagicMock(type="response.created")],
+        error=MidStreamFallbackError(
+            message="anthropic socket timeout",
+            model="anthropic/claude-sonnet-4-6",
+            llm_provider="anthropic",
+            is_pre_first_chunk=False,
+            generated_content="",
+        ),
+        model="anthropic/claude-sonnet-4-6",
+        hidden_params={"model_id": "src-deployment-1"},
+    )
+    fallback_chunks = [
+        MagicMock(type="response.output_text.delta"),
+        MagicMock(type="response.completed"),
+    ]
+
+    with patch.object(
+        router,
+        "async_function_with_fallbacks_common_utils",
+        return_value=_AsyncList(fallback_chunks),
+    ) as mock_fallback_utils:
+        wrapped = await router._aresponses_streaming_iterator(
+            response=src,
+            initial_kwargs={
+                "model": "anthropic/claude-sonnet-4-6",
+                "stream": True,
+                "input": "Hi",
+                "original_generic_function": litellm.aresponses,
+            },
+        )
+        assert isinstance(wrapped, BaseResponsesAPIStreamingIterator)
+        assert wrapped._hidden_params.get("model_id") == "src-deployment-1"
+        collected = [c async for c in wrapped]
+
+    assert len(collected) == 3  # 1 primary chunk + 2 fallback chunks
+    call_kwargs = mock_fallback_utils.call_args.kwargs
+    fbk = call_kwargs["kwargs"]
+    # Bound methods compare equal when they share the same instance + __func__.
+    assert fbk["original_function"] == router._ageneric_api_call_with_fallbacks_helper
+    assert fbk["original_generic_function"] is litellm.aresponses
+    assert call_kwargs["model_group"] == "anthropic/claude-sonnet-4-6"
+    assert call_kwargs["disable_fallbacks"] is False
+
+
+@pytest.mark.asyncio
+async def test_aresponses_streaming_iterator_writes_litellm_metadata_on_fallback():
+    """Regression: model_group must land under "litellm_metadata" (the key
+    litellm.aresponses reads), not the default "metadata"."""
+    from litellm.exceptions import MidStreamFallbackError
+
+    router = _make_router_with_fallback()
+    src = _make_responses_iterator(
+        error=MidStreamFallbackError(
+            message="boom",
+            model="gpt-4",
+            llm_provider="anthropic",
+            is_pre_first_chunk=True,
+            generated_content="",
+        )
+    )
+
+    with patch.object(
+        router,
+        "async_function_with_fallbacks_common_utils",
+        return_value=_AsyncList(),
+    ) as mock_fallback_utils:
+        wrapped = await router._aresponses_streaming_iterator(
+            response=src,
+            initial_kwargs={
+                "model": "gpt-4",
+                "stream": True,
+                "input": "Hello",
+                "original_generic_function": litellm.aresponses,
+            },
+        )
+        async for _ in wrapped:
+            pass
+
+    fbk = mock_fallback_utils.call_args.kwargs["kwargs"]
+    assert "litellm_metadata" in fbk, "wrong metadata_variable_name"
+    assert fbk["litellm_metadata"]["model_group"] == "gpt-4"
+    assert "model_group" not in fbk.get(
+        "metadata", {}
+    ), "model_group leaked into 'metadata' instead of 'litellm_metadata'"
+
+
+@pytest.mark.asyncio
+async def test_aresponses_streaming_iterator_pre_first_chunk_skips_continuation():
+    """Pre-first-chunk error: original input is preserved unchanged."""
+    from litellm.exceptions import MidStreamFallbackError
+
+    router = _make_router_with_fallback()
+    src = _make_responses_iterator(
+        error=MidStreamFallbackError(
+            message="socket timeout before first chunk",
+            model="gpt-4",
+            llm_provider="anthropic",
+            is_pre_first_chunk=True,
+            generated_content="",
+        )
+    )
+
+    with patch.object(
+        router,
+        "async_function_with_fallbacks_common_utils",
+        return_value=_AsyncList(),
+    ) as mock_fallback_utils:
+        wrapped = await router._aresponses_streaming_iterator(
+            response=src,
+            initial_kwargs={
+                "model": "gpt-4",
+                "stream": True,
+                "input": "Hello",
+                "original_generic_function": litellm.aresponses,
+            },
+        )
+        async for _ in wrapped:
+            pass
+
+    fbk = mock_fallback_utils.call_args.kwargs["kwargs"]
+    assert fbk["input"] == "Hello"  # original input, no continuation messages
+
+
+@pytest.mark.asyncio
+async def test_aresponses_streaming_iterator_partial_content_injects_continuation():
+    """Mid-stream error: input is rewritten to include user prompt +
+    developer instruction + prior assistant message with partial output."""
+    from litellm.exceptions import MidStreamFallbackError
+
+    router = _make_router_with_fallback()
+    src = _make_responses_iterator(
+        chunks=[MagicMock(type="response.output_text.delta")],
+        error=MidStreamFallbackError(
+            message="socket reset mid-stream",
+            model="gpt-4",
+            llm_provider="anthropic",
+            is_pre_first_chunk=False,
+            generated_content="The capital of France is",
+        ),
+    )
+
+    with patch.object(
+        router,
+        "async_function_with_fallbacks_common_utils",
+        return_value=_AsyncList(),
+    ) as mock_fallback_utils:
+        wrapped = await router._aresponses_streaming_iterator(
+            response=src,
+            initial_kwargs={
+                "model": "gpt-4",
+                "stream": True,
+                "input": "What's the capital of France?",
+                "original_generic_function": litellm.aresponses,
+            },
+        )
+        async for _ in wrapped:
+            pass
+
+    new_input = mock_fallback_utils.call_args.kwargs["kwargs"]["input"]
+    assert isinstance(new_input, list)
+    assert new_input[0]["role"] == "user"
+    assert new_input[0]["content"][0]["text"] == "What's the capital of France?"
+    assert new_input[1]["role"] == "developer"
+    assert "do not repeat" in new_input[1]["content"][0]["text"].lower()
+    assert new_input[2]["role"] == "assistant"
+    assert new_input[2]["content"][0]["type"] == "output_text"
+    assert new_input[2]["content"][0]["text"] == "The capital of France is"
+
+
+@pytest.mark.asyncio
+async def test_aresponses_streaming_iterator_combines_partial_usage():
+    """Partial usage from the bridge path is normalized to ResponseAPIUsage
+    and summed onto the fallback's response.completed event — no token-name
+    split, clean ResponseAPIUsage on output."""
+    from types import SimpleNamespace
+
+    from litellm.exceptions import MidStreamFallbackError
+    from litellm.types.llms.openai import (
+        ResponseAPIUsage,
+        ResponseCompletedEvent,
+        ResponsesAPIResponse,
+        ResponsesAPIStreamEvents,
+    )
+
+    router = _make_router_with_fallback()
+    src = _make_responses_iterator(
+        bridge=True,
+        chat_chunks=[MagicMock()],
+        chunks=[MagicMock(type="response.output_text.delta")],
+        error=MidStreamFallbackError(
+            message="boom",
+            model="gpt-4",
+            llm_provider="anthropic",
+            is_pre_first_chunk=False,
+            generated_content="hello",
+        ),
+    )
+
+    fallback_response_object = ResponsesAPIResponse(
+        id="resp_test", created_at=0, model="gpt-4", object="response", output=[]
+    )
+    fallback_response_object.usage = ResponseAPIUsage(
+        input_tokens=20, output_tokens=15, total_tokens=35
+    )
+    fallback_event = ResponseCompletedEvent(
+        type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED,
+        response=fallback_response_object,
+    )
+
+    with (
+        patch(
+            "litellm.main.stream_chunk_builder",
+            return_value=SimpleNamespace(
+                usage=SimpleNamespace(prompt_tokens=10, completion_tokens=4)
+            ),
+        ),
+        patch.object(
+            router,
+            "async_function_with_fallbacks_common_utils",
+            return_value=_AsyncList([fallback_event]),
+        ),
+    ):
+        wrapped = await router._aresponses_streaming_iterator(
+            response=src,
+            initial_kwargs={
+                "model": "gpt-4",
+                "stream": True,
+                "input": "hi",
+                "original_generic_function": litellm.aresponses,
+            },
+        )
+        async for _ in wrapped:
+            pass
+
+    merged = fallback_response_object.usage
+    assert isinstance(merged, ResponseAPIUsage)
+    assert merged.input_tokens == 30  # 10 (translated from prompt_tokens) + 20
+    assert merged.output_tokens == 19  # 4 (translated from completion_tokens) + 15
+    assert merged.total_tokens == 49
+
+
 @pytest.mark.asyncio
 async def test_async_function_with_fallbacks_common_utils():
     """Test the async_function_with_fallbacks_common_utils method"""
@@ -3697,3 +4053,181 @@ def test_try_early_resolve_deployments_for_model_not_in_names():
         default_router.default_deployment["litellm_params"]["model"]
         == "openai/will-be-overridden"
     )
+
+
+def _router_with_two_deployments(blocked_flags):
+    import litellm
+
+    model_list = []
+    for idx, blocked in enumerate(blocked_flags):
+        model_list.append(
+            {
+                "model_name": "gpt-4o",
+                "litellm_params": {"model": f"openai/gpt-4o-{idx}"},
+                "model_info": {"id": f"dep-{idx}", "blocked": blocked},
+            }
+        )
+    return litellm.Router(model_list=model_list)
+
+
+def test_get_fully_blocked_model_names_marks_name_when_all_deployments_blocked():
+    router = _router_with_two_deployments([True, True])
+    assert router.get_fully_blocked_model_names() == {"gpt-4o"}
+
+
+def test_get_fully_blocked_model_names_keeps_name_when_partial_blocked():
+    router = _router_with_two_deployments([True, False])
+    assert router.get_fully_blocked_model_names() == set()
+
+
+def test_get_fully_blocked_model_names_treats_missing_key_as_unblocked():
+    import litellm
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "gpt-4o",
+                "litellm_params": {"model": "openai/gpt-4o"},
+                "model_info": {"id": "dep-0"},
+            }
+        ]
+    )
+    assert router.get_fully_blocked_model_names() == set()
+
+
+@pytest.mark.asyncio
+async def test_async_get_healthy_deployments_skips_blocked_deployment():
+    router = _router_with_two_deployments([True, False])
+    healthy, all_dep = await router._async_get_healthy_deployments(
+        model="gpt-4o", parent_otel_span=None
+    )
+    healthy_ids = [d["model_info"]["id"] for d in healthy]
+    assert "dep-0" not in healthy_ids
+    assert "dep-1" in healthy_ids
+    assert len(all_dep) == 2
+
+
+def test_get_healthy_deployments_sync_skips_blocked_deployment():
+    router = _router_with_two_deployments([False, True])
+    healthy, all_dep = router._get_healthy_deployments(
+        model="gpt-4o", parent_otel_span=None
+    )
+    healthy_ids = [d["model_info"]["id"] for d in healthy]
+    assert "dep-0" in healthy_ids
+    assert "dep-1" not in healthy_ids
+    assert len(all_dep) == 2
+
+
+def test_filter_blocked_deployments_drops_blocked_keeps_unblocked():
+    router = _router_with_two_deployments([True, False])
+    filtered = router._filter_blocked_deployments(router.get_model_list() or [])
+    ids = [d["model_info"]["id"] for d in filtered]
+    assert ids == ["dep-1"]
+
+
+@pytest.mark.asyncio
+async def test_public_async_get_healthy_deployments_skips_blocked_on_primary_path():
+    router = _router_with_two_deployments([True, False])
+    deployments = await router.async_get_healthy_deployments(
+        model="gpt-4o", request_kwargs={}
+    )
+    assert isinstance(deployments, list)
+    ids = [d["model_info"]["id"] for d in deployments]
+    assert "dep-0" not in ids
+    assert "dep-1" in ids
+
+
+def test_public_get_available_deployment_skips_blocked_on_primary_path():
+    router = _router_with_two_deployments([True, False])
+    deployment = router.get_available_deployment(model="gpt-4o", request_kwargs={})
+    assert deployment["model_info"]["id"] == "dep-1"
+
+
+def test_get_available_deployment_raises_when_addressed_dict_is_blocked():
+    import litellm
+
+    router = _router_with_two_deployments([True, True])
+    with pytest.raises(litellm.ServiceUnavailableError):
+        router.get_available_deployment(model="dep-0", request_kwargs={})
+
+
+def _router_with_two_pass_through_deployments(blocked_flags):
+    import litellm
+
+    model_list = []
+    for idx, blocked in enumerate(blocked_flags):
+        model_list.append(
+            {
+                "model_name": "gpt-4o",
+                "litellm_params": {
+                    "model": f"openai/gpt-4o-{idx}",
+                    "api_key": "sk-fake-for-tests",
+                    "use_in_pass_through": True,
+                },
+                "model_info": {"id": f"pt-{idx}", "blocked": blocked},
+            }
+        )
+    return litellm.Router(model_list=model_list)
+
+
+def test_get_available_deployment_for_pass_through_skips_blocked():
+    router = _router_with_two_pass_through_deployments([True, False])
+    deployment = router.get_available_deployment_for_pass_through(
+        model="gpt-4o", request_kwargs={}
+    )
+    assert deployment["model_info"]["id"] == "pt-1"
+
+
+def test_get_available_deployment_for_pass_through_raises_when_dict_blocked():
+    import litellm
+
+    router = _router_with_two_pass_through_deployments([True, True])
+    with pytest.raises(litellm.ServiceUnavailableError):
+        router.get_available_deployment_for_pass_through(
+            model="pt-0", request_kwargs={}
+        )
+
+
+def test_get_deployment_credentials_returns_none_for_blocked_deployment():
+    router = _router_with_two_deployments([True, False])
+    assert router.get_deployment_credentials(model_id="dep-0") is None
+    assert router.get_deployment_credentials(model_id="dep-1") is not None
+
+
+def test_get_deployment_credentials_with_provider_returns_none_for_blocked_deployment():
+    router = _router_with_two_deployments([True, False])
+    assert router.get_deployment_credentials_with_provider(model_id="dep-0") is None
+    assert router.get_deployment_credentials_with_provider(model_id="dep-1") is not None
+
+
+def test_is_deployment_blocked_static_helper_reflects_blocked_flag():
+    """
+    Exercises Router._is_deployment_blocked so router_code_coverage.py (AST call graph)
+    marks the helper as covered by router-named tests.
+    """
+    import types
+
+    import litellm
+
+    router = _router_with_two_deployments([True, False])
+    blocked_dep = router.get_deployment("dep-0")
+    unblocked_dep = router.get_deployment("dep-1")
+    assert blocked_dep is not None and unblocked_dep is not None
+    assert litellm.Router._is_deployment_blocked(blocked_dep) is True
+    assert litellm.Router._is_deployment_blocked(unblocked_dep) is False
+
+    # No model_info on deployment object → treated as not blocked
+    assert litellm.Router._is_deployment_blocked(object()) is False
+    missing_blocked = types.SimpleNamespace()
+    assert (
+        litellm.Router._is_deployment_blocked(
+            types.SimpleNamespace(model_info=missing_blocked)
+        )
+        is False
+    )
+    assert (
+        litellm.Router._is_deployment_blocked(
+            types.SimpleNamespace(model_info=types.SimpleNamespace(blocked=True))
+        )
+        is True
+    )
diff --git a/tests/test_litellm/test_router_model_cost_isolation.py b/tests/test_litellm/test_router_model_cost_isolation.py
index c3f93078557..9454e03e918 100644
--- a/tests/test_litellm/test_router_model_cost_isolation.py
+++ b/tests/test_litellm/test_router_model_cost_isolation.py
@@ -7,8 +7,10 @@ and one has explicit zero-cost pricing in model_info, the other deployment
 should still use the built-in pricing.
 """
 
+import copy
 import os
 import sys
+from unittest.mock import patch
 
 import pytest
 
@@ -19,6 +21,16 @@ sys.path.insert(
 import litellm
 from litellm import Router
 from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
+from litellm.utils import _invalidate_model_cost_lowercase_map
+
+
+def _restore_model_cost_entries(original_entries):
+    for key, value in original_entries.items():
+        if value is None:
+            litellm.model_cost.pop(key, None)
+        else:
+            litellm.model_cost[key] = value
+    _invalidate_model_cost_lowercase_map()
 
 
 def test_should_not_pollute_shared_key_with_zero_cost_pricing():
@@ -323,3 +335,70 @@ def test_responses_prefix_stripped_alias_registered_for_add_deployment():
         )
         is True
     )
+
+
+def test_should_not_downgrade_chatgpt_shared_key_mode_with_alias_override():
+    """
+    ChatGPT aliases that share the same backend model should not be able to
+    downgrade the shared backend key from responses -> chat during router setup.
+    """
+    from litellm.main import responses_api_bridge_check
+
+    backend_model = "chatgpt/gpt-5.4"
+    model_keys = {
+        backend_model: copy.deepcopy(litellm.model_cost.get(backend_model)),
+        "chatgpt-shared-mode-base": copy.deepcopy(
+            litellm.model_cost.get("chatgpt-shared-mode-base")
+        ),
+        "chatgpt-shared-mode-alias": copy.deepcopy(
+            litellm.model_cost.get("chatgpt-shared-mode-alias")
+        ),
+    }
+
+    try:
+        backend_entry = copy.deepcopy(model_keys[backend_model]) or {}
+        backend_entry["litellm_provider"] = "chatgpt"
+        backend_entry["mode"] = "responses"
+        litellm.model_cost[backend_model] = backend_entry
+        _invalidate_model_cost_lowercase_map()
+
+        router = Router(model_list=[])
+        with patch.object(
+            Router, "_add_deployment", lambda self, deployment: deployment
+        ):
+            router._create_deployment(
+                deployment_info={},
+                _model_name="chatgpt/gpt-5.4",
+                _litellm_params={
+                    "model": "gpt-5.4",
+                    "custom_llm_provider": "chatgpt",
+                },
+                _model_info={
+                    "id": "chatgpt-shared-mode-base",
+                    "mode": "responses",
+                },
+            )
+            router._create_deployment(
+                deployment_info={},
+                _model_name="chatgpt/gpt-5.4-medium",
+                _litellm_params={
+                    "model": "gpt-5.4",
+                    "custom_llm_provider": "chatgpt",
+                },
+                _model_info={
+                    "id": "chatgpt-shared-mode-alias",
+                    "mode": "chat",
+                },
+            )
+
+        assert litellm.model_cost[backend_model]["mode"] == "responses"
+        assert "mode" in litellm.model_cost[backend_model]
+
+        bridge_model_info, bridge_model = responses_api_bridge_check(
+            model="gpt-5.4",
+            custom_llm_provider="chatgpt",
+        )
+        assert bridge_model == "gpt-5.4"
+        assert bridge_model_info["mode"] == "responses"
+    finally:
+        _restore_model_cost_entries(model_keys)
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index bc60375f906..40d9cf3231e 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -706,6 +706,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "cache_read_input_audio_token_cost": {"type": "number"},
                 "cache_read_input_token_cost_per_audio_token": {"type": "number"},
                 "cache_read_input_image_token_cost": {"type": "number"},
+                "audio_transcription_config": {"type": "string"},
                 "deprecation_date": {"type": "string"},
                 "input_cost_per_audio_per_second": {"type": "number"},
                 "input_cost_per_audio_per_second_above_128k_tokens": {"type": "number"},
@@ -754,6 +755,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "input_dbu_cost_per_token": {"type": "number"},
                 "annotation_cost_per_page": {"type": "number"},
                 "ocr_cost_per_page": {"type": "number"},
+                "ocr_cost_per_credit": {"type": "number"},
                 "code_interpreter_cost_per_session": {"type": "number"},
                 "inference_geo": {"type": "string"},
                 "litellm_provider": {"type": "string"},
@@ -855,6 +857,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "supports_adaptive_thinking": {"type": "boolean"},
                 "supports_service_tier": {"type": "boolean"},
                 "supports_preset": {"type": "boolean"},
+                    "supports_output_config": {"type": "boolean"},
                 "tool_use_system_prompt_tokens": {"type": "number"},
                 "tpm": {"type": "number"},
                 "provider_specific_entry": {"type": "object"},
@@ -1138,6 +1141,34 @@ def test_check_provider_match():
     assert litellm.utils._check_provider_match(model_info, "openai") is False
 
 
+def test_check_provider_match_none_value_matches_any_provider():
+    """
+    A ``litellm_provider`` of None must be treated the same as a missing
+    key: both mean "no provider constraint" and should match any
+    ``custom_llm_provider``.
+
+    Regression test for https://github.com/BerriAI/litellm/issues/28336.
+    Before the fix, ``register_model`` persisted ``litellm_provider: None``
+    via ``get_model_info`` for deployments registered without a provider
+    (e.g. ``Router.add_deployment``), which caused ``_check_provider_match``
+    to drop custom pricing intermittently.
+    """
+    # Missing key already returned True; None must behave identically.
+    assert litellm.utils._check_provider_match({}, "openai") is True
+    assert (
+        litellm.utils._check_provider_match({"litellm_provider": None}, "openai")
+        is True
+    )
+    assert (
+        litellm.utils._check_provider_match({"litellm_provider": None}, "anthropic")
+        is True
+    )
+    # When custom_llm_provider is also None nothing constrains the match.
+    assert (
+        litellm.utils._check_provider_match({"litellm_provider": None}, None) is True
+    )
+
+
 def test_get_provider_rerank_config():
     """
     Test the get_provider_rerank_config function for various providers
diff --git a/tests/test_spend_logs.py b/tests/test_spend_logs.py
index 8aec1d5cc60..c575fa07551 100644
--- a/tests/test_spend_logs.py
+++ b/tests/test_spend_logs.py
@@ -100,6 +100,9 @@ async def get_spend_logs(session, request_id=None, api_key=None):
         return await response.json()
 
 
+@pytest.mark.skip(
+    reason="Flaky in CI: /spend/logs?request_id=... returns 500 even after a 20s wait for the spend log to be written. Spend-log accuracy is covered by tests/test_litellm/proxy/spend_tracking/ and the proxy_spend_accuracy_tests CircleCI job."
+)
 @pytest.mark.asyncio
 async def test_spend_logs():
     """
@@ -155,6 +158,9 @@ async def generate_team(session: aiohttp.ClientSession, org_id: str) -> dict:
         return await response.json()
 
 
+@pytest.mark.skip(
+    reason="Flaky in CI: /spend/logs?request_id=... returns 500 even after a 20s wait for the spend log to be written. Same write-then-read race against the spend logs DB as test_spend_logs. Spend-log accuracy is covered by tests/test_litellm/proxy/spend_tracking/ and the proxy_spend_accuracy_tests CircleCI job."
+)
 @pytest.mark.asyncio
 async def test_spend_logs_with_org_id():
     """
diff --git a/tests/test_team_members.py b/tests/test_team_members.py
index a3d64eae803..4cf85af6410 100644
--- a/tests/test_team_members.py
+++ b/tests/test_team_members.py
@@ -136,6 +136,9 @@ def test_add_single_member(api_client, new_team):
     ), f"Team size did not increase by 1 (was {initial_size}, now {updated_size})"
 
 
+@pytest.mark.skip(
+    reason="Flaky in CI: /team/info?team_id=... intermittently returns 404/400 mid-loop after add_team_member calls. Single-member coverage in test_add_single_member is sufficient; team-member CRUD is also covered by tests/test_litellm/proxy/management_endpoints/."
+)
 def test_add_multiple_members(api_client, new_team):
     """Test adding multiple members to a new team"""
     # Get initial team size
@@ -203,6 +206,9 @@ def test_error_handling(api_client):
         api_client.get_team_info("invalid-team-id")
 
 
+@pytest.mark.skip(
+    reason="Flaky in CI: /team/info?team_id=... intermittently returns 404 after add_team_member calls, same race documented for test_add_multiple_members. Duplicate-prevention is covered by test_update_team_members_list_duplicate_prevention in tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py."
+)
 def test_duplicate_user_addition(api_client, new_team):
     """Test that adding the same user twice is handled appropriately"""
     # Add user first time
diff --git a/tests/unified_google_tests/conftest.py b/tests/unified_google_tests/conftest.py
index d28f89a77b0..5b4f57b8036 100644
--- a/tests/unified_google_tests/conftest.py
+++ b/tests/unified_google_tests/conftest.py
@@ -12,14 +12,17 @@ sys.path.insert(
 )  # Adds the parent directory to the system path
 import litellm  # noqa: E402,F401
 
-from tests._vcr_conftest_common import (  # noqa: E402
+from tests._vcr_conftest_common import (  # noqa: E402,F401
     VerboseReporterState,
+    _pin_multipart_boundary,
     apply_vcr_auto_marker_to_items,
     emit_cassette_cache_session_banner,
     emit_vcr_classification_summary,
+    emit_vcr_diagnostic_log,
     install_live_call_probe,
     record_vcr_outcome,
     register_persister_if_enabled,
+    reset_vcr_diag_dir,
     vcr_config_dict,
 )
 
@@ -84,6 +87,7 @@ def _vcr_outcome_gate(request, vcr):
 
 def pytest_configure(config):
     _verbose_state.remember_pluginmanager(config)
+    reset_vcr_diag_dir()
 
 
 def pytest_runtest_logreport(report):
@@ -110,3 +114,4 @@ def pytest_collection_modifyitems(config, items):
 def pytest_terminal_summary(terminalreporter, exitstatus, config):
     emit_cassette_cache_session_banner(terminalreporter)
     emit_vcr_classification_summary(terminalreporter)
+    emit_vcr_diagnostic_log(terminalreporter)
diff --git a/ui/litellm-dashboard/e2e_tests/playwright.config.ts b/ui/litellm-dashboard/e2e_tests/playwright.config.ts
index ec4d3a6ddb0..6964fe52a14 100644
--- a/ui/litellm-dashboard/e2e_tests/playwright.config.ts
+++ b/ui/litellm-dashboard/e2e_tests/playwright.config.ts
@@ -28,6 +28,11 @@ export default defineConfig({
     /* Action timeout for clicks, fills, waitForSelector, etc. */
     actionTimeout: 15 * 1000,
     navigationTimeout: 30 * 1000,
+
+    /* Slow down actions when SLOWMO=<ms> is set, useful for headed local debugging */
+    launchOptions: {
+      slowMo: process.env.SLOWMO ? (parseInt(process.env.SLOWMO, 10) || 0) : 0,
+    },
   },
 
   /* Configure projects for major browsers */
diff --git a/ui/litellm-dashboard/e2e_tests/run_e2e.sh b/ui/litellm-dashboard/e2e_tests/run_e2e.sh
index 4e3a47edfbd..36619dce9b2 100755
--- a/ui/litellm-dashboard/e2e_tests/run_e2e.sh
+++ b/ui/litellm-dashboard/e2e_tests/run_e2e.sh
@@ -15,7 +15,7 @@ set -euo pipefail
 # In CI (CI=true), expects:
 #   - PostgreSQL already running on 127.0.0.1:5432
 #   - DATABASE_URL already set
-#   - Python/Poetry already installed
+#   - Python/uv already installed
 #   - Node.js/npx already available
 # ================================================================
 
@@ -48,7 +48,7 @@ cleanup() {
 trap cleanup EXIT INT TERM
 
 # --- Pre-flight checks ---
-for cmd in python3 npx poetry; do
+for cmd in python3 npx uv; do
   command -v "$cmd" >/dev/null 2>&1 || { echo "Error: $cmd not found."; exit 1; }
 done
 
@@ -95,6 +95,10 @@ export DISABLE_SCHEMA_UPDATE="true"
 export SERVER_ROOT_PATH=""
 # Prevent logout from redirecting to an external URL
 export PROXY_LOGOUT_URL=""
+# Forward LITELLM_LICENSE if set in the outer env so premium-gated UI flows
+# (e.g. Team-BYOK Model switch) can be exercised. Tests that depend on a
+# premium proxy gate themselves on process.env.LITELLM_LICENSE.
+export LITELLM_LICENSE="${LITELLM_LICENSE:-}"
 
 # --- Rebuild UI from source ---
 echo "=== Building UI from source ==="
@@ -117,19 +121,15 @@ echo "UI build copied and restructured"
 # --- Python environment ---
 echo "=== Setting up Python environment ==="
 cd "$REPO_ROOT"
-if ! poetry run python3 -c "import prisma" 2>/dev/null; then
-  echo "Installing Python dependencies (first run)..."
-  poetry install --with dev,proxy-dev --extras "proxy" --quiet
-  poetry run pip install nodejs-wheel-binaries 2>/dev/null || true
-  poetry run prisma generate --schema litellm/proxy/schema.prisma
-fi
+uv sync --group dev --group proxy-dev --extra proxy --frozen --quiet
+uv run --no-sync python -m prisma generate --schema litellm/proxy/schema.prisma
 
 echo "=== Pushing Prisma schema to database ==="
-poetry run prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss
+uv run --no-sync python -m prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss
 
 # --- Mock LLM server ---
 echo "=== Starting mock LLM server ==="
-poetry run python3 "$SCRIPT_DIR/fixtures/mock_llm_server/server.py" &
+uv run --no-sync python "$SCRIPT_DIR/fixtures/mock_llm_server/server.py" &
 MOCK_PID=$!
 
 for i in $(seq 1 15); do
@@ -140,7 +140,7 @@ done
 # --- LiteLLM proxy ---
 echo "=== Starting LiteLLM proxy ==="
 cd "$REPO_ROOT"
-poetry run python3 -m litellm.proxy.proxy_cli \
+uv run --no-sync python -m litellm.proxy.proxy_cli \
   --config "$SCRIPT_DIR/fixtures/config.yml" \
   --port 4000 &
 PROXY_PID=$!
diff --git a/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/keys.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/keys.spec.ts
index 14ceb1a4a6b..1e44d9a25a0 100644
--- a/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/keys.spec.ts
+++ b/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/keys.spec.ts
@@ -126,4 +126,84 @@ test.describe("Proxy Admin - Keys", () => {
 
     await expect(page.getByText(E2E_INTERNAL_USER_KEY_ALIAS)).toBeVisible({ timeout: 10_000 });
   });
+
+  test("Create a key with All Proxy Models (no team)", async ({ page }) => {
+    await navigateToPage(page, Page.ApiKeys);
+    await dismissFeedbackPopup(page);
+
+    await page.getByRole("button", { name: /Create New Key/i }).click();
+
+    await expect(page.getByText("Key Ownership")).toBeVisible({ timeout: 10_000 });
+
+    const keyName = `e2e-admin-allproxy-${Date.now()}`;
+    await page.getByTestId("base-input").fill(keyName);
+
+    // No team selection — leave team dropdown empty so the key is owned by the admin user
+
+    // Select models — open the multi-select and pick the all-models meta-option.
+    // The Create Key modal labels this "All Team Models" even when no team is selected
+    // (see src/components/organisms/create_key_button.tsx:944), unlike the team/user
+    // settings screens which use "All Proxy Models".
+    await page.locator(".ant-select-selection-overflow").click();
+    await page.locator(".ant-select-dropdown:visible").getByText("All Team Models").click();
+    await page.keyboard.press("Escape");
+
+    await page.getByRole("button", { name: "Create Key", exact: true }).click();
+
+    await expect(page.getByText("Save your Key")).toBeVisible({ timeout: 10_000 });
+    await page.keyboard.press("Escape");
+
+    await expect(page.getByText(keyName)).toBeVisible({ timeout: 10_000 });
+  });
+
+  test("Create a key with a specific proxy model (no team)", async ({ page }) => {
+    await navigateToPage(page, Page.ApiKeys);
+    await dismissFeedbackPopup(page);
+
+    await page.getByRole("button", { name: /Create New Key/i }).click();
+
+    await expect(page.getByText("Key Ownership")).toBeVisible({ timeout: 10_000 });
+
+    const keyName = `e2e-admin-specific-${Date.now()}`;
+    await page.getByTestId("base-input").fill(keyName);
+
+    // Open the model multi-select and pick a single specific model. Use
+    // getByRole("option", ...) to avoid the strict-mode collision between
+    // the option container and its inner text node.
+    const modelName = "fake-openai-gpt-4";
+    await page.locator(".ant-select-selection-overflow").click();
+    const option = page.locator(".ant-select-dropdown:visible").getByRole("option", { name: modelName, exact: true });
+    await option.waitFor({ state: "attached" });
+    // Dispatch the click via the DOM — antd's dropdown can render the option
+    // off-viewport during the open animation, which trips Playwright's
+    // visibility/stability checks. The click handler fires regardless.
+    await option.evaluate((el: HTMLElement) => el.click());
+    await page.keyboard.press("Escape");
+
+    await page.getByRole("button", { name: "Create Key", exact: true }).click();
+
+    await expect(page.getByText("Save your Key")).toBeVisible({ timeout: 10_000 });
+
+    // Grab the new key from the success modal (rendered inside a <pre>) and
+    // verify it can call /chat/completions for the model it was scoped to.
+    // The mock LLM server (fixtures/mock_llm_server/server.py) replies with
+    // a fixed "This is a mock response." body.
+    const apiKey = (await page.locator(".ant-modal:visible pre").innerText()).trim();
+    expect(apiKey).toMatch(/^sk-/);
+
+    const response = await page.request.post("/chat/completions", {
+      headers: { Authorization: `Bearer ${apiKey}` },
+      data: {
+        model: modelName,
+        messages: [{ role: "user", content: "ping" }],
+      },
+    });
+    expect(response.status()).toBe(200);
+    const body = await response.json();
+    expect(body.choices?.[0]?.message?.content).toBe("This is a mock response.");
+
+    await page.keyboard.press("Escape");
+
+    await expect(page.getByText(keyName)).toBeVisible({ timeout: 10_000 });
+  });
 });
diff --git a/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/license.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/license.spec.ts
new file mode 100644
index 00000000000..579b3cede7c
--- /dev/null
+++ b/ui/litellm-dashboard/e2e_tests/tests/proxy-admin/license.spec.ts
@@ -0,0 +1,37 @@
+import { test, expect } from "@playwright/test";
+import * as fs from "fs";
+import { ADMIN_STORAGE_PATH } from "../../constants";
+
+/**
+ * Sanity check that LITELLM_LICENSE is being forwarded to the proxy when set
+ * in the environment (e.g. CircleCI's `e2e_ui_testing` job). The login JWT's
+ * `premium_user` claim is the same value the dashboard reads to enable
+ * premium-gated UI surfaces (Team-BYOK switch, etc.), so asserting it here
+ * catches any future regression where the env var stops being plumbed
+ * through `run_e2e.sh` / `.circleci/config.yml`.
+ *
+ * Skips locally when no license is configured.
+ */
+test.describe("Premium license wiring", () => {
+  test("admin session JWT carries premium_user=true when LITELLM_LICENSE is set", () => {
+    test.skip(
+      !process.env.LITELLM_LICENSE,
+      "LITELLM_LICENSE not set in test env — proxy is running unlicensed",
+    );
+
+    const storage = JSON.parse(fs.readFileSync(ADMIN_STORAGE_PATH, "utf-8"));
+    const tokenCookie = storage.cookies?.find((c: { name: string }) => c.name === "token");
+    expect(tokenCookie, "token cookie missing from admin storage state").toBeDefined();
+
+    // Decode the JWT payload (no signature check — we trust globalSetup ran
+    // against our own proxy). Payload is the middle base64url segment.
+    const jwtParts = tokenCookie.value.split(".");
+    expect(jwtParts.length, "token cookie is not a 3-part JWT").toBe(3);
+    const [, payloadB64] = jwtParts;
+    const payload = JSON.parse(
+      Buffer.from(payloadB64, "base64url").toString("utf-8"),
+    );
+
+    expect(payload.premium_user).toBe(true);
+  });
+});
diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json
index e17cf023012..97bc797fd54 100644
--- a/ui/litellm-dashboard/package-lock.json
+++ b/ui/litellm-dashboard/package-lock.json
@@ -23,7 +23,7 @@
         "jwt-decode": "4.0.0",
         "lucide-react": "0.513.0",
         "moment": "2.30.1",
-        "next": "16.2.4",
+        "next": "16.2.6",
         "openai": "4.104.0",
         "papaparse": "5.5.3",
         "react": "18.3.1",
@@ -1883,9 +1883,9 @@
       }
     },
     "node_modules/@next/env": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/env/-/env-16.2.4.tgz",
-      "integrity": "sha512-dKkkOzOSwFYe5RX6y26fZgkSpVAlIOJKQHIiydQcrWH6y/97+RceSOAdjZ14Qa3zLduVUy0TXcn+EiM6t4rPgw==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/env/-/env-16.2.6.tgz",
+      "integrity": "sha512-gd8HoHN4ufj73WmR3JmVolrpJR47ILK6LouP5xElPglaVxir6e1a7VzvTvDWkOoPXT9rkkTzyCxBu4yeZfZwcw==",
       "license": "MIT"
     },
     "node_modules/@next/eslint-plugin-next": {
@@ -1899,9 +1899,9 @@
       }
     },
     "node_modules/@next/swc-darwin-arm64": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-16.2.4.tgz",
-      "integrity": "sha512-OXTFFox5EKN1Ym08vfrz+OXxmCcEjT4SFMbNRsWZE99dMqt2Kcusl5MqPXcW232RYkMLQTy0hqgAMEsfEd/l2A==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-16.2.6.tgz",
+      "integrity": "sha512-ZJGkkcNfYgrrMkqOdZ7zoLa1TOy0qpcMfk/z4Mh/FKUz40gVO+HNQWqmLxf67Z5WB64DRp0dhEbyHfel+6sJUg==",
       "cpu": [
         "arm64"
       ],
@@ -1915,9 +1915,9 @@
       }
     },
     "node_modules/@next/swc-darwin-x64": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-16.2.4.tgz",
-      "integrity": "sha512-XhpVnUfmYWvD3YrXu55XdcAkQtOnvaI6wtQa8fuF5fGoKoxIUZ0kWPtcOfqJEWngFF/lOS9l3+O9CcownhiQxQ==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-16.2.6.tgz",
+      "integrity": "sha512-v/YLBHIY132Ced3puBJ7YJKw1lqsCrgcNo2aRJlCEyQrrCeRJlvGlnmxhPxNQI3KE3N1DN5r9TPNPvka3nq5RQ==",
       "cpu": [
         "x64"
       ],
@@ -1931,9 +1931,9 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-gnu": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-16.2.4.tgz",
-      "integrity": "sha512-Mx/tjlNA3G8kg14QvuGAJ4xBwPk1tUHq56JxZ8CXnZwz1Etz714soCEzGQQzVMz4bEnGPowzkV6Xrp6wAkEWOQ==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-16.2.6.tgz",
+      "integrity": "sha512-RPOvqlYBbcQjkz9VQQDZ2T2bARIjXZV1KFlt+V2Mr6SW/e4I9fcKsaA0hdyf2FHoTlsV2xnBd5Y912rP/1Ce6w==",
       "cpu": [
         "arm64"
       ],
@@ -1947,9 +1947,9 @@
       }
     },
     "node_modules/@next/swc-linux-arm64-musl": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-16.2.4.tgz",
-      "integrity": "sha512-iVMMp14514u7Nup2umQS03nT/bN9HurK8ufylC3FZNykrwjtx7V1A7+4kvhbDSCeonTVqV3Txnv0Lu+m2oDXNg==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-16.2.6.tgz",
+      "integrity": "sha512-URUTu1+dMkxJsPFgm+OeEvq9wf5sujw0EvgYy80TDGHTSLTnIHeqb0Eu8A3sC95IRgjejQL+kC4mw+4yPxiAXA==",
       "cpu": [
         "arm64"
       ],
@@ -1963,9 +1963,9 @@
       }
     },
     "node_modules/@next/swc-linux-x64-gnu": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-16.2.4.tgz",
-      "integrity": "sha512-EZOvm1aQWgnI/N/xcWOlnS3RQBk0VtVav5Zo7n4p0A7UKyTDx047k8opDbXgBpHl4CulRqRfbw3QrX2w5UOXMQ==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-16.2.6.tgz",
+      "integrity": "sha512-DOj182mPV8G3UkrayLoREM5YEYI+Dk5wv7Ox9xl1fFibAELEsFD0lDPfHIeILlutMMfdyhlzYPELG3peuKaurw==",
       "cpu": [
         "x64"
       ],
@@ -1979,9 +1979,9 @@
       }
     },
     "node_modules/@next/swc-linux-x64-musl": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-16.2.4.tgz",
-      "integrity": "sha512-h9FxsngCm9cTBf71AR4fGznDEDx1hS7+kSEiIRjq5kO1oXWm07DxVGZjCvk0SGx7TSjlUqhI8oOyz7NfwAdPoA==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-16.2.6.tgz",
+      "integrity": "sha512-HKQ5SP/V/ub73UvF7n/zeJlxk2kLmtL7Wzrg4WfmkjmNos5onJ2tKu7yZOPdL18A6Svfn3max29ym+ry7NkK4g==",
       "cpu": [
         "x64"
       ],
@@ -1995,9 +1995,9 @@
       }
     },
     "node_modules/@next/swc-win32-arm64-msvc": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-16.2.4.tgz",
-      "integrity": "sha512-3NdJV5OXMSOeJYijX+bjaLge3mJBlh4ybydbT4GFoB/2hAojWHtMhl3CYlYoMrjPuodp0nzFVi4Tj2+WaMg+Ow==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-16.2.6.tgz",
+      "integrity": "sha512-LZXpTlPyS5v7HhSmnvsLGP3iIYgYOBnc8r8ArlT55sGHV89bR2HlDdBjWQ+PY6SJMmk8TuVGFuxalnP3k/0Dwg==",
       "cpu": [
         "arm64"
       ],
@@ -2011,9 +2011,9 @@
       }
     },
     "node_modules/@next/swc-win32-x64-msvc": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-16.2.4.tgz",
-      "integrity": "sha512-kMVGgsqhO5YTYODD9IPGGhA6iprWidQckK3LmPeW08PIFENRmgfb4MjXHO+p//d+ts2rpjvK5gXWzXSMrPl9cw==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-16.2.6.tgz",
+      "integrity": "sha512-F0+4i0h9J6C4eE3EAPWsoCk7UW/dbzOjyzxY0qnDUOYFu6FFmdZ6l97/XdV3/Nz3VYyO7UWjyEJUXkGqcoXfMA==",
       "cpu": [
         "x64"
       ],
@@ -9304,12 +9304,12 @@
       "license": "MIT"
     },
     "node_modules/next": {
-      "version": "16.2.4",
-      "resolved": "https://registry.npmjs.org/next/-/next-16.2.4.tgz",
-      "integrity": "sha512-kPvz56wF5frc+FxlHI5qnklCzbq53HTwORaWBGdT0vNoKh1Aya9XC8aPauH4NJxqtzbWsS5mAbctm4cr+EkQ2Q==",
+      "version": "16.2.6",
+      "resolved": "https://registry.npmjs.org/next/-/next-16.2.6.tgz",
+      "integrity": "sha512-qOVgKJg1+At15NpeUP+eJgCHvTCgXsogweq87Ri/Ix7PkqQHg4sdaXmSFqKlgaIXE4kW0g25LE68W87UANlHtw==",
       "license": "MIT",
       "dependencies": {
-        "@next/env": "16.2.4",
+        "@next/env": "16.2.6",
         "@swc/helpers": "0.5.15",
         "baseline-browser-mapping": "^2.9.19",
         "caniuse-lite": "^1.0.30001579",
@@ -9323,14 +9323,14 @@
         "node": ">=20.9.0"
       },
       "optionalDependencies": {
-        "@next/swc-darwin-arm64": "16.2.4",
-        "@next/swc-darwin-x64": "16.2.4",
-        "@next/swc-linux-arm64-gnu": "16.2.4",
-        "@next/swc-linux-arm64-musl": "16.2.4",
-        "@next/swc-linux-x64-gnu": "16.2.4",
-        "@next/swc-linux-x64-musl": "16.2.4",
-        "@next/swc-win32-arm64-msvc": "16.2.4",
-        "@next/swc-win32-x64-msvc": "16.2.4",
+        "@next/swc-darwin-arm64": "16.2.6",
+        "@next/swc-darwin-x64": "16.2.6",
+        "@next/swc-linux-arm64-gnu": "16.2.6",
+        "@next/swc-linux-arm64-musl": "16.2.6",
+        "@next/swc-linux-x64-gnu": "16.2.6",
+        "@next/swc-linux-x64-musl": "16.2.6",
+        "@next/swc-win32-arm64-msvc": "16.2.6",
+        "@next/swc-win32-x64-msvc": "16.2.6",
         "sharp": "^0.34.5"
       },
       "peerDependencies": {
diff --git a/ui/litellm-dashboard/package.json b/ui/litellm-dashboard/package.json
index 32c00ac62a8..72b9bc2a159 100644
--- a/ui/litellm-dashboard/package.json
+++ b/ui/litellm-dashboard/package.json
@@ -35,7 +35,7 @@
     "jwt-decode": "4.0.0",
     "lucide-react": "0.513.0",
     "moment": "2.30.1",
-    "next": "16.2.4",
+    "next": "16.2.6",
     "openai": "4.104.0",
     "papaparse": "5.5.3",
     "react": "18.3.1",
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx
index 5431c196883..2626ace86d5 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AllModelsTab.tsx
@@ -7,7 +7,7 @@ import { columns } from "@/components/molecules/models/columns";
 import { getDisplayModelName } from "@/components/view_model/model_name_display";
 import DeleteResourceModal from "@/components/common_components/DeleteResourceModal";
 import NotificationsManager from "@/components/molecules/notifications_manager";
-import { modelDeleteCall } from "@/components/networking";
+import { modelDeleteCall, modelPatchUpdateCall } from "@/components/networking";
 import { InfoCircleOutlined, SettingOutlined } from "@ant-design/icons";
 import { PaginationState, SortingState } from "@tanstack/react-table";
 import { useQueryClient } from "@tanstack/react-query";
@@ -220,6 +220,25 @@ const AllModelsTab = ({
     }
   };
 
+  const [pausingModelId, setPausingModelId] = useState<string | null>(null);
+
+  const handleTogglePause = async (modelId: string, blocked: boolean) => {
+    if (!accessToken) return;
+    try {
+      setPausingModelId(modelId);
+      await modelPatchUpdateCall(accessToken, { blocked }, modelId);
+      NotificationsManager.success(blocked ? "Model paused" : "Model resumed");
+      // invalidateQueries already schedules a refetch for active observers
+      // on this key — no need to also call refetchModels() (would double-fetch).
+      queryClient.invalidateQueries({ queryKey: ["models", "list"] });
+    } catch (error) {
+      console.error("Error toggling model pause state:", error);
+      NotificationsManager.fromBackend(error);
+    } finally {
+      setPausingModelId(null);
+    }
+  };
+
   return (
     <TabPanel>
       <Grid>
@@ -536,6 +555,8 @@ const AllModelsTab = ({
                 expandedRows,
                 setExpandedRows,
                 setDeleteModalModelId,
+                handleTogglePause,
+                pausingModelId,
               )}
               data={filteredData}
               isLoading={isLoadingModelsInfo}
diff --git a/ui/litellm-dashboard/src/app/mcp/oauth/callback/page.tsx b/ui/litellm-dashboard/src/app/mcp/oauth/callback/page.tsx
index 0539d6d8f19..3b3729c1ac9 100644
--- a/ui/litellm-dashboard/src/app/mcp/oauth/callback/page.tsx
+++ b/ui/litellm-dashboard/src/app/mcp/oauth/callback/page.tsx
@@ -4,11 +4,12 @@ import { Suspense, useEffect, useMemo } from "react";
 import { useSearchParams } from "next/navigation";
 import { getSecureItem, setSecureItem } from "@/utils/secureStorage";
 
-// Written to sessionStorage so both the admin hook (useMcpOAuthFlow) and the
-// user hook (useUserMcpOAuthFlow) can pick up the result.  Each hook reads
-// its own namespace to avoid cross-flow collisions.
+// Written to sessionStorage so the admin hook (useMcpOAuthFlow), the user hook
+// (useUserMcpOAuthFlow), and the tools re-auth hook (useToolsOAuthFlow) can each
+// pick up the result.  Each hook reads its own namespace to avoid cross-flow collisions.
 const ADMIN_RESULT_KEY = "litellm-mcp-oauth-result";
 const USER_RESULT_KEY = "litellm-user-mcp-oauth-result";
+const TOOLS_RESULT_KEY = "litellm-tools-mcp-oauth-result";
 const RETURN_URL_STORAGE_KEY = "litellm-mcp-oauth-return-url";
 
 const resolveDefaultRedirect = () => {
@@ -50,11 +51,12 @@ const McpOAuthCallbackContent = () => {
     }
 
     try {
-      // Write to both namespace keys (admin and user) so whichever hook is
-      // active can consume the result.  sessionStorage only — no localStorage.
+      // Write to all namespace keys so whichever hook is active can consume
+      // the result.  sessionStorage only — no localStorage.
       const serialized = JSON.stringify(payload);
       setSecureItem(ADMIN_RESULT_KEY, serialized);
       setSecureItem(USER_RESULT_KEY, serialized);
+      setSecureItem(TOOLS_RESULT_KEY, serialized);
     } catch (err) {
       // Silently ignore storage errors
     }
diff --git a/ui/litellm-dashboard/src/components/OldTeams.tsx b/ui/litellm-dashboard/src/components/OldTeams.tsx
index b9305e4723a..da00ad911b0 100644
--- a/ui/litellm-dashboard/src/components/OldTeams.tsx
+++ b/ui/litellm-dashboard/src/components/OldTeams.tsx
@@ -45,6 +45,7 @@ import OrganizationDropdown from "./common_components/OrganizationDropdown";
 import TableIconActionButton from "./common_components/IconActionButton/TableIconActionButtons/TableIconActionButton";
 import { teamListCall as v2TeamListCall, type TeamsResponse } from "@/app/(dashboard)/hooks/teams/useTeams";
 import AccessGroupSelector from "./common_components/AccessGroupSelector";
+import PassThroughRoutesSelector from "./common_components/PassThroughRoutesSelector";
 import AgentSelector from "./agent_management/AgentSelector";
 import ModelAliasManager from "./common_components/ModelAliasManager";
 import PremiumLoggingSettings from "./common_components/PremiumLoggingSettings";
@@ -1446,6 +1447,30 @@ const Teams: React.FC<TeamProps> = ({
                           placeholder="Select vector stores (optional)"
                         />
                       </Form.Item>
+                      <Form.Item
+                        label="Allowed Pass Through Routes"
+                        name="allowed_passthrough_routes"
+                        className="mt-8"
+                      >
+                        <Tooltip
+                          title={
+                            !premiumUser
+                              ? "Premium feature - Upgrade to set allowed pass through routes"
+                              : !isProxyAdminRole(userRole || "")
+                                ? "Only proxy admins can set allowed pass through routes"
+                                : ""
+                          }
+                          placement="top"
+                        >
+                          <PassThroughRoutesSelector
+                            onChange={(values: string[]) => form.setFieldValue("allowed_passthrough_routes", values)}
+                            value={form.getFieldValue("allowed_passthrough_routes")}
+                            accessToken={accessToken || ""}
+                            placeholder="Select pass through routes (optional)"
+                            disabled={!premiumUser || !isProxyAdminRole(userRole || "")}
+                          />
+                        </Tooltip>
+                      </Form.Item>
                     </AccordionBody>
                   </Accordion>
 
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx
index 50dd5e78f37..41dfdb21d1b 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx
@@ -654,12 +654,7 @@ const UsagePage: React.FC<UsagePageProps> = ({ teams, organizations }) => {
                               <Card>
                                 <Title>Input Tokens</Title>
                                 <Text className="text-2xl font-bold mt-2 text-blue-600">
-                                  {Math.max(
-                                    0,
-                                    (userSpendData.metadata?.total_prompt_tokens || 0) -
-                                      (userSpendData.metadata?.total_cache_read_input_tokens || 0) -
-                                      (userSpendData.metadata?.total_cache_creation_input_tokens || 0)
-                                  ).toLocaleString()}
+                                  {(userSpendData.metadata?.total_prompt_tokens || 0).toLocaleString()}
                                 </Text>
                               </Card>
                               <Card>
diff --git a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx
index 8ae90c1cbbc..554fe86bf66 100644
--- a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx
@@ -52,6 +52,8 @@ const AdvancedSettings: React.FC<AdvancedSettingsProps> = ({
       form.setFieldsValue({
         input_cost_per_token: undefined,
         output_cost_per_token: undefined,
+        cache_read_input_token_cost: undefined,
+        cache_creation_input_token_cost: undefined,
         input_cost_per_second: undefined,
       });
     }
@@ -211,6 +213,24 @@ const AdvancedSettings: React.FC<AdvancedSettingsProps> = ({
                     >
                       <TextInput />
                     </Form.Item>
+                    <Form.Item
+                      label="Cache Read Cost (per 1M tokens)"
+                      name="cache_read_input_token_cost"
+                      rules={[{ validator: validateNumber }]}
+                      tooltip="If left blank, defaults to Input Cost."
+                      className="mb-4"
+                    >
+                      <TextInput placeholder="Defaults to Input Cost if blank" />
+                    </Form.Item>
+                    <Form.Item
+                      label="Cache Write Cost (per 1M tokens)"
+                      name="cache_creation_input_token_cost"
+                      rules={[{ validator: validateNumber }]}
+                      tooltip="If left blank, defaults to Input Cost (the backend falls back to input_cost_per_token when no cache-write rate is set)."
+                      className="mb-4"
+                    >
+                      <TextInput placeholder="Defaults to Input Cost if blank" />
+                    </Form.Item>
                   </>
                 ) : (
                   <Form.Item
diff --git a/ui/litellm-dashboard/src/components/add_model/handle_add_model_submit.tsx b/ui/litellm-dashboard/src/components/add_model/handle_add_model_submit.tsx
index 5137a302dd5..8dc8dfbaeee 100644
--- a/ui/litellm-dashboard/src/components/add_model/handle_add_model_submit.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/handle_add_model_submit.tsx
@@ -45,6 +45,39 @@ export const prepareModelAddRequest = async (formValues: Record<string, any>, ac
       if (formValues.output_cost_per_token !== undefined && formValues.output_cost_per_token !== null && formValues.output_cost_per_token !== "") {
         formValues.output_cost_per_token = Number(formValues.output_cost_per_token) / 1000000;
       }
+
+      // Cache Read Cost: if blank, default to Input Cost (already token-unit converted above)
+      if (
+        formValues.cache_read_input_token_cost !== undefined &&
+        formValues.cache_read_input_token_cost !== null &&
+        formValues.cache_read_input_token_cost !== ""
+      ) {
+        formValues.cache_read_input_token_cost =
+          Number(formValues.cache_read_input_token_cost) / 1000000;
+      } else if (
+        formValues.input_cost_per_token !== undefined &&
+        formValues.input_cost_per_token !== null &&
+        formValues.input_cost_per_token !== ""
+      ) {
+        formValues.cache_read_input_token_cost = Number(formValues.input_cost_per_token);
+      } else {
+        delete formValues.cache_read_input_token_cost;
+      }
+
+      // Cache Write Cost: explicit value if provided, else leave unset so the
+      // backend keeps the model-level default (per-second pricing, model_prices
+      // entries, etc.). Sending 0 here would overwrite that default.
+      // The backend falls back to input_cost_per_token when this key is absent.
+      if (
+        formValues.cache_creation_input_token_cost !== undefined &&
+        formValues.cache_creation_input_token_cost !== null &&
+        formValues.cache_creation_input_token_cost !== ""
+      ) {
+        formValues.cache_creation_input_token_cost =
+          Number(formValues.cache_creation_input_token_cost) / 1000000;
+      } else {
+        delete formValues.cache_creation_input_token_cost;
+      }
       // Keep input_cost_per_second as is, no conversion needed
 
       // Iterate through the key-value pairs in formValues
@@ -119,7 +152,13 @@ export const prepareModelAddRequest = async (formValues: Record<string, any>, ac
         }
 
         // Handle the pricing fields
-        else if (key === "input_cost_per_token" || key === "output_cost_per_token" || key === "input_cost_per_second") {
+        else if (
+          key === "input_cost_per_token" ||
+          key === "output_cost_per_token" ||
+          key === "input_cost_per_second" ||
+          key === "cache_read_input_token_cost" ||
+          key === "cache_creation_input_token_cost"
+        ) {
           if (value !== undefined && value !== null && value !== "") {
             litellmParamsObj[key] = Number(value);
           }
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx b/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx
index 7a592785a44..e98226b86bc 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/ToolTestPanel.tsx
@@ -182,16 +182,18 @@ export function ToolTestPanel({
 
     Object.entries(values).forEach(([key, value]) => {
       const prop = schemaToUse.properties?.[key];
-      if (prop && value !== null && value !== undefined && value !== "") {
+      // Strip leading/trailing whitespace from string inputs before submitting
+      const normalizedValue = typeof value === "string" ? value.trim() : value;
+      if (prop && normalizedValue !== null && normalizedValue !== undefined && normalizedValue !== "") {
         switch (prop.type) {
           case "boolean":
-            convertedValues[key] = value === "true" || value === true;
+            convertedValues[key] = normalizedValue === "true" || normalizedValue === true;
             break;
           case "number":
           case "integer": {
-            const numericValue = Number(value);
+            const numericValue = Number(normalizedValue);
             convertedValues[key] = Number.isNaN(numericValue)
-              ? value
+              ? normalizedValue
               : prop.type === "integer"
                 ? Math.trunc(numericValue)
                 : numericValue;
@@ -200,28 +202,28 @@ export function ToolTestPanel({
           case "object":
           case "array": {
             try {
-              const parsed = typeof value === "string" ? JSON.parse(value) : value;
+              const parsed = typeof normalizedValue === "string" ? JSON.parse(normalizedValue) : normalizedValue;
               const isValidObject =
                 prop.type === "object" && parsed !== null && typeof parsed === "object" && !Array.isArray(parsed);
               const isValidArray = prop.type === "array" && Array.isArray(parsed);
               if ((prop.type === "object" && isValidObject) || (prop.type === "array" && isValidArray)) {
                 convertedValues[key] = parsed;
               } else {
-                convertedValues[key] = value;
+                convertedValues[key] = normalizedValue;
               }
             } catch (err) {
-              convertedValues[key] = value;
+              convertedValues[key] = normalizedValue;
             }
             break;
           }
           case "string":
-            convertedValues[key] = String(value);
+            convertedValues[key] = String(normalizedValue);
             break;
           default:
-            convertedValues[key] = value;
+            convertedValues[key] = normalizedValue;
         }
-      } else if (value !== null && value !== undefined && value !== "") {
-        convertedValues[key] = value;
+      } else if (normalizedValue !== null && normalizedValue !== undefined && normalizedValue !== "") {
+        convertedValues[key] = normalizedValue;
       }
     });
 
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx b/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx
index f8b0141b25d..108911bdbf1 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/create_mcp_server.tsx
@@ -3,6 +3,7 @@ import { Modal, Tooltip, Form, Select, Input, Switch, Collapse } from "antd";
 import { InfoCircleOutlined } from "@ant-design/icons";
 import { Button, TextInput } from "@tremor/react";
 import { createMCPServer, registerMCPServer } from "../networking";
+import { setToken } from "@/utils/mcpTokenStore";
 import { AUTH_TYPE, DiscoverableMCPServer, OAUTH_FLOW, MCPServer, MCPServerCostInfo, TRANSPORT } from "./types";
 import OAuthFormFields from "./OAuthFormFields";
 import MCPServerCostConfig from "./mcp_server_cost_config";
@@ -24,6 +25,7 @@ export const mcpLogoImg = `${asset_logos_folder}mcp_logo.png`;
 
 interface CreateMCPServerProps {
   userRole: string;
+  userID?: string | null;
   accessToken: string | null;
   onCreateSuccess: (newMcpServer: MCPServer) => void;
   isModalVisible: boolean;
@@ -47,6 +49,7 @@ const reduceStaticHeaders = (list: unknown): Record<string, string> => {
 };
 
 const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
+  userID,
   userRole,
   accessToken,
   onCreateSuccess,
@@ -409,6 +412,21 @@ const CreateMCPServer: React.FC<CreateMCPServerProps> = ({
           ? await createMCPServer(accessToken, payload)
           : await registerMCPServer(accessToken, payload);
 
+        // Cache the OAuth token in sessionStorage so the Tools tab can use it
+        // immediately without re-authenticating.  No backend DB write.
+        if (oauthTokenResponse?.access_token && response?.server_id) {
+          setToken(
+            response.server_id,
+            {
+              access_token: oauthTokenResponse.access_token,
+              expires_in: oauthTokenResponse.expires_in,
+              refresh_token: oauthTokenResponse.refresh_token,
+              token_type: oauthTokenResponse.token_type,
+            },
+            userID,
+          );
+        }
+
         NotificationsManager.success(
           isAdmin
             ? "MCP Server created successfully"
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_view.tsx b/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_view.tsx
index 1f8f7f68d33..5a8035d4e0b 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_view.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/mcp_server_view.tsx
@@ -174,6 +174,7 @@ export const MCPServerView: React.FC<MCPServerViewProps> = ({
               serverId={mcpServer.server_id}
               accessToken={accessToken}
               auth_type={mcpServer.auth_type}
+              tokenUrl={mcpServer.token_url}
               userRole={userRole}
               userID={userID}
               serverAlias={mcpServer.alias}
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_servers.tsx b/ui/litellm-dashboard/src/components/mcp_tools/mcp_servers.tsx
index 72d5e4b5aa8..42583fdab07 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_servers.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/mcp_servers.tsx
@@ -287,6 +287,7 @@ const MCPServers: React.FC<MCPServerProps> = ({ accessToken, userRole, userID })
       </Modal>
       <CreateMCPServer
         userRole={userRole}
+        userID={userID}
         accessToken={accessToken}
         onCreateSuccess={handleCreateSuccess}
         isModalVisible={isModalVisible}
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx b/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx
index 198bf96c3f0..888316f0c3f 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/mcp_tools.tsx
@@ -1,17 +1,21 @@
-import React, { useState } from "react";
+import React, { useEffect, useState } from "react";
 import { useQuery, useMutation } from "@tanstack/react-query";
 import { ToolTestPanel } from "./ToolTestPanel";
 import { MCPTool, MCPToolsViewerProps, MCPContent, CallMCPToolResponse } from "./types";
 import { listMCPTools, callMCPTool } from "../networking";
+import { isTokenValid, getToken, removeToken } from "@/utils/mcpTokenStore";
+import { sanitizeMcpAliasForHeader } from "@/utils/mcpHeaderUtils";
+import { useToolsOAuthFlow } from "@/hooks/useToolsOAuthFlow";
 
 import { Card, Title, Text } from "@tremor/react";
-import { RobotOutlined, ToolOutlined, SearchOutlined, KeyOutlined } from "@ant-design/icons";
+import { RobotOutlined, ToolOutlined, SearchOutlined, KeyOutlined, LockOutlined } from "@ant-design/icons";
 import { Input, Button as AntdButton } from "antd";
 
 const MCPToolsViewer = ({
   serverId,
   accessToken,
   auth_type,
+  tokenUrl,
   userRole,
   userID,
   serverAlias,
@@ -21,29 +25,85 @@ const MCPToolsViewer = ({
   const [toolResult, setToolResult] = useState<MCPContent[] | null>(null);
   const [toolError, setToolError] = useState<Error | null>(null);
   const [toolSearchTerm, setToolSearchTerm] = useState("");
-  
+
   // State for passthrough headers
   const [passthroughHeaders, setPassthroughHeaders] = useState<Record<string, string>>({});
   const [showHeaderInput, setShowHeaderInput] = useState(false);
 
+  // OAuth session token (sessionStorage-backed, cleared on tab/browser close).
+  // Only the interactive (authorization_code/PKCE) flow needs a user-facing
+  // auth gate. M2M (client_credentials) servers are also `auth_type === "oauth2"`,
+  // but the backend fetches their token internally — gating tool listing on
+  // them would force users through a non-existent authorization endpoint.
+  // We detect M2M via the presence of `tokenUrl`, matching the heuristic in
+  // `mcp_server_edit.tsx`.
+  const isOAuth = auth_type === "oauth2" && !tokenUrl;
+  const [oauthToken, setOauthToken] = useState<string | null>(() =>
+    isOAuth && isTokenValid(serverId, userID)
+      ? (getToken(serverId, userID)?.access_token ?? null)
+      : null
+  );
+
+  // Re-sync token when serverId/userID changes (useState initializer only runs on mount).
+  useEffect(() => {
+    if (!isOAuth) {
+      setOauthToken(null);
+      return;
+    }
+    setOauthToken(
+      isTokenValid(serverId, userID)
+        ? (getToken(serverId, userID)?.access_token ?? null)
+        : null
+    );
+  }, [serverId, userID, isOAuth]);
+
+  const { startOAuthFlow, status: oauthStatus, error: oauthError } = useToolsOAuthFlow({
+    accessToken: accessToken ?? "",
+    serverId,
+    serverAlias,
+    userId: userID,
+    onSuccess: setOauthToken,
+  });
+
   // Check if this server has extra headers configured
   const hasExtraHeaders = extraHeaders && extraHeaders.length > 0;
 
   // Build custom headers for MCP server requests
   const buildCustomHeaders = () => {
-    if (!serverAlias || !hasExtraHeaders) return undefined;
-    
     const customHeaders: Record<string, string> = {};
-    
-    // Add passthrough headers with server-specific prefix
-    Object.entries(passthroughHeaders).forEach(([headerName, headerValue]) => {
-      if (headerValue && headerValue.trim()) {
-        // Format: x-mcp-{alias}-{header_name}
-        const mcpHeaderName = `x-mcp-${serverAlias}-${headerName.toLowerCase()}`;
-        customHeaders[mcpHeaderName] = headerValue;
+
+    // Include the session OAuth token using MCP-specific headers so it doesn't
+    // conflict with the Authorization header used by the LiteLLM proxy itself.
+    // The backend's _get_mcp_server_auth_headers_from_headers() picks up the
+    // x-mcp-{alias}-{header} pattern and forwards it to the upstream MCP server.
+    // When no alias is available, fall back to x-mcp-auth (legacy but still supported).
+    if (oauthToken) {
+      if (serverAlias) {
+        const safeAlias = sanitizeMcpAliasForHeader(serverAlias);
+        if (safeAlias) {
+          customHeaders[`x-mcp-${safeAlias}-authorization`] = `Bearer ${oauthToken}`;
+        } else {
+          customHeaders["x-mcp-auth"] = `Bearer ${oauthToken}`;
+        }
+      } else {
+        customHeaders["x-mcp-auth"] = `Bearer ${oauthToken}`;
       }
-    });
-    
+    }
+
+    // Add passthrough headers with server-specific prefix
+    if (serverAlias && hasExtraHeaders) {
+      const safeAlias = sanitizeMcpAliasForHeader(serverAlias);
+      if (safeAlias) {
+        Object.entries(passthroughHeaders).forEach(([headerName, headerValue]) => {
+          if (headerValue && headerValue.trim()) {
+            // Format: x-mcp-{alias}-{header_name}
+            const mcpHeaderName = `x-mcp-${safeAlias}-${headerName.toLowerCase()}`;
+            customHeaders[mcpHeaderName] = headerValue;
+          }
+        });
+      }
+    }
+
     return Object.keys(customHeaders).length > 0 ? customHeaders : undefined;
   };
 
@@ -54,15 +114,55 @@ const MCPToolsViewer = ({
     error: mcpToolsError,
     refetch: refetchTools,
   } = useQuery({
-    queryKey: ["mcpTools", serverId, passthroughHeaders],
-    queryFn: () => {
+    queryKey: ["mcpTools", serverId, passthroughHeaders, oauthToken],
+    queryFn: async () => {
       if (!accessToken) throw new Error("Access Token required");
-      return listMCPTools(accessToken, serverId, buildCustomHeaders());
+      const result = await listMCPTools(accessToken, serverId, buildCustomHeaders());
+      // listMCPTools never throws — surface error responses as thrown errors
+      // here so useQuery's retry/onError can react (e.g. clear the cached
+      // OAuth token on 401).
+      if (result?.error) {
+        const status = (result as { status?: number }).status;
+        if (status === 401) {
+          removeToken(serverId, userID);
+        }
+        const enhancedError = new Error(
+          result.message || result.error || "Failed to fetch MCP tools",
+        ) as Error & {
+          status?: number;
+          statusText?: string;
+          details?: any;
+        };
+        enhancedError.status = status;
+        enhancedError.statusText = (result as any).statusText;
+        enhancedError.details = (result as any).details;
+        throw enhancedError;
+      }
+      return result;
     },
-    enabled: !!accessToken,
+    // For OAuth servers, block the query until a session token is available
+    enabled: !!accessToken && (!isOAuth || oauthToken !== null),
     staleTime: 30000, // Consider data fresh for 30 seconds
+    retry: (failureCount, error: any) => {
+      // Don't retry on 401 — token is invalid, user must re-authenticate
+      if (error?.status === 401 || error?.response?.status === 401) return false;
+      return failureCount < 2;
+    },
   });
 
+  // If the tools query fails with 401, the cached OAuth token is invalid —
+  // clear it so the auth gate is shown again and the user can re-authenticate.
+  useEffect(() => {
+    const err = mcpToolsError as
+      | (Error & { status?: number; response?: { status?: number } })
+      | null;
+    const status = err?.status ?? err?.response?.status;
+    if (status === 401) {
+      removeToken(serverId, userID);
+      setOauthToken(null);
+    }
+  }, [mcpToolsError, serverId, userID]);
+
   // Mutation for calling a tool
   const { mutate: executeTool, isPending: isCallingTool } = useMutation({
     mutationFn: async (args: { tool: MCPTool; arguments: Record<string, any> }) => {
@@ -85,9 +185,14 @@ const MCPToolsViewer = ({
       setToolResult(data.content);
       setToolError(null);
     },
-    onError: (error: Error) => {
+    onError: (error: Error & { status?: number; response?: { status?: number } }) => {
       setToolError(error);
       setToolResult(null);
+      // On 401, clear the cached token so the auth gate is shown again
+      if (error?.status === 401 || (error as any)?.response?.status === 401) {
+        removeToken(serverId, userID);
+        setOauthToken(null);
+      }
     },
   });
 
@@ -197,7 +302,31 @@ const MCPToolsViewer = ({
                   )}
                 </Text>
 
-                {/* Search Bar */}
+                {/* OAuth Auth Gate — shown when token is absent for OAuth servers */}
+                {isOAuth && !oauthToken && (
+                  <div className="p-4 text-center bg-white border border-gray-200 rounded-lg">
+                    <LockOutlined className="text-2xl text-gray-400 mb-2" />
+                    <p className="text-xs font-medium text-gray-700 mb-1">Authentication required</p>
+                    <p className="text-xs text-gray-500 mb-3">
+                      Authenticate to view available tools
+                    </p>
+                    <AntdButton
+                      size="small"
+                      type="primary"
+                      loading={oauthStatus === "authorizing" || oauthStatus === "exchanging"}
+                      onClick={startOAuthFlow}
+                      disabled={!accessToken}
+                    >
+                      Authorize
+                    </AntdButton>
+                    {oauthError && (
+                      <p className="text-xs text-red-500 mt-2">{oauthError}</p>
+                    )}
+                  </div>
+                )}
+
+                {/* Search Bar — only shown when tools are loaded */}
+                {!isOAuth || oauthToken ? <>
                 {toolsData.length > 0 && (
                   <div className="mb-3">
                     <Input
@@ -224,14 +353,16 @@ const MCPToolsViewer = ({
                 )}
 
                 {/* Error State */}
-                {mcpToolsResponse?.error && !isLoadingTools && !toolsData.length && (
+                {(mcpToolsResponse?.error || mcpToolsError) && !isLoadingTools && !toolsData.length && (
                   <div className="p-3 text-xs text-red-800 rounded-lg bg-red-50 border border-red-200">
-                    <p className="font-medium">Error: {mcpToolsResponse.message}</p>
+                    <p className="font-medium">
+                      Error: {mcpToolsResponse?.message || (mcpToolsError as Error)?.message}
+                    </p>
                   </div>
                 )}
 
                 {/* No Tools State */}
-                {!isLoadingTools && !mcpToolsResponse?.error && (!toolsData || toolsData.length === 0) && (
+                {!isLoadingTools && !mcpToolsResponse?.error && !mcpToolsError && (!toolsData || toolsData.length === 0) && (
                   <div className="p-4 text-center bg-white border border-gray-200 rounded-lg">
                     <div className="mx-auto w-8 h-8 bg-gray-200 rounded-full flex items-center justify-center mb-2">
                       <svg className="w-4 h-4 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
@@ -315,6 +446,7 @@ const MCPToolsViewer = ({
                     )}
                   </>
                 )}
+                </> : null}
               </div>
             </div>
           </div>
diff --git a/ui/litellm-dashboard/src/components/mcp_tools/types.tsx b/ui/litellm-dashboard/src/components/mcp_tools/types.tsx
index 7cfe08d9ee5..9a8f2e8f514 100644
--- a/ui/litellm-dashboard/src/components/mcp_tools/types.tsx
+++ b/ui/litellm-dashboard/src/components/mcp_tools/types.tsx
@@ -163,6 +163,13 @@ export interface MCPToolsViewerProps {
   serverId: string;
   accessToken: string | null;
   auth_type?: string | null;
+  /**
+   * When set, indicates the server uses the OAuth2 M2M (client_credentials)
+   * flow — the backend handles token acquisition internally, so the UI must
+   * not gate tool listing behind an interactive PKCE authorization. Mirrors
+   * the heuristic used in `mcp_server_edit.tsx` (`token_url` set => M2M).
+   */
+  tokenUrl?: string | null;
   userRole: string | null;
   userID: string | null;
   serverAlias?: string | null;
diff --git a/ui/litellm-dashboard/src/components/model_dashboard/types.ts b/ui/litellm-dashboard/src/components/model_dashboard/types.ts
index b1447a0634b..77a03d2c039 100644
--- a/ui/litellm-dashboard/src/components/model_dashboard/types.ts
+++ b/ui/litellm-dashboard/src/components/model_dashboard/types.ts
@@ -6,6 +6,7 @@ export interface ModelInfo {
   team_id: string;
   db_model: boolean;
   access_groups: string[] | null;
+  blocked?: boolean;
 }
 
 export interface LiteLLMParams {
diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx
index 95a43862de5..5ed4c0468b8 100644
--- a/ui/litellm-dashboard/src/components/model_info_view.tsx
+++ b/ui/litellm-dashboard/src/components/model_info_view.tsx
@@ -263,6 +263,34 @@ export default function ModelInfoView({
         updatedLitellmParams.output_cost_per_token = Number(values.output_cost) / 1_000_000;
       }
 
+      // Cache Read Cost: explicit value if provided, else fall back to input cost (when input cost touched).
+      if (form.isFieldTouched("cache_read_cost") || form.isFieldTouched("input_cost")) {
+        if (
+          values.cache_read_cost !== undefined &&
+          values.cache_read_cost !== null &&
+          values.cache_read_cost !== ""
+        ) {
+          updatedLitellmParams.cache_read_input_token_cost = Number(values.cache_read_cost) / 1_000_000;
+        } else if (updatedLitellmParams.input_cost_per_token !== undefined) {
+          updatedLitellmParams.cache_read_input_token_cost = updatedLitellmParams.input_cost_per_token;
+        }
+      }
+
+      // Cache Write Cost: explicit value if provided, else clear the override
+      // so the backend falls back to the model-level default. Sending 0 here
+      // would persist a zero rate even when the user intended to unset it.
+      if (form.isFieldTouched("cache_write_cost")) {
+        if (
+          values.cache_write_cost !== undefined &&
+          values.cache_write_cost !== null &&
+          values.cache_write_cost !== ""
+        ) {
+          updatedLitellmParams.cache_creation_input_token_cost = Number(values.cache_write_cost) / 1_000_000;
+        } else {
+          delete updatedLitellmParams.cache_creation_input_token_cost;
+        }
+      }
+
       if (values.litellm_credential_name) {
         updatedLitellmParams.litellm_credential_name = values.litellm_credential_name;
       } else {
@@ -638,6 +666,22 @@ export default function ModelInfoView({
                     output_cost: localModelData.litellm_params?.output_cost_per_token
                       ? localModelData.litellm_params.output_cost_per_token * 1_000_000
                       : localModelData.model_info?.output_cost_per_token * 1_000_000 || null,
+                    cache_read_cost:
+                      localModelData.litellm_params?.cache_read_input_token_cost !== undefined &&
+                      localModelData.litellm_params?.cache_read_input_token_cost !== null
+                        ? localModelData.litellm_params.cache_read_input_token_cost * 1_000_000
+                        : localModelData.model_info?.cache_read_input_token_cost !== undefined &&
+                            localModelData.model_info?.cache_read_input_token_cost !== null
+                          ? localModelData.model_info.cache_read_input_token_cost * 1_000_000
+                          : null,
+                    cache_write_cost:
+                      localModelData.litellm_params?.cache_creation_input_token_cost !== undefined &&
+                      localModelData.litellm_params?.cache_creation_input_token_cost !== null
+                        ? localModelData.litellm_params.cache_creation_input_token_cost * 1_000_000
+                        : localModelData.model_info?.cache_creation_input_token_cost !== undefined &&
+                            localModelData.model_info?.cache_creation_input_token_cost !== null
+                          ? localModelData.model_info.cache_creation_input_token_cost * 1_000_000
+                          : null,
                     cache_control: localModelData.litellm_params?.cache_control_injection_points ? true : false,
                     cache_control_injection_points: localModelData.litellm_params?.cache_control_injection_points || [],
                     model_access_group: Array.isArray(localModelData.model_info?.access_groups)
@@ -725,6 +769,52 @@ export default function ModelInfoView({
                         )}
                       </div>
 
+                      <div>
+                        <Text className="font-medium">Cache Read Cost (per 1M tokens)</Text>
+                        {isEditing ? (
+                          <Form.Item
+                            name="cache_read_cost"
+                            className="mb-0"
+                            tooltip="If left blank on save, defaults to Input Cost."
+                          >
+                            <NumericalInput placeholder="Defaults to Input Cost if blank" />
+                          </Form.Item>
+                        ) : (
+                          <div className="mt-1 p-2 bg-gray-50 rounded">
+                            {localModelData?.litellm_params?.cache_read_input_token_cost !== undefined &&
+                            localModelData?.litellm_params?.cache_read_input_token_cost !== null
+                              ? (localModelData.litellm_params.cache_read_input_token_cost * 1_000_000).toFixed(4)
+                              : localModelData?.model_info?.cache_read_input_token_cost !== undefined &&
+                                  localModelData?.model_info?.cache_read_input_token_cost !== null
+                                ? (localModelData.model_info.cache_read_input_token_cost * 1_000_000).toFixed(4)
+                                : "Not Set"}
+                          </div>
+                        )}
+                      </div>
+
+                      <div>
+                        <Text className="font-medium">Cache Write Cost (per 1M tokens)</Text>
+                        {isEditing ? (
+                          <Form.Item
+                            name="cache_write_cost"
+                            className="mb-0"
+                            tooltip="If left blank on save, defaults to Input Cost (backend falls back to input_cost_per_token)."
+                          >
+                            <NumericalInput placeholder="Defaults to Input Cost if blank" />
+                          </Form.Item>
+                        ) : (
+                          <div className="mt-1 p-2 bg-gray-50 rounded">
+                            {localModelData?.litellm_params?.cache_creation_input_token_cost !== undefined &&
+                            localModelData?.litellm_params?.cache_creation_input_token_cost !== null
+                              ? (localModelData.litellm_params.cache_creation_input_token_cost * 1_000_000).toFixed(4)
+                              : localModelData?.model_info?.cache_creation_input_token_cost !== undefined &&
+                                  localModelData?.model_info?.cache_creation_input_token_cost !== null
+                                ? (localModelData.model_info.cache_creation_input_token_cost * 1_000_000).toFixed(4)
+                                : "Not Set"}
+                          </div>
+                        )}
+                      </div>
+
                       <div>
                         <Text className="font-medium">API Base</Text>
                         {isEditing ? (
diff --git a/ui/litellm-dashboard/src/components/molecules/models/columns.test.tsx b/ui/litellm-dashboard/src/components/molecules/models/columns.test.tsx
index a3dbbb2783f..c08dca1b8ce 100644
--- a/ui/litellm-dashboard/src/components/molecules/models/columns.test.tsx
+++ b/ui/litellm-dashboard/src/components/molecules/models/columns.test.tsx
@@ -944,4 +944,108 @@ describe("columns", () => {
     expect(screen.getByText("Out: $0.03")).toBeInTheDocument();
     expect(screen.queryByText(/In:/)).not.toBeInTheDocument();
   });
+
+  describe("pause/resume toggle", () => {
+    const renderWithToggle = (
+      overrides: Partial<ReturnType<typeof createMockModel>["model_info"]> = {},
+      togglePauseHandler?: ReturnType<typeof vi.fn>,
+      userRole: string = "Admin",
+    ) => {
+      const handler = togglePauseHandler ?? vi.fn();
+      const cols = columns(
+        userRole,
+        defaultProps.userID,
+        defaultProps.premiumUser,
+        defaultProps.setSelectedModelId,
+        defaultProps.setSelectedTeamId,
+        defaultProps.getDisplayModelName,
+        defaultProps.handleEditClick,
+        defaultProps.handleRefreshClick,
+        defaultProps.expandedRows,
+        defaultProps.setExpandedRows,
+        vi.fn(),
+        handler,
+      );
+      const model = createMockModel({
+        model_info: { ...createMockModel().model_info, ...overrides },
+      });
+      render(<TestTable data={[model]} columns={cols} />);
+      return { handler };
+    };
+
+    it("renders the toggle ON for a db_model that is not blocked", () => {
+      renderWithToggle({ db_model: true, blocked: false });
+      const toggle = screen.getByRole("switch", { name: /pause model/i });
+      expect(toggle).toBeEnabled();
+      expect(toggle).toHaveAttribute("aria-checked", "true");
+    });
+
+    it("renders the toggle OFF for a db_model that is blocked", () => {
+      renderWithToggle({ db_model: true, blocked: true });
+      const toggle = screen.getByRole("switch", { name: /resume model/i });
+      expect(toggle).toBeEnabled();
+      expect(toggle).toHaveAttribute("aria-checked", "false");
+    });
+
+    it("calls the handler with blocked=true when an admin flips an active toggle off", async () => {
+      const handler = vi.fn();
+      renderWithToggle({ db_model: true, blocked: false }, handler);
+      await userEvent.click(screen.getByRole("switch", { name: /pause model/i }));
+      expect(handler).toHaveBeenCalledWith("test-model-id", true);
+    });
+
+    it("calls the handler with blocked=false when an admin flips a paused toggle on", async () => {
+      const handler = vi.fn();
+      renderWithToggle({ db_model: true, blocked: true }, handler);
+      await userEvent.click(screen.getByRole("switch", { name: /resume model/i }));
+      expect(handler).toHaveBeenCalledWith("test-model-id", false);
+    });
+
+    it("disables the toggle for non-admin users", () => {
+      const handler = vi.fn();
+      renderWithToggle({ db_model: true, blocked: false }, handler, "User");
+      const toggle = screen.getByRole("switch", { name: /pause model/i });
+      expect(toggle).toBeDisabled();
+    });
+
+    it("disables the toggle for config models", () => {
+      const handler = vi.fn();
+      renderWithToggle({ db_model: false, blocked: false }, handler, "Admin");
+      const toggle = screen.getByRole("switch", { name: /pause model/i });
+      expect(toggle).toBeDisabled();
+    });
+
+    it("disables the toggle while a PATCH for the same row is in-flight", () => {
+      // Regression for Greptile P1 on PR #28151 — antd's `loading` prop is
+      // visual only and does not prevent click events, so the row needs to
+      // be explicitly disabled while its PATCH is pending to avoid
+      // racing/conflicting PATCH calls on double-click.
+      const handler = vi.fn();
+      const model = createMockModel({
+        model_info: {
+          ...createMockModel().model_info,
+          db_model: true,
+          blocked: false,
+        },
+      });
+      const cols = columns(
+        "Admin",
+        defaultProps.userID,
+        defaultProps.premiumUser,
+        defaultProps.setSelectedModelId,
+        defaultProps.setSelectedTeamId,
+        defaultProps.getDisplayModelName,
+        defaultProps.handleEditClick,
+        defaultProps.handleRefreshClick,
+        defaultProps.expandedRows,
+        defaultProps.setExpandedRows,
+        vi.fn(),
+        handler,
+        model.model_info.id, // pausingModelId matches this row
+      );
+      render(<TestTable data={[model]} columns={cols} />);
+      const toggle = screen.getByRole("switch", { name: /pause model/i });
+      expect(toggle).toBeDisabled();
+    });
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/molecules/models/columns.tsx b/ui/litellm-dashboard/src/components/molecules/models/columns.tsx
index a303e1b1a44..4563e9c80dd 100644
--- a/ui/litellm-dashboard/src/components/molecules/models/columns.tsx
+++ b/ui/litellm-dashboard/src/components/molecules/models/columns.tsx
@@ -2,7 +2,7 @@ import { EditOutlined, InfoCircleOutlined, SyncOutlined } from "@ant-design/icon
 import { TrashIcon } from "@heroicons/react/outline";
 import { ColumnDef } from "@tanstack/react-table";
 import { Badge, Button, Icon } from "@tremor/react";
-import { Divider, Flex, Popover, Space, Tooltip, Typography } from "antd";
+import { Divider, Flex, Popover, Space, Switch, Tooltip, Typography } from "antd";
 import { ModelData } from "../../model_dashboard/types";
 import { ProviderLogo } from "./ProviderLogo";
 
@@ -53,6 +53,8 @@ export const columns = (
   expandedRows: Set<string>,
   setExpandedRows: (expandedRows: Set<string>) => void,
   onDeleteClick?: (modelId: string) => void,
+  onTogglePauseClick?: (modelId: string, blocked: boolean) => void | Promise<void>,
+  pausingModelId?: string | null,
 ): ColumnDef<ModelData>[] => [
     {
       header: () => <span className="text-sm font-semibold">Model ID</span>,
@@ -398,15 +400,48 @@ export const columns = (
     {
       id: "actions",
       header: () => <span className="text-sm font-semibold">Actions</span>,
-      size: 60,
-      minSize: 40,
+      size: 100,
+      minSize: 80,
       enableResizing: false,
       cell: ({ row }) => {
         const model = row.original;
         const canEditModel = userRole === "Admin" || model.model_info?.created_by === userID;
         const isConfigModel = !model.model_info?.db_model;
+        const isAdmin = userRole === "Admin";
+        const isBlocked = model.model_info?.blocked === true;
+        const isPauseToggleable = !isConfigModel && isAdmin && Boolean(onTogglePauseClick);
+        const pauseTooltip = isConfigModel
+          ? "Config models cannot be paused from the dashboard. Pause is DB-backed."
+          : !isAdmin
+            ? "Only proxy admins can pause or resume a model."
+            : isBlocked
+              ? "Resume model — restore normal routing."
+              : "Pause model — stop routing requests until resumed.";
+        // antd's `loading` prop on Switch is purely cosmetic — it does not block
+        // clicks. Pair `loading` with `disabled` derived from the same condition
+        // so a double-click during a pending PATCH cannot send a second,
+        // conflicting `blocked` value.
+        const isPausing = pausingModelId === model.model_info?.id;
         return (
           <div className="flex items-center justify-end gap-2 pr-4">
+            <Tooltip title={pauseTooltip}>
+              <Switch
+                size="small"
+                checked={!isBlocked}
+                disabled={!isPauseToggleable || isPausing}
+                loading={isPausing}
+                aria-label={isBlocked ? "Resume model" : "Pause model"}
+                onClick={(_, e) => {
+                  e.stopPropagation();
+                }}
+                onChange={(nextChecked) => {
+                  const modelId = model.model_info?.id;
+                  if (isPauseToggleable && onTogglePauseClick && modelId) {
+                    void onTogglePauseClick(modelId, !nextChecked);
+                  }
+                }}
+              />
+            </Tooltip>
             {isConfigModel ? (
               <Tooltip title="Config model cannot be deleted on the dashboard. Please delete it from the config file.">
                 <Icon icon={TrashIcon} size="sm" className="opacity-50 cursor-not-allowed" />
diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx
index 756348f4937..57e7d51123e 100644
--- a/ui/litellm-dashboard/src/components/networking.tsx
+++ b/ui/litellm-dashboard/src/components/networking.tsx
@@ -7068,46 +7068,33 @@ export const testSearchToolConnection = async (accessToken: string, litellmParam
 };
 
 export const listMCPTools = async (
-  accessToken: string, 
+  accessToken: string,
   serverId: string,
-  customHeaders?: Record<string, string>
+  customHeaders?: Record<string, string>,
 ) => {
+  // Construct base URL
+  let url = proxyBaseUrl
+    ? `${proxyBaseUrl}/mcp-rest/tools/list?server_id=${serverId}`
+    : `/mcp-rest/tools/list?server_id=${serverId}`;
+
+  console.log("Fetching MCP tools from:", url);
+
+  const headers: Record<string, string> = {
+    [globalLitellmHeaderName]: `Bearer ${accessToken}`,
+    "Content-Type": "application/json",
+    ...customHeaders, // Merge custom headers for passthrough auth
+  };
+
+  let response: Response;
   try {
-    // Construct base URL
-    let url = proxyBaseUrl
-      ? `${proxyBaseUrl}/mcp-rest/tools/list?server_id=${serverId}`
-      : `/mcp-rest/tools/list?server_id=${serverId}`;
-
-    console.log("Fetching MCP tools from:", url);
-
-    const headers: Record<string, string> = {
-      [globalLitellmHeaderName]: `Bearer ${accessToken}`,
-      "Content-Type": "application/json",
-      ...customHeaders, // Merge custom headers for passthrough auth
-    };
-
-    const response = await fetch(url, {
+    response = await fetch(url, {
       method: "GET",
       headers,
     });
-
-    const data = await response.json();
-    console.log("Fetched MCP tools response:", data);
-
-    if (!response.ok) {
-      // If the server returned an error response, use it
-      if (data.error && data.message) {
-        throw new Error(data.message);
-      }
-      // Otherwise use a generic error
-      throw new Error("Failed to fetch MCP tools");
-    }
-
-    // Return the full response object which includes tools, error, message, and stack_trace
-    return data;
   } catch (error) {
-    console.error("Failed to fetch MCP tools:", error);
-    // Return an error response in the same format as the API
+    // Network-level failure (no HTTP response). Preserve legacy shape so the
+    // caller can render a generic error message without crashing.
+    console.error("Failed to fetch MCP tools (network error):", error);
     return {
       tools: [],
       error: "network_error",
@@ -7115,6 +7102,44 @@ export const listMCPTools = async (
       stack_trace: null,
     };
   }
+
+  let data: any = null;
+  try {
+    data = await response.json();
+  } catch (parseError) {
+    console.error("Failed to parse MCP tools response:", parseError);
+    return {
+      tools: [],
+      error: "parse_error",
+      message: "Failed to parse MCP tools response",
+      status: response.status,
+      statusText: response.statusText,
+      stack_trace: null,
+    };
+  }
+  console.log("Fetched MCP tools response:", data);
+
+  if (!response.ok) {
+    // Preserve the legacy "never throws" contract so existing callers
+    // (e.g. MCPToolPermissions, MCPAppsPanel, MCPConnectPicker) can continue
+    // to inspect `result.error` / `result.message`. Attach `status` so
+    // callers that need to react to auth failures (e.g. the useQuery in
+    // mcp_tools.tsx) can still detect 401s from the returned object.
+    const errorMessage =
+      (data && (data.message || data.error)) || "Failed to fetch MCP tools";
+    return {
+      tools: [],
+      error: (data && data.error) || `http_${response.status}`,
+      message: errorMessage,
+      status: response.status,
+      statusText: response.statusText,
+      details: data,
+      stack_trace: null,
+    };
+  }
+
+  // Return the full response object which includes tools, error, message, and stack_trace
+  return data;
 };
 
 interface CallMCPToolOptions {
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx
index 06a09ca2c37..a80cf935c4b 100644
--- a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx
@@ -49,6 +49,7 @@ import { fetchAvailableModels, ModelGroup } from "../llm_calls/fetch_models";
 import { makeOpenAIImageEditsRequest } from "../llm_calls/image_edits";
 import { makeOpenAIImageGenerationRequest } from "../llm_calls/image_generation";
 import { makeOpenAIResponsesRequest } from "../llm_calls/responses_api";
+import { makeInteractionsRequest } from "../llm_calls/interactions_api";
 import A2AMetrics from "./A2AMetrics";
 import AdditionalModelSettings from "./AdditionalModelSettings";
 import AudioRenderer from "./AudioRenderer";
@@ -649,6 +650,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
       EndpointType.ANTHROPIC_MESSAGES,
       EndpointType.EMBEDDINGS,
       EndpointType.TRANSCRIPTION,
+      EndpointType.INTERACTIONS,
     ];
 
     if (modelRequiredEndpoints.includes(endpointType as EndpointType) && !selectedModel) {
@@ -914,6 +916,16 @@ const ChatUI: React.FC<ChatUIProps> = ({
               customProxyBaseUrl || undefined,
             );
           }
+        } else if (endpointType === EndpointType.INTERACTIONS) {
+          await makeInteractionsRequest(
+            inputMessage,
+            (text, model) => updateTextUI("assistant", text, model),
+            selectedModel,
+            effectiveApiKey,
+            selectedTags,
+            signal,
+            customProxyBaseUrl || undefined,
+          );
         }
       }
 
@@ -1241,10 +1253,11 @@ const ChatUI: React.FC<ChatUIProps> = ({
                                 return true;
                               }
                               const optionEndpoint = getEndpointType(option.mode);
-                              // Show chat models for responses/anthropic_messages endpoints as they are compatible
+                              // Show chat models for responses/anthropic_messages/interactions endpoints as they are compatible
                               if (
                                 endpointType === EndpointType.RESPONSES ||
-                                endpointType === EndpointType.ANTHROPIC_MESSAGES
+                                endpointType === EndpointType.ANTHROPIC_MESSAGES ||
+                                endpointType === EndpointType.INTERACTIONS
                               ) {
                                 return optionEndpoint === endpointType || optionEndpoint === EndpointType.CHAT;
                               }
@@ -2089,7 +2102,8 @@ const ChatUI: React.FC<ChatUIProps> = ({
                         endpointType === EndpointType.CHAT ||
                         endpointType === EndpointType.EMBEDDINGS ||
                         endpointType === EndpointType.RESPONSES ||
-                        endpointType === EndpointType.ANTHROPIC_MESSAGES
+                        endpointType === EndpointType.ANTHROPIC_MESSAGES ||
+                        endpointType === EndpointType.INTERACTIONS
                           ? "Type your message... (Shift+Enter for new line)"
                           : endpointType === EndpointType.A2A_AGENTS
                             ? "Send a message to the A2A agent..."
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts b/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts
index 919e3bc1c65..9592a521250 100644
--- a/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts
@@ -45,4 +45,5 @@ export const ENDPOINT_OPTIONS = [
   { value: EndpointType.A2A_AGENTS, label: "/v1/a2a/message/send" },
   { value: EndpointType.MCP, label: "/mcp-rest/tools/call" },
   { value: EndpointType.REALTIME, label: "/v1/realtime" },
+  { value: EndpointType.INTERACTIONS, label: "/v1beta/interactions" },
 ];
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx
index f354efe641e..3b2a5e0fd60 100644
--- a/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx
@@ -28,6 +28,7 @@ export enum EndpointType {
   A2A_AGENTS = "a2a_agents",
   MCP = "mcp",
   REALTIME = "realtime",
+  INTERACTIONS = "interactions",
 }
 
 // Create a mapping between the model mode and the corresponding endpoint type
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx
new file mode 100644
index 00000000000..6a6e4a2cfa0
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx
@@ -0,0 +1,124 @@
+import NotificationManager from "@/components/molecules/notifications_manager";
+import { getGlobalLitellmHeaderName, getProxyBaseUrl } from "@/components/networking";
+
+export async function makeInteractionsRequest(
+  input: string,
+  updateUI: (text: string, model?: string) => void,
+  selectedModel: string,
+  accessToken: string,
+  tags?: string[],
+  signal?: AbortSignal,
+  customBaseUrl?: string,
+  previousInteractionId?: string,
+): Promise<void> {
+  if (!accessToken) {
+    throw new Error("Virtual Key is required");
+  }
+
+  const isLocal = process.env.NODE_ENV === "development";
+  if (isLocal !== true) {
+    console.log = function () {};
+  }
+
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
+  const normalizedBaseUrl = proxyBaseUrl.endsWith("/") ? proxyBaseUrl.slice(0, -1) : proxyBaseUrl;
+  const requestUrl = `${normalizedBaseUrl}/v1beta/interactions`;
+
+  const headers: Record<string, string> = {
+    "Content-Type": "application/json",
+    [getGlobalLitellmHeaderName()]: `Bearer ${accessToken}`,
+  };
+  if (tags && tags.length > 0) {
+    headers["x-litellm-tags"] = tags.join(",");
+  }
+
+  const body: Record<string, unknown> = {
+    model: selectedModel,
+    input,
+    stream: true,
+  };
+  if (previousInteractionId) {
+    body.previous_interaction_id = previousInteractionId;
+  }
+
+  try {
+    const response = await fetch(requestUrl, {
+      method: "POST",
+      headers,
+      body: JSON.stringify(body),
+      signal,
+    });
+
+    if (!response.ok) {
+      const errorText = await response.text();
+      throw new Error(errorText || `Request failed with status ${response.status}`);
+    }
+
+    if (!response.body) {
+      throw new Error("No response body received");
+    }
+
+    const reader = response.body.getReader();
+    const decoder = new TextDecoder();
+    let responseModel: string | undefined;
+    let buffer = "";
+
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      buffer += decoder.decode(value, { stream: true });
+
+      // SSE lines are separated by double newlines; split on single newlines and
+      // look for "data: " prefixed lines.
+      const lines = buffer.split("\n");
+      // Keep the last (potentially incomplete) line in the buffer
+      buffer = lines.pop() ?? "";
+
+      for (const line of lines) {
+        const trimmed = line.trim();
+        if (!trimmed.startsWith("data:")) continue;
+
+        const jsonStr = trimmed.slice("data:".length).trim();
+        if (!jsonStr || jsonStr === "[DONE]") continue;
+
+        let event: Record<string, unknown>;
+        try {
+          event = JSON.parse(jsonStr);
+        } catch {
+          continue;
+        }
+
+        const eventType = event.event_type as string | undefined;
+
+        if (eventType === "interaction.start" || eventType === "interaction.complete") {
+          // Capture model from either the native Gemini shape (nested under
+          // `interaction`) or the bridge shape (top-level `model` field).
+          const interaction = event.interaction as Record<string, unknown> | undefined;
+          if (typeof interaction?.model === "string" && interaction.model) {
+            responseModel = interaction.model;
+          } else if (typeof event.model === "string" && event.model) {
+            responseModel = event.model;
+          }
+        } else if (eventType === "content.delta" || eventType === "content.start") {
+          const delta = event.delta as Record<string, unknown> | undefined;
+          // Accept both native Gemini format {"type":"text","text":"..."} and bridge
+          // format {"text":"..."} (no type discriminator)
+          if (typeof delta?.text === "string" && delta.text) {
+            updateUI(delta.text, responseModel ?? selectedModel);
+          }
+        }
+        // content.start, content.stop, interaction.status_update — no UI action needed
+      }
+    }
+  } catch (error: unknown) {
+    if (signal?.aborted) {
+      console.log("Interactions request was cancelled");
+      throw error;
+    }
+    NotificationManager.fromBackend(
+      `Error occurred while making Interactions API request. Error: ${error}`,
+    );
+    throw error;
+  }
+}
diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx
index fa014de4e62..ab22b0ef49a 100644
--- a/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx
+++ b/ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx
@@ -218,14 +218,83 @@ describe("provider_info_helpers", () => {
       expect(result).toEqual(["gpt-3.5-turbo", "gpt-4"]);
     });
 
-    it("should return models when litellm_provider includes the provider string", () => {
+    it("should return models whose litellm_provider is a prefix-anchored variant of the provider", () => {
       const modelMap = {
-        "custom-openai-model": { litellm_provider: "custom_openai_endpoint" },
-        "another-model": { litellm_provider: "openai" },
+        "anthropic-text-model": { litellm_provider: "anthropic_text" },
+        "claude-3-opus": { litellm_provider: "anthropic" },
+      };
+      const result = getProviderModels(Providers.Anthropic, modelMap);
+      expect(result).toContain("anthropic-text-model");
+      expect(result).toContain("claude-3-opus");
+    });
+
+    it("should not leak vertex_ai-anthropic_models into the Anthropic provider", () => {
+      const modelMap = {
+        "claude-3-opus": { litellm_provider: "anthropic" },
+        "vertex_ai/claude-3-5-sonnet": { litellm_provider: "vertex_ai-anthropic_models" },
+        "vertex_ai/claude-haiku-4-5": { litellm_provider: "vertex_ai-anthropic_models" },
+      };
+      const result = getProviderModels(Providers.Anthropic, modelMap);
+      expect(result).toEqual(["claude-3-opus"]);
+      expect(result).not.toContain("vertex_ai/claude-3-5-sonnet");
+      expect(result).not.toContain("vertex_ai/claude-haiku-4-5");
+    });
+
+    it("should not leak vertex_ai-openai_models into the OpenAI provider", () => {
+      const modelMap = {
+        "gpt-4": { litellm_provider: "openai" },
+        "vertex_ai/openai-something": { litellm_provider: "vertex_ai-openai_models" },
       };
       const result = getProviderModels(Providers.OpenAI, modelMap);
-      expect(result).toContain("custom-openai-model");
-      expect(result).toContain("another-model");
+      expect(result).toEqual(["gpt-4"]);
+      expect(result).not.toContain("vertex_ai/openai-something");
+    });
+
+    // Note on the next three tests: in production, AddModelForm passes the
+    // backend `provider` field (the provider_map *key*, e.g. "Vertex_AI",
+    // "Bedrock", "FireworksAI") into getProviderModels, not the Providers
+    // enum value. The `as Providers` cast in callers is misleading. We mirror
+    // the production shape here by passing the key directly.
+    it("should include all vertex_ai variants when called with 'Vertex_AI' provider key", () => {
+      const modelMap = {
+        "vertex_ai/gemini-pro": { litellm_provider: "vertex_ai" },
+        "vertex_ai/claude-3-5-sonnet": { litellm_provider: "vertex_ai-anthropic_models" },
+        "vertex_ai/text-bison": { litellm_provider: "vertex_ai-text-models" },
+        "vertex_ai_beta/something": { litellm_provider: "vertex_ai_beta" },
+        "anthropic-native": { litellm_provider: "anthropic" },
+      };
+      const result = getProviderModels("Vertex_AI" as Providers, modelMap);
+      expect(result).toContain("vertex_ai/gemini-pro");
+      expect(result).toContain("vertex_ai/claude-3-5-sonnet");
+      expect(result).toContain("vertex_ai/text-bison");
+      expect(result).toContain("vertex_ai_beta/something");
+      expect(result).not.toContain("anthropic-native");
+    });
+
+    it("should include bedrock variants (converse, mantle) when called with 'Bedrock' provider key", () => {
+      const modelMap = {
+        "bedrock-base": { litellm_provider: "bedrock" },
+        "bedrock-converse-model": { litellm_provider: "bedrock_converse" },
+        "bedrock-mantle-model": { litellm_provider: "bedrock_mantle" },
+        "openai-model": { litellm_provider: "openai" },
+      };
+      const result = getProviderModels("Bedrock" as Providers, modelMap);
+      expect(result).toContain("bedrock-base");
+      expect(result).toContain("bedrock-converse-model");
+      expect(result).toContain("bedrock-mantle-model");
+      expect(result).not.toContain("openai-model");
+    });
+
+    it("should include fireworks_ai-embedding-models when called with 'FireworksAI' provider key", () => {
+      const modelMap = {
+        "fireworks-base": { litellm_provider: "fireworks_ai" },
+        "fireworks-embed": { litellm_provider: "fireworks_ai-embedding-models" },
+        "openai-model": { litellm_provider: "openai" },
+      };
+      const result = getProviderModels("FireworksAI" as Providers, modelMap);
+      expect(result).toContain("fireworks-base");
+      expect(result).toContain("fireworks-embed");
+      expect(result).not.toContain("openai-model");
     });
 
     it("should filter out models with null values", () => {
diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx
index 62c0633d117..105951114ca 100644
--- a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx
+++ b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx
@@ -389,7 +389,9 @@ export const getProviderModels = (provider: Providers, modelMap: any): Array<str
         const litellmProvider = (value as any)["litellm_provider"];
         if (
           litellmProvider === custom_llm_provider ||
-          (typeof litellmProvider === "string" && litellmProvider.includes(custom_llm_provider))
+          (typeof litellmProvider === "string" &&
+            (litellmProvider.startsWith(`${custom_llm_provider}_`) ||
+              litellmProvider.startsWith(`${custom_llm_provider}-`)))
         ) {
           providerModels.push(key);
         }
diff --git a/ui/litellm-dashboard/src/components/team/TeamInfo.tsx b/ui/litellm-dashboard/src/components/team/TeamInfo.tsx
index 8f3553a8395..ce9d15e1e19 100644
--- a/ui/litellm-dashboard/src/components/team/TeamInfo.tsx
+++ b/ui/litellm-dashboard/src/components/team/TeamInfo.tsx
@@ -502,6 +502,14 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
             (n) => !(values.guardrails || []).includes(n),
           );
 
+      // Non-proxy-admins can't set allowed_passthrough_routes; preserve the
+      // stored value so an unrelated save can't wipe it.
+      const passthroughRoutesMetadata = is_proxy_admin
+        ? { allowed_passthrough_routes: values.allowed_passthrough_routes || [] }
+        : info.metadata?.allowed_passthrough_routes
+          ? { allowed_passthrough_routes: info.metadata.allowed_passthrough_routes }
+          : {};
+
       const updateData: any = {
         team_id: teamId,
         team_alias: values.team_alias,
@@ -515,6 +523,7 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
         budget_duration: values.budget_duration,
         metadata: {
           ...parsedMetadata,
+          ...passthroughRoutesMetadata,
           guardrails: (values.guardrails || []).filter((n: string) => !globalGuardrailNames.has(n)),
           opted_out_global_guardrails: optedOutGlobalGuardrails,
           ...(values.logging_settings?.length > 0 ? { logging: values.logging_settings } : {}),
@@ -961,7 +970,7 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
                           : "",
                       metadata: info.metadata
                         ? JSON.stringify(
-                          (({ logging, secret_manager_settings, soft_budget_alerting_emails, model_tpm_limit, model_rpm_limit, ...rest }) => rest)(info.metadata),
+                          (({ logging, secret_manager_settings, soft_budget_alerting_emails, model_tpm_limit, model_rpm_limit, allowed_passthrough_routes, ...rest }) => rest)(info.metadata),
                           null,
                           2,
                         )
@@ -986,6 +995,7 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
                       },
                       access_group_ids: info.access_group_ids || [],
                       default_team_member_models: info.default_team_member_models || [],
+                      allowed_passthrough_routes: info.metadata?.allowed_passthrough_routes || [],
                     }}
                     layout="vertical"
                   >
@@ -1338,12 +1348,24 @@ const TeamInfoView: React.FC<TeamInfoProps> = ({
                     </Form.Item>
 
                     <Form.Item label="Allowed Pass Through Routes" name="allowed_passthrough_routes">
-                      <PassThroughRoutesSelector
-                        onChange={(values: string[]) => form.setFieldValue("allowed_passthrough_routes", values)}
-                        value={form.getFieldValue("allowed_passthrough_routes")}
-                        accessToken={accessToken || ""}
-                        placeholder="Select pass through routes"
-                      />
+                      <Tooltip
+                        title={
+                          !premiumUser
+                            ? "Premium feature - Upgrade to set allowed pass through routes"
+                            : !is_proxy_admin
+                              ? "Only proxy admins can set allowed pass through routes"
+                              : ""
+                        }
+                        placement="top"
+                      >
+                        <PassThroughRoutesSelector
+                          onChange={(values: string[]) => form.setFieldValue("allowed_passthrough_routes", values)}
+                          value={form.getFieldValue("allowed_passthrough_routes")}
+                          accessToken={accessToken || ""}
+                          placeholder="Select pass through routes"
+                          disabled={!premiumUser || !is_proxy_admin}
+                        />
+                      </Tooltip>
                     </Form.Item>
 
                     <Form.Item label="MCP Servers / Access Groups" name="mcp_servers_and_groups">
diff --git a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx
index a2da5136755..85a38e26977 100644
--- a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx
@@ -172,12 +172,7 @@ describe("LogDetailContent", () => {
   });
 
   it("should display loading state when isLoadingDetails is true", () => {
-    render(
-      <LogDetailContent
-        logEntry={createLogEntry()}
-        isLoadingDetails={true}
-      />,
-    );
+    render(<LogDetailContent logEntry={createLogEntry()} isLoadingDetails={true} />);
 
     expect(screen.getByText("Loading request & response data...")).toBeInTheDocument();
   });
@@ -298,6 +293,37 @@ describe("LogDetailContent", () => {
     expect(screen.getByText("42.50 ms")).toBeInTheDocument();
   });
 
+  it("should not display LiteLLM Overhead when litellm_overhead_time_ms is absent from metadata", () => {
+    render(<LogDetailContent logEntry={createLogEntry({ metadata: { status: "success" } })} />);
+
+    expect(screen.queryByText("LiteLLM Overhead")).not.toBeInTheDocument();
+  });
+
+  const retriesItem = () => screen.getByText("Retries").closest(".ant-descriptions-item") as HTMLElement;
+
+  it("should display attempted_retries / max_retries for Retries when attempted_retries > 0", () => {
+    render(
+      <LogDetailContent
+        logEntry={createLogEntry({ metadata: { status: "success", attempted_retries: 2, max_retries: 3 } })}
+      />,
+    );
+
+    expect(within(retriesItem()).getByText("2 / 3")).toBeInTheDocument();
+  });
+
+  it("should display a green 'None' tag for Retries when attempted_retries is 0", () => {
+    render(<LogDetailContent logEntry={createLogEntry({ metadata: { status: "success", attempted_retries: 0 } })} />);
+
+    const noneTag = within(retriesItem()).getByText("None");
+    expect(noneTag.closest(".ant-tag")).toHaveClass("ant-tag-green");
+  });
+
+  it("should display '-' for Retries when attempted_retries is absent from metadata", () => {
+    render(<LogDetailContent logEntry={createLogEntry({ metadata: { status: "success" } })} />);
+
+    expect(within(retriesItem()).getByText("-")).toBeInTheDocument();
+  });
+
   it("should display start and end time in ISO format", () => {
     render(
       <LogDetailContent
diff --git a/ui/litellm-dashboard/src/components/view_logs/LogsTableToolbar.tsx b/ui/litellm-dashboard/src/components/view_logs/LogsTableToolbar.tsx
new file mode 100644
index 00000000000..81ebea8e7d5
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/view_logs/LogsTableToolbar.tsx
@@ -0,0 +1,243 @@
+import moment from "moment";
+import { useEffect, useRef, useState } from "react";
+import { SyncOutlined } from "@ant-design/icons";
+import { Button, Switch } from "antd";
+import { QUICK_SELECT_OPTIONS } from "./constants";
+import { getTimeRangeDisplay } from "./logs_utils";
+import type { PaginatedResponse } from "./log_filter_logic";
+
+interface LogsTableToolbarProps {
+  searchTerm: string;
+  onSearchChange: (value: string) => void;
+  startTime: string;
+  onStartTimeChange: (value: string) => void;
+  endTime: string;
+  onEndTimeChange: (value: string) => void;
+  isCustomDate: boolean;
+  onIsCustomDateChange: (value: boolean) => void;
+  selectedTimeInterval: { value: number; unit: string };
+  onSelectedTimeIntervalChange: (value: { value: number; unit: string }) => void;
+  isLiveTail: boolean;
+  onIsLiveTailChange: (value: boolean) => void;
+  currentPage: number;
+  onCurrentPageChange: (updater: number | ((prev: number) => number)) => void;
+  pageSize: number;
+  isLoading: boolean;
+  isButtonLoading: boolean;
+  onRefetch: () => void;
+  filteredLogs: PaginatedResponse;
+}
+
+export function LogsTableToolbar({
+  searchTerm,
+  onSearchChange,
+  startTime,
+  onStartTimeChange,
+  endTime,
+  onEndTimeChange,
+  isCustomDate,
+  onIsCustomDateChange,
+  selectedTimeInterval,
+  onSelectedTimeIntervalChange,
+  isLiveTail,
+  onIsLiveTailChange,
+  currentPage,
+  onCurrentPageChange,
+  pageSize,
+  isLoading,
+  isButtonLoading,
+  onRefetch,
+  filteredLogs,
+}: LogsTableToolbarProps) {
+  const [quickSelectOpen, setQuickSelectOpen] = useState(false);
+  const quickSelectRef = useRef<HTMLDivElement>(null);
+
+  useEffect(() => {
+    function handleClickOutside(event: MouseEvent) {
+      if (quickSelectRef.current && !quickSelectRef.current.contains(event.target as Node)) {
+        setQuickSelectOpen(false);
+      }
+    }
+    document.addEventListener("mousedown", handleClickOutside);
+    return () => document.removeEventListener("mousedown", handleClickOutside);
+  }, []);
+
+  const selectedOption = QUICK_SELECT_OPTIONS.find(
+    (option) => option.value === selectedTimeInterval.value && option.unit === selectedTimeInterval.unit,
+  );
+  const displayLabel = isCustomDate ? getTimeRangeDisplay(isCustomDate, startTime, endTime) : selectedOption?.label;
+
+  return (
+    <>
+      <div className="border-b px-6 py-4 w-full max-w-full box-border">
+        <div className="flex flex-col md:flex-row items-start md:items-center justify-between space-y-4 md:space-y-0 w-full max-w-full box-border">
+          <div className="flex flex-wrap items-center gap-3 w-full max-w-full box-border">
+            <div className="relative w-64 min-w-0 flex-shrink-0">
+              <input
+                type="text"
+                placeholder="Search by Request ID"
+                className="w-full px-3 py-2 pl-8 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
+                value={searchTerm}
+                onChange={(e) => onSearchChange(e.target.value)}
+              />
+              <svg
+                className="absolute left-2.5 top-2.5 h-4 w-4 text-gray-500"
+                fill="none"
+                stroke="currentColor"
+                viewBox="0 0 24 24"
+              >
+                <path
+                  strokeLinecap="round"
+                  strokeLinejoin="round"
+                  strokeWidth={2}
+                  d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z"
+                />
+              </svg>
+            </div>
+
+            <div className="flex items-center gap-2 min-w-0 flex-shrink">
+              <div className="relative z-50" ref={quickSelectRef}>
+                <button
+                  onClick={() => setQuickSelectOpen(!quickSelectOpen)}
+                  className="px-3 py-2 text-sm border rounded-md hover:bg-gray-50 flex items-center gap-2"
+                >
+                  <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                    <path
+                      strokeLinecap="round"
+                      strokeLinejoin="round"
+                      strokeWidth={2}
+                      d="M8 7V3m8 4V3m-9 8h10M5 21h14a2 2 0 002-2V7a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z"
+                    />
+                  </svg>
+                  {displayLabel}
+                </button>
+
+                {quickSelectOpen && (
+                  <div className="absolute left-0 mt-2 w-64 bg-white rounded-lg shadow-lg border p-2 z-50">
+                    <div className="space-y-1">
+                      {QUICK_SELECT_OPTIONS.map((option) => (
+                        <button
+                          key={option.label}
+                          className={`w-full px-3 py-2 text-left text-sm hover:bg-gray-50 rounded-md ${displayLabel === option.label ? "bg-blue-50 text-blue-600" : ""}`}
+                          onClick={() => {
+                            onCurrentPageChange(1);
+                            onEndTimeChange(moment().format("YYYY-MM-DDTHH:mm"));
+                            onStartTimeChange(
+                              moment()
+                                .subtract(option.value, option.unit as any)
+                                .format("YYYY-MM-DDTHH:mm"),
+                            );
+                            onSelectedTimeIntervalChange({ value: option.value, unit: option.unit });
+                            onIsCustomDateChange(false);
+                            setQuickSelectOpen(false);
+                          }}
+                        >
+                          {option.label}
+                        </button>
+                      ))}
+                      <div className="border-t my-2" />
+                      <button
+                        className={`w-full px-3 py-2 text-left text-sm hover:bg-gray-50 rounded-md ${isCustomDate ? "bg-blue-50 text-blue-600" : ""}`}
+                        onClick={() => onIsCustomDateChange(!isCustomDate)}
+                      >
+                        Custom Range
+                      </button>
+                    </div>
+                  </div>
+                )}
+              </div>
+
+              <div className="flex items-center gap-2">
+                <span className="text-sm font-medium text-gray-900">Live Tail</span>
+                <Switch checked={isLiveTail} defaultChecked={true} onChange={onIsLiveTailChange} />
+              </div>
+
+              <Button
+                type="default"
+                icon={<SyncOutlined spin={isButtonLoading} />}
+                onClick={onRefetch}
+                disabled={isButtonLoading}
+                title="Fetch data"
+              >
+                {isButtonLoading ? "Fetching" : "Fetch"}
+              </Button>
+            </div>
+
+            {isCustomDate && (
+              <div className="flex items-center gap-2">
+                <div>
+                  <input
+                    type="datetime-local"
+                    value={startTime}
+                    onChange={(e) => {
+                      onStartTimeChange(e.target.value);
+                      onCurrentPageChange(1);
+                    }}
+                    className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
+                  />
+                </div>
+                <span className="text-gray-500">to</span>
+                <div>
+                  <input
+                    type="datetime-local"
+                    value={endTime}
+                    onChange={(e) => {
+                      onEndTimeChange(e.target.value);
+                      onCurrentPageChange(1);
+                    }}
+                    className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
+                  />
+                </div>
+              </div>
+            )}
+          </div>
+
+          <div className="flex items-center space-x-4">
+            <span className="text-sm text-gray-700 whitespace-nowrap">
+              Showing {isLoading ? "..." : filteredLogs ? (currentPage - 1) * pageSize + 1 : 0} -{" "}
+              {isLoading
+                ? "..."
+                : filteredLogs
+                  ? Math.min(currentPage * pageSize, filteredLogs.total)
+                  : 0}{" "}
+              of {isLoading ? "..." : filteredLogs ? filteredLogs.total : 0} results
+            </span>
+            <div className="flex items-center space-x-2">
+              <span className="text-sm text-gray-700 min-w-[90px]">
+                Page {isLoading ? "..." : currentPage} of{" "}
+                {isLoading ? "..." : filteredLogs ? filteredLogs.total_pages : 1}
+              </span>
+              <button
+                onClick={() => onCurrentPageChange((p: number) => Math.max(1, p - 1))}
+                disabled={isLoading || currentPage === 1}
+                className="px-3 py-1 text-sm border rounded-md hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed"
+              >
+                Previous
+              </button>
+              <button
+                onClick={() => onCurrentPageChange((p: number) => Math.min(filteredLogs.total_pages || 1, p + 1))}
+                disabled={isLoading || currentPage === (filteredLogs.total_pages || 1)}
+                className="px-3 py-1 text-sm border rounded-md hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed"
+              >
+                Next
+              </button>
+            </div>
+          </div>
+        </div>
+      </div>
+      {isLiveTail && currentPage === 1 && (
+        <div className="mb-4 px-4 py-2 bg-green-50 border border-green-200 rounded-md flex items-center justify-between">
+          <div className="flex items-center gap-2">
+            <span className="text-sm text-green-700">Auto-refreshing every 15 seconds</span>
+          </div>
+          <button
+            onClick={() => onIsLiveTailChange(false)}
+            className="text-sm text-green-600 hover:text-green-800"
+          >
+            Stop
+          </button>
+        </div>
+      )}
+    </>
+  );
+}
diff --git a/ui/litellm-dashboard/src/components/view_logs/filter_options.ts b/ui/litellm-dashboard/src/components/view_logs/filter_options.ts
new file mode 100644
index 00000000000..59ac58b6745
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/view_logs/filter_options.ts
@@ -0,0 +1,77 @@
+import FilterTeamDropdown from "../common_components/FilterTeamDropdown";
+import { PaginatedKeyAliasSelect } from "../KeyAliasSelect/PaginatedKeyAliasSelect/PaginatedKeyAliasSelect";
+import { PaginatedModelSelect } from "../ModelSelect/PaginatedModelSelect/PaginatedModelSelect";
+import { FilterOption } from "../molecules/filter";
+import { allEndUsersCall } from "../networking";
+import { ERROR_CODE_OPTIONS } from "./constants";
+import { FILTER_KEYS } from "./log_filter_logic";
+
+export function getLogFilterOptions(accessToken: string): FilterOption[] {
+  return [
+    {
+      name: "Team ID",
+      label: "Team ID",
+      customComponent: FilterTeamDropdown,
+    },
+    {
+      name: "Status",
+      label: "Status",
+      isSearchable: false,
+      options: [
+        { label: "Success", value: "success" },
+        { label: "Failure", value: "failure" },
+      ],
+    },
+    {
+      name: "Model",
+      label: "Model",
+      customComponent: PaginatedModelSelect,
+    },
+    {
+      name: FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL,
+      label: "Public model / search tool",
+      isSearchable: false,
+    },
+    {
+      name: "Key Alias",
+      label: "Key Alias",
+      customComponent: PaginatedKeyAliasSelect,
+    },
+    {
+      name: "End User",
+      label: "End User",
+      isSearchable: true,
+      searchFn: async (searchText: string) => {
+        const data = await allEndUsersCall(accessToken);
+        const users = data?.map((u: any) => u.user_id) || [];
+        const filtered = users.filter((u: string) => u.toLowerCase().includes(searchText.toLowerCase()));
+        return filtered.map((u: string) => ({ label: u, value: u }));
+      },
+    },
+    {
+      name: "Error Code",
+      label: "Error Code",
+      isSearchable: true,
+      searchFn: async (searchText: string) => {
+        if (!searchText) return ERROR_CODE_OPTIONS;
+        const lower = searchText.toLowerCase();
+        const filtered = ERROR_CODE_OPTIONS.filter((opt) => opt.label.toLowerCase().includes(lower));
+        const isExactValue = ERROR_CODE_OPTIONS.some((opt) => opt.value === searchText.trim());
+        if (!isExactValue && searchText.trim()) {
+          filtered.push({ label: `Use custom code: ${searchText.trim()}`, value: searchText.trim() });
+        }
+        return filtered;
+      },
+    },
+    {
+      name: "Key Hash",
+      label: "Key Hash",
+      isSearchable: false,
+    },
+    {
+      name: "Error Message",
+      label: "Error Message",
+      isSearchable: false,
+    },
+  ];
+}
diff --git a/ui/litellm-dashboard/src/components/view_logs/index.test.tsx b/ui/litellm-dashboard/src/components/view_logs/index.test.tsx
index 7a9a541d3e0..aed194a2972 100644
--- a/ui/litellm-dashboard/src/components/view_logs/index.test.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/index.test.tsx
@@ -1,12 +1,8 @@
-import { render, screen, waitFor } from "@testing-library/react";
+import { screen, waitFor } from "@testing-library/react";
 import userEvent from "@testing-library/user-event";
-import moment from "moment";
 import { beforeEach, describe, expect, it, vi } from "vitest";
-import SpendLogsTable, { RequestViewer } from "./index";
-import type { LogEntry } from "./columns";
-import type { Row } from "@tanstack/react-table";
+import SpendLogsTable from "./index";
 import { renderWithProviders } from "../../../tests/test-utils";
-import { uiSpendLogsCall } from "../networking";
 
 const mockHandleFilterResetFromHook = vi.fn();
 vi.mock("./log_filter_logic", async (importOriginal) => {
@@ -14,14 +10,8 @@ vi.mock("./log_filter_logic", async (importOriginal) => {
   return {
     ...actual,
     useLogFilterLogic: vi.fn(() => ({
-      filters: {},
-      filteredLogs: {
-        data: [],
-        total: 0,
-        page: 1,
-        page_size: 50,
-        total_pages: 1,
-      },
+      logsQuery: { isLoading: false, isFetching: false, refetch: vi.fn() },
+      filteredLogs: { data: [], total: 0, page: 1, page_size: 50, total_pages: 1 },
       allTeams: [],
       handleFilterChange: vi.fn(),
       handleFilterReset: mockHandleFilterResetFromHook,
@@ -50,139 +40,6 @@ vi.mock("../key_team_helpers/filter_helpers", () => ({
   fetchAllTeams: vi.fn().mockResolvedValue([]),
 }));
 
-const baseLogEntry: LogEntry = {
-  request_id: "chatcmpl-test-id",
-  api_key: "api-key",
-  team_id: "team-id",
-  model: "gpt-4",
-  model_id: "gpt-4",
-  call_type: "chat",
-  spend: 0,
-  total_tokens: 0,
-  prompt_tokens: 0,
-  completion_tokens: 0,
-  startTime: "2025-11-14T00:00:00Z",
-  endTime: "2025-11-14T00:00:00Z",
-  cache_hit: "miss",
-  request_duration_ms: 1000,
-  messages: [{ role: "user", content: "hello" }],
-  response: { status: "ok" },
-  metadata: {
-    status: "success",
-    additional_usage_values: {
-      cache_read_input_tokens: 0,
-      cache_creation_input_tokens: 0,
-    },
-  },
-  request_tags: {},
-  custom_llm_provider: "openai",
-  api_base: "https://api.example.com",
-};
-
-const createRow = (overrides: Partial<LogEntry> = {}): Row<LogEntry> =>
-  ({
-    original: {
-      ...baseLogEntry,
-      ...overrides,
-    },
-  }) as unknown as Row<LogEntry>;
-
-describe("Request Viewer", () => {
-  it("renders the request details heading", () => {
-    render(<RequestViewer row={createRow()} />);
-    expect(screen.getByText("Request Details")).toBeInTheDocument();
-  });
-
-  it("should truncate the request id if it is longer than 64 characters", () => {
-    const LONG_REQUEST_ID = "a".repeat(128);
-    const TRUNCATED_REQUEST_ID = `${"a".repeat(64)}...`;
-    render(
-      <RequestViewer
-        row={createRow({
-          request_id: LONG_REQUEST_ID,
-        })}
-      />,
-    );
-
-    expect(screen.getByText(TRUNCATED_REQUEST_ID)).toBeInTheDocument();
-  });
-
-  it("should display LiteLLM Overhead when litellm_overhead_time_ms is present in metadata", () => {
-    render(
-      <RequestViewer
-        row={createRow({
-          metadata: {
-            status: "success",
-            litellm_overhead_time_ms: 150,
-            additional_usage_values: {
-              cache_read_input_tokens: 0,
-              cache_creation_input_tokens: 0,
-            },
-          },
-        })}
-      />,
-    );
-
-    expect(screen.getByText("LiteLLM Overhead:")).toBeInTheDocument();
-    expect(screen.getByText("150 ms")).toBeInTheDocument();
-  });
-
-  it("should not display LiteLLM Overhead when litellm_overhead_time_ms is not present in metadata", () => {
-    render(<RequestViewer row={createRow()} />);
-
-    expect(screen.queryByText("LiteLLM Overhead:")).not.toBeInTheDocument();
-  });
-
-  it("should display retry count when attempted_retries > 0 in metadata", () => {
-    render(
-      <RequestViewer
-        row={createRow({
-          metadata: {
-            status: "success",
-            attempted_retries: 2,
-            max_retries: 3,
-            additional_usage_values: {
-              cache_read_input_tokens: 0,
-              cache_creation_input_tokens: 0,
-            },
-          },
-        })}
-      />,
-    );
-
-    expect(screen.getByText("Retries:")).toBeInTheDocument();
-    expect(screen.getByText("2 / 3")).toBeInTheDocument();
-  });
-
-  it("should display green 'None' tag when attempted_retries is 0", () => {
-    render(
-      <RequestViewer
-        row={createRow({
-          metadata: {
-            status: "success",
-            attempted_retries: 0,
-            max_retries: 3,
-            additional_usage_values: {
-              cache_read_input_tokens: 0,
-              cache_creation_input_tokens: 0,
-            },
-          },
-        })}
-      />,
-    );
-
-    expect(screen.getByText("Retries:")).toBeInTheDocument();
-    expect(screen.getByText("None")).toBeInTheDocument();
-  });
-
-  it("should display '-' for Retries when attempted_retries is not present in metadata", () => {
-    render(<RequestViewer row={createRow()} />);
-
-    expect(screen.getByText("Retries:")).toBeInTheDocument();
-    expect(screen.getByText("-")).toBeInTheDocument();
-  });
-});
-
 describe("SpendLogsTable", () => {
   const defaultProps = {
     accessToken: "test-token",
@@ -215,7 +72,9 @@ describe("SpendLogsTable", () => {
     renderWithProviders(<SpendLogsTable {...defaultProps} />);
 
     // Open the time range quick select dropdown (button shows current range like "Last 24 Hours")
-    const quickSelectButton = screen.getByRole("button", { name: /Last 24 Hours|Last 15 Minutes|Last Hour|Last 4 Hours|Last 7 Days/i });
+    const quickSelectButton = screen.getByRole("button", {
+      name: /Last 24 Hours|Last 15 Minutes|Last Hour|Last 4 Hours|Last 7 Days/i,
+    });
     await user.click(quickSelectButton);
 
     // Click "Custom Range" to enable custom date selection
@@ -241,51 +100,19 @@ describe("SpendLogsTable", () => {
     });
   });
 
-  describe("Quick Select time range", () => {
-    const waitForWindowSeconds = async (minMinutes: number) => {
-      let diff = -1;
-      await waitFor(() => {
-        const lastCall = vi.mocked(uiSpendLogsCall).mock.calls.at(-1)?.[0];
-        if (!lastCall) throw new Error("uiSpendLogsCall was not called");
-        diff = moment
-          .utc(lastCall.end_date, "YYYY-MM-DD HH:mm:ss")
-          .diff(moment.utc(lastCall.start_date, "YYYY-MM-DD HH:mm:ss"), "seconds");
-        // start_date is rounded down to the minute boundary; end_date is current time
-        expect(diff).toBeGreaterThanOrEqual(minMinutes * 60);
-        expect(diff).toBeLessThan((minMinutes + 1) * 60);
-      });
-      return diff;
-    };
+  describe("auth-not-ready guard", () => {
+    it("shows a loading spinner when credentials are not yet resolved", () => {
+      renderWithProviders(<SpendLogsTable {...defaultProps} accessToken={null} />);
 
-    it("should pass a ~1-minute window to uiSpendLogsCall when 'Last Minute' is selected", async () => {
-      const user = userEvent.setup();
-      renderWithProviders(<SpendLogsTable {...defaultProps} />);
-
-      await user.click(screen.getByRole("button", { name: /Last 24 Hours/i }));
-      await user.click(await screen.findByRole("button", { name: "Last Minute" }));
-
-      await waitForWindowSeconds(1);
+      expect(document.querySelector(".ant-spin")).toBeInTheDocument();
+      expect(screen.queryByRole("button", { name: "Reset Filters" })).not.toBeInTheDocument();
     });
 
-    it("should pass a ~15-minute window to uiSpendLogsCall when 'Last 15 Minutes' is selected", async () => {
-      const user = userEvent.setup();
+    it("renders the table (no spinner) once all credentials are present", () => {
       renderWithProviders(<SpendLogsTable {...defaultProps} />);
 
-      await user.click(screen.getByRole("button", { name: /Last 24 Hours/i }));
-      await user.click(await screen.findByRole("button", { name: "Last 15 Minutes" }));
-
-      await waitForWindowSeconds(15);
-    });
-
-    it("should update the time-range button label to 'Last Minute' after selecting it", async () => {
-      const user = userEvent.setup();
-      renderWithProviders(<SpendLogsTable {...defaultProps} />);
-
-      await user.click(screen.getByRole("button", { name: /Last 24 Hours/i }));
-      await user.click(await screen.findByRole("button", { name: "Last Minute" }));
-
-      expect(screen.getByRole("button", { name: "Last Minute" })).toBeInTheDocument();
-      expect(screen.queryByRole("button", { name: /Last 24 Hours/i })).not.toBeInTheDocument();
+      expect(document.querySelector(".ant-spin")).not.toBeInTheDocument();
+      expect(screen.getByRole("button", { name: "Reset Filters" })).toBeInTheDocument();
     });
   });
 });
diff --git a/ui/litellm-dashboard/src/components/view_logs/index.tsx b/ui/litellm-dashboard/src/components/view_logs/index.tsx
index 2f9e8fe8780..6c5fd03f0a0 100644
--- a/ui/litellm-dashboard/src/components/view_logs/index.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/index.tsx
@@ -1,35 +1,22 @@
-import { keepPreviousData, useQuery, useQueryClient } from "@tanstack/react-query";
 import moment from "moment";
-import { useCallback, useDeferredValue, useEffect, useRef, useState } from "react";
-import GuardrailViewer from "@/components/view_logs/GuardrailViewer/GuardrailViewer";
-import { formatNumberWithCommas } from "@/utils/dataUtils";
-import { truncateString } from "@/utils/textUtils";
-import { SyncOutlined } from "@ant-design/icons";
-import { Row } from "@tanstack/react-table";
-import { Switch, Tab, TabGroup, TabList, TabPanel, TabPanels } from "@tremor/react";
-import { Button, Tag, Tooltip } from "antd";
+import { useCallback, useDeferredValue, useEffect, useMemo, useState } from "react";
+import { Tab, TabGroup, TabList, TabPanel, TabPanels } from "@tremor/react";
 import { internalUserRoles } from "../../utils/roles";
 import DeletedKeysPage from "../DeletedKeysPage/DeletedKeysPage";
 import DeletedTeamsPage from "../DeletedTeamsPage/DeletedTeamsPage";
-import FilterTeamDropdown from "../common_components/FilterTeamDropdown";
 import { KeyResponse } from "../key_team_helpers/key_list";
-import { PaginatedKeyAliasSelect } from "../KeyAliasSelect/PaginatedKeyAliasSelect/PaginatedKeyAliasSelect";
-import { PaginatedModelSelect } from "../ModelSelect/PaginatedModelSelect/PaginatedModelSelect";
-import FilterComponent, { FilterOption } from "../molecules/filter";
-import { allEndUsersCall, keyInfoV1Call, uiSpendLogsCall } from "../networking";
+import FilterComponent from "../molecules/filter";
+import { keyInfoV1Call } from "../networking";
 import KeyInfoView from "../templates/key_info_view";
 import AuditLogs from "./audit_logs";
 import { createColumns, LogEntry, type LogsSortField } from "./columns";
-import { ConfigInfoMessage } from "./ConfigInfoMessage";
-import { AGENT_CALL_TYPES, ERROR_CODE_OPTIONS, MCP_CALL_TYPES, QUICK_SELECT_OPTIONS } from "./constants";
-import { CostBreakdownViewer } from "./CostBreakdownViewer";
-import { ErrorViewer } from "./ErrorViewer";
-import { FILTER_KEYS, useLogFilterLogic } from "./log_filter_logic";
+import { AGENT_CALL_TYPES, MCP_CALL_TYPES } from "./constants";
+import { getLogFilterOptions } from "./filter_options";
+import { useLogFilterLogic, defaultFilters, type LogFilterState } from "./log_filter_logic";
 import { LogDetailsDrawer } from "./LogDetailsDrawer";
-import { getTimeRangeDisplay } from "./logs_utils";
-import { RequestResponsePanel } from "./RequestResponsePanel";
+import { LogsTableToolbar } from "./LogsTableToolbar";
 import { DataTable } from "./table";
-import { VectorStoreViewer } from "./VectorStoreViewer";
+import { AntDLoadingSpinner } from "../ui/AntDLoadingSpinner";
 
 interface SpendLogsTableProps {
   accessToken: string | null;
@@ -39,45 +26,19 @@ interface SpendLogsTableProps {
   premiumUser: boolean;
 }
 
-export interface PaginatedResponse {
-  data: LogEntry[];
-  total: number;
-  page: number;
-  page_size: number;
-  total_pages: number;
-}
-
-export default function SpendLogsTable({
-  accessToken,
-  token,
-  userRole,
-  userID,
-  premiumUser,
-}: SpendLogsTableProps) {
+export default function SpendLogsTable({ accessToken, token, userRole, userID, premiumUser }: SpendLogsTableProps) {
   const [searchTerm, setSearchTerm] = useState("");
-  const [showFilters, setShowFilters] = useState(false);
-  const [showColumnDropdown, setShowColumnDropdown] = useState(false);
   const [currentPage, setCurrentPage] = useState(1);
   const [pageSize] = useState(50);
-  const dropdownRef = useRef<HTMLDivElement>(null);
-  const filtersRef = useRef<HTMLDivElement>(null);
-  const quickSelectRef = useRef<HTMLDivElement>(null);
 
   // New state variables for Start and End Time
   const [startTime, setStartTime] = useState<string>(moment().subtract(24, "hours").format("YYYY-MM-DDTHH:mm"));
   const [endTime, setEndTime] = useState<string>(moment().format("YYYY-MM-DDTHH:mm"));
 
   const [isCustomDate, setIsCustomDate] = useState(false);
-  const [quickSelectOpen, setQuickSelectOpen] = useState(false);
-  const [tempTeamId, setTempTeamId] = useState("");
-  const [tempKeyHash, setTempKeyHash] = useState("");
-  const [selectedTeamId, setSelectedTeamId] = useState("");
-  const [selectedKeyHash, setSelectedKeyHash] = useState("");
-  const [selectedModelId, setSelectedModelId] = useState("");
+  const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
   const [selectedKeyInfo, setSelectedKeyInfo] = useState<KeyResponse | null>(null);
   const [selectedKeyIdInfoView, setSelectedKeyIdInfoView] = useState<string | null>(null);
-  const [selectedStatus, setSelectedStatus] = useState("");
-  const [selectedEndUser, setSelectedEndUser] = useState("");
   const [filterByCurrentUser, setFilterByCurrentUser] = useState(userRole && internalUserRoles.includes(userRole));
   const [activeTab, setActiveTab] = useState("request logs");
 
@@ -88,12 +49,10 @@ export default function SpendLogsTable({
   const [sortBy, setSortBy] = useState<LogsSortField>("startTime");
   const [sortOrder, setSortOrder] = useState<"asc" | "desc">("desc");
 
-  // Tracks whether any filter that uses performSearch (backend) is active.
-  // Used to disable the main query so it doesn't fire redundant unfiltered requests
-  // when time range / sort / page changes while a backend filter is in effect.
-  const [isMainQueryEnabled, setIsMainQueryEnabled] = useState(true);
-
-  const queryClient = useQueryClient();
+  const [selectedTimeInterval, setSelectedTimeInterval] = useState<{ value: number; unit: string }>({
+    value: 24,
+    unit: "hours",
+  });
 
   const [isLiveTail, setIsLiveTail] = useState<boolean>(() => {
     const storedValue = sessionStorage.getItem("isLiveTail");
@@ -105,11 +64,6 @@ export default function SpendLogsTable({
     sessionStorage.setItem("isLiveTail", JSON.stringify(isLiveTail));
   }, [isLiveTail]);
 
-  const [selectedTimeInterval, setSelectedTimeInterval] = useState<{ value: number; unit: string }>({
-    value: 24,
-    unit: "hours",
-  });
-
   useEffect(() => {
     const fetchKeyInfo = async () => {
       if (selectedKeyIdInfoView && accessToken) {
@@ -126,132 +80,33 @@ export default function SpendLogsTable({
     fetchKeyInfo();
   }, [selectedKeyIdInfoView, accessToken]);
 
-  // Close dropdown when clicking outside
-  useEffect(() => {
-    function handleClickOutside(event: MouseEvent) {
-      if (dropdownRef.current && !dropdownRef.current.contains(event.target as Node)) {
-        setShowColumnDropdown(false);
-      }
-      if (filtersRef.current && !filtersRef.current.contains(event.target as Node)) {
-        setShowFilters(false);
-      }
-      if (quickSelectRef.current && !quickSelectRef.current.contains(event.target as Node)) {
-        setQuickSelectOpen(false);
-      }
-    }
-
-    document.addEventListener("mousedown", handleClickOutside);
-    return () => document.removeEventListener("mousedown", handleClickOutside);
-  }, []);
-
   useEffect(() => {
     if (userRole && internalUserRoles.includes(userRole)) {
       setFilterByCurrentUser(true);
     }
   }, [userRole]);
 
-  const LiveTailControls = () => {
-    return (
-      <div className="flex items-center gap-2">
-        <span className="text-sm font-medium text-gray-900">Live Tail</span>
-        <Switch color="green" checked={isLiveTail} defaultChecked={true} onChange={setIsLiveTail} />
-      </div>
-    );
-  };
-
-  const logs = useQuery<PaginatedResponse>({
-    queryKey: [
-      "logs",
-      "table",
-      currentPage,
-      pageSize,
-      startTime,
-      endTime,
-      selectedTeamId,
-      selectedKeyHash,
-      filterByCurrentUser ? userID : null,
-      selectedStatus,
-      selectedModelId,
-      sortBy,
-      sortOrder,
-    ],
-    queryFn: async () => {
-      if (!accessToken || !token || !userRole || !userID) {
-        return {
-          data: [],
-          total: 0,
-          page: 1,
-          page_size: pageSize,
-          total_pages: 0,
-        };
-      }
-
-      const formattedStartTime = moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss");
-      const formattedEndTime = isCustomDate
-        ? moment(endTime).utc().format("YYYY-MM-DD HH:mm:ss")
-        : moment().utc().format("YYYY-MM-DD HH:mm:ss");
-
-      // Get base response from API
-      // NOTE: We only fetch the list of logs here (lightweight).
-      // Log details (messages/response) are fetched on-demand when user clicks a row.
-      const response = await uiSpendLogsCall({
-        accessToken,
-        start_date: formattedStartTime,
-        end_date: formattedEndTime,
-        page: currentPage,
-        page_size: pageSize,
-        params: {
-          api_key: selectedKeyHash || undefined,
-          team_id: selectedTeamId || undefined,
-          user_id: filterByCurrentUser ? userID ?? undefined : undefined,
-          end_user: selectedEndUser || undefined,
-          status_filter: selectedStatus || undefined,
-          model_id: selectedModelId || undefined,
-          sort_by: sortBy,
-          sort_order: sortOrder,
-        },
-      });
-
-      return response;
-    },
-    enabled: !!accessToken && !!token && !!userRole && !!userID && activeTab === "request logs" && isMainQueryEnabled,
-    refetchInterval: isLiveTail && currentPage === 1 ? 15000 : false,
-    placeholderData: keepPreviousData,
-    refetchIntervalInBackground: true,
-  });
-
-  // Defer the transition from "Fetching" to "Fetch" so the button stays loading until
-  // the table has rendered with the new data (avoids the visual gap where the button
-  // exits loading state before the table updates)
-  const isFetchingDeferred = useDeferredValue(logs.isFetching);
-  const isButtonLoading = logs.isFetching || isFetchingDeferred;
-
-  const logsData = logs.data || {
-    data: [],
-    total: 0,
-    page: 1,
-    page_size: pageSize || 10,
-    total_pages: 1,
-  };
-
   const {
-    filters,
+    logsQuery,
     filteredLogs,
-    hasBackendFilters,
     allTeams,
     handleFilterChange,
     handleFilterReset: handleFilterResetFromHook,
-    refetchWithFilters,
   } = useLogFilterLogic({
-    logs: logsData,
     accessToken,
+    token,
+    userRole,
+    userID,
+    filters,
+    setFilters,
+    filterByCurrentUser: !!filterByCurrentUser,
+    activeTab,
+    isLiveTail,
     startTime,
     endTime,
     pageSize,
     isCustomDate,
     setCurrentPage,
-    userID,
-    userRole,
     sortBy,
     sortOrder,
     currentPage,
@@ -259,7 +114,6 @@ export default function SpendLogsTable({
 
   const handleFilterReset = useCallback(() => {
     handleFilterResetFromHook();
-    // Reset custom time range to default (last 24 hours)
     setStartTime(moment().subtract(24, "hours").format("YYYY-MM-DDTHH:mm"));
     setEndTime(moment().format("YYYY-MM-DDTHH:mm"));
     setIsCustomDate(false);
@@ -267,111 +121,104 @@ export default function SpendLogsTable({
     setCurrentPage(1);
   }, [handleFilterResetFromHook]);
 
-  // Disable the main query whenever backend filters are active so it doesn't fire
-  // redundant unfiltered requests when time range / sort / page changes.
-  useEffect(() => {
-    setIsMainQueryEnabled(!hasBackendFilters);
-  }, [hasBackendFilters]);
+  const handleSortChange = useCallback((newSortBy: LogsSortField, newSortOrder: "asc" | "desc") => {
+    setSortBy(newSortBy);
+    setSortOrder(newSortOrder);
+    setCurrentPage(1);
+  }, []);
 
-  // Sync filter state into the individual selectedX state variables used by the main query
-  useEffect(() => {
-    if (!accessToken) return;
+  const columns = useMemo(
+    () => createColumns({ sortBy, sortOrder, onSortChange: handleSortChange }),
+    [sortBy, sortOrder, handleSortChange],
+  );
 
-    if (filters["Team ID"]) {
-      setSelectedTeamId(filters["Team ID"]);
-    } else {
-      setSelectedTeamId("");
+  const filteredData = useMemo(() => {
+    const searchedLogs = filteredLogs.data.filter((log) => {
+      const matchesSearch =
+        !searchTerm ||
+        log.request_id.includes(searchTerm) ||
+        log.model.includes(searchTerm) ||
+        (log.user && log.user.includes(searchTerm));
+
+      // No need for additional filtering since we're now handling this in the API call
+      return matchesSearch;
+    });
+
+    const sessionCompositionById = searchedLogs.reduce<Record<string, { llm: number; agent: number; mcp: number }>>(
+      (acc, log) => {
+        if (!log.session_id) return acc;
+        if (!acc[log.session_id]) {
+          acc[log.session_id] = { llm: 0, agent: 0, mcp: 0 };
+        }
+        if (MCP_CALL_TYPES.includes(log.call_type)) {
+          acc[log.session_id].mcp += 1;
+        } else if (AGENT_CALL_TYPES.includes(log.call_type)) {
+          acc[log.session_id].agent += 1;
+        } else {
+          acc[log.session_id].llm += 1;
+        }
+        return acc;
+      },
+      {},
+    );
+
+    // Build a single-pass map of session_id → representative request_id.
+    // Prefers an LLM row over an MCP row as the representative.
+    const sessionRepresentativeMap = new Map<string, { requestId: string; isMcp: boolean }>();
+    for (const log of searchedLogs) {
+      if (!log.session_id || (log.session_total_count || 1) <= 1) continue;
+      const isMcp = MCP_CALL_TYPES.includes(log.call_type);
+      const existing = sessionRepresentativeMap.get(log.session_id);
+      if (!existing || (existing.isMcp && !isMcp)) {
+        sessionRepresentativeMap.set(log.session_id, { requestId: log.request_id, isMcp });
+      }
     }
-    setSelectedStatus(filters["Status"] || "");
-    setSelectedModelId(filters["Model"] || "");
-    setSelectedEndUser(filters["End User"] || "");
 
-    // Key Alias filtering is handled server-side by performSearch via the key_alias param.
-    // We intentionally do not translate the alias to a hash here to avoid firing a
-    // redundant main-query request (api_key=hash) alongside performSearch's key_alias request.
-    setSelectedKeyHash(filters["Key Hash"] || "");
-  }, [filters, accessToken]);
+    return (
+      searchedLogs
+        .map((log) => {
+          const sessionComposition = log.session_id ? sessionCompositionById[log.session_id] : undefined;
+          return {
+            ...log,
+            request_duration_ms: log.request_duration_ms,
+            session_llm_count: sessionComposition?.llm ?? undefined,
+            session_mcp_count: sessionComposition?.mcp ?? undefined,
+            session_agent_count: sessionComposition?.agent ?? undefined,
+            onKeyHashClick: (keyHash: string) => setSelectedKeyIdInfoView(keyHash),
+            onSessionClick: (sessionId: string) => {
+              if (sessionId) {
+                setSelectedSessionId(sessionId);
+                setSelectedLog(log);
+                setIsDrawerOpen(true);
+              }
+            },
+          };
+        })
+        // Deduplicate multi-call sessions using the pre-built map (O(1) per row).
+        .filter((log) => {
+          if (!log.session_id || (log.session_total_count || 1) <= 1) return true;
+          return sessionRepresentativeMap.get(log.session_id)?.requestId === log.request_id;
+        })
+    );
+  }, [filteredLogs.data, searchTerm]);
+
+  // Keep the Fetch button busy until the table has actually committed the new
+  // rows. `keepPreviousData` leaves logsQuery.isLoading false on refetch, so
+  // without this the button clears while stale rows are still on screen.
+  const deferredData = useDeferredValue(filteredData);
+  const isStale = deferredData !== filteredData;
+  const isButtonLoading = logsQuery.isFetching || isStale;
+  const isRefiltering = logsQuery.isPlaceholderData;
+  const isLogsLoading = logsQuery.isLoading || isRefiltering;
 
   if (!accessToken || !token || !userRole || !userID) {
-    return null;
+    return (
+      <div className="flex items-center justify-center h-64">
+        <AntDLoadingSpinner size="large" />
+      </div>
+    );
   }
 
-  const searchedLogs = filteredLogs.data.filter((log) => {
-    const matchesSearch =
-      !searchTerm ||
-      log.request_id.includes(searchTerm) ||
-      log.model.includes(searchTerm) ||
-      (log.user && log.user.includes(searchTerm));
-
-    // No need for additional filtering since we're now handling this in the API call
-    return matchesSearch;
-  });
-
-  const sessionCompositionById = searchedLogs.reduce<Record<string, { llm: number; agent: number; mcp: number }>>((acc, log) => {
-    if (!log.session_id) return acc;
-    if (!acc[log.session_id]) {
-      acc[log.session_id] = { llm: 0, agent: 0, mcp: 0 };
-    }
-    if (MCP_CALL_TYPES.includes(log.call_type)) {
-      acc[log.session_id].mcp += 1;
-    } else if (AGENT_CALL_TYPES.includes(log.call_type)) {
-      acc[log.session_id].agent += 1;
-    } else {
-      acc[log.session_id].llm += 1;
-    }
-    return acc;
-  }, {});
-
-  // Build a single-pass map of session_id → representative request_id.
-  // Prefers an LLM row over an MCP row as the representative.
-  const sessionRepresentativeMap = new Map<string, { requestId: string; isMcp: boolean }>();
-  for (const log of searchedLogs) {
-    if (!log.session_id || (log.session_total_count || 1) <= 1) continue;
-    const isMcp = MCP_CALL_TYPES.includes(log.call_type);
-    const existing = sessionRepresentativeMap.get(log.session_id);
-    if (!existing || (existing.isMcp && !isMcp)) {
-      sessionRepresentativeMap.set(log.session_id, { requestId: log.request_id, isMcp });
-    }
-  }
-
-  const filteredData =
-    searchedLogs
-      .map((log) => {
-        const sessionComposition = log.session_id ? sessionCompositionById[log.session_id] : undefined;
-        return {
-          ...log,
-          request_duration_ms: log.request_duration_ms,
-          session_llm_count: sessionComposition?.llm ?? undefined,
-          session_mcp_count: sessionComposition?.mcp ?? undefined,
-          session_agent_count: sessionComposition?.agent ?? undefined,
-          onKeyHashClick: (keyHash: string) => setSelectedKeyIdInfoView(keyHash),
-          onSessionClick: (sessionId: string) => {
-            if (sessionId) {
-              setSelectedSessionId(sessionId);
-              setSelectedLog(log);
-              setIsDrawerOpen(true);
-            }
-          },
-        };
-      })
-      // Deduplicate multi-call sessions using the pre-built map (O(1) per row).
-      .filter((log) => {
-        if (!log.session_id || (log.session_total_count || 1) <= 1) return true;
-        return sessionRepresentativeMap.get(log.session_id)?.requestId === log.request_id;
-      }) || [];
-
-  // Add this function to handle manual refresh
-  const handleRefresh = () => {
-    if (hasBackendFilters) {
-      // When backend filters (e.g. Key Alias) are active the main TanStack Query
-      // is disabled and its params do not include filter values like key_alias.
-      // Route through the filter-aware refetch so all active filters are preserved.
-      refetchWithFilters();
-    } else {
-      logs.refetch();
-    }
-  };
-
   const handleRowClick = (log: LogEntry) => {
     // Multi-call session row: open in the same right-side drawer (session mode)
     if (log.session_id && (log.session_total_count || 1) > 1) {
@@ -386,100 +233,6 @@ export default function SpendLogsTable({
     setIsDrawerOpen(true);
   };
 
-  const handleCloseDrawer = () => {
-    setIsDrawerOpen(false);
-    setSelectedSessionId(null);
-  };
-
-  const handleSelectLog = (log: LogEntry) => {
-    setSelectedLog(log);
-  };
-
-  const logFilterOptions: FilterOption[] = [
-    {
-      name: "Team ID",
-      label: "Team ID",
-      customComponent: FilterTeamDropdown,
-    },
-    {
-      name: "Status",
-      label: "Status",
-      isSearchable: false,
-      options: [
-        { label: "Success", value: "success" },
-        { label: "Failure", value: "failure" },
-      ],
-    },
-    {
-      name: "Model",
-      label: "Model",
-      customComponent: PaginatedModelSelect,
-    },
-    {
-      name: FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL,
-      label: "Public model / search tool",
-      isSearchable: false,
-    },
-    {
-      name: "Key Alias",
-      label: "Key Alias",
-      customComponent: PaginatedKeyAliasSelect,
-    },
-    {
-      name: "End User",
-      label: "End User",
-      isSearchable: true,
-      searchFn: async (searchText: string) => {
-        if (!accessToken) return [];
-        const data = await allEndUsersCall(accessToken);
-        // data if set, is a list of objects, with key = user_id
-        const users = data?.map((u: any) => u.user_id) || [];
-        const filtered = users.filter((u: string) => u.toLowerCase().includes(searchText.toLowerCase()));
-        return filtered.map((u: string) => ({ label: u, value: u }));
-      },
-    },
-    {
-      name: "Error Code",
-      label: "Error Code",
-      isSearchable: true,
-      searchFn: async (searchText: string) => {
-        if (!searchText) return ERROR_CODE_OPTIONS;
-        const lower = searchText.toLowerCase();
-        const filtered = ERROR_CODE_OPTIONS.filter((opt) => opt.label.toLowerCase().includes(lower));
-        const isExactValue = ERROR_CODE_OPTIONS.some((opt) => opt.value === searchText.trim());
-        if (!isExactValue && searchText.trim()) {
-          filtered.push({ label: `Use custom code: ${searchText.trim()}`, value: searchText.trim() });
-        }
-        return filtered;
-      },
-    },
-    {
-      name: "Key Hash",
-      label: "Key Hash",
-      isSearchable: false,
-    },
-    {
-      name: "Error Message",
-      label: "Error Message",
-      isSearchable: false,
-    },
-  ];
-
-  const formatTimeUnit = (value: number, unit: string) => {
-    if (value === 1) {
-      if (unit === "minutes") return "minute";
-      if (unit === "hours") return "hour";
-      if (unit === "days") return "day";
-    }
-    return unit;
-  };
-
-  const selectedOption = QUICK_SELECT_OPTIONS.find(
-    (option) => option.value === selectedTimeInterval.value && option.unit === selectedTimeInterval.unit,
-  );
-
-  const displayLabel = isCustomDate ? getTimeRangeDisplay(isCustomDate, startTime, endTime) : selectedOption?.label;
-
   return (
     <div className="w-full max-w-screen p-6 overflow-x-hidden box-border">
       <TabGroup defaultIndex={0} onIndexChange={(index) => setActiveTab(index === 0 ? "request logs" : "audit logs")}>
@@ -505,192 +258,37 @@ export default function SpendLogsTable({
             ) : (
               <>
                 <FilterComponent
-                  options={logFilterOptions}
+                  options={getLogFilterOptions(accessToken)}
                   onApplyFilters={handleFilterChange}
                   onResetFilters={handleFilterReset}
                 />
                 <div className="bg-white rounded-lg shadow w-full max-w-full box-border">
-                  <div className="border-b px-6 py-4 w-full max-w-full box-border">
-                    <div className="flex flex-col md:flex-row items-start md:items-center justify-between space-y-4 md:space-y-0 w-full max-w-full box-border">
-                      <div className="flex flex-wrap items-center gap-3 w-full max-w-full box-border">
-                        <div className="relative w-64 min-w-0 flex-shrink-0">
-                          <input
-                            type="text"
-                            placeholder="Search by Request ID"
-                            className="w-full px-3 py-2 pl-8 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
-                            value={searchTerm}
-                            onChange={(e) => setSearchTerm(e.target.value)}
-                          />
-                          <svg
-                            className="absolute left-2.5 top-2.5 h-4 w-4 text-gray-500"
-                            fill="none"
-                            stroke="currentColor"
-                            viewBox="0 0 24 24"
-                          >
-                            <path
-                              strokeLinecap="round"
-                              strokeLinejoin="round"
-                              strokeWidth={2}
-                              d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z"
-                            />
-                          </svg>
-                        </div>
-
-                        <div className="flex items-center gap-2 min-w-0 flex-shrink">
-                          <div className="relative z-50" ref={quickSelectRef}>
-                            <button
-                              onClick={() => setQuickSelectOpen(!quickSelectOpen)}
-                              className="px-3 py-2 text-sm border rounded-md hover:bg-gray-50 flex items-center gap-2"
-                            >
-                              <svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
-                                <path
-                                  strokeLinecap="round"
-                                  strokeLinejoin="round"
-                                  strokeWidth={2}
-                                  d="M8 7V3m8 4V3m-9 8h10M5 21h14a2 2 0 002-2V7a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z"
-                                />
-                              </svg>
-                              {displayLabel}
-                            </button>
-
-                            {quickSelectOpen && (
-                              <div className="absolute right-0 mt-2 w-64 bg-white rounded-lg shadow-lg border p-2 z-50">
-                                <div className="space-y-1">
-                                  {QUICK_SELECT_OPTIONS.map((option) => (
-                                    <button
-                                      key={option.label}
-                                      className={`w-full px-3 py-2 text-left text-sm hover:bg-gray-50 rounded-md ${displayLabel === option.label ? "bg-blue-50 text-blue-600" : ""
-                                        }`}
-                                      onClick={() => {
-                                        setCurrentPage(1);
-                                        setEndTime(moment().format("YYYY-MM-DDTHH:mm"));
-                                        setStartTime(
-                                          moment()
-                                            .subtract(option.value, option.unit as any)
-                                            .format("YYYY-MM-DDTHH:mm"),
-                                        );
-                                        setSelectedTimeInterval({ value: option.value, unit: option.unit });
-                                        setIsCustomDate(false);
-                                        setQuickSelectOpen(false);
-                                      }}
-                                    >
-                                      {option.label}
-                                    </button>
-                                  ))}
-                                  <div className="border-t my-2" />
-                                  <button
-                                    className={`w-full px-3 py-2 text-left text-sm hover:bg-gray-50 rounded-md ${isCustomDate ? "bg-blue-50 text-blue-600" : ""
-                                      }`}
-                                    onClick={() => setIsCustomDate(!isCustomDate)}
-                                  >
-                                    Custom Range
-                                  </button>
-                                </div>
-                              </div>
-                            )}
-                          </div>
-
-                          <LiveTailControls />
-
-                          <Button
-                            type="default"
-                            icon={<SyncOutlined spin={isButtonLoading} />}
-                            onClick={handleRefresh}
-                            disabled={isButtonLoading}
-                            title="Fetch data"
-                          >
-                            {isButtonLoading ? "Fetching" : "Fetch"}
-                          </Button>
-                        </div>
-
-                        {isCustomDate && (
-                          <div className="flex items-center gap-2">
-                            <div>
-                              <input
-                                type="datetime-local"
-                                value={startTime}
-                                onChange={(e) => {
-                                  setStartTime(e.target.value);
-                                  setCurrentPage(1);
-                                }}
-                                className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
-                              />
-                            </div>
-                            <span className="text-gray-500">to</span>
-                            <div>
-                              <input
-                                type="datetime-local"
-                                value={endTime}
-                                onChange={(e) => {
-                                  setEndTime(e.target.value);
-                                  setCurrentPage(1);
-                                }}
-                                className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500"
-                              />
-                            </div>
-                          </div>
-                        )}
-                      </div>
-
-                      <div className="flex items-center space-x-4">
-                        <span className="text-sm text-gray-700 whitespace-nowrap">
-                          Showing {logs.isLoading ? "..." : filteredLogs ? (currentPage - 1) * pageSize + 1 : 0} -{" "}
-                          {logs.isLoading
-                            ? "..."
-                            : filteredLogs
-                              ? Math.min(currentPage * pageSize, filteredLogs.total)
-                              : 0}{" "}
-                          of {logs.isLoading ? "..." : filteredLogs ? filteredLogs.total : 0} results
-                        </span>
-                        <div className="flex items-center space-x-2">
-                          <span className="text-sm text-gray-700 min-w-[90px]">
-                            Page {logs.isLoading ? "..." : currentPage} of{" "}
-                            {logs.isLoading ? "..." : filteredLogs ? filteredLogs.total_pages : 1}
-                          </span>
-                          <button
-                            onClick={() => setCurrentPage((p) => Math.max(1, p - 1))}
-                            disabled={logs.isLoading || currentPage === 1}
-                            className="px-3 py-1 text-sm border rounded-md hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed"
-                          >
-                            Previous
-                          </button>
-                          <button
-                            onClick={() => setCurrentPage((p) => Math.min(filteredLogs.total_pages || 1, p + 1))}
-                            disabled={logs.isLoading || currentPage === (filteredLogs.total_pages || 1)}
-                            className="px-3 py-1 text-sm border rounded-md hover:bg-gray-50 disabled:opacity-50 disabled:cursor-not-allowed"
-                          >
-                            Next
-                          </button>
-                        </div>
-                      </div>
-                    </div>
-                  </div>
-                  {isLiveTail && currentPage === 1 && isMainQueryEnabled && (
-                    <div className="mb-4 px-4 py-2 bg-green-50 border border-greem-200 rounded-md flex items-center justify-between">
-                      <div className="flex items-center gap-2">
-                        <span className="text-sm text-green-700">Auto-refreshing every 15 seconds</span>
-                      </div>
-                      <button
-                        onClick={() => setIsLiveTail(false)}
-                        className="text-sm text-green-600 hover:text-green-800"
-                      >
-                        Stop
-                      </button>
-                    </div>
-                  )}
+                  <LogsTableToolbar
+                    searchTerm={searchTerm}
+                    onSearchChange={setSearchTerm}
+                    startTime={startTime}
+                    onStartTimeChange={setStartTime}
+                    endTime={endTime}
+                    onEndTimeChange={setEndTime}
+                    isCustomDate={isCustomDate}
+                    onIsCustomDateChange={setIsCustomDate}
+                    selectedTimeInterval={selectedTimeInterval}
+                    onSelectedTimeIntervalChange={setSelectedTimeInterval}
+                    isLiveTail={isLiveTail}
+                    onIsLiveTailChange={setIsLiveTail}
+                    currentPage={currentPage}
+                    onCurrentPageChange={setCurrentPage}
+                    pageSize={pageSize}
+                    isLoading={isLogsLoading}
+                    isButtonLoading={isButtonLoading}
+                    onRefetch={() => logsQuery.refetch()}
+                    filteredLogs={filteredLogs}
+                  />
                   <DataTable
-                    columns={createColumns({
-                      sortBy,
-                      sortOrder,
-                      onSortChange: (newSortBy, newSortOrder) => {
-                        setSortBy(newSortBy);
-                        setSortOrder(newSortOrder);
-                        setCurrentPage(1);
-                      },
-                    })}
-                    data={filteredData}
+                    columns={columns}
+                    data={deferredData}
                     onRowClick={handleRowClick}
-                    isLoading={logs.isLoading}
+                    isLoading={isLogsLoading}
                   />
                 </div>
               </>
@@ -706,330 +304,29 @@ export default function SpendLogsTable({
               premiumUser={premiumUser}
             />
           </TabPanel>
-          <TabPanel><DeletedKeysPage /></TabPanel>
-          <TabPanel><DeletedTeamsPage /></TabPanel>
+          <TabPanel>
+            <DeletedKeysPage />
+          </TabPanel>
+          <TabPanel>
+            <DeletedTeamsPage />
+          </TabPanel>
         </TabPanels>
       </TabGroup>
 
       {/* Log Details Drawer */}
       <LogDetailsDrawer
         open={isDrawerOpen}
-        onClose={handleCloseDrawer}
+        onClose={() => {
+          setIsDrawerOpen(false);
+          setSelectedSessionId(null);
+        }}
         logEntry={selectedLog}
         sessionId={selectedSessionId}
         accessToken={accessToken}
         allLogs={filteredData}
-        onSelectLog={handleSelectLog}
+        onSelectLog={setSelectedLog}
         startTime={moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss")}
       />
     </div>
   );
 }
-
-export function RequestViewer({ row }: { row: Row<LogEntry> }) {
-  // Helper function to clean metadata by removing specific fields
-  const formatData = (input: any) => {
-    if (typeof input === "string") {
-      try {
-        return JSON.parse(input);
-      } catch {
-        return input;
-      }
-    }
-    return input;
-  };
-
-  // New helper function to get raw request
-  const getRawRequest = () => {
-    // First check if proxy_server_request exists in metadata
-    if (row.original?.proxy_server_request) {
-      return formatData(row.original.proxy_server_request);
-    }
-    // Fall back to messages if proxy_server_request is empty
-    return formatData(row.original.messages);
-  };
-
-  // Extract error information from metadata if available
-  const metadata = row.original.metadata || {};
-  const hasError = metadata.status === "failure";
-  const errorInfo = hasError ? metadata.error_information : null;
-
-  // Check if request/response data is missing
-  const hasMessages =
-    row.original.messages &&
-    (Array.isArray(row.original.messages)
-      ? row.original.messages.length > 0
-      : Object.keys(row.original.messages).length > 0);
-  const hasResponse = row.original.response && Object.keys(formatData(row.original.response)).length > 0;
-  const missingData = !hasMessages && !hasResponse && !hasError;
-
-  // Format the response with error details if present
-  const formattedResponse = () => {
-    if (hasError && errorInfo) {
-      return {
-        error: {
-          message: errorInfo.error_message || "An error occurred",
-          type: errorInfo.error_class || "error",
-          code: errorInfo.error_code || "unknown",
-          param: null,
-        },
-      };
-    }
-    return formatData(row.original.response);
-  };
-
-  // Extract vector store request metadata if available
-  const hasVectorStoreData =
-    metadata.vector_store_request_metadata &&
-    Array.isArray(metadata.vector_store_request_metadata) &&
-    metadata.vector_store_request_metadata.length > 0;
-
-  // Extract guardrail information from metadata if available
-  const guardrailInfo = row.original.metadata?.guardrail_information;
-  const guardrailEntries = Array.isArray(guardrailInfo) ? guardrailInfo : guardrailInfo ? [guardrailInfo] : [];
-  const hasGuardrailData = guardrailEntries.length > 0;
-
-  // Calculate total masked entities if guardrail data exists
-  const totalMaskedEntities = guardrailEntries.reduce((sum, entry) => {
-    const maskedCounts = entry?.masked_entity_count;
-    if (!maskedCounts) {
-      return sum;
-    }
-    return (
-      sum +
-      Object.values(maskedCounts).reduce<number>((acc, count) => (typeof count === "number" ? acc + count : acc), 0)
-    );
-  }, 0);
-
-  const primaryGuardrailLabel =
-    guardrailEntries.length === 1
-      ? guardrailEntries[0]?.guardrail_name ?? "-"
-      : guardrailEntries.length > 1
-        ? `${guardrailEntries.length} guardrails`
-        : "-";
-
-  const truncatedRequestId = truncateString(row.original.request_id, 64);
-
-  return (
-    <div className="p-6 bg-gray-50 space-y-6 w-full max-w-full overflow-hidden box-border">
-      {/* Combined Info Card */}
-      <div className="bg-white rounded-lg shadow w-full max-w-full overflow-hidden">
-        <div className="p-4 border-b">
-          <h3 className="text-lg font-medium">Request Details</h3>
-        </div>
-        <div className="grid grid-cols-1 md:grid-cols-2 gap-4 p-4 w-full max-w-full overflow-hidden">
-          <div className="space-y-2">
-            <div className="flex">
-              <span className="font-medium w-1/3">Request ID:</span>
-              {row.original.request_id.length > 64 ? (
-                <Tooltip title={row.original.request_id}>
-                  <span className="font-mono text-sm">{truncatedRequestId}</span>
-                </Tooltip>
-              ) : (
-                <span className="font-mono text-sm">{row.original.request_id}</span>
-              )}
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Model:</span>
-              <span>{row.original.model}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Model ID:</span>
-              <span>{row.original.model_id}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Call Type:</span>
-              <span>{row.original.call_type}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Provider:</span>
-              <span>{row.original.custom_llm_provider || "-"}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">API Base:</span>
-              <Tooltip title={row.original.api_base || "-"}>
-                <span className="max-w-[15ch] truncate block">{row.original.api_base || "-"}</span>
-              </Tooltip>
-            </div>
-            {row?.original?.requester_ip_address && (
-              <div className="flex">
-                <span className="font-medium w-1/3">IP Address:</span>
-                <span>{row?.original?.requester_ip_address}</span>
-              </div>
-            )}
-            {hasGuardrailData && (
-              <div className="flex">
-                <span className="font-medium w-1/3">Guardrail:</span>
-                <div>
-                  <span className="font-mono">{primaryGuardrailLabel}</span>
-                  {totalMaskedEntities > 0 && (
-                    <span className="ml-2 px-2 py-0.5 bg-blue-50 text-blue-700 rounded-md text-xs font-medium">
-                      {totalMaskedEntities} masked
-                    </span>
-                  )}
-                </div>
-              </div>
-            )}
-          </div>
-          <div className="space-y-2">
-            <div className="flex">
-              <span className="font-medium w-1/3">Tokens:</span>
-              <span>
-                {row.original.total_tokens} ({row.original.prompt_tokens} prompt tokens +{" "}
-                {row.original.completion_tokens} completion tokens)
-              </span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Cache Read Tokens:</span>
-              <span>
-                {formatNumberWithCommas(row.original.metadata?.additional_usage_values?.cache_read_input_tokens || 0)}
-              </span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Cache Creation Tokens:</span>
-              <span>
-                {formatNumberWithCommas(row.original.metadata?.additional_usage_values.cache_creation_input_tokens)}
-              </span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Cost:</span>
-              <span>${formatNumberWithCommas(row.original.spend || 0, 6)}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Cache Hit:</span>
-              <span>{row.original.cache_hit}</span>
-            </div>
-
-            <div className="flex">
-              <span className="font-medium w-1/3">Status:</span>
-              <span
-                className={`px-2 py-1 rounded-md text-xs font-medium inline-block text-center w-16 ${(row.original.metadata?.status || "Success").toLowerCase() !== "failure"
-                  ? "bg-green-100 text-green-800"
-                  : "bg-red-100 text-red-800"
-                  }`}
-              >
-                {(row.original.metadata?.status || "Success").toLowerCase() !== "failure" ? "Success" : "Failure"}
-              </span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Start Time:</span>
-              <span>{row.original.startTime}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">End Time:</span>
-              <span>{row.original.endTime}</span>
-            </div>
-            <div className="flex">
-              <span className="font-medium w-1/3">Duration:</span>
-              <span>{row.original.request_duration_ms != null ? (row.original.request_duration_ms / 1000).toFixed(3) : "-"} s.</span>
-            </div>
-            {row.original.metadata?.litellm_overhead_time_ms !== undefined && (
-              <div className="flex">
-                <span className="font-medium w-1/3">LiteLLM Overhead:</span>
-                <span>{row.original.metadata.litellm_overhead_time_ms} ms</span>
-              </div>
-            )}
-            <div className="flex">
-              <span className="font-medium w-1/3">Retries:</span>
-              <span>
-                {row.original.metadata?.attempted_retries !== undefined && row.original.metadata?.attempted_retries !== null
-                  ? row.original.metadata.attempted_retries > 0
-                    ? `${row.original.metadata.attempted_retries}${row.original.metadata.max_retries !== undefined && row.original.metadata.max_retries !== null ? ` / ${row.original.metadata.max_retries}` : ''}`
-                    : <Tag color="green">None</Tag>
-                  : '-'}
-              </span>
-            </div>
-          </div>
-        </div>
-      </div>
-
-      {/* Cost Breakdown - Show if cost breakdown data is available */}
-      <CostBreakdownViewer
-        costBreakdown={row.original.metadata?.cost_breakdown}
-        totalSpend={row.original.spend ?? 0}
-        promptTokens={row.original.prompt_tokens}
-        completionTokens={row.original.completion_tokens}
-        cacheHit={row.original.cache_hit}
-      />
-
-      {/* Configuration Info Message - Show when data is missing */}
-      <ConfigInfoMessage show={missingData} />
-
-      {/* Request/Response Panel */}
-      <div className="w-full max-w-full overflow-hidden">
-        <RequestResponsePanel
-          row={row}
-          hasMessages={hasMessages}
-          hasResponse={hasResponse}
-          hasError={hasError}
-          errorInfo={errorInfo}
-          getRawRequest={getRawRequest}
-          formattedResponse={formattedResponse}
-        />
-      </div>
-
-      {/* Guardrail Data - Show only if present */}
-      {hasGuardrailData && <GuardrailViewer data={guardrailInfo} />}
-
-      {/* Vector Store Request Data - Show only if present */}
-      {hasVectorStoreData && <VectorStoreViewer data={metadata.vector_store_request_metadata} />}
-
-      {/* Error Card - Only show for failures */}
-      {hasError && errorInfo && <ErrorViewer errorInfo={errorInfo} />}
-
-      {/* Tags Card - Only show if there are tags */}
-      {row.original.request_tags && Object.keys(row.original.request_tags).length > 0 && (
-        <div className="bg-white rounded-lg shadow">
-          <div className="flex justify-between items-center p-4 border-b">
-            <h3 className="text-lg font-medium">Request Tags</h3>
-          </div>
-          <div className="p-4">
-            <div className="flex flex-wrap gap-2">
-              {Object.entries(row.original.request_tags).map(([key, value]) => (
-                <span key={key} className="px-2 py-1 bg-gray-100 rounded-full text-xs">
-                  {key}: {String(value)}
-                </span>
-              ))}
-            </div>
-          </div>
-        </div>
-      )}
-
-      {/* Metadata Card - Only show if there's metadata */}
-      {row.original.metadata && Object.keys(row.original.metadata).length > 0 && (
-        <div className="bg-white rounded-lg shadow">
-          <div className="flex justify-between items-center p-4 border-b">
-            <h3 className="text-lg font-medium">Metadata</h3>
-            <button
-              onClick={() => {
-                navigator.clipboard.writeText(JSON.stringify(row.original.metadata, null, 2));
-              }}
-              className="p-1 hover:bg-gray-200 rounded"
-              title="Copy metadata"
-            >
-              <svg
-                xmlns="http://www.w3.org/2000/svg"
-                width="16"
-                height="16"
-                viewBox="0 0 24 24"
-                fill="none"
-                stroke="currentColor"
-                strokeWidth="2"
-                strokeLinecap="round"
-                strokeLinejoin="round"
-              >
-                <rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect>
-                <path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path>
-              </svg>
-            </button>
-          </div>
-          <div className="p-4 overflow-auto max-h-64">
-            <pre className="text-xs font-mono whitespace-pre-wrap break-all">
-              {JSON.stringify(row.original.metadata, null, 2)}
-            </pre>
-          </div>
-        </div>
-      )}
-    </div>
-  );
-}
diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx
index 17c50771526..cbe37e0b70f 100644
--- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx
@@ -1,10 +1,16 @@
 import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
 import { act, renderHook, waitFor } from "@testing-library/react";
-import React, { ReactNode } from "react";
+import React, { ReactNode, useState } from "react";
 import { beforeEach, describe, expect, it, vi } from "vitest";
-import type { PaginatedResponse } from ".";
-import type { LogEntry, LogsSortField } from "./columns";
-import { useLogFilterLogic } from "./log_filter_logic";
+import type { LogsSortField } from "./columns";
+import {
+  defaultFilters,
+  getLiveTailRefetchInterval,
+  LIVE_TAIL_INTERVAL_MS,
+  useLogFilterLogic,
+  type LogFilterState,
+  type PaginatedResponse,
+} from "./log_filter_logic";
 
 vi.mock("../networking", () => ({
   uiSpendLogsCall: vi.fn(),
@@ -16,767 +22,642 @@ vi.mock("@/components/key_team_helpers/filter_helpers", () => ({
 
 import { uiSpendLogsCall } from "../networking";
 
-const createLogEntry = (overrides: Partial<LogEntry> = {}): LogEntry =>
-({
-  request_id: "req-1",
-  api_key: "key-1",
-  team_id: "team-1",
-  model: "gpt-4",
-  model_id: "gpt-4",
-  call_type: "chat",
-  spend: 0,
-  total_tokens: 0,
-  prompt_tokens: 0,
-  completion_tokens: 0,
-  startTime: "2025-01-01T00:00:00Z",
-  endTime: "2025-01-01T00:01:00Z",
-  cache_hit: "miss",
-  messages: [],
-  response: {},
-  metadata: {},
-  request_tags: {},
-  ...overrides,
-} as LogEntry);
-
-const createPaginatedResponse = (data: LogEntry[]): PaginatedResponse => ({
-  data,
-  total: data.length,
+const emptyResponse: PaginatedResponse = {
+  data: [],
+  total: 0,
   page: 1,
   page_size: 50,
-  total_pages: 1,
-});
+  total_pages: 0,
+};
 
 const defaultProps = {
-  logs: createPaginatedResponse([]),
-  accessToken: "test-token",
+  accessToken: "test-token" as string | null,
+  token: "test-token" as string | null,
+  userRole: "Admin" as string | null,
+  userID: "user-1" as string | null,
+  filterByCurrentUser: false,
+  activeTab: "request logs",
+  isLiveTail: false,
   startTime: "2025-01-01T00:00:00",
   endTime: "2025-01-01T23:59:59",
   isCustomDate: true,
-  setCurrentPage: vi.fn(),
-  userID: "user-1",
-  userRole: "Admin",
+  sortBy: "startTime" as LogsSortField,
+  sortOrder: "desc" as "asc" | "desc",
+  currentPage: 1,
 };
 
+type HookOverrides = Partial<Omit<Parameters<typeof useLogFilterLogic>[0], "filters" | "setFilters">>;
+
 describe("useLogFilterLogic", () => {
   let queryClient: QueryClient;
 
   beforeEach(() => {
     queryClient = new QueryClient({
-      defaultOptions: {
-        queries: {
-          retry: false,
-        },
-      },
+      defaultOptions: { queries: { retry: false } },
     });
     vi.clearAllMocks();
-    vi.mocked(uiSpendLogsCall).mockResolvedValue({
-      data: [],
-      total: 0,
-      page: 1,
-      page_size: 50,
-      total_pages: 0,
-    });
+    vi.mocked(uiSpendLogsCall).mockResolvedValue(emptyResponse);
   });
 
   const wrapper = ({ children }: { children: ReactNode }) =>
     React.createElement(QueryClientProvider, { client: queryClient }, children);
 
-  it("should return filters, filteredLogs, allTeams, handleFilterChange, and handleFilterReset", () => {
-    const { result } = renderHook(
-      () =>
-        useLogFilterLogic({
+  function renderFilterHook(overrides: HookOverrides = {}) {
+    const setCurrentPage = overrides.setCurrentPage ?? vi.fn();
+    const rendered = renderHook(
+      () => {
+        const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
+        const hook = useLogFilterLogic({
           ...defaultProps,
-          logs: createPaginatedResponse([createLogEntry()]),
-        }),
+          ...overrides,
+          filters,
+          setFilters,
+          setCurrentPage,
+        });
+        return { ...hook, filters, setFilters };
+      },
       { wrapper },
     );
+    return { ...rendered, setCurrentPage };
+  }
 
-    expect(result.current.filters).toBeDefined();
-    expect(result.current.filteredLogs).toBeDefined();
-    expect(result.current).toHaveProperty("allTeams");
-    expect(result.current.handleFilterChange).toBeDefined();
-    expect(result.current.handleFilterReset).toBeDefined();
-  });
+  describe("return shape", () => {
+    it("exposes filteredLogs, allTeams, handleFilterChange, handleFilterReset", () => {
+      const { result } = renderFilterHook();
 
-  it("should initialize filters with all keys empty", () => {
-    const { result } = renderHook(() => useLogFilterLogic(defaultProps), { wrapper });
-
-    const filters = result.current.filters;
-    expect(filters["Team ID"]).toBe("");
-    expect(filters["Key Hash"]).toBe("");
-    expect(filters["Request ID"]).toBe("");
-    expect(filters["Model"]).toBe("");
-    expect(filters["User ID"]).toBe("");
-    expect(filters["End User"]).toBe("");
-    expect(filters["Status"]).toBe("");
-    expect(filters["Key Alias"]).toBe("");
-    expect(filters["Error Code"]).toBe("");
-    expect(filters["Error Message"]).toBe("");
-    expect(filters["Public model / search tool"]).toBe("");
-  });
-
-  it("should return all logs when no filters are applied", () => {
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1" }),
-      createLogEntry({ request_id: "req-2" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    expect(result.current.filteredLogs.data).toHaveLength(2);
-    expect(result.current.filteredLogs.data).toEqual(logs.data);
-  });
-
-  it("should filter logs by team_id when Team ID filter is set", () => {
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1", team_id: "team-a" }),
-      createLogEntry({ request_id: "req-2", team_id: "team-b" }),
-      createLogEntry({ request_id: "req-3", team_id: "team-a" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Team ID": "team-a" });
-    });
-
-    expect(result.current.filteredLogs.data).toHaveLength(2);
-    expect(result.current.filteredLogs.data.every((log) => log.team_id === "team-a")).toBe(true);
-  });
-
-  it("should filter logs by status when Status filter is set to success", () => {
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1", status: "success" }),
-      createLogEntry({ request_id: "req-2" }),
-      createLogEntry({ request_id: "req-3", status: "error" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ Status: "success" });
-    });
-
-    expect(result.current.filteredLogs.data).toHaveLength(2);
-    expect(result.current.filteredLogs.data.every((log) => !log.status || log.status === "success")).toBe(true);
-  });
-
-  it("should filter logs by status when Status filter is set to error", () => {
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1", status: "success" }),
-      createLogEntry({ request_id: "req-2", status: "error" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ Status: "error" });
-    });
-
-    expect(result.current.filteredLogs.data).toHaveLength(1);
-    expect(result.current.filteredLogs.data[0].status).toBe("error");
-  });
-
-  it("should filter logs by model_id when Model filter is set", async () => {
-    const filteredLogs = [
-      createLogEntry({ request_id: "req-1", model_id: "gpt-4" }),
-      createLogEntry({ request_id: "req-3", model_id: "gpt-4" }),
-    ];
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse(filteredLogs),
-    );
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1", model_id: "gpt-4" }),
-      createLogEntry({ request_id: "req-2", model_id: "gpt-3.5" }),
-      createLogEntry({ request_id: "req-3", model_id: "gpt-4" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ Model: "gpt-4" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data).toHaveLength(2);
-        expect(result.current.filteredLogs.data.every((log) => log.model_id === "gpt-4")).toBe(true);
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should pass model param and filter search-tool rows by spend log model column", async () => {
-    const searchRows = [
-      createLogEntry({
-        request_id: "s1",
-        call_type: "asearch",
-        model: "tavily-marketing",
-        model_id: "",
-        team_id: "team-x",
-      }),
-    ];
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(createPaginatedResponse(searchRows));
-    const logs = createPaginatedResponse([
-      ...searchRows,
-      createLogEntry({
-        request_id: "c1",
-        call_type: "chat",
-        model: "gpt-4o",
-        model_id: "mid-1",
-        team_id: "team-x",
-      }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Public model / search tool": "tavily-marketing" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data).toHaveLength(1);
-        expect(result.current.filteredLogs.data[0].model).toBe("tavily-marketing");
-        expect(result.current.filteredLogs.data[0].call_type).toBe("asearch");
-      },
-      { timeout: 500 },
-    );
-
-    expect(vi.mocked(uiSpendLogsCall)).toHaveBeenCalledWith(
-      expect.objectContaining({
-        params: expect.objectContaining({
-          model: "tavily-marketing",
-        }),
-      }),
-    );
-  });
-
-  it("should filter logs by api_key when Key Hash filter is set", async () => {
-    const filteredLog = createLogEntry({ request_id: "req-1", api_key: "key-x" });
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([filteredLog]),
-    );
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1", api_key: "key-x" }),
-      createLogEntry({ request_id: "req-2", api_key: "key-y" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Hash": "key-x" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data).toHaveLength(1);
-        expect(result.current.filteredLogs.data[0].api_key).toBe("key-x");
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should filter logs by end_user when End User filter is set", async () => {
-    const filteredLog = createLogEntry({ request_id: "req-1", end_user: "user-a" });
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([filteredLog]),
-    );
-    const logs = createPaginatedResponse([
-      createLogEntry({ request_id: "req-1", end_user: "user-a" }),
-      createLogEntry({ request_id: "req-2", end_user: "user-b" }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "End User": "user-a" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data).toHaveLength(1);
-        expect(result.current.filteredLogs.data[0].end_user).toBe("user-a");
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should filter logs by error_code when Error Code filter is set", async () => {
-    const filteredLog = createLogEntry({
-      request_id: "req-1",
-      metadata: { error_information: { error_code: "429" } },
-    });
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([filteredLog]),
-    );
-    const logs = createPaginatedResponse([
-      createLogEntry({
-        request_id: "req-1",
-        metadata: { error_information: { error_code: "429" } },
-      }),
-      createLogEntry({
-        request_id: "req-2",
-        metadata: { error_information: { error_code: "500" } },
-      }),
-    ]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Error Code": "429" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data).toHaveLength(1);
-        expect(result.current.filteredLogs.data[0].metadata?.error_information?.error_code).toBe("429");
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should return empty data when logs is null or has no data", () => {
-    const { result } = renderHook(
-      () =>
-        useLogFilterLogic({
-          ...defaultProps,
-          logs: { data: [], total: 0, page: 1, page_size: 50, total_pages: 0 },
-        }),
-      { wrapper },
-    );
-
-    expect(result.current.filteredLogs.data).toEqual([]);
-    expect(result.current.filteredLogs.total).toBe(0);
-  });
-
-  it("should reset filters when handleFilterReset is called", () => {
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Team ID": "team-1", Status: "success" });
-    });
-
-    expect(result.current.filters["Team ID"]).toBe("team-1");
-    expect(result.current.filters["Status"]).toBe("success");
-
-    act(() => {
-      result.current.handleFilterReset();
-    });
-
-    expect(result.current.filters["Team ID"]).toBe("");
-    expect(result.current.filters["Status"]).toBe("");
-  });
-
-  it("should call setCurrentPage with 1 when handleFilterChange is invoked", () => {
-    const setCurrentPage = vi.fn();
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(
-      () => useLogFilterLogic({ ...defaultProps, logs, setCurrentPage }),
-      { wrapper },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Team ID": "team-1" });
-    });
-
-    expect(setCurrentPage).toHaveBeenCalledWith(1);
-  });
-
-  it("should call uiSpendLogsCall when backend filter is set and debounce elapses", async () => {
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(
-      () => {
-        expect(uiSpendLogsCall).toHaveBeenCalled();
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should not call uiSpendLogsCall when accessToken is null", async () => {
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(
-      () => useLogFilterLogic({ ...defaultProps, logs, accessToken: null }),
-      { wrapper },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await new Promise((resolve) => setTimeout(resolve, 350));
-
-    expect(uiSpendLogsCall).not.toHaveBeenCalled();
-  });
-
-  it("should use backend filtered logs when backend filters are active and API returns data", async () => {
-    const backendLog = createLogEntry({ request_id: "backend-req" });
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([backendLog]),
-    );
-    const logs = createPaginatedResponse([createLogEntry({ request_id: "client-req" })]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data).toHaveLength(1);
-        expect(result.current.filteredLogs.data[0].request_id).toBe("backend-req");
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should call uiSpendLogsCall with request_id when Request ID filter is set", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry({ request_id: "req-xyz" })]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Request ID": "req-xyz" });
-    });
-
-    await waitFor(
-      () => {
-        expect(uiSpendLogsCall).toHaveBeenCalledWith(
-          expect.objectContaining({
-            params: expect.objectContaining({ request_id: "req-xyz" }),
-          }),
-        );
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should call uiSpendLogsCall with user_id when User ID filter is set", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "User ID": "user-123" });
-    });
-
-    await waitFor(
-      () => {
-        expect(uiSpendLogsCall).toHaveBeenCalledWith(
-          expect.objectContaining({
-            params: expect.objectContaining({ user_id: "user-123" }),
-          }),
-        );
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should call uiSpendLogsCall with error_message when Error Message filter is set", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Error Message": "rate limit exceeded" });
-    });
-
-    await waitFor(
-      () => {
-        expect(uiSpendLogsCall).toHaveBeenCalledWith(
-          expect.objectContaining({
-            params: expect.objectContaining({ error_message: "rate limit exceeded" }),
-          }),
-        );
-      },
-      { timeout: 500 },
-    );
-  });
-
-  it("should return empty results when backend filters are active but API returns empty", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue({
-      data: [],
-      total: 0,
-      page: 1,
-      page_size: 50,
-      total_pages: 0,
-    });
-    const clientLog = createLogEntry({ request_id: "client-req" });
-    const logs = createPaginatedResponse([clientLog]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(
-      () => {
-        expect(uiSpendLogsCall).toHaveBeenCalled();
-      },
-      { timeout: 500 },
-    );
-
-    expect(result.current.filteredLogs.data).toHaveLength(0);
-  });
-
-  it("should refetch when sortBy changes and backend filters are active", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result, rerender } = renderHook(
-      (props: { sortBy?: LogsSortField }) =>
-        useLogFilterLogic({ ...defaultProps, logs, ...props }),
-      { wrapper, initialProps: { sortBy: "startTime" as LogsSortField } },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), {
-      timeout: 500,
-    });
-
-    rerender({ sortBy: "spend" as LogsSortField });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), {
-      timeout: 500,
-    });
-    expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
-      expect.objectContaining({
-        params: expect.objectContaining({ sort_by: "spend" }),
-      }),
-    );
-  });
-
-  it("should refetch when sortOrder changes and backend filters are active", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result, rerender } = renderHook(
-      (props: { sortOrder?: "asc" | "desc" }) =>
-        useLogFilterLogic({ ...defaultProps, logs, ...props }),
-      { wrapper, initialProps: { sortOrder: "desc" } },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), {
-      timeout: 500,
-    });
-
-    rerender({ sortOrder: "asc" });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), {
-      timeout: 500,
-    });
-    expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
-      expect.objectContaining({
-        params: expect.objectContaining({ sort_order: "asc" }),
-      }),
-    );
-  });
-
-  it("should refetch when currentPage changes and backend filters are active", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result, rerender } = renderHook(
-      (props) => useLogFilterLogic({ ...defaultProps, logs, ...props }),
-      { wrapper, initialProps: { currentPage: 1 } },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), {
-      timeout: 500,
-    });
-
-    rerender({ currentPage: 2 });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), {
-      timeout: 500,
-    });
-    expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
-      expect.objectContaining({ page: 2 }),
-    );
-  });
-
-  it("should refetch when startTime changes and backend filters are active", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result, rerender } = renderHook(
-      (props: { startTime?: string }) =>
-        useLogFilterLogic({ ...defaultProps, logs, ...props }),
-      { wrapper, initialProps: { startTime: "2025-01-01T00:00:00Z" } },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), {
-      timeout: 500,
-    });
-
-    rerender({ startTime: "2025-01-02T00:00:00Z" });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), {
-      timeout: 500,
-    });
-    expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
-      expect.objectContaining({
-        start_date: "2025-01-02 00:00:00",
-      }),
-    );
-  });
-
-  it("should refetch when isCustomDate changes and backend filters are active", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result, rerender } = renderHook(
-      (props: { isCustomDate?: boolean }) =>
-        useLogFilterLogic({ ...defaultProps, logs, ...props }),
-      { wrapper, initialProps: { isCustomDate: false } },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), {
-      timeout: 500,
-    });
-
-    rerender({ isCustomDate: true });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), {
-      timeout: 500,
+      expect(result.current.filteredLogs).toBeDefined();
+      expect(result.current).toHaveProperty("allTeams");
+      expect(result.current.handleFilterChange).toBeInstanceOf(Function);
+      expect(result.current.handleFilterReset).toBeInstanceOf(Function);
     });
   });
 
-  it("should not call setCurrentPage when handleFilterChange receives identical filters", async () => {
-    const setCurrentPage = vi.fn();
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(
-      () => useLogFilterLogic({ ...defaultProps, logs, setCurrentPage }),
-      { wrapper },
-    );
+  describe("handleFilterReset", () => {
+    it("restores filters to defaults after changes", () => {
+      const { result } = renderFilterHook();
 
-    act(() => {
-      result.current.handleFilterChange({ "Team ID": "team-1" });
+      act(() => {
+        result.current.handleFilterChange({ "Team ID": "team-1", Status: "success" });
+      });
+
+      expect(result.current.filters["Team ID"]).toBe("team-1");
+      expect(result.current.filters["Status"]).toBe("success");
+
+      act(() => {
+        result.current.handleFilterReset();
+      });
+
+      expect(result.current.filters["Team ID"]).toBe("");
+      expect(result.current.filters["Status"]).toBe("");
     });
 
-    await waitFor(() => expect(setCurrentPage).toHaveBeenCalledTimes(1), {
-      timeout: 500,
+    it("calls setCurrentPage(1)", () => {
+      const setCurrentPage = vi.fn();
+      const { result } = renderFilterHook({ setCurrentPage });
+
+      act(() => {
+        result.current.handleFilterReset();
+      });
+
+      expect(setCurrentPage).toHaveBeenCalledWith(1);
     });
 
-    setCurrentPage.mockClear();
+    it("triggers a fetch with all filter params undefined", async () => {
+      vi.mocked(uiSpendLogsCall).mockResolvedValue(emptyResponse);
+      const { result } = renderFilterHook();
 
-    await act(async () => {
-      result.current.handleFilterChange({ "Team ID": "team-1" });
-      await new Promise((resolve) => setTimeout(resolve, 350));
-    });
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
 
-    expect(setCurrentPage).not.toHaveBeenCalled();
-  });
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
 
-  it("should not crash when uiSpendLogsCall throws", async () => {
-    vi.mocked(uiSpendLogsCall).mockRejectedValue(new Error("Network error"));
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
+      act(() => {
+        result.current.handleFilterReset();
+      });
 
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), {
-      timeout: 500,
-    });
-
-    expect(result.current.filteredLogs).toBeDefined();
-    expect(result.current.filters).toBeDefined();
-  });
-
-  it("should clear backendFilteredLogs when handleFilterReset is called", async () => {
-    const backendLog = createLogEntry({ request_id: "backend-req" });
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([backendLog]),
-    );
-    const logs = createPaginatedResponse([createLogEntry({ request_id: "client-req" })]);
-    const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper });
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(
-      () => {
-        expect(result.current.filteredLogs.data[0].request_id).toBe("backend-req");
-      },
-      { timeout: 500 },
-    );
-
-    act(() => {
-      result.current.handleFilterReset();
-    });
-
-    expect(result.current.filteredLogs.data).toEqual(logs.data);
-    expect(result.current.filteredLogs.data[0].request_id).toBe("client-req");
-  });
-
-  it("should pass correct start_date, end_date, sort_by, and sort_order to uiSpendLogsCall", async () => {
-    vi.mocked(uiSpendLogsCall).mockResolvedValue(
-      createPaginatedResponse([createLogEntry()]),
-    );
-    const logs = createPaginatedResponse([createLogEntry()]);
-    const { result } = renderHook(
-      () =>
-        useLogFilterLogic({
-          ...defaultProps,
-          logs,
-          startTime: "2025-01-15T00:00:00Z",
-          endTime: "2025-01-15T23:59:59Z",
-          isCustomDate: true,
-          sortBy: "spend",
-          sortOrder: "asc",
-        }),
-      { wrapper },
-    );
-
-    act(() => {
-      result.current.handleFilterChange({ "Key Alias": "alias-1" });
-    });
-
-    await waitFor(
-      () => {
-        expect(uiSpendLogsCall).toHaveBeenCalledWith(
-          expect.objectContaining({
-            start_date: "2025-01-15 00:00:00",
-            end_date: "2025-01-15 23:59:59",
-            params: expect.objectContaining({
-              sort_by: "spend",
-              sort_order: "asc",
+      await waitFor(
+        () => {
+          expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
+            expect.objectContaining({
+              params: expect.objectContaining({
+                team_id: undefined,
+                api_key: undefined,
+                request_id: undefined,
+                user_id: undefined,
+                end_user: undefined,
+                status_filter: undefined,
+                model_id: undefined,
+                key_alias: undefined,
+                error_code: undefined,
+                error_message: undefined,
+              }),
             }),
-          }),
+          );
+        },
+        { timeout: 500 },
+      );
+    });
+  });
+
+  describe("handleFilterChange", () => {
+    it("calls setCurrentPage(1) when filters change", () => {
+      const setCurrentPage = vi.fn();
+      const { result } = renderFilterHook({ setCurrentPage });
+
+      act(() => {
+        result.current.handleFilterChange({ "Team ID": "team-1" });
+      });
+
+      expect(setCurrentPage).toHaveBeenCalledWith(1);
+    });
+
+    it("merges partial updates without clobbering other filter keys", () => {
+      const { result } = renderFilterHook();
+
+      act(() => {
+        result.current.handleFilterChange({ "Team ID": "team-a" });
+      });
+      expect(result.current.filters["Team ID"]).toBe("team-a");
+
+      act(() => {
+        result.current.handleFilterChange({ Model: "gpt-4" });
+      });
+
+      expect(result.current.filters["Team ID"]).toBe("team-a");
+      expect(result.current.filters["Model"]).toBe("gpt-4");
+    });
+
+    it("does not call setCurrentPage when filters are identical", async () => {
+      const setCurrentPage = vi.fn();
+      const { result } = renderFilterHook({ setCurrentPage });
+
+      act(() => {
+        result.current.handleFilterChange({ "Team ID": "team-1" });
+      });
+
+      await waitFor(() => expect(setCurrentPage).toHaveBeenCalledTimes(1), { timeout: 500 });
+
+      setCurrentPage.mockClear();
+
+      await act(async () => {
+        result.current.handleFilterChange({ "Team ID": "team-1" });
+        await new Promise((resolve) => setTimeout(resolve, 350));
+      });
+
+      expect(setCurrentPage).not.toHaveBeenCalled();
+    });
+  });
+
+  describe("query params — filter keys", () => {
+    const filterCases: Array<{
+      filterKey: keyof LogFilterState;
+      paramName: string;
+      value: string;
+    }> = [
+      { filterKey: "Team ID", paramName: "team_id", value: "team-a" },
+      { filterKey: "Key Hash", paramName: "api_key", value: "key-x" },
+      { filterKey: "Request ID", paramName: "request_id", value: "req-xyz" },
+      { filterKey: "User ID", paramName: "user_id", value: "user-123" },
+      { filterKey: "End User", paramName: "end_user", value: "user-a" },
+      { filterKey: "Status", paramName: "status_filter", value: "error" },
+      { filterKey: "Model", paramName: "model_id", value: "gpt-4" },
+      { filterKey: "Public model / search tool", paramName: "model", value: "tavily-marketing" },
+      { filterKey: "Error Code", paramName: "error_code", value: "429" },
+      { filterKey: "Error Message", paramName: "error_message", value: "rate limit exceeded" },
+    ];
+
+    it.each(filterCases)(
+      "forwards $filterKey as params.$paramName to uiSpendLogsCall",
+      async ({ filterKey, paramName, value }) => {
+        const { result } = renderFilterHook();
+
+        act(() => {
+          result.current.handleFilterChange({ [filterKey]: value } as Partial<LogFilterState>);
+        });
+
+        await waitFor(
+          () => {
+            expect(uiSpendLogsCall).toHaveBeenCalledWith(
+              expect.objectContaining({
+                params: expect.objectContaining({ [paramName]: value }),
+              }),
+            );
+          },
+          { timeout: 500 },
         );
       },
-      { timeout: 500 },
     );
   });
+
+  describe("query params — date & sort", () => {
+    it("passes start_date, end_date, sort_by, and sort_order to uiSpendLogsCall", async () => {
+      const { result } = renderFilterHook({
+        startTime: "2025-01-15T00:00:00Z",
+        endTime: "2025-01-15T23:59:59Z",
+        isCustomDate: true,
+        sortBy: "spend" as LogsSortField,
+        sortOrder: "asc",
+      });
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await waitFor(
+        () => {
+          expect(uiSpendLogsCall).toHaveBeenCalledWith(
+            expect.objectContaining({
+              start_date: "2025-01-15 00:00:00",
+              end_date: "2025-01-15 23:59:59",
+              params: expect.objectContaining({
+                sort_by: "spend",
+                sort_order: "asc",
+              }),
+            }),
+          );
+        },
+        { timeout: 500 },
+      );
+    });
+  });
+
+  describe("debounce", () => {
+    it("calls uiSpendLogsCall after the debounce elapses for text filters", async () => {
+      const { result } = renderFilterHook();
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Hash": "hash-1" });
+      });
+
+      await waitFor(
+        () =>
+          expect(uiSpendLogsCall).toHaveBeenCalledWith(
+            expect.objectContaining({
+              params: expect.objectContaining({ api_key: "hash-1" }),
+            }),
+          ),
+        { timeout: 500 },
+      );
+    });
+
+    it("does not call uiSpendLogsCall with a text filter before the debounce elapses", async () => {
+      const { result } = renderFilterHook();
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
+      vi.mocked(uiSpendLogsCall).mockClear();
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Hash": "hash-1" });
+      });
+
+      await new Promise((resolve) => setTimeout(resolve, 100));
+      expect(uiSpendLogsCall).not.toHaveBeenCalledWith(
+        expect.objectContaining({
+          params: expect.objectContaining({ api_key: "hash-1" }),
+        }),
+      );
+
+      await waitFor(
+        () =>
+          expect(uiSpendLogsCall).toHaveBeenCalledWith(
+            expect.objectContaining({
+              params: expect.objectContaining({ api_key: "hash-1" }),
+            }),
+          ),
+        { timeout: 500 },
+      );
+    });
+
+    it("applies dropdown filter changes without waiting for the debounce", async () => {
+      const { result } = renderFilterHook();
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
+      vi.mocked(uiSpendLogsCall).mockClear();
+
+      act(() => {
+        result.current.handleFilterChange({ "Team ID": "team-instant" });
+      });
+
+      await waitFor(
+        () =>
+          expect(uiSpendLogsCall).toHaveBeenCalledWith(
+            expect.objectContaining({
+              params: expect.objectContaining({ team_id: "team-instant" }),
+            }),
+          ),
+        { timeout: 100 },
+      );
+    });
+
+    // Guards the TEXT_FILTER_KEYS fix: this free-text filter must debounce, not fire per keystroke.
+    it("debounces the 'Public model / search tool' text filter", async () => {
+      const { result } = renderFilterHook();
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
+      vi.mocked(uiSpendLogsCall).mockClear();
+
+      act(() => {
+        result.current.handleFilterChange({ "Public model / search tool": "tavily-marketing" });
+      });
+
+      await new Promise((resolve) => setTimeout(resolve, 100));
+      expect(uiSpendLogsCall).not.toHaveBeenCalledWith(
+        expect.objectContaining({
+          params: expect.objectContaining({ model: "tavily-marketing" }),
+        }),
+      );
+
+      await waitFor(
+        () =>
+          expect(uiSpendLogsCall).toHaveBeenCalledWith(
+            expect.objectContaining({
+              params: expect.objectContaining({ model: "tavily-marketing" }),
+            }),
+          ),
+        { timeout: 500 },
+      );
+    });
+  });
+
+  describe("handleFilterReset", () => {
+    it("flushes the text-filter debounce so a pending typed value is not sent", async () => {
+      const { result } = renderFilterHook();
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
+      vi.mocked(uiSpendLogsCall).mockClear();
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Hash": "pending-hash" });
+      });
+
+      act(() => {
+        result.current.handleFilterReset();
+      });
+
+      await new Promise((resolve) => setTimeout(resolve, 400));
+
+      for (const call of vi.mocked(uiSpendLogsCall).mock.calls) {
+        expect(call[0].params?.api_key).toBeUndefined();
+      }
+    });
+  });
+
+  describe("backend filtered logs", () => {
+    it("returns the query payload as filteredLogs when backend filters are active", async () => {
+      const backendLog = { request_id: "backend-req" };
+      vi.mocked(uiSpendLogsCall).mockResolvedValue({
+        data: [backendLog],
+        total: 1,
+        page: 1,
+        page_size: 50,
+        total_pages: 1,
+      } as PaginatedResponse);
+
+      const { result } = renderFilterHook();
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await waitFor(
+        () => {
+          expect(result.current.filteredLogs.data).toHaveLength(1);
+          expect(result.current.filteredLogs.data[0].request_id).toBe("backend-req");
+        },
+        { timeout: 500 },
+      );
+    });
+
+    it("returns empty data when the API returns an empty payload", async () => {
+      vi.mocked(uiSpendLogsCall).mockResolvedValue(emptyResponse);
+      const { result } = renderFilterHook();
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
+
+      expect(result.current.filteredLogs.data).toHaveLength(0);
+    });
+  });
+
+  describe("refetch triggers", () => {
+    it("refetches when sortBy changes", async () => {
+      const { rerender } = renderHook(
+        (props: { sortBy: LogsSortField }) => {
+          const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
+          return useLogFilterLogic({
+            ...defaultProps,
+            filters,
+            setFilters,
+            setCurrentPage: vi.fn(),
+            sortBy: props.sortBy,
+          });
+        },
+        { wrapper, initialProps: { sortBy: "startTime" } },
+      );
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 });
+
+      rerender({ sortBy: "spend" });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 });
+      expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
+        expect.objectContaining({
+          params: expect.objectContaining({ sort_by: "spend" }),
+        }),
+      );
+    });
+
+    it("refetches when sortOrder changes", async () => {
+      const { rerender } = renderHook(
+        (props: { sortOrder: "asc" | "desc" }) => {
+          const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
+          return useLogFilterLogic({
+            ...defaultProps,
+            filters,
+            setFilters,
+            setCurrentPage: vi.fn(),
+            sortOrder: props.sortOrder,
+          });
+        },
+        { wrapper, initialProps: { sortOrder: "desc" } },
+      );
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 });
+
+      rerender({ sortOrder: "asc" });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 });
+      expect(uiSpendLogsCall).toHaveBeenLastCalledWith(
+        expect.objectContaining({
+          params: expect.objectContaining({ sort_order: "asc" }),
+        }),
+      );
+    });
+
+    it("refetches when currentPage changes", async () => {
+      const { rerender } = renderHook(
+        (props: { currentPage: number }) => {
+          const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
+          return useLogFilterLogic({
+            ...defaultProps,
+            filters,
+            setFilters,
+            setCurrentPage: vi.fn(),
+            currentPage: props.currentPage,
+          });
+        },
+        { wrapper, initialProps: { currentPage: 1 } },
+      );
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 });
+
+      rerender({ currentPage: 2 });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 });
+      expect(uiSpendLogsCall).toHaveBeenLastCalledWith(expect.objectContaining({ page: 2 }));
+    });
+
+    it("refetches when startTime changes", async () => {
+      const { rerender } = renderHook(
+        (props: { startTime: string }) => {
+          const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
+          return useLogFilterLogic({
+            ...defaultProps,
+            filters,
+            setFilters,
+            setCurrentPage: vi.fn(),
+            startTime: props.startTime,
+          });
+        },
+        { wrapper, initialProps: { startTime: "2025-01-01T00:00:00Z" } },
+      );
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 });
+
+      rerender({ startTime: "2025-01-02T00:00:00Z" });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 });
+      expect(uiSpendLogsCall).toHaveBeenLastCalledWith(expect.objectContaining({ start_date: "2025-01-02 00:00:00" }));
+    });
+
+    it("refetches with a different end_date when isCustomDate toggles", async () => {
+      const customEndTime = "2025-01-15T23:59:59Z";
+      const customEndFormatted = "2025-01-15 23:59:59";
+
+      const { rerender } = renderHook(
+        (props: { isCustomDate: boolean }) => {
+          const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
+          return useLogFilterLogic({
+            ...defaultProps,
+            endTime: customEndTime,
+            filters,
+            setFilters,
+            setCurrentPage: vi.fn(),
+            isCustomDate: props.isCustomDate,
+          });
+        },
+        { wrapper, initialProps: { isCustomDate: false } },
+      );
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 });
+      const firstEndDate = vi.mocked(uiSpendLogsCall).mock.calls[0][0].end_date;
+      expect(firstEndDate).not.toBe(customEndFormatted);
+
+      rerender({ isCustomDate: true });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 });
+      expect(vi.mocked(uiSpendLogsCall).mock.calls[1][0].end_date).toBe(customEndFormatted);
+    });
+  });
+
+  describe("query enablement", () => {
+    const nullCredentialCases: Array<{ name: string; override: HookOverrides }> = [
+      { name: "accessToken", override: { accessToken: null } },
+      { name: "token", override: { token: null } },
+      { name: "userRole", override: { userRole: null } },
+      { name: "userID", override: { userID: null } },
+    ];
+
+    it.each(nullCredentialCases)("does not call uiSpendLogsCall when $name is null", async ({ override }) => {
+      const { result } = renderFilterHook(override);
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await new Promise((resolve) => setTimeout(resolve, 350));
+
+      expect(uiSpendLogsCall).not.toHaveBeenCalled();
+    });
+
+    it("does not call uiSpendLogsCall when activeTab is not 'request logs'", async () => {
+      const { result } = renderFilterHook({ activeTab: "audit logs" });
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await new Promise((resolve) => setTimeout(resolve, 350));
+
+      expect(uiSpendLogsCall).not.toHaveBeenCalled();
+    });
+  });
+
+  describe("filterByCurrentUser", () => {
+    it("sends user_id: userID when the User ID filter is blank", async () => {
+      const { result } = renderFilterHook({
+        filterByCurrentUser: true,
+        userID: "me-123",
+      });
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await waitFor(
+        () => {
+          expect(uiSpendLogsCall).toHaveBeenCalledWith(
+            expect.objectContaining({
+              params: expect.objectContaining({ user_id: "me-123" }),
+            }),
+          );
+        },
+        { timeout: 500 },
+      );
+    });
+  });
+
+  describe("error handling", () => {
+    it("does not crash when uiSpendLogsCall throws", async () => {
+      vi.mocked(uiSpendLogsCall).mockRejectedValue(new Error("Network error"));
+      const { result } = renderFilterHook();
+
+      act(() => {
+        result.current.handleFilterChange({ "Key Alias": "alias-1" });
+      });
+
+      await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 });
+
+      expect(result.current.filteredLogs).toBeDefined();
+      expect(result.current.filteredLogs.data).toEqual([]);
+    });
+  });
+});
+
+describe("getLiveTailRefetchInterval", () => {
+  it("polls every 15s when live tail is on and on page 1", () => {
+    expect(getLiveTailRefetchInterval(true, 1)).toBe(LIVE_TAIL_INTERVAL_MS);
+  });
+
+  it("does not poll when live tail is off", () => {
+    expect(getLiveTailRefetchInterval(false, 1)).toBe(false);
+  });
+
+  it("does not poll when not on page 1, even with live tail on", () => {
+    expect(getLiveTailRefetchInterval(true, 2)).toBe(false);
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx
index 8f916999c16..d9830a83f6e 100644
--- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx
+++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx
@@ -1,13 +1,28 @@
 import moment from "moment";
-import { useCallback, useEffect, useState, useRef, useMemo } from "react";
+import { useEffect, useMemo, useState } from "react";
 import { uiSpendLogsCall } from "../networking";
 import { Team } from "../key_team_helpers/key_list";
-import { useQuery } from "@tanstack/react-query";
+import { keepPreviousData, useQuery } from "@tanstack/react-query";
 import { fetchAllTeams } from "../../components/key_team_helpers/filter_helpers";
-import { debounce } from "lodash";
 import { defaultPageSize } from "../constants";
-import { PaginatedResponse } from ".";
-import type { LogsSortField } from "./columns";
+import type { LogEntry, LogsSortField } from "./columns";
+
+export interface PaginatedResponse {
+  data: LogEntry[];
+  total: number;
+  page: number;
+  page_size: number;
+  total_pages: number;
+}
+
+function useDebouncedValue<T>(value: T, delayMs: number): [T, React.Dispatch<React.SetStateAction<T>>] {
+  const [debounced, setDebounced] = useState(value);
+  useEffect(() => {
+    const timer = setTimeout(() => setDebounced(value), delayMs);
+    return () => clearTimeout(timer);
+  }, [value, delayMs]);
+  return [debounced, setDebounced];
+}
 
 /** Spend log `model` column (LLM public model name or `search_tool_name` for /search). */
 export const FILTER_KEYS = {
@@ -28,324 +43,188 @@ export const FILTER_KEYS = {
 export type FilterKey = keyof typeof FILTER_KEYS;
 export type LogFilterState = Record<(typeof FILTER_KEYS)[FilterKey], string>;
 
+// Keys whose UI is a free-form text input; only these need debouncing.
+const TEXT_FILTER_KEYS: readonly (keyof LogFilterState)[] = [
+  FILTER_KEYS.KEY_HASH,
+  FILTER_KEYS.ERROR_MESSAGE,
+  FILTER_KEYS.REQUEST_ID,
+  FILTER_KEYS.USER_ID,
+  FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL,
+];
+
+// Live-tail polls every 15s, but only on page 1 (newest) while live tail is on.
+export const LIVE_TAIL_INTERVAL_MS = 15000;
+export const getLiveTailRefetchInterval = (isLiveTail: boolean, currentPage: number): number | false =>
+  isLiveTail && currentPage === 1 ? LIVE_TAIL_INTERVAL_MS : false;
+
+export const defaultFilters: LogFilterState = {
+  [FILTER_KEYS.TEAM_ID]: "",
+  [FILTER_KEYS.KEY_HASH]: "",
+  [FILTER_KEYS.REQUEST_ID]: "",
+  [FILTER_KEYS.MODEL]: "",
+  [FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]: "",
+  [FILTER_KEYS.USER_ID]: "",
+  [FILTER_KEYS.END_USER]: "",
+  [FILTER_KEYS.STATUS]: "",
+  [FILTER_KEYS.KEY_ALIAS]: "",
+  [FILTER_KEYS.ERROR_CODE]: "",
+  [FILTER_KEYS.ERROR_MESSAGE]: "",
+};
+
 export function useLogFilterLogic({
-  logs,
   accessToken,
-  startTime, // Receive from SpendLogsTable
-  endTime, // Receive from SpendLogsTable
+  token,
+  userRole,
+  userID,
+  filters,
+  setFilters,
+  filterByCurrentUser,
+  activeTab,
+  isLiveTail,
+  startTime,
+  endTime,
   pageSize = defaultPageSize,
   isCustomDate,
   setCurrentPage,
-  userID,
-  userRole,
   sortBy = "startTime",
   sortOrder = "desc",
   currentPage = 1,
 }: {
-  logs: PaginatedResponse;
   accessToken: string | null;
+  token: string | null;
+  userRole: string | null;
+  userID: string | null;
+  filters: LogFilterState;
+  setFilters: React.Dispatch<React.SetStateAction<LogFilterState>>;
+  filterByCurrentUser: boolean | null;
+  activeTab: string;
+  isLiveTail: boolean;
   startTime: string;
   endTime: string;
   pageSize?: number;
   isCustomDate: boolean;
   setCurrentPage: (page: number) => void;
-  userID: string | null;
-  userRole: string | null;
   sortBy?: LogsSortField;
   sortOrder?: "asc" | "desc";
   currentPage?: number;
 }) {
-  const defaultFilters = useMemo<LogFilterState>(
-    () => ({
-      [FILTER_KEYS.TEAM_ID]: "",
-      [FILTER_KEYS.KEY_HASH]: "",
-      [FILTER_KEYS.REQUEST_ID]: "",
-      [FILTER_KEYS.MODEL]: "",
-      [FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]: "",
-      [FILTER_KEYS.USER_ID]: "",
-      [FILTER_KEYS.END_USER]: "",
-      [FILTER_KEYS.STATUS]: "",
-      [FILTER_KEYS.KEY_ALIAS]: "",
-      [FILTER_KEYS.ERROR_CODE]: "",
-      [FILTER_KEYS.ERROR_MESSAGE]: "",
-    }),
-    [],
-  );
+  const [debouncedFilters, setDebouncedFilters] = useDebouncedValue(filters, 300);
 
-  const [filters, setFilters] = useState<LogFilterState>(defaultFilters);
-  const [backendFilteredLogs, setBackendFilteredLogs] = useState<PaginatedResponse | null>(null);
-  const lastSearchTimestamp = useRef(0);
+  // Live values for dropdown keys, debounced for text keys.
+  const effectiveFilters = useMemo(() => {
+    const merged = { ...filters };
+    for (const k of TEXT_FILTER_KEYS) {
+      merged[k] = debouncedFilters[k];
+    }
+    return merged;
+  }, [filters, debouncedFilters]);
 
-  // Refs that always hold the latest filters and hasBackendFilters values.
-  // The sort/page/time effect below intentionally omits these from its dep array
-  // to avoid double-fetches when a filter changes; reading from refs instead of
-  // the closure prevents stale-closure bugs (e.g. the effect using a snapshot of
-  // filters taken before the user selected Key Alias).
-  const filtersRef = useRef(filters);
-  const hasBackendFiltersRef = useRef(false);
-  const performSearch = useCallback(
-    async (filters: LogFilterState, page = 1) => {
-      if (!accessToken) return;
-
-      console.log("Filters being sent to API:", filters);
-      const currentTimestamp = Date.now();
-      lastSearchTimestamp.current = currentTimestamp;
+  const logsQuery = useQuery<PaginatedResponse>({
+    queryKey: [
+      "logs",
+      "table",
+      currentPage,
+      pageSize,
+      startTime,
+      endTime,
+      isCustomDate,
+      effectiveFilters,
+      filterByCurrentUser ? userID : null,
+      sortBy,
+      sortOrder,
+    ],
+    queryFn: async () => {
+      if (!accessToken || !token || !userRole || !userID) {
+        return {
+          data: [],
+          total: 0,
+          page: 1,
+          page_size: pageSize,
+          total_pages: 0,
+        };
+      }
 
       const formattedStartTime = moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss");
       const formattedEndTime = isCustomDate
         ? moment(endTime).utc().format("YYYY-MM-DD HH:mm:ss")
         : moment().utc().format("YYYY-MM-DD HH:mm:ss");
 
-      try {
-        const response = await uiSpendLogsCall({
-          accessToken,
-          start_date: formattedStartTime,
-          end_date: formattedEndTime,
-          page,
-          page_size: pageSize,
-          params: {
-            api_key: filters[FILTER_KEYS.KEY_HASH] || undefined,
-            team_id: filters[FILTER_KEYS.TEAM_ID] || undefined,
-            request_id: filters[FILTER_KEYS.REQUEST_ID] || undefined,
-            user_id: filters[FILTER_KEYS.USER_ID] || undefined,
-            end_user: filters[FILTER_KEYS.END_USER] || undefined,
-            status_filter: filters[FILTER_KEYS.STATUS] || undefined,
-            model_id: filters[FILTER_KEYS.MODEL] || undefined,
-            model: filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL] || undefined,
-            key_alias: filters[FILTER_KEYS.KEY_ALIAS] || undefined,
-            error_code: filters[FILTER_KEYS.ERROR_CODE] || undefined,
-            error_message: filters[FILTER_KEYS.ERROR_MESSAGE] || undefined,
-            sort_by: sortBy,
-            sort_order: sortOrder,
-          },
-        });
+      const response = await uiSpendLogsCall({
+        accessToken,
+        start_date: formattedStartTime,
+        end_date: formattedEndTime,
+        page: currentPage,
+        page_size: pageSize,
+        params: {
+          api_key: effectiveFilters[FILTER_KEYS.KEY_HASH] || undefined,
+          team_id: effectiveFilters[FILTER_KEYS.TEAM_ID] || undefined,
+          request_id: effectiveFilters[FILTER_KEYS.REQUEST_ID] || undefined,
+          user_id: effectiveFilters[FILTER_KEYS.USER_ID] || (filterByCurrentUser ? userID ?? undefined : undefined),
+          end_user: effectiveFilters[FILTER_KEYS.END_USER] || undefined,
+          status_filter: effectiveFilters[FILTER_KEYS.STATUS] || undefined,
+          model_id: effectiveFilters[FILTER_KEYS.MODEL] || undefined,
+          model: effectiveFilters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL] || undefined,
+          key_alias: effectiveFilters[FILTER_KEYS.KEY_ALIAS] || undefined,
+          error_code: effectiveFilters[FILTER_KEYS.ERROR_CODE] || undefined,
+          error_message: effectiveFilters[FILTER_KEYS.ERROR_MESSAGE] || undefined,
+          sort_by: sortBy,
+          sort_order: sortOrder,
+        },
+      });
 
-        if (currentTimestamp === lastSearchTimestamp.current) {
-          setBackendFilteredLogs({
-            ...response,
-            data: response.data ?? [],
-          });
-        }
-      } catch (error) {
-        console.error("Error searching users:", error);
-        setBackendFilteredLogs({
-          data: [],
-          total: 0,
-          page: 1,
-          page_size: pageSize,
-          total_pages: 0,
-        });
-      }
+      return response;
     },
-    [accessToken, startTime, endTime, isCustomDate, pageSize, sortBy, sortOrder],
-  );
+    enabled: !!accessToken && !!token && !!userRole && !!userID && activeTab === "request logs",
+    refetchInterval: getLiveTailRefetchInterval(isLiveTail, currentPage),
+    placeholderData: keepPreviousData,
+    // Only live-tail-poll while the tab is visible.
+    refetchIntervalInBackground: false,
+  });
 
-  const debouncedSearch = useMemo(
-    () => debounce((filters: LogFilterState, page: number) => performSearch(filters, page), 300),
-    [performSearch],
-  );
+  const filteredLogs: PaginatedResponse = logsQuery.data ?? {
+    data: [],
+    total: 0,
+    page: 1,
+    page_size: pageSize,
+    total_pages: 0,
+  };
 
-  useEffect(() => {
-    return () => debouncedSearch.cancel();
-  }, [debouncedSearch]);
-
-  // Determine when backend filters are active (server-side filtering)
-  const hasBackendFilters = useMemo(
-    () =>
-      !!(
-        filters[FILTER_KEYS.KEY_ALIAS] ||
-        filters[FILTER_KEYS.KEY_HASH] ||
-        filters[FILTER_KEYS.REQUEST_ID] ||
-        filters[FILTER_KEYS.USER_ID] ||
-        filters[FILTER_KEYS.END_USER] ||
-        filters[FILTER_KEYS.ERROR_CODE] ||
-        filters[FILTER_KEYS.ERROR_MESSAGE] ||
-        filters[FILTER_KEYS.MODEL] ||
-        filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]
-      ),
-    [filters],
-  );
-
-  // Keep refs in sync on every render so the sort/page/time effect always reads
-  // the latest values without those values being in its dep array.
-  useEffect(() => {
-    filtersRef.current = filters;
-    hasBackendFiltersRef.current = hasBackendFilters;
-  }, [filters, hasBackendFilters]);
-
-  // Refetch when sort, page, or time range changes (backend filters use their own fetch, not the main query)
-  useEffect(() => {
-    if (hasBackendFiltersRef.current && accessToken) {
-      // Cancel any pending debounced search to prevent it from overwriting this page's results
-      debouncedSearch.cancel();
-      performSearch(filtersRef.current, currentPage);
-    }
-    // filters / hasBackendFilters are read via refs — avoids stale-closure bugs
-    // when sort/page/time changes after a filter (e.g. Key Alias) was set.
-    // debouncedSearch / performSearch: filter changes go through handleFilterChange
-    // → debouncedSearch; adding them here would cause double-fetches on filter apply.
-    // accessToken: stable across sort/page/time changes.
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [sortBy, sortOrder, currentPage, startTime, endTime, isCustomDate]);
-
-  // Compute client-side filtered logs directly from incoming logs and filters
-  const clientDerivedFilteredLogs: PaginatedResponse = useMemo(() => {
-    if (!logs || !logs.data) {
-      return {
-        data: [],
-        total: 0,
-        page: 1,
-        page_size: pageSize,
-        total_pages: 0,
-      };
-    }
-
-    // If backend filters are on, don't perform client-side filtering here
-    if (hasBackendFilters) {
-      return logs;
-    }
-
-    let filteredData = [...logs.data];
-
-    if (filters[FILTER_KEYS.TEAM_ID]) {
-      filteredData = filteredData.filter((log) => log.team_id === filters[FILTER_KEYS.TEAM_ID]);
-    }
-
-    if (filters[FILTER_KEYS.STATUS]) {
-      filteredData = filteredData.filter((log) => {
-        if (filters[FILTER_KEYS.STATUS] === "success") {
-          return !log.status || log.status === "success";
-        }
-        return log.status === filters[FILTER_KEYS.STATUS];
-      });
-    }
-
-    if (filters[FILTER_KEYS.MODEL]) {
-      filteredData = filteredData.filter((log) => log.model_id === filters[FILTER_KEYS.MODEL]);
-    }
-
-    if (filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]) {
-      const m = filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL];
-      filteredData = filteredData.filter((log) => log.model === m);
-    }
-
-    if (filters[FILTER_KEYS.KEY_HASH]) {
-      filteredData = filteredData.filter((log) => log.api_key === filters[FILTER_KEYS.KEY_HASH]);
-    }
-
-    if (filters[FILTER_KEYS.END_USER]) {
-      filteredData = filteredData.filter((log) => log.end_user === filters[FILTER_KEYS.END_USER]);
-    }
-
-    if (filters[FILTER_KEYS.ERROR_CODE]) {
-      filteredData = filteredData.filter((log) => {
-        const metadata = log.metadata || {};
-        const errorInfo = metadata.error_information;
-        return errorInfo && errorInfo.error_code === filters[FILTER_KEYS.ERROR_CODE];
-      });
-    }
-
-    return {
-      data: filteredData,
-      total: logs.total,
-      page: logs.page,
-      page_size: logs.page_size,
-      total_pages: logs.total_pages,
-    };
-  }, [logs, filters, hasBackendFilters]);
-
-  // Choose which filtered logs to expose: backend result when active, otherwise client-derived
-  const filteredLogs: PaginatedResponse = useMemo(() => {
-    if (hasBackendFilters) {
-      // When backend filters are active, only show backend results.
-      // If search hasn't completed yet (null), show empty state rather than
-      // falling back to unfiltered logs — that caused filtered views to
-      // display mismatched data when the filter matched zero rows.
-      if (backendFilteredLogs !== null) {
-        return backendFilteredLogs;
-      }
-      return {
-        data: [],
-        total: 0,
-        page: 1,
-        page_size: pageSize,
-        total_pages: 0,
-      };
-    }
-    return clientDerivedFilteredLogs;
-  }, [hasBackendFilters, backendFilteredLogs, clientDerivedFilteredLogs]);
-
-  // Fetch all teams and users for potential filter dropdowns (optional, can be adapted)
   const { data: allTeams } = useQuery<Team[], Error>({
     queryKey: ["allTeamsForLogFilters", accessToken],
     queryFn: async () => {
       if (!accessToken) return [];
-      // Use fetchAllTeams helper function for consistency and abstraction
-      // Assuming fetchAllTeams returns Team[] directly
       const teamsData = await fetchAllTeams(accessToken);
-      return teamsData || []; // Ensure it returns an array
+      return teamsData || [];
     },
     enabled: !!accessToken,
   });
 
-  // Update filters state
   const handleFilterChange = (newFilters: Partial<LogFilterState>) => {
     setFilters((prev) => {
       const updatedFilters = { ...prev, ...newFilters };
-
-      // Ensure all keys in LogFilterState are present, defaulting to '' if not in newFilters
       for (const key of Object.keys(defaultFilters) as Array<keyof LogFilterState>) {
         if (!(key in updatedFilters)) {
           updatedFilters[key] = defaultFilters[key];
         }
       }
-
-      // Only call debouncedSearch if filters have actually changed
       if (JSON.stringify(updatedFilters) !== JSON.stringify(prev)) {
         setCurrentPage(1);
-        setBackendFilteredLogs(null);
-        debouncedSearch(updatedFilters, 1);
       }
-
       return updatedFilters as LogFilterState;
     });
   };
 
   const handleFilterReset = () => {
-    // Reset filters state
     setFilters(defaultFilters);
-
-    // Clear backend filtered logs to ensure fresh render
-    setBackendFilteredLogs(null);
-
-    // Cancel any in-flight debounced search
-    debouncedSearch.cancel();
-
-    // Reset to first page so the unfiltered view starts at page 1
+    setDebouncedFilters(defaultFilters);
     setCurrentPage(1);
   };
 
-  // Expose a filter-aware refetch so callers (e.g. the manual Fetch button) can
-  // refresh results while keeping all active backend filters intact.  The plain
-  // `logs.refetch()` in the parent only re-runs the main TanStack Query, which
-  // does not carry key_alias or other backend-only filter params.
-  const refetchWithFilters = useCallback(
-    (page = currentPage) => {
-      if (hasBackendFilters && accessToken) {
-        debouncedSearch.cancel();
-        performSearch(filters, page);
-      }
-    },
-    [hasBackendFilters, accessToken, filters, currentPage, performSearch, debouncedSearch],
-  );
-
   return {
-    filters,
+    logsQuery,
     filteredLogs,
-    hasBackendFilters,
     allTeams,
     handleFilterChange,
     handleFilterReset,
-    refetchWithFilters,
   };
 }
diff --git a/ui/litellm-dashboard/src/components/view_logs/logs_utils.test.tsx b/ui/litellm-dashboard/src/components/view_logs/logs_utils.test.tsx
new file mode 100644
index 00000000000..b0b74df4c24
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/view_logs/logs_utils.test.tsx
@@ -0,0 +1,45 @@
+import moment from "moment";
+import { describe, expect, it } from "vitest";
+import { getTimeRangeDisplay } from "./logs_utils";
+
+// startTime built relative to "now"; getTimeRangeDisplay computes now() internally.
+const ago = (amount: number, unit: moment.unitOfTime.DurationConstructor) =>
+  moment().subtract(amount, unit).toISOString();
+
+describe("getTimeRangeDisplay", () => {
+  it("labels a ~1-minute window as 'Last 1 Minute'", () => {
+    expect(getTimeRangeDisplay(false, ago(1, "minutes"), "")).toBe("Last 1 Minute");
+  });
+
+  it("labels a ~10-minute window as 'Last 15 Minutes'", () => {
+    expect(getTimeRangeDisplay(false, ago(10, "minutes"), "")).toBe("Last 15 Minutes");
+  });
+
+  it("labels a ~30-minute window as 'Last Hour'", () => {
+    expect(getTimeRangeDisplay(false, ago(30, "minutes"), "")).toBe("Last Hour");
+  });
+
+  it("labels a ~2-hour window as 'Last 4 Hours'", () => {
+    expect(getTimeRangeDisplay(false, ago(2, "hours"), "")).toBe("Last 4 Hours");
+  });
+
+  it("labels a ~10-hour window as 'Last 24 Hours'", () => {
+    expect(getTimeRangeDisplay(false, ago(10, "hours"), "")).toBe("Last 24 Hours");
+  });
+
+  it("labels a ~3-day window as 'Last 7 Days'", () => {
+    expect(getTimeRangeDisplay(false, ago(3, "days"), "")).toBe("Last 7 Days");
+  });
+
+  it("falls back to a 'MMM D - MMM D' range beyond 7 days", () => {
+    const label = getTimeRangeDisplay(false, ago(30, "days"), "");
+    expect(label).toMatch(/^[A-Z][a-z]{2} \d{1,2} - [A-Z][a-z]{2} \d{1,2}$/);
+  });
+
+  it("renders an explicit start - end range when isCustomDate is true", () => {
+    const start = "2025-01-02T03:04:00Z";
+    const end = "2025-01-05T06:07:00Z";
+    const expected = `${moment(start).format("MMM D, h:mm A")} - ${moment(end).format("MMM D, h:mm A")}`;
+    expect(getTimeRangeDisplay(true, start, end)).toBe(expected);
+  });
+});
diff --git a/ui/litellm-dashboard/src/hooks/mcpOAuthUtils.ts b/ui/litellm-dashboard/src/hooks/mcpOAuthUtils.ts
new file mode 100644
index 00000000000..3aff8af6eef
--- /dev/null
+++ b/ui/litellm-dashboard/src/hooks/mcpOAuthUtils.ts
@@ -0,0 +1,39 @@
+/**
+ * Shared utilities for MCP OAuth2 PKCE flow hooks.
+ *
+ * These helpers are used by both useToolsOAuthFlow and useUserMcpOAuthFlow
+ * to avoid divergence in URL construction and storage cleanup logic.
+ */
+
+import { getProxyBaseUrl, serverRootPath } from "@/components/networking";
+
+/**
+ * Build the OAuth callback URL for the current UI deployment.
+ *
+ * In the browser, derive the `/ui` prefix from the current pathname so the
+ * callback works regardless of how the proxy is mounted.  Outside the browser
+ * (SSR), fall back to the configured proxy base URL and server root path.
+ */
+export const buildCallbackUrl = (): string => {
+  if (typeof window !== "undefined") {
+    const path = window.location.pathname || "";
+    const idx = path.indexOf("/ui");
+    const prefix = idx >= 0 ? path.slice(0, idx + 3).replace(/\/+$/, "") : "";
+    return `${window.location.origin}${prefix}/mcp/oauth/callback`;
+  }
+  const base = (getProxyBaseUrl() || "").replace(/\/+$/, "");
+  const root = serverRootPath && serverRootPath !== "/" ? serverRootPath : "";
+  return `${base}${root}/ui/mcp/oauth/callback`;
+};
+
+/**
+ * Remove the given keys from sessionStorage, ignoring errors (e.g. storage
+ * disabled by browser privacy settings).
+ */
+export const clearStorage = (...keys: string[]): void => {
+  keys.forEach((k) => {
+    try {
+      window.sessionStorage.removeItem(k);
+    } catch (_) {}
+  });
+};
diff --git a/ui/litellm-dashboard/src/hooks/useMcpOAuthFlow.tsx b/ui/litellm-dashboard/src/hooks/useMcpOAuthFlow.tsx
index 7edeade4cbd..11efaba53b3 100644
--- a/ui/litellm-dashboard/src/hooks/useMcpOAuthFlow.tsx
+++ b/ui/litellm-dashboard/src/hooks/useMcpOAuthFlow.tsx
@@ -224,12 +224,21 @@ export const useMcpOAuthFlow = ({
       if (!storedPayload) {
         return;
       }
-      
+
+      // Guard: the callback page writes to the admin result key for *all* OAuth
+      // flows (including the tools re-auth flow).  Only proceed if this hook's
+      // own flow state exists, meaning startOAuthFlow() was actually called here.
+      // Without this guard, a tools re-auth redirect triggers a spurious
+      // "OAuth session state was lost" error from this hook.
+      const storedFlowState = getStorageItem(FLOW_STATE_KEY);
+      if (!storedFlowState) {
+        return;
+      }
+
       // Mark as processing
       processingRef.current = true;
       payload = JSON.parse(storedPayload);
-      const storedFlowState = getStorageItem(FLOW_STATE_KEY);
-      flowState = storedFlowState ? JSON.parse(storedFlowState) : null;
+      flowState = JSON.parse(storedFlowState);
     } catch (err) {
       clearStoredFlow();
       processingRef.current = false;
diff --git a/ui/litellm-dashboard/src/hooks/useToolsOAuthFlow.tsx b/ui/litellm-dashboard/src/hooks/useToolsOAuthFlow.tsx
new file mode 100644
index 00000000000..66e59b80db4
--- /dev/null
+++ b/ui/litellm-dashboard/src/hooks/useToolsOAuthFlow.tsx
@@ -0,0 +1,232 @@
+"use client";
+
+/**
+ * OAuth2 PKCE flow for the Tools screen re-authentication path.
+ *
+ * Unlike useUserMcpOAuthFlow (used in the chat panel), this hook:
+ * - stores the resulting token in sessionStorage via mcpTokenStore only
+ * - does NOT call storeMCPOAuthUserCredential (no backend DB write)
+ * - uses "litellm-tools-mcp-oauth-result" as its result key to avoid
+ *   collisions with the admin and user flows
+ *
+ * The OAuth callback page (src/app/mcp/oauth/callback/page.tsx) writes
+ * to this key so this hook can pick up the result after the redirect.
+ */
+
+import { useCallback, useEffect, useRef, useState } from "react";
+import {
+  buildMcpOAuthAuthorizeUrl,
+  exchangeMcpOAuthToken,
+  registerMcpOAuthClient,
+} from "@/components/networking";
+import NotificationsManager from "@/components/molecules/notifications_manager";
+import { extractErrorMessage } from "@/utils/errorUtils";
+import { generateCodeChallenge, generateCodeVerifier } from "@/utils/pkce";
+import { getSecureItem, setSecureItem } from "@/utils/secureStorage";
+import { setToken } from "@/utils/mcpTokenStore";
+import { buildCallbackUrl, clearStorage } from "./mcpOAuthUtils";
+
+export type ToolsOAuthStatus = "idle" | "authorizing" | "exchanging" | "success" | "error";
+
+interface UseToolsOAuthFlowOptions {
+  accessToken: string;
+  serverId: string;
+  serverAlias?: string | null;
+  userId?: string | null;
+  scopes?: string[];
+  clientId?: string | null;
+  onSuccess: (accessToken: string) => void;
+}
+
+interface UseToolsOAuthFlowResult {
+  startOAuthFlow: () => Promise<void>;
+  status: ToolsOAuthStatus;
+  error: string | null;
+}
+
+const FLOW_STATE_KEY = "litellm-tools-mcp-oauth-flow-state";
+const RESULT_KEY = "litellm-tools-mcp-oauth-result";
+const RETURN_URL_KEY = "litellm-mcp-oauth-return-url";
+
+type StoredFlowState = {
+  state: string;
+  codeVerifier: string;
+  serverId: string;
+  redirectUri: string;
+  clientId?: string;
+  clientSecret?: string;
+  scopes?: string[];
+};
+
+export const useToolsOAuthFlow = ({
+  accessToken,
+  serverId,
+  serverAlias,
+  userId,
+  scopes,
+  clientId: preClientId,
+  onSuccess,
+}: UseToolsOAuthFlowOptions): UseToolsOAuthFlowResult => {
+  const [status, setStatus] = useState<ToolsOAuthStatus>("idle");
+  const [error, setError] = useState<string | null>(null);
+  const processingRef = useRef(false);
+  const onSuccessRef = useRef(onSuccess);
+  onSuccessRef.current = onSuccess;
+
+  const startOAuthFlow = useCallback(async () => {
+    if (typeof window === "undefined") return;
+    try {
+      setStatus("authorizing");
+      setError(null);
+
+      let clientId: string | undefined = preClientId ?? undefined;
+      let clientSecret: string | undefined;
+
+      if (!clientId) {
+        try {
+          const reg = await registerMcpOAuthClient(accessToken, serverId, {
+            client_name: serverAlias || serverId,
+            grant_types: ["authorization_code", "refresh_token"],
+            response_types: ["code"],
+            token_endpoint_auth_method: "none",
+          });
+          clientId = reg?.client_id;
+          clientSecret = reg?.client_secret;
+        } catch (_) {
+          // Registration is optional; proceed without client_id
+        }
+      }
+
+      const verifier = generateCodeVerifier();
+      const challenge = await generateCodeChallenge(verifier);
+      const state = crypto.randomUUID();
+      const redirectUri = buildCallbackUrl();
+      const scopeString = scopes?.filter((s) => s.trim()).join(" ");
+
+      const authorizeUrl = buildMcpOAuthAuthorizeUrl({
+        serverId,
+        clientId,
+        redirectUri,
+        state,
+        codeChallenge: challenge,
+        scope: scopeString,
+      });
+
+      const flowState: StoredFlowState = {
+        state,
+        codeVerifier: verifier,
+        serverId,
+        redirectUri,
+        clientId,
+        clientSecret,
+        scopes,
+      };
+
+      setSecureItem(FLOW_STATE_KEY, JSON.stringify(flowState));
+      // Return to the current page (Tools tab) after the OAuth redirect
+      setSecureItem(RETURN_URL_KEY, window.location.href);
+
+      window.location.href = authorizeUrl;
+    } catch (err) {
+      const msg = extractErrorMessage(err);
+      setError(msg);
+      setStatus("error");
+      NotificationsManager.error(msg);
+    }
+  }, [accessToken, serverId, serverAlias, scopes, preClientId]);
+
+  const resumeOAuthFlow = useCallback(async () => {
+    if (typeof window === "undefined" || processingRef.current) return;
+
+    const storedResult = getSecureItem(RESULT_KEY);
+    if (!storedResult) return;
+
+    // The callback page writes to this result key for every OAuth flow (including
+    // the admin server-creation flow).  Guard: only proceed if *this* hook's flow
+    // state exists, meaning startOAuthFlow() was actually called from the Tools screen.
+    // Without this guard, a stale result written during server creation would trigger
+    // "OAuth session state was lost" when the user navigates to the Tools tab.
+    const rawFlowState = getSecureItem(FLOW_STATE_KEY);
+    if (!rawFlowState) return;
+
+    let peeked: StoredFlowState | null = null;
+    try {
+      peeked = JSON.parse(rawFlowState) as StoredFlowState;
+      if (peeked.serverId && peeked.serverId !== serverId) return;
+    } catch (_) {}
+
+    processingRef.current = true;
+    clearStorage(RESULT_KEY);
+
+    let payload: Record<string, unknown> | null = null;
+    let flowState: StoredFlowState | null = null;
+
+    try {
+      payload = JSON.parse(storedResult);
+      flowState = peeked;
+    } catch (_) {
+      setError("Failed to resume OAuth flow. Please retry.");
+      setStatus("error");
+      processingRef.current = false;
+      clearStorage(FLOW_STATE_KEY);
+      return;
+    }
+
+    try {
+      if (!flowState?.state || !flowState.codeVerifier || !flowState.serverId) {
+        throw new Error("OAuth session state was lost. Please retry.");
+      }
+      if (!payload?.state || payload.state !== flowState.state) {
+        throw new Error("OAuth state mismatch. Please retry.");
+      }
+      if (payload.error) {
+        throw new Error((payload.error_description as string) || (payload.error as string));
+      }
+      if (!payload.code) {
+        throw new Error("Authorization code missing in callback.");
+      }
+
+      setStatus("exchanging");
+      const token = await exchangeMcpOAuthToken({
+        serverId: flowState.serverId,
+        code: payload.code as string,
+        clientId: flowState.clientId,
+        clientSecret: flowState.clientSecret,
+        codeVerifier: flowState.codeVerifier,
+        redirectUri: flowState.redirectUri,
+        accessToken,
+      });
+
+      // Store in sessionStorage only — no backend DB write
+      setToken(
+        flowState.serverId,
+        {
+          access_token: token.access_token,
+          expires_in: token.expires_in,
+          refresh_token: token.refresh_token,
+          token_type: token.token_type,
+        },
+        userId,
+      );
+
+      setStatus("success");
+      setError(null);
+      NotificationsManager.success("Connected successfully");
+      onSuccessRef.current(token.access_token);
+    } catch (err) {
+      const msg = extractErrorMessage(err);
+      setError(msg);
+      setStatus("error");
+      NotificationsManager.error(msg);
+    } finally {
+      clearStorage(FLOW_STATE_KEY);
+      setTimeout(() => { processingRef.current = false; }, 1000);
+    }
+  }, [accessToken, serverId, userId]);
+
+  useEffect(() => {
+    resumeOAuthFlow();
+  }, [resumeOAuthFlow]);
+
+  return { startOAuthFlow, status, error };
+};
diff --git a/ui/litellm-dashboard/src/hooks/useUserMcpOAuthFlow.tsx b/ui/litellm-dashboard/src/hooks/useUserMcpOAuthFlow.tsx
index cf0a81dcadf..1dc7a5ee54a 100644
--- a/ui/litellm-dashboard/src/hooks/useUserMcpOAuthFlow.tsx
+++ b/ui/litellm-dashboard/src/hooks/useUserMcpOAuthFlow.tsx
@@ -16,15 +16,14 @@ import { useCallback, useEffect, useRef, useState } from "react";
 import {
   buildMcpOAuthAuthorizeUrl,
   exchangeMcpOAuthToken,
-  getProxyBaseUrl,
   registerMcpOAuthClient,
-  serverRootPath,
   storeMCPOAuthUserCredential,
 } from "@/components/networking";
 import NotificationsManager from "@/components/molecules/notifications_manager";
 import { extractErrorMessage } from "@/utils/errorUtils";
 import { generateCodeChallenge, generateCodeVerifier } from "@/utils/pkce";
 import { getSecureItem, setSecureItem } from "@/utils/secureStorage";
+import { buildCallbackUrl, clearStorage } from "./mcpOAuthUtils";
 
 export type UserMcpOAuthStatus = "idle" | "authorizing" | "exchanging" | "success" | "error";
 
@@ -69,26 +68,6 @@ const getStorage = (key: string): string | null => {
   return getSecureItem(key);
 };
 
-const clearStorage = (...keys: string[]) => {
-  keys.forEach((k) => {
-    try {
-      window.sessionStorage.removeItem(k);
-    } catch (_) {}
-  });
-};
-
-const buildCallbackUrl = (): string => {
-  if (typeof window !== "undefined") {
-    const path = window.location.pathname || "";
-    const idx = path.indexOf("/ui");
-    const prefix = idx >= 0 ? path.slice(0, idx + 3).replace(/\/+$/, "") : "";
-    return `${window.location.origin}${prefix}/mcp/oauth/callback`;
-  }
-  const base = (getProxyBaseUrl() || "").replace(/\/+$/, "");
-  const root = serverRootPath && serverRootPath !== "/" ? serverRootPath : "";
-  return `${base}${root}/ui/mcp/oauth/callback`;
-};
-
 export const useUserMcpOAuthFlow = ({
   accessToken,
   serverId,
@@ -176,13 +155,17 @@ export const useUserMcpOAuthFlow = ({
     // mount and would compete for the same RESULT_KEY.  Peek at the stored
     // flow state first: only the hook instance whose serverId matches the one
     // that initiated the OAuth flow should consume the result.
+    // Guard: only proceed if this hook's flow state exists (startOAuthFlow was
+    // called from this hook).  Without the guard, a tools re-auth redirect writes
+    // to the user result key too, and every OAuth2ConnectButton instance would try
+    // to resume a flow that was never started here.
     const rawFlowState = getStorage(FLOW_STATE_KEY);
-    if (rawFlowState) {
-      try {
-        const peeked = JSON.parse(rawFlowState) as StoredFlowState;
-        if (peeked.serverId && peeked.serverId !== serverId) return;
-      } catch (_) {}
-    }
+    if (!rawFlowState) return;
+
+    try {
+      const peeked = JSON.parse(rawFlowState) as StoredFlowState;
+      if (peeked.serverId && peeked.serverId !== serverId) return;
+    } catch (_) {}
 
     processingRef.current = true;
     clearStorage(RESULT_KEY);
diff --git a/ui/litellm-dashboard/src/utils/cookieUtils.test.ts b/ui/litellm-dashboard/src/utils/cookieUtils.test.ts
index c7bd27a6a85..28e2fc771c2 100644
--- a/ui/litellm-dashboard/src/utils/cookieUtils.test.ts
+++ b/ui/litellm-dashboard/src/utils/cookieUtils.test.ts
@@ -1,5 +1,6 @@
 import { describe, it, expect, beforeEach, vi } from "vitest";
 import { clearTokenCookies, getCookie, storeLoginToken } from "./cookieUtils";
+import { getToken, setToken } from "./mcpTokenStore";
 
 describe("cookieUtils", () => {
   beforeEach(() => {
@@ -20,6 +21,15 @@ describe("cookieUtils", () => {
       expect(getCookie("token")).toBeNull();
     });
 
+    it("should clear MCP session tokens on logout", () => {
+      setToken("server-1", { access_token: "mcp-tok" }, "user-a");
+      expect(getToken("server-1", "user-a")).not.toBeNull();
+
+      clearTokenCookies();
+
+      expect(getToken("server-1", "user-a")).toBeNull();
+    });
+
     it("should clear token cookie from /ui path", () => {
       document.cookie = "token=test-token-value; path=/ui";
       clearTokenCookies();
diff --git a/ui/litellm-dashboard/src/utils/cookieUtils.ts b/ui/litellm-dashboard/src/utils/cookieUtils.ts
index b4493744ad4..da232e72e2a 100644
--- a/ui/litellm-dashboard/src/utils/cookieUtils.ts
+++ b/ui/litellm-dashboard/src/utils/cookieUtils.ts
@@ -2,6 +2,8 @@
  * Utility functions for managing cookies
  */
 
+import { clearAllMcpTokens } from "./mcpTokenStore";
+
 /**
  * Returns the cookie path for the UI.
  * Derives the path from window.location.pathname so it works when
@@ -67,6 +69,7 @@ export function clearTokenCookies() {
     // sessionStorage may be unavailable
   }
 
+  clearAllMcpTokens();
 }
 
 /**
diff --git a/ui/litellm-dashboard/src/utils/mcpHeaderUtils.test.ts b/ui/litellm-dashboard/src/utils/mcpHeaderUtils.test.ts
new file mode 100644
index 00000000000..b730b9c097c
--- /dev/null
+++ b/ui/litellm-dashboard/src/utils/mcpHeaderUtils.test.ts
@@ -0,0 +1,16 @@
+import { describe, expect, it } from "vitest";
+import { sanitizeMcpAliasForHeader } from "./mcpHeaderUtils";
+
+describe("sanitizeMcpAliasForHeader", () => {
+  it("lowercases and replaces spaces with underscores", () => {
+    expect(sanitizeMcpAliasForHeader("My Server")).toBe("my_server");
+  });
+
+  it("replaces invalid characters for header token segments", () => {
+    expect(sanitizeMcpAliasForHeader("GitHub-MCP!")).toBe("github_mcp");
+  });
+
+  it("preserves underscores and digits", () => {
+    expect(sanitizeMcpAliasForHeader("github_mcp2")).toBe("github_mcp2");
+  });
+});
diff --git a/ui/litellm-dashboard/src/utils/mcpHeaderUtils.ts b/ui/litellm-dashboard/src/utils/mcpHeaderUtils.ts
new file mode 100644
index 00000000000..76c752a4b61
--- /dev/null
+++ b/ui/litellm-dashboard/src/utils/mcpHeaderUtils.ts
@@ -0,0 +1,14 @@
+/**
+ * Sanitize an MCP server alias for use in HTTP header names (x-mcp-{alias}-...).
+ * RFC 7230 tchar allows token chars; aliases with spaces or hyphens break parsing
+ * because the backend splits on the first dash after the x-mcp- prefix.
+ * Keep in sync with litellm.proxy._experimental.mcp_server.utils.sanitize_mcp_alias_for_header.
+ */
+export function sanitizeMcpAliasForHeader(alias: string): string {
+  return alias
+    .toLowerCase()
+    .trim()
+    .replace(/[^a-z0-9_]/g, "_")
+    .replace(/_+/g, "_")
+    .replace(/^_|_$/g, "");
+}
diff --git a/ui/litellm-dashboard/src/utils/mcpTokenStore.test.ts b/ui/litellm-dashboard/src/utils/mcpTokenStore.test.ts
new file mode 100644
index 00000000000..1c61e9b1a26
--- /dev/null
+++ b/ui/litellm-dashboard/src/utils/mcpTokenStore.test.ts
@@ -0,0 +1,46 @@
+import { afterEach, beforeEach, describe, expect, it } from "vitest";
+import {
+  clearAllMcpTokens,
+  getToken,
+  isTokenValid,
+  removeToken,
+  setToken,
+} from "./mcpTokenStore";
+
+describe("mcpTokenStore", () => {
+  beforeEach(() => {
+    sessionStorage.clear();
+  });
+
+  afterEach(() => {
+    sessionStorage.clear();
+  });
+
+  it("scopes tokens by user id", () => {
+    setToken("server-a", { access_token: "user1-token" }, "user-1");
+    setToken("server-a", { access_token: "user2-token" }, "user-2");
+
+    expect(getToken("server-a", "user-1")?.access_token).toBe("user1-token");
+    expect(getToken("server-a", "user-2")?.access_token).toBe("user2-token");
+    expect(getToken("server-a", "user-3")).toBeNull();
+  });
+
+  it("validates expiry per user scope", () => {
+    setToken("server-a", { access_token: "tok", expires_in: 3600 }, "user-1");
+    expect(isTokenValid("server-a", "user-1")).toBe(true);
+    removeToken("server-a", "user-1");
+    expect(isTokenValid("server-a", "user-1")).toBe(false);
+  });
+
+  it("clearAllMcpTokens removes every mcp-session-token entry", () => {
+    setToken("s1", { access_token: "a" }, "u1");
+    setToken("s2", { access_token: "b" }, "u2");
+    sessionStorage.setItem("unrelated", "keep");
+
+    clearAllMcpTokens();
+
+    expect(getToken("s1", "u1")).toBeNull();
+    expect(getToken("s2", "u2")).toBeNull();
+    expect(sessionStorage.getItem("unrelated")).toBe("keep");
+  });
+});
diff --git a/ui/litellm-dashboard/src/utils/mcpTokenStore.ts b/ui/litellm-dashboard/src/utils/mcpTokenStore.ts
new file mode 100644
index 00000000000..0279922cd07
--- /dev/null
+++ b/ui/litellm-dashboard/src/utils/mcpTokenStore.ts
@@ -0,0 +1,93 @@
+/**
+ * Session-storage-backed OAuth token store for MCP servers.
+ * Tokens are keyed by LiteLLM user id + server_id and cleared when the browser
+ * session ends (tab/window close). Never written to localStorage.
+ */
+
+const KEY_PREFIX = "mcp-session-token:";
+
+interface StoredToken {
+  access_token: string;
+  expires_at: number;
+  refresh_token?: string;
+  token_type: string;
+}
+
+interface TokenInput {
+  access_token: string;
+  expires_in?: number;
+  refresh_token?: string;
+  token_type?: string;
+}
+
+const DEFAULT_TTL_MS = 3600 * 1000; // 1 hour
+
+function storageKey(serverId: string, userId?: string | null): string {
+  const userPart = userId?.trim() || "_anonymous";
+  return `${KEY_PREFIX}${userPart}:${serverId}`;
+}
+
+export function setToken(
+  serverId: string,
+  data: TokenInput,
+  userId?: string | null,
+): void {
+  if (typeof window === "undefined") return;
+  const stored: StoredToken = {
+    access_token: data.access_token,
+    expires_at: Date.now() + (data.expires_in != null ? data.expires_in * 1000 : DEFAULT_TTL_MS),
+    token_type: data.token_type ?? "bearer",
+    ...(data.refresh_token ? { refresh_token: data.refresh_token } : {}),
+  };
+  try {
+    window.sessionStorage.setItem(storageKey(serverId, userId), JSON.stringify(stored));
+  } catch {
+    // Silently ignore storage errors (private browsing, quota exceeded, etc.)
+  }
+}
+
+export function getToken(
+  serverId: string,
+  userId?: string | null,
+): StoredToken | null {
+  if (typeof window === "undefined") return null;
+  try {
+    const raw = window.sessionStorage.getItem(storageKey(serverId, userId));
+    if (!raw) return null;
+    return JSON.parse(raw) as StoredToken;
+  } catch {
+    return null;
+  }
+}
+
+export function removeToken(serverId: string, userId?: string | null): void {
+  if (typeof window === "undefined") return;
+  try {
+    window.sessionStorage.removeItem(storageKey(serverId, userId));
+  } catch {
+    // Silently ignore
+  }
+}
+
+export function isTokenValid(serverId: string, userId?: string | null): boolean {
+  const token = getToken(serverId, userId);
+  if (!token) return false;
+  return token.expires_at > Date.now();
+}
+
+/** Remove all MCP session tokens (e.g. on logout or user switch). */
+export function clearAllMcpTokens(): void {
+  if (typeof window === "undefined") return;
+  try {
+    const keysToRemove: string[] = [];
+    for (let i = 0; i < window.sessionStorage.length; i++) {
+      const key = window.sessionStorage.key(i);
+      if (key?.startsWith(KEY_PREFIX)) {
+        keysToRemove.push(key);
+      }
+    }
+    keysToRemove.forEach((key) => window.sessionStorage.removeItem(key));
+  } catch {
+    // Silently ignore
+  }
+}
diff --git a/ui/litellm-dashboard/tests/view_logs/useLogFilterLogic.min.test.tsx b/ui/litellm-dashboard/tests/view_logs/useLogFilterLogic.min.test.tsx
deleted file mode 100644
index faede84f4c2..00000000000
--- a/ui/litellm-dashboard/tests/view_logs/useLogFilterLogic.min.test.tsx
+++ /dev/null
@@ -1,62 +0,0 @@
-import React from "react";
-import { describe, it, expect } from "vitest";
-import { render, screen } from "@testing-library/react";
-import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
-import { useLogFilterLogic } from "../../src/components/view_logs/log_filter_logic";
-
-// Minimal mocks to avoid real network during hook init
-vi.mock("../../src/components/key_team_helpers/filter_helpers", () => ({
-  fetchAllKeyAliases: vi.fn().mockResolvedValue([]),
-  fetchAllTeams: vi.fn().mockResolvedValue([]),
-}));
-
-const createQueryClient = () =>
-  new QueryClient({
-    defaultOptions: { queries: { retry: false, gcTime: 0 } },
-  });
-
-function Harness({ logs }: { logs: any }) {
-  const { filteredLogs } = useLogFilterLogic({
-    logs,
-    accessToken: "token",
-    startTime: "2025-01-01 00:00:00",
-    endTime: "2025-01-02 00:00:00",
-    pageSize: 50,
-    isCustomDate: true,
-    setCurrentPage: () => {},
-    userID: "user-1",
-    userRole: "admin",
-  });
-
-  return <div data-testid="count">{filteredLogs.data.length}</div>;
-}
-
-describe("useLogFilterLogic (minimal)", () => {
-  it("useLogFilterLogic minimal: updates filteredLogs when logs change", async () => {
-    const qc = createQueryClient();
-    const logsA = { data: [{ request_id: "a" }], total: 1, page: 1, page_size: 50, total_pages: 1 };
-    const logsB = {
-      data: [{ request_id: "a" }, { request_id: "b" }],
-      total: 2,
-      page: 1,
-      page_size: 50,
-      total_pages: 1,
-    };
-
-    const { rerender } = render(
-      <QueryClientProvider client={qc}>
-        <Harness logs={logsA} />
-      </QueryClientProvider>,
-    );
-
-    expect(await screen.findByTestId("count")).toHaveTextContent("1");
-
-    rerender(
-      <QueryClientProvider client={qc}>
-        <Harness logs={logsB} />
-      </QueryClientProvider>,
-    );
-
-    expect(await screen.findByTestId("count")).toHaveTextContent("2");
-  });
-});
diff --git a/uv.lock b/uv.lock
index f3eaf6ca88c..fe3e0e037cf 100644
--- a/uv.lock
+++ b/uv.lock
@@ -9,7 +9,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
+exclude-newer = "2026-05-19T01:14:41.559325863Z"
 exclude-newer-span = "P3D"
 
 [manifest]
@@ -539,7 +539,7 @@ wheels = [
 
 [[package]]
 name = "black"
-version = "24.10.0"
+version = "26.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
@@ -547,28 +547,33 @@ dependencies = [
     { name = "packaging" },
     { name = "pathspec" },
     { name = "platformdirs" },
+    { name = "pytokens" },
     { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d8/0d/cc2fb42b8c50d80143221515dd7e4766995bd07c56c9a3ed30baf080b6dc/black-24.10.0.tar.gz", hash = "sha256:846ea64c97afe3bc677b761787993be4991810ecc7a4a937816dd6bddedc4875", size = 645813, upload-time = "2024-10-07T19:20:50.361Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e1/c5/61175d618685d42b005847464b8fb4743a67b1b8fdb75e50e5a96c31a27a/black-26.3.1.tar.gz", hash = "sha256:2c50f5063a9641c7eed7795014ba37b0f5fa227f3d408b968936e24bc0566b07", size = 666155, upload-time = "2026-03-12T03:36:03.593Z" }
 wheels = [
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