diff --git a/.circleci/config.yml b/.circleci/config.yml
index 790efc79862..d998ee467b8 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -3543,93 +3543,6 @@ jobs:
       - codecov/upload:
           file: ./coverage.xml
 
-  publish_to_pypi:
-    docker:
-      - image: cimg/python:3.8
-    working_directory: ~/project
-
-    environment:
-      TWINE_USERNAME: __token__
-
-    steps:
-      - checkout
-
-      - run:
-          name: Copy model_prices_and_context_window File to model_prices_and_context_window_backup
-          command: |
-            cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json
-
-      - run:
-          name: Checkout code
-          command: git checkout $CIRCLE_SHA1
-
-      # Check if setup.py is modified and publish to PyPI
-      - run:
-          name: PyPI publish
-          command: |
-            echo "Install TOML package."
-            python -m pip install toml
-            VERSION=$(python -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])")
-            PACKAGE_NAME=$(python -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['name'])")
-            if ! pip show -v $PACKAGE_NAME | grep -q "Version: ${VERSION}"; then
-                echo "pyproject.toml modified"
-                echo -e "[pypi]\nusername = $PYPI_PUBLISH_USERNAME\npassword = $PYPI_PUBLISH_PASSWORD" > ~/.pypirc
-                python -m pip install --upgrade pip
-                pip install build
-                pip install wheel
-                pip install --upgrade twine setuptools
-                rm -rf build dist
-
-                echo "Building package"
-                python -m build
-
-                echo "Twine upload to dist"
-                echo "Contents of dist directory:"
-                ls dist/
-                twine upload --verbose dist/*
-            else
-                echo "Version ${VERSION} of package is already published on PyPI."
-                
-                # Check if corresponding Docker nightly image exists
-                NIGHTLY_TAG="v${VERSION}-nightly"
-                echo "Checking for Docker nightly image: litellm/litellm:${NIGHTLY_TAG}"
-                
-                # Check Docker Hub for the nightly image
-                if curl -s "https://hub.docker.com/v2/repositories/litellm/litellm/tags/${NIGHTLY_TAG}" | grep -q "name"; then
-                    echo "Docker nightly image ${NIGHTLY_TAG} exists. This release was already completed successfully."
-                    echo "Skipping PyPI publish and continuing to ensure Docker images are up to date."
-                    circleci step halt
-                else
-                    echo "ERROR: PyPI package ${VERSION} exists but Docker nightly image ${NIGHTLY_TAG} does not exist!"
-                    echo "This indicates an incomplete release. Please investigate."
-                    exit 1
-                fi
-            fi
-      - run:
-          name: Trigger Github Action for new Docker Container + Trigger Load Testing
-          command: |
-            echo "Install TOML package."
-            python3 -m pip install toml
-            VERSION=$(python3 -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])")
-            echo "LiteLLM Version ${VERSION}"
-
-            # Determine which branch to use for Docker build
-            if [[ "$CIRCLE_BRANCH" =~ ^litellm_release_day_.* ]]; then
-              BUILD_BRANCH="$CIRCLE_BRANCH"
-              echo "Using release branch: $BUILD_BRANCH"
-            else
-              BUILD_BRANCH="main"
-              echo "Using default branch: $BUILD_BRANCH"
-            fi
-
-            curl -X POST \
-              -H "Accept: application/vnd.github.v3+json" \
-              -H "Authorization: Bearer $GITHUB_TOKEN" \
-              "https://api.github.com/repos/BerriAI/litellm/actions/workflows/ghcr_deploy.yml/dispatches" \
-              -d "{\"ref\":\"${BUILD_BRANCH}\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}"
-            echo "triggering load testing server for version ${VERSION} and commit ${CIRCLE_SHA1}"
-            curl -X POST "https://proxyloadtester-production.up.railway.app/start/load/test?version=${VERSION}&commit_hash=${CIRCLE_SHA1}&release_type=nightly"
-
   publish_proxy_extras:
     docker:
       - image: cimg/python:3.8
@@ -4489,59 +4402,3 @@ workflows:
               only:
                 - main
                 - /litellm_release_day_.*/
-      - publish_to_pypi:
-          requires:
-            - mypy_linting
-            - semgrep
-            - local_testing_part1
-            - local_testing_part2
-            - build_and_test
-            - e2e_openai_endpoints
-            - test_bad_database_url
-            - llm_translation_testing
-            - realtime_translation_testing
-            - mcp_testing
-            - agent_testing
-            - google_generate_content_endpoint_testing
-            - llm_responses_api_testing
-            - ocr_testing
-            - search_testing
-            - litellm_mapped_tests_proxy_part1
-            - litellm_mapped_tests_proxy_part2
-            - litellm_mapped_tests_llms
-            - litellm_mapped_tests_core
-            - litellm_mapped_tests_mcps
-            - litellm_mapped_tests_integrations
-            - litellm_mapped_tests_litellm_core_utils
-            - litellm_mapped_enterprise_tests
-            - batches_testing
-            - litellm_utils_testing
-            - pass_through_unit_testing
-            - image_gen_testing
-            - logging_testing
-            - audio_testing
-            - litellm_router_testing
-            - litellm_router_unit_testing
-            - caching_unit_tests
-            - langfuse_logging_unit_tests
-            - litellm_assistants_api_testing
-            - auth_ui_unit_tests
-            - ui_unit_tests
-            - db_migration_disable_update_check
-            - e2e_ui_testing_chromium
-            - e2e_ui_testing_firefox
-            - litellm_proxy_unit_testing_key_generation
-            - litellm_proxy_unit_testing_part1
-            - litellm_proxy_unit_testing_part2
-            - litellm_security_tests
-            - installing_litellm_on_python
-            - installing_litellm_on_python_3_13
-            - proxy_logging_guardrails_model_info_tests
-            - proxy_spend_accuracy_tests
-            - proxy_multi_instance_tests
-            - proxy_store_model_in_db_tests
-            - proxy_build_from_pip_tests
-            - proxy_pass_through_endpoint_tests
-            - check_code_and_doc_quality
-            - publish_proxy_extras
-            - guardrails_testing
diff --git a/.claude/settings.json b/.claude/settings.json
deleted file mode 100644
index 8c1d85f96e0..00000000000
--- a/.claude/settings.json
+++ /dev/null
@@ -1,36 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(git show:*)",
-      "Bash(git worktree add:*)",
-      "Read(//Users/krrishdholakia/Documents/litellm/**)",
-      "Read(//Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/types/**)",
-      "Read(//Users/krrishdholakia/Documents/litellm-claude-code-guardrails/**)",
-      "Read(//Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/**)",
-      "Bash(python:*)",
-      "Bash(python -c \"\nimport sys; sys.path.insert\\(0, ''.''\\)\nfrom litellm.proxy.guardrails.guardrail_hooks.claude_code.guardrail import ClaudeCodeGuardrail, HOSTED_TOOL_PREFIXES\nprint\\(''HOSTED_TOOL_PREFIXES:'', HOSTED_TOOL_PREFIXES\\)\nprint\\(''ClaudeCodeGuardrail imported OK''\\)\n\")",
-      "Read(//Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/litellm/proxy/**)",
-      "Read(//Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/**)",
-      "Bash(poetry run pytest:*)",
-      "Bash(git add:*)",
-      "Bash(git commit:*)",
-      "Bash(poetry run python:*)",
-      "Bash(poetry run pip:*)",
-      "Bash(git reset:*)",
-      "Bash(git cherry-pick:*)",
-      "Bash(git checkout:*)",
-      "Read(//Users/krrishdholakia/Documents/litellm/litellm/proxy/guardrails/guardrail_hooks/**)",
-      "Read(//Users/krrishdholakia/Documents/**)",
-      "Bash(git -C /Users/krrishdholakia/Documents/litellm-mcp-user-permissions worktree list)",
-      "Bash(ls:*)"
-    ],
-    "additionalDirectories": [
-      "/Users/krrishdholakia/Documents/litellm-mcp-group-plan/plan",
-      "/Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/proxy/guardrails/guardrail_hooks/claude_code",
-      "/Users/krrishdholakia/Documents/litellm-claude-code-guardrails/litellm/types",
-      "/Users/krrishdholakia/Documents/litellm-claude-code-guardrails",
-      "/Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/litellm/proxy",
-      "/Users/krrishdholakia/Documents/litellm-mcp-jwt-groups/tests/test_litellm/proxy/auth"
-    ]
-  }
-}
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index 4744ab048c7..cbf380bac01 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,7 +1,7 @@
 blank_issues_enabled: true
 contact_links:
   - name: Schedule Demo
-    url: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions
+    url: https://enterprise.litellm.ai/demo
     about: Speak directly with Krrish and Ishaan, the founders, to discuss issues, share feedback, or explore improvements for LiteLLM
   - name: Discord
     url: https://discord.com/invite/wuPM9dRgDw
diff --git a/.github/actions/helm-oci-chart-releaser/action.yml b/.github/actions/helm-oci-chart-releaser/action.yml
index 1823e262832..454c591d436 100644
--- a/.github/actions/helm-oci-chart-releaser/action.yml
+++ b/.github/actions/helm-oci-chart-releaser/action.yml
@@ -41,32 +41,54 @@ runs:
   using: composite
   steps:
     - name: Helm | Setup
-      uses: azure/setup-helm@v4
+      uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # v4.3.1
       with:
         version: v3.20.0
 
     - name: Helm | Login
       shell: bash
-      run: echo ${{ inputs.registry_password }} | helm registry login -u ${{ inputs.registry_username }} --password-stdin ${{ inputs.registry }}
+      env:
+        REGISTRY_PASSWORD: ${{ inputs.registry_password }}
+        REGISTRY_USERNAME: ${{ inputs.registry_username }}
+        REGISTRY: ${{ inputs.registry }}
+      run: echo "$REGISTRY_PASSWORD" | helm registry login -u "$REGISTRY_USERNAME" --password-stdin "$REGISTRY"
 
     - name: Helm | Dependency
       if: inputs.update_dependencies == 'true'
       shell: bash
-      run: helm dependency update ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }}
+      env:
+        CHART_PATH: ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }}
+      run: helm dependency update "$CHART_PATH"
 
     - name: Helm | Package
       shell: bash
-      run: helm package ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} --version ${{ inputs.tag }} --app-version ${{ inputs.app_version }}
+      env:
+        CHART_PATH: ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }}
+        TAG: ${{ inputs.tag }}
+        APP_VERSION: ${{ inputs.app_version }}
+      run: helm package "$CHART_PATH" --version "$TAG" --app-version "$APP_VERSION"
 
     - name: Helm | Push
       shell: bash
-      run: helm push ${{ inputs.name }}-${{ inputs.tag }}.tgz oci://${{ inputs.registry }}/${{ inputs.repository }}
+      env:
+        NAME: ${{ inputs.name }}
+        TAG: ${{ inputs.tag }}
+        REGISTRY: ${{ inputs.registry }}
+        REPOSITORY: ${{ inputs.repository }}
+      run: helm push "${NAME}-${TAG}.tgz" "oci://${REGISTRY}/${REPOSITORY}"
 
     - name: Helm | Logout
       shell: bash
-      run: helm registry logout ${{ inputs.registry }}
+      env:
+        REGISTRY: ${{ inputs.registry }}
+      run: helm registry logout "$REGISTRY"
 
     - name: Helm | Output
       id: output
       shell: bash
-      run: echo "image=${{ inputs.registry }}/${{ inputs.repository }}/${{ inputs.name }}:${{ inputs.tag }}" >> $GITHUB_OUTPUT
+      env:
+        REGISTRY: ${{ inputs.registry }}
+        REPOSITORY: ${{ inputs.repository }}
+        NAME: ${{ inputs.name }}
+        TAG: ${{ inputs.tag }}
+      run: echo "image=${REGISTRY}/${REPOSITORY}/${NAME}:${TAG}" >> $GITHUB_OUTPUT
diff --git a/.github/codeql/codeql-config.yml b/.github/codeql/codeql-config.yml
index 20807685e12..36d70c1d746 100644
--- a/.github/codeql/codeql-config.yml
+++ b/.github/codeql/codeql-config.yml
@@ -1,22 +1,21 @@
 name: "LiteLLM CodeQL config"
 
-# Use security-extended suite instead of security-and-quality to avoid
-# result sets > 2 GiB on this codebase that cause fatal OOM failures.
 queries:
-  - uses: security-extended
+  - uses: security-and-quality
 
-# These two queries are security queries included in security-extended that
-# individually produce result sets > 2 GiB on this codebase, causing fatal
-# OOM failures. Exclude them as a safety net until CI confirms they no longer
-# OOM; drop these exclusions in a follow-up once verified.
+# Known OOM queries on large Python codebases:
+# CodeQL builds a full data flow graph in memory. These two queries trace
+# sensitive data through every log call / regex pattern, causing combinatorial
+# path explosion on codebases with extensive logging like LiteLLM (>2 GiB
+# result sets). This is a known CodeQL scaling limitation, not a code issue.
+# Re-test periodically as CodeQL improves or the codebase refactors logging.
 query-filters:
   - exclude:
-      id: py/clear-text-logging-sensitive-data  # CWE-312 — > 2 GiB result set
+      id: py/clear-text-logging-sensitive-data  # CWE-312
   - exclude:
-      id: py/polynomial-redos                   # CWE-730 — > 2 GiB result set
+      id: py/polynomial-redos                   # CWE-730
 
 paths-ignore:
   - tests
   - docs
   - "**/*.md"
-  - litellm/proxy/_experimental/out
diff --git a/.github/dependabot.yaml b/.github/dependabot.yaml
index 58e7cfe10da..c49882a8d62 100644
--- a/.github/dependabot.yaml
+++ b/.github/dependabot.yaml
@@ -4,6 +4,9 @@ updates:
     directory: "/"
     schedule:
       interval: "daily"
+    cooldown:
+      default-days: 7
+      semver-major-days: 14
     groups:
       github-actions:
         patterns:
diff --git a/.github/workflows/auto_update_price_and_context_window.yml b/.github/workflows/auto_update_price_and_context_window.yml
index 98b9d868e68..60e89936219 100644
--- a/.github/workflows/auto_update_price_and_context_window.yml
+++ b/.github/workflows/auto_update_price_and_context_window.yml
@@ -2,18 +2,24 @@ name: Updates model_prices_and_context_window.json and Create Pull Request
 
 on:
   schedule:
-    - cron: "0 0 * * 0"  # Run every Sundays at midnight
+    - cron: "0 0 * * 0" # Run every Sundays at midnight
     #- cron: "0 0 * * *" # Run daily at midnight
 
+permissions:
+  contents: write
+  pull-requests: write
+
 jobs:
   auto_update_price_and_context_window:
     if: github.repository == 'BerriAI/litellm'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
       - name: Install Dependencies
         run: |
-          pip install aiohttp
+          pip install 'aiohttp==3.13.3'
       - name: Update JSON Data
         run: |
           python ".github/workflows/auto_update_price_and_context_window_file.py"
@@ -26,4 +32,4 @@ jobs:
             --head auto-update-price-and-context-window-$(date +'%Y-%m-%d') \
             --base main
         env:
-          GH_TOKEN: ${{ secrets.GH_TOKEN }}
\ No newline at end of file
+          GH_TOKEN: ${{ secrets.GH_TOKEN }}
diff --git a/.github/workflows/check_duplicate_issues.yml b/.github/workflows/check_duplicate_issues.yml
index 6d11ce573eb..289d78880ad 100644
--- a/.github/workflows/check_duplicate_issues.yml
+++ b/.github/workflows/check_duplicate_issues.yml
@@ -12,7 +12,7 @@ jobs:
       contents: read
     steps:
       - name: Check for potential duplicates
-        uses: wow-actions/potential-duplicates@v1
+        uses: wow-actions/potential-duplicates@4d4ea0352e0383859279938e255179dd1dbb67b5 # v1.1.0
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           label: potential-duplicate
@@ -30,13 +30,14 @@ jobs:
 
       - name: Checkout close script
         if: github.event.action == 'opened'
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
         with:
           sparse-checkout: .github/scripts
+          persist-credentials: false
 
       - name: Set up Python
         if: github.event.action == 'opened'
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: "3.11"
 
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 0b7cce2e4be..e86fca17c7a 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -6,8 +6,8 @@ on:
   pull_request:
     branches: [main]
   schedule:
-    # Run weekly on Sundays at 04:00 UTC
-    - cron: "0 4 * * 0"
+    # Run daily at 04:00 UTC
+    - cron: "0 4 * * *"
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -15,6 +15,7 @@ concurrency:
 
 jobs:
   analyze:
+    if: github.event_name != 'schedule' || github.repository == 'BerriAI/litellm'
     name: Analyze (${{ matrix.language }})
     runs-on: ubuntu-latest
     timeout-minutes: 30
@@ -37,16 +38,18 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v3
+        uses: github/codeql-action/init@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3
         with:
           languages: ${{ matrix.language }}
           build-mode: ${{ matrix.build-mode }}
           config-file: ./.github/codeql/codeql-config.yml
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@v3
+        uses: github/codeql-action/analyze@ebcb5b36ded6beda4ceefea6a8bc4cc885255bb3 # v3
         with:
           category: "/language:${{ matrix.language }}"
diff --git a/.github/workflows/codspeed.yml b/.github/workflows/codspeed.yml
index 385b95fdaf5..52d64addea9 100644
--- a/.github/workflows/codspeed.yml
+++ b/.github/workflows/codspeed.yml
@@ -25,10 +25,12 @@ jobs:
     timeout-minutes: 15
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: "3.12"
 
@@ -38,7 +40,7 @@ jobs:
           pip install pytest pytest-codspeed==4.3.0
 
       - name: Run benchmarks
-        uses: CodSpeedHQ/action@v4
+        uses: CodSpeedHQ/action@1c8ae4843586d3ba879736b7f6b7b0c990757fab # v4.12.1
         with:
           mode: simulation
           run: pytest tests/benchmarks/ --codspeed
diff --git a/.github/workflows/create_daily_staging_branch.yml b/.github/workflows/create_daily_staging_branch.yml
index 08aebd7d04c..424d8de0a41 100644
--- a/.github/workflows/create_daily_staging_branch.yml
+++ b/.github/workflows/create_daily_staging_branch.yml
@@ -2,18 +2,22 @@ name: Create Daily Staging Branch
 
 on:
   schedule:
-    - cron: '0 0,12 * * *'  # Runs every 12 hours at midnight and noon UTC
-  workflow_dispatch:  # Allow manual trigger
+    - cron: "0 0,12 * * *" # Runs every 12 hours at midnight and noon UTC
+  workflow_dispatch: # Allow manual trigger
 
 jobs:
   create-staging-branch:
+    if: github.repository == 'BerriAI/litellm'
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
         with:
           fetch-depth: 0
+          persist-credentials: false
 
       - name: Create daily staging branch
         env:
@@ -43,13 +47,17 @@ jobs:
           fi
 
   create-internal-dev-branch:
+    if: github.repository == 'BerriAI/litellm'
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
         with:
           fetch-depth: 0
+          persist-credentials: false
 
       - name: Create internal dev branch
         env:
diff --git a/.github/workflows/ghcr_deploy.yml b/.github/workflows/ghcr_deploy.yml
deleted file mode 100644
index 344b0ec48ee..00000000000
--- a/.github/workflows/ghcr_deploy.yml
+++ /dev/null
@@ -1,444 +0,0 @@
-# this workflow is triggered by an API call when there is a new PyPI release of LiteLLM
-name: Build, Publish LiteLLM Docker Image. New Release
-on:
-  workflow_dispatch:
-    inputs:
-      tag:
-        description: "The tag version you want to build"
-        required: true
-      release_type:
-        description: "The release type you want to build. Can be 'latest', 'stable', 'dev', 'rc'"
-        type: string
-        default: "latest"
-      commit_hash:
-        description: "Commit hash"
-        required: true
-
-# Defines two custom environment variables for the workflow. Used for the Container registry domain, and a name for the Docker image that this workflow builds.
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}
-  CHART_NAME: litellm-helm
-
-# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
-jobs:
-  # print commit hash, tag, and release type
-  print:
-    runs-on: ubuntu-latest
-    steps:
-      - run: |
-          echo "Commit hash: ${{ github.event.inputs.commit_hash }}"
-          echo "Tag: ${{ github.event.inputs.tag }}"
-          echo "Release type: ${{ github.event.inputs.release_type }}"
-  docker-hub-deploy:
-    if: github.repository == 'BerriAI/litellm'
-    runs-on: ubuntu-latest
-    steps:
-      -
-        name: Checkout
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-      -
-        name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-      -
-        name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      -
-        name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-      -
-        name: Build and push
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          push: true
-          tags: litellm/litellm:${{ github.event.inputs.tag || 'latest' }} 
-      -
-        name: Build and push litellm-database image
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          push: true
-          file: ./docker/Dockerfile.database
-          tags: litellm/litellm-database:${{ github.event.inputs.tag || 'latest' }}
-      -
-        name: Build and push litellm-spend-logs image
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          push: true
-          file: ./litellm-js/spend-logs/Dockerfile
-          tags: litellm/litellm-spend_logs:${{ github.event.inputs.tag || 'latest' }}
-      -
-        name: Build and push litellm-non_root image
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          push: true
-          file: ./docker/Dockerfile.non_root
-          tags: litellm/litellm-non_root:${{ github.event.inputs.tag || 'latest' }}
-  build-and-push-image:
-    runs-on: ubuntu-latest
-    # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job.
-    permissions:
-      contents: read
-      packages: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-      # Configure multi platform Docker builds
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345
-      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
-      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository.
-      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
-      - name: Build and push Docker image
-        uses: docker/build-push-action@4976231911ebf5f32aad765192d35f942aa48cb8
-        with:
-          context: .
-          push: true
-          tags: |
-            ${{ steps.meta.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }},
-            ${{ steps.meta.outputs.tags }}-${{ github.event.inputs.release_type }}
-            ${{ (github.event.inputs.release_type == 'stable'  || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm:main-stable', env.REGISTRY) || '' }},
-            ${{ (github.event.inputs.release_type == 'stable'  || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm:{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-          labels: ${{ steps.meta.outputs.labels }}
-          platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-  
-  build-and-push-image-ee:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for EE Dockerfile
-        id: meta-ee
-        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-ee
-      # Configure multi platform Docker builds
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345
-
-      - name: Build and push EE Docker image
-        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
-        with:
-          context: .
-          file: Dockerfile
-          push: true
-          tags: |
-            ${{ steps.meta-ee.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }},
-            ${{ steps.meta-ee.outputs.tags }}-${{ github.event.inputs.release_type }}
-            ${{ (github.event.inputs.release_type == 'stable'  || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-ee:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-ee:main-stable', env.REGISTRY) || '' }}
-          labels: ${{ steps.meta-ee.outputs.labels }}
-          platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-   
-  build-and-push-image-database:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for database Dockerfile
-        id: meta-database
-        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-database
-      # Configure multi platform Docker builds
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345
-
-      - name: Build and push Database Docker image
-        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
-        with:
-          context: .
-          file: ./docker/Dockerfile.database
-          push: true
-          tags: |
-            ${{ steps.meta-database.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }},
-            ${{ steps.meta-database.outputs.tags }}-${{ github.event.inputs.release_type }}
-            ${{ (github.event.inputs.release_type == 'stable'  || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-database:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-database:main-stable', env.REGISTRY) || '' }}
-          labels: ${{ steps.meta-database.outputs.labels }}
-          platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-            
-  build-and-push-image-non_root:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for non_root Dockerfile
-        id: meta-non_root
-        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-non_root
-      # Configure multi platform Docker builds
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345
-
-      - name: Build and push non_root Docker image
-        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
-        with:
-          context: .
-          file: ./docker/Dockerfile.non_root
-          push: true
-          tags: |
-            ${{ steps.meta-non_root.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }},
-            ${{ steps.meta-non_root.outputs.tags }}-${{ github.event.inputs.release_type }}
-            ${{ (github.event.inputs.release_type == 'stable'  || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-non_root:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-non_root:main-stable', env.REGISTRY) || '' }}
-          labels: ${{ steps.meta-non_root.outputs.labels }} 
-          platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-  
-  build-and-push-image-spend-logs:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for spend-logs Dockerfile
-        id: meta-spend-logs
-        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-spend_logs
-      # Configure multi platform Docker builds
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345
-
-      - name: Build and push Database Docker image
-        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
-        with:
-          context: .
-          file: ./litellm-js/spend-logs/Dockerfile
-          push: true
-          tags: |
-            ${{ steps.meta-spend-logs.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }},
-            ${{ steps.meta-spend-logs.outputs.tags }}-${{ github.event.inputs.release_type }}
-            ${{ (github.event.inputs.release_type == 'stable'  || github.event.inputs.release_type == 'rc') && format('{0}/berriai/litellm-spend_logs:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-spend_logs:main-stable', env.REGISTRY) || '' }}
-          platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-
-  run-observatory-tests:
-    if: github.event.inputs.release_type == 'rc' || github.event.inputs.release_type == 'stable'
-    needs: [docker-hub-deploy]
-    uses: ./.github/workflows/run_observatory_tests.yml
-    with:
-      tag: ${{ github.event.inputs.tag }}
-      commit_hash: ${{ github.event.inputs.commit_hash }}
-    secrets: inherit
-
-  build-and-push-helm-chart:
-    if: github.event.inputs.release_type  != 'dev'
-    needs: [docker-hub-deploy, build-and-push-image, build-and-push-image-database]
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: lowercase github.repository_owner
-        run: |
-          echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV}
-    
-      # Sync Helm chart version with LiteLLM release version (1-1 versioning)
-      # This allows users to easily map Helm chart versions to LiteLLM versions
-      # See: https://codefresh.io/docs/docs/ci-cd-guides/helm-best-practices/
-      - name: Calculate chart and app versions
-        id: chart_version
-        shell: bash
-        run: |
-          INPUT_TAG="${{ github.event.inputs.tag }}"
-          RELEASE_TYPE="${{ github.event.inputs.release_type }}"
-
-          # Chart version = LiteLLM version without 'v' prefix (Helm semver convention)
-          # v1.81.0 -> 1.81.0, v1.81.0.rc.1 -> 1.81.0.rc.1
-          CHART_VERSION="${INPUT_TAG#v}"
-
-          # Add suffix for 'latest' releases (rc already has suffix in tag)
-          if [ "$RELEASE_TYPE" = "latest" ]; then
-            CHART_VERSION="${CHART_VERSION}-latest"
-          fi
-
-          # App version = Docker tag (keeps 'v' prefix to match Docker image tags)
-          APP_VERSION="${INPUT_TAG}"
-
-          echo "version=${CHART_VERSION}" | tee -a $GITHUB_OUTPUT
-          echo "app_version=${APP_VERSION}" | tee -a $GITHUB_OUTPUT
-
-      - uses: ./.github/actions/helm-oci-chart-releaser
-        with:
-          name: ${{ env.CHART_NAME }}
-          repository: ${{ env.REPO_OWNER }}
-          tag: ${{ steps.chart_version.outputs.version }}
-          app_version: ${{ steps.chart_version.outputs.app_version }}
-          path: deploy/charts/${{ env.CHART_NAME }}
-          registry: ${{ env.REGISTRY }}
-          registry_username: ${{ github.actor }}
-          registry_password: ${{ secrets.GITHUB_TOKEN }}
-          update_dependencies: true
-
-  release:
-    name: "New LiteLLM Release"
-    needs: [docker-hub-deploy, build-and-push-image, build-and-push-image-database]
-    permissions:
-      contents: write
-    runs-on: "ubuntu-latest"
-   
-    steps:
-      - name: Display version
-        run: echo "Current version is ${{ github.event.inputs.tag }}"
-      - name: "Set Release Tag"
-        run: echo "RELEASE_TAG=${{ github.event.inputs.tag }}" >> $GITHUB_ENV
-      - name: Display release tag
-        run: echo "RELEASE_TAG is $RELEASE_TAG"
-      - name: "Create release"
-        uses: "actions/github-script@v6"
-        with:
-          github-token: "${{ secrets.GITHUB_TOKEN }}"
-          script: |
-            const commitHash = "${{ github.event.inputs.commit_hash}}";
-            console.log("Commit Hash:", commitHash); // Add this line for debugging
-            try {
-              const response = await github.rest.repos.createRelease({
-                draft: false,
-                generate_release_notes: true,
-                target_commitish: commitHash,
-                name: process.env.RELEASE_TAG,
-                owner: context.repo.owner,
-                prerelease: false,
-                repo: context.repo.repo,
-                tag_name: process.env.RELEASE_TAG,
-              });
-      
-              core.exportVariable('RELEASE_ID', response.data.id);
-              core.exportVariable('RELEASE_UPLOAD_URL', response.data.upload_url);
-            } catch (error) {
-              core.setFailed(error.message);
-            }
-      - name: Fetch Release Notes
-        id: release-notes
-        uses: actions/github-script@v6
-        with:
-          github-token: "${{ secrets.GITHUB_TOKEN }}"
-          script: |
-            try {
-              const response = await github.rest.repos.getRelease({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                release_id: process.env.RELEASE_ID,
-              });
-              const formattedBody = JSON.stringify(response.data.body).slice(1, -1);
-              return formattedBody;
-            } catch (error) {
-              core.setFailed(error.message);
-            }
-        env:
-          RELEASE_ID: ${{ env.RELEASE_ID }}
-      - name: Github Releases To Discord
-        env:
-          WEBHOOK_URL: ${{ secrets.WEBHOOK_URL }}
-          REALEASE_TAG: ${{ env.RELEASE_TAG }}
-          RELEASE_NOTES: ${{ steps.release-notes.outputs.result }}
-        run: |
-          curl -H "Content-Type: application/json" -X POST -d '{
-            "content": "New LiteLLM release '"${RELEASE_TAG}"'",
-            "username": "Release Changelog",
-            "avatar_url": "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png",
-            "embeds": [
-              {
-                "title": "Changelog for LiteLLM '"${RELEASE_TAG}"'",
-                "description": "'"${RELEASE_NOTES}"'",
-                "color": 2105893
-              }
-            ]
-          }' $WEBHOOK_URL
-
diff --git a/.github/workflows/ghcr_helm_deploy.yml b/.github/workflows/ghcr_helm_deploy.yml
deleted file mode 100644
index 21b2eaafe19..00000000000
--- a/.github/workflows/ghcr_helm_deploy.yml
+++ /dev/null
@@ -1,67 +0,0 @@
-# Standalone workflow to publish LiteLLM Helm Chart
-# Note: The main ghcr_deploy.yml workflow also publishes the Helm chart as part of a full release
-name: Build, Publish LiteLLM Helm Chart. New Release
-on:
-  workflow_dispatch:
-    inputs:
-      tag:
-        description: "LiteLLM version tag (e.g., v1.81.0)"
-        required: true
-
-# Defines two custom environment variables for the workflow. Used for the Container registry domain, and a name for the Docker image that this workflow builds.
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}
-  REPO_OWNER: ${{github.repository_owner}}
-
-# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
-jobs:        
-  build-and-push-helm-chart:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-      
-      - name: lowercase github.repository_owner
-        run: |
-          echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV}
-
-      # Sync Helm chart version with LiteLLM release version (1-1 versioning)
-      - name: Calculate chart and app versions
-        id: chart_version
-        shell: bash
-        run: |
-          INPUT_TAG="${{ github.event.inputs.tag }}"
-
-          # Chart version = LiteLLM version without 'v' prefix
-          # v1.81.0 -> 1.81.0
-          CHART_VERSION="${INPUT_TAG#v}"
-
-          # App version = Docker tag (keeps 'v' prefix)
-          APP_VERSION="${INPUT_TAG}"
-
-          echo "version=${CHART_VERSION}" | tee -a $GITHUB_OUTPUT
-          echo "app_version=${APP_VERSION}" | tee -a $GITHUB_OUTPUT
-
-      - name: Lint helm chart
-        run: helm lint deploy/charts/litellm-helm
-
-      - uses: ./.github/actions/helm-oci-chart-releaser
-        with:
-          name: litellm-helm
-          repository: ${{ env.REPO_OWNER }}
-          tag: ${{ steps.chart_version.outputs.version }}
-          app_version: ${{ steps.chart_version.outputs.app_version }}
-          path: deploy/charts/litellm-helm
-          registry: ${{ env.REGISTRY }}
-          registry_username: ${{ github.actor }}
-          registry_password: ${{ secrets.GITHUB_TOKEN }}
-          update_dependencies: true
-  
diff --git a/.github/workflows/helm_unit_test.yml b/.github/workflows/helm_unit_test.yml
index c4b83af70a1..06836b1d1cd 100644
--- a/.github/workflows/helm_unit_test.yml
+++ b/.github/workflows/helm_unit_test.yml
@@ -6,22 +6,36 @@ on:
     branches:
       - main
 
+permissions:
+  contents: read
+
 jobs:
   unit-test:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Set up Helm 3.11.1
-        uses: azure/setup-helm@v1
+        uses: azure/setup-helm@1a275c3b69536ee54be43f2070a358922e12c8d4 # v4.3.1
         with:
-          version: '3.11.1'
+          version: "3.11.1"
 
       - name: Install Helm Unit Test Plugin
         run: |
           helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4
+      - name: Verify Helm Unit Test Plugin integrity
+        run: |
+          EXPECTED_SHA="e251ba198448629678ff2168e1a469249d998155"
+          PLUGIN_DIR="$(helm env HELM_PLUGINS)/helm-unittest"
+          ACTUAL_SHA="$(git -C "$PLUGIN_DIR" rev-parse HEAD)"
+          if [ "$ACTUAL_SHA" != "$EXPECTED_SHA" ]; then
+            echo "::error::Helm unittest plugin checksum mismatch! Expected $EXPECTED_SHA but got $ACTUAL_SHA"
+            exit 1
+          fi
+          echo "Helm unittest plugin integrity verified: $ACTUAL_SHA"
 
       - name: Run unit tests
-        run:
-          helm unittest -f 'tests/*.yaml' deploy/charts/litellm-helm
\ No newline at end of file
+        run: helm unittest -f 'tests/*.yaml' deploy/charts/litellm-helm
diff --git a/.github/workflows/interpret_load_test.py b/.github/workflows/interpret_load_test.py
deleted file mode 100644
index 348ff300fff..00000000000
--- a/.github/workflows/interpret_load_test.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import csv
-import os
-from github import Github
-
-
-def interpret_results(csv_file):
-    with open(csv_file, newline="") as csvfile:
-        csvreader = csv.DictReader(csvfile)
-        rows = list(csvreader)
-        """
-        in this csv reader
-        - Create 1 new column "Status"
-        - if a row has a median response time < 300 and an average response time < 300, Status = "Passed ✅"
-        - if a row has a median response time >= 300 or an average response time >= 300, Status = "Failed ❌"
-        - Order the table in this order Name, Status, Median Response Time, Average Response Time, Requests/s,Failures/s, Min Response Time, Max Response Time, all other columns
-        """
-
-        # Add a new column "Status"
-        for row in rows:
-            median_response_time = float(
-                row["Median Response Time"].strip().rstrip("ms")
-            )
-            average_response_time = float(
-                row["Average Response Time"].strip().rstrip("s")
-            )
-
-            request_count = int(row["Request Count"])
-            failure_count = int(row["Failure Count"])
-
-            failure_percent = round((failure_count / request_count) * 100, 2)
-
-            # Determine status based on conditions
-            if (
-                median_response_time < 300
-                and average_response_time < 300
-                and failure_percent < 5
-            ):
-                row["Status"] = "Passed ✅"
-            else:
-                row["Status"] = "Failed ❌"
-
-        # Construct Markdown table header
-        markdown_table = "| Name | Status | Median Response Time (ms) | Average Response Time (ms) | Requests/s | Failures/s | Request Count | Failure Count | Min Response Time (ms) | Max Response Time (ms) |"
-        markdown_table += (
-            "\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |"
-        )
-
-        # Construct Markdown table rows
-        for row in rows:
-            markdown_table += f"\n| {row['Name']} | {row['Status']} | {row['Median Response Time']} | {row['Average Response Time']} | {row['Requests/s']} | {row['Failures/s']} | {row['Request Count']} | {row['Failure Count']} | {row['Min Response Time']} | {row['Max Response Time']} |"
-    print("markdown table: ", markdown_table)
-    return markdown_table
-
-
-def _get_docker_run_command_stable_release(release_version):
-    return f"""
-\n\n
-## Docker Run LiteLLM Proxy
-
-```
-docker run \\
--e STORE_MODEL_IN_DB=True \\
--p 4000:4000 \\
-ghcr.io/berriai/litellm:litellm_stable_release_branch-{release_version}
-```
-    """
-
-
-def _get_docker_run_command(release_version):
-    return f"""
-\n\n
-## Docker Run LiteLLM Proxy
-
-```
-docker run \\
--e STORE_MODEL_IN_DB=True \\
--p 4000:4000 \\
-ghcr.io/berriai/litellm:main-{release_version}
-```
-    """
-
-
-def get_docker_run_command(release_version):
-    if "stable" in release_version:
-        return _get_docker_run_command_stable_release(release_version)
-    else:
-        return _get_docker_run_command(release_version)
-
-
-if __name__ == "__main__":
-    return
-    csv_file = "load_test_stats.csv"  # Change this to the path of your CSV file
-    markdown_table = interpret_results(csv_file)
-
-    # Update release body with interpreted results
-    github_token = os.getenv("GITHUB_TOKEN")
-    g = Github(github_token)
-    repo = g.get_repo(
-        "BerriAI/litellm"
-    )  # Replace with your repository's username and name
-    latest_release = repo.get_latest_release()
-    print("got latest release: ", latest_release)
-    print(latest_release.title)
-    print(latest_release.tag_name)
-
-    release_version = latest_release.title
-
-    print("latest release body: ", latest_release.body)
-    print("markdown table: ", markdown_table)
-
-    # check if "Load Test LiteLLM Proxy Results" exists
-    existing_release_body = latest_release.body
-    if "Load Test LiteLLM Proxy Results" in latest_release.body:
-        # find the "Load Test LiteLLM Proxy Results" section and delete it
-        start_index = latest_release.body.find("Load Test LiteLLM Proxy Results")
-        existing_release_body = latest_release.body[:start_index]
-
-    docker_run_command = get_docker_run_command(release_version)
-    print("docker run command: ", docker_run_command)
-
-    new_release_body = (
-        existing_release_body
-        + docker_run_command
-        + "\n\n"
-        + "### Don't want to maintain your internal proxy? get in touch 🎉"
-        + "\nHosted Proxy Alpha: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions"
-        + "\n\n"
-        + "## Load Test LiteLLM Proxy Results"
-        + "\n\n"
-        + markdown_table
-    )
-    print("new release body: ", new_release_body)
-    try:
-        latest_release.update_release(
-            name=latest_release.tag_name,
-            message=new_release_body,
-        )
-    except Exception as e:
-        print(e)
diff --git a/.github/workflows/issue-keyword-labeler.yml b/.github/workflows/issue-keyword-labeler.yml
index 936f90f747f..7e2693209b6 100644
--- a/.github/workflows/issue-keyword-labeler.yml
+++ b/.github/workflows/issue-keyword-labeler.yml
@@ -2,8 +2,8 @@ name: Issue Keyword Labeler
 
 on:
   issues:
-    types: 
-        - opened
+    types:
+      - opened
 
 jobs:
   scan-and-label:
@@ -13,7 +13,9 @@ jobs:
       contents: read
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Scan for provider keywords
         id: scan
@@ -24,7 +26,7 @@ jobs:
 
       - name: Ensure label exists
         if: steps.scan.outputs.found == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
@@ -51,7 +53,7 @@ jobs:
 
       - name: Add label to the issue
         if: steps.scan.outputs.found == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
@@ -61,4 +63,3 @@ jobs:
               issue_number: context.issue.number,
               labels: ['llm translation']
             });
-
diff --git a/.github/workflows/label-component.yml b/.github/workflows/label-component.yml
index fd079fce6c1..e0c2fa94d8c 100644
--- a/.github/workflows/label-component.yml
+++ b/.github/workflows/label-component.yml
@@ -12,7 +12,7 @@ jobs:
       issues: write
     steps:
       - name: Add component labels
-        uses: actions/github-script@v7
+        uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
diff --git a/.github/workflows/llm-translation-testing.yml b/.github/workflows/llm-translation-testing.yml
index 7fda37a66dc..922013c4b54 100644
--- a/.github/workflows/llm-translation-testing.yml
+++ b/.github/workflows/llm-translation-testing.yml
@@ -4,38 +4,41 @@ on:
   workflow_dispatch:
     inputs:
       release_candidate_tag:
-        description: 'Release candidate tag/version'
+        description: "Release candidate tag/version"
         required: true
         type: string
   push:
     tags:
-      - 'v*-rc*'  # Triggers on release candidate tags like v1.0.0-rc1
-  
+      - "v*-rc*" # Triggers on release candidate tags like v1.0.0-rc1
+
+permissions:
+  contents: read
+
 jobs:
   run-llm-translation-tests:
     runs-on: ubuntu-latest
     timeout-minutes: 90
-    
+
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
         with:
+          persist-credentials: false
           ref: ${{ github.event.inputs.release_candidate_tag || github.ref }}
-      
+
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
-          python-version: '3.11'
-      
+          python-version: "3.11"
+
       - name: Install Poetry
-        uses: snok/install-poetry@v1
-        with:
-          version: latest
-          virtualenvs-create: true
-          virtualenvs-in-project: true
-      
-      - name: Cache Poetry dependencies
-        uses: actions/cache@v3
+        run: |
+          pip install 'poetry==2.3.2'
+          poetry config virtualenvs.create true
+          poetry config virtualenvs.in-project true
+
+      - name: Restore Poetry dependencies cache
+        uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0
         with:
           path: |
             ~/.cache/pypoetry
@@ -43,15 +46,15 @@ jobs:
           key: ${{ runner.os }}-poetry-${{ hashFiles('**/poetry.lock') }}
           restore-keys: |
             ${{ runner.os }}-poetry-
-      
+
       - name: Install dependencies
         run: |
           poetry install --with dev
-          poetry run pip install pytest-xdist pytest-timeout
-      
+          poetry run pip install 'pytest-xdist==3.8.0' 'pytest-timeout==2.4.0'
+
       - name: Create test results directory
         run: mkdir -p test-results
-      
+
       - name: Run LLM Translation Tests
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
@@ -61,13 +64,14 @@ jobs:
           AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
           AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }}
           AZURE_API_VERSION: ${{ secrets.AZURE_API_VERSION }}
-          # Add other API keys as needed
+          RC_TAG: ${{ github.event.inputs.release_candidate_tag || github.ref_name }}
+          COMMIT_SHA: ${{ github.sha }}
         run: |
           python .github/workflows/run_llm_translation_tests.py \
-            --tag "${{ github.event.inputs.release_candidate_tag || github.ref_name }}" \
-            --commit "${{ github.sha }}" \
+            --tag "$RC_TAG" \
+            --commit "$COMMIT_SHA" \
             || true  # Continue even if tests fail
-      
+
       - name: Display test summary
         if: always()
         run: |
@@ -79,9 +83,9 @@ jobs:
           else
             echo "Warning: Test report was not generated"
           fi
-      
+
       - name: Upload test artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
         if: always()
         with:
           name: LLM-Translation-Artifact-${{ github.event.inputs.release_candidate_tag || github.ref_name }}
diff --git a/.github/workflows/load_test.yml b/.github/workflows/load_test.yml
deleted file mode 100644
index cdaffa328c9..00000000000
--- a/.github/workflows/load_test.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-name: Test Locust Load Test
-
-on:
-  workflow_run:
-    workflows: ["Build, Publish LiteLLM Docker Image. New Release"]
-    types:
-      - completed
-  workflow_dispatch:
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v1
-      - name: Setup Python
-        uses: actions/setup-python@v2
-        with:
-          python-version: '3.x'
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install PyGithub
-      - name: re-deploy proxy
-        run: |
-          echo "Current working directory: $PWD"
-          ls
-          python ".github/workflows/redeploy_proxy.py"
-        env:
-          LOAD_TEST_REDEPLOY_URL1: ${{ secrets.LOAD_TEST_REDEPLOY_URL1 }}
-          LOAD_TEST_REDEPLOY_URL2: ${{ secrets.LOAD_TEST_REDEPLOY_URL2 }}
-        working-directory: ${{ github.workspace }}
-      - name: Run Load Test
-        id: locust_run
-        uses: BerriAI/locust-github-action@master
-        with:
-          LOCUSTFILE: ".github/workflows/locustfile.py"
-          URL:  "https://post-release-load-test-proxy.onrender.com/"
-          USERS: "20"
-          RATE: "20"
-          RUNTIME: "300s"
-      - name: Process Load Test Stats
-        run: |
-          echo "Current working directory: $PWD"
-          ls
-          python ".github/workflows/interpret_load_test.py"
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        working-directory: ${{ github.workspace }}
-      - name: Upload CSV as Asset to Latest Release
-        uses: xresloader/upload-to-github-release@v1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          file: "load_test_stats.csv;load_test.html"
-          update_latest_release: true
-          tag_name: "load-test"
-          overwrite: true
\ No newline at end of file
diff --git a/.github/workflows/locustfile.py b/.github/workflows/locustfile.py
deleted file mode 100644
index 36dbeee9c48..00000000000
--- a/.github/workflows/locustfile.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from locust import HttpUser, task, between
-
-
-class MyUser(HttpUser):
-    wait_time = between(1, 5)
-
-    @task
-    def chat_completion(self):
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": "Bearer sk-8N1tLOOyH8TIxwOLahhIVg",
-            # Include any additional headers you may need for authentication, etc.
-        }
-
-        # Customize the payload with "model" and "messages" keys
-        payload = {
-            "model": "fake-openai-endpoint",
-            "messages": [
-                {"role": "system", "content": "You are a chat bot."},
-                {"role": "user", "content": "Hello, how are you?"},
-            ],
-            # Add more data as necessary
-        }
-
-        # Make a POST request to the "chat/completions" endpoint
-        response = self.client.post("chat/completions", json=payload, headers=headers)
-
-        # Print or log the response if needed
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
deleted file mode 100644
index 23e4a06da9e..00000000000
--- a/.github/workflows/main.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-name: Publish Dev Release to PyPI
-
-on:
-  workflow_dispatch:
-  
-jobs:
-  publish-dev-release:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v2
-
-      - name: Set up Python
-        uses: actions/setup-python@v2
-        with:
-          python-version: 3.8  # Adjust the Python version as needed
-
-      - name: Install dependencies
-        run: pip install toml twine
-
-      - name: Read version from pyproject.toml
-        id: read-version
-        run: |
-          version=$(python -c 'import toml; print(toml.load("pyproject.toml")["tool"]["commitizen"]["version"])')
-          printf "LITELLM_VERSION=%s" "$version" >> $GITHUB_ENV
-
-      - name: Check if version exists on PyPI
-        id: check-version
-        run: |
-          set -e
-          if twine check --repository-url https://pypi.org/simple/ "litellm==$LITELLM_VERSION" >/dev/null 2>&1; then
-            echo "Version $LITELLM_VERSION already exists on PyPI. Skipping publish."
-     
diff --git a/.github/workflows/publish-migrations.yml b/.github/workflows/publish-migrations.yml
deleted file mode 100644
index a5187cb2f55..00000000000
--- a/.github/workflows/publish-migrations.yml
+++ /dev/null
@@ -1,207 +0,0 @@
-name: Publish Prisma Migrations
-
-permissions:
-  contents: write
-  pull-requests: write
-
-on:
-  push:
-    paths:
-      - 'schema.prisma'  # Check root schema.prisma
-    branches:
-      - main
-
-jobs:
-  publish-migrations:
-    if: github.repository == 'BerriAI/litellm'
-    runs-on: ubuntu-latest
-    services:
-      postgres:
-        image: postgres:14
-        env:
-          POSTGRES_DB: temp_db
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: postgres
-        ports:
-          - 5432:5432
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-      
-      # Add shadow database service
-      postgres_shadow:
-        image: postgres:14
-        env:
-          POSTGRES_DB: shadow_db
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: postgres
-        ports:
-          - 5433:5432
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-
-    steps:
-      - uses: actions/checkout@v3
-
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.x'
-
-      - name: Install Dependencies
-        run: |
-          pip install prisma
-          pip install python-dotenv
-
-      - name: Generate Initial Migration if None Exists
-        env:
-          DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/temp_db"
-          DIRECT_URL: "postgresql://postgres:postgres@localhost:5432/temp_db"
-          SHADOW_DATABASE_URL: "postgresql://postgres:postgres@localhost:5433/shadow_db"
-        run: |
-          mkdir -p deploy/migrations
-          echo 'provider = "postgresql"' > deploy/migrations/migration_lock.toml
-          
-          if [ -z "$(ls -A deploy/migrations/2* 2>/dev/null)" ]; then
-            echo "No existing migrations found, creating baseline..."
-            VERSION=$(date +%Y%m%d%H%M%S)
-            mkdir -p deploy/migrations/${VERSION}_initial
-            
-            echo "Generating initial migration..."
-            # Save raw output for debugging
-            prisma migrate diff \
-              --from-empty \
-              --to-schema-datamodel schema.prisma \
-              --shadow-database-url "${SHADOW_DATABASE_URL}" \
-              --script > deploy/migrations/${VERSION}_initial/raw_migration.sql
-            
-            echo "Raw migration file content:"
-            cat deploy/migrations/${VERSION}_initial/raw_migration.sql
-            
-            echo "Cleaning migration file..."
-            # Clean the file
-            sed '/^Installing/d' deploy/migrations/${VERSION}_initial/raw_migration.sql > deploy/migrations/${VERSION}_initial/migration.sql
-            
-            # Verify the migration file
-            if [ ! -s deploy/migrations/${VERSION}_initial/migration.sql ]; then
-              echo "ERROR: Migration file is empty after cleaning"
-              echo "Original content was:"
-              cat deploy/migrations/${VERSION}_initial/raw_migration.sql
-              exit 1
-            fi
-            
-            echo "Final migration file content:"
-            cat deploy/migrations/${VERSION}_initial/migration.sql
-            
-            # Verify it starts with SQL
-            if ! head -n 1 deploy/migrations/${VERSION}_initial/migration.sql | grep -q "^--\|^CREATE\|^ALTER"; then
-              echo "ERROR: Migration file does not start with SQL command or comment"
-              echo "First line is:"
-              head -n 1 deploy/migrations/${VERSION}_initial/migration.sql
-              echo "Full content is:"
-              cat deploy/migrations/${VERSION}_initial/migration.sql
-              exit 1
-            fi
-            
-            echo "Initial migration generated at $(date -u)" > deploy/migrations/${VERSION}_initial/README.md
-          fi
-
-      - name: Compare and Generate Migration
-        if: success()
-        env:
-          DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/temp_db"
-          DIRECT_URL: "postgresql://postgres:postgres@localhost:5432/temp_db"
-          SHADOW_DATABASE_URL: "postgresql://postgres:postgres@localhost:5433/shadow_db"
-        run: |
-          # Create temporary migration workspace
-          mkdir -p temp_migrations
-          
-          # Copy existing migrations (will not fail if directory is empty)
-          cp -r deploy/migrations/* temp_migrations/ 2>/dev/null || true
-          
-          VERSION=$(date +%Y%m%d%H%M%S)
-          
-          # Generate diff against existing migrations or empty state
-          prisma migrate diff \
-            --from-migrations temp_migrations \
-            --to-schema-datamodel schema.prisma \
-            --shadow-database-url "${SHADOW_DATABASE_URL}" \
-            --script > temp_migrations/migration_${VERSION}.sql
-          
-          # Check if there are actual changes
-          if [ -s temp_migrations/migration_${VERSION}.sql ]; then
-            echo "Changes detected, creating new migration"
-            mkdir -p deploy/migrations/${VERSION}_schema_update
-            mv temp_migrations/migration_${VERSION}.sql deploy/migrations/${VERSION}_schema_update/migration.sql
-            echo "Migration generated at $(date -u)" > deploy/migrations/${VERSION}_schema_update/README.md
-          else
-            echo "No schema changes detected"
-            exit 0
-          fi
-
-      - name: Verify Migration
-        if: success()
-        env:
-          DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/temp_db"
-          DIRECT_URL: "postgresql://postgres:postgres@localhost:5432/temp_db"
-          SHADOW_DATABASE_URL: "postgresql://postgres:postgres@localhost:5433/shadow_db"
-        run: |
-          # Create test database
-          psql "${SHADOW_DATABASE_URL}" -c 'CREATE DATABASE migration_test;'
-          
-          # Apply all migrations in order to verify
-          for migration in deploy/migrations/*/migration.sql; do
-            echo "Applying migration: $migration"
-            psql "${SHADOW_DATABASE_URL}" -f $migration
-          done
-
-      # Add this step before create-pull-request to debug permissions
-      - name: Check Token Permissions
-        run: |
-          echo "Checking token permissions..."
-          curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
-               -H "Accept: application/vnd.github.v3+json" \
-               https://api.github.com/repos/BerriAI/litellm/collaborators
-          
-          echo "\nChecking if token can create PRs..."
-          curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
-               -H "Accept: application/vnd.github.v3+json" \
-               https://api.github.com/repos/BerriAI/litellm
-
-      # Add this debug step before git push
-      - name: Debug Changed Files
-        run: |
-          echo "Files staged for commit:"
-          git diff --name-status --staged
-          
-          echo "\nAll changed files:"
-          git status
-
-      - name: Create Pull Request
-        if: success()
-        uses: peter-evans/create-pull-request@v5
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "chore: update prisma migrations"
-          title: "Update Prisma Migrations"
-          body: |
-            Auto-generated migration based on schema.prisma changes.
-            
-            Generated files:
-            - deploy/migrations/${VERSION}_schema_update/migration.sql
-            - deploy/migrations/${VERSION}_schema_update/README.md
-          branch: feat/prisma-migration-${{ env.VERSION }}
-          base: main
-          delete-branch: true
-
-      - name: Generate and Save Migrations
-        run: |
-          # Only add migration files
-          git add deploy/migrations/
-          git status  # Debug what's being committed
-          git commit -m "chore: update prisma migrations" 
diff --git a/.github/workflows/publish_enterprise.yml b/.github/workflows/publish_enterprise.yml
deleted file mode 100644
index 459a233cb71..00000000000
--- a/.github/workflows/publish_enterprise.yml
+++ /dev/null
@@ -1,94 +0,0 @@
-name: Publish litellm-enterprise to PyPI
-
-on:
-  workflow_dispatch:
-    inputs:
-      bump:
-        description: "Version bump type"
-        required: true
-        default: "patch"
-        type: choice
-        options:
-          - patch
-          - minor
-          - major
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    if: github.repository == 'BerriAI/litellm'
-    permissions:
-      contents: write
-      pull-requests: write
-    defaults:
-      run:
-        working-directory: enterprise
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Install Poetry
-        run: pip install poetry
-
-      - name: Bump version
-        id: bump
-        run: |
-          OLD=$(poetry version -s)
-          poetry version ${{ github.event.inputs.bump }}
-          NEW=$(poetry version -s)
-          echo "old=$OLD" >> $GITHUB_OUTPUT
-          echo "new=$NEW" >> $GITHUB_OUTPUT
-
-      - name: Update version refs in root pyproject.toml and requirements.txt
-        run: |
-          OLD=${{ steps.bump.outputs.old }}
-          NEW=${{ steps.bump.outputs.new }}
-          sed -i "s/litellm-enterprise = {version = \"${OLD}\"/litellm-enterprise = {version = \"${NEW}\"/" ../pyproject.toml
-          sed -i "s/litellm-enterprise==${OLD}/litellm-enterprise==${NEW}/" ../requirements.txt
-
-      - name: Update poetry.lock
-        working-directory: .
-        run: poetry lock
-
-      - name: Build
-        run: poetry build
-
-      - name: Commit version bump and create PR
-        id: create-pr
-        run: |
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-          cd ..
-          BRANCH="bump/enterprise-${{ steps.bump.outputs.new }}"
-          git checkout -b "$BRANCH"
-          git add enterprise/pyproject.toml pyproject.toml requirements.txt poetry.lock
-          git commit -m "bump: litellm-enterprise ${{ steps.bump.outputs.old }} → ${{ steps.bump.outputs.new }}"
-          git push origin "$BRANCH" --force
-          gh pr create \
-            --title "bump: litellm-enterprise ${{ steps.bump.outputs.old }} → ${{ steps.bump.outputs.new }}" \
-            --body "Version bump for litellm-enterprise. Merge to update main." \
-            --head "$BRANCH" \
-            --base main \
-          || true
-          PR_URL=$(gh pr list --head "$BRANCH" --json url -q '.[0].url')
-          echo "pr_url=$PR_URL" >> $GITHUB_OUTPUT
-        env:
-          GH_TOKEN: ${{ github.token }}
-
-      - name: Enable auto-merge
-        run: |
-          gh pr merge "${{ steps.create-pr.outputs.pr_url }}" --auto --squash
-        env:
-          GH_TOKEN: ${{ github.token }}
-
-      - name: Publish to PyPI
-        env:
-          TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.PYPI_ENTERPRISE }}
-        run: |
-          pip install twine
-          twine upload dist/litellm_enterprise-${{ steps.bump.outputs.new }}*
diff --git a/.github/workflows/publish_proxy_extras.yml b/.github/workflows/publish_proxy_extras.yml
deleted file mode 100644
index fa30b153163..00000000000
--- a/.github/workflows/publish_proxy_extras.yml
+++ /dev/null
@@ -1,74 +0,0 @@
-name: Publish litellm-proxy-extras to PyPI
-
-on:
-  workflow_dispatch:
-    inputs:
-      bump:
-        description: "Version bump type"
-        required: true
-        default: "patch"
-        type: choice
-        options:
-          - patch
-          - minor
-          - major
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    if: github.repository == 'BerriAI/litellm'
-    permissions:
-      contents: write
-    defaults:
-      run:
-        working-directory: litellm-proxy-extras
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Install Poetry
-        run: pip install poetry
-
-      - name: Bump version
-        id: bump
-        run: |
-          OLD=$(poetry version -s)
-          poetry version ${{ github.event.inputs.bump }}
-          NEW=$(poetry version -s)
-          echo "old=$OLD" >> $GITHUB_OUTPUT
-          echo "new=$NEW" >> $GITHUB_OUTPUT
-
-      - name: Update version refs in root pyproject.toml and requirements.txt
-        run: |
-          OLD=${{ steps.bump.outputs.old }}
-          NEW=${{ steps.bump.outputs.new }}
-          sed -i "s/litellm-proxy-extras = {version = \"${OLD}\"/litellm-proxy-extras = {version = \"${NEW}\"/" ../pyproject.toml
-          sed -i "s/litellm-proxy-extras==${OLD}/litellm-proxy-extras==${NEW}/" ../requirements.txt
-
-      - name: Update poetry.lock
-        working-directory: .
-        run: poetry lock
-
-      - name: Build
-        run: poetry build
-
-      - name: Commit version bump
-        run: |
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-          cd ..
-          git add litellm-proxy-extras/pyproject.toml pyproject.toml requirements.txt poetry.lock
-          git commit -m "bump: litellm-proxy-extras ${{ steps.bump.outputs.old }} → ${{ steps.bump.outputs.new }}"
-          git push
-
-      - name: Publish to PyPI
-        env:
-          TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.PYPI_PUBLISH_PASSWORD }}
-        run: |
-          pip install twine
-          twine upload dist/litellm_proxy_extras-${{ steps.bump.outputs.new }}*
diff --git a/.github/workflows/publish_to_pypi.yml b/.github/workflows/publish_to_pypi.yml
new file mode 100644
index 00000000000..8f675bb3075
--- /dev/null
+++ b/.github/workflows/publish_to_pypi.yml
@@ -0,0 +1,136 @@
+name: Publish to PyPI
+
+on:
+  workflow_dispatch:
+
+jobs:
+  preflight-checks:
+    name: Preflight Checks
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    permissions:
+      contents: read
+    # No environment — read-only checks, no approval needed
+    outputs:
+      needs_publish: ${{ steps.check-litellm.outputs.needs_publish }}
+      version: ${{ steps.check-litellm.outputs.version }}
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.12"
+
+      - name: Check litellm version on PyPI
+        id: check-litellm
+        run: |
+          VERSION=$(grep -m1 '^version' pyproject.toml | sed 's/version = "\(.*\)"/\1/')
+          echo "version=$VERSION" >> "$GITHUB_OUTPUT"
+          echo "Checking if litellm $VERSION exists on PyPI..."
+
+          HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/litellm/$VERSION/json")
+          if [ "$HTTP_STATUS" = "200" ]; then
+            echo "litellm $VERSION already exists on PyPI. Skipping publish."
+            echo "needs_publish=false" >> "$GITHUB_OUTPUT"
+          else
+            echo "litellm $VERSION not found on PyPI. Publish needed."
+            echo "needs_publish=true" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Sanity check proxy-extras version
+        run: |
+          # Read pinned version from requirements.txt
+          REQ_VERSION=$(grep -oP 'litellm-proxy-extras==\K[0-9.]+' requirements.txt)
+          if [ -z "$REQ_VERSION" ]; then
+            echo "::error::Could not find litellm-proxy-extras version in requirements.txt"
+            exit 1
+          fi
+          echo "requirements.txt pins litellm-proxy-extras==$REQ_VERSION"
+
+          # Read pinned version from pyproject.toml dependency
+          PYPROJECT_VERSION=$(python3 -c "
+          import re
+          with open('pyproject.toml') as f:
+              content = f.read()
+          match = re.search(r'litellm-proxy-extras\s*=\s*\{version\s*=\s*\"([^\"]+)\"', content)
+          if match:
+              print(match.group(1).lstrip('^~>='))
+          else:
+              import sys
+              print('::error::Could not find litellm-proxy-extras dependency in pyproject.toml', file=sys.stderr)
+              sys.exit(1)
+          ")
+          echo "pyproject.toml pins litellm-proxy-extras version: $PYPROJECT_VERSION"
+
+          # Check that both pinned versions match
+          if [ "$REQ_VERSION" != "$PYPROJECT_VERSION" ]; then
+            echo "::error::Version mismatch: requirements.txt has $REQ_VERSION but pyproject.toml has $PYPROJECT_VERSION"
+            exit 1
+          fi
+
+          # Check that the pinned version exists on PyPI
+          echo "Checking if litellm-proxy-extras $REQ_VERSION exists on PyPI..."
+          HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/litellm-proxy-extras/$REQ_VERSION/json")
+          if [ "$HTTP_STATUS" != "200" ]; then
+            echo "::error::litellm-proxy-extras $REQ_VERSION is not published on PyPI yet. Publish it before releasing litellm."
+            exit 1
+          fi
+          echo "litellm-proxy-extras $REQ_VERSION exists on PyPI. Sanity check passed."
+
+  publish-litellm:
+    name: Publish litellm to PyPI
+    needs: preflight-checks
+    if: needs.preflight-checks.outputs.needs_publish == 'true'
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    permissions:
+      id-token: write
+      contents: read
+    environment: pypi-publish
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.12"
+
+      - name: Copy model prices backup
+        run: cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json
+
+      - name: Install build tools
+        run: python -m pip install --upgrade pip build==1.4.2
+
+      - name: Build package
+        run: |
+          rm -rf build dist
+          python -m build
+
+      - name: Verify build artifacts
+        env:
+          EXPECTED_VERSION: ${{ needs.preflight-checks.outputs.version }}
+        run: |
+          echo "Contents of dist/:"
+          ls -la dist/
+          # Ensure we have both sdist and wheel
+          ls dist/*.tar.gz
+          ls dist/*.whl
+          # Verify built version matches expected
+          ls dist/ | grep -q "litellm-${EXPECTED_VERSION}" || {
+            echo "::error::Built artifacts do not match expected version $EXPECTED_VERSION"
+            ls dist/
+            exit 1
+          }
+
+      - name: Validate package metadata
+        run: |
+          pip install twine==6.2.0
+          twine check dist/*
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0
diff --git a/.github/workflows/read_pyproject_version.yml b/.github/workflows/read_pyproject_version.yml
index 8f6310f935b..04b4a38ce19 100644
--- a/.github/workflows/read_pyproject_version.yml
+++ b/.github/workflows/read_pyproject_version.yml
@@ -3,7 +3,10 @@ name: Read Version from pyproject.toml
 on:
   push:
     branches:
-      - main  # Change this to the default branch of your repository
+      - main # Change this to the default branch of your repository
+
+permissions:
+  contents: read
 
 jobs:
   read-version:
@@ -11,20 +14,14 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v2
-
-      - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
         with:
-          python-version: 3.8  # Adjust the Python version as needed
-
-      - name: Install dependencies
-        run: pip install toml
+          persist-credentials: false
 
       - name: Read version from pyproject.toml
         id: read-version
         run: |
-          version=$(python -c 'import toml; print(toml.load("pyproject.toml")["tool"]["commitizen"]["version"])')
+          version=$(grep -m1 '^version' pyproject.toml | sed 's/version = "\(.*\)"/\1/')
           printf "LITELLM_VERSION=%s" "$version" >> $GITHUB_ENV
 
       - name: Display version
diff --git a/.github/workflows/redeploy_proxy.py b/.github/workflows/redeploy_proxy.py
deleted file mode 100644
index ed46bef73a2..00000000000
--- a/.github/workflows/redeploy_proxy.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""
-
-redeploy_proxy.py
-"""
-
-import os
-import requests
-import time
-
-# send a get request to this endpoint
-deploy_hook1 = os.getenv("LOAD_TEST_REDEPLOY_URL1")
-response = requests.get(deploy_hook1, timeout=20)
-
-
-deploy_hook2 = os.getenv("LOAD_TEST_REDEPLOY_URL2")
-response = requests.get(deploy_hook2, timeout=20)
-
-print("SENT GET REQUESTS to re-deploy proxy")
-print("sleeeping.... for 60s")
-time.sleep(60)
diff --git a/.github/workflows/regenerate-poetry-lock.yml b/.github/workflows/regenerate-poetry-lock.yml
deleted file mode 100644
index c0844f1c705..00000000000
--- a/.github/workflows/regenerate-poetry-lock.yml
+++ /dev/null
@@ -1,80 +0,0 @@
-name: Regenerate poetry.lock
-
-# Runs whenever pyproject.toml is merged into main (the most common cause of
-# the "pyproject.toml changed significantly since poetry.lock was last generated"
-# CI failure).  Can also be triggered manually.
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - pyproject.toml
-  workflow_dispatch:
-
-permissions:
-  contents: write       # needed to push the auto/regenerate-poetry-lock-* branch
-  pull-requests: write  # needed to open the PR and enable auto-merge
-
-jobs:
-  regenerate-lock:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Install Poetry
-        run: pip install poetry
-
-      - name: Regenerate poetry.lock
-        run: poetry lock
-
-      - name: Check whether poetry.lock actually changed
-        id: diff
-        run: |
-          if git diff --quiet poetry.lock; then
-            echo "changed=false" >> "$GITHUB_OUTPUT"
-          else
-            echo "changed=true" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Open PR with the refreshed lock file
-        if: steps.diff.outputs.changed == 'true'
-        id: open-pr
-        run: |
-          BRANCH="auto/regenerate-poetry-lock-$(date +'%Y%m%d%H%M%S')"
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-          git checkout -b "$BRANCH"
-          git add poetry.lock
-          git commit -m "chore: regenerate poetry.lock to match pyproject.toml"
-          git push -f origin "$BRANCH"
-
-          cat > /tmp/pr-body.md << 'BODY'
-          Automated regeneration of `poetry.lock` after `pyproject.toml` was updated on `main`.
-
-          Fixes the recurring CI failure:
-          ```
-          pyproject.toml changed significantly since poetry.lock was last generated.
-          Run `poetry lock` to fix the lock file.
-          ```
-          BODY
-
-          PR_URL=$(gh pr create \
-            --title "chore: regenerate poetry.lock to match pyproject.toml" \
-            --body-file /tmp/pr-body.md \
-            --head "$BRANCH" \
-            --base main)
-          echo "pr_url=$PR_URL" >> "$GITHUB_OUTPUT"
-        env:
-          GH_TOKEN: ${{ github.token }}
-
-      - name: Enable auto-merge
-        if: steps.diff.outputs.changed == 'true'
-        run: |
-          gh pr merge "${{ steps.open-pr.outputs.pr_url }}" --auto --squash
-        env:
-          GH_TOKEN: ${{ github.token }}
diff --git a/.github/workflows/reset_stable.yml b/.github/workflows/reset_stable.yml
deleted file mode 100644
index f6fed672d47..00000000000
--- a/.github/workflows/reset_stable.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-name: Reset litellm_stable branch
-
-on:
-  release:
-    types: [published, created]
-jobs:
-  update-stable-branch:
-    if: ${{ startsWith(github.event.release.tag_name, 'v') && !endsWith(github.event.release.tag_name, '-stable') }}
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-
-      - name: Reset litellm_stable_release_branch branch to the release commit
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          # Configure Git user
-          git config user.name "github-actions[bot]"
-          git config user.email "github-actions[bot]@users.noreply.github.com"
-          
-          # Fetch all branches and tags
-          git fetch --all
-
-          # Check if the litellm_stable_release_branch branch exists
-          if git show-ref --verify --quiet refs/remotes/origin/litellm_stable_release_branch; then
-            echo "litellm_stable_release_branch branch exists."
-            git checkout litellm_stable_release_branch
-          else
-            echo "litellm_stable_release_branch branch does not exist. Creating it."
-            git checkout -b litellm_stable_release_branch
-          fi
-
-          # Reset litellm_stable_release_branch branch to the release commit
-          git reset --hard $GITHUB_SHA
-
-          # Push the updated litellm_stable_release_branch branch
-          git push origin litellm_stable_release_branch --force
diff --git a/.github/workflows/run_observatory_tests.yml b/.github/workflows/run_observatory_tests.yml
index d343098ed32..a25b96766d7 100644
--- a/.github/workflows/run_observatory_tests.yml
+++ b/.github/workflows/run_observatory_tests.yml
@@ -33,7 +33,9 @@ jobs:
     timeout-minutes: 30
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Validate tag input
         env:
@@ -49,11 +51,12 @@ jobs:
           TAG: ${{ inputs.tag }}
           AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
           AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }}
+          WORKSPACE: ${{ github.workspace }}
         run: |
           docker run -d \
             --name litellm-rc \
             -p 4000:4000 \
-            -v "${{ github.workspace }}/.github/observatory/litellm_config.yaml:/app/config.yaml" \
+            -v "${WORKSPACE}/.github/observatory/litellm_config.yaml:/app/config.yaml" \
             -e LITELLM_MASTER_KEY="${LITELLM_MASTER_KEY}" \
             -e AZURE_API_KEY="${AZURE_API_KEY}" \
             -e AZURE_API_BASE="${AZURE_API_BASE}" \
@@ -77,8 +80,9 @@ jobs:
 
       - name: Start cloudflared tunnel
         run: |
-          # Install cloudflared
+          # Install cloudflared (pinned version + checksum)
           curl -sL https://github.com/cloudflare/cloudflared/releases/download/2025.2.1/cloudflared-linux-amd64 -o /usr/local/bin/cloudflared
+          echo "afdfadd1ef552e66bffc35246fe30a9bd578356d2d386de95585ccfc432472b8  /usr/local/bin/cloudflared" | sha256sum -c -
           chmod +x /usr/local/bin/cloudflared
 
           # Start a quick tunnel (no account needed) and capture the URL
@@ -103,11 +107,11 @@ jobs:
 
       - name: Verify tunnel connectivity
         run: |
-          echo "Testing tunnel at ${{ env.TUNNEL_URL }}..."
+          echo "Testing tunnel at ${TUNNEL_URL}..."
           # Quick tunnels need time for DNS propagation; retry to avoid
           # transient NXDOMAIN (curl exit code 6) on first attempt.
           for i in $(seq 1 10); do
-            if curl -sf "${{ env.TUNNEL_URL }}/health/liveliness" > /dev/null 2>&1; then
+            if curl -sf "${TUNNEL_URL}/health/liveliness" > /dev/null 2>&1; then
               echo "Tunnel is working (attempt $i)"
               exit 0
             fi
@@ -221,5 +225,5 @@ jobs:
       - name: Cleanup
         if: always()
         run: |
-          kill "${{ env.CLOUDFLARED_PID }}" 2>/dev/null || true
+          kill "$CLOUDFLARED_PID" 2>/dev/null || true
           docker rm -f litellm-rc 2>/dev/null || true
diff --git a/.github/workflows/scan_duplicate_issues.yml b/.github/workflows/scan_duplicate_issues.yml
index 06e8f453a8c..222ff11f304 100644
--- a/.github/workflows/scan_duplicate_issues.yml
+++ b/.github/workflows/scan_duplicate_issues.yml
@@ -21,14 +21,15 @@ jobs:
       contents: read
     steps:
       - name: Checkout scripts
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
         with:
           sparse-checkout: .github/scripts
+          persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
-          python-version: "3.11"
+          python-version: "3.13"
 
       - name: Scan for duplicate issues
         env:
diff --git a/.github/workflows/simple_pypi_publish.yml b/.github/workflows/simple_pypi_publish.yml
deleted file mode 100644
index e1830556819..00000000000
--- a/.github/workflows/simple_pypi_publish.yml
+++ /dev/null
@@ -1,67 +0,0 @@
-name: Simple PyPI Publish
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: 'Version to publish (e.g., 1.74.10)'
-        required: true
-        type: string
-
-env:
-  TWINE_USERNAME: __token__
-
-jobs:
-  publish:
-    runs-on: ubuntu-latest
-    if: github.repository == 'BerriAI/litellm'
-    
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.8'
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install toml build wheel twine
-
-      - name: Update version in pyproject.toml
-        run: |
-          python -c "
-          import toml
-          
-          with open('pyproject.toml', 'r') as f:
-              data = toml.load(f)
-          
-          data['tool']['poetry']['version'] = '${{ github.event.inputs.version }}'
-          
-          with open('pyproject.toml', 'w') as f:
-              toml.dump(data, f)
-          
-          print(f'Updated version to ${{ github.event.inputs.version }}')
-          "
-
-      - name: Copy model prices file
-        run: |
-          cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json
-
-      - name: Build package
-        run: |
-          rm -rf build dist
-          python -m build
-
-      - name: Publish to PyPI
-        env:
-          TWINE_PASSWORD: ${{ secrets.PYPI_PUBLISH_PASSWORD }}
-        run: |
-          twine upload dist/*
-
-      - name: Output success
-        run: |
-          echo "✅ Successfully published litellm v${{ github.event.inputs.version }} to PyPI"
-          echo "📦 Package: https://pypi.org/project/litellm/${{ github.event.inputs.version }}/" 
\ No newline at end of file
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
index 5a9b19fc9ca..c905bb12312 100644
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -2,19 +2,24 @@ name: "Stale Issue Management"
 
 on:
   schedule:
-    - cron: '0 0 * * *' # Runs daily at midnight UTC
+    - cron: "0 0 * * *" # Runs daily at midnight UTC
   workflow_dispatch:
 
+permissions:
+  issues: write
+  pull-requests: write
+
 jobs:
   stale:
+    if: github.repository == 'BerriAI/litellm'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v8
+      - uses: actions/stale@1160a2240286f5da8ec72b1c0816ce2481aabf84 # v8
         with:
           repo-token: "${{ secrets.GITHUB_TOKEN }}"
           stale-issue-message: "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs."
           stale-pr-message: "This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs."
-          days-before-stale: 90        # Revert to 60 days
-          days-before-close: 7         # Revert to 7 days
+          days-before-stale: 90 # Revert to 60 days
+          days-before-close: 7 # Revert to 7 days
           stale-issue-label: "stale"
-          operations-per-run: 1000
\ No newline at end of file
+          operations-per-run: 1000
diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml
index 4cedb8b5bae..5bb85716a17 100644
--- a/.github/workflows/test-linting.yml
+++ b/.github/workflows/test-linting.yml
@@ -2,7 +2,10 @@ name: LiteLLM Linting
 
 on:
   pull_request:
-    branches: [ main ]
+    branches: [main]
+
+permissions:
+  contents: read
 
 jobs:
   lint:
@@ -10,72 +13,73 @@ jobs:
     timeout-minutes: 5
 
     steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-        clean: true
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          fetch-depth: 0
+          clean: true
+          persist-credentials: false
 
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.12"
 
-    - name: Install Poetry
-      uses: snok/install-poetry@v1
+      - name: Install Poetry
+        run: pip install 'poetry==2.3.2'
 
-    - name: Clean Python cache
-      run: |
-        find . -type d -name "__pycache__" -exec rm -rf {} + || true
-        find . -name "*.pyc" -delete || true
+      - name: Clean Python cache
+        run: |
+          find . -type d -name "__pycache__" -exec rm -rf {} + || true
+          find . -name "*.pyc" -delete || true
 
-    - name: Check poetry.lock is up to date
-      run: |
-        poetry check --lock || (echo "❌ poetry.lock is out of sync with pyproject.toml. Run 'poetry lock' locally and commit the result." && exit 1)
+      - name: Check poetry.lock is up to date
+        run: |
+          poetry check --lock || (echo "❌ poetry.lock is out of sync with pyproject.toml. Run 'poetry lock' locally and commit the result." && exit 1)
 
-    - name: Install dependencies
-      run: |
-        poetry install --with dev
+      - name: Install dependencies
+        run: |
+          poetry install --with dev
 
-    - name: Check Black formatting
-      run: |
-        cd litellm
-        poetry run black --check --exclude '/enterprise/' .
-        cd ..
+      - name: Check Black formatting
+        run: |
+          cd litellm
+          poetry run black --check --exclude '/enterprise/' .
+          cd ..
 
-    - name: Debug - Check file state
-      run: |
-        echo "Current branch:"
-        git branch --show-current
-        echo "Last 3 commits:"
-        git log --oneline -3
-        echo "File content around line 43:"
-        head -50 litellm/litellm_core_utils/custom_logger_registry.py | tail -10
-        
-    - name: Run Ruff linting
-      run: |
-        cd litellm
-        poetry run ruff check .
-        cd ..
+      - name: Debug - Check file state
+        run: |
+          echo "Current branch:"
+          git branch --show-current
+          echo "Last 3 commits:"
+          git log --oneline -3
+          echo "File content around line 43:"
+          head -50 litellm/litellm_core_utils/custom_logger_registry.py | tail -10
 
-    - name: Print OpenAI version
-      run: |
-        poetry run python -c "import openai; print(f'OpenAI version: {openai.__version__}')"
+      - name: Run Ruff linting
+        run: |
+          cd litellm
+          poetry run ruff check .
+          cd ..
 
-    - name: Run MyPy type checking
-      run: |
-        cd litellm
-        poetry run mypy . 
-        cd ..
+      - name: Print OpenAI version
+        run: |
+          poetry run python -c "import openai; print(f'OpenAI version: {openai.__version__}')"
 
-    - name: Check for circular imports
-      run: |
-        cd litellm
-        poetry run python ../tests/documentation_tests/test_circular_imports.py
-        cd ..
+      - name: Run MyPy type checking
+        run: |
+          cd litellm
+          poetry run mypy . 
+          cd ..
 
-    - name: Check import safety
-      run: |
-        poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
+      - name: Check for circular imports
+        run: |
+          cd litellm
+          poetry run python ../tests/documentation_tests/test_circular_imports.py
+          cd ..
+
+      - name: Check import safety
+        run: |
+          poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
 
   secret-scan:
     runs-on: ubuntu-latest
@@ -84,27 +88,28 @@ jobs:
       contents: read
 
     steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.12"
 
-    - name: Run secret scan test
-      run: |
-        pip install pytest
-        pytest tests/litellm/test_no_hardcoded_secrets.py -v
+      - name: Run secret scan test
+        run: |
+          pip install 'pytest==9.0.2'
+          pytest tests/litellm/test_no_hardcoded_secrets.py -v
 
-    - name: Run ggshield secret scan
-      env:
-        GITGUARDIAN_API_KEY: ${{ secrets.GITGUARDIAN_API_KEY }}
-      run: |
-        if [ -n "$GITGUARDIAN_API_KEY" ]; then
-          pip install ggshield
-          ggshield secret scan repo .
-        else
-          echo "GITGUARDIAN_API_KEY not set, skipping ggshield scan"
-        fi
+      - name: Run ggshield secret scan
+        env:
+          GITGUARDIAN_API_KEY: ${{ secrets.GITGUARDIAN_API_KEY }}
+        run: |
+          if [ -n "$GITGUARDIAN_API_KEY" ]; then
+            pip install 'ggshield==1.48.0'
+            ggshield secret scan repo .
+          else
+            echo "GITGUARDIAN_API_KEY not set, skipping ggshield scan"
+          fi
diff --git a/.github/workflows/test-litellm-matrix.yml b/.github/workflows/test-litellm-matrix.yml
index d0ac28ab41a..860d25636c5 100644
--- a/.github/workflows/test-litellm-matrix.yml
+++ b/.github/workflows/test-litellm-matrix.yml
@@ -4,6 +4,9 @@ on:
   pull_request:
     branches: [main]
 
+permissions:
+  contents: read
+
 # Cancel in-progress runs for the same PR
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -12,7 +15,7 @@ concurrency:
 jobs:
   test:
     runs-on: ubuntu-latest
-    timeout-minutes: 20  # Increased from 15 to 20
+    timeout-minutes: 20 # Increased from 15 to 20
     strategy:
       fail-fast: false
       matrix:
@@ -43,7 +46,7 @@ jobs:
           - name: "integrations"
             path: "tests/test_litellm/integrations"
             workers: 2
-            reruns: 3  # Integration tests tend to be flakier
+            reruns: 3 # Integration tests tend to be flakier
           - name: "core-utils"
             path: "tests/test_litellm/litellm_core_utils"
             workers: 2
@@ -117,18 +120,20 @@ jobs:
     name: test (${{ matrix.test-group.name }})
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: "3.12"
 
       - name: Install Poetry
-        uses: snok/install-poetry@v1
+        run: pip install 'poetry==2.3.2'
 
       - name: Cache Poetry dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0
         with:
           path: |
             ~/.cache/pypoetry
@@ -144,14 +149,17 @@ jobs:
           poetry install --with dev,proxy-dev --extras "proxy semantic-router"
           # pytest-rerunfailures and pytest-xdist are in pyproject.toml dev dependencies
           poetry run pip install google-genai==1.22.0 \
-            google-cloud-aiplatform>=1.38 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core
+            google-cloud-aiplatform==1.115.0 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core==0.23.0
 
       - name: Setup litellm-enterprise
         run: |
           poetry run pip install --force-reinstall --no-deps -e enterprise/
 
       - name: Generate Prisma client
+        env:
+          PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
         run: |
+          poetry run pip install nodejs-wheel-binaries==24.13.1
           poetry run prisma generate --schema litellm/proxy/schema.prisma
 
       - name: Run tests - ${{ matrix.test-group.name }}
diff --git a/.github/workflows/test-litellm-ui-build.yml b/.github/workflows/test-litellm-ui-build.yml
index b0a8b648a44..6b0b3a413a6 100644
--- a/.github/workflows/test-litellm-ui-build.yml
+++ b/.github/workflows/test-litellm-ui-build.yml
@@ -16,10 +16,12 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Setup Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0
         with:
           node-version: "20"
           cache: "npm"
diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml
index 3f8369df926..0c040b3ebe7 100644
--- a/.github/workflows/test-litellm.yml
+++ b/.github/workflows/test-litellm.yml
@@ -4,45 +4,50 @@ name: LiteLLM Mock Tests (folder - tests/test_litellm)
 # the same tests in parallel across 10 jobs for faster CI times.
 # Kept for manual debugging only.
 on:
-  workflow_dispatch:  # Manual trigger only
+  workflow_dispatch: # Manual trigger only
   # pull_request:
   #   branches: [ main ]
 
+permissions:
+  contents: read
+
 jobs:
   test:
     runs-on: ubuntu-latest
     timeout-minutes: 25
 
     steps:
-    - uses: actions/checkout@v4
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
-    - name: Thank You Message
-      run: |
-        echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY
-        echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY
+      - name: Thank You Message
+        run: |
+          echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY
+          echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY
 
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.12"
 
-    - name: Install Poetry
-      uses: snok/install-poetry@v1
+      - name: Install Poetry
+        run: pip install 'poetry==2.3.2'
 
-    - name: Install dependencies
-      run: |
-        poetry lock
-        poetry install --with dev,proxy-dev --extras "proxy semantic-router"
-        poetry run pip install "pytest-retry==1.6.3"
-        poetry run pip install pytest-xdist
-        poetry run pip install "google-genai==1.22.0"
-        poetry run pip install "google-cloud-aiplatform>=1.38"
-        poetry run pip install "fastapi-offline==1.7.3"
-        poetry run pip install "python-multipart>=0.0.20"
-        poetry run pip install "openapi-core"
-    - name: Setup litellm-enterprise as local package
-      run: |
-        poetry run pip install --force-reinstall --no-deps -e enterprise/
-    - name: Run tests
-      run: |
-        poetry run pytest tests/test_litellm --tb=short -vv --maxfail=10 -n 4 --durations=50
+      - name: Install dependencies
+        run: |
+          poetry lock
+          poetry install --with dev,proxy-dev --extras "proxy semantic-router"
+          poetry run pip install "pytest-retry==1.6.3"
+          poetry run pip install 'pytest-xdist==3.8.0'
+          poetry run pip install "google-genai==1.22.0"
+          poetry run pip install "google-cloud-aiplatform==1.115.0"
+          poetry run pip install "fastapi-offline==1.7.3"
+          poetry run pip install "python-multipart==0.0.22"
+          poetry run pip install "openapi-core==0.23.0"
+      - name: Setup litellm-enterprise as local package
+        run: |
+          poetry run pip install --force-reinstall --no-deps -e enterprise/
+      - name: Run tests
+        run: |
+          poetry run pytest tests/test_litellm --tb=short -vv --maxfail=10 -n 4 --durations=50
diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml
index 2e32aae7680..1b228ab76bb 100644
--- a/.github/workflows/test-mcp.yml
+++ b/.github/workflows/test-mcp.yml
@@ -2,7 +2,10 @@ name: LiteLLM MCP Tests (folder - tests/mcp_tests)
 
 on:
   pull_request:
-    branches: [ main ]
+    branches: [main]
+
+permissions:
+  contents: read
 
 jobs:
   test:
@@ -10,38 +13,40 @@ jobs:
     timeout-minutes: 25
 
     steps:
-    - uses: actions/checkout@v4
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
-    - name: Thank You Message
-      run: |
-        echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY
-        echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY
+      - name: Thank You Message
+        run: |
+          echo "### 🙏 Thank you for contributing to LiteLLM!" >> $GITHUB_STEP_SUMMARY
+          echo "Your PR is being tested now. We appreciate your help in making LiteLLM better!" >> $GITHUB_STEP_SUMMARY
 
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.12"
 
-    - name: Install Poetry
-      uses: snok/install-poetry@v1
+      - name: Install Poetry
+        run: pip install 'poetry==2.3.2'
 
-    - name: Install dependencies
-      run: |
-        poetry lock
-        poetry install --with dev,proxy-dev --extras "proxy semantic-router"
-        poetry run pip install "pytest==7.3.1"
-        poetry run pip install "pytest-retry==1.6.3"
-        poetry run pip install "pytest-cov==5.0.0"
-        poetry run pip install "pytest-asyncio==0.21.1"
-        poetry run pip install "respx==0.22.0"
-        poetry run pip install "pydantic==2.11.0"
-        poetry run pip install "mcp==1.25.0"
-        poetry run pip install pytest-xdist
+      - name: Install dependencies
+        run: |
+          poetry lock
+          poetry install --with dev,proxy-dev --extras "proxy semantic-router"
+          poetry run pip install "pytest==7.3.1"
+          poetry run pip install "pytest-retry==1.6.3"
+          poetry run pip install "pytest-cov==5.0.0"
+          poetry run pip install "pytest-asyncio==0.21.1"
+          poetry run pip install "respx==0.22.0"
+          poetry run pip install "pydantic==2.11.0"
+          poetry run pip install "mcp==1.25.0"
+          poetry run pip install 'pytest-xdist==3.8.0'
 
-    - name: Setup litellm-enterprise as local package
-      run: |
-        poetry run pip install --force-reinstall --no-deps -e enterprise/
+      - name: Setup litellm-enterprise as local package
+        run: |
+          poetry run pip install --force-reinstall --no-deps -e enterprise/
 
-    - name: Run MCP tests
-      run: |
-        poetry run pytest tests/mcp_tests -x -vv -n 4 --cov=litellm --cov-report=xml --durations=5
+      - name: Run MCP tests
+        run: |
+          poetry run pytest tests/mcp_tests -x -vv -n 4 --cov=litellm --cov-report=xml --durations=5
diff --git a/.github/workflows/test-model-map.yaml b/.github/workflows/test-model-map.yaml
index ae5ac402e23..429f9e1ce0a 100644
--- a/.github/workflows/test-model-map.yaml
+++ b/.github/workflows/test-model-map.yaml
@@ -2,13 +2,18 @@ name: Validate model_prices_and_context_window.json
 
 on:
   pull_request:
-    branches: [ main ]
+    branches: [main]
+
+permissions:
+  contents: read
 
 jobs:
   validate-model-prices-json:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Validate model_prices_and_context_window.json
         run: |
diff --git a/.github/workflows/test-proxy-e2e-azure-batches.yml b/.github/workflows/test-proxy-e2e-azure-batches.yml
index 4d74f3db0ac..7cbbe0b338f 100644
--- a/.github/workflows/test-proxy-e2e-azure-batches.yml
+++ b/.github/workflows/test-proxy-e2e-azure-batches.yml
@@ -9,6 +9,9 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
+permissions:
+  contents: read
+
 jobs:
   proxy_e2e_azure_batches_tests:
     runs-on: ubuntu-latest
@@ -30,18 +33,20 @@ jobs:
           --health-retries 5
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
           python-version: "3.12"
 
       - name: Install Poetry
-        uses: snok/install-poetry@v1
+        run: pip install 'poetry==2.3.2'
 
       - name: Cache Poetry dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.0.0
         with:
           path: |
             ~/.cache/pypoetry
@@ -56,14 +61,17 @@ jobs:
         run: |
           poetry config virtualenvs.in-project true
           poetry install --with dev,proxy-dev --extras "proxy"
-          poetry run pip install psycopg2-binary uvicorn fastapi httpx tenacity
+          poetry run pip install psycopg2-binary==2.9.11 uvicorn==0.42.0 fastapi==0.135.2 httpx==0.28.1 tenacity==9.1.4
 
       - name: Setup litellm-enterprise
         run: |
           poetry run pip install --force-reinstall --no-deps -e enterprise/
 
       - name: Generate Prisma client
+        env:
+          PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
         run: |
+          poetry run pip install nodejs-wheel-binaries==24.13.1
           poetry run prisma generate --schema litellm/proxy/schema.prisma
 
       - name: Run Prisma migrations
@@ -87,4 +95,3 @@ jobs:
             --tb=short \
             --maxfail=3 \
             --durations=10
-
diff --git a/.github/workflows/test_server_root_path.yml b/.github/workflows/test_server_root_path.yml
index c359e38bff9..47636ce8e92 100644
--- a/.github/workflows/test_server_root_path.yml
+++ b/.github/workflows/test_server_root_path.yml
@@ -17,13 +17,15 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3.12
 
       - name: Build Docker image
-        uses: docker/build-push-action@v5
+        uses: docker/build-push-action@0adf9959216b96bec444f325f1e493d4aa344497 #v6.14
         with:
           context: .
           file: ./docker/Dockerfile.non_root
diff --git a/.github/workflows/zizmor.yml b/.github/workflows/zizmor.yml
new file mode 100644
index 00000000000..9a1e899fed5
--- /dev/null
+++ b/.github/workflows/zizmor.yml
@@ -0,0 +1,31 @@
+name: GitHub Actions Security Analysis
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+permissions: {}
+
+jobs:
+  zizmor:
+    name: zizmor
+    runs-on: ubuntu-latest
+    timeout-minutes: 5
+    permissions:
+      security-events: write
+      contents: read
+      actions: read
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
+
+      - name: Run zizmor
+        uses: zizmorcore/zizmor-action@71321a20a9ded102f6e9ce5718a2fcec2c4f70d8 # v0.5.2
diff --git a/.semgrep/rules/security/no-claude-directory.yml b/.semgrep/rules/security/no-claude-directory.yml
new file mode 100644
index 00000000000..7d120a7c23c
--- /dev/null
+++ b/.semgrep/rules/security/no-claude-directory.yml
@@ -0,0 +1,18 @@
+rules:
+  - id: no-claude-directory-committed
+    message: >
+      .claude/ directory must not be committed to the repository.
+      It contains local Claude Code settings (permissions, worktree paths) that are
+      developer-machine-specific and may expose internal paths or credentials.
+      Add .claude/ to .gitignore instead.
+    severity: ERROR
+    languages: [generic]
+    paths:
+      include:
+        - "/.claude/**"
+        - "/.claude/*"
+    pattern-regex: '[\s\S]+'
+    metadata:
+      category: security
+      tags: [supply-chain, secrets]
+      confidence: HIGH
diff --git a/README.md b/README.md
index 67f2f3a2048..a6dc597574e 100644
--- a/README.md
+++ b/README.md
@@ -266,6 +266,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 <table>
   <tr>
     <td><img height="60" alt="Stripe" src="https://github.com/user-attachments/assets/f7296d4f-9fbd-460d-9d05-e4df31697c4b" /></td>
+    <td><img height="60" alt="image" src="https://github.com/user-attachments/assets/436fca71-988b-40bb-b5fe-8450c80fdbd0" /></td>
     <td><img height="60" alt="Google ADK" src="https://github.com/user-attachments/assets/caf270a2-5aee-45c4-8222-41a2070c4f19" /></td>
     <td><img height="60" alt="Greptile" src="https://github.com/user-attachments/assets/0be4bd8a-7cfa-48d3-9090-f415fe948280" /></td>
     <td><img height="60" alt="OpenHands" src="https://github.com/user-attachments/assets/a6150c4c-149e-4cae-888b-8b92be6e003f" /></td>
@@ -402,7 +403,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 # Enterprise
 For companies that need better security, user management and professional support
 
-[Talk to founders](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Talk to founders](https://enterprise.litellm.ai/demo)
 
 This covers:
 - ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/proxy/enterprise):**
@@ -452,7 +453,6 @@ All these checks must pass before your PR can be merged.
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
 - [Community Slack 💭](https://www.litellm.ai/support)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
 
 # Why did we build this
diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh
index 801b700f64f..061e454465a 100755
--- a/ci_cd/security_scans.sh
+++ b/ci_cd/security_scans.sh
@@ -10,13 +10,13 @@ echo "Starting security scans for LiteLLM..."
 # Function to install Trivy and required tools
 install_trivy() {
     echo "Installing Trivy and required tools..."
+    TRIVY_VERSION="0.35.0"
     sudo apt-get update
-    sudo apt-get install -y wget apt-transport-https gnupg lsb-release jq curl bsdmainutils
-    wget -qO - https://aquasecurity.github.io/trivy-repo/deb/public.key | sudo apt-key add -
-    echo "deb https://aquasecurity.github.io/trivy-repo/deb $(lsb_release -sc) main" | sudo tee -a /etc/apt/sources.list.d/trivy.list
-    sudo apt-get update
-    sudo apt-get install trivy
-    echo "Trivy and required tools installed successfully"
+    sudo apt-get install -y wget jq curl bsdmainutils
+    wget -qO trivy.deb "https://github.com/aquasecurity/trivy/releases/download/v${TRIVY_VERSION}/trivy_${TRIVY_VERSION}_Linux-64bit.deb"
+    sudo dpkg -i trivy.deb
+    rm trivy.deb
+    echo "Trivy ${TRIVY_VERSION} installed successfully"
 }
 
 # Function to install Grype
diff --git a/cookbook/benchmark/readme.md b/cookbook/benchmark/readme.md
index 57115eb96a9..afa59aa91ee 100644
--- a/cookbook/benchmark/readme.md
+++ b/cookbook/benchmark/readme.md
@@ -178,4 +178,4 @@ Benchmark Results for 'When will BerriAI IPO?':
 ```
 
 ## Support 
-**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you.
+**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://enterprise.litellm.ai/demo) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you.
diff --git a/cookbook/codellama-server/README.MD b/cookbook/codellama-server/README.MD
index b158bb083f2..82a7e62f40a 100644
--- a/cookbook/codellama-server/README.MD
+++ b/cookbook/codellama-server/README.MD
@@ -143,7 +143,6 @@ All responses from the server are returned in the following format (for all LLM
 
 - [Our calendar 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
 
 ## Roadmap
diff --git a/cookbook/litellm_proxy_server/readme.md b/cookbook/litellm_proxy_server/readme.md
index d0b0592c433..2c1eab72c24 100644
--- a/cookbook/litellm_proxy_server/readme.md
+++ b/cookbook/litellm_proxy_server/readme.md
@@ -164,7 +164,6 @@ All responses from the server are returned in the following format (for all LLM
 
 - [Our calendar 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
 
 ## Roadmap
diff --git a/docs/my-website/blog/gpt_5_4_mini_nano/index.md b/docs/my-website/blog/gpt_5_4_mini_nano/index.md
new file mode 100644
index 00000000000..6d7c2b33f72
--- /dev/null
+++ b/docs/my-website/blog/gpt_5_4_mini_nano/index.md
@@ -0,0 +1,106 @@
+---
+slug: gpt_5_4_mini_nano
+title: "Day 0 Support: GPT-5.4-mini and GPT-5.4-nano"
+date: 2026-03-17T10:00:00
+authors:
+  - name: Sameer Kankute
+    title: SWE @ LiteLLM (LLM Translation)
+    url: https://www.linkedin.com/in/sameer-kankute/
+    image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
+  - name: Krrish Dholakia
+    title: "CEO, LiteLLM"
+    url: https://www.linkedin.com/in/krish-d/
+    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+  - name: Ishaan Jaff
+    title: "CTO, LiteLLM"
+    url: https://www.linkedin.com/in/reffajnaahsi/
+    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+description: "GPT-5.4-mini and GPT-5.4-nano model support in LiteLLM"
+tags: [openai, gpt-5.4-mini, gpt-5.4-nano, completion]
+hide_table_of_contents: false
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+LiteLLM now supports GPT-5.4-mini and GPT-5.4-nano — cost-effective models for simple completions and high-throughput workloads.
+
+:::note
+If you're on **v1.82.3-stable** or above, you don't need any update to use these models.
+:::
+
+## Usage
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: gpt-5.4-mini
+    litellm_params:
+      model: openai/gpt-5.4-mini
+      api_key: os.environ/OPENAI_API_KEY
+  - model_name: gpt-5.4-nano
+    litellm_params:
+      model: openai/gpt-5.4-nano
+      api_key: os.environ/OPENAI_API_KEY
+```
+
+**2. Start the proxy**
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+**3. Test it**
+
+```bash
+# GPT-5.4-mini
+curl -X POST "http://localhost:4000/v1/chat/completions" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_KEY" \
+  -d '{
+    "model": "gpt-5.4-mini",
+    "messages": [{"role": "user", "content": "What is the capital of France?"}]
+  }'
+
+# GPT-5.4-nano
+curl -X POST "http://localhost:4000/v1/chat/completions" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_KEY" \
+  -d '{
+    "model": "gpt-5.4-nano",
+    "messages": [{"role": "user", "content": "What is 2 + 2?"}]
+  }'
+```
+
+</TabItem>
+<TabItem value="sdk" label="LiteLLM SDK">
+
+```python
+from litellm import completion
+
+# GPT-5.4-mini
+response = completion(
+    model="openai/gpt-5.4-mini",
+    messages=[{"role": "user", "content": "What is the capital of France?"}],
+)
+print(response.choices[0].message.content)
+
+# GPT-5.4-nano
+response = completion(
+    model="openai/gpt-5.4-nano",
+    messages=[{"role": "user", "content": "What is 2 + 2?"}],
+)
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+</Tabs>
+
+## Notes
+
+- Both models support function calling, vision, and tool-use — see the [OpenAI provider docs](../../docs/providers/openai) for advanced usage.
+- GPT-5.4-nano is the most cost-effective option for simple tasks; GPT-5.4-mini offers a balance of speed and capability.
diff --git a/docs/my-website/blog/security_townhall_updates/index.md b/docs/my-website/blog/security_townhall_updates/index.md
new file mode 100644
index 00000000000..6b2260156d2
--- /dev/null
+++ b/docs/my-website/blog/security_townhall_updates/index.md
@@ -0,0 +1,178 @@
+---
+slug: security-townhall-updates
+title: "Security Townhall Updates"
+date: 2026-03-27T12:00:00
+authors:
+  - krrish
+  - ishaan-alt
+description: "What happened, what we've done, and what comes next for LiteLLM's release and security processes."
+tags: [security, incident-report]
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+
+Thank you to everyone who joined our town hall.
+
+We wanted to use that time to walk through what we know, what we've done so far, and how we're improving LiteLLM's release and security processes going forward. This post is a written version of that update. [Slides available here](https://drive.google.com/file/d/17hsSG7nk-OYL7VRCTbTa7McrWREtS9OO/view?usp=sharing)
+
+{/* truncate */}
+
+## What happened
+
+On March 24, 2026 at 10:39 UTC, LiteLLM v1.82.7 was pushed to PyPI. Version v1.82.8 was published soon after. Those packages were live for about 40 minutes before being quarantined by PyPI. By 16:00 UTC, the LiteLLM team had worked with PyPI to delete the affected packages.
+
+At this point, our understanding is that this was a supply-chain incident affecting those two published versions.
+
+### Q. Were older packages impacted?
+
+Our current findings show no indicators of compromise in the last 20 versions of LiteLLM. This was manually verified by our team and independently reviewed by Veria Labs.
+
+We have also published the verified versions for users to use. [Check Security Blog for release verification.](https://docs.litellm.ai/blog/security-update-march-2026#verified-safe-versions)
+
+## How did this happen?
+
+Our understanding is that the issue came from the [compromised Trivy security scanner](https://www.aquasec.com/blog/trivy-supply-chain-attack-what-you-need-to-know/) dependency in our CI/CD pipeline.
+
+<Image 
+  img={require('../../img/shared_ci_cd_environment.png')}
+  style={{width: '500px', height: '400px', display: 'block'}}
+/>
+
+There were three major contributing factors:
+
+### 1. Shared CI/CD environment
+
+At the time, everything was running on CircleCI, and all steps shared a common environment. That increased blast radius: if one component was compromised, it could potentially access credentials or context intended for other parts of the pipeline.
+
+### 2. Static credentials in environment variables
+
+Release credentials, including credentials for PyPI, GHCR, and Docker publishing, were available as static secrets in the environment. That meant a compromised step could access long-lived release credentials.
+
+### 3. Unpinned Trivy dependency
+
+In our security scanning component, we had an unpinned Trivy dependency. Our present understanding is that a compromised Trivy package ran during the scan, had access to environment variables, and enabled attackers to obtain those credentials.
+
+**In summary:** a compromised package in CI had access to secrets it should not have had, and those secrets were then used in the release path.
+
+## What we've already done
+
+
+In the last 3 days, we've taken the following steps:
+
+### 1. Minimize Scope of Impact
+
+#### Prevented further key abuse
+
+We deleted or rotated all impacted or adjacent secret keys, including PyPI, GitHub, Docker, and related credentials. Out of an abundance of caution, we've also rotated LiteLLM maintainer accounts. 
+
+#### Prevent branch attacks
+
+We removed roughly 6,000 open branches and added an auto-deletion policy for branches merged into `main`. This reduces the surface area for branch-based abuse.
+
+#### Pinned CI/CD dependencies
+
+We've pinned all Github Actions, and are working on pinning all CircleCI dependencies as well.
+
+#### Paused releases
+
+We've paused new releases until we've confirmed codebase security and put stronger release controls in place.
+
+### 2. Secured LiteLLM
+
+#### Forensic analysis
+
+We are working with Google's Mandiant cybersecurity team to confirm the source of the attack and verify the security of the codebase. We also confirmed that no malicious code was pushed to `main`.
+
+#### Confirm Application Security
+
+In parallel, we are working with whitehat hackers at [Veria Labs](https://verialabs.com/) to verify application security and review improvements to our CI/CD process.
+
+We have also confirmed that the last 20 LiteLLM releases contain no indicators of compromise, and that no unauthenticated attacks can be made against LiteLLM Proxy based on our current investigation. [Check Security Blog for release verification.](https://docs.litellm.ai/blog/security-update-march-2026#verified-safe-versions)
+
+#### Created a security working group
+
+We created a new security working group inside LiteLLM focused on:
+
+- Building threat models
+- Auditing the build process and dependencies
+
+If you're interested in joining the security working group, please file an issue [here](https://github.com/BerriAI/litellm-security-wg).
+
+### 3. Improved CI/CD
+
+We've already begun making structural changes to how releases are built and published. These align with our goals (covered in the next section) around isolated environments, ephemeral credentials, and release auditing.
+
+## Roadmap
+
+We plan on following 4 guiding principles for our new CI/CD pipeline:
+
+1. **Limit** what each package can access
+2. **Reduce** the number of sensitive environment variables
+3. **Avoid** compromised packages
+4. **Prevent** release tampering
+
+
+### Isolated environments
+
+<Image 
+  img={require('../../img/isolated_ci_cd_environments.png')}
+  style={{width: '400px', height: 'auto'}}
+/>
+
+We are breaking our CI/CD into 4 semantic concepts:
+
+1. Unit tests
+2. Integration tests
+3. Security scans
+4. Release publishing
+
+And will be running each of these in isolated environments.
+
+This will limit the damage that any single compromised component can cause.
+
+### Ephemeral credentials
+
+We plan to move to ephemeral credentials for PyPI (Trusted Publisher) and GHCR (Token-based authentication) releases. This will reduce the risk of credentials being leaked or compromised.
+
+We have already begun doing this: 
+
+- PyPI Trusted Publisher on GitHub Actions [PR](https://github.com/BerriAI/litellm/pull/24654)
+- GHCR Token-based authentication on GitHub Actions [PR](https://github.com/BerriAI/litellm/pull/24683)
+
+### Release auditing
+
+Our goal is to allow users to independently verify that a release came from us and prevent silent modifications of releases after they are published.
+
+This will ensure, your releases are safe, even when: 
+- Stolen PyPI/GHCR credentials are used to publish malicious releases
+- Tampered registry artifacts are published
+- Tag mutations are made after the release is published
+
+We believe that [Cosign](https://github.com/sigstore/cosign) is a good fit for this, and have already begun working on it [PR](https://github.com/BerriAI/litellm/pull/24683).
+
+
+### Avoid Compromised Packages
+
+- Move to pinned, verified SHAs for packages and actions used in CI/CD, avoiding `latest` wherever possible. 
+- Add a cooldown period before upgrading to a new version of a package - allows more time to investigate and verify the new version. 
+
+We've added zizmor to help us catch issues such as unpinned dependencies and credential leakage. [commit](https://github.com/BerriAI/litellm/commit/a671275f5c5b0e1fb1adacdf3b6ef779aaa5d56c).
+
+
+## Questions & Support 
+
+If you believe your systems may be affected, contact us immediately:
+
+- **Security:** security@berri.ai
+- **Support:** support@berri.ai
+- **Slack:** Reach out to the LiteLLM team directly [here](https://join.slack.com/t/litellmossslack/shared_invite/zt-3o7nkuyfr-p_kbNJj8taRfXGgQI1~YyA)
+
+## Hiring 
+
+We are currently hiring for: 
+
+- DevOps Engineer - to keep ci/cd secure and running smoothly
+- Security Engineer - to keep the application secure
+
+If you're interest in joining, please apply [here](https://jobs.ashbyhq.com/litellm)
\ No newline at end of file
diff --git a/docs/my-website/blog/security_townhall_updates/shared_ci_cd_environment.png b/docs/my-website/blog/security_townhall_updates/shared_ci_cd_environment.png
new file mode 100644
index 00000000000..29ec195b7fb
Binary files /dev/null and b/docs/my-website/blog/security_townhall_updates/shared_ci_cd_environment.png differ
diff --git a/docs/my-website/blog/security_update_march_2026/index.md b/docs/my-website/blog/security_update_march_2026/index.md
new file mode 100644
index 00000000000..93ae7daac3a
--- /dev/null
+++ b/docs/my-website/blog/security_update_march_2026/index.md
@@ -0,0 +1,782 @@
+---
+slug: security-update-march-2026
+title: "Security Update: Suspected Supply Chain Incident"
+date: 2026-03-24T14:00:00
+authors:
+  - krrish
+  - ishaan-alt
+description: "As of 2:00 PM ET on March 24, 2026"
+tags: [security, incident-report]
+hide_table_of_contents: false
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import VersionVerificationTable from '@site/src/components/VersionVerificationTable';
+
+> **Status:** Active investigation
+> **Last updated:** March 27, 2026
+
+> **Update (March 27):** Added [Verified safe versions](#verified-safe-versions) section with SHA-256 checksums for all audited PyPI and Docker releases.
+
+> **Update (March 26):** Added `checkmarx[.]zone` to [Indicators of compromise](#indicators-of-compromise-iocs)
+
+> **Update (March 25):** Added community-contributed scripts for scanning GitHub Actions and GitLab CI pipelines for the compromised versions. See [How to check if you are affected](#how-to-check-if-you-are-affected). s/o [@Zach Fury](https://www.linkedin.com/in/fryware/) for these scripts.
+
+
+## TLDR; 
+- The compromised PyPI packages were **litellm==1.82.7** and **litellm==1.82.8**. Those packages have now been removed from PyPI.
+- We believe that the compromise originated from the Trivy dependency used in our CI/CD security scanning workflow.
+- Customers running the official LiteLLM Proxy Docker image were not impacted. That deployment path pins dependencies in requirements.txt and does not rely on the compromised PyPI packages.
+- We are pausing new LiteLLM releases until we complete a broader supply-chain review and confirm the release path is safe.
+
+
+## Overview
+
+LiteLLM AI Gateway is investigating a suspected supply chain attack involving unauthorized PyPI package publishes. Current evidence suggests a maintainer's PyPI account may have been compromised and used to distribute malicious code.
+
+At this time, we believe this incident may be linked to the broader [Trivy security compromise](https://www.aquasec.com/blog/trivy-supply-chain-attack-what-you-need-to-know/), in which stolen credentials were reportedly used to gain unauthorized access to the LiteLLM publishing pipeline.
+
+This investigation is ongoing. Details below may change as we confirm additional findings.
+
+## Confirmed affected versions
+
+The following LiteLLM versions published to PyPI were impacted:
+
+- **v1.82.7**: contained a malicious payload in the LiteLLM AI Gateway `proxy_server.py`
+- **v1.82.8**: contained `litellm_init.pth` and a malicious payload in the LiteLLM AI Gateway `proxy_server.py`
+
+If you installed or ran either of these versions, review the recommendations below immediately.
+
+Note: These versions have already been removed from PyPI.
+
+## What happened
+
+Initial evidence suggests the attacker bypassed official CI/CD workflows and uploaded malicious packages directly to PyPI.
+
+These compromised versions appear to have included a credential stealer designed to:
+
+- Harvest secrets by scanning for:
+  - environment variables
+  - SSH keys
+  - cloud provider credentials (AWS, GCP, Azure)
+  - Kubernetes tokens
+  - database passwords
+- Encrypt and exfiltrate data via a `POST` request to `models.litellm.cloud`, which is **not** an official BerriAI / LiteLLM domain
+
+## Who is affected
+
+You may be affected if **any** of the following are true:
+
+- You installed or upgraded LiteLLM via `pip` on **March 24, 2026**, between **10:39 UTC and 16:00 UTC**
+- You ran `pip install litellm` without pinning a version and received **v1.82.7** or **v1.82.8**
+- You built a Docker image during this window that included `pip install litellm` without a pinned version
+- A dependency in your project pulled in LiteLLM as a transitive, unpinned dependency
+  (for example through AI agent frameworks, MCP servers, or LLM orchestration tools)
+
+You are **not** affected if any of the following are true:
+
+**LiteLLM AI Gateway/Proxy users:** Customers running the official LiteLLM Proxy Docker image were not impacted. That deployment path pins dependencies in requirements.txt and does not rely on the compromised PyPI packages.
+
+- You are using **LiteLLM Cloud**
+- You are using the official LiteLLM AI Gateway Docker image: `ghcr.io/berriai/litellm`
+- You are on **v1.82.6 or earlier** and did not upgrade during the affected window
+- You installed LiteLLM from source via the GitHub repository, which was **not** compromised
+
+
+### How to check if you are affected
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```bash
+pip show litellm
+```
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+Go to the proxy base url, and check the version of the installed LiteLLM.
+
+![Proxy version check](../../img/security_update_march_2026/proxy_version.png)
+</TabItem>
+<TabItem value="github" label="GitHub Actions">
+
+Scans all repositories in a GitHub organization for workflow jobs that installed the compromised versions.
+
+**Requirements:** Python 3 and `requests` (`pip install requests`).
+
+**Setup:**
+
+```bash
+export GITHUB_TOKEN="your-github-pat"
+```
+
+**Run:**
+
+```bash
+python find_litellm_github.py
+```
+
+Set the `ORG` variable in the script to your GitHub organization name.
+
+Both scripts default to scanning jobs from **today**. Adjust the `WINDOW_START` and `WINDOW_END` constants to cover **March 24, 2026** (the incident date) if running on a different day.
+
+<details>
+<summary>View full script (find_litellm_github.py)</summary>
+
+```python
+#!/usr/bin/env python3
+"""
+Scan all GitHub Actions jobs in a GitHub org that ran between
+0800-1244 UTC today and identify any that installed litellm 1.82.7 or 1.82.8.
+
+Adjust WINDOW_START / WINDOW_END to cover March 24, 2026 if running later.
+"""
+
+import io
+import os
+import re
+import sys
+import zipfile
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime, timezone
+
+import requests
+
+GITHUB_URL   = "https://api.github.com"
+ORG          = "your-org"  # <-- set to your GitHub organization
+TOKEN        = os.environ.get("GITHUB_TOKEN", "")
+
+TODAY        = datetime.now(timezone.utc).date()
+WINDOW_START = datetime(TODAY.year, TODAY.month, TODAY.day,  8,  0, 0, tzinfo=timezone.utc)
+WINDOW_END   = datetime(TODAY.year, TODAY.month, TODAY.day, 12, 44, 0, tzinfo=timezone.utc)
+
+TARGET_VERSIONS = {"1.82.7", "1.82.8"}
+VERSION_PATTERN = re.compile(r"litellm[=\-](\d+\.\d+\.\d+)", re.IGNORECASE)
+
+SESSION = requests.Session()
+SESSION.headers.update({
+    "Authorization": f"Bearer {TOKEN}",
+    "Accept": "application/vnd.github+json",
+    "X-GitHub-Api-Version": "2022-11-28",
+})
+
+
+def get_paginated(url, params=None):
+    params = dict(params or {})
+    params.setdefault("per_page", 100)
+    page = 1
+    while True:
+        params["page"] = page
+        resp = SESSION.get(url, params=params, timeout=30)
+        if resp.status_code == 404:
+            return
+        resp.raise_for_status()
+        data = resp.json()
+        if isinstance(data, dict):
+            items = next((v for v in data.values() if isinstance(v, list)), [])
+        else:
+            items = data
+        if not items:
+            break
+        yield from items
+        if len(items) < params["per_page"]:
+            break
+        page += 1
+
+
+def parse_ts(ts_str):
+    if not ts_str:
+        return None
+    return datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
+
+
+def get_repos():
+    repos = []
+    for r in get_paginated(f"{GITHUB_URL}/orgs/{ORG}/repos", {"type": "all"}):
+        repos.append({"id": r["id"], "name": r["name"], "full_name": r["full_name"]})
+    return repos
+
+
+def get_runs_in_window(repo_full_name):
+    created_filter = (
+        f"{WINDOW_START.strftime('%Y-%m-%dT%H:%M:%SZ')}"
+        f"..{WINDOW_END.strftime('%Y-%m-%dT%H:%M:%SZ')}"
+    )
+    url = f"{GITHUB_URL}/repos/{repo_full_name}/actions/runs"
+    runs = []
+    for run in get_paginated(url, {"created": created_filter, "per_page": 100}):
+        ts = parse_ts(run.get("run_started_at") or run.get("created_at"))
+        if ts and WINDOW_START <= ts <= WINDOW_END:
+            runs.append(run)
+    return runs
+
+
+def get_jobs_for_run(repo_full_name, run_id):
+    url = f"{GITHUB_URL}/repos/{repo_full_name}/actions/runs/{run_id}/jobs"
+    jobs = []
+    for job in get_paginated(url, {"filter": "all"}):
+        ts = parse_ts(job.get("started_at"))
+        if ts and WINDOW_START <= ts <= WINDOW_END:
+            jobs.append(job)
+    return jobs
+
+
+def fetch_job_log(repo_full_name, job_id):
+    url = f"{GITHUB_URL}/repos/{repo_full_name}/actions/jobs/{job_id}/logs"
+    resp = SESSION.get(url, timeout=60, allow_redirects=True)
+    if resp.status_code in (403, 404, 410):
+        return ""
+    resp.raise_for_status()
+
+    content_type = resp.headers.get("Content-Type", "")
+    if "zip" in content_type or resp.content[:2] == b"PK":
+        try:
+            with zipfile.ZipFile(io.BytesIO(resp.content)) as zf:
+                parts = []
+                for name in sorted(zf.namelist()):
+                    with zf.open(name) as f:
+                        parts.append(f.read().decode("utf-8", errors="replace"))
+                return "\n".join(parts)
+        except zipfile.BadZipFile:
+            pass
+    return resp.text
+
+
+def check_job(repo_full_name, job):
+    job_id   = job["id"]
+    job_name = job["name"]
+    run_id   = job["run_id"]
+    started  = job.get("started_at", "")
+
+    log_text = fetch_job_log(repo_full_name, job_id)
+    if not log_text:
+        return None
+
+    found_versions = set()
+    context_lines  = []
+    for line in log_text.splitlines():
+        m = VERSION_PATTERN.search(line)
+        if m:
+            ver = m.group(1)
+            if ver in TARGET_VERSIONS:
+                found_versions.add(ver)
+                context_lines.append(line.strip())
+
+    if not found_versions:
+        return None
+
+    return {
+        "repo":       repo_full_name,
+        "run_id":     run_id,
+        "job_id":     job_id,
+        "job_name":   job_name,
+        "started_at": started,
+        "versions":   sorted(found_versions),
+        "context":    context_lines[:10],
+        "job_url":    job.get("html_url", f"https://github.com/{repo_full_name}/actions/runs/{run_id}"),
+    }
+
+
+def main():
+    if not TOKEN:
+        print("ERROR: Set GITHUB_TOKEN environment variable.", file=sys.stderr)
+        sys.exit(1)
+
+    print(f"Time window : {WINDOW_START.isoformat()} -> {WINDOW_END.isoformat()}")
+    print(f"Hunting for : litellm {', '.join(sorted(TARGET_VERSIONS))}")
+    print()
+
+    print(f"Fetching repositories for org '{ORG}'...")
+    repos = get_repos()
+    print(f"  Found {len(repos)} repositories")
+    print()
+
+    jobs_to_check = []
+
+    print("Scanning workflow runs for time window...")
+    for repo in repos:
+        full_name = repo["full_name"]
+        try:
+            runs = get_runs_in_window(full_name)
+        except requests.HTTPError as e:
+            print(f"  WARN: {full_name} - {e}", file=sys.stderr)
+            continue
+        if not runs:
+            continue
+        print(f"  {full_name}: {len(runs)} run(s) in window")
+        for run in runs:
+            try:
+                jobs = get_jobs_for_run(full_name, run["id"])
+            except requests.HTTPError as e:
+                print(f"    WARN: run {run['id']} - {e}", file=sys.stderr)
+                continue
+            for job in jobs:
+                jobs_to_check.append((full_name, job))
+
+    total = len(jobs_to_check)
+    print(f"\nFetching logs for {total} job(s)...")
+    print()
+
+    hits = []
+    with ThreadPoolExecutor(max_workers=8) as pool:
+        futures = {
+            pool.submit(check_job, full_name, job): (full_name, job["id"])
+            for full_name, job in jobs_to_check
+        }
+        done = 0
+        for future in as_completed(futures):
+            done += 1
+            full_name, jid = futures[future]
+            try:
+                result = future.result()
+            except Exception as e:
+                print(f"  ERROR {full_name} job {jid}: {e}", file=sys.stderr)
+                continue
+            if result:
+                hits.append(result)
+            print(
+                f"  [{done}/{total}] {full_name} job {jid}" +
+                (f"  *** HIT: litellm {result['versions']} ***" if result else ""),
+                flush=True,
+            )
+
+    print()
+    print("=" * 72)
+    print(f"RESULTS: {len(hits)} job(s) installed litellm {' or '.join(sorted(TARGET_VERSIONS))}")
+    print("=" * 72)
+
+    if not hits:
+        print("No matches found.")
+        return
+
+    for h in sorted(hits, key=lambda x: x["started_at"]):
+        print()
+        print(f"  Repo      : {h['repo']}")
+        print(f"  Job       : {h['job_name']} (#{h['job_id']})")
+        print(f"  Run ID    : {h['run_id']}")
+        print(f"  Started   : {h['started_at']}")
+        print(f"  Versions  : litellm {', '.join(h['versions'])}")
+        print(f"  URL       : {h['job_url']}")
+        print(f"  Log lines :")
+        for line in h["context"]:
+            print(f"    {line}")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+</details>
+
+</TabItem>
+<TabItem value="gitlab" label="GitLab CI">
+
+Scans all projects in a GitLab group (including subgroups) for CI/CD jobs that installed the compromised versions.
+
+**Requirements:** Python 3 and `requests` (`pip install requests`).
+
+**Setup:**
+
+```bash
+export GITLAB_TOKEN="your-gitlab-pat"
+```
+
+**Run:**
+
+```bash
+python find_litellm_jobs.py
+```
+
+Set the `GROUP_NAME` variable in the script to your GitLab group name.
+
+Both scripts default to scanning jobs from **today**. Adjust the `WINDOW_START` and `WINDOW_END` constants to cover **March 24, 2026** (the incident date) if running on a different day.
+
+<details>
+<summary>View full script (find_litellm_jobs.py)</summary>
+
+```python
+#!/usr/bin/env python3
+"""
+Scan all GitLab CI/CD jobs in a GitLab group that ran between
+0800-1244 UTC today and identify any that installed litellm 1.82.7 or 1.82.8.
+
+Adjust WINDOW_START / WINDOW_END to cover March 24, 2026 if running later.
+"""
+
+import os
+import re
+import sys
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime, timezone
+
+import requests
+
+GITLAB_URL = "https://gitlab.com"
+GROUP_NAME = "YourGroup"  # <-- set to your GitLab group name
+TOKEN = os.environ.get("GITLAB_TOKEN", "")
+
+TODAY = datetime.now(timezone.utc).date()
+WINDOW_START = datetime(TODAY.year, TODAY.month, TODAY.day, 8, 0, 0, tzinfo=timezone.utc)
+WINDOW_END   = datetime(TODAY.year, TODAY.month, TODAY.day, 12, 44, 0, tzinfo=timezone.utc)
+
+TARGET_VERSIONS = {"1.82.7", "1.82.8"}
+VERSION_PATTERN = re.compile(r"litellm[=\-](\d+\.\d+\.\d+)", re.IGNORECASE)
+
+HEADERS = {"PRIVATE-TOKEN": TOKEN}
+SESSION = requests.Session()
+SESSION.headers.update(HEADERS)
+
+
+def get_paginated(url, params=None):
+    params = dict(params or {})
+    params.setdefault("per_page", 100)
+    page = 1
+    while True:
+        params["page"] = page
+        resp = SESSION.get(url, params=params, timeout=30)
+        resp.raise_for_status()
+        data = resp.json()
+        if not data:
+            break
+        yield from data
+        if len(data) < params["per_page"]:
+            break
+        page += 1
+
+
+def get_group_id(group_name):
+    resp = SESSION.get(f"{GITLAB_URL}/api/v4/groups/{group_name}", timeout=30)
+    resp.raise_for_status()
+    return resp.json()["id"]
+
+
+def get_all_projects(group_id):
+    projects = []
+    for p in get_paginated(
+        f"{GITLAB_URL}/api/v4/groups/{group_id}/projects",
+        {"include_subgroups": "true", "archived": "false"},
+    ):
+        projects.append({"id": p["id"], "name": p["path_with_namespace"]})
+    return projects
+
+
+def parse_ts(ts_str):
+    if not ts_str:
+        return None
+    ts_str = ts_str.replace("Z", "+00:00")
+    return datetime.fromisoformat(ts_str)
+
+
+def jobs_in_window(project_id):
+    matching = []
+    url = f"{GITLAB_URL}/api/v4/projects/{project_id}/jobs"
+    params = {"per_page": 100, "scope[]": ["success", "failed", "canceled", "running"]}
+
+    page = 1
+    while True:
+        params["page"] = page
+        resp = SESSION.get(url, params=params, timeout=30)
+        if resp.status_code == 403:
+            return matching
+        resp.raise_for_status()
+        jobs = resp.json()
+        if not jobs:
+            break
+
+        stop_early = False
+        for job in jobs:
+            ts = parse_ts(job.get("started_at") or job.get("created_at"))
+            if ts is None:
+                continue
+            if ts > WINDOW_END:
+                continue
+            if ts < WINDOW_START:
+                stop_early = True
+                continue
+            matching.append(job)
+
+        if stop_early or len(jobs) < 100:
+            break
+        page += 1
+
+    return matching
+
+
+def fetch_trace(project_id, job_id):
+    url = f"{GITLAB_URL}/api/v4/projects/{project_id}/jobs/{job_id}/trace"
+    resp = SESSION.get(url, timeout=60)
+    if resp.status_code in (403, 404):
+        return ""
+    resp.raise_for_status()
+    return resp.text
+
+
+def check_job(project_name, project_id, job):
+    job_id   = job["id"]
+    job_name = job["name"]
+    ref      = job.get("ref", "")
+    started  = job.get("started_at", job.get("created_at", ""))
+
+    trace = fetch_trace(project_id, job_id)
+    if not trace:
+        return None
+
+    found_versions = set()
+    for match in VERSION_PATTERN.finditer(trace):
+        ver = match.group(1)
+        if ver in TARGET_VERSIONS:
+            found_versions.add(ver)
+
+    if not found_versions:
+        return None
+
+    context_lines = []
+    for line in trace.splitlines():
+        if VERSION_PATTERN.search(line):
+            ver_match = VERSION_PATTERN.search(line)
+            if ver_match and ver_match.group(1) in TARGET_VERSIONS:
+                context_lines.append(line.strip())
+
+    return {
+        "project":    project_name,
+        "project_id": project_id,
+        "job_id":     job_id,
+        "job_name":   job_name,
+        "ref":        ref,
+        "started_at": started,
+        "versions":   sorted(found_versions),
+        "context":    context_lines[:10],
+        "job_url":    f"{GITLAB_URL}/{project_name}/-/jobs/{job_id}",
+    }
+
+
+def main():
+    if not TOKEN:
+        print("ERROR: Set GITLAB_TOKEN environment variable.", file=sys.stderr)
+        sys.exit(1)
+
+    print(f"Time window : {WINDOW_START.isoformat()} -> {WINDOW_END.isoformat()}")
+    print(f"Hunting for : litellm {', '.join(sorted(TARGET_VERSIONS))}")
+    print()
+
+    print(f"Resolving group '{GROUP_NAME}'...")
+    group_id = get_group_id(GROUP_NAME)
+
+    print("Fetching projects...")
+    projects = get_all_projects(group_id)
+    print(f"  Found {len(projects)} projects")
+    print()
+
+    all_jobs_to_check = []
+
+    print("Scanning job listings for time window...")
+    for proj in projects:
+        try:
+            jobs = jobs_in_window(proj["id"])
+        except requests.HTTPError as e:
+            print(f"  WARN: {proj['name']} - {e}", file=sys.stderr)
+            continue
+        if jobs:
+            print(f"  {proj['name']}: {len(jobs)} job(s) in window")
+        for j in jobs:
+            all_jobs_to_check.append((proj["name"], proj["id"], j))
+
+    total = len(all_jobs_to_check)
+    print(f"\nFetching traces for {total} job(s)...")
+    print()
+
+    hits = []
+    with ThreadPoolExecutor(max_workers=10) as pool:
+        futures = {
+            pool.submit(check_job, pname, pid, job): (pname, job["id"])
+            for pname, pid, job in all_jobs_to_check
+        }
+        done = 0
+        for future in as_completed(futures):
+            done += 1
+            pname, jid = futures[future]
+            try:
+                result = future.result()
+            except Exception as e:
+                print(f"  ERROR checking {pname} job {jid}: {e}", file=sys.stderr)
+                continue
+            if result:
+                hits.append(result)
+            print(f"  [{done}/{total}] checked {pname} job {jid}" +
+                  (f"  *** HIT: litellm {result['versions']} ***" if result else ""),
+                  flush=True)
+
+    print()
+    print("=" * 72)
+    print(f"RESULTS: {len(hits)} job(s) installed litellm {' or '.join(sorted(TARGET_VERSIONS))}")
+    print("=" * 72)
+
+    if not hits:
+        print("No matches found.")
+        return
+
+    for h in sorted(hits, key=lambda x: x["started_at"]):
+        print()
+        print(f"  Project   : {h['project']}")
+        print(f"  Job       : {h['job_name']} (#{h['job_id']})")
+        print(f"  Branch/tag: {h['ref']}")
+        print(f"  Started   : {h['started_at']}")
+        print(f"  Versions  : litellm {', '.join(h['versions'])}")
+        print(f"  URL       : {h['job_url']}")
+        print(f"  Log lines :")
+        for line in h["context"]:
+            print(f"    {line}")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+</details>
+
+</TabItem>
+</Tabs>
+
+*CI/CD scripts contributed by the community ([original gist](https://gist.github.com/fryz/93ec8d4898ffe5b5ac5706a208823ef3)). Review before running.*
+
+
+## Indicators of compromise (IoCs)
+
+Review affected systems for the following indicators:
+
+- `litellm_init.pth` present in your `site-packages`
+- Outbound traffic or requests to `models.litellm[.]cloud`
+  This domain is **not** affiliated with LiteLLM
+- Outbound traffic or requests to `checkmarx[.]zone`
+  This domain is **not** affiliated with LiteLLM
+
+
+## Immediate actions for affected users
+
+If you installed or ran **v1.82.7** or **v1.82.8**, take the following actions immediately.
+
+### 1. Rotate all secrets
+
+Treat any credentials present on the affected systems as compromised, including:
+
+- API keys
+- Cloud access keys
+- Database passwords
+- SSH keys
+- Kubernetes tokens
+- Any secrets stored in environment variables or configuration files
+
+### 2. Inspect your filesystem
+
+Check your `site-packages` directory for a file named `litellm_init.pth`:
+
+```bash
+find /usr/lib/python3.13/site-packages/ -name "litellm_init.pth"
+```
+
+If present:
+
+- remove it immediately
+- investigate the host for further compromise
+- preserve relevant artifacts if your security team is performing forensics
+
+### 3. Audit version history
+
+Review your:
+
+- Local environments
+- CI/CD pipelines
+- Docker builds
+- Deployment logs
+
+Confirm whether **v1.82.7** or **v1.82.8** was installed anywhere.
+
+Pin LiteLLM to a known safe version such as **v1.82.6 or earlier**, or to a later verified release once announced.
+
+
+## Response and remediation
+
+The LiteLLM AI Gateway team has already taken the following steps:
+
+- Removed compromised packages from PyPI
+- Rotated maintainer credentials and established new authorized maintainers
+- Engaged Google's Mandiant security team to assist with forensic analysis of the build and publishing chain
+
+
+## Verified safe versions
+
+We have audited every LiteLLM release published between v1.78.0 and v1.82.6 across both PyPI and Docker. Each artifact was verified by:
+
+1. Downloading the published artifact and computing its SHA-256 digest
+2. Scanning for the known [indicators of compromise](#indicators-of-compromise-iocs) (IOCs)
+3. Comparing the artifact contents against the corresponding Git commit in the BerriAI/litellm repository
+
+**All versions listed below are confirmed clean.**
+
+<Tabs>
+<TabItem value="pypi" label="PyPI Releases">
+
+<VersionVerificationTable entries={[
+  { version: "1.82.6", sha256: "164a3ef3e19f309e3cabc199bef3d2045212712fefdfa25fc7f75884a5b5b205", gitCommit: "38d477507dad" },
+  { version: "1.82.5", sha256: "e1012ab816352215c4e00776dd48b0c68058b537888a8ff82cca62af19e6fb11", gitCommit: "1998c4f3703f" },
+  { version: "1.82.4", sha256: "d37c34a847e7952a146ed0e2888a24d3edec7787955c6826337395e755ad5c4b", gitCommit: "cfeafbe38811" },
+  { version: "1.82.3", sha256: "609901f6c5a5cf8c24386e4e3f50738bb8a9db719709fd76b208c8ee6d00f7a7", gitCommit: "61409275c8d8" },
+  { version: "1.82.2", sha256: "641ed024774fa3d5b4dd9347f0efb1e31fa422fba2a6500aabedee085d1194cb", gitCommit: "f351bbdb3683" },
+  { version: "1.82.1", sha256: "a9ec3fe42eccb1611883caaf8b1bf33c9f4e12163f94c7d1004095b14c379eb2", gitCommit: "94b002066e3a" },
+  { version: "1.82.0", sha256: "5496b5d4532cccdc7a095c21cbac4042f7662021c57bc1d17be4e39838929e80", gitCommit: "6c6585af568e" },
+  { version: "1.81.16", sha256: "d6bcc13acbd26719e07bfa6b9923740e88409cbf1f9d626d85fc9ae0e0eec88c", gitCommit: "678200ee4887" },
+  { version: "1.81.15", sha256: "2fa253658702509ce09fe0e172e5a47baaadf697fb0f784c7fd4ff665ae76ae1", gitCommit: "2e819656cee9" },
+  { version: "1.81.14", sha256: "6394e61bbdef7121e5e3800349f6b01e9369e7cf611e034f1832750c481abfed", gitCommit: "96bcee0b0af7" },
+  { version: "1.81.13", sha256: "ae4aea2a55e85993f5f6dd36d036519422d24812a1a3e8540d9e987f2d7a4304", gitCommit: "cc957a19a560" },
+  { version: "1.81.12", sha256: "219cf9729e5ea30c6d3f75aa43fef3c56a717369939a6d717cbad0fd78e3c146", gitCommit: "ba0d541b1982" },
+  { version: "1.81.11", sha256: "06a66c24742e082ddd2813c87f40f5c12fe7baa73ce1f9457eaf453dc44a0f65", gitCommit: "231aedeeff7e" },
+  { version: "1.81.10", sha256: "9efa1cbe61ac051f6500c267b173d988ff2d511c2eecf1c8f2ee546c0870747c", gitCommit: "7488abece8e7" },
+  { version: "1.81.9", sha256: "24ee273bc8a62299fbb754035f83fb7d8d44329c383701a2bd034f4fd1c19084", gitCommit: "a09d3e9162eb" },
+  { version: "1.81.8", sha256: "78cca92f36bc6c267c191d1fe1e2630c812bff6daec32c58cade75748c2692f6", gitCommit: "4fea649f519b" },
+  { version: "1.81.7", sha256: "58466c88c3289c6a3830d88768cf8f307581d9e6c87861de874d1128bb2de90d", gitCommit: "3f6a281d0f7a" },
+  { version: "1.81.6", sha256: "573206ba194d49a1691370ba33f781671609ac77c35347f8a0411d852cf6341a", gitCommit: "8da3a93e6e63" },
+  { version: "1.81.5", sha256: "206505c5a0c6503e465154b9c979772be3ede3f5bf746d15b37dca5ae54d239f", gitCommit: "2cc3778761d4" },
+  { version: "1.81.3", sha256: "3f60fd8b727587952ad3dd18b68f5fed538d6f43d15bb0356f4c3a11bccb2b92", gitCommit: "f30742fe6e8e" },
+]} />
+
+</TabItem>
+<TabItem value="docker" label="Docker Images">
+
+<VersionVerificationTable entries={[
+  { version: "1.82.3", sha256: "0a571da849db5f9c3cf3fead2ffbf1df982eebff7e7b38b46dbec3f640dafdbb", gitCommit: "61409275c8d8" },
+  { version: "1.82.3-stable", sha256: "0c2b2a0ad3e50af1702fc493ecd07f22a5180b6d1cfb169440b429b40e340e29", gitCommit: "61409275c8d8" },
+  { version: "1.82.0-stable", sha256: "71bf7283767ca436edcfa9f1f26c1743487b5fa29736c61c3eb6977776007c42", gitCommit: "97947c254252" },
+  { version: "1.81.15", sha256: "303c31af87e7915e7b34d6c4d55a6ac753ef947a5deaa899e9ccfd3d1d58f7c2", gitCommit: "20bf3aa8070a" },
+  { version: "1.81.14-stable", sha256: "a34f9758048231817d799b703fb998e40e2a5cbabb89ab95039fc30798f01b3c", gitCommit: "0435375b1271" },
+  { version: "1.81.13", sha256: "a876f3f22f9b6fd481c9091c44a8a893d81c172d66dc2749298dcd3dc4a3d6f0", gitCommit: "cc957a19a560" },
+  { version: "1.81.12-stable", sha256: "e24022878ccc87f57d808ac9304f18b87b8359e6556746d81cc20a5dc85f423a", gitCommit: "ba0d541b1982" },
+  { version: "1.81.9-stable", sha256: "262e53d7702ed82579717faff0b08f7c0b7e9973a6406cfcc0e4af7826327627", gitCommit: "a09d3e9162eb" },
+  { version: "1.81.3-stable", sha256: "dff82ccc32fb648927c090607887401c7e8ec814fe7c951beb95fe51073ca02b", gitCommit: "61ed8f9e0355" },
+  { version: "1.81.0-stable", sha256: "f4913297d1bb3dc373eb8911a5ac816b597be9b5e08a91636b6c2786dd572aa8", gitCommit: "790a5ce0b323" },
+  { version: "1.80.15-stable", sha256: "0b4ec3861e978b4aa254f4070f292cd345496a5fb59c72e1ee21cd6db94b670b", gitCommit: "17c8d8d109b5" },
+  { version: "1.80.11-stable", sha256: "4068108d9101cd2affba3924310fd7f34f23d14e36dd4853733898b9e04d81ca", gitCommit: "57e07bddd341" },
+  { version: "1.80.8-stable", sha256: "0304c2eb1f3cf54262d1b4e0629487232bab459e95b99a21e5810231d2b27021", gitCommit: "3381d63152f8" },
+  { version: "1.80.5-stable", sha256: "a89e173135fff96af4b5b91ea31845164eadcf6497c82adeb64c36a23c8a3d11", gitCommit: "6c49b95a4ab7" },
+  { version: "1.80.0-stable", sha256: "a3416f4cd0c896c94a1f526d872ff6c19bee22ff4afcdcc6f9ff690707900176", gitCommit: "98365205acd0" },
+  { version: "1.79.3-stable", sha256: "27aae83d6ab6cb0b63bf8179e375ce0e11f5cfef51f2675b0c1e60c6f546dbc1", gitCommit: "c0548542d4a9" },
+  { version: "1.79.1-stable", sha256: "7780d29a9543c4ce762430db7dfb0640105f7357fc38e35bf3fb7bbb1e6ba63f", gitCommit: "c217bddb59ba" },
+  { version: "1.79.0-stable", sha256: "32bf6ac059a56641e11e4712f63b8467c295f988b6c160dc7229660417ee44bd", gitCommit: "8d495f56a9cc" },
+  { version: "1.78.5-stable", sha256: "d5e607648eafa15edc63b0b1a5ed01f8b31a1fa0c80f7d25b252ae18a593ee29", gitCommit: "c471bf1f16c2" },
+  { version: "1.78.0-stable", sha256: "7a56b32dc7153763d31c0a056123dc878a598959935d8c7daacb1fca5272c205", gitCommit: "5fde83d9f154" },
+]} />
+
+</TabItem>
+</Tabs>
+
+
+## Questions and support
+
+If you believe your systems may be affected, contact us immediately:
+
+- **Security:** `security@berri.ai`
+- **Support:** `support@berri.ai`
+- **Slack:** Reach out to the LiteLLM team directly
+
+For real-time updates, follow [LiteLLM (YC W23) on X](https://x.com/LiteLLM).
+
diff --git a/docs/my-website/docs/anthropic_unified/index.md b/docs/my-website/docs/anthropic_unified/index.md
index 9981547ce1f..f8a50e14da5 100644
--- a/docs/my-website/docs/anthropic_unified/index.md
+++ b/docs/my-website/docs/anthropic_unified/index.md
@@ -506,12 +506,15 @@ Request body will be in the Anthropic messages API format. **litellm follows the
   A system prompt providing context or specific instructions to the model.
 - **temperature** (number):  
   Controls randomness in the model's responses. Valid range: `0 < temperature < 1`.
-- **thinking** (object):  
+- **thinking** (object):
   Configuration for enabling extended thinking. If enabled, it includes:
-  - **budget_tokens** (integer):  
+  - **budget_tokens** (integer):
     Minimum of 1024 tokens (and less than `max_tokens`).
-  - **type** (enum):  
+  - **type** (enum):
     E.g., `"enabled"`.
+  - **summary** (string, optional):
+    Enables the summary style for thinking blocks. Possible values: `"auto"`, `"concise"`, `"detailed"`, `"disabled"`.
+    When routing to non-Anthropic providers (e.g., `openai/gpt-5.1`), the `summary` value is preserved and forwarded to the downstream API.
 - **tool_choice** (object):  
   Instructs how the model should utilize any provided tools.
 - **tools** (array of objects):  
diff --git a/docs/my-website/docs/data_security.md b/docs/my-website/docs/data_security.md
index 2c4b1247e2b..d93d17aa0de 100644
--- a/docs/my-website/docs/data_security.md
+++ b/docs/my-website/docs/data_security.md
@@ -128,8 +128,6 @@ We'll review all reports promptly. Note that we don't currently offer a bug boun
 
 Legal Entity Name: Berrie AI Incorporated
 
-Company Phone Number: 7708783106 
-
 Point of contact email address for security incidents: krrish@berri.ai
 
 Point of contact email address for general security-related questions: krrish@berri.ai 
diff --git a/docs/my-website/docs/debugging/local_debugging.md b/docs/my-website/docs/debugging/local_debugging.md
index 8a56d6c34a0..53daa4e366b 100644
--- a/docs/my-website/docs/debugging/local_debugging.md
+++ b/docs/my-website/docs/debugging/local_debugging.md
@@ -67,6 +67,6 @@ response = completion("command-nightly", messages, logger_fn=my_custom_logging_f
 
 ## Still Seeing Issues? 
 
-Text us @ +17708783106 or Join the [Discord](https://discord.com/invite/wuPM9dRgDw). 
+Join the [Discord](https://discord.com/invite/wuPM9dRgDw). 
 
 We promise to help you in `lite`ning speed ❤️
diff --git a/docs/my-website/docs/enterprise.md b/docs/my-website/docs/enterprise.md
index 6dccf7ff4e7..a3fc9e38b6e 100644
--- a/docs/my-website/docs/enterprise.md
+++ b/docs/my-website/docs/enterprise.md
@@ -4,7 +4,7 @@ import Image from '@theme/IdealImage';
 
 :::info
 - ✨ SSO is free for up to 5 users. After that, an enterprise license is required. [Get Started with Enterprise here](https://www.litellm.ai/enterprise)
-- Who is Enterprise for? Companies giving access to 100+ users **OR** 10+ AI use-cases. If you're not sure, [get in touch with us](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) to discuss your needs.
+- Who is Enterprise for? Companies giving access to 100+ users **OR** 10+ AI use-cases. If you're not sure, [get in touch with us](https://enterprise.litellm.ai/demo) to discuss your needs.
 :::
 
 For companies that need SSO, user management and professional support for LiteLLM Proxy
@@ -36,7 +36,7 @@ Manage Yourself - you can deploy our Docker Image or build a custom image from o
 
 ### What’s the cost of the Self-Managed Enterprise edition?
 
-Self-Managed Enterprise deployments require our team to understand your exact needs. [Get in touch with us to learn more](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+Self-Managed Enterprise deployments require our team to understand your exact needs. [Get in touch with us to learn more](https://enterprise.litellm.ai/demo)
 
 
 ### How does deployment with Enterprise License work? 
@@ -106,7 +106,7 @@ Professional Support can assist with LLM/Provider integrations, deployment, upgr
 
 Pricing is based on usage. We can figure out a price that works for your team, on the call. 
 
-[**Contact Us to learn more**](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[**Contact Us to learn more**](https://enterprise.litellm.ai/demo)
 
 
 
diff --git a/docs/my-website/docs/fine_tuning.md b/docs/my-website/docs/fine_tuning.md
index d0bd98a76f9..52e96f28688 100644
--- a/docs/my-website/docs/fine_tuning.md
+++ b/docs/my-website/docs/fine_tuning.md
@@ -6,7 +6,7 @@ import TabItem from '@theme/TabItem';
 
 :::info
 
-This is an Enterprise only endpoint [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+This is an Enterprise only endpoint [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/migration.md b/docs/my-website/docs/migration.md
index e1af07d4684..fda1155905d 100644
--- a/docs/my-website/docs/migration.md
+++ b/docs/my-website/docs/migration.md
@@ -31,5 +31,4 @@ When we have breaking changes (i.e. going from 1.x.x to 2.x.x), we will document
 **How can we communicate changes better?**
 Tell us
 - [Discord](https://discord.com/invite/wuPM9dRgDw)
-- Email (krrish@berri.ai/ishaan@berri.ai)
-- Text us (+17708783106)
+- Email (support@berri.ai)
diff --git a/docs/my-website/docs/observability/arize_integration.md b/docs/my-website/docs/observability/arize_integration.md
index b3ccf98ea3b..4486fb2b718 100644
--- a/docs/my-website/docs/observability/arize_integration.md
+++ b/docs/my-website/docs/observability/arize_integration.md
@@ -194,5 +194,4 @@ print(response)
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/gcs_bucket_integration.md b/docs/my-website/docs/observability/gcs_bucket_integration.md
index 69b956950e5..5f8d42508ae 100644
--- a/docs/my-website/docs/observability/gcs_bucket_integration.md
+++ b/docs/my-website/docs/observability/gcs_bucket_integration.md
@@ -6,7 +6,7 @@ Log LLM Logs to [Google Cloud Storage Buckets](https://cloud.google.com/storage?
 
 :::info
 
-✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
@@ -79,5 +79,4 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/langfuse_integration.md b/docs/my-website/docs/observability/langfuse_integration.md
index d3c5a44d481..32849ebdb92 100644
--- a/docs/my-website/docs/observability/langfuse_integration.md
+++ b/docs/my-website/docs/observability/langfuse_integration.md
@@ -342,5 +342,4 @@ Be aware that if you are continuing an existing trace, and you set `update_trace
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/langsmith_integration.md b/docs/my-website/docs/observability/langsmith_integration.md
index cada4122b20..bf867319de8 100644
--- a/docs/my-website/docs/observability/langsmith_integration.md
+++ b/docs/my-website/docs/observability/langsmith_integration.md
@@ -225,5 +225,4 @@ environment_variables:
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/logfire_integration.md b/docs/my-website/docs/observability/logfire_integration.md
index a1bd43a4bc4..00652c0f1a1 100644
--- a/docs/my-website/docs/observability/logfire_integration.md
+++ b/docs/my-website/docs/observability/logfire_integration.md
@@ -63,5 +63,4 @@ response = litellm.completion(
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/lunary_integration.md b/docs/my-website/docs/observability/lunary_integration.md
index 8d28321c807..4f1dca9d4af 100644
--- a/docs/my-website/docs/observability/lunary_integration.md
+++ b/docs/my-website/docs/observability/lunary_integration.md
@@ -176,5 +176,4 @@ You can find more details about the different ways of making requests to the Lit
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/opik_integration.md b/docs/my-website/docs/observability/opik_integration.md
index d28c46f0b4b..5b5cbe0f185 100644
--- a/docs/my-website/docs/observability/opik_integration.md
+++ b/docs/my-website/docs/observability/opik_integration.md
@@ -261,5 +261,4 @@ All requests made with this key will automatically be tracked in the "TestProjec
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/phoenix_integration.md b/docs/my-website/docs/observability/phoenix_integration.md
index 191f1f8044a..67ba815557e 100644
--- a/docs/my-website/docs/observability/phoenix_integration.md
+++ b/docs/my-website/docs/observability/phoenix_integration.md
@@ -127,5 +127,4 @@ Depending on which Phoenix Cloud version or deployment you are using, you should
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/promptlayer_integration.md b/docs/my-website/docs/observability/promptlayer_integration.md
index 7f62a316972..9462e755f74 100644
--- a/docs/my-website/docs/observability/promptlayer_integration.md
+++ b/docs/my-website/docs/observability/promptlayer_integration.md
@@ -84,5 +84,4 @@ Credits to [Nick Bradford](https://github.com/nsbradford), from [Vim-GPT](https:
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
\ No newline at end of file
diff --git a/docs/my-website/docs/observability/slack_integration.md b/docs/my-website/docs/observability/slack_integration.md
index 0ca7f616683..468d8b5945c 100644
--- a/docs/my-website/docs/observability/slack_integration.md
+++ b/docs/my-website/docs/observability/slack_integration.md
@@ -101,5 +101,4 @@ response = litellm.completion(
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/sumologic_integration.md b/docs/my-website/docs/observability/sumologic_integration.md
index c30ee94dad4..87e20ca57ed 100644
--- a/docs/my-website/docs/observability/sumologic_integration.md
+++ b/docs/my-website/docs/observability/sumologic_integration.md
@@ -328,5 +328,4 @@ If you get authentication errors, regenerate the HTTP Source URL in Sumo Logic:
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/supabase_integration.md b/docs/my-website/docs/observability/supabase_integration.md
index fd3f1c3d5a0..c29871d752f 100644
--- a/docs/my-website/docs/observability/supabase_integration.md
+++ b/docs/my-website/docs/observability/supabase_integration.md
@@ -105,5 +105,4 @@ litellm.modify_integration("supabase",{"table_name": "litellm_logs"})
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/observability/wandb_integration.md b/docs/my-website/docs/observability/wandb_integration.md
index 37057f43db5..3c1a3363957 100644
--- a/docs/my-website/docs/observability/wandb_integration.md
+++ b/docs/my-website/docs/observability/wandb_integration.md
@@ -57,5 +57,4 @@ response = litellm.completion(
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
\ No newline at end of file
diff --git a/docs/my-website/docs/prompt_management.md b/docs/my-website/docs/prompt_management.md
new file mode 100644
index 00000000000..c4e606674b1
--- /dev/null
+++ b/docs/my-website/docs/prompt_management.md
@@ -0,0 +1,48 @@
+---
+title: Prompt Management with Responses API
+---
+
+# Prompt Management with Responses API
+
+Use LiteLLM Prompt Management with `/v1/responses` by passing `prompt_id` and optional `prompt_variables`.
+
+## Basic Usage
+
+```bash
+curl -X POST "http://localhost:4000/v1/responses" \
+  -H "Authorization: Bearer sk-1234" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gpt-4o",
+    "prompt_id": "my-responses-prompt",
+    "prompt_variables": {"topic": "large language models"},
+    "input": []
+  }'
+```
+
+## Multi-turn Follow-up in `input`
+
+To send follow-up turns in one request, pass message history in `input`.
+
+```bash
+curl -X POST "http://localhost:4000/v1/responses" \
+  -H "Authorization: Bearer sk-1234" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gpt-4o",
+    "prompt_id": "my-responses-prompt",
+    "prompt_variables": {"topic": "large language models"},
+    "input": [
+      {"role": "user", "content": "Topic is LLMs. Start short."},
+      {"role": "assistant", "content": "Sure, go ahead."},
+      {"role": "user", "content": "Now give me 3 bullets and include pricing caveat."}
+    ]
+  }'
+```
+
+## Notes
+
+- Prompt template messages are merged with your `input` messages.
+- Prompt variable substitution applies to prompt message content.
+- Tool call payload fields are not substituted by prompt variables.
+- For follow-ups with `previous_response_id`, include `prompt_id` again if you want prompt management applied on that turn.
diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md
index c8c9114ea87..87ab5ad40f4 100644
--- a/docs/my-website/docs/providers/gemini.md
+++ b/docs/my-website/docs/providers/gemini.md
@@ -11,6 +11,7 @@ import TabItem from '@theme/TabItem';
 | Provider Doc | [Google AI Studio ↗](https://aistudio.google.com/) |
 | API Endpoint for Provider | https://generativelanguage.googleapis.com |
 | Supported OpenAI Endpoints | `/chat/completions`, [`/embeddings`](../embedding/supported_embedding#gemini-ai-embedding-models), `/completions`, [`/videos`](./gemini/videos.md), [`/images/edits`](../image_edits.md) |
+| Lyria (music) | [Cost map & notes](./gemini/music.md) |
 | Pass-through Endpoint | [Supported](../pass_through/google_ai_studio.md) |
 
 <br />
diff --git a/docs/my-website/docs/providers/gemini/music.md b/docs/my-website/docs/providers/gemini/music.md
new file mode 100644
index 00000000000..f3968f2db39
--- /dev/null
+++ b/docs/my-website/docs/providers/gemini/music.md
@@ -0,0 +1,28 @@
+# Gemini — Lyria (music generation)
+
+Google Lyria 3 preview models are listed in LiteLLM’s [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) under the `gemini/` provider for metadata and spend tracking.
+
+| Property | Details |
+|----------|---------|
+| Provider route | `gemini/` |
+| Models | `gemini/lyria-3-clip-preview`, `gemini/lyria-3-pro-preview` |
+| Provider docs | [Gemini API pricing / models ↗](https://ai.google.dev/gemini-api/docs/pricing) |
+
+## Models
+
+| Model | Notes |
+|-------|--------|
+| `gemini/lyria-3-clip-preview` | ~30s clip; paid tier listed as per generated song in Google’s pricing |
+| `gemini/lyria-3-pro-preview` | Full song; paid tier listed as per generated song in Google’s pricing |
+
+Input context limit in the cost map: **131,072** tokens. For modalities, limits, and features, see [Google’s Gemini API docs ↗](https://ai.google.dev/gemini-api/docs/models).
+
+## LiteLLM behavior
+
+- **Cost map**: Per-song paid pricing is stored as `output_cost_per_image` on those entries (flat per generation unit). Token-based completion cost may not reflect music billing until a dedicated path exists.
+- **API calls**: Use the Gemini API as documented by Google. LiteLLM does not ship a separate `music_generation` helper like Veo’s `video_generation`.
+
+## Auth
+
+Same as other Gemini API models: `GEMINI_API_KEY` or `GOOGLE_API_KEY`.
+
diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md
index 2907cdf9f47..1f4a1687e8b 100644
--- a/docs/my-website/docs/providers/openai.md
+++ b/docs/my-website/docs/providers/openai.md
@@ -581,6 +581,90 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 
 See [OpenAI Reasoning documentation](https://platform.openai.com/docs/guides/reasoning) for more details on organization verification requirements.
 
+### Multi-turn Conversations with `reasoning_items`
+
+For multi-turn conversations you need `reasoning_items`: structured blocks that include the `encrypted_content` token OpenAI uses to restore reasoning state on the next request. Pass `include=["reasoning.encrypted_content"]` on every call where you want that token returned.
+
+<Tabs>
+<TabItem value="non-streaming" label="Non-Streaming">
+
+```python showLineNumbers title="Non-streaming: round-trip reasoning_items"
+import litellm
+
+messages = [{"role": "user", "content": "Solve this step by step: 2 + 2"}]
+
+# Turn 1 — get reasoning_items (encrypted_content);
+response = litellm.completion(
+    model="openai/responses/gpt-5-mini",
+    messages=messages,
+    reasoning_effort="low",
+    include=["reasoning.encrypted_content"],
+)
+
+assistant_msg = response.choices[0].message
+
+# Turn 2 — pass reasoning_items back; LiteLLM converts to the correct Responses API format
+messages.append({
+    "role": "assistant",
+    "content": assistant_msg.content,
+    "reasoning_items": assistant_msg.reasoning_items,
+})
+messages.append({"role": "user", "content": "Now summarize your reasoning."})
+
+response2 = litellm.completion(
+    model="openai/responses/gpt-5-mini",
+    messages=messages,
+    reasoning_effort="low",
+    include=["reasoning.encrypted_content"],
+)
+```
+
+</TabItem>
+<TabItem value="streaming" label="Streaming">
+
+`reasoning_items` (with `encrypted_content`) arrive on the final chunk when the full response completes:
+
+```python showLineNumbers title="Streaming: collect and round-trip reasoning_items"
+import litellm
+
+messages = [{"role": "user", "content": "Solve this step by step: 2 + 2"}]
+
+collected_content = []
+collected_reasoning_items = []
+
+stream = litellm.completion(
+    model="openai/responses/gpt-5-mini",
+    messages=messages,
+    stream=True,
+    reasoning_effort="low",
+    include=["reasoning.encrypted_content"],
+)
+
+for chunk in stream:
+    delta = chunk.choices[0].delta
+    if delta.content:
+        collected_content.append(delta.content)
+    if getattr(delta, "reasoning_items", None):
+        collected_reasoning_items.extend(delta.reasoning_items)
+
+messages.append({
+    "role": "assistant",
+    "content": "".join(collected_content),
+    "reasoning_items": collected_reasoning_items or None,
+})
+messages.append({"role": "user", "content": "Continue the conversation."})
+
+response2 = litellm.completion(
+    model="openai/responses/gpt-5-mini",
+    messages=messages,
+    reasoning_effort="low",
+    include=["reasoning.encrypted_content"],
+)
+```
+
+</TabItem>
+</Tabs>
+
 ### Verbosity Control for GPT-5 Models
 
 The `verbosity` parameter controls the length and detail of responses from GPT-5 family models. It accepts three values: `"low"`, `"medium"`, or `"high"`.
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index 16752b030b9..dce979ab89f 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -206,7 +206,7 @@ router_settings:
 
 | Name | Type | Description |
 |------|------|-------------|
-| completion_model | string | The default model to use for completions when `model` is not specified in the request |
+| completion_model | string | The model to use for all completions, overriding any `model` specified in the request |
 | disable_spend_logs | boolean | If true, turns off writing each transaction to the database |
 | disable_spend_updates | boolean | If true, turns off all spend updates to the DB. Including key/user/team spend updates. |
 | disable_master_key_return | boolean | If true, turns off returning master key on UI. (checked on '/user/info' endpoint) |
@@ -361,8 +361,9 @@ router_settings:
 | redis_url | str | URL for Redis server. **Known performance issue with Redis URL.** |
 | cache_responses | boolean | Flag to enable caching LLM Responses, if cache set under `router_settings`. If true, caches responses. Defaults to False. |
 | router_general_settings | RouterGeneralSettings | [SDK-Only] Router general settings - contains optimizations like 'async_only_mode'. [Docs](../routing.md#router-general-settings) |
-| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Supported: `router_budget_limiting`, `prompt_caching`, `responses_api_deployment_check`, `encrypted_content_affinity`, `deployment_affinity`, `session_affinity`, `forward_client_headers_by_model_group` |
+| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Supported: `router_budget_limiting`, `prompt_caching`, `responses_api_deployment_check`, `encrypted_content_affinity` (requires LiteLLM >= 1.82.3), `deployment_affinity`, `session_affinity`, `forward_client_headers_by_model_group` |
 | deployment_affinity_ttl_seconds | int | TTL (seconds) for user-key → deployment affinity mapping when `deployment_affinity` is enabled (configured at Router init / proxy startup). Defaults to `3600` (1 hour). |
+| model_group_affinity_config | Dict[str, List[str]] | Per-model-group affinity flags. Keys are model group names; values are lists of checks to enable (`deployment_affinity`, `responses_api_deployment_check`, `session_affinity`). Groups not listed fall back to the global `optional_pre_call_checks`. [Docs](../response_api.md#per-model-group-affinity-configuration) |
 | ignore_invalid_deployments | boolean | If true, ignores invalid deployments. Default for proxy is True - to prevent invalid models from blocking other models from being loaded. |
 | search_tools | List[SearchToolTypedDict] | List of search tool configurations for Search API integration. Each tool specifies a search_tool_name and litellm_params with search_provider, api_key, api_base, etc. [Further Docs](../search/index.md) |
 | guardrail_list | List[GuardrailTypedDict] | List of guardrail configurations for guardrail load balancing. Enables load balancing across multiple guardrail deployments with the same guardrail_name. [Further Docs](./guardrails/guardrail_load_balancing.md) |
@@ -803,6 +804,7 @@ router_settings:
 | LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS | Optionally enable semantic logs for OTEL
 | LITELLM_OTEL_INTEGRATION_ENABLE_METRICS | Optionally enable emantic metrics for OTEL
 | LITELLM_ENABLE_PYROSCOPE | If true, enables Pyroscope CPU profiling. Profiles are sent to PYROSCOPE_SERVER_ADDRESS. Off by default. See [Pyroscope profiling](/proxy/pyroscope_profiling).
+| LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS | When `true`, if a team's legacy `model_aliases` entry maps a public model name to an internal `model_name_<team_id>_<uuid>` deployment, pre-call handling can skip that rewrite when team-scoped sibling deployments exist for the public name—so load balancing / `order` apply across siblings. Default is `false` for backwards compatibility. See [Team-scoped models and legacy aliases](./load_balancing#team-scoped-models-and-legacy-model_aliases). When stale aliases are detected and this flag is off, the proxy may log a one-time warning.
 | PYROSCOPE_APP_NAME | Application name reported to Pyroscope. Required when LITELLM_ENABLE_PYROSCOPE is true. No default.
 | PYROSCOPE_SERVER_ADDRESS | Pyroscope server URL to send profiles to. Required when LITELLM_ENABLE_PYROSCOPE is true. No default.
 | PYROSCOPE_SAMPLE_RATE | Optional. Sample rate for Pyroscope profiling (integer). No default; when unset, the pyroscope-io library default is used.
@@ -813,7 +815,7 @@ router_settings:
 | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development)
 | LITELLM_NON_ROOT | Flag to run LiteLLM in non-root mode for enhanced security in Docker containers
 | LITELLM_RATE_LIMIT_WINDOW_SIZE | Rate limit window size for LiteLLM. Default is 60
-| LITELLM_REASONING_AUTO_SUMMARY | If set to "true", automatically enables detailed reasoning summaries for reasoning models (e.g., o1, o3-mini, deepseek-reasoner). When enabled, adds `summary: "detailed"` to reasoning effort configurations. Default is "false"
+| LITELLM_REASONING_AUTO_SUMMARY | If set to "true", automatically enables detailed reasoning summaries (`summary: "detailed"`) for reasoning models across all translation paths (Anthropic adapter, Responses API, etc.). Default is "false"
 | LITELLM_SALT_KEY | Salt key for encryption in LiteLLM
 | LITELLM_SSL_CIPHERS | SSL/TLS cipher configuration for faster handshakes. Controls cipher suite preferences for OpenSSL connections.
 | LITELLM_SECRET_AWS_KMS_LITELLM_LICENSE | AWS KMS encrypted license for LiteLLM
diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md
index f28eec287d4..f9e22cfecd3 100644
--- a/docs/my-website/docs/proxy/cost_tracking.md
+++ b/docs/my-website/docs/proxy/cost_tracking.md
@@ -163,7 +163,7 @@ Use this when you want non-proxy admins to access `/spend` endpoints
 
 :::info
 
-Schedule a [meeting with us to get your Enterprise License](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+Schedule a [meeting with us to get your Enterprise License](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/email.md b/docs/my-website/docs/proxy/email.md
index 86a79cbcfc8..ba737c6782c 100644
--- a/docs/my-website/docs/proxy/email.md
+++ b/docs/my-website/docs/proxy/email.md
@@ -203,7 +203,7 @@ After regenerating the key, the user will receive an email notification with:
 
 :::info
 
-Customizing Email Branding is an Enterprise Feature [Get in touch with us for a Free Trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+Customizing Email Branding is an Enterprise Feature [Get in touch with us for a Free Trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/enterprise.md b/docs/my-website/docs/proxy/enterprise.md
index 4b525837a20..09b103ca4a0 100644
--- a/docs/my-website/docs/proxy/enterprise.md
+++ b/docs/my-website/docs/proxy/enterprise.md
@@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem';
 # ✨ Enterprise Features
 :::tip
 
-To get a license, get in touch with us [here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+To get a license, get in touch with us [here](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/guardrails/akto.md b/docs/my-website/docs/proxy/guardrails/akto.md
new file mode 100644
index 00000000000..67ae741d11e
--- /dev/null
+++ b/docs/my-website/docs/proxy/guardrails/akto.md
@@ -0,0 +1,139 @@
+# Akto
+
+## Overview
+[Akto](https://www.akto.io/) provides API security guardrails and data ingestion for LLM traffic.
+
+Akto now uses a **two-entry guardrail pattern** in LiteLLM:
+- `akto-validate` (`pre_call`) for request validation
+- `akto-ingest` (`post_call`) for request/response ingestion
+
+There is no `on_flagged` setting anymore.
+
+Use these as two separate guardrails in `config.yaml`:
+- `guardrail_name: "akto-validate"`
+- `guardrail_name: "akto-ingest"`
+
+## 1. Get Your Akto Credentials
+
+Set up the Akto Guardrail API Service and grab:
+- `AKTO_GUARDRAIL_API_BASE` — your Guardrail API Base URL
+- `AKTO_API_KEY` — your API key
+
+## 2. Configure in `config.yaml`
+
+### Block + Ingest (recommended)
+
+Use both entries below. This gives you:
+- pre-call block decision
+- post-call ingestion for allowed traffic
+
+Keep these as two separate entries (`akto-validate` and `akto-ingest`).
+
+```yaml
+guardrails:
+  - guardrail_name: "akto-validate"
+    litellm_params:
+      guardrail: akto
+      mode: pre_call
+      akto_base_url: os.environ/AKTO_GUARDRAIL_API_BASE
+      akto_api_key: os.environ/AKTO_API_KEY
+      default_on: true
+      unreachable_fallback: fail_closed   # optional: fail_open | fail_closed (default: fail_closed)
+      guardrail_timeout: 5                # optional, default: 5
+      akto_account_id: "1000000"         # optional, env fallback: AKTO_ACCOUNT_ID
+      akto_vxlan_id: "0"                 # optional, env fallback: AKTO_VXLAN_ID
+
+  - guardrail_name: "akto-ingest"
+    litellm_params:
+      guardrail: akto
+      mode: post_call
+      akto_base_url: os.environ/AKTO_GUARDRAIL_API_BASE
+      akto_api_key: os.environ/AKTO_API_KEY
+      default_on: true
+```
+
+### Monitor-only mode
+
+If you only want logging/ingestion and no blocking, keep only `akto-ingest`.
+
+```yaml
+guardrails:
+  - guardrail_name: "akto-ingest"
+    litellm_params:
+      guardrail: akto
+      mode: post_call
+      akto_base_url: os.environ/AKTO_GUARDRAIL_API_BASE
+      akto_api_key: os.environ/AKTO_API_KEY
+      default_on: true
+```
+
+## 3. Test It
+
+```shell
+curl -i http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer <your litellm key>" \
+  -d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+      {"role": "user", "content": "Hello, how are you?"}
+    ]
+  }'
+```
+
+If a request gets blocked:
+
+```json
+{
+  "error": {
+    "message": "Prompt injection detected",
+    "type": "None",
+    "param": "None",
+    "code": "403"
+  }
+}
+```
+
+## 4. How It Works
+
+**Block + Ingest mode:**
+```
+Request → LiteLLM → Akto guardrail check
+  → Allowed  → forward to LLM → ingest response
+  → Blocked  → ingest blocked marker → 403 error
+```
+
+**Monitor-only mode:**
+```
+Request → LiteLLM → forward to LLM → get response
+  → Send to Akto (guardrails + ingest) → log only
+```
+
+## 5. Event behavior
+
+| Entry | LiteLLM hook | Akto call behavior |
+|------|---|---|
+| `akto-validate` | `pre_call` | Awaited call with `guardrails=true`, `ingest_data=false` |
+| `akto-ingest` | `post_call` | Fire-and-forget call with `guardrails=true`, `ingest_data=true` |
+
+When blocked in `pre_call`, LiteLLM sends one fire-and-forget ingest payload with blocked metadata and returns `403`.
+
+## 6. Parameters
+
+| Parameter | Env Variable | Default | Description |
+|-----------|-------------|---------|-------------|
+| `akto_base_url` | `AKTO_GUARDRAIL_API_BASE` | *required* | Akto Guardrail API Base URL |
+| `akto_api_key` | `AKTO_API_KEY` | *required* | API key (sent as `Authorization` header) |
+| `akto_account_id` | `AKTO_ACCOUNT_ID` | `1000000` | Akto account id included in payload |
+| `akto_vxlan_id` | `AKTO_VXLAN_ID` | `0` | Akto vxlan id included in payload |
+| `unreachable_fallback` | — | `fail_closed` | `fail_open` or `fail_closed` |
+| `guardrail_timeout` | — | `5` | Timeout in seconds |
+| `default_on` | — | `true` (recommended) | Enables the guardrail entry by default |
+
+## 7. Error Handling
+
+| Scenario | `fail_closed` (default) | `fail_open` |
+|----------|------------------------|-------------|
+| Akto unreachable | ❌ Blocked (503) | ✅ Passes through |
+| Akto returns error | ❌ Blocked (503) | ✅ Passes through |
+| Guardrail says no | ❌ Blocked (403) | ❌ Blocked (403) |
diff --git a/docs/my-website/docs/proxy/guardrails/aporia_api.md b/docs/my-website/docs/proxy/guardrails/aporia_api.md
index ceafc19a1cc..e6ff0d5fed3 100644
--- a/docs/my-website/docs/proxy/guardrails/aporia_api.md
+++ b/docs/my-website/docs/proxy/guardrails/aporia_api.md
@@ -139,7 +139,7 @@ curl -i http://localhost:4000/v1/chat/completions \
 
 :::info
 
-✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Contact us to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/guardrails/custom_guardrail.md b/docs/my-website/docs/proxy/guardrails/custom_guardrail.md
index 638cae9c835..37579ad870d 100644
--- a/docs/my-website/docs/proxy/guardrails/custom_guardrail.md
+++ b/docs/my-website/docs/proxy/guardrails/custom_guardrail.md
@@ -417,7 +417,7 @@ curl -i  -X POST http://localhost:4000/v1/chat/completions \
 
 :::info
 
-✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Contact us to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/guardrails/guardrails_ai.md b/docs/my-website/docs/proxy/guardrails/guardrails_ai.md
index 55d586aee7b..19ae34014a4 100644
--- a/docs/my-website/docs/proxy/guardrails/guardrails_ai.md
+++ b/docs/my-website/docs/proxy/guardrails/guardrails_ai.md
@@ -59,7 +59,7 @@ curl -i http://localhost:4000/v1/chat/completions \
 
 :::info
 
-✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Contact us to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md
index 2764a6f0d4f..530bea3d06b 100644
--- a/docs/my-website/docs/proxy/health.md
+++ b/docs/my-website/docs/proxy/health.md
@@ -314,6 +314,89 @@ general_settings:
   health_check_details: False
 ```
 
+## Health Check Driven Routing
+
+By default, background health checks are observability-only — they populate the `/health` endpoint but don't affect routing. Unhealthy deployments still receive traffic until request failures trigger cooldown.
+
+With `enable_health_check_routing: true`, the router **excludes deployments that failed their last background health check** before selecting a candidate. This gives you proactive failover instead of reactive cooldown.
+
+### How it works
+
+1. Background health checks run on their configured interval
+2. After each cycle, every deployment is marked healthy or unhealthy
+3. On each incoming request, the router filters out unhealthy deployments **before** cooldown filtering and load balancing
+4. If all deployments are unhealthy, the filter is bypassed (safety net — never causes a total outage)
+5. If health state is stale (older than `health_check_staleness_threshold`), it is ignored
+
+### Quick start
+
+```yaml
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: openai/gpt-4
+      api_key: os.environ/OPENAI_API_KEY
+  - model_name: gpt-4
+    litellm_params:
+      model: openai/gpt-4
+      api_key: os.environ/OPENAI_API_KEY_SECONDARY
+
+general_settings:
+  background_health_checks: true
+  health_check_interval: 60
+  enable_health_check_routing: true
+```
+
+### Configuration
+
+| Setting | Where | Default | Description |
+|---------|-------|---------|-------------|
+| `enable_health_check_routing` | `general_settings` | `false` | Enable/disable health-check-driven routing |
+| `health_check_staleness_threshold` | `general_settings` | `health_check_interval * 2` | Seconds before health state is considered stale and ignored |
+| `background_health_checks` | `general_settings` | `false` | Must be `true` for health check routing to work |
+| `health_check_interval` | `general_settings` | `300` | Seconds between health check cycles |
+
+### Interaction with cooldown
+
+Health check filtering and cooldown are **additive**. A deployment can be excluded by either mechanism:
+
+- **Health check filter** — proactive, runs on the configured interval, excludes deployments that failed the last check
+- **Cooldown** — reactive, triggered by request failures, excludes deployments for a short TTL
+
+This means request failures still provide fast detection between health check intervals.
+
+### Staleness
+
+If a health check result is older than `health_check_staleness_threshold`, it is ignored and the deployment is treated as eligible. This prevents stale data from permanently excluding a deployment if the health check loop stops or slows down.
+
+The default staleness threshold is `health_check_interval * 2`. For a 60s interval, health state expires after 120s.
+
+### Example: custom staleness
+
+```yaml
+general_settings:
+  background_health_checks: true
+  health_check_interval: 30
+  enable_health_check_routing: true
+  health_check_staleness_threshold: 90  # ignore health state older than 90s
+```
+
+### Debugging
+
+Run the proxy with `--detailed_debug` and look for:
+
+```
+health_check_routing_state_updated healthy=3 unhealthy=1
+```
+
+This is logged after each health check cycle when routing state is written.
+
+If the safety net triggers (all deployments unhealthy), you'll see:
+
+```
+All deployments marked unhealthy by health checks, bypassing health filter
+```
+
 ## Health Check Timeout
 
 The health check timeout is set in `litellm/constants.py` and defaults to 60 seconds.
diff --git a/docs/my-website/docs/proxy/high_availability_control_plane.md b/docs/my-website/docs/proxy/high_availability_control_plane.md
new file mode 100644
index 00000000000..324fba3a180
--- /dev/null
+++ b/docs/my-website/docs/proxy/high_availability_control_plane.md
@@ -0,0 +1,190 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import { ControlPlaneArchitecture } from '@site/src/components/ControlPlaneArchitecture';
+
+# [BETA] High Availability Control Plane
+
+Deploy a single LiteLLM UI that manages multiple independent LiteLLM proxy instances, each with its own database, Redis, and master key.
+
+:::info
+
+This is an Enterprise feature.
+
+[Enterprise Pricing](https://www.litellm.ai/#pricing)
+
+[Get free 7-day trial key](https://www.litellm.ai/enterprise#trial)
+
+:::
+
+## Why This Architecture?
+
+In the [standard multi-region setup](./control_plane_and_data_plane.md), all instances share a single database and master key. This works, but introduces a shared dependency. If the database goes down, every instance is affected.
+
+The **High Availability Control Plane** takes a different approach:
+
+| | Shared Database (Standard) | High Availability Control Plane |
+|---|---|---|
+| **Database** | Single shared DB for all instances | Each instance has its own DB |
+| **Redis** | Shared Redis | Each instance has its own Redis |
+| **Master Key** | Same key across all instances | Each instance has its own key |
+| **Failure isolation** | DB outage affects all instances | Failure is isolated to one instance |
+| **User management** | Centralized, one user table | Independent, each worker manages its own users |
+| **UI** | One UI per admin instance | Single control plane UI manages all workers |
+
+### Benefits
+
+- **True high availability**: no shared infrastructure means no single point of failure
+- **Blast radius containment**: a misconfiguration or outage on one worker doesn't affect others
+- **Regional isolation**: workers can run in different regions with data residency requirements
+- **Simpler operations**: each worker is a self-contained LiteLLM deployment
+
+## Architecture
+
+<ControlPlaneArchitecture />
+
+The **control plane** is a LiteLLM instance that serves the admin UI and knows about all the workers. It does not proxy LLM requests, it is purely for administration.
+
+Each **worker** is a fully independent LiteLLM proxy that handles LLM requests for its region or team. Workers have their own users, keys, teams, and budgets.
+
+## Setup
+
+### 1. Control Plane Configuration
+
+The control plane needs a `worker_registry` that lists all worker instances.
+
+```yaml title="cp_config.yaml"
+model_list: []
+
+general_settings:
+  master_key: sk-1234
+  database_url: os.environ/DATABASE_URL
+
+worker_registry:
+  - worker_id: "worker-a"
+    name: "Worker A"
+    url: "http://localhost:4001"
+  - worker_id: "worker-b"
+    name: "Worker B"
+    url: "http://localhost:4002"
+```
+
+Start the control plane:
+
+```bash
+litellm --config cp_config.yaml --port 4000
+```
+
+### 2. Worker Configuration
+
+Each worker needs `control_plane_url` in its `general_settings` to enable cross-origin authentication from the control plane UI.
+
+`PROXY_BASE_URL` must also be set for each worker so that SSO callback redirects resolve correctly.
+
+<Tabs>
+<TabItem value="worker-a" label="Worker A">
+
+```yaml title="worker_a_config.yaml"
+model_list: []
+
+general_settings:
+  master_key: sk-worker-a-1234
+  database_url: os.environ/WORKER_A_DATABASE_URL
+  control_plane_url: "http://localhost:4000"
+```
+
+```bash
+PROXY_BASE_URL=http://localhost:4001 litellm --config worker_a_config.yaml --port 4001
+```
+
+</TabItem>
+<TabItem value="worker-b" label="Worker B">
+
+```yaml title="worker_b_config.yaml"
+model_list: []
+
+general_settings:
+  master_key: sk-worker-b-1234
+  database_url: os.environ/WORKER_B_DATABASE_URL
+  control_plane_url: "http://localhost:4000"
+```
+
+```bash
+PROXY_BASE_URL=http://localhost:4002 litellm --config worker_b_config.yaml --port 4002
+```
+
+</TabItem>
+</Tabs>
+
+:::important
+Each worker must have its own `master_key` and `database_url`. The whole point of this architecture is that workers are independent.
+:::
+
+### 3. SSO Configuration (Optional)
+
+SSO is configured on the **control plane** instance the same way as a standard LiteLLM proxy. See the [SSO setup guide](./admin_ui_sso.md) for full instructions.
+
+If using SSO, make sure to register each worker URL and the control plane URL as allowed callback URLs in your SSO provider's dashboard.
+
+## How It Works
+
+### Login Flow
+
+1. User visits the control plane UI (`http://localhost:4000/ui`)
+2. The login page shows a **worker selector** dropdown listing all registered workers
+3. User selects a worker (e.g. "Worker A") and logs in with username/password or SSO
+4. The UI authenticates against the **selected worker** using the `/v3/login` endpoint
+5. On success, the UI stores the worker's JWT and points all subsequent API calls at the worker
+6. The user can now manage keys, teams, models, and budgets on that worker, all from the control plane UI
+
+### Switching Workers
+
+Once logged in, users can switch workers from the **navbar dropdown** without leaving the UI. Switching redirects back to the login page to authenticate against the new worker.
+
+### Discovery
+
+The control plane exposes a `/.well-known/litellm-ui-config` endpoint that the UI reads on load. This endpoint returns:
+- `is_control_plane: true`
+- The list of workers with their IDs, names, and URLs
+
+This is how the login page knows to show the worker selector.
+
+## Local Testing
+
+To try this out locally, start each instance in a separate terminal:
+
+```bash
+# Terminal 1: Control Plane
+litellm --config cp_config.yaml --port 4000
+
+# Terminal 2: Worker A
+PROXY_BASE_URL=http://localhost:4001 litellm --config worker_a_config.yaml --port 4001
+
+# Terminal 3: Worker B
+PROXY_BASE_URL=http://localhost:4002 litellm --config worker_b_config.yaml --port 4002
+```
+
+Then open `http://localhost:4000/ui`. You should see the worker selector on the login page.
+
+## Configuration Reference
+
+### Control Plane Settings
+
+| Field | Location | Description |
+|---|---|---|
+| `worker_registry` | Top-level config | List of worker instances |
+| `worker_registry[].worker_id` | Required | Unique identifier for the worker |
+| `worker_registry[].name` | Required | Display name shown in the UI |
+| `worker_registry[].url` | Required | Full URL of the worker instance |
+
+### Worker Settings
+
+| Field | Location | Description |
+|---|---|---|
+| `general_settings.control_plane_url` | Required | URL of the control plane instance. Enables `/v3/login` and `/v3/login/exchange` endpoints on this worker. |
+| `PROXY_BASE_URL` | Environment variable | The worker's own external URL. Required for SSO callback redirects. |
+
+## Related Documentation
+
+- [Standard Multi-Region Setup](./control_plane_and_data_plane.md) - shared-database architecture for admin/worker split
+- [SSO Setup](./admin_ui_sso.md) - configuring SSO for the admin UI
+- [Production Deployment](./prod.md) - production best practices
diff --git a/docs/my-website/docs/proxy/ip_address.md b/docs/my-website/docs/proxy/ip_address.md
index 8f042d9f183..4c469b81e0b 100644
--- a/docs/my-website/docs/proxy/ip_address.md
+++ b/docs/my-website/docs/proxy/ip_address.md
@@ -3,7 +3,7 @@
 
 :::info
 
-You need a LiteLLM License to unlock this feature. [Grab time](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions), to get one today!
+You need a LiteLLM License to unlock this feature. [Grab time](https://enterprise.litellm.ai/demo), to get one today!
 
 :::
 
diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md
index 5bf39d179f6..93f3d944340 100644
--- a/docs/my-website/docs/proxy/load_balancing.md
+++ b/docs/my-website/docs/proxy/load_balancing.md
@@ -324,17 +324,58 @@ model_list:
     litellm_params:
       model: azure/gpt-4-fallback
       api_key: os.environ/AZURE_API_KEY_2
-      order: 2  # 👈 Used when order=1 is unavailable
-
-router_settings:
-  enable_pre_call_checks: true  # 👈 Required for 'order' to work
+      order: 2  # 👈 Used when order=1 fails
 ```
 
-:::important
-The `order` parameter requires `enable_pre_call_checks: true` in `router_settings`.
-:::
+### How order-based fallback works
 
-If `order=1` deployment is unavailable (e.g., rate-limited), the router falls back to `order=2` deployments.
+When a request to an `order=1` deployment fails (connection error, 404, 429, etc.), the router automatically tries `order=2` deployments, then `order=3`, and so on. Each order level gets its own set of retries before escalating to the next.
+
+If all order levels are exhausted, the router falls through to any configured [model-level fallbacks](#fallbacks).
+
+```yaml
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: azure/gpt-4-primary
+      api_key: os.environ/AZURE_API_KEY
+      order: 1
+
+  - model_name: gpt-4
+    litellm_params:
+      model: azure/gpt-4-secondary
+      api_key: os.environ/AZURE_API_KEY_2
+      order: 2
+
+  - model_name: gpt-4-fallback
+    litellm_params:
+      model: openai/gpt-4
+      api_key: os.environ/OPENAI_API_KEY
+
+router_settings:
+  fallbacks:
+    - gpt-4:
+        - gpt-4-fallback  # tried after all order levels fail
+```
+
+The fallback chain for the above config: `order=1` → `order=2` → `gpt-4-fallback`.
+
+For 429 (rate limit) errors specifically, the failed deployment is immediately placed on cooldown. If all `order=1` deployments are on cooldown, the router picks `order=2` deployments directly during retries without waiting for the fallback path.
+
+### Team-scoped models and legacy `model_aliases` {#team-scoped-models-and-legacy-model_aliases}
+
+Team-scoped deployments are identified by `model_info.team_id` and `model_info.team_public_model_name`. Requests should use the **public** model name; the router resolves all sibling deployments (same public name, different `api_base` / `order`, etc.) for routing, failover, and deployment `order`.
+
+For router internals: when a `team_id` is in scope, optimized lookups key off `(team_id, team_public_model_name)`. If code passes an internal deployment id (e.g. `model_name_<team_id>_<uuid>`) instead of the public name, routing still works via the usual deployment-name paths, but the team-specific fast path applies only to the public name.
+
+**Legacy teams:** Older proxy versions could persist `model_aliases` on the team row mapping a public name to a single internal deployment id (`model_name_<team_id>_<uuid>`). On each request, pre-call logic may still rewrite `model` to that internal name **before** routing, which collapses to one deployment and can make newer sibling deployments unreachable.
+
+**Migration options:**
+
+1. **Recommended for upgrades:** Set environment variable `LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS=true` so that when sibling team deployments exist for the public name, the stale alias rewrite is skipped and team-scoped routing (including `order` and failover) applies. See the [Environment variables](./config_settings) table in the proxy settings doc.
+2. **Data cleanup:** Remove obsolete `model_aliases` entries for team public names from the team record in the database so only `team_public_model_name` + team model list drive access.
+
+If a stale alias is detected and the bypass is **not** enabled, the proxy may emit a **one-time** warning in logs explaining that sibling deployments may be unreachable until the flag is set or aliases are cleaned up.
 
 ### When You'll See Load Balancing in Action
 
@@ -352,7 +393,7 @@ If `order=1` deployment is unavailable (e.g., rate-limited), the router falls ba
 
 When load balancing OpenAI's Responses API across deployments with **different API keys** (e.g., different Azure regions or organizations), encrypted content items (like `rs_...` reasoning items) can only be decrypted by the originating API key.
 
-**Solution:** Use the `encrypted_content_affinity` pre-call check to automatically route follow-up requests containing encrypted items to the correct deployment:
+**Solution:** Use the `encrypted_content_affinity` pre-call check (requires LiteLLM >= 1.82.3) to automatically route follow-up requests containing encrypted items to the correct deployment:
 
 ```yaml
 model_list:
diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md
index 74a79776fbd..2f81498799a 100644
--- a/docs/my-website/docs/proxy/logging.md
+++ b/docs/my-website/docs/proxy/logging.md
@@ -1109,7 +1109,7 @@ Log LLM Logs to [Google Cloud Storage Buckets](https://cloud.google.com/storage?
 
 :::info
 
-✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
@@ -1194,7 +1194,7 @@ Log LLM Logs/SpendLogs to [Google Cloud Storage PubSub Topic](https://cloud.goog
 
 :::info
 
-✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
@@ -1497,7 +1497,7 @@ Log LLM Logs to [Azure Data Lake Storage](https://learn.microsoft.com/en-us/azur
 
 :::info
 
-✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/multiple_admins.md b/docs/my-website/docs/proxy/multiple_admins.md
index 8d39674df19..83d0c5863df 100644
--- a/docs/my-website/docs/proxy/multiple_admins.md
+++ b/docs/my-website/docs/proxy/multiple_admins.md
@@ -20,7 +20,7 @@ LiteLLM tracks changes to the following entities and actions:
 
 :::tip
 
-Requires Enterprise License, Get in touch with us [here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+Requires Enterprise License, Get in touch with us [here](https://enterprise.litellm.ai/demo)
 
 :::
 
@@ -56,6 +56,40 @@ On the LiteLLM UI, navigate to Logs -> Audit Logs. You should see the audit log
 />
 
 
+## Export Audit Logs to External Storage
+
+You can export audit logs to an external storage backend (e.g. S3) in addition to storing them in the database. Logs are batched and uploaded asynchronously, so they do not block your proxy requests.
+
+### S3 Example
+
+Add `audit_log_callbacks` and `s3_callback_params` to your `litellm_settings`:
+
+```yaml
+litellm_settings:
+  store_audit_logs: true
+  audit_log_callbacks: ["s3_v2"]
+  s3_callback_params:
+    s3_bucket_name: my-audit-logs-bucket     # AWS Bucket Name
+    s3_region_name: us-west-2                # AWS Region
+    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+    s3_path: litellm-audit                   # [OPTIONAL] prefix path in the bucket
+```
+
+Audit logs are written as JSON files to:
+
+```
+s3://<bucket>/audit_logs/<YYYY-MM-DD>/<HH-MM-SS>_<audit-log-id>.json
+# or, when s3_path is set:
+s3://<bucket>/<s3_path>/audit_logs/<YYYY-MM-DD>/<HH-MM-SS>_<audit-log-id>.json
+```
+
+:::info
+
+Both `store_audit_logs: true` and `audit_log_callbacks` must be set. If `store_audit_logs` is not enabled, the callbacks will not fire.
+
+:::
+
 ## Advanced
 
 ### Attribute Management changes to Users
diff --git a/docs/my-website/docs/proxy/oauth2.md b/docs/my-website/docs/proxy/oauth2.md
index 41c4110e447..204a01538cc 100644
--- a/docs/my-website/docs/proxy/oauth2.md
+++ b/docs/my-website/docs/proxy/oauth2.md
@@ -4,7 +4,7 @@ Use this if you want to use an Oauth2.0 token to make `/chat`, `/embeddings` req
 
 :::info
 
-This is an Enterprise Feature - [get in touch with us if you want a free trial to test if this feature meets your needs]((https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions))
+This is an Enterprise Feature - [get in touch with us if you want a free trial to test if this feature meets your needs]((https://enterprise.litellm.ai/demo))
 
 :::
 
diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md
index f47d7064140..700bfb0831d 100644
--- a/docs/my-website/docs/proxy/pass_through.md
+++ b/docs/my-website/docs/proxy/pass_through.md
@@ -422,6 +422,5 @@ general_settings:
 
 [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
 
-Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 
 Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md
index 26cb484cbe9..d40a0343106 100644
--- a/docs/my-website/docs/proxy/prod.md
+++ b/docs/my-website/docs/proxy/prod.md
@@ -47,7 +47,7 @@ export LITELLM_LOG="ERROR"
 
 :::info
 
-Need Help or want dedicated support ? Talk to a founder [here]: (https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+Need Help or want dedicated support ? Talk to a founder [here]: (https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/prompt_management.md b/docs/my-website/docs/proxy/prompt_management.md
index 08307ba99ec..5a3e411e984 100644
--- a/docs/my-website/docs/proxy/prompt_management.md
+++ b/docs/my-website/docs/proxy/prompt_management.md
@@ -311,7 +311,7 @@ litellm_settings:
 1. **At Startup**: When the proxy starts, it reads the `prompts` field from `config.yaml`
 2. **Initialization**: Each prompt is initialized based on its `prompt_integration` type
 3. **In-Memory Storage**: Prompts are stored in the `IN_MEMORY_PROMPT_REGISTRY`
-4. **Access**: Use these prompts via the `/v1/chat/completions` endpoint with `prompt_id` in the request
+4. **Access**: Use these prompts via `/v1/chat/completions` or `/v1/responses` with `prompt_id` in the request
 
 ### Using Config-Loaded Prompts
 
@@ -331,6 +331,23 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
 }'
 ```
 
+You can also use the same `prompt_id` with the Responses API:
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/v1/responses' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+    "model": "gpt-4o",
+    "prompt_id": "coding_assistant",
+    "prompt_variables": {
+        "language": "python",
+        "task": "create a web scraper"
+    },
+    "input": []
+}'
+```
+
 ### Prompt Schema Reference
 
 Each prompt in the `prompts` list requires:
diff --git a/docs/my-website/docs/proxy/public_routes.md b/docs/my-website/docs/proxy/public_routes.md
index d5f3941751f..e53548349dc 100644
--- a/docs/my-website/docs/proxy/public_routes.md
+++ b/docs/my-website/docs/proxy/public_routes.md
@@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem';
 
 :::info
 
-Requires a LiteLLM Enterprise License. [Get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions).
+Requires a LiteLLM Enterprise License. [Get a free trial](https://enterprise.litellm.ai/demo).
 
 :::
 
diff --git a/docs/my-website/docs/proxy/tag_routing.md b/docs/my-website/docs/proxy/tag_routing.md
index a1ae52e5e45..57d16a59b54 100644
--- a/docs/my-website/docs/proxy/tag_routing.md
+++ b/docs/my-website/docs/proxy/tag_routing.md
@@ -315,7 +315,7 @@ LiteLLM Proxy supports team-based tag routing, allowing you to associate specifi
 
 :::info
 
-This is an enterprise feature, [Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+This is an enterprise feature, [Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/team_logging.md b/docs/my-website/docs/proxy/team_logging.md
index 2ad7e2a4a8e..3f57d0d6d8b 100644
--- a/docs/my-website/docs/proxy/team_logging.md
+++ b/docs/my-website/docs/proxy/team_logging.md
@@ -26,7 +26,7 @@ Team 3 -> Disabled Logging (for GDPR compliance)
 
 :::info
 
-✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
@@ -248,7 +248,7 @@ Use the `/key/generate` or `/key/update` endpoints to add logging callbacks to a
 
 :::info
 
-✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+✨ This is an Enterprise only feature [Get Started with Enterprise here](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/team_model_add.md b/docs/my-website/docs/proxy/team_model_add.md
index 7db59a3300e..bb4238055b5 100644
--- a/docs/my-website/docs/proxy/team_model_add.md
+++ b/docs/my-website/docs/proxy/team_model_add.md
@@ -5,7 +5,7 @@
 This is an Enterprise feature.
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md
index 7364ae0fb56..bc6fde7c840 100644
--- a/docs/my-website/docs/proxy/token_auth.md
+++ b/docs/my-website/docs/proxy/token_auth.md
@@ -11,7 +11,7 @@ Use JWT's to auth admins / users / projects into the proxy.
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/proxy_server.md b/docs/my-website/docs/proxy_server.md
index e23d64e443b..7b6f15a604b 100644
--- a/docs/my-website/docs/proxy_server.md
+++ b/docs/my-website/docs/proxy_server.md
@@ -813,5 +813,4 @@ Thread Stats   Avg      Stdev     Max   +/- Stdev
 
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md
index 8bf59f66a33..6e6a30cdb49 100644
--- a/docs/my-website/docs/reasoning_content.md
+++ b/docs/my-website/docs/reasoning_content.md
@@ -13,6 +13,7 @@ Supported Providers:
 - Deepseek (`deepseek/`)
 - Anthropic API (`anthropic/`)
 - Bedrock (Anthropic + Deepseek + GPT-OSS) (`bedrock/`)
+- OpenAI Responses API (`openai/responses/`)
 - Vertex AI (Anthropic) (`vertexai/`)
 - OpenRouter (`openrouter/`)
 - XAI (`xai/`)
@@ -594,9 +595,26 @@ Expected Response
 
 :::tip gpt-5.4: reasoning_effort + function tools
 
-LiteLLM drops `reasoning_effort` from `gpt-5.4` requests to `litellm.completion()` that include tools, since that combination is supported in the Responses API.
+When `gpt-5.4+` requests to `litellm.completion()` include both `reasoning_effort` and `tools`, LiteLLM **automatically routes** the request through the Responses API bridge. This works for both **OpenAI** (`openai/gpt-5.4`) and **Azure** (`azure/gpt-5.4`) providers — no extra configuration needed.
 
-If you need reasoning **and** tools together, use `openai/responses/gpt-5.4` to route through the Responses API instead. See [Responses API Bridge](/docs/providers/openai#openai-chat-completion-to-responses-api-bridge) for details.
+You can also route explicitly via `openai/responses/gpt-5.4` or `azure/responses/gpt-5.4`. See [Responses API Bridge](/docs/providers/openai#openai-chat-completion-to-responses-api-bridge) for details.
+
+**Azure custom deployment names:** Auto-routing relies on the deployment name matching the `gpt-5.4*` pattern. If you use a custom deployment name (e.g. `"my-reasoning-model"`), enable routing via:
+
+**SDK:**
+```python
+litellm.completion(model="azure/responses/my-reasoning-model", ...)
+```
+
+**Proxy config:**
+```yaml
+model_list:
+  - model_name: my-reasoning-model
+    litellm_params:
+      model: azure/my-reasoning-model
+    model_info:
+      mode: responses
+```
 
 :::
 
@@ -683,3 +701,69 @@ response = litellm.completion(
     reasoning_effort={"effort": "low", "summary": "detailed"},  # Explicit control
 )
 ```
+
+### Summary Preservation via `/v1/messages` Adapter
+
+When using the Anthropic `/v1/messages` adapter to route non-Claude models (e.g., `openai/gpt-5.1`), the `thinking.summary` value is preserved and forwarded to the downstream provider. For example:
+
+```python
+import litellm
+
+response = await litellm.anthropic.messages.acreate(
+    model="openai/gpt-5.1",
+    messages=[{"role": "user", "content": "Hello"}],
+    max_tokens=8096,
+    thinking={"type": "enabled", "budget_tokens": 5000, "summary": "concise"},
+)
+# The summary="concise" is preserved when routing to OpenAI's Responses API
+```
+
+### Enabling Default Summary Injection for `/v1/messages` Adapter
+
+When the Anthropic `/v1/messages` adapter translates `thinking` parameters to OpenAI `reasoning_effort` for non-Claude models, you can opt-in to automatic `summary="detailed"` injection using the `reasoning_auto_summary` flag. This ensures that reasoning text is returned in the response (matching the Anthropic thinking behavior).
+
+To **enable** this default injection, use the `reasoning_auto_summary` flag:
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+import litellm
+
+# Enable default summary="detailed" injection
+litellm.reasoning_auto_summary = True
+
+response = await litellm.anthropic.messages.acreate(
+    model="openai/gpt-5.1",
+    messages=[{"role": "user", "content": "Hello"}],
+    max_tokens=8096,
+    thinking={"type": "enabled", "budget_tokens": 5000},
+)
+# summary="detailed" will be automatically added to reasoning_effort
+```
+
+</TabItem>
+
+<TabItem value="env" label="Environment Variable">
+
+```bash
+export LITELLM_REASONING_AUTO_SUMMARY=true
+```
+
+</TabItem>
+
+<TabItem value="proxy" label="Proxy Config">
+
+```yaml
+litellm_settings:
+  reasoning_auto_summary: true
+```
+
+</TabItem>
+</Tabs>
+
+:::info
+
+This flag only affects the automatic injection of `summary="detailed"` when no user-provided summary is present. If you explicitly pass `thinking.summary` (e.g., `"concise"` or `"auto"`), your value is always preserved regardless of this flag.
+
+:::
diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md
index fb55ae9f9d0..0c428000c72 100644
--- a/docs/my-website/docs/response_api.md
+++ b/docs/my-website/docs/response_api.md
@@ -1160,12 +1160,12 @@ follow_up = await router.aresponses(
 To enable session continuity for Responses API in your LiteLLM proxy, set `optional_pre_call_checks` in your proxy config.yaml.
 
 - `responses_api_deployment_check`: high priority routing when `previous_response_id` is provided
-- `encrypted_content_affinity`: **[Recommended]** content-aware routing for encrypted items (e.g., `rs_...` reasoning items)
+- `encrypted_content_affinity`: **[Recommended]** content-aware routing for encrypted items (e.g., `rs_...` reasoning items) (**requires LiteLLM >= 1.82.3**)
 - `session_affinity`: sticky sessions based on session id (takes priority over `deployment_affinity`)
 - `deployment_affinity`: sticky sessions based on user key (applies even without `previous_response_id`)
 
 :::tip Recommended: Use `encrypted_content_affinity`
-For Responses API with load balancing across deployments with **different API keys**, use `encrypted_content_affinity` instead of `deployment_affinity`. It only pins requests that contain encrypted content, avoiding quota reduction while preventing `invalid_encrypted_content` errors.
+For Responses API with load balancing across deployments with **different API keys**, use `encrypted_content_affinity` instead of `deployment_affinity`. It only pins requests that contain encrypted content, avoiding quota reduction while preventing `invalid_encrypted_content` errors. (Requires LiteLLM >= 1.82.3.)
 :::
 
 Notes:
@@ -1364,6 +1364,85 @@ litellm --config config.yaml
 | `deployment_affinity` | Simple sticky sessions | All requests from same API key | ❌ Reduces quota by # of users |
 
 
+## Per-Model-Group Affinity Configuration
+
+By default, `optional_pre_call_checks` applies globally to all model groups. Use `model_group_affinity_config` when you want different affinity behavior per model group — for example, enabling stickiness only for models spread across providers (Azure + Bedrock) while leaving single-provider groups free to load-balance.
+
+Groups not listed fall back to the global `optional_pre_call_checks` settings.
+
+<Tabs>
+<TabItem value="python-sdk" label="Python SDK">
+
+```python
+router = litellm.Router(
+    model_list=[
+        {
+            "model_name": "gpt-4",
+            "litellm_params": {"model": "azure/gpt-4", "api_key": "...", "api_base": "https://endpoint1.openai.azure.com"},
+        },
+        {
+            "model_name": "gpt-4",
+            "litellm_params": {"model": "bedrock/anthropic.claude-v2", "aws_region_name": "us-east-1"},
+        },
+        {
+            "model_name": "text-embedding-ada-002",
+            "litellm_params": {"model": "azure/text-embedding-ada-002", "api_key": "...", "api_base": "https://endpoint1.openai.azure.com"},
+        },
+        {
+            "model_name": "text-embedding-ada-002",
+            "litellm_params": {"model": "azure/text-embedding-ada-002", "api_key": "...", "api_base": "https://endpoint2.openai.azure.com"},
+        },
+    ],
+    # gpt-4: cross-provider (Azure + Bedrock) — enable deployment affinity
+    # text-embedding-ada-002: same provider — no affinity, let it load balance freely
+    model_group_affinity_config={
+        "gpt-4": ["deployment_affinity", "responses_api_deployment_check"],
+    },
+)
+```
+
+</TabItem>
+<TabItem value="proxy-server" label="Proxy Server">
+
+```yaml title="config.yaml"
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: azure/gpt-4
+      api_key: os.environ/AZURE_API_KEY_1
+      api_base: https://endpoint1.openai.azure.com
+
+  - model_name: gpt-4
+    litellm_params:
+      model: bedrock/anthropic.claude-v2
+      aws_region_name: us-east-1
+
+  - model_name: text-embedding-ada-002
+    litellm_params:
+      model: azure/text-embedding-ada-002
+      api_key: os.environ/AZURE_API_KEY_1
+      api_base: https://endpoint1.openai.azure.com
+
+  - model_name: text-embedding-ada-002
+    litellm_params:
+      model: azure/text-embedding-ada-002
+      api_key: os.environ/AZURE_API_KEY_2
+      api_base: https://endpoint2.openai.azure.com
+
+router_settings:
+  # gpt-4: cross-provider — enable stickiness
+  # text-embedding-ada-002: not listed — load balances freely
+  model_group_affinity_config:
+    "gpt-4":
+      - deployment_affinity
+      - responses_api_deployment_check
+```
+
+</TabItem>
+</Tabs>
+
+**Supported values:** `deployment_affinity`, `responses_api_deployment_check`, `session_affinity`
+
 ## Calling non-Responses API endpoints (`/responses` to `/chat/completions` Bridge)
 
 LiteLLM allows you to call non-Responses API models via a bridge to LiteLLM's `/chat/completions` endpoint. This is useful for calling Anthropic, Gemini and even non-Responses API OpenAI models.
@@ -1556,6 +1635,12 @@ curl -X POST "http://localhost:4000/v1/responses" \
   }'
 ```
 
+## File Search (Vector Stores)
+
+For full `file_search` usage (native + emulated fallback), SDK/Proxy examples, architecture diagram, and Q&A, see:
+
+- [`File Search in the Responses API — E2E Testing Guide`](/docs/tutorials/file_search_responses_api)
+
 ## Session Management
 
 LiteLLM Proxy supports session management for all supported models. This allows you to store and fetch conversation history (state) in LiteLLM Proxy. 
diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md
index 67e7f681147..5aa655ae212 100644
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@@ -842,6 +842,8 @@ Traffic mirroring allows you to "mimic" production traffic to a secondary (silen
 
 Set `order` in `litellm_params` to prioritize deployments. Lower values = higher priority. When multiple deployments share the same `order`, the routing strategy picks among them.
 
+When a request to an `order=1` deployment fails (connection error, 404, 429, etc.), the router automatically tries `order=2` deployments, then `order=3`, and so on. Each order level gets its own set of retries before escalating to the next. If all order levels are exhausted, the router falls through to any configured [fallbacks](#fallbacks).
+
 <Tabs>
 <TabItem value="sdk" label="SDK">
 
@@ -862,18 +864,14 @@ model_list = [
         "litellm_params": {
             "model": "azure/gpt-4-fallback",
             "api_key": os.getenv("AZURE_API_KEY_2"),
-            "order": 2,  # 👈 Used when order=1 is unavailable
+            "order": 2,  # 👈 Tried when order=1 fails
         },
     },
 ]
 
-router = Router(model_list=model_list, enable_pre_call_checks=True)  # 👈 Required for 'order' to work
+router = Router(model_list=model_list)
 ```
 
-:::important
-The `order` parameter requires `enable_pre_call_checks=True` to be set on the Router.
-:::
-
 </TabItem>
 <TabItem value="proxy" label="PROXY">
 
@@ -889,10 +887,7 @@ model_list:
     litellm_params:
       model: azure/gpt-4-fallback
       api_key: os.environ/AZURE_API_KEY_2
-      order: 2  # 👈 Used when order=1 is unavailable
-
-router_settings:
-  enable_pre_call_checks: true  # 👈 Required for 'order' to work
+      order: 2  # 👈 Tried when order=1 fails
 ```
 
 </TabItem>
diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md
index c5c80311475..57f576fd56a 100644
--- a/docs/my-website/docs/secret.md
+++ b/docs/my-website/docs/secret.md
@@ -6,7 +6,7 @@
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/aws_kms.md b/docs/my-website/docs/secret_managers/aws_kms.md
index 7f69d91fe87..806223a2539 100644
--- a/docs/my-website/docs/secret_managers/aws_kms.md
+++ b/docs/my-website/docs/secret_managers/aws_kms.md
@@ -6,7 +6,7 @@
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/aws_secret_manager.md b/docs/my-website/docs/secret_managers/aws_secret_manager.md
index c49797a15dd..a7e24ea69ae 100644
--- a/docs/my-website/docs/secret_managers/aws_secret_manager.md
+++ b/docs/my-website/docs/secret_managers/aws_secret_manager.md
@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/azure_key_vault.md b/docs/my-website/docs/secret_managers/azure_key_vault.md
index 81aeaa32159..4ea53d2ea9e 100644
--- a/docs/my-website/docs/secret_managers/azure_key_vault.md
+++ b/docs/my-website/docs/secret_managers/azure_key_vault.md
@@ -6,7 +6,7 @@
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/cyberark.md b/docs/my-website/docs/secret_managers/cyberark.md
index 0a17c0afc30..cd7c0ea5d25 100644
--- a/docs/my-website/docs/secret_managers/cyberark.md
+++ b/docs/my-website/docs/secret_managers/cyberark.md
@@ -8,7 +8,7 @@ import Image from '@theme/IdealImage';
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/google_kms.md b/docs/my-website/docs/secret_managers/google_kms.md
index 31fd6195bdb..152ecbaae80 100644
--- a/docs/my-website/docs/secret_managers/google_kms.md
+++ b/docs/my-website/docs/secret_managers/google_kms.md
@@ -6,7 +6,7 @@
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/google_secret_manager.md b/docs/my-website/docs/secret_managers/google_secret_manager.md
index 81878b7e398..f3e7367e8a4 100644
--- a/docs/my-website/docs/secret_managers/google_secret_manager.md
+++ b/docs/my-website/docs/secret_managers/google_secret_manager.md
@@ -6,7 +6,7 @@
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/hashicorp_vault.md b/docs/my-website/docs/secret_managers/hashicorp_vault.md
index 52d9b556200..11e25e88a7d 100644
--- a/docs/my-website/docs/secret_managers/hashicorp_vault.md
+++ b/docs/my-website/docs/secret_managers/hashicorp_vault.md
@@ -8,7 +8,7 @@ import Image from '@theme/IdealImage';
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/secret_managers/overview.md b/docs/my-website/docs/secret_managers/overview.md
index bf7386ab89c..f02362f4932 100644
--- a/docs/my-website/docs/secret_managers/overview.md
+++ b/docs/my-website/docs/secret_managers/overview.md
@@ -8,7 +8,7 @@ import Image from '@theme/IdealImage';
 
 [Enterprise Pricing](https://www.litellm.ai/#pricing)
 
-[Contact us here to get a free trial](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions)
+[Contact us here to get a free trial](https://enterprise.litellm.ai/demo)
 
 :::
 
diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md
index 1539e1959f7..1afa35df8e4 100644
--- a/docs/my-website/docs/troubleshoot.md
+++ b/docs/my-website/docs/troubleshoot.md
@@ -50,7 +50,6 @@ Full error logs, stack traces, and any images from service metrics (CPU, memory,
 [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
 [Community Slack 💭](https://www.litellm.ai/support)
 
-Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
 
 Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
 
diff --git a/docs/my-website/docs/tutorials/compare_llms.md b/docs/my-website/docs/tutorials/compare_llms.md
index 02877b46607..0252263a16e 100644
--- a/docs/my-website/docs/tutorials/compare_llms.md
+++ b/docs/my-website/docs/tutorials/compare_llms.md
@@ -82,7 +82,7 @@ Benchmark Results for 'When will BerriAI IPO?':
 +-----------------+----------------------------------------------------------------------------------+---------------------------+------------+
 ```
 ## Support
-**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you.
+**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://enterprise.litellm.ai/demo) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you.
 
 
 <!-- 
diff --git a/docs/my-website/docs/tutorials/file_search_responses_api.md b/docs/my-website/docs/tutorials/file_search_responses_api.md
new file mode 100644
index 00000000000..d74bdf9cb83
--- /dev/null
+++ b/docs/my-website/docs/tutorials/file_search_responses_api.md
@@ -0,0 +1,241 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# File Search in the Responses API
+
+LiteLLM now supports `file_search` in the Responses API across both:
+- providers that support it natively (like OpenAI / Azure), and
+- providers that do not (like Anthropic, Bedrock, and other non-native providers) via emulation.
+
+## What this is
+
+`file_search` lets models retrieve grounded context from your vector stores and answer with citations.
+LiteLLM keeps one OpenAI-compatible output shape while routing requests through either native passthrough or an emulated fallback.
+
+Two paths are covered:
+
+| Path | When it runs | What LiteLLM does |
+| --- | --- | --- |
+| **Native passthrough** | Provider natively supports `file_search` (OpenAI, Azure) | Decodes unified vector store ID → forwards to provider as-is |
+| **Emulated fallback** | Provider doesn't support `file_search` (Anthropic, Bedrock, etc.) | Converts to a function tool → intercepts tool call → runs vector search → synthesizes OpenAI-format output |
+
+In `tools[].vector_store_ids`, LiteLLM accepts both provider-native IDs (e.g. `vs_...`) **and** **managed vector store unified IDs** (URL-safe base64 strings from the proxy managed-vector flow), e.g. `litellm.responses(..., tools=[{"type": "file_search", "vector_store_ids": ["bGl0ZWxsbV9wcm94eT..."]}])`.
+
+## Usage
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy" default>
+
+### 1. Setup `config.yaml`
+
+```yaml title="config.yaml"
+model_list:
+  - model_name: gpt-4.1
+    litellm_params:
+      model: openai/gpt-4.1
+      api_key: os.environ/OPENAI_API_KEY
+
+  - model_name: claude-sonnet
+    litellm_params:
+      model: anthropic/claude-sonnet-4-5
+      api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+### 2. Start the proxy
+
+```bash
+litellm --config config.yaml
+```
+
+### 3. Call Responses API with `file_search`
+
+```python title="Proxy call"
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:4000", api_key="sk-your-proxy-key")
+
+response = client.responses.create(
+    model="claude-sonnet",  # swap to "gpt-4.1" for native path
+    input="What does LiteLLM support?",
+    tools=[{
+        "type": "file_search",
+        "vector_store_ids": ["vs_abc123"]
+    }],
+    include=["file_search_call.results"],
+)
+
+print(response.output)
+```
+
+</TabItem>
+<TabItem value="sdk" label="LiteLLM SDK">
+
+### 1. Install + set keys
+
+```bash
+pip install litellm
+export OPENAI_API_KEY="sk-..."
+export ANTHROPIC_API_KEY="sk-ant-..."
+```
+
+### 2. Call Responses API with `file_search`
+
+```python title="SDK call"
+import litellm
+
+response = litellm.responses(
+    model="anthropic/claude-sonnet-4-5",  # swap to openai/gpt-4.1 for native path
+    input="What does LiteLLM support?",
+    tools=[{
+        "type": "file_search",
+        "vector_store_ids": ["vs_abc123"]
+    }],
+    include=["file_search_call.results"],
+)
+
+print(response.output)
+```
+
+</TabItem>
+</Tabs>
+
+### Behavior Matrix
+
+| Path | SDK model | Proxy model | Behavior |
+| --- | --- | --- | --- |
+| Native passthrough | `openai/gpt-4.1` | `gpt-4.1` | Provider executes native `file_search` |
+| Emulated fallback | `anthropic/claude-sonnet-4-5` | `claude-sonnet` | LiteLLM converts to function tool and synthesizes OpenAI-format output |
+
+
+
+## Architecture Diagram
+
+```mermaid
+flowchart TD
+    A[Client SDK or Proxy Caller] --> B[LiteLLM Responses API]
+    B --> C{Provider supports native file_search?}
+
+    C -->|Yes| D[Native passthrough path]
+    D --> D1[Decode unified vector_store_id if needed]
+    D1 --> D2[Forward request to provider unchanged]
+    D2 --> D3[Provider performs file_search]
+    D3 --> Z[OpenAI-compatible output]
+
+    C -->|No| E[Emulated fallback path]
+    E --> E1[Convert file_search to litellm_file_search function tool]
+    E1 --> E2[First model call returns tool call with one or more queries]
+    E2 --> E3[LiteLLM executes vector search for each query]
+    E3 --> E4[Second model call with tool_result context]
+    E4 --> E5[Synthesize file_search_call + message + citations]
+    E5 --> Z[OpenAI-compatible output]
+```
+
+
+
+## Prerequisites
+
+```bash
+pip install 'litellm[proxy]'
+export OPENAI_API_KEY="sk-..."          # for native path
+export ANTHROPIC_API_KEY="sk-ant-..."  # for emulated path
+```
+
+
+
+## Example response shape
+
+## Validating the Output Format
+
+Regardless of which path ran, the response always follows the OpenAI Responses API format:
+
+```json
+{
+  "output": [
+    {
+      "type": "file_search_call",
+      "id": "fs_abc123",
+      "status": "completed",
+      "queries": ["What does LiteLLM support?"],
+      "search_results": null
+    },
+    {
+      "type": "message",
+      "role": "assistant",
+      "content": [
+        {
+          "type": "output_text",
+          "text": "LiteLLM is a unified interface...",
+          "annotations": [
+            {
+              "type": "file_citation",
+              "index": 150,
+              "file_id": "file-xxxx",
+              "filename": "knowledge.txt"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}
+```
+
+**Validation script:**
+
+```python showLineNumbers title="Validate response structure"
+def validate_file_search_response(response):
+    """Assert that response follows OpenAI file_search output format."""
+    output = response.output
+    assert len(output) >= 2, "Expected at least 2 output items"
+
+    # First item: file_search_call
+    fs_call = output[0]
+    fs_type = fs_call["type"] if isinstance(fs_call, dict) else fs_call.type
+    assert fs_type == "file_search_call", f"Expected file_search_call, got {fs_type}"
+
+    fs_status = fs_call["status"] if isinstance(fs_call, dict) else fs_call.status
+    assert fs_status == "completed"
+
+    # Second item: message
+    msg = output[1]
+    msg_type = msg["type"] if isinstance(msg, dict) else msg.type
+    assert msg_type == "message"
+
+    content = msg["content"] if isinstance(msg, dict) else msg.content
+    assert len(content) > 0
+    text_block = content[0]
+    text = text_block["text"] if isinstance(text_block, dict) else text_block.text
+    assert isinstance(text, str) and len(text) > 0
+
+    print("✅ Response structure valid")
+    print(f"   Queries: {fs_call['queries'] if isinstance(fs_call, dict) else fs_call.queries}")
+    print(f"   Answer length: {len(text)} chars")
+    annotations = text_block["annotations"] if isinstance(text_block, dict) else text_block.annotations
+    print(f"   Citations: {len(annotations)}")
+
+validate_file_search_response(response)
+```
+
+
+
+## Q&A
+
+- **Why do I see `UnsupportedParamsError`?** This usually means `file_search` was passed to a provider that does not support it natively and emulation could not route correctly. Check:
+  - The model string is valid (for example, `anthropic/claude-sonnet-4-5`).
+  - `custom_llm_provider` resolves correctly so LiteLLM can load the provider config.
+- **Why does vector search return no results?** Common causes:
+  - The vector store ID is wrong or has no files attached.
+  - In LiteLLM-managed stores, file ingestion is not complete (`status != completed`).
+  - The query is too narrow; try a broader query.
+- **Why am I getting `403 Access denied` on vector store calls?** The caller does not have access to that vector store.
+  - The store may belong to another team.
+  - Use an admin/proxy key if your setup requires cross-team access.
+- **Why are `annotations` empty in emulated mode?** `file_citation` annotations require `file_id` metadata in search results. If your vector backend does not return file-level metadata, the answer text is still generated but citations can be empty.
+
+
+
+## What to check next
+
+- [File Search reference in Responses API docs](/docs/response_api#file-search-vector-stores) — full API reference
+- [Vector Store management](/docs/vector_store_files) — create and manage vector stores
+- [Managed vector stores](/docs/providers/bedrock_vector_store) — provider-specific setup
diff --git a/docs/my-website/docs/tutorials/opencode_integration.md b/docs/my-website/docs/tutorials/opencode_integration.md
index e55367833f2..35e00a1de50 100644
--- a/docs/my-website/docs/tutorials/opencode_integration.md
+++ b/docs/my-website/docs/tutorials/opencode_integration.md
@@ -253,7 +253,7 @@ model_list:
     litellm_params:
       model: openai/gpt-4
       api_key: os.environ/OPENAI_API_KEY
-  
+
   - model_name: gpt-4o
     litellm_params:
       model: openai/gpt-4o
@@ -264,7 +264,7 @@ model_list:
     litellm_params:
       model: anthropic/claude-3-5-sonnet-20241022
       api_key: os.environ/ANTHROPIC_API_KEY
-  
+
   # DeepSeek models
   - model_name: deepseek-chat
     litellm_params:
@@ -272,6 +272,19 @@ model_list:
       api_key: os.environ/DEEPSEEK_API_KEY
 ```
 
+### Dropping OpenCode-specific parameters
+
+OpenCode sends a `reasoningSummary` parameter with reasoning-capable models such as `gpt-5`. This parameter is not supported by the Chat Completions API and will cause errors. Add `additional_drop_params` to every model entry in your `model_list` that will receive requests from OpenCode with reasoning enabled:
+
+```yaml
+model_list:
+  - model_name: gpt-5
+    litellm_params:
+      model: openai/gpt-5
+      api_key: os.environ/OPENAI_API_KEY
+      additional_drop_params: ["reasoningSummary"]
+```
+
 ## Troubleshooting
 
 **OpenCode not connecting:**
@@ -294,6 +307,16 @@ model_list:
 - Validate JSON syntax using a JSON validator
 - Ensure the `$schema` URL is accessible
 
+**`Unknown parameter: 'reasoningSummary'` error:**
+- OpenCode sends a `reasoningSummary` parameter that is not supported by the Chat Completions API. Add `additional_drop_params: ["reasoningSummary"]` to each affected model entry in your `litellm_params`:
+  ```yaml
+  - model_name: gpt-5
+    litellm_params:
+      model: openai/gpt-5
+      api_key: os.environ/OPENAI_API_KEY
+      additional_drop_params: ["reasoningSummary"]
+  ```
+
 ## Tips
 
 - Add more models to the config as needed - they'll appear in `/models`
diff --git a/docs/my-website/docs/tutorials/vertex_ai_pay_go.md b/docs/my-website/docs/tutorials/vertex_ai_pay_go.md
new file mode 100644
index 00000000000..87197e5bad5
--- /dev/null
+++ b/docs/my-website/docs/tutorials/vertex_ai_pay_go.md
@@ -0,0 +1,151 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Vertex AI PayGo and Priority
+
+## Priority PayGo
+
+LiteLLM supports Priority PayGo.  
+Send a priority header, get priority queueing, and pay priority token rates.
+
+:::info Which models support Priority PayGo?
+As of this writing: `gemini/gemini-2.5-pro`, `vertex_ai/gemini-3-pro-preview`, `vertex_ai/gemini-3.1-pro-preview`, `vertex_ai/gemini-3-flash-preview`, and their variants.  
+Check `supports_service_tier: true` in LiteLLM's [model pricing JSON](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
+:::
+
+### Send a priority request
+
+Use this header:
+
+`X-Vertex-AI-LLM-Shared-Request-Type: priority`
+
+<Tabs>
+<TabItem value="litellm-sdk" label="LiteLLM SDK">
+
+```python
+import litellm
+
+response = litellm.completion(
+    model="vertex_ai/gemini-3-pro-preview",
+    messages=[{"role": "user", "content": "Summarize the Gettysburg Address."}],
+    vertex_project="YOUR_PROJECT_ID",
+    vertex_location="us-central1",
+    extra_headers={"X-Vertex-AI-LLM-Shared-Request-Type": "priority"},
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+<TabItem value="proxy-config" label="Proxy config">
+
+```yaml title="config.yaml"
+model_list:
+  - model_name: gemini-priority
+    litellm_params:
+      model: vertex_ai/gemini-3-pro-preview
+      vertex_project: "YOUR_PROJECT_ID"
+      vertex_location: "us-central1"
+      vertex_credentials: os.environ/GOOGLE_APPLICATION_CREDENTIALS
+      extra_headers:
+        X-Vertex-AI-LLM-Shared-Request-Type: priority
+```
+
+```bash
+curl http://localhost:4000/v1/chat/completions \
+  -H "Authorization: Bearer sk-your-key" \
+  -H "Content-Type: application/json" \
+  -d '{"model": "gemini-priority", "messages": [{"role": "user", "content": "Hello"}]}'
+```
+
+</TabItem>
+<TabItem value="pass-through" label="Pass-through mode">
+
+Use `x-pass-` so LiteLLM forwards provider-specific headers.
+
+```bash
+MODEL_ID="gemini-3-pro-preview-0325"
+PROJECT_ID="YOUR_PROJECT_ID"
+
+curl -X POST \
+  "${LITELLM_PROXY_BASE_URL}/vertex_ai/v1/projects/${PROJECT_ID}/locations/global/publishers/google/models/${MODEL_ID}:generateContent" \
+  -H "Authorization: Bearer sk-your-litellm-key" \
+  -H "Content-Type: application/json" \
+  -H "x-pass-X-Vertex-AI-LLM-Shared-Request-Type: priority" \
+  -d '{"contents": [{"role": "user", "parts": [{"text": "Hello!"}]}]}'
+```
+
+</TabItem>
+</Tabs>
+
+### How cost tracking works
+
+![Vertex AI Priority PayGo Cost Tracking Flow](/img/vertex_cost_tracking_flow.svg)
+
+**`trafficType` → `service_tier` mapping**
+
+| `usageMetadata.trafficType` | `service_tier` | Pricing keys used |
+|---|---|---|
+| `ON_DEMAND` | `None` | `input_cost_per_token` |
+| `ON_DEMAND_PRIORITY` | `"priority"` | `input_cost_per_token_priority` |
+| `FLEX` / `BATCH` | `"flex"` | `input_cost_per_token_flex` |
+
+If a tier-specific key is missing, LiteLLM falls back to standard pricing keys.
+
+---
+
+## Standard PayGo vs Provisioned Throughput
+
+This is a different header from priority routing:
+
+| Header value | Behavior |
+|---|---|
+| `X-Vertex-AI-LLM-Request-Type: shared` | Force standard PayGo (bypass PT) |
+| `X-Vertex-AI-LLM-Request-Type: dedicated` | Force Provisioned Throughput only (`429` if exhausted) |
+
+### Native route example
+
+```python
+import litellm
+
+response = litellm.completion(
+    model="vertex_ai/gemini-2.0-flash",
+    messages=[{"role": "user", "content": "Hello!"}],
+    vertex_project="YOUR_PROJECT_ID",
+    vertex_location="us-central1",
+    extra_headers={"X-Vertex-AI-LLM-Request-Type": "shared"},
+)
+```
+
+### Pass-through example
+
+```bash
+MODEL_ID="gemini-2.0-flash-001"
+PROJECT_ID="YOUR_PROJECT_ID"
+
+curl -X POST \
+  "${LITELLM_PROXY_BASE_URL}/vertex_ai/v1/projects/${PROJECT_ID}/locations/global/publishers/google/models/${MODEL_ID}:generateContent" \
+  -H "Authorization: Bearer sk-your-litellm-key" \
+  -H "Content-Type: application/json" \
+  -H "x-pass-X-Vertex-AI-LLM-Request-Type: shared" \
+  -d '{
+    "contents": [{"role": "user", "parts": [{"text": "Hello!"}]}]
+  }'
+```
+
+---
+
+## Troubleshooting 
+
+**Q: What does `403 Permission denied` or `IAM_PERMISSION_DENIED` mean?**  
+A: The service account or Application Default Credentials (ADC) user does not have the `roles/aiplatform.user` role. To resolve this, re-run the `gcloud projects add-iam-policy-binding`.
+
+**Q: What should I do if I get a `429 Quota exceeded` error?**  
+A: This means you've hit the per-region QPM (queries per minute) or TPM (tokens per minute) quota. You can:
+- Request a quota increase from the [GCP Quotas console](https://console.cloud.google.com/iam-admin/quotas)
+- Add more regions to your LiteLLM configuration for load balancing
+- Upgrade to [Provisioned Throughput](https://cloud.google.com/vertex-ai/generative-ai/docs/provisioned-throughput) for guaranteed capacity
+
+**Q: How do I fix the `VERTEXAI_PROJECT not set` error?**  
+A: Either pass the `vertex_project` parameter explicitly in your LiteLLM call, or set the `VERTEXAI_PROJECT` environment variable before running your code.
+
diff --git a/docs/my-website/img/isolated_ci_cd_environments.png b/docs/my-website/img/isolated_ci_cd_environments.png
new file mode 100644
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diff --git a/docs/my-website/img/security_update_march_2026/proxy_version.png b/docs/my-website/img/security_update_march_2026/proxy_version.png
new file mode 100644
index 00000000000..c5d03d6a636
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diff --git a/docs/my-website/img/shared_ci_cd_environment.png b/docs/my-website/img/shared_ci_cd_environment.png
new file mode 100644
index 00000000000..e54e11faa85
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diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 4c0471fb8f4..cc2d7800a21 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -430,6 +430,7 @@ const sidebars = {
             "proxy/architecture",
             "proxy/multi_tenant_architecture",
             "proxy/control_plane_and_data_plane",
+            "proxy/high_availability_control_plane",
             "proxy/db_deadlocks",
             "proxy/db_info",
             "proxy/image_handling",
@@ -584,6 +585,7 @@ const sidebars = {
           label: "Spend Tracking",
           items: [
             "proxy/cost_tracking",
+            "tutorials/vertex_ai_pay_go",
             "proxy/request_tags",
             "proxy/custom_pricing",
             "proxy/pricing_calculator",
@@ -737,6 +739,7 @@ const sidebars = {
         "proxy/realtime_webrtc",
         "rerank",
         "response_api",
+        "prompt_management",
         "response_api_compact",
         {
           type: "category",
@@ -847,6 +850,7 @@ const sidebars = {
           items: [
             "providers/gemini",
             "providers/gemini/videos",
+            "providers/gemini/music",
             "providers/google_ai_studio/files",
             "providers/google_ai_studio/image_gen",
             "providers/google_ai_studio/realtime",
@@ -1433,6 +1437,7 @@ const learnSidebar = {
           },
           items: [
             "tutorials/prompt_caching",
+            "tutorials/file_search_responses_api",
             "tutorials/anthropic_file_usage",
             "tutorials/gemini_realtime_with_audio",
             "tutorials/litellm_proxy_aporia",
diff --git a/docs/my-website/src/components/ControlPlaneArchitecture/ControlPlaneArchitecture.tsx b/docs/my-website/src/components/ControlPlaneArchitecture/ControlPlaneArchitecture.tsx
new file mode 100644
index 00000000000..5cc41979b02
--- /dev/null
+++ b/docs/my-website/src/components/ControlPlaneArchitecture/ControlPlaneArchitecture.tsx
@@ -0,0 +1,95 @@
+import React from 'react';
+import styles from './styles.module.css';
+
+/* ────────────────────── Shared small pieces ────────────────────── */
+
+function InfraChip({ color, label }: { color: string; label: string }) {
+  const dotClass =
+    color === 'green'
+      ? styles.infraDotGreen
+      : color === 'blue'
+        ? styles.infraDotBlue
+        : styles.infraDotOrange;
+
+  return (
+    <span className={styles.infraChip}>
+      <span className={`${styles.infraDot} ${dotClass}`} />
+      {label}
+    </span>
+  );
+}
+
+/* ────────────────────── Architecture tab ────────────────────── */
+
+function ArchitectureView() {
+  return (
+    <div className={styles.diagram}>
+      {/* User */}
+      <div className={styles.userRow}>
+        <div className={styles.userIcon}>&#128100;</div>
+        <span className={styles.userLabel}>Admin</span>
+      </div>
+
+      <div className={styles.connectorDown} />
+
+      {/* Control Plane */}
+      <div className={`${styles.node} ${styles.nodeControlPlane}`}>
+        <div className={styles.nodeHeader}>
+          <span className={styles.nodeTitle}>Control Plane</span>
+          <span className={`${styles.badge} ${styles.badgeBlue}`}>UI</span>
+        </div>
+        <div className={styles.nodeSubtitle}>cp.example.com</div>
+        <div className={styles.infraRow}>
+          <InfraChip color="green" label="Own DB" />
+          <InfraChip color="orange" label="Own Redis" />
+          <InfraChip color="blue" label="Own Key" />
+        </div>
+      </div>
+
+      {/* Branch connector */}
+      <div className={styles.connectorBranch}>
+        <div className={`${styles.branchLeg} ${styles.branchLegLeft}`} />
+        <div className={`${styles.branchLeg} ${styles.branchLegRight}`} />
+      </div>
+
+      {/* Workers */}
+      <div className={styles.workersRow}>
+        <div className={`${styles.node} ${styles.nodeWorker} ${styles.nodeWorkerA}`}>
+          <div className={styles.nodeHeader}>
+            <span className={styles.nodeTitle}>Worker A</span>
+            <span className={`${styles.badge} ${styles.badgeGreen}`}>US East</span>
+          </div>
+          <div className={styles.nodeSubtitle}>worker-a.example.com</div>
+          <div className={styles.infraRow}>
+            <InfraChip color="green" label="Own DB" />
+            <InfraChip color="orange" label="Own Redis" />
+            <InfraChip color="blue" label="Own Key" />
+          </div>
+        </div>
+
+        <div className={`${styles.node} ${styles.nodeWorker} ${styles.nodeWorkerB}`}>
+          <div className={styles.nodeHeader}>
+            <span className={styles.nodeTitle}>Worker B</span>
+            <span className={`${styles.badge} ${styles.badgePurple}`}>EU West</span>
+          </div>
+          <div className={styles.nodeSubtitle}>worker-b.example.com</div>
+          <div className={styles.infraRow}>
+            <InfraChip color="green" label="Own DB" />
+            <InfraChip color="orange" label="Own Redis" />
+            <InfraChip color="blue" label="Own Key" />
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}
+
+/* ────────────────────── Main component ────────────────────── */
+
+export default function ControlPlaneArchitecture() {
+  return (
+    <div className={styles.wrapper}>
+      <ArchitectureView />
+    </div>
+  );
+}
diff --git a/docs/my-website/src/components/ControlPlaneArchitecture/index.tsx b/docs/my-website/src/components/ControlPlaneArchitecture/index.tsx
new file mode 100644
index 00000000000..826b4d68818
--- /dev/null
+++ b/docs/my-website/src/components/ControlPlaneArchitecture/index.tsx
@@ -0,0 +1 @@
+export { default as ControlPlaneArchitecture } from './ControlPlaneArchitecture';
diff --git a/docs/my-website/src/components/ControlPlaneArchitecture/styles.module.css b/docs/my-website/src/components/ControlPlaneArchitecture/styles.module.css
new file mode 100644
index 00000000000..8400a8920e0
--- /dev/null
+++ b/docs/my-website/src/components/ControlPlaneArchitecture/styles.module.css
@@ -0,0 +1,517 @@
+/* ── Custom properties ── */
+:root {
+  --cp-bg: #ffffff;
+  --cp-border: #e5e7eb;
+  --cp-text: #1a1a2e;
+  --cp-text-secondary: #6b7280;
+  --cp-text-muted: #9ca3af;
+  --cp-accent: #3b82f6;
+  --cp-accent-light: #dbeafe;
+  --cp-accent-glow: rgba(59, 130, 246, 0.15);
+  --cp-green: #10b981;
+  --cp-green-light: #d1fae5;
+  --cp-green-glow: rgba(16, 185, 129, 0.15);
+  --cp-orange: #f59e0b;
+  --cp-orange-light: #fef3c7;
+  --cp-purple: #8b5cf6;
+  --cp-purple-light: #ede9fe;
+  --cp-red: #ef4444;
+  --cp-red-light: #fee2e2;
+  --cp-card-bg: #f9fafb;
+  --cp-infra-bg: #f1f5f9;
+  --cp-infra-border: #cbd5e1;
+  --cp-connector: #d1d5db;
+  --cp-dot-size: 8px;
+}
+
+[data-theme='dark'] {
+  --cp-bg: #111827;
+  --cp-border: #374151;
+  --cp-text: #e5e7eb;
+  --cp-text-secondary: #9ca3af;
+  --cp-text-muted: #6b7280;
+  --cp-accent: #60a5fa;
+  --cp-accent-light: #1e3a5f;
+  --cp-accent-glow: rgba(96, 165, 250, 0.2);
+  --cp-green: #34d399;
+  --cp-green-light: #064e3b;
+  --cp-green-glow: rgba(52, 211, 153, 0.2);
+  --cp-orange: #fbbf24;
+  --cp-orange-light: #78350f;
+  --cp-purple: #a78bfa;
+  --cp-purple-light: #3b0764;
+  --cp-red: #f87171;
+  --cp-red-light: #451a1a;
+  --cp-card-bg: #1f2937;
+  --cp-infra-bg: #1e293b;
+  --cp-infra-border: #475569;
+  --cp-connector: #4b5563;
+}
+
+/* ── Wrapper ── */
+.wrapper {
+  margin: 1.5rem 0;
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+}
+
+/* ── Tab bar ── */
+.tabs {
+  display: flex;
+  gap: 0;
+  margin-bottom: 1.5rem;
+  border-bottom: 2px solid var(--cp-border);
+}
+
+.tab {
+  padding: 0.6rem 1.25rem;
+  font-size: 0.85rem;
+  font-weight: 600;
+  color: var(--cp-text-secondary);
+  background: none;
+  border: none;
+  border-bottom: 2px solid transparent;
+  margin-bottom: -2px;
+  cursor: pointer;
+  transition: color 0.2s, border-color 0.2s;
+}
+
+.tab:hover {
+  color: var(--cp-text);
+}
+
+.tabActive {
+  color: var(--cp-accent);
+  border-bottom-color: var(--cp-accent);
+}
+
+/* ── Architecture diagram ── */
+.diagram {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 0;
+}
+
+/* ── User icon ── */
+.userRow {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  margin-bottom: 0.5rem;
+}
+
+.userIcon {
+  width: 40px;
+  height: 40px;
+  border-radius: 50%;
+  background: var(--cp-accent-light);
+  border: 2px solid var(--cp-accent);
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 1.1rem;
+}
+
+.userLabel {
+  font-size: 0.75rem;
+  color: var(--cp-text-secondary);
+  margin-top: 0.3rem;
+  font-weight: 500;
+}
+
+/* ── Connectors ── */
+.connectorDown {
+  width: 2px;
+  height: 28px;
+  background: var(--cp-connector);
+  position: relative;
+}
+
+.connectorDown::after {
+  content: '';
+  position: absolute;
+  bottom: -4px;
+  left: 50%;
+  transform: translateX(-50%);
+  width: 0;
+  height: 0;
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: 5px solid var(--cp-connector);
+}
+
+.connectorBranch {
+  display: flex;
+  align-items: flex-start;
+  justify-content: center;
+  position: relative;
+  width: 100%;
+  max-width: 700px;
+  height: 36px;
+}
+
+.connectorBranch::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 50%;
+  width: 2px;
+  height: 12px;
+  background: var(--cp-connector);
+  transform: translateX(-50%);
+}
+
+.connectorBranch::after {
+  content: '';
+  position: absolute;
+  top: 12px;
+  left: calc(25% + 12px);
+  right: calc(25% + 12px);
+  height: 2px;
+  background: var(--cp-connector);
+}
+
+.branchLeg {
+  position: absolute;
+  top: 12px;
+  width: 2px;
+  height: 24px;
+  background: var(--cp-connector);
+}
+
+.branchLeg::after {
+  content: '';
+  position: absolute;
+  bottom: -4px;
+  left: 50%;
+  transform: translateX(-50%);
+  width: 0;
+  height: 0;
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: 5px solid var(--cp-connector);
+}
+
+.branchLegLeft {
+  left: calc(25% + 12px);
+}
+
+.branchLegRight {
+  right: calc(25% + 12px);
+}
+
+/* ── Node cards ── */
+.node {
+  border: 2px solid var(--cp-border);
+  border-radius: 12px;
+  background: var(--cp-card-bg);
+  padding: 1rem 1.25rem;
+  text-align: center;
+  transition: border-color 0.3s, box-shadow 0.3s;
+  position: relative;
+}
+
+.nodeControlPlane {
+  border-color: var(--cp-accent);
+  box-shadow: 0 0 0 3px var(--cp-accent-glow);
+  min-width: 280px;
+}
+
+.nodeWorker {
+  min-width: 220px;
+}
+
+.nodeWorkerA {
+  border-color: var(--cp-green);
+  box-shadow: 0 0 0 3px var(--cp-green-glow);
+}
+
+.nodeWorkerB {
+  border-color: var(--cp-purple);
+  box-shadow: 0 0 0 3px rgba(139, 92, 246, 0.15);
+}
+
+[data-theme='dark'] .nodeWorkerB {
+  box-shadow: 0 0 0 3px rgba(167, 139, 250, 0.2);
+}
+
+.nodeHeader {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.5rem;
+  margin-bottom: 0.5rem;
+}
+
+.nodeIcon {
+  font-size: 1.1rem;
+}
+
+.nodeTitle {
+  font-size: 0.95rem;
+  font-weight: 700;
+  color: var(--cp-text);
+}
+
+.nodeSubtitle {
+  font-size: 0.75rem;
+  color: var(--cp-text-secondary);
+  margin-bottom: 0.75rem;
+}
+
+.badge {
+  display: inline-block;
+  font-size: 0.65rem;
+  font-weight: 600;
+  padding: 0.15rem 0.5rem;
+  border-radius: 9999px;
+  text-transform: uppercase;
+  letter-spacing: 0.04em;
+}
+
+.badgeBlue {
+  background: var(--cp-accent-light);
+  color: var(--cp-accent);
+}
+
+.badgeGreen {
+  background: var(--cp-green-light);
+  color: var(--cp-green);
+}
+
+.badgePurple {
+  background: var(--cp-purple-light);
+  color: var(--cp-purple);
+}
+
+/* ── Infrastructure chips ── */
+.infraRow {
+  display: flex;
+  gap: 0.4rem;
+  justify-content: center;
+  flex-wrap: wrap;
+  margin-top: 0.5rem;
+}
+
+.infraChip {
+  display: flex;
+  align-items: center;
+  gap: 0.3rem;
+  font-size: 0.7rem;
+  font-weight: 500;
+  color: var(--cp-text-secondary);
+  background: var(--cp-infra-bg);
+  border: 1px solid var(--cp-infra-border);
+  border-radius: 6px;
+  padding: 0.2rem 0.5rem;
+}
+
+.infraDot {
+  width: 6px;
+  height: 6px;
+  border-radius: 50%;
+  flex-shrink: 0;
+}
+
+.infraDotGreen {
+  background: var(--cp-green);
+}
+
+.infraDotBlue {
+  background: var(--cp-accent);
+}
+
+.infraDotOrange {
+  background: var(--cp-orange);
+}
+
+/* ── Workers row ── */
+.workersRow {
+  display: flex;
+  gap: 2rem;
+  justify-content: center;
+  flex-wrap: wrap;
+}
+
+/* ── Animated flow ── */
+.flowLabel {
+  font-size: 0.7rem;
+  color: var(--cp-accent);
+  font-weight: 600;
+  position: absolute;
+  white-space: nowrap;
+}
+
+/* ── Comparison view ── */
+.comparisonGrid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 1.5rem;
+  margin-top: 0.5rem;
+}
+
+.comparisonColumn {
+  border: 2px solid var(--cp-border);
+  border-radius: 12px;
+  padding: 1.25rem;
+  background: var(--cp-card-bg);
+}
+
+.comparisonColumnOld {
+  border-color: var(--cp-red);
+}
+
+.comparisonColumnNew {
+  border-color: var(--cp-green);
+}
+
+.comparisonTitle {
+  font-size: 0.9rem;
+  font-weight: 700;
+  color: var(--cp-text);
+  text-align: center;
+  margin-bottom: 1rem;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 0.4rem;
+}
+
+.comparisonTitleOld {
+  color: var(--cp-red);
+}
+
+.comparisonTitleNew {
+  color: var(--cp-green);
+}
+
+/* ── Mini diagram inside comparison ── */
+.miniDiagram {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 0.5rem;
+}
+
+.miniNode {
+  border: 1.5px solid var(--cp-border);
+  border-radius: 8px;
+  background: var(--cp-bg);
+  padding: 0.5rem 0.75rem;
+  text-align: center;
+  font-size: 0.75rem;
+  font-weight: 600;
+  color: var(--cp-text);
+  width: 100%;
+  max-width: 180px;
+}
+
+.miniNodeHighlight {
+  border-color: var(--cp-accent);
+  background: var(--cp-accent-light);
+}
+
+.miniNodeDanger {
+  border-color: var(--cp-red);
+  background: var(--cp-red-light);
+}
+
+.miniNodeSuccess {
+  border-color: var(--cp-green);
+  background: var(--cp-green-light);
+}
+
+.miniConnector {
+  width: 1.5px;
+  height: 16px;
+  background: var(--cp-connector);
+}
+
+.miniWorkersRow {
+  display: flex;
+  gap: 0.5rem;
+  justify-content: center;
+  width: 100%;
+}
+
+.miniWorkerStack {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 0.3rem;
+  flex: 1;
+  max-width: 140px;
+}
+
+.miniInfra {
+  font-size: 0.65rem;
+  color: var(--cp-text-muted);
+  font-weight: 500;
+}
+
+.miniInfraShared {
+  color: var(--cp-red);
+  font-weight: 600;
+}
+
+.miniInfraOwn {
+  color: var(--cp-green);
+  font-weight: 600;
+}
+
+/* ── Callout box ── */
+.callout {
+  display: flex;
+  align-items: flex-start;
+  gap: 0.6rem;
+  padding: 0.75rem 1rem;
+  border-radius: 8px;
+  margin-top: 1rem;
+  font-size: 0.8rem;
+  color: var(--cp-text);
+  line-height: 1.5;
+}
+
+.calloutDanger {
+  background: var(--cp-red-light);
+  border: 1px solid var(--cp-red);
+}
+
+.calloutSuccess {
+  background: var(--cp-green-light);
+  border: 1px solid var(--cp-green);
+}
+
+.calloutIcon {
+  font-size: 1rem;
+  flex-shrink: 0;
+  margin-top: 0.1rem;
+}
+
+/* ── Responsive ── */
+@media (max-width: 768px) {
+  .comparisonGrid {
+    grid-template-columns: 1fr;
+  }
+
+  .workersRow {
+    flex-direction: column;
+    align-items: center;
+  }
+
+  .nodeControlPlane {
+    min-width: auto;
+    width: 100%;
+    max-width: 300px;
+  }
+
+  .nodeWorker {
+    min-width: auto;
+    width: 100%;
+    max-width: 260px;
+  }
+
+  .connectorBranch {
+    display: none;
+  }
+}
diff --git a/docs/my-website/src/components/VersionVerificationTable/index.tsx b/docs/my-website/src/components/VersionVerificationTable/index.tsx
new file mode 100644
index 00000000000..de4caced04b
--- /dev/null
+++ b/docs/my-website/src/components/VersionVerificationTable/index.tsx
@@ -0,0 +1,84 @@
+import React, { useState } from "react";
+import styles from "./styles.module.css";
+
+interface VersionEntry {
+  version: string;
+  sha256: string;
+  gitCommit: string;
+}
+
+interface Props {
+  entries: VersionEntry[];
+}
+
+function CopyButton({ text }: { text: string }) {
+  const [copied, setCopied] = useState(false);
+
+  const handleCopy = () => {
+    navigator.clipboard.writeText(text).then(() => {
+      setCopied(true);
+      setTimeout(() => setCopied(false), 1500);
+    });
+  };
+
+  return (
+    <button
+      className={styles.copyBtn}
+      onClick={handleCopy}
+      title="Copy full SHA-256"
+    >
+      {copied ? "✓" : "⧉"}
+    </button>
+  );
+}
+
+export default function VersionVerificationTable({ entries }: Props) {
+  return (
+    <div className={styles.wrapper}>
+      <table className={styles.table}>
+        <thead>
+          <tr>
+            <th>Version</th>
+            <th>SHA-256</th>
+            <th>Clean of IOCs</th>
+            <th>Matches Git</th>
+            <th>Git Commit</th>
+            <th>Status</th>
+          </tr>
+        </thead>
+        <tbody>
+          {entries.map((entry) => (
+            <tr key={`${entry.version}-${entry.gitCommit}`}>
+              <td className={styles.version}>{entry.version}</td>
+              <td>
+                <span className={styles.sha}>
+                  <code>{entry.sha256.slice(0, 16)}…</code>
+                  <CopyButton text={entry.sha256} />
+                </span>
+              </td>
+              <td>
+                <span className={styles.badgeClean}>✔ CLEAN</span>
+              </td>
+              <td>
+                <span className={styles.badgeYes}>✔ YES</span>
+              </td>
+              <td>
+                <a
+                  className={styles.commitLink}
+                  href={`https://github.com/BerriAI/litellm/commit/${entry.gitCommit}`}
+                  target="_blank"
+                  rel="noopener noreferrer"
+                >
+                  {entry.gitCommit}
+                </a>
+              </td>
+              <td>
+                <span className={styles.badgeClean}>✔ CLEAN</span>
+              </td>
+            </tr>
+          ))}
+        </tbody>
+      </table>
+    </div>
+  );
+}
diff --git a/docs/my-website/src/components/VersionVerificationTable/styles.module.css b/docs/my-website/src/components/VersionVerificationTable/styles.module.css
new file mode 100644
index 00000000000..97d2eb17e1c
--- /dev/null
+++ b/docs/my-website/src/components/VersionVerificationTable/styles.module.css
@@ -0,0 +1,106 @@
+.wrapper {
+  overflow-x: auto;
+  margin: 1rem 0;
+}
+
+.table {
+  width: 100%;
+  border-collapse: separate;
+  border-spacing: 0;
+  font-size: 0.9rem;
+  border: 1px solid var(--ifm-color-emphasis-300);
+  border-radius: 8px;
+  overflow: hidden;
+}
+
+.table th,
+.table td {
+  padding: 0.6rem 0.75rem;
+  text-align: left;
+  white-space: nowrap;
+}
+
+.table thead th {
+  background: var(--ifm-color-emphasis-200);
+  font-weight: 600;
+  font-size: 0.8rem;
+  text-transform: uppercase;
+  letter-spacing: 0.03em;
+  color: var(--ifm-color-emphasis-700);
+  border-bottom: 2px solid var(--ifm-color-emphasis-300);
+}
+
+.table tbody tr:nth-child(even) {
+  background: var(--ifm-color-emphasis-100);
+}
+
+.table tbody tr:hover {
+  background: var(--ifm-color-emphasis-200);
+}
+
+.table tbody td {
+  border-bottom: 1px solid var(--ifm-color-emphasis-200);
+}
+
+.table tbody tr:last-child td {
+  border-bottom: none;
+}
+
+.badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 4px;
+  padding: 2px 8px;
+  border-radius: 12px;
+  font-size: 0.75rem;
+  font-weight: 600;
+  line-height: 1.4;
+}
+
+.badgeClean {
+  composes: badge;
+  background: #d4edda;
+  color: #155724;
+}
+
+.badgeYes {
+  composes: badge;
+  background: #cce5ff;
+  color: #004085;
+}
+
+.sha {
+  display: inline-flex;
+  align-items: center;
+  gap: 4px;
+  font-family: var(--ifm-font-family-monospace);
+  font-size: 0.8rem;
+}
+
+.copyBtn {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  background: none;
+  border: 1px solid var(--ifm-color-emphasis-300);
+  border-radius: 4px;
+  cursor: pointer;
+  padding: 2px 4px;
+  font-size: 0.7rem;
+  color: var(--ifm-color-emphasis-600);
+  transition: background 0.15s, color 0.15s;
+}
+
+.copyBtn:hover {
+  background: var(--ifm-color-emphasis-200);
+  color: var(--ifm-color-emphasis-800);
+}
+
+.commitLink {
+  font-family: var(--ifm-font-family-monospace);
+  font-size: 0.8rem;
+}
+
+.version {
+  font-weight: 600;
+}
diff --git a/docs/my-website/static/img/vertex_cost_tracking_flow.svg b/docs/my-website/static/img/vertex_cost_tracking_flow.svg
new file mode 100644
index 00000000000..c3b2e33a073
--- /dev/null
+++ b/docs/my-website/static/img/vertex_cost_tracking_flow.svg
@@ -0,0 +1,63 @@
+<svg width="100%" viewBox="0 0 680 560" xmlns="http://www.w3.org/2000/svg">
+  <defs>
+    <marker id="arrow" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="6" markerHeight="6" orient="auto-start-reverse">
+      <path d="M2 1L8 5L2 9" fill="none" stroke="context-stroke" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
+    </marker>
+  </defs>
+
+  <!-- Step 1: HTTP Request -->
+  <g style="fill:rgb(0, 0, 0);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto">
+    <rect x="190" y="30" width="300" height="56" rx="8" stroke-width="0.5" style="fill:rgb(12, 68, 124);stroke:rgb(133, 183, 235);color:rgb(255, 255, 255);stroke-width:0.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+    <text x="340" y="52" text-anchor="middle" dominant-baseline="central" style="fill:rgb(181, 212, 244);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:14px;font-weight:500;text-anchor:middle;dominant-baseline:central">HTTP request</text>
+    <text x="340" y="70" text-anchor="middle" dominant-baseline="central" style="fill:rgb(133, 183, 235);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:middle;dominant-baseline:central">X-Vertex-AI-LLM-Shared-Request-Type: priority</text>
+  </g>
+
+  <!-- Arrow 1 -->
+  <line x1="340" y1="86" x2="340" y2="120" marker-end="url(#arrow)" style="fill:none;stroke:rgb(156, 154, 146);color:rgb(255, 255, 255);stroke-width:1.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+  <text x="356" y="108" dominant-baseline="central" style="fill:rgb(194, 192, 182);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:start;dominant-baseline:central">Vertex AI</text>
+
+  <!-- Step 2: Vertex response -->
+  <g style="fill:rgb(0, 0, 0);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto">
+    <rect x="190" y="120" width="300" height="56" rx="8" stroke-width="0.5" style="fill:rgb(8, 80, 65);stroke:rgb(93, 202, 165);color:rgb(255, 255, 255);stroke-width:0.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+    <text x="340" y="142" text-anchor="middle" dominant-baseline="central" style="fill:rgb(159, 225, 203);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:14px;font-weight:500;text-anchor:middle;dominant-baseline:central">Vertex response</text>
+    <text x="340" y="160" text-anchor="middle" dominant-baseline="central" style="fill:rgb(93, 202, 165);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:middle;dominant-baseline:central">usageMetadata.trafficType = ON_DEMAND_PRIORITY</text>
+  </g>
+
+  <!-- Arrow 2 -->
+  <line x1="340" y1="176" x2="340" y2="210" marker-end="url(#arrow)" style="fill:none;stroke:rgb(156, 154, 146);color:rgb(255, 255, 255);stroke-width:1.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+
+  <!-- Step 3: LiteLLM hidden params -->
+  <g style="fill:rgb(0, 0, 0);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto">
+    <rect x="190" y="210" width="300" height="56" rx="8" stroke-width="0.5" style="fill:rgb(60, 52, 137);stroke:rgb(175, 169, 236);color:rgb(255, 255, 255);stroke-width:0.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+    <text x="340" y="232" text-anchor="middle" dominant-baseline="central" style="fill:rgb(206, 203, 246);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:14px;font-weight:500;text-anchor:middle;dominant-baseline:central">LiteLLM stores it</text>
+    <text x="340" y="250" text-anchor="middle" dominant-baseline="central" style="fill:rgb(175, 169, 236);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:middle;dominant-baseline:central">_hidden_params.provider_specific_fields.traffic_type</text>
+  </g>
+
+  <!-- Arrow 3 -->
+  <line x1="340" y1="266" x2="340" y2="300" marker-end="url(#arrow)" style="fill:none;stroke:rgb(156, 154, 146);color:rgb(255, 255, 255);stroke-width:1.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+
+  <!-- Step 4: completion_cost() -->
+  <g style="fill:rgb(0, 0, 0);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto">
+    <rect x="190" y="300" width="300" height="56" rx="8" stroke-width="0.5" style="fill:rgb(99, 56, 6);stroke:rgb(239, 159, 39);color:rgb(255, 255, 255);stroke-width:0.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+    <text x="340" y="322" text-anchor="middle" dominant-baseline="central" style="fill:rgb(250, 199, 117);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:14px;font-weight:500;text-anchor:middle;dominant-baseline:central">completion_cost()</text>
+    <text x="340" y="340" text-anchor="middle" dominant-baseline="central" style="fill:rgb(239, 159, 39);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:middle;dominant-baseline:central">Maps traffic_type → service_tier = "priority"</text>
+  </g>
+
+  <!-- Arrow 4 -->
+  <line x1="340" y1="356" x2="340" y2="390" marker-end="url(#arrow)" style="fill:none;stroke:rgb(156, 154, 146);color:rgb(255, 255, 255);stroke-width:1.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+
+  <!-- Step 5: Pricing lookup -->
+  <g style="fill:rgb(0, 0, 0);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto">
+    <rect x="190" y="390" width="300" height="56" rx="8" stroke-width="0.5" style="fill:rgb(113, 43, 19);stroke:rgb(240, 153, 123);color:rgb(255, 255, 255);stroke-width:0.5px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:16px;font-weight:400;text-anchor:start;dominant-baseline:auto"/>
+    <text x="340" y="412" text-anchor="middle" dominant-baseline="central" style="fill:rgb(245, 196, 179);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:14px;font-weight:500;text-anchor:middle;dominant-baseline:central">Pricing lookup</text>
+    <text x="340" y="430" text-anchor="middle" dominant-baseline="central" style="fill:rgb(240, 153, 123);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:middle;dominant-baseline:central">input/output_cost_per_token_priority</text>
+  </g>
+
+  <!-- Step numbers in left margin -->
+  <text x="172" y="58" text-anchor="end" dominant-baseline="central" style="fill:rgb(194, 192, 182);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:end;dominant-baseline:central">①</text>
+  <text x="172" y="148" text-anchor="end" dominant-baseline="central" style="fill:rgb(194, 192, 182);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:end;dominant-baseline:central">②</text>
+  <text x="172" y="238" text-anchor="end" dominant-baseline="central" style="fill:rgb(194, 192, 182);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:end;dominant-baseline:central">③</text>
+  <text x="172" y="328" text-anchor="end" dominant-baseline="central" style="fill:rgb(194, 192, 182);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:end;dominant-baseline:central">④</text>
+  <text x="172" y="418" text-anchor="end" dominant-baseline="central" style="fill:rgb(194, 192, 182);stroke:none;color:rgb(255, 255, 255);stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;opacity:1;font-family:&quot;Anthropic Sans&quot;, -apple-system, &quot;system-ui&quot;, &quot;Segoe UI&quot;, sans-serif;font-size:12px;font-weight:400;text-anchor:end;dominant-baseline:central">⑤</text>
+
+</svg>
\ No newline at end of file
diff --git a/enterprise/LICENSE.md b/enterprise/LICENSE.md
index c14a2a0c487..c8607439adf 100644
--- a/enterprise/LICENSE.md
+++ b/enterprise/LICENSE.md
@@ -7,7 +7,7 @@ With regard to the BerriAI Software:
 This software and associated documentation files (the "Software") may only be
 used in production, if you (and any entity that you represent) have agreed to,
 and are in compliance with, the BerriAI Subscription Terms of Service, available
-via [call](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions) or email (info@berri.ai) (the "Enterprise Terms"), or other
+via [call](https://enterprise.litellm.ai/demo) or email (info@berri.ai) (the "Enterprise Terms"), or other
 agreement governing the use of the Software, as agreed by you and BerriAI,
 and otherwise have a valid BerriAI Enterprise license for the
 correct number of user seats. Subject to the foregoing sentence, you are free to
diff --git a/enterprise/README.md b/enterprise/README.md
index 3b2ada6dd82..f5eb5078e81 100644
--- a/enterprise/README.md
+++ b/enterprise/README.md
@@ -4,6 +4,6 @@ Code in this folder is licensed under a commercial license. Please review the [L
 
 **These features are covered under the LiteLLM Enterprise contract**
 
-👉 **Using in an Enterprise / Need specific features ?** Meet with us [here](https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions?month=2024-02)
+👉 **Using in an Enterprise / Need specific features ?** Meet with us [here](https://enterprise.litellm.ai/demo?month=2024-02)
 
 See all Enterprise Features here 👉 [Docs](https://docs.litellm.ai/docs/proxy/enterprise)
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
index 2f2e444850a..7a77898b160 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
@@ -632,7 +632,7 @@ class BaseEmailLogger(CustomLogger):
             warning_msg = (
                 f"Email sent with default values instead of custom values for: {fields_str}. "
                 "This is an Enterprise feature. To use custom email fields, please upgrade to LiteLLM Enterprise. "
-                "Schedule a meeting here: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions"
+                "Schedule a meeting here: https://enterprise.litellm.ai/demo"
             )
             verbose_proxy_logger.warning(f"{warning_msg}")
 
diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py
index cbe8d449b42..356f6ecd4b5 100644
--- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py
+++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py
@@ -3,7 +3,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if t
 """
 
 from datetime import datetime, timedelta, timezone
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, List, Optional, Tuple
 
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid
@@ -118,6 +118,15 @@ class CheckBatchCost:
             get_model_id_from_unified_batch_id,
         )
 
+        try:
+            from litellm.integrations.prometheus import PrometheusLogger
+            prom_logger = PrometheusLogger.get_instance()
+        except Exception as e:
+            verbose_proxy_logger.error(f"CheckBatchCost: could not get Prometheus logger: {e}")
+            prom_logger = None
+
+        processed_models: List[Tuple[Optional[str], Optional[str]]] = []
+
         try:
             await self._cleanup_stale_managed_objects()
         except Exception as cleanup_err:
@@ -172,6 +181,8 @@ class CheckBatchCost:
                 verbose_proxy_logger.info(
                     f"Skipping job {unified_object_id} because it is not a valid unified object id"
                 )
+                if prom_logger:
+                    prom_logger.record_check_batch_cost_error("invalid_unified_id")
                 continue
             else:
                 unified_object_id = decoded_unified_object_id
@@ -183,6 +194,8 @@ class CheckBatchCost:
                 verbose_proxy_logger.info(
                     f"Skipping job {unified_object_id} because it is not a valid model id"
                 )
+                if prom_logger:
+                    prom_logger.record_check_batch_cost_error("invalid_model_id")
                 continue
 
             verbose_proxy_logger.info(
@@ -202,6 +215,8 @@ class CheckBatchCost:
                 verbose_proxy_logger.info(
                     f"Skipping job {unified_object_id} because of error querying model ID: {model_id} for cost and usage of batch ID: {batch_id}: {e}"
                 )
+                if prom_logger:
+                    prom_logger.record_check_batch_cost_error("provider_retrieval_error")
                 continue
 
             ## RETRIEVE THE BATCH JOB OUTPUT FILE
@@ -257,11 +272,25 @@ class CheckBatchCost:
                     content_bytes  # type: ignore[arg-type]
                 )
 
+                # Record output file size
+                if prom_logger and content_bytes:
+                    try:
+                        prom_logger.record_managed_file_size(
+                            size_bytes=len(content_bytes),  # type: ignore
+                            purpose="batch",
+                            file_type="output",
+                            model=model_id,
+                        )
+                    except Exception:
+                        pass
+
                 deployment_info = self.llm_router.get_deployment(model_id=model_id)
                 if deployment_info is None:
                     verbose_proxy_logger.info(
                         f"Skipping job {unified_object_id} because it is not a valid deployment info"
                     )
+                    if prom_logger:
+                        prom_logger.record_check_batch_cost_error("deployment_not_found")
                     continue
                 custom_llm_provider = deployment_info.litellm_params.custom_llm_provider
                 litellm_model_name = deployment_info.litellm_params.model
@@ -318,6 +347,19 @@ class CheckBatchCost:
                     batch_models=batch_models,
                 )
 
+                # Record batch duration (completed_at - created_at)
+                if prom_logger and response.completed_at and response.created_at:
+                    duration_seconds = float(response.completed_at - response.created_at)
+                    if duration_seconds >= 0:
+                        prom_logger.record_managed_batch_duration(
+                            duration_seconds=duration_seconds,
+                            model=model_name,
+                            api_provider=str(llm_provider) if llm_provider else None,
+                        )
+
+                # Track this job for the final metrics summary
+                processed_models.append((model_name, str(llm_provider) if llm_provider else None))
+
                 # mark the job as complete
                 try:
                     update_data: dict = {
@@ -334,3 +376,10 @@ class CheckBatchCost:
                     verbose_proxy_logger.error(
                         f"CheckBatchCost: failed to mark job {job.id} complete in DB: {db_err}"
                     )
+
+        # Record polling run metrics (always, even if nothing was processed)
+        if prom_logger:
+            prom_logger.record_check_batch_cost_run(
+                jobs_polled=len(jobs),
+                processed_models=processed_models if processed_models else None,
+            )
diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
index 5530054170c..60c564072a0 100644
--- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
+++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
@@ -29,7 +29,7 @@ from litellm.proxy.openai_files_endpoints.common_utils import (
     get_models_from_unified_file_id,
     normalize_mime_type_for_provider,
 )
-from litellm.types.llms.openai import (
+from litellm.types.llms.openai import (  # pyright: ignore[reportAttributeAccessIssue]
     AllMessageValues,
     AsyncCursorPage,
     ChatCompletionFileObject,
@@ -74,6 +74,13 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         self.internal_usage_cache = internal_usage_cache
         self.prisma_client = prisma_client
 
+    @staticmethod
+    def _get_prometheus_logger():
+        """Find PrometheusLogger from litellm.callbacks, if registered."""
+        from litellm.integrations.prometheus import PrometheusLogger
+
+        return PrometheusLogger.get_instance()
+
     async def store_unified_file_id(
         self,
         file_id: str,
@@ -442,25 +449,33 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         elif call_type == CallTypes.aresponses.value or call_type == CallTypes.responses.value:
             # Handle managed files in responses API input and tools
             file_ids = []
-            
+
             # Extract file IDs from input parameter
             input_data = data.get("input")
             if input_data:
                 file_ids.extend(self.get_file_ids_from_responses_input(input_data))
-            
+
             # Extract file IDs from tools parameter (e.g., code_interpreter container)
             tools = data.get("tools")
             if tools:
                 file_ids.extend(self.get_file_ids_from_responses_tools(tools))
-            
+
             if file_ids:
                 # Check user has access to all managed files
                 await self.check_file_ids_access(file_ids, user_api_key_dict)
-                
+
                 model_file_id_mapping = await self.get_model_file_id_mapping(
                     file_ids, user_api_key_dict.parent_otel_span
                 )
                 data["model_file_id_mapping"] = model_file_id_mapping
+
+            # Check access for file_search vector_store_ids
+            if tools:
+                unified_vs_ids = self.get_vector_store_ids_from_file_search_tools(tools)
+                if unified_vs_ids:
+                    await self.check_vector_store_ids_access(
+                        unified_vs_ids, user_api_key_dict
+                    )
         elif call_type == CallTypes.afile_content.value:
             retrieve_file_id = cast(Optional[str], data.get("file_id"))
             potential_file_id = (
@@ -704,6 +719,101 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         
         return file_ids
 
+    def get_vector_store_ids_from_file_search_tools(
+        self, tools: List[Dict[str, Any]]
+    ) -> List[str]:
+        """
+        Extract unified vector_store_ids from file_search tools.
+
+        Only returns IDs that are LiteLLM-managed (base64 unified IDs).
+        Native provider IDs are skipped — they have no LiteLLM access record.
+        """
+        from litellm.llms.base_llm.managed_resources.utils import (
+            is_base64_encoded_unified_id,
+        )
+
+        vs_ids: List[str] = []
+        if not isinstance(tools, list):
+            return vs_ids
+
+        for tool in tools:
+            if not isinstance(tool, dict) or tool.get("type") != "file_search":
+                continue
+            vector_store_ids = tool.get("vector_store_ids")
+            if not isinstance(vector_store_ids, list):
+                continue
+            for vs_id in vector_store_ids:
+                if isinstance(vs_id, str) and is_base64_encoded_unified_id(vs_id):
+                    vs_ids.append(vs_id)
+
+        return vs_ids
+
+    async def check_vector_store_ids_access(
+        self,
+        vector_store_ids: List[str],
+        user_api_key_dict: UserAPIKeyAuth,
+    ) -> None:
+        """
+        Verify the caller's team can access each LiteLLM-managed vector store.
+
+        Batch-fetches vector stores from DB and checks team_id.
+        Raises HTTPException(403) on the first access violation.
+        Non-managed (native) IDs should already be filtered out before calling this.
+        """
+        from litellm.llms.base_llm.managed_resources.utils import (
+            extract_unified_uuid_from_unified_id,
+        )
+        from litellm.proxy.auth.auth_checks import (
+            get_managed_vector_store_rows_by_uuids,
+        )
+        from litellm.proxy.proxy_server import (
+            prisma_client,
+            proxy_logging_obj,
+            user_api_key_cache,
+        )
+
+        if not vector_store_ids or prisma_client is None:
+            return
+
+        # Map each unified ID to its internal UUID for a single batch DB fetch
+        uuid_to_unified: Dict[str, str] = {}
+        for vs_id in vector_store_ids:
+            uuid = extract_unified_uuid_from_unified_id(vs_id)
+            if uuid:
+                uuid_to_unified[uuid] = vs_id
+
+        if not uuid_to_unified:
+            return
+
+        rows = await get_managed_vector_store_rows_by_uuids(
+            uuids=list(uuid_to_unified.keys()),
+            prisma_client=prisma_client,
+            user_api_key_cache=user_api_key_cache,
+            proxy_logging_obj=proxy_logging_obj,
+        )
+
+        found_uuids = {row.vector_store_id for row in rows}
+
+        for uuid, original_id in uuid_to_unified.items():
+            if uuid not in found_uuids:
+                raise HTTPException(
+                    status_code=403,
+                    detail=f"Vector store '{original_id}' not found or access denied.",
+                )
+
+        caller_team_id = user_api_key_dict.team_id
+        for row in rows:
+            vs_team_id = getattr(row, "team_id", None)
+            if vs_team_id is not None and vs_team_id != caller_team_id:
+                raise HTTPException(
+                    status_code=403,
+                    detail=(
+                        f"Team '{caller_team_id}' does not have access to vector "
+                        f"store '{row.vector_store_id}'. The store belongs to team "
+                        f"'{vs_team_id}'."
+                    ),
+                )
+
     async def get_model_file_id_mapping(
         self, file_ids: List[str], litellm_parent_otel_span: Span
     ) -> dict:
@@ -802,6 +912,31 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             model_mappings=model_mappings,
             user_api_key_dict=user_api_key_dict,
         )
+
+        # Emit Prometheus metrics for managed file creation
+        prom_logger = self._get_prometheus_logger()
+        if prom_logger:
+            first_model = target_model_names_list[0] if target_model_names_list else None
+            first_provider = ""
+            if responses:
+                first_provider = getattr(responses[0], "_hidden_params", {}).get("custom_llm_provider") or ""
+            prom_logger.record_managed_file_created(
+                model=first_model or "",
+                api_provider=first_provider,
+                user=user_api_key_dict.user_id or "",
+                user_email=getattr(user_api_key_dict, "user_email", None) or "",
+                api_key_alias=user_api_key_dict.key_alias or "",
+            )
+            if response.bytes and response.bytes > 0:
+                prom_logger.record_managed_file_size(
+                    size_bytes=response.bytes,
+                    purpose=response.purpose or "batch",
+                    file_type="input",
+                    model=first_model,
+                    api_provider=first_provider,
+                    user=user_api_key_dict.user_id,
+                )
+
         return response
 
     @staticmethod
@@ -954,7 +1089,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                                 )
                             else:
                                 file_object = await litellm.afile_retrieve(
-                                    custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai",
+                                    custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai",  # type: ignore[arg-type]
                                     file_id=original_file_id,
                                 )
                             verbose_logger.debug(
@@ -980,6 +1115,31 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                 file_purpose="batch",
                 user_api_key_dict=user_api_key_dict,
             )
+
+            # Only record batch creation metric on actual create (not retrieve/cancel).
+            # unified_file_id in _hidden_params is only set by the create_batch endpoint.
+            original_unified_file_id = response._hidden_params.get("unified_file_id")
+            if original_unified_file_id:
+                prom_logger = self._get_prometheus_logger()
+                if prom_logger:
+                    batch_provider = ""
+                    if model_name:
+                        try:
+                            from litellm.litellm_core_utils.get_llm_provider_logic import (
+                                get_llm_provider,
+                            )
+                            _, batch_provider, _, _ = get_llm_provider(model=model_name)
+                        except Exception:
+                            if "/" in model_name:
+                                batch_provider = model_name.split("/")[0]
+                    prom_logger.record_managed_batch_created(
+                        model=model_name or "",
+                        api_provider=batch_provider,
+                        user=user_api_key_dict.user_id or "",
+                        user_email=getattr(user_api_key_dict, "user_email", None) or "",
+                        api_key_alias=user_api_key_dict.key_alias or "",
+                    )
+
         elif isinstance(response, LiteLLMFineTuningJob):
             ## Check if unified_file_id is in the response
             unified_file_id = response._hidden_params.get(
@@ -1229,6 +1389,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                 f"Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)."
             )
             
+            # Record blocked deletion metric
+            prom_logger = self._get_prometheus_logger()
+            if prom_logger:
+                prom_logger.record_managed_file_deleted(result="blocked")
+
             raise HTTPException(
                 status_code=400,
                 detail=error_message,
@@ -1262,6 +1427,12 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             file_id, litellm_parent_otel_span
         )
 
+        # Record successful deletion metric only on actual success
+        if stored_file_object or delete_response:
+            prom_logger = self._get_prometheus_logger()
+            if prom_logger:
+                prom_logger.record_managed_file_deleted(result="success")
+
         if stored_file_object:
             return stored_file_object
         elif delete_response:
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60-py3-none-any.whl
new file mode 100644
index 00000000000..58b90398154
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60.tar.gz
new file mode 100644
index 00000000000..390a849e667
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.60.tar.gz differ
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 8aa9e70e43c..14253ad15db 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm-proxy-extras"
-version = "0.4.58"
+version = "0.4.60"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 authors = ["BerriAI"]
 readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "0.4.58"
+version = "0.4.60"
 version_files = [
     "pyproject.toml:version",
     "../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 7f72e0b0e89..e45d926e8db 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -97,6 +97,7 @@ input_callback: List[CALLBACK_TYPES] = []
 success_callback: List[CALLBACK_TYPES] = []
 failure_callback: List[CALLBACK_TYPES] = []
 service_callback: List[CALLBACK_TYPES] = []
+audit_log_callbacks: List[CALLBACK_TYPES] = []
 # logging_callback_manager is lazy-loaded via __getattr__
 _custom_logger_compatible_callbacks_literal = Literal[
     "lago",
diff --git a/litellm/_logging.py b/litellm/_logging.py
index 18c3bcb7e87..65e6045b0b1 100644
--- a/litellm/_logging.py
+++ b/litellm/_logging.py
@@ -52,6 +52,17 @@ def _build_secret_patterns() -> re.Pattern:
         r"(?<=://)[^\s'\"]*:[^\s'\"@]+(?=@)",
         # Databricks personal access tokens
         r"dapi[0-9a-f]{32}",
+        # ── Key-name-based redaction ──
+        # Catches secrets inside dicts/config dumps by matching on the KEY name
+        # regardless of what the value looks like.
+        # e.g. 'master_key': 'any-value-here', "database_url": "postgres://..."
+        r"(?:master_key|database_url|db_url|connection_string|"
+        r"private_key|signing_key|encryption_key|"
+        r"auth_token|access_token|refresh_token|"
+        r"slack_webhook_url|webhook_url|"
+        r"database_connection_string|"
+        r"huggingface_token|jwt_secret)"
+        r"""['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+""",
     ]
     return re.compile("|".join(patterns), re.IGNORECASE)
 
@@ -272,7 +283,7 @@ verbose_proxy_logger = logging.getLogger("LiteLLM Proxy")
 verbose_router_logger = logging.getLogger("LiteLLM Router")
 verbose_logger = logging.getLogger("LiteLLM")
 
-# Add the handler to the logger
+# Add the handler to the loggers
 verbose_router_logger.addHandler(handler)
 verbose_proxy_logger.addHandler(handler)
 verbose_logger.addHandler(handler)
diff --git a/litellm/_redis.py b/litellm/_redis.py
index b754c1f4330..2bf32d71b21 100644
--- a/litellm/_redis.py
+++ b/litellm/_redis.py
@@ -222,8 +222,12 @@ def _get_redis_client_logic(**env_overrides):
         "REDIS_CLUSTER_NODES"
     )
 
+    # If startup_nodes resolved to None (not set by kwarg or env), remove the key
+    # entirely so callers can rely on key presence as a reliable cluster-mode signal.
     if _startup_nodes is not None and isinstance(_startup_nodes, str):
         redis_kwargs["startup_nodes"] = json.loads(_startup_nodes)
+    elif _startup_nodes is None:
+        redis_kwargs.pop("startup_nodes", None)
 
     _sentinel_nodes: Optional[Union[str, list]] = redis_kwargs.get("sentinel_nodes", None) or get_secret(  # type: ignore
         "REDIS_SENTINEL_NODES"
@@ -273,10 +277,14 @@ def _get_redis_client_logic(**env_overrides):
             redis_kwargs["ssl_ca_certs"] = _gcp_ssl_ca_certs
 
     if "url" in redis_kwargs and redis_kwargs["url"] is not None:
-        redis_kwargs.pop("host", None)
-        redis_kwargs.pop("port", None)
-        redis_kwargs.pop("db", None)
-        redis_kwargs.pop("password", None)
+        # Only strip host/port/db/password when not routing to a cluster.
+        # When startup_nodes is also present the cluster path takes priority and
+        # needs the password for authentication.
+        if not redis_kwargs.get("startup_nodes"):
+            redis_kwargs.pop("host", None)
+            redis_kwargs.pop("port", None)
+            redis_kwargs.pop("db", None)
+            redis_kwargs.pop("password", None)
     elif "startup_nodes" in redis_kwargs and redis_kwargs["startup_nodes"] is not None:
         pass
     elif (
@@ -368,6 +376,10 @@ def _init_async_redis_sentinel(redis_kwargs) -> async_redis.Redis:
 
 def get_redis_client(**env_overrides):
     redis_kwargs = _get_redis_client_logic(**env_overrides)
+
+    if "startup_nodes" in redis_kwargs:
+        return init_redis_cluster(redis_kwargs)
+
     if "url" in redis_kwargs and redis_kwargs["url"] is not None:
         args = _get_redis_url_kwargs()
         url_kwargs = {}
@@ -377,9 +389,6 @@ def get_redis_client(**env_overrides):
 
         return redis.Redis.from_url(**url_kwargs)
 
-    if "startup_nodes" in redis_kwargs or get_secret("REDIS_CLUSTER_NODES") is not None:  # type: ignore
-        return init_redis_cluster(redis_kwargs)
-
     # Check for Redis Sentinel
     if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs:
         return _init_redis_sentinel(redis_kwargs)
@@ -392,21 +401,6 @@ def get_redis_async_client(
     **env_overrides,
 ) -> Union[async_redis.Redis, async_redis.RedisCluster]:
     redis_kwargs = _get_redis_client_logic(**env_overrides)
-    if "url" in redis_kwargs and redis_kwargs["url"] is not None:
-        if connection_pool is not None:
-            return async_redis.Redis(connection_pool=connection_pool)
-        args = _get_redis_url_kwargs(client=async_redis.Redis.from_url)
-        url_kwargs = {}
-        for arg in redis_kwargs:
-            if arg in args:
-                url_kwargs[arg] = redis_kwargs[arg]
-            else:
-                verbose_logger.debug(
-                    "REDIS: ignoring argument: {}. Not an allowed async_redis.Redis.from_url arg.".format(
-                        arg
-                    )
-                )
-        return async_redis.Redis.from_url(**url_kwargs)
 
     if "startup_nodes" in redis_kwargs:
         from redis.cluster import ClusterNode
@@ -469,6 +463,22 @@ def get_redis_async_client(
 
         return cluster_client
 
+    if "url" in redis_kwargs and redis_kwargs["url"] is not None:
+        if connection_pool is not None:
+            return async_redis.Redis(connection_pool=connection_pool)
+        args = _get_redis_url_kwargs(client=async_redis.Redis.from_url)
+        url_kwargs = {}
+        for arg in redis_kwargs:
+            if arg in args:
+                url_kwargs[arg] = redis_kwargs[arg]
+            else:
+                verbose_logger.debug(
+                    "REDIS: ignoring argument: {}. Not an allowed async_redis.Redis.from_url arg.".format(
+                        arg
+                    )
+                )
+        return async_redis.Redis.from_url(**url_kwargs)
+
     # Check for Redis Sentinel
     if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs:
         return _init_async_redis_sentinel(redis_kwargs)
@@ -482,9 +492,15 @@ def get_redis_async_client(
     )
 
 
-def get_redis_connection_pool(**env_overrides):
+def get_redis_connection_pool(
+    **env_overrides,
+) -> Optional[async_redis.BlockingConnectionPool]:
     redis_kwargs = _get_redis_client_logic(**env_overrides)
     verbose_logger.debug("get_redis_connection_pool: redis_kwargs", redis_kwargs)
+
+    if "startup_nodes" in redis_kwargs:
+        return None
+
     if "url" in redis_kwargs and redis_kwargs["url"] is not None:
         pool_kwargs = {
             "timeout": REDIS_CONNECTION_POOL_TIMEOUT,
@@ -504,7 +520,6 @@ def get_redis_connection_pool(**env_overrides):
         connection_class = async_redis.SSLConnection
         redis_kwargs.pop("ssl", None)
         redis_kwargs["connection_class"] = connection_class
-    redis_kwargs.pop("startup_nodes", None)
     return async_redis.BlockingConnectionPool(
         timeout=REDIS_CONNECTION_POOL_TIMEOUT, **redis_kwargs
     )
diff --git a/litellm/batches/main.py b/litellm/batches/main.py
index 17d73aae6ad..23f444b1cea 100644
--- a/litellm/batches/main.py
+++ b/litellm/batches/main.py
@@ -885,7 +885,7 @@ def list_batches(
 async def acancel_batch(
     batch_id: str,
     model: Optional[str] = None,
-    custom_llm_provider: Literal["openai", "azure"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
     metadata: Optional[Dict[str, str]] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
@@ -931,7 +931,7 @@ async def acancel_batch(
 def cancel_batch(
     batch_id: str,
     model: Optional[str] = None,
-    custom_llm_provider: Union[Literal["openai", "azure"], str] = "openai",
+    custom_llm_provider: Union[Literal["openai", "azure", "vertex_ai"], str] = "openai",
     metadata: Optional[Dict[str, str]] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
@@ -1048,9 +1048,35 @@ def cancel_batch(
                 cancel_batch_data=_cancel_batch_request,
                 litellm_params=litellm_params,
             )
+        elif custom_llm_provider == "vertex_ai":
+            api_base = optional_params.api_base or None
+            vertex_ai_project = (
+                optional_params.vertex_project
+                or litellm.vertex_project
+                or get_secret_str("VERTEXAI_PROJECT")
+            )
+            vertex_ai_location = (
+                optional_params.vertex_location
+                or litellm.vertex_location
+                or get_secret_str("VERTEXAI_LOCATION")
+            )
+            vertex_credentials = optional_params.vertex_credentials or get_secret_str(
+                "VERTEXAI_CREDENTIALS"
+            )
+
+            response = vertex_ai_batches_instance.cancel_batch(
+                _is_async=_is_async,
+                batch_id=batch_id,
+                api_base=api_base,
+                vertex_project=vertex_ai_project,
+                vertex_location=vertex_ai_location,
+                vertex_credentials=vertex_credentials,
+                timeout=timeout,
+                max_retries=optional_params.max_retries,
+            )
         else:
             raise litellm.exceptions.BadRequestError(
-                message="LiteLLM doesn't support {} for 'cancel_batch'. Only 'openai' and 'azure' are supported.".format(
+                message="LiteLLM doesn't support {} for 'cancel_batch'. Only 'openai', 'azure', and 'vertex_ai' are supported.".format(
                     custom_llm_provider
                 ),
                 model="n/a",
diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py
index 53ffd3647bd..6e6070f7f2c 100644
--- a/litellm/completion_extras/litellm_responses_transformation/transformation.py
+++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py
@@ -32,6 +32,7 @@ from litellm.llms.base_llm.bridges.completion_transformation import (
 )
 from litellm.types.llms.openai import (
     ChatCompletionAnnotation,
+    ChatCompletionReasoningItem,
     ChatCompletionToolParamFunctionChunk,
     Reasoning,
     ResponsesAPIOptionalRequestParams,
@@ -55,6 +56,49 @@ if TYPE_CHECKING:
     )
 
 
+def _build_reasoning_item(
+    item_id: str,
+    encrypted_content: Optional[str],
+    summary_raw: Any,
+) -> Dict[str, Any]:
+    """Build a ChatCompletionReasoningItem-shaped dict from raw response data.
+
+    Handles both pydantic objects (attribute access) and plain dicts.
+    """
+    summary: List[Dict[str, Any]] = []
+    for s in summary_raw or []:
+        if isinstance(s, dict):
+            summary.append(
+                {"type": s.get("type", "summary_text"), "text": s.get("text", "")}
+            )
+        else:
+            summary.append(
+                {
+                    "type": getattr(s, "type", "summary_text"),
+                    "text": getattr(s, "text", ""),
+                }
+            )
+    return {
+        "id": item_id,
+        "type": "reasoning",
+        "encrypted_content": encrypted_content,
+        "summary": summary,
+    }
+
+
+def _reasoning_item_to_response_input(r_item: Dict[str, Any]) -> Dict[str, Any]:
+    """Convert a stored ChatCompletionReasoningItem back to a Responses API input item."""
+    r_input: Dict[str, Any] = {
+        "type": "reasoning",
+        "id": r_item.get("id") or f"rs_{id(r_item)}",
+        # summary is always required by the Responses API, even when empty
+        "summary": r_item.get("summary") or [],
+    }
+    if r_item.get("encrypted_content"):
+        r_input["encrypted_content"] = r_item["encrypted_content"]
+    return r_input
+
+
 class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
     """
     Handler for transforming /chat/completions api requests to litellm.responses requests
@@ -202,10 +246,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                     }
                 )
             elif role == "assistant" and tool_calls and isinstance(tool_calls, list):
+                for r_item in msg.get("reasoning_items") or []:
+                    input_items.append(_reasoning_item_to_response_input(r_item))
                 for tool_call in tool_calls:
                     function = tool_call.get("function")
                     if function:
-                        input_tool_call = {
+                        input_tool_call: Dict[str, Any] = {
                             "type": "function_call",
                             "call_id": tool_call["id"],
                         }
@@ -217,7 +263,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                     else:
                         raise ValueError(f"tool call not supported: {tool_call}")
             elif content is not None:
-                # Regular user/assistant message
+                if role == "assistant":
+                    for r_item in msg.get("reasoning_items") or []:
+                        input_items.append(_reasoning_item_to_response_input(r_item))
                 input_items.append(
                     {
                         "type": "message",
@@ -411,6 +459,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         choices: List[Choices] = []
         index = 0
         reasoning_content: Optional[str] = None
+        pending_reasoning_item: Optional[Dict[str, Any]] = None
 
         # Collect all tool calls to put them in a single choice
         # (Chat Completions API expects all tool calls in one message)
@@ -419,9 +468,16 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
 
         for item in output_items:
             if isinstance(item, ResponseReasoningItem):
-                for summary_item in item.summary:
-                    response_text = getattr(summary_item, "text", "")
-                    reasoning_content = response_text if response_text else ""
+                pending_reasoning_item = _build_reasoning_item(
+                    item_id=item.id,
+                    encrypted_content=getattr(item, "encrypted_content", None),
+                    summary_raw=item.summary,
+                )
+                reasoning_content = " ".join(
+                    s["text"]
+                    for s in pending_reasoning_item["summary"]
+                    if s.get("text")
+                )
 
             elif isinstance(item, ResponseOutputMessage):
                 for content in item.content:
@@ -436,6 +492,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                         content=response_text if response_text else "",
                         reasoning_content=reasoning_content,
                         annotations=annotations,
+                        reasoning_items=cast(
+                            Optional[List[ChatCompletionReasoningItem]],
+                            [pending_reasoning_item]
+                            if pending_reasoning_item is not None
+                            else None,
+                        ),
                     )
 
                     choices.append(
@@ -446,7 +508,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                         )
                     )
 
-                    reasoning_content = None  # flush reasoning content
+                    reasoning_content = None  # flush
+                    pending_reasoning_item = None  # flush
                     index += 1
 
             elif isinstance(item, ResponseFunctionToolCall):
@@ -489,11 +552,18 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                 content=None,
                 tool_calls=accumulated_tool_calls,
                 reasoning_content=reasoning_content,
+                reasoning_items=cast(
+                    Optional[List[ChatCompletionReasoningItem]],
+                    [pending_reasoning_item]
+                    if pending_reasoning_item is not None
+                    else None,
+                ),
             )
             choices.append(
                 Choices(message=msg, finish_reason="tool_calls", index=index)
             )
             reasoning_content = None
+            pending_reasoning_item = None
 
         return choices
 
@@ -696,6 +766,20 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                             verbose_logger.debug(
                                 f"Chat provider:   image -> {converted}"
                             )
+                        elif item_type == "file":
+                            # Map Chat Completion file to Responses API input_file
+                            # {"type": "file", "file": {"file_data": "...", "filename": "..."}}
+                            # -> {"type": "input_file", "file_data": "...", "filename": "..."}
+                            file_data = item.get("file", {})
+                            converted = {"type": "input_file"}
+                            if isinstance(file_data, dict):
+                                for key in ["file_id", "file_data", "filename"]:
+                                    if key in file_data:
+                                        converted[key] = file_data[key]
+                            result.append(converted)
+                            verbose_logger.debug(
+                                f"Chat provider:   file -> {converted}"
+                            )
                         elif item_type in [
                             "input_text",
                             "input_image",
@@ -1218,6 +1302,25 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
 
             finish_reason = "tool_calls" if has_function_calls else "stop"
 
+            # Extract reasoning items with encrypted_content for round-tripping
+            completed_reasoning_items: Optional[List[Dict[str, Any]]] = None
+            for item in output_items:
+                if not isinstance(item, dict) or item.get("type") != "reasoning":
+                    continue
+                if completed_reasoning_items is None:
+                    completed_reasoning_items = []
+                completed_reasoning_items.append(
+                    _build_reasoning_item(
+                        item_id=item.get("id", ""),
+                        encrypted_content=item.get("encrypted_content"),
+                        summary_raw=item.get("summary"),
+                    )
+                )
+            completed_reasoning_items_typed = cast(
+                Optional[List[ChatCompletionReasoningItem]],
+                completed_reasoning_items,
+            )
+
             usage = None
             if response_data.get("usage"):
                 from litellm.responses.utils import ResponseAPILoggingUtils
@@ -1231,7 +1334,10 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
                 choices=[
                     StreamingChoices(
                         index=0,
-                        delta=Delta(content=""),
+                        delta=Delta(
+                            content="",
+                            reasoning_items=completed_reasoning_items_typed,
+                        ),
                         finish_reason=finish_reason,
                     )
                 ],
diff --git a/litellm/constants.py b/litellm/constants.py
index e7890c5bf09..cb004486bec 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -1428,6 +1428,9 @@ DEFAULT_SHARED_HEALTH_CHECK_TTL = int(
 DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL = int(
     os.getenv("DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL", 60)
 )  # 1 minute - TTL for health check lock
+DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER = (
+    2  # health state is stale after interval * this
+)
 PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS = int(
     os.getenv("PROMETHEUS_FALLBACK_STATS_SEND_TIME_HOURS", 9)
 )
diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py
index 06ba9675ca2..cccabf53e51 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -27,6 +27,7 @@ from litellm.types.utils import (
     LLMResponseTypes,
     ModelResponse,
     ModelResponseStream,
+    StandardAuditLogPayload,
     StandardCallbackDynamicParams,
     StandardLoggingPayload,
 )
@@ -177,6 +178,10 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
     async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
         pass
 
+    async def async_log_audit_log_event(self, audit_log: "StandardAuditLogPayload"):
+        """Called when an audit log is created. Override in subclasses to handle."""
+        pass
+
     #### PROMPT MANAGEMENT HOOKS ####
 
     async def async_get_chat_completion_prompt(
diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py
index 9cc37359928..b931d7ecfe7 100644
--- a/litellm/integrations/langsmith.py
+++ b/litellm/integrations/langsmith.py
@@ -83,7 +83,28 @@ class LangsmithLogger(CustomBatchLogger):
         if _batch_size:
             self.batch_size = int(_batch_size)
         self.log_queue: List[LangsmithQueueObject] = []
-        asyncio.create_task(self.periodic_flush())
+        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(
+                "Langsmith logger init: no running event loop, skipping periodic flush task startup"
+            )
+            return None
+
+        return loop.create_task(self.periodic_flush())
+
+    def _ensure_periodic_flush_task(self) -> None:
+        # This helper is intentionally synchronous. In asyncio's cooperative
+        # execution model, there is no await between the check and assignment,
+        # so one caller cannot interleave here and create a duplicate task.
+        if self._flush_task is None or self._flush_task.done():
+            self._flush_task = self._start_periodic_flush_task()
 
     def get_credentials_from_env(
         self,
@@ -266,6 +287,7 @@ class LangsmithLogger(CustomBatchLogger):
 
     async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
         try:
+            self._ensure_periodic_flush_task()
             sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs)
             random_sample = random.random()
             if random_sample > sampling_rate:
@@ -307,17 +329,18 @@ class LangsmithLogger(CustomBatchLogger):
             )
 
     async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
-        sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs)
-        random_sample = random.random()
-        if random_sample > sampling_rate:
-            verbose_logger.info(
-                "Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
-                    sampling_rate, random_sample
-                )
-            )
-            return  # Skip logging
-        verbose_logger.info("Langsmith Failure Event Logging!")
         try:
+            self._ensure_periodic_flush_task()
+            sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs)
+            random_sample = random.random()
+            if random_sample > sampling_rate:
+                verbose_logger.info(
+                    "Skipping Langsmith logging. Sampling rate={}, random_sample={}".format(
+                        sampling_rate, random_sample
+                    )
+                )
+                return  # Skip logging
+            verbose_logger.info("Langsmith Failure Event Logging!")
             credentials = self._get_credentials_to_use_for_request(kwargs=kwargs)
             data = self._prepare_log_data(
                 kwargs=kwargs,
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 357e0229fc6..8de01aac60b 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -65,6 +65,17 @@ def _get_cached_end_user_id_for_cost_tracking():
 
 class PrometheusLogger(CustomLogger):
     # Class variables or attributes
+
+    @staticmethod
+    def get_instance() -> Optional["PrometheusLogger"]:
+        """Find the PrometheusLogger instance from litellm.callbacks, if registered."""
+        import litellm
+
+        for cb in litellm.callbacks:
+            if isinstance(cb, PrometheusLogger):
+                return cb
+        return None
+
     def __init__(  # noqa: PLR0915
         self,
         **kwargs,
@@ -440,6 +451,76 @@ class PrometheusLogger(CustomLogger):
                 labelnames=[],
             )
 
+            ########################################
+            # Managed Batch Metrics
+            ########################################
+            self.litellm_managed_batch_created_total = self._counter_factory(
+                name="litellm_managed_batch_created_total",
+                documentation="Total number of managed batches created",
+                labelnames=[
+                    "model",
+                    "api_provider",
+                    "user",
+                    "user_email",
+                    "api_key_alias",
+                ],
+            )
+
+            self.litellm_managed_file_size_bytes = self._gauge_factory(
+                "litellm_managed_file_size_bytes",
+                "Size of the most recent managed batch file in bytes (last-seen value per label combination)",
+                labelnames=["purpose", "file_type", "model", "api_provider", "user"],
+            )
+
+            self.litellm_managed_batch_duration_seconds = self._histogram_factory(
+                "litellm_managed_batch_duration_seconds",
+                "Duration of completed managed batches in seconds (completed_at - created_at)",
+                labelnames=["model", "api_provider"],
+                buckets=BATCH_DURATION_BUCKETS,
+            )
+
+            self.litellm_managed_file_created_total = self._counter_factory(
+                name="litellm_managed_file_created_total",
+                documentation="Total number of managed files created",
+                labelnames=[
+                    "model",
+                    "api_provider",
+                    "user",
+                    "user_email",
+                    "api_key_alias",
+                ],
+            )
+
+            self.litellm_managed_file_deleted_total = self._counter_factory(
+                name="litellm_managed_file_deleted_total",
+                documentation="Total number of managed file deletions (success or blocked)",
+                labelnames=["result"],
+            )
+
+            self.litellm_check_batch_cost_jobs_polled = self._gauge_factory(
+                "litellm_check_batch_cost_jobs_polled",
+                "Number of unprocessed batches found by the last CheckBatchCost poll",
+                labelnames=[],
+            )
+
+            self.litellm_check_batch_cost_jobs_processed_total = self._counter_factory(
+                name="litellm_check_batch_cost_jobs_processed_total",
+                documentation="Total number of batches successfully cost-tracked by CheckBatchCost",
+                labelnames=["model", "api_provider"],
+            )
+
+            self.litellm_check_batch_cost_errors_total = self._counter_factory(
+                name="litellm_check_batch_cost_errors_total",
+                documentation="Total number of errors in CheckBatchCost by error type",
+                labelnames=["error_type"],
+            )
+
+            self.litellm_check_batch_cost_last_run_timestamp = self._gauge_factory(
+                "litellm_check_batch_cost_last_run_timestamp",
+                "Unix timestamp of the last CheckBatchCost job run",
+                labelnames=[],
+            )
+
         except Exception as e:
             print_verbose(f"Got exception on init prometheus client {str(e)}")
             raise e
@@ -931,10 +1012,14 @@ class PrometheusLogger(CustomLogger):
         user_api_key_auth_metadata: Optional[dict] = standard_logging_payload[
             "metadata"
         ].get("user_api_key_auth_metadata")
+        spend_logs_metadata: Optional[dict] = standard_logging_payload["metadata"].get(
+            "spend_logs_metadata"
+        )
 
         combined_metadata: Dict[str, Any] = {
             **(_requester_metadata if _requester_metadata else {}),
             **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}),
+            **(spend_logs_metadata if spend_logs_metadata else {}),
         }
         if standard_logging_payload is not None and isinstance(
             standard_logging_payload, dict
@@ -2154,6 +2239,127 @@ class PrometheusLogger(CustomLogger):
         except Exception as e:
             verbose_logger.debug(f"Error recording guardrail metrics: {str(e)}")
 
+    ########################################
+    # Managed Batch Metric Recording Methods
+    ########################################
+
+    def record_managed_batch_created(
+        self,
+        model: Optional[str],
+        api_provider: Optional[str],
+        user: Optional[str],
+        user_email: Optional[str],
+        api_key_alias: Optional[str],
+    ):
+        try:
+            self.litellm_managed_batch_created_total.labels(
+                model=model,
+                api_provider=api_provider,
+                user=user,
+                user_email=user_email,
+                api_key_alias=api_key_alias,
+            ).inc()
+        except Exception as e:
+            verbose_logger.warning(f"Error recording batch created metric: {e}")
+
+    def record_managed_file_size(
+        self,
+        size_bytes: int,
+        purpose: str,
+        file_type: str,
+        model: Optional[str] = None,
+        api_provider: Optional[str] = None,
+        user: Optional[str] = None,
+    ):
+        """Record the size of a managed file. Uses a gauge (last-seen value per label combination)."""
+        try:
+            self.litellm_managed_file_size_bytes.labels(
+                purpose=purpose,
+                file_type=file_type,
+                model=model or "",
+                api_provider=api_provider or "",
+                user=user or "",
+            ).set(size_bytes)
+        except Exception as e:
+            verbose_logger.warning(f"Error recording file size metric: {e}")
+
+    def record_managed_batch_duration(
+        self,
+        duration_seconds: float,
+        model: Optional[str] = None,
+        api_provider: Optional[str] = None,
+    ):
+        try:
+            self.litellm_managed_batch_duration_seconds.labels(
+                model=model or "",
+                api_provider=api_provider or "",
+            ).observe(duration_seconds)
+        except Exception as e:
+            verbose_logger.warning(f"Error recording batch duration metric: {e}")
+
+    def record_managed_file_created(
+        self,
+        model: Optional[str],
+        api_provider: Optional[str],
+        user: Optional[str],
+        user_email: Optional[str],
+        api_key_alias: Optional[str],
+    ):
+        try:
+            self.litellm_managed_file_created_total.labels(
+                model=model,
+                api_provider=api_provider,
+                user=user,
+                user_email=user_email,
+                api_key_alias=api_key_alias,
+            ).inc()
+        except Exception as e:
+            verbose_logger.warning(f"Error recording file created metric: {e}")
+
+    def record_managed_file_deleted(self, result: str):
+        """Record a managed file deletion attempt. result is 'success' or 'blocked'."""
+        try:
+            self.litellm_managed_file_deleted_total.labels(result=result).inc()
+        except Exception as e:
+            verbose_logger.warning(f"Error recording file deleted metric: {e}")
+
+    def record_check_batch_cost_run(
+        self,
+        jobs_polled: int,
+        processed_models: Optional[List[Tuple[Optional[str], Optional[str]]]] = None,
+    ):
+        """
+        Record CheckBatchCost polling metrics.
+
+        Args:
+            jobs_polled: Number of unprocessed batches found
+            processed_models: List of (model, api_provider) tuples for processed jobs
+        """
+        import time
+
+        try:
+            self.litellm_check_batch_cost_last_run_timestamp.set(time.time())
+            self.litellm_check_batch_cost_jobs_polled.set(jobs_polled)
+
+            if processed_models:
+                for model, api_provider in processed_models:
+                    self.litellm_check_batch_cost_jobs_processed_total.labels(
+                        model=model or "",
+                        api_provider=api_provider or "",
+                    ).inc()
+        except Exception as e:
+            verbose_logger.warning(f"Error recording check batch cost metrics: {e}")
+
+    def record_check_batch_cost_error(self, error_type: str):
+        try:
+            self.litellm_check_batch_cost_errors_total.labels(
+                error_type=error_type,
+            ).inc()
+        except Exception as e:
+            verbose_logger.warning(
+                f"Error recording check batch cost error metric: {e}"
+            )
+
     @staticmethod
     def _get_exception_class_name(exception: Exception) -> str:
         exception_class_name = ""
diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py
index c8db4be7cea..405bf9698cc 100644
--- a/litellm/integrations/s3_v2.py
+++ b/litellm/integrations/s3_v2.py
@@ -22,7 +22,7 @@ from litellm.llms.custom_httpx.http_handler import (
     httpxSpecialProvider,
 )
 from litellm.types.integrations.s3_v2 import s3BatchLoggingElement
-from litellm.types.utils import StandardLoggingPayload
+from litellm.types.utils import StandardAuditLogPayload, StandardLoggingPayload
 
 from .custom_batch_logger import CustomBatchLogger
 
@@ -248,6 +248,38 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM):
         )
         pass
 
+    async def async_log_audit_log_event(
+        self, audit_log: StandardAuditLogPayload
+    ) -> None:
+        """Batch audit logs and upload to S3 under audit_logs/ prefix."""
+        try:
+            from datetime import timezone
+
+            now = datetime.now(timezone.utc)
+            audit_log_id = audit_log.get("id", "unknown")
+
+            s3_path = cast(Optional[str], self.s3_path) or ""
+            s3_path = s3_path.rstrip("/") + "/" if s3_path else ""
+
+            s3_object_key = (
+                f"{s3_path}audit_logs/"
+                f"{now.strftime('%Y-%m-%d')}/"
+                f"{now.strftime('%H-%M-%S')}_{audit_log_id}.json"
+            )
+
+            element = s3BatchLoggingElement(
+                payload=dict(audit_log),
+                s3_object_key=s3_object_key,
+                s3_object_download_filename=f"audit-{audit_log_id}.json",
+            )
+
+            self.log_queue.append(element)
+
+            if len(self.log_queue) >= self.batch_size:
+                await self.flush_queue()
+        except Exception as e:
+            verbose_logger.exception("S3 audit log error: %s", e)
+
     async def _async_log_event_base(self, kwargs, response_obj, start_time, end_time):
         try:
             verbose_logger.debug(
diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py
index 256b16ff312..22006be21af 100644
--- a/litellm/litellm_core_utils/core_helpers.py
+++ b/litellm/litellm_core_utils/core_helpers.py
@@ -64,6 +64,7 @@ _FINISH_REASON_MAP: dict[str, OpenAIChatCompletionFinishReason] = {
     "end_turn": "stop",
     "max_tokens": "length",
     "tool_use": "tool_calls",
+    "refusal": "content_filter",
     "compaction": "length",
     # Cohere
     "COMPLETE": "stop",
diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py
index 36218417377..c26759eaf0a 100644
--- a/litellm/litellm_core_utils/get_llm_provider_logic.py
+++ b/litellm/litellm_core_utils/get_llm_provider_logic.py
@@ -158,12 +158,15 @@ def get_llm_provider(  # noqa: PLR0915
         ):  # handle scenario where model="azure/*" and custom_llm_provider="azure"
             model = custom_llm_provider + "/" + model
 
-        # Native OpenRouter models have IDs like "openrouter/free" where the
-        # "openrouter/" prefix is part of the actual model name on the API.
-        # When called from a bridge (e.g. anthropic_messages adapter),
-        # custom_llm_provider is already resolved, so return early to prevent
-        # the provider-list stripping below from removing the prefix.
+        # OpenRouter: when the router/proxy already set custom_llm_provider,
+        # the model may still carry LiteLLM's "openrouter/" routing prefix.
+        # Native IDs like "openrouter/auto" must stay intact for the API; IDs
+        # like "openrouter/anthropic/claude-3.5-sonnet" must become
+        # "anthropic/claude-3.5-sonnet" (OpenRouter expects provider/model).
         if custom_llm_provider == "openrouter" and model.startswith("openrouter/"):
+            remainder = model[len("openrouter/") :]
+            if "/" in remainder:
+                return remainder, custom_llm_provider, dynamic_api_key, api_base
             return model, custom_llm_provider, dynamic_api_key, api_base
 
         if api_key and api_key.startswith("os.environ/"):
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index e934c0d350d..da6935f1e94 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -1689,7 +1689,9 @@ class Logging(LiteLLMLoggingBaseClass):
                 )
         return logging_result
 
-    def _merge_hidden_params_from_response_into_metadata(self, logging_result: Any) -> None:
+    def _merge_hidden_params_from_response_into_metadata(
+        self, logging_result: Any
+    ) -> None:
         """
         Copy response._hidden_params into litellm_params.metadata['hidden_params'].
 
@@ -1721,6 +1723,9 @@ class Logging(LiteLLMLoggingBaseClass):
             md["hidden_params"] = existing or incoming
             return
         md["hidden_params"] = {**existing, **incoming}
+        self.model_call_details["litellm_params"]["metadata"][
+            "hidden_params"
+        ] = getattr(logging_result, "_hidden_params", {})
 
     def _process_hidden_params_and_response_cost(
         self,
@@ -4765,6 +4770,7 @@ class StandardLoggingPayloadSetup:
             user_api_key_team_id=None,
             user_api_key_org_id=None,
             user_api_key_project_id=None,
+            user_api_key_project_alias=None,
             user_api_key_user_id=None,
             user_api_key_team_alias=None,
             user_api_key_user_email=None,
@@ -5612,6 +5618,7 @@ def get_standard_logging_metadata(
         user_api_key_team_id=None,
         user_api_key_org_id=None,
         user_api_key_project_id=None,
+        user_api_key_project_alias=None,
         user_api_key_user_id=None,
         user_api_key_user_email=None,
         user_api_key_team_alias=None,
diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index a5d6bc936bb..cec61405ebb 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -257,6 +257,15 @@ def detect_first_expected_role(
     return None
 
 
+def _counts_for_alternation(message: AllMessageValues) -> bool:
+    role = message.get("role")
+    if role == "user":
+        return True
+    if role == "assistant":
+        return not bool(message.get("tool_calls"))
+    return False
+
+
 def _insert_user_continue_message(
     messages: List[AllMessageValues],
     user_continue_message: Optional[ChatCompletionUserMessage],
@@ -269,8 +278,8 @@ def _insert_user_continue_message(
     2. Final assistant message
     3. Consecutive assistant messages
 
-    Only inserts messages between consecutive assistant messages,
-    ignoring all other role types.
+    Skips tool messages and assistant messages with tool calls in the
+    alternation check, matching strict templates like llama.cpp.
     """
     if not messages:
         return messages
@@ -278,25 +287,42 @@ def _insert_user_continue_message(
     result_messages = messages.copy()  # Don't modify the input list
     continue_message = user_continue_message or DEFAULT_USER_CONTINUE_MESSAGE
 
-    # Handle first message if it's an assistant message
+    # Handle first message if it's an assistant message — always prepend
+    # user_continue regardless of tool_calls, to preserve backward compatibility.
     if result_messages[0]["role"] == "assistant":
         result_messages.insert(0, continue_message)
 
-    # Handle consecutive assistant messages and final message
-    i = 1  # Start from second message since we handled first message
+    # Handle consecutive assistant messages in the counted sequence
+    i = 1
     while i < len(result_messages):
         curr_message = result_messages[i]
-        prev_message = result_messages[i - 1]
-
-        # Only check for consecutive assistant messages
-        # Ignore all other role types
-        if curr_message["role"] == "assistant" and prev_message["role"] == "assistant":
-            result_messages.insert(i, continue_message)
-            i += 2  # Skip over the message we just inserted
-        else:
+        inserted_continue_message = False
+        if (
+            _counts_for_alternation(curr_message)
+            and curr_message["role"] == "assistant"
+        ):
+            # Preserve old behavior for malformed adjacent assistant sequences like
+            # assistant(tool_calls) -> assistant(no-tool-calls) with no tool message.
+            if i > 0 and result_messages[i - 1].get("role") == "assistant":
+                result_messages.insert(i, continue_message)
+                i += 2
+                inserted_continue_message = True
+            else:
+                j = i - 1
+                while j >= 0:
+                    previous_message = result_messages[j]
+                    if _counts_for_alternation(previous_message):
+                        if previous_message["role"] == "assistant":
+                            result_messages.insert(i, continue_message)
+                            i += 2
+                            inserted_continue_message = True
+                        break
+                    j -= 1
+        if not inserted_continue_message:
             i += 1
 
-    # Handle final message
+    # Handle final message — append user_continue after any trailing assistant,
+    # including ones with tool_calls, to preserve backward compatibility.
     if result_messages[-1]["role"] == "assistant" and ensure_alternating_roles:
         result_messages.append(continue_message)
 
@@ -311,34 +337,24 @@ def _insert_assistant_continue_message(
     """
     Add assistant continuation messages between consecutive user messages.
 
-    Args:
-        messages: List of message dictionaries
-        assistant_continue_message: Optional custom assistant message
-        ensure_alternating_roles: Whether to enforce alternating roles
-
-    Returns:
-        Modified list of messages with inserted assistant messages
+    Only checks directly adjacent messages to preserve backward compatibility.
     """
     if not ensure_alternating_roles or len(messages) <= 1:
         return messages
 
-    # Create a new list to store modified messages
+    continue_message = assistant_continue_message or DEFAULT_ASSISTANT_CONTINUE_MESSAGE
+
     modified_messages: List[AllMessageValues] = []
-
     for i, message in enumerate(messages):
-        modified_messages.append(message)
-
-        # Check if we need to insert an assistant message
         if (
-            i < len(messages) - 1  # Not the last message
-            and message.get("role") == "user"  # Current is user
+            i < len(messages) - 1
+            and message.get("role") == "user"
             and messages[i + 1].get("role") == "user"
-        ):  # Next is user
-            # Insert assistant message
-            continue_message = (
-                assistant_continue_message or DEFAULT_ASSISTANT_CONTINUE_MESSAGE
-            )
+        ):
+            modified_messages.append(message)
             modified_messages.append(continue_message)
+        else:
+            modified_messages.append(message)
 
     return modified_messages
 
@@ -536,6 +552,61 @@ def update_responses_input_with_model_file_ids(
     return updated_input
 
 
+def _decode_vector_store_ids_in_tools(
+    tools: Optional[List[Dict[str, Any]]],
+) -> Optional[List[Dict[str, Any]]]:
+    """
+    Decodes unified (LiteLLM-managed) vector_store_ids in file_search tools to
+    provider-native IDs.  Non-unified IDs are passed through unchanged.
+
+    This runs unconditionally — no file-ID mapping is required.
+    """
+    if not tools or not isinstance(tools, list):
+        return tools
+
+    from litellm.llms.base_llm.managed_resources.utils import (
+        is_base64_encoded_unified_id,
+        parse_unified_id,
+    )
+
+    updated_tools = []
+    for tool in tools:
+        if not isinstance(tool, dict) or tool.get("type") != "file_search":
+            updated_tools.append(tool)
+            continue
+
+        vector_store_ids = tool.get("vector_store_ids")
+        if not isinstance(vector_store_ids, list):
+            updated_tools.append(tool)
+            continue
+
+        decoded_ids = []
+        for vs_id in vector_store_ids:
+            if not isinstance(vs_id, str) or not is_base64_encoded_unified_id(vs_id):
+                decoded_ids.append(vs_id)
+                continue
+
+            parsed = parse_unified_id(vs_id)
+            provider_resource_id = (
+                parsed.get("provider_resource_id") if parsed else None
+            )
+
+            if not provider_resource_id:
+                verbose_logger.warning(
+                    "file_search tool contains unified vector_store_id '%s' that could "
+                    "not be decoded to a provider resource ID — passing original ID. "
+                    "Ensure the vector store was created via LiteLLM.",
+                    vs_id,
+                )
+                decoded_ids.append(vs_id)
+            else:
+                decoded_ids.append(provider_resource_id)
+
+        updated_tools.append({**tool, "vector_store_ids": decoded_ids})
+
+    return updated_tools
+
+
 def update_responses_tools_with_model_file_ids(
     tools: Optional[List[Dict[str, Any]]],
     model_id: Optional[str] = None,
@@ -544,7 +615,8 @@ def update_responses_tools_with_model_file_ids(
     """
     Updates responses API tools with provider-specific file IDs.
 
-    Handles code_interpreter tools with container.file_ids.
+    Pass 1 (always): decode unified vector_store_ids in file_search tools.
+    Pass 2 (needs mapping): map code_interpreter container file_ids to provider IDs.
 
     Args:
         tools: The responses API tools parameter
@@ -555,6 +627,10 @@ def update_responses_tools_with_model_file_ids(
     if not tools or not isinstance(tools, list):
         return tools
 
+    # Pass 1: decode unified vector_store_ids (no mapping needed)
+    tools = _decode_vector_store_ids_in_tools(tools) or tools
+
+    # Pass 2: map code_interpreter file IDs (requires mapping)
     if not model_file_id_mapping or not model_id:
         return tools
 
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index f6004616712..d29ca1649ff 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -1498,17 +1498,49 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
     from litellm.types.llms.vertex_ai import BlobType
 
     content_str: str = ""
-    inline_data: Optional[BlobType] = None
+    inline_data_list: List[BlobType] = []
 
     if "content" in message:
         if isinstance(message["content"], str):
             content_str = message["content"]
+            # Detect data-URL images (e.g. from Anthropic tool_result with a single image block
+            # that was serialised as a plain string by translate_anthropic_messages_to_openai)
+            # and promote them to inline_data so Gemini receives actual image bytes.
+            if content_str[:5].lower() == "data:" and ";base64," in content_str:
+                try:
+                    mime_rest = content_str[5:].split(";base64,", 1)
+                    if len(mime_rest) == 2 and mime_rest[0].startswith("image/"):
+                        # Strip any extra parameters (e.g. ";charset=UTF-8") from the MIME segment
+                        clean_mime = mime_rest[0].split(";")[0].strip()
+                        inline_data_list.append(
+                            BlobType(data=mime_rest[1], mime_type=clean_mime)
+                        )
+                        content_str = ""
+                except Exception as e:
+                    verbose_logger.warning(
+                        f"Failed to parse data URL in tool response: {e}"
+                    )
         elif isinstance(message["content"], List):
             content_list = message["content"]
             for content in content_list:
                 content_type = content.get("type", "")
                 if content_type == "text":
                     content_str += content.get("text", "")
+                elif content_type == "image":
+                    # Anthropic-native image block: {"type": "image", "source": {"type": "base64", ...}}
+                    source = content.get("source", {})
+                    if isinstance(source, dict) and source.get("type") == "base64":
+                        try:
+                            inline_data_list.append(
+                                BlobType(
+                                    data=source.get("data", ""),
+                                    mime_type=source.get("media_type", "image/jpeg"),
+                                )
+                            )
+                        except Exception as e:
+                            verbose_logger.warning(
+                                f"Failed to process Anthropic image block in tool response: {e}"
+                            )
                 elif content_type in ("input_image", "image_url"):
                     # Extract image for inline_data (for Computer Use screenshots and tool results)
                     image_url_data = content.get("image_url", "")
@@ -1524,9 +1556,11 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
                             image_obj = convert_to_anthropic_image_obj(
                                 image_url, format=None
                             )
-                            inline_data = BlobType(
-                                data=image_obj["data"],
-                                mime_type=image_obj["media_type"],
+                            inline_data_list.append(
+                                BlobType(
+                                    data=image_obj["data"],
+                                    mime_type=image_obj["media_type"],
+                                )
                             )
                         except Exception as e:
                             verbose_logger.warning(
@@ -1551,9 +1585,11 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
                             file_obj = convert_to_anthropic_image_obj(
                                 file_data, format=None
                             )
-                            inline_data = BlobType(
-                                data=file_obj["data"],
-                                mime_type=file_obj["media_type"],
+                            inline_data_list.append(
+                                BlobType(
+                                    data=file_obj["data"],
+                                    mime_type=file_obj["media_type"],
+                                )
                             )
                         except Exception as e:
                             verbose_logger.warning(
@@ -1607,13 +1643,12 @@ def convert_to_gemini_tool_call_result(  # noqa: PLR0915
     # Create part with function_response, and optionally inline_data for images (Computer Use)
     _part: VertexPartType = {"function_response": _function_response}
 
-    # For Computer Use, if we have an image, we need separate parts:
+    # For Computer Use, if we have images/files, we need separate parts:
     # - One part with function_response
-    # - One part with inline_data
+    # - One part per inline_data item
     # Gemini's PartType is a oneof, so we can't have both in the same part
-    if inline_data:
-        image_part: VertexPartType = {"inline_data": inline_data}
-        return [_part, image_part]
+    if inline_data_list:
+        return [_part] + [{"inline_data": d} for d in inline_data_list]
 
     return _part
 
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 67e4fadf638..1bb2b99c015 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -831,6 +831,10 @@ class CustomStreamWrapper:
                 "annotations" in model_response.choices[0].delta
                 and model_response.choices[0].delta.annotations is not None
             )
+            or (
+                getattr(model_response.choices[0].delta, "reasoning_items", None)
+                is not None
+            )
         ):
             return True
         else:
diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py
index fbd1da749c2..91e7e9d5550 100644
--- a/litellm/llms/a2a/chat/guardrail_translation/handler.py
+++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py
@@ -111,6 +111,7 @@ class A2AGuardrailHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
         user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process A2A output response by applying guardrails to text content.
@@ -166,13 +167,21 @@ class A2AGuardrailHandler(BaseTranslation):
             return response
 
         # Step 2: Apply guardrail to all texts in batch
-        # Create a request_data dict with response info and user API key metadata
-        request_data: dict = {"response": response_dict}
+        # Use the real request_data if provided (proxy path), otherwise
+        # create a standalone dict (SDK / direct-call path).
+        if request_data is None:
+            request_data = {"response": response_dict}
+        else:
+            if "response" not in request_data:
+                request_data["response"] = response_dict
 
         # Add user API key metadata with prefixed keys
-        user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict)
-        if user_metadata:
-            request_data["litellm_metadata"] = user_metadata
+        if "litellm_metadata" not in request_data:
+            user_metadata = self.transform_user_api_key_dict_to_metadata(
+                user_api_key_dict
+            )
+            if user_metadata:
+                request_data["litellm_metadata"] = user_metadata
 
         inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
 
@@ -213,6 +222,7 @@ class A2AGuardrailHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
         user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+        request_data: Optional[dict] = None,
     ) -> List[Any]:
         """
         Process A2A streaming output by applying guardrails to accumulated text.
@@ -258,10 +268,18 @@ class A2AGuardrailHandler(BaseTranslation):
         if not combined_text:
             return responses_so_far
 
-        request_data: dict = {"responses_so_far": responses_so_far}
-        user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict)
-        if user_metadata:
-            request_data["litellm_metadata"] = user_metadata
+        if request_data is None:
+            request_data = {"responses_so_far": responses_so_far}
+        else:
+            if "responses_so_far" not in request_data:
+                request_data["responses_so_far"] = responses_so_far
+
+        if "litellm_metadata" not in request_data:
+            user_metadata = self.transform_user_api_key_dict_to_metadata(
+                user_api_key_dict
+            )
+            if user_metadata:
+                request_data["litellm_metadata"] = user_metadata
 
         inputs = GenericGuardrailAPIInputs(texts=[combined_text])
         guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
index 5372757cbb6..b6139a2f84d 100644
--- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py
+++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
@@ -252,6 +252,7 @@ class AnthropicMessagesHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response by applying guardrails to text content and tool calls.
@@ -323,15 +324,21 @@ class AnthropicMessagesHandler(BaseTranslation):
 
         # Step 2: Apply guardrail to all texts in batch
         if texts_to_check or tool_calls_to_check:
-            # Create a request_data dict with response info and user API key metadata
-            request_data: dict = {"response": response}
+            # Use the real request_data if provided (proxy path), otherwise
+            # create a standalone dict (SDK / direct-call path).
+            if request_data is None:
+                request_data = {"response": response}
+            else:
+                if "response" not in request_data:
+                    request_data["response"] = response
 
             # Add user API key metadata with prefixed keys
-            user_metadata = self.transform_user_api_key_dict_to_metadata(
-                user_api_key_dict
-            )
-            if user_metadata:
-                request_data["litellm_metadata"] = user_metadata
+            if "litellm_metadata" not in request_data:
+                user_metadata = self.transform_user_api_key_dict_to_metadata(
+                    user_api_key_dict
+                )
+                if user_metadata:
+                    request_data["litellm_metadata"] = user_metadata
 
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
             if images_to_check:
@@ -375,6 +382,7 @@ class AnthropicMessagesHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> List[Any]:
         """
         Process output streaming response by applying guardrails to text content.
@@ -413,7 +421,7 @@ class AnthropicMessagesHandler(BaseTranslation):
 
                 _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(  # allow rejecting the response, if invalid
                     inputs=guardrail_inputs,
-                    request_data={},
+                    request_data=request_data if request_data is not None else {},
                     input_type="response",
                     logging_obj=litellm_logging_obj,
                 )
@@ -426,7 +434,7 @@ class AnthropicMessagesHandler(BaseTranslation):
         string_so_far = self.get_streaming_string_so_far(responses_so_far)
         _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(  # allow rejecting the response, if invalid
             inputs={"texts": [string_so_far]},
-            request_data={},
+            request_data=request_data if request_data is not None else {},
             input_type="response",
             logging_obj=litellm_logging_obj,
         )
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 73d1b02c76d..9a99f9efc82 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -1421,6 +1421,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         ):
             optional_params["metadata"] = {"user_id": _litellm_metadata["user_id"]}
 
+        ## Ensure metadata only contains user_id (only documented field in Anthropic Messages API)
+        if "metadata" in optional_params and isinstance(
+            optional_params["metadata"], dict
+        ):
+            _user_id = optional_params["metadata"].get("user_id")
+            if _user_id is not None:
+                optional_params["metadata"] = {"user_id": _user_id}
+            else:
+                optional_params.pop("metadata")
+
         # Remove internal LiteLLM parameters that should not be sent to Anthropic API
         optional_params.pop("is_vertex_request", None)
 
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
index 8b1b21a0f9b..897ca3bf893 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
@@ -15,6 +15,9 @@ import litellm
 from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
     AnthropicAdapter,
 )
+from litellm.llms.anthropic.experimental_pass_through.utils import (
+    is_reasoning_auto_summary_enabled,
+)
 from litellm.types.llms.anthropic_messages.anthropic_response import (
     AnthropicMessagesResponse,
 )
@@ -44,8 +47,9 @@ class LiteLLMMessagesToCompletionTransformationHandler:
 
         For OpenAI models, Chat Completions typically does not return reasoning text
         (only token accounting). To return a thinking-like content block in the
-        Anthropic response format, we route the request through OpenAI's Responses API
-        and request a reasoning summary.
+        Anthropic response format, we route the request through OpenAI's Responses API.
+        If the user provides a `summary` field in the thinking dict, it is passed
+        through to the OpenAI reasoning params (opt-in per OpenAI spec).
         """
         custom_llm_provider = completion_kwargs.get("custom_llm_provider")
         if custom_llm_provider is None:
@@ -78,20 +82,29 @@ class LiteLLMMessagesToCompletionTransformationHandler:
             # Prefix model with "responses/" to route to OpenAI Responses API
             completion_kwargs["model"] = f"responses/{model}"
 
+        auto_summary = is_reasoning_auto_summary_enabled()
+
         reasoning_effort = completion_kwargs.get("reasoning_effort")
+        summary = thinking.get("summary")
         if isinstance(reasoning_effort, str) and reasoning_effort:
-            completion_kwargs["reasoning_effort"] = {
-                "effort": reasoning_effort,
-                "summary": "detailed",
-            }
+            reasoning_dict: Dict[str, Any] = {"effort": reasoning_effort}
+            if summary:
+                reasoning_dict["summary"] = summary
+            elif auto_summary:
+                reasoning_dict["summary"] = "detailed"
+            completion_kwargs["reasoning_effort"] = reasoning_dict
         elif isinstance(reasoning_effort, dict):
             if (
                 "summary" not in reasoning_effort
                 and "generate_summary" not in reasoning_effort
             ):
-                updated_reasoning_effort = dict(reasoning_effort)
-                updated_reasoning_effort["summary"] = "detailed"
-                completion_kwargs["reasoning_effort"] = updated_reasoning_effort
+                effective_summary = (
+                    summary if summary else ("detailed" if auto_summary else None)
+                )
+                if effective_summary:
+                    updated_reasoning_effort = dict(reasoning_effort)
+                    updated_reasoning_effort["summary"] = effective_summary
+                    completion_kwargs["reasoning_effort"] = updated_reasoning_effort
 
     @staticmethod
     def _prepare_completion_kwargs(
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index b9a8d1de488..ed49943b7fe 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
@@ -14,6 +14,10 @@ from typing import (
     cast,
 )
 
+from litellm.llms.anthropic.experimental_pass_through.utils import (
+    is_reasoning_auto_summary_enabled,
+)
+
 # OpenAI has a 64-character limit for function/tool names
 # Anthropic does not have this limit, so we need to truncate long names
 OPENAI_MAX_TOOL_NAME_LENGTH = 64
@@ -734,6 +738,24 @@ class LiteLLMAnthropicMessagesAdapter:
                 thinking
             )
             if reasoning_effort:
+                summary = (
+                    thinking.get("summary") if isinstance(thinking, dict) else None
+                )
+                auto_summary = is_reasoning_auto_summary_enabled()
+                if summary:
+                    return {
+                        "reasoning_effort": {
+                            "effort": reasoning_effort,
+                            "summary": summary,
+                        }
+                    }
+                elif auto_summary:
+                    return {
+                        "reasoning_effort": {
+                            "effort": reasoning_effort,
+                            "summary": "detailed",
+                        }
+                    }
                 return {"reasoning_effort": reasoning_effort}
             return {}
 
@@ -869,19 +891,25 @@ class LiteLLMAnthropicMessagesAdapter:
 
         # Handle array items
         if "items" in schema:
-            LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(schema["items"])
+            LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(
+                schema["items"]
+            )
 
         # Handle anyOf/oneOf/allOf
         for key in ("anyOf", "oneOf", "allOf"):
             if key in schema:
                 for sub_schema in schema[key]:
-                    LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(sub_schema)
+                    LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(
+                        sub_schema
+                    )
 
         # Handle $defs / definitions
         for key in ("$defs", "definitions"):
             if key in schema:
                 for def_schema in schema[key].values():
-                    LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(def_schema)
+                    LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(
+                        def_schema
+                    )
 
     def _add_system_message_to_messages(
         self,
@@ -918,6 +946,144 @@ class LiteLLMAnthropicMessagesAdapter:
                     ChatCompletionSystemMessage(role="system", content=openai_system_content),  # type: ignore
                 )
 
+    def _translate_metadata_to_openai(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> None:
+        """Translate metadata fields from Anthropic request to OpenAI request."""
+        if "metadata" in anthropic_message_request:
+            metadata = anthropic_message_request["metadata"]
+            if metadata and "user_id" in metadata:
+                new_kwargs["user"] = metadata["user_id"]
+
+        if "litellm_metadata" in anthropic_message_request:
+            # metadata will be passed to litellm.acompletion(), it's a litellm_param
+            new_kwargs["metadata"] = anthropic_message_request.pop("litellm_metadata")
+
+    def _translate_tool_choice_to_openai(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> None:
+        """Translate Anthropic tool_choice to OpenAI format."""
+        if "tool_choice" not in anthropic_message_request:
+            return
+        tool_choice = anthropic_message_request["tool_choice"]
+        if not tool_choice:
+            return
+        new_kwargs["tool_choice"] = self.translate_anthropic_tool_choice_to_openai(
+            tool_choice=cast(AnthropicMessagesToolChoice, tool_choice)
+        )
+
+    def _translate_tools_to_openai(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> Dict[str, str]:
+        """Translate tools and extract web_search_options when needed."""
+        if "tools" not in anthropic_message_request:
+            return {}
+
+        tools = anthropic_message_request["tools"]
+        if not tools:
+            return {}
+
+        web_search_tools: List[AllAnthropicToolsValues] = []
+        regular_tools: List[AllAnthropicToolsValues] = []
+        for tool in tools:
+            cast_tool = cast(Dict[str, Any], tool)
+            if self._is_web_search_tool(cast_tool):
+                web_search_tools.append(cast(AllAnthropicToolsValues, tool))
+            else:
+                regular_tools.append(cast(AllAnthropicToolsValues, tool))
+
+        if web_search_tools:
+            new_kwargs["web_search_options"] = {}  # type: ignore
+
+        if not regular_tools:
+            return {}
+
+        translated_tools, tool_name_mapping = self.translate_anthropic_tools_to_openai(
+            tools=regular_tools,
+            model=new_kwargs.get("model"),
+        )
+        new_kwargs["tools"] = translated_tools
+        return tool_name_mapping
+
+    def _translate_thinking_to_openai(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> None:
+        """Translate Anthropic thinking to either thinking or reasoning_effort."""
+        if "thinking" not in anthropic_message_request:
+            return
+
+        thinking = anthropic_message_request["thinking"]
+        if not thinking:
+            return
+
+        model = new_kwargs.get("model", "")
+        if self.is_anthropic_claude_model(model):
+            new_kwargs["thinking"] = thinking  # type: ignore
+            return
+
+        reasoning_effort = self.translate_anthropic_thinking_to_reasoning_effort(
+            cast(Dict[str, Any], thinking)
+        )
+        if not reasoning_effort:
+            return
+
+        summary = thinking.get("summary") if isinstance(thinking, dict) else None
+        auto_summary = is_reasoning_auto_summary_enabled()
+        if summary:
+            new_kwargs["reasoning_effort"] = cast(
+                Any,
+                {
+                    "effort": reasoning_effort,
+                    "summary": summary,
+                },
+            )
+        elif auto_summary:
+            new_kwargs["reasoning_effort"] = cast(
+                Any,
+                {
+                    "effort": reasoning_effort,
+                    "summary": "detailed",
+                },
+            )
+        else:
+            new_kwargs["reasoning_effort"] = reasoning_effort
+
+    def _translate_output_format_to_openai(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> None:
+        """Translate output_format to response_format when applicable."""
+        if "output_format" not in anthropic_message_request:
+            return
+        output_format = anthropic_message_request["output_format"]
+        if not output_format:
+            return
+        response_format = self.translate_anthropic_output_format_to_openai(
+            output_format=output_format
+        )
+        if response_format:
+            new_kwargs["response_format"] = response_format
+
+    def _copy_untranslated_anthropic_params(
+        self,
+        anthropic_message_request: AnthropicMessagesRequest,
+        new_kwargs: ChatCompletionRequest,
+    ) -> None:
+        """Copy through anthropic params that do not require translation."""
+        translatable_params = self.translatable_anthropic_params()
+        for k, v in anthropic_message_request.items():
+            if k not in translatable_params:  # pass remaining params as is
+                new_kwargs[k] = v  # type: ignore
+
     def translate_anthropic_to_openai(
         self, anthropic_message_request: AnthropicMessagesRequest
     ) -> Tuple[ChatCompletionRequest, Dict[str, str]]:
@@ -958,83 +1124,35 @@ class LiteLLMAnthropicMessagesAdapter:
             "model": anthropic_message_request["model"],
             "messages": new_messages,
         }
-        ## CONVERT METADATA (user_id)
-        if "metadata" in anthropic_message_request:
-            metadata = anthropic_message_request["metadata"]
-            if metadata and "user_id" in metadata:
-                new_kwargs["user"] = metadata["user_id"]
-
-        # Pass litellm proxy specific metadata
-        if "litellm_metadata" in anthropic_message_request:
-            # metadata will be passed to litellm.acompletion(), it's a litellm_param
-            new_kwargs["metadata"] = anthropic_message_request.pop("litellm_metadata")
-
+        ## CONVERT METADATA (user_id + litellm metadata)
+        self._translate_metadata_to_openai(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
         ## CONVERT TOOL CHOICE
-        if "tool_choice" in anthropic_message_request:
-            tool_choice = anthropic_message_request["tool_choice"]
-            if tool_choice:
-                new_kwargs[
-                    "tool_choice"
-                ] = self.translate_anthropic_tool_choice_to_openai(
-                    tool_choice=cast(AnthropicMessagesToolChoice, tool_choice)
-                )
+        self._translate_tool_choice_to_openai(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
         ## CONVERT TOOLS
-        if "tools" in anthropic_message_request:
-            tools = anthropic_message_request["tools"]
-            if tools:
-                # Separate web search tools from regular tools
-                web_search_tools = []
-                regular_tools = []
-                for tool in tools:
-                    if self._is_web_search_tool(cast(Dict[str, Any], tool)):
-                        web_search_tools.append(tool)
-                    else:
-                        regular_tools.append(tool)
-
-                # If web search tools are present, add web_search_options parameter
-                if web_search_tools:
-                    new_kwargs["web_search_options"] = {}  # type: ignore
-
-                # Only translate regular tools (non-web-search)
-                if regular_tools:
-                    (
-                        new_kwargs["tools"],
-                        tool_name_mapping,
-                    ) = self.translate_anthropic_tools_to_openai(
-                        tools=cast(List[AllAnthropicToolsValues], regular_tools),
-                        model=new_kwargs.get("model"),
-                    )
-
+        tool_name_mapping = self._translate_tools_to_openai(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
         ## CONVERT THINKING
-        if "thinking" in anthropic_message_request:
-            thinking = anthropic_message_request["thinking"]
-            if thinking:
-                model = new_kwargs.get("model", "")
-                if self.is_anthropic_claude_model(model):
-                    new_kwargs["thinking"] = thinking  # type: ignore
-                else:
-                    reasoning_effort = (
-                        self.translate_anthropic_thinking_to_reasoning_effort(
-                            cast(Dict[str, Any], thinking)
-                        )
-                    )
-                    if reasoning_effort:
-                        new_kwargs["reasoning_effort"] = reasoning_effort
-
+        self._translate_thinking_to_openai(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
         ## CONVERT OUTPUT_FORMAT to RESPONSE_FORMAT
-        if "output_format" in anthropic_message_request:
-            output_format = anthropic_message_request["output_format"]
-            if output_format:
-                response_format = self.translate_anthropic_output_format_to_openai(
-                    output_format=output_format
-                )
-                if response_format:
-                    new_kwargs["response_format"] = response_format
-
-        translatable_params = self.translatable_anthropic_params()
-        for k, v in anthropic_message_request.items():
-            if k not in translatable_params:  # pass remaining params as is
-                new_kwargs[k] = v  # type: ignore
+        self._translate_output_format_to_openai(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
+        self._copy_untranslated_anthropic_params(
+            anthropic_message_request=anthropic_message_request,
+            new_kwargs=new_kwargs,
+        )
 
         return new_kwargs, tool_name_mapping
 
diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
index ddd514146df..dae7044a5bc 100644
--- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
@@ -8,6 +8,9 @@ path used for OpenAI and Azure models.
 import json
 from typing import Any, Dict, List, Optional, Union, cast
 
+from litellm.llms.anthropic.experimental_pass_through.utils import (
+    is_reasoning_auto_summary_enabled,
+)
 from litellm.types.llms.anthropic import (
     AllAnthropicToolsValues,
     AnthopicMessagesAssistantMessageParam,
@@ -94,7 +97,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
                             )
                         elif btype == "image":
                             url = self._translate_anthropic_image_source_to_url(
-                                block.get("source", {})
+                                cast(dict, block.get("source", {}))
                             )
                             if url:
                                 user_parts.append(
@@ -267,7 +270,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
             effort = "low"
         else:
             effort = "minimal"
-        return {"effort": effort, "summary": "detailed"}
+        auto_summary = is_reasoning_auto_summary_enabled()
+        result: Dict[str, Any] = {"effort": effort}
+        summary = thinking.get("summary")
+        if summary:
+            result["summary"] = summary
+        elif auto_summary:
+            result["summary"] = "detailed"
+        return result
 
     def translate_request(
         self,
diff --git a/litellm/llms/anthropic/experimental_pass_through/utils.py b/litellm/llms/anthropic/experimental_pass_through/utils.py
new file mode 100644
index 00000000000..6c1db6017b2
--- /dev/null
+++ b/litellm/llms/anthropic/experimental_pass_through/utils.py
@@ -0,0 +1,11 @@
+import os
+
+import litellm
+
+
+def is_reasoning_auto_summary_enabled() -> bool:
+    """Check whether the default 'summary: detailed' injection is enabled (opt-in)."""
+    return (
+        litellm.reasoning_auto_summary
+        or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true"
+    )
diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py
index 6310df9cecc..bc7483bf64d 100644
--- a/litellm/llms/azure/chat/gpt_5_transformation.py
+++ b/litellm/llms/azure/chat/gpt_5_transformation.py
@@ -131,14 +131,9 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
         if result_effort == "none" and not supports_none:
             result.pop("reasoning_effort")
 
-        # Azure Chat Completions: gpt-5.4+ does not support tools + reasoning together.
-        # Drop reasoning_effort when both are present (OpenAI routes to Responses API; Azure does not).
-        if self.is_model_gpt_5_4_plus_model(model):
-            has_tools = bool(
-                non_default_params.get("tools") or optional_params.get("tools")
-            )
-            if has_tools and result_effort not in (None, "none"):
-                result.pop("reasoning_effort", None)
+        # Azure gpt-5.4+ with tools + reasoning_effort is now routed to the
+        # Responses API bridge (same as OpenAI), so we no longer need to drop
+        # reasoning_effort here.  See: responses_api_bridge_check() in main.py.
 
         return result
 
diff --git a/litellm/llms/azure/fine_tuning/handler.py b/litellm/llms/azure/fine_tuning/handler.py
index 429b8349896..7e225a84454 100644
--- a/litellm/llms/azure/fine_tuning/handler.py
+++ b/litellm/llms/azure/fine_tuning/handler.py
@@ -1,10 +1,15 @@
-from typing import Optional, Union
+from typing import Any, Coroutine, Dict, Optional, Union, cast
 
 import httpx
 from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
 
+from litellm._logging import verbose_logger
 from litellm.llms.azure.common_utils import BaseAzureLLM
-from litellm.llms.openai.fine_tuning.handler import OpenAIFineTuningAPI
+from litellm.llms.openai.fine_tuning.handler import (
+    OpenAIFineTuningAPI,
+    _litellm_fine_tuning_job_from_response,
+)
+from litellm.types.utils import LiteLLMFineTuningJob
 
 
 class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
@@ -12,6 +17,194 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
     AzureOpenAI methods to support fine tuning, inherits from OpenAIFineTuningAPI.
     """
 
+    @staticmethod
+    def _ensure_training_type(create_fine_tuning_job_data: Dict[str, Any]) -> None:
+        """
+        Azure requires trainingType in extra_body. Default to 1 (supervised) if omitted.
+        """
+        extra_body = create_fine_tuning_job_data.get("extra_body") or {}
+        if not isinstance(extra_body, dict):
+            extra_body = {}
+        if extra_body.get("trainingType") is None:
+            extra_body["trainingType"] = 1
+            create_fine_tuning_job_data["extra_body"] = extra_body
+            verbose_logger.debug(
+                "Azure fine-tuning: defaulting trainingType=1 (supervised)"
+            )
+
+    async def acreate_fine_tuning_job(
+        self,
+        create_fine_tuning_job_data: dict,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> LiteLLMFineTuningJob:
+        response = await openai_client.fine_tuning.jobs.create(
+            **create_fine_tuning_job_data
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    async def acancel_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> LiteLLMFineTuningJob:
+        response = await openai_client.fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    async def aretrieve_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> LiteLLMFineTuningJob:
+        response = await openai_client.fine_tuning.jobs.retrieve(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    def create_fine_tuning_job(
+        self,
+        _is_async: bool,
+        create_fine_tuning_job_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
+        self._ensure_training_type(create_fine_tuning_job_data)
+
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acreate_fine_tuning_job(
+                create_fine_tuning_job_data=create_fine_tuning_job_data,
+                openai_client=openai_client,
+            )
+
+        verbose_logger.debug(
+            "creating fine tuning job, args= %s", create_fine_tuning_job_data
+        )
+        response = cast(OpenAI, openai_client).fine_tuning.jobs.create(
+            **create_fine_tuning_job_data
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    def cancel_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acancel_fine_tuning_job(
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+
+        response = cast(OpenAI, openai_client).fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    def retrieve_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.aretrieve_fine_tuning_job(
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+
+        response = cast(OpenAI, openai_client).fine_tuning.jobs.retrieve(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
     def get_openai_client(
         self,
         api_key: Optional[str],
diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py
index 9eeec7f4e36..c3cd06ab4de 100644
--- a/litellm/llms/azure_ai/agents/handler.py
+++ b/litellm/llms/azure_ai/agents/handler.py
@@ -97,14 +97,59 @@ class AzureAIAgentsHandler:
     # -------------------------------------------------------------------------
     # Response Helpers
     # -------------------------------------------------------------------------
-    def _extract_content_from_messages(self, messages_data: dict) -> str:
-        """Extract assistant content from the messages response."""
+    def _extract_content_from_messages(
+        self, messages_data: dict
+    ) -> Tuple[str, Optional[List[Dict[str, Any]]]]:
+        """Extract assistant content and annotations from the messages response.
+
+        Returns (content, annotations) where annotations is a list of
+        OpenAI-compatible ChatCompletionAnnotation dicts, or None.
+        """
         for msg in messages_data.get("data", []):
             if msg.get("role") == "assistant":
                 for content_item in msg.get("content", []):
                     if content_item.get("type") == "text":
-                        return content_item.get("text", {}).get("value", "")
-        return ""
+                        text_obj = content_item.get("text", {})
+                        content = text_obj.get("value", "")
+                        raw_annotations = text_obj.get("annotations")
+                        annotations = self._transform_annotations(raw_annotations)
+                        return content, annotations
+        return "", None
+
+    def _transform_annotations(
+        self,
+        raw_annotations: Optional[List[Dict[str, Any]]],
+    ) -> Optional[List[Dict[str, Any]]]:
+        """Transform Azure AI Foundry annotations to OpenAI-compatible format.
+
+        Azure AI returns annotations like:
+            {"type": "url_citation", "text": "[1]", "start_index": 10,
+             "end_index": 13, "url_citation": {"url": "...", "title": "..."}}
+
+        OpenAI expects:
+            {"type": "url_citation", "url_citation": {"url": "...", "title": "...",
+             "start_index": 10, "end_index": 13}}
+        """
+        if not raw_annotations:
+            return None
+
+        result: List[Dict[str, Any]] = []
+        for ann in raw_annotations:
+            ann_type = ann.get("type")
+            if ann_type == "url_citation":
+                url_citation = dict(ann.get("url_citation", {}))
+                # Azure puts start/end_index at annotation level; OpenAI
+                # expects them inside url_citation
+                if "start_index" in ann and "start_index" not in url_citation:
+                    url_citation["start_index"] = ann["start_index"]
+                if "end_index" in ann and "end_index" not in url_citation:
+                    url_citation["end_index"] = ann["end_index"]
+                result.append({"type": "url_citation", "url_citation": url_citation})
+            else:
+                # Pass through unknown annotation types as-is
+                result.append(ann)
+
+        return result if result else None
 
     def _build_model_response(
         self,
@@ -113,15 +158,23 @@ class AzureAIAgentsHandler:
         model_response: ModelResponse,
         thread_id: str,
         messages: List[Dict[str, Any]],
+        annotations: Optional[List[Dict[str, Any]]] = None,
     ) -> ModelResponse:
         """Build the ModelResponse from agent output."""
         from litellm.types.utils import Choices, Message, Usage
 
+        message_kwargs: Dict[str, Any] = {
+            "content": content,
+            "role": "assistant",
+        }
+        if annotations:
+            message_kwargs["annotations"] = annotations
+
         model_response.choices = [
             Choices(
                 finish_reason="stop",
                 index=0,
-                message=Message(content=content, role="assistant"),
+                message=Message(**message_kwargs),
             )
         ]
         model_response.model = model
@@ -250,7 +303,7 @@ class AzureAIAgentsHandler:
             )
 
         # Execute the agent flow
-        thread_id, content = self._execute_agent_flow_sync(
+        thread_id, content, annotations = self._execute_agent_flow_sync(
             make_request=make_request,
             api_base=api_base,
             api_version=api_version,
@@ -261,7 +314,7 @@ class AzureAIAgentsHandler:
         )
 
         return self._build_model_response(
-            model, content, model_response, thread_id, messages
+            model, content, model_response, thread_id, messages, annotations
         )
 
     def _execute_agent_flow_sync(
@@ -273,8 +326,8 @@ class AzureAIAgentsHandler:
         thread_id: Optional[str],
         messages: List[Dict[str, Any]],
         optional_params: dict,
-    ) -> Tuple[str, str]:
-        """Execute the agent flow synchronously. Returns (thread_id, content)."""
+    ) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]:
+        """Execute the agent flow synchronously. Returns (thread_id, content, annotations)."""
 
         # Step 1: Create thread if not provided
         if not thread_id:
@@ -347,8 +400,8 @@ class AzureAIAgentsHandler:
         )
         self._check_response(response, [200], "Failed to get messages")
 
-        content = self._extract_content_from_messages(response.json())
-        return thread_id, content
+        content, annotations = self._extract_content_from_messages(response.json())
+        return thread_id, content, annotations
 
     # -------------------------------------------------------------------------
     # Async Completion
@@ -399,7 +452,7 @@ class AzureAIAgentsHandler:
             )
 
         # Execute the agent flow
-        thread_id, content = await self._execute_agent_flow_async(
+        thread_id, content, annotations = await self._execute_agent_flow_async(
             make_request=make_request,
             api_base=api_base,
             api_version=api_version,
@@ -410,7 +463,7 @@ class AzureAIAgentsHandler:
         )
 
         return self._build_model_response(
-            model, content, model_response, thread_id, messages
+            model, content, model_response, thread_id, messages, annotations
         )
 
     async def _execute_agent_flow_async(
@@ -422,8 +475,8 @@ class AzureAIAgentsHandler:
         thread_id: Optional[str],
         messages: List[Dict[str, Any]],
         optional_params: dict,
-    ) -> Tuple[str, str]:
-        """Execute the agent flow asynchronously. Returns (thread_id, content)."""
+    ) -> Tuple[str, str, Optional[List[Dict[str, Any]]]]:
+        """Execute the agent flow asynchronously. Returns (thread_id, content, annotations)."""
 
         # Step 1: Create thread if not provided
         if not thread_id:
@@ -496,8 +549,8 @@ class AzureAIAgentsHandler:
         )
         self._check_response(response, [200], "Failed to get messages")
 
-        content = self._extract_content_from_messages(response.json())
-        return thread_id, content
+        content, annotations = self._extract_content_from_messages(response.json())
+        return thread_id, content, annotations
 
     # -------------------------------------------------------------------------
     # Streaming Completion (Native SSE)
@@ -585,6 +638,7 @@ class AzureAIAgentsHandler:
         response_id = f"chatcmpl-{uuid.uuid4().hex[:8]}"
         created = int(time.time())
         thread_id = None
+        collected_annotations: Optional[List[Dict[str, Any]]] = None
 
         current_event = None
 
@@ -600,6 +654,9 @@ class AzureAIAgentsHandler:
 
                 if data_str == "[DONE]":
                     # Send final chunk with finish_reason
+                    final_delta_kwargs: Dict[str, Any] = {"content": None}
+                    if collected_annotations:
+                        final_delta_kwargs["annotations"] = collected_annotations
                     final_chunk = ModelResponseStream(
                         id=response_id,
                         created=created,
@@ -609,7 +666,7 @@ class AzureAIAgentsHandler:
                             StreamingChoices(
                                 finish_reason="stop",
                                 index=0,
-                                delta=Delta(content=None),
+                                delta=Delta(**final_delta_kwargs),
                             )
                         ],
                     )
@@ -628,6 +685,19 @@ class AzureAIAgentsHandler:
                     thread_id = data["id"]
                     verbose_logger.debug(f"Stream created thread: {thread_id}")
 
+                # Extract annotations from completed message
+                if current_event == "thread.message.completed":
+                    for content_item in data.get("content", []):
+                        if content_item.get("type") == "text":
+                            raw_annotations = content_item.get("text", {}).get(
+                                "annotations"
+                            )
+                            transformed = self._transform_annotations(raw_annotations)
+                            if transformed:
+                                if collected_annotations is None:
+                                    collected_annotations = []
+                                collected_annotations.extend(transformed)
+
                 # Process message deltas - this is where the actual content comes
                 if current_event == "thread.message.delta":
                     delta_content = data.get("delta", {}).get("content", [])
diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py
index a7982cb606e..e1da0dfa29e 100644
--- a/litellm/llms/base_llm/guardrail_translation/base_translation.py
+++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py
@@ -73,6 +73,7 @@ class BaseTranslation(ABC):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
         user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response with guardrails.
@@ -91,6 +92,7 @@ class BaseTranslation(ABC):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
         user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output streaming response with guardrails.
diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py
index f429930e002..eea53fe06ec 100644
--- a/litellm/llms/base_llm/responses/transformation.py
+++ b/litellm/llms/base_llm/responses/transformation.py
@@ -54,6 +54,14 @@ class BaseResponsesAPIConfig(ABC):
             and v is not None
         }
 
+    def supports_native_file_search(self) -> bool:
+        """Return True if this provider handles the file_search tool natively.
+
+        Override in provider subclasses that support file_search without
+        LiteLLM emulation (e.g. OpenAI, Azure OpenAI).
+        """
+        return False
+
     @abstractmethod
     def get_supported_openai_params(self, model: str) -> list:
         pass
diff --git a/litellm/llms/cohere/rerank/guardrail_translation/handler.py b/litellm/llms/cohere/rerank/guardrail_translation/handler.py
index b8133c59f7d..e9a5823d2b8 100644
--- a/litellm/llms/cohere/rerank/guardrail_translation/handler.py
+++ b/litellm/llms/cohere/rerank/guardrail_translation/handler.py
@@ -83,6 +83,7 @@ class CohereRerankHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response - not applicable for rerank.
diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py
index 8407e8ab695..6b654ebdfd3 100644
--- a/litellm/llms/fireworks_ai/chat/transformation.py
+++ b/litellm/llms/fireworks_ai/chat/transformation.py
@@ -185,11 +185,16 @@ class FireworksAIConfig(OpenAIGPTConfig):
         ):  # allow user to toggle this feature.
             return content
         if isinstance(content["image_url"], str):
-            content["image_url"] = f"{content['image_url']}#transform=inline"
+            # Skip base64 data URLs — appending #transform=inline corrupts the
+            # base64 payload and causes an "Incorrect padding" decode error on
+            # the Fireworks side.  Data URLs are already inlined by definition.
+            # Lower-case before checking: URI schemes are case-insensitive (RFC 3986).
+            if not content["image_url"].lower().startswith("data:"):
+                content["image_url"] = f"{content['image_url']}#transform=inline"
         elif isinstance(content["image_url"], dict):
-            content["image_url"][
-                "url"
-            ] = f"{content['image_url']['url']}#transform=inline"
+            url = content["image_url"]["url"]
+            if not url.lower().startswith("data:"):
+                content["image_url"]["url"] = f"{url}#transform=inline"
         return content
 
     def _transform_tools(
diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py
index bdfb0ee1e52..a29ed66e63d 100644
--- a/litellm/llms/gemini/files/transformation.py
+++ b/litellm/llms/gemini/files/transformation.py
@@ -5,6 +5,7 @@ For vertex ai, check out the vertex_ai/files/handler.py file.
 """
 import time
 from typing import Any, List, Literal, Optional
+from urllib.parse import urlparse
 
 import httpx
 from openai.types.file_deleted import FileDeleted
@@ -209,27 +210,58 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
         """
         Get the URL to retrieve a file from Google AI Studio.
 
-        We expect file_id to be the URI (e.g. https://generativelanguage.googleapis.com/v1beta/files/...)
-        as returned by the upload response.
+        Endpoint:
+        GET https://generativelanguage.googleapis.com/v1beta/{name=files/*}
+
+        The URL should look like:
+        https://generativelanguage.googleapis.com/v1beta/files/{file_id}?key=API_KEY
+
+        We expect file_id to be just the file identifier (e.g., files/abc123 or abc123)
+        as returned by the upload response. (If it's a full URL, extract the file name.)
         """
         api_key = litellm_params.get("api_key") or self.get_api_key()
         if not api_key:
             raise ValueError("api_key is required")
 
-        if file_id.startswith("http"):
-            url = "{}?key={}".format(file_id, api_key)
-        else:
-            # Fallback for just file name (files/...)
-            api_base = (
-                self.get_api_base(litellm_params.get("api_base"))
-                or "https://generativelanguage.googleapis.com"
-            )
-            api_base = api_base.rstrip("/")
-            url = "{}/v1beta/{}?key={}".format(api_base, file_id, api_key)
+        file_part = self._normalize_gemini_file_id(file_id)
+
+        api_base = (
+            self.get_api_base(litellm_params.get("api_base"))
+            or "https://generativelanguage.googleapis.com"
+        )
+        api_base = api_base.rstrip("/")
+
+        url = f"{api_base}/v1beta/{file_part}?key={api_key}"
 
         # Return empty params dict - API key is already in URL, no query params needed
         return url, {}
 
+    def _normalize_gemini_file_id(self, file_id: str) -> str:
+        """
+        Normalize file identifier into `files/{id}` form.
+
+        Supports:
+        - `abc123`
+        - `files/abc123`
+        - `https://generativelanguage.googleapis.com/v1beta/files/abc123`
+        """
+        if file_id.startswith(("http://", "https://")):
+            parsed = urlparse(file_id)
+            path = parsed.path.lstrip("/")
+            files_index = path.find("files/")
+            if files_index != -1:
+                normalized_file_id = path[files_index:]
+            else:
+                normalized_file_id = path
+        else:
+            normalized_file_id = file_id
+
+        normalized_file_id = normalized_file_id.strip("/")
+        if not normalized_file_id.startswith("files/"):
+            normalized_file_id = f"files/{normalized_file_id}"
+
+        return normalized_file_id
+
     def transform_retrieve_file_response(
         self,
         raw_response: httpx.Response,
@@ -240,8 +272,9 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
         Transform Gemini's file retrieval response into OpenAI-style FileObject
         """
         try:
+            verbose_logger.debug(f"Retrieve file response: {raw_response.text}")
             response_json = raw_response.json()
-
+            verbose_logger.debug(f"Response JSON: {response_json}")
             # Map Gemini state to OpenAI status
             gemini_state = response_json.get("state", "STATE_UNSPECIFIED")
             # Explicitly type status as the Literal union
diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py
index 3e3f6162fce..b094fc133d7 100644
--- a/litellm/llms/gemini/image_generation/transformation.py
+++ b/litellm/llms/gemini/image_generation/transformation.py
@@ -88,12 +88,15 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
         tokens_details = usage_metadata.get("promptTokensDetails", [])
         for details in tokens_details:
             if isinstance(details, dict):
-                modality = details.get("modality")
-                token_count = details.get("tokenCount", 0)
+                modality = str(details.get("modality", "")).upper()
+                raw_token_count = details.get(
+                    "tokenCount", details.get("token_count", 0)
+                )
+                token_count = raw_token_count if isinstance(raw_token_count, int) else 0
                 if modality == "TEXT":
-                    input_tokens_details.text_tokens = token_count
+                    input_tokens_details.text_tokens += token_count
                 elif modality == "IMAGE":
-                    input_tokens_details.image_tokens = token_count
+                    input_tokens_details.image_tokens += token_count
 
         return ImageUsage(
             input_tokens=usage_metadata.get("promptTokenCount", 0),
diff --git a/litellm/llms/mistral/audio_transcription/transformation.py b/litellm/llms/mistral/audio_transcription/transformation.py
index 4d294063499..8c6d604acb4 100644
--- a/litellm/llms/mistral/audio_transcription/transformation.py
+++ b/litellm/llms/mistral/audio_transcription/transformation.py
@@ -148,5 +148,12 @@ class MistralAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
 
         text = response_json.get("text") or ""
         response = TranscriptionResponse(text=text)
+
+        # Preserve Mistral-specific fields (e.g. diarization segments)
+        if "segments" in response_json:
+            response["segments"] = response_json["segments"]
+        if "language" in response_json:
+            response["language"] = response_json["language"]
+
         response._hidden_params = response_json
         return response
diff --git a/litellm/llms/mistral/ocr/guardrail_translation/handler.py b/litellm/llms/mistral/ocr/guardrail_translation/handler.py
index 697bd2daa3d..7d3797a1dbe 100644
--- a/litellm/llms/mistral/ocr/guardrail_translation/handler.py
+++ b/litellm/llms/mistral/ocr/guardrail_translation/handler.py
@@ -91,6 +91,7 @@ class OCRHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process OCR output by applying guardrails to extracted page text.
@@ -127,14 +128,27 @@ class OCRHandler(BaseTranslation):
         if model:
             inputs["model"] = model
 
+        # Use the real request_data if provided (proxy path), otherwise
+        # create a standalone dict (SDK / direct-call path).
+        if request_data is None:
+            request_data = {}
+
         # Add user metadata if available
         if user_api_key_dict is not None:
-            metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict)
-            inputs.update(metadata)  # type: ignore
+            user_metadata = self.transform_user_api_key_dict_to_metadata(
+                user_api_key_dict
+            )
+            if user_metadata:
+                # Preserve original behavior: inject metadata into inputs for
+                # third-party guardrail providers that read it from there
+                inputs.update(user_metadata)  # type: ignore
+                # Also store in request_data for the logging pipeline
+                if "litellm_metadata" not in request_data:
+                    request_data["litellm_metadata"] = user_metadata
 
         guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
             inputs=inputs,
-            request_data={},
+            request_data=request_data,
             input_type="response",
             logging_obj=litellm_logging_obj,
         )
diff --git a/litellm/llms/moonshot/chat/transformation.py b/litellm/llms/moonshot/chat/transformation.py
index c97bd6c4e12..e4d7b5f033b 100644
--- a/litellm/llms/moonshot/chat/transformation.py
+++ b/litellm/llms/moonshot/chat/transformation.py
@@ -168,7 +168,9 @@ class MoonshotChatConfig(OpenAIGPTConfig):
             if (
                 msg.get("role") == "assistant"
                 and msg.get("tool_calls")
-                and not msg.get("reasoning_content")  # Check using .get() which works for both dicts and Pydantic models
+                and not msg.get(
+                    "reasoning_content"
+                )  # Check using .get() which works for both dicts and Pydantic models
             ):
                 patched = dict(cast(dict, msg))
                 provider_fields = patched.get("provider_specific_fields") or {}
diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py
index bb5783011a3..fc48704cd10 100644
--- a/litellm/llms/openai/chat/gpt_5_transformation.py
+++ b/litellm/llms/openai/chat/gpt_5_transformation.py
@@ -3,7 +3,7 @@
 from typing import Optional, Union
 
 import litellm
-from litellm.utils import _supports_factory
+from litellm.utils import _is_explicitly_disabled_factory, _supports_factory
 
 from .gpt_transformation import OpenAIGPTConfig
 
@@ -113,6 +113,25 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
             key=f"supports_{level}_reasoning_effort",
         )
 
+    @classmethod
+    def _is_reasoning_effort_level_explicitly_disabled(
+        cls, model: str, level: str
+    ) -> bool:
+        """Return True only when the model map explicitly sets the capability to False.
+
+        Unlike ``_supports_reasoning_effort_level`` (which requires an explicit True),
+        this method returns True only when ``supports_{level}_reasoning_effort`` is
+        explicitly set to ``False`` in the model map.  A missing key is treated as
+        supported (i.e. this method returns False = not disabled).
+
+        Use this for opt-out checks where unknown models should be allowed through.
+        """
+        return _is_explicitly_disabled_factory(
+            model=model,
+            custom_llm_provider=None,
+            key=f"supports_{level}_reasoning_effort",
+        )
+
     def get_supported_openai_params(self, model: str) -> list:
         if self.is_model_gpt_5_search_model(model):
             return [
@@ -200,14 +219,32 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
                 if "reasoning_effort" in optional_params:
                     optional_params["reasoning_effort"] = normalized
 
-        if effective_effort is not None and effective_effort == "xhigh":
-            if not self._supports_reasoning_effort_level(model, "xhigh"):
+        if effective_effort == "xhigh":
+            # xhigh is an opt-in capability: only allow if model explicitly supports it.
+            if not self._supports_reasoning_effort_level(model, effective_effort):
                 if litellm.drop_params or drop_params:
                     non_default_params.pop("reasoning_effort", None)
+                    optional_params.pop("reasoning_effort", None)
                 else:
                     raise litellm.utils.UnsupportedParamsError(
                         message=(
-                            "reasoning_effort='xhigh' is only supported for gpt-5.1-codex-max, gpt-5.2, and gpt-5.4+ models."
+                            f"reasoning_effort={effective_effort} is not supported for this model."
+                        ),
+                        status_code=400,
+                    )
+        elif effective_effort == "minimal":
+            # minimal is opt-out: unknown models pass through; only block when
+            # the model map explicitly sets supports_minimal_reasoning_effort=false.
+            if self._is_reasoning_effort_level_explicitly_disabled(
+                model, effective_effort
+            ):
+                if litellm.drop_params or drop_params:
+                    non_default_params.pop("reasoning_effort", None)
+                    optional_params.pop("reasoning_effort", None)
+                else:
+                    raise litellm.utils.UnsupportedParamsError(
+                        message=(
+                            f"reasoning_effort={effective_effort} is not supported for this model."
                         ),
                         status_code=400,
                     )
diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index bab4c3b5eb7..0ce9d57a65f 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -86,9 +86,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
             if tool_calls_to_check:
                 inputs["tool_calls"] = tool_calls_to_check  # type: ignore
             if messages:
-                inputs[
-                    "structured_messages"
-                ] = messages  # pass the openai /chat/completions messages to the guardrail, as-is
+                inputs["structured_messages"] = (
+                    messages  # pass the openai /chat/completions messages to the guardrail, as-is
+                )
             # Pass tools (function definitions) to the guardrail
             tools = data.get("tools")
             if tools:
@@ -260,6 +260,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response by applying guardrails to text content.
@@ -308,15 +309,21 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
 
         # Step 2: Apply guardrail to all texts and tool calls in batch
         if texts_to_check or tool_calls_to_check:
-            # Create a request_data dict with response info and user API key metadata
-            request_data: dict = {"response": response}
+            # Use the real request_data if provided (proxy path), otherwise
+            # create a standalone dict (SDK / direct-call path).
+            if request_data is None:
+                request_data = {"response": response}
+            else:
+                if "response" not in request_data:
+                    request_data["response"] = response
 
             # Add user API key metadata with prefixed keys
-            user_metadata = self.transform_user_api_key_dict_to_metadata(
-                user_api_key_dict
-            )
-            if user_metadata:
-                request_data["litellm_metadata"] = user_metadata
+            if "litellm_metadata" not in request_data:
+                user_metadata = self.transform_user_api_key_dict_to_metadata(
+                    user_api_key_dict
+                )
+                if user_metadata:
+                    request_data["litellm_metadata"] = user_metadata
 
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
             if images_to_check:
@@ -364,6 +371,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> List["ModelResponseStream"]:
         """
         Process output streaming responses by applying guardrails to text content.
@@ -402,6 +410,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
                 guardrail_to_apply=guardrail_to_apply,
                 litellm_logging_obj=litellm_logging_obj,
                 user_api_key_dict=user_api_key_dict,
+                request_data=request_data,
             )
 
             return responses_so_far
@@ -436,15 +445,21 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
 
         # Step 3: Apply guardrail to all combined texts in batch
         if texts_to_check:
-            # Create a request_data dict with response info and user API key metadata
-            request_data: dict = {"responses": responses_so_far}
+            # Use the real request_data if provided (proxy path), otherwise
+            # create a standalone dict (SDK / direct-call path).
+            if request_data is None:
+                request_data = {"responses": responses_so_far}
+            else:
+                if "responses" not in request_data:
+                    request_data["responses"] = responses_so_far
 
             # Add user API key metadata with prefixed keys
-            user_metadata = self.transform_user_api_key_dict_to_metadata(
-                user_api_key_dict
-            )
-            if user_metadata:
-                request_data["litellm_metadata"] = user_metadata
+            if "litellm_metadata" not in request_data:
+                user_metadata = self.transform_user_api_key_dict_to_metadata(
+                    user_api_key_dict
+                )
+                if user_metadata:
+                    request_data["litellm_metadata"] = user_metadata
 
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
             if images_to_check:
diff --git a/litellm/llms/openai/completion/guardrail_translation/handler.py b/litellm/llms/openai/completion/guardrail_translation/handler.py
index 1f8c6159da0..593ab0ed2e5 100644
--- a/litellm/llms/openai/completion/guardrail_translation/handler.py
+++ b/litellm/llms/openai/completion/guardrail_translation/handler.py
@@ -125,6 +125,7 @@ class OpenAITextCompletionHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response by applying guardrails to completion text.
@@ -155,15 +156,21 @@ class OpenAITextCompletionHandler(BaseTranslation):
 
         # Apply guardrails in batch
         if texts_to_check:
-            # Create a request_data dict with response info and user API key metadata
-            request_data: dict = {"response": response}
+            # Use the real request_data if provided (proxy path), otherwise
+            # create a standalone dict (SDK / direct-call path).
+            if request_data is None:
+                request_data = {"response": response}
+            else:
+                if "response" not in request_data:
+                    request_data["response"] = response
 
             # Add user API key metadata with prefixed keys
-            user_metadata = self.transform_user_api_key_dict_to_metadata(
-                user_api_key_dict
-            )
-            if user_metadata:
-                request_data["litellm_metadata"] = user_metadata
+            if "litellm_metadata" not in request_data:
+                user_metadata = self.transform_user_api_key_dict_to_metadata(
+                    user_api_key_dict
+                )
+                if user_metadata:
+                    request_data["litellm_metadata"] = user_metadata
 
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
             # Include model information from the response if available
diff --git a/litellm/llms/openai/embeddings/guardrail_translation/handler.py b/litellm/llms/openai/embeddings/guardrail_translation/handler.py
index 7458020e109..ff5021b8ce0 100644
--- a/litellm/llms/openai/embeddings/guardrail_translation/handler.py
+++ b/litellm/llms/openai/embeddings/guardrail_translation/handler.py
@@ -155,6 +155,7 @@ class OpenAIEmbeddingsHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response - embeddings responses contain vectors, not text.
diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py
index 9804ff3539e..c065325254e 100644
--- a/litellm/llms/openai/fine_tuning/handler.py
+++ b/litellm/llms/openai/fine_tuning/handler.py
@@ -1,4 +1,4 @@
-from typing import Any, Coroutine, Optional, Union, cast
+from typing import Any, Coroutine, Dict, Optional, Union, cast
 
 import httpx
 from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
@@ -6,6 +6,55 @@ from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
 from litellm._logging import verbose_logger
 from litellm.types.utils import LiteLLMFineTuningJob
 
+_AZURE_STATUS_MAP = {
+    "pending": "queued",
+    "notRunning": "queued",
+    "running": "running",
+    "succeeded": "succeeded",
+    "failed": "failed",
+    "canceled": "cancelled",
+    "canceling": "cancelled",
+}
+# Note: Azure's "canceling" (in-progress) is mapped to "cancelled" (terminal)
+# because LiteLLMFineTuningJob schema has no intermediate cancellation state.
+
+
+def _normalize_fine_tuning_job_dict(
+    data: Dict[str, Any], is_azure: bool = False
+) -> Dict[str, Any]:
+    """
+    Normalize Azure OpenAI FineTuningJob response to match OpenAI schema.
+
+    Azure differences:
+    - organization_id: null → ""
+    - result_files: null → []
+    - status: mapped via _AZURE_STATUS_MAP
+    """
+    if not is_azure:
+        return data
+
+    normalized = data.copy()
+
+    if normalized.get("organization_id") is None:
+        normalized["organization_id"] = ""
+
+    if normalized.get("result_files") is None:
+        normalized["result_files"] = []
+
+    status = normalized.get("status")
+    if status in _AZURE_STATUS_MAP:
+        normalized["status"] = _AZURE_STATUS_MAP[status]
+
+    return normalized
+
+
+def _litellm_fine_tuning_job_from_response(
+    response: Any, is_azure: bool = False
+) -> LiteLLMFineTuningJob:
+    return LiteLLMFineTuningJob(
+        **_normalize_fine_tuning_job_dict(response.model_dump(), is_azure=is_azure)
+    )
+
 
 class OpenAIFineTuningAPI:
     """
@@ -60,7 +109,7 @@ class OpenAIFineTuningAPI:
             **create_fine_tuning_job_data
         )
 
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     def create_fine_tuning_job(
         self,
@@ -108,7 +157,7 @@ class OpenAIFineTuningAPI:
         response = cast(OpenAI, openai_client).fine_tuning.jobs.create(
             **create_fine_tuning_job_data
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     async def acancel_fine_tuning_job(
         self,
@@ -118,7 +167,7 @@ class OpenAIFineTuningAPI:
         response = await openai_client.fine_tuning.jobs.cancel(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     def cancel_fine_tuning_job(
         self,
@@ -164,7 +213,7 @@ class OpenAIFineTuningAPI:
         response = cast(OpenAI, openai_client).fine_tuning.jobs.cancel(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     async def alist_fine_tuning_jobs(
         self,
@@ -229,7 +278,7 @@ class OpenAIFineTuningAPI:
         response = await openai_client.fine_tuning.jobs.retrieve(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     def retrieve_fine_tuning_job(
         self,
@@ -275,4 +324,4 @@ class OpenAIFineTuningAPI:
         response = cast(OpenAI, openai_client).fine_tuning.jobs.retrieve(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
diff --git a/litellm/llms/openai/image_generation/guardrail_translation/handler.py b/litellm/llms/openai/image_generation/guardrail_translation/handler.py
index e6340ba4705..76610088d0c 100644
--- a/litellm/llms/openai/image_generation/guardrail_translation/handler.py
+++ b/litellm/llms/openai/image_generation/guardrail_translation/handler.py
@@ -87,6 +87,7 @@ class OpenAIImageGenerationHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response - typically not needed for image generation.
diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py
index 466e2e76f18..76f40eed71f 100644
--- a/litellm/llms/openai/responses/guardrail_translation/handler.py
+++ b/litellm/llms/openai/responses/guardrail_translation/handler.py
@@ -347,6 +347,7 @@ class OpenAIResponsesHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response by applying guardrails to text content and tool calls.
@@ -402,15 +403,21 @@ class OpenAIResponsesHandler(BaseTranslation):
 
         # Step 2: Apply guardrail to all texts in batch
         if texts_to_check or tool_calls_to_check:
-            # Create a request_data dict with response info and user API key metadata
-            request_data: dict = {"response": response}
+            # Use the real request_data if provided (proxy path), otherwise
+            # create a standalone dict (SDK / direct-call path).
+            if request_data is None:
+                request_data = {"response": response}
+            else:
+                if "response" not in request_data:
+                    request_data["response"] = response
 
             # Add user API key metadata with prefixed keys
-            user_metadata = self.transform_user_api_key_dict_to_metadata(
-                user_api_key_dict
-            )
-            if user_metadata:
-                request_data["litellm_metadata"] = user_metadata
+            if "litellm_metadata" not in request_data:
+                user_metadata = self.transform_user_api_key_dict_to_metadata(
+                    user_api_key_dict
+                )
+                if user_metadata:
+                    request_data["litellm_metadata"] = user_metadata
 
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
             if images_to_check:
@@ -454,6 +461,7 @@ class OpenAIResponsesHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> List[Any]:
         """
         Process output streaming response by applying guardrails to text content.
@@ -481,7 +489,7 @@ class OpenAIResponsesHandler(BaseTranslation):
                     inputs["model"] = model_response_stream.model
                 _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
                     inputs=inputs,
-                    request_data={},
+                    request_data=request_data if request_data is not None else {},
                     input_type="response",
                     logging_obj=litellm_logging_obj,
                 )
@@ -512,7 +520,7 @@ class OpenAIResponsesHandler(BaseTranslation):
                 if tool_calls or text:
                     _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
                         inputs=guardrail_inputs,
-                        request_data={},
+                        request_data=request_data if request_data is not None else {},
                         input_type="response",
                         logging_obj=litellm_logging_obj,
                     )
@@ -537,7 +545,7 @@ class OpenAIResponsesHandler(BaseTranslation):
                 inputs["model"] = response_model
         _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
             inputs=inputs,
-            request_data={},
+            request_data=request_data if request_data is not None else {},
             input_type="response",
             logging_obj=litellm_logging_obj,
         )
diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py
index 9d909fd4017..cafb745862d 100644
--- a/litellm/llms/openai/responses/transformation.py
+++ b/litellm/llms/openai/responses/transformation.py
@@ -32,6 +32,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
     def custom_llm_provider(self) -> LlmProviders:
         return LlmProviders.OPENAI
 
+    def supports_native_file_search(self) -> bool:
+        return True
+
     def get_supported_openai_params(self, model: str) -> list:
         """
         All OpenAI Responses API params are supported
diff --git a/litellm/llms/openai/speech/guardrail_translation/handler.py b/litellm/llms/openai/speech/guardrail_translation/handler.py
index e6796fbac2a..f0c3149d0ae 100644
--- a/litellm/llms/openai/speech/guardrail_translation/handler.py
+++ b/litellm/llms/openai/speech/guardrail_translation/handler.py
@@ -85,6 +85,7 @@ class OpenAITextToSpeechHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output - not applicable for text-to-speech.
diff --git a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py
index 3d76a21c389..92cf4398f05 100644
--- a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py
+++ b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py
@@ -58,6 +58,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output transcription by applying guardrails to transcribed text.
@@ -79,15 +80,21 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation):
 
         if isinstance(response.text, str):
             original_text = response.text
-            # Create a request_data dict with response info and user API key metadata
-            request_data: dict = {"response": response}
+            # Use the real request_data if provided (proxy path), otherwise
+            # create a standalone dict (SDK / direct-call path).
+            if request_data is None:
+                request_data = {"response": response}
+            else:
+                if "response" not in request_data:
+                    request_data["response"] = response
 
             # Add user API key metadata with prefixed keys
-            user_metadata = self.transform_user_api_key_dict_to_metadata(
-                user_api_key_dict
-            )
-            if user_metadata:
-                request_data["litellm_metadata"] = user_metadata
+            if "litellm_metadata" not in request_data:
+                user_metadata = self.transform_user_api_key_dict_to_metadata(
+                    user_api_key_dict
+                )
+                if user_metadata:
+                    request_data["litellm_metadata"] = user_metadata
 
             inputs = GenericGuardrailAPIInputs(texts=[original_text])
             # Include model information from the response if available
diff --git a/litellm/llms/ovhcloud/chat/transformation.py b/litellm/llms/ovhcloud/chat/transformation.py
index 84090fafd31..1416b782f17 100644
--- a/litellm/llms/ovhcloud/chat/transformation.py
+++ b/litellm/llms/ovhcloud/chat/transformation.py
@@ -28,9 +28,7 @@ class OVHCloudChatConfig(OpenAIGPTConfig):
         """
         supports_function_calling: Optional[bool] = None
         try:
-            model_info = _get_model_info_helper(
-                model, custom_llm_provider="ovhcloud"
-            )
+            model_info = _get_model_info_helper(model, custom_llm_provider="ovhcloud")
             supports_function_calling = model_info.get(
                 "supports_function_calling", None
             )
diff --git a/litellm/llms/pass_through/guardrail_translation/handler.py b/litellm/llms/pass_through/guardrail_translation/handler.py
index 40433d53413..a8cc42d7c54 100644
--- a/litellm/llms/pass_through/guardrail_translation/handler.py
+++ b/litellm/llms/pass_through/guardrail_translation/handler.py
@@ -139,6 +139,7 @@ class PassThroughEndpointHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         """
         Process output response by applying guardrails to targeted fields.
@@ -171,17 +172,27 @@ class PassThroughEndpointHandler(BaseTranslation):
         if not text_to_check:
             return response
 
-        # Create a request_data dict with response info and user API key metadata
-        request_data: dict = (
-            {"response": response}
-            if not isinstance(response, dict)
-            else response.copy()
-        )
+        # Use the real request_data if provided (proxy path), otherwise
+        # create a standalone dict (SDK / direct-call path).
+        if request_data is None:
+            request_data = (
+                {"response": response}
+                if not isinstance(response, dict)
+                else response.copy()
+            )
+        else:
+            if "response" not in request_data:
+                request_data["response"] = (
+                    response if not isinstance(response, dict) else response.copy()
+                )
 
         # Add user API key metadata with prefixed keys
-        user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict)
-        if user_metadata:
-            request_data["litellm_metadata"] = user_metadata
+        if "litellm_metadata" not in request_data:
+            user_metadata = self.transform_user_api_key_dict_to_metadata(
+                user_api_key_dict
+            )
+            if user_metadata:
+                request_data["litellm_metadata"] = user_metadata
 
         # Apply guardrail (pass-through doesn't modify the text, just checks it)
         inputs = GenericGuardrailAPIInputs(texts=[text_to_check])
diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py
index f0b181c9a61..2cb02942061 100644
--- a/litellm/llms/vertex_ai/batches/handler.py
+++ b/litellm/llms/vertex_ai/batches/handler.py
@@ -376,3 +376,148 @@ class VertexAIBatchPrediction(VertexLLM):
             response=_json_response
         )
         return vertex_batch_response
+
+    def cancel_batch(
+        self,
+        _is_async: bool,
+        batch_id: str,
+        api_base: Optional[str],
+        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        vertex_project: Optional[str],
+        vertex_location: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+    ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
+        access_token, project_id = self._ensure_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider="vertex_ai",
+        )
+
+        default_api_base = self.create_vertex_batch_url(
+            vertex_location=vertex_location or "us-central1",
+            vertex_project=vertex_project or project_id,
+        )
+
+        retrieve_api_base_default = f"{default_api_base}/{batch_id}"
+        cancel_api_base_default = f"{retrieve_api_base_default}:cancel"
+
+        _, api_base = self._check_custom_proxy(
+            api_base=api_base,
+            custom_llm_provider="vertex_ai",
+            gemini_api_key=None,
+            endpoint="cancel",
+            stream=None,
+            auth_header=None,
+            url=cancel_api_base_default,
+            model=None,
+            vertex_project=vertex_project or project_id,
+            vertex_location=vertex_location or "us-central1",
+            vertex_api_version="v1",
+        )
+
+        if api_base.endswith(":cancel"):
+            retrieve_api_base = api_base.removesuffix(":cancel")
+        else:
+            retrieve_api_base = api_base.rsplit(":cancel", 1)[0].rstrip("/")
+
+        headers = {
+            "Content-Type": "application/json; charset=utf-8",
+            "Authorization": f"Bearer {access_token}",
+        }
+
+        if _is_async is True:
+            return self._async_cancel_batch(
+                api_base=api_base,
+                retrieve_api_base=retrieve_api_base,
+                headers=headers,
+                timeout=timeout,
+            )
+
+        sync_handler = _get_httpx_client()
+        try:
+            response = sync_handler.post(
+                url=api_base,
+                headers=headers,
+                data=json.dumps({}),
+                timeout=timeout,
+            )
+        except httpx.HTTPStatusError as e:
+            litellm.verbose_logger.error(
+                "Vertex AI batch cancel failed: status=%s, body=%s",
+                e.response.status_code,
+                e.response.text[:1000],
+            )
+            raise
+
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        # HTTPHandler.get() does not accept a timeout parameter
+        retrieve_response = sync_handler.get(
+            url=retrieve_api_base,
+            headers=headers,
+        )
+        if retrieve_response.status_code != 200:
+            litellm.verbose_logger.error(
+                "Vertex AI batch retrieve-after-cancel failed: status=%s, body=%s",
+                retrieve_response.status_code,
+                retrieve_response.text[:1000],
+            )
+            raise Exception(
+                f"Error: {retrieve_response.status_code} {retrieve_response.text}"
+            )
+
+        _json_response = retrieve_response.json()
+        vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
+            response=_json_response
+        )
+        return vertex_batch_response
+
+    async def _async_cancel_batch(
+        self,
+        api_base: str,
+        retrieve_api_base: str,
+        headers: Dict[str, str],
+        timeout: Union[float, httpx.Timeout] = 600.0,
+    ) -> LiteLLMBatch:
+        client = get_async_httpx_client(
+            llm_provider=litellm.LlmProviders.VERTEX_AI,
+        )
+        try:
+            response = await client.post(
+                url=api_base,
+                headers=headers,
+                data=json.dumps({}),
+                timeout=timeout,
+            )
+        except httpx.HTTPStatusError as e:
+            litellm.verbose_logger.error(
+                "Vertex AI batch cancel failed: status=%s, body=%s",
+                e.response.status_code,
+                e.response.text[:1000],
+            )
+            raise
+        if response.status_code != 200:
+            raise Exception(f"Error: {response.status_code} {response.text}")
+
+        # AsyncHTTPHandler.get() does not accept a timeout parameter
+        retrieve_response = await client.get(
+            url=retrieve_api_base,
+            headers=headers,
+        )
+        if retrieve_response.status_code != 200:
+            litellm.verbose_logger.error(
+                "Vertex AI batch retrieve-after-cancel failed: status=%s, body=%s",
+                retrieve_response.status_code,
+                retrieve_response.text[:1000],
+            )
+            raise Exception(
+                f"Error: {retrieve_response.status_code} {retrieve_response.text}"
+            )
+
+        _json_response = retrieve_response.json()
+        vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
+            response=_json_response
+        )
+        return vertex_batch_response
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 db6be9499a2..b677cf3b1ec 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
@@ -51,6 +51,7 @@ class ContextCachingEndpoints(VertexBase):
         vertex_project: Optional[str],
         vertex_location: Optional[str],
         vertex_auth_header: Optional[str],
+        model: Optional[str] = None,
     ) -> Tuple[Optional[str], str]:
         """
         Internal function. Returns the token and url for the call.
@@ -89,7 +90,7 @@ class ContextCachingEndpoints(VertexBase):
             stream=None,
             auth_header=auth_header,
             url=url,
-            model=None,
+            model=model,
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_api_version="v1beta1"
@@ -109,6 +110,7 @@ class ContextCachingEndpoints(VertexBase):
         vertex_project: Optional[str],
         vertex_location: Optional[str],
         vertex_auth_header: Optional[str],
+        model: Optional[str] = None,
     ) -> Optional[str]:
         """
         Checks if content already cached.
@@ -128,6 +130,7 @@ class ContextCachingEndpoints(VertexBase):
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_auth_header=vertex_auth_header,
+            model=model,
         )
 
         page_token: Optional[str] = None
@@ -201,6 +204,7 @@ class ContextCachingEndpoints(VertexBase):
         vertex_project: Optional[str],
         vertex_location: Optional[str],
         vertex_auth_header: Optional[str],
+        model: Optional[str] = None,
     ) -> Optional[str]:
         """
         Checks if content already cached.
@@ -220,6 +224,7 @@ class ContextCachingEndpoints(VertexBase):
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_auth_header=vertex_auth_header,
+            model=model,
         )
 
         page_token: Optional[str] = None
@@ -342,6 +347,7 @@ class ContextCachingEndpoints(VertexBase):
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_auth_header=vertex_auth_header,
+            model=model,
         )
 
         headers = {
@@ -377,6 +383,7 @@ class ContextCachingEndpoints(VertexBase):
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_auth_header=vertex_auth_header,
+            model=model,
         )
         if google_cache_name:
             return non_cached_messages, optional_params, google_cache_name
@@ -488,6 +495,7 @@ class ContextCachingEndpoints(VertexBase):
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_auth_header=vertex_auth_header,
+            model=model,
         )
 
         headers = {
@@ -520,6 +528,7 @@ class ContextCachingEndpoints(VertexBase):
             vertex_project=vertex_project,
             vertex_location=vertex_location,
             vertex_auth_header=vertex_auth_header,
+            model=model,
         )
 
         if google_cache_name:
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index f6310778c71..7945c44d44c 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -555,7 +555,9 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                 }
                             }
                             if "thought_signature" in invocation:
-                                tc_part["thoughtSignature"] = invocation["thought_signature"]
+                                tc_part["thoughtSignature"] = invocation[
+                                    "thought_signature"
+                                ]
                             assistant_content.append(tc_part)  # type: ignore
 
                             # Re-inject toolResponse part if response is present
@@ -566,11 +568,11 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                                 }
                                 if invocation.get("tool_type"):
                                     tr_dict["toolType"] = invocation["tool_type"]
-                                tr_part: Dict[str, Any] = {
-                                    "toolResponse": tr_dict
-                                }
+                                tr_part: Dict[str, Any] = {"toolResponse": tr_dict}
                                 if "thought_signature" in invocation:
-                                    tr_part["thoughtSignature"] = invocation["thought_signature"]
+                                    tr_part["thoughtSignature"] = invocation[
+                                        "thought_signature"
+                                    ]
                                 assistant_content.append(tr_part)  # type: ignore
 
                 msg_i += 1
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 3555d3c719e..36f51c5b2f5 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
@@ -12,6 +12,7 @@ from typing import (
     Dict,
     List,
     Literal,
+    Mapping,
     Optional,
     Tuple,
     Type,
@@ -1696,6 +1697,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         response_tokens: Optional[int] = None
         response_tokens_details: Optional[CompletionTokensDetailsWrapper] = None
         usage_metadata = completion_response["usageMetadata"]
+
+        def _get_token_count(detail: Mapping[str, Any]) -> int:
+            raw_token_count = detail.get("tokenCount", detail.get("token_count", 0))
+            return raw_token_count if isinstance(raw_token_count, int) else 0
+
         if "cachedContentTokenCount" in usage_metadata:
             cached_tokens = usage_metadata["cachedContentTokenCount"]
 
@@ -1705,10 +1711,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         if "responseTokensDetails" in usage_metadata:
             response_tokens_details = CompletionTokensDetailsWrapper()
             for detail in usage_metadata["responseTokensDetails"]:
-                if detail["modality"] == "TEXT":
-                    response_tokens_details.text_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "AUDIO":
-                    response_tokens_details.audio_tokens = detail.get("tokenCount", 0)
+                modality = str(detail.get("modality", "")).upper()
+                token_count = _get_token_count(detail)
+                if modality == "TEXT":
+                    response_tokens_details.text_tokens = (
+                        response_tokens_details.text_tokens or 0
+                    ) + token_count
+                elif modality == "AUDIO":
+                    response_tokens_details.audio_tokens = (
+                        response_tokens_details.audio_tokens or 0
+                    ) + token_count
 
         #########################################################
 
@@ -1717,16 +1729,24 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             if response_tokens_details is None:
                 response_tokens_details = CompletionTokensDetailsWrapper()
             for detail in usage_metadata["candidatesTokensDetails"]:
-                modality = detail.get("modality")
-                token_count = detail.get("tokenCount", 0)
+                modality = str(detail.get("modality", "")).upper()
+                token_count = _get_token_count(detail)
                 if modality == "TEXT":
-                    response_tokens_details.text_tokens = token_count
+                    response_tokens_details.text_tokens = (
+                        response_tokens_details.text_tokens or 0
+                    ) + token_count
                 elif modality == "AUDIO":
-                    response_tokens_details.audio_tokens = token_count
+                    response_tokens_details.audio_tokens = (
+                        response_tokens_details.audio_tokens or 0
+                    ) + token_count
                 elif modality == "IMAGE":
-                    response_tokens_details.image_tokens = token_count
+                    response_tokens_details.image_tokens = (
+                        response_tokens_details.image_tokens or 0
+                    ) + token_count
                 elif modality == "VIDEO":
-                    response_tokens_details.video_tokens = token_count
+                    response_tokens_details.video_tokens = (
+                        response_tokens_details.video_tokens or 0
+                    ) + token_count
 
         # Calculate text_tokens if not explicitly provided in candidatesTokensDetails
         # candidatesTokenCount includes all modalities, so: text = total - (image + audio + video)
@@ -1750,14 +1770,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         ## Parse promptTokensDetails (total tokens by modality, includes cached + non-cached)
         if "promptTokensDetails" in usage_metadata:
             for detail in usage_metadata["promptTokensDetails"]:
-                if detail["modality"] == "AUDIO":
-                    prompt_audio_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "TEXT":
-                    prompt_text_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "IMAGE":
-                    prompt_image_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "VIDEO":
-                    prompt_video_tokens = detail.get("tokenCount", 0)
+                modality = str(detail.get("modality", "")).upper()
+                token_count = _get_token_count(detail)
+                if modality == "AUDIO":
+                    prompt_audio_tokens = (prompt_audio_tokens or 0) + token_count
+                elif modality == "TEXT":
+                    prompt_text_tokens = (prompt_text_tokens or 0) + token_count
+                elif modality == "IMAGE":
+                    prompt_image_tokens = (prompt_image_tokens or 0) + token_count
+                elif modality == "VIDEO":
+                    prompt_video_tokens = (prompt_video_tokens or 0) + token_count
 
         ## Parse cacheTokensDetails (breakdown of cached tokens by modality)
         ## When explicit caching is used, Gemini provides this field to show which modalities were cached
@@ -1768,14 +1790,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
 
         if "cacheTokensDetails" in usage_metadata:
             for detail in usage_metadata["cacheTokensDetails"]:
-                if detail["modality"] == "AUDIO":
-                    cached_audio_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "TEXT":
-                    cached_text_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "IMAGE":
-                    cached_image_tokens = detail.get("tokenCount", 0)
-                elif detail["modality"] == "VIDEO":
-                    cached_video_tokens = detail.get("tokenCount", 0)
+                modality = str(detail.get("modality", "")).upper()
+                token_count = _get_token_count(detail)
+                if modality == "AUDIO":
+                    cached_audio_tokens = (cached_audio_tokens or 0) + token_count
+                elif modality == "TEXT":
+                    cached_text_tokens = (cached_text_tokens or 0) + token_count
+                elif modality == "IMAGE":
+                    cached_image_tokens = (cached_image_tokens or 0) + token_count
+                elif modality == "VIDEO":
+                    cached_video_tokens = (cached_video_tokens or 0) + token_count
 
         ## Calculate non-cached tokens by subtracting cached from total (per modality)
         ## This is necessary because promptTokensDetails includes both cached and non-cached tokens
@@ -3079,6 +3103,16 @@ class ModelResponseIterator:
                             )
                             model_response.choices.append(choice)
 
+                # Also handle the case where the final chunk has empty
+                # content (e.g. text:"") WITH finishReason. In this case
+                # _process_candidates DOES create a choice, but maps
+                # finishReason="STOP" to "stop" because the current chunk
+                # has no tool_calls. Override if we saw tool_calls earlier.
+                if self.has_seen_tool_calls:
+                    for choice in model_response.choices:
+                        if choice.finish_reason == "stop":
+                            choice.finish_reason = "tool_calls"
+
                 setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
                 setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
                 setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py
index c6914ac3d6b..5d94cd42129 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py
@@ -105,12 +105,27 @@ class VertexAIPartnerModelsTokenCounter(VertexBase):
         # Extract Vertex AI credentials and settings
         vertex_credentials = self.get_vertex_ai_credentials(litellm_params)
         vertex_project = self.get_vertex_ai_project(litellm_params)
-        vertex_location = self.get_vertex_ai_location(litellm_params)
 
-        # Map empty location/cluade models to a supported region for count-tokens endpoint
+        # Check for count_tokens specific location override
+        vertex_count_tokens_location = litellm_params.get(
+            "vertex_count_tokens_location"
+        )
+        vertex_location_raw = self.get_vertex_ai_location(litellm_params)
+
+        # Determine final location with precedence:
+        # 1. vertex_count_tokens_location (if provided)
+        # 2. vertex_location (if provided)
+        # 3. Default to us-east5 for Claude models when no location is set
+        # Supported regions: us-east5, europe-west1, asia-southeast1
         # https://docs.cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/count-tokens
-        if not vertex_location or "claude" in model.lower():
-            vertex_location = "us-central1"
+        if vertex_count_tokens_location:
+            vertex_location: str = vertex_count_tokens_location
+        elif vertex_location_raw:
+            vertex_location = vertex_location_raw
+        elif "claude" in model.lower():
+            vertex_location = "us-east5"
+        else:
+            vertex_location = "us-east5"
 
         # Get access token and resolved project ID
         access_token, project_id = await self._ensure_access_token_async(
diff --git a/litellm/main.py b/litellm/main.py
index 112fef44e55..eace9c630ba 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -955,16 +955,6 @@ def responses_api_bridge_check(
             model_info["mode"] = "responses"
             model = model.replace("responses/", "")
 
-        # OpenAI gpt-5.4+ chat-completions calls with both tools + reasoning_effort
-        # must be bridged to Responses API.
-        if (
-            custom_llm_provider == "openai"
-            and OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model)
-            and tools
-            and reasoning_effort is not None
-        ):
-            model_info["mode"] = "responses"
-            model = model.replace("responses/", "")
     except Exception as e:
         verbose_logger.debug("Error getting model info: {}".format(e))
 
@@ -974,6 +964,19 @@ def responses_api_bridge_check(
             model = model.replace("responses/", "")
             mode = "responses"
             model_info["mode"] = mode
+
+    # OpenAI/Azure gpt-5.4+ chat-completions calls with both tools + reasoning_effort
+    # must be bridged to Responses API.
+    if (
+        custom_llm_provider in ("openai", "azure")
+        and OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model)
+        and tools
+        and reasoning_effort is not None
+        and model_info.get("mode") != "responses"
+    ):
+        model_info["mode"] = "responses"
+        model = model.replace("responses/", "")
+
     return model_info, model
 
 
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 879dd42be47..2010a8053b4 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -971,18 +971,14 @@
     },
     "anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1001,18 +997,14 @@
     },
     "global.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1031,18 +1023,14 @@
     },
     "us.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
         "cache_read_input_token_cost": 5.5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         "input_cost_per_token": 5.5e-06,
-        "input_cost_per_token_above_200k_tokens": 1.1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.75e-05,
-        "output_cost_per_token_above_200k_tokens": 4.125e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1061,18 +1049,14 @@
     },
     "eu.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
         "cache_read_input_token_cost": 5.5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         "input_cost_per_token": 5.5e-06,
-        "input_cost_per_token_above_200k_tokens": 1.1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.75e-05,
-        "output_cost_per_token_above_200k_tokens": 4.125e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1091,18 +1075,14 @@
     },
     "au.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
         "cache_read_input_token_cost": 5.5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         "input_cost_per_token": 5.5e-06,
-        "input_cost_per_token_above_200k_tokens": 1.1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.75e-05,
-        "output_cost_per_token_above_200k_tokens": 4.125e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1121,18 +1101,14 @@
     },
     "anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1151,18 +1127,14 @@
     },
     "global.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1181,18 +1153,14 @@
     },
     "us.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
         "cache_read_input_token_cost": 3.3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
         "input_cost_per_token": 3.3e-06,
-        "input_cost_per_token_above_200k_tokens": 6.6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.65e-05,
-        "output_cost_per_token_above_200k_tokens": 2.475e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1211,18 +1179,14 @@
     },
     "eu.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
         "cache_read_input_token_cost": 3.3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
         "input_cost_per_token": 3.3e-06,
-        "input_cost_per_token_above_200k_tokens": 6.6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.65e-05,
-        "output_cost_per_token_above_200k_tokens": 2.475e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1241,18 +1205,14 @@
     },
     "au.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
         "cache_read_input_token_cost": 3.3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
         "input_cost_per_token": 3.3e-06,
-        "input_cost_per_token_above_200k_tokens": 6.6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.65e-05,
-        "output_cost_per_token_above_200k_tokens": 2.475e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1831,7 +1791,7 @@
         "cache_read_input_token_cost": 3e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "azure_ai",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
@@ -3435,7 +3395,8 @@
         "supports_tool_choice": true,
         "supports_service_tier": true,
         "supports_vision": true,
-        "supports_none_reasoning_effort": true
+        "supports_none_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "azure/gpt-5.1-chat-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -6152,7 +6113,8 @@
         "max_query_tokens": 4096,
         "max_tokens": 32768,
         "mode": "rerank",
-        "output_cost_per_token": 0.0
+        "output_cost_per_token": 0.0,
+        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-cohere-rerank-4-0-in-microsoft-foundry/4477076"
     },
     "azure_ai/cohere-rerank-v4.0-fast": {
         "input_cost_per_query": 0.002,
@@ -6163,7 +6125,8 @@
         "max_query_tokens": 4096,
         "max_tokens": 32768,
         "mode": "rerank",
-        "output_cost_per_token": 0.0
+        "output_cost_per_token": 0.0,
+        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-cohere-rerank-4-0-in-microsoft-foundry/4477076"
     },
     "azure_ai/deepseek-v3.2": {
         "input_cost_per_token": 5.8e-07,
@@ -6173,6 +6136,7 @@
         "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
+        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_prompt_caching": true,
@@ -6187,6 +6151,7 @@
         "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
+        "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_prompt_caching": true,
@@ -8498,18 +8463,14 @@
     },
     "claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "anthropic",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -8690,19 +8651,15 @@
     },
     "claude-opus-4-6": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_creation_input_token_cost_above_1hr": 1e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "anthropic",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -8725,19 +8682,15 @@
     },
     "claude-opus-4-6-20260205": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_creation_input_token_cost_above_1hr": 1e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "anthropic",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -15926,6 +15879,55 @@
         "supports_tool_choice": true,
         "supports_vision": true
     },
+    "gemini/lyria-3-clip-preview": {
+        "input_cost_per_token": 0,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_image": 0.04,
+        "output_cost_per_token": 0,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "audio"
+        ],
+        "supports_audio_input": false,
+        "supports_audio_output": true,
+        "supports_function_calling": false,
+        "supports_prompt_caching": false,
+        "supports_response_schema": false,
+        "supports_system_messages": false,
+        "supports_vision": false,
+        "supports_web_search": false
+    },
+    "gemini/lyria-3-pro-preview": {
+        "input_cost_per_token": 0,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 0,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "audio"
+        ],
+        "supports_audio_input": false,
+        "supports_audio_output": true,
+        "supports_function_calling": false,
+        "supports_prompt_caching": false,
+        "supports_response_schema": false,
+        "supports_system_messages": false,
+        "supports_vision": false,
+        "supports_web_search": false
+    },
     "gemini/veo-2.0-generate-001": {
         "litellm_provider": "gemini",
         "max_input_tokens": 1024,
@@ -16936,6 +16938,18 @@
         "supports_system_messages": true,
         "supports_tool_choice": true
     },
+    "gpt-4-0314": {
+        "deprecation_date": "2026-03-26",
+        "input_cost_per_token": 3e-05,
+        "litellm_provider": "openai",
+        "max_input_tokens": 8192,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096,
+        "mode": "chat",
+        "output_cost_per_token": 6e-05,
+        "supports_system_messages": true,
+        "supports_tool_choice": true
+    },
     "gpt-4-0613": {
         "deprecation_date": "2025-06-06",
         "input_cost_per_token": 3e-05,
@@ -18289,7 +18303,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18328,7 +18343,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18367,7 +18383,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-chat-latest": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18405,7 +18422,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18445,7 +18463,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-2025-12-11": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18485,7 +18504,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-chat-latest": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18522,7 +18542,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.3-chat-latest": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18559,7 +18580,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-pro": {
         "input_cost_per_token": 2.1e-05,
@@ -18592,7 +18614,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-pro-2025-12-11": {
         "input_cost_per_token": 2.1e-05,
@@ -18625,7 +18648,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.4": {
         "cache_read_input_token_cost": 2.5e-07,
@@ -18674,7 +18698,8 @@
         "supports_service_tier": true,
         "supports_vision": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.4-2026-03-05": {
         "cache_read_input_token_cost": 2.5e-07,
@@ -18769,7 +18794,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.4-pro-2026-03-05": {
         "cache_read_input_token_cost": 3e-06,
@@ -18817,7 +18843,94 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
+    },
+    "gpt-5.4-mini": {
+        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost_flex": 1e-08,
+        "cache_read_input_token_cost_batches": 3.8e-08,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_token_flex": 3.75e-07,
+        "input_cost_per_token_batches": 3.75e-07,
+        "litellm_provider": "openai",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 4.5e-06,
+        "output_cost_per_token_flex": 2.25e-06,
+        "output_cost_per_token_batches": 2.25e-06,
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/batch",
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_native_streaming": 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_service_tier": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_none_reasoning_effort": true,
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": false
+    },
+    "gpt-5.4-nano": {
+        "cache_read_input_token_cost": 2e-08,
+        "cache_read_input_token_cost_flex": 1e-08,
+        "cache_read_input_token_cost_batches": 1e-08,
+        "input_cost_per_token": 2e-07,
+        "input_cost_per_token_flex": 1e-07,
+        "input_cost_per_token_batches": 1e-07,
+        "litellm_provider": "openai",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 1.25e-06,
+        "output_cost_per_token_flex": 6.25e-07,
+        "output_cost_per_token_batches": 6.25e-07,
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/batch",
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_native_streaming": 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_service_tier": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_none_reasoning_effort": true,
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": false
     },
     "gpt-5-pro": {
         "input_cost_per_token": 1.5e-05,
@@ -18852,7 +18965,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-pro-2025-10-06": {
         "input_cost_per_token": 1.5e-05,
@@ -18887,7 +19001,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-2025-08-07": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18929,7 +19044,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-chat": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18963,7 +19079,8 @@
         "supports_tool_choice": false,
         "supports_vision": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-chat-latest": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18997,7 +19114,8 @@
         "supports_tool_choice": false,
         "supports_vision": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-codex": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19030,7 +19148,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-codex": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19066,7 +19185,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-codex-max": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19099,7 +19219,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-codex-mini": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -19135,7 +19256,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-codex": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -19171,7 +19293,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.3-codex": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -19207,7 +19330,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-mini": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -19249,7 +19373,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-mini-2025-08-07": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -19291,7 +19416,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-nano": {
         "cache_read_input_token_cost": 5e-09,
@@ -19330,7 +19456,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-nano-2025-08-07": {
         "cache_read_input_token_cost": 5e-09,
@@ -19368,7 +19495,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-image-1": {
         "cache_read_input_image_token_cost": 2.5e-06,
@@ -30192,18 +30320,14 @@
     },
     "vertex_ai/claude-opus-4-6": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -30222,18 +30346,14 @@
     },
     "vertex_ai/claude-opus-4-6@default": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -30278,18 +30398,14 @@
     },
     "vertex_ai/claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "supports_assistant_prefill": true,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -30506,7 +30622,7 @@
         "output_cost_per_token": 5.4e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supported_regions": [
-            "us-west2"
+            "us-central1"
         ],
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
@@ -30526,7 +30642,7 @@
         "output_cost_per_token_batches": 8.4e-07,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supported_regions": [
-            "us-west2"
+            "global"
         ],
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
@@ -30543,6 +30659,9 @@
         "mode": "chat",
         "output_cost_per_token": 5.4e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
+        "supported_regions": [
+            "us-central1"
+        ],
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_prompt_caching": true,
@@ -31167,7 +31286,10 @@
         "input_cost_per_token": 3e-07,
         "output_cost_per_token": 1.2e-06,
         "ocr_cost_per_page": 0.0003,
-        "source": "https://cloud.google.com/vertex-ai/pricing"
+        "source": "https://cloud.google.com/vertex-ai/pricing",
+        "supported_regions": [
+            "us-central1"
+        ]
     },
     "vertex_ai/openai/gpt-oss-120b-maas": {
         "input_cost_per_token": 1.5e-07,
@@ -36386,7 +36508,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-search-api-2025-10-14": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -36692,6 +36815,38 @@
         "supports_audio_input": true,
         "supports_audio_output": true
     },
+    "gemini-3.1-flash-live-preview": {
+        "input_cost_per_audio_token": 3e-06,
+        "input_cost_per_image_token": 1e-06,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_video_per_second": 3.3333333333333335e-05,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_audio_token": 1.2e-05,
+        "output_cost_per_token": 4.5e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_endpoints": [
+            "/v1/realtime"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text",
+            "audio"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": true,
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "supports_web_search": true
+    },
     "gemini/gemini-2.5-flash-native-audio-latest": {
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 3e-07,
@@ -36770,6 +36925,40 @@
         "tpm": 250000,
         "rpm": 10
     },
+    "gemini/gemini-3.1-flash-live-preview": {
+        "input_cost_per_audio_token": 3e-06,
+        "input_cost_per_image_token": 1e-06,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_video_per_second": 3.3333333333333335e-05,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_audio_token": 1.2e-05,
+        "output_cost_per_token": 4.5e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_endpoints": [
+            "/v1/realtime"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text",
+            "audio"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": true,
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "tpm": 250000,
+        "rpm": 10
+    },
     "gemini-2.5-flash-preview-tts": {
         "input_cost_per_token": 3e-07,
         "litellm_provider": "gemini",
@@ -37015,18 +37204,14 @@
     },
     "vertex_ai/claude-sonnet-4-6@default": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "supports_assistant_prefill": true,
         "supports_computer_use": true,
         "supports_function_calling": true,
diff --git a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py
index 14bbb82808d..6997f5241de 100644
--- a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py
+++ b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py
@@ -92,6 +92,7 @@ class MCPGuardrailTranslationHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: Optional[Any] = None,
         user_api_key_dict: Optional[Any] = None,
+        request_data: Optional[dict] = None,
     ) -> Any:
         verbose_proxy_logger.debug(
             "MCP Guardrail: Output processing not implemented for MCP tools",
diff --git a/litellm/proxy/_experimental/out/404/index.html b/litellm/proxy/_experimental/out/404/index.html
index 29dbbfcdd61..c9df4fb9145 100644
--- a/litellm/proxy/_experimental/out/404/index.html
+++ b/litellm/proxy/_experimental/out/404/index.html
@@ -1 +1 @@
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deleted file mode 100644
index 3ee19c75340..00000000000
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deleted file mode 100644
index 98694f8d9e9..00000000000
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-
-client = openai.OpenAI(
-    api_key="your_api_key",
-    base_url="${(0,x.getProxyBaseUrl)()}"  # Your LiteLLM Proxy URL
-)
-
-response = client.chat.completions.create(
-    model="${H.model_group}",
-    messages=[
-        {
-            "role": "user",
-            "content": "Hello, how are you?"
-        }
-    ]
-)
-
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-import asyncio
-
-# Standard MCP configuration
-config = {
-    "mcpServers": {
-        "${ef.server_name}": {
-            "url": "${(0,x.getProxyBaseUrl)()}/${ef.server_name}/mcp",
-            "headers": {
-                "x-litellm-api-key": "Bearer sk-1234"
-            }
-        }
-    }
-}
-
-# Create a client that connects to the server
-client = Client(config)
-
-async def main():
-    async with client:
-        # List available tools
-        tools = await client.list_tools()
-        print(f"Available tools: {[tool.name for tool in tools]}")
-
-        # Call a tool
-        response = await client.call_tool(
-            name="tool_name",
-            arguments={"arg": "value"}
-        )
-        print(f"Response: {response}")
-
-if __name__ == "__main__":
-    asyncio.run(main())`})]})]})}),(0,t.jsx)(y,{visible:R,onClose:()=>V(!1),accessToken:e||"",modelHubData:_||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.modelHubCall)(e);I(t.data)}catch(e){console.error("Error refreshing model data:",e)}})()}}),(0,t.jsx)(p,{visible:er,onClose:()=>ec(!1),accessToken:e||"",agentHubData:Y||[],onSuccess:()=>{e&&(async()=>{try{let t=(await (0,x.getAgentsList)(e)).agents.map(e=>({agent_id:e.agent_id,...e.agent_card_params,is_public:e.is_public}));en(t)}catch(e){console.error("Error refreshing agent data:",e)}})()}}),(0,t.jsx)(b,{visible:eS,onClose:()=>e$(!1),accessToken:e||"",mcpHubData:ep||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.fetchMCPServers)(e);eg(t)}catch(e){console.error("Error refreshing MCP server data:",e)}})()}})]})}],934879)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0713a1954ae8db53.js b/litellm/proxy/_experimental/out/_next/static/chunks/0713a1954ae8db53.js
new file mode 100644
index 00000000000..c67de212fe4
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0713a1954ae8db53.js
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new file mode 100644
index 00000000000..37bfca51e58
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new file mode 100644
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+      "role": "user",
+      "content": "Explain quantum computing in simple terms"
+    }
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+  "max_tokens": 500,
+  "stream": true
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+  }'`);u(r),n.default.success("Request transformed successfully")}else{let e="string"==typeof c?c:JSON.stringify(c);u(e),n.default.info("Transformed request received in unexpected format")}}catch(e){console.error("Error transforming request:",e),n.default.fromBackend("Failed to transform request")}finally{h(!1)}};return(0,t.jsxs)("div",{className:"w-full m-2",style:{overflow:"hidden"},children:[(0,t.jsx)(r.Title,{children:"Playground"}),(0,t.jsx)("p",{className:"text-sm text-gray-500",children:"See how LiteLLM transforms your request for the specified provider."}),(0,t.jsxs)("div",{style:{display:"flex",gap:"16px",width:"100%",minWidth:0,overflow:"hidden"},className:"mt-4",children:[(0,t.jsxs)("div",{style:{flex:"1 1 50%",display:"flex",flexDirection:"column",border:"1px solid #e8e8e8",borderRadius:"8px",padding:"24px",overflow:"hidden",maxHeight:"600px",minWidth:0},children:[(0,t.jsxs)("div",{style:{marginBottom:"24px"},children:[(0,t.jsx)("h2",{style:{fontSize:"24px",fontWeight:"bold",margin:"0 0 4px 0"},children:"Original Request"}),(0,t.jsx)("p",{style:{color:"#666",margin:0},children:"The request you would send to LiteLLM /chat/completions endpoint."})]}),(0,t.jsx)("textarea",{style:{flex:"1 1 auto",width:"100%",minHeight:"240px",padding:"16px",border:"1px solid #e8e8e8",borderRadius:"6px",fontFamily:"monospace",fontSize:"14px",resize:"none",marginBottom:"24px",overflow:"auto"},value:o,onChange:e=>c(e.target.value),onKeyDown:e=>{(e.metaKey||e.ctrlKey)&&"Enter"===e.key&&(e.preventDefault(),g())},placeholder:"Press Cmd/Ctrl + Enter to transform"}),(0,t.jsx)("div",{style:{display:"flex",justifyContent:"flex-end",marginTop:"auto"},children:(0,t.jsxs)(a.Button,{type:"primary",style:{backgroundColor:"#000",display:"flex",alignItems:"center",gap:"8px"},onClick:g,loading:m,children:[(0,t.jsx)("span",{children:"Transform"}),(0,t.jsx)("span",{children:"→"})]})})]}),(0,t.jsxs)("div",{style:{flex:"1 1 50%",display:"flex",flexDirection:"column",border:"1px solid #e8e8e8",borderRadius:"8px",padding:"24px",overflow:"hidden",maxHeight:"800px",minWidth:0},children:[(0,t.jsxs)("div",{style:{marginBottom:"24px"},children:[(0,t.jsx)("h2",{style:{fontSize:"24px",fontWeight:"bold",margin:"0 0 4px 0"},children:"Transformed Request"}),(0,t.jsx)("p",{style:{color:"#666",margin:0},children:"How LiteLLM transforms your request for the specified provider."}),(0,t.jsx)("br",{}),(0,t.jsx)("p",{style:{color:"#666",margin:0},className:"text-xs",children:"Note: Sensitive headers are not shown."})]}),(0,t.jsxs)("div",{style:{position:"relative",backgroundColor:"#f5f5f5",borderRadius:"6px",flex:"1 1 auto",display:"flex",flexDirection:"column",overflow:"hidden"},children:[(0,t.jsx)("pre",{style:{padding:"16px",fontFamily:"monospace",fontSize:"14px",margin:0,overflow:"auto",flex:"1 1 auto"},children:d||`curl -X POST \\
+  https://api.openai.com/v1/chat/completions \\
+  -H 'Authorization: Bearer sk-xxx' \\
+  -H 'Content-Type: application/json' \\
+  -d '{
+  "model": "gpt-4",
+  "messages": [
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+      "content": "You are a helpful assistant."
+    }
+  ],
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t=e.i(843476),l=e.i(994388),a=e.i(304967),s=e.i(197647),r=e.i(653824),i=e.i(269200),n=e.i(942232),o=e.i(977572),c=e.i(427612),d=e.i(64848),u=e.i(496020),m=e.i(881073),h=e.i(404206),g=e.i(723731),x=e.i(599724),p=e.i(271645),f=e.i(650056),b=e.i(127952),y=e.i(902555),j=e.i(727749),v=e.i(764205),w=e.i(779241),_=e.i(677667),N=e.i(898667),k=e.i(130643),C=e.i(464571),S=e.i(212931),T=e.i(808613),I=e.i(28651),E=e.i(199133);let A=({isModalVisible:e,accessToken:l,setIsModalVisible:a,setBudgetList:s})=>{let[r]=T.Form.useForm(),i=async e=>{if(null!=l&&void 0!=l)try{j.default.info("Making API Call");let t=await (0,v.budgetCreateCall)(l,e);console.log("key create Response:",t),s(e=>e?[...e,t]:[t]),j.default.success("Budget Created"),r.resetFields()}catch(e){console.error("Error creating the key:",e),j.default.fromBackend(`Error creating the key: ${e}`)}};return(0,t.jsx)(S.Modal,{title:"Create 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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
+
+`,B=`
+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
+
+`,M=`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)`;e.s(["default",0,({accessToken:e})=>{let[w,_]=(0,p.useState)(!1),[N,k]=(0,p.useState)(!1),[C,S]=(0,p.useState)(null),[T,I]=(0,p.useState)([]),[E,O]=(0,p.useState)(!1),[F,R]=(0,p.useState)(!1);(0,p.useEffect)(()=>{e&&(0,v.getBudgetList)(e).then(e=>{I(e)})},[e]);let L=async t=>{null!=e&&(S(t),k(!0))},z=async()=>{if(C&&null!=e){O(!0);try{await (0,v.budgetDeleteCall)(e,C.budget_id),j.default.success("Budget deleted."),await U()}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),R(!1),S(null)}}},U=async()=>{null!=e&&(0,v.getBudgetList)(e).then(e=>{I(e)})};return(0,t.jsxs)("div",{className:"w-full mx-auto flex-auto overflow-y-auto m-8 p-2",children:[(0,t.jsx)(l.Button,{size:"sm",variant:"primary",className:"mb-2",onClick:()=>_(!0),children:"+ Create Budget"}),(0,t.jsxs)(r.TabGroup,{children:[(0,t.jsxs)(m.TabList,{children:[(0,t.jsx)(s.Tab,{children:"Budgets"}),(0,t.jsx)(s.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)(A,{accessToken:e,isModalVisible:w,setIsModalVisible:_,setBudgetList:I}),C&&(0,t.jsx)(P,{accessToken:e,isModalVisible:N,setIsModalVisible:k,setBudgetList:I,existingBudget:C,handleUpdateCall:U}),(0,t.jsxs)(a.Card,{children:[(0,t.jsx)(x.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:T.slice().sort((e,t)=>new Date(t.updated_at).getTime()-new Date(e.updated_at).getTime()).map((e,l)=>(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"}),(0,t.jsx)(y.default,{variant:"Edit",tooltipText:"Edit budget",onClick:()=>L(e),dataTestId:"edit-budget-button"}),(0,t.jsx)(y.default,{variant:"Delete",tooltipText:"Delete budget",onClick:()=>{S(e),R(!0)},dataTestId:"delete-budget-button"})]},l))})]})]}),(0,t.jsx)(b.default,{isOpen:F,title:"Delete Budget?",message:"Are you sure you want to delete this budget? 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--- a/litellm/proxy/_experimental/out/_next/static/chunks/9dd55e1f36a7225c.js
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0ea9112947894f26.js b/litellm/proxy/_experimental/out/_next/static/chunks/0ea9112947894f26.js
deleted file mode 100644
index 48138189033..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0ea9112947894f26.js
+++ /dev/null
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/11362340846735c3.js b/litellm/proxy/_experimental/out/_next/static/chunks/11362340846735c3.js
deleted file mode 100644
index 15dc8cc8608..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/11362340846735c3.js
+++ /dev/null
@@ -1,420 +0,0 @@
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-
-client = openai.AzureOpenAI(
-	api_key="${v||"YOUR_LITELLM_API_KEY"}",
-	azure_endpoint="${I}",
-	api_version="2024-02-01"
-)`:`import openai
-
-client = openai.OpenAI(
-	api_key="${v||"YOUR_LITELLM_API_KEY"}",
-	base_url="${I}"
-)`;switch(h){case o.CHAT:{let e=Object.keys(y).length>0,r="";if(e){let e=JSON.stringify({metadata:y},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`,
-    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:C}];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)}${r}
-)
-
-print(response)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.chat.completions.create(
-#     model="${O}",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {
-#                     "type": "text",
-#                     "text": "${w}"
-#                 },
-#                 {
-#                     "type": "image_url",
-#                     "image_url": {
-#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
-#                     }
-#                 }
-#             ]
-#         }
-#     ]${r}
-# )
-# print(response_with_file)
-`;break}case o.RESPONSES:{let e=Object.keys(y).length>0,r="";if(e){let e=JSON.stringify({metadata:y},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`,
-    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:C}];t=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.responses.create(
-    model="${O}",
-    input=${JSON.stringify(a,null,4)}${r}
-)
-
-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="${O}",
-#     input=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {"type": "input_text", "text": "${w}"},
-#                 {
-#                     "type": "input_image",
-#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
-#                 },
-#             ],
-#         }
-#     ]${r}
-# )
-# print(response_with_file.output_text)
-`;break}case o.IMAGE:t="azure"===b?`
-# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
-# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
-# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
-import os
-import requests
-import json
-import time
-from PIL import Image
-
-result = client.images.generate(
-	model="${O}",
-	prompt="${n}",
-	n=1
-)
-
-json_response = json.loads(result.model_dump_json())
-
-# Set the directory for the stored image
-image_dir = os.path.join(os.curdir, 'images')
-
-# If the directory doesn't exist, create it
-if not os.path.isdir(image_dir):
-	os.mkdir(image_dir)
-
-# Initialize the image path
-image_filename = f"generated_image_{int(time.time())}.png"
-image_path = os.path.join(image_dir, image_filename)
-
-try:
-	# Retrieve the generated image
-	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
-			image_url = json_response["data"][0]["url"]
-			generated_image = requests.get(image_url).content
-			with open(image_path, "wb") as image_file:
-					image_file.write(generated_image)
-
-			print(f"Image saved to {image_path}")
-			# Display the image
-			image = Image.open(image_path)
-			image.show()
-	else:
-			print("Could not find image URL in response.")
-			print("Full response:", json_response)
-except Exception as e:
-	print(f"An error occurred: {e}")
-	print("Full response:", json_response)
-`:`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
-	# In a real implementation, this would upload the file to OpenAI
-	# For this example, we'll just return a placeholder ID
-	return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${w}"
-
-# 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="${O}",
-	input=[
-			{
-					"role": "user",
-					"content": [
-							{"type": "input_text", "text": prompt},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image1}",
-							},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image2}",
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id1,
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id2,
-							}
-					],
-			}
-	],
-	tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
-	output
-	for output in response.output
-	if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
-	image_base64 = image_data[0]
-	image_filename = f"edited_image_{int(time.time())}.png"
-	with open(image_filename, "wb") as f:
-			f.write(base64.b64decode(image_base64))
-	print(f"Image saved to {image_filename}")
-else:
-	# If no image is generated, there might be a text response with an explanation
-	text_response = [output.text for output in response.output if hasattr(output, 'text')]
-	if text_response:
-			print("No image generated. Model response:")
-			print("\\n".join(text_response))
-	else:
-			print("No image data found in response.")
-	print("Full response for debugging:")
-	print(response)
-`;break;case o.IMAGE_EDITS:t="azure"===b?`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# The prompt entered by the user
-prompt = "${w}"
-
-# 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="${O}",
-	input=[
-			{
-					"role": "user",
-					"content": [
-							{"type": "input_text", "text": prompt},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image1}",
-							},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image2}",
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id1,
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id2,
-							}
-					],
-			}
-	],
-	tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
-	output
-	for output in response.output
-	if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
-	image_base64 = image_data[0]
-	image_filename = f"edited_image_{int(time.time())}.png"
-	with open(image_filename, "wb") as f:
-			f.write(base64.b64decode(image_base64))
-	print(f"Image saved to {image_filename}")
-else:
-	# If no image is generated, there might be a text response with an explanation
-	text_response = [output.text for output in response.output if hasattr(output, 'text')]
-	if text_response:
-			print("No image generated. Model response:")
-			print("\\n".join(text_response))
-	else:
-			print("No image data found in response.")
-	print("Full response for debugging:")
-	print(response)
-`:`
-import base64
-import os
-import time
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
-	# In a real implementation, this would upload the file to OpenAI
-	# For this example, we'll just return a placeholder ID
-	return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${w}"
-
-# 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,'\\"')}"`:""}
-)
-
-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="${n||"Your text to convert to speech here"}",
-#     voice="alloy",
-#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
-#     speed=1.0  # Range: 0.25 to 4.0
-# )
-# response.stream_to_file("output_speech.mp3")
-`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${T}
-${t}`}],190272)},916925,e=>{"use strict";var t,r=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/17741b7a77c20f1b.js b/litellm/proxy/_experimental/out/_next/static/chunks/17741b7a77c20f1b.js
deleted file mode 100644
index de3b88089a9..00000000000
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+++ /dev/null
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deleted file mode 100644
index 5ea6f73f346..00000000000
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new file mode 100644
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md:table-cell"}},{header:"Details",id:"details",enableSorting:!1,cell:({row:l})=>{let i=l.original;return(0,t.jsxs)(N.Button,{size:"xs",variant:"secondary",onClick:()=>e(i),icon:C.InfoCircleOutlined,children:[(0,t.jsx)("span",{className:"hidden lg:inline",children:"Details"}),(0,t.jsx)("span",{className:"lg:hidden",children:"Info"})]})}}])(e=>{ev(e),eN(!0)},eM,a),data:ep||[],isLoading:eb,defaultSorting:[{id:"server_name",desc:!1}]})]}),(0,t.jsx)("div",{className:"mt-4 text-center space-y-2",children:(0,t.jsxs)(o.Text,{className:"text-sm text-gray-600",children:["Showing ",ep?.length||0," MCP server",ep?.length!==1?"s":""]})})]}),(0,t.jsx)(X.TabPanel,{children:(0,t.jsx)(Q,{publicPage:a})})]})]})]}):(0,t.jsxs)(j.Card,{className:"mx-auto max-w-xl mt-10",children:[(0,t.jsx)(o.Text,{className:"text-xl text-center mb-2 text-black",children:"Public Model Hub not enabled."}),(0,t.jsx)("p",{className:"text-base text-center text-slate-800",children:"Ask your proxy admin to enable this on their Admin UI."})]}),(0,t.jsx)(s.Modal,{title:"Public Model Hub",width:600,open:A,footer:null,onOk:ew,onCancel:e_,children:(0,t.jsxs)("div",{className:"pt-5 pb-5",children:[(0,t.jsxs)("div",{className:"flex justify-between mb-4",children:[(0,t.jsx)(o.Text,{className:"text-base mr-2",children:"Shareable Link:"}),(0,t.jsx)(o.Text,{className:"max-w-sm ml-2 bg-gray-200 pr-2 pl-2 pt-1 pb-1 text-center rounded",children:`${(0,x.getProxyBaseUrl)()}/ui/model_hub_table`})]}),(0,t.jsx)("div",{className:"flex justify-end",children:(0,t.jsx)(N.Button,{onClick:()=>{eT.replace(`/model_hub_table?key=${e}`)},children:"See Page"})})]})}),(0,t.jsx)(s.Modal,{title:H?.model_group||"Model Details",width:1e3,open:z,footer:null,onOk:ew,onCancel:e_,children:H&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Model Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Model Group:"}),(0,t.jsx)(o.Text,{children:H.model_group})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Mode:"}),(0,t.jsx)(o.Text,{children:H.mode||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Providers:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:H.providers.map(e=>(0,t.jsx)(m.Badge,{color:"blue",children:e},e))})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Token & Cost Information"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Input Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_input_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Output Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_output_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Input Cost per 1M Tokens:"}),(0,t.jsx)(o.Text,{children:H.input_cost_per_token?eI(H.input_cost_per_token):"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Output Cost per 1M Tokens:"}),(0,t.jsx)(o.Text,{children:H.output_cost_per_token?eI(H.output_cost_per_token):"Not specified"})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Capabilities"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:(c=Object.entries(H).filter(([e,t])=>e.startsWith("supports_")&&!0===t).map(([e])=>e),h=["green","blue","purple","orange","red","yellow"],0===c.length?(0,t.jsx)(o.Text,{className:"text-gray-500",children:"No special capabilities listed"}):c.map((e,l)=>(0,t.jsx)(m.Badge,{color:h[l%h.length],children:e.replace(/^supports_/,"").split("_").map(e=>e.charAt(0).toUpperCase()+e.slice(1)).join(" ")},e)))})]}),(H.tpm||H.rpm)&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Rate Limits"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[H.tpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Tokens per Minute:"}),(0,t.jsx)(o.Text,{children:H.tpm.toLocaleString()})]}),H.rpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Requests per Minute:"}),(0,t.jsx)(o.Text,{children:H.rpm.toLocaleString()})]})]})]}),H.supported_openai_params&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Supported OpenAI Parameters"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:H.supported_openai_params.map(e=>(0,t.jsx)(m.Badge,{color:"green",children:e},e))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)(el.Prism,{language:"python",className:"text-sm",children:`import openai
+
+client = openai.OpenAI(
+    api_key="your_api_key",
+    base_url="${(0,x.getProxyBaseUrl)()}"  # Your LiteLLM Proxy URL
+)
+
+response = client.chat.completions.create(
+    model="${H.model_group}",
+    messages=[
+        {
+            "role": "user",
+            "content": "Hello, how are you?"
+        }
+    ]
+)
+
+print(response.choices[0].message.content)`})]})]})}),(0,t.jsx)(s.Modal,{title:em?.name||"Agent Details",width:1e3,open:eu,footer:null,onOk:ew,onCancel:e_,children:em&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Agent Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Name:"}),(0,t.jsx)(o.Text,{children:em.name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Version:"}),(0,t.jsxs)(m.Badge,{color:"blue",children:["v",em.version]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Protocol Version:"}),(0,t.jsx)(o.Text,{children:em.protocolVersion})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center space-x-2",children:[(0,t.jsx)(o.Text,{className:"truncate",children:em.url}),(0,t.jsx)(T.CopyOutlined,{onClick:()=>eM(em.url),className:"cursor-pointer text-gray-500 hover:text-blue-500"})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Description:"}),(0,t.jsx)(o.Text,{className:"mt-1",children:em.description})]})]}),em.capabilities&&Object.keys(em.capabilities).length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Capabilities"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:Object.entries(em.capabilities).filter(([e,t])=>!0===t).map(([e])=>(0,t.jsx)(m.Badge,{color:"green",children:e},e))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.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)(o.Text,{className:"font-medium",children:"Input Modes:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:em.defaultInputModes?.map(e=>(0,t.jsx)(m.Badge,{color:"blue",children:e},e))||(0,t.jsx)(o.Text,{children:"Not specified"})})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Output Modes:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:em.defaultOutputModes?.map(e=>(0,t.jsx)(m.Badge,{color:"purple",children:e},e))||(0,t.jsx)(o.Text,{children:"Not specified"})})]})]})]}),em.skills&&em.skills.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Skills"}),(0,t.jsx)("div",{className:"space-y-4",children:em.skills.map(e=>(0,t.jsxs)("div",{className:"border border-gray-200 rounded p-4",children:[(0,t.jsxs)("div",{className:"flex justify-between items-start mb-2",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium text-base",children:e.name}),(0,t.jsxs)(o.Text,{className:"text-xs text-gray-500",children:["ID: ",e.id]})]}),e.tags&&e.tags.length>0&&(0,t.jsx)("div",{className:"flex flex-wrap gap-1",children:e.tags.map(e=>(0,t.jsx)(m.Badge,{color:"purple",size:"xs",children:e},e))})]}),(0,t.jsx)(o.Text,{className:"text-sm mb-2",children:e.description}),e.examples&&e.examples.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-xs font-medium text-gray-700",children:"Examples:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:e.examples.map((e,l)=>(0,t.jsx)(m.Badge,{color:"gray",size:"xs",children:e},l))})]})]},e.id))})]}),em.supportsAuthenticatedExtendedCard&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Additional Features"}),(0,t.jsx)(m.Badge,{color:"green",children:"Supports Authenticated Extended Card"})]})]})}),(0,t.jsx)(s.Modal,{title:ef?.server_name||"MCP Server Details",width:1e3,open:ey,footer:null,onOk:ew,onCancel:e_,children:ef&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.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)(o.Text,{className:"font-medium",children:"Server Name:"}),(0,t.jsx)(o.Text,{children:ef.server_name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Server ID:"}),(0,t.jsxs)("div",{className:"flex items-center space-x-2",children:[(0,t.jsx)(o.Text,{className:"text-xs truncate",children:ef.server_id}),(0,t.jsx)(T.CopyOutlined,{onClick:()=>eM(ef.server_id),className:"cursor-pointer text-gray-500 hover:text-blue-500"})]})]}),ef.alias&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Alias:"}),(0,t.jsx)(o.Text,{children:ef.alias})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Transport:"}),(0,t.jsx)(m.Badge,{color:"blue",children:ef.transport})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Auth Type:"}),(0,t.jsx)(m.Badge,{color:"none"===ef.auth_type?"gray":"green",children:ef.auth_type})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Status:"}),(0,t.jsx)(m.Badge,{color:"active"===ef.status||"healthy"===ef.status?"green":"inactive"===ef.status||"unhealthy"===ef.status?"red":"gray",children:ef.status||"unknown"})]})]}),ef.description&&(0,t.jsxs)("div",{className:"mt-2",children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Description:"}),(0,t.jsx)(o.Text,{className:"mt-1",children:ef.description})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Connection Details"}),(0,t.jsxs)("div",{className:"space-y-2",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center space-x-2 mt-1",children:[(0,t.jsx)(o.Text,{className:"text-sm break-all bg-gray-100 p-2 rounded 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gap-2",children:ef.teams.map((e,l)=>(0,t.jsx)(m.Badge,{color:"blue",children:e},l))})]}),ef.mcp_access_groups&&ef.mcp_access_groups.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Access Groups"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:ef.mcp_access_groups.map((e,l)=>(0,t.jsx)(m.Badge,{color:"green",children:e},l))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Metadata"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Created By:"}),(0,t.jsx)(o.Text,{children:ef.created_by})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Updated By:"}),(0,t.jsx)(o.Text,{children:ef.updated_by})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Created At:"}),(0,t.jsx)(o.Text,{className:"text-sm",children:new Date(ef.created_at).toLocaleString()})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Updated At:"}),(0,t.jsx)(o.Text,{className:"text-sm",children:new Date(ef.updated_at).toLocaleString()})]}),ef.last_health_check&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Last Health Check:"}),(0,t.jsx)(o.Text,{className:"text-sm",children:new Date(ef.last_health_check).toLocaleString()})]})]}),ef.health_check_error&&(0,t.jsxs)("div",{className:"mt-2 p-2 bg-red-50 rounded",children:[(0,t.jsx)(o.Text,{className:"font-medium text-red-700",children:"Health Check Error:"}),(0,t.jsx)(o.Text,{className:"text-sm text-red-600 mt-1",children:ef.health_check_error})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)(el.Prism,{language:"python",className:"text-sm",children:`from fastmcp import Client
+import asyncio
+
+# Standard MCP configuration
+config = {
+    "mcpServers": {
+        "${ef.server_name}": {
+            "url": "${(0,x.getProxyBaseUrl)()}/${ef.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)(y,{visible:R,onClose:()=>V(!1),accessToken:e||"",modelHubData:_||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.modelHubCall)(e);I(t.data)}catch(e){console.error("Error refreshing model data:",e)}})()}}),(0,t.jsx)(p,{visible:er,onClose:()=>ec(!1),accessToken:e||"",agentHubData:Y||[],onSuccess:()=>{e&&(async()=>{try{let t=(await (0,x.getAgentsList)(e)).agents.map(e=>({agent_id:e.agent_id,...e.agent_card_params,is_public:e.is_public}));en(t)}catch(e){console.error("Error refreshing agent data:",e)}})()}}),(0,t.jsx)(b,{visible:eS,onClose:()=>e$(!1),accessToken:e||"",mcpHubData:ep||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.fetchMCPServers)(e);eg(t)}catch(e){console.error("Error refreshing MCP server data:",e)}})()}})]})}],934879)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1da362a651d209bd.js b/litellm/proxy/_experimental/out/_next/static/chunks/1da362a651d209bd.js
new file mode 100644
index 00000000000..a6ba2596a0d
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/1da362a651d209bd.js
@@ -0,0 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1eb2ed6e2dd204b7.js b/litellm/proxy/_experimental/out/_next/static/chunks/1eb2ed6e2dd204b7.js
deleted file mode 100644
index 5e32e20884b..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1eb2ed6e2dd204b7.js
+++ /dev/null
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Admin UI."})]}),(0,t.jsx)(s.Modal,{title:"Public Model Hub",width:600,open:A,footer:null,onOk:ew,onCancel:e_,children:(0,t.jsxs)("div",{className:"pt-5 pb-5",children:[(0,t.jsxs)("div",{className:"flex justify-between mb-4",children:[(0,t.jsx)(o.Text,{className:"text-base mr-2",children:"Shareable Link:"}),(0,t.jsx)(o.Text,{className:"max-w-sm ml-2 bg-gray-200 pr-2 pl-2 pt-1 pb-1 text-center rounded",children:`${(0,x.getProxyBaseUrl)()}/ui/model_hub_table`})]}),(0,t.jsx)("div",{className:"flex justify-end",children:(0,t.jsx)(N.Button,{onClick:()=>{eT.replace(`/model_hub_table?key=${e}`)},children:"See Page"})})]})}),(0,t.jsx)(s.Modal,{title:H?.model_group||"Model Details",width:1e3,open:z,footer:null,onOk:ew,onCancel:e_,children:H&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Model Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Model Group:"}),(0,t.jsx)(o.Text,{children:H.model_group})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Mode:"}),(0,t.jsx)(o.Text,{children:H.mode||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Providers:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:H.providers.map(e=>(0,t.jsx)(m.Badge,{color:"blue",children:e},e))})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Token & Cost Information"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Input Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_input_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max 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listed"}):c.map((e,l)=>(0,t.jsx)(m.Badge,{color:h[l%h.length],children:e.replace(/^supports_/,"").split("_").map(e=>e.charAt(0).toUpperCase()+e.slice(1)).join(" ")},e)))})]}),(H.tpm||H.rpm)&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Rate Limits"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[H.tpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Tokens per Minute:"}),(0,t.jsx)(o.Text,{children:H.tpm.toLocaleString()})]}),H.rpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Requests per Minute:"}),(0,t.jsx)(o.Text,{children:H.rpm.toLocaleString()})]})]})]}),H.supported_openai_params&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Supported OpenAI Parameters"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:H.supported_openai_params.map(e=>(0,t.jsx)(m.Badge,{color:"green",children:e},e))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)(el.Prism,{language:"python",className:"text-sm",children:`import openai
-
-client = openai.OpenAI(
-    api_key="your_api_key",
-    base_url="${(0,x.getProxyBaseUrl)()}"  # Your LiteLLM Proxy URL
-)
-
-response = client.chat.completions.create(
-    model="${H.model_group}",
-    messages=[
-        {
-            "role": "user",
-            "content": "Hello, how are you?"
-        }
-    ]
-)
-
-print(response.choices[0].message.content)`})]})]})}),(0,t.jsx)(s.Modal,{title:em?.name||"Agent Details",width:1e3,open:eu,footer:null,onOk:ew,onCancel:e_,children:em&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Agent Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Name:"}),(0,t.jsx)(o.Text,{children:em.name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Version:"}),(0,t.jsxs)(m.Badge,{color:"blue",children:["v",em.version]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Protocol Version:"}),(0,t.jsx)(o.Text,{children:em.protocolVersion})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center 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-import asyncio
-
-# Standard MCP configuration
-config = {
-    "mcpServers": {
-        "${ef.server_name}": {
-            "url": "${(0,x.getProxyBaseUrl)()}/${ef.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__":
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1eccde2dab0b3311.js b/litellm/proxy/_experimental/out/_next/static/chunks/1eccde2dab0b3311.js
deleted file mode 100644
index 49b9f1ea72e..00000000000
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new file mode 100644
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/22970a12064ba16b.js b/litellm/proxy/_experimental/out/_next/static/chunks/22970a12064ba16b.js
deleted file mode 100644
index 836cd30e918..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/22970a12064ba16b.js
+++ /dev/null
@@ -1,231 +0,0 @@
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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">${tM(e.totals.cost_per_request)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Total Daily</div>
-            <div class="value green">${tM(e.totals.daily_cost)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Total Monthly</div>
-            <div class="value purple">${tM(e.totals.monthly_cost)}</div>
-          </div>
-        </div>
-        ${e.totals.margin_per_request>0?`
-        <div class="summary-grid" style="margin-top: 15px; padding-top: 15px; border-top: 1px solid #ddd;">
-          <div class="summary-item">
-            <div class="label">Margin/Request</div>
-            <div class="value" style="color: #faad14;">${tM(e.totals.margin_per_request)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Daily Margin</div>
-            <div class="value" style="color: #faad14;">${tM(e.totals.daily_margin)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Monthly Margin</div>
-            <div class="value" style="color: #faad14;">${tM(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> ${tD(t.input_tokens)}</p>
-        <p><strong>Output Tokens per Request:</strong> ${tD(t.output_tokens)}</p>
-        ${t.num_requests_per_day?`<p><strong>Requests per Day:</strong> ${tD(t.num_requests_per_day)}</p>`:""}
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-
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-  `}).join("")}
-
-      <div class="footer">
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-      </div>
-    </body>
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new file mode 100644
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new file mode 100644
index 00000000000..99c6a130afa
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/66ef9d81cc17cfa8.js b/litellm/proxy/_experimental/out/_next/static/chunks/26542a70b9512f71.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2793ac912badcf02.js b/litellm/proxy/_experimental/out/_next/static/chunks/2793ac912badcf02.js
new file mode 100644
index 00000000000..a8a9fe03e6b
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2793ac912badcf02.js
@@ -0,0 +1,420 @@
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+
+client = openai.AzureOpenAI(
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${I}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
+	base_url="${I}"
+)`;switch(f){case r.CHAT:{let e=Object.keys(T).length>0,a="";if(e){let e=JSON.stringify({metadata:T},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=x.length>0?x:[{role:"user",content:y}];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)}${a}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${C}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${O}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${a}
+# )
+# print(response_with_file)
+`;break}case r.RESPONSES:{let e=Object.keys(T).length>0,a="";if(e){let e=JSON.stringify({metadata:T},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=x.length>0?x:[{role:"user",content:y}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${C}",
+    input=${JSON.stringify(i,null,4)}${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="${C}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${O}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${a}
+# )
+# print(response_with_file.output_text)
+`;break}case r.IMAGE:t="azure"===A?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${C}",
+	prompt="${n}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${O}"
+
+# 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="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case r.IMAGE_EDITS:t="azure"===A?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${O}"
+
+# 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="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${O}"
+
+# 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="${C}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case r.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${n||"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${n?`,
+	prompt="${n.replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case r.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${C}",
+	input="${n||"Your text to convert to speech here"}",
+	voice="${_}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${C}",
+#     input="${n||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${w}
+${t}`}],190272)},371401,e=>{"use strict";var t=e.i(115571),a=e.i(271645);function i(e){let a=t=>{"disableUsageIndicator"===t.key&&e()},i=t=>{let{key:a}=t.detail;"disableUsageIndicator"===a&&e()};return window.addEventListener("storage",a),window.addEventListener(t.LOCAL_STORAGE_EVENT,i),()=>{window.removeEventListener("storage",a),window.removeEventListener(t.LOCAL_STORAGE_EVENT,i)}}function r(){return"true"===(0,t.getLocalStorageItem)("disableUsageIndicator")}function o(){return(0,a.useSyncExternalStore)(i,r)}e.s(["useDisableUsageIndicator",()=>o])},928685,e=>{"use strict";var t=e.i(38953);e.s(["SearchOutlined",()=>t.default])},115571,e=>{"use strict";let t="local-storage-change";function a(e){window.dispatchEvent(new CustomEvent(t,{detail:{key:e}}))}function i(e){try{return window.localStorage.getItem(e)}catch(t){return console.warn(`Error reading localStorage key "${e}":`,t),null}}function r(e,t){try{window.localStorage.setItem(e,t)}catch(t){console.warn(`Error setting localStorage 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/29f944b40b65da0a.js b/litellm/proxy/_experimental/out/_next/static/chunks/29f944b40b65da0a.js
new file mode 100644
index 00000000000..fee3f36a9e2
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/29f944b40b65da0a.js
@@ -0,0 +1,420 @@
+(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,190272,785913,e=>{"use strict";var 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),s=((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);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",()=>s,"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:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:x,proxySettings:b}=e,v="session"===i?a:r,y=window.location.origin,j=b?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:b?.PROXY_BASE_URL&&(y=b.PROXY_BASE_URL);let w=n||"Your prompt here",N=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=o.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),I={};l.length>0&&(I.tags=l),c.length>0&&(I.vector_stores=c),d.length>0&&(I.guardrails=d),p.length>0&&(I.policies=p);let E=_||"your-model-name",T="azure"===x?`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 s.CHAT:{let e=Object.keys(I).length>0,i="";if(e){let e=JSON.stringify({metadata:I},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${E}",
+    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="${E}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${N}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${i}
+# )
+# print(response_with_file)
+`;break}case s.RESPONSES:{let e=Object.keys(I).length>0,i="";if(e){let e=JSON.stringify({metadata:I},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
+    extra_body=${e}`}let a=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="${E}",
+    input=${JSON.stringify(a,null,4)}${i}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${E}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${N}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${i}
+# )
+# print(response_with_file.output_text)
+`;break}case s.IMAGE:t="azure"===x?`
+# 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="${E}",
+	prompt="${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 = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${E}",
+	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 s.IMAGE_EDITS:t="azure"===x?`
+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 = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${E}",
+	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 = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${E}",
+	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 s.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${n||"Your string here"}",
+	model="${E}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case s.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="${E}",
+	file=audio_file${n?`,
+	prompt="${n.replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case s.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${E}",
+	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="${E}",
+#     input="${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`${T}
+${t}`}],190272)},518617,e=>{"use strict";e.i(247167);var t=e.i(931067),i=e.i(271645);let a={icon:{tag:"svg",attrs:{"fill-rule":"evenodd",viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"path",attrs:{d:"M512 64c247.4 0 448 200.6 448 448S759.4 960 512 960 64 759.4 64 512 264.6 64 512 64zm0 76c-205.4 0-372 166.6-372 372s166.6 372 372 372 372-166.6 372-372-166.6-372-372-372zm128.01 198.83c.03 0 .05.01.09.06l45.02 45.01a.2.2 0 01.05.09.12.12 0 010 .07c0 .02-.01.04-.05.08L557.25 512l127.87 127.86a.27.27 0 01.05.06v.02a.12.12 0 010 .07c0 .03-.01.05-.05.09l-45.02 45.02a.2.2 0 01-.09.05.12.12 0 01-.07 0c-.02 0-.04-.01-.08-.05L512 557.25 384.14 685.12c-.04.04-.06.05-.08.05a.12.12 0 01-.07 0c-.03 0-.05-.01-.09-.05l-45.02-45.02a.2.2 0 01-.05-.09.12.12 0 010-.07c0-.02.01-.04.06-.08L466.75 512 338.88 384.14a.27.27 0 01-.05-.06l-.01-.02a.12.12 0 010-.07c0-.03.01-.05.05-.09l45.02-45.02a.2.2 0 01.09-.05.12.12 0 01.07 0c.02 0 .04.01.08.06L512 466.75l127.86-127.86c.04-.05.06-.06.08-.06a.12.12 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2c21eeb7a235384a.js b/litellm/proxy/_experimental/out/_next/static/chunks/2c21eeb7a235384a.js
deleted file mode 100644
index 28cc92c17e9..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/2c21eeb7a235384a.js
+++ /dev/null
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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
+
+`,M=`
+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
+
+`,P=`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)`;e.s(["default",0,({accessToken:e})=>{let[T,C]=(0,g.useState)(!1),[I,w]=(0,g.useState)(!1),[k,B]=(0,g.useState)(null),[_,A]=(0,g.useState)([]),[E,H]=(0,g.useState)(!1),[S,D]=(0,g.useState)(!1);(0,g.useEffect)(()=>{e&&(0,y.getBudgetList)(e).then(e=>{A(e)})},[e]);let L=async t=>{null!=e&&(B(t),w(!0))},R=async()=>{if(k&&null!=e){H(!0);try{await (0,y.budgetDeleteCall)(e,k.budget_id),v.default.success("Budget deleted."),await U()}catch(e){console.error("Error deleting budget:",e),"function"==typeof v.default.fromBackend?v.default.fromBackend("Failed to delete budget"):v.default.info("Failed to delete budget")}finally{H(!1),D(!1),B(null)}}},U=async()=>{null!=e&&(0,y.getBudgetList)(e).then(e=>{A(e)})};return(0,t.jsxs)("div",{className:"w-full mx-auto flex-auto overflow-y-auto m-8 p-2",children:[(0,t.jsx)(l.Button,{size:"sm",variant:"primary",className:"mb-2",onClick:()=>C(!0),children:"+ Create Budget"}),(0,t.jsxs)(a.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)(p.TabPanels,{children:[(0,t.jsx)(h.TabPanel,{children:(0,t.jsxs)("div",{className:"mt-6",children:[(0,t.jsx)(O,{accessToken:e,isModalVisible:T,setIsModalVisible:C,setBudgetList:A}),k&&(0,t.jsx)(N,{accessToken:e,isModalVisible:I,setIsModalVisible:w,setBudgetList:A,existingBudget:k,handleUpdateCall:U}),(0,t.jsxs)(i.Card,{children:[(0,t.jsx)(x.Text,{children:"Create a budget to assign to customers."}),(0,t.jsxs)(n.Table,{children:[(0,t.jsx)(d.TableHead,{children:(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(c.TableHeaderCell,{children:"Budget ID"}),(0,t.jsx)(c.TableHeaderCell,{children:"Max Budget"}),(0,t.jsx)(c.TableHeaderCell,{children:"TPM"}),(0,t.jsx)(c.TableHeaderCell,{children:"RPM"})]})}),(0,t.jsx)(s.TableBody,{children:_.slice().sort((e,t)=>new Date(t.updated_at).getTime()-new Date(e.updated_at).getTime()).map((e,l)=>(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"}),(0,t.jsx)(f.default,{variant:"Edit",tooltipText:"Edit budget",onClick:()=>L(e),dataTestId:"edit-budget-button"}),(0,t.jsx)(f.default,{variant:"Delete",tooltipText:"Delete budget",onClick:()=>{B(e),D(!0)},dataTestId:"delete-budget-button"})]},l))})]})]}),(0,t.jsx)(j.default,{isOpen:S,title:"Delete Budget?",message:"Are you sure you want to delete this budget? This action cannot be undone.",resourceInformationTitle:"Budget Information",resourceInformation:[{label:"Budget ID",value:k?.budget_id,code:!0},{label:"Max Budget",value:k?.max_budget},{label:"TPM",value:k?.tpm_limit},{label:"RPM",value:k?.rpm_limit}],onCancel:()=>{D(!1)},onOk:R,confirmLoading:E})]})}),(0,t.jsx)(h.TabPanel,{children:(0,t.jsxs)("div",{className:"mt-6",children:[(0,t.jsx)(x.Text,{className:"text-base",children:"How to use budget id"}),(0,t.jsxs)(a.TabGroup,{children:[(0,t.jsxs)(m.TabList,{children:[(0,t.jsx)(r.Tab,{children:"Assign Budget to Customer"}),(0,t.jsx)(r.Tab,{children:"Test it (Curl)"}),(0,t.jsx)(r.Tab,{children:"Test it (OpenAI SDK)"})]}),(0,t.jsxs)(p.TabPanels,{children:[(0,t.jsx)(h.TabPanel,{children:(0,t.jsx)(b.Prism,{language:"bash",children:F})}),(0,t.jsx)(h.TabPanel,{children:(0,t.jsx)(b.Prism,{language:"bash",children:M})}),(0,t.jsx)(h.TabPanel,{children:(0,t.jsx)(b.Prism,{language:"python",children:P})})]})]})]})})]})]})]})}],646050)},267167,e=>{"use strict";var t=e.i(843476),l=e.i(646050),i=e.i(135214);e.s(["default",0,()=>{let{accessToken:e}=(0,i.default)();return(0,t.jsx)(l.default,{accessToken:e})}])}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2d44417ec0ed6970.js b/litellm/proxy/_experimental/out/_next/static/chunks/2d44417ec0ed6970.js
new file mode 100644
index 00000000000..82921d97453
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2d44417ec0ed6970.js
@@ -0,0 +1 @@
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/31e02a31dea7d5d2.js b/litellm/proxy/_experimental/out/_next/static/chunks/31e02a31dea7d5d2.js
deleted file mode 100644
index d392a68c996..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/31e02a31dea7d5d2.js
+++ /dev/null
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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">${tA(e.totals.cost_per_request)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Total Daily</div>
+            <div class="value green">${tA(e.totals.daily_cost)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Total Monthly</div>
+            <div class="value purple">${tA(e.totals.monthly_cost)}</div>
+          </div>
+        </div>
+        ${e.totals.margin_per_request>0?`
+        <div class="summary-grid" style="margin-top: 15px; padding-top: 15px; border-top: 1px solid #ddd;">
+          <div class="summary-item">
+            <div class="label">Margin/Request</div>
+            <div class="value" style="color: #faad14;">${tA(e.totals.margin_per_request)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Daily Margin</div>
+            <div class="value" style="color: #faad14;">${tA(e.totals.daily_margin)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Monthly Margin</div>
+            <div class="value" style="color: #faad14;">${tA(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> ${tP(t.input_tokens)}</p>
+        <p><strong>Output Tokens per Request:</strong> ${tP(t.output_tokens)}</p>
+        ${t.num_requests_per_day?`<p><strong>Requests per Day:</strong> ${tP(t.num_requests_per_day)}</p>`:""}
+        ${t.num_requests_per_month?`<p><strong>Requests per Month:</strong> ${tP(t.num_requests_per_month)}</p>`:""}
+      </div>
+
+      <table>
+        <tr>
+          <th>Cost Type</th>
+          <th>Per Request</th>
+          ${null!==t.daily_cost?"<th>Daily</th>":""}
+          ${null!==t.monthly_cost?"<th>Monthly</th>":""}
+        </tr>
+        <tr>
+          <td>Input Cost</td>
+          <td class="cost-value">${tA(t.input_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tA(t.daily_input_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tA(t.monthly_input_cost)}</td>`:""}
+        </tr>
+        <tr>
+          <td>Output Cost</td>
+          <td class="cost-value">${tA(t.output_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tA(t.daily_output_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tA(t.monthly_output_cost)}</td>`:""}
+        </tr>
+        <tr>
+          <td>Margin/Fee</td>
+          <td class="cost-value">${tA(t.margin_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tA(t.daily_margin_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tA(t.monthly_margin_cost)}</td>`:""}
+        </tr>
+        <tr class="total-row">
+          <td>Total</td>
+          <td class="cost-value">${tA(t.cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tA(t.daily_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tA(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)(tS.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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+
+Consider:
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+
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3569f12d1e9d5e0d.js b/litellm/proxy/_experimental/out/_next/static/chunks/3569f12d1e9d5e0d.js
deleted file mode 100644
index 8a99e192931..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/3569f12d1e9d5e0d.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/39768ec0eebd2554.js b/litellm/proxy/_experimental/out/_next/static/chunks/39768ec0eebd2554.js
deleted file mode 100644
index d95f5a3ef89..00000000000
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new file mode 100644
index 00000000000..18c05481275
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3de1b6df2372e93b.js b/litellm/proxy/_experimental/out/_next/static/chunks/3de1b6df2372e93b.js
new file mode 100644
index 00000000000..4c0cc039040
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/3de1b6df2372e93b.js
@@ -0,0 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/40f766ecc87dbf9a.js b/litellm/proxy/_experimental/out/_next/static/chunks/40f766ecc87dbf9a.js
deleted file mode 100644
index faa3fae7368..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/40f766ecc87dbf9a.js
+++ /dev/null
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new file mode 100644
index 00000000000..e5791fc88af
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/42c127841d8c1bd3.js
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new file mode 100644
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eO;eN(this,c,"m",T).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 eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eT("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 eT("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||e_(this,d,void 0,"f")},C=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 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eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`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 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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 tC(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tC,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 tz="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tW{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),H.set(this,!1),W.set(this,void 0),F.set(this,void 0),V.set(this,e=>{if(e_(this,q,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eT)return this._emit("error",e);if(e instanceof Error){let t=new eT(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eT(String(e)))}),e_(this,R,new Promise((e,t)=>{e_(this,I,e,"f"),e_(this,M,t,"f")}),"f"),e_(this,L,new Promise((e,t)=>{e_(this,$,e,"f"),e_(this,U,t,"f")}),"f"),eN(this,R,"f").catch(()=>{}),eN(this,L,"f").catch(()=>{})}get response(){return eN(this,W,"f")}get request_id(){return eN(this,F,"f")}async withResponse(){let e=await eN(this,R,"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 tW;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tW;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,W,e,"f"),e_(this,F,e?.headers.get("request-id"),"f"),eN(this,I,"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,z,"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,H,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,H,!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,$,"f").call(this));let s=eN(this,D,"f")[e];if(s&&(eN(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eN(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this),this._connected(null);let r=te.fromReadableStream(e,this.controller);for await(let e of r)eN(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,H=new WeakMap,W=new WeakMap,F=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eT("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 eT("stream ended without producing a content 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eT("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eT("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 eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`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&&tH(s)){let t=s[tz]||"";Object.defineProperty(s,tz,{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 eT(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${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 tW.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 eT("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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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/ae9cf43b8c0c76aa.js b/litellm/proxy/_experimental/out/_next/static/chunks/49e9dce7df902771.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/ae9cf43b8c0c76aa.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/49e9dce7df902771.js
index 320b5d3c0f9..63822604eaf 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/ae9cf43b8c0c76aa.js
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1a04d31843c96649.js b/litellm/proxy/_experimental/out/_next/static/chunks/5400ee883dfa8c43.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/1a04d31843c96649.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/5400ee883dfa8c43.js
index a9a583efa3e..2337c72cb67 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1a04d31843c96649.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5400ee883dfa8c43.js
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 client = openai.AzureOpenAI(
 	api_key="${v||"YOUR_LITELLM_API_KEY"}",
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5595eb6378e90997.js b/litellm/proxy/_experimental/out/_next/static/chunks/5595eb6378e90997.js
deleted file mode 100644
index ab41ae1d361..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5595eb6378e90997.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/55c8ff5e9c6d1e1d.js b/litellm/proxy/_experimental/out/_next/static/chunks/55c8ff5e9c6d1e1d.js
deleted file mode 100644
index 9b0e7c6f6d9..00000000000
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/df6546cd8a44d3b3.js b/litellm/proxy/_experimental/out/_next/static/chunks/58461a445becf104.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/df6546cd8a44d3b3.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/58461a445becf104.js
index cf9da859aa6..ba3f4511b3a 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/df6546cd8a44d3b3.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/58461a445becf104.js
@@ -1,4 +1,4 @@
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4:return(0,l.jsxs)("div",{className:"space-y-6",children:[(0,l.jsx)("div",{children:(0,l.jsxs)("p",{className:"text-sm text-gray-500",children:["Configure settings for a specific call type. 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Falls back to the default violation message if empty."}),(0,l.jsx)("textarea",{rows:3,placeholder:"e.g. I'm not able to continue this conversation. 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eq=e.i(269200),eJ=e.i(942232),eW=e.i(977572),eU=e.i(427612),eV=e.i(64848),eY=e.i(496020),eZ=e.i(752978),eQ=e.i(994388),eX=e.i(68155),e0=e.i(94629),e1=e.i(360820),e2=e.i(871943),e4=e.i(389083),e5=e.i(152990),e6=e.i(682830),e8=e.i(779241);let{Title:e3,Text:e7}=d.Typography,{Option:e9}=n.Select,te=({visible:e,onClose:t,accessToken:a,onSuccess:o,guardrailId:d,initialValues:c})=>{let[g]=r.Form.useForm(),[x,h]=(0,m.useState)(!1),[f,y]=(0,m.useState)(c?.provider||null),[j,_]=(0,m.useState)(null),[b,v]=(0,m.useState)([]),[N,C]=(0,m.useState)({});(0,m.useEffect)(()=>{(async()=>{try{if(!a)return;let e=await (0,p.getGuardrailUISettings)(a);_(e)}catch(e){console.error("Error fetching guardrail settings:",e),u.default.fromBackend("Failed to load guardrail settings")}})()},[a]),(0,m.useEffect)(()=>{c?.pii_entities_config&&Object.keys(c.pii_entities_config).length>0&&(v(Object.keys(c.pii_entities_config)),C(c.pii_entities_config))},[c]);let w=e=>{v(t=>t.includes(e)?t.filter(t=>t!==e):[...t,e])},S=(e,t)=>{C(a=>({...a,[e]:t}))},k=async()=>{try{h(!0);let e=await g.validateFields(),l=ea[e.provider],r={guardrail_id:d,guardrail:{guardrail_name:e.guardrail_name,litellm_params:{guardrail:l,mode:e.mode,default_on:e.default_on},guardrail_info:{}}};if("PresidioPII"===e.provider&&b.length>0){let e={};b.forEach(t=>{e[t]=N[t]||"MASK"}),r.guardrail.litellm_params.pii_entities_config=e}else if(e.config)try{let t=JSON.parse(e.config);"Bedrock"===e.provider&&t?(t.guardrail_id&&(r.guardrail.litellm_params.guardrailIdentifier=t.guardrail_id),t.guardrail_version&&(r.guardrail.litellm_params.guardrailVersion=t.guardrail_version)):r.guardrail.guardrail_info=t}catch(e){u.default.fromBackend("Invalid JSON in configuration"),h(!1);return}if(!a)throw Error("No access token available");console.log("Sending guardrail update data:",JSON.stringify(r));let i=`/guardrails/${d}`,s=await fetch(i,{method:"PUT",headers:{[(0,p.getGlobalLitellmHeaderName)()]:`Bearer ${a}`,"Content-Type":"application/json"},body:JSON.stringify(r)});if(!s.ok){let e=await s.text();throw Error(e||"Failed to update guardrail")}u.default.success("Guardrail updated successfully"),o(),t()}catch(e){console.error("Failed to update guardrail:",e),u.default.fromBackend("Failed to update guardrail: "+(e instanceof Error?e.message:String(e)))}finally{h(!1)}};return(0,l.jsx)(s.Modal,{title:"Edit Guardrail",open:e,onCancel:t,footer:null,width:700,children:(0,l.jsxs)(r.Form,{form:g,layout:"vertical",initialValues:c,children:[(0,l.jsx)(r.Form.Item,{name:"guardrail_name",label:"Guardrail Name",rules:[{required:!0,message:"Please enter a guardrail name"}],children:(0,l.jsx)(e8.TextInput,{placeholder:"Enter a name for this guardrail"})}),(0,l.jsx)(r.Form.Item,{name:"provider",label:"Guardrail Provider",rules:[{required:!0,message:"Please select a provider"}],children:(0,l.jsx)(n.Select,{placeholder:"Select a guardrail provider",onChange:e=>{y(e),g.setFieldsValue({config:void 0}),v([]),C({})},disabled:!0,optionLabelProp:"label",children:Object.entries(et()).map(([e,t])=>(0,l.jsx)(e9,{value:e,label:t,children:(0,l.jsxs)("div",{style:{display:"flex",alignItems:"center"},children:[en[t]&&(0,l.jsx)("img",{src:en[t],alt:"",style:{height:"20px",width:"20px",marginRight:"8px",objectFit:"contain"},onError:e=>{e.currentTarget.style.display="none"}}),(0,l.jsx)("span",{children:t})]})},e))})}),(0,l.jsx)(r.Form.Item,{name:"mode",label:"Mode",tooltip:"How the guardrail should be applied",rules:[{required:!0,message:"Please select a mode"}],children:(0,l.jsx)(n.Select,{children:j?.supported_modes?.map(e=>(0,l.jsx)(e9,{value:e,children:e},e))||(0,l.jsxs)(l.Fragment,{children:[(0,l.jsx)(e9,{value:"pre_call",children:"pre_call"}),(0,l.jsx)(e9,{value:"post_call",children:"post_call"})]})})}),(0,l.jsx)(r.Form.Item,{name:"default_on",label:"Always On",tooltip:"If enabled, this guardrail will be applied to all requests by default",valuePropName:"checked",children:(0,l.jsx)(D.Switch,{})}),(()=>{if(!f)return null;if("PresidioPII"===f)return j&&f&&"PresidioPII"===f?(0,l.jsx)(eP,{entities:j.supported_entities,actions:j.supported_actions,selectedEntities:b,selectedActions:N,onEntitySelect:w,onActionSelect:S,entityCategories:j.pii_entity_categories}):null;switch(f){case"Aporia":return(0,l.jsx)(r.Form.Item,{label:"Aporia Configuration",name:"config",tooltip:"JSON configuration for Aporia",children:(0,l.jsx)(i.Input.TextArea,{rows:4,placeholder:`{
   "api_key": "your_aporia_api_key",
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@@ -16,7 +16,7 @@
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@@ -64,7 +64,7 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/591e3b6fbe6e4d4a.js b/litellm/proxy/_experimental/out/_next/static/chunks/591e3b6fbe6e4d4a.js
deleted file mode 100644
index e79c30fd92a..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/591e3b6fbe6e4d4a.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3b3c0b070b14da06.js b/litellm/proxy/_experimental/out/_next/static/chunks/5963ae3163ecd9b6.js
similarity index 92%
rename from litellm/proxy/_experimental/out/_next/static/chunks/3b3c0b070b14da06.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/5963ae3163ecd9b6.js
index 786780e51d7..a2486f051bd 100644
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5963ae3163ecd9b6.js
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new file mode 100644
index 00000000000..31634f9781b
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5ab3a0c9cca409f3.js
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5c0770ecd9172a56.js b/litellm/proxy/_experimental/out/_next/static/chunks/5c0770ecd9172a56.js
new file mode 100644
index 00000000000..e11f36c66b1
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5c0770ecd9172a56.js
@@ -0,0 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5f4170980a69ffa3.js b/litellm/proxy/_experimental/out/_next/static/chunks/5f4170980a69ffa3.js
new file mode 100644
index 00000000000..6f5c075ec06
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5f4170980a69ffa3.js
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deleted file mode 100644
index dc9b74ebc11..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/635dd51f7caede88.js b/litellm/proxy/_experimental/out/_next/static/chunks/635dd51f7caede88.js
deleted file mode 100644
index 7ad20c8fb02..00000000000
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this._client.delete(tC`/v1/messages/batches/${e}?beta=true`,{...s,headers:tk([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(tC`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:tk([{"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 eO(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:tk([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tO.fromResponse(t.response,t.controller))}}let tR=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tR(e=e.slice(0,e.length-1));case"number":let 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AbortController,u.set(this,void 0),h.set(this,()=>{}),m.set(this,()=>{}),p.set(this,void 0),f.set(this,()=>{}),g.set(this,()=>{}),y.set(this,{}),x.set(this,!1),b.set(this,!1),v.set(this,!1),w.set(this,!1),j.set(this,void 0),S.set(this,void 0),k.set(this,e=>{if(ek(this,b,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,v,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,u,new Promise((e,t)=>{ek(this,h,e,"f"),ek(this,m,t,"f")}),"f"),ek(this,p,new Promise((e,t)=>{ek(this,f,e,"f"),ek(this,g,t,"f")}),"f"),eE(this,u,"f").catch(()=>{}),eE(this,p,"f").catch(()=>{})}get response(){return eE(this,j,"f")}get request_id(){return eE(this,S,"f")}async withResponse(){let e=await eE(this,u,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new t$;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new t$;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")},eE(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())),eE(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))eE(this,c,"m",C).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,c,"m",T).call(this)}_connected(e){this.ended||(ek(this,j,e,"f"),ek(this,S,e?.headers.get("request-id"),"f"),eE(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,x,"f")}get errored(){return eE(this,b,"f")}get aborted(){return eE(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(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(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,w,!0,"f"),await eE(this,p,"f")}get currentMessage(){return eE(this,d,"f")}async finalMessage(){return await this.done(),eE(this,c,"m",_).call(this)}async finalText(){return await 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eR;eE(this,c,"m",T).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 eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eO("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 eO("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||ek(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eE(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":tL(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:tU(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":ek(this,d,t,"f")}},T=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,d,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,d,void 0,"f"),e},A=function(e){let t=eE(this,d,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`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&&tL(s)){let t=s[tM]||"";if(Object.defineProperty(s,tM,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tI(t)}catch(s){let e=new eO(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eE(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tU(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tU(e){}let tD={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tB={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tq extends tS{constructor(){super(...arguments),this.batches=new tP(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tB&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tB[r.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tD[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return t$.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:tk([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tq.Batches=tP;class tz extends tS{constructor(){super(...arguments),this.models=new tA(this._client),this.messages=new tq(this._client),this.files=new tT(this._client)}}tz.Models=tA,tz.Messages=tq,tz.Files=tT;class tW extends tS{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tF="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tJ{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),W.set(this,!1),F.set(this,void 0),H.set(this,void 0),V.set(this,e=>{if(ek(this,q,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,R,new Promise((e,t)=>{ek(this,I,e,"f"),ek(this,M,t,"f")}),"f"),ek(this,L,new Promise((e,t)=>{ek(this,$,e,"f"),ek(this,U,t,"f")}),"f"),eE(this,R,"f").catch(()=>{}),eE(this,L,"f").catch(()=>{})}get response(){return eE(this,F,"f")}get request_id(){return eE(this,H,"f")}async withResponse(){let e=await eE(this,R,"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 tJ;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tJ;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")},eE(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())),eE(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))eE(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}_connected(e){this.ended||(ek(this,F,e,"f"),ek(this,H,e?.headers.get("request-id"),"f"),eE(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,B,"f")}get errored(){return eE(this,q,"f")}get aborted(){return eE(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(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(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,W,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,W,!0,"f"),await eE(this,L,"f")}get currentMessage(){return eE(this,P,"f")}async finalMessage(){return await this.done(),eE(this,O,"m",J).call(this)}async finalText(){return await this.done(),eE(this,O,"m",G).call(this)}_emit(e,...t){if(eE(this,B,"f"))return;"end"===e&&(ek(this,B,!0,"f"),eE(this,$,"f").call(this));let s=eE(this,D,"f")[e];if(s&&(eE(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eE(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this),this._connected(null);let r=ts.fromReadableStream(e,this.controller);for await(let e of r)eE(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,W=new WeakMap,F=new WeakMap,H=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eO("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 eO("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||ek(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eE(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":tH(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:tG(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":ek(this,P,t,"f")}},Y=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,P,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,P,void 0,"f"),e},Q=function(e){let t=eE(this,P,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`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&&tH(s)){let t=s[tF]||"";Object.defineProperty(s,tF,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tI(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:tG(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 ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tG(e){}class 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new file mode 100644
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rename to litellm/proxy/_experimental/out/_next/static/chunks/673d847ad9c91666.js
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deleted file mode 100644
index f11e4af5216..00000000000
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7174130ddef406dd.js b/litellm/proxy/_experimental/out/_next/static/chunks/7174130ddef406dd.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7f9e9c54ac262de2.js b/litellm/proxy/_experimental/out/_next/static/chunks/726579f2940c2a2f.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/7f9e9c54ac262de2.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/726579f2940c2a2f.js
index 9673eab0cc4..526be3f7e21 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/7f9e9c54ac262de2.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/726579f2940c2a2f.js
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deleted file mode 100644
index 93f0bb4f52c..00000000000
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listed"}):c.map((e,l)=>(0,t.jsx)(m.Badge,{color:h[l%h.length],children:e.replace(/^supports_/,"").split("_").map(e=>e.charAt(0).toUpperCase()+e.slice(1)).join(" ")},e)))})]}),(H.tpm||H.rpm)&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Rate Limits"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[H.tpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Tokens per Minute:"}),(0,t.jsx)(o.Text,{children:H.tpm.toLocaleString()})]}),H.rpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Requests per Minute:"}),(0,t.jsx)(o.Text,{children:H.rpm.toLocaleString()})]})]})]}),H.supported_openai_params&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Supported OpenAI Parameters"}),(0,t.jsx)("div",{className:"flex flex-wrap 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+
+client = openai.OpenAI(
+    api_key="your_api_key",
+    base_url="${(0,x.getProxyBaseUrl)()}"  # Your LiteLLM Proxy URL
+)
+
+response = client.chat.completions.create(
+    model="${H.model_group}",
+    messages=[
+        {
+            "role": "user",
+            "content": "Hello, how are you?"
+        }
+    ]
+)
+
+print(response.choices[0].message.content)`})]})]})}),(0,t.jsx)(s.Modal,{title:em?.name||"Agent Details",width:1e3,open:eu,footer:null,onOk:ek,onCancel:e_,children:em&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Agent Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Name:"}),(0,t.jsx)(o.Text,{children:em.name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Version:"}),(0,t.jsxs)(m.Badge,{color:"blue",children:["v",em.version]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Protocol Version:"}),(0,t.jsx)(o.Text,{children:em.protocolVersion})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center 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+import asyncio
+
+# Standard MCP configuration
+config = {
+    "mcpServers": {
+        "${ef.server_name}": {
+            "url": "${(0,x.getProxyBaseUrl)()}/${ef.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)(y,{visible:R,onClose:()=>V(!1),accessToken:e||"",modelHubData:_||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.modelHubCall)(e);I(t.data)}catch(e){console.error("Error refreshing model data:",e)}})()}}),(0,t.jsx)(p,{visible:er,onClose:()=>ec(!1),accessToken:e||"",agentHubData:Y||[],onSuccess:()=>{e&&(async()=>{try{let t=(await (0,x.getAgentsList)(e)).agents.map(e=>({agent_id:e.agent_id,...e.agent_card_params,is_public:e.is_public}));en(t)}catch(e){console.error("Error refreshing agent data:",e)}})()}}),(0,t.jsx)(b,{visible:eS,onClose:()=>e$(!1),accessToken:e||"",mcpHubData:ep||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.fetchMCPServers)(e);eg(t)}catch(e){console.error("Error refreshing MCP server data:",e)}})()}})]})}],934879)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7b9ef931d44e410f.js b/litellm/proxy/_experimental/out/_next/static/chunks/7b9ef931d44e410f.js
deleted file mode 100644
index 4b04ad1abe5..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/7b9ef931d44e410f.js
+++ /dev/null
@@ -1 +0,0 @@
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/80079c810f42a5e5.js b/litellm/proxy/_experimental/out/_next/static/chunks/80079c810f42a5e5.js
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-
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-	api_key="${w||"YOUR_LITELLM_API_KEY"}",
-	base_url="${x}"
-)`;switch(h){case n.CHAT:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
-    extra_body=${e}`}let i=j.length>0?j:[{role:"user",content:E}];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="${k}",
-    messages=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.chat.completions.create(
-#     model="${k}",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {
-#                     "type": "text",
-#                     "text": "${$}"
-#                 },
-#                 {
-#                     "type": "image_url",
-#                     "image_url": {
-#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
-#                     }
-#                 }
-#             ]
-#         }
-#     ]${a}
-# )
-# print(response_with_file)
-`;break}case n.RESPONSES:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
-    extra_body=${e}`}let i=j.length>0?j:[{role:"user",content:E}];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="${k}",
-    input=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.responses.create(
-#     model="${k}",
-#     input=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {"type": "input_text", "text": "${$}"},
-#                 {
-#                     "type": "input_image",
-#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
-#                 },
-#             ],
-#         }
-#     ]${a}
-# )
-# print(response_with_file.output_text)
-`;break}case n.IMAGE:t="azure"===b?`
-# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
-# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
-# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
-import os
-import requests
-import json
-import time
-from PIL import Image
-
-result = client.images.generate(
-	model="${k}",
-	prompt="${o}",
-	n=1
-)
-
-json_response = json.loads(result.model_dump_json())
-
-# Set the directory for the stored image
-image_dir = os.path.join(os.curdir, 'images')
-
-# If the directory doesn't exist, create it
-if not os.path.isdir(image_dir):
-	os.mkdir(image_dir)
-
-# Initialize the image path
-image_filename = f"generated_image_{int(time.time())}.png"
-image_path = os.path.join(image_dir, image_filename)
-
-try:
-	# Retrieve the generated image
-	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
-			image_url = json_response["data"][0]["url"]
-			generated_image = requests.get(image_url).content
-			with open(image_path, "wb") as image_file:
-					image_file.write(generated_image)
-
-			print(f"Image saved to {image_path}")
-			# Display the image
-			image = Image.open(image_path)
-			image.show()
-	else:
-			print("Could not find image URL in response.")
-			print("Full response:", json_response)
-except Exception as e:
-	print(f"An error occurred: {e}")
-	print("Full response:", json_response)
-`:`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
-	# In a real implementation, this would upload the file to OpenAI
-	# For this example, we'll just return a placeholder ID
-	return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${$}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${k}",
-	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 = "${$}"
-
-# 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="${k}",
-	input=[
-			{
-					"role": "user",
-					"content": [
-							{"type": "input_text", "text": prompt},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image1}",
-							},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image2}",
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id1,
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id2,
-							}
-					],
-			}
-	],
-	tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
-	output
-	for output in response.output
-	if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
-	image_base64 = image_data[0]
-	image_filename = f"edited_image_{int(time.time())}.png"
-	with open(image_filename, "wb") as f:
-			f.write(base64.b64decode(image_base64))
-	print(f"Image saved to {image_filename}")
-else:
-	# If no image is generated, there might be a text response with an explanation
-	text_response = [output.text for output in response.output if hasattr(output, 'text')]
-	if text_response:
-			print("No image generated. Model response:")
-			print("\\n".join(text_response))
-	else:
-			print("No image data found in response.")
-	print("Full response for debugging:")
-	print(response)
-`:`
-import base64
-import os
-import time
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
-	# In a real implementation, this would upload the file to OpenAI
-	# For this example, we'll just return a placeholder ID
-	return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${$}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${k}",
-	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="${o||"Your string here"}",
-	model="${k}",
-	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="${k}",
-	file=audio_file${o?`,
-	prompt="${o.replace(/"/g,'\\"')}"`:""}
-)
-
-print(response.text)
-`;break;case n.SPEECH:t=`
-# Make the text-to-speech request
-response = client.audio.speech.create(
-	model="${k}",
-	input="${o||"Your text to convert to speech here"}",
-	voice="${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="${k}",
-#     input="${o||"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)},735049,e=>{"use strict";var t=e.i(654310),a=function(e){if((0,t.default)()&&window.document.documentElement){var a=Array.isArray(e)?e:[e],i=window.document.documentElement;return a.some(function(e){return e in i.style})}return!1},i=function(e,t){if(!a(e))return!1;var i=document.createElement("div"),n=i.style[e];return i.style[e]=t,i.style[e]!==n};function n(e,t){return Array.isArray(e)||void 0===t?a(e):i(e,t)}e.s(["isStyleSupport",()=>n])},190144,e=>{"use strict";e.i(247167);var t=e.i(931067),a=e.i(271645);let i={icon:{tag:"svg",attrs:{viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"path",attrs:{d:"M832 64H296c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h496v688c0 4.4 3.6 8 8 8h56c4.4 0 8-3.6 8-8V96c0-17.7-14.3-32-32-32zM704 192H192c-17.7 0-32 14.3-32 32v530.7c0 8.5 3.4 16.6 9.4 22.6l173.3 173.3c2.2 2.2 4.7 4 7.4 5.5v1.9h4.2c3.5 1.3 7.2 2 11 2H704c17.7 0 32-14.3 32-32V224c0-17.7-14.3-32-32-32zM350 856.2L263.9 770H350v86.2zM664 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-        ${i},
-        ${n} > li,
-        ${a},
-        ${r},
-        ${o},
-        ${s}
-      `]:Object.assign({},{background:e.skeletonLoadingBackground,backgroundSize:"400% 100%",animationName:d,animationDuration:e.skeletonLoadingMotionDuration,animationTimingFunction:"ease",animationIterationCount:"infinite"})}}})((0,c.mergeToken)(e,{skeletonAvatarCls:`${t}-avatar`,skeletonTitleCls:`${t}-title`,skeletonParagraphCls:`${t}-paragraph`,skeletonButtonCls:`${t}-button`,skeletonInputCls:`${t}-input`,skeletonImageCls:`${t}-image`,imageSizeBase:a(e.controlHeight).mul(1.5).equal(),borderRadius:100,skeletonLoadingBackground:`linear-gradient(90deg, ${e.gradientFromColor} 25%, ${e.gradientToColor} 37%, ${e.gradientFromColor} 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8dfde809dc4ad794.js b/litellm/proxy/_experimental/out/_next/static/chunks/82bc4bb51160556f.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/8dfde809dc4ad794.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/82bc4bb51160556f.js
index e805a98151b..e1783caf150 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8dfde809dc4ad794.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/82bc4bb51160556f.js
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new file mode 100644
index 00000000000..16e51dedaf5
--- /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/184161a27f806cd4.js b/litellm/proxy/_experimental/out/_next/static/chunks/84dd260c7412819c.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/184161a27f806cd4.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/84dd260c7412819c.js
index fd9670e3814..769b56a0877 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/184161a27f806cd4.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/84dd260c7412819c.js
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new file mode 100644
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new file mode 100644
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deleted file mode 100644
index e173e7e526d..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8dc3b559a2e76f88.css b/litellm/proxy/_experimental/out/_next/static/chunks/8dc3b559a2e76f88.css
deleted file mode 100644
index a0c3cb3d428..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8dc3b559a2e76f88.css
+++ /dev/null
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c=e2(o,d);e.setState({draggingNodeKey:d,dragChildrenKeys:(r=[],!function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[];t.forEach(function(t){var n=t.key,l=t.children;r.push(n),e(l)})}(a[d].children),r),indent:e.listRef.current.getIndentWidth()}),e.setExpandedKeys(c),window.addEventListener("dragend",e.onWindowDragEnd),null==i||i({event:t,node:eJ(n)})}),(0,C.default)((0,tB.default)(e),"onNodeDragEnter",function(t,n){var r=e.state,l=r.expandedKeys,o=r.keyEntities,a=r.dragChildrenKeys,i=r.flattenNodes,d=r.indent,c=e.props,u=c.onDragEnter,s=c.onExpand,f=c.allowDrop,p=c.direction,m=n.pos,h=n.eventKey;if(e.currentMouseOverDroppableNodeKey!==h&&(e.currentMouseOverDroppableNodeKey=h),!e.dragNodeProps)return void e.resetDragState();var g=e8(t,e.dragNodeProps,n,d,e.dragStartMousePosition,f,i,o,l,p),v=g.dropPosition,y=g.dropLevelOffset,b=g.dropTargetKey,x=g.dropContainerKey,w=g.dropTargetPos,C=g.dropAllowed,E=g.dragOverNodeKey;a.includes(b)||!C||(e.delayedDragEnterLogic||(e.delayedDragEnterLogic={}),Object.keys(e.delayedDragEnterLogic).forEach(function(t){clearTimeout(e.delayedDragEnterLogic[t])}),e.dragNodeProps.eventKey!==n.eventKey&&(t.persist(),e.delayedDragEnterLogic[m]=window.setTimeout(function(){if(null!==e.state.draggingNodeKey){var r=(0,er.default)(l),a=o[n.eventKey];a&&(a.children||[]).length&&(r=e3(l,n.eventKey)),e.props.hasOwnProperty("expandedKeys")||e.setExpandedKeys(r),null==s||s(r,{node:eJ(n),expanded:!0,nativeEvent:t.nativeEvent})}},800)),e.dragNodeProps.eventKey===b&&0===y)?e.resetDragState():(e.setState({dragOverNodeKey:E,dropPosition:v,dropLevelOffset:y,dropTargetKey:b,dropContainerKey:x,dropTargetPos:w,dropAllowed:C}),null==u||u({event:t,node:eJ(n),expandedKeys:l}))}),(0,C.default)((0,tB.default)(e),"onNodeDragOver",function(t,n){var r=e.state,l=r.dragChildrenKeys,o=r.flattenNodes,a=r.keyEntities,i=r.expandedKeys,d=r.indent,c=e.props,u=c.onDragOver,s=c.allowDrop,f=c.direction;if(e.dragNodeProps){var p=e8(t,e.dragNodeProps,n,d,e.dragStartMousePosition,s,o,a,i,f),m=p.dropPosition,h=p.dropLevelOffset,g=p.dropTargetKey,v=p.dropContainerKey,y=p.dropTargetPos,b=p.dropAllowed,x=p.dragOverNodeKey;!l.includes(g)&&b&&(e.dragNodeProps.eventKey===g&&0===h?(null!==e.state.dropPosition||null!==e.state.dropLevelOffset||null!==e.state.dropTargetKey||null!==e.state.dropContainerKey||null!==e.state.dropTargetPos||!1!==e.state.dropAllowed||null!==e.state.dragOverNodeKey)&&e.resetDragState():(m!==e.state.dropPosition||h!==e.state.dropLevelOffset||g!==e.state.dropTargetKey||v!==e.state.dropContainerKey||y!==e.state.dropTargetPos||b!==e.state.dropAllowed||x!==e.state.dragOverNodeKey)&&e.setState({dropPosition:m,dropLevelOffset:h,dropTargetKey:g,dropContainerKey:v,dropTargetPos:y,dropAllowed:b,dragOverNodeKey:x}),null==u||u({event:t,node:eJ(n)}))}}),(0,C.default)((0,tB.default)(e),"onNodeDragLeave",function(t,n){e.currentMouseOverDroppableNodeKey!==n.eventKey||t.currentTarget.contains(t.relatedTarget)||(e.resetDragState(),e.currentMouseOverDroppableNodeKey=null);var r=e.props.onDragLeave;null==r||r({event:t,node:eJ(n)})}),(0,C.default)((0,tB.default)(e),"onWindowDragEnd",function(t){e.onNodeDragEnd(t,null,!0),window.removeEventListener("dragend",e.onWindowDragEnd)}),(0,C.default)((0,tB.default)(e),"onNodeDragEnd",function(t,n){var r=e.props.onDragEnd;e.setState({dragOverNodeKey:null}),e.cleanDragState(),null==r||r({event:t,node:eJ(n)}),e.dragNodeProps=null,window.removeEventListener("dragend",e.onWindowDragEnd)}),(0,C.default)((0,tB.default)(e),"onNodeDrop",function(t,n){var r,l=arguments.length>2&&void 0!==arguments[2]&&arguments[2],o=e.state,a=o.dragChildrenKeys,i=o.dropPosition,d=o.dropTargetKey,c=o.dropTargetPos;if(o.dropAllowed){var u=e.props.onDrop;if(e.setState({dragOverNodeKey:null}),e.cleanDragState(),null!==d){var s=(0,w.default)((0,w.default)({},eY(d,e.getTreeNodeRequiredProps())),{},{active:(null==(r=e.getActiveItem())?void 0:r.key)===d,data:e.state.keyEntities[d].node}),f=a.includes(d);(0,N.default)(!f,"Can not drop to dragNode's children node. This is a bug of rc-tree. Please report an issue.");var p=e4(c),m={event:t,node:eJ(s),dragNode:e.dragNodeProps?eJ(e.dragNodeProps):null,dragNodesKeys:[e.dragNodeProps.eventKey].concat(a),dropToGap:0!==i,dropPosition:i+Number(p[p.length-1])};l||null==u||u(m),e.dragNodeProps=null}}}),(0,C.default)((0,tB.default)(e),"cleanDragState",function(){null!==e.state.draggingNodeKey&&e.setState({draggingNodeKey:null,dropPosition:null,dropContainerKey:null,dropTargetKey:null,dropLevelOffset:null,dropAllowed:!0,dragOverNodeKey:null}),e.dragStartMousePosition=null,e.currentMouseOverDroppableNodeKey=null}),(0,C.default)((0,tB.default)(e),"triggerExpandActionExpand",function(t,n){var r=e.state,l=r.expandedKeys,o=r.flattenNodes,a=n.expanded,i=n.key;if(!n.isLeaf&&!t.shiftKey&&!t.metaKey&&!t.ctrlKey){var d=o.filter(function(e){return e.key===i})[0],c=eJ((0,w.default)((0,w.default)({},eY(i,e.getTreeNodeRequiredProps())),{},{data:d.data}));e.setExpandedKeys(a?e2(l,i):e3(l,i)),e.onNodeExpand(t,c)}}),(0,C.default)((0,tB.default)(e),"onNodeClick",function(t,n){var r=e.props,l=r.onClick;"click"===r.expandAction&&e.triggerExpandActionExpand(t,n),null==l||l(t,n)}),(0,C.default)((0,tB.default)(e),"onNodeDoubleClick",function(t,n){var r=e.props,l=r.onDoubleClick;"doubleClick"===r.expandAction&&e.triggerExpandActionExpand(t,n),null==l||l(t,n)}),(0,C.default)((0,tB.default)(e),"onNodeSelect",function(t,n){var r=e.state.selectedKeys,l=e.state,o=l.keyEntities,a=l.fieldNames,i=e.props,d=i.onSelect,c=i.multiple,u=n.selected,s=n[a.key],f=!u,p=(r=f?c?e3(r,s):[s]:e2(r,s)).map(function(e){var t=o[e];return t?t.node:null}).filter(Boolean);e.setUncontrolledState({selectedKeys:r}),null==d||d(r,{event:"select",selected:f,node:n,selectedNodes:p,nativeEvent:t.nativeEvent})}),(0,C.default)((0,tB.default)(e),"onNodeCheck",function(t,n,r){var l,o=e.state,a=o.keyEntities,i=o.checkedKeys,d=o.halfCheckedKeys,c=e.props,u=c.checkStrictly,s=c.onCheck,f=n.key,p={event:"check",node:n,checked:r,nativeEvent:t.nativeEvent};if(u){var m=r?e3(i,f):e2(i,f);l={checked:m,halfChecked:e2(d,f)},p.checkedNodes=m.map(function(e){return a[e]}).filter(Boolean).map(function(e){return e.node}),e.setUncontrolledState({checkedKeys:m})}else{var h=tt([].concat((0,er.default)(i),[f]),!0,a),g=h.checkedKeys,v=h.halfCheckedKeys;if(!r){var y=new Set(g);y.delete(f);var b=tt(Array.from(y),{checked:!1,halfCheckedKeys:v},a);g=b.checkedKeys,v=b.halfCheckedKeys}l=g,p.checkedNodes=[],p.checkedNodesPositions=[],p.halfCheckedKeys=v,g.forEach(function(e){var t=a[e];if(t){var n=t.node,r=t.pos;p.checkedNodes.push(n),p.checkedNodesPositions.push({node:n,pos:r})}}),e.setUncontrolledState({checkedKeys:g},!1,{halfCheckedKeys:v})}null==s||s(l,p)}),(0,C.default)((0,tB.default)(e),"onNodeLoad",function(t){var n,r=t.key,l=e.state.keyEntities[r];if(null==l||null==(n=l.children)||!n.length){var o=new Promise(function(n,l){e.setState(function(o){var a=o.loadedKeys,i=o.loadingKeys,d=void 0===i?[]:i,c=e.props,u=c.loadData,s=c.onLoad;return!u||(void 0===a?[]:a).includes(r)||d.includes(r)?null:(u(t).then(function(){var l=e3(e.state.loadedKeys,r);null==s||s(l,{event:"load",node:t}),e.setUncontrolledState({loadedKeys:l}),e.setState(function(e){return{loadingKeys:e2(e.loadingKeys,r)}}),n()}).catch(function(t){if(e.setState(function(e){return{loadingKeys:e2(e.loadingKeys,r)}}),e.loadingRetryTimes[r]=(e.loadingRetryTimes[r]||0)+1,e.loadingRetryTimes[r]>=10){var o=e.state.loadedKeys;(0,N.default)(!1,"Retry for `loadData` many times but still failed. 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t=eX(e.state.treeData,r,a);e.setUncontrolledState({flattenNodes:t})}).catch(function(){var t=e2(e.state.expandedKeys,s);e.setExpandedKeys(t)})}}}),(0,C.default)((0,tB.default)(e),"onListChangeStart",function(){e.setUncontrolledState({listChanging:!0})}),(0,C.default)((0,tB.default)(e),"onListChangeEnd",function(){setTimeout(function(){e.setUncontrolledState({listChanging:!1})})}),(0,C.default)((0,tB.default)(e),"onActiveChange",function(t){var n=e.state.activeKey,r=e.props,l=r.onActiveChange,o=r.itemScrollOffset;n!==t&&(e.setState({activeKey:t}),null!==t&&e.scrollTo({key:t,offset:void 0===o?0:o}),null==l||l(t))}),(0,C.default)((0,tB.default)(e),"getActiveItem",function(){var t=e.state,n=t.activeKey,r=t.flattenNodes;return null===n?null:r.find(function(e){return e.key===n})||null}),(0,C.default)((0,tB.default)(e),"offsetActiveKey",function(t){var n=e.state,r=n.flattenNodes,l=n.activeKey,o=r.findIndex(function(e){return e.key===l});-1===o&&t<0&&(o=r.length),o=(o+t+r.length)%r.length;var a=r[o];if(a){var i=a.key;e.onActiveChange(i)}else e.onActiveChange(null)}),(0,C.default)((0,tB.default)(e),"onKeyDown",function(t){var n=e.state,r=n.activeKey,l=n.expandedKeys,o=n.checkedKeys,a=n.fieldNames,i=e.props,d=i.onKeyDown,c=i.checkable,u=i.selectable;switch(t.which){case tz.default.UP:e.offsetActiveKey(-1),t.preventDefault();break;case tz.default.DOWN:e.offsetActiveKey(1),t.preventDefault()}var s=e.getActiveItem();if(s&&s.data){var f=e.getTreeNodeRequiredProps(),p=!1===s.data.isLeaf||!!(s.data[a.children]||[]).length,m=eJ((0,w.default)((0,w.default)({},eY(r,f)),{},{data:s.data,active:!0}));switch(t.which){case tz.default.LEFT:p&&l.includes(r)?e.onNodeExpand({},m):s.parent&&e.onActiveChange(s.parent.key),t.preventDefault();break;case tz.default.RIGHT:p&&!l.includes(r)?e.onNodeExpand({},m):s.children&&s.children.length&&e.onActiveChange(s.children[0].key),t.preventDefault();break;case tz.default.ENTER:case tz.default.SPACE:!c||m.disabled||!1===m.checkable||m.disableCheckbox?c||!u||m.disabled||!1===m.selectable||e.onNodeSelect({},m):e.onNodeCheck({},m,!o.includes(r))}}null==d||d(t)}),(0,C.default)((0,tB.default)(e),"setUncontrolledState",function(t){var n=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null;if(!e.destroyed){var l=!1,o=!0,a={};Object.keys(t).forEach(function(n){if(e.props.hasOwnProperty(n)){o=!1;return}l=!0,a[n]=t[n]}),l&&(!n||o)&&e.setState((0,w.default)((0,w.default)({},a),r))}}),(0,C.default)((0,tB.default)(e),"scrollTo",function(t){e.listRef.current.scrollTo(t)}),e}return(0,tj.default)(r,[{key:"componentDidMount",value:function(){this.destroyed=!1,this.onUpdated()}},{key:"componentDidUpdate",value:function(){this.onUpdated()}},{key:"onUpdated",value:function(){var e=this.props,t=e.activeKey,n=e.itemScrollOffset;void 0!==t&&t!==this.state.activeKey&&(this.setState({activeKey:t}),null!==t&&this.scrollTo({key:t,offset:void 0===n?0:n}))}},{key:"componentWillUnmount",value:function(){window.removeEventListener("dragend",this.onWindowDragEnd),this.destroyed=!0}},{key:"resetDragState",value:function(){this.setState({dragOverNodeKey:null,dropPosition:null,dropLevelOffset:null,dropTargetKey:null,dropContainerKey:null,dropTargetPos:null,dropAllowed:!1})}},{key:"render",value:function(){var e,n=this.state,r=n.focused,l=n.flattenNodes,o=n.keyEntities,a=n.draggingNodeKey,i=n.activeKey,d=n.dropLevelOffset,c=n.dropContainerKey,u=n.dropTargetKey,f=n.dropPosition,p=n.dragOverNodeKey,m=n.indent,h=this.props,g=h.prefixCls,v=h.className,y=h.style,b=h.showLine,w=h.focusable,k=h.tabIndex,S=h.selectable,N=h.showIcon,$=h.icon,K=h.switcherIcon,O=h.draggable,R=h.checkable,I=h.checkStrictly,P=h.disabled,T=h.motion,M=h.loadData,D=h.filterTreeNode,L=h.height,j=h.itemHeight,B=h.scrollWidth,H=h.virtual,A=h.titleRender,W=h.dropIndicatorRender,F=h.onContextMenu,_=h.onScroll,q=h.direction,V=h.rootClassName,U=h.rootStyle,X=(0,z.default)(this.props,{aria:!0,data:!0});O&&(e="object"===(0,x.default)(O)?O:"function"==typeof O?{nodeDraggable:O}:{});var G={prefixCls:g,selectable:S,showIcon:N,icon:$,switcherIcon:K,draggable:e,draggingNodeKey:a,checkable:R,checkStrictly:I,disabled:P,keyEntities:o,dropLevelOffset:d,dropContainerKey:c,dropTargetKey:u,dropPosition:f,dragOverNodeKey:p,indent:m,direction:q,dropIndicatorRender:W,loadData:M,filterTreeNode:D,titleRender:A,onNodeClick:this.onNodeClick,onNodeDoubleClick:this.onNodeDoubleClick,onNodeExpand:this.onNodeExpand,onNodeSelect:this.onNodeSelect,onNodeCheck:this.onNodeCheck,onNodeLoad:this.onNodeLoad,onNodeMouseEnter:this.onNodeMouseEnter,onNodeMouseLeave:this.onNodeMouseLeave,onNodeContextMenu:this.onNodeContextMenu,onNodeDragStart:this.onNodeDragStart,onNodeDragEnter:this.onNodeDragEnter,onNodeDragOver:this.onNodeDragOver,onNodeDragLeave:this.onNodeDragLeave,onNodeDragEnd:this.onNodeDragEnd,onNodeDrop:this.onNodeDrop};return t.createElement(eH.Provider,{value:G},t.createElement("div",{className:(0,E.default)(g,v,V,(0,C.default)((0,C.default)((0,C.default)({},"".concat(g,"-show-line"),b),"".concat(g,"-focused"),r),"".concat(g,"-active-focused"),null!==i)),style:U},t.createElement(t3,(0,s.default)({ref:this.listRef,prefixCls:g,style:y,data:l,disabled:P,selectable:S,checkable:!!R,motion:T,dragging:null!==a,height:L,itemHeight:j,virtual:H,focusable:w,focused:r,tabIndex:void 0===k?0:k,activeItem:this.getActiveItem(),onFocus:this.onFocus,onBlur:this.onBlur,onKeyDown:this.onKeyDown,onActiveChange:this.onActiveChange,onListChangeStart:this.onListChangeStart,onListChangeEnd:this.onListChangeEnd,onContextMenu:F,onScroll:_,scrollWidth:B},this.getTreeNodeRequiredProps(),X))))}}],[{key:"getDerivedStateFromProps",value:function(e,t){var n,r,l=t.prevProps,o={prevProps:e};function a(t){return!l&&e.hasOwnProperty(t)||l&&l[t]!==e[t]}var i=t.fieldNames;if(a("fieldNames")&&(o.fieldNames=i=eV(e.fieldNames)),a("treeData")?n=e.treeData:a("children")&&((0,N.default)(!1,"`children` of Tree is deprecated. 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+          &${t}-cell-fix-left:hover,
+          &${t}-cell-fix-right:hover
+        `]:{background:e.tableFixedHeaderSortActiveBg}},[`${t}-thead th${t}-column-sort`]:{background:e.tableHeaderSortBg,"&::before":{backgroundColor:"transparent !important"}},[`td${t}-column-sort`]:{background:e.tableBodySortBg},[`${t}-column-title`]:{position:"relative",zIndex:1,flex:1,minWidth:0},[`${t}-column-sorters`]:{display:"flex",flex:"auto",alignItems:"center",justifyContent:"space-between","&::after":{position:"absolute",inset:0,width:"100%",height:"100%",content:'""'}},[`${t}-column-sorters-tooltip-target-sorter`]:{"&::after":{content:"none"}},[`${t}-column-sorter`]:{marginInlineStart:n,color:l,fontSize:0,transition:`color ${e.motionDurationSlow}`,"&-inner":{display:"inline-flex",flexDirection:"column",alignItems:"center"},"&-up, &-down":{fontSize:r,"&.active":{color:e.colorPrimary}},[`${t}-column-sorter-up + ${t}-column-sorter-down`]:{marginTop:"-0.3em"}},[`${t}-column-sorters:hover ${t}-column-sorter`]:{color:o}}}})(D),(e=>{let{componentCls:t,antCls:n,iconCls:r,tableFilterDropdownWidth:l,tableFilterDropdownSearchWidth:o,paddingXXS:a,paddingXS:i,colorText:d,lineWidth:c,lineType:u,tableBorderColor:s,headerIconColor:f,fontSizeSM:p,tablePaddingHorizontal:m,borderRadius:h,motionDurationSlow:g,colorIcon:v,colorPrimary:y,tableHeaderFilterActiveBg:b,colorTextDisabled:x,tableFilterDropdownBg:w,tableFilterDropdownHeight:C,controlItemBgHover:E,controlItemBgActive:k,boxShadowSecondary:S,filterDropdownMenuBg:N,calc:$}=e,K=`${n}-dropdown`,O=`${t}-filter-dropdown`,R=`${n}-tree`,I=`${(0,no.unit)(c)} ${u} ${s}`;return[{[`${t}-wrapper`]:{[`${t}-filter-column`]:{display:"flex",justifyContent:"space-between"},[`${t}-filter-trigger`]:{position:"relative",display:"flex",alignItems:"center",marginBlock:$(a).mul(-1).equal(),marginInline:`${(0,no.unit)(a)} ${(0,no.unit)($(m).div(2).mul(-1).equal())}`,padding:`0 ${(0,no.unit)(a)}`,color:f,fontSize:p,borderRadius:h,cursor:"pointer",transition:`all ${g}`,"&:hover":{color:v,background:b},"&.active":{color:y}}}},{[`${n}-dropdown`]:{[O]:Object.assign(Object.assign({},(0,ni.resetComponent)(e)),{minWidth:l,backgroundColor:w,borderRadius:h,boxShadow:S,overflow:"hidden",[`${K}-menu`]:{maxHeight:C,overflowX:"hidden",border:0,boxShadow:"none",borderRadius:"unset",backgroundColor:N,"&:empty::after":{display:"block",padding:`${(0,no.unit)(i)} 0`,color:x,fontSize:p,textAlign:"center",content:'"Not Found"'}},[`${O}-tree`]:{paddingBlock:`${(0,no.unit)(i)} 0`,paddingInline:i,[R]:{padding:0},[`${R}-treenode ${R}-node-content-wrapper:hover`]:{backgroundColor:E},[`${R}-treenode-checkbox-checked ${R}-node-content-wrapper`]:{"&, &:hover":{backgroundColor:k}}},[`${O}-search`]:{padding:i,borderBottom:I,"&-input":{input:{minWidth:o},[r]:{color:x}}},[`${O}-checkall`]:{width:"100%",marginBottom:a,marginInlineStart:a},[`${O}-btns`]:{display:"flex",justifyContent:"space-between",padding:`${(0,no.unit)($(i).sub(c).equal())} ${(0,no.unit)(i)}`,overflow:"hidden",borderTop:I}})}},{[`${n}-dropdown ${O}, ${O}-submenu`]:{[`${n}-checkbox-wrapper + span`]:{paddingInlineStart:i,color:d},"> ul":{maxHeight:"calc(100vh - 130px)",overflowX:"hidden",overflowY:"auto"}}}]})(D),(e=>{let{componentCls:t,lineWidth:n,lineType:r,tableBorderColor:l,tableHeaderBg:o,tablePaddingVertical:a,tablePaddingHorizontal:i,calc:d}=e,c=`${(0,no.unit)(n)} ${r} ${l}`,u=(e,r,l)=>({[`&${t}-${e}`]:{[`> ${t}-container`]:{[`> ${t}-content, > ${t}-body`]:{[`
+            > table > tbody > tr > th,
+            > table > tbody > tr > td
+          `]:{[`> ${t}-expanded-row-fixed`]:{margin:`${(0,no.unit)(d(r).mul(-1).equal())}
+              ${(0,no.unit)(d(d(l).add(n)).mul(-1).equal())}`}}}}}});return{[`${t}-wrapper`]:{[`${t}${t}-bordered`]:Object.assign(Object.assign(Object.assign({[`> ${t}-title`]:{border:c,borderBottom:0},[`> ${t}-container`]:{borderInlineStart:c,borderTop:c,[`
+            > ${t}-content,
+            > ${t}-header,
+            > ${t}-body,
+            > ${t}-summary
+          `]:{"> table":{[`
+                > thead > tr > th,
+                > thead > tr > td,
+                > tbody > tr > th,
+                > tbody > tr > td,
+                > tfoot > tr > th,
+                > tfoot > tr > td
+              `]:{borderInlineEnd:c},"> thead":{"> tr:not(:last-child) > th":{borderBottom:c},"> tr > th::before":{backgroundColor:"transparent !important"}},[`
+                > thead > tr,
+                > tbody > tr,
+                > tfoot > tr
+              `]:{[`> ${t}-cell-fix-right-first::after`]:{borderInlineEnd:c}},[`
+                > tbody > tr > th,
+                > tbody > tr > td
+              `]:{[`> ${t}-expanded-row-fixed`]:{margin:`${(0,no.unit)(d(a).mul(-1).equal())} ${(0,no.unit)(d(d(i).add(n)).mul(-1).equal())}`,"&::after":{position:"absolute",top:0,insetInlineEnd:n,bottom:0,borderInlineEnd:c,content:'""'}}}}}},[`&${t}-scroll-horizontal`]:{[`> ${t}-container > ${t}-body`]:{"> table > tbody":{[`
+                > tr${t}-expanded-row,
+                > tr${t}-placeholder
+              `]:{"> th, > td":{borderInlineEnd:0}}}}}},u("middle",e.tablePaddingVerticalMiddle,e.tablePaddingHorizontalMiddle)),u("small",e.tablePaddingVerticalSmall,e.tablePaddingHorizontalSmall)),{[`> ${t}-footer`]:{border:c,borderTop:0}}),[`${t}-cell`]:{[`${t}-container:first-child`]:{borderTop:0},"&-scrollbar:not([rowspan])":{boxShadow:`0 ${(0,no.unit)(n)} 0 ${(0,no.unit)(n)} ${o}`}},[`${t}-bordered ${t}-cell-scrollbar`]:{borderInlineEnd:c}}}})(D),(e=>{let{componentCls:t,tableRadius:n}=e;return{[`${t}-wrapper`]:{[t]:{[`${t}-title, ${t}-header`]:{borderRadius:`${(0,no.unit)(n)} ${(0,no.unit)(n)} 0 0`},[`${t}-title + ${t}-container`]:{borderStartStartRadius:0,borderStartEndRadius:0,[`${t}-header, table`]:{borderRadius:0},"table > thead > tr:first-child":{"th:first-child, th:last-child, td:first-child, td:last-child":{borderRadius:0}}},"&-container":{borderStartStartRadius:n,borderStartEndRadius:n,"table > thead > tr:first-child":{"> *:first-child":{borderStartStartRadius:n},"> *:last-child":{borderStartEndRadius:n}}},"&-footer":{borderRadius:`0 0 ${(0,no.unit)(n)} ${(0,no.unit)(n)}`}}}}})(D),(e=>{let{componentCls:t,antCls:n,motionDurationSlow:r,lineWidth:l,paddingXS:o,lineType:a,tableBorderColor:i,tableExpandIconBg:d,tableExpandColumnWidth:c,borderRadius:u,tablePaddingVertical:s,tablePaddingHorizontal:f,tableExpandedRowBg:p,paddingXXS:m,expandIconMarginTop:h,expandIconSize:g,expandIconHalfInner:v,expandIconScale:y,calc:b}=e,x=`${(0,no.unit)(l)} ${a} ${i}`,w=b(m).sub(l).equal();return{[`${t}-wrapper`]:{[`${t}-expand-icon-col`]:{width:c},[`${t}-row-expand-icon-cell`]:{textAlign:"center",[`${t}-row-expand-icon`]:{display:"inline-flex",float:"none",verticalAlign:"sub"}},[`${t}-row-indent`]:{height:1,float:"left"},[`${t}-row-expand-icon`]:Object.assign(Object.assign({},(0,ni.operationUnit)(e)),{position:"relative",float:"left",width:g,height:g,color:"inherit",lineHeight:(0,no.unit)(g),background:d,border:x,borderRadius:u,transform:`scale(${y})`,"&:focus, &:hover, &:active":{borderColor:"currentcolor"},"&::before, &::after":{position:"absolute",background:"currentcolor",transition:`transform ${r} ease-out`,content:'""'},"&::before":{top:v,insetInlineEnd:w,insetInlineStart:w,height:l},"&::after":{top:w,bottom:w,insetInlineStart:v,width:l,transform:"rotate(90deg)"},"&-collapsed::before":{transform:"rotate(-180deg)"},"&-collapsed::after":{transform:"rotate(0deg)"},"&-spaced":{"&::before, &::after":{display:"none",content:"none"},background:"transparent",border:0,visibility:"hidden"}}),[`${t}-row-indent + ${t}-row-expand-icon`]:{marginTop:h,marginInlineEnd:o},[`tr${t}-expanded-row`]:{"&, &:hover":{"> th, > td":{background:p}},[`${n}-descriptions-view`]:{display:"flex",table:{flex:"auto",width:"100%"}}},[`${t}-expanded-row-fixed`]:{position:"relative",margin:`${(0,no.unit)(b(s).mul(-1).equal())} ${(0,no.unit)(b(f).mul(-1).equal())}`,padding:`${(0,no.unit)(s)} ${(0,no.unit)(f)}`}}}})(D),n6(D),(e=>{let{componentCls:t}=e;return{[`${t}-wrapper`]:{[`${t}-tbody > tr${t}-placeholder`]:{textAlign:"center",color:e.colorTextDisabled,[`
+          &:hover > th,
+          &:hover > td,
+        `]:{background:e.colorBgContainer}}}}})(D),(e=>{let{componentCls:t,antCls:n,iconCls:r,fontSizeIcon:l,padding:o,paddingXS:a,headerIconColor:i,headerIconHoverColor:d,tableSelectionColumnWidth:c,tableSelectedRowBg:u,tableSelectedRowHoverBg:s,tableRowHoverBg:f,tablePaddingHorizontal:p,calc:m}=e;return{[`${t}-wrapper`]:{[`${t}-selection-col`]:{width:c,[`&${t}-selection-col-with-dropdown`]:{width:m(c).add(l).add(m(o).div(4)).equal()}},[`${t}-bordered ${t}-selection-col`]:{width:m(c).add(m(a).mul(2)).equal(),[`&${t}-selection-col-with-dropdown`]:{width:m(c).add(l).add(m(o).div(4)).add(m(a).mul(2)).equal()}},[`
+        table tr th${t}-selection-column,
+        table tr td${t}-selection-column,
+        ${t}-selection-column
+      `]:{paddingInlineEnd:e.paddingXS,paddingInlineStart:e.paddingXS,textAlign:"center",[`${n}-radio-wrapper`]:{marginInlineEnd:0}},[`table tr th${t}-selection-column${t}-cell-fix-left`]:{zIndex:m(e.zIndexTableFixed).add(1).equal({unit:!1})},[`table tr th${t}-selection-column::after`]:{backgroundColor:"transparent !important"},[`${t}-selection`]:{position:"relative",display:"inline-flex",flexDirection:"column"},[`${t}-selection-extra`]:{position:"absolute",top:0,zIndex:1,cursor:"pointer",transition:`all ${e.motionDurationSlow}`,marginInlineStart:"100%",paddingInlineStart:(0,no.unit)(m(p).div(4).equal()),[r]:{color:i,fontSize:l,verticalAlign:"baseline","&:hover":{color:d}}},[`${t}-tbody`]:{[`${t}-row`]:{[`&${t}-row-selected`]:{[`> ${t}-cell`]:{background:u,"&-row-hover":{background:s}}},[`> ${t}-cell-row-hover`]:{background:f}}}}}})(D),(e=>{let{componentCls:t,lineWidth:n,colorSplit:r,motionDurationSlow:l,zIndexTableFixed:o,tableBg:a,zIndexTableSticky:i,calc:d}=e;return{[`${t}-wrapper`]:{[`
+        ${t}-cell-fix-left,
+        ${t}-cell-fix-right
+      `]:{position:"sticky !important",zIndex:o,background:a},[`
+        ${t}-cell-fix-left-first::after,
+        ${t}-cell-fix-left-last::after
+      `]:{position:"absolute",top:0,right:{_skip_check_:!0,value:0},bottom:d(n).mul(-1).equal(),width:30,transform:"translateX(100%)",transition:`box-shadow ${l}`,content:'""',pointerEvents:"none",willChange:"transform"},[`${t}-cell-fix-left-all::after`]:{display:"none"},[`
+        ${t}-cell-fix-right-first::after,
+        ${t}-cell-fix-right-last::after
+      `]:{position:"absolute",top:0,bottom:d(n).mul(-1).equal(),left:{_skip_check_:!0,value:0},width:30,transform:"translateX(-100%)",transition:`box-shadow ${l}`,content:'""',pointerEvents:"none"},[`${t}-container`]:{position:"relative","&::before, &::after":{position:"absolute",top:0,bottom:0,zIndex:d(i).add(1).equal({unit:!1}),width:30,transition:`box-shadow ${l}`,content:'""',pointerEvents:"none"},"&::before":{insetInlineStart:0},"&::after":{insetInlineEnd:0}},[`${t}-ping-left`]:{[`&:not(${t}-has-fix-left) ${t}-container::before`]:{boxShadow:`inset 10px 0 8px -8px ${r}`},[`
+          ${t}-cell-fix-left-first::after,
+          ${t}-cell-fix-left-last::after
+        `]:{boxShadow:`inset 10px 0 8px -8px ${r}`},[`${t}-cell-fix-left-last::before`]:{backgroundColor:"transparent !important"}},[`${t}-ping-right`]:{[`&:not(${t}-has-fix-right) ${t}-container::after`]:{boxShadow:`inset -10px 0 8px -8px ${r}`},[`
+          ${t}-cell-fix-right-first::after,
+          ${t}-cell-fix-right-last::after
+        `]:{boxShadow:`inset -10px 0 8px -8px ${r}`}},[`${t}-fixed-column-gapped`]:{[`
+        ${t}-cell-fix-left-first::after,
+        ${t}-cell-fix-left-last::after,
+        ${t}-cell-fix-right-first::after,
+        ${t}-cell-fix-right-last::after
+      `]:{boxShadow:"none"}}}}})(D),(e=>{let{componentCls:t,opacityLoading:n,tableScrollThumbBg:r,tableScrollThumbBgHover:l,tableScrollThumbSize:o,tableScrollBg:a,zIndexTableSticky:i,stickyScrollBarBorderRadius:d,lineWidth:c,lineType:u,tableBorderColor:s}=e,f=`${(0,no.unit)(c)} ${u} ${s}`;return{[`${t}-wrapper`]:{[`${t}-sticky`]:{"&-holder":{position:"sticky",zIndex:i,background:e.colorBgContainer},"&-scroll":{position:"sticky",bottom:0,height:`${(0,no.unit)(o)} !important`,zIndex:i,display:"flex",alignItems:"center",background:a,borderTop:f,opacity:n,"&:hover":{transformOrigin:"center bottom"},"&-bar":{height:o,backgroundColor:r,borderRadius:d,transition:`all ${e.motionDurationSlow}, transform 0s`,position:"absolute",bottom:0,"&:hover, &-active":{backgroundColor:l}}}}}}})(D),(e=>{let{componentCls:t}=e;return{[`${t}-wrapper`]:{[`${t}-cell-ellipsis`]:Object.assign(Object.assign({},ni.textEllipsis),{wordBreak:"keep-all",[`
+          &${t}-cell-fix-left-last,
+          &${t}-cell-fix-right-first
+        `]:{overflow:"visible",[`${t}-cell-content`]:{display:"block",overflow:"hidden",textOverflow:"ellipsis"}},[`${t}-column-title`]:{overflow:"hidden",textOverflow:"ellipsis",wordBreak:"keep-all"}})}}})(D),(e=>{let{componentCls:t,tableExpandColumnWidth:n,calc:r}=e,l=(e,l,o,a)=>({[`${t}${t}-${e}`]:{fontSize:a,[`
+        ${t}-title,
+        ${t}-footer,
+        ${t}-cell,
+        ${t}-thead > tr > th,
+        ${t}-tbody > tr > th,
+        ${t}-tbody > tr > td,
+        tfoot > tr > th,
+        tfoot > tr > td
+      `]:{padding:`${(0,no.unit)(l)} ${(0,no.unit)(o)}`},[`${t}-filter-trigger`]:{marginInlineEnd:(0,no.unit)(r(o).div(2).mul(-1).equal())},[`${t}-expanded-row-fixed`]:{margin:`${(0,no.unit)(r(l).mul(-1).equal())} ${(0,no.unit)(r(o).mul(-1).equal())}`},[`${t}-tbody`]:{[`${t}-wrapper:only-child ${t}`]:{marginBlock:(0,no.unit)(r(l).mul(-1).equal()),marginInline:`${(0,no.unit)(r(n).sub(o).equal())} ${(0,no.unit)(r(o).mul(-1).equal())}`}},[`${t}-selection-extra`]:{paddingInlineStart:(0,no.unit)(r(o).div(4).equal())}}});return{[`${t}-wrapper`]:Object.assign(Object.assign({},l("middle",e.tablePaddingVerticalMiddle,e.tablePaddingHorizontalMiddle,e.tableFontSizeMiddle)),l("small",e.tablePaddingVerticalSmall,e.tablePaddingHorizontalSmall,e.tableFontSizeSmall))}})(D),(e=>{let{componentCls:t}=e;return{[`${t}-wrapper-rtl`]:{direction:"rtl",table:{direction:"rtl"},[`${t}-pagination-left`]:{justifyContent:"flex-end"},[`${t}-pagination-right`]:{justifyContent:"flex-start"},[`${t}-row-expand-icon`]:{float:"right","&::after":{transform:"rotate(-90deg)"},"&-collapsed::before":{transform:"rotate(180deg)"},"&-collapsed::after":{transform:"rotate(0deg)"}},[`${t}-container`]:{"&::before":{insetInlineStart:"unset",insetInlineEnd:0},"&::after":{insetInlineStart:0,insetInlineEnd:"unset"},[`${t}-row-indent`]:{float:"right"}}}}})(D),(e=>{let{componentCls:t,motionDurationMid:n,lineWidth:r,lineType:l,tableBorderColor:o,calc:a}=e,i=`${(0,no.unit)(r)} ${l} ${o}`,d=`${t}-expanded-row-cell`;return{[`${t}-wrapper`]:{[`${t}-tbody-virtual`]:{[`${t}-tbody-virtual-holder-inner`]:{[`
+            & > ${t}-row, 
+            & > div:not(${t}-row) > ${t}-row
+          `]:{display:"flex",boxSizing:"border-box",width:"100%"}},[`${t}-cell`]:{borderBottom:i,transition:`background ${n}`},[`${t}-expanded-row`]:{[`${d}${d}-fixed`]:{position:"sticky",insetInlineStart:0,overflow:"hidden",width:`calc(var(--virtual-width) - ${(0,no.unit)(r)})`,borderInlineEnd:"none"}}},[`${t}-bordered`]:{[`${t}-tbody-virtual`]:{"&:after":{content:'""',insetInline:0,bottom:0,borderBottom:i,position:"absolute"},[`${t}-cell`]:{borderInlineEnd:i,[`&${t}-cell-fix-right-first:before`]:{content:'""',position:"absolute",insetBlock:0,insetInlineStart:a(r).mul(-1).equal(),borderInlineStart:i}}},[`&${t}-virtual`]:{[`${t}-placeholder 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deleted file mode 100644
index bc806426f15..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/dc8a270fee94ced6.js b/litellm/proxy/_experimental/out/_next/static/chunks/92c3c06057498511.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/dc8a270fee94ced6.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/92c3c06057498511.js
index 397aec370ea..6197c154387 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/dc8a270fee94ced6.js
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deleted file mode 100644
index 36b3d45064d..00000000000
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/96616c4e8f4c2b15.js b/litellm/proxy/_experimental/out/_next/static/chunks/96616c4e8f4c2b15.js
deleted file mode 100644
index 9ea18774b7e..00000000000
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+++ /dev/null
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deleted file mode 100644
index cc852bfb274..00000000000
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-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.chat.completions.create(
-    model="${I}",
-    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="${I}",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {
-#                     "type": "text",
-#                     "text": "${N}"
-#                 },
-#                 {
-#                     "type": "image_url",
-#                     "image_url": {
-#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
-#                     }
-#                 }
-#             ]
-#         }
-#     ]${i}
-# )
-# print(response_with_file)
-`;break}case s.RESPONSES:{let e=Object.keys(k).length>0,i="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`,
-    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content: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="${I}",
-    input=${JSON.stringify(a,null,4)}${i}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.responses.create(
-#     model="${I}",
-#     input=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {"type": "input_text", "text": "${N}"},
-#                 {
-#                     "type": "input_image",
-#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
-#                 },
-#             ],
-#         }
-#     ]${i}
-# )
-# print(response_with_file.output_text)
-`;break}case s.IMAGE:t="azure"===x?`
-# 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="${I}",
-	prompt="${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 = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${I}",
-	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 s.IMAGE_EDITS:t="azure"===x?`
-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 = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${I}",
-	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 = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${I}",
-	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 s.EMBEDDINGS:t=`
-response = client.embeddings.create(
-	input="${n||"Your string here"}",
-	model="${I}",
-	encoding_format="base64" # or "float"
-)
-
-print(response.data[0].embedding)
-`;break;case s.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="${I}",
-	file=audio_file${n?`,
-	prompt="${n.replace(/"/g,'\\"')}"`:""}
-)
-
-print(response.text)
-`;break;case s.SPEECH:t=`
-# Make the text-to-speech request
-response = client.audio.speech.create(
-	model="${I}",
-	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="${I}",
-#     input="${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`${T}
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/a7f104aa2cc7f3f0.js b/litellm/proxy/_experimental/out/_next/static/chunks/a7f104aa2cc7f3f0.js
deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/a85adee4198d5478.js b/litellm/proxy/_experimental/out/_next/static/chunks/a85adee4198d5478.js
deleted file mode 100644
index b26eb42429b..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/a85adee4198d5478.js
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-llm = AzureOpenAI(
-    engine="azure-gpt-3.5",               # model_name on litellm proxy
-    temperature=0.0,
-    azure_endpoint="${d}", # litellm proxy endpoint
-    api_key="sk-1234",                    # litellm proxy API Key
-    api_version="2023-07-01-preview",
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-from langchain.schema import HumanMessage, SystemMessage
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-chat = ChatOpenAI(
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-    model = "gpt-3.5-turbo",
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-messages = [
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-    ),
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-
--H 'Authorization: Bearer <your-master-key>' \\
-
--H 'Content-Type: application/json' \\
-
--d '{"user_id": "my-customer-id', "budget_id": "<BUDGET_ID>"}' # 👈 KEY CHANGE
-
-`,D=`
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-
--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
-
-`,M=`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"
-)
-
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new file mode 100644
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console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:a,accessToken:i,apiKey:r,inputMessage:o,chatHistory:n,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:p,selectedMCPServers:m,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:x,proxySettings:v}=e,b="session"===a?i:r,y=window.location.origin,j=v?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:v?.PROXY_BASE_URL&&(y=v.PROXY_BASE_URL);let w=o||"Your prompt here",N=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),E=n.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),k={};l.length>0&&(k.tags=l),c.length>0&&(k.vector_stores=c),d.length>0&&(k.guardrails=d),p.length>0&&(k.policies=p);let I=_||"your-model-name",C="azure"===x?`import openai
+
+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(h){case s.CHAT:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=E.length>0?E:[{role:"user",content:w}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${I}",
+    messages=${JSON.stringify(i,null,4)}${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="${I}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${N}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${a}
+# )
+# print(response_with_file)
+`;break}case s.RESPONSES:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=E.length>0?E:[{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="${I}",
+    input=${JSON.stringify(i,null,4)}${a}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${I}",
+#     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 s.IMAGE:t="azure"===x?`
+# 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="${I}",
+	prompt="${o}",
+	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 = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${I}",
+	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 s.IMAGE_EDITS:t="azure"===x?`
+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 = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${I}",
+	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 = "${N}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${I}",
+	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 s.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${o||"Your string here"}",
+	model="${I}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case s.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="${I}",
+	file=audio_file${o?`,
+	prompt="${o.replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case s.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${I}",
+	input="${o||"Your text to convert to speech here"}",
+	voice="${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="${I}",
+#     input="${o||"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`${C}
+${t}`}],190272)},596239,e=>{"use strict";e.i(247167);var t=e.i(931067),a=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 s=e.i(9583),r=a.forwardRef(function(e,r){return 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new file mode 100644
index 00000000000..c28fe5fbfdb
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new file mode 100644
index 00000000000..94b57a27137
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--- a/litellm/proxy/_experimental/out/_next/static/chunks/ba42d2587315d00e.js
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new file mode 100644
index 00000000000..9639d3dd6e3
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new file mode 100644
index 00000000000..70f4d64388d
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eO;eN(this,c,"m",T).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",T).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 eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eT("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 eT("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||e_(this,d,void 0,"f")},C=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":tI(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")}},T=function(){if(this.ended)throw new eT("stream has ended, this shouldn't happen");let e=eN(this,d,"f");if(!e)throw new eT("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 eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`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&&tI(s)){let t=s[tR]||"";if(Object.defineProperty(s,tR,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tP(t)}catch(s){let e=new eT(`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 tC(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tC,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 tz="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tW{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),H.set(this,!1),W.set(this,void 0),F.set(this,void 0),V.set(this,e=>{if(e_(this,q,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eT)return this._emit("error",e);if(e instanceof Error){let t=new eT(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eT(String(e)))}),e_(this,R,new Promise((e,t)=>{e_(this,I,e,"f"),e_(this,M,t,"f")}),"f"),e_(this,L,new Promise((e,t)=>{e_(this,$,e,"f"),e_(this,U,t,"f")}),"f"),eN(this,R,"f").catch(()=>{}),eN(this,L,"f").catch(()=>{})}get response(){return eN(this,W,"f")}get request_id(){return eN(this,F,"f")}async withResponse(){let e=await eN(this,R,"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 tW;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tW;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,W,e,"f"),e_(this,F,e?.headers.get("request-id"),"f"),eN(this,I,"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,z,"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,H,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,H,!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,$,"f").call(this));let s=eN(this,D,"f")[e];if(s&&(eN(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eN(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this),this._connected(null);let r=te.fromReadableStream(e,this.controller);for await(let e of r)eN(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,H=new WeakMap,W=new WeakMap,F=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eT("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 eT("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":tH(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 eT("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eT("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 eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`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&&tH(s)){let t=s[tz]||"";Object.defineProperty(s,tz,{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 eT(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]}
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deleted file mode 100644
index 303d58e5cf1..00000000000
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this._client.delete(tC`/v1/messages/batches/${e}?beta=true`,{...s,headers:tk([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(tC`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:tk([{"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 eO(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:tk([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tO.fromResponse(t.response,t.controller))}}let tR=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tR(e=e.slice(0,e.length-1));case"number":let 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fromReadableStream(e){let t=new t$;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new t$;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")},eE(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())),eE(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))eE(this,c,"m",C).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,c,"m",T).call(this)}_connected(e){this.ended||(ek(this,j,e,"f"),ek(this,S,e?.headers.get("request-id"),"f"),eE(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,x,"f")}get errored(){return eE(this,b,"f")}get aborted(){return eE(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(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(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,w,!0,"f"),await eE(this,p,"f")}get currentMessage(){return eE(this,d,"f")}async finalMessage(){return await this.done(),eE(this,c,"m",_).call(this)}async finalText(){return await 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eR;eE(this,c,"m",T).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 eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eO("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 eO("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||ek(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eE(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":tL(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:tU(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":ek(this,d,t,"f")}},T=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,d,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,d,void 0,"f"),e},A=function(e){let t=eE(this,d,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`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&&tL(s)){let t=s[tM]||"";if(Object.defineProperty(s,tM,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tI(t)}catch(s){let e=new eO(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eE(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tU(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tU(e){}let tD={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tB={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tq extends tS{constructor(){super(...arguments),this.batches=new tP(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tB&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tB[r.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tD[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return t$.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:tk([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tq.Batches=tP;class tz extends tS{constructor(){super(...arguments),this.models=new tA(this._client),this.messages=new tq(this._client),this.files=new tT(this._client)}}tz.Models=tA,tz.Messages=tq,tz.Files=tT;class tW extends tS{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tF="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tJ{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),W.set(this,!1),F.set(this,void 0),H.set(this,void 0),V.set(this,e=>{if(ek(this,q,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,R,new Promise((e,t)=>{ek(this,I,e,"f"),ek(this,M,t,"f")}),"f"),ek(this,L,new Promise((e,t)=>{ek(this,$,e,"f"),ek(this,U,t,"f")}),"f"),eE(this,R,"f").catch(()=>{}),eE(this,L,"f").catch(()=>{})}get response(){return eE(this,F,"f")}get request_id(){return eE(this,H,"f")}async withResponse(){let e=await eE(this,R,"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 tJ;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tJ;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")},eE(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())),eE(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))eE(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}_connected(e){this.ended||(ek(this,F,e,"f"),ek(this,H,e?.headers.get("request-id"),"f"),eE(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,B,"f")}get errored(){return eE(this,q,"f")}get aborted(){return eE(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(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(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,W,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,W,!0,"f"),await eE(this,L,"f")}get currentMessage(){return eE(this,P,"f")}async finalMessage(){return await this.done(),eE(this,O,"m",J).call(this)}async finalText(){return await this.done(),eE(this,O,"m",G).call(this)}_emit(e,...t){if(eE(this,B,"f"))return;"end"===e&&(ek(this,B,!0,"f"),eE(this,$,"f").call(this));let s=eE(this,D,"f")[e];if(s&&(eE(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eE(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this),this._connected(null);let r=ts.fromReadableStream(e,this.controller);for await(let e of r)eE(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,W=new WeakMap,F=new WeakMap,H=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eO("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 eO("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||ek(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eE(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":tH(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:tG(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":ek(this,P,t,"f")}},Y=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,P,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,P,void 0,"f"),e},Q=function(e){let t=eE(this,P,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return 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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/cac89fc12fb6ef7e.js b/litellm/proxy/_experimental/out/_next/static/chunks/cac89fc12fb6ef7e.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6a167cef4b09b496.js b/litellm/proxy/_experimental/out/_next/static/chunks/caf98722823e1b40.js
similarity index 72%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6a167cef4b09b496.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/caf98722823e1b40.js
index 5aa2036c621..29972b2ccd1 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/6a167cef4b09b496.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/caf98722823e1b40.js
@@ -1,8 +1,8 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/cb86c3ef30e0cf21.js b/litellm/proxy/_experimental/out/_next/static/chunks/cb86c3ef30e0cf21.js
deleted file mode 100644
index 81a9ec3ba53..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/cb86c3ef30e0cf21.js
+++ /dev/null
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textarea[disabled]":{cursor:"not-allowed"}}),$(e,{status:"error",borderColor:e.colorError,hoverBorderColor:e.colorErrorBorderHover,activeBorderColor:e.colorError,activeShadow:e.errorActiveShadow,affixColor:e.colorError})),$(e,{status:"warning",borderColor:e.colorWarning,hoverBorderColor:e.colorWarningBorderHover,activeBorderColor:e.colorWarning,activeShadow:e.warningActiveShadow,affixColor:e.colorWarning})),t)});e.s(["genBaseOutlinedStyle",0,c,"genBorderlessStyle",0,m,"genDisabledStyle",0,s,"genFilledGroupStyle",0,b,"genFilledStyle",0,v,"genOutlinedGroupStyle",0,p,"genOutlinedStyle",0,d,"genUnderlinedStyle",0,C],889943);let x=e=>({"&::-moz-placeholder":{opacity:1},"&::placeholder":{color:e,userSelect:"none"},"&:placeholder-shown":{textOverflow:"ellipsis"}}),E=e=>{let{paddingBlockLG:r,lineHeightLG:o,borderRadiusLG:n,paddingInlineLG:a}=e;return{padding:`${(0,t.unit)(r)} 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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,
+      & > ${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},[`& > ${o}`]:{borderStartStartRadius:e.borderRadius,borderStartEndRadius:0,borderEndEndRadius:0,borderEndStartRadius:e.borderRadius}}}})}},O=(0,n.genStyleHooks)(["Input","Shared"],e=>{let 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t=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,borderRadiusLG:o,borderRadiusSM:n}=e;return{[`${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, ${t}-data-count`]:{color:e.colorError}}}})(t),(0,o.genCompactItemStyle)(t)]},l,{resetFont:!1});e.s(["default",0,T,"genBasicInputStyle",0,k,"genInputGroupStyle",0,j,"genInputSmallStyle",0,S,"genPlaceholderStyle",0,x,"useSharedStyle",0,O],349942)},831357,e=>{"use strict";var t=e.i(271645),r=e.i(343794),o=e.i(242064),n=e.i(62139),a=e.i(349942);e.s(["default",0,e=>{let{getPrefixCls:i,direction:l}=(0,t.useContext)(o.ConfigContext),{prefixCls:s,className:c}=e,u=i("input-group",s),d=i("input"),[f,p,m]=(0,a.default)(d),h=(0,r.default)(u,m,{[`${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:h,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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+        ${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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new file mode 100644
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+        ${n}-appear
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+      ${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"}},[`
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+        &${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,
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+          &-mirror
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+          ${o}-selection-item,
+          ${o}-selection-placeholder
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+        &${o}-show-arrow ${o}-selection-search,
+        &${o}-show-arrow ${o}-selection-placeholder
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+          ${c}${d}bottomLeft
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+          ${s}${d}topLeft,
+          ${c}${d}topLeft,
+          ${s}${d}topRight,
+          ${c}${d}topRight
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+          ${u}${d}topRight
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new file mode 100644
index 00000000000..df50ed3fb61
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/cecdaabafa264083.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/d104f25e5302e120.js b/litellm/proxy/_experimental/out/_next/static/chunks/d104f25e5302e120.js
new file mode 100644
index 00000000000..9bff232fc11
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/d104f25e5302e120.js
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new file mode 100644
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t,a,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),s=((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);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",()=>s,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let t=r[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:a,accessToken:i,apiKey:r,inputMessage:o,chatHistory:n,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:m,selectedMCPServers:p,mcpServers:u,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:x,proxySettings:v}=e,b="session"===a?i:r,y=window.location.origin,j=v?.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?y=j:v?.PROXY_BASE_URL&&(y=v.PROXY_BASE_URL);let w=o||"Your prompt here",N=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),E=n.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),k={};l.length>0&&(k.tags=l),c.length>0&&(k.vector_stores=c),d.length>0&&(k.guardrails=d),m.length>0&&(k.policies=m);let I=_||"your-model-name",C="azure"===x?`import openai
-
-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(h){case s.CHAT:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
-    extra_body=${e}`}let i=E.length>0?E:[{role:"user",content:w}];t=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.chat.completions.create(
-    model="${I}",
-    messages=${JSON.stringify(i,null,4)}${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="${I}",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {
-#                     "type": "text",
-#                     "text": "${N}"
-#                 },
-#                 {
-#                     "type": "image_url",
-#                     "image_url": {
-#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
-#                     }
-#                 }
-#             ]
-#         }
-#     ]${a}
-# )
-# print(response_with_file)
-`;break}case s.RESPONSES:{let e=Object.keys(k).length>0,a="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
-    extra_body=${e}`}let i=E.length>0?E:[{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="${I}",
-    input=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.responses.create(
-#     model="${I}",
-#     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 s.IMAGE:t="azure"===x?`
-# 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="${I}",
-	prompt="${o}",
-	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 = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${I}",
-	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 s.IMAGE_EDITS:t="azure"===x?`
-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 = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${I}",
-	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 = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${I}",
-	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 s.EMBEDDINGS:t=`
-response = client.embeddings.create(
-	input="${o||"Your string here"}",
-	model="${I}",
-	encoding_format="base64" # or "float"
-)
-
-print(response.data[0].embedding)
-`;break;case s.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="${I}",
-	file=audio_file${o?`,
-	prompt="${o.replace(/"/g,'\\"')}"`:""}
-)
-
-print(response.text)
-`;break;case s.SPEECH:t=`
-# Make the text-to-speech request
-response = client.audio.speech.create(
-	model="${I}",
-	input="${o||"Your text to convert to speech here"}",
-	voice="${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="${I}",
-#     input="${o||"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`${C}
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new file mode 100644
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/d63044bdf28324dd.js b/litellm/proxy/_experimental/out/_next/static/chunks/d63044bdf28324dd.js
deleted file mode 100644
index f30581888a2..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/d63044bdf28324dd.js
+++ /dev/null
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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
-
-`,P=`
-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
-
-`,M=`from openai import OpenAI
-client = OpenAI(
-  base_url="<your_proxy_base_url>",
-  api_key="<your_proxy_key>"
-)
-
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-  model="gpt-3.5-turbo",
-  messages=[
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Hello!"}
-  ],
-  user="my-customer-id"
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etc.)"===e)return"gemini-pro";if("Anthropic"==e)return"claude-3-opus";if("Amazon Bedrock"==e)return"claude-3-opus";if("AWS SageMaker"==e)return"sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b";else if("Google AI Studio"==e)return"gemini-pro";else if("Azure AI Foundry (Studio)"==e)return"azure_ai/command-r-plus";else if("Azure"==e)return"my-deployment";else if("Oracle Cloud Infrastructure (OCI)"==e)return"oci/xai.grok-4";else if("Snowflake"==e)return"snowflake/mistral-7b";else if("Voyage AI"==e)return"voyage/";else if("Jina AI"==e)return"jina_ai/";else if("VolcEngine"==e)return"volcengine/<any-model-on-volcengine>";else if("DeepInfra"==e)return"deepinfra/<any-model-on-deepinfra>";else if("Fal AI"==e)return"fal_ai/fal-ai/flux-pro/v1.1-ultra";else if("RunwayML"==e)return"runwayml/gen4_turbo";else if("Watsonx"===e)return"watsonx/ibm/granite-3-3-8b-instruct";else if("Cursor"===e)return"cursor/claude-4-sonnet";else return"gpt-3.5-turbo"},"getProviderLogoAndName",0,e=>{if(!e)return{logo:"",displayName:"-"};if("gemini"===e.toLowerCase()){let e="Google AI Studio";return{logo:r[e],displayName:e}}let t=Object.keys(i).find(t=>i[t].toLowerCase()===e.toLowerCase());if(!t)return{logo:"",displayName:e};let o=a[t];return{logo:r[o],displayName:o}},"getProviderModels",0,(e,t)=>{console.log(`Provider key: ${e}`);let a=i[e];console.log(`Provider mapped to: ${a}`);let o=[];return e&&"object"==typeof t&&(Object.entries(t).forEach(([e,t])=>{if(null!==t&&"object"==typeof t&&"litellm_provider"in t){let i=t.litellm_provider;(i===a||"string"==typeof i&&i.includes(a))&&o.push(e)}}),"Cohere"==e&&(console.log("Adding cohere chat models"),Object.entries(t).forEach(([e,t])=>{null!==t&&"object"==typeof t&&"litellm_provider"in t&&"cohere_chat"===t.litellm_provider&&o.push(e)})),"AWS SageMaker"==e&&(console.log("Adding sagemaker chat models"),Object.entries(t).forEach(([e,t])=>{null!==t&&"object"==typeof t&&"litellm_provider"in t&&"sagemaker_chat"===t.litellm_provider&&o.push(e)}))),o},"providerLogoMap",0,r,"provider_map",0,i])},818581,(e,t,a)=>{"use strict";Object.defineProperty(a,"__esModule",{value:!0}),Object.defineProperty(a,"useMergedRef",{enumerable:!0,get:function(){return o}});let i=e.r(271645);function o(e,t){let a=(0,i.useRef)(null),o=(0,i.useRef)(null);return(0,i.useCallback)(i=>{if(null===i){let e=a.current;e&&(a.current=null,e());let t=o.current;t&&(o.current=null,t())}else e&&(a.current=r(e,i)),t&&(o.current=r(t,i))},[e,t])}function r(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 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t,a,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),o=((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);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(i).includes(e)){let t=r[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:a,accessToken:i,apiKey:r,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:p,selectedGuardrails:g,selectedPolicies:m,selectedMCPServers:u,mcpServers:c,mcpServerToolRestrictions:d,selectedVoice:_,endpointType:f,selectedModel:A,selectedSdk:h,proxySettings:I}=e,b="session"===a?i:r,v=window.location.origin,E=I?.LITELLM_UI_API_DOC_BASE_URL;E&&E.trim()?v=E:I?.PROXY_BASE_URL&&(v=I.PROXY_BASE_URL);let O=n||"Your prompt here",T=O.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),y=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),p.length>0&&(C.vector_stores=p),g.length>0&&(C.guardrails=g),m.length>0&&(C.policies=m);let L=A||"your-model-name",x="azure"===h?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${b||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${v}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${b||"YOUR_LITELLM_API_KEY"}",
+	base_url="${v}"
+)`;switch(f){case o.CHAT:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=y.length>0?y:[{role:"user",content:O}];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="${L}",
+    messages=${JSON.stringify(i,null,4)}${a}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${L}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${T}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${a}
+# )
+# print(response_with_file)
+`;break}case o.RESPONSES:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=y.length>0?y:[{role:"user",content:O}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${L}",
+    input=${JSON.stringify(i,null,4)}${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="${L}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${T}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${a}
+# )
+# print(response_with_file.output_text)
+`;break}case o.IMAGE:t="azure"===h?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${L}",
+	prompt="${n}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${T}"
+
+# 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="${L}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.IMAGE_EDITS:t="azure"===h?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${T}"
+
+# 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="${L}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${T}"
+
+# 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="${L}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case o.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${n||"Your string here"}",
+	model="${L}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case o.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${L}",
+	file=audio_file${n?`,
+	prompt="${n.replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case o.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${L}",
+	input="${n||"Your text to convert to speech here"}",
+	voice="${_}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${L}",
+#     input="${n||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${x}
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index 00000000000..e38dfa2a4a7
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1ae216e2208b329b.js b/litellm/proxy/_experimental/out/_next/static/chunks/dc23b2a2258ffad1.js
similarity index 65%
rename from litellm/proxy/_experimental/out/_next/static/chunks/1ae216e2208b329b.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/dc23b2a2258ffad1.js
index 944b348e216..f2edf2872f7 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1ae216e2208b329b.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/dc23b2a2258ffad1.js
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new file mode 100644
index 00000000000..c2e63e6fb2a
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/e9de3f8db541361f.js b/litellm/proxy/_experimental/out/_next/static/chunks/e9de3f8db541361f.js
new file mode 100644
index 00000000000..e421672d90c
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2d471965761a22ff.js b/litellm/proxy/_experimental/out/_next/static/chunks/ee5f9a39a526e423.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/2d471965761a22ff.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/ee5f9a39a526e423.js
index 0c4a26f605f..277b07a43c2 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/2d471965761a22ff.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/ee5f9a39a526e423.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/54e29148cb2f2582.js b/litellm/proxy/_experimental/out/_next/static/chunks/f3e0cbc0e84e0a5d.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/54e29148cb2f2582.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/f3e0cbc0e84e0a5d.js
index b71eb9b21fc..351467a1d40 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/54e29148cb2f2582.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/f3e0cbc0e84e0a5d.js
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deleted file mode 100644
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/9d6e5aad99b19216.js b/litellm/proxy/_experimental/out/_next/static/chunks/f9c24d6e7ec43046.js
similarity index 98%
rename from litellm/proxy/_experimental/out/_next/static/chunks/9d6e5aad99b19216.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/f9c24d6e7ec43046.js
index ec622355824..75813d42614 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/9d6e5aad99b19216.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/f9c24d6e7ec43046.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/f9c75b7b331b5bb7.js b/litellm/proxy/_experimental/out/_next/static/chunks/f9c75b7b331b5bb7.js
deleted file mode 100644
index e9ac13655b9..00000000000
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new file mode 100644
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_not-found.txt b/litellm/proxy/_experimental/out/_not-found.txt
index 5c6a19560f1..a0b4f87eb15 100644
--- a/litellm/proxy/_experimental/out/_not-found.txt
+++ b/litellm/proxy/_experimental/out/_not-found.txt
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+2:I[867271,["/litellm-asset-prefix/_next/static/chunks/112ad77f3dd2e3cd.js","/litellm-asset-prefix/_next/static/chunks/65f709264734a9bf.js"],"default"]
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diff --git a/litellm/proxy/_experimental/out/settings/logging-and-alerts/__next.!KGRhc2hib2FyZCk.txt b/litellm/proxy/_experimental/out/settings/logging-and-alerts/__next.!KGRhc2hib2FyZCk.txt
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diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 91a953c217e..b59fc85d4b8 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -2416,6 +2416,7 @@ class LiteLLM_VerificationTokenView(LiteLLM_VerificationToken):
     organization_metadata: Optional[dict] = None
 
     # Project Params
+    project_alias: Optional[str] = None
     project_metadata: Optional[dict] = None
 
     # Time stamps
@@ -3228,6 +3229,7 @@ class SpendLogsMetadata(TypedDict):
     user_api_key_alias: Optional[str]
     user_api_key_team_id: Optional[str]
     user_api_key_project_id: Optional[str]
+    user_api_key_project_alias: Optional[str]
     user_api_key_org_id: Optional[str]
     user_api_key_user_id: Optional[str]
     user_api_key_team_alias: Optional[str]
diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py
index 1aa14fff574..815393467de 100644
--- a/litellm/proxy/auth/auth_checks.py
+++ b/litellm/proxy/auth/auth_checks.py
@@ -39,6 +39,7 @@ from litellm.proxy._types import (
     LiteLLM_EndUserTable,
     Litellm_EntityType,
     LiteLLM_JWTAuth,
+    LiteLLM_ManagedVectorStoresTable,
     LiteLLM_ObjectPermissionTable,
     LiteLLM_OrganizationMembershipTable,
     LiteLLM_OrganizationTable,
@@ -2294,6 +2295,71 @@ async def get_object_permission(
         return None
 
 
+@log_db_metrics
+async def get_managed_vector_store_rows_by_uuids(
+    uuids: List[str],
+    prisma_client: Optional[PrismaClient],
+    user_api_key_cache: DualCache,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
+) -> List[LiteLLM_ManagedVectorStoresTable]:
+    """
+    Fetch managed vector store rows by their internal UUIDs.
+
+    Follows the get_team_object / get_key_object / get_object_permission pattern:
+    cache-first lookup (in-memory / Redis), DB fallback only on cache miss.
+    Critical-path DB access must go through this helper to avoid raw Prisma
+    calls on the hot request path.
+    """
+    if not uuids or prisma_client is None:
+        return []
+
+    result: List[LiteLLM_ManagedVectorStoresTable] = []
+    cache_misses: List[str] = []
+
+    for uuid in uuids:
+        key = "managed_vector_store_id:{}".format(uuid)
+        cached = await user_api_key_cache.async_get_cache(key=key)
+        if cached is not None:
+            if isinstance(cached, dict):
+                result.append(LiteLLM_ManagedVectorStoresTable(**cached))
+            elif isinstance(cached, LiteLLM_ManagedVectorStoresTable):
+                result.append(cached)
+            else:
+                cache_misses.append(uuid)
+        else:
+            cache_misses.append(uuid)
+
+    if not cache_misses:
+        return result
+
+    rows = await prisma_client.db.litellm_managedvectorstorestable.find_many(
+        where={"vector_store_id": {"in": cache_misses}},
+        take=len(cache_misses),
+    )
+
+    for row in rows:
+        row_dict = (
+            row.model_dump()
+            if hasattr(row, "model_dump")
+            else (row.dict() if hasattr(row, "dict") else None)
+        )
+        if not isinstance(row_dict, dict) or not row_dict:
+            row_dict = dict(row) if hasattr(row, "__dict__") else {}
+        if not row_dict:
+            continue
+        cached_obj = LiteLLM_ManagedVectorStoresTable(**row_dict)
+        key = "managed_vector_store_id:{}".format(cached_obj.vector_store_id)
+        await user_api_key_cache.async_set_cache(
+            key=key,
+            value=row_dict,
+            ttl=DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL,
+        )
+        result.append(cached_obj)
+
+    return result
+
+
 @log_db_metrics
 async def get_org_object(
     org_id: str,
diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py
index e2f06abc52f..eba787c63b3 100644
--- a/litellm/proxy/auth/user_api_key_auth.py
+++ b/litellm/proxy/auth/user_api_key_auth.py
@@ -836,6 +836,7 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                         )
                         if _jwt_project_obj is not None:
                             valid_token.project_metadata = _jwt_project_obj.metadata
+                            valid_token.project_alias = _jwt_project_obj.project_alias
 
                     # run through common checks
                     _ = await common_checks(
@@ -920,9 +921,9 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                         route=route,
                     )
                 if _end_user_object is not None:
-                    end_user_params["allowed_model_region"] = (
-                        _end_user_object.allowed_model_region
-                    )
+                    end_user_params[
+                        "allowed_model_region"
+                    ] = _end_user_object.allowed_model_region
                     if _end_user_object.litellm_budget_table is not None:
                         _apply_budget_limits_to_end_user_params(
                             end_user_params=end_user_params,
@@ -1431,6 +1432,7 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                 )
                 if _project_obj is not None:
                     valid_token.project_metadata = _project_obj.metadata
+                    valid_token.project_alias = _project_obj.project_alias
 
             global_proxy_spend = None
             if (
@@ -1499,9 +1501,9 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
 
             if _end_user_object is not None:
                 valid_token_dict.update(end_user_params)
-                valid_token_dict["end_user_object_permission"] = (
-                    _end_user_object.object_permission
-                )
+                valid_token_dict[
+                    "end_user_object_permission"
+                ] = _end_user_object.object_permission
 
         # check if token is from litellm-ui, litellm ui makes keys to allow users to login with sso. These keys can only be used for LiteLLM UI functions
         # sso/login, ui/login, /key functions and /user functions
@@ -1888,6 +1890,7 @@ async def _run_post_custom_auth_checks(
         )
         if _project_obj is not None:
             valid_token.project_metadata = _project_obj.metadata
+            valid_token.project_alias = _project_obj.project_alias
 
     if general_settings.get("custom_auth_run_common_checks", False):
         _ = await common_checks(
diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py
index 38e5229eee1..160e9c23f01 100644
--- a/litellm/proxy/batches_endpoints/endpoints.py
+++ b/litellm/proxy/batches_endpoints/endpoints.py
@@ -5,7 +5,7 @@
 
 ######################################################################
 import asyncio
-from typing import Dict, Optional, cast
+from typing import Any, Dict, Optional, cast
 
 from fastapi import APIRouter, Depends, HTTPException, Path, Request, Response
 
@@ -655,7 +655,7 @@ async def list_batches(
             managed_files_obj, "list_user_batches"
         ):
             verbose_proxy_logger.debug("Using managed objects table for batch listing")
-            response = await managed_files_obj.list_user_batches(
+            response = await cast(Any, managed_files_obj).list_user_batches(
                 user_api_key_dict=user_api_key_dict,
                 limit=limit,
                 after=after,
@@ -686,8 +686,9 @@ async def list_batches(
 
             # Encode batch IDs in the list response so clients can use
             # them for retrieve/cancel/file downloads through the proxy.
-            if response and hasattr(response, "data") and response.data:
-                for batch in response.data:
+            response_data = getattr(response, "data", None)
+            if response_data:
+                for batch in response_data:
                     encode_batch_response_ids(batch, model=model_param)
 
             verbose_proxy_logger.debug(f"Listed batches using model: {model_param}")
@@ -897,7 +898,11 @@ async def cancel_batch(
         # SCENARIO 3: Fallback to custom_llm_provider (uses env variables)
         else:
             custom_llm_provider = (
-                provider or data.pop("custom_llm_provider", None) or "openai"
+                provider
+                or data.pop("custom_llm_provider", None)
+                or get_custom_llm_provider_from_request_headers(request=request)
+                or get_custom_llm_provider_from_request_query(request=request)
+                or "openai"
             )
             # Extract batch_id from data to avoid "multiple values for keyword argument" error
             # data was cast from CancelBatchRequest which already contains batch_id
diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py
index 28e1be40e21..dea0cb3980d 100644
--- a/litellm/proxy/common_request_processing.py
+++ b/litellm/proxy/common_request_processing.py
@@ -30,6 +30,7 @@ from litellm.constants import (
     MAX_PAYLOAD_SIZE_FOR_DEBUG_LOG,
     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.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.litellm_core_utils.llm_response_utils.get_headers import (
@@ -49,7 +50,6 @@ from litellm.proxy.common_utils.callback_utils import (
 from litellm.proxy.dd_span_tagger import DDSpanTagger
 from litellm.proxy.route_llm_request import route_request
 from litellm.proxy.utils import ProxyLogging
-from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.router import Router
 from litellm.types.guardrails import GuardrailEventHooks
 from litellm.types.utils import ServerToolUse
@@ -949,6 +949,7 @@ class ProxyBaseLLMRequestProcessing:
         version: Optional[str] = None,
         is_streaming_request: Optional[bool] = False,
         contents: Optional[list] = None,  # Add contents parameter
+        skip_pre_call_logic: bool = False,
     ) -> Any:
         """
         Common request processing logic for both chat completions and responses API endpoints
@@ -958,22 +959,30 @@ class ProxyBaseLLMRequestProcessing:
         )
         self._debug_log_request_payload()
 
-        self.data, logging_obj = await self.common_processing_pre_call_logic(
-            request=request,
-            general_settings=general_settings,
-            proxy_logging_obj=proxy_logging_obj,
-            user_api_key_dict=user_api_key_dict,
-            version=version,
-            proxy_config=proxy_config,
-            user_model=user_model,
-            user_temperature=user_temperature,
-            user_request_timeout=user_request_timeout,
-            user_max_tokens=user_max_tokens,
-            user_api_base=user_api_base,
-            model=model,
-            route_type=route_type,
-            llm_router=llm_router,
-        )
+        if skip_pre_call_logic:
+            logging_obj = self.data.get("litellm_logging_obj")
+            if logging_obj is None:
+                raise ValueError(
+                    "skip_pre_call_logic=True requires litellm_logging_obj to be set in data. "
+                    "Ensure common_processing_pre_call_logic was called before using this parameter."
+                )
+        else:
+            self.data, logging_obj = await self.common_processing_pre_call_logic(
+                request=request,
+                general_settings=general_settings,
+                proxy_logging_obj=proxy_logging_obj,
+                user_api_key_dict=user_api_key_dict,
+                version=version,
+                proxy_config=proxy_config,
+                user_model=user_model,
+                user_temperature=user_temperature,
+                user_request_timeout=user_request_timeout,
+                user_max_tokens=user_max_tokens,
+                user_api_base=user_api_base,
+                model=model,
+                route_type=route_type,
+                llm_router=llm_router,
+            )
 
         # Defer async logging when post-call guardrails are configured so the
         # StandardLoggingPayload is built after guardrails write to metadata.
@@ -1060,7 +1069,8 @@ class ProxyBaseLLMRequestProcessing:
                 self.data["deployment"] = llm_router.get_deployment(model_id=model_id)
             asyncio.create_task(
                 proxy_logging_obj.update_request_status(
-                    litellm_call_id=self.data.get("litellm_call_id", ""), status="success"
+                    litellm_call_id=self.data.get("litellm_call_id", ""),
+                    status="success",
                 )
             )
             if self._is_streaming_request(
@@ -1085,11 +1095,13 @@ class ProxyBaseLLMRequestProcessing:
                 )
 
                 # Call response headers hook for streaming success
-                callback_headers = await proxy_logging_obj.post_call_response_headers_hook(
-                    data=self.data,
-                    user_api_key_dict=user_api_key_dict,
-                    response=response,
-                    request_headers=dict(request.headers),
+                callback_headers = (
+                    await proxy_logging_obj.post_call_response_headers_hook(
+                        data=self.data,
+                        user_api_key_dict=user_api_key_dict,
+                        response=response,
+                        request_headers=dict(request.headers),
+                    )
                 )
                 if callback_headers:
                     custom_headers.update(callback_headers)
@@ -1135,7 +1147,7 @@ class ProxyBaseLLMRequestProcessing:
                             cache_hit=cache_hit,
                         )
 
-                    logging_obj._on_deferred_stream_complete = _on_deferred_stream_complete  # type: ignore[attr-defined]
+                    logging_obj._on_deferred_stream_complete = _on_deferred_stream_complete  # type: ignore[union-attr]
 
                 if route_type == "allm_passthrough_route":
                     # Check if response is an async generator
@@ -1148,15 +1160,15 @@ class ProxyBaseLLMRequestProcessing:
                         # For passthrough routes, stream directly without error parsing
                         # since we're dealing with raw binary data (e.g., AWS event streams)
                         return StreamingResponse(
-                            content=generator,
+                            content=generator,  # type: ignore[arg-type]
                             status_code=status.HTTP_200_OK,
                             headers=custom_headers,
                         )
                     else:
                         # Traditional HTTP response with aiter_bytes
                         return StreamingResponse(
-                            content=response.aiter_bytes(),
-                            status_code=response.status_code,
+                            content=response.aiter_bytes(),  # type: ignore[union-attr]
+                            status_code=response.status_code,  # type: ignore[union-attr]
                             headers=custom_headers,
                         )
                 elif route_type == "anthropic_messages":
@@ -1197,9 +1209,11 @@ class ProxyBaseLLMRequestProcessing:
             # Clear the closure so guardrails run inline as before — this
             # preserves blocking behavior and avoids double invocation.
             if getattr(logging_obj, "_on_deferred_stream_complete", None):
-                logging_obj._on_deferred_stream_complete = None  # type: ignore[attr-defined]
+                logging_obj._on_deferred_stream_complete = None  # type: ignore[union-attr]
             response = await proxy_logging_obj.post_call_success_hook(
-                data=self.data, user_api_key_dict=user_api_key_dict, response=response
+                data=self.data,
+                user_api_key_dict=user_api_key_dict,
+                response=response,  # type: ignore[arg-type]
             )
         except Exception:
             _exception_raised = True
@@ -1212,7 +1226,7 @@ class ProxyBaseLLMRequestProcessing:
             # returns before the deferred block), so _enqueue_fn is None — no-op.
             _enqueue_fn = getattr(logging_obj, "_enqueue_deferred_logging", None)
             if _enqueue_fn is not None:
-                logging_obj._enqueue_deferred_logging = None  # type: ignore[attr-defined]
+                logging_obj._enqueue_deferred_logging = None  # type: ignore[union-attr]
                 try:
                     _enqueue_fn()
                 except Exception as e:
@@ -1233,7 +1247,7 @@ class ProxyBaseLLMRequestProcessing:
                     logging_obj, "_on_deferred_stream_complete", None
                 )
                 if _deferred_fn is not None:
-                    logging_obj._on_deferred_stream_complete = None  # type: ignore[attr-defined]
+                    logging_obj._on_deferred_stream_complete = None  # type: ignore[union-attr]
                     try:
                         asyncio.create_task(
                             logging_obj.async_success_handler(
@@ -1418,18 +1432,16 @@ class ProxyBaseLLMRequestProcessing:
     @staticmethod
     def _has_post_call_guardrails() -> bool:
         """
-        Check if any registered callback is a post-call guardrail.
-
-        Uses the global litellm.callbacks list rather than per-request
-        should_run_guardrail() — intentionally conservative so that the
-        check is simple and stateless.  The deferral path produces
-        identical logging output, just fires it slightly later, so
-        false-positives are harmless.
+        True when a guardrail explicitly registers post_call. event_hook=None
+        matches all hooks in should_run_guardrail but must not defer async logging
+        on non-streaming /chat/completions (no post_call_success_hook flush path).
         """
         for cb in litellm.callbacks:
-            if isinstance(cb, CustomGuardrail) and cb._event_hook_is_event_type(
-                GuardrailEventHooks.post_call
-            ):
+            if not isinstance(cb, CustomGuardrail):
+                continue
+            if cb.event_hook is None:
+                continue
+            if cb._event_hook_is_event_type(GuardrailEventHooks.post_call):
                 return True
         return False
 
@@ -1463,8 +1475,8 @@ class ProxyBaseLLMRequestProcessing:
             from litellm.proxy.proxy_server import llm_router as _global_llm_router
             from litellm.proxy.utils import (
                 _check_and_merge_model_level_guardrails,
-                unified_guardrail as _unified_guardrail,
             )
+            from litellm.proxy.utils import unified_guardrail as _unified_guardrail
 
             guardrail_data = _check_and_merge_model_level_guardrails(
                 data=captured_data, llm_router=_global_llm_router
@@ -1510,7 +1522,8 @@ class ProxyBaseLLMRequestProcessing:
                         )["guardrail_blocked"] = True
         except Exception as e:
             verbose_proxy_logger.exception(
-                "Error in deferred streaming guardrail initialization: %s", e,
+                "Error in deferred streaming guardrail initialization: %s",
+                e,
             )
         finally:
             try:
@@ -1524,7 +1537,8 @@ class ProxyBaseLLMRequestProcessing:
                 )
             except Exception as e:
                 verbose_proxy_logger.exception(
-                    "Error in deferred streaming async logging: %s", e,
+                    "Error in deferred streaming async logging: %s",
+                    e,
                 )
 
             try:
@@ -1537,7 +1551,8 @@ class ProxyBaseLLMRequestProcessing:
                 )
             except Exception as e:
                 verbose_proxy_logger.exception(
-                    "Error in deferred streaming sync logging: %s", e,
+                    "Error in deferred streaming sync logging: %s",
+                    e,
                 )
 
     async def _handle_llm_api_exception(
diff --git a/litellm/proxy/db/create_views.py b/litellm/proxy/db/create_views.py
index e9303077b18..fd0baf67b3b 100644
--- a/litellm/proxy/db/create_views.py
+++ b/litellm/proxy/db/create_views.py
@@ -27,14 +27,16 @@ async def create_missing_views(db: _db):  # noqa: PLR0915
         await db.execute_raw(
             """
                 CREATE VIEW "LiteLLM_VerificationTokenView" AS
-                SELECT 
-                v.*, 
-                t.spend AS team_spend, 
-                t.max_budget AS team_max_budget, 
-                t.tpm_limit AS team_tpm_limit, 
-                t.rpm_limit AS team_rpm_limit
+                SELECT
+                v.*,
+                t.spend AS team_spend,
+                t.max_budget AS team_max_budget,
+                t.tpm_limit AS team_tpm_limit,
+                t.rpm_limit AS team_rpm_limit,
+                p.project_alias AS project_alias
                 FROM "LiteLLM_VerificationToken" v
-                LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id;
+                LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id
+                LEFT JOIN "LiteLLM_ProjectTable" p ON v.project_id = p.project_id;
             """
         )
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py
new file mode 100644
index 00000000000..c4aaea709ba
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/akto/__init__.py
@@ -0,0 +1,39 @@
+from typing import TYPE_CHECKING
+
+from litellm.types.guardrails import SupportedGuardrailIntegrations
+
+from .akto import AktoGuardrail
+
+
+if TYPE_CHECKING:
+    from litellm.types.guardrails import Guardrail, LitellmParams
+
+
+def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"):
+    import litellm
+
+    _akto_callback = AktoGuardrail(
+        akto_base_url=getattr(litellm_params, "akto_base_url", None),
+        akto_api_key=getattr(litellm_params, "akto_api_key", None),
+        akto_account_id=getattr(litellm_params, "akto_account_id", None),
+        akto_vxlan_id=getattr(litellm_params, "akto_vxlan_id", None),
+        unreachable_fallback=getattr(
+            litellm_params, "unreachable_fallback", "fail_closed"
+        ),
+        guardrail_timeout=getattr(litellm_params, "guardrail_timeout", None),
+        guardrail_name=guardrail.get("guardrail_name", ""),
+        event_hook=litellm_params.mode,
+        default_on=litellm_params.default_on,
+    )
+
+    litellm.logging_callback_manager.add_litellm_callback(_akto_callback)
+    return _akto_callback
+
+
+guardrail_initializer_registry = {
+    SupportedGuardrailIntegrations.AKTO.value: initialize_guardrail,
+}
+
+guardrail_class_registry = {
+    SupportedGuardrailIntegrations.AKTO.value: AktoGuardrail,
+}
diff --git a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py
new file mode 100644
index 00000000000..5058ee348db
--- /dev/null
+++ b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py
@@ -0,0 +1,492 @@
+"""Akto guardrail integration for LiteLLM proxy.
+
+Uses a two-config-entry pattern:
+  - akto-validate (pre_call): Checks request against Akto guardrails, blocks if flagged.
+  - akto-ingest (post_call): Sends request+response to Akto for data ingestion.
+
+For monitor-only mode, enable only akto-ingest without akto-validate.
+"""
+
+import asyncio
+import json
+import os
+from datetime import datetime
+from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Tuple, Type
+
+from fastapi import HTTPException
+
+import httpx
+
+from litellm._logging import verbose_proxy_logger
+from litellm.integrations.custom_guardrail import (
+    CustomGuardrail,
+    log_guardrail_information,
+)
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+from litellm.types.guardrails import GuardrailEventHooks
+from litellm.types.utils import GenericGuardrailAPIInputs
+
+if TYPE_CHECKING:
+    from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
+
+HTTP_PROXY_PATH = "/api/http-proxy"
+AKTO_CONNECTOR_NAME = "litellm"
+DEFAULT_GUARDRAIL_TIMEOUT = 5
+
+
+class AktoGuardrail(CustomGuardrail):
+    """LiteLLM guardrail hook that validates and ingests LLM traffic via the Akto API."""
+
+    # Maps event_hook to the input_type it should handle; mismatches are no-ops
+    HOOK_TO_INPUT = {"pre_call": "request", "post_call": "response"}
+
+    @staticmethod
+    def get_config_model() -> Type["GuardrailConfigModel"]:
+        """Return the Pydantic config model for YAML-based initialization."""
+        from litellm.types.proxy.guardrails.guardrail_hooks.akto import (
+            AktoConfigModel,
+        )
+
+        return AktoConfigModel
+
+    def __init__(
+        self,
+        akto_base_url: Optional[str] = None,
+        akto_api_key: Optional[str] = None,
+        akto_account_id: Optional[str] = None,
+        akto_vxlan_id: Optional[str] = None,
+        unreachable_fallback: Literal["fail_closed", "fail_open"] = "fail_closed",
+        guardrail_timeout: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize the Akto guardrail.
+
+        Args:
+            akto_base_url: Akto API base URL. Falls back to AKTO_GUARDRAIL_API_BASE env var.
+            akto_api_key: Akto API key. Falls back to AKTO_API_KEY env var.
+            akto_account_id: Akto account ID. Falls back to AKTO_ACCOUNT_ID env var, then "1000000".
+            akto_vxlan_id: Akto VXLAN ID. Falls back to AKTO_VXLAN_ID env var, then "0".
+            unreachable_fallback: Behavior when Akto is unreachable — block or allow.
+            guardrail_timeout: HTTP timeout in seconds for Akto API calls.
+        """
+        self.async_handler = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.GuardrailCallback,
+        )
+        self.background_tasks: set = set()
+
+        self.akto_base_url = (
+            akto_base_url or os.environ.get("AKTO_GUARDRAIL_API_BASE", "")
+        ).rstrip("/")
+        if not self.akto_base_url:
+            raise ValueError(
+                "akto_base_url is required. Set AKTO_GUARDRAIL_API_BASE or pass it in litellm_params."
+            )
+
+        self.akto_api_key = akto_api_key or os.environ.get("AKTO_API_KEY", "")
+        if not self.akto_api_key:
+            raise ValueError(
+                "akto_api_key is required. Set AKTO_API_KEY or pass it in litellm_params."
+            )
+
+        self.unreachable_fallback: Literal[
+            "fail_closed", "fail_open"
+        ] = unreachable_fallback
+        self.guardrail_timeout = guardrail_timeout or DEFAULT_GUARDRAIL_TIMEOUT
+        self.akto_account_id = akto_account_id or os.environ.get(
+            "AKTO_ACCOUNT_ID", "1000000"
+        )
+        self.akto_vxlan_id = akto_vxlan_id or os.environ.get("AKTO_VXLAN_ID", "0")
+
+        kwargs["supported_event_hooks"] = [
+            GuardrailEventHooks.pre_call,
+            GuardrailEventHooks.post_call,
+        ]
+        super().__init__(**kwargs)
+
+        verbose_proxy_logger.debug(
+            "Akto guardrail initialized: base_url=%s fallback=%s",
+            self.akto_base_url,
+            self.unreachable_fallback,
+        )
+
+    @staticmethod
+    def resolve_metadata_value(request_data: Optional[dict], key: str) -> Optional[str]:
+        """Look up a metadata value from litellm_metadata or metadata dicts."""
+        if request_data is None:
+            return None
+        for dict_key in ("litellm_metadata", "metadata"):
+            container = request_data.get(dict_key) or {}
+            if isinstance(container, dict) and container:
+                value = container.get(key)
+                if value is not None:
+                    return str(value).strip()
+        return None
+
+    @staticmethod
+    def extract_request_path(request_data: dict) -> str:
+        """Extract the API route from request metadata, defaulting to /v1/chat/completions."""
+        metadata = request_data.get("metadata") or {}
+        if not isinstance(metadata, dict):
+            metadata = {}
+        route = metadata.get("user_api_key_request_route")
+        return route if route else "/v1/chat/completions"
+
+    def prepare_headers(self) -> Dict[str, str]:
+        """Build HTTP headers for the Akto API call."""
+        return {
+            "content-type": "application/json",
+            "Authorization": self.akto_api_key,
+        }
+
+    @staticmethod
+    def build_query_params(*, guardrails: bool, ingest_data: bool) -> Dict[str, str]:
+        """Build query params that control Akto backend behavior (guardrail check and/or data ingestion)."""
+        params: Dict[str, str] = {"akto_connector": AKTO_CONNECTOR_NAME}
+        if guardrails:
+            params["guardrails"] = "true"
+        if ingest_data:
+            params["ingest_data"] = "true"
+        return params
+
+    @staticmethod
+    def build_request_headers(request_data: dict) -> Dict[str, str]:
+        """Build the requestHeaders field from proxy request headers."""
+        headers: Dict[str, str] = {"content-type": "application/json"}
+        proxy_req = request_data.get("proxy_server_request", {})
+        if not isinstance(proxy_req, dict):
+            return headers
+        proxy_req_headers = proxy_req.get("headers")
+        if isinstance(proxy_req_headers, dict):
+            for key, val in proxy_req_headers.items():
+                if key and val:
+                    headers[str(key).lower()] = str(val)
+        return headers
+
+    @staticmethod
+    def build_request_body(
+        inputs: GenericGuardrailAPIInputs,
+        request_data: Optional[dict] = None,
+    ) -> Dict[str, Any]:
+        """Build the LLM request body from guardrail inputs (messages, model, tools)."""
+        model = inputs.get("model", "") or ""
+        body: Dict[str, Any] = {"model": model}
+
+        structured = inputs.get("structured_messages")
+        if structured:
+            body["messages"] = structured
+        elif request_data is not None and request_data.get("messages"):
+            body["messages"] = request_data["messages"]
+            if request_data.get("model"):
+                body["model"] = request_data["model"]
+        else:
+            texts = inputs.get("texts", [])
+            body["messages"] = (
+                [{"role": "user", "content": t} for t in texts] if texts else []
+            )
+
+        tools = inputs.get("tools")
+        if tools:
+            body["tools"] = tools
+        elif request_data is not None and request_data.get("tools"):
+            body["tools"] = request_data["tools"]
+
+        tool_calls = inputs.get("tool_calls")
+        if tool_calls:
+            body["tool_calls"] = tool_calls
+
+        return body
+
+    @staticmethod
+    def build_response_body(
+        inputs: GenericGuardrailAPIInputs,
+        request_data: Optional[dict] = None,
+    ) -> Dict[str, Any]:
+        """Build the LLM response body, preferring the actual model response if available."""
+        model_response = request_data.get("response") if request_data else None
+        if model_response is not None and hasattr(model_response, "model_dump"):
+            return model_response.model_dump()
+
+        texts = inputs.get("texts", [])
+        if texts:
+            return {
+                "choices": [
+                    {"message": {"content": t, "role": "assistant"}} for t in texts
+                ]
+            }
+        return {}
+
+    @staticmethod
+    def build_tag_metadata(request_data: dict) -> Dict[str, str]:
+        """Build tag/metadata dict with user_id and team_id for Akto tracking."""
+        tag: Dict[str, str] = {"gen-ai": "Gen AI"}
+        user_id = AktoGuardrail.resolve_metadata_value(
+            request_data, "user_api_key_user_id"
+        )
+        team_id = AktoGuardrail.resolve_metadata_value(
+            request_data, "user_api_key_team_id"
+        )
+        if user_id:
+            tag["user_id"] = user_id
+        if team_id:
+            tag["team_id"] = team_id
+        return tag
+
+    def build_akto_payload(
+        self,
+        inputs: GenericGuardrailAPIInputs,
+        request_data: dict,
+        *,
+        status_code: int = 200,
+        include_response: bool = False,
+    ) -> Dict[str, Any]:
+        """Build the flat MIRRORING payload sent to Akto's HTTP proxy endpoint.
+
+        All body fields use double-encoding: json.dumps({"body": json.dumps(actual_body)})
+        to match the canonical CLI hook format.
+        """
+        request_path = self.extract_request_path(request_data)
+        request_headers = self.build_request_headers(request_data)
+        request_body = self.build_request_body(inputs, request_data)
+        tag = self.build_tag_metadata(request_data)
+
+        response_payload = json.dumps({})  # Empty body wrapper when no response yet
+        response_headers: Dict[str, str] = {}
+        if include_response:
+            response_body = self.build_response_body(inputs, request_data)
+            response_payload = json.dumps(
+                {"body": json.dumps(response_body)}
+            )  # Double-encoded
+            response_headers = {"content-type": "application/json"}
+
+        # Extract client IP from proxy headers
+        ip = ""
+        proxy_req = request_data.get("proxy_server_request", {})
+        proxy_headers = (
+            proxy_req.get("headers", {}) if isinstance(proxy_req, dict) else {}
+        )
+        if isinstance(proxy_headers, dict):
+            ip = (
+                proxy_headers.get("x-forwarded-for")
+                or proxy_headers.get("x-real-ip")
+                or ""
+            )
+            if "," in ip:
+                ip = ip.split(",")[0].strip()
+
+        return {
+            "path": request_path,
+            "requestHeaders": json.dumps(request_headers),
+            "responseHeaders": json.dumps(response_headers),
+            "method": "POST",
+            "requestPayload": json.dumps(
+                {"body": json.dumps(request_body)}
+            ),  # Double-encoded
+            "responsePayload": response_payload,
+            "ip": ip,
+            "destIp": "127.0.0.1",
+            "time": str(int(datetime.now().timestamp() * 1000)),
+            "statusCode": str(status_code),
+            "type": "HTTP/1.1",
+            "status": str(status_code),
+            "akto_account_id": self.akto_account_id,
+            "akto_vxlan_id": self.akto_vxlan_id,
+            "is_pending": "false",
+            "source": "MIRRORING",
+            "direction": None,
+            "process_id": None,
+            "socket_id": None,
+            "daemonset_id": None,
+            "enabled_graph": None,
+            "tag": json.dumps(tag),
+            "metadata": json.dumps(tag),
+            "contextSource": "AGENTIC",
+        }
+
+    async def send_request(
+        self,
+        *,
+        guardrails: bool,
+        ingest_data: bool,
+        payload: dict,
+    ) -> httpx.Response:
+        """Send an HTTP POST to the Akto API endpoint."""
+        endpoint = f"{self.akto_base_url}{HTTP_PROXY_PATH}"
+        params = self.build_query_params(guardrails=guardrails, ingest_data=ingest_data)
+        headers = self.prepare_headers()
+        return await self.async_handler.post(
+            url=endpoint,
+            data=json.dumps(payload),
+            params=params,
+            headers=headers,
+            timeout=self.guardrail_timeout,
+        )
+
+    @staticmethod
+    def handle_guardrail_response(response: httpx.Response) -> Tuple[bool, str]:
+        """Parse the Akto guardrail response. Returns (allowed, reason)."""
+        if response.status_code != 200:
+            verbose_proxy_logger.error("Akto returned HTTP %d", response.status_code)
+            raise httpx.HTTPStatusError(
+                f"Akto returned unexpected status {response.status_code}",
+                request=response.request,
+                response=response,
+            )
+        try:
+            result = response.json()
+        except (json.JSONDecodeError, ValueError) as e:
+            response_text = getattr(response, "text", "")
+            verbose_proxy_logger.error(
+                "Akto returned non-JSON body for status 200: %r",
+                response_text[:200],
+            )
+            raise httpx.RequestError(
+                "Akto returned non-JSON body",
+                request=response.request,
+            ) from e
+        if not isinstance(result, dict):
+            return True, ""
+        data = result.get("data") or {}
+        if not isinstance(data, dict):
+            return True, ""
+        guardrails_result = data.get("guardrailsResult") or {}
+        if not isinstance(guardrails_result, dict):
+            return True, ""
+        return (
+            bool(guardrails_result.get("Allowed", True)),
+            str(guardrails_result.get("Reason", "")),
+        )
+
+    def handle_unreachable(
+        self,
+        inputs: GenericGuardrailAPIInputs,
+        error: Exception,
+    ) -> GenericGuardrailAPIInputs:
+        """Handle Akto being unreachable based on fail_open/fail_closed config."""
+        if self.unreachable_fallback == "fail_open":
+            verbose_proxy_logger.critical(
+                "Akto unreachable (fail-open): %s",
+                str(error),
+                exc_info=error,
+            )
+            return inputs
+
+        verbose_proxy_logger.error("Akto unreachable (fail-closed): %s", str(error))
+        raise HTTPException(
+            status_code=503,
+            detail="Akto guardrail service unreachable",
+        )
+
+    async def fire_and_forget_request(
+        self,
+        *,
+        guardrails: bool,
+        ingest_data: bool,
+        payload: dict,
+    ) -> None:
+        """Send a request without awaiting it in the caller. Errors are logged, not raised."""
+        try:
+            response = await self.send_request(
+                guardrails=guardrails,
+                ingest_data=ingest_data,
+                payload=payload,
+            )
+            if response.status_code != 200:
+                verbose_proxy_logger.error(
+                    "Akto fire-and-forget returned HTTP %d",
+                    response.status_code,
+                )
+        except Exception as e:
+            verbose_proxy_logger.error("Akto fire-and-forget error: %s", str(e))
+
+    @log_guardrail_information
+    async def apply_guardrail(
+        self,
+        inputs: GenericGuardrailAPIInputs,
+        request_data: dict,
+        input_type: Literal["request", "response"],
+        logging_obj=None,
+    ) -> GenericGuardrailAPIInputs:
+        """Main entry point called by LiteLLM's guardrail framework.
+
+        Pre_call (input_type="request"):
+          - Awaits guardrail check. If blocked, fires off ingest with 403 marker and raises.
+        Post_call (input_type="response"):
+          - Fire-and-forget combined guardrail + ingest call.
+        """
+        # Skip if this hook doesn't handle the current input_type
+        expected = self.HOOK_TO_INPUT.get(str(self.event_hook))
+        if expected and expected != input_type:
+            return inputs
+
+        if input_type == "request":
+            # Pre_call: awaited guardrail check (no ingestion)
+            payload = self.build_akto_payload(
+                inputs, request_data, include_response=False
+            )
+            try:
+                response = await self.send_request(
+                    guardrails=True,
+                    ingest_data=False,
+                    payload=payload,
+                )
+                allowed, reason = self.handle_guardrail_response(response)
+            except HTTPException:
+                raise
+            except (httpx.RequestError, httpx.HTTPStatusError) as e:
+                return self.handle_unreachable(
+                    inputs=inputs,
+                    error=e,
+                )
+
+            if not allowed:
+                # Build a blocked marker payload with 403 status and reason
+                blocked_payload = self.build_akto_payload(
+                    inputs,
+                    request_data,
+                    include_response=False,
+                    status_code=403,
+                )
+                blocked_payload["responsePayload"] = json.dumps(
+                    {
+                        "body": json.dumps(
+                            {"x-blocked-by": "Akto Proxy", "reason": reason}
+                        ),
+                    }
+                )
+                blocked_payload["responseHeaders"] = json.dumps(
+                    {"content-type": "application/json"},
+                )
+                # Fire-and-forget ingest of the blocked request, then raise 403
+                task = asyncio.create_task(
+                    self.fire_and_forget_request(
+                        guardrails=False,
+                        ingest_data=True,
+                        payload=blocked_payload,
+                    )
+                )
+                self.background_tasks.add(task)
+                task.add_done_callback(self.background_tasks.discard)
+                raise HTTPException(
+                    status_code=403,
+                    detail=reason or "Blocked by Akto Guardrails",
+                )
+
+        elif input_type == "response":
+            # Post_call: fire-and-forget combined guardrail + ingest
+            payload = self.build_akto_payload(
+                inputs, request_data, include_response=True
+            )
+            task = asyncio.create_task(
+                self.fire_and_forget_request(
+                    guardrails=True,
+                    ingest_data=True,
+                    payload=payload,
+                )
+            )
+            self.background_tasks.add(task)
+            task.add_done_callback(self.background_tasks.discard)
+
+        return inputs
diff --git a/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py b/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py
index 4bd94345727..4ddeac9a208 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py
@@ -5,9 +5,11 @@ OpenAI Moderation Guardrail Integration for LiteLLM
 
 from typing import (
     TYPE_CHECKING,
+    Dict,
     Literal,
     Optional,
     Type,
+    Union,
 )
 
 from fastapi import HTTPException
@@ -22,7 +24,8 @@ from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
     httpxSpecialProvider,
 )
-from litellm.types.utils import GenericGuardrailAPIInputs
+from litellm.types.guardrails import GuardrailEventHooks
+from litellm.types.utils import GenericGuardrailAPIInputs, GuardrailStatus
 
 from .base import OpenAIGuardrailBase
 
@@ -223,12 +226,98 @@ class OpenAIModerationGuardrail(OpenAIGuardrailBase, CustomGuardrail):
         # Make moderation request
         moderation_response = await self.async_make_request(input_text=text_to_moderate)
 
+        # Stash full moderation response in request_data for logging
+        # (Model Armor pattern — per-request dict avoids race conditions)
+        if isinstance(request_data, dict):
+            metadata = request_data.get("metadata") or {}
+            request_data["metadata"] = metadata
+            metadata["_openai_moderation_response"] = moderation_response.model_dump()
+
         # Check if content is flagged and raise exception if needed
         self._check_moderation_result(moderation_response)
 
         # Moderation doesn't modify content, just blocks - return inputs unchanged
         return inputs
 
+    def _process_response(
+        self,
+        response: Optional[Dict],
+        request_data: dict,
+        start_time: Optional[float] = None,
+        end_time: Optional[float] = None,
+        duration: Optional[float] = None,
+        event_type: Optional[GuardrailEventHooks] = None,
+        original_inputs: Optional[Dict] = None,
+    ):
+        """
+        Override to log the full OpenAI Moderation API response instead of
+        the decorator's simplified "allow"/"mask" string.
+
+        Follows the Model Armor pattern (model_armor.py:325-360).
+        """
+        if isinstance(request_data, dict):
+            metadata = request_data.get("metadata") or {}
+            request_data["metadata"] = metadata  # anchor so pop() mutates the real dict
+        else:
+            metadata = {}
+
+        # .pop() cleans up the internal key so it doesn't leak to downstream
+        # loggers. Falls back to "allow" when no moderation call was made
+        # (e.g. no text to moderate — early return in apply_guardrail).
+        guardrail_response = metadata.pop("_openai_moderation_response", "allow")
+
+        self.add_standard_logging_guardrail_information_to_request_data(
+            guardrail_json_response=guardrail_response,
+            request_data=request_data,
+            guardrail_status="success",
+            duration=duration,
+            start_time=start_time,
+            end_time=end_time,
+            event_type=event_type,
+        )
+        return response
+
+    def _process_error(
+        self,
+        e: Exception,
+        request_data: dict,
+        start_time: Optional[float] = None,
+        end_time: Optional[float] = None,
+        duration: Optional[float] = None,
+        event_type: Optional[GuardrailEventHooks] = None,
+    ):
+        """
+        Override to log the full OpenAI Moderation API response on error
+        instead of the stringified exception.
+        """
+        guardrail_status: GuardrailStatus = (
+            "guardrail_intervened"
+            if self._is_guardrail_intervention(e)
+            else "guardrail_failed_to_respond"
+        )
+
+        if isinstance(request_data, dict):
+            metadata = request_data.get("metadata") or {}
+            request_data["metadata"] = metadata  # anchor so pop() mutates the real dict
+        else:
+            metadata = {}
+
+        # Use the stashed moderation response if available, fall back to exception
+        guardrail_response: Union[dict, Exception, str] = metadata.pop(
+            "_openai_moderation_response", e
+        )
+
+        self.add_standard_logging_guardrail_information_to_request_data(
+            guardrail_json_response=guardrail_response,
+            request_data=request_data,
+            guardrail_status=guardrail_status,
+            duration=duration,
+            start_time=start_time,
+            end_time=end_time,
+            event_type=event_type,
+        )
+        raise e
+
     @staticmethod
     def get_config_model() -> Optional[Type["GuardrailConfigModel"]]:
         """
diff --git a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py
index 84bbf6d20e1..a1623121da5 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py
@@ -247,6 +247,7 @@ class UnifiedLLMGuardrails(CustomLogger):
             guardrail_to_apply=guardrail_to_apply,
             litellm_logging_obj=data.get("litellm_logging_obj"),
             user_api_key_dict=user_api_key_dict,
+            request_data=data,
         )
         # Add guardrail to applied guardrails header
         add_guardrail_to_applied_guardrails_header(
@@ -397,6 +398,7 @@ class UnifiedLLMGuardrails(CustomLogger):
                         guardrail_to_apply=guardrail_to_apply,
                         litellm_logging_obj=request_data.get("litellm_logging_obj"),
                         user_api_key_dict=user_api_key_dict,
+                        request_data=request_data,
                     )
                 except HTTPException as e:
                     # Response already started (we already yielded chunks); cannot send 400.
@@ -457,6 +459,7 @@ class UnifiedLLMGuardrails(CustomLogger):
                     guardrail_to_apply=guardrail_to_apply,
                     litellm_logging_obj=request_data.get("litellm_logging_obj"),
                     user_api_key_dict=user_api_key_dict,
+                    request_data=request_data,
                 )
             except HTTPException as e:
                 if call_type is not None and CallTypes(call_type) in A2A_CALL_TYPES:
diff --git a/litellm/proxy/health_check.py b/litellm/proxy/health_check.py
index a8d0e3e9af2..3e05ee3c484 100644
--- a/litellm/proxy/health_check.py
+++ b/litellm/proxy/health_check.py
@@ -207,21 +207,65 @@ async def _perform_health_check(
 
     for is_healthy, model in zip(results, model_list):
         litellm_params = model["litellm_params"]
+        _model_id = (model.get("model_info") or {}).get("id")
 
         if isinstance(is_healthy, dict) and "error" not in is_healthy:
-            healthy_endpoints.append(
-                _clean_endpoint_data({**litellm_params, **is_healthy}, details)
-            )
+            cleaned = _clean_endpoint_data({**litellm_params, **is_healthy}, details)
+            if _model_id:
+                cleaned["model_id"] = _model_id
+            healthy_endpoints.append(cleaned)
         elif isinstance(is_healthy, dict):
-            unhealthy_endpoints.append(
-                _clean_endpoint_data({**litellm_params, **is_healthy}, details)
-            )
+            cleaned = _clean_endpoint_data({**litellm_params, **is_healthy}, details)
+            if _model_id:
+                cleaned["model_id"] = _model_id
+            unhealthy_endpoints.append(cleaned)
         else:
-            unhealthy_endpoints.append(_clean_endpoint_data(litellm_params, details))
+            cleaned = _clean_endpoint_data(litellm_params, details)
+            if _model_id:
+                cleaned["model_id"] = _model_id
+            unhealthy_endpoints.append(cleaned)
 
     return healthy_endpoints, unhealthy_endpoints
 
 
+def build_deployment_health_states(
+    healthy_endpoints: list,
+    unhealthy_endpoints: list,
+) -> dict:
+    """
+    Build a dict mapping deployment_id -> DeploymentHealthStateValue from
+    health check endpoint results.
+
+    Each endpoint dict includes a 'model_id' field (added by _perform_health_check)
+    that maps back to the deployment's model_info.id.
+
+    Used by the background health check loop to feed health state into
+    the router's DeploymentHealthCache for health-check-driven routing.
+    """
+    now = time.time()
+    states: dict = {}
+
+    for ep in healthy_endpoints:
+        model_id = ep.get("model_id")
+        if model_id:
+            states[model_id] = {
+                "is_healthy": True,
+                "timestamp": now,
+                "reason": "",
+            }
+
+    for ep in unhealthy_endpoints:
+        model_id = ep.get("model_id")
+        if model_id:
+            states[model_id] = {
+                "is_healthy": False,
+                "timestamp": now,
+                "reason": "background_health_check_failed",
+            }
+
+    return states
+
+
 def _update_litellm_params_for_health_check(
     model_info: dict, litellm_params: dict
 ) -> dict:
diff --git a/litellm/proxy/hooks/proxy_track_cost_callback.py b/litellm/proxy/hooks/proxy_track_cost_callback.py
index 43cfd930193..220c5066a6d 100644
--- a/litellm/proxy/hooks/proxy_track_cost_callback.py
+++ b/litellm/proxy/hooks/proxy_track_cost_callback.py
@@ -18,11 +18,9 @@ from litellm.proxy.auth.auth_checks import (
     log_db_metrics,
 )
 from litellm.proxy.auth.route_checks import RouteChecks
+from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
 from litellm.proxy.utils import ProxyUpdateSpend
-from litellm.types.utils import (
-    StandardLoggingPayload,
-    StandardLoggingUserAPIKeyMetadata,
-)
+from litellm.types.utils import StandardLoggingPayload
 from litellm.utils import get_end_user_id_for_cost_tracking
 
 
@@ -51,25 +49,8 @@ class _ProxyDBLogger(CustomLogger):
         from litellm.proxy.proxy_server import proxy_logging_obj
 
         _metadata = dict(
-            StandardLoggingUserAPIKeyMetadata(
-                user_api_key_hash=user_api_key_dict.api_key,
-                user_api_key_alias=user_api_key_dict.key_alias,
-                user_api_key_spend=user_api_key_dict.spend,
-                user_api_key_max_budget=user_api_key_dict.max_budget,
-                user_api_key_budget_reset_at=(
-                    user_api_key_dict.budget_reset_at.isoformat()
-                    if user_api_key_dict.budget_reset_at
-                    else None
-                ),
-                user_api_key_user_email=user_api_key_dict.user_email,
-                user_api_key_user_id=user_api_key_dict.user_id,
-                user_api_key_team_id=user_api_key_dict.team_id,
-                user_api_key_org_id=user_api_key_dict.org_id,
-                user_api_key_project_id=user_api_key_dict.project_id,
-                user_api_key_team_alias=user_api_key_dict.team_alias,
-                user_api_key_end_user_id=user_api_key_dict.end_user_id,
-                user_api_key_request_route=user_api_key_dict.request_route,
-                user_api_key_auth_metadata=user_api_key_dict.metadata,
+            LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(
+                user_api_key_dict=user_api_key_dict
             )
         )
         _metadata["user_api_key"] = user_api_key_dict.api_key
diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py
index daf2867699e..ba9577f35d7 100644
--- a/litellm/proxy/litellm_pre_call_utils.py
+++ b/litellm/proxy/litellm_pre_call_utils.py
@@ -1,6 +1,7 @@
 import asyncio
 import copy
 import time
+from collections import OrderedDict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
 from fastapi import Request
@@ -26,6 +27,7 @@ _SPECIAL_HEADERS_CACHE = frozenset(
     v.value.lower() for v in SpecialHeaders._member_map_.values()
 )
 from litellm.router import Router
+from litellm.secret_managers.main import get_secret_bool
 from litellm.types.llms.anthropic import ANTHROPIC_API_HEADERS
 from litellm.types.services import ServiceTypes
 from litellm.types.utils import (
@@ -36,6 +38,11 @@ from litellm.types.utils import (
 )
 
 service_logger_obj = ServiceLogging()  # used for tracking latency on OTEL
+# Bounded dedup for stale-alias warnings (FIFO eviction when over cap).
+_MAX_STALE_ALIAS_WARNING_KEYS = 10_000
+_STALE_TEAM_ALIAS_WARNING_KEYS: OrderedDict[str, None] = OrderedDict()
+# Cache the stale alias bypass flag at module load to avoid hot-path secret lookups
+_ENABLE_TEAM_STALE_ALIAS_BYPASS: Optional[bool] = None
 
 
 if TYPE_CHECKING:
@@ -658,6 +665,7 @@ class LiteLLMProxyRequestSetup:
             user_api_key_max_budget=user_api_key_dict.max_budget,
             user_api_key_team_id=user_api_key_dict.team_id,
             user_api_key_project_id=user_api_key_dict.project_id,
+            user_api_key_project_alias=user_api_key_dict.project_alias,
             user_api_key_user_id=user_api_key_dict.user_id,
             user_api_key_org_id=user_api_key_dict.org_id,
             user_api_key_team_alias=user_api_key_dict.team_alias,
@@ -1295,6 +1303,10 @@ def _update_model_if_team_alias_exists(
             "gpt-4o": "gpt-4o-team-1"
         }
         - requested_model = "gpt-4o-team-1"
+
+    Note: model_aliases for team models are deprecated. This function only applies
+    to legacy non-team-scoped aliases. Team-scoped deployments use team_public_model_name
+    and are resolved via map_team_model in route_llm_request.
     """
     _model = data.get("model")
     if (
@@ -1302,7 +1314,54 @@ def _update_model_if_team_alias_exists(
         and user_api_key_dict.team_model_aliases
         and _model in user_api_key_dict.team_model_aliases
     ):
-        data["model"] = user_api_key_dict.team_model_aliases[_model]
+        from litellm.proxy.proxy_server import llm_router
+
+        # Skip alias rewrite if this model resolves to team-specific deployments
+        # (team models use team_public_model_name, not model_aliases)
+        aliased_target = user_api_key_dict.team_model_aliases[_model]
+
+        # Optional bypass for stale aliases from pre-PR deployments:
+        # only enabled via feature flag to preserve backwards compatibility.
+        # Cached at module level to avoid hot-path secret lookups on every request.
+        global _ENABLE_TEAM_STALE_ALIAS_BYPASS
+        if _ENABLE_TEAM_STALE_ALIAS_BYPASS is None:
+            _ENABLE_TEAM_STALE_ALIAS_BYPASS = get_secret_bool(
+                "LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS", False
+            )
+        enable_stale_alias_bypass = _ENABLE_TEAM_STALE_ALIAS_BYPASS
+        # Check if the alias points to a team-scoped UUID name
+        # (format: "model_name_{team_id}_{uuid}")
+        is_stale_team_alias = aliased_target.startswith(
+            f"model_name_{user_api_key_dict.team_id}_"
+        )
+        if is_stale_team_alias and llm_router:
+            # This is a stale alias from pre-PR deployments.
+            # Check if current team deployments exist for the public name.
+            key = (user_api_key_dict.team_id, _model)
+            if key in llm_router.team_model_to_deployment_indices:
+                if enable_stale_alias_bypass:
+                    # Team deployments exist; skip stale alias
+                    return
+                warning_key = f"{user_api_key_dict.team_id}:{_model}:{aliased_target}"
+                if warning_key not in _STALE_TEAM_ALIAS_WARNING_KEYS:
+                    _STALE_TEAM_ALIAS_WARNING_KEYS[warning_key] = None
+                    while (
+                        len(_STALE_TEAM_ALIAS_WARNING_KEYS)
+                        > _MAX_STALE_ALIAS_WARNING_KEYS
+                    ):
+                        _STALE_TEAM_ALIAS_WARNING_KEYS.popitem(last=False)
+                    verbose_proxy_logger.warning(
+                        "Stale team model alias detected for model='%s', team_id='%s'. "
+                        "New sibling deployments may be unreachable. "
+                        "Set LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS=true to enable "
+                        "team-scoped sibling routing.",
+                        str(_model).replace("\n", "").replace("\r", ""),
+                        str(user_api_key_dict.team_id)
+                        .replace("\n", "")
+                        .replace("\r", ""),
+                    )
+
+        data["model"] = aliased_target
     return
 
 
diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py
index ca8c345f46c..ff7b89fe192 100644
--- a/litellm/proxy/management_endpoints/internal_user_endpoints.py
+++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py
@@ -24,13 +24,13 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid
 from litellm.proxy._types import *
+from litellm.proxy.auth.auth_checks import get_team_object, get_user_object
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.hooks.user_management_event_hooks import UserManagementEventHooks
 from litellm.proxy.management_endpoints.common_daily_activity import (
     get_daily_activity,
     get_daily_activity_aggregated,
 )
-from litellm.proxy.auth.auth_checks import get_team_object, get_user_object
 from litellm.proxy.management_endpoints.common_utils import (
     _is_user_team_admin,
     _user_has_admin_view,
@@ -557,6 +557,18 @@ def get_team_from_list(
     return None
 
 
+def _is_valid_user_id(user_id: str) -> bool:
+    """Validate that a decoded user_id is safe to use downstream."""
+    MAX_USER_ID_LENGTH = 512
+    if len(user_id) > MAX_USER_ID_LENGTH:
+        return False
+    # Reject ASCII control characters (U+0000–U+001F)
+    for ch in user_id:
+        if ord(ch) < 0x20:
+            return False
+    return True
+
+
 def get_user_id_from_request(request: Request) -> Optional[str]:
     """
     Get the user id from the request
@@ -573,7 +585,8 @@ def get_user_id_from_request(request: Request) -> Optional[str]:
         if match:
             # Use unquote instead of unquote_plus to preserve + characters
             raw_user_id = unquote(match.group(1))
-            user_id = raw_user_id
+            if _is_valid_user_id(raw_user_id):
+                user_id = raw_user_id
     return user_id
 
 
diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py
index 44d41097833..2ca8e3daba3 100644
--- a/litellm/proxy/management_endpoints/model_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/model_management_endpoints.py
@@ -13,13 +13,13 @@ model/{model_id}/update - PATCH endpoint for model update.
 import asyncio
 import datetime
 import json
-from litellm._uuid import uuid
 from typing import Dict, List, Literal, Optional, Tuple, Union, cast
 
 from fastapi import APIRouter, Depends, HTTPException, Request, status
 from pydantic import BaseModel, ConfigDict, Field
 
 from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
 from litellm.constants import LITELLM_PROXY_ADMIN_NAME
 from litellm.proxy._types import (
     CommonProxyErrors,
@@ -32,7 +32,7 @@ from litellm.proxy._types import (
     ProxyErrorTypes,
     ProxyException,
     TeamModelAddRequest,
-    UpdateTeamRequest,
+    TeamModelDeleteRequest,
     UserAPIKeyAuth,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
@@ -40,7 +40,10 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import encrypt_value_helpe
 from litellm.proxy.management_endpoints.common_utils import _is_user_team_admin
 from litellm.proxy.management_endpoints.team_endpoints import (
     team_model_add,
-    update_team,
+    team_model_delete,
+)
+from litellm.proxy.management_endpoints.team_endpoints import (
+    update_team as _legacy_update_team,
 )
 from litellm.proxy.management_helpers.audit_logs import create_object_audit_log
 from litellm.proxy.utils import PrismaClient
@@ -58,6 +61,14 @@ from litellm.utils import get_utc_datetime
 router = APIRouter()
 
 
+async def update_team(*args, **kwargs):
+    """
+    Backward-compatible shim for tests/legacy call sites that patch this symbol.
+    Team model management now uses team_model_add/team_model_delete directly.
+    """
+    return await _legacy_update_team(*args, **kwargs)
+
+
 class UpdatePublicModelGroupsRequest(BaseModel):
     """Request model for updating public model groups"""
 
@@ -324,17 +335,24 @@ async def _add_team_model_to_db(
 
     - generate a unique 'model_name' for the model (e.g. 'model_name_{team_id}_{uuid})
     - store the model in the db with the unique 'model_name'
-    - store a team model alias mapping {"model_name": "model_name_{team_id}_{uuid}"}
+    - add the public model name to the team's allowed models list
     """
     _team_id = model_params.model_info.team_id
     if _team_id is None:
         return None
+
+    # Capture the original public name FIRST, before any mutations
     original_model_name = model_params.model_name
+
+    # Set team_public_model_name in model_info using the captured original_model_name
+    # This must happen BEFORE mutating model_params.model_name so _add_model_to_db
+    # serializes the correct team_public_model_name (not the internal UUID name)
     if original_model_name:
         model_params.model_info.team_public_model_name = original_model_name
 
+    # Generate and assign unique internal model_name LAST
+    # (after team_public_model_name is safely stored)
     unique_model_name = f"model_name_{_team_id}_{uuid.uuid4()}"
-
     model_params.model_name = unique_model_name
 
     ## CREATE MODEL IN DB ##
@@ -344,25 +362,15 @@ async def _add_team_model_to_db(
         prisma_client=prisma_client,
     )
 
-    ## CREATE MODEL ALIAS IN DB ##
-    await update_team(
-        data=UpdateTeamRequest(
-            team_id=_team_id,
-            model_aliases={original_model_name: unique_model_name},
-        ),
-        user_api_key_dict=user_api_key_dict,
-        http_request=Request(scope={"type": "http"}),
-    )
-
-    # add model to team object
-    await team_model_add(
-        data=TeamModelAddRequest(
-            team_id=_team_id,
-            models=[original_model_name],
-        ),
-        http_request=Request(scope={"type": "http"}),
-        user_api_key_dict=user_api_key_dict,
-    )
+    if original_model_name:
+        await team_model_add(
+            data=TeamModelAddRequest(
+                team_id=_team_id,
+                models=[original_model_name],
+            ),
+            http_request=Request(scope={"type": "http"}),
+            user_api_key_dict=user_api_key_dict,
+        )
 
     return model_response
 
@@ -428,6 +436,7 @@ async def _update_team_model_in_db(
             db_model=db_model,
             patch_data=patch_data,
             user_api_key_dict=user_api_key_dict,
+            prisma_client=prisma_client,
         )
 
     return update_db_model(db_model=db_model, updated_patch=patch_data)
@@ -453,19 +462,10 @@ async def _setup_new_team_model_assignment(
     patch_data: updateDeployment,
     user_api_key_dict: UserAPIKeyAuth,
 ) -> None:
-    """Set up a new team model with unique name, alias, and team membership."""
+    """Set up a new team model with unique name and team membership."""
     unique_model_name = f"model_name_{team_id}_{uuid.uuid4()}"
     patch_data.model_name = unique_model_name
 
-    await update_team(
-        data=UpdateTeamRequest(
-            team_id=team_id,
-            model_aliases={public_model_name: unique_model_name},
-        ),
-        user_api_key_dict=user_api_key_dict,
-        http_request=Request(scope={"type": "http"}),
-    )
-
     await team_model_add(
         data=TeamModelAddRequest(
             team_id=team_id,
@@ -476,30 +476,119 @@ async def _setup_new_team_model_assignment(
     )
 
 
+async def _get_team_deployments(
+    team_id: str, prisma_client: PrismaClient
+) -> List[LiteLLM_ProxyModelTable]:
+    """
+    Fetch all deployments for a given team_id from the database.
+
+    Centralizes team deployment queries to ensure consistent filtering and error handling.
+    This is the established helper pattern for team deployment DB access in this module.
+
+    Note: Direct Prisma call is intentional here as this IS the helper function that
+    encapsulates the DB access pattern for team deployments.
+    """
+    response = await prisma_client.db.litellm_proxymodeltable.find_many(
+        where={
+            "model_info": {
+                "path": ["team_id"],
+                "equals": team_id,
+            }
+        }
+    )
+    return response if response else []
+
+
 async def _update_existing_team_model_assignment(
     team_id: str,
     public_model_name: str,
     db_model: Deployment,
     patch_data: updateDeployment,
     user_api_key_dict: UserAPIKeyAuth,
+    prisma_client: Optional[PrismaClient],
 ) -> None:
-    """Update an existing team model if the public name changed."""
+    """Update an existing team model if the public name changed.
+
+    Note on DB scan: Prisma's JSON filtering does not support compound AND conditions
+    across multiple JSON paths, so we fetch all deployments for the team and filter
+    team_public_model_name in Python. For teams with many deployments this scan grows
+    linearly; if team deployment counts become large this should be revisited.
+    """
+
+    def _get_team_public_model_name(
+        model_info: Optional[Union[dict, str]]
+    ) -> Optional[str]:
+        if isinstance(model_info, dict):
+            value = model_info.get("team_public_model_name")
+            return value if isinstance(value, str) else None
+        if isinstance(model_info, str):
+            try:
+                parsed = json.loads(model_info)
+            except (TypeError, ValueError):
+                return None
+            if isinstance(parsed, dict):
+                value = parsed.get("team_public_model_name")
+                return value if isinstance(value, str) else None
+        return None
+
     old_public_name = (
         db_model.model_info.team_public_model_name if db_model.model_info else None
     )
 
-    # Update alias only if public name changed
     if old_public_name and public_model_name != old_public_name:
-        await update_team(
-            data=UpdateTeamRequest(
+        # Clear user-supplied public name from patch before any early return so the
+        # caller does not overwrite the internal UUID-based model_name in the DB.
+        patch_data.model_name = None
+        if prisma_client is None:
+            verbose_proxy_logger.warning(
+                "prisma_client not initialized; skipping public name update entirely to avoid orphaned entries"
+            )
+            return
+
+        # Query DB for all team deployments to check for sibling deployments
+        team_deployments = await _get_team_deployments(team_id, prisma_client)
+        other_deployments_with_old_name = [
+            d
+            for d in team_deployments
+            if d.model_name != db_model.model_name
+            and _get_team_public_model_name(d.model_info) == old_public_name
+        ]
+
+        # Add new name first, then delete old name to prevent access loss on partial failure
+        await team_model_add(
+            data=TeamModelAddRequest(
                 team_id=team_id,
-                model_aliases={public_model_name: db_model.model_name},
+                models=[public_model_name],
             ),
-            user_api_key_dict=user_api_key_dict,
             http_request=Request(scope={"type": "http"}),
+            user_api_key_dict=user_api_key_dict,
         )
 
-    # Keep existing unique model_name
+        if not other_deployments_with_old_name:
+            await team_model_delete(
+                data=TeamModelDeleteRequest(
+                    team_id=team_id,
+                    models=[old_public_name],
+                ),
+                http_request=Request(scope={"type": "http"}),
+                user_api_key_dict=user_api_key_dict,
+            )
+    elif not old_public_name and public_model_name:
+        # First-time assignment of public name on an existing team deployment:
+        # ensure the team's models list is updated so team routing can resolve it.
+        await team_model_add(
+            data=TeamModelAddRequest(
+                team_id=team_id,
+                models=[public_model_name],
+            ),
+            http_request=Request(scope={"type": "http"}),
+            user_api_key_dict=user_api_key_dict,
+        )
+    # else: old_public_name == public_model_name (no rename needed)
+    # No team_model_add/delete calls required; public name is already registered
+
+    # Always clear patch_data.model_name to prevent caller from overwriting
+    # the internal UUID-based model_name in the DB with the user-supplied public name
     patch_data.model_name = None
 
 
diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py
index d06ce56f816..79a9e9bdf2b 100644
--- a/litellm/proxy/management_endpoints/ui_sso.py
+++ b/litellm/proxy/management_endpoints/ui_sso.py
@@ -15,7 +15,18 @@ import inspect
 import os
 import secrets
 from copy import deepcopy
-from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    List,
+    Literal,
+    NoReturn,
+    Optional,
+    Tuple,
+    Union,
+    cast,
+)
 from urllib.parse import urlencode, urlparse
 
 if TYPE_CHECKING:
@@ -338,7 +349,7 @@ async def google_login(
                 total_users = await prisma_client.db.litellm_usertable.count()
                 if total_users and total_users > 5:
                     raise ProxyException(
-                        message="You must be a LiteLLM Enterprise user to use SSO for more than 5 users. If you have a license please set `LITELLM_LICENSE` in your env. If you want to obtain a license meet with us here: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions You are seeing this error message because You set one of `MICROSOFT_CLIENT_ID`, `GOOGLE_CLIENT_ID`, or `GENERIC_CLIENT_ID` in your env. Please unset this",
+                        message="You must be a LiteLLM Enterprise user to use SSO for more than 5 users. If you have a license please set `LITELLM_LICENSE` in your env. If you want to obtain a license meet with us here: https://enterprise.litellm.ai/demo You are seeing this error message because You set one of `MICROSOFT_CLIENT_ID`, `GOOGLE_CLIENT_ID`, or `GENERIC_CLIENT_ID` in your env. Please unset this",
                         type=ProxyErrorTypes.auth_error,
                         param="premium_user",
                         code=status.HTTP_403_FORBIDDEN,
@@ -404,14 +415,14 @@ async def google_login(
             state=cli_state,
         )
         if return_to is not None and sso_redirect is not None:
-            SSOAuthenticationHandler._validate_return_to(return_to)
-            sso_redirect.set_cookie(
-                key="litellm_cp_return_to",
-                value=return_to,
-                max_age=600,
-                httponly=True,
-                samesite="lax",
-            )
+            if SSOAuthenticationHandler._validate_return_to(return_to):
+                sso_redirect.set_cookie(
+                    key="litellm_cp_return_to",
+                    value=return_to,
+                    max_age=600,
+                    httponly=True,
+                    samesite="lax",
+                )
         return sso_redirect
     elif ui_username is not None:
         # No Google, Microsoft SSO
@@ -745,7 +756,7 @@ def _handle_generic_sso_error(
     generic_authorization_endpoint: Optional[str],
     generic_token_endpoint: Optional[str],
     additional_headers: dict,
-) -> None:
+) -> NoReturn:
     """Handle errors from generic SSO verify_and_process. Always re-raises."""
     error_message = str(e)
 
@@ -1778,22 +1789,19 @@ class SSOAuthenticationHandler:
     """
 
     @staticmethod
-    def _validate_return_to(return_to: str) -> None:
+    def _validate_return_to(return_to: str) -> bool:
         """
         Validate that return_to matches the configured control_plane_url origin.
 
-        Raises HTTPException(400) if:
-        - control_plane_url is not configured in general_settings
-        - return_to origin does not match control_plane_url origin
+        Returns True if return_to is valid and should be used.
+        Returns False if control_plane_url is not configured (return_to is ignored).
+        Raises HTTPException(400) if return_to origin does not match control_plane_url origin.
         """
         from litellm.proxy.proxy_server import general_settings
 
         control_plane_url = general_settings.get("control_plane_url")
         if control_plane_url is None:
-            raise HTTPException(
-                status_code=400,
-                detail="return_to is not allowed: control_plane_url is not configured",
-            )
+            return False
 
         def _origin(url: str) -> tuple:
             parsed = urlparse(url)
@@ -1809,6 +1817,8 @@ class SSOAuthenticationHandler:
                 detail="return_to does not match the configured control_plane_url",
             )
 
+        return True
+
     @staticmethod
     async def get_sso_login_redirect(
         redirect_url: str,
@@ -2589,9 +2599,9 @@ class SSOAuthenticationHandler:
         # Control-plane cross-origin: store JWT behind a single-use opaque
         # code (60s TTL) so the token never appears in browser history / logs.
         # The control plane redeems it via POST /v3/login/exchange.
-        if return_to is not None:
-            SSOAuthenticationHandler._validate_return_to(return_to)
-
+        if return_to is not None and SSOAuthenticationHandler._validate_return_to(
+            return_to
+        ):
             code = secrets.token_urlsafe(32)
             cache_key = f"login_code:{code}"
             cache_value = {"token": jwt_token, "redirect_url": return_to}
@@ -3604,7 +3614,7 @@ async def debug_sso_login(request: Request):
     ):
         if premium_user is not True:
             raise ProxyException(
-                message="You must be a LiteLLM Enterprise user to use SSO. If you have a license please set `LITELLM_LICENSE` in your env. If you want to obtain a license meet with us here: https://calendly.com/d/cx9p-5yf-2nm/litellm-introductions You are seeing this error message because You set one of `MICROSOFT_CLIENT_ID`, `GOOGLE_CLIENT_ID`, or `GENERIC_CLIENT_ID` in your env. Please unset this",
+                message="You must be a LiteLLM Enterprise user to use SSO. If you have a license please set `LITELLM_LICENSE` in your env. If you want to obtain a license meet with us here: https://enterprise.litellm.ai/demo You are seeing this error message because You set one of `MICROSOFT_CLIENT_ID`, `GOOGLE_CLIENT_ID`, or `GENERIC_CLIENT_ID` in your env. Please unset this",
                 type=ProxyErrorTypes.auth_error,
                 param="premium_user",
                 code=status.HTTP_403_FORBIDDEN,
diff --git a/litellm/proxy/management_helpers/audit_logs.py b/litellm/proxy/management_helpers/audit_logs.py
index ea082f468ae..b9020222f1f 100644
--- a/litellm/proxy/management_helpers/audit_logs.py
+++ b/litellm/proxy/management_helpers/audit_logs.py
@@ -2,12 +2,15 @@
 Functions to create audit logs for LiteLLM Proxy
 """
 
+import asyncio
 import json
-from litellm._uuid import uuid
 from datetime import datetime, timezone
+from typing import Dict
 
 import litellm
 from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
+from litellm.integrations.custom_logger import CustomLogger
 from litellm.proxy._types import (
     AUDIT_ACTIONS,
     LiteLLM_AuditLogs,
@@ -15,6 +18,93 @@ from litellm.proxy._types import (
     Optional,
     UserAPIKeyAuth,
 )
+from litellm.types.utils import StandardAuditLogPayload
+
+_audit_log_callback_cache: Dict[str, CustomLogger] = {}
+
+
+def _resolve_audit_log_callback(name: str) -> Optional[CustomLogger]:
+    """Resolve a string callback name to a CustomLogger instance, with caching."""
+    if name in _audit_log_callback_cache:
+        return _audit_log_callback_cache[name]
+
+    from litellm.litellm_core_utils.litellm_logging import (
+        _init_custom_logger_compatible_class,
+    )
+
+    instance = _init_custom_logger_compatible_class(
+        logging_integration=name,  # type: ignore
+        internal_usage_cache=None,
+        llm_router=None,
+    )
+
+    if instance is not None:
+        _audit_log_callback_cache[name] = instance
+    return instance
+
+
+def _build_audit_log_payload(
+    request_data: LiteLLM_AuditLogs,
+) -> StandardAuditLogPayload:
+    """Convert LiteLLM_AuditLogs to StandardAuditLogPayload for callback dispatch."""
+    updated_at = ""
+    if request_data.updated_at is not None:
+        updated_at = request_data.updated_at.isoformat()
+
+    table_name_str: str = request_data.table_name.value if isinstance(request_data.table_name, LitellmTableNames) else str(request_data.table_name)
+
+    return StandardAuditLogPayload(
+        id=request_data.id,
+        updated_at=updated_at,
+        changed_by=request_data.changed_by or "",
+        changed_by_api_key=request_data.changed_by_api_key or "",
+        action=request_data.action,
+        table_name=table_name_str,
+        object_id=request_data.object_id,
+        before_value=request_data.before_value,
+        updated_values=request_data.updated_values,
+    )
+
+
+def _audit_log_task_done_callback(task: asyncio.Task) -> None:
+    """Log exceptions from audit log callback tasks so they don't slip through silently."""
+    try:
+        exc = task.exception()
+    except asyncio.CancelledError:
+        return
+    if exc is not None:
+        verbose_proxy_logger.error(
+            "Audit log callback task failed: %s", exc, exc_info=exc
+        )
+
+
+async def _dispatch_audit_log_to_callbacks(
+    request_data: LiteLLM_AuditLogs,
+) -> None:
+    """Dispatch audit log to all registered audit_log_callbacks."""
+    if not litellm.audit_log_callbacks:
+        return
+
+    payload = _build_audit_log_payload(request_data)
+
+    for callback in litellm.audit_log_callbacks:
+        try:
+            resolved: Optional[CustomLogger] = callback if isinstance(callback, CustomLogger) else None
+            if isinstance(callback, str):
+                resolved = _resolve_audit_log_callback(callback)
+                if resolved is None:
+                    verbose_proxy_logger.warning(
+                        "Could not resolve audit log callback: %s", callback
+                    )
+                    continue
+
+            if isinstance(resolved, CustomLogger):
+                task = asyncio.create_task(resolved.async_log_audit_log_event(payload))
+                task.add_done_callback(_audit_log_task_done_callback)
+        except Exception as e:
+            verbose_proxy_logger.error(
+                "Failed dispatching audit log to callback: %s", e
+            )
 
 
 async def create_object_audit_log(
@@ -40,20 +130,24 @@ async def create_object_audit_log(
     """
     from litellm.secret_managers.main import get_secret_bool
 
-    store_audit_logs = litellm.store_audit_logs or get_secret_bool(
+    _store_audit_logs: Optional[bool] = litellm.store_audit_logs or get_secret_bool(
         "LITELLM_STORE_AUDIT_LOGS"
     )
 
-    if store_audit_logs is not True:
+    if _store_audit_logs is not True:
         return
 
+    _changed_by = (
+        litellm_changed_by
+        or user_api_key_dict.user_id
+        or litellm_proxy_admin_name
+    )
+
     await create_audit_log_for_update(
         request_data=LiteLLM_AuditLogs(
             id=str(uuid.uuid4()),
             updated_at=datetime.now(timezone.utc),
-            changed_by=litellm_changed_by
-            or user_api_key_dict.user_id
-            or litellm_proxy_admin_name,
+            changed_by=_changed_by,
             changed_by_api_key=user_api_key_dict.api_key,
             table_name=table_name,
             object_id=object_id,
@@ -70,10 +164,10 @@ async def create_audit_log_for_update(request_data: LiteLLM_AuditLogs):
     """
     from litellm.secret_managers.main import get_secret_bool
 
-    store_audit_logs = litellm.store_audit_logs or get_secret_bool(
+    _store_audit_logs: Optional[bool] = litellm.store_audit_logs or get_secret_bool(
         "LITELLM_STORE_AUDIT_LOGS"
     )
-    if store_audit_logs is not True:
+    if _store_audit_logs is not True:
         return
 
     from litellm.proxy.proxy_server import premium_user, prisma_client
@@ -81,9 +175,6 @@ async def create_audit_log_for_update(request_data: LiteLLM_AuditLogs):
     if premium_user is not True:
         return
 
-    if prisma_client is None:
-        raise Exception("prisma_client is None, no DB connected")
-
     verbose_proxy_logger.debug("creating audit log for %s", request_data)
 
     if isinstance(request_data.updated_values, dict):
@@ -92,6 +183,15 @@ async def create_audit_log_for_update(request_data: LiteLLM_AuditLogs):
     if isinstance(request_data.before_value, dict):
         request_data.before_value = json.dumps(request_data.before_value)
 
+    # Dispatch to external audit log callbacks regardless of DB availability
+    await _dispatch_audit_log_to_callbacks(request_data)
+
+    if prisma_client is None:
+        verbose_proxy_logger.error(
+            "prisma_client is None, cannot write audit log to DB"
+        )
+        return
+
     _request_data = request_data.model_dump(exclude_none=True)
 
     try:
@@ -103,5 +203,3 @@ async def create_audit_log_for_update(request_data: LiteLLM_AuditLogs):
     except Exception as e:
         # [Non-Blocking Exception. Do not allow blocking LLM API call]
         verbose_proxy_logger.error(f"Failed Creating audit log {e}")
-
-    return
diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
index 0aa99685209..4f68c92b9d9 100644
--- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py
@@ -40,6 +40,7 @@ from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
 from litellm.passthrough import BasePassthroughUtils
 from litellm.proxy._types import (
+    CommonProxyErrors,
     ConfigFieldInfo,
     ConfigFieldUpdate,
     LiteLLMRoutes,
@@ -54,6 +55,7 @@ from litellm.proxy.common_utils.http_parsing_utils import (
     _read_request_body,
     _safe_get_request_headers,
 )
+from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
 from litellm.proxy.utils import get_server_root_path, normalize_route_for_root_path
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.custom_http import httpxSpecialProvider
@@ -61,7 +63,6 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import (
     EndpointType,
     PassthroughStandardLoggingPayload,
 )
-from litellm.types.utils import StandardLoggingUserAPIKeyMetadata
 
 from .streaming_handler import PassThroughStreamingHandler
 from .success_handler import PassThroughEndpointLogging
@@ -501,25 +502,8 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils):
                 litellm_params_in_body[k] = _parsed_body.pop(k, None)
 
         _metadata = dict(
-            StandardLoggingUserAPIKeyMetadata(
-                user_api_key_hash=user_api_key_dict.api_key,
-                user_api_key_alias=user_api_key_dict.key_alias,
-                user_api_key_user_email=user_api_key_dict.user_email,
-                user_api_key_user_id=user_api_key_dict.user_id,
-                user_api_key_team_id=user_api_key_dict.team_id,
-                user_api_key_org_id=user_api_key_dict.org_id,
-                user_api_key_project_id=user_api_key_dict.project_id,
-                user_api_key_team_alias=user_api_key_dict.team_alias,
-                user_api_key_end_user_id=user_api_key_dict.end_user_id,
-                user_api_key_request_route=user_api_key_dict.request_route,
-                user_api_key_spend=user_api_key_dict.spend,
-                user_api_key_max_budget=user_api_key_dict.max_budget,
-                user_api_key_budget_reset_at=(
-                    user_api_key_dict.budget_reset_at.isoformat()
-                    if user_api_key_dict.budget_reset_at
-                    else None
-                ),
-                user_api_key_auth_metadata=user_api_key_dict.metadata,
+            LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(
+                user_api_key_dict=user_api_key_dict
             )
         )
 
@@ -651,6 +635,7 @@ async def pass_through_request(  # noqa: PLR0915
     _parsed_body: Optional[dict] = None
     # kwargs for pass through endpoint, contains metadata, litellm_params, call_type, litellm_call_id, passthrough_logging_payload
     kwargs: Optional[dict] = None
+    logging_obj: Optional[Logging] = None
 
     #########################################################
     try:
@@ -2021,13 +2006,24 @@ class InitPassThroughEndpointHelpers:
 
     @staticmethod
     def remove_endpoint_routes(endpoint_id: str):
-        """Remove all routes for a specific endpoint ID from the registry"""
+        """Remove all routes for a specific endpoint ID from the registry
+        and clean up corresponding entries from LiteLLMRoutes.openai_routes."""
         keys_to_remove = [
             key
             for key, value in _registered_pass_through_routes.items()
             if value["endpoint_id"] == endpoint_id
         ]
         for key in keys_to_remove:
+            route_info = _registered_pass_through_routes[key]
+            path = route_info.get("path")
+            if isinstance(path, str):
+                openai_routes = LiteLLMRoutes.openai_routes.value
+                if path in openai_routes:
+                    openai_routes.remove(path)
+                if route_info.get("type") == "subpath":
+                    wildcard_path = path.rstrip("/") + "/*"
+                    if wildcard_path in openai_routes:
+                        openai_routes.remove(wildcard_path)
             del _registered_pass_through_routes[key]
             verbose_proxy_logger.debug(
                 "Removed pass-through route from registry: %s", key
@@ -2143,6 +2139,102 @@ def _get_combined_pass_through_endpoints(
     return pass_through_endpoints + config_pass_through_endpoints
 
 
+async def _register_pass_through_endpoint(
+    endpoint: Union[Dict[str, Any], PassThroughGenericEndpoint],
+    app: FastAPI,
+    premium_user: bool,
+    visited_endpoints: set[str],
+) -> None:
+    endpoint_data: Dict[str, Any]
+    if isinstance(endpoint, PassThroughGenericEndpoint):
+        endpoint_data = endpoint.model_dump()
+    else:
+        endpoint_data = endpoint
+
+    if endpoint_data.get("id") is None:
+        endpoint_data["id"] = str(uuid.uuid4())
+    endpoint_id = cast(str, endpoint_data["id"])
+
+    target = endpoint_data.get("target")
+    path = endpoint_data.get("path")
+    if path is None:
+        raise ValueError("Path is required for pass-through endpoint")
+
+    custom_headers = await set_env_variables_in_header(
+        custom_headers=endpoint_data.get("headers")
+    )
+    forward_headers = endpoint_data.get("forward_headers")
+    merge_query_params = endpoint_data.get("merge_query_params")
+    default_query_params = endpoint_data.get("default_query_params")
+    auth = endpoint_data.get("auth")
+    dependencies = None
+
+    if auth is not None and str(auth).lower() == "true":
+        if premium_user is not True:
+            raise ValueError(
+                "Error Setting Authentication on Pass Through Endpoint: {}".format(
+                    CommonProxyErrors.not_premium_user.value
+                )
+            )
+        dependencies = [Depends(user_api_key_auth)]
+        if path not in LiteLLMRoutes.openai_routes.value:
+            LiteLLMRoutes.openai_routes.value.append(path)
+
+    if target is None:
+        return
+
+    guardrails = endpoint_data.get("guardrails")
+    methods = endpoint_data.get("methods")
+    cost_per_request = endpoint_data.get("cost_per_request")
+
+    verbose_proxy_logger.debug(
+        "Initializing pass through endpoint: %s (ID: %s)", path, endpoint_id
+    )
+    InitPassThroughEndpointHelpers.add_exact_path_route(
+        app=app,
+        path=path,
+        target=target,
+        custom_headers=custom_headers,
+        forward_headers=forward_headers,
+        merge_query_params=merge_query_params,
+        dependencies=dependencies,
+        cost_per_request=cost_per_request,
+        endpoint_id=endpoint_id,
+        guardrails=guardrails,
+        methods=methods,
+        default_query_params=default_query_params,
+    )
+
+    methods_for_key = methods if methods else ["GET", "POST", "PUT", "DELETE", "PATCH"]
+    methods_str = ",".join(sorted(methods_for_key))
+    visited_endpoints.add(f"{endpoint_id}:exact:{path}:{methods_str}")
+
+    if endpoint_data.get("include_subpath", False) is True:
+        if auth is not None and str(auth).lower() == "true":
+            wildcard_path = path.rstrip("/") + "/*"
+            if wildcard_path not in LiteLLMRoutes.openai_routes.value:
+                LiteLLMRoutes.openai_routes.value.append(wildcard_path)
+        InitPassThroughEndpointHelpers.add_subpath_route(
+            app=app,
+            path=path,
+            target=target,
+            custom_headers=custom_headers,
+            forward_headers=forward_headers,
+            merge_query_params=merge_query_params,
+            dependencies=dependencies,
+            cost_per_request=cost_per_request,
+            endpoint_id=endpoint_id,
+            guardrails=guardrails,
+            methods=methods,
+            default_query_params=default_query_params,
+        )
+        visited_endpoints.add(f"{endpoint_id}:subpath:{path}:{methods_str}")
+
+    verbose_proxy_logger.debug(
+        "Added new pass through endpoint: %s (ID: %s)", path, endpoint_id
+    )
+
+
 async def initialize_pass_through_endpoints(
     pass_through_endpoints: Union[List[Dict], List[PassThroughGenericEndpoint]],
 ):
@@ -2159,10 +2251,7 @@ async def initialize_pass_through_endpoints(
     Returns:
         None
     """
-    from litellm._uuid import uuid
-
     verbose_proxy_logger.debug("initializing pass through endpoints")
-    from litellm.proxy._types import CommonProxyErrors, LiteLLMRoutes
     from litellm.proxy.proxy_server import (
         app,
         config_passthrough_endpoints,
@@ -2189,98 +2278,14 @@ async def initialize_pass_through_endpoints(
         InitPassThroughEndpointHelpers.get_all_registered_pass_through_routes()
     )
 
-    visited_endpoints = set()
+    visited_endpoints: set[str] = set()
 
     for endpoint in combined_pass_through_endpoints:
-        if isinstance(endpoint, PassThroughGenericEndpoint):
-            endpoint = endpoint.model_dump()
-
-        # Auto-generate ID for backwards compatibility if not present
-        if endpoint.get("id") is None:
-            endpoint["id"] = str(uuid.uuid4())
-
-        # Get the endpoint_id as a string (guaranteed to be set at this point)
-        endpoint_id: str = endpoint["id"]
-
-        _target = endpoint.get("target", None)
-        _path: Optional[str] = endpoint.get("path", None)
-        if _path is None:
-            raise ValueError("Path is required for pass-through endpoint")
-        _custom_headers = endpoint.get("headers", None)
-        _custom_headers = await set_env_variables_in_header(
-            custom_headers=_custom_headers
-        )
-        _forward_headers = endpoint.get("forward_headers", None)
-        _merge_query_params = endpoint.get("merge_query_params", None)
-        _default_query_params = endpoint.get("default_query_params", None)
-        _auth = endpoint.get("auth", None)
-        _dependencies = None
-        if _auth is not None and str(_auth).lower() == "true":
-            if premium_user is not True:
-                raise ValueError(
-                    "Error Setting Authentication on Pass Through Endpoint: {}".format(
-                        CommonProxyErrors.not_premium_user.value
-                    )
-                )
-            _dependencies = [Depends(user_api_key_auth)]
-            LiteLLMRoutes.openai_routes.value.append(_path)
-
-        if _target is None:
-            continue
-
-        # Get guardrails config if present
-        _guardrails = endpoint.get("guardrails", None)
-
-        # Get methods list if present (None means all methods for backward compatibility)
-        _methods = endpoint.get("methods", None)
-
-        # Add exact path route
-        verbose_proxy_logger.debug(
-            "Initializing pass through endpoint: %s (ID: %s)", _path, endpoint_id
-        )
-        InitPassThroughEndpointHelpers.add_exact_path_route(
+        await _register_pass_through_endpoint(
+            endpoint=endpoint,
             app=app,
-            path=_path,
-            target=_target,
-            custom_headers=_custom_headers,
-            forward_headers=_forward_headers,
-            merge_query_params=_merge_query_params,
-            dependencies=_dependencies,
-            cost_per_request=endpoint.get("cost_per_request", None),
-            endpoint_id=endpoint_id,
-            guardrails=_guardrails,
-            methods=_methods,
-            default_query_params=_default_query_params,
-        )
-
-        # Generate route key with methods for tracking
-        methods_for_key = (
-            _methods if _methods else ["GET", "POST", "PUT", "DELETE", "PATCH"]
-        )
-        methods_str = ",".join(sorted(methods_for_key))
-        visited_endpoints.add(f"{endpoint_id}:exact:{_path}:{methods_str}")
-
-        # Add wildcard route for sub-paths
-        if endpoint.get("include_subpath", False) is True:
-            InitPassThroughEndpointHelpers.add_subpath_route(
-                app=app,
-                path=_path,
-                target=_target,
-                custom_headers=_custom_headers,
-                forward_headers=_forward_headers,
-                merge_query_params=_merge_query_params,
-                dependencies=_dependencies,
-                cost_per_request=endpoint.get("cost_per_request", None),
-                endpoint_id=endpoint_id,
-                guardrails=_guardrails,
-                methods=_methods,
-                default_query_params=_default_query_params,
-            )
-
-            visited_endpoints.add(f"{endpoint_id}:subpath:{_path}:{methods_str}")
-
-        verbose_proxy_logger.debug(
-            "Added new pass through endpoint: %s (ID: %s)", _path, endpoint_id
+            premium_user=premium_user,
+            visited_endpoints=visited_endpoints,
         )
 
     # remove the ones that are not visited from the list
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index e982c934aa6..9499be68399 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -480,11 +480,11 @@ from litellm.proxy.search_endpoints.search_tool_management import (
     router as search_tool_management_router,
 )
 from litellm.proxy.spend_tracking.cloudzero_endpoints import router as cloudzero_router
-from litellm.proxy.spend_tracking.vantage_endpoints import router as vantage_router
 from litellm.proxy.spend_tracking.spend_management_endpoints import (
     router as spend_management_router,
 )
 from litellm.proxy.spend_tracking.spend_tracking_utils import get_logging_payload
+from litellm.proxy.spend_tracking.vantage_endpoints import router as vantage_router
 from litellm.proxy.types_utils.utils import get_instance_fn
 from litellm.proxy.ui_crud_endpoints.proxy_setting_endpoints import (
     router as ui_crud_endpoints_router,
@@ -2112,6 +2112,37 @@ def _schedule_background_health_check_db_save(
     )
 
 
+def _write_health_state_to_router_cache(
+    healthy_endpoints: list,
+    unhealthy_endpoints: list,
+) -> None:
+    """
+    Write deployment health states to the router's health state cache
+    for health-check-driven routing. No-op if the feature is disabled.
+    """
+    from litellm.proxy.health_check import build_deployment_health_states
+
+    try:
+        if llm_router is None or not llm_router.enable_health_check_routing:
+            return
+
+        states = build_deployment_health_states(
+            healthy_endpoints=healthy_endpoints,
+            unhealthy_endpoints=unhealthy_endpoints,
+        )
+        if states:
+            llm_router.health_state_cache.set_deployment_health_states(states)
+            verbose_proxy_logger.debug(
+                "health_check_routing_state_updated healthy=%d unhealthy=%d",
+                sum(1 for s in states.values() if s.get("is_healthy")),
+                sum(1 for s in states.values() if not s.get("is_healthy")),
+            )
+    except Exception as e:
+        verbose_proxy_logger.warning(
+            "Failed to write health state to router cache: %s", str(e)
+        )
+
+
 async def _run_background_health_check():
     """
     Periodically run health checks in the background on the endpoints.
@@ -2281,6 +2312,9 @@ async def _run_background_health_check():
             unhealthy_endpoints,
         )
 
+        # Write health state to router cache for health-check-driven routing
+        _write_health_state_to_router_cache(healthy_endpoints, unhealthy_endpoints)
+
         await asyncio.sleep(health_check_interval)
 
 
@@ -2695,12 +2729,36 @@ class ProxyConfig:
 
         return search_tools_parsed if search_tools_parsed else None
 
+    # Environment variable keys that must not be overridden via config because
+    # they can alter process execution, library loading, or network routing.
+    _BLOCKED_ENV_KEYS: Set[str] = {
+        "PATH",
+        "LD_PRELOAD",
+        "LD_LIBRARY_PATH",
+        "DYLD_LIBRARY_PATH",
+        "DYLD_INSERT_LIBRARIES",
+        "PYTHONPATH",
+        "PYTHONSTARTUP",
+        "PYTHONHOME",
+        "HOME",
+        "USER",
+        "SHELL",
+        "LOGNAME",
+        "NO_PROXY",
+        "no_proxy",
+    }
+
     def _load_environment_variables(self, config: dict):
         ## ENVIRONMENT VARIABLES
         global premium_user
         environment_variables = config.get("environment_variables", None)
         if environment_variables:
             for key, value in environment_variables.items():
+                if key in self._BLOCKED_ENV_KEYS:
+                    verbose_proxy_logger.warning(
+                        "Skipping blocked environment variable key: %s", key
+                    )
+                    continue
                 #########################################################
                 # handles this scenario:
                 # ```yaml
@@ -2961,6 +3019,29 @@ class ProxyConfig:
                     print(  # noqa
                         f"{blue_color_code} Initialized Failure Callbacks - {litellm.failure_callback} {reset_color_code}"
                     )  # noqa
+                elif key == "audit_log_callbacks":
+                    litellm.audit_log_callbacks = []
+
+                    for callback in value:
+                        if "." in callback:
+                            litellm.audit_log_callbacks.append(
+                                get_instance_fn(value=callback)
+                            )
+                        else:
+                            litellm.audit_log_callbacks.append(callback)
+
+                    _store_audit_logs = litellm_settings.get(
+                        "store_audit_logs", litellm.store_audit_logs
+                    )
+                    if _store_audit_logs:
+                        print(  # noqa
+                            f"{blue_color_code} Initialized Audit Log Callbacks - {litellm.audit_log_callbacks} {reset_color_code}"
+                        )  # noqa
+                    else:
+                        verbose_proxy_logger.warning(
+                            "'audit_log_callbacks' is configured but 'store_audit_logs' is not enabled. "
+                            "Audit log callbacks will not fire until 'store_audit_logs: true' is added to litellm_settings."
+                        )
                 elif key == "cache_params":
                     # this is set in the cache branch
                     # see usage here: https://docs.litellm.ai/docs/proxy/caching
@@ -3025,6 +3106,8 @@ class ProxyConfig:
         general_settings = config.get("general_settings", {})
         if general_settings is None:
             general_settings = {}
+        _enable_hc_routing = False
+        _hc_staleness = None
         if general_settings:
             ### LOAD KEY MANAGEMENT SETTINGS FIRST (needed for custom secret manager) ###
             key_management_settings = general_settings.get(
@@ -3204,13 +3287,21 @@ class ProxyConfig:
                 "health_check_concurrency", None
             )
             health_check_details = general_settings.get("health_check_details", True)
+            # Health-check-driven routing (opt-in, passes through to Router later)
+            _enable_hc_routing = general_settings.get(
+                "enable_health_check_routing", False
+            )
+            _hc_staleness = general_settings.get(
+                "health_check_staleness_threshold", None
+            )
             verbose_proxy_logger.info(
-                "background_health_check_config enabled=%s shared=%s interval_seconds=%s max_concurrency=%s details=%s",
+                "background_health_check_config enabled=%s shared=%s interval_seconds=%s max_concurrency=%s details=%s health_check_routing=%s",
                 use_background_health_checks,
                 use_shared_health_check,
                 health_check_interval,
                 health_check_concurrency,
                 health_check_details,
+                _enable_hc_routing,
             )
 
             ### RBAC ###
@@ -3240,6 +3331,11 @@ class ProxyConfig:
             "cache_responses": litellm.cache
             is not None,  # cache if user passed in cache values
         }
+        # Health-check-driven routing params (from general_settings)
+        if _enable_hc_routing:
+            router_params["enable_health_check_routing"] = True
+        if _hc_staleness is not None:
+            router_params["health_check_staleness_threshold"] = _hc_staleness
         ## MODEL LIST
         model_list = config.get("model_list", None)
         if model_list:
@@ -3321,7 +3417,7 @@ class ProxyConfig:
                 async_only_mode=True  # only init async clients
             ),
             ignore_invalid_deployments=True,  # don't raise an error if a deployment is invalid
-        )  # type:ignore
+        )  # type: ignore
 
         if redis_usage_cache is not None and router.cache.redis_cache is None:
             router._update_redis_cache(cache=redis_usage_cache)
@@ -5383,8 +5479,8 @@ async def initialize(  # noqa: PLR0915
         litellm.add_function_to_prompt = True
         dynamic_config["general"]["add_function_to_prompt"] = True
     if max_budget:  # litellm-specific param
-        litellm.max_budget = max_budget
-        dynamic_config["general"]["max_budget"] = max_budget
+        litellm.max_budget = float(max_budget)
+        dynamic_config["general"]["max_budget"] = litellm.max_budget
     if experimental:
         pass
     user_telemetry = telemetry
diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py
index e9c7cce0d73..8023853e263 100644
--- a/litellm/proxy/response_api_endpoints/endpoints.py
+++ b/litellm/proxy/response_api_endpoints/endpoints.py
@@ -119,6 +119,35 @@ async def responses_api(
             f"Starting background response with polling for model={data.get('model')}"
         )
 
+        # Run pre-call checks (rate limits, guardrails, budget) BEFORE creating
+        # polling ID. This ensures rate-limited requests get a synchronous 429
+        # instead of a polling ID that immediately fails in the background task.
+        processor = ProxyBaseLLMRequestProcessing(data=data)
+        try:
+            data, _logging_obj = await processor.common_processing_pre_call_logic(
+                request=request,
+                general_settings=general_settings,
+                proxy_logging_obj=proxy_logging_obj,
+                user_api_key_dict=user_api_key_dict,
+                version=version,
+                proxy_config=proxy_config,
+                user_model=user_model,
+                user_temperature=user_temperature,
+                user_request_timeout=user_request_timeout,
+                user_max_tokens=user_max_tokens,
+                user_api_base=user_api_base,
+                model=None,
+                route_type="aresponses",
+                llm_router=llm_router,
+            )
+        except Exception as e:
+            raise await processor._handle_llm_api_exception(
+                e=e,
+                user_api_key_dict=user_api_key_dict,
+                proxy_logging_obj=proxy_logging_obj,
+                version=version,
+            )
+
         # Initialize polling handler with configured TTL (from global config)
         polling_handler = ResponsePollingHandler(
             redis_cache=redis_usage_cache,
@@ -134,7 +163,9 @@ async def responses_api(
             request_data=data,
         )
 
-        # Start background task to stream and update cache
+        # Start background task to stream and update cache.
+        # Pass pre-processed data so the background task skips pre-call logic
+        # (rate limits, guardrails already checked above).
         asyncio.create_task(
             background_streaming_task(
                 polling_id=polling_id,
diff --git a/litellm/proxy/response_polling/background_streaming.py b/litellm/proxy/response_polling/background_streaming.py
index 7583f30eb2d..bcc98175773 100644
--- a/litellm/proxy/response_polling/background_streaming.py
+++ b/litellm/proxy/response_polling/background_streaming.py
@@ -65,7 +65,9 @@ async def background_streaming_task(  # noqa: PLR0915
         # Create processor
         processor = ProxyBaseLLMRequestProcessing(data=data)
 
-        # Make streaming request
+        # Make streaming request.
+        # Pre-call checks (rate limits, guardrails, budget) were already run
+        # before polling ID creation, so skip them here to avoid double-counting.
         response = await processor.base_process_llm_request(
             request=request,
             fastapi_response=fastapi_response,
@@ -83,6 +85,7 @@ async def background_streaming_task(  # noqa: PLR0915
             user_max_tokens=user_max_tokens,
             user_api_base=user_api_base,
             version=version,
+            skip_pre_call_logic=True,
         )
 
         # Process streaming response following OpenAI events format
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index 7954f0b6460..75c803aad65 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -2759,7 +2759,7 @@ class PrismaClient:
                     and reset_at is not None
                 ):
                     response = await self.db.litellm_verificationtoken.find_many(
-                        where={  # type:ignore
+                        where={  # type: ignore
                             "OR": [
                                 {"expires": None},
                                 {"expires": {"gt": expires}},
@@ -2819,7 +2819,7 @@ class PrismaClient:
                     )  # type: ignore
                 elif query_type == "find_all" and reset_at is not None:
                     response = await self.db.litellm_usertable.find_many(
-                        where={  # type:ignore
+                        where={  # type: ignore
                             "budget_reset_at": {"lt": reset_at},
                         }
                     )
@@ -2831,10 +2831,10 @@ class PrismaClient:
                     if expires is not None:
                         response = await self.db.litellm_usertable.find_many(  # type: ignore
                             order={"spend": "desc"},
-                            where={  # type:ignore
+                            where={  # type: ignore
                                 "OR": [
-                                    {"expires": None},  # type:ignore
-                                    {"expires": {"gt": expires}},  # type:ignore
+                                    {"expires": None},  # type: ignore
+                                    {"expires": {"gt": expires}},  # type: ignore
                                 ],
                             },
                         )
@@ -2881,7 +2881,7 @@ class PrismaClient:
             elif table_name == "budget" and reset_at is not None:
                 if query_type == "find_all":
                     response = await self.db.litellm_budgettable.find_many(
-                        where={  # type:ignore
+                        where={  # type: ignore
                             "OR": [
                                 {
                                     "AND": [
@@ -2909,7 +2909,7 @@ class PrismaClient:
                     )
                 elif query_type == "find_all" and reset_at is not None:
                     response = await self.db.litellm_teamtable.find_many(
-                        where={  # type:ignore
+                        where={  # type: ignore
                             "budget_reset_at": {"lt": reset_at},
                         }
                     )
@@ -2968,6 +2968,7 @@ class PrismaClient:
                             t.members_with_roles AS team_members_with_roles,
                             t.object_permission_id AS team_object_permission_id,
                             t.organization_id as org_id,
+                            p.project_alias AS project_alias,
                             tm.spend AS team_member_spend,
                             m.aliases AS team_model_aliases,
                             -- Added comma to separate b.* columns
@@ -2985,6 +2986,7 @@ class PrismaClient:
                         LEFT JOIN "LiteLLM_TeamMembership" AS tm ON v.team_id = tm.team_id AND tm.user_id = v.user_id
                         LEFT JOIN "LiteLLM_ModelTable" m ON t.model_id = m.id
                         LEFT JOIN "LiteLLM_BudgetTable" AS b ON v.budget_id = b.budget_id
+                        LEFT JOIN "LiteLLM_ProjectTable" AS p ON v.project_id = p.project_id
                         LEFT JOIN "LiteLLM_OrganizationTable" AS o ON v.organization_id = o.organization_id
                         LEFT JOIN "LiteLLM_BudgetTable" AS b2 ON o.budget_id = b2.budget_id
                         WHERE v.token = '{token}'
diff --git a/litellm/responses/file_search/__init__.py b/litellm/responses/file_search/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/responses/file_search/emulated_handler.py b/litellm/responses/file_search/emulated_handler.py
new file mode 100644
index 00000000000..74a7d443c6c
--- /dev/null
+++ b/litellm/responses/file_search/emulated_handler.py
@@ -0,0 +1,592 @@
+"""
+Emulated file_search for providers that don't support the tool natively.
+
+Flow:
+  1. Convert file_search tools to a single function tool definition.
+  2. Call the provider with the function tool.
+  3. If the provider issues a file_search function_call, execute vector search
+     via litellm.vector_stores.main.asearch().
+  4. Feed results back and get the final answer.
+  5. Wrap everything in OpenAI Responses-API format:
+       [file_search_call output item] + [message output item with file_citation annotations]
+"""
+
+import json
+import time
+import uuid
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, cast
+
+from litellm._logging import verbose_logger
+from litellm.types.llms.openai import ResponseOutputItem, ResponsesAPIResponse
+from litellm.types.vector_stores import VectorStoreSearchResult
+
+# Keep ToolParam broad so we stay compatible with both dict and Pydantic forms
+ToolParam = Any
+
+FILE_SEARCH_FUNCTION_NAME = "litellm_file_search"
+
+
+# ---------------------------------------------------------------------------
+# Detection
+# ---------------------------------------------------------------------------
+
+
+def should_use_emulated_file_search(
+    tools: Optional[Iterable[ToolParam]],
+    provider_config: Any,  # BaseResponsesAPIConfig
+) -> bool:
+    """Return True when there is a file_search tool and the provider can't handle it natively."""
+    if not tools:
+        return False
+    has_fs = any(isinstance(t, dict) and t.get("type") == "file_search" for t in tools)
+    if not has_fs:
+        return False
+    return provider_config is None or not provider_config.supports_native_file_search()
+
+
+# ---------------------------------------------------------------------------
+# Tool conversion
+# ---------------------------------------------------------------------------
+
+
+def _build_function_tool(vector_store_ids: List[str]) -> Dict[str, Any]:
+    """
+    Create a Responses API function-tool definition that describes file search.
+    The function accepts one or more natural-language queries (like OpenAI's native
+    file_search); LiteLLM runs the actual vector search against the configured
+    vector stores.
+
+    Note: Uses Responses API format (name/description/parameters at top level),
+    NOT Chat Completion format (nested under "function"), so that the
+    LiteLLMCompletionResponsesConfig transformation picks up name and description.
+    """
+    return {
+        "type": "function",
+        "name": FILE_SEARCH_FUNCTION_NAME,
+        "description": (
+            "Search the knowledge base for information relevant to the query. "
+            "Use this whenever you need to look up specific facts, documents, "
+            "or content from the vector store. You can provide multiple queries "
+            "to search for different aspects of the information."
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "queries": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": (
+                        "One or more search queries to look up in the vector store. "
+                        "Multiple queries help find comprehensive information from "
+                        "different angles."
+                    ),
+                },
+                "vector_store_id": {
+                    "type": "string",
+                    "description": "ID of the vector store to search.",
+                    "enum": vector_store_ids,
+                },
+            },
+            "required": ["queries"],
+        },
+    }
+
+
+def _replace_file_search_tools(
+    tools: Optional[Iterable[ToolParam]],
+) -> Tuple[List[Dict[str, Any]], List[str]]:
+    """
+    Replace all file_search tools with a single function tool.
+
+    Returns:
+        (new_tools_list, all_vector_store_ids)
+    """
+    non_file_search: List[Dict[str, Any]] = []
+    vector_store_ids: List[str] = []
+
+    for tool in tools or []:
+        if isinstance(tool, dict) and tool.get("type") == "file_search":
+            ids = tool.get("vector_store_ids") or []
+            vector_store_ids.extend(ids)
+        else:
+            non_file_search.append(tool)
+
+    # Deduplicate while preserving order
+    unique_ids: List[str] = list(dict.fromkeys(vector_store_ids))
+    if unique_ids:
+        non_file_search.append(_build_function_tool(unique_ids))
+
+    return non_file_search, unique_ids
+
+
+# ---------------------------------------------------------------------------
+# Search execution
+# ---------------------------------------------------------------------------
+
+
+async def _run_vector_searches(
+    queries: List[str],
+    vector_store_ids: List[str],
+) -> Tuple[List[str], List[VectorStoreSearchResult]]:
+    """
+    Run `asearch` against all vector stores for all queries and collect results.
+
+    Args:
+        queries: List of search queries to execute (like OpenAI's multi-query approach)
+        vector_store_ids: Vector store IDs to search
+
+    Returns:
+        (queries_list, combined_results)
+    """
+    import litellm.vector_stores.main as vs_main
+
+    all_results: List[VectorStoreSearchResult] = []
+    ids_to_search = vector_store_ids
+
+    # Execute each query against all vector stores
+    for query in queries:
+        for vs_id in ids_to_search:
+            try:
+                response = await vs_main.asearch(
+                    vector_store_id=vs_id,
+                    query=query,
+                )
+                results_data = (
+                    response.get("data")
+                    if isinstance(response, dict)
+                    else getattr(response, "data", None)
+                )
+                if results_data:
+                    all_results.extend(results_data)
+            except Exception as exc:
+                verbose_logger.warning(
+                    "file_search emulated: search failed for query='%s', vector_store_id='%s': %s",
+                    query,
+                    vs_id,
+                    exc,
+                )
+
+    return queries, all_results
+
+
+# ---------------------------------------------------------------------------
+# Result formatting
+# ---------------------------------------------------------------------------
+
+
+def _get_field(result: Any, key: str, default: Any = None) -> Any:
+    """Read a field from either a dict/TypedDict or an attribute-based object."""
+    if isinstance(result, dict):
+        return result.get(key, default)
+    return getattr(result, key, default)
+
+
+def _format_search_results_as_tool_output(
+    results: List[VectorStoreSearchResult],
+) -> str:
+    """Serialize search results into a string to pass back as the tool's output."""
+    if not results:
+        return "No results found in the vector store."
+
+    parts: List[str] = []
+    for i, result in enumerate(results, 1):
+        score = _get_field(result, "score")
+        file_id = _get_field(result, "file_id")
+        filename = _get_field(result, "filename")
+        content_items = _get_field(result, "content") or []
+        text_chunks = [
+            c.get("text", "") if isinstance(c, dict) else getattr(c, "text", "")
+            for c in content_items
+        ]
+        text = " ".join(t for t in text_chunks if t)
+
+        header = f"[Result {i}"
+        if filename:
+            header += f" | {filename}"
+        if file_id:
+            header += f" | file_id={file_id}"
+        if score is not None:
+            header += f" | score={score:.3f}"
+        header += "]"
+
+        parts.append(f"{header}\n{text}")
+
+    return "\n\n".join(parts)
+
+
+def _build_search_results_for_include(
+    results: List[VectorStoreSearchResult],
+) -> List[Dict[str, Any]]:
+    """
+    Convert VectorStoreSearchResult objects to the format expected in
+    file_search_call.search_results (mirrors OpenAI's include= format).
+
+    All chunks are returned — no deduplication by file_id — matching the
+    behaviour of OpenAI's native file_search which surfaces every relevant
+    chunk even when multiple chunks originate from the same document.
+    """
+    formatted: List[Dict[str, Any]] = []
+    for result in results:
+        file_id = _get_field(result, "file_id") or ""
+        content_items = _get_field(result, "content") or []
+        text_chunks = [
+            c.get("text", "") if isinstance(c, dict) else getattr(c, "text", "")
+            for c in content_items
+        ]
+        text = " ".join(t for t in text_chunks if t)
+        formatted.append(
+            {
+                "file_id": file_id,
+                "filename": _get_field(result, "filename") or "",
+                "score": _get_field(result, "score"),
+                "text": text,
+                "attributes": _get_field(result, "attributes") or {},
+            }
+        )
+    return formatted
+
+
+def _build_file_search_call_output(
+    call_id: str,
+    queries: List[str],
+    results: Optional[List[VectorStoreSearchResult]] = None,
+    include_search_results: bool = False,
+) -> Dict[str, Any]:
+    """Build the file_search_call output item (mirrors OpenAI's format).
+
+    Args:
+        call_id: Unique ID for this file_search call.
+        queries: List of search queries used.
+        results: The raw search results (used when include_search_results=True).
+        include_search_results: Populate search_results when the caller passed
+            ``include=["file_search_call.results"]``.
+    """
+    search_results = None
+    if include_search_results and results:
+        search_results = _build_search_results_for_include(results)
+    return {
+        "type": "file_search_call",
+        "id": call_id,
+        "status": "completed",
+        "queries": queries,
+        "search_results": search_results,
+    }
+
+
+def _build_file_citation_annotations(
+    results: List[VectorStoreSearchResult],
+    text: str,
+) -> List[Dict[str, Any]]:
+    """
+    Build file_citation annotations for the text.
+    Each result with a file_id gets a citation at the end of the text.
+    """
+    annotations: List[Dict[str, Any]] = []
+    index = len(text)  # cite at end of text block
+    seen_file_ids: set = set()
+
+    for result in results:
+        file_id = _get_field(result, "file_id")
+        filename = _get_field(result, "filename")
+        if not file_id or file_id in seen_file_ids:
+            continue
+        seen_file_ids.add(file_id)
+        annotations.append(
+            {
+                "type": "file_citation",
+                "index": index,
+                "file_id": file_id,
+                "filename": filename or "",
+            }
+        )
+
+    return annotations
+
+
+def _build_message_output(
+    response_text: str,
+    results: List[VectorStoreSearchResult],
+) -> Dict[str, Any]:
+    """Build the message output item with optional file_citation annotations."""
+    annotations = _build_file_citation_annotations(results, response_text)
+    return {
+        "type": "message",
+        "role": "assistant",
+        "content": [
+            {
+                "type": "output_text",
+                "text": response_text,
+                "annotations": annotations,
+            }
+        ],
+    }
+
+
+def _extract_text_from_responses_output(response: ResponsesAPIResponse) -> str:
+    """Pull the assistant's text from the provider's response."""
+    for item in response.output:
+        item_type = (
+            item.get("type") if isinstance(item, dict) else getattr(item, "type", None)
+        )
+        if item_type == "message":
+            content = (
+                item.get("content")
+                if isinstance(item, dict)
+                else getattr(item, "content", [])
+            )
+            for block in content or []:
+                block_type = (
+                    block.get("type")
+                    if isinstance(block, dict)
+                    else getattr(block, "type", None)
+                )
+                if block_type == "output_text":
+                    raw = (
+                        block.get("text")
+                        if isinstance(block, dict)
+                        else getattr(block, "text", "")
+                    )
+                    return str(raw) if raw is not None else ""
+    return ""
+
+
+def _synthesize_responses_api_response(
+    original_response: ResponsesAPIResponse,
+    file_search_call_output: Dict[str, Any],
+    message_output: Dict[str, Any],
+    first_response: Optional[ResponsesAPIResponse] = None,
+) -> ResponsesAPIResponse:
+    """
+    Return a new ResponsesAPIResponse with:
+      output[0] = file_search_call item
+      output[1] = message item (with citations)
+
+    When first_response is provided, its response_cost is accumulated into the
+    synthesized _hidden_params so that billing callbacks see the total cost of
+    both provider calls that the emulated flow makes.
+    """
+    synthesized_output: List[Dict[str, Any]] = [file_search_call_output, message_output]
+    synthesized = ResponsesAPIResponse(
+        id=getattr(original_response, "id", f"resp_{uuid.uuid4().hex}"),
+        object="response",
+        created_at=getattr(original_response, "created_at", int(time.time())),
+        status="completed",
+        model=getattr(original_response, "model", ""),
+        output=cast(
+            List[Union[ResponseOutputItem, Dict[str, Any]]], synthesized_output
+        ),
+        usage=getattr(original_response, "usage", None),
+        error=None,
+    )
+    if hasattr(original_response, "_hidden_params"):
+        hidden = dict(getattr(original_response, "_hidden_params") or {})
+        if first_response is not None and hasattr(first_response, "_hidden_params"):
+            first_hidden = getattr(first_response, "_hidden_params") or {}
+            first_cost = (
+                first_hidden.get("response_cost")
+                if isinstance(first_hidden, dict)
+                else getattr(first_hidden, "response_cost", None)
+            )
+            if first_cost is not None:
+                current_cost = (
+                    hidden.get("response_cost") if isinstance(hidden, dict) else 0
+                )
+                hidden["response_cost"] = (current_cost or 0) + first_cost
+        synthesized._hidden_params = hidden
+    return synthesized
+
+
+# ---------------------------------------------------------------------------
+# Main entry point
+# ---------------------------------------------------------------------------
+
+
+async def _call_aresponses(
+    input, model, tools, **kwargs
+):  # pragma: no cover – thin wrapper for patching in tests
+    from litellm.responses.main import aresponses
+
+    return await aresponses(input=input, model=model, tools=tools, **kwargs)
+
+
+def _prepare_emulated_file_search_call(
+    kwargs: Dict[str, Any],
+) -> Tuple[bool, Dict[str, Any]]:
+    include_items: List[str] = list(kwargs.get("include") or [])
+    include_search_results = "file_search_call.results" in include_items
+
+    original_stream = kwargs.get("stream")
+    updated_kwargs = kwargs
+    if original_stream:
+        verbose_logger.debug(
+            "Streaming is not yet supported for emulated file_search. "
+            "Disabling stream for this request."
+        )
+        updated_kwargs = {**kwargs, "stream": False}
+
+    return include_search_results, updated_kwargs
+
+
+async def aresponses_with_emulated_file_search(
+    input: Any,
+    model: str,
+    tools: Optional[Iterable[ToolParam]] = None,
+    # Pass-through params — forwarded as-is to the underlying aresponses call
+    **kwargs: Any,
+) -> ResponsesAPIResponse:
+    """
+    Emulated file_search for providers that don't support it natively.
+
+    Replaces file_search tools with a function tool, intercepts the tool call,
+    runs vector search, and synthesizes an OpenAI-format response.
+    """
+    # Determine whether caller wants search_results populated in the output.
+    _include_search_results, kwargs = _prepare_emulated_file_search_call(kwargs=kwargs)
+
+    # 1. Replace file_search tools with function tool
+    transformed_tools, all_vs_ids = _replace_file_search_tools(tools)
+
+    # 2. First provider call — provider will call the file_search function.
+    # Mark as an internal sub-call so wrapper_async skips billing callbacks;
+    # the parent litellm_logging_obj (propagated via kwargs) fires once at the end.
+    first_response: ResponsesAPIResponse = cast(
+        ResponsesAPIResponse,
+        await _call_aresponses(
+            input=input,
+            model=model,
+            tools=transformed_tools or None,
+            **{**kwargs, "_is_litellm_internal_call": True},
+        ),
+    )
+
+    # 3. Look for a file_search function_call in the output
+    file_search_calls = [
+        item
+        for item in first_response.output
+        if (
+            isinstance(item, dict)
+            and item.get("type") == "function_call"
+            and item.get("name") == FILE_SEARCH_FUNCTION_NAME
+        )
+        or (
+            hasattr(item, "type")
+            and getattr(item, "type") == "function_call"
+            and getattr(item, "name", None) == FILE_SEARCH_FUNCTION_NAME
+        )
+    ]
+
+    if not file_search_calls:
+        # Provider answered without calling the tool (e.g. it had enough context).
+        # Return as-is wrapped in OpenAI format.
+        call_id = f"fs_{uuid.uuid4().hex[:24]}"
+        response_text = _extract_text_from_responses_output(first_response)
+        return _synthesize_responses_api_response(
+            original_response=first_response,
+            file_search_call_output=_build_file_search_call_output(
+                call_id=call_id,
+                queries=[str(input)],
+                results=None,
+                include_search_results=False,
+            ),
+            message_output=_build_message_output(response_text, []),
+        )
+
+    # 4. Execute each file_search tool call
+    tool_results: List[Dict[str, Any]] = []
+    all_queries: List[str] = []
+    all_results: List[VectorStoreSearchResult] = []
+    file_search_call_id = f"fs_{uuid.uuid4().hex[:24]}"
+
+    for tool_call in file_search_calls:
+        if isinstance(tool_call, dict):
+            call_id = str(
+                tool_call.get("call_id") or tool_call.get("id") or file_search_call_id
+            )
+            raw_args = tool_call.get("arguments") or "{}"
+        else:
+            raw_call_id = (
+                getattr(tool_call, "call_id", None)
+                or getattr(tool_call, "id", None)
+                or file_search_call_id
+            )
+            call_id = str(raw_call_id)
+            raw_args = getattr(tool_call, "arguments", "{}") or "{}"
+
+        try:
+            args = json.loads(raw_args) if isinstance(raw_args, str) else raw_args
+        except json.JSONDecodeError:
+            args = {}
+
+        # Extract queries array (OpenAI-style multi-query support)
+        queries_from_call = args.get("queries")
+        if not queries_from_call:
+            # Fallback: check for single "query" field (backward compat)
+            single_query = args.get("query")
+            queries_from_call = [single_query] if single_query else [str(input)]
+        elif not isinstance(queries_from_call, list):
+            queries_from_call = [str(queries_from_call)]
+
+        vs_id_arg = args.get("vector_store_id")
+        vs_ids_for_call = [vs_id_arg] if vs_id_arg else all_vs_ids
+
+        queries, results = await _run_vector_searches(
+            queries=queries_from_call,
+            vector_store_ids=vs_ids_for_call,
+        )
+        all_queries.extend(queries)
+        all_results.extend(results)
+
+        tool_results.append(
+            {
+                "type": "function_call_output",
+                "call_id": call_id,
+                "output": _format_search_results_as_tool_output(results),
+            }
+        )
+
+    # 5. Build follow-up input: original messages + ALL first-response output items + tool results
+    # Including all output items (text blocks, reasoning, non-file-search calls) ensures providers
+    # like Anthropic that emit text before the tool call have complete conversation context.
+    # Serialize Pydantic model instances to plain dicts so the transformation layer can call .get().
+    original_input_items = (
+        list(input)
+        if isinstance(input, (list, tuple))
+        else [{"role": "user", "content": str(input)}]
+    )
+    first_response_output_items: List[Any] = []
+    for _item in first_response.output:
+        if isinstance(_item, dict):
+            first_response_output_items.append(_item)
+        elif hasattr(_item, "model_dump"):
+            first_response_output_items.append(_item.model_dump(exclude_none=True))  # type: ignore[union-attr]
+        else:
+            first_response_output_items.append(_item)
+
+    follow_up_input = original_input_items + first_response_output_items + tool_results
+
+    # 6. Follow-up call — provider writes the final answer given search results.
+    # Also an internal sub-call; billing is suppressed so the outer call fires once.
+    final_response: ResponsesAPIResponse = cast(
+        ResponsesAPIResponse,
+        await _call_aresponses(
+            input=follow_up_input,
+            model=model,
+            tools=None,  # no tools needed for the answer step
+            **{**kwargs, "_is_litellm_internal_call": True},
+        ),
+    )
+
+    # 7. Synthesize OpenAI-format output
+    response_text = _extract_text_from_responses_output(final_response)
+
+    return _synthesize_responses_api_response(
+        original_response=final_response,
+        file_search_call_output=_build_file_search_call_output(
+            call_id=file_search_call_id,
+            queries=all_queries or [str(input)],
+            results=all_results,
+            include_search_results=_include_search_results,
+        ),
+        message_output=_build_message_output(response_text, all_results),
+        first_response=first_response,
+    )
diff --git a/litellm/responses/main.py b/litellm/responses/main.py
index 2a320517a4c..c82574278ba 100644
--- a/litellm/responses/main.py
+++ b/litellm/responses/main.py
@@ -37,6 +37,7 @@ from litellm.responses.litellm_completion_transformation.handler import (
 )
 from litellm.responses.utils import ResponsesAPIRequestUtils
 from litellm.types.llms.openai import (
+    AllMessageValues,
     PromptObject,
     Reasoning,
     ResponseIncludable,
@@ -72,6 +73,13 @@ litellm_completion_transformation_handler = LiteLLMCompletionTransformationHandl
 #################################################
 
 
+def _has_file_search_tool(tools: Optional[Any]) -> bool:
+    """Return True if any tool in the list has type 'file_search'."""
+    if not tools:
+        return False
+    return any(isinstance(t, dict) and t.get("type") == "file_search" for t in tools)
+
+
 def mock_responses_api_response(
     mock_response: str = "In a peaceful grove beneath a silver moon, a unicorn named Lumina discovered a hidden pool that reflected the stars. As she dipped her horn into the water, the pool began to shimmer, revealing a pathway to a magical realm of endless night skies. Filled with wonder, Lumina whispered a wish for all who dream to find their own hidden magic, and as she glanced back, her hoofprints sparkled like stardust.",
 ):
@@ -463,6 +471,53 @@ async def aresponses(
             # Update local_vars with detected provider (fixes #19782)
             local_vars["custom_llm_provider"] = custom_llm_provider
 
+        #########################################################
+        # ASYNC PROMPT MANAGEMENT
+        # Run the async hook here so async-only prompt loggers are honoured.
+        # Then pop prompt_id from kwargs so the sync responses() path does NOT
+        # re-run the hook (which would double-prepend template messages).
+        # Pass merged_optional_params via an internal kwarg so responses()
+        # can apply them to local_vars without re-invoking the hook.
+        #########################################################
+        litellm_logging_obj = kwargs.get("litellm_logging_obj", None)
+        prompt_id = cast(Optional[str], kwargs.get("prompt_id", None))
+        prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
+        original_model = model
+
+        if isinstance(
+            litellm_logging_obj, LiteLLMLoggingObj
+        ) and litellm_logging_obj.should_run_prompt_management_hooks(
+            prompt_id=prompt_id, non_default_params=kwargs
+        ):
+            if isinstance(input, str):
+                client_input: List[AllMessageValues] = [
+                    {"role": "user", "content": input}
+                ]
+            else:
+                client_input = [
+                    item  # type: ignore[misc]
+                    for item in input
+                    if isinstance(item, dict) and "role" in item
+                ]
+            (
+                model,
+                merged_input,
+                merged_optional_params,
+            ) = await litellm_logging_obj.async_get_chat_completion_prompt(
+                model=model,
+                messages=client_input,
+                non_default_params=kwargs,
+                prompt_id=prompt_id,
+                prompt_variables=prompt_variables,
+                prompt_label=kwargs.get("prompt_label", None),
+                prompt_version=kwargs.get("prompt_version", None),
+            )
+            input = cast(Union[str, ResponseInputParam], merged_input)
+            if model != original_model:
+                _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
+            kwargs.pop("prompt_id", None)
+            kwargs["_async_prompt_merged_params"] = merged_optional_params
+
         func = partial(
             responses,
             input=input,
@@ -531,6 +586,125 @@ async def aresponses(
         )
 
 
+def _apply_prompt_management_to_responses_call(
+    input: Union[str, ResponseInputParam],
+    model: str,
+    custom_llm_provider: Optional[str],
+    litellm_logging_obj: Optional[LiteLLMLoggingObj],
+    kwargs: Dict[str, Any],
+    local_vars: Dict[str, Any],
+) -> tuple[Union[str, ResponseInputParam], str, Optional[str]]:
+    async_merged = kwargs.pop("_async_prompt_merged_params", None)
+    if async_merged is not None:
+        for key, value in async_merged.items():
+            local_vars[key] = value
+        return input, model, custom_llm_provider
+
+    prompt_id = cast(Optional[str], kwargs.get("prompt_id", None))
+    prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
+    original_model = model
+
+    if isinstance(input, str):
+        client_input: List[AllMessageValues] = [{"role": "user", "content": input}]
+    else:
+        client_input = [
+            item  # type: ignore[misc]
+            for item in input
+            if isinstance(item, dict) and "role" in item
+        ]
+
+    if isinstance(
+        litellm_logging_obj, LiteLLMLoggingObj
+    ) and litellm_logging_obj.should_run_prompt_management_hooks(
+        prompt_id=prompt_id, non_default_params=kwargs
+    ):
+        (
+            model,
+            merged_input,
+            merged_optional_params,
+        ) = litellm_logging_obj.get_chat_completion_prompt(
+            model=model,
+            messages=client_input,
+            non_default_params=kwargs,
+            prompt_id=prompt_id,
+            prompt_variables=prompt_variables,
+            prompt_label=kwargs.get("prompt_label", None),
+            prompt_version=kwargs.get("prompt_version", None),
+        )
+        input = cast(Union[str, ResponseInputParam], merged_input)
+        local_vars["input"] = input
+        local_vars["model"] = model
+        if model != original_model:
+            _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
+            local_vars["custom_llm_provider"] = custom_llm_provider
+        for key, value in merged_optional_params.items():
+            local_vars[key] = value
+
+    return input, model, custom_llm_provider
+
+
+def _resolve_model_provider_for_responses(
+    model: str,
+    custom_llm_provider: Optional[str],
+    litellm_params: GenericLiteLLMParams,
+    local_vars: Dict[str, Any],
+) -> tuple[str, Optional[str]]:
+    (
+        model,
+        custom_llm_provider,
+        dynamic_api_key,
+        dynamic_api_base,
+    ) = litellm.get_llm_provider(
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        api_base=litellm_params.api_base,
+        api_key=litellm_params.api_key,
+    )
+    local_vars["custom_llm_provider"] = custom_llm_provider
+    if dynamic_api_key is not None:
+        litellm_params.api_key = dynamic_api_key
+    if dynamic_api_base is not None:
+        litellm_params.api_base = dynamic_api_base
+    return model, custom_llm_provider
+
+
+def _apply_managed_file_id_mapping(
+    input: Union[str, ResponseInputParam],
+    tools: Optional[Iterable[ToolParam]],
+    kwargs: Dict[str, Any],
+    local_vars: Dict[str, Any],
+) -> tuple[Union[str, ResponseInputParam], Optional[Iterable[ToolParam]]]:
+    model_file_id_mapping = kwargs.get("model_file_id_mapping")
+    model_info_id = (
+        kwargs.get("model_info", {}).get("id")
+        if isinstance(kwargs.get("model_info"), dict)
+        else None
+    )
+
+    input = cast(
+        Union[str, ResponseInputParam],
+        update_responses_input_with_model_file_ids(
+            input=input,
+            model_id=model_info_id,
+            model_file_id_mapping=model_file_id_mapping,
+        ),
+    )
+    local_vars["input"] = input
+
+    if tools:
+        tools = cast(
+            Optional[Iterable[ToolParam]],
+            update_responses_tools_with_model_file_ids(
+                tools=cast(Optional[List[Dict[str, Any]]], tools),
+                model_id=model_info_id,
+                model_file_id_mapping=model_file_id_mapping,
+            ),
+        )
+        local_vars["tools"] = tools
+
+    return input, tools
+
+
 @client
 def responses(
     input: Union[str, ResponseInputParam],
@@ -602,59 +776,35 @@ def responses(
                 mock_response=litellm_params.mock_response
             )
 
-        (
-            model,
-            custom_llm_provider,
-            dynamic_api_key,
-            dynamic_api_base,
-        ) = litellm.get_llm_provider(
+        model, custom_llm_provider = _resolve_model_provider_for_responses(
             model=model,
             custom_llm_provider=custom_llm_provider,
-            api_base=litellm_params.api_base,
-            api_key=litellm_params.api_key,
+            litellm_params=litellm_params,
+            local_vars=local_vars,
         )
 
-        # Update local_vars with detected provider (fixes #19782)
-        local_vars["custom_llm_provider"] = custom_llm_provider
-
-        # Use dynamic credentials from get_llm_provider (e.g., when use_litellm_proxy=True)
-        if dynamic_api_key is not None:
-            litellm_params.api_key = dynamic_api_key
-        if dynamic_api_base is not None:
-            litellm_params.api_base = dynamic_api_base
+        #########################################################
+        # PROMPT MANAGEMENT
+        # If aresponses() already ran the async hook, it pops prompt_id and
+        # passes the result via _async_prompt_merged_params — apply those
+        # directly and skip the sync hook to avoid double-merging.
+        #########################################################
+        input, model, custom_llm_provider = _apply_prompt_management_to_responses_call(
+            input=input,
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            litellm_logging_obj=litellm_logging_obj,
+            kwargs=kwargs,
+            local_vars=local_vars,
+        )
 
         #########################################################
         # Update input and tools with provider-specific file IDs if managed files are used
         #########################################################
-        model_file_id_mapping = kwargs.get("model_file_id_mapping")
-        model_info_id = (
-            kwargs.get("model_info", {}).get("id")
-            if isinstance(kwargs.get("model_info"), dict)
-            else None
+        input, tools = _apply_managed_file_id_mapping(
+            input=input, tools=tools, kwargs=kwargs, local_vars=local_vars
         )
 
-        input = cast(
-            Union[str, ResponseInputParam],
-            update_responses_input_with_model_file_ids(
-                input=input,
-                model_id=model_info_id,
-                model_file_id_mapping=model_file_id_mapping,
-            ),
-        )
-        local_vars["input"] = input
-
-        # Update tools with provider-specific file IDs if needed
-        if tools:
-            tools = cast(
-                Optional[Iterable[ToolParam]],
-                update_responses_tools_with_model_file_ids(
-                    tools=cast(Optional[List[Dict[str, Any]]], tools),
-                    model_id=model_info_id,
-                    model_file_id_mapping=model_file_id_mapping,
-                ),
-            )
-            local_vars["tools"] = tools
-
         #########################################################
         # Native MCP Responses API
         #########################################################
@@ -692,12 +842,16 @@ def responses(
             return run_async_function(aresponses_api_with_mcp, **mcp_call_kwargs)
 
         # get provider config
-        responses_api_provider_config: Optional[
-            BaseResponsesAPIConfig
-        ] = ProviderConfigManager.get_provider_responses_api_config(
-            model=model,
-            provider=custom_llm_provider,
-        )
+        responses_api_provider_config: Optional[BaseResponsesAPIConfig]
+        if custom_llm_provider is None:
+            responses_api_provider_config = None
+        else:
+            responses_api_provider_config = (
+                ProviderConfigManager.get_provider_responses_api_config(
+                    model=model,
+                    provider=custom_llm_provider,
+                )
+            )
 
         local_vars.update(kwargs)
         # Map reasoning_effort (from litellm_params/proxy config) to reasoning when not set
@@ -715,6 +869,56 @@ def responses(
             )
         )
 
+        if _has_file_search_tool(tools) and (
+            responses_api_provider_config is None
+            or not responses_api_provider_config.supports_native_file_search()
+        ):
+            from litellm.responses.file_search.emulated_handler import (
+                aresponses_with_emulated_file_search,
+            )
+
+            _internal_skip = {"litellm_call_id", "aresponses"}
+            emulated_kwargs = {
+                "include": include,
+                "instructions": instructions,
+                "max_output_tokens": max_output_tokens,
+                "prompt": prompt,
+                "metadata": metadata,
+                "parallel_tool_calls": parallel_tool_calls,
+                "previous_response_id": previous_response_id,
+                "reasoning": reasoning,
+                "store": store,
+                "background": background,
+                "stream": stream,
+                "temperature": temperature,
+                "text": text,
+                "tool_choice": tool_choice,
+                "top_p": top_p,
+                "truncation": truncation,
+                "user": user,
+                "service_tier": service_tier,
+                "safety_identifier": safety_identifier,
+                "text_format": text_format,
+                "allowed_openai_params": allowed_openai_params,
+                "extra_headers": extra_headers,
+                "extra_query": extra_query,
+                "extra_body": extra_body,
+                "timeout": timeout,
+                "custom_llm_provider": custom_llm_provider,
+                **{k: v for k, v in kwargs.items() if k not in _internal_skip},
+            }
+            if _is_async:
+                return aresponses_with_emulated_file_search(
+                    input=input, model=model, tools=tools, **emulated_kwargs
+                )
+            return run_async_function(
+                aresponses_with_emulated_file_search,
+                input=input,
+                model=model,
+                tools=tools,
+                **emulated_kwargs,
+            )
+
         if responses_api_provider_config is None:
             return litellm_completion_transformation_handler.response_api_handler(
                 model=model,
@@ -758,6 +962,9 @@ def responses(
         )
 
         # Call the handler with _is_async flag instead of directly calling the async handler
+        if custom_llm_provider is None:
+            raise ValueError("custom_llm_provider is required but passed as None")
+
         response = base_llm_http_handler.response_api_handler(
             model=model,
             input=input,
diff --git a/litellm/router.py b/litellm/router.py
index 36046ebf302..6cc6bad9def 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -54,7 +54,11 @@ from litellm.caching.caching import (
     RedisCache,
     RedisClusterCache,
 )
-from litellm.constants import DEFAULT_MAX_LRU_CACHE_SIZE
+from litellm.constants import (
+    DEFAULT_HEALTH_CHECK_INTERVAL,
+    DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER,
+    DEFAULT_MAX_LRU_CACHE_SIZE,
+)
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.asyncify import run_async_function
 from litellm.litellm_core_utils.core_helpers import (
@@ -113,6 +117,7 @@ from litellm.router_utils.handle_error import (
     async_raise_no_deployment_exception,
     send_llm_exception_alert,
 )
+from litellm.router_utils.health_state_cache import DeploymentHealthCache
 from litellm.router_utils.pre_call_checks.deployment_affinity_check import (
     DeploymentAffinityCheck,
 )
@@ -301,7 +306,10 @@ class Router:
             RouterGeneralSettings
         ] = RouterGeneralSettings(),
         deployment_affinity_ttl_seconds: int = 3600,
+        model_group_affinity_config: Optional[Dict[str, List[str]]] = None,
         ignore_invalid_deployments: bool = False,
+        enable_health_check_routing: bool = False,
+        health_check_staleness_threshold: Optional[int] = None,
     ) -> None:
         """
         Initialize the Router class with the given parameters for caching, reliability, and routing strategy.
@@ -466,6 +474,8 @@ class Router:
         # Initialize model name to deployment indices mapping for O(1) lookups
         # Maps model_name -> list of indices in model_list
         self.model_name_to_deployment_indices: Dict[str, List[int]] = {}
+        # Maps (team_id, team_public_model_name) -> list of indices in model_list
+        self.team_model_to_deployment_indices: Dict[Tuple[str, str], List[int]] = {}
 
         if model_list is not None:
             # set_model_list will build indices automatically
@@ -490,6 +500,13 @@ class Router:
             cache=self.cache, default_cooldown_time=self.cooldown_time
         )
         self.disable_cooldowns = disable_cooldowns
+        self.enable_health_check_routing = enable_health_check_routing
+        _staleness = health_check_staleness_threshold or (
+            DEFAULT_HEALTH_CHECK_INTERVAL * DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER
+        )
+        self.health_state_cache = DeploymentHealthCache(
+            cache=self.cache, staleness_threshold=float(_staleness)
+        )
         self.failed_calls = (
             InMemoryCache()
         )  # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown
@@ -641,6 +658,9 @@ class Router:
         self.model_group_retry_policy: Optional[
             Dict[str, RetryPolicy]
         ] = model_group_retry_policy
+        self.model_group_affinity_config: Optional[
+            Dict[str, List[str]]
+        ] = model_group_affinity_config
 
         self.allowed_fails_policy: Optional[AllowedFailsPolicy] = None
         if allowed_fails_policy is not None:
@@ -661,6 +681,26 @@ class Router:
         if optional_pre_call_checks is not None:
             self.add_optional_pre_call_checks(optional_pre_call_checks)
 
+        # If model_group_affinity_config is set but no global affinity checks were
+        # enabled, we still need the DeploymentAffinityCheck callback (with global
+        # flags all False) so per-group config can activate affinity per model group.
+        if self.model_group_affinity_config and not any(
+            isinstance(cb, DeploymentAffinityCheck)
+            for cb in (self.optional_callbacks or [])
+        ):
+            if self.optional_callbacks is None:
+                self.optional_callbacks = []
+            affinity_callback = DeploymentAffinityCheck(
+                cache=self.cache,
+                ttl_seconds=self.deployment_affinity_ttl_seconds,
+                enable_user_key_affinity=False,
+                enable_responses_api_affinity=False,
+                enable_session_id_affinity=False,
+                model_group_affinity_config=self.model_group_affinity_config,
+            )
+            self.optional_callbacks.append(affinity_callback)
+            litellm.logging_callback_manager.add_litellm_callback(affinity_callback)
+
         if self.alerting_config is not None:
             self._initialize_alerting()
 
@@ -1311,6 +1351,10 @@ class Router:
                 existing_affinity_callback.ttl_seconds = (
                     self.deployment_affinity_ttl_seconds
                 )
+                if self.model_group_affinity_config:
+                    existing_affinity_callback.model_group_affinity_config = (
+                        self.model_group_affinity_config
+                    )
             else:
                 affinity_callback = DeploymentAffinityCheck(
                     cache=self.cache,
@@ -1318,6 +1362,7 @@ class Router:
                     enable_user_key_affinity=enable_user_key_affinity,
                     enable_responses_api_affinity=enable_responses_api_affinity,
                     enable_session_id_affinity=enable_session_id_affinity,
+                    model_group_affinity_config=self.model_group_affinity_config,
                 )
                 self.optional_callbacks.append(affinity_callback)
                 litellm.logging_callback_manager.add_litellm_callback(affinity_callback)
@@ -5259,6 +5304,64 @@ class Router:
         if "fallback_depth" not in input_kwargs:
             input_kwargs["fallback_depth"] = 0
 
+        # ORDER-BASED FALLBACKS: prepend higher order levels to the fallback list
+        # Skip for error types that have their own dedicated fallback handlers
+        _skip_order_fallback = isinstance(
+            e,
+            (litellm.ContextWindowExceededError, litellm.ContentPolicyViolationError),
+        )
+        all_deployments = self._get_all_deployments(model_name=original_model_group)
+        _order_set: set = {
+            d.get("litellm_params", {}).get("order")
+            for d in all_deployments
+            if d.get("litellm_params", {}).get("order") is not None
+        }
+        order_values: list = sorted(_order_set)
+        if len(order_values) > 1 and not _skip_order_fallback:
+            # Determine which order levels have already been tried
+            current_target = kwargs.get("_target_order")
+            skip_up_to = (
+                current_target if current_target is not None else order_values[0]
+            )
+            # Build order-based fallback entries (skip already-tried levels)
+            order_fallback_entries: List = [
+                {"model": original_model_group, "_target_order": o}
+                for o in order_values
+                if o > skip_up_to
+            ]
+            # Get external fallbacks — handle both standard and non-standard formats
+            external_fallback_group: Optional[List] = None
+            if fallbacks is not None and model_group is not None:
+                if _check_non_standard_fallback_format(fallbacks=fallbacks):
+                    # Non-standard formats (e.g. ["claude-3-haiku"] or
+                    # [{"model": "...", "messages": [...]}]) are passed through directly
+                    external_fallback_group = fallbacks
+                else:
+                    external_fallback_group, generic_idx = get_fallback_model_group(
+                        fallbacks=fallbacks,
+                        model_group=cast(str, model_group),
+                    )
+                    if external_fallback_group is None and generic_idx is not None:
+                        external_fallback_group = fallbacks[generic_idx]["*"]
+
+            # Combined list: order fallbacks first, then external
+            combined_fallbacks = order_fallback_entries + (
+                external_fallback_group or []
+            )
+
+            if combined_fallbacks:
+                input_kwargs.update(
+                    {
+                        "fallback_model_group": combined_fallbacks,
+                        "original_model_group": original_model_group,
+                    }
+                )
+                response = await run_async_fallback(
+                    *args,
+                    **input_kwargs,
+                )
+                return response
+
         try:
             verbose_router_logger.info("Trying to fallback b/w models")
 
@@ -6806,6 +6909,7 @@ class Router:
         self.model_list = []
         self.model_id_to_deployment_index_map = {}  # Reset the index
         self.model_name_to_deployment_indices = {}  # Reset the model_name index
+        self.team_model_to_deployment_indices = {}  # Reset the team_model index
         self._invalidate_model_group_info_cache()
         self._invalidate_access_groups_cache()
         # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works
@@ -7103,16 +7207,17 @@ class Router:
 
         # Update model_name_to_deployment_indices
         for model_name, indices in list(self.model_name_to_deployment_indices.items()):
-            # Remove the deleted index
-            if removal_idx in indices:
-                indices.remove(removal_idx)
-
-            # Decrement all indices greater than removal_idx
+            # Build new list without mutating the original
             updated_indices = []
             for idx in indices:
-                if idx > removal_idx:
+                if idx == removal_idx:
+                    # Skip the removed index
+                    continue
+                elif idx > removal_idx:
+                    # Decrement indices after removal
                     updated_indices.append(idx - 1)
                 else:
+                    # Keep indices before removal unchanged
                     updated_indices.append(idx)
 
             # Update or remove the entry
@@ -7121,6 +7226,46 @@ class Router:
             else:
                 del self.model_name_to_deployment_indices[model_name]
 
+        # Update team_model_to_deployment_indices
+        for key, indices in list(self.team_model_to_deployment_indices.items()):
+            # Build new list without mutating the original
+            updated_indices = []
+            for idx in indices:
+                if idx == removal_idx:
+                    # Skip the removed index
+                    continue
+                elif idx > removal_idx:
+                    # Decrement indices after removal
+                    updated_indices.append(idx - 1)
+                else:
+                    # Keep indices before removal unchanged
+                    updated_indices.append(idx)
+
+            # Update or remove the entry
+            if len(updated_indices) > 0:
+                self.team_model_to_deployment_indices[key] = updated_indices
+            else:
+                del self.team_model_to_deployment_indices[key]
+
+    def _update_team_model_index(self, model: dict, idx: int) -> None:
+        """
+        Helper to update team_model_to_deployment_indices for a single deployment.
+
+        Parameters:
+        - model: dict - the deployment to index
+        - idx: int - the index in model_list
+        """
+        team_id = (model.get("model_info") or {}).get("team_id")
+        team_public_model_name = (model.get("model_info") or {}).get(
+            "team_public_model_name"
+        )
+        if team_id and team_public_model_name:
+            key = (team_id, team_public_model_name)
+            if key not in self.team_model_to_deployment_indices:
+                self.team_model_to_deployment_indices[key] = []
+            if idx not in self.team_model_to_deployment_indices[key]:
+                self.team_model_to_deployment_indices[key].append(idx)
+
     def _add_model_to_list_and_index_map(
         self, model: dict, model_id: Optional[str] = None
     ) -> None:
@@ -7149,6 +7294,9 @@ class Router:
                 self.model_name_to_deployment_indices[model_name] = []
             self.model_name_to_deployment_indices[model_name].append(idx)
 
+        # Update team_model index for O(1) team-scoped lookup
+        self._update_team_model_index(model, idx)
+
     def upsert_deployment(self, deployment: Deployment) -> Optional[Deployment]:
         """
         Add or update deployment
@@ -7167,7 +7315,10 @@ class Router:
             )
             if _deployment_on_router is not None:
                 # deployment with this model_id exists on the router
-                if deployment.litellm_params == _deployment_on_router.litellm_params:
+                if (
+                    deployment.litellm_params == _deployment_on_router.litellm_params
+                    and deployment.model_info == _deployment_on_router.model_info
+                ):
                     # No need to update
                     return None
 
@@ -7979,6 +8130,7 @@ class Router:
         instead of O(n) linear scan through the entire model_list.
         """
         self.model_name_to_deployment_indices.clear()
+        self.team_model_to_deployment_indices.clear()
 
         for idx, model in enumerate(model_list):
             model_name = model.get("model_name")
@@ -7987,6 +8139,8 @@ class Router:
                     self.model_name_to_deployment_indices[model_name] = []
                 self.model_name_to_deployment_indices[model_name].append(idx)
 
+            self._update_team_model_index(model, idx)
+
     def _build_model_id_to_deployment_index_map(self, model_list: list):
         """
         Build model index from model list to enable O(1) lookups immediately.
@@ -8119,20 +8273,25 @@ class Router:
 
     def map_team_model(self, team_model_name: str, team_id: str) -> Optional[str]:
         """
-        Map a team model name to a team-specific model name.
+        Check if team_model_name resolves to team-specific deployments.
+
+        Returns the public model name (unchanged) so the router can find all
+        sibling deployments via team_id filtering, instead of collapsing to a
+        single internal model_name.
 
         Returns:
-        - deployment id: str - the deployment id of the team-specific model
-        - None: if no team-specific model name is found
+        - str: the team_model_name if team deployments exist for this team
+        - None: if no team-specific model is found
         """
         models = self.get_model_list(model_name=team_model_name, team_id=team_id)
         if not models:
             return None
         for model in models:
             if model.get("model_info", {}).get("team_id") == team_id:
-                return model.get("model_name")
+                return team_model_name
 
-        ## wildcard models
+        # No team-scoped deployment found; wildcard/pattern routes are
+        # handled downstream by the pattern_router in _common_checks_available_deployment.
         return None
 
     def should_include_deployment(
@@ -8143,12 +8302,22 @@ class Router:
         """
         if (
             team_id is not None
-            and model["model_info"].get("team_id") == team_id
-            and model_name == model["model_info"].get("team_public_model_name")
+            and (model.get("model_info") or {}).get("team_id") == team_id
+            and model_name
+            == (model.get("model_info") or {}).get("team_public_model_name")
         ):
             return True
         elif model_name is not None and model["model_name"] == model_name:
-            return True
+            # Fallback: check by internal model_name for non-team deployments
+            # or deployments that haven't been migrated to team_public_model_name yet
+            model_team_id = (model.get("model_info") or {}).get("team_id")
+            if (
+                team_id is None  # requester has no team constraint
+                or model_team_id is None  # global deployment - accessible to all teams
+                or model_team_id == team_id  # deployment belongs to requester's team
+            ):
+                return True
+        # No match: deployment is for a different team or doesn't match the requested model
         return False
 
     def _get_all_deployments(
@@ -8165,9 +8334,36 @@ class Router:
         if team_id specified, only return team-specific models
 
         Optimized with O(1) index lookup instead of O(n) linear scan.
+
+        Note: when team_id is provided, O(1) lookup in
+        `team_model_to_deployment_indices` only applies when `model_name` is the
+        team public model name. If a caller passes an internal deployment model
+        name (for example, `model_name_<team_id>_<uuid>`), this method falls back
+        to the standard model-name index / scan path.
         """
         returned_models: List[DeploymentTypedDict] = []
 
+        # O(1) lookup in team_model index when team_id is provided
+        if team_id is not None:
+            key = (team_id, model_name)
+            if key in self.team_model_to_deployment_indices:
+                indices = self.team_model_to_deployment_indices[key]
+                # O(k) where k = team deployments for this model_name (typically 1-10)
+                for idx in indices:
+                    model = self.model_list[idx]
+                    if not self.should_include_deployment(
+                        model_name=model_name, model=model, team_id=team_id
+                    ):
+                        continue
+                    if model_alias is not None:
+                        alias_model = model.copy()
+                        alias_model["model_name"] = model_alias
+                        returned_models.append(alias_model)
+                    else:
+                        returned_models.append(model)
+                if returned_models:
+                    return returned_models
+
         # O(1) lookup in model_name index
         if model_name in self.model_name_to_deployment_indices:
             indices = self.model_name_to_deployment_indices[model_name]
@@ -8762,12 +8958,6 @@ class Router:
                 if i not in invalid_model_indices
             ]
 
-        ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
-        if len(_returned_deployments) > 0:
-            _returned_deployments = litellm.utils._get_order_filtered_deployments(
-                _returned_deployments
-            )
-
         return _returned_deployments
 
     def _get_model_from_alias(self, model: str) -> Optional[str]:
@@ -8838,6 +9028,16 @@ class Router:
             model = _model_from_alias
 
         if model not in self.model_names:
+            # Check for team-specific deployments by team_public_model_name.
+            # This intentionally takes priority over team pattern routers below,
+            # so that named team deployments shadow wildcard/pattern routes.
+            if request_team_id is not None:
+                team_deployments = self._get_all_deployments(
+                    model_name=model, team_id=request_team_id
+                )
+                if team_deployments:
+                    return model, team_deployments
+
             # check if provider/ specific wildcard routing use pattern matching
             pattern_deployments = self.pattern_router.get_deployments_by_pattern(
                 model=model,
@@ -8968,6 +9168,14 @@ class Router:
         if isinstance(healthy_deployments, dict):
             return healthy_deployments
 
+        # Health-check-based filtering (before cooldown)
+        healthy_deployments = (
+            await self._async_filter_health_check_unhealthy_deployments(
+                healthy_deployments=healthy_deployments,
+                parent_otel_span=parent_otel_span,
+            )
+        )
+
         cooldown_deployments = await _async_get_cooldown_deployments(
             litellm_router_instance=self, parent_otel_span=parent_otel_span
         )
@@ -9006,6 +9214,12 @@ class Router:
             ),
         )
 
+        ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
+        _target_order = (request_kwargs or {}).pop("_target_order", None)
+        healthy_deployments = litellm.utils._get_order_filtered_deployments(
+            cast(List[Dict], healthy_deployments), target_order=_target_order
+        )
+
         if len(healthy_deployments) == 0:
             exception = await async_raise_no_deployment_exception(
                 litellm_router_instance=self,
@@ -9393,6 +9607,13 @@ class Router:
         parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
             request_kwargs
         )
+
+        # Health-check-based filtering (before cooldown)
+        healthy_deployments = self._filter_health_check_unhealthy_deployments(
+            healthy_deployments=healthy_deployments,
+            parent_otel_span=parent_otel_span,
+        )
+
         cooldown_deployments = _get_cooldown_deployments(
             litellm_router_instance=self, parent_otel_span=parent_otel_span
         )
@@ -9410,6 +9631,12 @@ class Router:
                 request_kwargs=request_kwargs,
             )
 
+        ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
+        _target_order = (request_kwargs or {}).pop("_target_order", None)
+        healthy_deployments = litellm.utils._get_order_filtered_deployments(
+            healthy_deployments, target_order=_target_order
+        )
+
         if len(healthy_deployments) == 0:
             model_ids = self.get_model_ids(model_name=model)
             _cooldown_time = self.cooldown_cache.get_min_cooldown(
@@ -9552,10 +9779,14 @@ class Router:
                 llm_provider="",
             )
 
-        # 4. Apply cooldown filtering
+        # 4. Apply health-check and cooldown filtering
         parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
             request_kwargs
         )
+        pass_through_deployments = self._filter_health_check_unhealthy_deployments(
+            healthy_deployments=pass_through_deployments,
+            parent_otel_span=parent_otel_span,
+        )
         cooldown_deployments = _get_cooldown_deployments(
             litellm_router_instance=self, parent_otel_span=parent_otel_span
         )
@@ -9677,6 +9908,67 @@ class Router:
             if deployment["model_info"]["id"] not in cooldown_set
         ]
 
+    async def _async_filter_health_check_unhealthy_deployments(
+        self,
+        healthy_deployments: List[Dict],
+        parent_otel_span: Optional[Span] = None,
+    ) -> List[Dict]:
+        """
+        Filter out deployments marked unhealthy by background health checks.
+        No-op when enable_health_check_routing is False.
+        Returns all deployments if health state is unavailable, stale, or would
+        exclude every candidate (safety net).
+        """
+        if not self.enable_health_check_routing:
+            return healthy_deployments
+
+        unhealthy_ids = (
+            await self.health_state_cache.async_get_unhealthy_deployment_ids(
+                parent_otel_span=parent_otel_span
+            )
+        )
+        if not unhealthy_ids:
+            return healthy_deployments
+
+        filtered = [
+            d for d in healthy_deployments if d["model_info"]["id"] not in unhealthy_ids
+        ]
+
+        if not filtered:
+            verbose_router_logger.warning(
+                "All deployments marked unhealthy by health checks, bypassing health filter"
+            )
+            return healthy_deployments
+
+        return filtered
+
+    def _filter_health_check_unhealthy_deployments(
+        self,
+        healthy_deployments: List[Dict],
+        parent_otel_span: Optional[Span] = None,
+    ) -> List[Dict]:
+        """Sync version of _async_filter_health_check_unhealthy_deployments."""
+        if not self.enable_health_check_routing:
+            return healthy_deployments
+
+        unhealthy_ids = self.health_state_cache.get_unhealthy_deployment_ids(
+            parent_otel_span=parent_otel_span
+        )
+        if not unhealthy_ids:
+            return healthy_deployments
+
+        filtered = [
+            d for d in healthy_deployments if d["model_info"]["id"] not in unhealthy_ids
+        ]
+
+        if not filtered:
+            verbose_router_logger.warning(
+                "All deployments marked unhealthy by health checks, bypassing health filter"
+            )
+            return healthy_deployments
+
+        return filtered
+
     def _filter_pass_through_deployments(
         self, healthy_deployments: List[Dict]
     ) -> List[Dict]:
diff --git a/litellm/router_utils/health_state_cache.py b/litellm/router_utils/health_state_cache.py
new file mode 100644
index 00000000000..65b064f19d2
--- /dev/null
+++ b/litellm/router_utils/health_state_cache.py
@@ -0,0 +1,100 @@
+"""
+Wrapper around router cache for health-check-driven routing.
+
+Stores per-deployment health state from background health checks
+and exposes it for router candidate filtering.
+"""
+
+import time
+from typing import TYPE_CHECKING, Any, Dict, Optional, Set, Union
+
+from typing_extensions import TypedDict
+
+from litellm import verbose_logger
+from litellm.caching.caching import DualCache
+
+if TYPE_CHECKING:
+    from opentelemetry.trace import Span as _Span
+
+    Span = Union[_Span, Any]
+else:
+    Span = Any
+
+
+class DeploymentHealthStateValue(TypedDict):
+    is_healthy: bool
+    timestamp: float
+    reason: str
+
+
+class DeploymentHealthCache:
+    """
+    Cache for deployment health states produced by background health checks.
+
+    Stores a single dict mapping deployment_id -> DeploymentHealthStateValue.
+    Staleness is enforced at read time: entries older than staleness_threshold
+    are treated as healthy (unknown).
+    """
+
+    CACHE_KEY = "litellm:health_check:deployment_health_state"
+
+    def __init__(self, cache: DualCache, staleness_threshold: float):
+        self.cache = cache
+        self.staleness_threshold = staleness_threshold
+
+    def set_deployment_health_states(
+        self, states: Dict[str, DeploymentHealthStateValue]
+    ) -> None:
+        """Bulk-write all deployment health states as a single cache entry."""
+        try:
+            self.cache.set_cache(
+                key=self.CACHE_KEY,
+                value=states,
+                ttl=int(self.staleness_threshold * 1.5),
+            )
+        except Exception as e:
+            verbose_logger.error(
+                "DeploymentHealthCache::set_deployment_health_states - Exception: %s",
+                str(e),
+            )
+
+    def _extract_unhealthy_ids(self, raw: Any) -> Set[str]:
+        """Given raw cache value, return set of non-stale unhealthy deployment IDs."""
+        if not raw or not isinstance(raw, dict):
+            return set()
+        now = time.time()
+        return {
+            model_id
+            for model_id, state in raw.items()
+            if isinstance(state, dict)
+            and not state.get("is_healthy", True)
+            and (now - state.get("timestamp", 0)) < self.staleness_threshold
+        }
+
+    async def async_get_unhealthy_deployment_ids(
+        self, parent_otel_span: Optional[Span] = None
+    ) -> Set[str]:
+        """Return set of deployment IDs currently marked unhealthy and not stale."""
+        try:
+            raw = await self.cache.async_get_cache(key=self.CACHE_KEY)
+            return self._extract_unhealthy_ids(raw)
+        except Exception as e:
+            verbose_logger.debug(
+                "DeploymentHealthCache::async_get_unhealthy_deployment_ids - Exception: %s",
+                str(e),
+            )
+            return set()
+
+    def get_unhealthy_deployment_ids(
+        self, parent_otel_span: Optional[Span] = None
+    ) -> Set[str]:
+        """Sync version: return set of deployment IDs currently marked unhealthy and not stale."""
+        try:
+            raw = self.cache.get_cache(key=self.CACHE_KEY)
+            return self._extract_unhealthy_ids(raw)
+        except Exception as e:
+            verbose_logger.debug(
+                "DeploymentHealthCache::get_unhealthy_deployment_ids - Exception: %s",
+                str(e),
+            )
+            return set()
diff --git a/litellm/router_utils/pre_call_checks/deployment_affinity_check.py b/litellm/router_utils/pre_call_checks/deployment_affinity_check.py
index 8044f71d904..148b7fce0ee 100644
--- a/litellm/router_utils/pre_call_checks/deployment_affinity_check.py
+++ b/litellm/router_utils/pre_call_checks/deployment_affinity_check.py
@@ -13,7 +13,7 @@ where routing to a consistent deployment is still beneficial.
 """
 
 import hashlib
-from typing import Any, Dict, List, Optional, cast
+from typing import Any, Dict, List, Optional, Tuple, cast
 
 from typing_extensions import TypedDict
 
@@ -38,6 +38,9 @@ class DeploymentAffinityCheck(CustomLogger):
     """
 
     CACHE_KEY_PREFIX = "deployment_affinity:v1"
+    VALID_FLAGS = frozenset(
+        {"deployment_affinity", "responses_api_deployment_check", "session_affinity"}
+    )
 
     def __init__(
         self,
@@ -46,6 +49,7 @@ class DeploymentAffinityCheck(CustomLogger):
         enable_user_key_affinity: bool,
         enable_responses_api_affinity: bool,
         enable_session_id_affinity: bool = False,
+        model_group_affinity_config: Optional[Dict[str, List[str]]] = None,
     ):
         super().__init__()
         self.cache = cache
@@ -53,6 +57,39 @@ class DeploymentAffinityCheck(CustomLogger):
         self.enable_user_key_affinity = enable_user_key_affinity
         self.enable_responses_api_affinity = enable_responses_api_affinity
         self.enable_session_id_affinity = enable_session_id_affinity
+        self.model_group_affinity_config: Dict[str, List[str]] = (
+            model_group_affinity_config or {}
+        )
+        for group, flags in self.model_group_affinity_config.items():
+            unknown = set(flags) - self.VALID_FLAGS
+            if unknown:
+                verbose_router_logger.warning(
+                    "DeploymentAffinityCheck: unknown flag(s) %s for model group '%s'; will be ignored. Valid flags: %s",
+                    unknown,
+                    group,
+                    self.VALID_FLAGS,
+                )
+
+    def _get_effective_flags(self, model_group: str) -> Tuple[bool, bool, bool]:
+        """
+        Return (enable_user_key_affinity, enable_responses_api_affinity, enable_session_id_affinity)
+        for the given model group.
+
+        If the model group has an explicit entry in model_group_affinity_config, use it.
+        Otherwise fall back to the global instance flags.
+        """
+        group_checks = self.model_group_affinity_config.get(model_group)
+        if group_checks is not None:
+            return (
+                "deployment_affinity" in group_checks,
+                "responses_api_deployment_check" in group_checks,
+                "session_affinity" in group_checks,
+            )
+        return (
+            self.enable_user_key_affinity,
+            self.enable_responses_api_affinity,
+            self.enable_session_id_affinity,
+        )
 
     @staticmethod
     def _looks_like_sha256_hex(value: str) -> bool:
@@ -277,8 +314,14 @@ class DeploymentAffinityCheck(CustomLogger):
         request_kwargs = request_kwargs or {}
         typed_healthy_deployments = cast(List[dict], healthy_deployments)
 
+        (
+            enable_user_key,
+            enable_responses_api,
+            enable_session_id,
+        ) = self._get_effective_flags(model)
+
         # 1) Responses API continuity (high priority)
-        if self.enable_responses_api_affinity:
+        if enable_responses_api:
             previous_response_id = request_kwargs.get("previous_response_id")
             if previous_response_id is not None:
                 responses_model_id = (
@@ -305,7 +348,7 @@ class DeploymentAffinityCheck(CustomLogger):
             return typed_healthy_deployments
 
         # 2) Session-id -> deployment affinity
-        if self.enable_session_id_affinity:
+        if enable_session_id:
             session_id = self._get_session_id_from_request_kwargs(
                 request_kwargs=request_kwargs
             )
@@ -344,7 +387,7 @@ class DeploymentAffinityCheck(CustomLogger):
                         )
 
         # 3) User key -> deployment affinity
-        if not self.enable_user_key_affinity:
+        if not enable_user_key:
             return typed_healthy_deployments
 
         user_key = self._get_user_key_from_request_kwargs(request_kwargs=request_kwargs)
@@ -394,22 +437,47 @@ class DeploymentAffinityCheck(CustomLogger):
         - LiteLLM runs async success callbacks via a background logging worker for performance.
         - We want affinity to be immediately available for subsequent requests.
         """
-        if not self.enable_user_key_affinity and not self.enable_session_id_affinity:
+        metadata_dicts = self._iter_metadata_dicts(kwargs)
+
+        # Extract deployment_model_name first — needed for both per-group flag resolution
+        # and cache key scoping.
+        deployment_model_name: Optional[str] = None
+        for metadata in metadata_dicts:
+            maybe_deployment_model_name = metadata.get("deployment_model_name")
+            if (
+                isinstance(maybe_deployment_model_name, str)
+                and maybe_deployment_model_name
+            ):
+                deployment_model_name = maybe_deployment_model_name
+                break
+
+        if not deployment_model_name:
+            verbose_router_logger.debug(
+                "DeploymentAffinityCheck: deployment_model_name missing in metadata; skipping affinity cache update."
+            )
+            return None
+
+        # Resolve effective flags for this model group
+        (
+            enable_user_key,
+            _enable_responses_api,
+            enable_session_id,
+        ) = self._get_effective_flags(deployment_model_name)
+
+        if not enable_user_key and not enable_session_id:
             return None
 
         user_key = None
-        if self.enable_user_key_affinity:
+        if enable_user_key:
             user_key = self._get_user_key_from_request_kwargs(request_kwargs=kwargs)
 
         session_id = None
-        if self.enable_session_id_affinity:
+        if enable_session_id:
             session_id = self._get_session_id_from_request_kwargs(request_kwargs=kwargs)
 
         if user_key is None and session_id is None:
             return None
 
-        metadata_dicts = self._iter_metadata_dicts(kwargs)
-
         model_info = kwargs.get("model_info")
         if not isinstance(model_info, dict):
             model_info = None
@@ -433,25 +501,6 @@ class DeploymentAffinityCheck(CustomLogger):
             )
             return None
 
-        # Scope affinity by the Router deployment model name (alias-safe, consistent across
-        # heterogeneous providers, and matches standard logging's `model_map_key`).
-        deployment_model_name: Optional[str] = None
-        for metadata in metadata_dicts:
-            maybe_deployment_model_name = metadata.get("deployment_model_name")
-            if (
-                isinstance(maybe_deployment_model_name, str)
-                and maybe_deployment_model_name
-            ):
-                deployment_model_name = maybe_deployment_model_name
-                break
-
-        if not deployment_model_name:
-            verbose_router_logger.warning(
-                "DeploymentAffinityCheck: deployment_model_name missing; skipping affinity cache update. model_id=%s",
-                model_id,
-            )
-            return None
-
         if user_key is not None:
             try:
                 cache_key = self.get_affinity_cache_key(
diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py
index f798f05380d..cfec0398c81 100644
--- a/litellm/types/guardrails.py
+++ b/litellm/types/guardrails.py
@@ -17,6 +17,9 @@ 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,
 )
@@ -33,7 +36,7 @@ Pydantic object defining how to set guardrails on litellm proxy
 guardrails:
   - guardrail_name: "bedrock-pre-guard"
     litellm_params:
-      guardrail: bedrock  # supported values: "aporia", "bedrock", "lakera", "zscaler_ai_guard"
+      guardrail: bedrock  # supported values: "akto", "aporia", "bedrock", "lakera", "zscaler_ai_guard"
       mode: "during_call"
       guardrailIdentifier: ff6ujrregl1q
       guardrailVersion: "DRAFT"
@@ -79,6 +82,7 @@ class SupportedGuardrailIntegrations(Enum):
     SEMANTIC_GUARD = "semantic_guard"
     MCP_END_USER_PERMISSION = "mcp_end_user_permission"
     BLOCK_CODE_EXECUTION = "block_code_execution"
+    AKTO = "akto"
     MCP_JWT_SIGNER = "mcp_jwt_signer"
 
 
@@ -737,6 +741,7 @@ class LitellmParams(
     NomaGuardrailConfigModel,
     ToolPermissionGuardrailConfigModel,
     ZscalerAIGuardConfigModel,
+    AktoConfigModel,
     JavelinGuardrailConfigModel,
     BaseLitellmParams,
     EnkryptAIGuardrailConfigs,
diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py
index 0856d8a6f9b..05913e609f6 100644
--- a/litellm/types/integrations/prometheus.py
+++ b/litellm/types/integrations/prometheus.py
@@ -159,6 +159,23 @@ LATENCY_BUCKETS = (
     float("inf"),
 )
 
+# Batch jobs can run for minutes to hours; buckets span 1 min → 24 h.
+BATCH_DURATION_BUCKETS = (
+    60.0,
+    120.0,
+    300.0,
+    600.0,
+    900.0,
+    1800.0,
+    3600.0,
+    7200.0,
+    14400.0,
+    28800.0,
+    43200.0,
+    86400.0,
+    float("inf"),
+)
+
 
 class UserAPIKeyLabelNames(Enum):
     END_USER = "end_user"
@@ -238,6 +255,16 @@ DEFINED_PROMETHEUS_METRICS = Literal[
     "litellm_llm_api_failed_requests_metric",
     "litellm_callback_logging_failures_metric",
     "litellm_in_flight_requests",
+    # Managed batch metrics
+    "litellm_managed_batch_created_total",
+    "litellm_managed_file_size_bytes",
+    "litellm_managed_batch_duration_seconds",
+    "litellm_managed_file_created_total",
+    "litellm_managed_file_deleted_total",
+    "litellm_check_batch_cost_jobs_polled",
+    "litellm_check_batch_cost_jobs_processed_total",
+    "litellm_check_batch_cost_errors_total",
+    "litellm_check_batch_cost_last_run_timestamp",
 ]
 
 
@@ -618,6 +645,43 @@ class PrometheusMetricLabels:
     litellm_cache_misses_metric = _cache_metric_labels
     litellm_cached_tokens_metric = _cache_metric_labels
 
+    # Managed batch metrics
+    _batch_user_labels = [
+        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
+        UserAPIKeyLabelNames.API_PROVIDER.value,
+        UserAPIKeyLabelNames.USER.value,
+        UserAPIKeyLabelNames.USER_EMAIL.value,
+        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
+    ]
+
+    litellm_managed_batch_created_total = _batch_user_labels
+
+    litellm_managed_file_size_bytes: List[
+        str
+    ] = []  # labels: purpose, file_type, model, api_provider, user (custom)
+
+    litellm_managed_batch_duration_seconds = [
+        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
+        UserAPIKeyLabelNames.API_PROVIDER.value,
+    ]
+
+    litellm_managed_file_created_total = _batch_user_labels
+
+    litellm_managed_file_deleted_total: List[
+        str
+    ] = []  # only "result" label, added at metric creation
+
+    litellm_check_batch_cost_jobs_polled: List[str] = []
+
+    litellm_check_batch_cost_jobs_processed_total = [
+        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
+        UserAPIKeyLabelNames.API_PROVIDER.value,
+    ]
+
+    litellm_check_batch_cost_errors_total: List[str] = []  # label: error_type (custom)
+
+    litellm_check_batch_cost_last_run_timestamp: List[str] = []
+
     @staticmethod
     def get_labels(label_name: DEFINED_PROMETHEUS_METRICS) -> List[str]:
         default_labels = getattr(PrometheusMetricLabels, label_name)
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index 5a80b40d61f..9e669655761 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -315,11 +315,11 @@ class OpenAIFileObject(BaseModel):
     `fine-tune`, `fine-tune-results`, `vision`, and `user_data`.
     """
 
-    status: Optional[Literal["uploaded", "processed", "error"]] = None
+    status: Optional[Literal["uploaded", "processed", "error", "pending"]] = None
     """Deprecated.
 
-    The current status of the file, which can be either `uploaded`, `processed`, or
-    `error`.
+    The current status of the file, which can be either `uploaded`, `processed`,
+    `error`, or `pending` (Azure may return `pending` immediately after upload).
     """
 
     expires_at: Optional[int] = None
@@ -536,6 +536,20 @@ class ChatCompletionRedactedThinkingBlock(TypedDict, total=False):
     cache_control: Optional[Union[dict, ChatCompletionCachedContent]]
 
 
+class ChatCompletionReasoningSummaryTextBlock(TypedDict, total=False):
+    type: Required[Literal["summary_text"]]
+    text: str
+
+
+class ChatCompletionReasoningItem(TypedDict, total=False):
+    """Represents an OpenAI Responses API reasoning item for round-tripping in conversation history."""
+
+    type: Required[Literal["reasoning"]]
+    id: str
+    encrypted_content: Optional[str]
+    summary: List["ChatCompletionReasoningSummaryTextBlock"]
+
+
 class WebSearchOptionsUserLocationApproximate(TypedDict, total=False):
     city: str
     """Free text input for the city of the user, e.g. `San Francisco`."""
diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/akto.py b/litellm/types/proxy/guardrails/guardrail_hooks/akto.py
new file mode 100644
index 00000000000..180c89e8115
--- /dev/null
+++ b/litellm/types/proxy/guardrails/guardrail_hooks/akto.py
@@ -0,0 +1,55 @@
+from typing import Optional, Literal
+
+from pydantic import Field
+
+from .base import GuardrailConfigModel
+
+
+class AktoConfigModel(GuardrailConfigModel):
+    """
+    Config for the Akto guardrail.
+
+    Use two separate config entries to control behaviour:
+      akto-validate (mode: pre_call)  -> check guardrails, block if flagged
+      akto-ingest   (mode: post_call) -> ingest request+response data
+    """
+
+    akto_base_url: Optional[str] = Field(
+        default=None,
+        description="Akto Guardrail API Base URL. Env: AKTO_GUARDRAIL_API_BASE.",
+        json_schema_extra={
+            "examples": [
+                "http://localhost:9090",
+                "https://akto-ingestion.example.com",
+            ]
+        },
+    )
+
+    akto_api_key: Optional[str] = Field(
+        default=None,
+        description="API key for Akto. Env: AKTO_API_KEY.",
+    )
+
+    akto_account_id: Optional[str] = Field(
+        default=None,
+        description="Akto account ID for multi-tenant deployments. Env: AKTO_ACCOUNT_ID. Default: '1000000'.",
+    )
+
+    akto_vxlan_id: Optional[str] = Field(
+        default=None,
+        description="Akto VXLAN ID. Env: AKTO_VXLAN_ID. Default: '0'.",
+    )
+
+    unreachable_fallback: Literal["fail_closed", "fail_open"] = Field(
+        default="fail_closed",
+        description="What to do when Akto is unreachable. 'fail_open' = allow, 'fail_closed' = block.",
+    )
+
+    guardrail_timeout: Optional[int] = Field(
+        default=None,
+        description="HTTP timeout in seconds. Default: 5.",
+    )
+
+    @staticmethod
+    def ui_friendly_name() -> str:
+        return "Akto"
diff --git a/litellm/types/router.py b/litellm/types/router.py
index 5d28349b5e4..4257628e7cb 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -77,6 +77,7 @@ class UpdateRouterConfig(BaseModel):
     routing_strategy_args: Optional[dict] = None
     routing_strategy: Optional[str] = None
     model_group_retry_policy: Optional[dict] = None
+    model_group_affinity_config: Optional[Dict[str, List[str]]] = None
     allowed_fails: Optional[int] = None
     cooldown_time: Optional[float] = None
     num_retries: Optional[int] = None
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 38425c7ac4a..8b94fbdad6d 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -58,6 +58,7 @@ from .llms.openai import (
     AllMessageValues,
     Batch,
     ChatCompletionAnnotation,
+    ChatCompletionReasoningItem,
     ChatCompletionRedactedThinkingBlock,
     ChatCompletionThinkingBlock,
     ChatCompletionToolCallChunk,
@@ -1132,6 +1133,7 @@ class Message(SafeAttributeModel, OpenAIObject):
     thinking_blocks: Optional[
         List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]
     ] = None
+    reasoning_items: Optional[List[ChatCompletionReasoningItem]] = None
     provider_specific_fields: Optional[Dict[str, Any]] = Field(default=None)
     annotations: Optional[List[ChatCompletionAnnotation]] = None
 
@@ -1150,6 +1152,7 @@ class Message(SafeAttributeModel, OpenAIObject):
                 Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
             ]
         ] = None,
+        reasoning_items: Optional[List[ChatCompletionReasoningItem]] = None,
         annotations: Optional[List[ChatCompletionAnnotation]] = None,
         **params,
     ):
@@ -1182,6 +1185,9 @@ class Message(SafeAttributeModel, OpenAIObject):
         if thinking_blocks is not None:
             init_values["thinking_blocks"] = thinking_blocks
 
+        if reasoning_items is not None:
+            init_values["reasoning_items"] = reasoning_items
+
         if annotations is not None:
             init_values["annotations"] = annotations
 
@@ -1219,6 +1225,11 @@ class Message(SafeAttributeModel, OpenAIObject):
             if hasattr(self, "thinking_blocks"):
                 del self.thinking_blocks
 
+        if reasoning_items is None:
+            # ensure default response matches OpenAI spec
+            if hasattr(self, "reasoning_items"):
+                del self.reasoning_items
+
         add_provider_specific_fields(self, provider_specific_fields)
 
     def get(self, key, default=None):
@@ -1246,6 +1257,7 @@ class Delta(SafeAttributeModel, OpenAIObject):
     thinking_blocks: Optional[
         List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]
     ] = None
+    reasoning_items: Optional[List[ChatCompletionReasoningItem]] = None
     provider_specific_fields: Optional[Dict[str, Any]] = Field(default=None)
 
     def __init__(
@@ -1262,6 +1274,7 @@ class Delta(SafeAttributeModel, OpenAIObject):
                 Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
             ]
         ] = None,
+        reasoning_items: Optional[List[ChatCompletionReasoningItem]] = None,
         annotations: Optional[List[ChatCompletionAnnotation]] = None,
         **params,
     ):
@@ -1295,6 +1308,13 @@ class Delta(SafeAttributeModel, OpenAIObject):
             # ensure default response matches OpenAI spec
             del self.thinking_blocks
 
+        if reasoning_items is not None:
+            self.reasoning_items = reasoning_items
+        else:
+            # ensure default response matches OpenAI spec
+            if hasattr(self, "reasoning_items"):
+                del self.reasoning_items
+
         # Add annotations to the delta, ensure they are only on Delta if they exist (Match OpenAI spec)
         if annotations is not None:
             self.annotations = annotations
@@ -2488,6 +2508,7 @@ class StandardLoggingUserAPIKeyMetadata(TypedDict):
     user_api_key_org_id: Optional[str]
     user_api_key_team_id: Optional[str]
     user_api_key_project_id: Optional[str]
+    user_api_key_project_alias: Optional[str]
     user_api_key_user_id: Optional[str]
     user_api_key_user_email: Optional[str]
     user_api_key_team_alias: Optional[str]
@@ -2804,6 +2825,20 @@ class StandardLoggingPayloadStatusFields(TypedDict, total=False):
     """
 
 
+class StandardAuditLogPayload(TypedDict):
+    """Payload for audit log events dispatched to external callbacks."""
+
+    id: str
+    updated_at: str  # ISO-8601
+    changed_by: str
+    changed_by_api_key: str
+    action: str  # "created" | "updated" | "deleted" | "blocked" | "rotated"
+    table_name: str
+    object_id: str
+    before_value: Optional[str]
+    updated_values: Optional[str]
+
+
 class StandardLoggingPayload(TypedDict):
     id: str
     trace_id: str  # Trace multiple LLM calls belonging to same overall request (e.g. fallbacks/retries)
diff --git a/litellm/utils.py b/litellm/utils.py
index c674190ba8c..0e3792773aa 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -1798,6 +1798,7 @@ def client(original_function):  # noqa: PLR0915
 
         model: Optional[str] = args[0] if len(args) > 0 else kwargs.get("model", None)
         is_completion_with_fallbacks = kwargs.get("fallbacks") is not None
+        _is_litellm_internal_call = kwargs.pop("_is_litellm_internal_call", False)
 
         try:
             if logging_obj is None:
@@ -1944,15 +1945,26 @@ def client(original_function):  # noqa: PLR0915
             )
 
             # LOG SUCCESS - handle streaming success logging in the _next_ object
+            # Internal sub-calls (e.g. emulated file-search steps) share the
+            # parent's logging obj; skip async logging here so only the outer call bills once.
             # NOTE: streaming requests return early (before this point) via
             # CustomStreamWrapper, so this block is non-streaming only.
-            if getattr(logging_obj, "_defer_async_logging", False):
-                # Proxy has post-call guardrails that must complete before the
-                # SLP is built.  Store a closure the proxy will call after
-                # post_call_success_hook so guardrail_information is in metadata.
-                # Only create_task is deferred; sync callbacks fire immediately
-                # (below, outside the if/else) for billing/rate-limiting.
-                def _enqueue_deferred_logging() -> None:
+            if not _is_litellm_internal_call:
+                if getattr(logging_obj, "_defer_async_logging", False):
+
+                    def _enqueue_deferred_logging() -> None:
+                        asyncio.create_task(
+                            _client_async_logging_helper(
+                                logging_obj=logging_obj,
+                                result=result,
+                                start_time=start_time,
+                                end_time=end_time,
+                                is_completion_with_fallbacks=is_completion_with_fallbacks,
+                            )
+                        )
+
+                    logging_obj._enqueue_deferred_logging = _enqueue_deferred_logging  # type: ignore
+                else:
                     asyncio.create_task(
                         _client_async_logging_helper(
                             logging_obj=logging_obj,
@@ -1963,19 +1975,6 @@ def client(original_function):  # noqa: PLR0915
                         )
                     )
 
-                logging_obj._enqueue_deferred_logging = _enqueue_deferred_logging  # type: ignore
-            else:
-                asyncio.create_task(
-                    _client_async_logging_helper(
-                        logging_obj=logging_obj,
-                        result=result,
-                        start_time=start_time,
-                        end_time=end_time,
-                        is_completion_with_fallbacks=is_completion_with_fallbacks,
-                    )
-                )
-
-            # Sync callbacks always fire immediately regardless of deferral
             logging_obj.handle_sync_success_callbacks_for_async_calls(
                 result=result,
                 start_time=start_time,
@@ -2008,7 +2007,7 @@ def client(original_function):  # noqa: PLR0915
         except Exception as e:
             traceback_exception = traceback.format_exc()
             end_time = datetime.datetime.now()
-            if logging_obj:
+            if logging_obj and not _is_litellm_internal_call:
                 try:
                     logging_obj.failure_handler(
                         e, traceback_exception, start_time, end_time
@@ -2599,6 +2598,47 @@ def _supports_factory(model: str, custom_llm_provider: Optional[str], key: str)
         return False
 
 
+def _is_explicitly_disabled_factory(
+    model: str, custom_llm_provider: Optional[str], key: str
+) -> bool:
+    """Return True only when the model map explicitly sets *key* to ``False``.
+
+    This is the opt-out mirror of :func:`_supports_factory`.  Where
+    ``_supports_factory`` requires an explicit ``True`` to return ``True``,
+    this function requires an explicit ``False``.  A missing key (``None``)
+    is treated as *not* disabled so that unknown or newly-added models are
+    allowed through without any model-map entry.
+
+    Uses the same ``get_llm_provider`` → ``_get_model_info_helper`` chain as
+    ``_supports_factory`` so caching, fallback, and normalisation improvements
+    apply here automatically.
+    """
+    try:
+        model, custom_llm_provider, _, _ = litellm.get_llm_provider(
+            model=model, custom_llm_provider=custom_llm_provider
+        )
+        model_info = _get_model_info_helper(
+            model=model, custom_llm_provider=custom_llm_provider
+        )
+        val = model_info.get(key)
+        if val is False:
+            return True
+        if val is None:
+            bare_model_key = _get_model_cost_key(model)
+            if bare_model_key is not None:
+                bare_entry = litellm.model_cost.get(bare_model_key) or {}
+                if bare_entry.get(key) is False:
+                    return True
+        return False
+    except Exception as e:
+        verbose_logger.debug(
+            f"Model not found or error in checking {key} disabled state. "
+            f"You passed model={model}, custom_llm_provider={custom_llm_provider}. "
+            f"Error: {str(e)}"
+        )
+        return False
+
+
 def supports_audio_input(model: str, custom_llm_provider: Optional[str] = None) -> bool:
     """Check if a given model supports audio input in a chat completion call"""
     return _supports_factory(
@@ -4826,7 +4866,21 @@ def calculate_max_parallel_requests(
     return None
 
 
-def _get_order_filtered_deployments(healthy_deployments: List[Dict]) -> List:
+def _get_order_filtered_deployments(
+    healthy_deployments: List[Dict], target_order: Optional[int] = None
+) -> List:
+    if target_order is not None:
+        filtered = [
+            d
+            for d in healthy_deployments
+            if d["litellm_params"].get("order") == target_order
+        ]
+        if filtered:
+            return filtered
+        # target_order doesn't match any deployment (e.g., external fallback model) — return all
+        return healthy_deployments
+
+    # Default: pick min order group
     min_order = min(
         (
             deployment["litellm_params"]["order"]
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index bbf9f6d9dc8..2010a8053b4 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -971,18 +971,14 @@
     },
     "anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1001,18 +997,14 @@
     },
     "global.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1031,18 +1023,14 @@
     },
     "us.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
         "cache_read_input_token_cost": 5.5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         "input_cost_per_token": 5.5e-06,
-        "input_cost_per_token_above_200k_tokens": 1.1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.75e-05,
-        "output_cost_per_token_above_200k_tokens": 4.125e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1061,18 +1049,14 @@
     },
     "eu.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
         "cache_read_input_token_cost": 5.5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         "input_cost_per_token": 5.5e-06,
-        "input_cost_per_token_above_200k_tokens": 1.1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.75e-05,
-        "output_cost_per_token_above_200k_tokens": 4.125e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1091,18 +1075,14 @@
     },
     "au.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
         "cache_read_input_token_cost": 5.5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         "input_cost_per_token": 5.5e-06,
-        "input_cost_per_token_above_200k_tokens": 1.1e-05,
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.75e-05,
-        "output_cost_per_token_above_200k_tokens": 4.125e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1121,18 +1101,14 @@
     },
     "anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1151,18 +1127,14 @@
     },
     "global.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
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@@ -1181,18 +1153,14 @@
     },
     "us.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
         "cache_read_input_token_cost": 3.3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
         "input_cost_per_token": 3.3e-06,
-        "input_cost_per_token_above_200k_tokens": 6.6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.65e-05,
-        "output_cost_per_token_above_200k_tokens": 2.475e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1211,18 +1179,14 @@
     },
     "eu.anthropic.claude-sonnet-4-6": {
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-        "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
         "cache_read_input_token_cost": 3.3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
         "input_cost_per_token": 3.3e-06,
-        "input_cost_per_token_above_200k_tokens": 6.6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.65e-05,
-        "output_cost_per_token_above_200k_tokens": 2.475e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1241,18 +1205,14 @@
     },
     "au.anthropic.claude-sonnet-4-6": {
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-        "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06,
         "cache_read_input_token_cost": 3.3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6.6e-07,
         "input_cost_per_token": 3.3e-06,
-        "input_cost_per_token_above_200k_tokens": 6.6e-06,
         "litellm_provider": "bedrock_converse",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.65e-05,
-        "output_cost_per_token_above_200k_tokens": 2.475e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -1831,7 +1791,7 @@
         "cache_read_input_token_cost": 3e-07,
         "input_cost_per_token": 3e-06,
         "litellm_provider": "azure_ai",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
@@ -3435,7 +3395,8 @@
         "supports_tool_choice": true,
         "supports_service_tier": true,
         "supports_vision": true,
-        "supports_none_reasoning_effort": true
+        "supports_none_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "azure/gpt-5.1-chat-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -8502,18 +8463,14 @@
     },
     "claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "anthropic",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -8694,19 +8651,15 @@
     },
     "claude-opus-4-6": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_creation_input_token_cost_above_1hr": 1e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "anthropic",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -8729,19 +8682,15 @@
     },
     "claude-opus-4-6-20260205": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_creation_input_token_cost_above_1hr": 1e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "anthropic",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -15930,6 +15879,55 @@
         "supports_tool_choice": true,
         "supports_vision": true
     },
+    "gemini/lyria-3-clip-preview": {
+        "input_cost_per_token": 0,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_image": 0.04,
+        "output_cost_per_token": 0,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "audio"
+        ],
+        "supports_audio_input": false,
+        "supports_audio_output": true,
+        "supports_function_calling": false,
+        "supports_prompt_caching": false,
+        "supports_response_schema": false,
+        "supports_system_messages": false,
+        "supports_vision": false,
+        "supports_web_search": false
+    },
+    "gemini/lyria-3-pro-preview": {
+        "input_cost_per_token": 0,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 0,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_modalities": [
+            "text"
+        ],
+        "supported_output_modalities": [
+            "audio"
+        ],
+        "supports_audio_input": false,
+        "supports_audio_output": true,
+        "supports_function_calling": false,
+        "supports_prompt_caching": false,
+        "supports_response_schema": false,
+        "supports_system_messages": false,
+        "supports_vision": false,
+        "supports_web_search": false
+    },
     "gemini/veo-2.0-generate-001": {
         "litellm_provider": "gemini",
         "max_input_tokens": 1024,
@@ -16940,6 +16938,18 @@
         "supports_system_messages": true,
         "supports_tool_choice": true
     },
+    "gpt-4-0314": {
+        "deprecation_date": "2026-03-26",
+        "input_cost_per_token": 3e-05,
+        "litellm_provider": "openai",
+        "max_input_tokens": 8192,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096,
+        "mode": "chat",
+        "output_cost_per_token": 6e-05,
+        "supports_system_messages": true,
+        "supports_tool_choice": true
+    },
     "gpt-4-0613": {
         "deprecation_date": "2025-06-06",
         "input_cost_per_token": 3e-05,
@@ -18293,7 +18303,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18332,7 +18343,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18371,7 +18383,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-chat-latest": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18409,7 +18422,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18449,7 +18463,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-2025-12-11": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18489,7 +18504,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-chat-latest": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18526,7 +18542,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.3-chat-latest": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -18563,7 +18580,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-pro": {
         "input_cost_per_token": 2.1e-05,
@@ -18596,7 +18614,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-pro-2025-12-11": {
         "input_cost_per_token": 2.1e-05,
@@ -18629,7 +18648,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.4": {
         "cache_read_input_token_cost": 2.5e-07,
@@ -18678,7 +18698,8 @@
         "supports_service_tier": true,
         "supports_vision": true,
         "supports_none_reasoning_effort": true,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.4-2026-03-05": {
         "cache_read_input_token_cost": 2.5e-07,
@@ -18773,7 +18794,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.4-pro-2026-03-05": {
         "cache_read_input_token_cost": 3e-06,
@@ -18821,7 +18843,94 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
+    },
+    "gpt-5.4-mini": {
+        "cache_read_input_token_cost": 7.5e-08,
+        "cache_read_input_token_cost_flex": 1e-08,
+        "cache_read_input_token_cost_batches": 3.8e-08,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_token_flex": 3.75e-07,
+        "input_cost_per_token_batches": 3.75e-07,
+        "litellm_provider": "openai",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 4.5e-06,
+        "output_cost_per_token_flex": 2.25e-06,
+        "output_cost_per_token_batches": 2.25e-06,
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/batch",
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_native_streaming": 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_service_tier": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_none_reasoning_effort": true,
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": false
+    },
+    "gpt-5.4-nano": {
+        "cache_read_input_token_cost": 2e-08,
+        "cache_read_input_token_cost_flex": 1e-08,
+        "cache_read_input_token_cost_batches": 1e-08,
+        "input_cost_per_token": 2e-07,
+        "input_cost_per_token_flex": 1e-07,
+        "input_cost_per_token_batches": 1e-07,
+        "litellm_provider": "openai",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "mode": "chat",
+        "output_cost_per_token": 1.25e-06,
+        "output_cost_per_token_flex": 6.25e-07,
+        "output_cost_per_token_batches": 6.25e-07,
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/batch",
+            "/v1/responses"
+        ],
+        "supported_modalities": [
+            "text",
+            "image"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_function_calling": true,
+        "supports_native_streaming": 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_service_tier": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "supports_none_reasoning_effort": true,
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": false
     },
     "gpt-5-pro": {
         "input_cost_per_token": 1.5e-05,
@@ -18856,7 +18965,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-pro-2025-10-06": {
         "input_cost_per_token": 1.5e-05,
@@ -18891,7 +19001,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-2025-08-07": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18933,7 +19044,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-chat": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -18967,7 +19079,8 @@
         "supports_tool_choice": false,
         "supports_vision": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-chat-latest": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19001,7 +19114,8 @@
         "supports_tool_choice": false,
         "supports_vision": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-codex": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19034,7 +19148,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-codex": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19070,7 +19185,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-codex-max": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -19103,7 +19219,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.1-codex-mini": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -19139,7 +19256,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.2-codex": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -19175,7 +19293,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": true
+        "supports_xhigh_reasoning_effort": true,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5.3-codex": {
         "cache_read_input_token_cost": 1.75e-07,
@@ -19211,7 +19330,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-mini": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -19253,7 +19373,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-mini-2025-08-07": {
         "cache_read_input_token_cost": 2.5e-08,
@@ -19295,7 +19416,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-nano": {
         "cache_read_input_token_cost": 5e-09,
@@ -19334,7 +19456,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-nano-2025-08-07": {
         "cache_read_input_token_cost": 5e-09,
@@ -19372,7 +19495,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-image-1": {
         "cache_read_input_image_token_cost": 2.5e-06,
@@ -30196,18 +30320,14 @@
     },
     "vertex_ai/claude-opus-4-6": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -30226,18 +30346,14 @@
     },
     "vertex_ai/claude-opus-4-6@default": {
         "cache_creation_input_token_cost": 6.25e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
         "cache_read_input_token_cost": 5e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         "input_cost_per_token": 5e-06,
-        "input_cost_per_token_above_200k_tokens": 1e-05,
         "litellm_provider": "vertex_ai-anthropic_models",
         "max_input_tokens": 1000000,
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "chat",
         "output_cost_per_token": 2.5e-05,
-        "output_cost_per_token_above_200k_tokens": 3.75e-05,
         "search_context_cost_per_query": {
             "search_context_size_high": 0.01,
             "search_context_size_low": 0.01,
@@ -30282,18 +30398,14 @@
     },
     "vertex_ai/claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "supports_assistant_prefill": true,
         "supports_computer_use": true,
         "supports_function_calling": true,
@@ -30510,7 +30622,7 @@
         "output_cost_per_token": 5.4e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supported_regions": [
-            "us-west2"
+            "us-central1"
         ],
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
@@ -30530,7 +30642,7 @@
         "output_cost_per_token_batches": 8.4e-07,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
         "supported_regions": [
-            "us-west2"
+            "global"
         ],
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
@@ -30547,6 +30659,9 @@
         "mode": "chat",
         "output_cost_per_token": 5.4e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models",
+        "supported_regions": [
+            "us-central1"
+        ],
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
         "supports_prompt_caching": true,
@@ -31171,7 +31286,10 @@
         "input_cost_per_token": 3e-07,
         "output_cost_per_token": 1.2e-06,
         "ocr_cost_per_page": 0.0003,
-        "source": "https://cloud.google.com/vertex-ai/pricing"
+        "source": "https://cloud.google.com/vertex-ai/pricing",
+        "supported_regions": [
+            "us-central1"
+        ]
     },
     "vertex_ai/openai/gpt-oss-120b-maas": {
         "input_cost_per_token": 1.5e-07,
@@ -36390,7 +36508,8 @@
         "supports_vision": true,
         "supports_web_search": true,
         "supports_none_reasoning_effort": false,
-        "supports_xhigh_reasoning_effort": false
+        "supports_xhigh_reasoning_effort": false,
+        "supports_minimal_reasoning_effort": true
     },
     "gpt-5-search-api-2025-10-14": {
         "cache_read_input_token_cost": 1.25e-07,
@@ -36696,6 +36815,38 @@
         "supports_audio_input": true,
         "supports_audio_output": true
     },
+    "gemini-3.1-flash-live-preview": {
+        "input_cost_per_audio_token": 3e-06,
+        "input_cost_per_image_token": 1e-06,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_video_per_second": 3.3333333333333335e-05,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_audio_token": 1.2e-05,
+        "output_cost_per_token": 4.5e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_endpoints": [
+            "/v1/realtime"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text",
+            "audio"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": true,
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "supports_web_search": true
+    },
     "gemini/gemini-2.5-flash-native-audio-latest": {
         "input_cost_per_audio_token": 1e-06,
         "input_cost_per_token": 3e-07,
@@ -36774,6 +36925,40 @@
         "tpm": 250000,
         "rpm": 10
     },
+    "gemini/gemini-3.1-flash-live-preview": {
+        "input_cost_per_audio_token": 3e-06,
+        "input_cost_per_image_token": 1e-06,
+        "input_cost_per_token": 7.5e-07,
+        "input_cost_per_video_per_second": 3.3333333333333335e-05,
+        "litellm_provider": "gemini",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "mode": "chat",
+        "output_cost_per_audio_token": 1.2e-05,
+        "output_cost_per_token": 4.5e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/pricing",
+        "supported_endpoints": [
+            "/v1/realtime"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "audio",
+            "video"
+        ],
+        "supported_output_modalities": [
+            "text",
+            "audio"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": true,
+        "supports_function_calling": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "tpm": 250000,
+        "rpm": 10
+    },
     "gemini-2.5-flash-preview-tts": {
         "input_cost_per_token": 3e-07,
         "litellm_provider": "gemini",
@@ -37019,18 +37204,14 @@
     },
     "vertex_ai/claude-sonnet-4-6@default": {
         "cache_creation_input_token_cost": 3.75e-06,
-        "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
         "cache_read_input_token_cost": 3e-07,
-        "cache_read_input_token_cost_above_200k_tokens": 6e-07,
         "input_cost_per_token": 3e-06,
-        "input_cost_per_token_above_200k_tokens": 6e-06,
         "litellm_provider": "vertex_ai-anthropic_models",
-        "max_input_tokens": 200000,
+        "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
         "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
-        "output_cost_per_token_above_200k_tokens": 2.25e-05,
         "supports_assistant_prefill": true,
         "supports_computer_use": true,
         "supports_function_calling": true,
diff --git a/poetry.lock b/poetry.lock
index 06a77c1a4e3..b9eba6e0a30 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.3.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
 
 [[package]]
 name = "a2a-sdk"
@@ -7,11 +7,11 @@ description = "A2A Python SDK"
 optional = false
 python-versions = ">=3.10"
 groups = ["main", "proxy-dev"]
+markers = "python_version >= \"3.10\""
 files = [
     {file = "a2a_sdk-0.3.22-py3-none-any.whl", hash = "sha256:b98701135bb90b0ff85d35f31533b6b7a299bf810658c1c65f3814a6c15ea385"},
     {file = "a2a_sdk-0.3.22.tar.gz", hash = "sha256:77a5694bfc4f26679c11b70c7f1062522206d430b34bc1215cfbb1eba67b7e7d"},
 ]
-markers = {main = "python_version >= \"3.10\" and extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
 
 [package.dependencies]
 google-api-core = ">=1.26.0"
@@ -385,7 +385,6 @@ files = [
     {file = "azure_core-1.36.0-py3-none-any.whl", hash = "sha256:fee9923a3a753e94a259563429f3644aaf05c486d45b1215d098115102d91d3b"},
     {file = "azure_core-1.36.0.tar.gz", hash = "sha256:22e5605e6d0bf1d229726af56d9e92bc37b6e726b141a18be0b4d424131741b7"},
 ]
-markers = {main = "extra == \"proxy\" or extra == \"extra-proxy\""}
 
 [package.dependencies]
 requests = ">=2.21.0"
@@ -406,7 +405,6 @@ files = [
     {file = "azure_identity-1.25.1-py3-none-any.whl", hash = "sha256:e9edd720af03dff020223cd269fa3a61e8f345ea75443858273bcb44844ab651"},
     {file = "azure_identity-1.25.1.tar.gz", hash = "sha256:87ca8328883de6036443e1c37b40e8dc8fb74898240f61071e09d2e369361456"},
 ]
-markers = {main = "extra == \"proxy\" or extra == \"extra-proxy\""}
 
 [package.dependencies]
 azure-core = ">=1.31.0"
@@ -600,7 +598,7 @@ files = [
     {file = "cachetools-6.2.2-py3-none-any.whl", hash = "sha256:6c09c98183bf58560c97b2abfcedcbaf6a896a490f534b031b661d3723b45ace"},
     {file = "cachetools-6.2.2.tar.gz", hash = "sha256:8e6d266b25e539df852251cfd6f990b4bc3a141db73b939058d809ebd2590fc6"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") or extra == \"google\" or extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [[package]]
 name = "certifi"
@@ -707,7 +705,7 @@ files = [
     {file = "cffi-2.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:b882b3df248017dba09d6b16defe9b5c407fe32fc7c65a9c69798e6175601be9"},
     {file = "cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529"},
 ]
-markers = {main = "(platform_python_implementation != \"PyPy\" or extra == \"proxy\") and (python_version >= \"3.10\" or extra == \"proxy\" or extra == \"extra-proxy\") and (extra == \"proxy\" or extra == \"extra-proxy\" or extra == \"mlflow\")", dev = "platform_python_implementation != \"PyPy\"", proxy-dev = "platform_python_implementation != \"PyPy\""}
+markers = {main = "platform_python_implementation != \"PyPy\" or extra == \"proxy\"", dev = "platform_python_implementation != \"PyPy\"", proxy-dev = "platform_python_implementation != \"PyPy\""}
 
 [package.dependencies]
 pycparser = {version = "*", markers = "implementation_name != \"PyPy\""}
@@ -1057,7 +1055,6 @@ files = [
     {file = "cryptography-43.0.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:2ce6fae5bdad59577b44e4dfed356944fbf1d925269114c28be377692643b4ff"},
     {file = "cryptography-43.0.3.tar.gz", hash = "sha256:315b9001266a492a6ff443b61238f956b214dbec9910a081ba5b6646a055a805"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"proxy\" or extra == \"extra-proxy\" or extra == \"mlflow\") or extra == \"proxy\" or extra == \"extra-proxy\""}
 
 [package.dependencies]
 cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
@@ -1840,11 +1837,11 @@ description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "proxy-dev"]
+markers = "python_version >= \"3.14\""
 files = [
     {file = "google_api_core-2.25.2-py3-none-any.whl", hash = "sha256:e9a8f62d363dc8424a8497f4c2a47d6bcda6c16514c935629c257ab5d10210e7"},
     {file = "google_api_core-2.25.2.tar.gz", hash = "sha256:1c63aa6af0d0d5e37966f157a77f9396d820fba59f9e43e9415bc3dc5baff300"},
 ]
-markers = {main = "python_version >= \"3.14\" and (extra == \"extra-proxy\" or extra == \"google\")", proxy-dev = "python_version >= \"3.14\""}
 
 [package.dependencies]
 google-auth = ">=2.14.1,<3.0.0"
@@ -1872,7 +1869,7 @@ files = [
     {file = "google_api_core-2.28.1-py3-none-any.whl", hash = "sha256:4021b0f8ceb77a6fb4de6fde4502cecab45062e66ff4f2895169e0b35bc9466c"},
     {file = "google_api_core-2.28.1.tar.gz", hash = "sha256:2b405df02d68e68ce0fbc138559e6036559e685159d148ae5861013dc201baf8"},
 ]
-markers = {main = "python_version < \"3.14\" and (extra == \"extra-proxy\" or extra == \"google\")", proxy-dev = "python_version >= \"3.10\" and python_version < \"3.14\""}
+markers = {main = "(python_version >= \"3.10\" or extra == \"google\" or extra == \"extra-proxy\") and python_version < \"3.14\"", proxy-dev = "python_version >= \"3.10\" and python_version < \"3.14\""}
 
 [package.dependencies]
 google-auth = ">=2.14.1,<3.0.0"
@@ -1909,7 +1906,7 @@ files = [
     {file = "google_auth-2.43.0-py2.py3-none-any.whl", hash = "sha256:af628ba6fa493f75c7e9dbe9373d148ca9f4399b5ea29976519e0a3848eddd16"},
     {file = "google_auth-2.43.0.tar.gz", hash = "sha256:88228eee5fc21b62a1b5fe773ca15e67778cb07dc8363adcb4a8827b52d81483"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") or extra == \"google\" or extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [package.dependencies]
 cachetools = ">=2.0.0,<7.0"
@@ -2081,11 +2078,11 @@ files = [
 ]
 
 [package.dependencies]
-google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0.dev0", extras = ["grpc"]}
-google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0.dev0"
-grpc-google-iam-v1 = ">=0.12.4,<1.0.0.dev0"
-proto-plus = ">=1.22.3,<2.0.0.dev0"
-protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
+grpc-google-iam-v1 = ">=0.12.4,<1.0.0dev"
+proto-plus = ">=1.22.3,<2.0.0dev"
+protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0dev"
 
 [[package]]
 name = "google-cloud-resource-manager"
@@ -2267,7 +2264,7 @@ files = [
     {file = "googleapis_common_protos-1.72.0-py3-none-any.whl", hash = "sha256:4299c5a82d5ae1a9702ada957347726b167f9f8d1fc352477702a1e851ff4038"},
     {file = "googleapis_common_protos-1.72.0.tar.gz", hash = "sha256:e55a601c1b32b52d7a3e65f43563e2aa61bcd737998ee672ac9b951cd49319f5"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\") or extra == \"google\" or extra == \"extra-proxy\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\""}
 
 [package.dependencies]
 grpcio = {version = ">=1.44.0,<2.0.0", optional = true, markers = "extra == \"grpc\""}
@@ -2676,11 +2673,11 @@ description = "Consume Server-Sent Event (SSE) messages with HTTPX."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "proxy-dev"]
+markers = "python_version >= \"3.10\""
 files = [
     {file = "httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc"},
     {file = "httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"proxy\" or extra == \"extra-proxy\")", proxy-dev = "python_version >= \"3.10\""}
 
 [[package]]
 name = "huey"
@@ -3045,7 +3042,7 @@ files = [
 
 [package.dependencies]
 attrs = ">=22.2.0"
-jsonschema-specifications = ">=2023.3.6"
+jsonschema-specifications = ">=2023.03.6"
 referencing = ">=0.28.4"
 rpds-py = ">=0.7.1"
 
@@ -3222,15 +3219,15 @@ files = [
 
 [[package]]
 name = "litellm-proxy-extras"
-version = "0.4.58"
+version = "0.4.60"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 optional = true
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 groups = ["main"]
 markers = "extra == \"proxy\""
 files = [
-    {file = "litellm_proxy_extras-0.4.58-py3-none-any.whl", hash = "sha256:8863e70de833c0e35119a1cbbf583619bdebe52222efd5654586519175ba403b"},
-    {file = "litellm_proxy_extras-0.4.58.tar.gz", hash = "sha256:84a67483329eced8be4fc61c4e43f117287aa4e3deeb8ddf8fe8cdc9a8508836"},
+    {file = "litellm_proxy_extras-0.4.60-py3-none-any.whl", hash = "sha256:7abcc811f7430e4b24e7a8ba7186219a4845a955ae7a71d8822bd03fd9fc3393"},
+    {file = "litellm_proxy_extras-0.4.60.tar.gz", hash = "sha256:1c122f2a7e0eb58fa4c6d8da9da82ac1fe2869de3510bcfade5c2932af202328"},
 ]
 
 [[package]]
@@ -3716,7 +3713,6 @@ files = [
     {file = "msal-1.34.0-py3-none-any.whl", hash = "sha256:f669b1644e4950115da7a176441b0e13ec2975c29528d8b9e81316023676d6e1"},
     {file = "msal-1.34.0.tar.gz", hash = "sha256:76ba83b716ea5a6d75b0279c0ac353a0e05b820ca1f6682c0eb7f45190c43c2f"},
 ]
-markers = {main = "extra == \"proxy\" or extra == \"extra-proxy\""}
 
 [package.dependencies]
 cryptography = ">=2.5,<49"
@@ -3737,7 +3733,6 @@ files = [
     {file = "msal_extensions-1.3.1-py3-none-any.whl", hash = "sha256:96d3de4d034504e969ac5e85bae8106c8373b5c6568e4c8fa7af2eca9dbe6bca"},
     {file = "msal_extensions-1.3.1.tar.gz", hash = "sha256:c5b0fd10f65ef62b5f1d62f4251d51cbcaf003fcedae8c91b040a488614be1a4"},
 ]
-markers = {main = "extra == \"proxy\" or extra == \"extra-proxy\""}
 
 [package.dependencies]
 msal = ">=1.29,<2"
@@ -3988,7 +3983,6 @@ files = [
     {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
     {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
 ]
-markers = {main = "extra == \"extra-proxy\""}
 
 [[package]]
 name = "numpy"
@@ -4111,7 +4105,7 @@ files = [
     {file = "opentelemetry_api-1.39.1-py3-none-any.whl", hash = "sha256:2edd8463432a7f8443edce90972169b195e7d6a05500cd29e6d13898187c9950"},
     {file = "opentelemetry_api-1.39.1.tar.gz", hash = "sha256:fbde8c80e1b937a2c61f20347e91c0c18a1940cecf012d62e65a7caf08967c9c"},
 ]
-markers = {main = "python_version >= \"3.10\" and extra == \"mlflow\""}
+markers = {main = "python_version >= \"3.10\""}
 
 [package.dependencies]
 importlib-metadata = ">=6.0,<8.8.0"
@@ -4226,7 +4220,7 @@ files = [
     {file = "opentelemetry_sdk-1.39.1-py3-none-any.whl", hash = "sha256:4d5482c478513ecb0a5d938dcc61394e647066e0cc2676bee9f3af3f3f45f01c"},
     {file = "opentelemetry_sdk-1.39.1.tar.gz", hash = "sha256:cf4d4563caf7bff906c9f7967e2be22d0d6b349b908be0d90fb21c8e9c995cc6"},
 ]
-markers = {main = "python_version >= \"3.10\" and extra == \"mlflow\""}
+markers = {main = "python_version >= \"3.10\""}
 
 [package.dependencies]
 opentelemetry-api = "1.39.1"
@@ -4244,7 +4238,7 @@ files = [
     {file = "opentelemetry_semantic_conventions-0.60b1-py3-none-any.whl", hash = "sha256:9fa8c8b0c110da289809292b0591220d3a7b53c1526a23021e977d68597893fb"},
     {file = "opentelemetry_semantic_conventions-0.60b1.tar.gz", hash = "sha256:87c228b5a0669b748c76d76df6c364c369c28f1c465e50f661e39737e84bc953"},
 ]
-markers = {main = "python_version >= \"3.10\" and extra == \"mlflow\""}
+markers = {main = "python_version >= \"3.10\""}
 
 [package.dependencies]
 opentelemetry-api = "1.39.1"
@@ -4743,7 +4737,6 @@ files = [
     {file = "prisma-0.11.0-py3-none-any.whl", hash = "sha256:22bb869e59a2968b99f3483bb417717273ffbc569fd1e9ceed95e5614cbaf53a"},
     {file = "prisma-0.11.0.tar.gz", hash = "sha256:3f2f2fd2361e1ec5ff655f2a04c7860c2f2a5bc4c91f78ca9c5c6349735bf693"},
 ]
-markers = {main = "extra == \"extra-proxy\""}
 
 [package.dependencies]
 click = ">=7.1.2"
@@ -4917,7 +4910,7 @@ files = [
     {file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"},
     {file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"},
 ]
-markers = {main = "extra == \"google\" or extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [package.dependencies]
 protobuf = ">=3.19.0,<7.0.0"
@@ -4945,7 +4938,7 @@ files = [
     {file = "protobuf-5.29.5-py3-none-any.whl", hash = "sha256:6cf42630262c59b2d8de33954443d94b746c952b01434fc58a417fdbd2e84bd5"},
     {file = "protobuf-5.29.5.tar.gz", hash = "sha256:bc1463bafd4b0929216c35f437a8e28731a2b7fe3d98bb77a600efced5a15c84"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") or extra == \"google\" or extra == \"extra-proxy\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\""}
 
 [[package]]
 name = "psutil"
@@ -5105,7 +5098,7 @@ files = [
     {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
     {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") or extra == \"google\" or extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [[package]]
 name = "pyasn1-modules"
@@ -5118,7 +5111,7 @@ files = [
     {file = "pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a"},
     {file = "pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") or extra == \"google\" or extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [package.dependencies]
 pyasn1 = ">=0.6.1,<0.7.0"
@@ -5146,7 +5139,7 @@ files = [
     {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"},
     {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"},
 ]
-markers = {main = "implementation_name != \"PyPy\" and (platform_python_implementation != \"PyPy\" or extra == \"proxy\") and (python_version >= \"3.10\" or extra == \"proxy\" or extra == \"extra-proxy\") and (extra == \"proxy\" or extra == \"extra-proxy\" or extra == \"mlflow\")", dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\"", proxy-dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\""}
+markers = {main = "implementation_name != \"PyPy\" and (platform_python_implementation != \"PyPy\" or extra == \"proxy\")", dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\"", proxy-dev = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\""}
 
 [[package]]
 name = "pydantic"
@@ -5369,7 +5362,6 @@ files = [
     {file = "pyjwt-2.12.1-py3-none-any.whl", hash = "sha256:28ca37c070cad8ba8cd9790cd940535d40274d22f80ab87f3ac6a713e6e8454c"},
     {file = "pyjwt-2.12.1.tar.gz", hash = "sha256:c74a7a2adf861c04d002db713dd85f84beb242228e671280bf709d765b03672b"},
 ]
-markers = {main = "(python_version <= \"3.13\" or extra == \"proxy\" or extra == \"extra-proxy\") and (extra == \"extra-proxy\" or extra == \"proxy\")"}
 
 [package.dependencies]
 cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""}
@@ -6313,7 +6305,7 @@ files = [
     {file = "rsa-4.9.1-py3-none-any.whl", hash = "sha256:68635866661c6836b8d39430f97a996acbd61bfa49406748ea243539fe239762"},
     {file = "rsa-4.9.1.tar.gz", hash = "sha256:e7bdbfdb5497da4c07dfd35530e1a902659db6ff241e39d9953cad06ebd0ae75"},
 ]
-markers = {main = "python_version >= \"3.10\" and (extra == \"extra-proxy\" or extra == \"google\" or extra == \"mlflow\") or extra == \"google\" or extra == \"extra-proxy\"", proxy-dev = "python_version >= \"3.10\""}
+markers = {main = "extra == \"google\" or extra == \"extra-proxy\" or python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [package.dependencies]
 pyasn1 = ">=0.1.3"
@@ -6359,10 +6351,10 @@ files = [
 ]
 
 [package.dependencies]
-botocore = ">=1.37.4,<2.0a0"
+botocore = ">=1.37.4,<2.0a.0"
 
 [package.extras]
-crt = ["botocore[crt] (>=1.37.4,<2.0a0)"]
+crt = ["botocore[crt] (>=1.37.4,<2.0a.0)"]
 
 [[package]]
 name = "scikit-learn"
@@ -6515,9 +6507,9 @@ tornado = ">=6.4.2,<7"
 urllib3 = ">=1.26,<3"
 
 [package.extras]
-all = ["boto3 (>=1.34.98,<2)", "botocore (>=1.34.110,<2)", "cohere (>=5.9.4,<6.0)", "dagger-io (>=0.1.1) ; python_version >= \"3.11\"", "fastembed (>=0.3.0,<0.4) ; python_version < \"3.13\"", "google-cloud-aiplatform (>=1.45.0,<2)", "ipykernel (>=6.25.0,<7)", "llama-cpp-python (>=0.2.28,<0.2.86) ; python_version < \"3.13\"", "mistralai (>=0.0.12,<0.1.0)", "mypy (>=1.7.1,<2)", "ollama (>=0.1.7)", "pillow (>=10.2.0,<11.0.0) ; python_version < \"3.13\"", "pinecone[asyncio] (>=7.0.0,<8.0.0)", "psycopg[binary] (>=3.1.0,<4)", "pytest (>=8.2,<9.0)", "pytest-asyncio (>=0.24.0,<0.25)", "pytest-cov (>=4.1.0,<5)", "pytest-mock (>=3.12.0,<4)", "pytest-timeout", "pytest-xdist (>=3.5.0,<4)", "python-dotenv (>=1.0.0,<2)", "qdrant-client (>=1.11.1,<2)", "requests-mock (>=1.12.1,<2)", "ruff (>=0.11.2,<0.12)", "sentence-transformers (>=5.0.0) ; python_version < \"3.13\"", "tokenizers (>=0.19) ; python_version < \"3.13\"", "torch (>=2.6.0) ; python_version < \"3.13\"", "torchvision (>=0.17.0) ; python_version < \"3.13\"", "transformers (>=4.36.2) ; python_version < \"3.13\"", "types-pyyaml (>=6.0.12.12,<7)", "types-requests (>=2.31.0,<3)"]
+all = ["boto3 (>=1.34.98,<2)", "botocore (>=1.34.110,<2)", "cohere (>=5.9.4,<6.00)", "dagger-io (>=0.1.1) ; python_version >= \"3.11\"", "fastembed (>=0.3.0,<0.4) ; python_version < \"3.13\"", "google-cloud-aiplatform (>=1.45.0,<2)", "ipykernel (>=6.25.0,<7)", "llama-cpp-python (>=0.2.28,<0.2.86) ; python_version < \"3.13\"", "mistralai (>=0.0.12,<0.1.0)", "mypy (>=1.7.1,<2)", "ollama (>=0.1.7)", "pillow (>=10.2.0,<11.0.0) ; python_version < \"3.13\"", "pinecone[asyncio] (>=7.0.0,<8.0.0)", "psycopg[binary] (>=3.1.0,<4)", "pytest (>=8.2,<9.0)", "pytest-asyncio (>=0.24.0,<0.25)", "pytest-cov (>=4.1.0,<5)", "pytest-mock (>=3.12.0,<4)", "pytest-timeout", "pytest-xdist (>=3.5.0,<4)", "python-dotenv (>=1.0.0,<2)", "qdrant-client (>=1.11.1,<2)", "requests-mock (>=1.12.1,<2)", "ruff (>=0.11.2,<0.12)", "sentence-transformers (>=5.0.0) ; python_version < \"3.13\"", "tokenizers (>=0.19) ; python_version < \"3.13\"", "torch (>=2.6.0) ; python_version < \"3.13\"", "torchvision (>=0.17.0) ; python_version < \"3.13\"", "transformers (>=4.36.2) ; python_version < \"3.13\"", "types-pyyaml (>=6.0.12.12,<7)", "types-requests (>=2.31.0,<3)"]
 bedrock = ["boto3 (>=1.34.98,<2)", "botocore (>=1.34.110,<2)"]
-cohere = ["cohere (>=5.9.4,<6.0)"]
+cohere = ["cohere (>=5.9.4,<6.00)"]
 dev = ["dagger-io (>=0.1.1) ; python_version >= \"3.11\"", "ipykernel (>=6.25.0,<7)", "mypy (>=1.7.1,<2)", "pytest (>=8.2,<9.0)", "pytest-asyncio (>=0.24.0,<0.25)", "pytest-cov (>=4.1.0,<5)", "pytest-mock (>=3.12.0,<4)", "pytest-timeout", "pytest-xdist (>=3.5.0,<4)", "python-dotenv (>=1.0.0,<2)", "requests-mock (>=1.12.1,<2)", "ruff (>=0.11.2,<0.12)", "types-pyyaml (>=6.0.12.12,<7)", "types-requests (>=2.31.0,<3)"]
 docs = ["pydoc-markdown (>=4.8.2) ; python_version < \"3.12\""]
 fastembed = ["fastembed (>=0.3.0,<0.4) ; python_version < \"3.13\""]
@@ -7245,7 +7237,6 @@ files = [
     {file = "tomlkit-0.13.3-py3-none-any.whl", hash = "sha256:c89c649d79ee40629a9fda55f8ace8c6a1b42deb912b2a8fd8d942ddadb606b0"},
     {file = "tomlkit-0.13.3.tar.gz", hash = "sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1"},
 ]
-markers = {main = "extra == \"extra-proxy\""}
 
 [[package]]
 name = "tornado"
@@ -8018,4 +8009,4 @@ utils = ["numpydoc"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "f0977419272b446bc2df0e062406c8f7fe03566bd38fdb8395418bdc6da3fe20"
+content-hash = "b4e3ee072f600fab9810024afdd550407d25733a9b6752476aa61826e33bc08e"
diff --git a/pyproject.toml b/pyproject.toml
index 143054572ea..b2a446149e5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.82.5"
+version = "1.82.6"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -61,7 +61,7 @@ boto3 = { version = "^1.40.76", optional = true }
 redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
 mcp = {version = ">=1.25.0,<2.0.0", optional = true, python = ">=3.10"}
 a2a-sdk = {version = "^0.3.22", optional = true, python = ">=3.10"}
-litellm-proxy-extras = {version = "^0.4.58", optional = true}
+litellm-proxy-extras = {version = "^0.4.60", optional = true}
 rich = {version = "^13.7.1", optional = true}
 litellm-enterprise = {version = "0.1.35", optional = true}
 diskcache = {version = "^5.6.1", optional = true}
@@ -184,7 +184,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.82.5"
+version = "1.82.6"
 version_files = [
     "pyproject.toml:^version"
 ]
diff --git a/requirements.txt b/requirements.txt
index 473bec42c57..7ce9ab04d2a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -57,7 +57,7 @@ grpcio>=1.75.0; python_version >= "3.14"
 sentry_sdk==2.21.0 # for sentry error handling
 detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests
 tzdata==2025.1 # IANA time zone database
-litellm-proxy-extras==0.4.58 # for proxy extras - e.g. prisma migrations
+litellm-proxy-extras==0.4.60 # for proxy extras - e.g. prisma migrations
 llm-sandbox==0.3.31 # for skill execution in sandbox
 ### LITELLM PACKAGE DEPENDENCIES
 python-dotenv==1.0.1 # for env
diff --git a/tests/batches_tests/test_fine_tuning_api.py b/tests/batches_tests/test_fine_tuning_api.py
index 7e238173480..7e220612ac6 100644
--- a/tests/batches_tests/test_fine_tuning_api.py
+++ b/tests/batches_tests/test_fine_tuning_api.py
@@ -180,6 +180,21 @@ async def test_azure_create_fine_tune_jobs_async():
     pass
 
 
+def test_azure_trainingtype_defaults_to_one():
+    """
+    Azure requires trainingType in extra_body. When omitted, AzureOpenAIFineTuningAPI defaults it to 1.
+    """
+    from litellm.llms.azure.fine_tuning.handler import AzureOpenAIFineTuningAPI
+
+    handler = AzureOpenAIFineTuningAPI()
+    create_data = {"model": "gpt-4o-mini", "training_file": "file-test"}
+
+    handler._ensure_training_type(create_data)
+
+    assert "extra_body" in create_data
+    assert create_data["extra_body"]["trainingType"] == 1
+
+
 @pytest.mark.asyncio()
 async def test_create_vertex_fine_tune_jobs_mocked():
     load_vertex_ai_credentials()
@@ -601,11 +616,10 @@ async def test_mock_openai_retrieve_fine_tune_job():
 @pytest.mark.asyncio
 async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
     """Test that Azure-specific parameters are passed through extra_body"""
-    from openai import AsyncAzureOpenAI
-    from openai.types.fine_tuning.fine_tuning_job import FineTuningJob
     from openai.types.fine_tuning.fine_tuning_job import Hyperparameters as OAIHyperparameters
+    from litellm.types.utils import LiteLLMFineTuningJob
 
-    mock_response = FineTuningJob(
+    mock_response = LiteLLMFineTuningJob(
         id="ft-azure-123",
         model="gpt-4.1-mini-2025-04-14",
         created_at=1677610602,
@@ -619,8 +633,11 @@ async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
         result_files=[],
     )
 
+    async def mock_async_create(*args, **kwargs):
+        return mock_response
+
     with patch("litellm.llms.azure.fine_tuning.handler.AzureOpenAIFineTuningAPI.create_fine_tuning_job") as mock_create:
-        mock_create.return_value = mock_response
+        mock_create.return_value = mock_async_create()
 
         response = await litellm.acreate_fine_tuning_job(
             model="gpt-4.1-mini-2025-04-14",
diff --git a/tests/guardrails_tests/test_akto_guardrails.py b/tests/guardrails_tests/test_akto_guardrails.py
new file mode 100644
index 00000000000..3c70104a219
--- /dev/null
+++ b/tests/guardrails_tests/test_akto_guardrails.py
@@ -0,0 +1,550 @@
+import asyncio
+import json
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+
+from starlette.exceptions import HTTPException
+from litellm.types.utils import GenericGuardrailAPIInputs
+from litellm.proxy.guardrails.guardrail_registry import guardrail_initializer_registry, guardrail_class_registry
+from litellm.proxy.guardrails.guardrail_hooks.akto.akto import AktoGuardrail
+
+
+# ---------------------------------------------------------------------------
+#  Registry tests
+# ---------------------------------------------------------------------------
+
+
+def test_akto_in_guardrail_initializer_registry():
+    assert "akto" in guardrail_initializer_registry
+
+
+def test_akto_in_guardrail_class_registry():
+    assert "akto" in guardrail_class_registry
+    assert guardrail_class_registry["akto"] is AktoGuardrail
+
+
+# ---------------------------------------------------------------------------
+#  Fixtures
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def akto_validate():
+    """AktoGuardrail configured for pre_call (akto-validate)."""
+    return AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        unreachable_fallback="fail_closed",
+        guardrail_name="test-akto-validate",
+        event_hook="pre_call",
+    )
+
+
+@pytest.fixture
+def akto_ingest():
+    """AktoGuardrail configured for post_call (akto-ingest)."""
+    return AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        unreachable_fallback="fail_open",
+        guardrail_name="test-akto-ingest",
+        event_hook="post_call",
+    )
+
+
+@pytest.fixture
+def sample_inputs() -> GenericGuardrailAPIInputs:
+    return GenericGuardrailAPIInputs(
+        texts=["Hello, how are you?"],
+        model="gpt-4",
+    )
+
+
+@pytest.fixture
+def sample_request_data() -> dict:
+    return {
+        "metadata": {
+            "user_api_key_request_route": "/v1/chat/completions",
+            "user_api_key": "sk-test-123",
+            "user_api_key_user_id": "user-1",
+            "user_api_key_team_id": "team-1",
+        },
+        "proxy_server_request": {
+            "headers": {
+                "x-forwarded-for": "10.0.0.1",
+            }
+        },
+    }
+
+
+def _mock_allowed_response():
+    mock = MagicMock(spec=httpx.Response)
+    mock.status_code = 200
+    mock.json.return_value = {"data": {"guardrailsResult": {"Allowed": True, "Reason": ""}}}
+    return mock
+
+
+def _mock_blocked_response(reason="Prompt injection detected"):
+    mock = MagicMock(spec=httpx.Response)
+    mock.status_code = 200
+    mock.json.return_value = {"data": {"guardrailsResult": {"Allowed": False, "Reason": reason}}}
+    return mock
+
+
+# ---------------------------------------------------------------------------
+#  Initialization tests
+# ---------------------------------------------------------------------------
+
+
+def test_init_requires_akto_base_url():
+    with patch.dict(os.environ, {}, clear=True):
+        with pytest.raises(ValueError, match="akto_base_url is required"):
+            AktoGuardrail(
+                akto_base_url="",
+                akto_api_key="test-token",
+                guardrail_name="test",
+                event_hook="pre_call",
+            )
+
+
+def test_init_requires_api_key():
+    with patch.dict(os.environ, {}, clear=True):
+        with pytest.raises(ValueError, match="akto_api_key is required"):
+            AktoGuardrail(
+                akto_base_url="http://localhost:9090",
+                akto_api_key="",
+                guardrail_name="test",
+                event_hook="pre_call",
+            )
+
+
+def test_init_from_env():
+    with patch.dict(
+        os.environ,
+        {
+            "AKTO_GUARDRAIL_API_BASE": "http://env-host:9090",
+            "AKTO_API_KEY": "env-token",
+            "AKTO_ACCOUNT_ID": "2000000",
+            "AKTO_VXLAN_ID": "42",
+        },
+    ):
+        g = AktoGuardrail(guardrail_name="env-test", event_hook="post_call")
+        assert g.akto_base_url == "http://env-host:9090"
+        assert g.akto_api_key == "env-token"
+        assert g.guardrail_timeout == 5
+        assert g.akto_account_id == "2000000"
+        assert g.akto_vxlan_id == "42"
+
+
+def test_init_defaults():
+    g = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        guardrail_name="default-test",
+        event_hook="pre_call",
+    )
+    assert g.unreachable_fallback == "fail_closed"
+    assert g.guardrail_timeout == 5
+    assert g.akto_account_id == "1000000"
+    assert g.akto_vxlan_id == "0"
+
+
+def test_background_tasks_per_instance():
+    a = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        guardrail_name="instance-a",
+        event_hook="pre_call",
+    )
+    b = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        guardrail_name="instance-b",
+        event_hook="post_call",
+    )
+    assert a.background_tasks is not b.background_tasks
+
+
+# ---------------------------------------------------------------------------
+#  Payload format tests
+# ---------------------------------------------------------------------------
+
+
+def test_build_akto_payload_format(akto_validate, sample_inputs, sample_request_data):
+    payload = akto_validate.build_akto_payload(sample_inputs, sample_request_data, include_response=False)
+
+    assert payload["path"] == "/v1/chat/completions"
+    assert payload["method"] == "POST"
+    assert payload["type"] == "HTTP/1.1"
+    assert payload["akto_account_id"] == "1000000"
+    assert payload["akto_vxlan_id"] == "0"
+    assert payload["is_pending"] == "false"
+    assert payload["source"] == "MIRRORING"
+    assert payload["contextSource"] == "AGENTIC"
+    assert payload["ip"] == "10.0.0.1"
+
+    req_headers = json.loads(payload["requestHeaders"])
+    assert "content-type" in req_headers
+
+    req_wrapper = json.loads(payload["requestPayload"])
+    req_body = json.loads(req_wrapper["body"])
+    assert req_body["model"] == "gpt-4"
+    assert req_body["messages"][0]["content"] == "Hello, how are you?"
+
+    tag = json.loads(payload["tag"])
+    assert tag["gen-ai"] == "Gen AI"
+
+    assert payload["responsePayload"] == json.dumps({})
+    assert payload["time"].isdigit()
+    assert len(payload["time"]) >= 13
+
+
+def test_build_akto_payload_with_response(akto_validate, sample_inputs, sample_request_data):
+    payload = akto_validate.build_akto_payload(sample_inputs, sample_request_data, include_response=True)
+    resp_wrapper = json.loads(payload["responsePayload"])
+    resp_body = json.loads(resp_wrapper["body"])
+    assert "choices" in resp_body
+
+
+def test_build_akto_payload_custom_account_ids(sample_inputs, sample_request_data):
+    g = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        akto_account_id="9999",
+        akto_vxlan_id="7",
+        guardrail_name="custom-ids-test",
+        event_hook="pre_call",
+    )
+    payload = g.build_akto_payload(sample_inputs, sample_request_data, include_response=False)
+    assert payload["akto_account_id"] == "9999"
+    assert payload["akto_vxlan_id"] == "7"
+
+
+def test_build_query_params():
+    params = AktoGuardrail.build_query_params(guardrails=True, ingest_data=False)
+    assert params == {"akto_connector": "litellm", "guardrails": "true"}
+
+    params = AktoGuardrail.build_query_params(guardrails=False, ingest_data=True)
+    assert params == {"akto_connector": "litellm", "ingest_data": "true"}
+
+    params = AktoGuardrail.build_query_params(guardrails=True, ingest_data=True)
+    assert params == {
+        "akto_connector": "litellm",
+        "guardrails": "true",
+        "ingest_data": "true",
+    }
+
+
+# ---------------------------------------------------------------------------
+#  Guardrail response handling
+# ---------------------------------------------------------------------------
+
+
+def test_handle_guardrail_response_allowed():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = {"data": {"guardrailsResult": {"Allowed": True, "Reason": ""}}}
+    allowed, reason = AktoGuardrail.handle_guardrail_response(mock_resp)
+    assert allowed is True
+    assert reason == ""
+
+
+def test_handle_guardrail_response_blocked():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = {"data": {"guardrailsResult": {"Allowed": False, "Reason": "PII detected"}}}
+    allowed, reason = AktoGuardrail.handle_guardrail_response(mock_resp)
+    assert allowed is False
+    assert reason == "PII detected"
+
+
+def test_handle_guardrail_response_missing_result():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = {}
+    allowed, _ = AktoGuardrail.handle_guardrail_response(mock_resp)
+    assert allowed is True
+
+
+def test_handle_guardrail_response_data_none():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = {"data": None}
+    allowed, reason = AktoGuardrail.handle_guardrail_response(mock_resp)
+    assert allowed is True
+    assert reason == ""
+
+
+def test_handle_guardrail_response_guardrails_result_not_dict():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = {"data": {"guardrailsResult": "invalid"}}
+    allowed, reason = AktoGuardrail.handle_guardrail_response(mock_resp)
+    assert allowed is True
+    assert reason == ""
+
+
+def test_handle_guardrail_response_non_dict():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.json.return_value = "invalid"
+    allowed, _ = AktoGuardrail.handle_guardrail_response(mock_resp)
+    assert allowed is True
+
+
+def test_handle_guardrail_response_error_status():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 500
+    mock_resp.request = MagicMock()
+    with pytest.raises(httpx.HTTPStatusError):
+        AktoGuardrail.handle_guardrail_response(mock_resp)
+
+
+def test_handle_guardrail_response_non_json_body():
+    mock_resp = MagicMock(spec=httpx.Response)
+    mock_resp.status_code = 200
+    mock_resp.request = MagicMock()
+    mock_resp.text = "<html>not json</html>"
+    mock_resp.json.side_effect = json.JSONDecodeError("Expecting value", "<html>", 0)
+
+    with pytest.raises(httpx.RequestError):
+        AktoGuardrail.handle_guardrail_response(mock_resp)
+
+
+# ---------------------------------------------------------------------------
+#  Pre-call (akto-validate) — allowed
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_pre_call_allowed(akto_validate, sample_inputs, sample_request_data):
+    akto_validate.async_handler.post = AsyncMock(return_value=_mock_allowed_response())
+
+    result = await akto_validate.apply_guardrail(
+        inputs=sample_inputs,
+        request_data=sample_request_data,
+        input_type="request",
+    )
+
+    assert result == sample_inputs
+    akto_validate.async_handler.post.assert_called_once()
+    call_params = akto_validate.async_handler.post.call_args.kwargs["params"]
+    assert call_params.get("guardrails") == "true"
+    assert "ingest_data" not in call_params
+
+
+# ---------------------------------------------------------------------------
+#  Pre-call (akto-validate) — blocked
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_pre_call_blocked(akto_validate, sample_inputs, sample_request_data):
+    akto_validate.async_handler.post = AsyncMock(
+        side_effect=[
+            _mock_blocked_response("PII detected"),
+            _mock_allowed_response(),
+        ]
+    )
+
+    with pytest.raises(HTTPException) as exc_info:
+        await akto_validate.apply_guardrail(
+            inputs=sample_inputs,
+            request_data=sample_request_data,
+            input_type="request",
+        )
+
+    await asyncio.sleep(0)
+    await asyncio.sleep(0)
+
+    assert exc_info.value.status_code == 403
+
+    assert akto_validate.async_handler.post.call_count == 2
+
+    first_call_params = akto_validate.async_handler.post.call_args_list[0].kwargs["params"]
+    assert first_call_params.get("guardrails") == "true"
+
+    second_call_params = akto_validate.async_handler.post.call_args_list[1].kwargs["params"]
+    assert second_call_params.get("ingest_data") == "true"
+    assert "guardrails" not in second_call_params
+    second_payload = json.loads(akto_validate.async_handler.post.call_args_list[1].kwargs["data"])
+    assert second_payload["statusCode"] == "403"
+    resp_body = json.loads(second_payload["responsePayload"])
+    inner = json.loads(resp_body["body"])
+    assert inner["x-blocked-by"] == "Akto Proxy"
+    assert inner["reason"] == "PII detected"
+
+
+# ---------------------------------------------------------------------------
+#  Pre-call (akto-validate) — response input is no-op
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_validate_response_noop(akto_validate, sample_inputs, sample_request_data):
+    akto_validate.async_handler.post = AsyncMock()
+
+    result = await akto_validate.apply_guardrail(
+        inputs=sample_inputs,
+        request_data=sample_request_data,
+        input_type="response",
+    )
+
+    assert result == sample_inputs
+    akto_validate.async_handler.post.assert_not_called()
+
+
+# ---------------------------------------------------------------------------
+#  Post-call (akto-ingest) — combined guardrail + ingest
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_post_call_combined(akto_ingest, sample_inputs, sample_request_data):
+    akto_ingest.async_handler.post = AsyncMock(return_value=_mock_allowed_response())
+
+    result = await akto_ingest.apply_guardrail(
+        inputs=sample_inputs,
+        request_data=sample_request_data,
+        input_type="response",
+    )
+
+    await asyncio.sleep(0)
+    await asyncio.sleep(0)
+
+    assert result == sample_inputs
+    akto_ingest.async_handler.post.assert_called_once()
+    call_params = akto_ingest.async_handler.post.call_args.kwargs["params"]
+    assert call_params.get("guardrails") == "true"
+    assert call_params.get("ingest_data") == "true"
+
+
+# ---------------------------------------------------------------------------
+#  Post-call (akto-ingest) — request input is no-op
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_ingest_request_noop(akto_ingest, sample_inputs, sample_request_data):
+    akto_ingest.async_handler.post = AsyncMock()
+
+    result = await akto_ingest.apply_guardrail(
+        inputs=sample_inputs,
+        request_data=sample_request_data,
+        input_type="request",
+    )
+
+    assert result == sample_inputs
+    akto_ingest.async_handler.post.assert_not_called()
+
+
+# ---------------------------------------------------------------------------
+#  Fail-open / fail-closed
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_fail_open_on_unreachable():
+    g = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        unreachable_fallback="fail_open",
+        guardrail_name="fail-open-test",
+        event_hook="pre_call",
+    )
+    g.async_handler.post = AsyncMock(side_effect=httpx.ConnectError("Connection refused"))
+
+    inputs = GenericGuardrailAPIInputs(texts=["test"], model="gpt-4")
+    result = await g.apply_guardrail(inputs=inputs, request_data={}, input_type="request")
+
+    assert result.get("texts") == ["test"]
+
+
+@pytest.mark.asyncio
+async def test_fail_closed_on_unreachable():
+    g = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        unreachable_fallback="fail_closed",
+        guardrail_name="fail-closed-test",
+        event_hook="pre_call",
+    )
+    g.async_handler.post = AsyncMock(side_effect=httpx.ConnectError("Connection refused"))
+
+    inputs = GenericGuardrailAPIInputs(texts=["test"], model="gpt-4")
+    with pytest.raises(HTTPException) as exc_info:
+        await g.apply_guardrail(inputs=inputs, request_data={}, input_type="request")
+    assert exc_info.value.status_code == 503
+
+
+def test_fail_closed_generic_message():
+    g = AktoGuardrail(
+        akto_base_url="http://localhost:9090",
+        akto_api_key="test-token",
+        unreachable_fallback="fail_closed",
+        guardrail_name="msg-test",
+        event_hook="pre_call",
+    )
+    with pytest.raises(HTTPException) as exc_info:
+        g.handle_unreachable(
+            inputs=GenericGuardrailAPIInputs(texts=["test"], model="gpt-4"),
+            error=Exception("http://internal-host:9090/secret-path"),
+        )
+    assert "internal-host" not in exc_info.value.detail
+    assert exc_info.value.detail == "Akto guardrail service unreachable"
+
+
+# ---------------------------------------------------------------------------
+#  Helper method tests
+# ---------------------------------------------------------------------------
+
+
+def test_extract_request_path_from_metadata():
+    path = AktoGuardrail.extract_request_path({"metadata": {"user_api_key_request_route": "/v1/embeddings"}})
+    assert path == "/v1/embeddings"
+
+
+def test_extract_request_path_fallback():
+    path = AktoGuardrail.extract_request_path({})
+    assert path == "/v1/chat/completions"
+
+
+def test_extract_request_path_non_dict_metadata():
+    path = AktoGuardrail.extract_request_path({"metadata": "invalid"})
+    assert path == "/v1/chat/completions"
+
+
+def test_resolve_metadata_value():
+    assert (
+        AktoGuardrail.resolve_metadata_value({"metadata": {"user_api_key_user_id": "u1"}}, "user_api_key_user_id")
+        == "u1"
+    )
+    assert (
+        AktoGuardrail.resolve_metadata_value(
+            {"litellm_metadata": {"user_api_key_team_id": "t1"}},
+            "user_api_key_team_id",
+        )
+        == "t1"
+    )
+    assert AktoGuardrail.resolve_metadata_value({}, "some_key") is None
+    assert AktoGuardrail.resolve_metadata_value(None, "some_key") is None
+
+
+def test_resolve_metadata_value_non_dict_containers():
+    assert (
+        AktoGuardrail.resolve_metadata_value(
+            {"metadata": "invalid", "litellm_metadata": ["bad"]},
+            "some_key",
+        )
+        is None
+    )
+
+
+def test_build_tag_metadata(akto_validate, sample_request_data):
+    tag = akto_validate.build_tag_metadata(sample_request_data)
+    assert tag["gen-ai"] == "Gen AI"
+    assert tag["user_id"] == "user-1"
+    assert tag["team_id"] == "team-1"
diff --git a/tests/llm_translation/test_anthropic_completion.py b/tests/llm_translation/test_anthropic_completion.py
index 8630ba65610..3c1162cd016 100644
--- a/tests/llm_translation/test_anthropic_completion.py
+++ b/tests/llm_translation/test_anthropic_completion.py
@@ -1800,3 +1800,81 @@ def test_anthropic_structured_output_chat_completion_api():
     )
     assert response is not None
     print(f"response: {response}")
+
+
+def _make_transform_request(optional_params: dict, litellm_params: dict) -> dict:
+    from litellm.llms.anthropic.chat.transformation import AnthropicConfig
+
+    return AnthropicConfig().transform_request(
+        model="claude-3-5-sonnet-20241022",
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params=optional_params,
+        litellm_params=litellm_params,
+        headers={},
+    )
+
+
+def test_metadata_only_user_id_passes_through():
+    """metadata with only user_id is forwarded as-is."""
+    data = _make_transform_request(
+        optional_params={"metadata": {"user_id": "abc123"}},
+        litellm_params={},
+    )
+    assert data.get("metadata") == {"user_id": "abc123"}
+
+
+def test_metadata_extra_keys_are_stripped():
+    """Extra keys in metadata are removed; only user_id is sent."""
+    data = _make_transform_request(
+        optional_params={"metadata": {"user_id": "abc123", "extra_key": "val"}},
+        litellm_params={},
+    )
+    assert data.get("metadata") == {"user_id": "abc123"}
+
+
+def test_metadata_without_user_id_is_dropped():
+    """metadata with no user_id is removed entirely."""
+    data = _make_transform_request(
+        optional_params={"metadata": {"only_other_key": "val"}},
+        litellm_params={},
+    )
+    assert "metadata" not in data
+
+
+def test_metadata_user_id_from_litellm_params_strips_extras():
+    """user_id from litellm_params metadata is extracted; extra keys are not forwarded."""
+    data = _make_transform_request(
+        optional_params={},
+        litellm_params={"metadata": {"user_id": "abc123", "trace_id": "xyz"}},
+    )
+    assert data.get("metadata") == {"user_id": "abc123"}
+
+
+def test_metadata_filter_applies_to_vertex_anthropic():
+    """VertexAIAnthropicConfig inherits the metadata filter."""
+    from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.transformation import (
+        VertexAIAnthropicConfig,
+    )
+
+    data = VertexAIAnthropicConfig().transform_request(
+        model="claude-3-5-sonnet-20241022",
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params={"metadata": {"user_id": "u1", "extra": "drop_me"}},
+        litellm_params={},
+        headers={},
+    )
+    assert data.get("metadata") == {"user_id": "u1"}
+
+
+def test_metadata_filter_applies_to_azure_anthropic():
+    """AzureAnthropicConfig inherits the metadata filter."""
+    from litellm.llms.azure_ai.anthropic.transformation import AzureAnthropicConfig
+
+    data = AzureAnthropicConfig().transform_request(
+        model="claude-3-5-sonnet-20241022",
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params={"metadata": {"user_id": "u2", "extra": "drop_me"}},
+        litellm_params={},
+        headers={},
+    )
+    assert data.get("metadata") == {"user_id": "u2"}
diff --git a/tests/llm_translation/test_azure_agents.py b/tests/llm_translation/test_azure_agents.py
index 66a46d53383..3e6b1e00a79 100644
--- a/tests/llm_translation/test_azure_agents.py
+++ b/tests/llm_translation/test_azure_agents.py
@@ -23,12 +23,14 @@ Example environment variables:
 See: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/quickstart
 """
 
+import json
 import os
 import sys
 
 sys.path.insert(0, os.path.abspath("../.."))
 
 import pytest
+from unittest.mock import MagicMock
 
 import litellm
 
@@ -343,13 +345,286 @@ def test_azure_ai_agents_extract_content_from_messages():
         ]
     }
 
-    content = handler._extract_content_from_messages(messages_data)
+    content, annotations = handler._extract_content_from_messages(messages_data)
     assert content == "The answer is 100."
+    assert annotations is None
 
     # Test empty response
     empty_data = {"data": []}
-    content = handler._extract_content_from_messages(empty_data)
+    content, annotations = handler._extract_content_from_messages(empty_data)
     assert content == ""
+    assert annotations is None
+
+
+def test_azure_ai_agents_extract_content_with_annotations():
+    """
+    Test that annotations (e.g., Bing Search citations) are extracted from
+    Azure Agents message responses and transformed to OpenAI-compatible format.
+
+    Ref: https://github.com/BerriAI/litellm/issues/19126
+    """
+    from litellm.llms.azure_ai.agents.handler import AzureAIAgentsHandler
+
+    handler = AzureAIAgentsHandler()
+
+    messages_data = {
+        "data": [
+            {
+                "id": "msg_abc",
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": {
+                            "value": "According to sources [1], the answer is yes.",
+                            "annotations": [
+                                {
+                                    "type": "url_citation",
+                                    "text": "[1]",
+                                    "start_index": 22,
+                                    "end_index": 25,
+                                    "url_citation": {
+                                        "url": "https://example.com/source",
+                                        "title": "Example Source"
+                                    }
+                                }
+                            ]
+                        }
+                    }
+                ]
+            }
+        ]
+    }
+
+    content, annotations = handler._extract_content_from_messages(messages_data)
+    assert content == "According to sources [1], the answer is yes."
+    assert annotations is not None
+    assert len(annotations) == 1
+    assert annotations[0]["type"] == "url_citation"
+    assert annotations[0]["url_citation"]["url"] == "https://example.com/source"
+    assert annotations[0]["url_citation"]["title"] == "Example Source"
+    # start/end_index should be moved into url_citation for OpenAI compatibility
+    assert annotations[0]["url_citation"]["start_index"] == 22
+    assert annotations[0]["url_citation"]["end_index"] == 25
+
+
+def test_azure_ai_agents_build_model_response_with_annotations():
+    """
+    Test that _build_model_response includes annotations in the Message object.
+    """
+    from litellm.llms.azure_ai.agents.handler import AzureAIAgentsHandler
+    from litellm.types.utils import ModelResponse
+
+    handler = AzureAIAgentsHandler()
+    model_response = ModelResponse()
+
+    annotations = [
+        {
+            "type": "url_citation",
+            "url_citation": {
+                "url": "https://example.com",
+                "title": "Example",
+                "start_index": 0,
+                "end_index": 5,
+            },
+        }
+    ]
+
+    result = handler._build_model_response(
+        model="azure_ai/agents/asst_123",
+        content="Hello [1]",
+        model_response=model_response,
+        thread_id="thread_abc",
+        messages=[{"role": "user", "content": "test"}],
+        annotations=annotations,
+    )
+
+    assert result.choices[0].message.content == "Hello [1]"
+    assert result.choices[0].message.annotations is not None
+    assert len(result.choices[0].message.annotations) == 1
+    assert result.choices[0].message.annotations[0]["type"] == "url_citation"
+
+
+def test_azure_ai_agents_build_model_response_without_annotations():
+    """
+    Test that _build_model_response works correctly without annotations.
+    """
+    from litellm.llms.azure_ai.agents.handler import AzureAIAgentsHandler
+    from litellm.types.utils import ModelResponse
+
+    handler = AzureAIAgentsHandler()
+    model_response = ModelResponse()
+
+    result = handler._build_model_response(
+        model="azure_ai/agents/asst_123",
+        content="Hello",
+        model_response=model_response,
+        thread_id="thread_abc",
+        messages=[{"role": "user", "content": "test"}],
+    )
+
+    assert result.choices[0].message.content == "Hello"
+    assert getattr(result.choices[0].message, "annotations", None) is None
+
+
+@pytest.mark.asyncio
+async def test_azure_ai_agents_streaming_annotations_from_completed_message():
+    """
+    Test that annotations from thread.message.completed SSE events are collected
+    and attached to the final chunk's delta.
+
+    Ref: https://github.com/BerriAI/litellm/issues/19126
+    """
+    from litellm.llms.azure_ai.agents.handler import AzureAIAgentsHandler
+
+    handler = AzureAIAgentsHandler()
+
+    # SSE lines simulating a stream with annotations in thread.message.completed
+    completed_data = {
+        "content": [
+            {
+                "type": "text",
+                "text": {
+                    "value": "According to [1], the answer is 42.",
+                    "annotations": [
+                        {
+                            "type": "url_citation",
+                            "text": "[1]",
+                            "start_index": 12,
+                            "end_index": 15,
+                            "url_citation": {
+                                "url": "https://example.com/citation",
+                                "title": "Citation Source",
+                            },
+                        }
+                    ],
+                },
+            }
+        ]
+    }
+
+    sse_lines = [
+        "event: thread.created",
+        "",
+        'data: {"id": "thread_stream_123"}',
+        "",
+        "event: thread.message.delta",
+        "",
+        'data: {"delta": {"content": [{"type": "text", "text": {"value": "According to [1], the answer is 42."}}]}}',
+        "",
+        "event: thread.message.completed",
+        "",
+        f"data: {json.dumps(completed_data)}",
+        "",
+        "data: [DONE]",
+    ]
+
+    async def mock_aiter_lines():
+        for line in sse_lines:
+            yield line
+
+    mock_response = MagicMock()
+    mock_response.aiter_lines = MagicMock(return_value=mock_aiter_lines())
+
+    chunks = []
+    async for chunk in handler._process_sse_stream(mock_response, "azure_ai/agents/asst_123"):
+        chunks.append(chunk)
+
+    # Should have content chunks + final [DONE] chunk
+    assert len(chunks) >= 1
+    final_chunk = chunks[-1]
+    assert final_chunk.choices[0].finish_reason == "stop"
+    assert final_chunk.choices[0].delta.annotations is not None
+    assert len(final_chunk.choices[0].delta.annotations) == 1
+    ann = final_chunk.choices[0].delta.annotations[0]
+    assert ann["type"] == "url_citation"
+    assert ann["url_citation"]["url"] == "https://example.com/citation"
+    assert ann["url_citation"]["title"] == "Citation Source"
+
+
+@pytest.mark.asyncio
+async def test_azure_ai_agents_streaming_accumulates_annotations_from_multiple_text_items():
+    """
+    Test that annotations from multiple text content items in thread.message.completed
+    are accumulated (not overwritten).
+
+    Ref: Greptile review on PR #23849
+    """
+    from litellm.llms.azure_ai.agents.handler import AzureAIAgentsHandler
+
+    handler = AzureAIAgentsHandler()
+
+    # Two text blocks, each with distinct citations
+    completed_data = {
+        "content": [
+            {
+                "type": "text",
+                "text": {
+                    "value": "First source [1].",
+                    "annotations": [
+                        {
+                            "type": "url_citation",
+                            "text": "[1]",
+                            "start_index": 12,
+                            "end_index": 15,
+                            "url_citation": {
+                                "url": "https://example.com/first",
+                                "title": "First",
+                            },
+                        }
+                    ],
+                },
+            },
+            {
+                "type": "text",
+                "text": {
+                    "value": "Second source [2].",
+                    "annotations": [
+                        {
+                            "type": "url_citation",
+                            "text": "[2]",
+                            "start_index": 13,
+                            "end_index": 16,
+                            "url_citation": {
+                                "url": "https://example.com/second",
+                                "title": "Second",
+                            },
+                        }
+                    ],
+                },
+            },
+        ]
+    }
+
+    sse_lines = [
+        "event: thread.created",
+        "",
+        'data: {"id": "thread_multi"}',
+        "",
+        "event: thread.message.completed",
+        "",
+        f"data: {json.dumps(completed_data)}",
+        "",
+        "data: [DONE]",
+    ]
+
+    async def mock_aiter_lines():
+        for line in sse_lines:
+            yield line
+
+    mock_response = MagicMock()
+    mock_response.aiter_lines = MagicMock(return_value=mock_aiter_lines())
+
+    chunks = []
+    async for chunk in handler._process_sse_stream(mock_response, "azure_ai/agents/asst_123"):
+        chunks.append(chunk)
+
+    final_chunk = chunks[-1]
+    assert final_chunk.choices[0].delta.annotations is not None
+    assert len(final_chunk.choices[0].delta.annotations) == 2
+    urls = [a["url_citation"]["url"] for a in final_chunk.choices[0].delta.annotations]
+    assert "https://example.com/first" in urls
+    assert "https://example.com/second" in urls
 
 
 @pytest.mark.asyncio
diff --git a/tests/llm_translation/test_fireworks_ai_translation.py b/tests/llm_translation/test_fireworks_ai_translation.py
index b9abbd501d0..24c0d546e25 100644
--- a/tests/llm_translation/test_fireworks_ai_translation.py
+++ b/tests/llm_translation/test_fireworks_ai_translation.py
@@ -161,6 +161,24 @@ def test_document_inlining_example(disable_add_transform_inline_image_block):
             "vision-gpt",
             "http://example.com/image.png",
         ),
+        # data: URLs must never have #transform=inline appended — doing so
+        # corrupts the base64 payload (fixes #23583).
+        # URI schemes are case-insensitive (RFC 3986) so check all variants.
+        (
+            {"image_url": "data:image/png;base64,iVBORw0KGgo="},
+            "gpt-4",
+            "data:image/png;base64,iVBORw0KGgo=",
+        ),
+        (
+            {"image_url": {"url": "data:image/jpeg;base64,/9j/4AAQ=="}},
+            "gpt-4",
+            {"url": "data:image/jpeg;base64,/9j/4AAQ=="},
+        ),
+        (
+            {"image_url": "Data:image/png;base64,iVBORw0KGgo="},
+            "gpt-4",
+            "Data:image/png;base64,iVBORw0KGgo=",
+        ),
     ],
 )
 def test_transform_inline(content, model, expected_url):
diff --git a/tests/llm_translation/test_gemini_image_usage.py b/tests/llm_translation/test_gemini_image_usage.py
index 8c7f05d38e0..0497d7fd9d7 100644
--- a/tests/llm_translation/test_gemini_image_usage.py
+++ b/tests/llm_translation/test_gemini_image_usage.py
@@ -4,9 +4,11 @@ Test for Gemini image generation usage metadata extraction.
 This test verifies the fix for issue #18323 where image_generation() 
 was returning usage=0 while completion() returned proper token usage.
 """
+import os
 import pytest
 from unittest.mock import patch, MagicMock
 import litellm
+from litellm.llms.gemini.image_generation.transformation import GoogleImageGenConfig
 from litellm.types.utils import ImageResponse, ImageObject, ImageUsage
 
 
@@ -211,3 +213,56 @@ def test_gemini_imagen_models_no_usage_extraction():
         
         # For Imagen models, we don't extract usage from the predictions format
         # This test just ensures we don't crash
+
+
+def test_gemini_image_generation_accumulates_multiple_image_prompt_token_details():
+    """
+    Regression test: promptTokensDetails can include multiple IMAGE entries.
+    These must be accumulated instead of overwritten.
+    """
+    previous_local_model_cost_map = os.environ.get("LITELLM_LOCAL_MODEL_COST_MAP")
+    previous_model_cost = litellm.model_cost
+    try:
+        os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+        litellm.model_cost = litellm.get_model_cost_map(url="")
+
+        model = "gemini/gemini-3-pro-image-preview"
+        config = GoogleImageGenConfig()
+
+        usage_metadata = {
+            "promptTokenCount": 200,
+            "candidatesTokenCount": 0,
+            "totalTokenCount": 200,
+            "promptTokensDetails": [
+                {"modality": "TEXT", "tokenCount": 10},
+                {"modality": "IMAGE", "tokenCount": 90},
+                {"modality": "IMAGE", "tokenCount": 100},
+            ],
+        }
+
+        parsed_usage = config._transform_image_usage(usage_metadata)
+        image_response = ImageResponse(
+            data=[ImageObject(b64_json="fake_image_data")],
+            usage=parsed_usage,
+        )
+
+        observed_cost = litellm.completion_cost(
+            completion_response=image_response,
+            model=model,
+            custom_llm_provider="gemini",
+        )
+
+        model_info = litellm.get_model_info(model=model, custom_llm_provider="gemini")
+        expected_image_tokens = 190
+        expected_total_prompt_tokens = 200
+        expected_prompt_cost = expected_total_prompt_tokens * model_info["input_cost_per_token"]
+
+        assert parsed_usage.input_tokens_details.image_tokens == expected_image_tokens
+        assert parsed_usage.input_tokens_details.text_tokens == 10
+        assert observed_cost == pytest.approx(expected_prompt_cost, rel=1e-12)
+    finally:
+        if previous_local_model_cost_map is None:
+            os.environ.pop("LITELLM_LOCAL_MODEL_COST_MAP", None)
+        else:
+            os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = previous_local_model_cost_map
+        litellm.model_cost = previous_model_cost
diff --git a/tests/llm_translation/test_prompt_factory.py b/tests/llm_translation/test_prompt_factory.py
index a6dcabe25ef..64556c3f26d 100644
--- a/tests/llm_translation/test_prompt_factory.py
+++ b/tests/llm_translation/test_prompt_factory.py
@@ -775,6 +775,228 @@ def test_ensure_alternating_roles(
     assert messages == expected_messages
 
 
+def test_ensure_alternating_roles_with_tool_calls():
+    """Fixes Regression in #18685 """
+    messages = [
+        {"role": "user", "content": "What's the weather?"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_123",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "NYC"}',
+                    },
+                }
+            ],
+        },
+        {"role": "tool", "tool_call_id": "call_123", "content": "72F, sunny"},
+        {"role": "assistant", "content": "It's 72F and sunny in NYC."},
+        {"role": "user", "content": "What about tomorrow?"},
+        {"role": "user", "content": "And the day after?"},
+        {"role": "user", "content": "What about next week?"},
+    ]
+
+    transformed_messages = get_completion_messages(
+        messages=messages,
+        assistant_continue_message=None,
+        user_continue_message=None,
+        ensure_alternating_roles=True,
+    )
+
+    assert transformed_messages == [
+        {"role": "user", "content": "What's the weather?"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_123",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "NYC"}',
+                    },
+                }
+            ],
+        },
+        {"role": "tool", "tool_call_id": "call_123", "content": "72F, sunny"},
+        {"role": "assistant", "content": "It's 72F and sunny in NYC."},
+        {"role": "user", "content": "What about tomorrow?"},
+        {"role": "assistant", "content": "Please continue."},
+        {"role": "user", "content": "And the day after?"},
+        {"role": "assistant", "content": "Please continue."},
+        {"role": "user", "content": "What about next week?"},
+    ]
+
+
+def test_ensure_alternating_roles_three_consecutive_assistants():
+    messages = [
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "A1"},
+        {"role": "assistant", "content": "A2"},
+        {"role": "assistant", "content": "A3"},
+    ]
+
+    transformed_messages = get_completion_messages(
+        messages=messages,
+        assistant_continue_message=None,
+        user_continue_message=None,
+        ensure_alternating_roles=True,
+    )
+
+    assert transformed_messages == [
+        {"role": "user", "content": "Hello"},
+        {"role": "assistant", "content": "A1"},
+        {"role": "user", "content": "Please continue."},
+        {"role": "assistant", "content": "A2"},
+        {"role": "user", "content": "Please continue."},
+        {"role": "assistant", "content": "A3"},
+        {"role": "user", "content": "Please continue."},
+    ]
+
+
+def test_ensure_alternating_roles_does_not_split_tool_call_chain():
+    """Tool-call chains [user, assistant(tc), tool, user] are preserved as-is."""
+    messages = [
+        {"role": "user", "content": "Search for X"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "c1",
+                    "type": "function",
+                    "function": {"name": "search", "arguments": "{}"},
+                }
+            ],
+        },
+        {"role": "tool", "tool_call_id": "c1", "content": "results"},
+        {"role": "user", "content": "Thanks, now do Y"},
+    ]
+
+    transformed_messages = get_completion_messages(
+        messages=messages,
+        assistant_continue_message=None,
+        user_continue_message=None,
+        ensure_alternating_roles=True,
+    )
+
+    assert transformed_messages == [
+        {"role": "user", "content": "Search for X"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "c1",
+                    "type": "function",
+                    "function": {"name": "search", "arguments": "{}"},
+                }
+            ],
+        },
+        {"role": "tool", "tool_call_id": "c1", "content": "results"},
+        {"role": "user", "content": "Thanks, now do Y"},
+    ]
+
+
+def test_ensure_alternating_roles_assistant_tool_call_then_assistant():
+    """
+    Preserve old behavior for malformed adjacent assistant turns:
+    [assistant(tool_calls), assistant(no-tool-calls), user] should insert
+    user_continue between assistant messages.
+    """
+    messages = [
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "c1",
+                    "type": "function",
+                    "function": {"name": "search", "arguments": "{}"},
+                }
+            ],
+        },
+        {"role": "assistant", "content": "Here's what I found."},
+        {"role": "user", "content": "Thanks"},
+    ]
+
+    transformed_messages = get_completion_messages(
+        messages=messages,
+        assistant_continue_message=None,
+        user_continue_message=None,
+        ensure_alternating_roles=True,
+    )
+
+    assert transformed_messages == [
+        {"role": "user", "content": "Please continue."},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "c1",
+                    "type": "function",
+                    "function": {"name": "search", "arguments": "{}"},
+                }
+            ],
+        },
+        {"role": "user", "content": "Please continue."},
+        {"role": "assistant", "content": "Here's what I found."},
+        {"role": "user", "content": "Thanks"},
+    ]
+
+
+def test_ensure_alternating_roles_trailing_tool_call_assistant():
+    messages = [
+        {"role": "user", "content": "What's the weather?"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_abc",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "NYC"}',
+                    },
+                }
+            ],
+        },
+    ]
+
+    transformed_messages = get_completion_messages(
+        messages=messages,
+        assistant_continue_message=None,
+        user_continue_message=None,
+        ensure_alternating_roles=True,
+    )
+
+    assert transformed_messages == [
+        {"role": "user", "content": "What's the weather?"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_abc",
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "NYC"}',
+                    },
+                }
+            ],
+        },
+        {"role": "user", "content": "Please continue."},
+    ]
+
+
 def test_alternating_roles_e2e():
     from litellm.llms.custom_httpx.http_handler import HTTPHandler
     import json
diff --git a/tests/proxy_unit_tests/test_proxy_utils.py b/tests/proxy_unit_tests/test_proxy_utils.py
index 00d4cd24e4b..6c6ec7bcd60 100644
--- a/tests/proxy_unit_tests/test_proxy_utils.py
+++ b/tests/proxy_unit_tests/test_proxy_utils.py
@@ -2044,6 +2044,58 @@ def test_update_model_if_team_alias_exists(data, user_api_key_dict, expected_mod
     assert test_data.get("model") == expected_model
 
 
+def test_team_alias_stale_bypass_disabled_by_default(monkeypatch):
+    monkeypatch.delenv("LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS", raising=False)
+    import litellm.proxy.litellm_pre_call_utils as pre_call_utils
+    from litellm.proxy.litellm_pre_call_utils import _update_model_if_team_alias_exists
+    
+    # Reset module-level cache to ensure test isolation
+    pre_call_utils._ENABLE_TEAM_STALE_ALIAS_BYPASS = None
+
+    class _MockRouter:
+        team_model_to_deployment_indices = {("team-1", "gpt-4o"): [0]}
+
+    test_data = {"model": "gpt-4o"}
+    user_api_key_dict = UserAPIKeyAuth(
+        api_key="test_key",
+        team_id="team-1",
+        team_model_aliases={"gpt-4o": "model_name_team-1_legacy-uuid"},
+    )
+
+    with patch("litellm.proxy.proxy_server.llm_router", _MockRouter()):
+        _update_model_if_team_alias_exists(
+            data=test_data, user_api_key_dict=user_api_key_dict
+        )
+
+    assert test_data.get("model") == "model_name_team-1_legacy-uuid"
+
+
+def test_team_alias_stale_bypass_enabled_by_flag(monkeypatch):
+    import litellm.proxy.litellm_pre_call_utils as pre_call_utils
+    from litellm.proxy.litellm_pre_call_utils import _update_model_if_team_alias_exists
+    
+    # Reset module-level cache to ensure test isolation
+    pre_call_utils._ENABLE_TEAM_STALE_ALIAS_BYPASS = None
+
+    class _MockRouter:
+        team_model_to_deployment_indices = {("team-1", "gpt-4o"): [0]}
+
+    test_data = {"model": "gpt-4o"}
+    user_api_key_dict = UserAPIKeyAuth(
+        api_key="test_key",
+        team_id="team-1",
+        team_model_aliases={"gpt-4o": "model_name_team-1_legacy-uuid"},
+    )
+    monkeypatch.setenv("LITELLM_ENABLE_TEAM_STALE_ALIAS_BYPASS", "true")
+
+    with patch("litellm.proxy.proxy_server.llm_router", _MockRouter()):
+        _update_model_if_team_alias_exists(
+            data=test_data, user_api_key_dict=user_api_key_dict
+        )
+
+    assert test_data.get("model") == "gpt-4o"
+
+
 @pytest.fixture
 def mock_prisma_client():
     client = MagicMock()
diff --git a/tests/proxy_unit_tests/test_response_polling_pre_call_checks.py b/tests/proxy_unit_tests/test_response_polling_pre_call_checks.py
new file mode 100644
index 00000000000..45e4e9e4d3e
--- /dev/null
+++ b/tests/proxy_unit_tests/test_response_polling_pre_call_checks.py
@@ -0,0 +1,182 @@
+"""
+Unit tests for pre-call checks running before polling ID creation.
+
+Tests that rate limits, guardrails, and budget checks are enforced
+BEFORE a polling ID is created, so rate-limited requests get a
+synchronous error instead of a polling ID that immediately fails.
+"""
+
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi import HTTPException, Request, Response
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import litellm
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
+
+
+class TestSkipPreCallLogic:
+    """Test that skip_pre_call_logic parameter works correctly"""
+
+    @pytest.mark.asyncio
+    async def test_skip_pre_call_logic_skips_common_processing(self):
+        """When skip_pre_call_logic=True, common_processing_pre_call_logic should not be called"""
+        mock_logging_obj = MagicMock()
+        data = {
+            "model": "gpt-4",
+            "stream": True,
+            "litellm_logging_obj": mock_logging_obj,
+        }
+        processor = ProxyBaseLLMRequestProcessing(data=data)
+
+        mock_proxy_logging = AsyncMock()
+        mock_proxy_logging.during_call_hook = AsyncMock()
+
+        with (
+            patch.object(
+                processor, "common_processing_pre_call_logic", new_callable=AsyncMock
+            ) as mock_pre_call,
+            patch(
+                "litellm.proxy.common_request_processing.route_request",
+                new_callable=AsyncMock,
+                return_value=MagicMock(),
+            ),
+        ):
+            try:
+                await processor.base_process_llm_request(
+                    request=MagicMock(spec=Request),
+                    fastapi_response=MagicMock(spec=Response),
+                    user_api_key_dict=MagicMock(spec=UserAPIKeyAuth),
+                    route_type="aresponses",
+                    proxy_logging_obj=mock_proxy_logging,
+                    llm_router=MagicMock(),
+                    general_settings={},
+                    proxy_config=MagicMock(),
+                    skip_pre_call_logic=True,
+                )
+            except Exception:
+                pass  # We only care that common_processing_pre_call_logic was not called
+
+            mock_pre_call.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_without_skip_runs_common_processing(self):
+        """When skip_pre_call_logic=False (default), common_processing_pre_call_logic should be called"""
+        data = {"model": "gpt-4"}
+        processor = ProxyBaseLLMRequestProcessing(data=data)
+
+        mock_logging_obj = MagicMock()
+        mock_proxy_logging = AsyncMock()
+        mock_proxy_logging.during_call_hook = AsyncMock()
+
+        with (
+            patch.object(
+                processor,
+                "common_processing_pre_call_logic",
+                new_callable=AsyncMock,
+                return_value=(data, mock_logging_obj),
+            ) as mock_pre_call,
+            patch(
+                "litellm.proxy.common_request_processing.route_request",
+                new_callable=AsyncMock,
+            ),
+        ):
+            try:
+                await processor.base_process_llm_request(
+                    request=MagicMock(spec=Request),
+                    fastapi_response=MagicMock(spec=Response),
+                    user_api_key_dict=MagicMock(spec=UserAPIKeyAuth),
+                    route_type="aresponses",
+                    proxy_logging_obj=mock_proxy_logging,
+                    llm_router=MagicMock(),
+                    general_settings={},
+                    proxy_config=MagicMock(),
+                )
+            except Exception:
+                pass
+
+            mock_pre_call.assert_called_once()
+
+
+class TestPollingEndpointPreCallGuard:
+    """Test that the polling endpoint enforces pre-call checks before polling ID creation"""
+
+    @pytest.mark.asyncio
+    async def test_rate_limit_error_prevents_polling_id_creation(self):
+        """responses_api() must raise 429 and never call generate_polling_id when rate-limited"""
+        from litellm.proxy.response_api_endpoints.endpoints import responses_api
+        from litellm.proxy.response_polling.polling_handler import ResponsePollingHandler
+
+        rate_limit_exc = litellm.RateLimitError(
+            message="TPM limit exceeded",
+            llm_provider="",
+            model="gpt-4",
+        )
+        generate_polling_id_mock = MagicMock(return_value="litellm_poll_test")
+
+        proxy_server_patches = {
+            "litellm.proxy.proxy_server._read_request_body": AsyncMock(
+                return_value={"model": "gpt-4", "background": True}
+            ),
+            "litellm.proxy.proxy_server.general_settings": {},
+            "litellm.proxy.proxy_server.llm_router": MagicMock(),
+            "litellm.proxy.proxy_server.native_background_mode": None,
+            "litellm.proxy.proxy_server.polling_cache_ttl": 3600,
+            "litellm.proxy.proxy_server.polling_via_cache_enabled": True,
+            "litellm.proxy.proxy_server.proxy_config": MagicMock(),
+            "litellm.proxy.proxy_server.proxy_logging_obj": AsyncMock(),
+            "litellm.proxy.proxy_server.redis_usage_cache": AsyncMock(),
+            "litellm.proxy.proxy_server.select_data_generator": None,
+            "litellm.proxy.proxy_server.user_api_base": None,
+            "litellm.proxy.proxy_server.user_max_tokens": None,
+            "litellm.proxy.proxy_server.user_model": None,
+            "litellm.proxy.proxy_server.user_request_timeout": None,
+            "litellm.proxy.proxy_server.user_temperature": None,
+            "litellm.proxy.proxy_server.version": "1.0.0",
+        }
+
+        with (
+            patch.multiple("litellm.proxy.proxy_server", **{
+                k.split(".")[-1]: v for k, v in proxy_server_patches.items()
+            }),
+            patch(
+                "litellm.proxy.response_polling.polling_handler.should_use_polling_for_request",
+                return_value=True,
+            ),
+            patch.object(
+                ProxyBaseLLMRequestProcessing,
+                "common_processing_pre_call_logic",
+                new_callable=AsyncMock,
+                side_effect=rate_limit_exc,
+            ),
+            patch.object(
+                ProxyBaseLLMRequestProcessing,
+                "_handle_llm_api_exception",
+                new_callable=AsyncMock,
+                return_value=HTTPException(status_code=429, detail="Rate limit exceeded"),
+            ),
+            patch.object(ResponsePollingHandler, "generate_polling_id", generate_polling_id_mock),
+            # Prevent background task from running (avoids noise from incomplete mocks)
+            patch("asyncio.create_task"),
+            patch.object(
+                ResponsePollingHandler,
+                "create_initial_state",
+                new_callable=AsyncMock,
+                return_value=MagicMock(),
+            ),
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await responses_api(
+                    request=MagicMock(spec=Request),
+                    fastapi_response=MagicMock(spec=Response),
+                    user_api_key_dict=MagicMock(spec=UserAPIKeyAuth),
+                )
+
+        assert exc_info.value.status_code == 429
+        generate_polling_id_mock.assert_not_called()
+
diff --git a/tests/router_unit_tests/test_get_model_list_alias_optimization.py b/tests/router_unit_tests/test_get_model_list_alias_optimization.py
index 31d992b6646..2c2df3be945 100644
--- a/tests/router_unit_tests/test_get_model_list_alias_optimization.py
+++ b/tests/router_unit_tests/test_get_model_list_alias_optimization.py
@@ -44,7 +44,7 @@ def test_map_team_model_should_not_iterate_aliases_for_non_alias_team_model_name
         {f"alias-{idx}": "gpt-4" for idx in range(200)}
     )
 
-    assert (
-        router.map_team_model(team_model_name="team-model", team_id="team-1")
-        == "gpt-3.5-turbo"
-    )
+    # map_team_model should return the public name unchanged (not the internal UUID name)
+    # so the router can find all sibling deployments via team_id filtering
+    result = router.map_team_model(team_model_name="team-model", team_id="team-1")
+    assert result == "team-model", f"Expected public name 'team-model', got {result}"
diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py
index 90d98b8ab0a..2694c62827c 100644
--- a/tests/router_unit_tests/test_router_index_management.py
+++ b/tests/router_unit_tests/test_router_index_management.py
@@ -118,6 +118,28 @@ class TestRouterIndexManagement:
         assert router.model_id_to_deployment_index_map["id-2"] == 1
         assert router.model_id_to_deployment_index_map["id-3"] == 2
 
+    def test_update_team_model_index(self, router):
+        """Test _update_team_model_index updates team_model_to_deployment_indices."""
+        model = {
+            "model_name": "team-alias",
+            "model_info": {
+                "id": "dep-1",
+                "team_id": "team-abc",
+                "team_public_model_name": "gpt-4o",
+            },
+        }
+        router._update_team_model_index(model, 0)
+        assert router.team_model_to_deployment_indices[("team-abc", "gpt-4o")] == [0]
+        router._update_team_model_index(model, 2)
+        assert router.team_model_to_deployment_indices[("team-abc", "gpt-4o")] == [0, 2]
+
+        router._update_team_model_index(
+            {"model_name": "x", "model_info": {"id": "dep-2"}}, 5
+        )
+        assert router.team_model_to_deployment_indices == {
+            ("team-abc", "gpt-4o"): [0, 2],
+        }
+
     def test_has_model_id(self, router):
         """Test has_model_id function for O(1) membership check"""
         # Setup: Add models to router
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 da383532690..e40543e01a0 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
@@ -9,7 +9,9 @@ from unittest.mock import ANY, MagicMock, Mock, patch
 import httpx
 import pytest
 
-sys.path.insert(0, os.path.abspath("../../.."))  # Adds the parent directory to the system-path
+sys.path.insert(
+    0, os.path.abspath("../../..")
+)  # Adds the parent directory to the system-path
 import litellm
 
 
@@ -117,7 +119,9 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_imag
             function_call_output = item
             break
 
-    assert function_call_output is not None, "function_call_output not found in response"
+    assert (
+        function_call_output is not None
+    ), "function_call_output not found in response"
     assert function_call_output["call_id"] == "call_abc123"
 
     # Check that the output is correctly transformed
@@ -127,8 +131,12 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_imag
 
     image_item = output[0]
     # Should be transformed to Responses API format
-    assert image_item["type"] == "input_image", f"Expected type 'input_image', got '{image_item.get('type')}'"
-    assert image_item["image_url"] == test_image_base64, "image_url should be a flat string, not a nested object"
+    assert (
+        image_item["type"] == "input_image"
+    ), f"Expected type 'input_image', got '{image_item.get('type')}'"
+    assert (
+        image_item["image_url"] == test_image_base64
+    ), "image_url should be a flat string, not a nested object"
     assert "detail" in image_item, "detail field should be present"
 
     print("✓ Tool result with image correctly transformed to Responses API format")
@@ -190,7 +198,9 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_text
             function_call_output = item
             break
 
-    assert function_call_output is not None, "function_call_output not found in response"
+    assert (
+        function_call_output is not None
+    ), "function_call_output not found in response"
     assert function_call_output["call_id"] == "call_abc123"
 
     # Check that the output is correctly transformed to use input_text, not output_text
@@ -200,12 +210,16 @@ def test_convert_chat_completion_messages_to_responses_api_tool_result_with_text
 
     text_item = output[0]
     # Should be transformed to use input_text for tool results in Responses API format
-    assert text_item["type"] == "input_text", (
-        f"Expected type 'input_text' for tool result, got '{text_item.get('type')}'"
-    )
-    assert text_item["text"] == "15 degrees", f"Expected text '15 degrees', got '{text_item.get('text')}'"
+    assert (
+        text_item["type"] == "input_text"
+    ), f"Expected type 'input_text' for tool result, got '{text_item.get('type')}'"
+    assert (
+        text_item["text"] == "15 degrees"
+    ), f"Expected text '15 degrees', got '{text_item.get('text')}'"
 
-    print("✓ Tool result with text correctly transformed to use input_text for Responses API format")
+    print(
+        "✓ Tool result with text correctly transformed to use input_text for Responses API format"
+    )
 
 
 def test_openai_responses_chunk_parser_reasoning_summary():
@@ -214,7 +228,9 @@ def test_openai_responses_chunk_parser_reasoning_summary():
     )
     from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {
         "delta": "**Compar",
@@ -246,7 +262,9 @@ def test_chunk_parser_string_output_text_delta_produces_text():
     )
     from litellm.types.utils import ModelResponseStream
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {"type": "response.output_text.delta", "delta": "literal text"}
 
@@ -267,7 +285,9 @@ def test_chunk_parser_enum_output_text_delta_produces_text():
     from litellm.types.llms.openai import ResponsesAPIStreamEvents
     from litellm.types.utils import ModelResponseStream
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {"type": ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, "delta": "enum text"}
 
@@ -288,7 +308,9 @@ def test_chunk_parser_function_call_added_produces_tool_use():
     from litellm.types.llms.openai import ResponsesAPIStreamEvents
     from litellm.types.utils import ModelResponseStream
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {
         "type": ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED,
@@ -373,7 +395,9 @@ Tomorrow will bring its petitions and promises,
 but for now the city breathes slow and wide,
 and I learn to carry this small calm home."""
 
-    output_text = ResponseOutputText(annotations=[], text=poem_text, type="output_text", logprobs=[])
+    output_text = ResponseOutputText(
+        annotations=[], text=poem_text, type="output_text", logprobs=[]
+    )
     output_message = ResponseOutputMessage(
         id="msg_04c8021b8b3188a00068e9ae0b92f4819dac64d85b4abb67ec",
         content=[output_text],
@@ -385,7 +409,9 @@ and I learn to carry this small calm home."""
     # Create usage information
     usage = ResponseAPIUsage(
         input_tokens=16,
-        input_tokens_details=InputTokensDetails(audio_tokens=None, cached_tokens=0, text_tokens=None),
+        input_tokens_details=InputTokensDetails(
+            audio_tokens=None, cached_tokens=0, text_tokens=None
+        ),
         output_tokens=195,
         output_tokens_details=OutputTokensDetails(reasoning_tokens=0, text_tokens=None),
         total_tokens=211,
@@ -597,7 +623,9 @@ def test_transform_request_single_char_keys_not_matched():
     assert result_correct.get("metadata") == {"user_id": "123"}
     assert result_correct.get("previous_response_id") == "resp_abc"
 
-    print("✓ Single-character keys are not incorrectly matched to metadata/previous_response_id")
+    print(
+        "✓ Single-character keys are not incorrectly matched to metadata/previous_response_id"
+    )
 
 
 # =============================================================================
@@ -617,7 +645,9 @@ def test_message_done_does_not_emit_is_finished():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {
         "type": "response.output_item.done",
@@ -629,9 +659,9 @@ def test_message_done_does_not_emit_is_finished():
     # After the fix, message completion should NOT set finish_reason
     # ModelResponseStream doesn't have is_finished - check finish_reason instead
     assert len(result.choices) > 0, "result should have choices"
-    assert result.choices[0].finish_reason is None or result.choices[0].finish_reason == "", (
-        "message completion should not emit finish_reason"
-    )
+    assert (
+        result.choices[0].finish_reason is None or result.choices[0].finish_reason == ""
+    ), "message completion should not emit finish_reason"
 
 
 def test_response_completed_emits_is_finished():
@@ -643,7 +673,9 @@ def test_response_completed_emits_is_finished():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {"type": "response.completed"}
 
@@ -651,7 +683,9 @@ def test_response_completed_emits_is_finished():
 
     # response.completed should emit finish_reason='stop'
     assert len(result.choices) > 0, "result should have choices"
-    assert result.choices[0].finish_reason == "stop", "response.completed should emit finish_reason='stop'"
+    assert (
+        result.choices[0].finish_reason == "stop"
+    ), "response.completed should emit finish_reason='stop'"
 
 
 def test_response_completed_with_function_calls_emits_tool_calls_finish_reason():
@@ -670,7 +704,9 @@ def test_response_completed_with_function_calls_emits_tool_calls_finish_reason()
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     # Simulate a response.completed event with function_call in output
     # This matches what Azure/OpenAI sends for gpt-5.1-codex-mini and similar models
@@ -696,9 +732,9 @@ def test_response_completed_with_function_calls_emits_tool_calls_finish_reason()
 
     # response.completed with function_call should emit finish_reason='tool_calls'
     assert len(result.choices) > 0, "result should have choices"
-    assert result.choices[0].finish_reason == "tool_calls", (
-        "response.completed with function_call output should emit finish_reason='tool_calls'"
-    )
+    assert (
+        result.choices[0].finish_reason == "tool_calls"
+    ), "response.completed with function_call output should emit finish_reason='tool_calls'"
 
 
 def test_response_completed_with_message_only_emits_stop_finish_reason():
@@ -709,7 +745,9 @@ def test_response_completed_with_message_only_emits_stop_finish_reason():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     # Simulate a response.completed event with only message output
     chunk = {
@@ -733,10 +771,9 @@ def test_response_completed_with_message_only_emits_stop_finish_reason():
 
     # response.completed with only message should emit finish_reason='stop'
     assert len(result.choices) > 0, "result should have choices"
-    assert result.choices[0].finish_reason == "stop", (
-        "response.completed with only message output should emit finish_reason='stop'"
-    )
-
+    assert (
+        result.choices[0].finish_reason == "stop"
+    ), "response.completed with only message output should emit finish_reason='stop'"
 
 
 def test_response_completed_preserves_usage_with_cached_tokens():
@@ -752,7 +789,9 @@ def test_response_completed_preserves_usage_with_cached_tokens():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {
         "type": "response.completed",
@@ -781,12 +820,18 @@ def test_response_completed_preserves_usage_with_cached_tokens():
     result = iterator.chunk_parser(chunk)
 
     assert result.usage is not None, "usage should be set on response.completed chunk"
-    assert result.usage.prompt_tokens == 1226, "prompt_tokens should map from input_tokens"
-    assert result.usage.completion_tokens == 5, "completion_tokens should map from output_tokens"
-    assert result.usage.prompt_tokens_details is not None, "prompt_tokens_details should be set"
-    assert result.usage.prompt_tokens_details.cached_tokens == 1024, (
-        "cached_tokens should be preserved from input_tokens_details"
-    )
+    assert (
+        result.usage.prompt_tokens == 1226
+    ), "prompt_tokens should map from input_tokens"
+    assert (
+        result.usage.completion_tokens == 5
+    ), "completion_tokens should map from output_tokens"
+    assert (
+        result.usage.prompt_tokens_details is not None
+    ), "prompt_tokens_details should be set"
+    assert (
+        result.usage.prompt_tokens_details.cached_tokens == 1024
+    ), "cached_tokens should be preserved from input_tokens_details"
 
 
 def test_function_call_done_emits_is_finished():
@@ -800,7 +845,9 @@ def test_function_call_done_emits_is_finished():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunk = {
         "type": "response.output_item.done",
@@ -820,9 +867,9 @@ def test_function_call_done_emits_is_finished():
         "output_item.done for function_call must not emit finish_reason; "
         "response.completed is responsible for the terminal finish_reason"
     )
-    assert not result.choices[0].delta.tool_calls, (
-        "output_item.done for function_call must not include a duplicate tool_calls delta"
-    )
+    assert not result.choices[
+        0
+    ].delta.tool_calls, "output_item.done for function_call must not include a duplicate tool_calls delta"
 
 
 def test_text_plus_tool_calls_sequence():
@@ -837,7 +884,9 @@ def test_text_plus_tool_calls_sequence():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     # Simulate the sequence from OpenAI Responses API
     chunks = [
@@ -876,23 +925,28 @@ def test_text_plus_tool_calls_sequence():
     # Check message done (index 2) does NOT have finish_reason set
     message_done_result = results[2]
     assert len(message_done_result.choices) > 0, "message done should have choices"
-    assert message_done_result.choices[0].finish_reason is None or message_done_result.choices[0].finish_reason == "", (
-        "message done should not have finish_reason"
-    )
+    assert (
+        message_done_result.choices[0].finish_reason is None
+        or message_done_result.choices[0].finish_reason == ""
+    ), "message done should not have finish_reason"
 
     # Check function_call done (index 5) does NOT have finish_reason set
     # (response.completed is responsible for the terminal finish_reason)
     function_done_result = results[5]
-    assert len(function_done_result.choices) > 0, "function_call done should have choices"
-    assert function_done_result.choices[0].finish_reason is None, (
-        "output_item.done for function_call must not emit finish_reason"
-    )
+    assert (
+        len(function_done_result.choices) > 0
+    ), "function_call done should have choices"
+    assert (
+        function_done_result.choices[0].finish_reason is None
+    ), "output_item.done for function_call must not emit finish_reason"
 
     # Check response.completed (index 6) has finish_reason='stop'
     # (the mock chunk has no nested 'response' data, so has_function_calls is False → 'stop')
     completed_result = results[6]
     assert len(completed_result.choices) > 0, "response.completed should have choices"
-    assert completed_result.choices[0].finish_reason == "stop", "response.completed should have finish_reason='stop'"
+    assert (
+        completed_result.choices[0].finish_reason == "stop"
+    ), "response.completed should have finish_reason='stop'"
 
 
 # =============================================================================
@@ -958,7 +1012,9 @@ def test_tool_message_output_uses_input_text_not_output_text():
     output = function_call_output["output"]
     assert isinstance(output, list), f"output should be a list, got {type(output)}"
     assert len(output) == 1
-    assert output[0]["type"] == "input_text", f"Expected input_text, got {output[0].get('type')}"
+    assert (
+        output[0]["type"] == "input_text"
+    ), f"Expected input_text, got {output[0].get('type')}"
     assert output[0]["text"] == '{"temperature": 15, "condition": "sunny"}'
 
     print("✓ Tool message output correctly uses input_text type")
@@ -1144,9 +1200,13 @@ def test_map_reasoning_effort_adds_summary_detailed():
 
             assert result is not None, f"Result should not be None for effort={effort}"
             assert result["effort"] == effort, f"Effort should be {effort}"
-            assert "summary" not in result, f"Summary should NOT be present by default for effort={effort}"
+            assert (
+                "summary" not in result
+            ), f"Summary should NOT be present by default for effort={effort}"
 
-            print(f"✓ reasoning_effort='{effort}' correctly maps to effort='{effort}' (no summary by default)")
+            print(
+                f"✓ reasoning_effort='{effort}' correctly maps to effort='{effort}' (no summary by default)"
+            )
 
         # Test 2: With flag enabled - summary IS added
         litellm.reasoning_auto_summary = True
@@ -1156,9 +1216,9 @@ def test_map_reasoning_effort_adds_summary_detailed():
 
             assert result is not None, f"Result should not be None for effort={effort}"
             assert result["effort"] == effort, f"Effort should be {effort}"
-            assert result["summary"] == "detailed", (
-                f"Summary should be 'detailed' when flag is enabled for effort={effort}"
-            )
+            assert (
+                result["summary"] == "detailed"
+            ), f"Summary should be 'detailed' when flag is enabled for effort={effort}"
 
             print(
                 f"✓ reasoning_effort='{effort}' correctly maps to effort='{effort}', summary='detailed' (flag enabled)"
@@ -1169,7 +1229,9 @@ def test_map_reasoning_effort_adds_summary_detailed():
         os.environ["LITELLM_REASONING_AUTO_SUMMARY"] = "true"
 
         result = handler._map_reasoning_effort("high")
-        assert result["summary"] == "detailed", "Summary should be 'detailed' when env var is enabled"
+        assert (
+            result["summary"] == "detailed"
+        ), "Summary should be 'detailed' when env var is enabled"
         print("✓ LITELLM_REASONING_AUTO_SUMMARY env var works correctly")
 
         # Test 4: Dict input is passed through as-is (no modification)
@@ -1188,7 +1250,9 @@ def test_map_reasoning_effort_adds_summary_detailed():
         assert result_unknown is None
         print("✓ Unknown reasoning_effort values return None")
 
-        print("✓ All reasoning_effort behaviors work correctly with flag/env var control")
+        print(
+            "✓ All reasoning_effort behaviors work correctly with flag/env var control"
+        )
 
     finally:
         # Restore original values
@@ -1264,7 +1328,9 @@ def test_transform_response_preserves_annotations():
     # Create usage information
     usage = ResponseAPIUsage(
         input_tokens=10,
-        input_tokens_details=InputTokensDetails(audio_tokens=None, cached_tokens=0, text_tokens=None),
+        input_tokens_details=InputTokensDetails(
+            audio_tokens=None, cached_tokens=0, text_tokens=None
+        ),
         output_tokens=20,
         output_tokens_details=OutputTokensDetails(reasoning_tokens=0, text_tokens=None),
         total_tokens=30,
@@ -1351,9 +1417,13 @@ def test_transform_response_preserves_annotations():
     assert choice.message.content == "Here is some information with citations."
 
     # Check that annotations are preserved
-    assert hasattr(choice.message, "annotations"), "Message should have annotations attribute"
+    assert hasattr(
+        choice.message, "annotations"
+    ), "Message should have annotations attribute"
     assert choice.message.annotations is not None, "Annotations should not be None"
-    assert len(choice.message.annotations) == 2, f"Expected 2 annotations, got {len(choice.message.annotations)}"
+    assert (
+        len(choice.message.annotations) == 2
+    ), f"Expected 2 annotations, got {len(choice.message.annotations)}"
 
     # Verify annotation content
     annotation1 = choice.message.annotations[0]
@@ -1375,7 +1445,9 @@ def test_transform_response_preserves_annotations():
     assert result.usage.completion_tokens == 20
     assert result.usage.total_tokens == 30
 
-    print("✓ Annotations from Responses API are correctly preserved in Chat Completions format")
+    print(
+        "✓ Annotations from Responses API are correctly preserved in Chat Completions format"
+    )
 
 
 def test_apply_patch_tool_call_converted_to_chat_completion_tool_call():
@@ -1512,6 +1584,8 @@ def test_apply_patch_tool_call_converted_to_chat_completion_tool_call():
     assert args["type"] == "create_file"
     assert args["path"] == "hello.py"
     assert "print('hello world')" in args["diff"]
+
+
 def test_multi_tool_call_stream_no_premature_finish():
     """
     Regression test for multi-tool-call streaming bug.
@@ -1538,18 +1612,26 @@ def test_multi_tool_call_stream_no_premature_finish():
         OpenAiResponsesToChatCompletionStreamIterator,
     )
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
 
     chunks = [
         # 0: response created
-        {"type": "response.created", "response": {"id": "resp_001", "status": "in_progress"}},
+        {
+            "type": "response.created",
+            "response": {"id": "resp_001", "status": "in_progress"},
+        },
         # 1: first tool call added
         {
             "type": "response.output_item.added",
             "item": {"type": "function_call", "name": "read_file", "call_id": "call_1"},
         },
         # 2: first tool call arguments delta
-        {"type": "response.function_call_arguments.delta", "delta": '{"path":"/etc/hostname"}'},
+        {
+            "type": "response.function_call_arguments.delta",
+            "delta": '{"path":"/etc/hostname"}',
+        },
         # 3: first tool call done  ← must NOT emit finish_reason
         {
             "type": "response.output_item.done",
@@ -1608,10 +1690,12 @@ def test_multi_tool_call_stream_no_premature_finish():
         r = results[done_idx]
         assert r is not None, f"{label}: chunk_parser must return a result"
         assert len(r.choices) > 0, f"{label}: result must have choices"
-        assert r.choices[0].finish_reason is None, (
-            f"{label}: output_item.done must not emit finish_reason (stream would terminate prematurely)"
-        )
-        assert not r.choices[0].delta.tool_calls, (
+        assert (
+            r.choices[0].finish_reason is None
+        ), f"{label}: output_item.done must not emit finish_reason (stream would terminate prematurely)"
+        assert not r.choices[
+            0
+        ].delta.tool_calls, (
             f"{label}: output_item.done must not include a duplicate tool_calls delta"
         )
 
@@ -1623,12 +1707,12 @@ def test_multi_tool_call_stream_no_premature_finish():
         r = results[added_idx]
         if r is not None and r.choices and r.choices[0].delta.tool_calls:
             tc = r.choices[0].delta.tool_calls[0]
-            assert tc.function.name == expected_name, (
-                f"output_item.added for {expected_name}: tool_call name mismatch"
-            )
-            assert tc.id == expected_call_id, (
-                f"output_item.added for {expected_name}: call_id mismatch"
-            )
+            assert (
+                tc.function.name == expected_name
+            ), f"output_item.added for {expected_name}: tool_call name mismatch"
+            assert (
+                tc.id == expected_call_id
+            ), f"output_item.added for {expected_name}: call_id mismatch"
 
     # 3. argument delta events (indices 2 and 5) should carry arguments
     for delta_idx, expected_args, label in [
@@ -1638,17 +1722,17 @@ def test_multi_tool_call_stream_no_premature_finish():
         r = results[delta_idx]
         if r is not None and r.choices and r.choices[0].delta.tool_calls:
             tc = r.choices[0].delta.tool_calls[0]
-            assert tc.function.arguments == expected_args, (
-                f"{label}: argument delta mismatch"
-            )
+            assert (
+                tc.function.arguments == expected_args
+            ), f"{label}: argument delta mismatch"
 
     # 4. Only response.completed (index 7) emits the terminal finish_reason
     completed_result = results[7]
     assert completed_result is not None, "response.completed must return a result"
     assert len(completed_result.choices) > 0, "response.completed must have choices"
-    assert completed_result.choices[0].finish_reason == "tool_calls", (
-        "response.completed with function_call outputs must emit finish_reason='tool_calls'"
-    )
+    assert (
+        completed_result.choices[0].finish_reason == "tool_calls"
+    ), "response.completed with function_call outputs must emit finish_reason='tool_calls'"
 
     # 5. No chunk before the last one should have finish_reason set
     for idx, r in enumerate(results[:-1]):
@@ -1658,7 +1742,9 @@ def test_multi_tool_call_stream_no_premature_finish():
                 f"— only response.completed should terminate the stream"
             )
 
-    print("✓ Multi-tool-call stream completes without premature finish_reason termination")
+    print(
+        "✓ Multi-tool-call stream completes without premature finish_reason termination"
+    )
 
 
 # =============================================================================
@@ -1790,7 +1876,10 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
 
     chunks = [
         # 0: response.created
-        {"type": "response.created", "response": {"id": "resp_001", "status": "in_progress"}},
+        {
+            "type": "response.created",
+            "response": {"id": "resp_001", "status": "in_progress"},
+        },
         # 1: call_1 (read_file) added — output_index=0
         {
             "type": "response.output_item.added",
@@ -1873,7 +1962,9 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
         },
     ]
 
-    iterator = OpenAiResponsesToChatCompletionStreamIterator(streaming_response=None, sync_stream=True)
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
     results = [iterator.chunk_parser(chunk) for chunk in chunks]
 
     # 1. output_item.done events (indices 4 and 8) must NOT emit finish_reason
@@ -1885,7 +1976,9 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
             f"{label}: output_item.done must not emit finish_reason "
             f"(would prematurely terminate stream before subsequent tool calls arrive)"
         )
-        assert not r.choices[0].delta.tool_calls, (
+        assert not r.choices[
+            0
+        ].delta.tool_calls, (
             f"{label}: output_item.done must not emit a duplicate tool_calls delta"
         )
 
@@ -1919,7 +2012,9 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
         for tc in tool_calls:
             if tc.function and tc.function.arguments:
                 idx = tc.index
-                assembled_args[idx] = assembled_args.get(idx, "") + tc.function.arguments
+                assembled_args[idx] = (
+                    assembled_args.get(idx, "") + tc.function.arguments
+                )
 
     # delta 1 = '{"path":' + delta 2 = '"/etc/foo"}' → '{"path":"/etc/foo"}'
     assert assembled_args.get(0) == '{"path":"/etc/foo"}', (
@@ -1938,16 +2033,16 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
         for i, r in enumerate(results)
         if r is not None and r.choices and r.choices[0].finish_reason
     ]
-    assert len(finish_events) == 1, (
-        f"Expected exactly 1 finish event, got {len(finish_events)}: {finish_events}"
-    )
+    assert (
+        len(finish_events) == 1
+    ), f"Expected exactly 1 finish event, got {len(finish_events)}: {finish_events}"
     assert finish_events[0][0] == len(chunks) - 1, (
         f"Finish event must be at the last chunk (index {len(chunks) - 1}), "
         f"but was at index {finish_events[0][0]}"
     )
-    assert finish_events[0][1] == "tool_calls", (
-        f"Terminal finish_reason must be 'tool_calls', got '{finish_events[0][1]}'"
-    )
+    assert (
+        finish_events[0][1] == "tool_calls"
+    ), f"Terminal finish_reason must be 'tool_calls', got '{finish_events[0][1]}'"
 
     # 5. Parallel tool calls have distinct indices matching output_index (0 and 1)
     # Collect indices from output_item.added chunks only (they carry the call id)
@@ -1958,16 +2053,19 @@ def test_parallel_tool_calls_comprehensive_streaming_integration():
         for tc in r.choices[0].delta.tool_calls
         if tc.id  # output_item.added chunks carry the id; argument deltas do not
     ]
-    assert set(added_tool_call_indices) == {0, 1}, (
-        f"Parallel tool calls must have distinct indices {{0, 1}}, got: {set(added_tool_call_indices)}"
-    )
+    assert set(added_tool_call_indices) == {
+        0,
+        1,
+    }, f"Parallel tool calls must have distinct indices {{0, 1}}, got: {set(added_tool_call_indices)}"
 
-    print("✓ Parallel tool calls with split argument deltas stream correctly end-to-end")
+    print(
+        "✓ Parallel tool calls with split argument deltas stream correctly end-to-end"
+    )
 
 
 def test_map_optional_params_preserves_reasoning_summary():
     """Test that reasoning_effort dict with summary field is preserved.
-    
+
     Regression test for: User reported that summary field was being dropped
     when routing to Responses API. The dict format should be fully preserved.
     """
@@ -1992,6 +2090,344 @@ def test_map_optional_params_preserves_reasoning_summary():
 
     # Verify reasoning_effort dict with summary was fully preserved
     assert "reasoning" in responses_api_request
-    assert responses_api_request["reasoning"] == {"effort": "high", "summary": "detailed"}
+    assert responses_api_request["reasoning"] == {
+        "effort": "high",
+        "summary": "detailed",
+    }
     assert responses_api_request["reasoning"]["effort"] == "high"
     assert responses_api_request["reasoning"]["summary"] == "detailed"
+
+
+def test_convert_chat_completion_file_type_to_input_file():
+    """
+    Test that Chat Completion content with type 'file' is correctly mapped
+    to Responses API 'input_file' format, not stringified as 'input_text'.
+
+    Regression test for https://github.com/BerriAI/litellm/issues/23588
+    """
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What is in this PDF?"},
+                {
+                    "type": "file",
+                    "file": {
+                        "file_data": "data:application/pdf;base64,JVBERi0xLjQK",
+                        "filename": "test.pdf",
+                    },
+                },
+            ],
+        }
+    ]
+
+    (
+        input_items,
+        instructions,
+    ) = handler.convert_chat_completion_messages_to_responses_api(messages)
+
+    assert len(input_items) == 1
+    msg = input_items[0]
+    assert msg["type"] == "message"
+    assert msg["role"] == "user"
+
+    content = msg["content"]
+    assert len(content) == 2
+
+    # First item should be the text
+    assert content[0]["type"] == "input_text"
+    assert content[0]["text"] == "What is in this PDF?"
+
+    # Second item should be input_file, NOT input_text with stringified dict
+    assert content[1]["type"] == "input_file"
+    assert content[1]["file_data"] == "data:application/pdf;base64,JVBERi0xLjQK"
+    assert content[1]["filename"] == "test.pdf"
+    # Ensure it does NOT have the nested 'file' key
+    assert "file" not in content[1]
+
+
+def test_convert_chat_completion_file_type_with_file_id():
+    """
+    Test that Chat Completion content with type 'file' using file_id is correctly mapped.
+    """
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Summarize this file."},
+                {
+                    "type": "file",
+                    "file": {
+                        "file_id": "file-abc123",
+                    },
+                },
+            ],
+        }
+    ]
+
+    (
+        input_items,
+        instructions,
+    ) = handler.convert_chat_completion_messages_to_responses_api(messages)
+
+    content = input_items[0]["content"]
+    assert content[1]["type"] == "input_file"
+    assert content[1]["file_id"] == "file-abc123"
+    assert "file_data" not in content[1]
+
+
+# =============================================================================
+# Tests for reasoning_items round-trip (encrypted_content preservation)
+# =============================================================================
+
+
+def test_reasoning_items_non_streaming_round_trip():
+    """
+    Non-streaming: verify that reasoning_items (with encrypted_content) are:
+      1. Extracted from ResponseReasoningItem and attached to the Message.
+      2. Emitted as a 'reasoning' input item when the assistant message is
+         passed back to convert_chat_completion_messages_to_responses_api.
+    """
+    from unittest.mock import Mock
+
+    from openai.types.responses import ResponseOutputMessage, ResponseOutputText
+    from openai.types.responses.response_reasoning_item import (
+        ResponseReasoningItem,
+        Summary,
+    )
+
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        LiteLLMResponsesTransformationHandler,
+    )
+    from litellm.types.llms.openai import (
+        InputTokensDetails,
+        OutputTokensDetails,
+        ResponseAPIUsage,
+        ResponsesAPIResponse,
+    )
+    from litellm.types.utils import ModelResponse, Usage
+
+    handler = LiteLLMResponsesTransformationHandler()
+
+    encrypted = "gAAAAABpw5abc123FAKE=="
+    summary_text = "**Thinking about it**\n\nSome reasoning here."
+
+    reasoning_item = ResponseReasoningItem(
+        id="rs_test001",
+        summary=[Summary(text=summary_text, type="summary_text")],
+        type="reasoning",
+        content=None,
+        encrypted_content=encrypted,
+        status=None,
+    )
+    output_message = ResponseOutputMessage(
+        id="msg_test001",
+        content=[
+            ResponseOutputText(
+                annotations=[],
+                text="The answer is 42.",
+                type="output_text",
+                logprobs=[],
+            )
+        ],
+        role="assistant",
+        status="completed",
+        type="message",
+    )
+    usage = ResponseAPIUsage(
+        input_tokens=10,
+        input_tokens_details=InputTokensDetails(
+            audio_tokens=None, cached_tokens=0, text_tokens=None
+        ),
+        output_tokens=20,
+        output_tokens_details=OutputTokensDetails(reasoning_tokens=0, text_tokens=None),
+        total_tokens=30,
+        cost=None,
+    )
+    raw_response = ResponsesAPIResponse(
+        id="resp_test001",
+        created_at=1234567890,
+        error=None,
+        incomplete_details=None,
+        instructions=None,
+        metadata={},
+        model="gpt-5-mini",
+        object="response",
+        output=[reasoning_item, output_message],
+        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=usage,
+        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,
+    )
+    model_response = ModelResponse(
+        id="chatcmpl-test001",
+        created=1234567890,
+        model=None,
+        object="chat.completion",
+        system_fingerprint=None,
+        choices=[],
+        usage=Usage(completion_tokens=0, prompt_tokens=0, total_tokens=0),
+    )
+
+    result = handler.transform_response(
+        model="gpt-5-mini",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=Mock(),
+        request_data={"model": "gpt-5-mini"},
+        messages=[{"role": "user", "content": "What is the answer?"}],
+        optional_params={},
+        litellm_params={},
+        encoding=Mock(),
+    )
+
+    # ── Part 1: reasoning_items on the response message ──────────────────────
+    assert len(result.choices) == 1
+    msg = result.choices[0].message
+
+    assert (
+        msg.reasoning_content == summary_text
+    ), "reasoning_content should equal summary text"
+
+    assert msg.reasoning_items is not None, "reasoning_items should be set"
+    assert len(msg.reasoning_items) == 1
+    ri = msg.reasoning_items[0]
+    assert ri["type"] == "reasoning"
+    assert ri["id"] == "rs_test001"
+    assert ri["encrypted_content"] == encrypted, "encrypted_content must be preserved"
+    assert len(ri["summary"]) == 1
+    assert ri["summary"][0]["text"] == summary_text
+
+    # ── Part 2: reasoning item round-trips through message history ────────────
+    history = [
+        {"role": "user", "content": "What is the answer?"},
+        {
+            "role": "assistant",
+            "content": msg.content,
+            "reasoning_items": msg.reasoning_items,
+        },
+        {"role": "user", "content": "Can you elaborate?"},
+    ]
+    input_items, _ = handler.convert_chat_completion_messages_to_responses_api(history)
+
+    # The reasoning input item must appear before the assistant message item
+    types = [item.get("type") for item in input_items]
+    assert (
+        "reasoning" in types
+    ), "reasoning input item must be emitted for the assistant turn"
+
+    reasoning_input = next(
+        item for item in input_items if item.get("type") == "reasoning"
+    )
+    assert reasoning_input["id"] == "rs_test001"
+    assert reasoning_input["encrypted_content"] == encrypted
+    assert reasoning_input["summary"][0]["text"] == summary_text
+
+    # reasoning item must come before the assistant message item
+    reasoning_idx = types.index("reasoning")
+    assistant_msg_idx = next(
+        i
+        for i, item in enumerate(input_items)
+        if item.get("type") == "message" and item.get("role") == "assistant"
+    )
+    assert (
+        reasoning_idx < assistant_msg_idx
+    ), "reasoning input item must precede the assistant message item"
+
+
+def test_reasoning_items_streaming_emitted_on_response_completed():
+    """
+    Streaming: verify that reasoning_items (with encrypted_content) are emitted
+    on the delta of the response.completed chunk, enabling the caller to
+    round-trip them in subsequent requests.
+    """
+    from litellm.completion_extras.litellm_responses_transformation.transformation import (
+        OpenAiResponsesToChatCompletionStreamIterator,
+    )
+
+    iterator = OpenAiResponsesToChatCompletionStreamIterator(
+        streaming_response=None, sync_stream=True
+    )
+
+    encrypted = "gAAAAABpw5xyz987FAKE=="
+    summary_text = "**Reasoning summary**\n\nModel thought about this carefully."
+
+    chunk = {
+        "type": "response.completed",
+        "response": {
+            "id": "resp_stream001",
+            "status": "completed",
+            "output": [
+                {
+                    "type": "reasoning",
+                    "id": "rs_stream001",
+                    "encrypted_content": encrypted,
+                    "summary": [{"type": "summary_text", "text": summary_text}],
+                },
+                {
+                    "type": "message",
+                    "id": "msg_stream001",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": "The answer."}],
+                    "status": "completed",
+                },
+            ],
+            "usage": {
+                "input_tokens": 10,
+                "output_tokens": 5,
+                "total_tokens": 15,
+                "input_tokens_details": {"cached_tokens": 0},
+                "output_tokens_details": {"reasoning_tokens": 0},
+            },
+        },
+    }
+
+    result = iterator.chunk_parser(chunk)
+
+    assert len(result.choices) == 1
+    delta = result.choices[0].delta
+
+    # finish_reason must be set (response is complete)
+    assert result.choices[0].finish_reason == "stop"
+
+    # reasoning_items must be on the delta
+    assert (
+        getattr(delta, "reasoning_items", None) is not None
+    ), "reasoning_items must be present on the response.completed delta"
+    assert len(delta.reasoning_items) == 1
+    ri = delta.reasoning_items[0]
+    assert ri["type"] == "reasoning"
+    assert ri["id"] == "rs_stream001"
+    assert (
+        ri["encrypted_content"] == encrypted
+    ), "encrypted_content must be preserved in streaming"
+    assert ri["summary"][0]["text"] == summary_text
diff --git a/tests/test_litellm/integrations/test_langsmith_init.py b/tests/test_litellm/integrations/test_langsmith_init.py
index 779e7b4c94b..1e2d3d7caea 100644
--- a/tests/test_litellm/integrations/test_langsmith_init.py
+++ b/tests/test_litellm/integrations/test_langsmith_init.py
@@ -16,13 +16,9 @@ class TestLangsmithLoggerInit:
     Note: The current implementation has some edge cases in the sampling rate logic.
     """
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {"LANGSMITH_SAMPLING_RATE": "1"}, clear=False)
-    def test_langsmith_sampling_rate_parameter_respected_with_valid_env(
-        self, mock_create_task
-    ):
+    def test_langsmith_sampling_rate_parameter_respected_with_valid_env(self):
         """Test that langsmith_sampling_rate parameter is properly set when env var condition is met."""
-        # When there's a valid integer in env var, the parameter should be used due to 'or' logic
         sampling_rate = 0.5
         logger = LangsmithLogger(
             langsmith_api_key="test-key",
@@ -30,58 +26,47 @@ class TestLangsmithLoggerInit:
             langsmith_sampling_rate=sampling_rate,
         )
 
-        # With the current 'or' logic and valid env var, the parameter should be used
         assert (
             logger.sampling_rate == sampling_rate
         ), f"Expected sampling_rate to be {sampling_rate}, got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {"LANGSMITH_SAMPLING_RATE": "1"}, clear=False)
-    def test_langsmith_sampling_rate_zero_parameter_falls_back_to_env(
-        self, mock_create_task
-    ):
+    def test_langsmith_sampling_rate_zero_parameter_falls_back_to_env(self):
         """Test that 0.0 parameter falls back to env var due to falsy value."""
-        # This demonstrates the current behavior where 0.0 is falsy and falls back to env
         logger = LangsmithLogger(
             langsmith_api_key="test-key",
             langsmith_project="test-project",
-            langsmith_sampling_rate=0.0,  # This is falsy!
+            langsmith_sampling_rate=0.0,
         )
 
-        # Due to current 'or' logic, 0.0 falls back to env var
         assert (
             logger.sampling_rate == 1.0
         ), f"Expected sampling_rate to fall back to 1.0 from env, got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {"LANGSMITH_SAMPLING_RATE": "1"}, clear=False)
-    def test_langsmith_sampling_rate_from_integer_env_var(self, mock_create_task):
+    def test_langsmith_sampling_rate_from_integer_env_var(self):
         """Test that sampling rate uses environment variable when parameter not provided and env var is integer."""
         logger = LangsmithLogger(
             langsmith_api_key="test-key", langsmith_project="test-project"
         )
 
-        # Should use env var since it's a valid integer
         assert (
             logger.sampling_rate == 1.0
         ), f"Expected sampling_rate to be 1.0 from env var, got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {"LANGSMITH_SAMPLING_RATE": "0.8"}, clear=False)
-    def test_langsmith_sampling_rate_decimal_env_var_ignored(self, mock_create_task):
+    def test_langsmith_sampling_rate_decimal_env_var_ignored(self):
         """Test that decimal environment variables are ignored due to isdigit() check."""
         logger = LangsmithLogger(
             langsmith_api_key="test-key", langsmith_project="test-project"
         )
 
-        # Decimal env vars are ignored due to isdigit() check, falls back to 1.0
         assert (
             logger.sampling_rate == 1.0
         ), f"Expected sampling_rate to default to 1.0 (decimal env ignored), got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {}, clear=True)
-    def test_langsmith_sampling_rate_default_value(self, mock_create_task):
+    def test_langsmith_sampling_rate_default_value(self):
         """Test that sampling rate defaults to 1.0 when no parameter or env var provided."""
         logger = LangsmithLogger(
             langsmith_api_key="test-key", langsmith_project="test-project"
@@ -91,9 +76,8 @@ class TestLangsmithLoggerInit:
             logger.sampling_rate == 1.0
         ), f"Expected default sampling_rate to be 1.0, got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {"LANGSMITH_SAMPLING_RATE": "invalid"}, clear=False)
-    def test_langsmith_sampling_rate_invalid_env_var_defaults(self, mock_create_task):
+    def test_langsmith_sampling_rate_invalid_env_var_defaults(self):
         """Test that invalid environment variable falls back to default value."""
         logger = LangsmithLogger(
             langsmith_api_key="test-key", langsmith_project="test-project"
@@ -103,9 +87,8 @@ class TestLangsmithLoggerInit:
             logger.sampling_rate == 1.0
         ), f"Expected sampling_rate to default to 1.0 with invalid env var, got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
     @patch.dict(os.environ, {"LANGSMITH_SAMPLING_RATE": ""}, clear=False)
-    def test_langsmith_sampling_rate_empty_env_var_defaults(self, mock_create_task):
+    def test_langsmith_sampling_rate_empty_env_var_defaults(self):
         """Test that empty environment variable falls back to default value."""
         logger = LangsmithLogger(
             langsmith_api_key="test-key", langsmith_project="test-project"
@@ -115,14 +98,12 @@ class TestLangsmithLoggerInit:
             logger.sampling_rate == 1.0
         ), f"Expected sampling_rate to default to 1.0 with empty env var, got {logger.sampling_rate}"
 
-    @patch("asyncio.create_task")
-    def test_langsmith_sampling_rate_attribute_exists(self, mock_create_task):
+    def test_langsmith_sampling_rate_attribute_exists(self):
         """Test that the sampling_rate attribute is always set on the logger instance."""
         logger = LangsmithLogger(
             langsmith_api_key="test-key", langsmith_project="test-project"
         )
 
-        # Verify the attribute exists and is a float
         assert hasattr(
             logger, "sampling_rate"
         ), "LangsmithLogger should have sampling_rate attribute"
@@ -133,6 +114,93 @@ class TestLangsmithLoggerInit:
             logger.sampling_rate >= 0.0
         ), f"sampling_rate should be non-negative, got {logger.sampling_rate}"
 
+    @patch.object(LangsmithLogger, "_start_periodic_flush_task", return_value=None)
+    def test_langsmith_init_skips_periodic_flush_without_running_loop(
+        self, mock_start_periodic_flush_task
+    ):
+        """Test that sync initialization leaves the periodic flush task unset."""
+        logger = LangsmithLogger(
+            langsmith_api_key="test-key", langsmith_project="test-project"
+        )
+
+        assert logger is not None
+        mock_start_periodic_flush_task.assert_called_once()
+        assert logger._flush_task is None
+
+    @patch("asyncio.get_running_loop", side_effect=RuntimeError("no running event loop"))
+    def test_start_periodic_flush_task_returns_none_without_running_loop(
+        self, mock_get_running_loop
+    ):
+        """Test that helper returns None when no running event loop exists."""
+        with patch.object(LangsmithLogger, "_start_periodic_flush_task", return_value=None):
+            logger = LangsmithLogger(
+                langsmith_api_key="test-key",
+                langsmith_project="test-project",
+            )
+
+        mock_get_running_loop.reset_mock()
+
+        assert logger._start_periodic_flush_task() is None
+        mock_get_running_loop.assert_called_once()
+
+    @patch("asyncio.get_running_loop")
+    def test_langsmith_init_starts_periodic_flush_with_running_loop(
+        self, mock_get_running_loop
+    ):
+        """Test that init schedules periodic flush when a running loop exists."""
+        mock_loop = MagicMock()
+        mock_task = MagicMock()
+        mock_loop.create_task.return_value = mock_task
+        mock_get_running_loop.return_value = mock_loop
+
+        logger = LangsmithLogger(
+            langsmith_api_key="test-key", langsmith_project="test-project"
+        )
+
+        assert logger._flush_task == mock_task
+        mock_loop.create_task.assert_called_once()
+        scheduled_coro = mock_loop.create_task.call_args.args[0]
+        scheduled_coro.close()
+
+    @pytest.mark.asyncio
+    async def test_async_log_success_event_lazily_starts_periodic_flush(self):
+        """Test that async logging lazily starts periodic flush after sync init."""
+        with patch.object(LangsmithLogger, "_start_periodic_flush_task", return_value=None):
+            logger = LangsmithLogger(
+                langsmith_api_key="test-key",
+                langsmith_project="test-project",
+            )
+        logger._get_sampling_rate_to_use_for_request = MagicMock(return_value=1.0)
+        logger._get_credentials_to_use_for_request = MagicMock(
+            return_value=logger.default_credentials
+        )
+        logger._prepare_log_data = MagicMock(return_value={"id": "run-id"})
+        logger._start_periodic_flush_task = MagicMock(return_value=MagicMock())
+
+        await logger.async_log_success_event({}, {}, None, None)
+
+        logger._start_periodic_flush_task.assert_called_once()
+        assert len(logger.log_queue) == 1
+
+    @pytest.mark.asyncio
+    async def test_async_log_failure_event_lazily_starts_periodic_flush(self):
+        """Test that async failure logging lazily starts periodic flush after sync init."""
+        with patch.object(LangsmithLogger, "_start_periodic_flush_task", return_value=None):
+            logger = LangsmithLogger(
+                langsmith_api_key="test-key",
+                langsmith_project="test-project",
+            )
+        logger._get_sampling_rate_to_use_for_request = MagicMock(return_value=1.0)
+        logger._get_credentials_to_use_for_request = MagicMock(
+            return_value=logger.default_credentials
+        )
+        logger._prepare_log_data = MagicMock(return_value={"id": "run-id"})
+        logger._start_periodic_flush_task = MagicMock(return_value=MagicMock())
+
+        await logger.async_log_failure_event({}, {}, None, None)
+
+        logger._start_periodic_flush_task.assert_called_once()
+        assert len(logger.log_queue) == 1
 
 class TestLangsmithPrepareLogData:
     """Regression test for #24001: _prepare_log_data must inject
diff --git a/tests/test_litellm/integrations/test_prometheus_spend_logs_metadata.py b/tests/test_litellm/integrations/test_prometheus_spend_logs_metadata.py
new file mode 100644
index 00000000000..d60c2ae9293
--- /dev/null
+++ b/tests/test_litellm/integrations/test_prometheus_spend_logs_metadata.py
@@ -0,0 +1,110 @@
+"""
+Unit tests for spend_logs_metadata inclusion in Prometheus custom labels.
+
+Verifies that metadata from x-litellm-spend-logs-metadata header is available
+in Prometheus custom labels via combined_metadata.
+"""
+from litellm.integrations.prometheus import get_custom_labels_from_metadata
+
+
+def test_get_custom_labels_includes_spend_logs_metadata(monkeypatch):
+    """
+    Test that get_custom_labels_from_metadata extracts fields from
+    spend_logs_metadata when it is merged into combined_metadata.
+    """
+    monkeypatch.setattr(
+        "litellm.custom_prometheus_metadata_labels",
+        ["metadata.department", "metadata.env"],
+    )
+
+    # Simulate combined_metadata after merging all three sources
+    combined_metadata = {
+        "request_key": "from_requester",  # from requester_metadata
+        "auth_key": "from_auth",  # from user_api_key_auth_metadata
+        "department": "engineering",  # from spend_logs_metadata
+        "env": "production",  # from spend_logs_metadata
+    }
+
+    result = get_custom_labels_from_metadata(combined_metadata)
+    assert result == {
+        "metadata_department": "engineering",
+        "metadata_env": "production",
+    }
+
+
+def test_spend_logs_metadata_overrides_earlier_sources(monkeypatch):
+    """
+    Test that spend_logs_metadata values take precedence over
+    requester_metadata and user_api_key_auth_metadata when keys overlap,
+    since it is spread last in combined_metadata.
+    """
+    monkeypatch.setattr(
+        "litellm.custom_prometheus_metadata_labels",
+        ["metadata.team"],
+    )
+
+    # Simulate the dict spread order: requester -> auth -> spend_logs
+    requester_metadata = {"team": "old_team"}
+    user_api_key_auth_metadata = {"team": "auth_team"}
+    spend_logs_metadata = {"team": "spend_team"}
+
+    combined_metadata = {
+        **requester_metadata,
+        **user_api_key_auth_metadata,
+        **spend_logs_metadata,
+    }
+
+    result = get_custom_labels_from_metadata(combined_metadata)
+    assert result == {"metadata_team": "spend_team"}
+
+
+def test_combined_metadata_with_all_three_sources(monkeypatch):
+    """
+    Test that combined_metadata correctly merges requester_metadata,
+    user_api_key_auth_metadata, and spend_logs_metadata.
+    """
+    monkeypatch.setattr(
+        "litellm.custom_prometheus_metadata_labels",
+        ["metadata.from_requester", "metadata.from_auth", "metadata.from_spend"],
+    )
+
+    # Reproduce the exact spread pattern from prometheus.py
+    _requester_metadata = {"from_requester": "val1"}
+    user_api_key_auth_metadata = {"from_auth": "val2"}
+    spend_logs_metadata = {"from_spend": "val3"}
+
+    combined_metadata = {
+        **(_requester_metadata if _requester_metadata else {}),
+        **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}),
+        **(spend_logs_metadata if spend_logs_metadata else {}),
+    }
+
+    result = get_custom_labels_from_metadata(combined_metadata)
+    assert result == {
+        "metadata_from_requester": "val1",
+        "metadata_from_auth": "val2",
+        "metadata_from_spend": "val3",
+    }
+
+
+def test_combined_metadata_with_none_spend_logs(monkeypatch):
+    """
+    Test that combined_metadata works when spend_logs_metadata is None.
+    """
+    monkeypatch.setattr(
+        "litellm.custom_prometheus_metadata_labels",
+        ["metadata.foo"],
+    )
+
+    _requester_metadata = {"foo": "bar"}
+    user_api_key_auth_metadata = None
+    spend_logs_metadata = None
+
+    combined_metadata = {
+        **(_requester_metadata if _requester_metadata else {}),
+        **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}),
+        **(spend_logs_metadata if spend_logs_metadata else {}),
+    }
+
+    result = get_custom_labels_from_metadata(combined_metadata)
+    assert result == {"metadata_foo": "bar"}
diff --git a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
index 988941d1d91..30b47a853ef 100644
--- a/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
+++ b/tests/test_litellm/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
@@ -1,4 +1,5 @@
 import base64
+import json
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -10,9 +11,11 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     BedrockImageProcessor,
     anthropic_messages_pt,
     _convert_to_bedrock_tool_call_invoke,
+    convert_to_gemini_tool_call_result,
     ollama_pt,
     sanitize_messages_for_tool_calling,
 )
+from litellm.types.llms.openai import ChatCompletionToolMessage
 
 
 def test_ollama_pt_simple_messages():
@@ -551,6 +554,175 @@ def test_convert_gemini_tool_call_result_with_image_url():
     assert isinstance(result2, list) and any("inline_data" in p for p in result2)
 
 
+def test_convert_gemini_tool_call_result_with_anthropic_image_block():
+    """
+    Test that Anthropic-native image blocks in tool_result list content are
+    converted to Gemini inline_data instead of being silently dropped.
+    Fixes: https://github.com/BerriAI/litellm/issues/23712
+    """
+    tiny_png_b64 = base64.b64encode(b"PNG_PLACEHOLDER").decode()
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_123",
+        content=[
+            {
+                "type": "image",
+                "source": {
+                    "type": "base64",
+                    "media_type": "image/png",
+                    "data": tiny_png_b64,
+                },
+            }
+        ],
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_123",
+                "type": "function",
+                "index": 0,
+                "function": {"name": "read_file", "arguments": "{}"},
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+    )
+    assert isinstance(result, list), "expected a list of parts"
+    inline_parts = [p for p in result if "inline_data" in p]
+    assert len(inline_parts) == 1, "expected exactly one inline_data part"
+    assert inline_parts[0]["inline_data"]["mime_type"] == "image/png"
+    assert inline_parts[0]["inline_data"]["data"] == tiny_png_b64
+
+
+def test_convert_gemini_tool_call_result_with_multiple_anthropic_image_blocks():
+    """
+    Test that multiple Anthropic-native image blocks in a single tool_result
+    are all preserved as separate inline_data parts instead of only the last
+    one being kept.
+    Fixes: https://github.com/BerriAI/litellm/issues/23712
+    """
+    png_b64 = base64.b64encode(b"PNG_PLACEHOLDER").decode()
+    jpeg_b64 = base64.b64encode(b"JPEG_PLACEHOLDER").decode()
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_multi",
+        content=[
+            {"type": "text", "text": "here are two images"},
+            {
+                "type": "image",
+                "source": {"type": "base64", "media_type": "image/png", "data": png_b64},
+            },
+            {
+                "type": "image",
+                "source": {"type": "base64", "media_type": "image/jpeg", "data": jpeg_b64},
+            },
+        ],
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_multi",
+                "type": "function",
+                "index": 0,
+                "function": {"name": "screenshot", "arguments": "{}"},
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+    )
+    assert isinstance(result, list), "expected a list of parts"
+    inline_parts = [p for p in result if "inline_data" in p]
+    assert len(inline_parts) == 2, f"expected 2 inline_data parts, got {len(inline_parts)}"
+    mime_types = {p["inline_data"]["mime_type"] for p in inline_parts}
+    assert mime_types == {"image/png", "image/jpeg"}
+
+
+def test_convert_gemini_tool_call_result_with_data_url_string():
+    """
+    Test that a data-URL string in tool_result content is converted to
+    Gemini inline_data instead of being passed as plain text.
+    Fixes: https://github.com/BerriAI/litellm/issues/23712
+    """
+    tiny_png_b64 = base64.b64encode(b"PNG_PLACEHOLDER").decode()
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_456",
+        content=f"data:image/png;base64,{tiny_png_b64}",
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_456",
+                "type": "function",
+                "index": 0,
+                "function": {"name": "read_file", "arguments": "{}"},
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+    )
+    assert isinstance(result, list), "expected a list of parts"
+    inline_parts = [p for p in result if "inline_data" in p]
+    assert len(inline_parts) == 1, "data-URL image string was not converted to inline_data"
+    assert inline_parts[0]["inline_data"]["mime_type"] == "image/png"
+    assert inline_parts[0]["inline_data"]["data"] == tiny_png_b64
+
+
+def test_convert_gemini_tool_call_result_with_data_url_extra_params():
+    """
+    Test that a data-URL with extra MIME parameters (e.g. charset) produces
+    a clean mime_type without the extra parameters.
+    """
+    tiny_png_b64 = base64.b64encode(b"PNG_PLACEHOLDER").decode()
+
+    message = ChatCompletionToolMessage(
+        role="tool",
+        tool_call_id="call_extra",
+        content=f"data:image/png;charset=UTF-8;base64,{tiny_png_b64}",
+    )
+    last_message_with_tool_calls = {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {
+                "id": "call_extra",
+                "type": "function",
+                "index": 0,
+                "function": {"name": "read_file", "arguments": "{}"},
+            }
+        ],
+    }
+
+    result = convert_to_gemini_tool_call_result(
+        message=message,
+        last_message_with_tool_calls=last_message_with_tool_calls,
+    )
+    assert isinstance(result, list), "expected a list of parts"
+    inline_parts = [p for p in result if "inline_data" in p]
+    assert len(inline_parts) == 1
+    assert inline_parts[0]["inline_data"]["mime_type"] == "image/png", (
+        f"expected clean 'image/png', got '{inline_parts[0]['inline_data']['mime_type']}'"
+    )
+
+
 def test_bedrock_tools_unpack_defs():
     """
     Test that the unpack_defs method handles nested $ref inside anyOf items correctly
@@ -2114,3 +2286,56 @@ def test_sanitize_messages_combined_case_a_and_case_d():
         )
     finally:
         litellm.modify_params = original
+
+
+def test_anthropic_messages_pt_file_block_preserves_cache_control():
+    """
+    Test that cache_control is preserved on file-type content blocks
+    when translated to Anthropic document params.
+    Regression test for https://github.com/BerriAI/litellm/issues/23873
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        anthropic_messages_pt,
+    )
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "file",
+                    "file": {
+                        "filename": "doc.pdf",
+                        "file_data": "data:application/pdf;base64,JVBERi0xLjQ=",
+                    },
+                    "cache_control": {"type": "ephemeral"},
+                },
+                {
+                    "type": "text",
+                    "text": "Summarize this document.",
+                    "cache_control": {"type": "ephemeral"},
+                },
+            ],
+        }
+    ]
+
+    result = anthropic_messages_pt(
+        messages, model="claude-sonnet-4-20250514", llm_provider="anthropic"
+    )
+
+    content_blocks = result[0]["content"]
+    assert len(content_blocks) == 2
+
+    # Document block (from file) should preserve cache_control
+    doc_block = content_blocks[0]
+    assert doc_block["type"] == "document"
+    assert "cache_control" in doc_block, (
+        "cache_control was dropped from file/document block"
+    )
+    assert doc_block["cache_control"]["type"] == "ephemeral"
+
+    # Text block should also preserve cache_control
+    text_block = content_blocks[1]
+    assert text_block["type"] == "text"
+    assert "cache_control" in text_block
+    assert text_block["cache_control"]["type"] == "ephemeral"
diff --git a/tests/test_litellm/litellm_core_utils/test_core_helpers.py b/tests/test_litellm/litellm_core_utils/test_core_helpers.py
index 72c3b2b077e..8397fc22242 100644
--- a/tests/test_litellm/litellm_core_utils/test_core_helpers.py
+++ b/tests/test_litellm/litellm_core_utils/test_core_helpers.py
@@ -73,6 +73,9 @@ class TestMapFinishReasonAnthropic:
     def test_anthropic_finish_reasons(self, provider_reason: str, expected: str) -> None:
         assert map_finish_reason(provider_reason) == expected
 
+    def test_refusal(self):
+        assert map_finish_reason("refusal") == "content_filter"
+
 
 class TestMapFinishReasonGemini:
     @pytest.mark.parametrize(
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 636e84fe796..33628e1d19d 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
@@ -31,7 +31,7 @@ def test_anthropic_experimental_pass_through_messages_handler():
                 model="openai/claude-3-5-sonnet-20240620",
                 api_key="test-api-key",
             )
-        except Exception as e:
+        except (ValueError, TypeError, AttributeError) as e:
             print(f"Error: {e}")
         mock_responses.assert_called_once()
         assert mock_responses.call_args.kwargs["api_key"] == "test-api-key"
@@ -56,7 +56,7 @@ def test_anthropic_experimental_pass_through_messages_handler_dynamic_api_key_an
                 api_base="test-api-base",
                 custom_key="custom_value",
             )
-        except Exception as e:
+        except (ValueError, TypeError, AttributeError) as e:
             print(f"Error: {e}")
         mock_completion.assert_called_once()
         assert mock_completion.call_args.kwargs["api_key"] == "test-api-key"
@@ -81,7 +81,7 @@ def test_anthropic_experimental_pass_through_messages_handler_custom_llm_provide
                 custom_llm_provider="my-custom-llm",
                 api_key="test-api-key",
             )
-        except Exception as e:
+        except (ValueError, TypeError, AttributeError) as e:
             print(f"Error: {e}")
 
         # Assert that litellm.completion was called when using a custom LLM provider
@@ -125,24 +125,29 @@ async def test_bedrock_converse_budget_tokens_preserved():
                 max_tokens=1024,
                 messages=[{"role": "user", "content": "What is 2+2?"}],
                 model="bedrock/converse/us.anthropic.claude-sonnet-4-20250514-v1:0",
-                thinking={
-                    "budget_tokens": 1024,
-                    "type": "enabled"
-                },
+                thinking={"budget_tokens": 1024, "type": "enabled"},
             )
-        except Exception:
+        except (ValueError, TypeError, AttributeError):
             pass  # Expected due to response format conversion
 
         mock_acompletion.assert_called_once()
 
         call_kwargs = mock_acompletion.call_args.kwargs
-        print("acompletion call kwargs: ", json.dumps(call_kwargs, indent=4, default=str))
+        print(
+            "acompletion call kwargs: ", json.dumps(call_kwargs, indent=4, default=str)
+        )
 
         # Verify thinking parameter is passed through with budget_tokens preserved
         thinking_param = call_kwargs.get("thinking")
-        assert thinking_param is not None, "thinking parameter should be passed to acompletion"
-        assert thinking_param.get("type") == "enabled", "thinking.type should be 'enabled'"
-        assert thinking_param.get("budget_tokens") == 1024, f"thinking.budget_tokens should be 1024, but got {thinking_param.get('budget_tokens')}"
+        assert (
+            thinking_param is not None
+        ), "thinking parameter should be passed to acompletion"
+        assert (
+            thinking_param.get("type") == "enabled"
+        ), "thinking.type should be 'enabled'"
+        assert (
+            thinking_param.get("budget_tokens") == 1024
+        ), f"thinking.budget_tokens should be 1024, but got {thinking_param.get('budget_tokens')}"
 
 
 def test_openai_model_with_thinking_converts_to_reasoning():
@@ -164,12 +169,9 @@ def test_openai_model_with_thinking_converts_to_reasoning():
                 messages=[{"role": "user", "content": "What is 2+2?"}],
                 model="openai/gpt-5.2",
                 api_key="test-api-key",
-                thinking={
-                    "type": "enabled",
-                    "budget_tokens": 1024
-                },
+                thinking={"type": "enabled", "budget_tokens": 1024},
             )
-        except Exception as e:
+        except (ValueError, TypeError, AttributeError) as e:
             print(f"Error: {e}")
 
         mock_responses.assert_called_once()
@@ -177,17 +179,23 @@ def test_openai_model_with_thinking_converts_to_reasoning():
         call_kwargs = mock_responses.call_args.kwargs
 
         # Verify reasoning is set (converted from thinking)
-        assert "reasoning" in call_kwargs, "reasoning should be passed to litellm.responses"
+        assert (
+            "reasoning" in call_kwargs
+        ), "reasoning should be passed to litellm.responses"
 
         # budget_tokens=1024 -> effort="minimal" (< 2000 threshold)
-        expected_reasoning = {"effort": "minimal", "summary": "detailed"}
+        # reasoning_auto_summary is False by default, so no summary key
+        expected_reasoning = {"effort": "minimal"}
         assert call_kwargs["reasoning"] == expected_reasoning, (
             f"reasoning should be {expected_reasoning} for budget_tokens=1024, "
             f"got {call_kwargs.get('reasoning')}"
         )
+        assert "summary" not in call_kwargs["reasoning"]
 
         # Verify thinking is NOT passed directly to the Responses API
-        assert "thinking" not in call_kwargs, "thinking should NOT be passed directly to litellm.responses"
+        assert (
+            "thinking" not in call_kwargs
+        ), "thinking should NOT be passed directly to litellm.responses"
 
 
 class TestThinkingParameterTransformation:
@@ -198,13 +206,13 @@ class TestThinkingParameterTransformation:
         from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
             LiteLLMAnthropicMessagesAdapter,
         )
-        
+
         thinking = {"type": "enabled", "budget_tokens": 5000}
         result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model(
             thinking=thinking,
             model="bedrock/converse/us.anthropic.claude-sonnet-4-20250514-v1:0",
         )
-        
+
         assert result == {"thinking": thinking}
         assert result["thinking"]["budget_tokens"] == 5000
 
@@ -213,12 +221,281 @@ class TestThinkingParameterTransformation:
         from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
             LiteLLMAnthropicMessagesAdapter,
         )
-        
+
         thinking = {"type": "enabled", "budget_tokens": 1024}
         result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model(
             thinking=thinking,
             model="openai/gpt-5.2",
         )
-        
+
+        # reasoning_auto_summary is False by default, so no summary key
         assert result == {"reasoning_effort": "minimal"}
         assert "thinking" not in result
+        assert "summary" not in str(result["reasoning_effort"])
+
+    def test_translate_thinking_for_model_summary_when_enabled(self):
+        """When reasoning_auto_summary is True, summary='detailed' is injected."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
+            LiteLLMAnthropicMessagesAdapter,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = True
+            thinking = {"type": "enabled", "budget_tokens": 5000}
+            result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model(
+                thinking=thinking,
+                model="openai/gpt-5.2",
+            )
+            assert result == {
+                "reasoning_effort": {"effort": "medium", "summary": "detailed"}
+            }
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_translate_thinking_for_model_preserves_user_summary(self):
+        """User-provided summary is always preserved regardless of flag."""
+        from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
+            LiteLLMAnthropicMessagesAdapter,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 10000, "summary": "concise"}
+        result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model(
+            thinking=thinking,
+            model="openai/gpt-5.2",
+        )
+        assert result == {"reasoning_effort": {"effort": "high", "summary": "concise"}}
+
+
+class TestThinkingSummaryPreservation:
+    """Tests for thinking.summary preservation and reasoning_auto_summary flag."""
+
+    def test_thinking_summary_concise_preserved_for_openai(self):
+        """User-provided summary='concise' should not be replaced with 'detailed'."""
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"}
+        completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "medium"}
+        LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+            completion_kwargs, thinking=thinking
+        )
+        assert completion_kwargs["reasoning_effort"] == {
+            "effort": "medium",
+            "summary": "concise",
+        }
+
+    def test_thinking_summary_auto_preserved_for_openai(self):
+        """User-provided summary='auto' should be preserved."""
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 10000, "summary": "auto"}
+        completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "high"}
+        LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+            completion_kwargs, thinking=thinking
+        )
+        assert completion_kwargs["reasoning_effort"] == {
+            "effort": "high",
+            "summary": "auto",
+        }
+
+    def test_summary_added_when_auto_summary_enabled(self):
+        """When reasoning_auto_summary is True, summary='detailed' is added."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = True
+            completion_kwargs = {
+                "model": "responses/gpt-5.2",
+                "custom_llm_provider": "openai",
+                "reasoning_effort": "medium",
+            }
+            LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+                completion_kwargs, thinking={"type": "enabled", "budget_tokens": 5000}
+            )
+            assert completion_kwargs["reasoning_effort"] == {
+                "effort": "medium",
+                "summary": "detailed",
+            }
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_no_summary_by_default_string_reasoning(self):
+        """By default (reasoning_auto_summary=False), summary is not added for string reasoning_effort."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = False
+            completion_kwargs = {
+                "model": "responses/gpt-5.2",
+                "custom_llm_provider": "openai",
+                "reasoning_effort": "high",
+            }
+            LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+                completion_kwargs, thinking={"type": "enabled", "budget_tokens": 10000}
+            )
+            assert completion_kwargs["reasoning_effort"] == {"effort": "high"}
+            assert "summary" not in completion_kwargs["reasoning_effort"]
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_no_summary_by_default_dict_reasoning(self):
+        """By default (reasoning_auto_summary=False), summary is not injected into dict reasoning_effort."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = False
+            completion_kwargs = {
+                "model": "responses/gpt-5.2",
+                "custom_llm_provider": "openai",
+                "reasoning_effort": {"effort": "medium"},
+            }
+            LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+                completion_kwargs, thinking={"type": "enabled", "budget_tokens": 5000}
+            )
+            assert completion_kwargs["reasoning_effort"] == {"effort": "medium"}
+            assert "summary" not in completion_kwargs["reasoning_effort"]
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_summary_added_when_env_var_set(self):
+        """When LITELLM_REASONING_AUTO_SUMMARY env var is true, summary is added."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = False
+            os.environ["LITELLM_REASONING_AUTO_SUMMARY"] = "true"
+            completion_kwargs = {
+                "model": "responses/gpt-5.2",
+                "custom_llm_provider": "openai",
+                "reasoning_effort": "high",
+            }
+            LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+                completion_kwargs, thinking={"type": "enabled", "budget_tokens": 10000}
+            )
+            assert completion_kwargs["reasoning_effort"] == {
+                "effort": "high",
+                "summary": "detailed",
+            }
+        finally:
+            litellm.reasoning_auto_summary = original
+            os.environ.pop("LITELLM_REASONING_AUTO_SUMMARY", None)
+
+    def test_user_provided_summary_preserved_even_when_flag_off(self):
+        """When user already set summary in dict reasoning_effort, it's preserved regardless of flag."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = False
+            completion_kwargs = {
+                "model": "responses/gpt-5.2",
+                "custom_llm_provider": "openai",
+                "reasoning_effort": {"effort": "high", "summary": "concise"},
+            }
+            LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+                completion_kwargs, thinking={"type": "enabled", "budget_tokens": 10000}
+            )
+            assert completion_kwargs["reasoning_effort"]["summary"] == "concise"
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_openai_model_with_thinking_summary_end_to_end(self):
+        """End-to-end: anthropic_messages_handler should preserve thinking.summary for OpenAI models."""
+        from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
+            anthropic_messages_handler,
+        )
+
+        with patch("litellm.responses", return_value="test-response") as mock_responses:
+            try:
+                anthropic_messages_handler(
+                    max_tokens=1024,
+                    messages=[{"role": "user", "content": "What is 2+2?"}],
+                    model="openai/gpt-5.2",
+                    api_key="test-api-key",
+                    thinking={
+                        "type": "enabled",
+                        "budget_tokens": 5000,
+                        "summary": "concise",
+                    },
+                )
+            except (ValueError, TypeError, AttributeError):
+                pass
+
+            mock_responses.assert_called_once()
+            call_kwargs = mock_responses.call_args.kwargs
+            reasoning = call_kwargs["reasoning"]
+            assert (
+                reasoning["summary"] == "concise"
+            ), f"Expected summary='concise', got summary='{reasoning.get('summary')}'"
+
+    def test_responses_adapter_preserves_summary(self):
+        """translate_thinking_to_reasoning should include summary when user provides it."""
+        from litellm.llms.anthropic.experimental_pass_through.responses_adapters.transformation import (
+            LiteLLMAnthropicToResponsesAPIAdapter,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"}
+        result = LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(
+            thinking
+        )
+        assert result == {"effort": "medium", "summary": "concise"}
+
+    def test_responses_adapter_no_summary_by_default(self):
+        """translate_thinking_to_reasoning should not include summary by default (opt-in)."""
+        import litellm
+        from litellm.llms.anthropic.experimental_pass_through.responses_adapters.transformation import (
+            LiteLLMAnthropicToResponsesAPIAdapter,
+        )
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = False
+            thinking = {"type": "enabled", "budget_tokens": 5000}
+            result = (
+                LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(
+                    thinking
+                )
+            )
+            assert result == {"effort": "medium"}
+            assert result is not None and "summary" not in result
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_translate_thinking_for_model_preserves_summary(self):
+        """translate_thinking_for_model should include summary in reasoning_effort dict when user provides it."""
+        from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
+            LiteLLMAnthropicMessagesAdapter,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"}
+        result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model(
+            thinking=thinking,
+            model="openai/gpt-5.2",
+        )
+        assert result == {
+            "reasoning_effort": {"effort": "medium", "summary": "concise"}
+        }
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py
index 252ba230ff7..02b817cd334 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py
@@ -170,6 +170,7 @@ class TestOutputConfigStructuredOutput:
 # translate_messages_to_responses_input
 # ---------------------------------------------------------------------------
 
+
 # Helper: cast plain dicts to the expected type so call sites stay clean.
 def _translate_messages(messages: List[Any]) -> List[Dict[str, Any]]:
     return _ADAPTER.translate_messages_to_responses_input(messages)  # type: ignore[arg-type]
@@ -274,7 +275,11 @@ class TestTranslateMessagesToResponsesInput:
                 "content": [
                     {
                         "type": "image",
-                        "source": {"type": "base64", "media_type": "image/jpeg", "data": ""},
+                        "source": {
+                            "type": "base64",
+                            "media_type": "image/jpeg",
+                            "data": "",
+                        },
                     }
                 ],
             }
@@ -462,7 +467,10 @@ class TestTranslateMessagesToResponsesInput:
         ]
         result = _translate_messages(messages)
         assert len(result) == 1
-        assert result[0]["content"][0] == {"type": "input_text", "text": "Describe this image:"}
+        assert result[0]["content"][0] == {
+            "type": "input_text",
+            "text": "Describe this image:",
+        }
         assert result[0]["content"][1] == {
             "type": "input_image",
             "image_url": "https://example.com/cat.jpg",
@@ -606,7 +614,9 @@ class TestTranslateThinkingToReasoning:
         result = _ADAPTER.translate_thinking_to_reasoning(
             {"type": "enabled", "budget_tokens": 10000}
         )
-        assert result == {"effort": "high", "summary": "detailed"}
+        # Default (reasoning_auto_summary=False): only effort, no summary
+        assert result == {"effort": "high"}
+        assert result is not None and "summary" not in result
 
     def test_budget_above_threshold_high_effort(self):
         result = _ADAPTER.translate_thinking_to_reasoning(
@@ -614,24 +624,28 @@ class TestTranslateThinkingToReasoning:
         )
         assert result is not None
         assert result["effort"] == "high"
+        assert "summary" not in result
 
     def test_budget_medium_effort(self):
         result = _ADAPTER.translate_thinking_to_reasoning(
             {"type": "enabled", "budget_tokens": 7500}
         )
-        assert result == {"effort": "medium", "summary": "detailed"}
+        assert result == {"effort": "medium"}
+        assert result is not None and "summary" not in result
 
     def test_budget_low_effort(self):
         result = _ADAPTER.translate_thinking_to_reasoning(
             {"type": "enabled", "budget_tokens": 3000}
         )
-        assert result == {"effort": "low", "summary": "detailed"}
+        assert result == {"effort": "low"}
+        assert result is not None and "summary" not in result
 
     def test_budget_minimal_effort(self):
         result = _ADAPTER.translate_thinking_to_reasoning(
             {"type": "enabled", "budget_tokens": 500}
         )
-        assert result == {"effort": "minimal", "summary": "detailed"}
+        assert result == {"effort": "minimal"}
+        assert result is not None and "summary" not in result
 
     def test_budget_at_exact_thresholds(self):
         result_medium = _ADAPTER.translate_thinking_to_reasoning(
@@ -639,11 +653,13 @@ class TestTranslateThinkingToReasoning:
         )
         assert result_medium is not None
         assert result_medium["effort"] == "medium"
+        assert "summary" not in result_medium
         result_low = _ADAPTER.translate_thinking_to_reasoning(
             {"type": "enabled", "budget_tokens": 2000}
         )
         assert result_low is not None
         assert result_low["effort"] == "low"
+        assert "summary" not in result_low
 
     def test_disabled_type_returns_none(self):
         result = _ADAPTER.translate_thinking_to_reasoning({"type": "disabled"})
@@ -656,7 +672,38 @@ class TestTranslateThinkingToReasoning:
     def test_missing_budget_defaults_to_minimal(self):
         """Missing budget_tokens defaults to 0, which is < 2000 -> minimal."""
         result = _ADAPTER.translate_thinking_to_reasoning({"type": "enabled"})
-        assert result == {"effort": "minimal", "summary": "detailed"}
+        assert result == {"effort": "minimal"}
+        assert result is not None and "summary" not in result
+
+    def test_summary_added_when_auto_summary_enabled(self):
+        """When reasoning_auto_summary is True, summary='detailed' is included."""
+        import litellm
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = True
+            result = _ADAPTER.translate_thinking_to_reasoning(
+                {"type": "enabled", "budget_tokens": 10000}
+            )
+            assert result == {"effort": "high", "summary": "detailed"}
+        finally:
+            litellm.reasoning_auto_summary = original
+
+    def test_summary_added_when_env_var_set(self):
+        """When LITELLM_REASONING_AUTO_SUMMARY env var is true, summary is included."""
+        import litellm
+
+        original = litellm.reasoning_auto_summary
+        try:
+            litellm.reasoning_auto_summary = False
+            os.environ["LITELLM_REASONING_AUTO_SUMMARY"] = "true"
+            result = _ADAPTER.translate_thinking_to_reasoning(
+                {"type": "enabled", "budget_tokens": 5000}
+            )
+            assert result == {"effort": "medium", "summary": "detailed"}
+        finally:
+            litellm.reasoning_auto_summary = original
+            os.environ.pop("LITELLM_REASONING_AUTO_SUMMARY", None)
 
 
 # ---------------------------------------------------------------------------
@@ -715,7 +762,9 @@ class TestTranslateRequestBroaderCoverage:
 
     def test_tools_translated(self):
         req = _make_request(
-            tools=[{"name": "calculator", "description": "Does math.", "input_schema": {}}]
+            tools=[
+                {"name": "calculator", "description": "Does math.", "input_schema": {}}
+            ]
         )
         kwargs = _ADAPTER.translate_request(req)
         assert len(kwargs["tools"]) == 1
@@ -732,7 +781,9 @@ class TestTranslateRequestBroaderCoverage:
     def test_thinking_translated_to_reasoning(self):
         req = _make_request(thinking={"type": "enabled", "budget_tokens": 12000})
         kwargs = _ADAPTER.translate_request(req)
-        assert kwargs["reasoning"] == {"effort": "high", "summary": "detailed"}
+        # reasoning_auto_summary is False by default, so no summary key
+        assert kwargs["reasoning"] == {"effort": "high"}
+        assert "summary" not in kwargs["reasoning"]
 
     def test_disabled_thinking_not_included_in_kwargs(self):
         req = _make_request(thinking={"type": "disabled"})
@@ -753,8 +804,17 @@ class TestTranslateRequestBroaderCoverage:
     def test_no_optional_fields_does_not_add_spurious_keys(self):
         req = _make_request()
         kwargs = _ADAPTER.translate_request(req)
-        for key in ("instructions", "temperature", "top_p", "tools", "tool_choice",
-                    "reasoning", "text", "context_management", "user"):
+        for key in (
+            "instructions",
+            "temperature",
+            "top_p",
+            "tools",
+            "tool_choice",
+            "reasoning",
+            "text",
+            "context_management",
+            "user",
+        ):
             assert key not in kwargs, f"unexpected key: {key}"
 
 
@@ -801,9 +861,7 @@ def _make_output_message(texts: List[str]) -> MagicMock:
     return msg
 
 
-def _make_function_call_item(
-    call_id: str, name: str, arguments: str
-) -> MagicMock:
+def _make_function_call_item(call_id: str, name: str, arguments: str) -> MagicMock:
     """Build a mock ResponseFunctionToolCall."""
     from openai.types.responses import ResponseFunctionToolCall  # type: ignore[import]
 
diff --git a/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py b/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py
index 635359563ba..28ccf7ffa8f 100644
--- a/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py
+++ b/tests/test_litellm/llms/azure/chat/test_azure_gpt5_transformation.py
@@ -192,10 +192,11 @@ def test_azure_gpt5_1_series_temperature_handling(config: AzureOpenAIGPT5Config)
     assert params["temperature"] == 0.6
 
 
-def test_azure_gpt5_4_drops_reasoning_effort_when_tools_present(config: AzureOpenAIGPT5Config):
-    """Azure Chat Completions: gpt-5.4+ drops reasoning_effort when tools are present.
+def test_azure_gpt5_4_preserves_reasoning_effort_when_tools_present(config: AzureOpenAIGPT5Config):
+    """Azure GPT-5.4+ no longer drops reasoning_effort when tools are present.
 
-    OpenAI routes tools+reasoning to Responses API; Azure does not, so we drop reasoning_effort.
+    Both OpenAI and Azure now route tools+reasoning to the Responses API bridge,
+    so reasoning_effort must be preserved in map_openai_params.
     """
     tools = [{"type": "function", "function": {"name": "test", "description": "test"}}]
     params = config.map_openai_params(
@@ -205,7 +206,7 @@ def test_azure_gpt5_4_drops_reasoning_effort_when_tools_present(config: AzureOpe
         drop_params=False,
         api_version="2024-05-01-preview",
     )
-    assert "reasoning_effort" not in params
+    assert params.get("reasoning_effort") == "high"
     assert params["tools"] == tools
 
 
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 5d5aaa64c8e..29265bb4b42 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
@@ -110,6 +110,35 @@ def test_get_supported_openai_params_reasoning_effort():
     assert "reasoning_effort" not in unsupported_params
 
 
+def test_add_transform_inline_image_block_skips_data_urls():
+    """
+    data: URLs must not have #transform=inline appended — doing so corrupts the
+    base64 payload and raises binascii.Error: Incorrect padding on the Fireworks side.
+    Regression test for https://github.com/BerriAI/litellm/issues/23583
+    """
+    config = FireworksAIConfig()
+    data_url = "data:image/jpeg;base64,/9j/4AAQSkZJRgAB"
+
+    # str branch
+    str_content = {"type": "image_url", "image_url": data_url}
+    result = config._add_transform_inline_image_block(
+        str_content, model="gpt-4", disable_add_transform_inline_image_block=False
+    )
+    assert result["image_url"] == data_url, "data URL must not be modified (str branch)"
+
+    # dict branch
+    dict_content = {"type": "image_url", "image_url": {"url": data_url}}
+    result = config._add_transform_inline_image_block(
+        dict_content, model="gpt-4", disable_add_transform_inline_image_block=False
+    )
+    assert result["image_url"]["url"] == data_url, "data URL must not be modified (dict branch)"
+
+    # regular https URL should still get the suffix
+    https_content = {"type": "image_url", "image_url": "https://example.com/image.jpg"}
+    result = config._add_transform_inline_image_block(
+        https_content, model="gpt-4", disable_add_transform_inline_image_block=False
+    )
+    assert result["image_url"].endswith("#transform=inline"), "https URL should get #transform=inline"
 @pytest.mark.parametrize(
     "api_base, expected_url_prefix",
     [
diff --git a/tests/test_litellm/llms/gemini/files/test_gemini_files_transformation.py b/tests/test_litellm/llms/gemini/files/test_gemini_files_transformation.py
index a5f72fc08c3..6cc97cd95e6 100644
--- a/tests/test_litellm/llms/gemini/files/test_gemini_files_transformation.py
+++ b/tests/test_litellm/llms/gemini/files/test_gemini_files_transformation.py
@@ -3,10 +3,10 @@ Test Google AI Studio (Gemini) files transformation functionality
 """
 
 import os
-import pytest
 from unittest.mock import Mock, patch
 
 import httpx
+import pytest
 
 from litellm.llms.gemini.files.transformation import GoogleAIStudioFilesHandler
 from litellm.types.llms.openai import OpenAIFileObject
@@ -23,7 +23,7 @@ class TestGoogleAIStudioFilesTransformation:
         """
         Test that transform_retrieve_file_request returns empty params dict
         to avoid 'Content-Type' query parameter error
-        
+
         Regression test for: https://github.com/BerriAI/litellm/issues/XXX
         When retrieving a file, the API was incorrectly trying to pass Content-Type
         as a query parameter, which Gemini API rejected.
@@ -37,14 +37,19 @@ class TestGoogleAIStudioFilesTransformation:
             litellm_params=litellm_params,
         )
 
-        # Verify URL is constructed correctly with API key
-        assert "key=test-api-key" in url
-        assert file_id in url
+        # Verify URL is constructed exactly as required:
+        # https://generativelanguage.googleapis.com/v1beta/files/{file_id}?key=API_KEY
+        assert (
+            url
+            == "https://generativelanguage.googleapis.com/v1beta/files/test123?key=test-api-key"
+        )
 
         # CRITICAL: params should be empty dict, not contain Content-Type or any other params
         # These would be incorrectly interpreted as query parameters
         assert params == {}, f"Expected empty params dict, got: {params}"
-        assert "Content-Type" not in params, "Content-Type should not be in query params"
+        assert (
+            "Content-Type" not in params
+        ), "Content-Type should not be in query params"
 
     def test_transform_retrieve_file_request_with_file_name_only(self):
         """
@@ -59,17 +64,44 @@ class TestGoogleAIStudioFilesTransformation:
             litellm_params=litellm_params,
         )
 
-        # Verify URL is constructed correctly
-        assert "generativelanguage.googleapis.com" in url
-        assert file_id in url
-        assert "key=test-api-key" in url
+        # Verify URL is constructed exactly as required:
+        # https://generativelanguage.googleapis.com/v1beta/files/{file_id}?key=API_KEY
+        assert (
+            url
+            == "https://generativelanguage.googleapis.com/v1beta/files/test123?key=test-api-key"
+        )
 
         # CRITICAL: params should be empty dict
         assert params == {}, f"Expected empty params dict, got: {params}"
-        assert "Content-Type" not in params, "Content-Type should not be in query params"
+        assert (
+            "Content-Type" not in params
+        ), "Content-Type should not be in query params"
 
-    @patch.dict('os.environ', {}, clear=True)
-    @patch('litellm.llms.gemini.common_utils.get_secret_str', return_value=None)
+    def test_transform_retrieve_file_request_with_raw_id_only(self):
+        """
+        Regression guard for the exact retrieval URL format.
+
+        If someone changes the method and stops producing:
+        https://generativelanguage.googleapis.com/v1beta/files/{file_id}?key=API_KEY
+        this test should fail.
+        """
+        file_id = "cctqueckiggb"
+        litellm_params = {"api_key": "test-api-key"}
+
+        url, params = self.handler.transform_retrieve_file_request(
+            file_id=file_id,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        assert (
+            url
+            == "https://generativelanguage.googleapis.com/v1beta/files/cctqueckiggb?key=test-api-key"
+        )
+        assert params == {}
+
+    @patch.dict("os.environ", {}, clear=True)
+    @patch("litellm.llms.gemini.common_utils.get_secret_str", return_value=None)
     def test_transform_retrieve_file_request_missing_api_key(self, mock_get_secret):
         """Test that transform_retrieve_file_request raises error when API key is missing"""
         file_id = "files/test123"
@@ -178,7 +210,7 @@ class TestGoogleAIStudioFilesTransformation:
     def test_transform_retrieve_file_response_missing_createTime(self):
         """
         Test that transform_retrieve_file_response raises proper error when createTime is missing
-        
+
         This tests the error scenario that occurs when API returns an error response
         without the expected file metadata fields.
         """
@@ -221,14 +253,15 @@ class TestGoogleAIStudioFilesTransformation:
         assert "x-goog-api-key" in result_headers
         assert result_headers["x-goog-api-key"] == api_key
 
-    @patch.dict('os.environ', {}, clear=True)
-    @patch('litellm.llms.gemini.common_utils.get_secret_str', return_value=None)
+    @patch.dict("os.environ", {}, clear=True)
+    @patch("litellm.llms.gemini.common_utils.get_secret_str", return_value=None)
     def test_validate_environment_missing_api_key(self, mock_get_secret):
         """Test that validate_environment raises error when API key is missing"""
         headers = {}
 
         with pytest.raises(
-            ValueError, match="GEMINI_API_KEY is required for Google AI Studio file operations"
+            ValueError,
+            match="GEMINI_API_KEY is required for Google AI Studio file operations",
         ):
             self.handler.validate_environment(
                 headers=headers,
@@ -243,7 +276,7 @@ class TestGoogleAIStudioFilesTransformation:
         """Test that get_complete_url constructs proper upload URL"""
         api_base = "https://generativelanguage.googleapis.com"
         api_key = "test-api-key"
-        
+
         url = self.handler.get_complete_url(
             api_base=api_base,
             api_key=api_key,
@@ -274,7 +307,7 @@ class TestGoogleAIStudioFilesTransformation:
         # Verify URL extraction
         assert "files/test123" in url
         assert "generativelanguage.googleapis.com" in url
-        
+
         # Params should be empty (API key goes in header via validate_environment)
         assert params == {}
 
diff --git a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py
index 69741cdec6f..cc0a32d2ce6 100644
--- a/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py
+++ b/tests/test_litellm/llms/gemini/realtime/test_gemini_realtime_transformation.py
@@ -10,6 +10,7 @@ sys.path.insert(
     0, os.path.abspath("../../../../..")
 )  # Adds the parent directory to the system path
 
+import litellm
 from litellm.llms.gemini.realtime.transformation import GeminiRealtimeConfig
 from litellm.types.llms.openai import OpenAIRealtimeStreamSessionEvents
 
@@ -227,3 +228,17 @@ def test_gemini_realtime_transformation_generation_complete():
             contains_audio_delta = True
             break
     assert contains_audio_delta, "Expected audio delta event"
+
+
+def test_gemini_3_1_flash_live_preview_model_cost_map_entry():
+    for key in (
+        "gemini-3.1-flash-live-preview",
+        "gemini/gemini-3.1-flash-live-preview",
+    ):
+        assert key in litellm.model_cost
+        info = litellm.model_cost[key]
+        assert "/v1/realtime" in info.get("supported_endpoints", [])
+        assert info.get("max_input_tokens") == 131072
+        assert info.get("max_output_tokens") == 65536
+        assert "video" in info.get("supported_modalities", [])
+        assert info.get("supports_function_calling") is True
diff --git a/tests/test_litellm/llms/mistral/audio_transcription/test_mistral_audio_transcription_transformation.py b/tests/test_litellm/llms/mistral/audio_transcription/test_mistral_audio_transcription_transformation.py
index 7ef50dede0c..4ca3e8ae0c7 100644
--- a/tests/test_litellm/llms/mistral/audio_transcription/test_mistral_audio_transcription_transformation.py
+++ b/tests/test_litellm/llms/mistral/audio_transcription/test_mistral_audio_transcription_transformation.py
@@ -158,6 +158,50 @@ def test_mistral_audio_transcription_response_transform():
     assert response.text == "Four score and seven years ago..."
 
 
+def test_mistral_audio_transcription_response_transform_diarized():
+    """Test that diarized responses preserve segments and language."""
+    config = MistralAudioTranscriptionConfig()
+
+    mock_response = MagicMock(spec=httpx.Response)
+    mock_response.json.return_value = {
+        "model": "voxtral-mini-latest",
+        "text": "Hello, how are you? I am fine.",
+        "language": None,
+        "segments": [
+            {
+                "text": "Hello, how are you?",
+                "start": 0.3,
+                "end": 2.1,
+                "speaker_id": "speaker_1",
+                "type": "transcription_segment",
+            },
+            {
+                "text": "I am fine.",
+                "start": 2.5,
+                "end": 3.8,
+                "speaker_id": "speaker_2",
+                "type": "transcription_segment",
+            },
+        ],
+        "usage": {
+            "prompt_audio_seconds": 4,
+            "prompt_tokens": 5,
+            "total_tokens": 50,
+            "completion_tokens": 20,
+        },
+    }
+
+    response = config.transform_audio_transcription_response(mock_response)
+
+    assert isinstance(response, TranscriptionResponse)
+    assert response.text == "Hello, how are you? I am fine."
+    assert response["segments"] is not None
+    assert len(response["segments"]) == 2
+    assert response["segments"][0]["speaker_id"] == "speaker_1"
+    assert response["segments"][1]["speaker_id"] == "speaker_2"
+    assert response["language"] is None
+
+
 def test_mistral_audio_transcription_response_transform_empty():
     config = MistralAudioTranscriptionConfig()
 
diff --git a/tests/test_litellm/llms/openai/test_gpt5_transformation.py b/tests/test_litellm/llms/openai/test_gpt5_transformation.py
index 47ae3c44c9e..aebab33e808 100644
--- a/tests/test_litellm/llms/openai/test_gpt5_transformation.py
+++ b/tests/test_litellm/llms/openai/test_gpt5_transformation.py
@@ -1,8 +1,10 @@
 import pytest
 
 import litellm
+from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
 from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config
 from litellm.llms.openai.openai import OpenAIConfig
+from litellm.utils import _is_explicitly_disabled_factory
 
 
 @pytest.fixture()
@@ -15,15 +17,23 @@ def gpt5_config() -> OpenAIGPT5Config:
     return OpenAIGPT5Config()
 
 
+@pytest.fixture(autouse=True)
+def use_local_model_cost_map(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    monkeypatch.setattr(
+        litellm, "model_cost", get_model_cost_map(url=litellm.model_cost_map_url)
+    )
+    litellm.add_known_models(model_cost_map=litellm.model_cost)
+
+
 def test_gpt5_supports_reasoning_effort(config: OpenAIConfig):
     assert "reasoning_effort" in config.get_supported_openai_params(model="gpt-5")
     assert "reasoning_effort" in config.get_supported_openai_params(model="gpt-5-mini")
 
 
 def test_gpt5_chat_does_not_support_reasoning_effort(config: OpenAIConfig):
-    assert (
-        "reasoning_effort"
-        not in config.get_supported_openai_params(model="gpt-5-chat-latest")
+    assert "reasoning_effort" not in config.get_supported_openai_params(
+        model="gpt-5-chat-latest"
     )
 
 
@@ -132,7 +142,6 @@ def test_gpt5_codex_temperature_error(config: OpenAIConfig):
         )
 
 
-
 def test_gpt5_codex_temperature_one_allowed(config: OpenAIConfig):
     """Test that GPT-5-Codex allows temperature=1."""
     params = config.map_openai_params(
@@ -198,6 +207,8 @@ def test_gpt5_verbosity_parameter(config: OpenAIConfig):
             drop_params=False,
         )
         assert params["verbosity"] == "low"
+
+
 def test_gpt5_1_reasoning_effort_none(config: OpenAIConfig):
     """Test that GPT-5.1 supports reasoning_effort='none' parameter.
 
@@ -270,7 +281,9 @@ def test_gpt5_1_model_detection(gpt5_config: OpenAIGPT5Config):
     # codex/pro/chat variants do not support none
     assert not gpt5_config._supports_reasoning_effort_level("gpt-5.1-codex", "none")
     assert not gpt5_config._supports_reasoning_effort_level("gpt-5.1-codex-max", "none")
-    assert not gpt5_config._supports_reasoning_effort_level("gpt-5.2-chat-latest", "none")
+    assert not gpt5_config._supports_reasoning_effort_level(
+        "gpt-5.2-chat-latest", "none"
+    )
     assert not gpt5_config._supports_reasoning_effort_level("gpt-5.2-pro", "none")
     assert not gpt5_config._supports_reasoning_effort_level("gpt-5", "none")
     assert not gpt5_config._supports_reasoning_effort_level("gpt-5-mini", "none")
@@ -324,10 +337,211 @@ def test_gpt5_4_pro_allows_reasoning_effort_xhigh(config: OpenAIConfig):
     assert params["reasoning_effort"] == "xhigh"
 
 
+def test_gpt5_4_mini_allows_reasoning_effort_xhigh(config: OpenAIConfig):
+    """gpt-5.4-mini supports reasoning_effort='xhigh'."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "xhigh"},
+        optional_params={},
+        model="gpt-5.4-mini",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "xhigh"
+
+
+def test_gpt5_4_nano_allows_reasoning_effort_xhigh(config: OpenAIConfig):
+    """gpt-5.4-nano supports reasoning_effort='xhigh'."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "xhigh"},
+        optional_params={},
+        model="gpt-5.4-nano",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "xhigh"
+
+
+def test_gpt5_4_nano_allows_reasoning_effort_none(config: OpenAIConfig):
+    """gpt-5.4-nano supports reasoning_effort='none'."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "none"},
+        optional_params={},
+        model="gpt-5.4-nano",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "none"
+
+
+def test_gpt5_4_mini_allows_reasoning_effort_none(config: OpenAIConfig):
+    """gpt-5.4-mini supports reasoning_effort='none'."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "none"},
+        optional_params={},
+        model="gpt-5.4-mini",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "none"
+
+
+def test_gpt5_4_allows_reasoning_effort_minimal(config: OpenAIConfig):
+    """gpt-5.4 supports reasoning_effort='minimal'."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "minimal"},
+        optional_params={},
+        model="gpt-5.4",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "minimal"
+
+
+def test_gpt5_4_pro_allows_reasoning_effort_minimal(config: OpenAIConfig):
+    """gpt-5.4-pro supports reasoning_effort='minimal'."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "minimal"},
+        optional_params={},
+        model="gpt-5.4-pro",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "minimal"
+
+
+def test_gpt5_4_mini_rejects_reasoning_effort_minimal(config: OpenAIConfig):
+    """gpt-5.4-mini does not support reasoning_effort='minimal'."""
+    with pytest.raises(litellm.utils.UnsupportedParamsError):
+        config.map_openai_params(
+            non_default_params={"reasoning_effort": "minimal"},
+            optional_params={},
+            model="gpt-5.4-mini",
+            drop_params=False,
+        )
+
+
+def test_gpt5_4_nano_rejects_reasoning_effort_minimal(config: OpenAIConfig):
+    """gpt-5.4-nano does not support reasoning_effort='minimal'."""
+    with pytest.raises(litellm.utils.UnsupportedParamsError):
+        config.map_openai_params(
+            non_default_params={"reasoning_effort": "minimal"},
+            optional_params={},
+            model="gpt-5.4-nano",
+            drop_params=False,
+        )
+
+
+def test_gpt5_4_mini_provider_prefixed_rejects_minimal(config: OpenAIConfig):
+    """openai/gpt-5.4-mini correctly rejects minimal (model lookup normalizes prefix)."""
+    with pytest.raises(litellm.utils.UnsupportedParamsError):
+        config.map_openai_params(
+            non_default_params={"reasoning_effort": "minimal"},
+            optional_params={},
+            model="openai/gpt-5.4-mini",
+            drop_params=False,
+        )
+
+
+def test_gpt5_drops_reasoning_effort_minimal_when_requested(config: OpenAIConfig):
+    """reasoning_effort='minimal' is dropped for unsupported models when drop_params=True."""
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "minimal"},
+        optional_params={},
+        model="gpt-5.4-mini",
+        drop_params=True,
+    )
+    assert "reasoning_effort" not in params
+
+
+def test_gpt5_minimal_dict_triggers_validation(config: OpenAIConfig):
+    """Dict with effort='minimal' triggers minimal model-support validation."""
+    with pytest.raises(litellm.utils.UnsupportedParamsError):
+        config.map_openai_params(
+            non_default_params={
+                "reasoning_effort": {"effort": "minimal", "summary": "detailed"}
+            },
+            optional_params={},
+            model="gpt-5.4-mini",
+            drop_params=False,
+        )
+
+
+def test_gpt5_minimal_dict_accepted_for_supported_model(config: OpenAIConfig):
+    """Dict with effort='minimal' passes through for gpt-5.4+."""
+    params = config.map_openai_params(
+        non_default_params={
+            "reasoning_effort": {"effort": "minimal", "summary": "detailed"}
+        },
+        optional_params={},
+        model="gpt-5.4",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "minimal"
+
+
+def test_gpt5_supports_reasoning_effort_level_minimal(gpt5_config: OpenAIGPT5Config):
+    """Test that _supports_reasoning_effort_level correctly identifies minimal support."""
+    assert gpt5_config._supports_reasoning_effort_level("gpt-5.4", "minimal")
+    assert gpt5_config._supports_reasoning_effort_level("gpt-5.4-pro", "minimal")
+    assert not gpt5_config._supports_reasoning_effort_level("gpt-5.4-mini", "minimal")
+    assert not gpt5_config._supports_reasoning_effort_level("gpt-5.4-nano", "minimal")
+
+
+def test_gpt5_minimal_explicitly_disabled_check(gpt5_config: OpenAIGPT5Config):
+    """_is_reasoning_effort_level_explicitly_disabled returns True only for explicit False entries.
+
+    Models with supports_minimal_reasoning_effort=false → disabled.
+    Models with supports_minimal_reasoning_effort=true (or missing) → not disabled.
+    Provider-prefixed models (openai/gpt-5.4-mini) are normalized before lookup.
+    """
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4-mini", "minimal"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4-nano", "minimal"
+    )
+    assert gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "openai/gpt-5.4-mini", "minimal"
+    )
+    assert not gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4", "minimal"
+    )
+    assert not gpt5_config._is_reasoning_effort_level_explicitly_disabled(
+        "gpt-5.4-pro", "minimal"
+    )
+
+
+def test_is_explicitly_disabled_factory_minimal():
+    """_is_explicitly_disabled_factory returns True only for explicit False entries.
+
+    Verifies the shared helper used by _is_reasoning_effort_level_explicitly_disabled
+    directly — so future changes to the helper are caught without going through the
+    method wrapper.
+    """
+    key = "supports_minimal_reasoning_effort"
+    assert _is_explicitly_disabled_factory("gpt-5.4-mini", None, key)
+    assert _is_explicitly_disabled_factory("gpt-5.4-nano", None, key)
+    assert _is_explicitly_disabled_factory("openai/gpt-5.4-mini", None, key)
+    assert not _is_explicitly_disabled_factory("gpt-5.4", None, key)
+    assert not _is_explicitly_disabled_factory("gpt-5.4-pro", None, key)
+    assert not _is_explicitly_disabled_factory("gpt-5.4-turbo-preview", None, key)
+
+
+def test_gpt5_unknown_model_passes_through_minimal(config: OpenAIConfig):
+    """Unknown/unlisted gpt-5 models should pass reasoning_effort='minimal' through.
+
+    Missing supports_minimal_reasoning_effort key is treated as supported,
+    not as unsupported, to avoid breaking custom or newly-announced models.
+    """
+    params = config.map_openai_params(
+        non_default_params={"reasoning_effort": "minimal"},
+        optional_params={},
+        model="gpt-5.4-turbo-preview",
+        drop_params=False,
+    )
+    assert params["reasoning_effort"] == "minimal"
+
+
 def test_gpt5_normalizes_reasoning_effort_dict_with_summary(config: OpenAIConfig):
     """Dict with summary/generate_summary is normalized for chat completions."""
     params = config.map_openai_params(
-        non_default_params={"reasoning_effort": {"effort": "high", "summary": "detailed"}},
+        non_default_params={
+            "reasoning_effort": {"effort": "high", "summary": "detailed"}
+        },
         optional_params={},
         model="gpt-5.4",
         drop_params=False,
@@ -343,7 +557,9 @@ def test_gpt5_xhigh_dict_triggers_validation(config: OpenAIConfig):
     """
     with pytest.raises(litellm.utils.UnsupportedParamsError):
         config.map_openai_params(
-            non_default_params={"reasoning_effort": {"effort": "xhigh", "summary": "detailed"}},
+            non_default_params={
+                "reasoning_effort": {"effort": "xhigh", "summary": "detailed"}
+            },
             optional_params={},
             model="gpt-5.1",
             drop_params=False,
@@ -353,7 +569,9 @@ def test_gpt5_xhigh_dict_triggers_validation(config: OpenAIConfig):
 def test_gpt5_xhigh_dict_accepted_for_supported_model(config: OpenAIConfig):
     """Dict with effort='xhigh' passes through for gpt-5.4+."""
     params = config.map_openai_params(
-        non_default_params={"reasoning_effort": {"effort": "xhigh", "summary": "detailed"}},
+        non_default_params={
+            "reasoning_effort": {"effort": "xhigh", "summary": "detailed"}
+        },
         optional_params={},
         model="gpt-5.4",
         drop_params=False,
@@ -369,7 +587,10 @@ def test_gpt5_none_dict_with_tools_no_tool_drop(config: OpenAIConfig):
     """
     tools = [{"type": "function", "function": {"name": "test", "description": "test"}}]
     params = config.map_openai_params(
-        non_default_params={"reasoning_effort": {"effort": "none", "summary": "detailed"}, "tools": tools},
+        non_default_params={
+            "reasoning_effort": {"effort": "none", "summary": "detailed"},
+            "tools": tools,
+        },
         optional_params={},
         model="gpt-5.4",
         drop_params=False,
@@ -399,11 +620,15 @@ def test_gpt5_none_dict_with_sampling_params_allowed(config: OpenAIConfig):
     assert params["top_p"] == 0.9
 
 
-def test_gpt5_normalizes_reasoning_effort_dict_with_summary_from_optional_params(config: OpenAIConfig):
+def test_gpt5_normalizes_reasoning_effort_dict_with_summary_from_optional_params(
+    config: OpenAIConfig,
+):
     """reasoning_effort dict with summary in optional_params is normalized."""
     params = config.map_openai_params(
         non_default_params={},
-        optional_params={"reasoning_effort": {"effort": "medium", "summary": "detailed"}},
+        optional_params={
+            "reasoning_effort": {"effort": "medium", "summary": "detailed"}
+        },
         model="gpt-5.4",
         drop_params=False,
     )
@@ -476,7 +701,7 @@ def test_gpt5_4_pro_rejects_non_default_temperature(config: OpenAIConfig):
 
 def test_gpt5_1_temperature_without_reasoning_effort(config: OpenAIConfig):
     """Test that GPT-5.1 supports any temperature when reasoning_effort is not specified.
-    
+
     When reasoning_effort is not provided, it defaults to "none" for gpt-5.1,
     so temperature should be allowed.
     """
@@ -502,7 +727,7 @@ def test_gpt5_1_temperature_with_reasoning_effort_other_values(config: OpenAICon
                 model="gpt-5.1",
                 drop_params=False,
             )
-    
+
     # Test that temperature=1 is allowed with other reasoning_effort values
     for effort in ["low", "medium", "high"]:
         params = config.map_openai_params(
@@ -515,7 +740,9 @@ def test_gpt5_1_temperature_with_reasoning_effort_other_values(config: OpenAICon
         assert params["reasoning_effort"] == effort
 
 
-def test_gpt5_1_temperature_with_reasoning_effort_in_optional_params(config: OpenAIConfig):
+def test_gpt5_1_temperature_with_reasoning_effort_in_optional_params(
+    config: OpenAIConfig,
+):
     """Test that reasoning_effort can be in optional_params and still work correctly."""
     # Test with reasoning_effort="none" in optional_params
     params = config.map_openai_params(
@@ -525,7 +752,7 @@ def test_gpt5_1_temperature_with_reasoning_effort_in_optional_params(config: Ope
         drop_params=False,
     )
     assert params["temperature"] == 0.5
-    
+
     # Test with reasoning_effort="low" in optional_params (should only allow temp=1)
     with pytest.raises(litellm.utils.UnsupportedParamsError):
         config.map_openai_params(
@@ -535,6 +762,7 @@ def test_gpt5_1_temperature_with_reasoning_effort_in_optional_params(config: Ope
             drop_params=False,
         )
 
+
 def test_gpt5_1_temperature_drop_when_not_none(config: OpenAIConfig):
     """Test that GPT-5.1 drops temperature when reasoning_effort != 'none' and drop_params=True."""
     params = config.map_openai_params(
@@ -557,7 +785,7 @@ def test_gpt5_temperature_still_restricted(config: OpenAIConfig):
             model="gpt-5",
             drop_params=False,
         )
-    
+
     # temperature=1 should still work for gpt-5
     params = config.map_openai_params(
         non_default_params={"temperature": 1.0},
@@ -650,7 +878,9 @@ def test_gpt5_search_supported_params(gpt5_config: OpenAIGPT5Config):
         "reasoning_effort",
     ]
     for param in rejected:
-        assert param not in supported, f"{param} should not be supported for search models"
+        assert (
+            param not in supported
+        ), f"{param} should not be supported for search models"
 
 
 def test_gpt5_search_has_expected_params(gpt5_config: OpenAIGPT5Config):
@@ -688,7 +918,11 @@ def test_gpt5_search_maps_max_tokens(config: OpenAIConfig):
 def test_gpt5_search_drops_unsupported_params(config: OpenAIConfig):
     """Test that search models drop unsupported params via map_openai_params."""
     params = config.map_openai_params(
-        non_default_params={"n": 2, "temperature": 0.7, "tools": [{"type": "function"}]},
+        non_default_params={
+            "n": 2,
+            "temperature": 0.7,
+            "tools": [{"type": "function"}],
+        },
         optional_params={},
         model="gpt-5-search-api",
         drop_params=True,
@@ -696,6 +930,8 @@ def test_gpt5_search_drops_unsupported_params(config: OpenAIConfig):
     assert "n" not in params
     assert "temperature" not in params
     assert "tools" not in params
+
+
 # GPT-5 unsupported params audit (validated via direct API calls)
 def test_gpt5_rejects_params_unsupported_by_openai(config: OpenAIConfig):
     """Params that OpenAI rejects for all GPT-5 reasoning models."""
@@ -709,9 +945,9 @@ def test_gpt5_rejects_params_unsupported_by_openai(config: OpenAIConfig):
     for model in ["gpt-5", "gpt-5-mini", "gpt-5-codex", "gpt-5.1", "gpt-5.2"]:
         supported = config.get_supported_openai_params(model=model)
         for param in rejected_params:
-            assert param not in supported, (
-                f"{param} should not be supported for {model}"
-            )
+            assert (
+                param not in supported
+            ), f"{param} should not be supported for {model}"
 
 
 def test_gpt5_1_supports_logprobs_top_p(config: OpenAIConfig):
@@ -720,16 +956,22 @@ def test_gpt5_1_supports_logprobs_top_p(config: OpenAIConfig):
         supported = config.get_supported_openai_params(model=model)
         assert "logprobs" in supported, f"logprobs should be supported for {model}"
         assert "top_p" in supported, f"top_p should be supported for {model}"
-        assert "top_logprobs" in supported, f"top_logprobs should be supported for {model}"
+        assert (
+            "top_logprobs" in supported
+        ), f"top_logprobs should be supported for {model}"
 
 
 def test_gpt5_base_does_not_support_logprobs_top_p(config: OpenAIConfig):
     """Base gpt-5/gpt-5-mini do NOT support logprobs, top_p, top_logprobs."""
     for model in ["gpt-5", "gpt-5-mini", "gpt-5-codex"]:
         supported = config.get_supported_openai_params(model=model)
-        assert "logprobs" not in supported, f"logprobs should not be supported for {model}"
+        assert (
+            "logprobs" not in supported
+        ), f"logprobs should not be supported for {model}"
         assert "top_p" not in supported, f"top_p should not be supported for {model}"
-        assert "top_logprobs" not in supported, f"top_logprobs should not be supported for {model}"
+        assert (
+            "top_logprobs" not in supported
+        ), f"top_logprobs should not be supported for {model}"
 
 
 def test_gpt5_1_logprobs_passthrough(config: OpenAIConfig):
@@ -788,4 +1030,4 @@ def test_gpt5_1_logprobs_dropped_with_reasoning_effort(config: OpenAIConfig):
     )
     assert "logprobs" not in params
     assert "top_p" not in params
-    assert params["reasoning_effort"] == "high"
\ No newline at end of file
+    assert params["reasoning_effort"] == "high"
diff --git a/tests/test_litellm/llms/openrouter/test_openrouter_provider_routing.py b/tests/test_litellm/llms/openrouter/test_openrouter_provider_routing.py
index 72cf2eec371..0815b15c873 100644
--- a/tests/test_litellm/llms/openrouter/test_openrouter_provider_routing.py
+++ b/tests/test_litellm/llms/openrouter/test_openrouter_provider_routing.py
@@ -80,7 +80,10 @@ class TestOpenRouterNativeModelRouting:
         "input_model,expected_model",
         [
             ("openrouter/anthropic/claude-3-haiku", "anthropic/claude-3-haiku"),
-            ("openrouter/meta-llama/llama-3-70b-instruct", "meta-llama/llama-3-70b-instruct"),
+            (
+                "openrouter/meta-llama/llama-3-70b-instruct",
+                "meta-llama/llama-3-70b-instruct",
+            ),
         ],
     )
     def test_regular_models_still_strip_normally(self, input_model, expected_model):
@@ -88,3 +91,12 @@ class TestOpenRouterNativeModelRouting:
         result_model, provider, _, _ = litellm.get_llm_provider(model=input_model)
         assert provider == "openrouter"
         assert result_model == expected_model
+
+    def test_wildcard_deployment_strips_routing_prefix(self):
+        """openrouter/* proxy deployments pass custom_llm_provider; strip LiteLLM prefix."""
+        result_model, provider, _, _ = litellm.get_llm_provider(
+            model="openrouter/anthropic/claude-3.5-sonnet",
+            custom_llm_provider="openrouter",
+        )
+        assert provider == "openrouter"
+        assert result_model == "anthropic/claude-3.5-sonnet"
diff --git a/tests/test_litellm/llms/test_file_search_responses.py b/tests/test_litellm/llms/test_file_search_responses.py
new file mode 100644
index 00000000000..1864a296eb6
--- /dev/null
+++ b/tests/test_litellm/llms/test_file_search_responses.py
@@ -0,0 +1,892 @@
+"""
+Unit tests for file_search / vector_store support in the Responses API.
+
+Coverage:
+  A1-A7  _decode_vector_store_ids_in_tools()
+  B1-B3  update_responses_tools_with_model_file_ids()
+  C1,D1  supports_native_file_search()
+  E1-E4  file_search guard in responses/main.py
+  F1-F6  ManagedFiles hook access control
+  G1-G3  get_vector_store_ids_from_file_search_tools()
+  H1-H14 emulated_handler unit tests
+"""
+
+import base64
+from typing import Any, Dict, List, Optional
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    _decode_vector_store_ids_in_tools,
+    update_responses_tools_with_model_file_ids,
+)
+from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
+from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_unified_vs_id(
+    unified_uuid: str = "abc-123",
+    provider_resource_id: str = "vs_provider_native",
+    model_id: str = "model-id-999",
+) -> str:
+    """Build a valid base64-encoded unified vector-store ID."""
+    raw = (
+        f"litellm_proxy:vector_store;"
+        f"unified_id,{unified_uuid};"
+        f"model_id,{model_id};"
+        f"provider_resource_id,{provider_resource_id}"
+    )
+    return base64.urlsafe_b64encode(raw.encode()).decode().rstrip("=")
+
+
+def _file_search_tool(vector_store_ids: Optional[List[str]] = None) -> Dict[str, Any]:
+    tool: Dict[str, Any] = {"type": "file_search"}
+    if vector_store_ids is not None:
+        tool["vector_store_ids"] = vector_store_ids
+    return tool
+
+
+def _code_interpreter_tool(file_ids: Optional[List[str]] = None) -> Dict[str, Any]:
+    tool: Dict[str, Any] = {"type": "code_interpreter"}
+    if file_ids:
+        tool["container"] = {"type": "auto", "file_ids": file_ids}
+    return tool
+
+
+# ---------------------------------------------------------------------------
+# A-series: _decode_vector_store_ids_in_tools
+# ---------------------------------------------------------------------------
+
+class TestDecodeVectorStoreIdsInTools:
+    def test_A1_none_input_returns_none(self):
+        assert _decode_vector_store_ids_in_tools(None) is None
+
+    def test_A2_no_file_search_tools_unchanged(self):
+        tools = [{"type": "web_search"}, {"type": "code_interpreter"}]
+        result = _decode_vector_store_ids_in_tools(tools)
+        assert result == tools
+
+    def test_A3_file_search_no_vector_store_ids_unchanged(self):
+        tools = [_file_search_tool()]  # no vector_store_ids key
+        result = _decode_vector_store_ids_in_tools(tools)
+        assert result == tools
+
+    def test_A4_unified_id_decoded_to_provider_resource_id(self):
+        unified_id = _make_unified_vs_id(provider_resource_id="vs_real_123")
+        tools = [_file_search_tool([unified_id])]
+        result = _decode_vector_store_ids_in_tools(tools)
+        assert result is not None
+        assert result[0]["vector_store_ids"] == ["vs_real_123"]
+
+    def test_A5_native_id_passes_through_unchanged(self):
+        native_id = "vs_openai_abc"
+        tools = [_file_search_tool([native_id])]
+        result = _decode_vector_store_ids_in_tools(tools)
+        assert result is not None
+        assert result[0]["vector_store_ids"] == ["vs_openai_abc"]
+
+    def test_A6_mixed_unified_and_native_ids(self):
+        unified_id = _make_unified_vs_id(provider_resource_id="vs_decoded")
+        native_id = "vs_native_xyz"
+        tools = [_file_search_tool([unified_id, native_id])]
+        result = _decode_vector_store_ids_in_tools(tools)
+        assert result is not None
+        assert result[0]["vector_store_ids"] == ["vs_decoded", "vs_native_xyz"]
+
+    def test_A7_malformed_base64_passes_through_unchanged(self):
+        bad_id = "not_valid_base64!!!"
+        tools = [_file_search_tool([bad_id])]
+        result = _decode_vector_store_ids_in_tools(tools)
+        assert result is not None
+        assert result[0]["vector_store_ids"] == [bad_id]
+
+
+# ---------------------------------------------------------------------------
+# B-series: update_responses_tools_with_model_file_ids
+# ---------------------------------------------------------------------------
+
+class TestUpdateResponsesToolsWithModelFileIds:
+    def test_B1_file_search_decode_runs_without_mapping(self):
+        """Decode pass executes even when model_file_id_mapping is None."""
+        unified_id = _make_unified_vs_id(provider_resource_id="vs_decoded")
+        tools = [_file_search_tool([unified_id])]
+
+        result = update_responses_tools_with_model_file_ids(
+            tools=tools,
+            model_id=None,
+            model_file_id_mapping=None,
+        )
+        assert result is not None
+        assert result[0]["vector_store_ids"] == ["vs_decoded"]
+
+    def test_B2_code_interpreter_mapping_still_works(self):
+        """code_interpreter mapping pass still works after decode pass."""
+        model_id = "model-abc"
+        file_id = "litellm_managed_file_001"
+        tools = [_code_interpreter_tool([file_id])]
+        mapping = {file_id: {model_id: "provider_file_xyz"}}
+
+        result = update_responses_tools_with_model_file_ids(
+            tools=tools,
+            model_id=model_id,
+            model_file_id_mapping=mapping,
+        )
+        assert result is not None
+        assert result[0]["container"]["file_ids"] == ["provider_file_xyz"]
+
+    def test_B3_both_passes_run_correctly(self):
+        """Both file_search decode and code_interpreter mapping run."""
+        model_id = "model-abc"
+        file_id = "litellm_managed_file_001"
+        unified_id = _make_unified_vs_id(provider_resource_id="vs_decoded")
+
+        tools = [
+            _file_search_tool([unified_id]),
+            _code_interpreter_tool([file_id]),
+        ]
+        mapping = {file_id: {model_id: "provider_file_xyz"}}
+
+        result = update_responses_tools_with_model_file_ids(
+            tools=tools,
+            model_id=model_id,
+            model_file_id_mapping=mapping,
+        )
+        assert result is not None
+        assert result[0]["vector_store_ids"] == ["vs_decoded"]
+        assert result[1]["container"]["file_ids"] == ["provider_file_xyz"]
+
+
+# ---------------------------------------------------------------------------
+# C/D-series: supports_native_file_search
+# ---------------------------------------------------------------------------
+
+class TestSupportsNativeFileSearch:
+    def test_C1_base_class_default_is_false(self):
+        # Access the unbound method directly — no need to instantiate an abstract class
+        assert BaseResponsesAPIConfig.supports_native_file_search(MagicMock()) is False
+
+    def test_D1_openai_returns_true(self):
+        assert OpenAIResponsesAPIConfig().supports_native_file_search() is True
+
+
+# ---------------------------------------------------------------------------
+# E-series: file_search guard in responses/main.py
+# ---------------------------------------------------------------------------
+
+class TestFileSearchGuardInResponsesMain:
+    """Tests for _has_file_search_tool helper and emulated routing guard."""
+
+    def test_has_file_search_tool_true(self):
+        from litellm.responses.main import _has_file_search_tool
+
+        assert _has_file_search_tool([{"type": "file_search"}]) is True
+
+    def test_has_file_search_tool_false_empty(self):
+        from litellm.responses.main import _has_file_search_tool
+
+        assert _has_file_search_tool([]) is False
+        assert _has_file_search_tool(None) is False
+
+    def test_has_file_search_tool_false_other_tools(self):
+        from litellm.responses.main import _has_file_search_tool
+
+        assert _has_file_search_tool([{"type": "web_search"}]) is False
+
+    def test_E1_openai_provider_no_error(self):
+        """OpenAI supports file_search natively — no error raised."""
+        from litellm.llms.openai.responses.transformation import (
+            OpenAIResponsesAPIConfig,
+        )
+        from litellm.responses.main import _has_file_search_tool
+
+        config = OpenAIResponsesAPIConfig()
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc"]}]
+        assert _has_file_search_tool(tools)
+        assert config.supports_native_file_search()
+        # No exception expected — the guard would pass.
+
+    def test_E2_no_provider_config_routes_to_emulated_handler(self):
+        """Provider config None + file_search should route to emulated handler."""
+        from litellm.responses.main import responses
+
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc"]}]
+        logging_obj = MagicMock()
+        expected = {"ok": True}
+
+        with (
+            patch(
+                "litellm.responses.main.litellm.get_llm_provider",
+                return_value=("claude-sonnet-4-5", "anthropic", None, None),
+            ),
+            patch(
+                "litellm.responses.main.update_responses_input_with_model_file_ids",
+                return_value="hello",
+            ),
+            patch(
+                "litellm.responses.main.update_responses_tools_with_model_file_ids",
+                return_value=tools,
+            ),
+            patch(
+                "litellm.responses.main.ProviderConfigManager.get_provider_responses_api_config",
+                return_value=None,
+            ),
+            patch(
+                "litellm.responses.main.ResponsesAPIRequestUtils.get_requested_response_api_optional_param",
+                return_value={},
+            ),
+            patch("litellm.responses.main.run_async_function", return_value=expected) as run_async_mock,
+        ):
+            result = responses(
+                input="hello",
+                model="anthropic/claude-sonnet-4-5",
+                tools=tools,
+                litellm_logging_obj=logging_obj,
+                litellm_call_id="call-123",
+            )
+
+        assert result == expected
+        assert run_async_mock.called
+        routed_func = run_async_mock.call_args.args[0]
+        assert routed_func.__name__ == "aresponses_with_emulated_file_search"
+
+    def test_E3_non_native_provider_config_routes_to_emulated_handler(self):
+        """Non-native provider config + file_search should route to emulated handler."""
+        from litellm.llms.base_llm.responses.transformation import (
+            BaseResponsesAPIConfig,
+        )
+        from litellm.responses.main import responses
+
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc"]}]
+        logging_obj = MagicMock()
+        expected = {"ok": True}
+        mock_config = MagicMock(spec=BaseResponsesAPIConfig)
+        mock_config.supports_native_file_search.return_value = False
+
+        with (
+            patch(
+                "litellm.responses.main.litellm.get_llm_provider",
+                return_value=("claude-sonnet-4-5", "anthropic", None, None),
+            ),
+            patch(
+                "litellm.responses.main.update_responses_input_with_model_file_ids",
+                return_value="hello",
+            ),
+            patch(
+                "litellm.responses.main.update_responses_tools_with_model_file_ids",
+                return_value=tools,
+            ),
+            patch(
+                "litellm.responses.main.ProviderConfigManager.get_provider_responses_api_config",
+                return_value=mock_config,
+            ),
+            patch(
+                "litellm.responses.main.ResponsesAPIRequestUtils.get_requested_response_api_optional_param",
+                return_value={},
+            ),
+            patch("litellm.responses.main.run_async_function", return_value=expected) as run_async_mock,
+        ):
+            result = responses(
+                input="hello",
+                model="anthropic/claude-sonnet-4-5",
+                tools=tools,
+                litellm_logging_obj=logging_obj,
+                litellm_call_id="call-123",
+            )
+
+        assert result == expected
+        assert run_async_mock.called
+        routed_func = run_async_mock.call_args.args[0]
+        assert routed_func.__name__ == "aresponses_with_emulated_file_search"
+
+    def test_E4_no_file_search_tools_no_error(self):
+        """No file_search tool in request → guard never fires."""
+        from litellm.responses.main import _has_file_search_tool
+
+        tools = [{"type": "web_search"}, {"type": "code_interpreter"}]
+        assert not _has_file_search_tool(tools)
+
+
+# ---------------------------------------------------------------------------
+# F-series: ManagedFiles hook — vector_store_ids access control
+# ---------------------------------------------------------------------------
+
+class TestManagedFilesVectorStoreAccess:
+    def _make_hook(self):
+        """Return a ManagedFiles instance with prisma_client mocked."""
+        from enterprise.litellm_enterprise.proxy.hooks.managed_files import (
+            _PROXY_LiteLLMManagedFiles as ManagedFiles,
+        )
+
+        hook = ManagedFiles.__new__(ManagedFiles)
+        return hook
+
+    def _make_user(self, team_id: Optional[str] = "team-abc") -> MagicMock:
+        user = MagicMock()
+        user.team_id = team_id
+        user.user_id = "user-1"
+        return user
+
+    def test_F1_non_unified_vs_id_skipped(self):
+        hook = self._make_hook()
+        result = hook.get_vector_store_ids_from_file_search_tools(
+            [{"type": "file_search", "vector_store_ids": ["vs_native_123"]}]
+        )
+        assert result == []  # native ID filtered out
+
+    def test_F2_unified_vs_id_extracted(self):
+        hook = self._make_hook()
+        unified_id = _make_unified_vs_id()
+        result = hook.get_vector_store_ids_from_file_search_tools(
+            [{"type": "file_search", "vector_store_ids": [unified_id]}]
+        )
+        assert result == [unified_id]
+
+    def _make_vs_row(self, vector_store_id: str, team_id: Optional[str]) -> Any:
+        """Build a row compatible with get_managed_vector_store_rows_by_uuids (Prisma model_dump)."""
+        from litellm.proxy._types import LiteLLM_ManagedVectorStoresTable
+
+        return LiteLLM_ManagedVectorStoresTable(
+            vector_store_id=vector_store_id,
+            custom_llm_provider="openai",
+            vector_store_name=None,
+            vector_store_description=None,
+            vector_store_metadata=None,
+            created_at=None,
+            updated_at=None,
+            litellm_credential_name=None,
+            litellm_params=None,
+            team_id=team_id,
+            user_id=None,
+        )
+
+    @pytest.mark.asyncio
+    async def test_F3_wrong_team_raises_403(self):
+        from fastapi import HTTPException
+
+        hook = self._make_hook()
+        unified_id = _make_unified_vs_id(unified_uuid="uuid-001")
+
+        mock_row = self._make_vs_row(vector_store_id="uuid-001", team_id="team-other")
+
+        async def mock_get_rows(uuids, prisma_client, user_api_key_cache, proxy_logging_obj=None):
+            return [mock_row]
+
+        with patch(
+            "litellm.proxy.proxy_server.prisma_client",
+            MagicMock(),
+        ), patch(
+            "litellm.proxy.auth.auth_checks.get_managed_vector_store_rows_by_uuids",
+            side_effect=mock_get_rows,
+        ):
+            with pytest.raises(HTTPException) as exc_info:
+                await hook.check_vector_store_ids_access(
+                    [unified_id], self._make_user(team_id="team-caller")
+                )
+            assert exc_info.value.status_code == 403
+
+    @pytest.mark.asyncio
+    async def test_F4_no_team_on_vs_allowed(self):
+        """Legacy vector store with no team_id — accessible to all."""
+        hook = self._make_hook()
+        unified_id = _make_unified_vs_id(unified_uuid="uuid-002")
+
+        mock_row = self._make_vs_row(vector_store_id="uuid-002", team_id=None)
+
+        async def mock_get_rows(uuids, prisma_client, user_api_key_cache, proxy_logging_obj=None):
+            return [mock_row]
+
+        with patch(
+            "litellm.proxy.proxy_server.prisma_client",
+            MagicMock(),
+        ), patch(
+            "litellm.proxy.auth.auth_checks.get_managed_vector_store_rows_by_uuids",
+            side_effect=mock_get_rows,
+        ):
+            await hook.check_vector_store_ids_access(
+                [unified_id], self._make_user(team_id="team-caller")
+            )
+
+    @pytest.mark.asyncio
+    async def test_F5_batch_lookup_single_db_call(self):
+        """Multiple unified IDs resolved in a single DB call (no N+1)."""
+        hook = self._make_hook()
+        ids = [
+            _make_unified_vs_id(unified_uuid=f"uuid-{i}", provider_resource_id=f"vs_{i}")
+            for i in range(3)
+        ]
+
+        rows = [
+            self._make_vs_row(vector_store_id=f"uuid-{i}", team_id="team-abc")
+            for i in range(3)
+        ]
+
+        get_rows_mock = AsyncMock(return_value=rows)
+
+        with patch(
+            "litellm.proxy.proxy_server.prisma_client",
+            MagicMock(),
+        ), patch(
+            "litellm.proxy.auth.auth_checks.get_managed_vector_store_rows_by_uuids",
+            get_rows_mock,
+        ):
+            await hook.check_vector_store_ids_access(ids, self._make_user("team-abc"))
+
+        get_rows_mock.assert_called_once()
+        call_args = get_rows_mock.call_args
+        assert set(call_args.kwargs["uuids"] or call_args.args[0]) == {"uuid-0", "uuid-1", "uuid-2"}
+
+    @pytest.mark.asyncio
+    async def test_F6_non_responses_call_type_skipped(self):
+        """Access check only runs for aresponses/responses call types."""
+        from enterprise.litellm_enterprise.proxy.hooks.managed_files import (
+            _PROXY_LiteLLMManagedFiles as ManagedFiles,
+        )
+        from litellm.proxy._types import CallTypes
+
+        # If call_type is acompletion, the vector_store check branch isn't reached.
+        # Smoke-test: hook runs without error for acompletion with file_search tools.
+        hook = MagicMock(spec=ManagedFiles)
+        hook.async_pre_call_hook = AsyncMock(return_value=None)
+
+        await hook.async_pre_call_hook(
+            user_api_key_dict=self._make_user(),
+            cache=MagicMock(),
+            data={"tools": [{"type": "file_search", "vector_store_ids": ["vs_native"]}]},
+            call_type=CallTypes.acompletion.value,
+        )
+        hook.async_pre_call_hook.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# G-series: get_vector_store_ids_from_file_search_tools helper
+# ---------------------------------------------------------------------------
+
+class TestGetVectorStoreIdsFromFileSearchTools:
+    def _make_hook(self):
+        from enterprise.litellm_enterprise.proxy.hooks.managed_files import (
+            _PROXY_LiteLLMManagedFiles as ManagedFiles,
+        )
+
+        return ManagedFiles.__new__(ManagedFiles)
+
+    def test_G1_tools_none_returns_empty(self):
+        hook = self._make_hook()
+        assert hook.get_vector_store_ids_from_file_search_tools([]) == []
+
+    def test_G2_no_file_search_tools_returns_empty(self):
+        hook = self._make_hook()
+        tools = [{"type": "code_interpreter"}, {"type": "web_search"}]
+        assert hook.get_vector_store_ids_from_file_search_tools(tools) == []
+
+    def test_G3_only_file_search_vs_ids_returned(self):
+        hook = self._make_hook()
+        unified_id = _make_unified_vs_id()
+        tools = [
+            {"type": "web_search"},
+            {"type": "file_search", "vector_store_ids": [unified_id, "vs_native"]},
+            {"type": "code_interpreter"},
+        ]
+        result = hook.get_vector_store_ids_from_file_search_tools(tools)
+        # Only the unified ID is included; native IDs are filtered
+        assert result == [unified_id]
+
+# ---------------------------------------------------------------------------
+# Phase 2: Emulated file_search handler
+# ---------------------------------------------------------------------------
+
+class TestEmulatedFileSearchHandler:
+    """Tests for litellm/responses/file_search/emulated_handler.py"""
+
+    def _make_mock_responses_api_response(
+        self,
+        text: str = "The answer is 42.",
+        output_type: str = "message",
+        include_function_call: bool = False,
+    ):
+        """Build a minimal ResponsesAPIResponse-like mock."""
+        if include_function_call:
+            output = [
+                {
+                    "type": "function_call",
+                    "name": "litellm_file_search",
+                    "call_id": "call_abc123",
+                    "arguments": '{"query": "what is X?", "vector_store_id": "vs_001"}',
+                }
+            ]
+        else:
+            output = [
+                {
+                    "type": "message",
+                    "role": "assistant",
+                    "content": [{"type": "output_text", "text": text}],
+                }
+            ]
+        resp = MagicMock()
+        resp.output = output
+        resp.id = "resp_test123"
+        resp.created_at = 1700000000
+        resp.model = "claude-3-5-sonnet"
+        resp.usage = None
+        return resp
+
+    # --- Tool conversion ---
+
+    def test_H1_file_search_replaced_with_function_tool(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _replace_file_search_tools,
+        )
+
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc", "vs_def"]}]
+        new_tools, vs_ids = _replace_file_search_tools(tools)
+
+        assert vs_ids == ["vs_abc", "vs_def"]
+        assert len(new_tools) == 1
+        assert new_tools[0]["type"] == "function"
+        assert new_tools[0]["name"] == "litellm_file_search"
+        # Both store IDs appear in the enum
+        enum_ids = new_tools[0]["parameters"]["properties"]["vector_store_id"]["enum"]
+        assert "vs_abc" in enum_ids
+        assert "vs_def" in enum_ids
+
+    def test_H2_non_file_search_tools_preserved(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _replace_file_search_tools,
+        )
+
+        tools = [
+            {"type": "web_search"},
+            {"type": "file_search", "vector_store_ids": ["vs_abc"]},
+        ]
+        new_tools, vs_ids = _replace_file_search_tools(tools)
+
+        assert len(new_tools) == 2  # web_search + generated function tool
+        assert new_tools[0]["type"] == "web_search"
+        assert new_tools[1]["type"] == "function"
+
+    def test_H3_no_file_search_tools_returns_unchanged(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _replace_file_search_tools,
+        )
+
+        tools = [{"type": "web_search"}]
+        new_tools, vs_ids = _replace_file_search_tools(tools)
+
+        assert vs_ids == []
+        assert new_tools == [{"type": "web_search"}]
+
+    def test_H4_empty_vector_store_ids_no_function_tool(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _replace_file_search_tools,
+        )
+
+        tools = [{"type": "file_search", "vector_store_ids": []}]
+        new_tools, vs_ids = _replace_file_search_tools(tools)
+
+        assert vs_ids == []
+        assert new_tools == []  # no function tool added without store IDs
+
+    # --- Detection ---
+
+    def test_H5_should_use_emulated_for_non_native_provider(self):
+        from litellm.responses.file_search.emulated_handler import (
+            should_use_emulated_file_search,
+        )
+
+        mock_config = MagicMock()
+        mock_config.supports_native_file_search.return_value = False
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc"]}]
+
+        assert should_use_emulated_file_search(tools, mock_config) is True
+
+    def test_H6_should_not_emulate_for_native_provider(self):
+        from litellm.llms.openai.responses.transformation import (
+            OpenAIResponsesAPIConfig,
+        )
+        from litellm.responses.file_search.emulated_handler import (
+            should_use_emulated_file_search,
+        )
+
+        config = OpenAIResponsesAPIConfig()
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc"]}]
+
+        assert should_use_emulated_file_search(tools, config) is False
+
+    def test_H7_should_not_emulate_without_file_search_tools(self):
+        from litellm.responses.file_search.emulated_handler import (
+            should_use_emulated_file_search,
+        )
+
+        mock_config = MagicMock()
+        mock_config.supports_native_file_search.return_value = False
+        tools = [{"type": "web_search"}]
+
+        assert should_use_emulated_file_search(tools, mock_config) is False
+
+    # --- Output synthesis ---
+
+    def test_H8_synthesized_output_has_file_search_call_and_message(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _build_file_search_call_output,
+            _build_message_output,
+        )
+
+        fs_call = _build_file_search_call_output("fs_abc123", ["what is X?"])
+        assert fs_call["type"] == "file_search_call"
+        assert fs_call["status"] == "completed"
+        assert fs_call["queries"] == ["what is X?"]
+
+        msg = _build_message_output("The answer is 42.", [])
+        assert msg["type"] == "message"
+        assert msg["role"] == "assistant"
+        assert msg["content"][0]["type"] == "output_text"
+        assert msg["content"][0]["text"] == "The answer is 42."
+
+    def test_H9_file_citations_added_for_results_with_file_ids(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _build_file_citation_annotations,
+        )
+
+        result = MagicMock()
+        result.file_id = "file-abc"
+        result.filename = "doc.pdf"
+
+        annotations = _build_file_citation_annotations([result], "some text")
+        assert len(annotations) == 1
+        assert annotations[0]["type"] == "file_citation"
+        assert annotations[0]["file_id"] == "file-abc"
+        assert annotations[0]["filename"] == "doc.pdf"
+
+    def test_H10_no_duplicate_citations_for_same_file(self):
+        from litellm.responses.file_search.emulated_handler import (
+            _build_file_citation_annotations,
+        )
+
+        r1, r2 = MagicMock(), MagicMock()
+        r1.file_id = "file-abc"
+        r1.filename = "doc.pdf"
+        r2.file_id = "file-abc"  # same file
+        r2.filename = "doc.pdf"
+
+        annotations = _build_file_citation_annotations([r1, r2], "text")
+        assert len(annotations) == 1
+
+    def test_H14_include_search_results_returns_all_chunks(self):
+        """All chunks are returned even when they originate from the same file,
+        matching OpenAI native file_search behaviour."""
+        from litellm.responses.file_search.emulated_handler import (
+            _build_search_results_for_include,
+        )
+
+        r1, r2 = MagicMock(), MagicMock()
+        r1.file_id = "file-abc"
+        r1.filename = "doc.pdf"
+        r1.score = 0.9
+        r1.attributes = {}
+        r1.content = [{"type": "text", "text": "first hit"}]
+        r2.file_id = "file-abc"  # same file, different chunk from a second query
+        r2.filename = "doc.pdf"
+        r2.score = 0.85
+        r2.attributes = {}
+        r2.content = [{"type": "text", "text": "second hit"}]
+
+        search_results = _build_search_results_for_include([r1, r2])
+        assert len(search_results) == 2, "Both chunks should be returned, not deduplicated"
+        assert search_results[0]["text"] == "first hit"
+        assert search_results[1]["text"] == "second hit"
+
+    # --- End-to-end (mocked) ---
+
+    @pytest.mark.asyncio
+    async def test_H11_emulated_full_flow_provider_calls_tool(self):
+        """Full flow: provider calls file_search function → search → follow-up → OpenAI output."""
+        from litellm.responses.file_search.emulated_handler import (
+            aresponses_with_emulated_file_search,
+        )
+
+        first_resp = self._make_mock_responses_api_response(include_function_call=True)
+        final_resp = self._make_mock_responses_api_response(text="Deep research enables multi-step queries.")
+
+        search_result = MagicMock()
+        search_result.file_id = "file-xyz"
+        search_result.filename = "research.pdf"
+        search_result.score = 0.95
+        search_result.content = [{"type": "text", "text": "deep research context..."}]
+
+        mock_search_response = MagicMock()
+        mock_search_response.data = [search_result]
+
+        with patch(
+            "litellm.responses.file_search.emulated_handler._call_aresponses",
+            new=AsyncMock(side_effect=[first_resp, final_resp]),
+        ), patch(
+            "litellm.vector_stores.main.asearch",
+            new=AsyncMock(return_value=mock_search_response),
+        ):
+            result = await aresponses_with_emulated_file_search(
+                input="What is deep research?",
+                model="anthropic/claude-3-5-sonnet",
+                tools=[{"type": "file_search", "vector_store_ids": ["vs_001"]}],
+            )
+
+        # output[0] is file_search_call, output[1] is message
+        # ResponsesAPIResponse converts dicts to Pydantic objects — use attribute access
+        def _get(item, key):
+            return item[key] if isinstance(item, dict) else getattr(item, key, None)
+
+        assert _get(result.output[0], "type") == "file_search_call"
+        assert _get(result.output[0], "status") == "completed"
+        assert _get(result.output[1], "type") == "message"
+        content0 = _get(result.output[1], "content")[0]
+        assert "Deep research" in _get(content0, "text")
+        annotations = _get(content0, "annotations")
+        assert any(_get(a, "file_id") == "file-xyz" for a in annotations)
+
+    @pytest.mark.asyncio
+    async def test_H11b_emulated_full_flow_primary_queries_schema(self):
+        """Primary path: provider returns queries (plural array) as defined in the tool schema."""
+        from litellm.responses.file_search.emulated_handler import (
+            aresponses_with_emulated_file_search,
+        )
+
+        # Use the primary schema: queries (plural, list) instead of the backward-compat query (singular)
+        first_resp_plural = MagicMock()
+        first_resp_plural.output = [
+            {
+                "type": "function_call",
+                "name": "litellm_file_search",
+                "call_id": "call_plural",
+                "arguments": '{"queries": ["what is deep research?", "multi-step reasoning"], "vector_store_id": "vs_001"}',
+            }
+        ]
+        first_resp_plural.id = "resp_plural"
+        first_resp_plural.created_at = 1700000000
+        first_resp_plural.model = "claude-3-5-sonnet"
+        first_resp_plural.usage = None
+
+        final_resp = self._make_mock_responses_api_response(text="Deep research uses multiple queries.")
+
+        search_result = MagicMock()
+        search_result.file_id = "file-multi"
+        search_result.filename = "multi.pdf"
+        search_result.score = 0.9
+        search_result.content = [{"type": "text", "text": "multi-query context"}]
+        mock_search_response = MagicMock()
+        mock_search_response.data = [search_result]
+
+        with patch(
+            "litellm.responses.file_search.emulated_handler._call_aresponses",
+            new=AsyncMock(side_effect=[first_resp_plural, final_resp]),
+        ), patch(
+            "litellm.vector_stores.main.asearch",
+            new=AsyncMock(return_value=mock_search_response),
+        ):
+            result = await aresponses_with_emulated_file_search(
+                input="What is deep research?",
+                model="anthropic/claude-3-5-sonnet",
+                tools=[{"type": "file_search", "vector_store_ids": ["vs_001"]}],
+            )
+
+        def _get(item, key):
+            return item[key] if isinstance(item, dict) else getattr(item, key, None)
+
+        assert _get(result.output[0], "type") == "file_search_call"
+        # Two queries were issued, both should appear in the output
+        assert len(_get(result.output[0], "queries")) == 2
+        assert _get(result.output[1], "type") == "message"
+
+    @pytest.mark.asyncio
+    async def test_H12_emulated_flow_provider_answers_without_tool_call(self):
+        """If provider answers directly (no tool call), still return OpenAI format."""
+        from litellm.responses.file_search.emulated_handler import (
+            aresponses_with_emulated_file_search,
+        )
+
+        direct_resp = self._make_mock_responses_api_response(text="I already know the answer.")
+
+        with patch(
+            "litellm.responses.file_search.emulated_handler._call_aresponses",
+            new=AsyncMock(return_value=direct_resp),
+        ):
+            result = await aresponses_with_emulated_file_search(
+                input="What is 2+2?",
+                model="anthropic/claude-3-5-sonnet",
+                tools=[{"type": "file_search", "vector_store_ids": ["vs_001"]}],
+            )
+
+        def _get(item, key):
+            return item[key] if isinstance(item, dict) else getattr(item, key, None)
+
+        assert _get(result.output[0], "type") == "file_search_call"
+        assert _get(result.output[1], "type") == "message"
+        assert "I already know" in _get(_get(result.output[1], "content")[0], "text")
+
+    def test_H13_should_use_emulated_when_provider_config_is_none(self):
+        """None provider config (chat fallback) also triggers emulation."""
+        from litellm.responses.file_search.emulated_handler import (
+            should_use_emulated_file_search,
+        )
+
+        tools = [{"type": "file_search", "vector_store_ids": ["vs_abc"]}]
+        assert should_use_emulated_file_search(tools, None) is True
+
+    @pytest.mark.asyncio
+    async def test_H15_sub_calls_carry_internal_call_flag(self):
+        """Both internal aresponses sub-calls receive _is_litellm_internal_call=True.
+
+        This ensures wrapper_async skips success/failure callbacks for sub-calls so
+        billing fires exactly once (on the outer call) with the synthesized result.
+        """
+        from litellm.responses.file_search.emulated_handler import (
+            aresponses_with_emulated_file_search,
+        )
+
+        first_resp = self._make_mock_responses_api_response(include_function_call=True)
+        final_resp = self._make_mock_responses_api_response(text="answer")
+
+        search_result = MagicMock()
+        search_result.file_id = "file-h15"
+        search_result.filename = "h15.pdf"
+        search_result.score = 0.9
+        search_result.content = [{"type": "text", "text": "context"}]
+        mock_search_response = MagicMock()
+        mock_search_response.data = [search_result]
+
+        captured_kwargs: list = []
+
+        async def _capture(*args, **kwargs):
+            captured_kwargs.append(dict(kwargs))
+            return captured_kwargs.__len__() == 1 and first_resp or final_resp
+
+        with patch(
+            "litellm.responses.file_search.emulated_handler._call_aresponses",
+            new=AsyncMock(side_effect=[first_resp, final_resp]),
+        ) as mock_call, patch(
+            "litellm.vector_stores.main.asearch",
+            new=AsyncMock(return_value=mock_search_response),
+        ):
+            # Intercept kwargs before the mock returns
+            original_side_effect = [first_resp, final_resp]
+            call_kwargs: list = []
+
+            async def _intercept(**kwargs):  # type: ignore[misc]
+                call_kwargs.append(dict(kwargs))
+                return original_side_effect.pop(0)
+
+            mock_call.side_effect = _intercept
+
+            await aresponses_with_emulated_file_search(
+                input="What is H15?",
+                model="anthropic/claude-3-5-sonnet",
+                tools=[{"type": "file_search", "vector_store_ids": ["vs_h15"]}],
+            )
+
+        assert len(call_kwargs) == 2, "Expected exactly 2 sub-calls"
+        for i, kw in enumerate(call_kwargs):
+            assert kw.get("_is_litellm_internal_call") is True, (
+                f"Sub-call {i} must carry _is_litellm_internal_call=True to suppress "
+                "billing callbacks in wrapper_async"
+            )
diff --git a/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py b/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
index 3f8cbf12361..11ccd34804a 100644
--- a/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
+++ b/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
@@ -1317,4 +1317,41 @@ class TestVertexAIGlobalLocation:
             # Assert correct URL format for global with beta API
             expected_url = "https://aiplatform.googleapis.com/v1beta1/projects/test-project/locations/global/cachedContents"
             assert url == expected_url, f"Expected {expected_url}, got {url}"
-            assert "global-aiplatform" not in url, "URL should not contain 'global-aiplatform' prefix"
\ No newline at end of file
+            assert "global-aiplatform" not in url, "URL should not contain 'global-aiplatform' prefix"
+
+    def test_gemini_context_caching_with_custom_api_base_passes_model(self):
+        """Gemini context caching with custom api_base must pass model to _check_custom_proxy.
+
+        Regression test for https://github.com/BerriAI/litellm/issues/23846
+        Previously model was hardcoded to None, causing ValueError when api_base was set.
+        """
+        caching = ContextCachingEndpoints()
+
+        auth_header, url = caching._get_token_and_url_context_caching(
+            gemini_api_key="test-key",
+            custom_llm_provider="gemini",
+            api_base="https://my-proxy.example.com",
+            vertex_project=None,
+            vertex_location=None,
+            vertex_auth_header=None,
+            model="gemini-1.5-pro",
+        )
+
+        assert "models/gemini-1.5-pro" in url
+        assert url.startswith("https://my-proxy.example.com/")
+
+    def test_gemini_context_caching_without_api_base_ignores_model(self):
+        """Without custom api_base, model param is not needed (default URL is used)."""
+        caching = ContextCachingEndpoints()
+
+        auth_header, url = caching._get_token_and_url_context_caching(
+            gemini_api_key="test-key",
+            custom_llm_provider="gemini",
+            api_base=None,
+            vertex_project=None,
+            vertex_location=None,
+            vertex_auth_header=None,
+        )
+
+        assert "generativelanguage.googleapis.com" in url
+        assert "cachedContents" in url
\ No newline at end of file
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_streaming_tool_call_finish_reason.py b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_streaming_tool_call_finish_reason.py
index 3f8efd47fa3..d4d76ab3079 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_streaming_tool_call_finish_reason.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_streaming_tool_call_finish_reason.py
@@ -230,3 +230,75 @@ def test_streaming_content_filter_finish_reason_preserved():
     assert response is not None
     assert len(response.choices) == 1
     assert response.choices[0].finish_reason == "content_filter"
+
+
+def test_streaming_tool_call_finish_reason_with_empty_content_in_final_chunk():
+    """
+    When Gemini streams tool calls and the final chunk has BOTH empty content
+    (e.g. parts: [{text: ""}]) AND finishReason="STOP", the finish_reason
+    must still be "tool_calls".
+
+    This covers models like gemini-3.1-flash-lite-preview that send the
+    final chunk with content (empty text) instead of omitting it entirely.
+
+    Ref: https://github.com/BerriAI/litellm/issues/22900
+    """
+    logging_obj = _make_logging_obj()
+    iterator = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    # Chunk 1: tool call with no finishReason
+    chunk_with_tool_calls = {
+        "candidates": [
+            {
+                "content": {
+                    "parts": [
+                        {
+                            "functionCall": {
+                                "name": "get_weather",
+                                "args": {"location": "San Francisco"},
+                            }
+                        }
+                    ],
+                    "role": "model",
+                },
+                "index": 0,
+            }
+        ],
+    }
+
+    # Chunk 2: finishReason="STOP" WITH empty content (text: "")
+    chunk_with_empty_content_and_finish = {
+        "candidates": [
+            {
+                "content": {
+                    "parts": [{"text": ""}],
+                    "role": "model",
+                },
+                "finishReason": "STOP",
+                "index": 0,
+            }
+        ],
+        "usageMetadata": {
+            "promptTokenCount": 50,
+            "candidatesTokenCount": 20,
+            "totalTokenCount": 70,
+        },
+    }
+
+    # Process chunk 1
+    response1 = iterator.chunk_parser(chunk_with_tool_calls)
+    assert response1 is not None
+    assert len(response1.choices) == 1
+    assert response1.choices[0].delta.tool_calls is not None
+    assert iterator.has_seen_tool_calls is True
+
+    # Process chunk 2 (final chunk with empty content)
+    response2 = iterator.chunk_parser(chunk_with_empty_content_and_finish)
+    assert response2 is not None
+    assert len(response2.choices) == 1
+    # Must be "tool_calls", NOT "stop"
+    assert response2.choices[0].finish_reason == "tool_calls"
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 965fc03a33d..3102a695961 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
@@ -860,6 +860,42 @@ def test_vertex_ai_usage_metadata_with_image_tokens_in_prompt():
     )
 
 
+def test_vertex_ai_usage_metadata_accumulates_duplicate_modalities():
+    """Ensure _calculate_usage accumulates repeated modality entries."""
+    v = VertexGeminiConfig()
+    usage_metadata = {
+        "promptTokenCount": 210,
+        "candidatesTokenCount": 50,
+        "totalTokenCount": 260,
+        "promptTokensDetails": [
+            {"modality": "TEXT", "tokenCount": 20},
+            {"modality": "IMAGE", "tokenCount": 90},
+            {"modality": "IMAGE", "token_count": 100},
+        ],
+        "candidatesTokensDetails": [
+            {"modality": "IMAGE", "tokenCount": 30},
+            {"modality": "TEXT", "tokenCount": 15},
+            {"modality": "TEXT", "token_count": 5},
+        ],
+        "cacheTokensDetails": [
+            {"modality": "TEXT", "tokenCount": 4},
+            {"modality": "IMAGE", "tokenCount": 40},
+            {"modality": "IMAGE", "token_count": 10},
+        ],
+    }
+    usage_metadata = UsageMetadata(**usage_metadata)
+    result = v._calculate_usage(completion_response={"usageMetadata": usage_metadata})
+
+    # prompt details are total - cached per modality
+    assert result.prompt_tokens_details.text_tokens == 16  # 20 - 4
+    assert result.prompt_tokens_details.image_tokens == 140  # (90 + 100) - (40 + 10)
+
+    # candidates details accumulate duplicate modalities
+    assert result.completion_tokens_details.text_tokens == 20  # 15 + 5
+    assert result.completion_tokens_details.image_tokens == 30
+    assert result.completion_tokens == 50
+
+
 def test_vertex_ai_map_thinking_param_with_budget_tokens_0():
     """
     If budget_tokens is 0, do not set includeThoughts to True
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_batch_transformation.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_batch_transformation.py
index 1aab74ddc26..7310c68b4e0 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_batch_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_batch_transformation.py
@@ -1,3 +1,9 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+import litellm
+from litellm.llms.vertex_ai.batches.handler import VertexAIBatchPrediction
 from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation
 
 
@@ -36,3 +42,124 @@ def test_output_file_id_falls_back_to_output_uri_prefix_with_predictions_jsonl()
         output_file_id
         == "gs://test-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-pro/prediction-model-456/predictions.jsonl"
     )
+
+
+def test_vertex_ai_cancel_batch():
+    """Test that vertex_ai cancel_batch calls the correct API endpoint"""
+    handler = VertexAIBatchPrediction(gcs_bucket_name="test-bucket")
+    
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = {
+        "name": "projects/test-project/locations/us-central1/batchPredictionJobs/123456",
+        "state": "JOB_STATE_CANCELLING",
+        "createTime": "2024-03-17T10:00:00.000000Z",
+        "inputConfig": {
+            "gcsSource": {
+                "uris": ["gs://test-bucket/input.jsonl"]
+            }
+        },
+        "outputConfig": {
+            "gcsDestination": {
+                "outputUriPrefix": "gs://test-bucket/output"
+            }
+        }
+    }
+    
+    with patch("litellm.llms.vertex_ai.batches.handler._get_httpx_client") as mock_client:
+        mock_client.return_value.post.return_value = mock_response
+        mock_client.return_value.get.return_value = mock_response
+        
+        with patch.object(handler, "_ensure_access_token") as mock_auth:
+            mock_auth.return_value = ("fake-token", "test-project")
+            
+            response = handler.cancel_batch(
+                _is_async=False,
+                batch_id="123456",
+                api_base=None,
+                vertex_credentials=None,
+                vertex_project="test-project",
+                vertex_location="us-central1",
+                timeout=600.0,
+                max_retries=None,
+            )
+            
+            assert response.id == "123456"
+            assert response.status == "cancelling"
+            
+            mock_client.return_value.post.assert_called_once()
+            mock_client.return_value.get.assert_called_once()
+            call_args = mock_client.return_value.post.call_args
+            assert ":cancel" in call_args.kwargs["url"]
+
+
+def test_vertex_ai_cancel_batch_forwards_timeout():
+    """Test that timeout is forwarded to the POST (cancel) HTTP call.
+
+    Note: the follow-up GET (retrieve) call does not accept a timeout
+    parameter in the underlying HTTP handler, so it is intentionally omitted.
+    """
+
+
+def test_vertex_ai_cancel_batch_custom_proxy_retrieve_url():
+    """Retrieve URL should go through the custom proxy, not bypass it"""
+    handler = VertexAIBatchPrediction(gcs_bucket_name="test-bucket")
+
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = {
+        "name": "projects/test-project/locations/us-central1/batchPredictionJobs/123456",
+        "state": "JOB_STATE_CANCELLING",
+        "createTime": "2024-03-17T10:00:00.000000Z",
+        "inputConfig": {"gcsSource": {"uris": ["gs://test-bucket/input.jsonl"]}},
+        "outputConfig": {"gcsDestination": {"outputUriPrefix": "gs://test-bucket/output"}},
+    }
+
+    with patch("litellm.llms.vertex_ai.batches.handler._get_httpx_client") as mock_client:
+        mock_client.return_value.post.return_value = mock_response
+        mock_client.return_value.get.return_value = mock_response
+
+        with patch.object(handler, "_ensure_access_token") as mock_auth:
+            mock_auth.return_value = ("fake-token", "test-project")
+
+            handler.cancel_batch(
+                _is_async=False,
+                batch_id="123456",
+                api_base="https://my-proxy.example.com",
+                vertex_credentials=None,
+                vertex_project="test-project",
+                vertex_location="us-central1",
+                timeout=600.0,
+                max_retries=None,
+            )
+
+            post_url = mock_client.return_value.post.call_args.kwargs["url"]
+            get_url = mock_client.return_value.get.call_args.kwargs["url"]
+
+            assert "my-proxy.example.com" in post_url
+            assert ":cancel" in post_url
+            assert "my-proxy.example.com" in get_url
+            assert ":cancel" not in get_url
+            assert "googleapis.com" not in get_url
+
+
+@pytest.mark.asyncio
+async def test_litellm_cancel_batch_vertex_ai():
+    """Test that litellm.cancel_batch works with vertex_ai provider"""
+    mock_response = MagicMock()
+    mock_response.id = "batch_123"
+    mock_response.status = "cancelling"
+    
+    with patch("litellm.batches.main.vertex_ai_batches_instance") as mock_instance:
+        mock_instance.cancel_batch.return_value = mock_response
+        
+        response = litellm.cancel_batch(
+            batch_id="batch_123",
+            custom_llm_provider="vertex_ai",
+            vertex_project="test-project",
+            vertex_location="us-central1",
+        )
+        
+        assert mock_instance.cancel_batch.called
+        assert response.id == "batch_123"
+        assert response.status == "cancelling"
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/__init__.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_location.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_location.py
new file mode 100644
index 00000000000..6487ea25f21
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_location.py
@@ -0,0 +1,164 @@
+"""
+Tests for Vertex AI partner models count_tokens location resolution.
+
+Ref: https://github.com/BerriAI/litellm/issues/23872
+"""
+import pytest
+
+from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import (
+    VertexAIPartnerModelsTokenCounter,
+)
+
+
+@pytest.fixture
+def counter():
+    return VertexAIPartnerModelsTokenCounter()
+
+
+class TestCountTokensLocationResolution:
+    """Verify that vertex_count_tokens_location is respected in handle_count_tokens_request."""
+
+    def _build_litellm_params(
+        self,
+        vertex_location=None,
+        vertex_count_tokens_location=None,
+    ):
+        params = {}
+        if vertex_location is not None:
+            params["vertex_location"] = vertex_location
+        if vertex_count_tokens_location is not None:
+            params["vertex_count_tokens_location"] = vertex_count_tokens_location
+        return params
+
+    @pytest.mark.asyncio
+    async def test_count_tokens_location_overrides_vertex_location(self, counter, monkeypatch):
+        """vertex_count_tokens_location should take precedence over vertex_location."""
+        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["vertex_location"] = vertex_location
+            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
+        )
+
+        # Mock the HTTP call to avoid real network requests
+        class FakeResponse:
+            status_code = 200
+            def json(self):
+                return {"input_tokens": 10}
+            def raise_for_status(self):
+                pass
+
+        class FakeClient:
+            async def post(self, url, headers=None, json=None, **kwargs):
+                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())
+
+        litellm_params = self._build_litellm_params(
+            vertex_location="us-east5",
+            vertex_count_tokens_location="europe-west1",
+        )
+
+        await counter.handle_count_tokens_request(
+            model="claude-sonnet-4-6",
+            request_data={"messages": [{"role": "user", "content": "hi"}]},
+            litellm_params=litellm_params,
+        )
+
+        assert captured["vertex_location"] == "europe-west1"
+
+    @pytest.mark.asyncio
+    async def test_claude_without_count_tokens_location_defaults_to_us_east5(self, counter, monkeypatch):
+        """Claude models without any location should default to us-east5."""
+        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["vertex_location"] = vertex_location
+            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": 10}
+            def raise_for_status(self):
+                pass
+
+        class FakeClient:
+            async def post(self, url, headers=None, json=None, **kwargs):
+                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())
+
+        litellm_params = self._build_litellm_params()  # no location at all
+
+        await counter.handle_count_tokens_request(
+            model="claude-sonnet-4-6",
+            request_data={"messages": [{"role": "user", "content": "hi"}]},
+            litellm_params=litellm_params,
+        )
+
+        assert captured["vertex_location"] == "us-east5"
+
+    @pytest.mark.asyncio
+    async def test_claude_with_vertex_location_uses_it(self, counter, monkeypatch):
+        """Claude models with vertex_location but no count_tokens_location should use vertex_location."""
+        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["vertex_location"] = vertex_location
+            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": 10}
+            def raise_for_status(self):
+                pass
+
+        class FakeClient:
+            async def post(self, url, headers=None, json=None, **kwargs):
+                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())
+
+        litellm_params = self._build_litellm_params(vertex_location="asia-southeast1")
+
+        await counter.handle_count_tokens_request(
+            model="claude-sonnet-4-6",
+            request_data={"messages": [{"role": "user", "content": "hi"}]},
+            litellm_params=litellm_params,
+        )
+
+        assert captured["vertex_location"] == "asia-southeast1"
diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py
index f20c14aa611..83703cd4edd 100644
--- a/tests/test_litellm/proxy/auth/test_route_checks.py
+++ b/tests/test_litellm/proxy/auth/test_route_checks.py
@@ -1329,3 +1329,78 @@ def test_non_org_admin_with_organizations_list():
         organization_memberships=[membership],
     )
     assert _user_is_org_admin({"organizations": ["org-1"]}, user_obj) is False
+
+
+@pytest.mark.asyncio
+async def test_initialize_pass_through_registers_wildcard_for_auth_subpath():
+    """
+    Test that initialize_pass_through_endpoints registers both base path and
+    wildcard path in openai_routes when auth=true and include_subpath=true,
+    and that subpath requests pass is_llm_api_route.
+
+    Also verifies:
+    - Dedup: calling init twice does not duplicate entries
+    - Cleanup: removing the endpoint cleans up openai_routes
+    """
+    from litellm.proxy._types import LiteLLMRoutes
+    from litellm.proxy.pass_through_endpoints.pass_through_endpoints import (
+        InitPassThroughEndpointHelpers,
+        initialize_pass_through_endpoints,
+    )
+
+    base_path = "/v1/ocr/nvidia/community/nemoretriever-ocr-v1"
+    wildcard_path = base_path + "/*"
+
+    endpoint_config = {
+        "path": base_path,
+        "target": "https://httpbin.org/post",
+        "include_subpath": True,
+        "auth": True,
+        "headers": {"content-type": "application/json"},
+    }
+
+    original_routes = LiteLLMRoutes.openai_routes.value[:]
+    try:
+        with patch(
+            "litellm.proxy.proxy_server.app",
+            MagicMock(),
+        ), patch(
+            "litellm.proxy.proxy_server.premium_user",
+            True,
+        ), patch(
+            "litellm.proxy.proxy_server.config_passthrough_endpoints",
+            None,
+        ):
+            await initialize_pass_through_endpoints([endpoint_config])
+
+            # Both base and wildcard paths should be registered
+            assert base_path in LiteLLMRoutes.openai_routes.value
+            assert wildcard_path in LiteLLMRoutes.openai_routes.value
+
+            # Subpath requests should pass the auth route check
+            assert RouteChecks.is_llm_api_route(base_path) is True
+            assert RouteChecks.is_llm_api_route(base_path + "/v1/infer") is True
+
+            # Calling init again should not duplicate entries
+            await initialize_pass_through_endpoints([endpoint_config])
+            assert LiteLLMRoutes.openai_routes.value.count(base_path) == 1
+            assert LiteLLMRoutes.openai_routes.value.count(wildcard_path) == 1
+
+            # Removing the endpoint should clean up openai_routes
+            # remove_endpoint_routes takes endpoint_id (UUID portion of
+            # the route key "{id}:exact:{path}:{methods}")
+            registered = InitPassThroughEndpointHelpers.get_all_registered_pass_through_routes()
+            endpoint_ids = {k.split(":")[0] for k in registered}
+            for eid in endpoint_ids:
+                InitPassThroughEndpointHelpers.remove_endpoint_routes(eid)
+            assert base_path not in LiteLLMRoutes.openai_routes.value
+            assert wildcard_path not in LiteLLMRoutes.openai_routes.value
+    finally:
+        LiteLLMRoutes.openai_routes.value[:] = original_routes
+        # Clean up any routes registered during this test to avoid
+        # polluting the module-level _registered_pass_through_routes
+        registered = InitPassThroughEndpointHelpers.get_all_registered_pass_through_routes()
+        for k in registered:
+            InitPassThroughEndpointHelpers.remove_endpoint_routes(
+                k.split(":")[0]
+            )
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py
index 3a17bbd0025..5c19e7189e0 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py
@@ -488,3 +488,330 @@ async def test_openai_moderation_guardrail_streaming_harmful_content():
 
             assert exc_info.value.status_code == 400
             assert "Violated OpenAI moderation policy" in str(exc_info.value.detail)
+
+
+@pytest.mark.asyncio
+async def test_openai_moderation_guardrail_logs_full_response_safe_content():
+    """Test that safe content logs the full moderation response (categories, scores)
+    in StandardLoggingGuardrailInformation, not just 'allow'."""
+    from litellm.types.utils import GenericGuardrailAPIInputs
+
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        guardrail = OpenAIModerationGuardrail(
+            guardrail_name="test-openai-moderation",
+        )
+
+        mock_response = OpenAIModerationResponse(
+            id="modr-123",
+            model="omni-moderation-latest",
+            results=[
+                OpenAIModerationResult(
+                    flagged=False,
+                    categories={
+                        "sexual": False,
+                        "hate": False,
+                        "harassment": False,
+                        "self-harm": False,
+                        "violence": False,
+                    },
+                    category_scores={
+                        "sexual": 0.001,
+                        "hate": 0.002,
+                        "harassment": 0.001,
+                        "self-harm": 0.001,
+                        "violence": 0.003,
+                    },
+                    category_applied_input_types={
+                        "sexual": [],
+                        "hate": [],
+                        "harassment": [],
+                        "self-harm": [],
+                        "violence": [],
+                    },
+                )
+            ],
+        )
+
+        with patch.object(guardrail, "async_make_request", return_value=mock_response):
+            inputs = GenericGuardrailAPIInputs(
+                structured_messages=[
+                    {"role": "user", "content": "Hello, how are you?"}
+                ]
+            )
+            request_data = {"metadata": {}}
+
+            await guardrail.apply_guardrail(
+                inputs=inputs,
+                request_data=request_data,
+                input_type="request",
+            )
+
+            guardrail_info_list = request_data["metadata"][
+                "standard_logging_guardrail_information"
+            ]
+            assert len(guardrail_info_list) == 1
+
+            info = guardrail_info_list[0]
+            assert info["guardrail_name"] == "test-openai-moderation"
+            assert info["guardrail_status"] == "success"
+
+            # Full moderation response, NOT "allow"
+            guardrail_resp = info["guardrail_response"]
+            assert isinstance(guardrail_resp, dict)
+            assert guardrail_resp["results"][0]["flagged"] is False
+            assert "category_scores" in guardrail_resp["results"][0]
+
+            # Internal key cleaned up (.pop())
+            assert "_openai_moderation_response" not in request_data["metadata"]
+
+
+@pytest.mark.asyncio
+async def test_openai_moderation_guardrail_logs_full_response_harmful_content():
+    """Test that harmful content logs guardrail_intervened status with the full
+    moderation response, not just the exception string."""
+    from litellm.types.utils import GenericGuardrailAPIInputs
+
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        guardrail = OpenAIModerationGuardrail(
+            guardrail_name="test-openai-moderation",
+        )
+
+        mock_response = OpenAIModerationResponse(
+            id="modr-456",
+            model="omni-moderation-latest",
+            results=[
+                OpenAIModerationResult(
+                    flagged=True,
+                    categories={
+                        "sexual": False,
+                        "hate": True,
+                        "harassment": False,
+                        "self-harm": False,
+                        "violence": False,
+                    },
+                    category_scores={
+                        "sexual": 0.001,
+                        "hate": 0.95,
+                        "harassment": 0.001,
+                        "self-harm": 0.001,
+                        "violence": 0.001,
+                    },
+                    category_applied_input_types={
+                        "sexual": [],
+                        "hate": ["text"],
+                        "harassment": [],
+                        "self-harm": [],
+                        "violence": [],
+                    },
+                )
+            ],
+        )
+
+        with patch.object(guardrail, "async_make_request", return_value=mock_response):
+            inputs = GenericGuardrailAPIInputs(
+                structured_messages=[
+                    {"role": "user", "content": "Hateful content"}
+                ]
+            )
+            request_data = {"metadata": {}}
+
+            from fastapi import HTTPException
+
+            with pytest.raises(HTTPException):
+                await guardrail.apply_guardrail(
+                    inputs=inputs,
+                    request_data=request_data,
+                    input_type="request",
+                )
+
+            guardrail_info_list = request_data["metadata"][
+                "standard_logging_guardrail_information"
+            ]
+            info = guardrail_info_list[0]
+            assert info["guardrail_status"] == "guardrail_intervened"
+
+            # Full moderation response, NOT stringified exception
+            guardrail_resp = info["guardrail_response"]
+            assert isinstance(guardrail_resp, dict)
+            assert guardrail_resp["results"][0]["flagged"] is True
+            assert guardrail_resp["results"][0]["category_scores"]["hate"] == 0.95
+
+            # Internal key cleaned up by _process_error (.pop())
+            assert "_openai_moderation_response" not in request_data["metadata"]
+
+
+@pytest.mark.asyncio
+async def test_openai_moderation_post_call_request_data_passthrough():
+    """Test that post-call guardrail info flows through to the real request_data
+    via the unified guardrail dispatcher (Bug 1 fix)."""
+    from unittest.mock import AsyncMock
+
+    from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import (
+        UnifiedLLMGuardrails,
+    )
+    from litellm.types.utils import ModelResponse
+
+    import litellm
+
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        guardrail = OpenAIModerationGuardrail(
+            guardrail_name="test-openai-moderation",
+            event_hook="post_call",
+        )
+        unified_guardrail = UnifiedLLMGuardrails()
+
+        mock_mod_response = OpenAIModerationResponse(
+            id="modr-789",
+            model="omni-moderation-latest",
+            results=[
+                OpenAIModerationResult(
+                    flagged=False,
+                    categories={
+                        "sexual": False,
+                        "hate": False,
+                        "harassment": False,
+                        "self-harm": False,
+                        "violence": False,
+                    },
+                    category_scores={
+                        "sexual": 0.001,
+                        "hate": 0.002,
+                        "harassment": 0.001,
+                        "self-harm": 0.001,
+                        "violence": 0.001,
+                    },
+                    category_applied_input_types={
+                        "sexual": [],
+                        "hate": [],
+                        "harassment": [],
+                        "self-harm": [],
+                        "violence": [],
+                    },
+                )
+            ],
+        )
+
+        llm_response = ModelResponse(
+            id="chatcmpl-test",
+            model="gpt-4",
+            choices=[
+                litellm.Choices(
+                    index=0,
+                    message=litellm.Message(
+                        role="assistant", content="Hello world"
+                    ),
+                    finish_reason="stop",
+                )
+            ],
+        )
+
+        request_data = {
+            "messages": [{"role": "user", "content": "Hello"}],
+            "guardrail_to_apply": guardrail,
+            "metadata": {"guardrails": ["test-openai-moderation"]},
+        }
+
+        mock_make_request = AsyncMock(return_value=mock_mod_response)
+        with patch.object(guardrail, "async_make_request", mock_make_request):
+            await unified_guardrail.async_post_call_success_hook(
+                data=request_data,
+                user_api_key_dict=UserAPIKeyAuth(
+                    api_key="test", request_route="/chat/completions"
+                ),
+                response=llm_response,
+            )
+
+        mock_make_request.assert_called_once()
+
+        # Guardrail info in the REAL request_data (not a throwaway)
+        guardrail_info_list = request_data["metadata"].get(
+            "standard_logging_guardrail_information"
+        )
+        assert guardrail_info_list is not None
+        assert isinstance(guardrail_info_list[0]["guardrail_response"], dict)
+        assert "results" in guardrail_info_list[0]["guardrail_response"]
+
+
+def test_openai_moderation_process_response_metadata_none_edge_case():
+    """
+    Test that _process_response anchors the metadata dict back into
+    request_data when metadata is None, so pop() doesn't operate on a
+    temporary and the moderation response is correctly logged.
+    """
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        guardrail = OpenAIModerationGuardrail(
+            guardrail_name="test-openai-moderation",
+            event_hook="post_call",
+        )
+
+        mod_dict = {"id": "modr-test", "model": "omni-moderation-latest", "results": []}
+
+        # Simulate apply_guardrail having stashed the response but metadata
+        # was None initially — apply_guardrail anchors it, so metadata is a
+        # real dict with the stashed key by the time _process_response runs.
+        request_data = {"metadata": {"_openai_moderation_response": mod_dict}}
+
+        guardrail._process_response(
+            response={"inputs": {}},
+            request_data=request_data,
+        )
+
+        # Full moderation dict should be logged, not "allow"
+        info_list = request_data["metadata"].get(
+            "standard_logging_guardrail_information"
+        )
+        assert info_list is not None
+        assert info_list[0]["guardrail_response"] == mod_dict
+
+        # Internal key should have been cleaned up by pop()
+        assert "_openai_moderation_response" not in request_data["metadata"]
+
+
+def test_openai_moderation_process_error_metadata_none_edge_case():
+    """
+    Test that _process_error anchors the metadata dict back into
+    request_data when metadata starts as None (or {}), so pop() doesn't
+    operate on a temporary.
+    """
+    from fastapi import HTTPException
+
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        guardrail = OpenAIModerationGuardrail(
+            guardrail_name="test-openai-moderation",
+            event_hook="post_call",
+        )
+
+        mod_dict = {
+            "id": "modr-test",
+            "model": "omni-moderation-latest",
+            "results": [{"flagged": True, "categories": {"hate": True}}],
+        }
+
+        # metadata is None — exercises the or {} anchor
+        request_data: dict = {"metadata": None}
+
+        # Simulate stashing the response then calling _process_error
+        # (normally apply_guardrail stashes, then the decorator calls
+        # _process_error on HTTPException)
+        # First anchor metadata like apply_guardrail does:
+        metadata = request_data.get("metadata") or {}
+        request_data["metadata"] = metadata
+        metadata["_openai_moderation_response"] = mod_dict
+
+        exc = HTTPException(status_code=400, detail="Violated policy")
+        with pytest.raises(HTTPException):
+            guardrail._process_error(
+                e=exc,
+                request_data=request_data,
+            )
+
+        # Full moderation dict should be logged, not the exception
+        info_list = request_data["metadata"].get(
+            "standard_logging_guardrail_information"
+        )
+        assert info_list is not None
+        assert info_list[0]["guardrail_response"] == mod_dict
+        assert info_list[0]["guardrail_status"] == "guardrail_intervened"
+
+        # Internal key cleaned up
+        assert "_openai_moderation_response" not in request_data["metadata"]
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py
index c77a5d07b3b..2595a1df7f1 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_openai_moderation_streaming.py
@@ -170,3 +170,106 @@ async def test_openai_moderation_guardrail_streaming_harmful_content():
 
             assert exc_info.value.status_code == 400
             assert "Violated OpenAI moderation policy" in str(exc_info.value.detail)
+
+
+@pytest.mark.asyncio
+async def test_openai_moderation_streaming_end_of_stream_request_data_passthrough():
+    """Test that streaming end-of-stream guardrail info flows through to the
+    real request_data (Bug 1 fix for streaming path)."""
+    from litellm.types.llms.openai import (
+        OpenAIModerationResponse,
+        OpenAIModerationResult,
+    )
+
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        openai_guardrail = OpenAIModerationGuardrail(
+            guardrail_name="test-openai-moderation",
+            event_hook="post_call",
+        )
+        unified_guardrail = UnifiedLLMGuardrails()
+
+        mock_mod_response = OpenAIModerationResponse(
+            id="modr-stream-test",
+            model="omni-moderation-latest",
+            results=[
+                OpenAIModerationResult(
+                    flagged=False,
+                    categories={"hate": False, "violence": False},
+                    category_scores={"hate": 0.001, "violence": 0.002},
+                    category_applied_input_types={"hate": [], "violence": []},
+                )
+            ],
+        )
+
+        async def mock_stream():
+            import litellm
+
+            chunks_data = ["Hello", " world"]
+            for i, content in enumerate(chunks_data):
+                chunk = MagicMock(spec=ModelResponseStream)
+                chunk.model = "gpt-4"
+                choice = MagicMock()
+                choice.delta = MagicMock()
+                choice.delta.content = content
+                choice.finish_reason = "stop" if i == len(chunks_data) - 1 else None
+                chunk.choices = [choice]
+                yield chunk
+
+        import litellm
+
+        mock_model_response = ModelResponse(
+            id="mock-stream-response",
+            model="gpt-4",
+            choices=[
+                litellm.Choices(
+                    index=0,
+                    message=litellm.Message(
+                        role="assistant", content="Hello world"
+                    ),
+                    finish_reason="stop",
+                )
+            ],
+        )
+
+        request_data = {
+            "messages": [{"role": "user", "content": "hi"}],
+            "guardrail_to_apply": openai_guardrail,
+            "metadata": {
+                "guardrails": ["test-openai-moderation"],
+                "guardrail_config": {"streaming_sampling_rate": 1},
+            },
+        }
+
+        with patch.object(
+            openai_guardrail, "async_make_request", return_value=mock_mod_response
+        ), patch(
+            "litellm.llms.openai.chat.guardrail_translation.handler.stream_chunk_builder",
+            return_value=mock_model_response,
+        ):
+            user_api_key_dict = UserAPIKeyAuth(
+                api_key="test", request_route="/chat/completions"
+            )
+
+            async for _ in unified_guardrail.async_post_call_streaming_iterator_hook(
+                user_api_key_dict=user_api_key_dict,
+                response=mock_stream(),
+                request_data=request_data,
+            ):
+                pass
+
+        # Verify guardrail info reached the REAL request_data (not a throwaway)
+        guardrail_info_list = request_data["metadata"].get(
+            "standard_logging_guardrail_information"
+        )
+        assert guardrail_info_list is not None, (
+            "Guardrail info should be in request_data after streaming"
+        )
+        info = guardrail_info_list[0]
+        assert info["guardrail_status"] == "success"
+
+        # Full moderation response dict, NOT the simplified "allow" string
+        guardrail_resp = info["guardrail_response"]
+        assert isinstance(guardrail_resp, dict), (
+            f"Expected full moderation response dict, got {type(guardrail_resp)}: {guardrail_resp}"
+        )
+        assert "results" in guardrail_resp
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py
index 7c29c8161db..bbba8e4d03d 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py
@@ -78,9 +78,7 @@ class TestUnifiedLLMGuardrails:
 
             data = {
                 "guardrail_to_apply": guardrail,
-                "messages": [
-                    {"role": "user", "content": "Tool: test\nArguments: {}"}
-                ],
+                "messages": [{"role": "user", "content": "Tool: test\nArguments: {}"}],
                 "model": "mcp-tool-call",
             }
 
@@ -102,9 +100,7 @@ class TestUnifiedLLMGuardrails:
 
             data = {
                 "guardrail_to_apply": guardrail,
-                "messages": [
-                    {"role": "user", "content": "Tool: test\nArguments: {}"}
-                ],
+                "messages": [{"role": "user", "content": "Tool: test\nArguments: {}"}],
                 "model": "mcp-tool-call",
             }
 
@@ -168,6 +164,7 @@ class TestUnifiedLLMGuardrails:
                     guardrail_to_apply,
                     litellm_logging_obj=None,
                     user_api_key_dict=None,
+                    request_data=None,
                 ):
                     # Simulate what the real handler does:
                     # put combined text in first chunk, clear the rest
@@ -200,10 +197,12 @@ class TestUnifiedLLMGuardrails:
             chunks = []
             for i in range(10):
                 chunk = ModelResponseStream(
-                    choices=[StreamingChoices(
-                        delta=Delta(content=f"word{i} ", role="assistant"),
-                        finish_reason=None,
-                    )],
+                    choices=[
+                        StreamingChoices(
+                            delta=Delta(content=f"word{i} ", role="assistant"),
+                            finish_reason=None,
+                        )
+                    ],
                 )
                 chunks.append(chunk)
 
@@ -228,7 +227,9 @@ class TestUnifiedLLMGuardrails:
                 response=mock_stream(),
                 request_data=request_data,
             ):
-                content = item.choices[0].delta.content if item.choices[0].delta else None
+                content = (
+                    item.choices[0].delta.content if item.choices[0].delta else None
+                )
                 yielded_contents.append(content)
 
             # Every chunk should have non-empty content
@@ -271,10 +272,15 @@ class TestUnifiedLLMGuardrails:
             assert guardrail.event_history == [GuardrailEventHooks.pre_call]
             assert len(guardrail.apply_calls) == 1
             assert guardrail.apply_calls[0]["input_type"] == "request"
-            assert "https://arxiv.org/pdf/2201.04234" in guardrail.apply_calls[0]["inputs"]["texts"]
+            assert (
+                "https://arxiv.org/pdf/2201.04234"
+                in guardrail.apply_calls[0]["inputs"]["texts"]
+            )
 
             # Data should be returned with document intact
-            assert result["document"]["document_url"] == "https://arxiv.org/pdf/2201.04234"
+            assert (
+                result["document"]["document_url"] == "https://arxiv.org/pdf/2201.04234"
+            )
 
         @pytest.mark.asyncio
         async def test_moderation_hook_invokes_ocr_handler(self):
@@ -302,7 +308,10 @@ class TestUnifiedLLMGuardrails:
 
             assert guardrail.event_history == [GuardrailEventHooks.during_call]
             assert len(guardrail.apply_calls) == 1
-            assert "https://example.com/scan.png" in guardrail.apply_calls[0]["inputs"]["texts"]
+            assert (
+                "https://example.com/scan.png"
+                in guardrail.apply_calls[0]["inputs"]["texts"]
+            )
 
         @pytest.mark.asyncio
         async def test_post_call_success_hook_guardrails_ocr_output(self):
@@ -318,7 +327,9 @@ class TestUnifiedLLMGuardrails:
                 def should_run_guardrail(self, data, event_type):  # type: ignore[override]
                     return True
 
-                async def apply_guardrail(self, inputs, request_data, input_type, **kwargs):
+                async def apply_guardrail(
+                    self, inputs, request_data, input_type, **kwargs
+                ):
                     texts = inputs.get("texts", [])
                     return {"texts": [t.replace("SECRET", "[REDACTED]") for t in texts]}
 
diff --git a/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py b/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py
index c4d2dce5876..ca22c5aab56 100644
--- a/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py
+++ b/tests/test_litellm/proxy/guardrails/test_deferred_guardrail_logging.py
@@ -32,7 +32,6 @@ from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessin
 from litellm.proxy.utils import ProxyLogging
 from litellm.types.guardrails import GuardrailEventHooks
 
-
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
@@ -86,10 +85,10 @@ class TestHasPostCallGuardrails:
         with patch("litellm.callbacks", [PostCallGuardrail()]):
             assert ProxyBaseLLMRequestProcessing._has_post_call_guardrails() is True
 
-    def test_returns_true_for_event_hook_none(self):
-        """event_hook=None means 'all events', including post_call."""
+    def test_returns_false_for_event_hook_none(self):
+        """event_hook=None is not an explicit post_call registration for deferral."""
         with patch("litellm.callbacks", [AllEventsGuardrail()]):
-            assert ProxyBaseLLMRequestProcessing._has_post_call_guardrails() is True
+            assert ProxyBaseLLMRequestProcessing._has_post_call_guardrails() is False
 
     def test_returns_false_for_pre_call_only(self):
         with patch("litellm.callbacks", [PreCallGuardrail()]):
@@ -112,7 +111,10 @@ class TestHasPostCallGuardrails:
                 super().__init__(
                     guardrail_name="list-post",
                     default_on=True,
-                    event_hook=[GuardrailEventHooks.pre_call, GuardrailEventHooks.post_call],
+                    event_hook=[
+                        GuardrailEventHooks.pre_call,
+                        GuardrailEventHooks.post_call,
+                    ],
                 )
 
         with patch("litellm.callbacks", [ListGuardrail()]):
@@ -418,7 +420,9 @@ class TestDeferredStreamingClosure:
             await asyncio.sleep(0)
 
         assert guardrail_called is True, "Guardrail hook should be called"
-        assert logger_called is False, "Non-guardrail logger should NOT be called by closure"
+        assert (
+            logger_called is False
+        ), "Non-guardrail logger should NOT be called by closure"
 
     @pytest.mark.asyncio
     async def test_closure_passes_guardrail_modified_response_to_logging(self):
@@ -463,8 +467,9 @@ class TestDeferredStreamingClosure:
         await asyncio.sleep(0)
         await asyncio.sleep(0)
 
-        assert logged_response is modified_response, \
-            "Logging must receive the guardrail-modified response"
+        assert (
+            logged_response is modified_response
+        ), "Logging must receive the guardrail-modified response"
 
     @pytest.mark.asyncio
     async def test_closure_logs_even_on_guardrail_exception(self):
@@ -511,11 +516,13 @@ class TestDeferredStreamingClosure:
         await asyncio.sleep(0)
         await asyncio.sleep(0)
 
-        assert logging_called is True, \
-            "Logging must fire even when guardrail raises HTTPException"
-        assert mock_logging_obj.model_call_details["metadata"].get(
-            "guardrail_blocked"
-        ) is True, "guardrail_blocked must be set for HTTPException"
+        assert (
+            logging_called is True
+        ), "Logging must fire even when guardrail raises HTTPException"
+        assert (
+            mock_logging_obj.model_call_details["metadata"].get("guardrail_blocked")
+            is True
+        ), "guardrail_blocked must be set for HTTPException"
 
     @pytest.mark.asyncio
     async def test_transient_error_does_not_set_guardrail_blocked(self):
@@ -556,9 +563,10 @@ class TestDeferredStreamingClosure:
 
         await asyncio.sleep(0)
 
-        assert mock_logging_obj.model_call_details["metadata"].get(
-            "guardrail_blocked"
-        ) is not True, "guardrail_blocked must NOT be set for transient errors"
+        assert (
+            mock_logging_obj.model_call_details["metadata"].get("guardrail_blocked")
+            is not True
+        ), "guardrail_blocked must NOT be set for transient errors"
 
     @pytest.mark.asyncio
     async def test_production_closure_integration(self):
@@ -620,10 +628,10 @@ class TestDeferredStreamingClosure:
             await asyncio.sleep(0)
             await asyncio.sleep(0)
 
-        assert hook_called is True, \
-            "Production closure must call guardrail hook"
-        assert logged_response is modified_response, \
-            "Production closure must pass guardrail-modified response to logging"
+        assert hook_called is True, "Production closure must call guardrail hook"
+        assert (
+            logged_response is modified_response
+        ), "Production closure must pass guardrail-modified response to logging"
 
     @pytest.mark.asyncio
     async def test_apply_guardrail_path_uses_unified_guardrail(self):
@@ -686,10 +694,12 @@ class TestDeferredStreamingClosure:
             await asyncio.sleep(0)
             await asyncio.sleep(0)
 
-        assert unified_hook_called is True, \
-            "apply_guardrail guardrails must be dispatched through UnifiedLLMGuardrails"
-        assert logged_response is not None, \
-            "Logging must fire after unified guardrail path"
+        assert (
+            unified_hook_called is True
+        ), "apply_guardrail guardrails must be dispatched through UnifiedLLMGuardrails"
+        assert (
+            logged_response is not None
+        ), "Logging must fire after unified guardrail path"
 
     @pytest.mark.asyncio
     async def test_hooks_receive_merged_guardrail_data(self):
@@ -742,11 +752,10 @@ class TestDeferredStreamingClosure:
             merged["_merged_marker"] = True
             return merged
 
-        with patch("litellm.callbacks", [guardrail]), \
-             patch(
-                "litellm.proxy.utils._check_and_merge_model_level_guardrails",
-                side_effect=mock_merge,
-             ):
+        with patch("litellm.callbacks", [guardrail]), patch(
+            "litellm.proxy.utils._check_and_merge_model_level_guardrails",
+            side_effect=mock_merge,
+        ):
             await ProxyBaseLLMRequestProcessing._run_deferred_stream_guardrails(
                 captured_data=captured_data,
                 captured_user_api_key_dict=UserAPIKeyAuth(api_key="test"),
@@ -756,8 +765,9 @@ class TestDeferredStreamingClosure:
             )
 
         assert hook_received_data is not None, "Guardrail hook must be called"
-        assert hook_received_data.get("_merged_marker") is True, \
-            "Hook must receive guardrail_data (merged), not original captured_data"
+        assert (
+            hook_received_data.get("_merged_marker") is True
+        ), "Hook must receive guardrail_data (merged), not original captured_data"
         assert "model-guardrail" in hook_received_data.get("metadata", {}).get(
             "guardrails", []
         ), "Hook data must contain model-level guardrails"
@@ -773,6 +783,7 @@ class TestDeferredStreamingClosure:
         be silently skipped at execution time if captured_data (unmerged) were
         passed instead of guardrail_data (merged)."""
         import copy
+
         from litellm.types.utils import GenericGuardrailAPIInputs
 
         unified_received_data = None
@@ -812,6 +823,7 @@ class TestDeferredStreamingClosure:
         from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import (
             UnifiedLLMGuardrails,
         )
+
         original_unified_hook = UnifiedLLMGuardrails.async_post_call_success_hook
 
         async def tracking_unified_hook(self, user_api_key_dict, data, response):
@@ -819,16 +831,14 @@ class TestDeferredStreamingClosure:
             unified_received_data = data
             return response
 
-        with patch("litellm.callbacks", [guardrail]), \
-             patch(
-                "litellm.proxy.utils._check_and_merge_model_level_guardrails",
-                side_effect=mock_merge,
-             ), \
-             patch.object(
-                UnifiedLLMGuardrails,
-                "async_post_call_success_hook",
-                tracking_unified_hook,
-             ):
+        with patch("litellm.callbacks", [guardrail]), patch(
+            "litellm.proxy.utils._check_and_merge_model_level_guardrails",
+            side_effect=mock_merge,
+        ), patch.object(
+            UnifiedLLMGuardrails,
+            "async_post_call_success_hook",
+            tracking_unified_hook,
+        ):
             await ProxyBaseLLMRequestProcessing._run_deferred_stream_guardrails(
                 captured_data=captured_data,
                 captured_user_api_key_dict=UserAPIKeyAuth(api_key="test"),
@@ -837,14 +847,15 @@ class TestDeferredStreamingClosure:
                 cache_hit=False,
             )
 
-        assert unified_received_data is not None, \
-            "UnifiedLLMGuardrails must be called for apply_guardrail guardrails"
-        assert unified_received_data.get("_merged_marker") is True, \
-            "UnifiedLLMGuardrails must receive guardrail_data (merged), not captured_data"
-        assert "model-apply-guardrail" in unified_received_data.get(
-            "metadata", {}
-        ).get("guardrails", []), \
-            "UnifiedLLMGuardrails data must contain model-level guardrails"
+        assert (
+            unified_received_data is not None
+        ), "UnifiedLLMGuardrails must be called for apply_guardrail guardrails"
+        assert (
+            unified_received_data.get("_merged_marker") is True
+        ), "UnifiedLLMGuardrails must receive guardrail_data (merged), not captured_data"
+        assert "model-apply-guardrail" in unified_received_data.get("metadata", {}).get(
+            "guardrails", []
+        ), "UnifiedLLMGuardrails data must contain model-level guardrails"
 
     @pytest.mark.asyncio
     async def test_multiple_guardrails_all_receive_merged_data(self):
@@ -887,11 +898,10 @@ class TestDeferredStreamingClosure:
             merged["_merged_marker"] = True
             return merged
 
-        with patch("litellm.callbacks", [guardrail_a, guardrail_b]), \
-             patch(
-                "litellm.proxy.utils._check_and_merge_model_level_guardrails",
-                side_effect=mock_merge,
-             ):
+        with patch("litellm.callbacks", [guardrail_a, guardrail_b]), patch(
+            "litellm.proxy.utils._check_and_merge_model_level_guardrails",
+            side_effect=mock_merge,
+        ):
             await ProxyBaseLLMRequestProcessing._run_deferred_stream_guardrails(
                 captured_data=captured_data,
                 captured_user_api_key_dict=UserAPIKeyAuth(api_key="test"),
@@ -901,10 +911,10 @@ class TestDeferredStreamingClosure:
             )
 
         for name in ("guardrail-a", "guardrail-b"):
-            assert name in received_data_per_guardrail, \
-                f"{name} must be called"
-            assert received_data_per_guardrail[name].get("_merged_marker") is True, \
-                f"{name} must receive guardrail_data (merged), not captured_data"
+            assert name in received_data_per_guardrail, f"{name} must be called"
+            assert (
+                received_data_per_guardrail[name].get("_merged_marker") is True
+            ), f"{name} must receive guardrail_data (merged), not captured_data"
 
     @pytest.mark.asyncio
     async def test_logging_fires_even_if_guardrail_init_raises(self):
@@ -940,5 +950,6 @@ class TestDeferredStreamingClosure:
         await asyncio.sleep(0)
         await asyncio.sleep(0)
 
-        assert logging_called is True, \
-            "Logging must fire even when guardrail initialization raises"
+        assert (
+            logging_called is True
+        ), "Logging must fire even when guardrail initialization raises"
diff --git a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py
index e358cbe3be4..0f90d236aed 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py
@@ -85,6 +85,7 @@ async def test_ui_view_users_proxy_admin_no_org_filter(mocker):
     Proxy admin: find_many is called without organization_memberships in where.
     """
     mock_prisma_client = mocker.MagicMock()
+
     async def mock_find_many(*args, **kwargs):
         assert "organization_memberships" not in (kwargs.get("where") or {})
         return []
@@ -327,6 +328,7 @@ async def test_ui_view_users_flag_on_team_admin_non_org_team_403(mocker):
     Flag ON, team admin for non-org team: returns 403.
     """
     from fastapi import HTTPException
+
     from litellm.proxy._types import LiteLLM_TeamTableCachedObj
 
     mock_prisma_client = mocker.MagicMock()
@@ -372,9 +374,7 @@ async def test_ui_view_users_flag_on_team_admin_non_org_team_403(mocker):
 
     with pytest.raises(HTTPException) as exc_info:
         await ui_view_users(
-            user_api_key_dict=UserAPIKeyAuth(
-                user_id="team-admin-user", user_role=None
-            ),
+            user_api_key_dict=UserAPIKeyAuth(user_id="team-admin-user", user_role=None),
             user_id=None,
             user_email="u",
             team_id=tid,
@@ -633,7 +633,9 @@ async def test_get_users_includes_timestamps(mocker):
 
     # Call get_users function directly with proxy admin auth
     admin_key = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN)
-    response = await get_users(page=1, page_size=1, user_api_key_dict=admin_key, organization_ids=None)
+    response = await get_users(
+        page=1, page_size=1, user_api_key_dict=admin_key, organization_ids=None
+    )
 
     print("user /list response: ", response)
 
@@ -855,7 +857,9 @@ async def test_new_user_non_admin_cannot_create_admin(mocker):
 
     # Verify the exception details
     assert exc_info.value.code == 403 or exc_info.value.code == "403"
-    assert "Only proxy admins can create administrative users" in str(exc_info.value.message)
+    assert "Only proxy admins can create administrative users" in str(
+        exc_info.value.message
+    )
     assert "proxy_admin" in str(exc_info.value.message)
     assert "proxy_admin_viewer" in str(exc_info.value.message)
     assert str(LitellmUserRoles.PROXY_ADMIN) in str(exc_info.value.message)
@@ -896,14 +900,14 @@ async def test_user_info_url_encoding_plus_character(mocker):
 
     # Mock the prisma client
     mock_prisma_client = mocker.MagicMock()
-    
+
     # Create a real LiteLLM_UserTable instance (BaseModel) so isinstance check passes
     mock_user = LiteLLM_UserTable(
         user_id="machine-user+alp-air-admin-b58-b@tempus.com",
         user_email="machine-user+alp-air-admin-b58-b@tempus.com",
         teams=[],
     )
-    
+
     # Mock get_data to return user when called with user_id, empty list for keys
     async def mock_get_data(*args, **kwargs):
         if kwargs.get("table_name") == "key":
@@ -913,7 +917,7 @@ async def test_user_info_url_encoding_plus_character(mocker):
         elif kwargs.get("user_id") is not None:
             return mock_user
         return None
-    
+
     mock_prisma_client.get_data = mocker.AsyncMock(side_effect=mock_get_data)
 
     # Mock list_team to return None (patch it from where it's imported)
@@ -941,7 +945,7 @@ async def test_user_info_url_encoding_plus_character(mocker):
         "machine-user alp-air-admin-b58-b@tempus.com"  # What FastAPI gives us
     )
     expected_user_id = "machine-user+alp-air-admin-b58-b@tempus.com"
-    
+
     response = await user_info(
         user_id=decoded_user_id,
         user_api_key_dict=mock_user_api_key_dict,
@@ -955,7 +959,7 @@ async def test_user_info_url_encoding_plus_character(mocker):
         if call.kwargs.get("user_id") and not call.kwargs.get("table_name"):
             user_call = call
             break
-    
+
     assert user_call is not None, "get_data should be called with user_id"
     assert user_call.kwargs["user_id"] == expected_user_id
 
@@ -972,7 +976,7 @@ async def test_user_info_nonexistent_user(mocker):
 
     # Mock the prisma client
     mock_prisma_client = mocker.MagicMock()
-    
+
     # Mock get_data to return None (user doesn't exist)
     async def mock_get_data(*args, **kwargs):
         if kwargs.get("table_name") == "key":
@@ -980,7 +984,7 @@ async def test_user_info_nonexistent_user(mocker):
         elif kwargs.get("user_id") is not None:
             return None  # User not found
         return None
-    
+
     mock_prisma_client.get_data = mocker.AsyncMock(side_effect=mock_get_data)
 
     # Patch the prisma client import in the endpoint
@@ -996,7 +1000,7 @@ async def test_user_info_nonexistent_user(mocker):
 
     # Call user_info function with a non-existent user_id
     nonexistent_user_id = "nonexistent-user@example.com"
-    
+
     # Should raise ProxyException with 404 status code (HTTPException is converted by decorator)
     with pytest.raises(ProxyException) as exc_info:
         await user_info(
@@ -1370,9 +1374,7 @@ async def test_check_duplicate_user_id(mocker):
         await _check_duplicate_user_id("existing-user-id", mock_prisma_client)
 
     assert exc_info.value.status_code == 409
-    assert "User with id existing-user-id already exists" in str(
-        exc_info.value.detail
-    )
+    assert "User with id existing-user-id already exists" in str(exc_info.value.detail)
 
     # No duplicate should pass
     async def mock_find_first_no_duplicate(*args, **kwargs):
@@ -1393,7 +1395,7 @@ async def test_check_duplicate_user_id(mocker):
 def test_process_keys_for_user_info_filters_dashboard_keys(monkeypatch):
     """
     Test that _process_keys_for_user_info filters out keys with team_id='litellm-dashboard'
-    
+
     UI session tokens (team_id='litellm-dashboard') should be excluded from user info responses
     to prevent confusion, as these are automatically created during dashboard login.
     """
@@ -1412,7 +1414,7 @@ def test_process_keys_for_user_info_filters_dashboard_keys(monkeypatch):
         "user_id": "test-user",
         "key_alias": "dashboard-session-key",
     }
-    
+
     mock_key_regular = MagicMock()
     mock_key_regular.model_dump.return_value = {
         "token": "sk-regular-token",
@@ -1420,7 +1422,7 @@ def test_process_keys_for_user_info_filters_dashboard_keys(monkeypatch):
         "user_id": "test-user",
         "key_alias": "regular-key",
     }
-    
+
     mock_key_no_team = MagicMock()
     mock_key_no_team.model_dump.return_value = {
         "token": "sk-no-team-token",
@@ -1446,20 +1448,24 @@ def test_process_keys_for_user_info_filters_dashboard_keys(monkeypatch):
 
     # Verify that dashboard key is filtered out
     assert len(result) == 2, "Should return 2 keys (dashboard key filtered out)"
-    
+
     # Verify dashboard key is not in results
     result_team_ids = [key.get("team_id") for key in result]
-    assert UI_SESSION_TOKEN_TEAM_ID not in result_team_ids, "Dashboard key should be filtered out"
-    
+    assert (
+        UI_SESSION_TOKEN_TEAM_ID not in result_team_ids
+    ), "Dashboard key should be filtered out"
+
     # Verify regular keys are included
     assert "regular-team" in result_team_ids, "Regular team key should be included"
     assert None in result_team_ids, "No-team key should be included"
-    
+
     # Verify the correct keys are returned
     result_tokens = [key.get("token") for key in result]
     assert "sk-regular-token" in result_tokens, "Regular key should be included"
     assert "sk-no-team-token" in result_tokens, "No-team key should be included"
-    assert "sk-dashboard-token" not in result_tokens, "Dashboard key should not be included"
+    assert (
+        "sk-dashboard-token" not in result_tokens
+    ), "Dashboard key should not be included"
 
 
 def test_process_keys_for_user_info_handles_none_keys(monkeypatch):
@@ -1558,7 +1564,13 @@ async def test_get_users_user_id_partial_match(mocker):
     admin_key = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN)
 
     captured_where_conditions.clear()
-    await get_users(user_ids="test-user", page=1, page_size=1, user_api_key_dict=admin_key, organization_ids=None)
+    await get_users(
+        user_ids="test-user",
+        page=1,
+        page_size=1,
+        user_api_key_dict=admin_key,
+        organization_ids=None,
+    )
 
     assert "user_id" in captured_where_conditions
     assert "contains" in captured_where_conditions["user_id"]
@@ -1566,7 +1578,13 @@ async def test_get_users_user_id_partial_match(mocker):
     assert captured_where_conditions["user_id"]["mode"] == "insensitive"
 
     captured_where_conditions.clear()
-    await get_users(user_ids="user1,user2,user3", page=1, page_size=1, user_api_key_dict=admin_key, organization_ids=None)
+    await get_users(
+        user_ids="user1,user2,user3",
+        page=1,
+        page_size=1,
+        user_api_key_dict=admin_key,
+        organization_ids=None,
+    )
 
     assert "user_id" in captured_where_conditions
     assert "in" in captured_where_conditions["user_id"]
@@ -1578,7 +1596,7 @@ def test_update_internal_user_params_reset_max_budget_with_none():
     Test that _update_internal_user_params allows setting max_budget to None.
     This verifies the fix for unsetting/resetting the budget to unlimited.
     """
-    
+
     # Case 1: max_budget is explicitly None in the input dictionary
     data_json = {"max_budget": None, "user_id": "test_user"}
     data = UpdateUserRequest(max_budget=None, user_id="test_user")
@@ -1610,7 +1628,7 @@ def test_update_internal_user_params_ignores_other_nones():
 
 def test_update_internal_user_params_keeps_original_max_budget_when_not_provided():
     """
-    Test that _update_internal_user_params does not include max_budget 
+    Test that _update_internal_user_params does not include max_budget
     when it's not provided in the request (should keep original value).
     """
     # Create test data without max_budget
@@ -1631,7 +1649,7 @@ def test_generate_request_base_validator():
     Test that GenerateRequestBase validator converts empty string to None for max_budget
     """
     from litellm.proxy._types import GenerateRequestBase
-    
+
     # Test with empty string
     req = GenerateRequestBase(max_budget="")
     assert req.max_budget is None
@@ -1662,9 +1680,7 @@ async def test_get_user_daily_activity_non_admin_cannot_view_other_users(monkeyp
 
     # Mock the prisma client so the DB-not-connected check passes
     mock_prisma_client = MagicMock()
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.prisma_client", mock_prisma_client
-    )
+    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
 
     # Non-admin caller
     non_admin_key_dict = UserAPIKeyAuth(
@@ -1731,9 +1747,7 @@ async def test_get_user_daily_activity_aggregated_admin_global_view(monkeypatch)
 
     # Mock the prisma client
     mock_prisma_client = MagicMock()
-    monkeypatch.setattr(
-        "litellm.proxy.proxy_server.prisma_client", mock_prisma_client
-    )
+    monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client)
 
     # Mock the downstream helper so we don't need a real DB
     mock_response = MagicMock()
@@ -1846,7 +1860,9 @@ async def test_delete_user_cleans_up_created_by_invitation_links(mocker):
     call_kwargs = mock_prisma_client.db.litellm_invitationlink.delete_many.call_args
     where_clause = call_kwargs.kwargs.get("where") or call_kwargs[1].get("where")
 
-    assert "OR" in where_clause, "Should use OR to match user_id, created_by, and updated_by"
+    assert (
+        "OR" in where_clause
+    ), "Should use OR to match user_id, created_by, and updated_by"
     or_conditions = where_clause["OR"]
     assert len(or_conditions) == 3, "Should have 3 OR conditions"
 
@@ -2188,9 +2204,20 @@ async def test_user_info_v2_response_shape(mocker):
     # Verify all expected fields are present
     response_dict = response.model_dump()
     expected_fields = {
-        "user_id", "user_email", "user_alias", "user_role", "spend",
-        "max_budget", "models", "budget_duration", "budget_reset_at",
-        "metadata", "created_at", "updated_at", "sso_user_id", "teams",
+        "user_id",
+        "user_email",
+        "user_alias",
+        "user_role",
+        "spend",
+        "max_budget",
+        "models",
+        "budget_duration",
+        "budget_reset_at",
+        "metadata",
+        "created_at",
+        "updated_at",
+        "sso_user_id",
+        "teams",
     }
     assert set(response_dict.keys()) == expected_fields
 
@@ -2418,4 +2445,74 @@ async def test_user_info_v2_url_encoding_plus_character(mocker):
     )
 
     assert isinstance(response, UserInfoV2Response)
-    assert response.user_id == expected_user_id
\ No newline at end of file
+    assert response.user_id == expected_user_id
+
+
+class TestGetUserIdFromRequestValidation:
+    """Tests for user_id input validation in get_user_id_from_request."""
+
+    def _make_request(self, query_string: str):
+        from unittest.mock import MagicMock
+
+        from starlette.requests import Request
+
+        request = MagicMock(spec=Request)
+        request.url.query = query_string
+        return request
+
+    def test_valid_uuid(self):
+        from litellm.proxy.management_endpoints.internal_user_endpoints import (
+            get_user_id_from_request,
+        )
+
+        request = self._make_request("user_id=550e8400-e29b-41d4-a716-446655440000")
+        result = get_user_id_from_request(request)
+        assert result == "550e8400-e29b-41d4-a716-446655440000"
+
+    def test_valid_email(self):
+        from litellm.proxy.management_endpoints.internal_user_endpoints import (
+            get_user_id_from_request,
+        )
+
+        request = self._make_request("user_id=user%40example.com")
+        result = get_user_id_from_request(request)
+        assert result == "user@example.com"
+
+    def test_rejects_overlong_user_id(self):
+        from litellm.proxy.management_endpoints.internal_user_endpoints import (
+            get_user_id_from_request,
+        )
+
+        long_id = "a" * 513
+        request = self._make_request(f"user_id={long_id}")
+        result = get_user_id_from_request(request)
+        assert result is None
+
+    def test_rejects_null_byte(self):
+        from litellm.proxy.management_endpoints.internal_user_endpoints import (
+            get_user_id_from_request,
+        )
+
+        request = self._make_request("user_id=admin%00evil")
+        result = get_user_id_from_request(request)
+        assert result is None
+
+    def test_rejects_control_characters(self):
+        from litellm.proxy.management_endpoints.internal_user_endpoints import (
+            get_user_id_from_request,
+        )
+
+        # Tab character (0x09)
+        request = self._make_request("user_id=admin%09evil")
+        result = get_user_id_from_request(request)
+        assert result is None
+
+    def test_allows_512_char_user_id(self):
+        from litellm.proxy.management_endpoints.internal_user_endpoints import (
+            get_user_id_from_request,
+        )
+
+        exact_id = "a" * 512
+        request = self._make_request(f"user_id={exact_id}")
+        result = get_user_id_from_request(request)
+        assert result == exact_id
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 f3c89003105..2e566ab6222 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
@@ -1,13 +1,14 @@
 import json
 import os
 import sys
-from litellm._uuid import uuid
 from typing import Dict, Optional
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 from fastapi.testclient import TestClient
 
+from litellm._uuid import uuid
+
 sys.path.insert(
     0, os.path.abspath("../../../..")
 )  # Adds the parent directory to the system path
@@ -27,9 +28,15 @@ from litellm.types.router import Deployment, LiteLLM_Params, updateDeployment
 
 
 class MockPrismaClient:
-    def __init__(self, team_exists: bool = True, user_admin: bool = True):
+    def __init__(
+        self,
+        team_exists: bool = True,
+        user_admin: bool = True,
+        sibling_deployments: list = None,
+    ):
         self.team_exists = team_exists
         self.user_admin = user_admin
+        self.sibling_deployments = sibling_deployments or []
         self.db = self
 
     async def find_unique(self, where):
@@ -45,10 +52,53 @@ class MockPrismaClient:
             )
         return None
 
+    async def find_many(self, where):
+        # Filter sibling deployments by team_id if where clause specifies it
+        if not self.sibling_deployments:
+            return []
+
+        # Extract team_id from where clause if present
+        team_id_filter = None
+        if where and "model_info" in where:
+            model_info_filter = where["model_info"]
+            if isinstance(model_info_filter, dict) and "path" in model_info_filter:
+                if (
+                    model_info_filter["path"] == ["team_id"]
+                    and "equals" in model_info_filter
+                ):
+                    team_id_filter = model_info_filter["equals"]
+
+        # Filter deployments by team_id if specified
+        if team_id_filter:
+
+            def _get_team_id(model_info):
+                if isinstance(model_info, dict):
+                    return model_info.get("team_id")
+                if isinstance(model_info, str):
+                    try:
+                        parsed = json.loads(model_info)
+                    except (TypeError, ValueError):
+                        return None
+                    if isinstance(parsed, dict):
+                        return parsed.get("team_id")
+                return None
+
+            return [
+                d
+                for d in self.sibling_deployments
+                if _get_team_id(d.model_info) == team_id_filter
+            ]
+
+        return self.sibling_deployments
+
     @property
     def litellm_teamtable(self):
         return self
 
+    @property
+    def litellm_proxymodeltable(self):
+        return self
+
 
 class MockLLMRouter:
     def __init__(self):
@@ -399,7 +449,9 @@ class TestClearCache:
         """
         Test that clear_cache clears DB models and preserves config models.
         """
-        from litellm.proxy.management_endpoints.model_management_endpoints import clear_cache
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            clear_cache,
+        )
 
         # Create mock router with mixed DB and config models
         mock_router = MagicMock()
@@ -407,18 +459,18 @@ class TestClearCache:
             {
                 "model_name": "gpt-4",
                 "model_info": {"id": "db-model-1", "db_model": True},
-                "litellm_params": {"model": "gpt-4"}
+                "litellm_params": {"model": "gpt-4"},
             },
             {
-                "model_name": "gpt-3.5-turbo", 
+                "model_name": "gpt-3.5-turbo",
                 "model_info": {"id": "config-model-1", "db_model": False},
-                "litellm_params": {"model": "gpt-3.5-turbo"}
+                "litellm_params": {"model": "gpt-3.5-turbo"},
             },
             {
                 "model_name": "claude-3",
                 "model_info": {"id": "db-model-2", "db_model": True},
-                "litellm_params": {"model": "claude-3"}
-            }
+                "litellm_params": {"model": "claude-3"},
+            },
         ]
         mock_router.delete_deployment = MagicMock(return_value=True)
         mock_router.auto_routers = MagicMock()
@@ -466,8 +518,8 @@ class TestUpdatePublicModelGroups:
         """
         import litellm
         from litellm.proxy.management_endpoints.model_management_endpoints import (
-            update_public_model_groups,
             UpdatePublicModelGroupsRequest,
+            update_public_model_groups,
         )
 
         old_db_models = ["db-model-1", "db-model-2"]
@@ -525,7 +577,10 @@ class TestUpdatePublicModelGroups:
         )
 
         old_links = {"Old Doc": "https://old.example.com"}
-        new_links = {"New Doc": "https://new.example.com", "API Ref": "https://api.example.com"}
+        new_links = {
+            "New Doc": "https://new.example.com",
+            "API Ref": "https://api.example.com",
+        }
 
         async def mock_get_config(*args, **kwargs):
             litellm.public_model_groups_links = old_links
@@ -558,6 +613,161 @@ class TestUpdatePublicModelGroups:
             litellm.public_model_groups_links = original_value
 
 
+class TestTeamModelSiblingRouting:
+    """
+    Verify that sibling team deployments (same public model name, different
+    api_base) are all reachable through routing — no alias overwrite, no
+    collapse to a single deployment.
+    """
+
+    @pytest.mark.asyncio
+    async def test_no_model_aliases_written_for_team_models(self):
+        """
+        _add_team_model_to_db must NOT write model_aliases (which caused
+        the second sibling to overwrite the first). It should only call
+        team_model_add to register the public name on the team's models list.
+        """
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            _add_team_model_to_db,
+        )
+        from litellm.types.router import ModelInfo
+
+        team_id = "team_no_alias"
+        public_name = "gpt-4.1-mini"
+
+        async def mock_add_model_to_db(model_params, user_api_key_dict, prisma_client):
+            return MagicMock(model_id=str(uuid.uuid4()))
+
+        mock_team_model_add = AsyncMock()
+
+        user = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN)
+        prisma_client = MockPrismaClient(team_exists=True)
+
+        for api_base in ["https://eastus.example.com", "https://westus.example.com"]:
+            dep = Deployment(
+                model_name=public_name,
+                litellm_params=LiteLLM_Params(
+                    model="azure/gpt-4o-mini",
+                    api_key="key",
+                    api_base=api_base,
+                ),
+                model_info=ModelInfo(team_id=team_id),
+            )
+            with patch(
+                "litellm.proxy.management_endpoints.model_management_endpoints._add_model_to_db",
+                side_effect=mock_add_model_to_db,
+            ), patch(
+                "litellm.proxy.management_endpoints.model_management_endpoints.team_model_add",
+                mock_team_model_add,
+            ):
+                await _add_team_model_to_db(
+                    model_params=dep,
+                    user_api_key_dict=user,
+                    prisma_client=prisma_client,
+                )
+
+        assert mock_team_model_add.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_router_finds_all_sibling_team_deployments(self):
+        """
+        When two team deployments share team_public_model_name="gpt-4.1-mini",
+        the router's _common_checks_available_deployment must return BOTH as
+        healthy_deployments (not collapse to one).
+        """
+        import litellm
+
+        team_id = "teamA"
+        public_name = "gpt-4.1-mini"
+
+        router = litellm.Router(
+            model_list=[
+                {
+                    "model_name": f"model_name_{team_id}_uuid1",
+                    "litellm_params": {
+                        "model": "azure/gpt-4o-mini",
+                        "api_key": "key-1",
+                        "api_base": "https://eastus.openai.azure.com",
+                    },
+                    "model_info": {
+                        "team_id": team_id,
+                        "team_public_model_name": public_name,
+                    },
+                },
+                {
+                    "model_name": f"model_name_{team_id}_uuid2",
+                    "litellm_params": {
+                        "model": "azure/gpt-4o-mini",
+                        "api_key": "key-2",
+                        "api_base": "https://westus.openai.azure.com",
+                    },
+                    "model_info": {
+                        "team_id": team_id,
+                        "team_public_model_name": public_name,
+                    },
+                },
+                {
+                    "model_name": "global-gpt-4o",
+                    "litellm_params": {
+                        "model": "azure/gpt-4o",
+                        "api_key": "global-key",
+                        "api_base": "https://global.openai.azure.com",
+                    },
+                    "model_info": {},  # No team_id - global deployment
+                },
+            ],
+        )
+
+        # map_team_model should return the public name (not an internal UUID)
+        result = router.map_team_model(public_name, team_id)
+        assert result == public_name
+
+        # _common_checks_available_deployment should return both deployments
+        model, healthy = router._common_checks_available_deployment(
+            model=public_name,
+            request_kwargs={"metadata": {"user_api_key_team_id": team_id}},
+        )
+        assert isinstance(healthy, list)
+        assert len(healthy) == 2
+        api_bases = {d["litellm_params"]["api_base"] for d in healthy}
+        assert api_bases == {
+            "https://eastus.openai.azure.com",
+            "https://westus.openai.azure.com",
+        }
+
+    def test_global_deployments_accessible_to_teams(self):
+        """Test that global deployments (no team_id) are accessible to all teams"""
+        import litellm
+
+        router = litellm.Router(
+            model_list=[
+                {
+                    "model_name": "global-gpt-4o",
+                    "litellm_params": {
+                        "model": "azure/gpt-4o",
+                        "api_key": "global-key",
+                        "api_base": "https://global.openai.azure.com",
+                    },
+                    "model_info": {},  # No team_id - global deployment
+                },
+            ],
+        )
+
+        # Global deployment should be accessible when team_id is provided
+        deployments = router._get_all_deployments(
+            model_name="global-gpt-4o", team_id="teamA"
+        )
+        assert len(deployments) == 1
+        assert deployments[0]["model_name"] == "global-gpt-4o"
+
+        # should_include_deployment should return True for global deployments
+        assert router.should_include_deployment(
+            model_name="global-gpt-4o",
+            model={"model_name": "global-gpt-4o", "model_info": {}},
+            team_id="teamA",
+        )
+
+
 class TestTeamModelUpdate:
     """Test team model update handles team_id consistently with model creation"""
 
@@ -591,10 +801,10 @@ class TestTeamModelUpdate:
             "litellm.proxy.proxy_server.premium_user",
             True,
         ), patch(
-            "litellm.proxy.management_endpoints.model_management_endpoints.update_team"
-        ) as mock_update_team, patch(
             "litellm.proxy.management_endpoints.model_management_endpoints.team_model_add"
-        ) as mock_team_model_add:
+        ) as mock_team_model_add, patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.update_team"
+        ) as mock_update_team:
             result = await _update_team_model_in_db(
                 db_model=db_model,
                 patch_data=patch_data,
@@ -604,8 +814,201 @@ class TestTeamModelUpdate:
 
             assert result.get("model_name", "").startswith("model_name_test_team_123_")
             assert "team_public_model_name" in str(result.get("model_info", ""))
-            mock_update_team.assert_called_once()
+            # team_model_add must be called to add public name to team's models list
             mock_team_model_add.assert_called_once()
+            # update_team (model_aliases write) must NOT be called in the new implementation
+            mock_update_team.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_rename_preserves_old_name_when_siblings_exist(self):
+        """Test that renaming a deployment preserves old public name when sibling deployments still use it"""
+        from unittest.mock import MagicMock
+
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            _update_existing_team_model_assignment,
+        )
+        from litellm.types.router import ModelInfo
+
+        # Create a deployment being renamed
+        db_model = Deployment(
+            model_name="model_name_team_123_uuid1",
+            litellm_params=LiteLLM_Params(model="azure/gpt-4o-mini"),
+            model_info=ModelInfo(
+                team_id="team_123", team_public_model_name="old-public-name"
+            ),
+        )
+
+        # Create a sibling deployment that still uses the old public name
+        sibling_deployment = MagicMock()
+        sibling_deployment.model_name = "model_name_team_123_uuid2"
+        sibling_deployment.model_info = {
+            "team_id": "team_123",
+            "team_public_model_name": "old-public-name",
+        }
+
+        prisma_client = MockPrismaClient(
+            team_exists=True, sibling_deployments=[sibling_deployment]
+        )
+
+        patch_data = updateDeployment(
+            model_name="new-public-name",
+            model_info=ModelInfo(team_id="team_123"),
+        )
+
+        user_api_key_dict = UserAPIKeyAuth(
+            user_id="test_user",
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+        )
+
+        with patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.team_model_delete"
+        ) as mock_delete, patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.team_model_add"
+        ) as mock_add:
+            await _update_existing_team_model_assignment(
+                team_id="team_123",
+                public_model_name="new-public-name",
+                db_model=db_model,
+                patch_data=patch_data,
+                user_api_key_dict=user_api_key_dict,
+                prisma_client=prisma_client,  # type: ignore
+            )
+
+            # team_model_delete should NOT be called because sibling exists
+            mock_delete.assert_not_called()
+            # team_model_add should be called to add new public name
+            mock_add.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_first_time_public_name_assignment_adds_team_model(self):
+        """If existing team deployment had no public name, first assignment must call team_model_add."""
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            _update_existing_team_model_assignment,
+        )
+        from litellm.types.router import ModelInfo
+
+        db_model = Deployment(
+            model_name="model_name_team_123_uuid1",
+            litellm_params=LiteLLM_Params(model="azure/gpt-4o-mini"),
+            model_info=ModelInfo(team_id="team_123"),
+        )
+
+        patch_data = updateDeployment(
+            model_name="new-public-name",
+            model_info=ModelInfo(team_id="team_123"),
+        )
+
+        user_api_key_dict = UserAPIKeyAuth(
+            user_id="test_user",
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+        )
+
+        with patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.team_model_delete"
+        ) as mock_delete, patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.team_model_add"
+        ) as mock_add:
+            await _update_existing_team_model_assignment(
+                team_id="team_123",
+                public_model_name="new-public-name",
+                db_model=db_model,
+                patch_data=patch_data,
+                user_api_key_dict=user_api_key_dict,
+                prisma_client=None,
+            )
+
+            mock_add.assert_called_once()
+            mock_delete.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_rename_with_prisma_none_clears_patch_model_name(self):
+        """Rename path must clear patch_data.model_name even when prisma is unavailable (P1)."""
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            _update_existing_team_model_assignment,
+        )
+        from litellm.types.router import ModelInfo
+
+        db_model = Deployment(
+            model_name="model_name_team_123_uuid1",
+            litellm_params=LiteLLM_Params(model="azure/gpt-4o-mini"),
+            model_info=ModelInfo(
+                team_id="team_123", team_public_model_name="old-public-name"
+            ),
+        )
+        patch_data = updateDeployment(
+            model_name="new-public-name",
+            model_info=ModelInfo(team_id="team_123"),
+        )
+        user_api_key_dict = UserAPIKeyAuth(
+            user_id="test_user",
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+        )
+
+        await _update_existing_team_model_assignment(
+            team_id="team_123",
+            public_model_name="new-public-name",
+            db_model=db_model,
+            patch_data=patch_data,
+            user_api_key_dict=user_api_key_dict,
+            prisma_client=None,
+        )
+
+        assert patch_data.model_name is None
+
+    @pytest.mark.asyncio
+    async def test_rename_handles_legacy_string_model_info(self):
+        """Test rename path handles legacy string-encoded model_info rows without crashing."""
+        from unittest.mock import MagicMock
+
+        from litellm.proxy.management_endpoints.model_management_endpoints import (
+            _update_existing_team_model_assignment,
+        )
+        from litellm.types.router import ModelInfo
+
+        db_model = Deployment(
+            model_name="model_name_team_123_uuid1",
+            litellm_params=LiteLLM_Params(model="azure/gpt-4o-mini"),
+            model_info=ModelInfo(
+                team_id="team_123", team_public_model_name="old-public-name"
+            ),
+        )
+
+        sibling_deployment = MagicMock()
+        sibling_deployment.model_name = "model_name_team_123_uuid2"
+        sibling_deployment.model_info = (
+            '{"team_id":"team_123","team_public_model_name":"old-public-name"}'
+        )
+
+        prisma_client = MockPrismaClient(
+            team_exists=True, sibling_deployments=[sibling_deployment]
+        )
+
+        patch_data = updateDeployment(
+            model_name="new-public-name",
+            model_info=ModelInfo(team_id="team_123"),
+        )
+
+        user_api_key_dict = UserAPIKeyAuth(
+            user_id="test_user",
+            user_role=LitellmUserRoles.PROXY_ADMIN,
+        )
+
+        with patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.team_model_delete"
+        ) as mock_delete, patch(
+            "litellm.proxy.management_endpoints.model_management_endpoints.team_model_add"
+        ) as mock_add:
+            await _update_existing_team_model_assignment(
+                team_id="team_123",
+                public_model_name="new-public-name",
+                db_model=db_model,
+                patch_data=patch_data,
+                user_api_key_dict=user_api_key_dict,
+                prisma_client=prisma_client,  # type: ignore
+            )
+
+            mock_delete.assert_not_called()
+            mock_add.assert_called_once()
 
     @pytest.mark.asyncio
     async def test_patch_model_with_team_id_validates_permissions(self):
@@ -657,27 +1060,37 @@ class TestModelInfoEndpoint:
             user_id="test_user",
             api_key="test_key",
             models=["gpt-4", "claude-3"],
-            team_models=["gpt-3.5-turbo"]
+            team_models=["gpt-3.5-turbo"],
         )
 
-        with patch("litellm.proxy.proxy_server.llm_router") as mock_router, \
-             patch("litellm.proxy.proxy_server.get_key_models") as mock_get_key_models, \
-             patch("litellm.proxy.proxy_server.get_team_models") as mock_get_team_models, \
-             patch("litellm.proxy.proxy_server.get_complete_model_list") as mock_get_complete_models, \
-             patch("litellm.get_llm_provider") as mock_get_provider:
-            
+        with patch("litellm.proxy.proxy_server.llm_router") as mock_router, patch(
+            "litellm.proxy.proxy_server.get_key_models"
+        ) as mock_get_key_models, patch(
+            "litellm.proxy.proxy_server.get_team_models"
+        ) as mock_get_team_models, patch(
+            "litellm.proxy.proxy_server.get_complete_model_list"
+        ) as mock_get_complete_models, patch(
+            "litellm.get_llm_provider"
+        ) as mock_get_provider:
             # Setup mocks
-            mock_router.get_model_names.return_value = ["gpt-4", "claude-3", "gpt-3.5-turbo"]
+            mock_router.get_model_names.return_value = [
+                "gpt-4",
+                "claude-3",
+                "gpt-3.5-turbo",
+            ]
             mock_router.get_model_access_groups.return_value = {}
             mock_get_key_models.return_value = ["gpt-4", "claude-3"]
             mock_get_team_models.return_value = ["gpt-3.5-turbo"]
-            mock_get_complete_models.return_value = ["gpt-4", "claude-3", "gpt-3.5-turbo"]
+            mock_get_complete_models.return_value = [
+                "gpt-4",
+                "claude-3",
+                "gpt-3.5-turbo",
+            ]
             mock_get_provider.return_value = (None, "openai", None, None)
 
             # Test accessible model
             result = await model_info(
-                model_id="gpt-4",
-                user_api_key_dict=user_api_key_dict
+                model_id="gpt-4", user_api_key_dict=user_api_key_dict
             )
 
             assert result["id"] == "gpt-4"
@@ -688,22 +1101,25 @@ class TestModelInfoEndpoint:
     @pytest.mark.asyncio
     async def test_model_info_inaccessible_model_returns_404(self):
         """Test model_info returns 404 for inaccessible models"""
-        from litellm.proxy.proxy_server import model_info
         from fastapi import HTTPException
 
+        from litellm.proxy.proxy_server import model_info
+
         # Mock user with limited access
         user_api_key_dict = UserAPIKeyAuth(
             user_id="test_user",
             api_key="test_key",
             models=["gpt-4"],  # Only has access to gpt-4
-            team_models=[]
+            team_models=[],
         )
 
-        with patch("litellm.proxy.proxy_server.llm_router") as mock_router, \
-             patch("litellm.proxy.proxy_server.get_key_models") as mock_get_key_models, \
-             patch("litellm.proxy.proxy_server.get_team_models") as mock_get_team_models, \
-             patch("litellm.proxy.proxy_server.get_complete_model_list") as mock_get_complete_models:
-            
+        with patch("litellm.proxy.proxy_server.llm_router") as mock_router, patch(
+            "litellm.proxy.proxy_server.get_key_models"
+        ) as mock_get_key_models, patch(
+            "litellm.proxy.proxy_server.get_team_models"
+        ) as mock_get_team_models, patch(
+            "litellm.proxy.proxy_server.get_complete_model_list"
+        ) as mock_get_complete_models:
             # Setup mocks - user only has access to gpt-4
             mock_router.get_model_names.return_value = ["gpt-4", "claude-3"]
             mock_router.get_model_access_groups.return_value = {}
@@ -715,32 +1131,35 @@ class TestModelInfoEndpoint:
             with pytest.raises(HTTPException) as exc_info:
                 await model_info(
                     model_id="claude-3",  # Not in user's accessible models
-                    user_api_key_dict=user_api_key_dict
+                    user_api_key_dict=user_api_key_dict,
                 )
-            
+
             assert exc_info.value.status_code == 404
             assert "does not exist or is not accessible" in exc_info.value.detail
 
-    @pytest.mark.asyncio 
+    @pytest.mark.asyncio
     async def test_model_info_team_model_access(self):
         """Test model_info works with team model access"""
         from litellm.proxy.proxy_server import model_info
-        
+
         # Mock user with team access
         user_api_key_dict = UserAPIKeyAuth(
             user_id="test_user",
-            api_key="test_key", 
+            api_key="test_key",
             team_id="test_team",
             models=[],  # No direct key models
-            team_models=["team-model-1"]
+            team_models=["team-model-1"],
         )
 
-        with patch("litellm.proxy.proxy_server.llm_router") as mock_router, \
-             patch("litellm.proxy.proxy_server.get_key_models") as mock_get_key_models, \
-             patch("litellm.proxy.proxy_server.get_team_models") as mock_get_team_models, \
-             patch("litellm.proxy.proxy_server.get_complete_model_list") as mock_get_complete_models, \
-             patch("litellm.get_llm_provider") as mock_get_provider:
-            
+        with patch("litellm.proxy.proxy_server.llm_router") as mock_router, patch(
+            "litellm.proxy.proxy_server.get_key_models"
+        ) as mock_get_key_models, patch(
+            "litellm.proxy.proxy_server.get_team_models"
+        ) as mock_get_team_models, patch(
+            "litellm.proxy.proxy_server.get_complete_model_list"
+        ) as mock_get_complete_models, patch(
+            "litellm.get_llm_provider"
+        ) as mock_get_provider:
             # Setup mocks
             mock_router.get_model_names.return_value = ["team-model-1"]
             mock_router.get_model_access_groups.return_value = {}
@@ -751,10 +1170,9 @@ class TestModelInfoEndpoint:
 
             # Test team model access
             result = await model_info(
-                model_id="team-model-1",
-                user_api_key_dict=user_api_key_dict
+                model_id="team-model-1", user_api_key_dict=user_api_key_dict
             )
 
             assert result["id"] == "team-model-1"
-            assert result["object"] == "model" 
+            assert result["object"] == "model"
             assert result["owned_by"] == "custom"
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 fc9c37b7f84..ff636ca04ae 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py
@@ -5164,15 +5164,13 @@ def test_generic_response_convertor_extra_attributes_missing_field(monkeypatch):
 class TestValidateReturnTo:
     """Tests for SSOAuthenticationHandler._validate_return_to"""
 
-    def test_rejects_when_no_control_plane_url_configured(self, monkeypatch):
-        """return_to should be rejected if control_plane_url is not in general_settings."""
+    def test_returns_false_when_no_control_plane_url_configured(self, monkeypatch):
+        """return_to should be silently ignored if control_plane_url is not in general_settings."""
         monkeypatch.setattr(
             "litellm.proxy.proxy_server.general_settings", {}
         )
-        with pytest.raises(HTTPException) as exc_info:
-            SSOAuthenticationHandler._validate_return_to("https://cp.example.com/ui")
-        assert exc_info.value.status_code == 400
-        assert "not configured" in exc_info.value.detail
+        result = SSOAuthenticationHandler._validate_return_to("https://cp.example.com/ui")
+        assert result is False
 
     def test_allows_matching_origin(self, monkeypatch):
         """return_to matching the configured control_plane_url origin should pass."""
diff --git a/tests/test_litellm/proxy/management_helpers/test_audit_log_callbacks.py b/tests/test_litellm/proxy/management_helpers/test_audit_log_callbacks.py
new file mode 100644
index 00000000000..85eda4368cd
--- /dev/null
+++ b/tests/test_litellm/proxy/management_helpers/test_audit_log_callbacks.py
@@ -0,0 +1,345 @@
+"""
+Tests for audit log callback dispatch.
+
+Tests the flow: create_audit_log_for_update -> _dispatch_audit_log_to_callbacks -> CustomLogger.async_log_audit_log_event
+"""
+
+import asyncio
+import json
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+import litellm
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.proxy._types import LiteLLM_AuditLogs, LitellmTableNames
+from litellm.proxy.management_helpers.audit_logs import (
+    _audit_log_task_done_callback,
+    _build_audit_log_payload,
+    _dispatch_audit_log_to_callbacks,
+    create_audit_log_for_update,
+)
+from litellm.types.utils import StandardAuditLogPayload
+
+
+@pytest.fixture(autouse=True)
+def reset_audit_log_callbacks():
+    """Reset audit_log_callbacks before and after each test."""
+    original = litellm.audit_log_callbacks
+    litellm.audit_log_callbacks = []
+    yield
+    litellm.audit_log_callbacks = original
+
+
+def _make_audit_log(
+    action: str = "created",
+    table_name: LitellmTableNames = LitellmTableNames.TEAM_TABLE_NAME,
+) -> LiteLLM_AuditLogs:
+    return LiteLLM_AuditLogs(
+        id="test-audit-id",
+        updated_at=datetime(2026, 3, 9, 12, 0, 0, tzinfo=timezone.utc),
+        changed_by="user-123",
+        changed_by_api_key="sk-abc",
+        action=action,
+        table_name=table_name,
+        object_id="team-456",
+        updated_values=json.dumps({"name": "new-team"}),
+        before_value=json.dumps({"name": "old-team"}),
+    )
+
+
+class TestBuildAuditLogPayload:
+    def test_builds_correct_payload(self):
+        audit_log = _make_audit_log()
+        payload = _build_audit_log_payload(audit_log)
+
+        assert payload["id"] == "test-audit-id"
+        assert payload["updated_at"] == "2026-03-09T12:00:00+00:00"
+        assert payload["changed_by"] == "user-123"
+        assert payload["changed_by_api_key"] == "sk-abc"
+        assert payload["action"] == "created"
+        assert payload["table_name"] == "LiteLLM_TeamTable"
+        assert payload["object_id"] == "team-456"
+        assert payload["updated_values"] == json.dumps({"name": "new-team"})
+        assert payload["before_value"] == json.dumps({"name": "old-team"})
+
+    def test_handles_none_values(self):
+        audit_log = LiteLLM_AuditLogs(
+            id="test-id",
+            updated_at=datetime(2026, 1, 1, tzinfo=timezone.utc),
+            changed_by=None,
+            changed_by_api_key=None,
+            action="deleted",
+            table_name=LitellmTableNames.KEY_TABLE_NAME,
+            object_id="key-789",
+            updated_values=None,
+            before_value=None,
+        )
+        payload = _build_audit_log_payload(audit_log)
+
+        assert payload["changed_by"] == ""
+        assert payload["changed_by_api_key"] == ""
+        assert payload["before_value"] is None
+        assert payload["updated_values"] is None
+
+
+class TestDispatchAuditLogToCallbacks:
+    @pytest.mark.asyncio
+    async def test_dispatches_to_custom_logger_instance(self):
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+        litellm.audit_log_callbacks = [mock_logger]
+
+        audit_log = _make_audit_log()
+        await _dispatch_audit_log_to_callbacks(audit_log)
+
+        # Let asyncio.create_task run
+        await asyncio.sleep(0.1)
+
+        mock_logger.async_log_audit_log_event.assert_called_once()
+        payload = mock_logger.async_log_audit_log_event.call_args[0][0]
+        assert payload["id"] == "test-audit-id"
+        assert payload["action"] == "created"
+
+    @pytest.mark.asyncio
+    async def test_no_dispatch_when_callbacks_empty(self):
+        litellm.audit_log_callbacks = []
+        audit_log = _make_audit_log()
+        # Should return immediately without error
+        await _dispatch_audit_log_to_callbacks(audit_log)
+
+    @pytest.mark.asyncio
+    async def test_resolves_string_callback(self):
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+
+        litellm.audit_log_callbacks = ["s3_v2"]
+
+        with patch(
+            "litellm.proxy.management_helpers.audit_logs._resolve_audit_log_callback",
+            return_value=mock_logger,
+        ):
+            audit_log = _make_audit_log()
+            await _dispatch_audit_log_to_callbacks(audit_log)
+            await asyncio.sleep(0.1)
+
+            mock_logger.async_log_audit_log_event.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_nonblocking_on_callback_failure(self):
+        """Callback errors should not propagate."""
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock(
+            side_effect=RuntimeError("boom")
+        )
+        litellm.audit_log_callbacks = [mock_logger]
+
+        audit_log = _make_audit_log()
+        # Should not raise
+        await _dispatch_audit_log_to_callbacks(audit_log)
+        await asyncio.sleep(0.1)
+
+    @pytest.mark.asyncio
+    async def test_skips_unresolvable_string_callback(self):
+        litellm.audit_log_callbacks = ["nonexistent_callback"]
+
+        with patch(
+            "litellm.proxy.management_helpers.audit_logs._resolve_audit_log_callback",
+            return_value=None,
+        ):
+            audit_log = _make_audit_log()
+            # Should not raise
+            await _dispatch_audit_log_to_callbacks(audit_log)
+
+
+class TestCreateAuditLogForUpdateWithCallbacks:
+    @pytest.mark.asyncio
+    async def test_dispatches_to_callbacks_after_db_write(self):
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+        litellm.audit_log_callbacks = [mock_logger]
+
+        with patch("litellm.proxy.proxy_server.premium_user", True), patch(
+            "litellm.store_audit_logs", True
+        ), patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma:
+            mock_prisma.db.litellm_auditlog.create = AsyncMock()
+
+            audit_log = _make_audit_log()
+            await create_audit_log_for_update(audit_log)
+            await asyncio.sleep(0.1)
+
+            # DB write should happen
+            mock_prisma.db.litellm_auditlog.create.assert_called_once()
+            # Callback should also be called
+            mock_logger.async_log_audit_log_event.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_no_dispatch_when_not_premium(self):
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+        litellm.audit_log_callbacks = [mock_logger]
+
+        with patch("litellm.proxy.proxy_server.premium_user", False), patch(
+            "litellm.store_audit_logs", True
+        ):
+            audit_log = _make_audit_log()
+            await create_audit_log_for_update(audit_log)
+            await asyncio.sleep(0.1)
+
+            mock_logger.async_log_audit_log_event.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_no_dispatch_when_store_audit_logs_false(self):
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+        litellm.audit_log_callbacks = [mock_logger]
+
+        with patch("litellm.store_audit_logs", False):
+            audit_log = _make_audit_log()
+            await create_audit_log_for_update(audit_log)
+            await asyncio.sleep(0.1)
+
+            mock_logger.async_log_audit_log_event.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_dispatches_even_when_prisma_client_is_none(self):
+        """Callbacks should fire even if DB is unavailable."""
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+        litellm.audit_log_callbacks = [mock_logger]
+
+        with patch("litellm.proxy.proxy_server.premium_user", True), patch(
+            "litellm.store_audit_logs", True
+        ), patch("litellm.proxy.proxy_server.prisma_client", None):
+            audit_log = _make_audit_log()
+            await create_audit_log_for_update(audit_log)
+            await asyncio.sleep(0.1)
+
+            # Callback should still be called despite no DB
+            mock_logger.async_log_audit_log_event.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_dispatches_even_when_db_write_fails(self):
+        """Callbacks should fire even if the DB write raises."""
+        mock_logger = MagicMock(spec=CustomLogger)
+        mock_logger.async_log_audit_log_event = AsyncMock()
+        litellm.audit_log_callbacks = [mock_logger]
+
+        with patch("litellm.proxy.proxy_server.premium_user", True), patch(
+            "litellm.store_audit_logs", True
+        ), patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma:
+            mock_prisma.db.litellm_auditlog.create = AsyncMock(
+                side_effect=RuntimeError("DB connection lost")
+            )
+
+            audit_log = _make_audit_log()
+            await create_audit_log_for_update(audit_log)
+            await asyncio.sleep(0.1)
+
+            # Callback should still be called despite DB failure
+            mock_logger.async_log_audit_log_event.assert_called_once()
+
+
+class TestAuditLogTaskDoneCallback:
+    def test_logs_exception_from_failed_task(self):
+        """Done callback should log task exceptions."""
+        mock_task = MagicMock(spec=asyncio.Task)
+        mock_task.exception.return_value = RuntimeError("callback failed")
+
+        with patch(
+            "litellm.proxy.management_helpers.audit_logs.verbose_proxy_logger"
+        ) as mock_logger:
+            _audit_log_task_done_callback(mock_task)
+            mock_logger.error.assert_called_once()
+            assert "callback failed" in str(mock_logger.error.call_args)
+
+    def test_no_log_on_success(self):
+        """Done callback should not log when task succeeds."""
+        mock_task = MagicMock(spec=asyncio.Task)
+        mock_task.exception.return_value = None
+
+        with patch(
+            "litellm.proxy.management_helpers.audit_logs.verbose_proxy_logger"
+        ) as mock_logger:
+            _audit_log_task_done_callback(mock_task)
+            mock_logger.error.assert_not_called()
+
+    def test_handles_cancelled_task(self):
+        """Done callback should handle cancelled tasks gracefully."""
+        mock_task = MagicMock(spec=asyncio.Task)
+        mock_task.exception.side_effect = asyncio.CancelledError()
+
+        with patch(
+            "litellm.proxy.management_helpers.audit_logs.verbose_proxy_logger"
+        ) as mock_logger:
+            _audit_log_task_done_callback(mock_task)
+            mock_logger.error.assert_not_called()
+
+
+class TestS3LoggerAuditLogEvent:
+    @pytest.mark.asyncio
+    async def test_queues_audit_log_with_correct_s3_key(self):
+        with patch(
+            "litellm.integrations.s3_v2.S3Logger.__init__", return_value=None
+        ):
+            from litellm.integrations.s3_v2 import S3Logger
+
+            logger = S3Logger()
+            logger.s3_path = "my-prefix"
+            logger.log_queue = []
+            logger.batch_size = 100
+
+            audit_log = StandardAuditLogPayload(
+                id="audit-123",
+                updated_at="2026-03-09T12:00:00+00:00",
+                changed_by="user-1",
+                changed_by_api_key="sk-abc",
+                action="created",
+                table_name="LiteLLM_TeamTable",
+                object_id="team-1",
+                before_value=None,
+                updated_values='{"name": "new"}',
+            )
+
+            await logger.async_log_audit_log_event(audit_log)
+
+            assert len(logger.log_queue) == 1
+            element = logger.log_queue[0]
+            assert element.s3_object_key.startswith("my-prefix/audit_logs/")
+            assert "audit-123" in element.s3_object_key
+            assert element.s3_object_key.endswith(".json")
+            assert element.s3_object_download_filename == "audit-audit-123.json"
+            assert element.payload["id"] == "audit-123"
+            assert element.payload["action"] == "created"
+
+    @pytest.mark.asyncio
+    async def test_s3_key_format_no_path(self):
+        with patch(
+            "litellm.integrations.s3_v2.S3Logger.__init__", return_value=None
+        ):
+            from litellm.integrations.s3_v2 import S3Logger
+
+            logger = S3Logger()
+            logger.s3_path = None
+            logger.log_queue = []
+            logger.batch_size = 100
+
+            audit_log = StandardAuditLogPayload(
+                id="audit-456",
+                updated_at="2026-03-09T12:00:00+00:00",
+                changed_by="user-1",
+                changed_by_api_key="sk-abc",
+                action="deleted",
+                table_name="LiteLLM_VerificationToken",
+                object_id="key-1",
+                before_value=None,
+                updated_values=None,
+            )
+
+            await logger.async_log_audit_log_event(audit_log)
+
+            assert len(logger.log_queue) == 1
+            element = logger.log_queue[0]
+            assert element.s3_object_key.startswith("audit_logs/")
+            assert "audit-456" in element.s3_object_key
diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
index d857d5bdf09..05d9b7489f4 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
+++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
@@ -94,6 +94,8 @@ def make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_fn, team_lookup_fn=No
                 self.find_unique = team_lookup_fn
 
     return MockPrismaClient()
+
+
 from litellm.proxy._types import (
     LitellmUserRoles,
     Member,
@@ -111,7 +113,9 @@ from litellm.types.utils import BudgetConfig
 async def test_is_admin_view_safe_true(monkeypatch):
     # Force underlying check to return True
     monkeypatch.setattr(
-        spend_management_endpoints, "_user_has_admin_view", lambda user_api_key_dict: True
+        spend_management_endpoints,
+        "_user_has_admin_view",
+        lambda user_api_key_dict: True,
     )
     auth = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user")
     assert spend_management_endpoints._is_admin_view_safe(auth) is True
@@ -121,7 +125,9 @@ async def test_is_admin_view_safe_true(monkeypatch):
 async def test_is_admin_view_safe_false(monkeypatch):
     # Force underlying check to return False
     monkeypatch.setattr(
-        spend_management_endpoints, "_user_has_admin_view", lambda user_api_key_dict: False
+        spend_management_endpoints,
+        "_user_has_admin_view",
+        lambda user_api_key_dict: False,
     )
     auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER, user_id="user_1")
     assert spend_management_endpoints._is_admin_view_safe(auth) is False
@@ -179,7 +185,9 @@ async def test_can_team_member_view_log_team_not_found(monkeypatch):
     prisma = MockPrisma()
     # Even if admin check would return True, no team means False
     monkeypatch.setattr(
-        spend_management_endpoints, "_is_user_team_admin", lambda user_api_key_dict, team_obj: True
+        spend_management_endpoints,
+        "_is_user_team_admin",
+        lambda user_api_key_dict, team_obj: True,
     )
     auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER, user_id="user_1")
     allowed = await spend_management_endpoints._can_team_member_view_log(
@@ -208,7 +216,9 @@ async def test_can_team_member_view_log_not_admin(monkeypatch):
 
     prisma = MockPrisma()
     monkeypatch.setattr(
-        spend_management_endpoints, "_is_user_team_admin", lambda user_api_key_dict, team_obj: False
+        spend_management_endpoints,
+        "_is_user_team_admin",
+        lambda user_api_key_dict, team_obj: False,
     )
     auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER, user_id="user_1")
     allowed = await spend_management_endpoints._can_team_member_view_log(
@@ -237,7 +247,9 @@ async def test_can_team_member_view_log_admin(monkeypatch):
 
     prisma = MockPrisma()
     monkeypatch.setattr(
-        spend_management_endpoints, "_is_user_team_admin", lambda user_api_key_dict, team_obj: True
+        spend_management_endpoints,
+        "_is_user_team_admin",
+        lambda user_api_key_dict, team_obj: True,
     )
     auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER, user_id="user_1")
     allowed = await spend_management_endpoints._can_team_member_view_log(
@@ -267,6 +279,7 @@ def test_can_user_view_spend_log_false_for_other_roles():
     auth = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin")
     assert spend_management_endpoints._can_user_view_spend_log(auth) is False
 
+
 ignored_keys = [
     "request_id",
     "session_id",
@@ -292,6 +305,7 @@ ignored_keys = [
     "metadata.user_api_key_alias",
     "metadata.user_api_key_team_id",
     "metadata.user_api_key_project_id",
+    "metadata.user_api_key_project_alias",
     "metadata.user_api_key_org_id",
     "metadata.user_api_key_user_id",
     "metadata.user_api_key_team_alias",
@@ -502,7 +516,11 @@ async def test_ui_view_spend_logs_sort_by_and_sort_order(
 
     async def mock_query_raw(sql_query, *params):
         # Endpoint uses raw SQL with ORDER BY startTime DESC; mock returns sorted data
-        order = {"startTime": "desc"} if sort_by is None else {sort_by: sort_order or "desc"}
+        order = (
+            {"startTime": "desc"}
+            if sort_by is None
+            else {sort_by: sort_order or "desc"}
+        )
         sorted_logs = _sort_logs(base_logs, order)
         page_size = params[-2] if len(params) >= 2 else 50
         skip = params[-1] if len(params) >= 1 else 0
@@ -568,6 +586,7 @@ async def test_ui_view_spend_logs_sort_validation_errors(
     client, monkeypatch, sort_by, sort_order
 ):
     """Test that invalid sort_by and sort_order return 400."""
+
     async def mock_count(*args, **kwargs):
         return 0
 
@@ -752,13 +771,33 @@ async def test_ui_view_spend_logs_with_team_id(client, monkeypatch):
 
 
 @pytest.mark.asyncio
-async def test_ui_view_spend_logs_internal_user_scoped_without_user_id(client, monkeypatch):
+async def test_ui_view_spend_logs_internal_user_scoped_without_user_id(
+    client, monkeypatch
+):
     """
     Internal users should only be able to view their own spend even if user_id is not provided.
     """
     mock_spend_logs = [
-        {"id": "log1", "request_id": "req1", "api_key": "sk-test-key", "user": "internal_user_1", "team_id": "team1", "spend": 0.05, "startTime": datetime.datetime.now(timezone.utc).isoformat(), "model": "gpt-3.5-turbo"},
-        {"id": "log2", "request_id": "req2", "api_key": "sk-test-key", "user": "internal_user_2", "team_id": "team1", "spend": 0.10, "startTime": datetime.datetime.now(timezone.utc).isoformat(), "model": "gpt-4"},
+        {
+            "id": "log1",
+            "request_id": "req1",
+            "api_key": "sk-test-key",
+            "user": "internal_user_1",
+            "team_id": "team1",
+            "spend": 0.05,
+            "startTime": datetime.datetime.now(timezone.utc).isoformat(),
+            "model": "gpt-3.5-turbo",
+        },
+        {
+            "id": "log2",
+            "request_id": "req2",
+            "api_key": "sk-test-key",
+            "user": "internal_user_2",
+            "team_id": "team1",
+            "spend": 0.10,
+            "startTime": datetime.datetime.now(timezone.utc).isoformat(),
+            "model": "gpt-4",
+        },
     ]
 
     def filter_by_user(where):
@@ -799,8 +838,26 @@ async def test_ui_view_spend_logs_team_admin_can_view_team_spend(client, monkeyp
     Team admins should be able to view team-wide spend when team_id is provided.
     """
     mock_spend_logs = [
-        {"id": "log1", "request_id": "req1", "api_key": "sk-test-key", "user": "member1", "team_id": "team_admin_team", "spend": 0.05, "startTime": datetime.datetime.now(timezone.utc).isoformat(), "model": "gpt-3.5-turbo"},
-        {"id": "log2", "request_id": "req2", "api_key": "sk-test-key", "user": "member2", "team_id": "team_other", "spend": 0.10, "startTime": datetime.datetime.now(timezone.utc).isoformat(), "model": "gpt-4"},
+        {
+            "id": "log1",
+            "request_id": "req1",
+            "api_key": "sk-test-key",
+            "user": "member1",
+            "team_id": "team_admin_team",
+            "spend": 0.05,
+            "startTime": datetime.datetime.now(timezone.utc).isoformat(),
+            "model": "gpt-3.5-turbo",
+        },
+        {
+            "id": "log2",
+            "request_id": "req2",
+            "api_key": "sk-test-key",
+            "user": "member2",
+            "team_id": "team_other",
+            "spend": 0.10,
+            "startTime": datetime.datetime.now(timezone.utc).isoformat(),
+            "model": "gpt-4",
+        },
     ]
 
     def filter_by_team(where):
@@ -827,7 +884,11 @@ async def test_ui_view_spend_logs_team_admin_can_view_team_spend(client, monkeyp
 
         response = client.get(
             "/spend/logs/ui",
-            params={"team_id": "team_admin_team", "start_date": start_date, "end_date": end_date},
+            params={
+                "team_id": "team_admin_team",
+                "start_date": start_date,
+                "end_date": end_date,
+            },
             headers={"Authorization": "Bearer sk-test"},
         )
 
@@ -839,6 +900,7 @@ async def test_ui_view_spend_logs_team_admin_can_view_team_spend(client, monkeyp
     finally:
         app.dependency_overrides.pop(ps.user_api_key_auth, None)
 
+
 @pytest.mark.asyncio
 async def test_ui_view_spend_logs_pagination(client, monkeypatch):
     mock_spend_logs = [
@@ -2163,7 +2225,9 @@ async def test_ui_view_spend_logs_with_error_code(client):
 
     try:
         with patch.object(
-            ps, "prisma_client", make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_by_error_code)
+            ps,
+            "prisma_client",
+            make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_by_error_code),
         ):
             start_date, end_date = _default_date_range()
 
@@ -2234,7 +2298,9 @@ async def test_ui_view_spend_logs_with_error_message(client):
 
     try:
         with patch.object(
-            ps, "prisma_client", make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_by_error_message)
+            ps,
+            "prisma_client",
+            make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_by_error_message),
         ):
             start_date, end_date = _default_date_range()
 
@@ -2255,7 +2321,9 @@ async def test_ui_view_spend_logs_with_error_message(client):
             assert data["data"][0]["id"] == "log1"
             metadata = json.loads(data["data"][0]["metadata"])
             assert "error_information" in metadata
-            assert "Rate limit exceeded" in metadata["error_information"]["error_message"]
+            assert (
+                "Rate limit exceeded" in metadata["error_information"]["error_message"]
+            )
     finally:
         app.dependency_overrides.pop(ps.user_api_key_auth, None)
 
@@ -2321,7 +2389,9 @@ async def test_ui_view_spend_logs_with_error_code_and_key_alias(client):
         with patch.object(
             ps,
             "prisma_client",
-            make_ui_spend_logs_mock_prisma(mock_spend_logs, filter_by_error_code_and_key_alias),
+            make_ui_spend_logs_mock_prisma(
+                mock_spend_logs, filter_by_error_code_and_key_alias
+            ),
         ):
             start_date, end_date = _default_date_range()
 
diff --git a/tests/test_litellm/proxy/test_max_budget_env_var.py b/tests/test_litellm/proxy/test_max_budget_env_var.py
new file mode 100644
index 00000000000..90dfb81f3ae
--- /dev/null
+++ b/tests/test_litellm/proxy/test_max_budget_env_var.py
@@ -0,0 +1,49 @@
+"""
+Test that max_budget from environment variable (string) is correctly
+converted to float.
+GitHub Issue: #23843
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+import litellm
+
+
+@pytest.mark.asyncio
+async def test_max_budget_string_converted_to_float():
+    """
+    When max_budget is set via os.environ/MAX_BUDGET, it arrives as a
+    string. initialize() should convert it to float so the comparison
+    `litellm.max_budget > 0` doesn't raise TypeError.
+    """
+    with patch("litellm.proxy.common_utils.banner.show_banner"), patch(
+        "litellm.proxy.proxy_server.generate_feedback_box"
+    ):
+        from litellm.proxy.proxy_server import initialize
+
+        original = litellm.max_budget
+        try:
+            await initialize(max_budget="100.5")
+            assert isinstance(litellm.max_budget, float)
+            assert litellm.max_budget == 100.5
+        finally:
+            litellm.max_budget = original
+
+
+@pytest.mark.asyncio
+async def test_max_budget_float_stays_float():
+    """max_budget as float should still work."""
+    with patch("litellm.proxy.common_utils.banner.show_banner"), patch(
+        "litellm.proxy.proxy_server.generate_feedback_box"
+    ):
+        from litellm.proxy.proxy_server import initialize
+
+        original = litellm.max_budget
+        try:
+            await initialize(max_budget=200.0)
+            assert isinstance(litellm.max_budget, float)
+            assert litellm.max_budget == 200.0
+        finally:
+            litellm.max_budget = original
diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py
index bd6162f225a..d7f1774fc1b 100644
--- a/tests/test_litellm/proxy/test_proxy_server.py
+++ b/tests/test_litellm/proxy/test_proxy_server.py
@@ -104,10 +104,7 @@ def test_login_v2_returns_redirect_url_and_sets_cookie(monkeypatch):
     )
 
     assert response.status_code == 200
-    assert (
-        response.json()
-        == {"redirect_url": "http://testserver/ui/?login=success"}
-    )
+    assert response.json() == {"redirect_url": "http://testserver/ui/?login=success"}
     assert response.cookies.get("token") == "signed-token"
 
     mock_authenticate_user.assert_awaited_once_with(
@@ -516,15 +513,11 @@ def test_restructure_ui_html_files_handles_nested_routes(tmp_path):
     assert (ui_root / "home" / "index.html").read_text() == "home"
     assert not (ui_root / "mcp" / "oauth" / "callback.html").exists()
     assert (
-        (ui_root / "mcp" / "oauth" / "callback" / "index.html").read_text()
-        == "callback"
-    )
+        ui_root / "mcp" / "oauth" / "callback" / "index.html"
+    ).read_text() == "callback"
     assert (ui_root / "existing" / "index.html").read_text() == "keep"
     assert (ui_root / "_next" / "ignore.html").read_text() == "asset"
-    assert (
-        (ui_root / "litellm-asset-prefix" / "ignore.html").read_text()
-        == "asset"
-    )
+    assert (ui_root / "litellm-asset-prefix" / "ignore.html").read_text() == "asset"
 
 
 def test_ui_extensionless_route_requires_restructure(tmp_path):
@@ -541,9 +534,7 @@ def test_ui_extensionless_route_requires_restructure(tmp_path):
     (ui_root / "login.html").write_text("login")
 
     fastapi_app = FastAPI()
-    fastapi_app.mount(
-        "/ui", StaticFiles(directory=str(ui_root), html=True), name="ui"
-    )
+    fastapi_app.mount("/ui", StaticFiles(directory=str(ui_root), html=True), name="ui")
     client = TestClient(fastapi_app)
 
     assert client.get("/ui/login.html").status_code == 200
@@ -564,37 +555,37 @@ def test_restructure_always_happens(monkeypatch):
     """
     # Test Case 1: is_non_root is True - restructuring happens in /var/lib/litellm/ui
     monkeypatch.setenv("LITELLM_NON_ROOT", "true")
-    
+
     runtime_ui_path = "/var/lib/litellm/ui"
     packaged_ui_path = "/some/packaged/ui/path"
-    
+
     # Simulate the logic from proxy_server.py
     is_non_root = os.getenv("LITELLM_NON_ROOT", "").lower() == "true"
     if is_non_root:
         ui_path = runtime_ui_path
     else:
         ui_path = packaged_ui_path
-    
+
     # Restructuring always happens now, regardless of ui_path vs packaged_ui_path
     should_restructure = True
-    
+
     assert is_non_root is True
     assert should_restructure is True
     assert ui_path == runtime_ui_path
-    
+
     # Test Case 2: is_non_root is False - restructuring happens directly in packaged_ui_path
     monkeypatch.delenv("LITELLM_NON_ROOT", raising=False)
-    
+
     # Simulate the logic from proxy_server.py
     is_non_root = os.getenv("LITELLM_NON_ROOT", "").lower() == "true"
     if is_non_root:
         ui_path = runtime_ui_path
     else:
         ui_path = packaged_ui_path
-    
+
     # Restructuring always happens now, even when ui_path == packaged_ui_path
     should_restructure = True
-    
+
     assert is_non_root is False
     assert should_restructure is True
     assert ui_path == packaged_ui_path
@@ -691,9 +682,7 @@ def test_update_config_fields_deep_merge_db_wins():
                 "hidden": True,
             },
             # Demonstrate that None values from DB are skipped (preserve existing)
-            "legacy-sonnet": {
-                "hidden": None  # should not clobber current True
-            },
+            "legacy-sonnet": {"hidden": None},  # should not clobber current True
         }
     }
 
@@ -743,9 +732,7 @@ def test_get_config_custom_callback_api_env_vars(monkeypatch):
     mock_router = MagicMock()
     mock_router.get_settings.return_value = {}
     monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr(
-        proxy_config, "get_config", AsyncMock(return_value=config_data)
-    )
+    monkeypatch.setattr(proxy_config, "get_config", AsyncMock(return_value=config_data))
 
     # Bypass auth dependency
     original_overrides = app.dependency_overrides.copy()
@@ -923,7 +910,9 @@ def test_embedding_input_array_of_tokens(client_no_auth):
             assert response.status_code == 200
             result = response.json()
             print(len(result["data"][0]["embedding"]))
-            assert len(result["data"][0]["embedding"]) > 10  # this usually has len==1536 so
+            assert (
+                len(result["data"][0]["embedding"]) > 10
+            )  # this usually has len==1536 so
     except Exception as e:
         pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}")
 
@@ -1180,7 +1169,6 @@ async def test_delete_deployment_type_mismatch():
     with patch("litellm.proxy.proxy_server.llm_router", mock_llm_router), patch(
         "litellm.proxy.proxy_server.user_config_file_path", "test_config.yaml"
     ):
-
         # Call the function under test
         deleted_count = await pc._delete_deployment(db_models=[])
 
@@ -1322,6 +1310,7 @@ def test_normalize_datetime_for_sorting():
 
     # Test Case 6: Timezone-aware datetime object (non-UTC)
     from datetime import timedelta
+
     aware_dt = datetime(2024, 1, 15, 10, 30, 0, tzinfo=timezone(timedelta(hours=5)))
     result = _normalize_datetime_for_sorting(aware_dt)
     assert result is not None
@@ -1574,6 +1563,91 @@ async def test_load_environment_variables_litellm_license_and_edge_cases():
             assert "FAILED_SECRET" not in os.environ
 
 
+@pytest.mark.asyncio
+async def test_load_environment_variables_blocks_dangerous_keys():
+    """
+    Test that _load_environment_variables rejects dangerous env var keys
+    like PATH, LD_PRELOAD, PYTHONPATH, etc.
+    """
+    import logging
+
+    from litellm.proxy.proxy_server import ProxyConfig
+
+    proxy_config = ProxyConfig()
+
+    original_path = os.environ.get("PATH", "")
+
+    test_config = {
+        "environment_variables": {
+            "PATH": "/tmp/evil",
+            "LD_PRELOAD": "/tmp/evil.so",
+            "PYTHONPATH": "/tmp/evil",
+            "SAFE_CUSTOM_VAR": "safe_value",
+        }
+    }
+
+    with patch.dict(os.environ, {}, clear=False):
+        proxy_config._load_environment_variables(test_config)
+
+        # Blocked keys should not be set to the attacker value
+        assert os.environ.get("PATH") != "/tmp/evil"
+        assert (
+            "LD_PRELOAD" not in os.environ or os.environ["LD_PRELOAD"] != "/tmp/evil.so"
+        )
+        assert os.environ.get("PYTHONPATH") != "/tmp/evil"
+
+        # Safe keys should still be set
+        assert os.environ["SAFE_CUSTOM_VAR"] == "safe_value"
+
+
+@pytest.mark.asyncio
+async def test_load_environment_variables_allows_proxy_keys():
+    """
+    Test that HTTP_PROXY/HTTPS_PROXY are allowed since they are commonly used
+    in corporate environments to route outbound API calls.
+    """
+    from litellm.proxy.proxy_server import ProxyConfig
+
+    proxy_config = ProxyConfig()
+
+    test_config = {
+        "environment_variables": {
+            "HTTP_PROXY": "http://corp-proxy:8080",
+            "HTTPS_PROXY": "http://corp-proxy:8080",
+        }
+    }
+
+    with patch.dict(os.environ, {}, clear=False):
+        proxy_config._load_environment_variables(test_config)
+
+        assert os.environ["HTTP_PROXY"] == "http://corp-proxy:8080"
+        assert os.environ["HTTPS_PROXY"] == "http://corp-proxy:8080"
+
+
+@pytest.mark.asyncio
+async def test_load_environment_variables_blocks_no_proxy():
+    """
+    Test that NO_PROXY/no_proxy are blocked to prevent bypassing proxy-based
+    network monitoring.
+    """
+    from litellm.proxy.proxy_server import ProxyConfig
+
+    proxy_config = ProxyConfig()
+
+    test_config = {
+        "environment_variables": {
+            "NO_PROXY": "internal-service",
+            "no_proxy": "internal-service",
+        }
+    }
+
+    with patch.dict(os.environ, {}, clear=False):
+        proxy_config._load_environment_variables(test_config)
+
+        assert os.environ.get("NO_PROXY") != "internal-service"
+        assert os.environ.get("no_proxy") != "internal-service"
+
+
 @pytest.mark.asyncio
 async def test_write_config_to_file(monkeypatch):
     """
@@ -1882,7 +1956,6 @@ async def test_chat_completion_result_no_nested_none_values():
         "litellm.proxy.proxy_server.ProxyBaseLLMRequestProcessing",
         return_value=mock_base_processor,
     ):
-
         # Call the chat_completion function
         result = await chat_completion(
             request=mock_request,
@@ -2027,9 +2100,7 @@ class TestPriceDataReloadAPI:
 
             # Mock the database connection
             with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma:
-                mock_prisma.db.litellm_config.find_unique = AsyncMock(
-                    return_value=None
-                )
+                mock_prisma.db.litellm_config.find_unique = AsyncMock(return_value=None)
                 mock_prisma.db.litellm_config.upsert = AsyncMock(return_value=None)
 
                 response = client_with_auth.post("/reload/model_cost_map")
@@ -2372,8 +2443,13 @@ class TestPriceDataReloadIntegration:
                 with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma:
                     # Simulate existing config with a schedule
                     mock_existing = MagicMock()
-                    mock_existing.param_value = {"interval_hours": 12, "force_reload": False}
-                    mock_prisma.db.litellm_config.find_unique = AsyncMock(return_value=mock_existing)
+                    mock_existing.param_value = {
+                        "interval_hours": 12,
+                        "force_reload": False,
+                    }
+                    mock_prisma.db.litellm_config.find_unique = AsyncMock(
+                        return_value=mock_existing
+                    )
                     mock_prisma.db.litellm_config.upsert = AsyncMock(return_value=None)
 
                     response = client.post("/reload/model_cost_map")
@@ -2415,7 +2491,9 @@ class TestPriceDataReloadIntegration:
         ) as mock_reload:
             mock_reload.return_value = {"anthropic": {"beta_header": "test-value"}}
 
-            asyncio.run(proxy_config._check_and_reload_anthropic_beta_headers(mock_prisma))
+            asyncio.run(
+                proxy_config._check_and_reload_anthropic_beta_headers(mock_prisma)
+            )
 
             # Verify the upsert update branch preserves interval_hours
             mock_prisma.db.litellm_config.upsert.assert_called()
@@ -2456,7 +2534,9 @@ class TestPriceDataReloadIntegration:
                 # Simulate existing config with a schedule
                 mock_existing = MagicMock()
                 mock_existing.param_value = {"interval_hours": 8, "force_reload": False}
-                mock_prisma.db.litellm_config.find_unique = AsyncMock(return_value=mock_existing)
+                mock_prisma.db.litellm_config.find_unique = AsyncMock(
+                    return_value=mock_existing
+                )
                 mock_prisma.db.litellm_config.upsert = AsyncMock(return_value=None)
 
                 response = client.post("/reload/anthropic_beta_headers")
@@ -2821,7 +2901,7 @@ async def test_model_info_v1_oci_secrets_not_leaked():
     mock_user_api_key_dict.api_key = "test-key"
     mock_user_api_key_dict.team_models = []
     mock_user_api_key_dict.models = ["oci-grok-test"]
-    
+
     # Mock model data with OCI sensitive information
     mock_model_data = {
         "model_name": "oci-grok-test",
@@ -2834,59 +2914,73 @@ async def test_model_info_v1_oci_secrets_not_leaked():
             "oci_tenancy": "ocid1.tenancy.oc1..aaaaaaaa7kbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbk",
             "oci_key_file": "/path/to/oci_api_key.pem",
             "oci_compartment_id": "ocid1.compartment.oc1..aaaaaaaa7kbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbk",
-            "drop_params": True
+            "drop_params": True,
         },
-        "model_info": {
-            "mode": "completion",
-            "id": "test-model-id"
-        }
+        "model_info": {"mode": "completion", "id": "test-model-id"},
     }
-    
+
     # Mock the llm_router to return our test data
     mock_router = MagicMock()
     mock_router.get_model_names.return_value = ["oci-grok-test"]
     mock_router.get_model_access_groups.return_value = {}
     mock_router.get_model_list.return_value = [mock_model_data]
-    
+
     # Mock global variables
-    with patch("litellm.proxy.proxy_server.llm_router", mock_router), \
-         patch("litellm.proxy.proxy_server.llm_model_list", [mock_model_data]), \
-         patch("litellm.proxy.proxy_server.general_settings", {"infer_model_from_keys": False}), \
-         patch("litellm.proxy.proxy_server.user_model", None):
-        
+    with patch("litellm.proxy.proxy_server.llm_router", mock_router), patch(
+        "litellm.proxy.proxy_server.llm_model_list", [mock_model_data]
+    ), patch(
+        "litellm.proxy.proxy_server.general_settings", {"infer_model_from_keys": False}
+    ), patch(
+        "litellm.proxy.proxy_server.user_model", None
+    ):
         # Call the model_info_v1 endpoint
         result = await model_info_v1(
-            user_api_key_dict=mock_user_api_key_dict,
-            litellm_model_id=None
+            user_api_key_dict=mock_user_api_key_dict, litellm_model_id=None
         )
-        
+
         # Verify the result structure
         assert "data" in result
         assert len(result["data"]) == 1
-        
+
         model_info = result["data"][0]
         litellm_params = model_info["litellm_params"]
-        
+
         # Verify that sensitive OCI fields are masked
         assert "****" in litellm_params["oci_key"], "oci_key should be masked"
-        assert "****" in litellm_params["oci_fingerprint"], "oci_fingerprint should be masked"
+        assert (
+            "****" in litellm_params["oci_fingerprint"]
+        ), "oci_fingerprint should be masked"
         assert "****" in litellm_params["oci_tenancy"], "oci_tenancy should be masked"
         assert "****" in litellm_params["oci_key_file"], "oci_key_file should be masked"
-        
+
         # Verify that non-sensitive fields are NOT masked
-        assert litellm_params["model"] == "oci/xai.grok-4", "model field should not be masked"
-        assert litellm_params["oci_region"] == "us-phoenix-1", "oci_region should not be masked"
+        assert (
+            litellm_params["model"] == "oci/xai.grok-4"
+        ), "model field should not be masked"
+        assert (
+            litellm_params["oci_region"] == "us-phoenix-1"
+        ), "oci_region should not be masked"
         assert litellm_params["drop_params"] is True, "drop_params should not be masked"
-        
+
         # Verify the model field specifically is not masked (this was the original issue)
-        assert "****" not in litellm_params["model"], "model field should never be masked"
-        assert litellm_params["model"].startswith("oci/"), "model should retain its full value"
-        
+        assert (
+            "****" not in litellm_params["model"]
+        ), "model field should never be masked"
+        assert litellm_params["model"].startswith(
+            "oci/"
+        ), "model should retain its full value"
+
         # Verify that actual secret values are not present in the response
         result_str = str(result)
-        assert "ocid1.api_key.oc1..aaaaaaaa7kbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbk" not in result_str
+        assert (
+            "ocid1.api_key.oc1..aaaaaaaa7kbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbk"
+            not in result_str
+        )
         assert "aa:bb:cc:dd:ee:ff:11:22:33:44:55:66:77:88:99:00" not in result_str
-        assert "ocid1.tenancy.oc1..aaaaaaaa7kbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbk" not in result_str
+        assert (
+            "ocid1.tenancy.oc1..aaaaaaaa7kbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbkbk"
+            not in result_str
+        )
         assert "/path/to/oci_api_key.pem" not in result_str
 
 
@@ -2898,17 +2992,17 @@ def test_add_callback_from_db_to_in_memory_litellm_callbacks():
     from unittest.mock import MagicMock, patch
 
     from litellm.proxy.proxy_server import ProxyConfig
-    
+
     proxy_config = ProxyConfig()
-    
+
     # Mock the callback manager
     mock_callback_manager = MagicMock()
-    
+
     with patch("litellm.proxy.proxy_server.litellm") as mock_litellm:
         # Set up mock litellm attributes
         mock_litellm._known_custom_logger_compatible_callbacks = []
         mock_litellm.logging_callback_manager = mock_callback_manager
-        
+
         # Test Case 1: Add success callback
         mock_success_callbacks = []
         proxy_config._add_callback_from_db_to_in_memory_litellm_callbacks(
@@ -2916,9 +3010,11 @@ def test_add_callback_from_db_to_in_memory_litellm_callbacks():
             event_types=["success"],
             existing_callbacks=mock_success_callbacks,
         )
-        mock_callback_manager.add_litellm_success_callback.assert_called_once_with("prometheus")
+        mock_callback_manager.add_litellm_success_callback.assert_called_once_with(
+            "prometheus"
+        )
         mock_callback_manager.reset_mock()
-        
+
         # Test Case 2: Add failure callback
         mock_failure_callbacks = []
         proxy_config._add_callback_from_db_to_in_memory_litellm_callbacks(
@@ -2926,9 +3022,11 @@ def test_add_callback_from_db_to_in_memory_litellm_callbacks():
             event_types=["failure"],
             existing_callbacks=mock_failure_callbacks,
         )
-        mock_callback_manager.add_litellm_failure_callback.assert_called_once_with("langfuse")
+        mock_callback_manager.add_litellm_failure_callback.assert_called_once_with(
+            "langfuse"
+        )
         mock_callback_manager.reset_mock()
-        
+
         # Test Case 3: Add callback for both success and failure
         mock_callbacks = []
         proxy_config._add_callback_from_db_to_in_memory_litellm_callbacks(
@@ -2938,7 +3036,7 @@ def test_add_callback_from_db_to_in_memory_litellm_callbacks():
         )
         mock_callback_manager.add_litellm_callback.assert_called_once_with("s3")
         mock_callback_manager.reset_mock()
-        
+
         # Test Case 4: Don't add callback if it already exists
         existing_callbacks_with_item = ["prometheus"]
         proxy_config._add_callback_from_db_to_in_memory_litellm_callbacks(
@@ -2952,7 +3050,7 @@ def test_add_callback_from_db_to_in_memory_litellm_callbacks():
 def test_should_load_db_object_with_supported_db_objects():
     """
     Test _should_load_db_object method with supported_db_objects configuration.
-    
+
     Verifies that when supported_db_objects is set, only specified object types
     are loaded from the database.
     """
@@ -3056,8 +3154,12 @@ async def test_tag_cache_update_called():
         "spend": 10.0,
     }
 
-    with patch.object(cache, "async_get_cache", new=AsyncMock(return_value=mock_tag_obj)) as mock_get_cache:
-        with patch.object(cache, "async_set_cache_pipeline", new=AsyncMock()) as mock_set_cache:
+    with patch.object(
+        cache, "async_get_cache", new=AsyncMock(return_value=mock_tag_obj)
+    ) as mock_get_cache:
+        with patch.object(
+            cache, "async_set_cache_pipeline", new=AsyncMock()
+        ) as mock_set_cache:
             await litellm.proxy.proxy_server.update_cache(
                 token=None,
                 user_id=None,
@@ -3108,8 +3210,12 @@ async def test_tag_cache_update_multiple_tags():
             return mock_tag2_obj
         return None
 
-    with patch.object(cache, "async_get_cache", new=AsyncMock(side_effect=mock_get_cache_side_effect)) as mock_get_cache:
-        with patch.object(cache, "async_set_cache_pipeline", new=AsyncMock()) as mock_set_cache:
+    with patch.object(
+        cache, "async_get_cache", new=AsyncMock(side_effect=mock_get_cache_side_effect)
+    ) as mock_get_cache:
+        with patch.object(
+            cache, "async_set_cache_pipeline", new=AsyncMock()
+        ) as mock_set_cache:
             await litellm.proxy.proxy_server.update_cache(
                 token=None,
                 user_id=None,
@@ -3130,7 +3236,9 @@ async def test_tag_cache_update_multiple_tags():
 
             assert len(cache_list) == 2
 
-            tag_updates = {cache_key: cache_value for cache_key, cache_value in cache_list}
+            tag_updates = {
+                cache_key: cache_value for cache_key, cache_value in cache_list
+            }
             assert "tag:tag1" in tag_updates
             assert "tag:tag2" in tag_updates
             assert tag_updates["tag:tag1"]["spend"] == 15.0
@@ -3250,7 +3358,9 @@ async def test_init_sso_settings_in_db_error_handling():
         assert True
     except Exception as e:
         # The exception should be caught and logged, not propagated
-        pytest.fail(f"Exception should have been caught and logged, but was raised: {e}")
+        pytest.fail(
+            f"Exception should have been caught and logged, but was raised: {e}"
+        )
 
 
 @pytest.mark.asyncio
@@ -3353,11 +3463,15 @@ def test_get_prompt_spec_for_db_prompt_with_versions():
     }
 
     # Test version 1
-    prompt_spec_v1 = proxy_config._get_prompt_spec_for_db_prompt(db_prompt=mock_prompt_v1)
+    prompt_spec_v1 = proxy_config._get_prompt_spec_for_db_prompt(
+        db_prompt=mock_prompt_v1
+    )
     assert prompt_spec_v1.prompt_id == "chat_prompt.v1"
 
     # Test version 2
-    prompt_spec_v2 = proxy_config._get_prompt_spec_for_db_prompt(db_prompt=mock_prompt_v2)
+    prompt_spec_v2 = proxy_config._get_prompt_spec_for_db_prompt(
+        db_prompt=mock_prompt_v2
+    )
     assert prompt_spec_v2.prompt_id == "chat_prompt.v2"
 
 
@@ -3372,15 +3486,15 @@ def test_root_redirect_when_docs_url_not_root_and_redirect_url_set(monkeypatch):
     config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml"
     # Ensure docs are mounted on a non-root path to trigger redirect logic
     monkeypatch.setenv("DOCS_URL", "/docs")
-    
+
     test_redirect_url = "/ui"
     monkeypatch.setenv("ROOT_REDIRECT_URL", test_redirect_url)
-    
+
     asyncio.run(initialize(config=config_fp, debug=True))
-    
+
     docs_url = _get_docs_url()
     root_redirect_url = os.getenv("ROOT_REDIRECT_URL")
-    
+
     # Remove any existing "/" route that might interfere
     routes_to_remove = []
     for route in app.routes:
@@ -3389,16 +3503,17 @@ def test_root_redirect_when_docs_url_not_root_and_redirect_url_set(monkeypatch):
                 routes_to_remove.append(route)
             elif not hasattr(route, "methods"):  # Catch-all routes
                 routes_to_remove.append(route)
-    
+
     for route in routes_to_remove:
         app.routes.remove(route)
-    
+
     # Add the redirect route if conditions are met (matching the actual implementation)
     if docs_url != "/" and root_redirect_url:
+
         @app.get("/", include_in_schema=False)
         async def root_redirect():
             return RedirectResponse(url=root_redirect_url)
-    
+
     client = TestClient(app)
     response = client.get("/", follow_redirects=False)
     assert response.status_code == 307
@@ -3422,12 +3537,13 @@ async def test_get_image_non_root_uses_var_lib_assets_dir(monkeypatch):
     def exists_side_effect(path):
         return False if path == "/var/lib/litellm/assets" else True
 
-    with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, \
-         patch("litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect), \
-         patch("litellm.proxy.proxy_server.os.access", return_value=True), \
-         patch("litellm.proxy.proxy_server.os.getenv") as mock_getenv, \
-         patch("litellm.proxy.proxy_server.FileResponse") as mock_file_response:
-
+    with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, patch(
+        "litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect
+    ), patch("litellm.proxy.proxy_server.os.access", return_value=True), patch(
+        "litellm.proxy.proxy_server.os.getenv"
+    ) as mock_getenv, patch(
+        "litellm.proxy.proxy_server.FileResponse"
+    ) as mock_file_response:
         # Setup mock_getenv to return empty string for UI_LOGO_PATH
         def getenv_side_effect(key, default=""):
             if key == "UI_LOGO_PATH":
@@ -3471,12 +3587,13 @@ async def test_get_image_non_root_fallback_to_default_logo(monkeypatch):
         return True
 
     # Mock os.path operations
-    with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, \
-         patch("litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect), \
-         patch("litellm.proxy.proxy_server.os.access", return_value=True), \
-         patch("litellm.proxy.proxy_server.os.getenv") as mock_getenv, \
-         patch("litellm.proxy.proxy_server.FileResponse") as mock_file_response:
-
+    with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, patch(
+        "litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect
+    ), patch("litellm.proxy.proxy_server.os.access", return_value=True), patch(
+        "litellm.proxy.proxy_server.os.getenv"
+    ) as mock_getenv, patch(
+        "litellm.proxy.proxy_server.FileResponse"
+    ) as mock_file_response:
         # Setup mock_getenv
         def getenv_side_effect(key, default=""):
             if key == "UI_LOGO_PATH":
@@ -3495,8 +3612,9 @@ async def test_get_image_non_root_fallback_to_default_logo(monkeypatch):
 
         # Verify that exists was called to check /var/lib/litellm/assets/logo.jpg
         assets_logo_path = "/var/lib/litellm/assets/logo.jpg"
-        assert any(assets_logo_path in str(call) for call in exists_calls), \
-            f"Should check if {assets_logo_path} exists"
+        assert any(
+            assets_logo_path in str(call) for call in exists_calls
+        ), f"Should check if {assets_logo_path} exists"
 
         # Verify FileResponse was called (with fallback logo)
         assert mock_file_response.called, "FileResponse should be called"
@@ -3516,11 +3634,11 @@ async def test_get_image_root_case_uses_current_dir(monkeypatch):
     monkeypatch.delenv("UI_LOGO_PATH", raising=False)
 
     # Mock os.path operations
-    with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, \
-         patch("litellm.proxy.proxy_server.os.path.exists", return_value=True), \
-         patch("litellm.proxy.proxy_server.os.getenv") as mock_getenv, \
-         patch("litellm.proxy.proxy_server.FileResponse") as mock_file_response:
-
+    with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, patch(
+        "litellm.proxy.proxy_server.os.path.exists", return_value=True
+    ), patch("litellm.proxy.proxy_server.os.getenv") as mock_getenv, patch(
+        "litellm.proxy.proxy_server.FileResponse"
+    ) as mock_file_response:
         # Setup mock_getenv
         def getenv_side_effect(key, default=""):
             if key == "UI_LOGO_PATH":
@@ -3536,10 +3654,13 @@ async def test_get_image_root_case_uses_current_dir(monkeypatch):
 
         # Verify makedirs was NOT called with /var/lib/litellm/assets (should not create it for root case)
         var_lib_assets_calls = [
-            call for call in mock_makedirs.call_args_list
+            call
+            for call in mock_makedirs.call_args_list
             if "/var/lib/litellm/assets" in str(call)
         ]
-        assert len(var_lib_assets_calls) == 0, "Should not create /var/lib/litellm/assets for root case"
+        assert (
+            len(var_lib_assets_calls) == 0
+        ), "Should not create /var/lib/litellm/assets for root case"
 
         # Verify FileResponse was called
         assert mock_file_response.called, "FileResponse should be called"
@@ -3569,13 +3690,14 @@ async def test_get_image_custom_local_logo_bypasses_cache(monkeypatch):
         calls_to_file_response.append(path)
         return MagicMock()
 
-    with patch("litellm.proxy.proxy_server.os.path.exists", return_value=True), \
-         patch("litellm.proxy.proxy_server.os.access", return_value=True), \
-         patch("litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response):
-
+    with patch("litellm.proxy.proxy_server.os.path.exists", return_value=True), patch(
+        "litellm.proxy.proxy_server.os.access", return_value=True
+    ), patch("litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response):
         await get_image()
 
-    assert len(calls_to_file_response) == 1, "FileResponse should be called exactly once"
+    assert (
+        len(calls_to_file_response) == 1
+    ), "FileResponse should be called exactly once"
     assert calls_to_file_response[0] == "/app/custom_logo.jpg", (
         f"Expected custom logo path, got {calls_to_file_response[0]}. "
         "A stale cached_logo.jpg may have been returned instead."
@@ -3602,17 +3724,18 @@ async def test_get_image_default_logo_still_uses_cache(monkeypatch):
         calls_to_file_response.append(path)
         return MagicMock()
 
-    with patch("litellm.proxy.proxy_server.os.path.exists", return_value=True), \
-         patch("litellm.proxy.proxy_server.os.access", return_value=True), \
-         patch("litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response):
-
+    with patch("litellm.proxy.proxy_server.os.path.exists", return_value=True), patch(
+        "litellm.proxy.proxy_server.os.access", return_value=True
+    ), patch("litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response):
         await get_image()
 
-    assert len(calls_to_file_response) == 1, "FileResponse should be called exactly once"
+    assert (
+        len(calls_to_file_response) == 1
+    ), "FileResponse should be called exactly once"
     served_path = calls_to_file_response[0]
-    assert served_path.endswith("cached_logo.jpg"), (
-        f"Expected cached_logo.jpg for default logo, got {served_path}"
-    )
+    assert served_path.endswith(
+        "cached_logo.jpg"
+    ), f"Expected cached_logo.jpg for default logo, got {served_path}"
 
 
 @pytest.mark.asyncio
@@ -3641,20 +3764,23 @@ async def test_get_image_custom_logo_missing_falls_through_to_default(monkeypatc
             return False
         return True
 
-    with patch("litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect), \
-         patch("litellm.proxy.proxy_server.os.access", return_value=True), \
-         patch("litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response):
-
+    with patch(
+        "litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect
+    ), patch("litellm.proxy.proxy_server.os.access", return_value=True), patch(
+        "litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response
+    ):
         await get_image()
 
-    assert len(calls_to_file_response) == 1, "FileResponse should be called exactly once"
+    assert (
+        len(calls_to_file_response) == 1
+    ), "FileResponse should be called exactly once"
     served_path = calls_to_file_response[0]
-    assert served_path != "/app/nonexistent_logo.jpg", (
-        "Should not attempt to serve a non-existent custom logo"
-    )
-    assert served_path.endswith("cached_logo.jpg"), (
-        f"Expected fallback to cached_logo.jpg, got {served_path}"
-    )
+    assert (
+        served_path != "/app/nonexistent_logo.jpg"
+    ), "Should not attempt to serve a non-existent custom logo"
+    assert served_path.endswith(
+        "cached_logo.jpg"
+    ), f"Expected fallback to cached_logo.jpg, got {served_path}"
 
 
 @pytest.mark.asyncio
@@ -3686,20 +3812,23 @@ async def test_get_image_custom_logo_missing_no_cache_serves_default(monkeypatch
             return False
         return True
 
-    with patch("litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect), \
-         patch("litellm.proxy.proxy_server.os.access", return_value=True), \
-         patch("litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response):
-
+    with patch(
+        "litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect
+    ), patch("litellm.proxy.proxy_server.os.access", return_value=True), patch(
+        "litellm.proxy.proxy_server.FileResponse", side_effect=fake_file_response
+    ):
         await get_image()
 
-    assert len(calls_to_file_response) == 1, "FileResponse should be called exactly once"
+    assert (
+        len(calls_to_file_response) == 1
+    ), "FileResponse should be called exactly once"
     served_path = calls_to_file_response[0]
-    assert served_path != "/app/nonexistent_logo.jpg", (
-        "Should not attempt to serve a non-existent custom logo"
-    )
-    assert served_path.endswith("logo.jpg"), (
-        f"Expected fallback to default logo.jpg, got {served_path}"
-    )
+    assert (
+        served_path != "/app/nonexistent_logo.jpg"
+    ), "Should not attempt to serve a non-existent custom logo"
+    assert served_path.endswith(
+        "logo.jpg"
+    ), f"Expected fallback to default logo.jpg, got {served_path}"
 
 
 def test_get_config_normalizes_string_callbacks(monkeypatch):
@@ -3721,9 +3850,7 @@ def test_get_config_normalizes_string_callbacks(monkeypatch):
     mock_router = MagicMock()
     mock_router.get_settings.return_value = {}
     monkeypatch.setattr("litellm.proxy.proxy_server.llm_router", mock_router)
-    monkeypatch.setattr(
-        proxy_config, "get_config", AsyncMock(return_value=config_data)
-    )
+    monkeypatch.setattr(proxy_config, "get_config", AsyncMock(return_value=config_data))
 
     original_overrides = app.dependency_overrides.copy()
     app.dependency_overrides[user_api_key_auth] = lambda: MagicMock()
@@ -4127,9 +4254,9 @@ async def test_update_general_settings_store_model_in_db_false():
 
     proxy_config = ProxyConfig()
 
-    with patch(
-        "litellm.proxy.proxy_server.store_model_in_db", True
-    ), patch("litellm.proxy.proxy_server.general_settings", {}):
+    with patch("litellm.proxy.proxy_server.store_model_in_db", True), patch(
+        "litellm.proxy.proxy_server.general_settings", {}
+    ):
         await proxy_config._update_general_settings(
             db_general_settings={"store_model_in_db": False}
         )
@@ -4150,9 +4277,9 @@ async def test_update_general_settings_store_model_in_db_string_normalization():
     proxy_config = ProxyConfig()
 
     # Test "true" string
-    with patch(
-        "litellm.proxy.proxy_server.store_model_in_db", False
-    ), patch("litellm.proxy.proxy_server.general_settings", {}):
+    with patch("litellm.proxy.proxy_server.store_model_in_db", False), patch(
+        "litellm.proxy.proxy_server.general_settings", {}
+    ):
         await proxy_config._update_general_settings(
             db_general_settings={"store_model_in_db": "true"}
         )
@@ -4161,9 +4288,9 @@ async def test_update_general_settings_store_model_in_db_string_normalization():
         assert ps.store_model_in_db is True
 
     # Test "True" string
-    with patch(
-        "litellm.proxy.proxy_server.store_model_in_db", False
-    ), patch("litellm.proxy.proxy_server.general_settings", {}):
+    with patch("litellm.proxy.proxy_server.store_model_in_db", False), patch(
+        "litellm.proxy.proxy_server.general_settings", {}
+    ):
         await proxy_config._update_general_settings(
             db_general_settings={"store_model_in_db": "True"}
         )
@@ -4172,9 +4299,9 @@ async def test_update_general_settings_store_model_in_db_string_normalization():
         assert ps.store_model_in_db is True
 
     # Test "false" string
-    with patch(
-        "litellm.proxy.proxy_server.store_model_in_db", True
-    ), patch("litellm.proxy.proxy_server.general_settings", {}):
+    with patch("litellm.proxy.proxy_server.store_model_in_db", True), patch(
+        "litellm.proxy.proxy_server.general_settings", {}
+    ):
         await proxy_config._update_general_settings(
             db_general_settings={"store_model_in_db": "false"}
         )
@@ -4194,9 +4321,9 @@ async def test_update_general_settings_store_model_in_db_none_keeps_current():
     proxy_config = ProxyConfig()
 
     # When current is True and DB sends None, should stay True
-    with patch(
-        "litellm.proxy.proxy_server.store_model_in_db", True
-    ), patch("litellm.proxy.proxy_server.general_settings", {}):
+    with patch("litellm.proxy.proxy_server.store_model_in_db", True), patch(
+        "litellm.proxy.proxy_server.general_settings", {}
+    ):
         await proxy_config._update_general_settings(
             db_general_settings={"store_model_in_db": None}
         )
@@ -4205,9 +4332,9 @@ async def test_update_general_settings_store_model_in_db_none_keeps_current():
         assert ps.store_model_in_db is True
 
     # When current is False and DB sends None, should stay False
-    with patch(
-        "litellm.proxy.proxy_server.store_model_in_db", False
-    ), patch("litellm.proxy.proxy_server.general_settings", {}):
+    with patch("litellm.proxy.proxy_server.store_model_in_db", False), patch(
+        "litellm.proxy.proxy_server.general_settings", {}
+    ):
         await proxy_config._update_general_settings(
             db_general_settings={"store_model_in_db": None}
         )
@@ -4238,13 +4365,9 @@ async def test_store_model_in_db_db_override_when_config_false():
     mock_proxy_logging.slack_alerting_instance = MagicMock()
     mock_proxy_config = AsyncMock()
 
-    with patch(
-        "litellm.proxy.proxy_server.proxy_config", mock_proxy_config
-    ), patch(
+    with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config), patch(
         "litellm.proxy.proxy_server.store_model_in_db", False
-    ), patch(
-        "litellm.proxy.proxy_server.get_secret_bool", return_value=False
-    ):
+    ), patch("litellm.proxy.proxy_server.get_secret_bool", return_value=False):
         await ProxyStartupEvent.initialize_scheduled_background_jobs(
             general_settings={},
             prisma_client=mock_prisma_client,
@@ -4282,13 +4405,9 @@ async def test_store_model_in_db_db_check_skipped_when_already_true(monkeypatch)
     mock_proxy_logging.slack_alerting_instance = MagicMock()
     mock_proxy_config = AsyncMock()
 
-    with patch(
-        "litellm.proxy.proxy_server.proxy_config", mock_proxy_config
-    ), patch(
+    with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config), patch(
         "litellm.proxy.proxy_server.store_model_in_db", True
-    ), patch(
-        "litellm.proxy.proxy_server.get_secret_bool", return_value=True
-    ):
+    ), patch("litellm.proxy.proxy_server.get_secret_bool", return_value=True):
         await ProxyStartupEvent.initialize_scheduled_background_jobs(
             general_settings={},
             prisma_client=mock_prisma_client,
@@ -4328,13 +4447,9 @@ async def test_store_model_in_db_db_failure_graceful(monkeypatch):
     mock_proxy_logging.slack_alerting_instance = MagicMock()
     mock_proxy_config = AsyncMock()
 
-    with patch(
-        "litellm.proxy.proxy_server.proxy_config", mock_proxy_config
-    ), patch(
+    with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config), patch(
         "litellm.proxy.proxy_server.store_model_in_db", False
-    ), patch(
-        "litellm.proxy.proxy_server.get_secret_bool", return_value=False
-    ):
+    ), patch("litellm.proxy.proxy_server.get_secret_bool", return_value=False):
         # Should not raise an exception
         await ProxyStartupEvent.initialize_scheduled_background_jobs(
             general_settings={},
diff --git a/tests/test_litellm/responses/test_responses_prompt_management.py b/tests/test_litellm/responses/test_responses_prompt_management.py
new file mode 100644
index 00000000000..f49679fc400
--- /dev/null
+++ b/tests/test_litellm/responses/test_responses_prompt_management.py
@@ -0,0 +1,383 @@
+"""
+Unit tests for prompt management support in the Responses API.
+
+Covers:
+  A) str input is coerced to a message list before merging with the template
+  B) list input is merged with the template
+  C) no prompt_id → hook is skipped, input is unchanged
+  D) model override from the prompt template is applied
+  E) prompt_template_optional_params flow into the request
+  F) non-message items in input are filtered out
+  G) model override re-resolves provider
+  H) async path calls async_get_chat_completion_prompt
+  I) async path propagates optional params to downstream handler
+"""
+
+import asyncio
+from typing import List
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.types.llms.openai import AllMessageValues
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_logging_obj(
+    merged_model: str,
+    merged_messages: List[AllMessageValues],
+    should_run: bool = True,
+    merged_optional_params: dict = None,
+) -> MagicMock:
+    """Return a mock LiteLLMLoggingObj pre-configured for prompt management."""
+    if merged_optional_params is None:
+        merged_optional_params = {}
+    logging_obj = MagicMock()
+    logging_obj.__class__ = LiteLLMLoggingObj
+    logging_obj.should_run_prompt_management_hooks.return_value = should_run
+    prompt_return = (merged_model, merged_messages, merged_optional_params)
+    logging_obj.get_chat_completion_prompt.return_value = prompt_return
+    logging_obj.async_get_chat_completion_prompt = AsyncMock(
+        return_value=prompt_return
+    )
+    logging_obj.model_call_details = {}
+    return logging_obj
+
+
+def _patch_responses_dispatch():
+    """Patch everything after the prompt management block so tests stay unit-level."""
+    return [
+        patch(
+            "litellm.responses.main.litellm.get_llm_provider",
+            return_value=("gpt-4o", "openai", None, None),
+        ),
+        patch(
+            "litellm.responses.mcp.litellm_proxy_mcp_handler."
+            "LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway",
+            return_value=False,
+        ),
+        patch(
+            "litellm.responses.main.ProviderConfigManager"
+            ".get_provider_responses_api_config",
+            return_value=None,
+        ),
+        patch(
+            "litellm.responses.main.litellm_completion_transformation_handler"
+            ".response_api_handler",
+            return_value=MagicMock(),
+        ),
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+class TestResponsesAPIPromptManagement:
+
+    def test_str_input_coerced_and_merged(self):
+        """[A] str input is wrapped into a message list before being passed to the hook."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "system", "content": "You are a summariser."},  # type: ignore[list-item]
+        ]
+        client_message: List[AllMessageValues] = [
+            {"role": "user", "content": "Tell me about AI."},  # type: ignore[list-item]
+        ]
+        expected_merged = template_messages + client_message
+
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=expected_merged,
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3]:
+            import litellm
+            litellm.responses(
+                input="Tell me about AI.",
+                model="gpt-4o",
+                prompt_id="summariser-prompt",
+                prompt_variables={},
+                litellm_logging_obj=logging_obj,
+            )
+
+        logging_obj.get_chat_completion_prompt.assert_called_once()
+        call_kwargs = logging_obj.get_chat_completion_prompt.call_args.kwargs
+        # str was coerced to a single user message before being passed to the hook
+        assert call_kwargs["messages"] == [
+            {"role": "user", "content": "Tell me about AI."}
+        ]
+        assert call_kwargs["prompt_id"] == "summariser-prompt"
+
+    def test_list_input_merged_with_template(self):
+        """[B] list input is passed directly to the hook and merged with the template."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "system", "content": "You are helpful."},  # type: ignore[list-item]
+        ]
+        client_messages = [
+            {"role": "user", "content": [{"type": "input_text", "text": "Hello"}]},
+        ]
+        expected_merged = template_messages + client_messages  # type: ignore[operator]
+
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=expected_merged,  # type: ignore[arg-type]
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3]:
+            import litellm
+            litellm.responses(
+                input=client_messages,  # type: ignore[arg-type]
+                model="gpt-4o",
+                prompt_id="helper-prompt",
+                litellm_logging_obj=logging_obj,
+            )
+
+        logging_obj.get_chat_completion_prompt.assert_called_once()
+        call_kwargs = logging_obj.get_chat_completion_prompt.call_args.kwargs
+        assert call_kwargs["messages"] == client_messages
+
+    def test_no_prompt_id_skips_hook(self):
+        """[C] When prompt_id is absent, prompt management hooks are not called."""
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=[],
+            should_run=False,
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3]:
+            import litellm
+            litellm.responses(
+                input="Hello",
+                model="gpt-4o",
+                litellm_logging_obj=logging_obj,
+            )
+
+        logging_obj.get_chat_completion_prompt.assert_not_called()
+
+    def test_optional_params_from_template_applied(self):
+        """[E] prompt_template_optional_params (e.g. temperature) flow into the request."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "user", "content": "Hello"},  # type: ignore[list-item]
+        ]
+        # Simulate get_chat_completion_prompt returning merged optional params
+        # that include a template-defined temperature
+        merged_kwargs = {"temperature": 0.2}
+
+        logging_obj = MagicMock()
+        logging_obj.__class__ = LiteLLMLoggingObj
+        logging_obj.should_run_prompt_management_hooks.return_value = True
+        logging_obj.get_chat_completion_prompt.return_value = (
+            "openai/gpt-4o",
+            template_messages,
+            merged_kwargs,
+        )
+        logging_obj.model_call_details = {}
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3] as mock_handler:
+            import litellm
+            litellm.responses(
+                input="Hello",
+                model="gpt-4o",
+                prompt_id="t",
+                litellm_logging_obj=logging_obj,
+            )
+
+        # temperature from the template should reach the downstream handler via local_vars
+        handler_call_kwargs = mock_handler.call_args.kwargs
+        request_params = handler_call_kwargs.get("responses_api_request", {})
+        assert request_params.get("temperature") == 0.2
+
+    def test_model_override_from_template(self):
+        """[D] Model returned by the prompt hook overrides the original request model."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "user", "content": "{{query}}"},  # type: ignore[list-item]
+        ]
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o-mini",  # overridden model from template
+            merged_messages=template_messages,
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3] as mock_handler:
+            import litellm
+            litellm.responses(
+                input="What is AI?",
+                model="gpt-4o",
+                prompt_id="query-prompt",
+                prompt_variables={"query": "What is AI?"},
+                litellm_logging_obj=logging_obj,
+            )
+
+        # The model passed to the downstream handler should be the overridden one
+        handler_call_kwargs = mock_handler.call_args.kwargs
+        assert handler_call_kwargs.get("model") == "openai/gpt-4o-mini"
+
+    def test_non_message_input_items_filtered(self):
+        """[F] Non-message items in ResponseInputParam (e.g. function_call_output) are
+        filtered out before being passed to the prompt hook, avoiding malformed merges."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "system", "content": "You are helpful."},  # type: ignore[list-item]
+        ]
+        mixed_input = [
+            {"role": "user", "content": "Hello"},
+            {"type": "function_call_output", "call_id": "abc", "output": "42"},
+        ]
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=template_messages + [{"role": "user", "content": "Hello"}],  # type: ignore[operator]
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3]:
+            import litellm
+            litellm.responses(
+                input=mixed_input,  # type: ignore[arg-type]
+                model="gpt-4o",
+                prompt_id="filter-test",
+                litellm_logging_obj=logging_obj,
+            )
+
+        call_kwargs = logging_obj.get_chat_completion_prompt.call_args.kwargs
+        passed_messages = call_kwargs["messages"]
+        assert all(isinstance(m, dict) and "role" in m for m in passed_messages)
+        assert len(passed_messages) == 1
+
+    def test_model_override_re_resolves_provider(self):
+        """[G] When the prompt template overrides the model to a different provider,
+        custom_llm_provider is re-resolved so downstream routing uses the correct provider."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "user", "content": "Hi"},  # type: ignore[list-item]
+        ]
+        logging_obj = _make_logging_obj(
+            merged_model="anthropic/claude-3-5-sonnet",
+            merged_messages=template_messages,
+        )
+
+        patches = _patch_responses_dispatch()
+        with (
+            patch(
+                "litellm.responses.main.litellm.get_llm_provider",
+                side_effect=[
+                    ("gpt-4o", "openai", None, None),
+                    ("claude-3-5-sonnet", "anthropic", None, None),
+                ],
+            ),
+            patches[1],
+            patches[2],
+            patches[3] as mock_handler,
+        ):
+            import litellm
+            litellm.responses(
+                input="Hi",
+                model="gpt-4o",
+                prompt_id="cross-provider",
+                litellm_logging_obj=logging_obj,
+            )
+
+        handler_call_kwargs = mock_handler.call_args.kwargs
+        assert handler_call_kwargs.get("custom_llm_provider") == "anthropic"
+
+
+class TestAsyncResponsesAPIPromptManagement:
+    """Tests for the async aresponses() prompt management path.
+
+    aresponses() calls async_get_chat_completion_prompt at the outer async
+    level, then pops prompt_id from kwargs and passes merged_optional_params
+    via an internal kwarg. The sync responses() path sees no prompt_id and
+    skips the sync hook entirely — preventing double-merge of template messages.
+    """
+
+    @pytest.mark.asyncio
+    async def test_async_calls_async_hook_not_sync(self):
+        """[H] aresponses() invokes async_get_chat_completion_prompt and the
+        sync get_chat_completion_prompt is NOT called (no double-merge)."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "system", "content": "You are helpful."},  # type: ignore[list-item]
+        ]
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=template_messages + [{"role": "user", "content": "Hi"}],  # type: ignore[list-item]
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3]:
+            import litellm
+            await litellm.aresponses(
+                input="Hi",
+                model="gpt-4o",
+                prompt_id="async-test",
+                prompt_variables={},
+                litellm_logging_obj=logging_obj,
+            )
+
+        logging_obj.async_get_chat_completion_prompt.assert_called_once()
+        logging_obj.get_chat_completion_prompt.assert_not_called()
+        call_kwargs = logging_obj.async_get_chat_completion_prompt.call_args.kwargs
+        assert call_kwargs["prompt_id"] == "async-test"
+
+    @pytest.mark.asyncio
+    async def test_async_optional_params_propagated(self):
+        """[I] Template-defined optional params (e.g. temperature) from the async
+        hook reach the downstream handler — they are NOT silently discarded."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "user", "content": "Hello"},  # type: ignore[list-item]
+        ]
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=template_messages,
+            merged_optional_params={"temperature": 0.7},
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3] as mock_handler:
+            import litellm
+            await litellm.aresponses(
+                input="Hello",
+                model="gpt-4o",
+                prompt_id="async-temp",
+                litellm_logging_obj=logging_obj,
+            )
+
+        logging_obj.get_chat_completion_prompt.assert_not_called()
+        handler_call_kwargs = mock_handler.call_args.kwargs
+        request_params = handler_call_kwargs.get("responses_api_request", {})
+        assert request_params.get("temperature") == 0.7
+
+    @pytest.mark.asyncio
+    async def test_async_non_message_items_filtered(self):
+        """[J] Non-message items are filtered in the async path too."""
+        template_messages: List[AllMessageValues] = [
+            {"role": "system", "content": "Be helpful."},  # type: ignore[list-item]
+        ]
+        mixed_input = [
+            {"role": "user", "content": "Hello"},
+            {"type": "function_call_output", "call_id": "abc", "output": "42"},
+        ]
+        logging_obj = _make_logging_obj(
+            merged_model="openai/gpt-4o",
+            merged_messages=template_messages + [{"role": "user", "content": "Hello"}],  # type: ignore[operator]
+        )
+
+        patches = _patch_responses_dispatch()
+        with patches[0], patches[1], patches[2], patches[3]:
+            import litellm
+            await litellm.aresponses(
+                input=mixed_input,  # type: ignore[arg-type]
+                model="gpt-4o",
+                prompt_id="async-filter",
+                litellm_logging_obj=logging_obj,
+            )
+
+        logging_obj.async_get_chat_completion_prompt.assert_called_once()
+        logging_obj.get_chat_completion_prompt.assert_not_called()
+        call_kwargs = logging_obj.async_get_chat_completion_prompt.call_args.kwargs
+        passed_messages = call_kwargs["messages"]
+        assert all(isinstance(m, dict) and "role" in m for m in passed_messages)
+        assert len(passed_messages) == 1
diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py
index e500ad3ca6e..28311a30c0d 100644
--- a/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py
+++ b/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py
@@ -657,3 +657,284 @@ def test_cache_key_does_not_double_hash_user_api_key_hash():
         user_key=user_api_key_hash,
     )
     assert key.endswith(user_api_key_hash)
+
+
+def test_get_effective_flags_returns_per_group_config():
+    """
+    _get_effective_flags should return per-group flags when the model group has an entry
+    in model_group_affinity_config, and global flags otherwise.
+    """
+    callback = DeploymentAffinityCheck(
+        cache=AsyncMock(),
+        ttl_seconds=60,
+        enable_user_key_affinity=True,
+        enable_responses_api_affinity=True,
+        enable_session_id_affinity=False,
+        model_group_affinity_config={
+            "gpt-4": ["deployment_affinity"],
+            "claude-3": ["session_affinity", "responses_api_deployment_check"],
+        },
+    )
+
+    # gpt-4: only deployment_affinity
+    user_key, responses_api, session_id = callback._get_effective_flags("gpt-4")
+    assert user_key is True
+    assert responses_api is False
+    assert session_id is False
+
+    # claude-3: session_affinity + responses_api_deployment_check
+    user_key, responses_api, session_id = callback._get_effective_flags("claude-3")
+    assert user_key is False
+    assert responses_api is True
+    assert session_id is True
+
+    # unconfigured-model: falls back to global flags
+    user_key, responses_api, session_id = callback._get_effective_flags(
+        "unconfigured-model"
+    )
+    assert user_key is True
+    assert responses_api is True
+    assert session_id is False
+
+
+@pytest.mark.asyncio
+async def test_model_group_affinity_config_only_applies_to_configured_group():
+    """
+    When model_group_affinity_config is set without global optional_pre_call_checks,
+    only configured model groups should get affinity behavior.
+    """
+    mock_response_data = {
+        "id": "resp_mock-resp-per-group",
+        "object": "response",
+        "created_at": 1741476542,
+        "status": "completed",
+        "model": "openai/gpt-4",
+        "output": [
+            {
+                "type": "message",
+                "id": "msg_pg",
+                "status": "completed",
+                "role": "assistant",
+                "content": [{"type": "output_text", "text": "Per-group response"}],
+            }
+        ],
+        "parallel_tool_calls": True,
+        "usage": {"input_tokens": 5, "output_tokens": 5, "total_tokens": 10},
+        "text": {"format": {"type": "text"}},
+        "error": None,
+        "previous_response_id": None,
+    }
+
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "gpt-4",
+                "litellm_params": {
+                    "model": "azure/gpt-4-deploy-1",
+                    "api_key": "mock-key-1",
+                    "api_base": "https://mock-gpt4-1.openai.azure.com",
+                    "api_version": "2024-02-01",
+                },
+                "model_info": {"base_model": "gpt-4"},
+            },
+            {
+                "model_name": "gpt-4",
+                "litellm_params": {
+                    "model": "azure/gpt-4-deploy-2",
+                    "api_key": "mock-key-2",
+                    "api_base": "https://mock-gpt4-2.openai.azure.com",
+                    "api_version": "2024-02-01",
+                },
+                "model_info": {"base_model": "gpt-4"},
+            },
+            {
+                "model_name": "claude-3",
+                "litellm_params": {
+                    "model": "azure/claude-3-deploy-1",
+                    "api_key": "mock-key-3",
+                    "api_base": "https://mock-claude-1.openai.azure.com",
+                    "api_version": "2024-02-01",
+                },
+                "model_info": {"base_model": "claude-3"},
+            },
+            {
+                "model_name": "claude-3",
+                "litellm_params": {
+                    "model": "azure/claude-3-deploy-2",
+                    "api_key": "mock-key-4",
+                    "api_base": "https://mock-claude-2.openai.azure.com",
+                    "api_version": "2024-02-01",
+                },
+                "model_info": {"base_model": "claude-3"},
+            },
+        ],
+        # No global optional_pre_call_checks — only per-group
+        model_group_affinity_config={
+            "gpt-4": ["deployment_affinity"],
+        },
+    )
+
+    user_api_key_hash = "test-per-group-key"
+    choice_calls = {"count": 0}
+
+    def deterministic_choice(seq):
+        choice_calls["count"] += 1
+        if choice_calls["count"] == 1:
+            return seq[0]
+        return seq[1] if len(seq) > 1 else seq[0]
+
+    with patch(
+        "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
+        new_callable=AsyncMock,
+    ) as mock_post, patch(
+        "litellm.router_strategy.simple_shuffle.random.choice",
+        side_effect=deterministic_choice,
+    ):
+        mock_post.return_value = MockResponse(mock_response_data, 200)
+
+        # gpt-4: affinity should work — second request pinned to same deployment
+        first = await router.aresponses(
+            model="gpt-4",
+            input="Hello",
+            truncation="auto",
+            litellm_metadata={"user_api_key_hash": user_api_key_hash},
+        )
+        first_model_id = first._hidden_params["model_id"]
+
+        second = await router.aresponses(
+            model="gpt-4",
+            input="Follow-up",
+            truncation="auto",
+            litellm_metadata={"user_api_key_hash": user_api_key_hash},
+        )
+        assert second._hidden_params["model_id"] == first_model_id
+
+        # claude-3: no affinity configured — should NOT be pinned
+        choice_calls["count"] = 0
+        first_claude = await router.aresponses(
+            model="claude-3",
+            input="Hello",
+            truncation="auto",
+            litellm_metadata={"user_api_key_hash": user_api_key_hash},
+        )
+        first_claude_id = first_claude._hidden_params["model_id"]
+
+        second_claude = await router.aresponses(
+            model="claude-3",
+            input="Follow-up",
+            truncation="auto",
+            litellm_metadata={"user_api_key_hash": user_api_key_hash},
+        )
+        # With deterministic choice and len>1, second call picks seq[1]
+        assert second_claude._hidden_params["model_id"] != first_claude_id
+
+
+@pytest.mark.asyncio
+async def test_model_group_affinity_config_falls_back_to_global():
+    """
+    When both global optional_pre_call_checks and model_group_affinity_config are set,
+    unconfigured model groups should use the global settings.
+    """
+    callback = DeploymentAffinityCheck(
+        cache=DualCache(),
+        ttl_seconds=60,
+        enable_user_key_affinity=True,
+        enable_responses_api_affinity=False,
+        enable_session_id_affinity=False,
+        model_group_affinity_config={
+            "claude-3": ["session_affinity"],
+        },
+    )
+
+    stable_model_map_key = "gpt-4"
+    user_key = "test-fallback-key"
+
+    healthy_deployments = [
+        {
+            "model_name": stable_model_map_key,
+            "litellm_params": {"model": "openai/gpt-4"},
+            "model_info": {"id": "deployment-1"},
+        },
+        {
+            "model_name": stable_model_map_key,
+            "litellm_params": {"model": "openai/gpt-4"},
+            "model_info": {"id": "deployment-2"},
+        },
+    ]
+
+    # Set up affinity cache for gpt-4 (should work since global has deployment_affinity)
+    await callback.async_pre_call_deployment_hook(
+        kwargs={
+            "model_info": {"id": "deployment-1"},
+            "metadata": {
+                "user_api_key_hash": user_key,
+                "deployment_model_name": stable_model_map_key,
+            },
+        },
+        call_type=None,
+    )
+
+    # gpt-4 not in model_group_affinity_config, so global flags apply (user_key affinity ON)
+    filtered = await callback.async_filter_deployments(
+        model="gpt-4",
+        healthy_deployments=healthy_deployments,
+        messages=None,
+        request_kwargs={"metadata": {"user_api_key_hash": user_key}},
+        parent_otel_span=None,
+    )
+    assert len(filtered) == 1
+    assert filtered[0]["model_info"]["id"] == "deployment-1"
+
+
+@pytest.mark.asyncio
+async def test_model_group_affinity_config_overrides_global():
+    """
+    When model_group_affinity_config specifies session_affinity for a model group,
+    user-key affinity (from global config) should NOT apply to that group.
+    """
+    callback = DeploymentAffinityCheck(
+        cache=DualCache(),
+        ttl_seconds=60,
+        enable_user_key_affinity=True,
+        enable_responses_api_affinity=False,
+        enable_session_id_affinity=False,
+        model_group_affinity_config={
+            "claude-3": ["session_affinity"],
+        },
+    )
+
+    stable_model_map_key = "claude-3"
+    user_key = "test-override-key"
+
+    healthy_deployments = [
+        {
+            "model_name": stable_model_map_key,
+            "litellm_params": {"model": "anthropic/claude-3-opus"},
+            "model_info": {"id": "deployment-1"},
+        },
+        {
+            "model_name": stable_model_map_key,
+            "litellm_params": {"model": "anthropic/claude-3-opus"},
+            "model_info": {"id": "deployment-2"},
+        },
+    ]
+
+    # Set up user-key affinity cache for claude-3
+    cache_key = DeploymentAffinityCheck.get_affinity_cache_key(
+        model_group=stable_model_map_key, user_key=user_key
+    )
+    await callback.cache.async_set_cache(
+        cache_key, {"model_id": "deployment-1"}, ttl=60
+    )
+
+    # claude-3 has per-group config (session_affinity only), so user-key affinity
+    # should NOT apply even though it's globally enabled
+    filtered = await callback.async_filter_deployments(
+        model="claude-3",
+        healthy_deployments=healthy_deployments,
+        messages=None,
+        request_kwargs={"metadata": {"user_api_key_hash": user_key}},
+        parent_otel_span=None,
+    )
+    # All deployments returned (user-key affinity disabled for this group)
+    assert len(filtered) == 2
diff --git a/tests/test_litellm/router_utils/test_health_check_routing.py b/tests/test_litellm/router_utils/test_health_check_routing.py
new file mode 100644
index 00000000000..f40144b44c9
--- /dev/null
+++ b/tests/test_litellm/router_utils/test_health_check_routing.py
@@ -0,0 +1,197 @@
+"""
+Tests for health-check-driven routing filter in the Router.
+"""
+
+import time
+
+import pytest
+
+from litellm.caching.caching import DualCache
+from litellm.router_utils.health_state_cache import DeploymentHealthCache
+
+
+def _make_deployment(model_id: str, model_name: str = "gpt-4") -> dict:
+    """Helper to create a deployment dict for testing."""
+    return {
+        "model_name": model_name,
+        "litellm_params": {"model": model_name, "api_key": "fake"},
+        "model_info": {"id": model_id},
+    }
+
+
+def _make_health_cache(
+    unhealthy_ids: set = None, staleness_threshold: float = 60.0
+) -> DeploymentHealthCache:
+    """Create a health cache pre-populated with unhealthy deployment IDs."""
+    cache = DualCache()
+    health_cache = DeploymentHealthCache(
+        cache=cache, staleness_threshold=staleness_threshold
+    )
+    if unhealthy_ids:
+        now = time.time()
+        states = {}
+        for uid in unhealthy_ids:
+            states[uid] = {
+                "is_healthy": False,
+                "timestamp": now,
+                "reason": "test_unhealthy",
+            }
+        health_cache.set_deployment_health_states(states)
+    return health_cache
+
+
+class TestFilterHealthCheckUnhealthyDeployments:
+    """Test the sync filter method."""
+
+    def _make_router_like(self, enable: bool, health_cache: DeploymentHealthCache):
+        """Create a minimal object that behaves like Router for filter testing."""
+
+        class FakeRouter:
+            def __init__(self):
+                self.enable_health_check_routing = enable
+                self.health_state_cache = health_cache
+
+        # Import the actual method and bind it
+        from litellm.router import Router
+
+        fake = FakeRouter()
+        # Use the unbound method
+        fake._filter_health_check_unhealthy_deployments = (
+            Router._filter_health_check_unhealthy_deployments.__get__(fake, FakeRouter)
+        )
+        return fake
+
+    def test_filter_removes_unhealthy_deployments(self):
+        """Unhealthy deployments should be removed from candidates."""
+        health_cache = _make_health_cache(unhealthy_ids={"deploy-2"})
+        router = self._make_router_like(enable=True, health_cache=health_cache)
+
+        deployments = [
+            _make_deployment("deploy-1"),
+            _make_deployment("deploy-2"),
+            _make_deployment("deploy-3"),
+        ]
+        result = router._filter_health_check_unhealthy_deployments(deployments)
+        assert len(result) == 2
+        assert all(d["model_info"]["id"] != "deploy-2" for d in result)
+
+    def test_filter_noop_when_disabled(self):
+        """When enable_health_check_routing=False, filter should be a no-op."""
+        health_cache = _make_health_cache(unhealthy_ids={"deploy-1"})
+        router = self._make_router_like(enable=False, health_cache=health_cache)
+
+        deployments = [
+            _make_deployment("deploy-1"),
+            _make_deployment("deploy-2"),
+        ]
+        result = router._filter_health_check_unhealthy_deployments(deployments)
+        assert len(result) == 2  # no filtering
+
+    def test_filter_returns_all_when_all_unhealthy(self):
+        """Safety net: if ALL deployments are unhealthy, return all (don't cause outage)."""
+        health_cache = _make_health_cache(
+            unhealthy_ids={"deploy-1", "deploy-2", "deploy-3"}
+        )
+        router = self._make_router_like(enable=True, health_cache=health_cache)
+
+        deployments = [
+            _make_deployment("deploy-1"),
+            _make_deployment("deploy-2"),
+            _make_deployment("deploy-3"),
+        ]
+        result = router._filter_health_check_unhealthy_deployments(deployments)
+        assert len(result) == 3  # all returned, safety net
+
+    def test_filter_returns_all_when_cache_empty(self):
+        """When cache is empty, all deployments should pass through."""
+        health_cache = _make_health_cache()  # empty
+        router = self._make_router_like(enable=True, health_cache=health_cache)
+
+        deployments = [
+            _make_deployment("deploy-1"),
+            _make_deployment("deploy-2"),
+        ]
+        result = router._filter_health_check_unhealthy_deployments(deployments)
+        assert len(result) == 2
+
+
+class TestAsyncFilterHealthCheckUnhealthyDeployments:
+    """Test the async filter method."""
+
+    def _make_router_like(self, enable: bool, health_cache: DeploymentHealthCache):
+        from litellm.router import Router
+
+        class FakeRouter:
+            def __init__(self):
+                self.enable_health_check_routing = enable
+                self.health_state_cache = health_cache
+
+        fake = FakeRouter()
+        fake._async_filter_health_check_unhealthy_deployments = (
+            Router._async_filter_health_check_unhealthy_deployments.__get__(
+                fake, FakeRouter
+            )
+        )
+        return fake
+
+    @pytest.mark.asyncio
+    async def test_async_filter_removes_unhealthy(self):
+        """Async version: unhealthy deployments removed."""
+        health_cache = _make_health_cache(unhealthy_ids={"deploy-2"})
+        router = self._make_router_like(enable=True, health_cache=health_cache)
+
+        deployments = [
+            _make_deployment("deploy-1"),
+            _make_deployment("deploy-2"),
+            _make_deployment("deploy-3"),
+        ]
+        result = await router._async_filter_health_check_unhealthy_deployments(
+            healthy_deployments=deployments
+        )
+        assert len(result) == 2
+        assert all(d["model_info"]["id"] != "deploy-2" for d in result)
+
+    @pytest.mark.asyncio
+    async def test_async_filter_safety_net(self):
+        """Async version: safety net when all unhealthy."""
+        health_cache = _make_health_cache(unhealthy_ids={"deploy-1", "deploy-2"})
+        router = self._make_router_like(enable=True, health_cache=health_cache)
+
+        deployments = [
+            _make_deployment("deploy-1"),
+            _make_deployment("deploy-2"),
+        ]
+        result = await router._async_filter_health_check_unhealthy_deployments(
+            healthy_deployments=deployments
+        )
+        assert len(result) == 2  # safety net
+
+
+class TestBuildDeploymentHealthStates:
+    """Test the build_deployment_health_states function."""
+
+    def test_builds_states_from_endpoints(self):
+        from litellm.proxy.health_check import build_deployment_health_states
+
+        healthy = [{"model": "gpt-4", "model_id": "deploy-1"}]
+        unhealthy = [{"model": "gpt-4", "model_id": "deploy-2", "error": "timeout"}]
+
+        states = build_deployment_health_states(healthy, unhealthy)
+        assert states["deploy-1"]["is_healthy"] is True
+        assert states["deploy-2"]["is_healthy"] is False
+
+    def test_no_model_id_skipped(self):
+        from litellm.proxy.health_check import build_deployment_health_states
+
+        healthy = [{"model": "gpt-4"}]  # no model_id
+        unhealthy = [{"model": "gpt-4", "model_id": "deploy-2"}]
+
+        states = build_deployment_health_states(healthy, unhealthy)
+        assert "deploy-1" not in states
+        assert states["deploy-2"]["is_healthy"] is False
+
+    def test_empty_endpoints(self):
+        from litellm.proxy.health_check import build_deployment_health_states
+
+        states = build_deployment_health_states([], [])
+        assert states == {}
diff --git a/tests/test_litellm/router_utils/test_health_state_cache.py b/tests/test_litellm/router_utils/test_health_state_cache.py
new file mode 100644
index 00000000000..1af61e899be
--- /dev/null
+++ b/tests/test_litellm/router_utils/test_health_state_cache.py
@@ -0,0 +1,113 @@
+"""
+Tests for DeploymentHealthCache - the cache layer for health-check-driven routing.
+"""
+
+import time
+
+import pytest
+
+from litellm.caching.caching import DualCache
+from litellm.router_utils.health_state_cache import DeploymentHealthCache
+
+
+@pytest.fixture
+def cache():
+    return DualCache()
+
+
+@pytest.fixture
+def health_cache(cache):
+    return DeploymentHealthCache(cache=cache, staleness_threshold=60.0)
+
+
+def test_set_and_get_unhealthy_ids(health_cache):
+    """Write states, verify unhealthy set is returned correctly."""
+    now = time.time()
+    states = {
+        "deploy-1": {"is_healthy": True, "timestamp": now, "reason": ""},
+        "deploy-2": {"is_healthy": False, "timestamp": now, "reason": "check_failed"},
+        "deploy-3": {"is_healthy": False, "timestamp": now, "reason": "timeout"},
+    }
+    health_cache.set_deployment_health_states(states)
+    result = health_cache.get_unhealthy_deployment_ids()
+    assert result == {"deploy-2", "deploy-3"}
+
+
+@pytest.mark.asyncio
+async def test_async_get_unhealthy_ids(health_cache):
+    """Async version of set and get."""
+    now = time.time()
+    states = {
+        "deploy-1": {"is_healthy": True, "timestamp": now, "reason": ""},
+        "deploy-2": {"is_healthy": False, "timestamp": now, "reason": "check_failed"},
+    }
+    health_cache.set_deployment_health_states(states)
+    result = await health_cache.async_get_unhealthy_deployment_ids()
+    assert result == {"deploy-2"}
+
+
+def test_staleness_filtering(health_cache):
+    """Entries older than staleness_threshold should be ignored."""
+    old_time = time.time() - 120  # 2 minutes ago, threshold is 60s
+    states = {
+        "deploy-1": {
+            "is_healthy": False,
+            "timestamp": old_time,
+            "reason": "check_failed",
+        },
+    }
+    health_cache.set_deployment_health_states(states)
+    result = health_cache.get_unhealthy_deployment_ids()
+    assert result == set()  # stale entry should be ignored
+
+
+def test_empty_cache_returns_empty_set(health_cache):
+    """No data in cache should return empty set."""
+    result = health_cache.get_unhealthy_deployment_ids()
+    assert result == set()
+
+
+def test_all_healthy_returns_empty_set(health_cache):
+    """All healthy deployments should return empty set."""
+    now = time.time()
+    states = {
+        "deploy-1": {"is_healthy": True, "timestamp": now, "reason": ""},
+        "deploy-2": {"is_healthy": True, "timestamp": now, "reason": ""},
+    }
+    health_cache.set_deployment_health_states(states)
+    result = health_cache.get_unhealthy_deployment_ids()
+    assert result == set()
+
+
+def test_mixed_stale_and_fresh(health_cache):
+    """Only fresh unhealthy entries should be returned."""
+    now = time.time()
+    old_time = now - 120  # stale
+    states = {
+        "deploy-1": {
+            "is_healthy": False,
+            "timestamp": old_time,
+            "reason": "stale",
+        },
+        "deploy-2": {
+            "is_healthy": False,
+            "timestamp": now,
+            "reason": "fresh",
+        },
+    }
+    health_cache.set_deployment_health_states(states)
+    result = health_cache.get_unhealthy_deployment_ids()
+    assert result == {"deploy-2"}
+
+
+def test_malformed_state_entries_are_skipped(health_cache):
+    """Non-dict entries in the cache should be skipped safely."""
+    now = time.time()
+    states = {
+        "deploy-1": {"is_healthy": False, "timestamp": now, "reason": "bad"},
+        "deploy-2": "not_a_dict",  # malformed
+        "deploy-3": None,  # malformed
+    }
+    health_cache.set_deployment_health_states(states)
+    result = health_cache.get_unhealthy_deployment_ids()
+    assert result == {"deploy-1"}
diff --git a/tests/test_litellm/test_claude_opus_4_6_config.py b/tests/test_litellm/test_claude_opus_4_6_config.py
index 7ee2ea33957..654ef1b9771 100644
--- a/tests/test_litellm/test_claude_opus_4_6_config.py
+++ b/tests/test_litellm/test_claude_opus_4_6_config.py
@@ -24,70 +24,82 @@ def test_claude_4_6_australia_region_uses_au_prefix_not_apac():
     Related: The 'apac.' prefix is valid for Asia-Pacific (Singapore) region models,
     but should not be used for Australia which has its own 'au.' prefix.
     """
-    json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json")
+    json_path = os.path.join(
+        os.path.dirname(__file__), "../../model_prices_and_context_window.json"
+    )
     with open(json_path) as f:
         model_data = json.load(f)
 
     # Verify au.anthropic.claude-opus-4-6-v1 exists (correct)
-    assert "au.anthropic.claude-opus-4-6-v1" in model_data, \
-        "Missing Australia region model: au.anthropic.claude-opus-4-6-v1"
+    assert (
+        "au.anthropic.claude-opus-4-6-v1" in model_data
+    ), "Missing Australia region model: au.anthropic.claude-opus-4-6-v1"
 
     # Verify apac.anthropic.claude-opus-4-6-v1 does NOT exist (incorrect)
-    assert "apac.anthropic.claude-opus-4-6-v1" not in model_data, \
-        "Incorrect model entry exists: apac.anthropic.claude-opus-4-6-v1 should be au.anthropic.claude-opus-4-6-v1"
+    assert (
+        "apac.anthropic.claude-opus-4-6-v1" not in model_data
+    ), "Incorrect model entry exists: apac.anthropic.claude-opus-4-6-v1 should be au.anthropic.claude-opus-4-6-v1"
 
     # Verify au.anthropic.claude-sonnet-4-6 exists (correct)
-    assert "au.anthropic.claude-sonnet-4-6" in model_data, \
-        "Missing Australia region model: au.anthropic.claude-sonnet-4-6"
+    assert (
+        "au.anthropic.claude-sonnet-4-6" in model_data
+    ), "Missing Australia region model: au.anthropic.claude-sonnet-4-6"
 
     # Verify apac.anthropic.claude-sonnet-4-6 does NOT exist (incorrect)
-    assert "apac.anthropic.claude-sonnet-4-6" not in model_data, \
-        "Incorrect model entry exists: apac.anthropic.claude-sonnet-4-6 should be au.anthropic.claude-sonnet-4-6"
+    assert (
+        "apac.anthropic.claude-sonnet-4-6" not in model_data
+    ), "Incorrect model entry exists: apac.anthropic.claude-sonnet-4-6 should be au.anthropic.claude-sonnet-4-6"
 
     # Verify the au. model is registered in bedrock_converse_models
-    assert "au.anthropic.claude-opus-4-6-v1" in litellm.bedrock_converse_models, \
-        "au.anthropic.claude-opus-4-6-v1 not registered in bedrock_converse_models"
+    assert (
+        "au.anthropic.claude-opus-4-6-v1" in litellm.bedrock_converse_models
+    ), "au.anthropic.claude-opus-4-6-v1 not registered in bedrock_converse_models"
 
     # Verify apac. is NOT registered for this model
-    assert "apac.anthropic.claude-opus-4-6-v1" not in litellm.bedrock_converse_models, \
-        "apac.anthropic.claude-opus-4-6-v1 should not be in bedrock_converse_models"
+    assert (
+        "apac.anthropic.claude-opus-4-6-v1" not in litellm.bedrock_converse_models
+    ), "apac.anthropic.claude-opus-4-6-v1 should not be in bedrock_converse_models"
 
     # Verify the au. model is registered in bedrock_converse_models
-    assert "au.anthropic.claude-sonnet-4-6" in litellm.bedrock_converse_models, \
-        "au.anthropic.claude-sonnet-4-6 not registered in bedrock_converse_models"
+    assert (
+        "au.anthropic.claude-sonnet-4-6" in litellm.bedrock_converse_models
+    ), "au.anthropic.claude-sonnet-4-6 not registered in bedrock_converse_models"
 
     # Verify apac. is NOT registered for this model
-    assert "apac.anthropic.claude-sonnet-4-6" not in litellm.bedrock_converse_models, \
-        "apac.anthropic.claude-sonnet-4-6 should not be in bedrock_converse_models"
+    assert (
+        "apac.anthropic.claude-sonnet-4-6" not in litellm.bedrock_converse_models
+    ), "apac.anthropic.claude-sonnet-4-6 should not be in bedrock_converse_models"
 
 
 def test_opus_4_6_model_pricing_and_capabilities():
-    json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json")
+    json_path = os.path.join(
+        os.path.dirname(__file__), "../../model_prices_and_context_window.json"
+    )
     with open(json_path) as f:
         model_data = json.load(f)
 
     expected_models = {
         "claude-opus-4-6": {
             "provider": "anthropic",
-            "has_long_context_pricing": True,
+            "has_long_context_pricing": False,
             "tool_use_system_prompt_tokens": 346,
             "max_input_tokens": 1000000,
         },
         "claude-opus-4-6-20260205": {
             "provider": "anthropic",
-            "has_long_context_pricing": True,
+            "has_long_context_pricing": False,
             "tool_use_system_prompt_tokens": 346,
             "max_input_tokens": 1000000,
         },
         "anthropic.claude-opus-4-6-v1": {
             "provider": "bedrock_converse",
-            "has_long_context_pricing": True,
+            "has_long_context_pricing": False,
             "tool_use_system_prompt_tokens": 346,
             "max_input_tokens": 1000000,
         },
         "vertex_ai/claude-opus-4-6": {
             "provider": "vertex_ai-anthropic_models",
-            "has_long_context_pricing": True,
+            "has_long_context_pricing": False,
             "tool_use_system_prompt_tokens": 346,
             "max_input_tokens": 1000000,
         },
@@ -119,6 +131,11 @@ def test_opus_4_6_model_pricing_and_capabilities():
             assert info["output_cost_per_token_above_200k_tokens"] == 3.75e-05
             assert info["cache_creation_input_token_cost_above_200k_tokens"] == 1.25e-05
             assert info["cache_read_input_token_cost_above_200k_tokens"] == 1e-06
+        else:
+            assert "input_cost_per_token_above_200k_tokens" not in info
+            assert "output_cost_per_token_above_200k_tokens" not in info
+            assert "cache_creation_input_token_cost_above_200k_tokens" not in info
+            assert "cache_read_input_token_cost_above_200k_tokens" not in info
 
         assert info["supports_assistant_prefill"] is False
         assert info["supports_function_calling"] is True
@@ -126,11 +143,16 @@ def test_opus_4_6_model_pricing_and_capabilities():
         assert info["supports_reasoning"] is True
         assert info["supports_tool_choice"] is True
         assert info["supports_vision"] is True
-        assert info["tool_use_system_prompt_tokens"] == config["tool_use_system_prompt_tokens"]
+        assert (
+            info["tool_use_system_prompt_tokens"]
+            == config["tool_use_system_prompt_tokens"]
+        )
 
 
 def test_opus_4_6_bedrock_regional_model_pricing():
-    json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json")
+    json_path = os.path.join(
+        os.path.dirname(__file__), "../../model_prices_and_context_window.json"
+    )
     with open(json_path) as f:
         model_data = json.load(f)
 
@@ -140,40 +162,24 @@ def test_opus_4_6_bedrock_regional_model_pricing():
             "output_cost_per_token": 2.5e-05,
             "cache_creation_input_token_cost": 6.25e-06,
             "cache_read_input_token_cost": 5e-07,
-            "input_cost_per_token_above_200k_tokens": 1e-05,
-            "output_cost_per_token_above_200k_tokens": 3.75e-05,
-            "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
-            "cache_read_input_token_cost_above_200k_tokens": 1e-06,
         },
         "us.anthropic.claude-opus-4-6-v1": {
             "input_cost_per_token": 5.5e-06,
             "output_cost_per_token": 2.75e-05,
             "cache_creation_input_token_cost": 6.875e-06,
             "cache_read_input_token_cost": 5.5e-07,
-            "input_cost_per_token_above_200k_tokens": 1.1e-05,
-            "output_cost_per_token_above_200k_tokens": 4.125e-05,
-            "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
-            "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         },
         "eu.anthropic.claude-opus-4-6-v1": {
             "input_cost_per_token": 5.5e-06,
             "output_cost_per_token": 2.75e-05,
             "cache_creation_input_token_cost": 6.875e-06,
             "cache_read_input_token_cost": 5.5e-07,
-            "input_cost_per_token_above_200k_tokens": 1.1e-05,
-            "output_cost_per_token_above_200k_tokens": 4.125e-05,
-            "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
-            "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         },
         "au.anthropic.claude-opus-4-6-v1": {
             "input_cost_per_token": 5.5e-06,
             "output_cost_per_token": 2.75e-05,
             "cache_creation_input_token_cost": 6.875e-06,
             "cache_read_input_token_cost": 5.5e-07,
-            "input_cost_per_token_above_200k_tokens": 1.1e-05,
-            "output_cost_per_token_above_200k_tokens": 4.125e-05,
-            "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
-            "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
         },
     }
 
@@ -186,12 +192,18 @@ def test_opus_4_6_bedrock_regional_model_pricing():
         assert info["max_tokens"] == 128000
         assert info["supports_assistant_prefill"] is False
         assert info["tool_use_system_prompt_tokens"] == 346
+        assert "input_cost_per_token_above_200k_tokens" not in info
+        assert "output_cost_per_token_above_200k_tokens" not in info
+        assert "cache_creation_input_token_cost_above_200k_tokens" not in info
+        assert "cache_read_input_token_cost_above_200k_tokens" not in info
         for key, value in expected.items():
             assert info[key] == value
 
 
 def test_opus_4_6_alias_and_dated_metadata_match():
-    json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json")
+    json_path = os.path.join(
+        os.path.dirname(__file__), "../../model_prices_and_context_window.json"
+    )
     with open(json_path) as f:
         model_data = json.load(f)
 
@@ -207,10 +219,6 @@ def test_opus_4_6_alias_and_dated_metadata_match():
         "cache_creation_input_token_cost",
         "cache_creation_input_token_cost_above_1hr",
         "cache_read_input_token_cost",
-        "input_cost_per_token_above_200k_tokens",
-        "output_cost_per_token_above_200k_tokens",
-        "cache_creation_input_token_cost_above_200k_tokens",
-        "cache_read_input_token_cost_above_200k_tokens",
         "supports_assistant_prefill",
         "tool_use_system_prompt_tokens",
     ]
diff --git a/tests/test_litellm/test_constants.py b/tests/test_litellm/test_constants.py
index 8fff3ec40d4..b3c13c6e26e 100644
--- a/tests/test_litellm/test_constants.py
+++ b/tests/test_litellm/test_constants.py
@@ -1,3 +1,4 @@
+import ast
 import inspect
 import json
 import os
@@ -17,57 +18,56 @@ import litellm
 from litellm import constants
 
 
-def test_all_numeric_constants_can_be_overridden():
+def _build_constant_env_var_map() -> dict[str, str]:
     """
-    Test that all integer and float constants in constants.py can be overridden with environment variables.
-    This ensures that any new constants added in the future will be configurable via environment variables.
+    Build a mapping of CONSTANT_NAME -> ENV_VAR_NAME by parsing constants.py.
+
+    This keeps the test resilient when a constant name and env var name differ
+    (e.g., aliases like LITELLM_* env vars).
     """
-    # Get all attributes from the constants module
-    constants_attributes = inspect.getmembers(constants)
+    env_var_map: dict[str, str] = {}
+    constants_source = inspect.getsource(constants)
+    parsed = ast.parse(constants_source)
 
-    # Filter for uppercase constants (by convention) that are integers or floats
-    # Exclude booleans since bool is a subclass of int in Python
-    numeric_constants = [
-        (name, value)
-        for name, value in constants_attributes
-        if name.isupper() and isinstance(value, (int, float)) and not isinstance(value, bool)
-    ]
-
-    # Ensure we found some constants to test
-    assert len(numeric_constants) > 0, "No numeric constants found to test"
-
-    print("all numeric constants", json.dumps(numeric_constants, indent=4))
-
-    # Constants that use a different env var name than the constant name
-    constant_to_env_var = {
-        "MAX_CALLBACKS": "LITELLM_MAX_CALLBACKS",
-        "MCP_CLIENT_TIMEOUT": "LITELLM_MCP_CLIENT_TIMEOUT",
-        "MCP_TOOL_LISTING_TIMEOUT": "LITELLM_MCP_TOOL_LISTING_TIMEOUT",
-        "MCP_METADATA_TIMEOUT": "LITELLM_MCP_METADATA_TIMEOUT",
-        "MCP_HEALTH_CHECK_TIMEOUT": "LITELLM_MCP_HEALTH_CHECK_TIMEOUT",
-    }
-
-    # Verify all numeric constants have environment variable support
-    for name, value in numeric_constants:
-        # Skip constants that are not meant to be overridden (if any)
-        if name.startswith("_"):
+    for node in parsed.body:
+        if not isinstance(node, ast.Assign):
             continue
 
-        # Create a test value that's different from the default
-        test_value = value + 1 if isinstance(value, int) else value + 0.1
+        if len(node.targets) != 1 or not isinstance(node.targets[0], ast.Name):
+            continue
 
-        # Use the env var name that the constants module actually reads
-        env_var_name = constant_to_env_var.get(name, name)
+        constant_name = node.targets[0].id
+        env_var_name = None
 
-        # Set the environment variable
-        with mock.patch.dict(os.environ, {env_var_name: str(test_value)}):
-            print("overriding", name, "with", test_value)
-            importlib.reload(constants)
+        for child in ast.walk(node.value):
+            if not isinstance(child, ast.Call):
+                continue
 
-            # Get the new value after reload
-            new_value = getattr(constants, name)
+            # os.getenv("ENV_NAME", default)
+            if (
+                isinstance(child.func, ast.Attribute)
+                and isinstance(child.func.value, ast.Name)
+                and child.func.value.id == "os"
+                and child.func.attr == "getenv"
+                and len(child.args) >= 1
+                and isinstance(child.args[0], ast.Constant)
+                and isinstance(child.args[0].value, str)
+            ):
+                env_var_name = child.args[0].value
+                break
 
-            # Verify the value was overridden
-            assert (
-                new_value == test_value
-            ), f"Failed to override {name} with environment variable. Expected {test_value}, got {new_value}"
+            # get_env_int("ENV_NAME", default)
+            if (
+                isinstance(child.func, ast.Name)
+                and child.func.id == "get_env_int"
+                and len(child.args) >= 1
+                and isinstance(child.args[0], ast.Constant)
+                and isinstance(child.args[0].value, str)
+            ):
+                env_var_name = child.args[0].value
+                break
+
+        if env_var_name:
+            env_var_map[constant_name] = env_var_name
+
+    return env_var_map
diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py
index 6ac988b2c21..ce5873f5063 100644
--- a/tests/test_litellm/test_main.py
+++ b/tests/test_litellm/test_main.py
@@ -661,6 +661,40 @@ def test_responses_api_bridge_check_gpt_5_5_tools_plus_reasoning_routes_to_respo
     assert model_info.get("mode") == "responses"
 
 
+def test_responses_api_bridge_check_azure_gpt_5_4_tools_plus_reasoning_routes_to_responses():
+    """Azure gpt-5.4 with both tools and reasoning_effort should route to Responses API."""
+    from litellm.main import responses_api_bridge_check
+
+    with patch("litellm.main._get_model_info_helper") as mock_get_model_info:
+        mock_get_model_info.return_value = {"max_tokens": 128000}
+        model_info, model = responses_api_bridge_check(
+            model="gpt-5.4",
+            custom_llm_provider="azure",
+            tools=[{"type": "function", "function": {"name": "get_capital"}}],
+            reasoning_effort="high",
+        )
+
+    assert model == "gpt-5.4"
+    assert model_info.get("mode") == "responses"
+
+
+def test_responses_api_bridge_check_azure_gpt_5_4_tools_without_reasoning_stays_chat():
+    """Azure gpt-5.4 with tools only should not be force-routed to Responses API."""
+    from litellm.main import responses_api_bridge_check
+
+    with patch("litellm.main._get_model_info_helper") as mock_get_model_info:
+        mock_get_model_info.return_value = {"max_tokens": 128000}
+        model_info, model = responses_api_bridge_check(
+            model="gpt-5.4",
+            custom_llm_provider="azure",
+            tools=[{"type": "function", "function": {"name": "get_capital"}}],
+            reasoning_effort=None,
+        )
+
+    assert model == "gpt-5.4"
+    assert model_info.get("mode") != "responses"
+
+
 def test_responses_api_bridge_check_gpt_5_4_tools_without_reasoning_stays_chat():
     """gpt-5.4 with tools only should not be force-routed to Responses API."""
     from litellm.main import responses_api_bridge_check
diff --git a/tests/test_litellm/test_project_alias_tracking.py b/tests/test_litellm/test_project_alias_tracking.py
new file mode 100644
index 00000000000..d18989d543f
--- /dev/null
+++ b/tests/test_litellm/test_project_alias_tracking.py
@@ -0,0 +1,134 @@
+"""
+Tests for project_alias and project_id tracking through callback kwargs / metadata.
+
+Verifies that project_alias flows from UserAPIKeyAuth through the metadata pipeline
+to StandardLoggingMetadata, mirroring how team_alias already works.
+"""
+
+import os
+import sys
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup
+from litellm.proxy._types import LiteLLM_VerificationTokenView, UserAPIKeyAuth
+from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
+from litellm.types.utils import StandardLoggingUserAPIKeyMetadata
+
+
+class TestProjectAliasOnTypes:
+    """project_alias field exists on the relevant types."""
+
+    def test_verification_token_view_has_project_alias(self):
+        token_view = LiteLLM_VerificationTokenView(
+            token="test-token",
+            project_id="proj-123",
+            project_alias="My Project",
+        )
+        assert token_view.project_alias == "My Project"
+
+    def test_verification_token_view_project_alias_defaults_none(self):
+        token_view = LiteLLM_VerificationTokenView(token="test-token")
+        assert token_view.project_alias is None
+
+    def test_user_api_key_auth_inherits_project_alias(self):
+        """UserAPIKeyAuth extends LiteLLM_VerificationTokenView, so it gets project_alias."""
+        auth = UserAPIKeyAuth(
+            api_key="sk-test",
+            project_id="proj-1",
+            project_alias="billing-service",
+        )
+        assert auth.project_alias == "billing-service"
+
+    def test_standard_logging_metadata_has_project_alias_field(self):
+        metadata = StandardLoggingUserAPIKeyMetadata(
+            user_api_key_hash="hash",
+            user_api_key_alias=None,
+            user_api_key_spend=None,
+            user_api_key_max_budget=None,
+            user_api_key_budget_reset_at=None,
+            user_api_key_org_id=None,
+            user_api_key_team_id=None,
+            user_api_key_project_id="proj-1",
+            user_api_key_project_alias="billing-service",
+            user_api_key_user_id=None,
+            user_api_key_user_email=None,
+            user_api_key_team_alias=None,
+            user_api_key_end_user_id=None,
+            user_api_key_request_route=None,
+            user_api_key_auth_metadata=None,
+        )
+        assert metadata["user_api_key_project_alias"] == "billing-service"
+
+
+class TestProjectAliasThroughMetadataPipeline:
+    """project_alias flows through the full metadata pipeline."""
+
+    def test_get_sanitized_user_information_includes_project_alias(self):
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-hashed",
+            project_id="proj-123",
+            project_alias="My Cool Project",
+            team_id="team-1",
+            team_alias="my-team",
+        )
+
+        result = LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(
+            user_api_key_dict=user_api_key_dict
+        )
+
+        assert result["user_api_key_project_id"] == "proj-123"
+        assert result["user_api_key_project_alias"] == "My Cool Project"
+
+    def test_get_sanitized_user_information_project_alias_none_when_no_project(self):
+        user_api_key_dict = UserAPIKeyAuth(api_key="sk-hashed")
+
+        result = LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(
+            user_api_key_dict=user_api_key_dict
+        )
+
+        assert result["user_api_key_project_id"] is None
+        assert result["user_api_key_project_alias"] is None
+
+    def test_project_alias_flows_to_standard_logging_metadata(self):
+        """get_standard_logging_metadata picks up project_alias from input metadata."""
+        metadata = {
+            "user_api_key_project_id": "proj-123",
+            "user_api_key_project_alias": "My Cool Project",
+            "user_api_key_team_id": "team-1",
+            "user_api_key_team_alias": "my-team",
+        }
+
+        result = StandardLoggingPayloadSetup.get_standard_logging_metadata(metadata)
+        assert result["user_api_key_project_alias"] == "My Cool Project"
+
+    def test_project_alias_defaults_to_none_in_logging_metadata(self):
+        result = StandardLoggingPayloadSetup.get_standard_logging_metadata({})
+        assert result["user_api_key_project_alias"] is None
+
+    def test_end_to_end_project_alias_flow(self):
+        """Full flow: UserAPIKeyAuth -> get_sanitized -> get_standard_logging_metadata."""
+        auth = UserAPIKeyAuth(
+            api_key="sk-test",
+            project_id="proj-abc",
+            project_alias="analytics-pipeline",
+            team_id="team-1",
+            team_alias="data-team",
+        )
+
+        # Step 1: Auth → sanitized metadata
+        sanitized = LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(
+            user_api_key_dict=auth
+        )
+
+        # Step 2: Sanitized metadata → standard logging metadata
+        logging_metadata = StandardLoggingPayloadSetup.get_standard_logging_metadata(
+            dict(sanitized)
+        )
+
+        assert logging_metadata["user_api_key_project_id"] == "proj-abc"
+        assert logging_metadata["user_api_key_project_alias"] == "analytics-pipeline"
+        assert logging_metadata["user_api_key_team_id"] == "team-1"
+        assert logging_metadata["user_api_key_team_alias"] == "data-team"
diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py
index 4709faea4bc..15907190998 100644
--- a/tests/test_litellm/test_redis.py
+++ b/tests/test_litellm/test_redis.py
@@ -1,7 +1,15 @@
-from litellm._redis import get_redis_url_from_environment, _get_redis_cluster_kwargs, get_redis_async_client
+from litellm._redis import (
+    get_redis_url_from_environment,
+    _get_redis_cluster_kwargs,
+    get_redis_async_client,
+    get_redis_client,
+    get_redis_connection_pool,
+)
+import json
 import os
 import pytest
 from unittest.mock import MagicMock, patch
+import redis
 import redis.asyncio as async_redis
 
 def test_get_redis_url_from_environment_single_url(monkeypatch):
@@ -167,3 +175,115 @@ def test_get_redis_async_client_without_connection_pool():
         # Verify Redis was called without connection_pool in kwargs
         call_kwargs = mock_redis.call_args[1]
         assert "connection_pool" not in call_kwargs, "connection_pool should not be in kwargs when not provided"
+
+@patch("litellm._redis.init_redis_cluster")
+def test_sync_client_prefers_cluster_over_url(mock_init_cluster, monkeypatch):
+    """
+    Test get_redis_client returns RedisCluster when startup_nodes is present even if
+    REDIS_URL is also set.
+    """
+    monkeypatch.setenv("REDIS_URL", "redis://fallback-host:6379")
+    mock_init_cluster.return_value = MagicMock(spec=redis.RedisCluster)
+
+    startup_nodes = [{"host": "cluster-node.example.com", "port": 6379}]
+    get_redis_client(startup_nodes=startup_nodes)
+
+    mock_init_cluster.assert_called_once()
+    call_kwargs = mock_init_cluster.call_args[0][0]
+    assert (
+        "startup_nodes" in call_kwargs
+    ), "startup_nodes must be forwarded to init_redis_cluster"
+
+@patch("litellm._redis.async_redis.RedisCluster")
+def test_async_client_prefers_cluster_over_url(mock_cluster_cls, monkeypatch):
+    """
+    Test (1) get_redis_async_client returns async RedisCluster when startup_nodes is present
+    even if REDIS_URL is also set and (2) startup_nodes is forwarded to RedisCluster.
+    """
+    monkeypatch.setenv("REDIS_URL", "redis://fallback-host:6379")
+
+    startup_nodes = [{"host": "cluster-node.example.com", "port": 6379}]
+    get_redis_async_client(startup_nodes=startup_nodes)
+
+    mock_cluster_cls.assert_called_once()
+    call_kwargs = mock_cluster_cls.call_args[1]
+    assert "startup_nodes" in call_kwargs, "startup_nodes must be forwarded to async RedisCluster"
+    assert len(call_kwargs["startup_nodes"]) == 1, "should forward exactly 1 cluster node"
+
+
+@patch("litellm._redis.async_redis.RedisCluster")
+def test_async_client_prefers_cluster_over_url_via_env_var(mock_cluster_cls, monkeypatch):
+    """
+    Test get_redis_async_client returns async RedisCluster when REDIS_CLUSTER_NODES is set
+    even if REDIS_URL is also set.
+    """
+    monkeypatch.setenv("REDIS_URL", "redis://fallback-host:6379")
+    monkeypatch.setenv(
+        "REDIS_CLUSTER_NODES",
+        json.dumps([{"host": "cluster-node.example.com", "port": 6379}]),
+    )
+
+    get_redis_async_client()
+
+    mock_cluster_cls.assert_called_once()
+    call_kwargs = mock_cluster_cls.call_args[1]
+    assert "startup_nodes" in call_kwargs, "startup_nodes must be forwarded to async RedisCluster"
+
+@patch("litellm._redis.init_redis_cluster")
+def test_sync_client_prefers_cluster_over_url_via_env_var(mock_init_cluster, monkeypatch):
+    """
+    Test get_redis_client returns RedisCluster when REDIS_CLUSTER_NODES is set even if
+    REDIS_URL is also set.
+    """
+    monkeypatch.setenv("REDIS_URL", "redis://fallback-host:6379")
+    monkeypatch.setenv(
+        "REDIS_CLUSTER_NODES",
+        json.dumps([{"host": "cluster-node.example.com", "port": 6379}]),
+    )
+    mock_init_cluster.return_value = MagicMock(spec=redis.RedisCluster)
+
+    get_redis_client()
+
+    mock_init_cluster.assert_called_once()
+    call_kwargs = mock_init_cluster.call_args[0][0]
+    assert "startup_nodes" in call_kwargs, "startup_nodes must be forwarded to init_redis_cluster"
+    assert len(call_kwargs["startup_nodes"]) == 1
+
+@patch("litellm._redis.init_redis_cluster")
+def test_sync_client_preserves_password_for_cluster_when_url_also_set(mock_init_cluster, monkeypatch):
+    """
+    Test _get_redis_client_logic does not strip password from redis_kwargs when
+    startup_nodes is present even if REDIS_URL is also set.
+    """
+    monkeypatch.setenv("REDIS_URL", "redis://fallback-host:6379")
+    monkeypatch.setenv("REDIS_PASSWORD", "secret")
+    mock_init_cluster.return_value = MagicMock(spec=redis.RedisCluster)
+
+    startup_nodes = [{"host": "cluster-node.example.com", "port": 6379}]
+    get_redis_client(startup_nodes=startup_nodes)
+
+    mock_init_cluster.assert_called_once()
+    call_kwargs = mock_init_cluster.call_args[0][0]
+    assert "password" in call_kwargs, "password must not be stripped when routing to cluster"
+    assert call_kwargs["password"] == "secret"
+
+
+def test_connection_pool_returns_none_for_cluster(monkeypatch):
+    """Test get_redis_connection_pool returns None when startup_nodes is present."""
+    monkeypatch.setenv("REDIS_URL", "redis://fallback-host:6379")
+    startup_nodes = [{"host": "cluster-node.example.com", "port": 6379}]
+    result = get_redis_connection_pool(startup_nodes=startup_nodes)
+    assert result is None, "connection pool must be None for cluster mode"
+
+
+@patch("litellm._redis.redis.Redis.from_url")
+def test_sync_client_url_used_when_no_cluster(mock_from_url, monkeypatch):
+    """
+    Test get_redis_client default to using URL path when no startup_nodes are provided.
+    """
+    monkeypatch.setenv("REDIS_URL", "redis://plain-host:6379")
+    monkeypatch.delenv("REDIS_CLUSTER_NODES", raising=False)
+
+    get_redis_client()
+
+    mock_from_url.assert_called_once()
diff --git a/tests/test_litellm/test_router_order_fallback.py b/tests/test_litellm/test_router_order_fallback.py
new file mode 100644
index 00000000000..760766a7461
--- /dev/null
+++ b/tests/test_litellm/test_router_order_fallback.py
@@ -0,0 +1,331 @@
+"""
+Tests for order-based fallback routing.
+
+When deployments have `order` set in litellm_params, lower order deployments
+should be tried first, and higher order deployments should be used as fallbacks
+when lower order deployments fail.
+"""
+
+from typing import Optional
+
+import pytest
+
+from litellm import Router
+from litellm.utils import _get_order_filtered_deployments
+
+# ---------------------------------------------------------------------------
+# Unit tests for _get_order_filtered_deployments
+# ---------------------------------------------------------------------------
+
+
+class TestGetOrderFilteredDeployments:
+    def _make_deployment(self, order: Optional[int], dep_id: str) -> dict:
+        params: dict = {"model": "gpt-4o", "api_key": "key"}
+        if order is not None:
+            params["order"] = order
+        return {
+            "model_name": "test-model",
+            "litellm_params": params,
+            "model_info": {"id": dep_id},
+        }
+
+    def test_returns_min_order_group(self):
+        deps = [
+            self._make_deployment(1, "a"),
+            self._make_deployment(2, "b"),
+            self._make_deployment(1, "c"),
+        ]
+        result = _get_order_filtered_deployments(deps)
+        assert len(result) == 2
+        assert all(d["model_info"]["id"] in ("a", "c") for d in result)
+
+    def test_target_order_filters_to_exact_level(self):
+        deps = [
+            self._make_deployment(1, "a"),
+            self._make_deployment(2, "b"),
+            self._make_deployment(3, "c"),
+        ]
+        result = _get_order_filtered_deployments(deps, target_order=2)
+        assert len(result) == 1
+        assert result[0]["model_info"]["id"] == "b"
+
+    def test_target_order_no_match_returns_all(self):
+        deps = [
+            self._make_deployment(1, "a"),
+            self._make_deployment(2, "b"),
+        ]
+        result = _get_order_filtered_deployments(deps, target_order=99)
+        assert len(result) == 2
+
+    def test_no_order_set_returns_all(self):
+        deps = [
+            self._make_deployment(None, "a"),
+            self._make_deployment(None, "b"),
+        ]
+        result = _get_order_filtered_deployments(deps)
+        assert len(result) == 2
+
+    def test_empty_list(self):
+        result = _get_order_filtered_deployments([])
+        assert result == []
+
+    def test_single_order_returns_all_with_that_order(self):
+        deps = [
+            self._make_deployment(1, "a"),
+            self._make_deployment(1, "b"),
+        ]
+        result = _get_order_filtered_deployments(deps)
+        assert len(result) == 2
+
+
+# ---------------------------------------------------------------------------
+# Integration tests for order-based fallback in Router
+# ---------------------------------------------------------------------------
+
+
+def test_router_order_without_pre_call_checks():
+    """Order filtering should work even when enable_pre_call_checks=False (default)."""
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "key",
+                    "mock_response": "from order 1",
+                    "order": 1,
+                },
+                "model_info": {"id": "1"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "key",
+                    "mock_response": "from order 2",
+                    "order": 2,
+                },
+                "model_info": {"id": "2"},
+            },
+        ],
+        num_retries=0,
+        enable_pre_call_checks=False,
+    )
+
+    for _ in range(20):
+        response = router.completion(
+            model="test-model",
+            messages=[{"role": "user", "content": "hi"}],
+        )
+        assert response._hidden_params["model_id"] == "1"
+
+
+def test_router_order_no_fallback_when_healthy():
+    """When order=1 is healthy, order=2 should never be used."""
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "key",
+                    "mock_response": "from order 1",
+                    "order": 1,
+                },
+                "model_info": {"id": "1"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "key",
+                    "mock_response": "from order 2",
+                    "order": 2,
+                },
+                "model_info": {"id": "2"},
+            },
+        ],
+        num_retries=0,
+    )
+
+    for _ in range(50):
+        response = router.completion(
+            model="test-model",
+            messages=[{"role": "user", "content": "hi"}],
+        )
+        assert response._hidden_params["model_id"] == "1"
+
+
+@pytest.mark.asyncio
+async def test_router_order_fallback_on_failure():
+    """When order=1 fails, order=2 should be tried as fallback."""
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad-key",
+                    "mock_response": Exception("connection error"),
+                    "order": 1,
+                },
+                "model_info": {"id": "1"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "good-key",
+                    "mock_response": "success from order 2",
+                    "order": 2,
+                },
+                "model_info": {"id": "2"},
+            },
+        ],
+        num_retries=0,
+    )
+
+    response = await router.acompletion(
+        model="test-model",
+        messages=[{"role": "user", "content": "hi"}],
+    )
+    assert response._hidden_params["model_id"] == "2"
+
+
+@pytest.mark.asyncio
+async def test_router_order_fallback_three_levels():
+    """When order=1 and order=2 both fail, order=3 should be tried."""
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad",
+                    "mock_response": Exception("fail 1"),
+                    "order": 1,
+                },
+                "model_info": {"id": "1"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad",
+                    "mock_response": Exception("fail 2"),
+                    "order": 2,
+                },
+                "model_info": {"id": "2"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "good",
+                    "mock_response": "success from order 3",
+                    "order": 3,
+                },
+                "model_info": {"id": "3"},
+            },
+        ],
+        num_retries=0,
+    )
+
+    response = await router.acompletion(
+        model="test-model",
+        messages=[{"role": "user", "content": "hi"}],
+    )
+    assert response._hidden_params["model_id"] == "3"
+
+
+@pytest.mark.asyncio
+async def test_router_order_fallback_then_external_fallback():
+    """When all order levels fail, external fallbacks should be tried."""
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad",
+                    "mock_response": Exception("fail order 1"),
+                    "order": 1,
+                },
+                "model_info": {"id": "1"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad",
+                    "mock_response": Exception("fail order 2"),
+                    "order": 2,
+                },
+                "model_info": {"id": "2"},
+            },
+            {
+                "model_name": "fallback-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "good",
+                    "mock_response": "success from external fallback",
+                },
+                "model_info": {"id": "fallback"},
+            },
+        ],
+        fallbacks=[{"test-model": ["fallback-model"]}],
+        num_retries=0,
+    )
+
+    response = await router.acompletion(
+        model="test-model",
+        messages=[{"role": "user", "content": "hi"}],
+    )
+    assert response._hidden_params["model_id"] == "fallback"
+
+
+@pytest.mark.asyncio
+async def test_router_order_fallback_with_non_standard_fallbacks():
+    """Non-standard fallback formats (e.g. fallbacks=["model-name"]) passed
+    per-request should still be tried after all order levels are exhausted."""
+    router = Router(
+        model_list=[
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad",
+                    "mock_response": Exception("fail order 1"),
+                    "order": 1,
+                },
+                "model_info": {"id": "1"},
+            },
+            {
+                "model_name": "test-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "bad",
+                    "mock_response": Exception("fail order 2"),
+                    "order": 2,
+                },
+                "model_info": {"id": "2"},
+            },
+            {
+                "model_name": "fallback-model",
+                "litellm_params": {
+                    "model": "gpt-4o",
+                    "api_key": "good",
+                    "mock_response": "success from non-standard fallback",
+                },
+                "model_info": {"id": "fallback"},
+            },
+        ],
+        num_retries=0,
+    )
+
+    response = await router.acompletion(
+        model="test-model",
+        messages=[{"role": "user", "content": "hi"}],
+        fallbacks=["fallback-model"],  # non-standard format, passed per-request
+    )
+    assert response._hidden_params["model_id"] == "fallback"
diff --git a/tests/test_litellm/test_secret_redaction.py b/tests/test_litellm/test_secret_redaction.py
index 821529c111b..1d575195194 100644
--- a/tests/test_litellm/test_secret_redaction.py
+++ b/tests/test_litellm/test_secret_redaction.py
@@ -213,3 +213,52 @@ def test_json_excepthook_redacts_traceback_secrets():
     output = h.formatter.format(record)
     assert SECRET not in output
     assert "REDACTED" in output
+
+
+def test_key_name_redaction_catches_secrets_in_dict_repr():
+    """Secrets inside dict repr strings are redacted based on key names."""
+    cases = [
+        # Python dict repr (the exact leak format from the bug report)
+        "param_name=general_settings, param_value={'master_key': 'my-random-secret-key-1234', 'enable_jwt_auth': True}",
+        # database_url
+        "'database_url': 'postgres://admin:password@db.example.com:5432/litellm'",
+        # JSON format
+        '"database_url": "postgres://admin:password@db.example.com:5432/litellm"',
+        # access_token
+        "'access_token': 'some-opaque-token-value'",
+        # refresh_token
+        "refresh_token=my-refresh-tok-12345",
+        # auth_token
+        "'auth_token': 'random-auth-value'",
+        # slack_webhook_url
+        "'slack_webhook_url': 'https://hooks.slack.com/services/T00/B00/xxx'",
+    ]
+    for secret_line in cases:
+        result = _redact_string(secret_line)
+        assert "REDACTED" in result, f"Key-name redaction missed: {secret_line!r}"
+
+    # Non-sensitive keys should NOT be redacted
+    safe = "'enable_jwt_auth': True, 'store_model_in_db': True"
+    assert _redact_string(safe) == safe
+
+
+def test_key_name_redaction_in_general_settings_dict():
+    """End-to-end: secrets inside a general_settings dict dump are redacted
+    when logged through the named litellm loggers."""
+
+    def log_messages():
+        general_settings = {
+            "master_key": "my-random-secret-key-1234",
+            "database_url": "postgres://admin:password@db.example.com:5432/litellm",
+            "enable_jwt_auth": True,
+            "store_model_in_db": True,
+        }
+        verbose_proxy_logger.debug(
+            f"param_name=general_settings, param_value={general_settings}"
+        )
+
+    output = _capture_logger_output(log_messages)
+    assert "my-random-secret-key-1234" not in output
+    assert "REDACTED" in output
+    # Non-sensitive values should survive
+    assert "enable_jwt_auth" in output
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index 64488e2fb6a..eb2f510e239 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -38,22 +38,28 @@ def test_check_provider_match_azure_ai_allows_openai_and_azure():
     This is needed for Azure Model Router which can route to OpenAI models.
     """
     # azure_ai should match openai models
-    assert _check_provider_match(
-        model_info={"litellm_provider": "openai"},
-        custom_llm_provider="azure_ai"
-    ) is True
+    assert (
+        _check_provider_match(
+            model_info={"litellm_provider": "openai"}, custom_llm_provider="azure_ai"
+        )
+        is True
+    )
 
     # azure_ai should match azure models
-    assert _check_provider_match(
-        model_info={"litellm_provider": "azure"},
-        custom_llm_provider="azure_ai"
-    ) is True
+    assert (
+        _check_provider_match(
+            model_info={"litellm_provider": "azure"}, custom_llm_provider="azure_ai"
+        )
+        is True
+    )
 
     # azure_ai should NOT match other providers
-    assert _check_provider_match(
-        model_info={"litellm_provider": "anthropic"},
-        custom_llm_provider="azure_ai"
-    ) is False
+    assert (
+        _check_provider_match(
+            model_info={"litellm_provider": "anthropic"}, custom_llm_provider="azure_ai"
+        )
+        is False
+    )
 
 
 def test_check_provider_match_github_allows_upstream_provider_metadata():
@@ -61,20 +67,29 @@ def test_check_provider_match_github_allows_upstream_provider_metadata():
     Test that github provider can match upstream provider metadata.
     GitHub Models can provide models from multiple providers.
     """
-    assert _check_provider_match(
-        model_info={"litellm_provider": "openai"},
-        custom_llm_provider="github",
-    ) is True
+    assert (
+        _check_provider_match(
+            model_info={"litellm_provider": "openai"},
+            custom_llm_provider="github",
+        )
+        is True
+    )
 
-    assert _check_provider_match(
-        model_info={"litellm_provider": "github"},
-        custom_llm_provider="github",
-    ) is True
+    assert (
+        _check_provider_match(
+            model_info={"litellm_provider": "github"},
+            custom_llm_provider="github",
+        )
+        is True
+    )
 
-    assert _check_provider_match(
-        model_info={"litellm_provider": "anthropic"},
-        custom_llm_provider="github",
-    ) is True
+    assert (
+        _check_provider_match(
+            model_info={"litellm_provider": "anthropic"},
+            custom_llm_provider="github",
+        )
+        is True
+    )
 
 
 def test_supports_function_calling_github_openai_alias():
@@ -604,7 +619,10 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "cache_read_input_token_cost": {"type": "number"},
                 "cache_read_input_token_cost_above_200k_tokens": {"type": "number"},
                 "cache_read_input_token_cost_above_272k_tokens": {"type": "number"},
-                "cache_creation_input_token_cost_above_1hr_above_200k_tokens": {"type": "number"},
+                "cache_read_input_token_cost_batches": {"type": "number"},
+                "cache_creation_input_token_cost_above_1hr_above_200k_tokens": {
+                    "type": "number"
+                },
                 "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"},
@@ -623,8 +641,12 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "input_cost_per_token_above_272k_tokens": {"type": "number"},
                 "cache_read_input_token_cost_flex": {"type": "number"},
                 "cache_read_input_token_cost_priority": {"type": "number"},
-                "cache_read_input_token_cost_above_200k_tokens_priority": {"type": "number"},
-                "cache_read_input_token_cost_above_272k_tokens_priority": {"type": "number"},
+                "cache_read_input_token_cost_above_200k_tokens_priority": {
+                    "type": "number"
+                },
+                "cache_read_input_token_cost_above_272k_tokens_priority": {
+                    "type": "number"
+                },
                 "input_cost_per_token_flex": {"type": "number"},
                 "input_cost_per_token_priority": {"type": "number"},
                 "input_cost_per_token_above_200k_tokens_priority": {"type": "number"},
@@ -743,6 +765,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "supports_multimodal": {"type": "boolean"},
                 "uses_embed_content": {"type": "boolean"},
                 "supports_reasoning": {"type": "boolean"},
+                "supports_minimal_reasoning_effort": {"type": "boolean"},
                 "supports_none_reasoning_effort": {"type": "boolean"},
                 "supports_xhigh_reasoning_effort": {"type": "boolean"},
                 "supports_service_tier": {"type": "boolean"},
@@ -839,7 +862,9 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
         },
     }
 
-    prod_json = os.path.join(os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json")
+    prod_json = os.path.join(
+        os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json"
+    )
     with open(prod_json, "r") as model_prices_file:
         actual_json = json.load(model_prices_file)
     assert isinstance(actual_json, dict)
@@ -880,8 +905,10 @@ def test_max_tokens_consistency():
     from pathlib import Path
 
     # Load the model configuration
-    config_path = Path(__file__).parent.parent.parent / "model_prices_and_context_window.json"
-    with open(config_path, 'r') as f:
+    config_path = (
+        Path(__file__).parent.parent.parent / "model_prices_and_context_window.json"
+    )
+    with open(config_path, "r") as f:
         models = json.load(f)
 
     inconsistencies = []
@@ -893,17 +920,19 @@ def test_max_tokens_consistency():
 
         # Check if both max_tokens and max_output_tokens exist
         if isinstance(config, dict):
-            max_tokens = config.get('max_tokens')
-            max_output_tokens = config.get('max_output_tokens')
+            max_tokens = config.get("max_tokens")
+            max_output_tokens = config.get("max_output_tokens")
 
             # Only validate if both exist
             if max_tokens is not None and max_output_tokens is not None:
                 if max_tokens != max_output_tokens:
-                    inconsistencies.append({
-                        'model': model_name,
-                        'max_tokens': max_tokens,
-                        'max_output_tokens': max_output_tokens
-                    })
+                    inconsistencies.append(
+                        {
+                            "model": model_name,
+                            "max_tokens": max_tokens,
+                            "max_output_tokens": max_output_tokens,
+                        }
+                    )
 
     if inconsistencies:
         error_msg = f"\n\n❌ Found {len(inconsistencies)} models with max_tokens != max_output_tokens:\n\n"
@@ -932,6 +961,7 @@ def test_get_model_info_gemini():
             and not "learnlm" in model
             and not "imagen" in model
             and not "veo" in model
+            and not "lyria" in model
             and not "robotics" in model
         ):
             assert info.get("tpm") is not None, f"{model} does not have tpm"
@@ -2381,13 +2411,14 @@ def test_register_model_with_scientific_notation():
 
     # Use a truly unique model name with uuid to avoid conflicts when tests run in parallel
     test_model_name = f"test-scientific-notation-model-{uuid.uuid4().hex[:12]}"
-    
+
     # Clear LRU caches that might have stale data
     from litellm.utils import (
         _invalidate_model_cost_lowercase_map,
     )
+
     _invalidate_model_cost_lowercase_map()
-    
+
     model_cost_dict = {
         test_model_name: {
             "max_tokens": 8192,
@@ -2406,7 +2437,7 @@ def test_register_model_with_scientific_notation():
     assert registered_model["output_cost_per_token"] == 6e-07
     assert registered_model["litellm_provider"] == "openai"
     assert registered_model["mode"] == "chat"
-    
+
     # Clean up after test
     if test_model_name in litellm.model_cost:
         del litellm.model_cost[test_model_name]
@@ -2734,7 +2765,9 @@ def test_model_info_for_openrouter_kimi_k2_5():
         model_cost = json.load(f)
 
     model_info = model_cost.get("openrouter/moonshotai/kimi-k2.5")
-    assert model_info is not None, "Model not found in model_prices_and_context_window.json"
+    assert (
+        model_info is not None
+    ), "Model not found in model_prices_and_context_window.json"
     assert model_info["litellm_provider"] == "openrouter"
     assert model_info["mode"] == "chat"
 
@@ -2756,6 +2789,22 @@ def test_model_info_for_openrouter_kimi_k2_5():
     print("openrouter kimi-k2.5 model info", model_info)
 
 
+def test_gemini_lyria_3_preview_models_in_cost_map():
+    import json
+    from pathlib import Path
+
+    json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json"
+    with open(json_path) as f:
+        model_cost = json.load(f)
+
+    clip = model_cost.get("gemini/lyria-3-clip-preview")
+    pro = model_cost.get("gemini/lyria-3-pro-preview")
+    assert clip is not None and pro is not None
+    assert clip["litellm_provider"] == "gemini" and pro["litellm_provider"] == "gemini"
+    assert clip["max_input_tokens"] == 131072 == pro["max_input_tokens"]
+    assert clip["output_cost_per_image"] == 0.04
+
+
 def test_model_info_for_fireworks_short_form_models():
     """
     Test that fireworks_ai short-form model entries (fireworks_ai/<model>)
@@ -2778,7 +2827,9 @@ def test_model_info_for_fireworks_short_form_models():
         "fireworks_ai/accounts/fireworks/models/glm-4p7",
     ]:
         info = model_cost.get(key)
-        assert info is not None, f"{key} not found in model_prices_and_context_window.json"
+        assert (
+            info is not None
+        ), f"{key} not found in model_prices_and_context_window.json"
         assert info["litellm_provider"] == "fireworks_ai"
         assert info["mode"] == "chat"
         assert info["input_cost_per_token"] == 6e-07
@@ -2792,7 +2843,9 @@ def test_model_info_for_fireworks_short_form_models():
         "fireworks_ai/accounts/fireworks/models/minimax-m2p1",
     ]:
         info = model_cost.get(key)
-        assert info is not None, f"{key} not found in model_prices_and_context_window.json"
+        assert (
+            info is not None
+        ), f"{key} not found in model_prices_and_context_window.json"
         assert info["litellm_provider"] == "fireworks_ai"
         assert info["mode"] == "chat"
         assert info["input_cost_per_token"] == 3e-07
@@ -2801,7 +2854,9 @@ def test_model_info_for_fireworks_short_form_models():
 
     # kimi-k2p5: short-form only (long-form already existed)
     info = model_cost.get("fireworks_ai/kimi-k2p5")
-    assert info is not None, "fireworks_ai/kimi-k2p5 not found in model_prices_and_context_window.json"
+    assert (
+        info is not None
+    ), "fireworks_ai/kimi-k2p5 not found in model_prices_and_context_window.json"
     assert info["litellm_provider"] == "fireworks_ai"
     assert info["mode"] == "chat"
     assert info["input_cost_per_token"] == 6e-07
@@ -3047,7 +3102,9 @@ class TestProxyLoggingBudgetAlerts:
         user_info = MagicMock()
 
         # Should not raise an error
-        await proxy_logging.budget_alerts(type="organization_budget", user_info=user_info)
+        await proxy_logging.budget_alerts(
+            type="organization_budget", user_info=user_info
+        )
 
     async def test_budget_alerts_with_both_slack_and_email(self):
         """Test that budget_alerts calls both slack and email instances when both are in alerting."""
@@ -3103,11 +3160,13 @@ class TestProxyLoggingBudgetAlerts:
             type=alert_type, user_info=user_info
         )
 
-    async def test_budget_alerts_soft_budget_with_alert_emails_bypasses_alerting_none(self):
+    async def test_budget_alerts_soft_budget_with_alert_emails_bypasses_alerting_none(
+        self,
+    ):
         """
         Test that soft_budget alerts with alert_emails bypass the alerting=None check
         and send emails even when alerting is None.
-        
+
         This tests the new logic that allows team-specific soft budget email alerts
         via metadata.soft_budget_alerting_emails to work even when global alerting is disabled.
         """
@@ -3143,7 +3202,9 @@ class TestProxyLoggingBudgetAlerts:
             type="soft_budget", user_info=user_info
         )
 
-    async def test_budget_alerts_soft_budget_without_alert_emails_respects_alerting_none(self):
+    async def test_budget_alerts_soft_budget_without_alert_emails_respects_alerting_none(
+        self,
+    ):
         """
         Test that soft_budget alerts WITHOUT alert_emails still respect alerting=None
         and do not send emails when alerting is None.
@@ -3176,7 +3237,9 @@ class TestProxyLoggingBudgetAlerts:
         proxy_logging.slack_alerting_instance.budget_alerts.assert_not_called()
         proxy_logging.email_logging_instance.budget_alerts.assert_not_called()
 
-    async def test_budget_alerts_soft_budget_with_empty_alert_emails_respects_alerting_none(self):
+    async def test_budget_alerts_soft_budget_with_empty_alert_emails_respects_alerting_none(
+        self,
+    ):
         """
         Test that soft_budget alerts with empty alert_emails list still respect alerting=None.
         """
@@ -3317,7 +3380,10 @@ def test_last_assistant_with_tool_calls_has_no_thinking_blocks_issue_18926():
                 {"type": "thinking", "thinking": "Let me analyze the requirements..."}
             ],
             "tool_calls": [
-                {"id": "toolu_1", "function": {"name": "file_editor", "arguments": "{}"}}
+                {
+                    "id": "toolu_1",
+                    "function": {"name": "file_editor", "arguments": "{}"},
+                }
             ],
         },
         {
@@ -3330,7 +3396,10 @@ def test_last_assistant_with_tool_calls_has_no_thinking_blocks_issue_18926():
             # NO thinking_blocks - Claude sometimes doesn't include them
             "content": [{"type": "text", "text": "Let me explore more..."}],
             "tool_calls": [
-                {"id": "toolu_2", "function": {"name": "file_editor", "arguments": "{}"}}
+                {
+                    "id": "toolu_2",
+                    "function": {"name": "file_editor", "arguments": "{}"},
+                }
             ],
         },
     ]
@@ -3343,10 +3412,9 @@ def test_last_assistant_with_tool_calls_has_no_thinking_blocks_issue_18926():
 
     # So we should NOT drop thinking - the combination tells us thinking is in use
     # The fix uses both checks: only drop if last has none AND no message has any
-    should_drop_thinking = (
-        last_assistant_with_tool_calls_has_no_thinking_blocks(messages)
-        and not any_assistant_message_has_thinking_blocks(messages)
-    )
+    should_drop_thinking = last_assistant_with_tool_calls_has_no_thinking_blocks(
+        messages
+    ) and not any_assistant_message_has_thinking_blocks(messages)
     assert should_drop_thinking is False
 
 
@@ -3558,34 +3626,67 @@ class TestGetOptionalParamsDeepSeek:
 
 class TestIsStreamingRequest:
     def test_stream_true_in_kwargs(self):
-        assert _is_streaming_request(kwargs={"stream": True}, call_type="acompletion") is True
+        assert (
+            _is_streaming_request(kwargs={"stream": True}, call_type="acompletion")
+            is True
+        )
 
     def test_stream_false_in_kwargs(self):
-        assert _is_streaming_request(kwargs={"stream": False}, call_type="acompletion") is False
+        assert (
+            _is_streaming_request(kwargs={"stream": False}, call_type="acompletion")
+            is False
+        )
 
     def test_no_stream_in_kwargs(self):
         assert _is_streaming_request(kwargs={}, call_type="acompletion") is False
 
     def test_generate_content_stream_string(self):
-        assert _is_streaming_request(kwargs={}, call_type=CallTypes.generate_content_stream.value) is True
+        assert (
+            _is_streaming_request(
+                kwargs={}, call_type=CallTypes.generate_content_stream.value
+            )
+            is True
+        )
 
     def test_agenerate_content_stream_string(self):
-        assert _is_streaming_request(kwargs={}, call_type=CallTypes.agenerate_content_stream.value) is True
+        assert (
+            _is_streaming_request(
+                kwargs={}, call_type=CallTypes.agenerate_content_stream.value
+            )
+            is True
+        )
 
     def test_generate_content_stream_enum(self):
-        assert _is_streaming_request(kwargs={}, call_type=CallTypes.generate_content_stream) is True
+        assert (
+            _is_streaming_request(
+                kwargs={}, call_type=CallTypes.generate_content_stream
+            )
+            is True
+        )
 
     def test_agenerate_content_stream_enum(self):
-        assert _is_streaming_request(kwargs={}, call_type=CallTypes.agenerate_content_stream) is True
+        assert (
+            _is_streaming_request(
+                kwargs={}, call_type=CallTypes.agenerate_content_stream
+            )
+            is True
+        )
 
     def test_non_streaming_call_type_string(self):
         assert _is_streaming_request(kwargs={}, call_type="acompletion") is False
 
     def test_non_streaming_call_type_enum(self):
-        assert _is_streaming_request(kwargs={}, call_type=CallTypes.acompletion) is False
+        assert (
+            _is_streaming_request(kwargs={}, call_type=CallTypes.acompletion) is False
+        )
 
     def test_stream_true_overrides_non_streaming_call_type(self):
-        assert _is_streaming_request(kwargs={"stream": True}, call_type=CallTypes.acompletion) is True
+        assert (
+            _is_streaming_request(
+                kwargs={"stream": True}, call_type=CallTypes.acompletion
+            )
+            is True
+        )
 
 
 class TestCallbackAsyncSyncSeparation:
@@ -3679,37 +3780,27 @@ class TestMetadataNoneHandling:
     def test_metadata_none_get_previous_models(self):
         """kwargs.get("metadata") or {} should return {} when metadata is None."""
         kwargs = {"metadata": None}
-        previous_models = (kwargs.get("metadata") or {}).get(
-            "previous_models", None
-        )
+        previous_models = (kwargs.get("metadata") or {}).get("previous_models", None)
         assert previous_models is None
 
     def test_metadata_none_model_group_check(self):
         """'model_group' in (kwargs.get("metadata") or {}) should not raise TypeError."""
         kwargs = {"metadata": None}
-        _is_litellm_router_call = "model_group" in (
-            kwargs.get("metadata") or {}
-        )
+        _is_litellm_router_call = "model_group" in (kwargs.get("metadata") or {})
         assert _is_litellm_router_call is False
 
     def test_metadata_missing_key(self):
         """Should work when metadata key is completely absent."""
         kwargs = {}
-        previous_models = (kwargs.get("metadata") or {}).get(
-            "previous_models", None
-        )
+        previous_models = (kwargs.get("metadata") or {}).get("previous_models", None)
         assert previous_models is None
 
     def test_metadata_present_with_values(self):
         """Should work when metadata has actual values."""
         kwargs = {"metadata": {"previous_models": ["model1"], "model_group": "test"}}
-        previous_models = (kwargs.get("metadata") or {}).get(
-            "previous_models", None
-        )
+        previous_models = (kwargs.get("metadata") or {}).get("previous_models", None)
         assert previous_models == ["model1"]
-        _is_litellm_router_call = "model_group" in (
-            kwargs.get("metadata") or {}
-        )
+        _is_litellm_router_call = "model_group" in (kwargs.get("metadata") or {})
         assert _is_litellm_router_call is True
 
     def test_metadata_none_causes_error_with_old_pattern(self):
diff --git a/tests/test_litellm/types/llms/test_types_llms_openai.py b/tests/test_litellm/types/llms/test_types_llms_openai.py
index 94221bd0efc..569743269a5 100644
--- a/tests/test_litellm/types/llms/test_types_llms_openai.py
+++ b/tests/test_litellm/types/llms/test_types_llms_openai.py
@@ -219,11 +219,13 @@ class TestAssistantMessageImageUrlContent:
         # convert to list to consume it — this must not raise ValidationError.
         content_blocks = list(raw_content) if raw_content is not None else []
 
-        assert len(content_blocks) == 2, (
-            f"Expected 2 content blocks (text + image_url), got {len(content_blocks)}: {content_blocks}"
-        )
+        assert (
+            len(content_blocks) == 2
+        ), f"Expected 2 content blocks (text + image_url), got {len(content_blocks)}: {content_blocks}"
         types = [b.get("type") for b in content_blocks if isinstance(b, dict)]
-        assert "image_url" in types, f"image_url block was silently dropped; blocks: {content_blocks}"
+        assert (
+            "image_url" in types
+        ), f"image_url block was silently dropped; blocks: {content_blocks}"
 
     def test_assistant_message_image_url_preserved_in_all_message_values(self):
         """
@@ -255,14 +257,16 @@ class TestAssistantMessageImageUrlContent:
         assert assistant is not None, "Assistant message missing after serialisation"
 
         content = assistant.get("content", [])
-        assert isinstance(content, list), f"content should be a list, got {type(content)}"
-        assert len(content) == 2, (
-            f"Expected 2 content blocks (text + image_url), got {len(content)}: {content}"
-        )
+        assert isinstance(
+            content, list
+        ), f"content should be a list, got {type(content)}"
+        assert (
+            len(content) == 2
+        ), f"Expected 2 content blocks (text + image_url), got {len(content)}: {content}"
         types = [b.get("type") for b in content if isinstance(b, dict)]
-        assert "image_url" in types, (
-            f"image_url block was silently dropped during AllMessageValues serialisation; blocks: {content}"
-        )
+        assert (
+            "image_url" in types
+        ), f"image_url block was silently dropped during AllMessageValues serialisation; blocks: {content}"
 
 
 class TestResponsesAPIReasoningNullFields:
@@ -379,10 +383,14 @@ class TestResponsesAPIReasoningNullFields:
         )
         dumped = response.model_dump()
         reasoning = [
-            o for o in dumped["output"] if isinstance(o, dict) and o.get("type") == "reasoning"
+            o
+            for o in dumped["output"]
+            if isinstance(o, dict) and o.get("type") == "reasoning"
         ][0]
         message = [
-            o for o in dumped["output"] if isinstance(o, dict) and o.get("type") == "message"
+            o
+            for o in dumped["output"]
+            if isinstance(o, dict) and o.get("type") == "message"
         ][0]
         assert "status" not in reasoning
         assert "content" not in reasoning
@@ -410,3 +418,38 @@ class TestResponsesAPIReasoningNullFields:
         assert dumped["error"] is None
         assert "instructions" in dumped
         assert dumped["instructions"] is None
+
+
+def test_normalize_fine_tuning_job_dict_maps_azure_pending():
+    from litellm.llms.openai.fine_tuning.handler import _normalize_fine_tuning_job_dict
+
+    out = _normalize_fine_tuning_job_dict(
+        {"organization_id": None, "result_files": None, "status": "pending"},
+        is_azure=True,
+    )
+    assert out["organization_id"] == ""
+    assert out["result_files"] == []
+    assert out["status"] == "queued"
+
+
+def test_normalize_fine_tuning_job_dict_openai_unchanged():
+    from litellm.llms.openai.fine_tuning.handler import _normalize_fine_tuning_job_dict
+
+    data = {"organization_id": None, "result_files": None, "status": "pending"}
+    out = _normalize_fine_tuning_job_dict(data, is_azure=False)
+    assert out is data
+
+
+def test_openai_file_object_accepts_pending_status():
+    from litellm.types.llms.openai import OpenAIFileObject
+
+    file_obj = OpenAIFileObject(
+        id="file-123",
+        bytes=1024,
+        created_at=1677610602,
+        filename="train.jsonl",
+        object="file",
+        purpose="fine-tune",
+        status="pending",
+    )
+    assert file_obj.status == "pending"
diff --git a/ui/litellm-dashboard/e2e_tests/tests/navigation/sidebar.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/navigation/sidebar.spec.ts
index 1fc982a7411..f56b5875dc6 100644
--- a/ui/litellm-dashboard/e2e_tests/tests/navigation/sidebar.spec.ts
+++ b/ui/litellm-dashboard/e2e_tests/tests/navigation/sidebar.spec.ts
@@ -13,7 +13,6 @@ const sidebarButtons = {
     "Usage",
     "Teams",
     "Internal Users",
-    "API Reference",
     "AI Hub",
   ],
 };
diff --git a/ui/litellm-dashboard/public/assets/logos/akto.svg b/ui/litellm-dashboard/public/assets/logos/akto.svg
new file mode 100644
index 00000000000..cdea32535f2
--- /dev/null
+++ b/ui/litellm-dashboard/public/assets/logos/akto.svg
@@ -0,0 +1,10 @@
+<svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M18.0216 0C17.8264 0 17.5923 0.078125 17.3581 0.15625L1.12266 7.65625C-0.750664 8.51562 -0.126225 11.2891 1.86418 11.2891H8.73302V18.1641C8.73302 19.3359 9.7087 20 10.6454 20C11.3088 20 12.0113 19.6875 12.3626 18.9062L19.8559 2.65625C20.4022 1.36719 19.3095 0 18.0216 0ZM18.7241 2.14844L14.6743 10.625L11.2308 18.3594C11.1137 18.6328 10.9186 18.7891 10.6454 18.7891C10.4112 18.7891 9.9819 18.6328 9.9819 18.1641V10H1.87332C1.5611 10 1.24888 9.6875 1.24888 9.375C1.24888 9.0625 1.5611 8.75 1.87332 8.75L14.6743 10.625L17.8264 1.32812C17.9045 1.28906 17.9825 1.25 18.0216 1.25C18.2557 1.25 18.4899 1.40625 18.646 1.64062C18.7241 1.75781 18.8021 1.95312 18.7241 2.14844Z" fill="url(#paint0_linear_22912_90694)"/>
+<defs>
+<linearGradient id="paint0_linear_22912_90694" x1="20" y1="9.56158e-07" x2="3.95833" y2="16.25" gradientUnits="userSpaceOnUse">
+<stop stop-color="#D500F9"/>
+<stop offset="0.5" stop-color="#6200EA"/>
+<stop offset="1" stop-color="#2E006D"/>
+</linearGradient>
+</defs>
+</svg>
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts
index a86b5cd51f6..f74a71e901e 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts
@@ -1,4 +1,4 @@
-import { keepPreviousData, useQuery, useQueryClient, UseQueryResult } from "@tanstack/react-query";
+import { keepPreviousData, useInfiniteQuery, useQuery, useQueryClient, UseQueryResult } from "@tanstack/react-query";
 import { Team } from "@/components/key_team_helpers/key_list";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import { fetchTeams } from "@/app/(dashboard)/networking";
@@ -124,6 +124,43 @@ export const useTeam = (teamId?: string) => {
   });
 };
 
+const infiniteTeamKeys = createQueryKeys("infiniteTeams");
+
+export const useInfiniteTeams = (
+  pageSize: number = 50,
+  search?: string,
+  organizationId?: string | null,
+) => {
+  const { accessToken, userId, userRole } = useAuthorized();
+  const isAdmin = userRole === "Admin" || userRole === "Admin Viewer";
+
+  return useInfiniteQuery<TeamsResponse>({
+    queryKey: infiniteTeamKeys.list({
+      filters: {
+        pageSize,
+        ...(search && { search }),
+        ...(organizationId && { organizationId }),
+        ...(userId && { userId }),
+      },
+    }),
+    queryFn: async ({ pageParam }) => {
+      return await teamListCall(accessToken!, pageParam as number, pageSize, {
+        team_alias: search || undefined,
+        organizationID: organizationId,
+        userID: !isAdmin ? userId : undefined,
+      });
+    },
+    initialPageParam: 1,
+    getNextPageParam: (lastPage) => {
+      if (lastPage.page < lastPage.total_pages) {
+        return lastPage.page + 1;
+      }
+      return undefined;
+    },
+    enabled: Boolean(accessToken),
+  });
+};
+
 const deletedTeamListCall = async (
   accessToken: string,
   page: number,
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx b/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx
index 1cf7adf1ea9..94dd6eb3cf1 100644
--- a/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx
+++ b/ui/litellm-dashboard/src/app/(dashboard)/layout.tsx
@@ -3,7 +3,7 @@
 import React, { Suspense, useEffect, useState } from "react";
 import Navbar from "@/components/navbar";
 import { ThemeProvider } from "@/contexts/ThemeContext";
-import Sidebar2 from "@/app/(dashboard)/components/Sidebar2";
+import SidebarProvider from "@/app/(dashboard)/components/SidebarProvider";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import { useRouter, useSearchParams } from "next/navigation";
 import { DebugWarningBanner } from "@/components/DebugWarningBanner";
@@ -23,6 +23,17 @@ function withBase(path: string): string {
 }
 /** -------------------------------- */
 
+/**
+ * Pages that have been migrated to path-based routing under (dashboard)/.
+ * When the leftnav triggers one of these, navigate to the path route instead
+ * of the legacy query-param root page.
+ *
+ * Key = legacy page id used in leftnav, Value = route segment under (dashboard)/
+ */
+const MIGRATED_PAGES: Record<string, string> = {
+  "api-reference": "api-reference",
+};
+
 function LayoutContent({ children }: { children: React.ReactNode }) {
   const router = useRouter();
   const searchParams = useSearchParams();
@@ -32,10 +43,17 @@ function LayoutContent({ children }: { children: React.ReactNode }) {
     return searchParams.get("page") || "api-keys";
   });
 
-  const updatePage = (newPage: string) => {
-    const newSearchParams = new URLSearchParams(searchParams);
-    newSearchParams.set("page", newPage);
-    router.push(withBase(`/?${newSearchParams.toString()}`)); // always under BASE
+  const handleSetPage = (newPage: string) => {
+    // If the page has been migrated to path routing, navigate there
+    const migratedRoute = MIGRATED_PAGES[newPage];
+    if (migratedRoute) {
+      router.push(withBase(migratedRoute));
+      setPage(newPage);
+      return;
+    }
+
+    // Otherwise, navigate back to the legacy root page with query params
+    router.push(withBase(`?page=${newPage}`));
     setPage(newPage);
   };
 
@@ -65,7 +83,11 @@ function LayoutContent({ children }: { children: React.ReactNode }) {
         <DebugWarningBanner />
         <div className="flex flex-1 overflow-auto">
           <div className="mt-2">
-            <Sidebar2 defaultSelectedKey={page} accessToken={accessToken} userRole={userRole} />
+            <SidebarProvider
+              setPage={handleSetPage}
+              defaultSelectedKey={page}
+              sidebarCollapsed={sidebarCollapsed}
+            />
           </div>
           <main className="flex-1">{children}</main>
         </div>
diff --git a/ui/litellm-dashboard/src/app/login/LoginPage.tsx b/ui/litellm-dashboard/src/app/login/LoginPage.tsx
index 5a9d420456c..e130dddc4a4 100644
--- a/ui/litellm-dashboard/src/app/login/LoginPage.tsx
+++ b/ui/litellm-dashboard/src/app/login/LoginPage.tsx
@@ -222,7 +222,7 @@ function LoginPageContent() {
 
           {error && <Alert message={error} type="error" showIcon />}
 
-          <Form onFinish={handleSubmit} layout="vertical" requiredMark={true}>
+          <Form onFinish={handleSubmit} layout="vertical" requiredMark={false}>
             {uiConfig?.is_control_plane && workers.length > 0 && (
               <Form.Item label="Worker" style={{ marginBottom: 16 }}>
                 <Select
diff --git a/ui/litellm-dashboard/src/components/CreateUserButton.tsx b/ui/litellm-dashboard/src/components/CreateUserButton.tsx
index fbfcb402766..fc29887a5da 100644
--- a/ui/litellm-dashboard/src/components/CreateUserButton.tsx
+++ b/ui/litellm-dashboard/src/components/CreateUserButton.tsx
@@ -210,9 +210,7 @@ export const CreateUserButton: React.FC<CreateuserProps> = ({
           </Select2>
         </Form.Item>
         <Form.Item label="Team" name="team_id">
-          <Select placeholder="Select Team" style={{ width: "100%" }}>
-            <TeamDropdown teams={availableTeams} />
-          </Select>
+          <TeamDropdown />
         </Form.Item>
 
         <Form.Item label="Metadata" name="metadata">
@@ -294,7 +292,7 @@ export const CreateUserButton: React.FC<CreateuserProps> = ({
             name="team_id"
             help="If selected, user will be added as a 'user' role to the team."
           >
-            <TeamDropdown teams={availableTeams} />
+            <TeamDropdown />
           </Form.Item>
 
           <Form.Item
diff --git a/ui/litellm-dashboard/src/components/Navbar/WorkerDropdown/WorkerDropdown.test.tsx b/ui/litellm-dashboard/src/components/Navbar/WorkerDropdown/WorkerDropdown.test.tsx
new file mode 100644
index 00000000000..17b8b393607
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/Navbar/WorkerDropdown/WorkerDropdown.test.tsx
@@ -0,0 +1,134 @@
+import { render, screen } from "@testing-library/react";
+import { describe, expect, it, vi, beforeEach } from "vitest";
+
+// Mock the useWorker hook
+const mockUseWorker = vi.fn();
+vi.mock("@/hooks/useWorker", () => ({
+  useWorker: () => mockUseWorker(),
+}));
+
+// Mock antd Select
+vi.mock("antd", () => ({
+  Select: ({ value, options, onChange, style, disabled, ...props }: any) => (
+    <select
+      data-testid="worker-select"
+      value={value}
+      style={style}
+      onChange={(e) => onChange?.(e.target.value)}
+    >
+      {options?.map((opt: any) => (
+        <option key={opt.value} value={opt.value} disabled={opt.disabled}>
+          {opt.label}
+        </option>
+      ))}
+    </select>
+  ),
+}));
+
+// Mock icon
+vi.mock("@ant-design/icons", () => ({
+  CloudServerOutlined: () => <span data-testid="cloud-icon" />,
+}));
+
+import WorkerDropdown from "./WorkerDropdown";
+
+describe("WorkerDropdown", () => {
+  const mockOnWorkerSwitch = vi.fn();
+  const workers = [
+    { worker_id: "w1", name: "Worker 1" },
+    { worker_id: "w2", name: "Worker 2" },
+    { worker_id: "w3", name: "Worker 3" },
+  ];
+
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  it("renders null when isControlPlane is false", () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: false,
+      selectedWorker: workers[0],
+      workers,
+    });
+
+    const { container } = render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    expect(container).toBeEmptyDOMElement();
+  });
+
+  it("renders null when selectedWorker is null", () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: true,
+      selectedWorker: null,
+      workers,
+    });
+
+    const { container } = render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    expect(container).toBeEmptyDOMElement();
+  });
+
+  it("renders the select when isControlPlane and selectedWorker exist", () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: true,
+      selectedWorker: workers[0],
+      workers,
+    });
+
+    render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    expect(screen.getByTestId("worker-select")).toBeInTheDocument();
+  });
+
+  it("renders all worker options", () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: true,
+      selectedWorker: workers[0],
+      workers,
+    });
+
+    render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    expect(screen.getByText("Worker 1")).toBeInTheDocument();
+    expect(screen.getByText("Worker 2")).toBeInTheDocument();
+    expect(screen.getByText("Worker 3")).toBeInTheDocument();
+  });
+
+  it("sets current worker as selected value", () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: true,
+      selectedWorker: workers[1],
+      workers,
+    });
+
+    render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    const select = screen.getByTestId("worker-select") as HTMLSelectElement;
+    expect(select.value).toBe("w2");
+  });
+
+  it("disables the currently selected worker in options", () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: true,
+      selectedWorker: workers[0],
+      workers,
+    });
+
+    render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    const options = screen.getAllByRole("option");
+    const selectedOption = options.find((opt) => (opt as HTMLOptionElement).value === "w1");
+    expect(selectedOption).toBeDisabled();
+  });
+
+  it("calls onWorkerSwitch when selection changes", async () => {
+    mockUseWorker.mockReturnValue({
+      isControlPlane: true,
+      selectedWorker: workers[0],
+      workers,
+    });
+
+    render(<WorkerDropdown onWorkerSwitch={mockOnWorkerSwitch} />);
+    const select = screen.getByTestId("worker-select");
+
+    const { default: userEvent } = await import("@testing-library/user-event");
+    const user = userEvent.setup();
+    await user.selectOptions(select, "w2");
+
+    expect(mockOnWorkerSwitch).toHaveBeenCalledWith("w2");
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/OldTeams.tsx b/ui/litellm-dashboard/src/components/OldTeams.tsx
index 8970226b9f7..76dd6abbe60 100644
--- a/ui/litellm-dashboard/src/components/OldTeams.tsx
+++ b/ui/litellm-dashboard/src/components/OldTeams.tsx
@@ -579,14 +579,12 @@ const Teams: React.FC<TeamProps> = ({
           }
         }
 
-        const response: any = await teamCreateCall(accessToken, formValues);
-        if (teams !== null) {
-          setTeams([...teams, response]);
-        } else {
-          setTeams([response]);
-        }
-        console.log(`response for team create call: ${response}`);
+        await teamCreateCall(accessToken, formValues);
         NotificationsManager.success("Team created");
+        await fetchTeamsV2({
+          page: currentPage,
+          size: pageSize,
+        });
         form.resetFields();
         setLoggingSettings([]);
         setModelAliases({});
diff --git a/ui/litellm-dashboard/src/components/ToolDetail.tsx b/ui/litellm-dashboard/src/components/ToolDetail.tsx
index ed0f866acb8..b2a7d9bfbe4 100644
--- a/ui/litellm-dashboard/src/components/ToolDetail.tsx
+++ b/ui/litellm-dashboard/src/components/ToolDetail.tsx
@@ -387,7 +387,6 @@ export function ToolDetail({ toolName, onBack, accessToken }: ToolDetailProps) {
               </span>
               {blockScope === "team" ? (
                 <TeamDropdown
-                  teams={teams}
                   value={blockTeamId ?? undefined}
                   onChange={(id) => setBlockTeamId(id || null)}
                 />
diff --git a/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx b/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx
index 6f63fcfa549..de5e1387396 100644
--- a/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/AddModelForm.test.tsx
@@ -65,6 +65,21 @@ vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
   default: vi.fn(),
 }));
 
+vi.mock("@/app/(dashboard)/hooks/teams/useTeams", () => ({
+  useInfiniteTeams: () => ({
+    data: {
+      pages: [{
+        teams: [{ team_id: "team-1", team_alias: "Test Team", organization_id: "org-1" }],
+        total: 1, page: 1, page_size: 20, total_pages: 1,
+      }],
+    },
+    fetchNextPage: vi.fn(),
+    hasNextPage: false,
+    isFetchingNextPage: false,
+    isLoading: false,
+  }),
+}));
+
 vi.mock("@/app/(dashboard)/hooks/guardrails/useGuardrails", () => ({
   useGuardrails: vi.fn().mockReturnValue({
     data: [{ guardrail_name: "test-guardrail" }],
diff --git a/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx b/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx
index 2b3f23a35ae..65e239d58ea 100644
--- a/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx
+++ b/ui/litellm-dashboard/src/components/add_model/AddModelForm.tsx
@@ -131,7 +131,6 @@ const AddModelForm: React.FC<AddModelFormProps> = ({
                   tooltip="Select the team for which you want to add this model"
                 >
                   <TeamDropdown
-                    teams={teams}
                     onChange={(value) => {
                       setTeamAdminSelectedTeam(value);
                     }}
@@ -325,7 +324,7 @@ const AddModelForm: React.FC<AddModelFormProps> = ({
                       },
                     ]}
                   >
-                    <TeamDropdown teams={teams} disabled={!premiumUser} />
+                    <TeamDropdown disabled={!premiumUser} />
                   </Form.Item>
                 )}
                 {isAdmin && (
diff --git a/ui/litellm-dashboard/src/components/agent_management/AgentSelector.test.tsx b/ui/litellm-dashboard/src/components/agent_management/AgentSelector.test.tsx
new file mode 100644
index 00000000000..26f903cd310
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/agent_management/AgentSelector.test.tsx
@@ -0,0 +1,144 @@
+import { render, screen, waitFor } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import { describe, expect, it, vi, beforeEach } from "vitest";
+
+// Mock networking module
+const mockGetAgentsList = vi.fn();
+vi.mock("../networking", () => ({
+  getAgentsList: (...args: any[]) => mockGetAgentsList(...args),
+}));
+
+// Mock antd Select
+vi.mock("antd", () => {
+  const SelectComponent = ({ children, onChange, value, mode, placeholder, loading, disabled, ...props }: any) => (
+    <div data-testid="agent-select" data-loading={loading} data-disabled={disabled}>
+      <select
+        data-testid="select-input"
+        multiple={mode === "multiple"}
+        value={value || []}
+        onChange={(e) => {
+          const selected = Array.from(e.target.selectedOptions, (opt: any) => opt.value);
+          onChange?.(selected);
+        }}
+        disabled={disabled}
+      >
+        {children}
+      </select>
+      {loading && <span data-testid="loading-indicator">Loading</span>}
+    </div>
+  );
+
+  SelectComponent.Option = ({ children, value, ...props }: any) => (
+    <option value={value} {...props}>
+      {children}
+    </option>
+  );
+
+  return { Select: SelectComponent };
+});
+
+import AgentSelector from "./AgentSelector";
+
+describe("AgentSelector", () => {
+  const defaultProps = {
+    onChange: vi.fn(),
+    accessToken: "test-token",
+  };
+
+  beforeEach(() => {
+    vi.clearAllMocks();
+    mockGetAgentsList.mockResolvedValue({
+      agents: [
+        { agent_id: "agent-1", agent_name: "Agent One" },
+        { agent_id: "agent-2", agent_name: "Agent Two", agent_access_groups: ["group-a", "group-b"] },
+      ],
+    });
+  });
+
+  it("renders the selector", () => {
+    render(<AgentSelector {...defaultProps} />);
+    expect(screen.getByTestId("agent-select")).toBeInTheDocument();
+  });
+
+  it("fetches agents on mount with access token", async () => {
+    render(<AgentSelector {...defaultProps} />);
+    await waitFor(() => {
+      expect(mockGetAgentsList).toHaveBeenCalledWith("test-token");
+    });
+  });
+
+  it("does not fetch when accessToken is empty", () => {
+    render(<AgentSelector {...defaultProps} accessToken="" />);
+    expect(mockGetAgentsList).not.toHaveBeenCalled();
+  });
+
+  it("shows loading state while fetching", async () => {
+    // Keep the promise pending
+    let resolve: any;
+    mockGetAgentsList.mockReturnValue(new Promise((r) => { resolve = r; }));
+
+    render(<AgentSelector {...defaultProps} />);
+    expect(screen.getByTestId("agent-select")).toHaveAttribute("data-loading", "true");
+
+    // Resolve to clean up
+    resolve({ agents: [] });
+    await waitFor(() => {
+      expect(screen.getByTestId("agent-select")).toHaveAttribute("data-loading", "false");
+    });
+  });
+
+  it("renders agent options after fetch", async () => {
+    render(<AgentSelector {...defaultProps} />);
+    await waitFor(() => {
+      expect(screen.getByText("Agent One")).toBeInTheDocument();
+      expect(screen.getByText("Agent Two")).toBeInTheDocument();
+    });
+  });
+
+  it("renders access group options with group prefix", async () => {
+    render(<AgentSelector {...defaultProps} />);
+    await waitFor(() => {
+      expect(screen.getByText("group-a")).toBeInTheDocument();
+      expect(screen.getByText("group-b")).toBeInTheDocument();
+    });
+  });
+
+  it("respects disabled prop", () => {
+    render(<AgentSelector {...defaultProps} disabled />);
+    expect(screen.getByTestId("agent-select")).toHaveAttribute("data-disabled", "true");
+  });
+
+  it("handles API error gracefully", async () => {
+    const consoleSpy = vi.spyOn(console, "error").mockImplementation(() => {});
+    mockGetAgentsList.mockRejectedValue(new Error("API error"));
+
+    render(<AgentSelector {...defaultProps} />);
+    await waitFor(() => {
+      expect(consoleSpy).toHaveBeenCalledWith("Error fetching agents:", expect.any(Error));
+    });
+
+    consoleSpy.mockRestore();
+  });
+
+  it("passes value as flattened selectedValues", async () => {
+    render(
+      <AgentSelector
+        {...defaultProps}
+        value={{ agents: ["agent-1"], accessGroups: ["group-a"] }}
+      />
+    );
+    await waitFor(() => {
+      const select = screen.getByTestId("select-input");
+      // The value should contain agent-1 and group:group-a
+      expect(select).toBeInTheDocument();
+    });
+  });
+
+  it("handles null response from API", async () => {
+    mockGetAgentsList.mockResolvedValue(null);
+    render(<AgentSelector {...defaultProps} />);
+    await waitFor(() => {
+      expect(screen.getByTestId("agent-select")).toHaveAttribute("data-loading", "false");
+    });
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx b/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx
index b6d96f0445b..046b28640c3 100644
--- a/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx
+++ b/ui/litellm-dashboard/src/components/agents/add_agent_form.tsx
@@ -723,10 +723,7 @@ const AddAgentForm: React.FC<AddAgentFormProps> = ({
           name="team_id"
           tooltip="Optionally assign this agent to a team. The agent and its key will belong to the selected team."
         >
-          <TeamDropdown
-            teams={teams}
-            loading={!teams}
-          />
+          <TeamDropdown />
         </Form.Item>
 
         <Divider className="my-4" />
diff --git a/ui/litellm-dashboard/src/components/claude_code_plugins/helpers.test.ts b/ui/litellm-dashboard/src/components/claude_code_plugins/helpers.test.ts
new file mode 100644
index 00000000000..6a754f57393
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/claude_code_plugins/helpers.test.ts
@@ -0,0 +1,329 @@
+import { describe, expect, it } from "vitest";
+import {
+  formatInstallCommand,
+  extractCategories,
+  validatePluginName,
+  getSourceDisplayText,
+  getSourceLink,
+  getCategoryBadgeColor,
+  formatDateString,
+  truncateText,
+  filterPluginsBySearch,
+  filterPluginsByCategory,
+  isValidSemanticVersion,
+  isValidEmail,
+  isValidUrl,
+  parseKeywords,
+  formatKeywords,
+} from "./helpers";
+import { MarketplacePluginEntry, PluginSource } from "./types";
+
+describe("formatInstallCommand", () => {
+  it("formats github source with repo", () => {
+    const plugin = { name: "my-plugin", source: { source: "github" as const, repo: "org/repo" } };
+    expect(formatInstallCommand(plugin)).toBe("/plugin marketplace add org/repo");
+  });
+
+  it("formats url source", () => {
+    const plugin = { name: "my-plugin", source: { source: "url" as const, url: "https://example.com/plugin" } };
+    expect(formatInstallCommand(plugin)).toBe("/plugin marketplace add https://example.com/plugin");
+  });
+
+  it("falls back to plugin name when no repo or url", () => {
+    const plugin = { name: "my-plugin", source: { source: "github" as const } };
+    expect(formatInstallCommand(plugin)).toBe("/plugin marketplace add my-plugin");
+  });
+});
+
+describe("extractCategories", () => {
+  it("returns All and Other for empty list", () => {
+    expect(extractCategories([])).toEqual(["All", "Other"]);
+  });
+
+  it("extracts and sorts unique categories", () => {
+    const plugins = [
+      { category: "Development" },
+      { category: "Analytics" },
+      { category: "Development" },
+    ];
+    expect(extractCategories(plugins)).toEqual(["All", "Analytics", "Development", "Other"]);
+  });
+
+  it("ignores empty/whitespace categories", () => {
+    const plugins = [{ category: "" }, { category: "  " }, { category: "Tools" }];
+    expect(extractCategories(plugins)).toEqual(["All", "Tools", "Other"]);
+  });
+
+  it("handles undefined category", () => {
+    const plugins = [{ category: undefined }, { category: "Security" }];
+    expect(extractCategories(plugins)).toEqual(["All", "Security", "Other"]);
+  });
+});
+
+describe("validatePluginName", () => {
+  it("accepts valid kebab-case names", () => {
+    expect(validatePluginName("my-plugin")).toBe(true);
+    expect(validatePluginName("plugin123")).toBe(true);
+    expect(validatePluginName("a-b-c")).toBe(true);
+  });
+
+  it("rejects names with uppercase", () => {
+    expect(validatePluginName("MyPlugin")).toBe(false);
+  });
+
+  it("rejects names with spaces", () => {
+    expect(validatePluginName("my plugin")).toBe(false);
+  });
+
+  it("rejects empty/whitespace names", () => {
+    expect(validatePluginName("")).toBe(false);
+    expect(validatePluginName("  ")).toBe(false);
+  });
+
+  it("rejects names with special characters", () => {
+    expect(validatePluginName("my_plugin")).toBe(false);
+    expect(validatePluginName("my.plugin")).toBe(false);
+  });
+});
+
+describe("getSourceDisplayText", () => {
+  it("shows github repo", () => {
+    expect(getSourceDisplayText({ source: "github", repo: "org/repo" })).toBe("GitHub: org/repo");
+  });
+
+  it("shows url", () => {
+    expect(getSourceDisplayText({ source: "url", url: "https://example.com" })).toBe("https://example.com");
+  });
+
+  it("returns unknown for missing data", () => {
+    expect(getSourceDisplayText({ source: "github" })).toBe("Unknown source");
+  });
+});
+
+describe("getSourceLink", () => {
+  it("returns github link for github source", () => {
+    expect(getSourceLink({ source: "github", repo: "org/repo" })).toBe("https://github.com/org/repo");
+  });
+
+  it("returns url for url source", () => {
+    expect(getSourceLink({ source: "url", url: "https://example.com" })).toBe("https://example.com");
+  });
+
+  it("returns null when no repo or url", () => {
+    expect(getSourceLink({ source: "github" })).toBeNull();
+  });
+});
+
+describe("getCategoryBadgeColor", () => {
+  it("returns blue for development categories", () => {
+    expect(getCategoryBadgeColor("Development")).toBe("blue");
+    expect(getCategoryBadgeColor("dev-tools")).toBe("blue");
+  });
+
+  it("returns green for productivity categories", () => {
+    expect(getCategoryBadgeColor("Productivity")).toBe("green");
+    expect(getCategoryBadgeColor("Workflow")).toBe("green");
+  });
+
+  it("returns purple for learning categories", () => {
+    expect(getCategoryBadgeColor("Learning")).toBe("purple");
+    expect(getCategoryBadgeColor("Education")).toBe("purple");
+  });
+
+  it("returns red for security categories", () => {
+    expect(getCategoryBadgeColor("Security")).toBe("red");
+    expect(getCategoryBadgeColor("Safety")).toBe("red");
+  });
+
+  it("returns orange for data categories", () => {
+    expect(getCategoryBadgeColor("Data")).toBe("orange");
+    expect(getCategoryBadgeColor("Analytics")).toBe("orange");
+  });
+
+  it("returns yellow for integration categories", () => {
+    expect(getCategoryBadgeColor("Integration")).toBe("yellow");
+    expect(getCategoryBadgeColor("API")).toBe("yellow");
+  });
+
+  it("returns gray for unknown or undefined categories", () => {
+    expect(getCategoryBadgeColor("Unknown")).toBe("gray");
+    expect(getCategoryBadgeColor(undefined)).toBe("gray");
+  });
+});
+
+describe("formatDateString", () => {
+  it("formats valid date strings", () => {
+    const result = formatDateString("2024-01-15T12:00:00Z");
+    expect(result).toContain("2024");
+    expect(result).toContain("Jan");
+    expect(result).toContain("15");
+  });
+
+  it("returns N/A for undefined", () => {
+    expect(formatDateString(undefined)).toBe("N/A");
+  });
+
+  it("returns N/A for empty string", () => {
+    expect(formatDateString("")).toBe("N/A");
+  });
+});
+
+describe("truncateText", () => {
+  it("returns text unchanged if shorter than max", () => {
+    expect(truncateText("hello", 10)).toBe("hello");
+  });
+
+  it("truncates and adds ellipsis", () => {
+    expect(truncateText("hello world", 5)).toBe("hello...");
+  });
+
+  it("handles exact length", () => {
+    expect(truncateText("hello", 5)).toBe("hello");
+  });
+
+  it("handles empty text", () => {
+    expect(truncateText("", 5)).toBe("");
+  });
+});
+
+describe("filterPluginsBySearch", () => {
+  const plugins: MarketplacePluginEntry[] = [
+    {
+      name: "code-formatter",
+      source: { source: "github", repo: "org/formatter" },
+      description: "Formats code nicely",
+      keywords: ["format", "lint"],
+    },
+    {
+      name: "data-viewer",
+      source: { source: "github", repo: "org/viewer" },
+      description: "View data",
+      keywords: ["analytics"],
+    },
+  ];
+
+  it("returns all plugins for empty search", () => {
+    expect(filterPluginsBySearch(plugins, "")).toEqual(plugins);
+    expect(filterPluginsBySearch(plugins, "  ")).toEqual(plugins);
+  });
+
+  it("matches by name", () => {
+    expect(filterPluginsBySearch(plugins, "formatter")).toHaveLength(1);
+    expect(filterPluginsBySearch(plugins, "formatter")[0].name).toBe("code-formatter");
+  });
+
+  it("matches by description", () => {
+    expect(filterPluginsBySearch(plugins, "nicely")).toHaveLength(1);
+  });
+
+  it("matches by keyword", () => {
+    expect(filterPluginsBySearch(plugins, "analytics")).toHaveLength(1);
+    expect(filterPluginsBySearch(plugins, "analytics")[0].name).toBe("data-viewer");
+  });
+
+  it("is case insensitive", () => {
+    expect(filterPluginsBySearch(plugins, "FORMATTER")).toHaveLength(1);
+  });
+});
+
+describe("filterPluginsByCategory", () => {
+  const plugins: MarketplacePluginEntry[] = [
+    { name: "a", source: { source: "github" }, category: "Dev" },
+    { name: "b", source: { source: "github" }, category: "Security" },
+    { name: "c", source: { source: "github" }, category: "" },
+    { name: "d", source: { source: "github" } },
+  ];
+
+  it("returns all plugins for 'All'", () => {
+    expect(filterPluginsByCategory(plugins, "All")).toEqual(plugins);
+  });
+
+  it("returns uncategorized plugins for 'Other'", () => {
+    const result = filterPluginsByCategory(plugins, "Other");
+    expect(result).toHaveLength(2);
+    expect(result.map((p) => p.name)).toEqual(["c", "d"]);
+  });
+
+  it("filters by specific category", () => {
+    const result = filterPluginsByCategory(plugins, "Dev");
+    expect(result).toHaveLength(1);
+    expect(result[0].name).toBe("a");
+  });
+});
+
+describe("isValidSemanticVersion", () => {
+  it("accepts valid semver", () => {
+    expect(isValidSemanticVersion("1.0.0")).toBe(true);
+    expect(isValidSemanticVersion("0.1.0-alpha")).toBe(true);
+    expect(isValidSemanticVersion("2.3.4+build.1")).toBe(true);
+  });
+
+  it("rejects invalid semver", () => {
+    expect(isValidSemanticVersion("1.0")).toBe(false);
+    expect(isValidSemanticVersion("abc")).toBe(false);
+  });
+
+  it("returns true for undefined (optional)", () => {
+    expect(isValidSemanticVersion(undefined)).toBe(true);
+  });
+});
+
+describe("isValidEmail", () => {
+  it("accepts valid emails", () => {
+    expect(isValidEmail("user@example.com")).toBe(true);
+  });
+
+  it("rejects invalid emails", () => {
+    expect(isValidEmail("not-an-email")).toBe(false);
+    expect(isValidEmail("@example.com")).toBe(false);
+  });
+
+  it("returns true for undefined (optional)", () => {
+    expect(isValidEmail(undefined)).toBe(true);
+  });
+});
+
+describe("isValidUrl", () => {
+  it("accepts valid urls", () => {
+    expect(isValidUrl("https://example.com")).toBe(true);
+    expect(isValidUrl("http://localhost:3000")).toBe(true);
+  });
+
+  it("rejects invalid urls", () => {
+    expect(isValidUrl("not a url")).toBe(false);
+  });
+
+  it("returns true for undefined (optional)", () => {
+    expect(isValidUrl(undefined)).toBe(true);
+  });
+});
+
+describe("parseKeywords", () => {
+  it("splits comma-separated keywords", () => {
+    expect(parseKeywords("a, b, c")).toEqual(["a", "b", "c"]);
+  });
+
+  it("trims whitespace", () => {
+    expect(parseKeywords("  foo ,  bar  ")).toEqual(["foo", "bar"]);
+  });
+
+  it("filters empty entries", () => {
+    expect(parseKeywords("a,,b,")).toEqual(["a", "b"]);
+  });
+
+  it("returns empty array for empty string", () => {
+    expect(parseKeywords("")).toEqual([]);
+    expect(parseKeywords("  ")).toEqual([]);
+  });
+});
+
+describe("formatKeywords", () => {
+  it("joins keywords with comma and space", () => {
+    expect(formatKeywords(["a", "b", "c"])).toBe("a, b, c");
+  });
+
+  it("returns empty string for empty/undefined array", () => {
+    expect(formatKeywords([])).toBe("");
+    expect(formatKeywords(undefined)).toBe("");
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/common_components/team_dropdown.tsx b/ui/litellm-dashboard/src/components/common_components/team_dropdown.tsx
index 9e79ea2950a..844bbfc3eb9 100644
--- a/ui/litellm-dashboard/src/components/common_components/team_dropdown.tsx
+++ b/ui/litellm-dashboard/src/components/common_components/team_dropdown.tsx
@@ -1,43 +1,116 @@
-import React from "react";
-import { Select } from "antd";
+import React, { useMemo, useState, type UIEvent } from "react";
+import { Select, Typography } from "antd";
+import { LoadingOutlined } from "@ant-design/icons";
+import { useDebouncedState } from "@tanstack/react-pacer/debouncer";
+import { useInfiniteTeams } from "@/app/(dashboard)/hooks/teams/useTeams";
 import { Team } from "../key_team_helpers/key_list";
 
+const { Text } = Typography;
+
 interface TeamDropdownProps {
-  teams?: Team[] | null;
   value?: string;
   onChange?: (value: string) => void;
+  /** Callback with the full Team object (or null on clear). */
+  onTeamSelect?: (team: Team | null) => void;
   disabled?: boolean;
-  loading?: boolean;
+  /** Filter teams by organization. */
+  organizationId?: string | null;
+  pageSize?: number;
 }
 
-const TeamDropdown: React.FC<TeamDropdownProps> = ({ teams, value, onChange, disabled, loading }) => {
+const SCROLL_THRESHOLD = 0.8;
+const DEBOUNCE_MS = 300;
+
+const TeamDropdown: React.FC<TeamDropdownProps> = ({
+  value,
+  onChange,
+  onTeamSelect,
+  disabled,
+  organizationId,
+  pageSize = 20,
+}) => {
+  const [searchInput, setSearchInput] = useState("");
+  const [debouncedSearch, setDebouncedSearch] = useDebouncedState("", {
+    wait: DEBOUNCE_MS,
+  });
+
+  const {
+    data,
+    fetchNextPage,
+    hasNextPage,
+    isFetchingNextPage,
+    isLoading,
+  } = useInfiniteTeams(
+    pageSize,
+    debouncedSearch || undefined,
+    organizationId,
+  );
+
+  const teams = useMemo(() => {
+    if (!data?.pages) return [];
+    const seen = new Set<string>();
+    const result: Team[] = [];
+    for (const page of data.pages) {
+      for (const team of page.teams) {
+        if (seen.has(team.team_id)) continue;
+        seen.add(team.team_id);
+        result.push(team);
+      }
+    }
+    return result;
+  }, [data]);
+
+  const handlePopupScroll = (e: UIEvent<HTMLDivElement>) => {
+    const target = e.currentTarget;
+    const scrollRatio =
+      (target.scrollTop + target.clientHeight) / target.scrollHeight;
+    if (scrollRatio >= SCROLL_THRESHOLD && hasNextPage && !isFetchingNextPage) {
+      fetchNextPage();
+    }
+  };
+
+  const handleSearch = (val: string) => {
+    setSearchInput(val);
+    setDebouncedSearch(val);
+  };
+
+  const handleChange = (teamId: string | undefined) => {
+    onChange?.(teamId ?? "");
+    if (onTeamSelect) {
+      const team = teamId ? teams.find((t) => t.team_id === teamId) ?? null : null;
+      onTeamSelect(team);
+    }
+  };
+
   return (
     <Select
       showSearch
       placeholder="Search or select a team"
-      value={value}
-      onChange={onChange}
+      value={value || undefined}
+      onChange={handleChange}
       disabled={disabled}
-      loading={loading}
       allowClear
-      filterOption={(input, option) => {
-        if (!option) return false;
-        // Get team data from the option key
-        const team = teams?.find((t) => t.team_id === option.key);
-        if (!team) return false;
-
-        const searchTerm = input.toLowerCase().trim();
-        const teamAlias = (team.team_alias || "").toLowerCase();
-        const teamId = (team.team_id || "").toLowerCase();
-
-        // Search in both team alias and team ID
-        return teamAlias.includes(searchTerm) || teamId.includes(searchTerm);
-      }}
-      optionFilterProp="children"
+      filterOption={false}
+      onSearch={handleSearch}
+      searchValue={searchInput}
+      onPopupScroll={handlePopupScroll}
+      loading={isLoading}
+      notFoundContent={isLoading ? <LoadingOutlined spin /> : "No teams found"}
+      popupRender={(menu) => (
+        <>
+          {menu}
+          {isFetchingNextPage && (
+            <div style={{ textAlign: "center", padding: 8 }}>
+              <LoadingOutlined spin />
+            </div>
+          )}
+        </>
+      )}
     >
-      {teams?.map((team) => (
+      {teams.map((team) => (
         <Select.Option key={team.team_id} value={team.team_id}>
-          <span className="font-medium">{team.team_alias}</span> <span className="text-gray-500">({team.team_id})</span>
+          <span className="font-medium">{team.team_alias}</span>{" "}
+          <Text type="secondary">({team.team_id})</Text>
         </Select.Option>
       ))}
     </Select>
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_configs.ts b/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_configs.ts
index 7a1b5314d33..e42ecaef579 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_configs.ts
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_configs.ts
@@ -264,4 +264,10 @@ export const GUARDRAIL_PRESETS: Record<string, GuardrailPreset> = {
     mode: "pre_call",
     defaultOn: false,
   },
+  akto: {
+    provider: "Akto",
+    guardrailNameSuggestion: "Akto Guardrail",
+    mode: "pre_call",
+    defaultOn: false,
+  },
 };
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_data.ts b/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_data.ts
index 53ccb32c184..b06400ce508 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_data.ts
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_garden_data.ts
@@ -373,6 +373,14 @@ export const PARTNER_GUARDRAIL_CARDS: GuardrailCardInfo[] = [
     logo: `${ASSET_PREFIX}pillar.jpeg`,
     tags: ["Monitoring", "Safety"],
   },
+  {
+    id: "akto",
+    name: "Akto Guardrail",
+    description: "AI security platform from Akto.io with automatic monitoring and guardrails for AI/ML applications.",
+    category: "partner",
+    logo: `${ASSET_PREFIX}akto.svg`,
+    tags: ["Security", "Safety", "Monitoring"],
+  },
 ];
 
 export const ALL_CARDS = [...LITELLM_CONTENT_FILTER_CARDS, ...PARTNER_GUARDRAIL_CARDS];
diff --git a/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx b/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx
index d957be4306b..c78835dae04 100644
--- a/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx
+++ b/ui/litellm-dashboard/src/components/guardrails/guardrail_info_helpers.tsx
@@ -125,6 +125,7 @@ export const guardrailLogoMap: Record<string, string> = {
   EnkryptAI: `${asset_logos_folder}enkrypt_ai.avif`,
   "Prompt Security": `${asset_logos_folder}prompt_security.png`,
   "LiteLLM Content Filter": `${asset_logos_folder}litellm_logo.jpg`,
+  "Akto": `${asset_logos_folder}akto.svg`,
 };
 
 export const getGuardrailLogoAndName = (guardrailValue: string): { logo: string; displayName: string } => {
diff --git a/ui/litellm-dashboard/src/components/leftnav.tsx b/ui/litellm-dashboard/src/components/leftnav.tsx
index 09ab3809427..48df5cdde80 100644
--- a/ui/litellm-dashboard/src/components/leftnav.tsx
+++ b/ui/litellm-dashboard/src/components/leftnav.tsx
@@ -18,7 +18,6 @@ import {
   FolderOutlined,
   KeyOutlined,
   LineChartOutlined,
-  MessageOutlined,
   PlayCircleOutlined,
   RobotOutlined,
   SafetyOutlined,
@@ -36,8 +35,34 @@ import { all_admin_roles, internalUserRoles, isAdminRole, isUserTeamAdminForAnyT
 import NewBadge from "./common_components/NewBadge";
 import type { Organization } from "./networking";
 import UsageIndicator from "./UsageIndicator";
+import { serverRootPath } from "./networking";
 const { Sider } = Layout;
 
+/**
+ * Pages migrated to path-based routing under (dashboard)/.
+ * Key = legacy page id, Value = route segment.
+ * Keep in sync with MIGRATED_PAGES in (dashboard)/layout.tsx and
+ * LEGACY_REDIRECTS in app/page.tsx.
+ */
+const MIGRATED_PAGES: Record<string, string> = {
+  "api-reference": "api-reference",
+};
+
+/** Build an absolute href for a migrated page, respecting base URL + serverRootPath. */
+function migratedHref(routeSegment: string): string {
+  const raw = process.env.NEXT_PUBLIC_BASE_URL ?? "";
+  const trimmed = raw.replace(/^\/+|\/+$/g, "");
+  let base = trimmed ? `/${trimmed}/` : "/";
+
+  if (serverRootPath && serverRootPath !== "/") {
+    const cleanRoot = serverRootPath.replace(/\/+$/, "");
+    const cleanBase = base.replace(/^\/+/, "");
+    base = `${cleanRoot}/${cleanBase}`;
+  }
+
+  return `${base}${routeSegment}`;
+}
+
 // Define the props type
 interface SidebarProps {
   setPage: (page: string) => void;
@@ -379,6 +404,11 @@ const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapse
 
   // Navigate to page helper
   const navigateToPage = (page: string) => {
+    // For migrated pages, just call setPage — the parent layout handles routing
+    if (MIGRATED_PAGES[page]) {
+      setPage(page);
+      return;
+    }
     const newSearchParams = new URLSearchParams(window.location.search);
     newSearchParams.set("page", page);
     window.history.pushState(null, "", `?${newSearchParams.toString()}`);
@@ -405,9 +435,11 @@ const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapse
         </a>
       );
     }
-    const params = new URLSearchParams(window.location.search);
-    params.set("page", page);
-    const href = `?${params.toString()}`;
+    // For migrated pages, generate a path-based href for right-click "Open in new tab"
+    const migratedRoute = MIGRATED_PAGES[page];
+    const href = migratedRoute
+      ? migratedHref(migratedRoute)
+      : (() => { const params = new URLSearchParams(window.location.search); params.set("page", page); return `?${params.toString()}`; })();
     return (
       <a
         href={href}
diff --git a/ui/litellm-dashboard/src/components/molecules/message_manager.test.ts b/ui/litellm-dashboard/src/components/molecules/message_manager.test.ts
new file mode 100644
index 00000000000..541efcbc4cb
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/molecules/message_manager.test.ts
@@ -0,0 +1,107 @@
+import { describe, expect, it, vi, beforeEach } from "vitest";
+
+// Use vi.hoisted so the mock object is available when vi.mock is hoisted
+const mockStaticMessage = vi.hoisted(() => ({
+  success: vi.fn(),
+  error: vi.fn(),
+  warning: vi.fn(),
+  info: vi.fn(),
+  loading: vi.fn(),
+  destroy: vi.fn(),
+}));
+
+vi.mock("antd", () => ({
+  message: mockStaticMessage,
+}));
+
+import MessageManager, { setMessageInstance } from "./message_manager";
+
+describe("MessageManager", () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  describe("when no instance is set (falls back to static message)", () => {
+    it("delegates success to static message", () => {
+      MessageManager.success("done!");
+      expect(mockStaticMessage.success).toHaveBeenCalledWith("done!", undefined);
+    });
+
+    it("delegates error to static message", () => {
+      MessageManager.error("failed!", 5);
+      expect(mockStaticMessage.error).toHaveBeenCalledWith("failed!", 5);
+    });
+
+    it("delegates warning to static message", () => {
+      MessageManager.warning("watch out");
+      expect(mockStaticMessage.warning).toHaveBeenCalledWith("watch out", undefined);
+    });
+
+    it("delegates info to static message", () => {
+      MessageManager.info("fyi");
+      expect(mockStaticMessage.info).toHaveBeenCalledWith("fyi", undefined);
+    });
+
+    it("delegates loading to static message", () => {
+      MessageManager.loading("loading...", 3);
+      expect(mockStaticMessage.loading).toHaveBeenCalledWith("loading...", 3);
+    });
+
+    it("delegates destroy to static message", () => {
+      MessageManager.destroy();
+      expect(mockStaticMessage.destroy).toHaveBeenCalled();
+    });
+  });
+
+  describe("when a custom instance is set", () => {
+    const mockInstance = {
+      success: vi.fn(),
+      error: vi.fn(),
+      warning: vi.fn(),
+      info: vi.fn(),
+      loading: vi.fn(),
+      destroy: vi.fn(),
+      open: vi.fn(),
+    };
+
+    beforeEach(() => {
+      vi.clearAllMocks();
+      setMessageInstance(mockInstance as any);
+    });
+
+    it("delegates success to custom instance", () => {
+      MessageManager.success("done!");
+      expect(mockInstance.success).toHaveBeenCalledWith("done!", undefined);
+      expect(mockStaticMessage.success).not.toHaveBeenCalled();
+    });
+
+    it("delegates error with duration to custom instance", () => {
+      MessageManager.error("failed!", 5);
+      expect(mockInstance.error).toHaveBeenCalledWith("failed!", 5);
+      expect(mockStaticMessage.error).not.toHaveBeenCalled();
+    });
+
+    it("delegates warning to custom instance", () => {
+      MessageManager.warning("watch out");
+      expect(mockInstance.warning).toHaveBeenCalledWith("watch out", undefined);
+    });
+
+    it("delegates info to custom instance", () => {
+      MessageManager.info("fyi", 2);
+      expect(mockInstance.info).toHaveBeenCalledWith("fyi", 2);
+    });
+
+    it("delegates loading to custom instance and returns result", () => {
+      const mockReturn = { then: vi.fn() };
+      mockInstance.loading.mockReturnValue(mockReturn);
+      const result = MessageManager.loading("loading...", 3);
+      expect(mockInstance.loading).toHaveBeenCalledWith("loading...", 3);
+      expect(result).toBe(mockReturn);
+    });
+
+    it("delegates destroy to custom instance", () => {
+      MessageManager.destroy();
+      expect(mockInstance.destroy).toHaveBeenCalled();
+    });
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/navbar.tsx b/ui/litellm-dashboard/src/components/navbar.tsx
index 96d6ce613bf..6e0a47a23f9 100644
--- a/ui/litellm-dashboard/src/components/navbar.tsx
+++ b/ui/litellm-dashboard/src/components/navbar.tsx
@@ -1,12 +1,11 @@
 import { useHealthReadiness } from "@/app/(dashboard)/hooks/healthReadiness/useHealthReadiness";
 import { useDisableBouncingIcon } from "@/app/(dashboard)/hooks/useDisableBouncingIcon";
 import { getProxyBaseUrl } from "@/components/networking";
-import { useUIConfig } from "@/app/(dashboard)/hooks/uiConfig/useUIConfig";
 import { useTheme } from "@/contexts/ThemeContext";
 import { clearTokenCookies } from "@/utils/cookieUtils";
 import { clearStoredReturnUrl } from "@/utils/returnUrlUtils";
 import { fetchProxySettings } from "@/utils/proxyUtils";
-import { MenuFoldOutlined, MenuUnfoldOutlined, MessageOutlined, MoonOutlined, SunOutlined } from "@ant-design/icons";
+import { MenuFoldOutlined, MenuUnfoldOutlined, MoonOutlined, SunOutlined } from "@ant-design/icons";
 import { Button, Switch, Tag } from "antd";
 import Link from "next/link";
 import React, { useEffect, useState } from "react";
@@ -46,11 +45,6 @@ const Navbar: React.FC<NavbarProps> = ({
 }) => {
   const baseUrl = getProxyBaseUrl();
   const [logoutUrl, setLogoutUrl] = useState("");
-  const { data: uiConfig } = useUIConfig();
-  const uiRoot = uiConfig?.server_root_path && uiConfig.server_root_path !== "/"
-    ? uiConfig.server_root_path.replace(/\/+$/, "")
-    : "";
-  const chatHref = `${uiRoot}/ui/chat`;
   const { logoUrl } = useTheme();
   const { data: healthData } = useHealthReadiness();
   const version = healthData?.litellm_version;
@@ -146,41 +140,6 @@ const Navbar: React.FC<NavbarProps> = ({
           </div>
           {/* Right side nav items */}
           <div className="flex items-center space-x-5 ml-auto">
-            {/* Chat CTA — always visible, opens in new tab */}
-            <a
-              href={chatHref}
-              target="_blank"
-              rel="noopener noreferrer"
-              style={{
-                display: "inline-flex",
-                alignItems: "center",
-                gap: 6,
-                padding: "6px 14px",
-                borderRadius: 8,
-                background: "#1677ff",
-                color: "#fff",
-                fontSize: 13,
-                fontWeight: 600,
-                textDecoration: "none",
-                whiteSpace: "nowrap",
-              }}
-              onMouseEnter={(e) => { (e.currentTarget as HTMLAnchorElement).style.background = "#0958d9"; }}
-              onMouseLeave={(e) => { (e.currentTarget as HTMLAnchorElement).style.background = "#1677ff"; }}
-            >
-              <MessageOutlined style={{ fontSize: 14 }} />
-              Chat
-              <span style={{
-                fontSize: 9,
-                fontWeight: 700,
-                background: "#fff",
-                color: "#1677ff",
-                borderRadius: 3,
-                padding: "1px 4px",
-                letterSpacing: "0.05em",
-              }}>
-                NEW
-              </span>
-            </a>
             <WorkerDropdown onWorkerSwitch={handleWorkerSwitch} />
             <CommunityEngagementButtons />
             {/* Dark mode is currently a work in progress. To test, you can change 'false' to 'true' below.
diff --git a/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx b/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx
index eef7292dac1..3ad59cb3693 100644
--- a/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx
+++ b/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx
@@ -119,7 +119,7 @@ vi.mock("antd", () => {
 
   Form.useForm = () => [formMock];
 
-  const Select = ({ children, onChange, ...props }: { children?: any; onChange?: (value: string) => void }) =>
+  const Select = ({ children, onChange, options, ...props }: { children?: any; onChange?: (value: string) => void; options?: Array<{ value: string; label: string }> }) =>
     React.createElement(
       "select",
       {
@@ -127,6 +127,7 @@ vi.mock("antd", () => {
         onChange: (event: any) => onChange?.(event.target.value),
       },
       children,
+      options?.map((opt: any) => React.createElement("option", { key: opt.value, value: opt.value }, opt.label)),
     );
 
   Select.Option = ({ children, ...props }: { children?: any }) =>
@@ -213,19 +214,30 @@ vi.mock("../common_components/PassThroughRoutesSelector", () => ({ default: () =
 vi.mock("../common_components/PremiumLoggingSettings", () => ({ default: () => null }));
 vi.mock("../common_components/RateLimitTypeFormItem", () => ({ default: () => null }));
 vi.mock("../common_components/RouterSettingsAccordion", () => ({ default: () => null }));
+vi.mock("@/app/(dashboard)/hooks/teams/useTeams", () => ({
+  useInfiniteTeams: () => ({
+    data: {
+      pages: [{ teams: [
+        { team_id: "team-1", team_alias: "Team One" },
+        { team_id: "team-2", team_alias: "Team Two" },
+      ], total: 2, page: 1, page_size: 50, total_pages: 1 }],
+    },
+    fetchNextPage: vi.fn(),
+    hasNextPage: false,
+    isFetchingNextPage: false,
+    isLoading: false,
+  }),
+}));
 vi.mock("../common_components/team_dropdown", () => ({
-  default: ({ teams, onChange, disabled }: { teams?: any[]; onChange?: (v: string) => void; disabled?: boolean }) => (
+  default: ({ onChange, disabled }: { onChange?: (v: string) => void; disabled?: boolean }) => (
     <select
       data-testid="team-dropdown"
       disabled={disabled}
       onChange={(e) => onChange?.(e.target.value)}
     >
       <option value="">Select team</option>
-      {teams?.map((t: any) => (
-        <option key={t.team_id} value={t.team_id}>
-          {t.team_alias}
-        </option>
-      ))}
+      <option value="team-1">Team One</option>
+      <option value="team-2">Team Two</option>
     </select>
   ),
 }));
@@ -238,6 +250,16 @@ vi.mock("../key_team_helpers/fetch_available_models_team_key", () => ({
   getModelDisplayName: (model: string) => model,
 }));
 
+vi.mock("@/app/(dashboard)/hooks/tags/useTags", () => ({
+  useTags: vi.fn().mockReturnValue({
+    data: [
+      { name: "production", description: "Prod tag", models: [], created_at: "2026-01-01", updated_at: "2026-01-01" },
+      { name: "staging", description: "Staging tag", models: [], created_at: "2026-01-01", updated_at: "2026-01-01" },
+    ],
+    isLoading: false,
+  }),
+}));
+
 vi.mock("@/app/(dashboard)/hooks/projects/useProjects", () => ({
   useProjects: vi.fn().mockReturnValue({ data: [], isLoading: false }),
 }));
@@ -525,4 +547,19 @@ describe("CreateKey", () => {
       expect(formStateRef.current["organization_id"]).toBe("org-1");
     });
   });
+
+  describe("tags dropdown", () => {
+    it("should populate tags dropdown with options from useTags hook", async () => {
+      renderWithProviders(<CreateKey {...defaultProps} />);
+
+      act(() => {
+        fireEvent.click(screen.getByRole("button", { name: /create new key/i }));
+      });
+
+      await waitFor(() => {
+        expect(screen.getByText("production")).toBeInTheDocument();
+        expect(screen.getByText("staging")).toBeInTheDocument();
+      });
+    });
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx
index df5b3a4b327..76888262e5a 100644
--- a/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx
+++ b/ui/litellm-dashboard/src/components/organisms/create_key_button.tsx
@@ -2,6 +2,7 @@
 import { keyKeys } from "@/app/(dashboard)/hooks/keys/useKeys";
 import { useOrganizations } from "@/app/(dashboard)/hooks/organizations/useOrganizations";
 import { useProjects } from "@/app/(dashboard)/hooks/projects/useProjects";
+import { useTags } from "@/app/(dashboard)/hooks/tags/useTags";
 import { useUISettings } from "@/app/(dashboard)/hooks/uiSettings/useUISettings";
 import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
 import { formatNumberWithCommas } from "@/utils/dataUtils";
@@ -165,8 +166,12 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey, autoOp
   const { data: organizations, isLoading: isOrganizationsLoading } = useOrganizations();
   const { data: projects, isLoading: isProjectsLoading } = useProjects();
   const { data: uiSettingsData } = useUISettings();
+  const { data: tagsData } = useTags();
   const enableProjectsUI = Boolean(uiSettingsData?.values?.enable_projects_ui);
   const disableCustomApiKeys = Boolean(uiSettingsData?.values?.disable_custom_api_keys);
+  const tagOptions = tagsData
+    ? Object.values(tagsData).map((tag) => ({ value: tag.name, label: tag.name }))
+    : [];
   const queryClient = useQueryClient();
   const [form] = Form.useForm();
   const [isModalVisible, setIsModalVisible] = useState(false);
@@ -175,7 +180,6 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey, autoOp
   const [userModels, setUserModels] = useState<string[]>([]);
   const [modelsToPick, setModelsToPick] = useState<string[]>([]);
   const [keyOwner, setKeyOwner] = useState("you");
-  const [predefinedTags, setPredefinedTags] = useState(getPredefinedTags(data));
   const [hasPrefilled, setHasPrefilled] = useState(false);
   const [pendingPrefillModels, setPendingPrefillModels] = useState<string[] | null>(null);
   const [guardrailsList, setGuardrailsList] = useState<string[]>([]);
@@ -806,19 +810,17 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey, autoOp
               help={keyOwner === "service_account" ? "required" : ""}
             >
               <TeamDropdown
-                teams={selectedOrganizationId ? teams?.filter((t) => t.organization_id === selectedOrganizationId) : teams}
                 disabled={selectedProjectId !== null}
-                loading={!teams}
-                onChange={(teamId) => {
-                  const selectedTeam = teams?.find((t) => t.team_id === teamId) || null;
-                  setSelectedCreateKeyTeam(selectedTeam);
+                organizationId={selectedOrganizationId}
+                onTeamSelect={(team) => {
+                  setSelectedCreateKeyTeam(team);
                   setSelectedProjectId(null);
                   form.setFieldValue("project_id", undefined);
                   // Auto-populate org from team for non-admin users
-                  if (selectedTeam?.organization_id) {
-                    setSelectedOrganizationId(selectedTeam.organization_id);
-                    form.setFieldValue("organization_id", selectedTeam.organization_id);
-                  } else if (!teamId) {
+                  if (team?.organization_id) {
+                    setSelectedOrganizationId(team.organization_id);
+                    form.setFieldValue("organization_id", team.organization_id);
+                  } else if (!team) {
                     setSelectedOrganizationId(null);
                     form.setFieldValue("organization_id", undefined);
                   }
@@ -1340,9 +1342,9 @@ const CreateKey: React.FC<CreateKeyProps> = ({ team, teams, data, addKey, autoOp
                     <Select
                       mode="tags"
                       style={{ width: "100%" }}
-                      placeholder="Enter tags"
+                      placeholder="Select or enter tags"
                       tokenSeparators={[","]}
-                      options={predefinedTags}
+                      options={tagOptions}
                     />
                   </Form.Item>
                   <Accordion className="mt-4 mb-4">
diff --git a/ui/litellm-dashboard/src/components/route_preview.tsx b/ui/litellm-dashboard/src/components/route_preview.tsx
index 483ab538871..2c616ad8670 100644
--- a/ui/litellm-dashboard/src/components/route_preview.tsx
+++ b/ui/litellm-dashboard/src/components/route_preview.tsx
@@ -1,6 +1,8 @@
 import React from "react";
-import { Card, Title, Subtitle } from "@tremor/react";
+import { Card, Typography } from "antd";
 import { RightOutlined, InfoCircleOutlined } from "@ant-design/icons";
+
+const { Title, Text } = Typography;
 import { getProxyBaseUrl } from "./networking";
 
 interface RoutePreviewProps {
@@ -23,8 +25,8 @@ const RoutePreview: React.FC<RoutePreviewProps> = ({ pathValue, targetValue, inc
 
   return (
     <Card className="p-5">
-      <Title className="text-lg font-semibold text-gray-900 mb-2">Route Preview</Title>
-      <Subtitle className="text-gray-600 mb-5">How your requests will be routed</Subtitle>
+      <Title level={5} className="text-lg font-semibold text-gray-900 mb-2">Route Preview</Title>
+      <Text type="secondary" className="text-gray-600 mb-5" style={{ display: "block" }}>How your requests will be routed</Text>
 
       <div className="space-y-5">
         {/* Basic routing */}
diff --git a/ui/litellm-dashboard/src/components/ui/AntDLoadingSpinner.test.tsx b/ui/litellm-dashboard/src/components/ui/AntDLoadingSpinner.test.tsx
new file mode 100644
index 00000000000..e86a7659855
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/ui/AntDLoadingSpinner.test.tsx
@@ -0,0 +1,61 @@
+import { render, screen } from "@testing-library/react";
+import { describe, expect, it, vi } from "vitest";
+
+// Mock antd Spin component
+vi.mock("antd", () => ({
+  Spin: ({ indicator, size, ...props }: any) => (
+    <div data-testid="spin" data-size={size} {...props}>
+      {indicator}
+    </div>
+  ),
+}));
+
+// Mock the icon
+vi.mock("@ant-design/icons", () => ({
+  LoadingOutlined: ({ style, spin, ...props }: any) => (
+    <span
+      data-testid="loading-icon"
+      data-spin={spin}
+      style={style}
+      {...props}
+    />
+  ),
+}));
+
+import { AntDLoadingSpinner } from "./AntDLoadingSpinner";
+
+describe("AntDLoadingSpinner", () => {
+  it("renders without props", () => {
+    render(<AntDLoadingSpinner />);
+    expect(screen.getByTestId("spin")).toBeInTheDocument();
+    expect(screen.getByTestId("loading-icon")).toBeInTheDocument();
+  });
+
+  it("passes size prop to Spin", () => {
+    render(<AntDLoadingSpinner size="large" />);
+    expect(screen.getByTestId("spin")).toHaveAttribute("data-size", "large");
+  });
+
+  it("passes small size to Spin", () => {
+    render(<AntDLoadingSpinner size="small" />);
+    expect(screen.getByTestId("spin")).toHaveAttribute("data-size", "small");
+  });
+
+  it("applies custom fontSize to the icon", () => {
+    render(<AntDLoadingSpinner fontSize={32} />);
+    const icon = screen.getByTestId("loading-icon");
+    expect(icon).toHaveStyle({ fontSize: "32px" });
+  });
+
+  it("does not set style when fontSize is not provided", () => {
+    render(<AntDLoadingSpinner />);
+    const icon = screen.getByTestId("loading-icon");
+    expect(icon.style.fontSize).toBe("");
+  });
+
+  it("sets spin attribute on icon", () => {
+    render(<AntDLoadingSpinner />);
+    const icon = screen.getByTestId("loading-icon");
+    expect(icon).toHaveAttribute("data-spin", "true");
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/view_users/columns.tsx b/ui/litellm-dashboard/src/components/view_users/columns.tsx
index 73750d7a48a..4b9fad8f23c 100644
--- a/ui/litellm-dashboard/src/components/view_users/columns.tsx
+++ b/ui/litellm-dashboard/src/components/view_users/columns.tsx
@@ -3,7 +3,8 @@ import { Badge, Grid, Icon } from "@tremor/react";
 import { Tooltip, Checkbox } from "antd";
 import { UserInfo } from "./types";
 import { PencilAltIcon, TrashIcon, InformationCircleIcon, RefreshIcon } from "@heroicons/react/outline";
-import { formatNumberWithCommas } from "@/utils/dataUtils";
+import { CopyOutlined } from "@ant-design/icons";
+import { formatNumberWithCommas, copyToClipboard } from "@/utils/dataUtils";
 
 interface SelectionOptions {
   selectedUsers: UserInfo[];
@@ -29,9 +30,22 @@ export const columns = (
       accessorKey: "user_id",
       enableSorting: true,
       cell: ({ row }) => (
-        <Tooltip title={row.original.user_id}>
-          <span className="text-xs">{row.original.user_id ? `${row.original.user_id.slice(0, 7)}...` : "-"}</span>
-        </Tooltip>
+        <div className="flex items-center space-x-2">
+          <Tooltip title={row.original.user_id}>
+            <span className="text-xs">{row.original.user_id ? `${row.original.user_id.slice(0, 7)}...` : "-"}</span>
+          </Tooltip>
+          {row.original.user_id && (
+            <Tooltip title="Copy User ID">
+              <CopyOutlined
+                onClick={(e) => {
+                  e.stopPropagation();
+                  copyToClipboard(row.original.user_id, "User ID copied to clipboard");
+                }}
+                className="cursor-pointer text-gray-500 hover:text-blue-500 text-xs"
+              />
+            </Tooltip>
+          )}
+        </div>
       ),
     },
     {