diff --git a/.circleci/config.yml b/.circleci/config.yml
index 476f138b1d4..dc6e3bbd3cc 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -2038,6 +2038,39 @@ jobs:
       - run: python ./tests/code_coverage_tests/memory_test.py
       - run: helm lint ./deploy/charts/litellm-helm
 
+  memory_leak_tests:
+    docker:
+      - image: cimg/python:3.11
+        auth:
+          username: ${DOCKERHUB_USERNAME}
+          password: ${DOCKERHUB_PASSWORD}
+    working_directory: ~/project
+    resource_class: large
+    steps:
+      - setup_litellm_test_deps
+      - run:
+          name: Install Memory Test Dependencies
+          command: |
+            pip install "psutil>=5.9.0"
+            pip install "fastapi>=0.100.0"
+            pip install "httpx>=0.24.0"
+            pip install "uvicorn>=0.23.0"
+      - run:
+          name: Run Linear Memory Growth Tests
+          command: |
+            echo "Running memory leak tests individually to avoid baseline drift..."
+            echo "Running test_memory_baseline_1k..."
+            python -m pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_1k -v -s --tb=short
+            echo "Running test_memory_baseline_2k..."
+            python -m pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_2k -v -s --tb=short
+            echo "Running test_memory_baseline_4k..."
+            python -m pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_4k -v -s --tb=short
+            echo "Running test_memory_baseline_10k..."
+            python -m pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_10k -v -s --tb=short
+            echo "Running test_memory_baseline_30k..."
+            python -m pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_30k -v -s --tb=short
+          no_output_timeout: 60m
+
   db_migration_disable_update_check:
     machine:
       image: ubuntu-2204:2023.10.1
@@ -2102,10 +2135,11 @@ jobs:
           name: Check container logs for expected message
           command: |
             echo "=== Printing Full Container Startup Logs ==="
-            docker logs my-app
+            LOG_OUTPUT="$(docker logs my-app 2>&1)"
+            printf '%s\n' "$LOG_OUTPUT"
             echo "=== End of Full Container Startup Logs ==="
 
-            if docker logs my-app 2>&1 | grep -q "prisma schema out of sync with db. Consider running these sql_commands to sync the two"; then
+            if printf '%s\n' "$LOG_OUTPUT" | grep -q "prisma schema out of sync with db. Consider running these sql_commands to sync the two"; then
               echo "Expected message found in logs. Test passed."
             else
               echo "Expected message not found in logs. Test failed."
@@ -3557,12 +3591,34 @@ jobs:
           name: Install Playwright Browsers
           command: |
             npx playwright install
+      - run:
+          name: Install Neon CLI
+          command: |
+            npm i -g neonctl
+      - run:
+          name: Create Neon branch
+          command: |
+            export EXPIRES_AT=$(date -u -d "+3 hours" +"%Y-%m-%dT%H:%M:%SZ")
+            echo "Expires at: $EXPIRES_AT"
+            neon branches create \
+              --project-id $NEON_PROJECT_ID \
+              --name preview/commit-${CIRCLE_SHA1:0:7} \
+              --expires-at $EXPIRES_AT \
+              --parent br-fancy-paper-ad1olsb3 \
+              --api-key $NEON_API_KEY || true
       - run:
           name: Run Docker container
           command: |
+            E2E_UI_TEST_DATABASE_URL=$(neon connection-string \
+              --project-id $NEON_PROJECT_ID \
+              --api-key $NEON_API_KEY \
+              --branch preview/commit-${CIRCLE_SHA1:0:7} \
+              --database-name yuneng-trial-db \
+              --role neondb_owner)
+            echo $E2E_UI_TEST_DATABASE_URL
             docker run -d \
               -p 4000:4000 \
-              -e DATABASE_URL=$SMALL_DATABASE_URL \
+              -e DATABASE_URL=$E2E_UI_TEST_DATABASE_URL \
               -e LITELLM_MASTER_KEY="sk-1234" \
               -e OPENAI_API_KEY=$OPENAI_API_KEY \
               -e UI_USERNAME="admin" \
@@ -3765,6 +3821,12 @@ workflows:
               only:
                 - main
                 - /litellm_.*/
+      - memory_leak_tests:
+          filters:
+            branches:
+              only:
+                - main
+                - /litellm_.*/
       - ui_build:
           filters:
             branches:
@@ -3792,6 +3854,7 @@ workflows:
                 - main
                 - /litellm_.*/
       - e2e_ui_testing:
+          context: e2e_ui_tests
           requires:
             - ui_build
             - build_docker_database_image
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 905ebd3dba4..a3da5a85c65 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -16,6 +16,21 @@ body:
       value: "A bug happened!"
     validations:
       required: true
+  - type: textarea
+    id: steps-to-reproduce
+    attributes:
+      label: Steps to Reproduce
+      description: Please provide detailed steps to reproduce this bug(A curl/python code to reproduce the bug)
+      placeholder: |
+        1. config.yaml file/ .env file/ etc.
+        2. Run the following code...
+        3. Observe the error...
+      value: |
+        1. 
+        2. 
+        3. 
+    validations:
+      required: true
   - type: textarea
     id: logs
     attributes:
diff --git a/.github/workflows/ghcr_deploy.yml b/.github/workflows/ghcr_deploy.yml
index f574ec9c202..aa032972b80 100644
--- a/.github/workflows/ghcr_deploy.yml
+++ b/.github/workflows/ghcr_deploy.yml
@@ -5,6 +5,7 @@ on:
     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
@@ -336,9 +337,9 @@ jobs:
         run: |
           CHART_LIST=$(helm show chart oci://${{ env.REGISTRY }}/${{ env.REPO_OWNER }}/${{ env.CHART_NAME }} 2>/dev/null || true)
           if [ -z "${CHART_LIST}" ]; then
-            echo "current-version=0.1.0" | tee -a $GITHUB_OUTPUT
+            echo "current-version=1.0.0" | tee -a $GITHUB_OUTPUT
           else
-            # Extract version and strip any prerelease suffix (e.g., 0.1.827-latest -> 0.1.827)
+            # Extract version and strip any prerelease suffix (e.g., 1.0.5-latest -> 1.0.5)
             VERSION=$(printf '%s' "${CHART_LIST}" | grep '^version:' | awk 'BEGIN{FS=":"}{print $2}' | tr -d " " | cut -d'-' -f1)
             echo "current-version=${VERSION}" | tee -a $GITHUB_OUTPUT
           fi
@@ -350,28 +351,42 @@ jobs:
         id: bump_version
         uses: christian-draeger/increment-semantic-version@1.1.0
         with:
-          current-version: ${{ steps.current_version.outputs.current-version || '0.1.0' }}
+          current-version: ${{ steps.current_version.outputs.current-version || '1.0.0' }}
           version-fragment: 'bug'
 
       # Add suffix for non-stable releases (semantic versioning)
-      - name: Calculate chart version with prerelease suffix
+      - name: Calculate chart and app versions
         id: chart_version
         shell: bash
         run: |
-          BASE_VERSION="${{ steps.bump_version.outputs.next-version || '0.1.0' }}"
+          BASE_VERSION="${{ steps.bump_version.outputs.next-version || '1.0.0' }}"
           RELEASE_TYPE="${{ github.event.inputs.release_type }}"
+          INPUT_TAG="${{ github.event.inputs.tag }}"
+
+          # Chart version (independent Helm chart versioning with release type suffix)
           if [ "$RELEASE_TYPE" = "stable" ]; then
             echo "version=${BASE_VERSION}" | tee -a $GITHUB_OUTPUT
           else
             echo "version=${BASE_VERSION}-${RELEASE_TYPE}" | tee -a $GITHUB_OUTPUT
           fi
 
+          # App version (must match Docker tags)
+          # stable/rc releases: Docker creates main-{tag}, so use the tag
+          # latest/dev releases: Docker only creates main-{release_type}, so use release_type
+          if [ "$RELEASE_TYPE" = "stable" ] || [ "$RELEASE_TYPE" = "rc" ]; then
+            APP_VERSION="${INPUT_TAG}"
+          else
+            APP_VERSION="${RELEASE_TYPE}"
+          fi
+
+          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: ${{ github.event.inputs.chartVersion || steps.chart_version.outputs.version || '0.1.0' }}
-          app_version: ${{ steps.current_app_tag.outputs.latest_tag }}
+          tag: ${{ github.event.inputs.chartVersion || steps.chart_version.outputs.version || '1.0.0' }}
+          app_version: ${{ steps.chart_version.outputs.app_version }}
           path: deploy/charts/${{ env.CHART_NAME }}
           registry: ${{ env.REGISTRY }}
           registry_username: ${{ github.actor }}
diff --git a/.github/workflows/label-component.yml b/.github/workflows/label-component.yml
index 9a547c162a6..76b8316790c 100644
--- a/.github/workflows/label-component.yml
+++ b/.github/workflows/label-component.yml
@@ -11,134 +11,72 @@ jobs:
     permissions:
       issues: write
     steps:
-      - name: Add SDK label
-        if: contains(github.event.issue.body, 'What part of LiteLLM is this about?\n\nSDK (litellm Python package)')
+      - name: Add component labels
         uses: actions/github-script@v7
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           script: |
-            const labelName = 'sdk';
-            try {
-              await github.rest.issues.getLabel({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                name: labelName
-              });
-            } catch (error) {
-              if (error.status === 404) {
-                await github.rest.issues.createLabel({
-                  owner: context.repo.owner,
-                  repo: context.repo.repo,
-                  name: labelName,
-                  color: '0E7C86',
-                  description: 'Issues related to the litellm Python SDK'
-                });
-              } else {
-                throw error;
-              }
-            }
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              labels: [labelName]
-            });
+            const body = context.payload.issue.body;
+            if (!body) return;
 
-      - name: Add Proxy label
-        if: contains(github.event.issue.body, 'What part of LiteLLM is this about?\n\nProxy')
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const labelName = 'proxy';
-            try {
-              await github.rest.issues.getLabel({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                name: labelName
-              });
-            } catch (error) {
-              if (error.status === 404) {
-                await github.rest.issues.createLabel({
-                  owner: context.repo.owner,
-                  repo: context.repo.repo,
-                  name: labelName,
-                  color: '5319E7',
-                  description: 'Issues related to the LiteLLM Proxy'
-                });
-              } else {
-                throw error;
+            // Define component mappings with regex patterns that handle flexible whitespace
+            const components = [
+              {
+                pattern: /What part of LiteLLM is this about\?\s*SDK \(litellm Python package\)/,
+                label: 'sdk',
+                color: '0E7C86',
+                description: 'Issues related to the litellm Python SDK'
+              },
+              {
+                pattern: /What part of LiteLLM is this about\?\s*Proxy/,
+                label: 'proxy',
+                color: '5319E7',
+                description: 'Issues related to the LiteLLM Proxy'
+              },
+              {
+                pattern: /What part of LiteLLM is this about\?\s*UI Dashboard/,
+                label: 'ui-dashboard',
+                color: 'D876E3',
+                description: 'Issues related to the LiteLLM UI Dashboard'
+              },
+              {
+                pattern: /What part of LiteLLM is this about\?\s*Docs/,
+                label: 'docs',
+                color: 'FBCA04',
+                description: 'Issues related to LiteLLM documentation'
               }
-            }
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              labels: [labelName]
-            });
+            ];
 
-      - name: Add UI Dashboard label
-        if: contains(github.event.issue.body, 'What part of LiteLLM is this about?\n\nUI Dashboard')
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const labelName = 'ui-dashboard';
-            try {
-              await github.rest.issues.getLabel({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                name: labelName
-              });
-            } catch (error) {
-              if (error.status === 404) {
-                await github.rest.issues.createLabel({
-                  owner: context.repo.owner,
-                  repo: context.repo.repo,
-                  name: labelName,
-                  color: 'D876E3',
-                  description: 'Issues related to the LiteLLM UI Dashboard'
-                });
-              } else {
-                throw error;
-              }
-            }
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              labels: [labelName]
-            });
+            // Find matching component
+            for (const component of components) {
+              if (component.pattern.test(body)) {
+                // Ensure label exists
+                try {
+                  await github.rest.issues.getLabel({
+                    owner: context.repo.owner,
+                    repo: context.repo.repo,
+                    name: component.label
+                  });
+                } catch (error) {
+                  if (error.status === 404) {
+                    await github.rest.issues.createLabel({
+                      owner: context.repo.owner,
+                      repo: context.repo.repo,
+                      name: component.label,
+                      color: component.color,
+                      description: component.description
+                    });
+                  }
+                }
 
-      - name: Add Docs label
-        if: contains(github.event.issue.body, 'What part of LiteLLM is this about?\n\nDocs')
-        uses: actions/github-script@v7
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const labelName = 'docs';
-            try {
-              await github.rest.issues.getLabel({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                name: labelName
-              });
-            } catch (error) {
-              if (error.status === 404) {
-                await github.rest.issues.createLabel({
+                // Add label to issue
+                await github.rest.issues.addLabels({
                   owner: context.repo.owner,
                   repo: context.repo.repo,
-                  name: labelName,
-                  color: 'FBCA04',
-                  description: 'Issues related to LiteLLM documentation'
+                  issue_number: context.issue.number,
+                  labels: [component.label]
                 });
-              } else {
-                throw error;
+
+                break;
               }
             }
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: context.issue.number,
-              labels: [labelName]
-            });
diff --git a/.github/workflows/publish-migrations.yml b/.github/workflows/publish-migrations.yml
index 8e5a67bcf85..a5187cb2f55 100644
--- a/.github/workflows/publish-migrations.yml
+++ b/.github/workflows/publish-migrations.yml
@@ -13,6 +13,7 @@ on:
 
 jobs:
   publish-migrations:
+    if: github.repository == 'BerriAI/litellm'
     runs-on: ubuntu-latest
     services:
       postgres:
diff --git a/README.md b/README.md
index a020bd80898..75a23faa5c1 100644
--- a/README.md
+++ b/README.md
@@ -262,6 +262,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 
 | Provider                                                                            | `/chat/completions` | `/messages` | `/responses` | `/embeddings` | `/image/generations` | `/audio/transcriptions` | `/audio/speech` | `/moderations` | `/batches` | `/rerank` |
 |-------------------------------------------------------------------------------------|---------------------|-------------|--------------|---------------|----------------------|-------------------------|-----------------|----------------|-----------|-----------|
+| [Abliteration (`abliteration`)](https://docs.litellm.ai/docs/providers/abliteration) | ✅ |  |  |  |  |  |  |  |  |  |
 | [AI/ML API (`aiml`)](https://docs.litellm.ai/docs/providers/aiml) | ✅ | ✅ | ✅ | ✅ | ✅ |  |  |  |  |  |
 | [AI21 (`ai21`)](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
 | [AI21 Chat (`ai21_chat`)](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
@@ -455,4 +456,3 @@ All these checks must pass before your PR can be merged.
   <img src="https://contrib.rocks/image?repo=BerriAI/litellm" />
 </a>
 
-
diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml
index b77693ba8d5..b37597c7c82 100644
--- a/deploy/charts/litellm-helm/Chart.yaml
+++ b/deploy/charts/litellm-helm/Chart.yaml
@@ -18,13 +18,13 @@ type: application
 # This is the chart version. This version number should be incremented each time you make changes
 # to the chart and its templates, including the app version.
 # Versions are expected to follow Semantic Versioning (https://semver.org/)
-version: 0.4.10
+version: 1.0.0
 
 # This is the version number of the application being deployed. This version number should be
 # incremented each time you make changes to the application. Versions are not expected to
 # follow Semantic Versioning. They should reflect the version the application is using.
 # It is recommended to use it with quotes.
-appVersion: v1.50.2
+appVersion: v1.80.12
 
 dependencies:
   - name: "postgresql"
diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md
index 7df4f77017a..2f6da4bedcd 100644
--- a/docs/my-website/docs/completion/input.md
+++ b/docs/my-website/docs/completion/input.md
@@ -142,7 +142,47 @@ def completion(
 - `tool_call_id`: *str (optional)* - Tool call that this message is responding to.
 
 
-[**See All Message Values**](https://github.com/BerriAI/litellm/blob/8600ec77042dacad324d3879a2bd918fc6a719fa/litellm/types/llms/openai.py#L392)
+[**See All Message Values**](https://github.com/BerriAI/litellm/blob/main/litellm/types/llms/openai.py#L664)
+
+#### Content Types
+
+`content` can be a string (text only) or a list of content blocks (multimodal):
+
+| Type | Description | Docs |
+|------|-------------|------|
+| `text` | Text content | [Type Definition](https://github.com/BerriAI/litellm/blob/main/litellm/types/llms/openai.py#L598) |
+| `image_url` | Images | [Vision](./vision.md) |
+| `input_audio` | Audio input | [Audio](./audio.md) |
+| `video_url` | Video input | [Type Definition](https://github.com/BerriAI/litellm/blob/main/litellm/types/llms/openai.py#L625) |
+| `file` | Files | [Document Understanding](./document_understanding.md) |
+| `document` | Documents/PDFs | [Document Understanding](./document_understanding.md) |
+
+**Examples:**
+```python
+# Text
+messages=[{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
+
+# Image
+messages=[{"role": "user", "content": [{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}]}]
+
+# Audio
+messages=[{"role": "user", "content": [{"type": "input_audio", "input_audio": {"data": "<base64>", "format": "wav"}}]}]
+
+# Video
+messages=[{"role": "user", "content": [{"type": "video_url", "video_url": {"url": "https://example.com/video.mp4"}}]}]
+
+# File
+messages=[{"role": "user", "content": [{"type": "file", "file": {"file_id": "https://example.com/doc.pdf"}}]}]
+
+# Document
+messages=[{"role": "user", "content": [{"type": "document", "source": {"type": "text", "media_type": "application/pdf", "data": "<base64>"}}]}]
+
+# Combining multiple types (multimodal)
+messages=[{"role": "user", "content": [
+    {"type": "text", "text": "Generate a product description based on this image"},
+    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+]}]
+```
 
 ## Optional Fields
 
diff --git a/docs/my-website/docs/mcp_control.md b/docs/my-website/docs/mcp_control.md
index a7d66a6b7fc..96c71ef9278 100644
--- a/docs/my-website/docs/mcp_control.md
+++ b/docs/my-website/docs/mcp_control.md
@@ -649,3 +649,16 @@ general_settings:
 ```
 
 This is useful when you want discoverability for MCP offerings without granting additional execution privileges.
+
+
+## Publish MCP Registry
+
+If you want other systems—for example external agent frameworks such as MCP-capable IDEs running outside your network—to automatically discover the MCP servers hosted on LiteLLM, you can expose a Model Context Protocol Registry endpoint. This registry lists the built-in LiteLLM MCP server and every server you have configured, using the [official MCP Registry spec](https://github.com/modelcontextprotocol/registry).
+
+1. Set `enable_mcp_registry: true` under `general_settings` in your proxy config (or DB settings) and restart the proxy.
+2. LiteLLM will serve the registry at `GET /v1/mcp/registry.json`.
+3. Each entry points to either `/mcp` (built-in server) or `/{mcp_server_name}/mcp` for your custom servers, so clients can connect directly using the advertised Streamable HTTP URL.
+
+:::note Permissions still apply
+The registry only advertises server URLs. Actual access control is still enforced by LiteLLM when the client connects to `/mcp` or `/{server}/mcp`, so publishing the registry does not bypass per-key permissions.
+:::
diff --git a/docs/my-website/docs/observability/focus.md b/docs/my-website/docs/observability/focus.md
new file mode 100644
index 00000000000..c282f4a220c
--- /dev/null
+++ b/docs/my-website/docs/observability/focus.md
@@ -0,0 +1,93 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Focus Export (Experimental)
+
+:::caution Experimental feature
+Focus Format export is under active development and currently considered experimental.
+Interfaces, schema mappings, and configuration options may change as we iterate based on user feedback.
+Please treat this integration as a preview and report any issues or suggestions to help us stabilize and improve the workflow.
+:::
+
+LiteLLM can emit usage data in the [FinOps FOCUS format](https://focus.finops.org/focus-specification/v1-2/) and push artifacts (for example Parquet files) to destinations such as Amazon S3. This enables downstream cost-analysis tooling to ingest a standardised dataset directly from LiteLLM.
+
+LiteLLM currently conforms to the FinOps FOCUS v1.2 specification when emitting this dataset.
+
+## Overview
+
+| Property | Details |
+|----------|---------|
+| Destination | Export LiteLLM usage data in FOCUS format to managed storage (currently S3) |
+| Callback name | `focus` |
+| Supported operations | Automatic scheduled export |
+| Data format | FOCUS Normalised Dataset (Parquet) |
+
+## Environment Variables
+
+### Common settings
+
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `FOCUS_PROVIDER` | No | Destination provider (defaults to `s3`). |
+| `FOCUS_FORMAT` | No | Output format (currently only `parquet`). |
+| `FOCUS_FREQUENCY` | No | Export cadence. Prefer `hourly` or `daily` for production; `interval` is intended for short test loops. Defaults to `hourly`. |
+| `FOCUS_CRON_OFFSET` | No | Minute offset used for hourly/daily cron triggers. Defaults to `5`. |
+| `FOCUS_INTERVAL_SECONDS` | No | Interval (seconds) when `FOCUS_FREQUENCY="interval"`. |
+| `FOCUS_PREFIX` | No | Object key prefix/folder. Defaults to `focus_exports`. |
+
+### S3 destination
+
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `FOCUS_S3_BUCKET_NAME` | Yes | Destination bucket for exported files. |
+| `FOCUS_S3_REGION_NAME` | No | AWS region for the bucket. |
+| `FOCUS_S3_ENDPOINT_URL` | No | Custom endpoint (useful for S3-compatible storage). |
+| `FOCUS_S3_ACCESS_KEY` | Yes | AWS access key for uploads. |
+| `FOCUS_S3_SECRET_KEY` | Yes | AWS secret key for uploads. |
+| `FOCUS_S3_SESSION_TOKEN` | No | AWS session token if using temporary credentials. |
+
+## Setup via Config
+
+### Configure environment variables
+
+```bash
+export FOCUS_PROVIDER="s3"
+export FOCUS_PREFIX="focus_exports"
+
+# S3 example
+export FOCUS_S3_BUCKET_NAME="my-litellm-focus-bucket"
+export FOCUS_S3_REGION_NAME="us-east-1"
+export FOCUS_S3_ACCESS_KEY="AKIA..."
+export FOCUS_S3_SECRET_KEY="..."
+```
+
+### Update LiteLLM config
+
+```yaml
+model_list:
+  - model_name: gpt-4o
+    litellm_params:
+      model: openai/gpt-4o
+      api_key: sk-your-key
+
+litellm_settings:
+  callbacks: ["focus"]
+```
+
+### Start the proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+During boot LiteLLM registers the Focus logger and a background job that runs according to the configured frequency.
+
+## Planned Enhancements
+- Add "Setup on UI" flow alongside the current configuration-based setup.
+- Add GCS / Azure Blob to the Destination options.
+- Support CSV output alongside Parquet.
+
+## Related Links
+
+- [Focus](https://focus.finops.org/)
+
diff --git a/docs/my-website/docs/observability/qualifire_integration.md b/docs/my-website/docs/observability/qualifire_integration.md
new file mode 100644
index 00000000000..cf866f467bf
--- /dev/null
+++ b/docs/my-website/docs/observability/qualifire_integration.md
@@ -0,0 +1,122 @@
+import Image from '@theme/IdealImage';
+
+# Qualifire - LLM Evaluation, Guardrails & Observability
+
+[Qualifire](https://qualifire.ai/) provides real-time Agentic evaluations, guardrails and observability for production AI applications.
+
+**Key Features:**
+
+- **Evaluation** - Systematically assess AI behavior to detect hallucinations, jailbreaks, policy breaches, and other vulnerabilities
+- **Guardrails** - Real-time interventions to prevent risks like brand damage, data leaks, and compliance breaches
+- **Observability** - Complete tracing and logging for RAG pipelines, chatbots, and AI agents
+- **Prompt Management** - Centralized prompt management with versioning and no-code studio
+
+:::tip
+
+Looking for Qualifire Guardrails? Check out the [Qualifire Guardrails Integration](../proxy/guardrails/qualifire.md) for real-time content moderation, prompt injection detection, PII checks, and more.
+
+:::
+
+## Pre-Requisites
+
+1. Create an account on [Qualifire](https://app.qualifire.ai/)
+2. Get your API key and webhook URL from the Qualifire dashboard
+
+```bash
+pip install litellm
+```
+
+## Quick Start
+
+Use just 2 lines of code to instantly log your responses **across all providers** with Qualifire.
+
+```python
+litellm.callbacks = ["qualifire_eval"]
+```
+
+```python
+import litellm
+import os
+
+# Set Qualifire credentials
+os.environ["QUALIFIRE_API_KEY"] = "your-qualifire-api-key"
+os.environ["QUALIFIRE_WEBHOOK_URL"] = "https://your-qualifire-webhook-url"
+
+# LLM API Keys
+os.environ['OPENAI_API_KEY'] = "your-openai-api-key"
+
+# Set qualifire_eval as a callback & LiteLLM will send the data to Qualifire
+litellm.callbacks = ["qualifire_eval"]
+
+# OpenAI call
+response = litellm.completion(
+  model="gpt-5",
+  messages=[
+    {"role": "user", "content": "Hi 👋 - i'm openai"}
+  ]
+)
+```
+
+## Using with LiteLLM Proxy
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: gpt-4o
+    litellm_params:
+      model: openai/gpt-4o
+      api_key: os.environ/OPENAI_API_KEY
+
+litellm_settings:
+  callbacks: ["qualifire_eval"]
+
+general_settings:
+  master_key: "sk-1234"
+
+environment_variables:
+  QUALIFIRE_API_KEY: "your-qualifire-api-key"
+  QUALIFIRE_WEBHOOK_URL: "https://app.qualifire.ai/api/v1/webhooks/evaluations"
+```
+
+2. Start the proxy
+
+```bash
+litellm --config config.yaml
+```
+
+3. Test it!
+
+```bash
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{ "model": "gpt-4o", "messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}]}'
+```
+
+## Environment Variables
+
+| Variable                | Description                                            |
+| ----------------------- | ------------------------------------------------------ |
+| `QUALIFIRE_API_KEY`     | Your Qualifire API key for authentication              |
+| `QUALIFIRE_WEBHOOK_URL` | The Qualifire webhook endpoint URL from your dashboard |
+
+## What Gets Logged?
+
+The [LiteLLM Standard Logging Payload](https://docs.litellm.ai/docs/proxy/logging_spec) is sent to your Qualifire endpoint on each successful LLM API call.
+
+This includes:
+
+- Request messages and parameters
+- Response content and metadata
+- Token usage statistics
+- Latency metrics
+- Model information
+- Cost data
+
+Once data is in Qualifire, you can:
+
+- Run evaluations to detect hallucinations, toxicity, and policy violations
+- Set up guardrails to block or modify responses in real-time
+- View traces across your entire AI pipeline
+- Track performance and quality metrics over time
diff --git a/docs/my-website/docs/providers/abliteration.md b/docs/my-website/docs/providers/abliteration.md
new file mode 100644
index 00000000000..a0fc7f39310
--- /dev/null
+++ b/docs/my-website/docs/providers/abliteration.md
@@ -0,0 +1,109 @@
+# Abliteration
+
+## Overview
+
+| Property | Details |
+|-------|-------|
+| Description | Abliteration provides an OpenAI-compatible `/chat/completions` endpoint. |
+| Provider Route on LiteLLM | `abliteration/` |
+| Link to Provider Doc | [Abliteration](https://abliteration.ai) |
+| Base URL | `https://api.abliteration.ai/v1` |
+| Supported Operations | [`/chat/completions`](#sample-usage) |
+
+<br />
+
+## Required Variables
+
+```python showLineNumbers title="Environment Variables"
+os.environ["ABLITERATION_API_KEY"] = ""  # your Abliteration API key
+```
+
+## Sample Usage
+
+```python showLineNumbers title="Abliteration Completion"
+import os
+from litellm import completion
+
+os.environ["ABLITERATION_API_KEY"] = ""
+
+response = completion(
+    model="abliteration/abliterated-model",
+    messages=[{"role": "user", "content": "Hello from LiteLLM"}],
+)
+
+print(response)
+```
+
+## Sample Usage - Streaming
+
+```python showLineNumbers title="Abliteration Streaming Completion"
+import os
+from litellm import completion
+
+os.environ["ABLITERATION_API_KEY"] = ""
+
+response = completion(
+    model="abliteration/abliterated-model",
+    messages=[{"role": "user", "content": "Stream a short reply"}],
+    stream=True,
+)
+
+for chunk in response:
+    print(chunk)
+```
+
+## Usage with LiteLLM Proxy Server
+
+1. Add the model to your proxy config:
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: abliteration-chat
+    litellm_params:
+      model: abliteration/abliterated-model
+      api_key: os.environ/ABLITERATION_API_KEY
+```
+
+2. Start the proxy:
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+## Direct API Usage (Bearer Token)
+
+Use the environment variable as a Bearer token against the OpenAI-compatible endpoint:
+`https://api.abliteration.ai/v1/chat/completions`.
+
+```bash showLineNumbers title="cURL"
+export ABLITERATION_API_KEY=""
+curl https://api.abliteration.ai/v1/chat/completions \
+  -H "Authorization: Bearer ${ABLITERATION_API_KEY}" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "abliterated-model",
+    "messages": [{"role": "user", "content": "Hello from Abliteration"}]
+  }'
+```
+
+```python showLineNumbers title="Python (requests)"
+import os
+import requests
+
+api_key = os.environ["ABLITERATION_API_KEY"]
+
+response = requests.post(
+    "https://api.abliteration.ai/v1/chat/completions",
+    headers={
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+    },
+    json={
+        "model": "abliterated-model",
+        "messages": [{"role": "user", "content": "Hello from Abliteration"}],
+    },
+    timeout=60,
+)
+
+print(response.json())
+```
diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md
index cae8657f1a0..446d663c5ac 100644
--- a/docs/my-website/docs/providers/anthropic.md
+++ b/docs/my-website/docs/providers/anthropic.md
@@ -1692,9 +1692,9 @@ Assistant:
 ```
 
 
-## Usage - PDF 
+## Usage - PDF
 
-Pass base64 encoded PDF files to Anthropic models using the `image_url` field.
+Pass base64 encoded PDF files to Anthropic models using the `file` content type with a `file_data` field.
 
 <Tabs>
 <TabItem value="sdk" label="SDK">
diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md
index f1eed4b4d52..487212ad655 100644
--- a/docs/my-website/docs/providers/bedrock.md
+++ b/docs/my-website/docs/providers/bedrock.md
@@ -7,7 +7,7 @@ ALL Bedrock models (Anthropic, Meta, Deepseek, Mistral, Amazon, etc.) are Suppor
 | Property | Details |
 |-------|-------|
 | Description | Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs). |
-| Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models), [`bedrock/qwen2/`](./bedrock_imported.md#qwen2-imported-models), [`bedrock/openai/`](./bedrock_imported.md#openai-compatible-imported-models-qwen-25-vl-etc) |
+| Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models), [`bedrock/qwen2/`](./bedrock_imported.md#qwen2-imported-models), [`bedrock/openai/`](./bedrock_imported.md#openai-compatible-imported-models-qwen-25-vl-etc), [`bedrock/moonshot`](./bedrock_imported.md#moonshot-kimi-k2-thinking) |
 | Provider Doc | [Amazon Bedrock ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) |
 | Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings`, `/images/generations` |
 | Rerank Endpoint | `/rerank` |
@@ -967,6 +967,30 @@ Control the processing tier for your Bedrock requests using `serviceTier`. Valid
 
 [Bedrock ServiceTier API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ServiceTier.html)
 
+### OpenAI-compatible `service_tier` parameter
+
+LiteLLM also supports the OpenAI-style `service_tier` parameter, which is automatically translated to Bedrock's native `serviceTier` format:
+
+| OpenAI `service_tier` | Bedrock `serviceTier` |
+|-----------------------|----------------------|
+| `"priority"` | `{"type": "priority"}` |
+| `"default"` | `{"type": "default"}` |
+| `"flex"` | `{"type": "flex"}` |
+| `"auto"` | `{"type": "default"}` |
+
+```python
+from litellm import completion
+
+# Using OpenAI-style service_tier parameter
+response = completion(
+    model="bedrock/converse/anthropic.claude-3-sonnet-20240229-v1:0",
+    messages=[{"role": "user", "content": "Hello!"}],
+    service_tier="priority"  # Automatically translated to serviceTier={"type": "priority"}
+)
+```
+
+### Native Bedrock `serviceTier` parameter
+
 <Tabs>
 <TabItem value="sdk" label="SDK">
 
@@ -1941,6 +1965,7 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re
 | Mixtral 8x7B Instruct      | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)`   | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
 | TwelveLabs Pegasus 1.2 (US) | `completion(model='bedrock/us.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})`   | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
 | TwelveLabs Pegasus 1.2 (EU) | `completion(model='bedrock/eu.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})`   | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
+| Moonshot Kimi K2 Thinking | `completion(model='bedrock/moonshot.kimi-k2-thinking', messages=messages)` or `completion(model='bedrock/invoke/moonshot.kimi-k2-thinking', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
 
 
 ## Bedrock Embedding
diff --git a/docs/my-website/docs/providers/bedrock_imported.md b/docs/my-website/docs/providers/bedrock_imported.md
index 0784f716925..709736e6109 100644
--- a/docs/my-website/docs/providers/bedrock_imported.md
+++ b/docs/my-website/docs/providers/bedrock_imported.md
@@ -431,4 +431,180 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
             "max_tokens": 300,
             "temperature": 0.5
         }'
-```
\ No newline at end of file
+```
+
+### Moonshot Kimi K2 Thinking
+
+Moonshot AI's Kimi K2 Thinking model is now available on Amazon Bedrock. This model features advanced reasoning capabilities with automatic reasoning content extraction.
+
+| Property | Details |
+|----------|---------|
+| Provider Route | `bedrock/moonshot.kimi-k2-thinking`, `bedrock/invoke/moonshot.kimi-k2-thinking` |
+| Provider Documentation | [AWS Bedrock Moonshot Announcement ↗](https://aws.amazon.com/about-aws/whats-new/2025/12/amazon-bedrock-fully-managed-open-weight-models/) |
+| Supported Parameters | `temperature`, `max_tokens`, `top_p`, `stream`, `tools`, `tool_choice` |
+| Special Features | Reasoning content extraction, Tool calling |
+
+#### Supported Features
+
+- **Reasoning Content Extraction**: Automatically extracts `<reasoning>` tags and returns them as `reasoning_content` (similar to OpenAI's o1 models)
+- **Tool Calling**: Full support for function/tool calling with tool responses
+- **Streaming**: Both streaming and non-streaming responses
+- **System Messages**: System message support
+
+#### Basic Usage
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python title="Moonshot Kimi K2 SDK Usage" showLineNumbers
+from litellm import completion
+import os
+
+os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key"
+os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your preferred region
+
+# Basic completion
+response = completion(
+    model="bedrock/moonshot.kimi-k2-thinking",  # or bedrock/invoke/moonshot.kimi-k2-thinking
+    messages=[
+        {"role": "user", "content": "What is 2+2? Think step by step."}
+    ],
+    temperature=0.7,
+    max_tokens=200
+)
+
+print(response.choices[0].message.content)
+
+# Access reasoning content if present
+if response.choices[0].message.reasoning_content:
+    print("Reasoning:", response.choices[0].message.reasoning_content)
+```
+
+</TabItem>
+<TabItem value="proxy" label="Proxy">
+
+**1. Add to config**
+
+```yaml title="config.yaml" showLineNumbers
+model_list:
+  - model_name: kimi-k2
+    litellm_params:
+      model: bedrock/moonshot.kimi-k2-thinking
+      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+      aws_region_name: us-west-2
+```
+
+**2. Start proxy**
+
+```bash title="Start LiteLLM Proxy" showLineNumbers
+litellm --config /path/to/config.yaml
+
+# RUNNING at http://0.0.0.0:4000
+```
+
+**3. Test it!**
+
+```bash title="Test Kimi K2 via Proxy" showLineNumbers
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+  --header 'Authorization: Bearer sk-1234' \
+  --header 'Content-Type: application/json' \
+  --data '{
+    "model": "kimi-k2",
+    "messages": [
+      {
+        "role": "user",
+        "content": "What is 2+2? Think step by step."
+      }
+    ],
+    "temperature": 0.7,
+    "max_tokens": 200
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+#### Tool Calling Example
+
+```python title="Kimi K2 with Tool Calling" showLineNumbers
+from litellm import completion
+import os
+
+os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key"
+os.environ["AWS_REGION_NAME"] = "us-west-2"
+
+# Tool calling example
+response = completion(
+    model="bedrock/moonshot.kimi-k2-thinking",
+    messages=[
+        {"role": "user", "content": "What's the weather in Tokyo?"}
+    ],
+    tools=[
+        {
+            "type": "function",
+            "function": {
+                "name": "get_weather",
+                "description": "Get the current weather in a location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city name"
+                        }
+                    },
+                    "required": ["location"]
+                }
+            }
+        }
+    ]
+)
+
+if response.choices[0].message.tool_calls:
+    tool_call = response.choices[0].message.tool_calls[0]
+    print(f"Tool called: {tool_call.function.name}")
+    print(f"Arguments: {tool_call.function.arguments}")
+```
+
+#### Streaming Example
+
+```python title="Kimi K2 Streaming" showLineNumbers
+from litellm import completion
+import os
+
+os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key"
+os.environ["AWS_REGION_NAME"] = "us-west-2"
+
+response = completion(
+    model="bedrock/moonshot.kimi-k2-thinking",
+    messages=[
+        {"role": "user", "content": "Explain quantum computing in simple terms."}
+    ],
+    stream=True,
+    temperature=0.7
+)
+
+for chunk in response:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="")
+    
+    # Check for reasoning content in streaming
+    if hasattr(chunk.choices[0].delta, 'reasoning_content') and chunk.choices[0].delta.reasoning_content:
+        print(f"\n[Reasoning: {chunk.choices[0].delta.reasoning_content}]")
+```
+
+#### Supported Parameters
+
+| Parameter | Type | Description | Supported |
+|-----------|------|-------------|-----------|
+| `temperature` | float (0-1) | Controls randomness in output | ✅ |
+| `max_tokens` | integer | Maximum tokens to generate | ✅ |
+| `top_p` | float | Nucleus sampling parameter | ✅ |
+| `stream` | boolean | Enable streaming responses | ✅ |
+| `tools` | array | Tool/function definitions | ✅ |
+| `tool_choice` | string/object | Tool choice specification | ✅ |
+| `stop` | array | Stop sequences | ❌ (Not supported on Bedrock) |
\ No newline at end of file
diff --git a/docs/my-website/docs/providers/manus.md b/docs/my-website/docs/providers/manus.md
new file mode 100644
index 00000000000..92bf2b9b966
--- /dev/null
+++ b/docs/my-website/docs/providers/manus.md
@@ -0,0 +1,369 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Manus
+
+Use Manus AI agents through LiteLLM's OpenAI-compatible Responses API.
+
+| Property | Details |
+|----------|---------|
+| Description | Manus is an AI agent platform for complex reasoning tasks, document analysis, and multi-step workflows with asynchronous task execution. |
+| Provider Route on LiteLLM | `manus/{agent_profile}` |
+| Supported Operations | `/responses` (Responses API), `/files` (Files API) |
+| Provider Doc | [Manus API ↗](https://open.manus.im/docs/openai-compatibility) |
+
+## Model Format
+
+```shell
+manus/{agent_profile}
+```
+
+**Examples:**
+- `manus/manus-1.6` - General purpose agent
+- `manus/manus-1.6-lite` - Lightweight agent for simple tasks
+- `manus/manus-1.6-max` - Advanced agent for complex analysis
+
+## LiteLLM Python SDK
+
+```python showLineNumbers title="Basic Usage"
+import litellm
+import os
+import time
+
+# Set API key
+os.environ["MANUS_API_KEY"] = "your-manus-api-key"
+
+# Create task
+response = litellm.responses(
+    model="manus/manus-1.6",
+    input="What's the capital of France?",
+)
+
+print(f"Task ID: {response.id}")
+print(f"Status: {response.status}")  # "running"
+
+# Poll until complete
+task_id = response.id
+while response.status == "running":
+    time.sleep(5)
+    response = litellm.get_response(
+        response_id=task_id,
+        custom_llm_provider="manus",
+    )
+    print(f"Status: {response.status}")
+
+# Get results
+if response.status == "completed":
+    for message in response.output:
+        if message.role == "assistant":
+            print(message.content[0].text)
+```
+
+## LiteLLM AI Gateway
+
+### Setup
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: manus-agent
+    litellm_params:
+      model: manus/manus-1.6
+      api_key: os.environ/MANUS_API_KEY
+```
+
+```bash title="Start Proxy"
+litellm --config config.yaml
+```
+
+### Usage
+
+<Tabs>
+<TabItem value="curl" label="cURL">
+
+```bash showLineNumbers title="Create Task"
+# Create task
+curl -X POST http://localhost:4000/responses \
+  -H "Authorization: Bearer your-proxy-key" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "manus-agent",
+    "input": "What is the capital of France?"
+  }'
+
+# Response
+{
+  "id": "task_abc123",
+  "status": "running",
+  "metadata": {
+    "task_url": "https://manus.im/app/task_abc123"
+  }
+}
+```
+
+```bash showLineNumbers title="Poll for Completion"
+# Check status (repeat until status is "completed")
+curl http://localhost:4000/responses/task_abc123 \
+  -H "Authorization: Bearer your-proxy-key"
+
+# When completed
+{
+  "id": "task_abc123",
+  "status": "completed",
+  "output": [
+    {
+      "role": "user",
+      "content": [{"text": "What is the capital of France?"}]
+    },
+    {
+      "role": "assistant",
+      "content": [{"text": "The capital of France is Paris."}]
+    }
+  ]
+}
+```
+
+</TabItem>
+<TabItem value="openai" label="OpenAI SDK">
+
+```python showLineNumbers title="Create Task and Poll"
+import openai
+import time
+
+client = openai.OpenAI(
+    base_url="http://localhost:4000",
+    api_key="your-proxy-key"
+)
+
+# Create task
+response = client.responses.create(
+    model="manus-agent",
+    input="What is the capital of France?"
+)
+
+print(f"Task ID: {response.id}")
+print(f"Status: {response.status}")  # "running"
+
+# Poll until complete
+task_id = response.id
+while response.status == "running":
+    time.sleep(5)
+    response = client.responses.retrieve(response_id=task_id)
+    print(f"Status: {response.status}")
+
+# Get results
+if response.status == "completed":
+    for message in response.output:
+        if message.role == "assistant":
+            print(message.content[0].text)
+```
+
+</TabItem>
+</Tabs>
+
+## How It Works
+
+Manus operates as an **asynchronous agent API**:
+
+1. **Create Task**: When you call `litellm.responses()`, Manus creates a task and returns immediately with `status: "running"`
+2. **Task Executes**: The agent works on your request in the background
+3. **Poll for Completion**: You must repeatedly call `litellm.get_response()` or `client.responses.retrieve()` until the status changes to `"completed"`
+4. **Get Results**: Once completed, the `output` field contains the full conversation
+
+**Task Statuses:**
+- `running` - Agent is actively working
+- `pending` - Agent is waiting for input
+- `completed` - Task finished successfully
+- `error` - Task failed
+
+:::tip Production Usage
+For production applications, use [webhooks](https://open.manus.im/docs/webhooks) instead of polling to get notified when tasks complete.
+:::
+
+## Supported Parameters
+
+| Parameter | Supported | Notes |
+|-----------|-----------|-------|
+| `input` | ✅ | Text, images, or structured content |
+| `stream` | ✅ | Fake streaming (task runs async) |
+| `max_output_tokens` | ✅ | Limits response length |
+| `previous_response_id` | ✅ | For multi-turn conversations |
+
+## Files API
+
+Manus supports file uploads for document analysis and processing. Files can be uploaded and then referenced in Responses API calls.
+
+### LiteLLM Python SDK
+
+```python showLineNumbers title="Upload, Use, Retrieve, and Delete Files"
+import litellm
+import os
+
+# Set API key
+os.environ["MANUS_API_KEY"] = "your-manus-api-key"
+
+# Upload file
+file_content = b"This is a document for analysis."
+created_file = await litellm.acreate_file(
+    file=("document.txt", file_content),
+    purpose="assistants",
+    custom_llm_provider="manus",
+)
+print(f"Uploaded file: {created_file.id}")
+
+# Use file with Responses API
+response = await litellm.aresponses(
+    model="manus/manus-1.6",
+    input=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "input_text", "text": "Summarize this document."},
+                {"type": "input_file", "file_id": created_file.id},
+            ],
+        },
+    ],
+    extra_body={"task_mode": "agent", "agent_profile": "manus-1.6-agent"},
+)
+print(f"Response: {response.id}")
+
+# Retrieve file
+retrieved_file = await litellm.afile_retrieve(
+    file_id=created_file.id,
+    custom_llm_provider="manus",
+)
+print(f"File details: {retrieved_file.filename}, {retrieved_file.bytes} bytes")
+
+# Delete file
+deleted_file = await litellm.afile_delete(
+    file_id=created_file.id,
+    custom_llm_provider="manus",
+)
+print(f"Deleted: {deleted_file.deleted}")
+```
+
+### LiteLLM AI Gateway
+
+<Tabs>
+<TabItem value="curl" label="cURL">
+
+```bash showLineNumbers title="Upload File"
+# Upload file
+curl -X POST http://localhost:4000/v1/files \
+  -H "Authorization: Bearer your-proxy-key" \
+  -F "file=@document.txt" \
+  -F "purpose=assistants" \
+  -F "custom_llm_provider=manus"
+
+# Response
+{
+  "id": "file_abc123",
+  "object": "file",
+  "bytes": 1024,
+  "created_at": 1234567890,
+  "filename": "document.txt",
+  "purpose": "assistants",
+  "status": "uploaded"
+}
+```
+
+```bash showLineNumbers title="Use File with Responses API"
+# Create response with file
+curl -X POST http://localhost:4000/responses \
+  -H "Authorization: Bearer your-proxy-key" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "manus-agent",
+    "input": [
+      {
+        "role": "user",
+        "content": [
+          {"type": "input_text", "text": "Summarize this document."},
+          {"type": "input_file", "file_id": "file_abc123"}
+        ]
+      }
+    ]
+  }'
+```
+
+```bash showLineNumbers title="Retrieve File"
+# Get file details
+curl http://localhost:4000/v1/files/file_abc123 \
+  -H "Authorization: Bearer your-proxy-key"
+
+# Response
+{
+  "id": "file_abc123",
+  "object": "file",
+  "bytes": 1024,
+  "created_at": 1234567890,
+  "filename": "document.txt",
+  "purpose": "assistants",
+  "status": "uploaded"
+}
+```
+
+```bash showLineNumbers title="Delete File"
+# Delete file
+curl -X DELETE http://localhost:4000/v1/files/file_abc123 \
+  -H "Authorization: Bearer your-proxy-key"
+
+# Response
+{
+  "id": "file_abc123",
+  "object": "file",
+  "deleted": true
+}
+```
+
+</TabItem>
+<TabItem value="openai" label="OpenAI SDK">
+
+```python showLineNumbers title="Upload, Use, Retrieve, and Delete Files"
+import openai
+
+client = openai.OpenAI(
+    base_url="http://localhost:4000",
+    api_key="your-proxy-key"
+)
+
+# Upload file
+with open("document.txt", "rb") as f:
+    created_file = client.files.create(
+        file=f,
+        purpose="assistants",
+        extra_body={"custom_llm_provider": "manus"}
+    )
+print(f"Uploaded file: {created_file.id}")
+
+# Use file with Responses API
+response = client.responses.create(
+    model="manus-agent",
+    input=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "input_text", "text": "Summarize this document."},
+                {"type": "input_file", "file_id": created_file.id}
+            ]
+        }
+    ]
+)
+print(f"Response: {response.id}")
+
+# Retrieve file
+retrieved_file = client.files.retrieve(created_file.id)
+print(f"File: {retrieved_file.filename}, {retrieved_file.bytes} bytes")
+
+# Delete file
+deleted_file = client.files.delete(created_file.id)
+print(f"Deleted: {deleted_file.deleted}")
+```
+
+</TabItem>
+</Tabs>
+
+## Related Documentation
+
+- [LiteLLM Responses API](/docs/response_api)
+- [LiteLLM Files API](/docs/proxy/litellm_managed_files)
+- [Manus OpenAI Compatibility](https://open.manus.im/docs/openai-compatibility)
diff --git a/docs/my-website/docs/providers/openrouter.md b/docs/my-website/docs/providers/openrouter.md
index 327634909b3..a1ed6c4466e 100644
--- a/docs/my-website/docs/providers/openrouter.md
+++ b/docs/my-website/docs/providers/openrouter.md
@@ -1,5 +1,5 @@
 # OpenRouter
-LiteLLM supports all the text / chat / vision models from [OpenRouter](https://openrouter.ai/docs)
+LiteLLM supports all the text / chat / vision / embedding models from [OpenRouter](https://openrouter.ai/docs)
 
 <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/LiteLLM_OpenRouter.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
@@ -78,3 +78,18 @@ response = completion(
             route= ""
         )
 ```
+
+## Embedding
+
+```python
+from litellm import embedding
+import os
+
+os.environ["OPENROUTER_API_KEY"] = "your-api-key"
+
+response = embedding(
+    model="openrouter/openai/text-embedding-3-small",
+    input=["good morning from litellm", "this is another item"],
+)
+print(response)
+```
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index dfc0efd37ad..42494c1e5f5 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -146,6 +146,7 @@ router_settings:
   cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails
   disable_cooldowns: True                  # bool - Disable cooldowns for all models 
   enable_tag_filtering: True                # bool - Use tag based routing for requests
+  tag_filtering_match_any: True             # bool - Tag matching behavior (only when enable_tag_filtering=true). `true`: match if deployment has ANY requested tag; `false`: match only if deployment has ALL requested tags
   retry_policy: {                          # Dict[str, int]: retry policy for different types of exceptions
     "AuthenticationErrorRetries": 3,
     "TimeoutErrorRetries": 3,
@@ -293,6 +294,7 @@ router_settings:
   cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails
   disable_cooldowns: True                  # bool - Disable cooldowns for all models
   enable_tag_filtering: True                # bool - Use tag based routing for requests
+  tag_filtering_match_any: True             # bool - Tag matching behavior (only when enable_tag_filtering=true). `true`: match if deployment has ANY requested tag; `false`: match only if deployment has ALL requested tags
   retry_policy: {                          # Dict[str, int]: retry policy for different types of exceptions
     "AuthenticationErrorRetries": 3,
     "TimeoutErrorRetries": 3,
@@ -322,6 +324,7 @@ router_settings:
 | content_policy_fallbacks | array of objects | Specifies fallback models for content policy violations. [More information here](reliability) |
 | fallbacks | array of objects | Specifies fallback models for all types of errors. [More information here](reliability) |
 | enable_tag_filtering | boolean | If true, uses tag based routing for requests [Tag Based Routing](tag_routing) |
+| tag_filtering_match_any | boolean | Tag matching behavior (only when enable_tag_filtering=true). `true`: match if deployment has ANY requested tag; `false`: match only if deployment has ALL requested tags |
 | cooldown_time | integer | The duration (in seconds) to cooldown a model if it exceeds the allowed failures. |
 | disable_cooldowns | boolean | If true, disables cooldowns for all models. [More information here](reliability) |
 | retry_policy | object | Specifies the number of retries for different types of exceptions. [More information here](reliability) |
@@ -578,6 +581,18 @@ router_settings:
 | FIREWORKS_AI_56_B_MOE | Size parameter for Fireworks AI 56B MOE model. Default is 56
 | FIREWORKS_AI_80_B | Size parameter for Fireworks AI 80B model. Default is 80
 | FIREWORKS_AI_176_B_MOE | Size parameter for Fireworks AI 176B MOE model. Default is 176
+| FOCUS_PROVIDER | Destination provider for Focus exports (e.g., `s3`). Defaults to `s3`.
+| FOCUS_FORMAT | Output format for Focus exports. Defaults to `parquet`.
+| FOCUS_FREQUENCY | Frequency for scheduled Focus exports (`hourly`, `daily`, or `interval`). Defaults to `hourly`.
+| FOCUS_CRON_OFFSET | Minute offset used when scheduling hourly/daily Focus exports. Defaults to `5` minutes.
+| FOCUS_INTERVAL_SECONDS | Interval (in seconds) for Focus exports when `frequency` is `interval`.
+| FOCUS_PREFIX | Object key prefix (or folder) used when uploading Focus export files. Defaults to `focus_exports`.
+| FOCUS_S3_BUCKET_NAME | S3 bucket to upload Focus export files when using the S3 destination.
+| FOCUS_S3_REGION_NAME | AWS region for the Focus export S3 bucket.
+| FOCUS_S3_ENDPOINT_URL | Custom endpoint for the Focus export S3 client (optional; useful for S3-compatible storage).
+| FOCUS_S3_ACCESS_KEY | AWS access key ID used by the Focus export S3 client.
+| FOCUS_S3_SECRET_KEY | AWS secret access key used by the Focus export S3 client.
+| FOCUS_S3_SESSION_TOKEN | AWS session token used by the Focus export S3 client (optional).
 | FUNCTION_DEFINITION_TOKEN_COUNT | Token count for function definitions. Default is 9
 | GALILEO_BASE_URL | Base URL for Galileo platform
 | GALILEO_PASSWORD | Password for Galileo authentication
diff --git a/docs/my-website/docs/proxy/db_deadlocks.md b/docs/my-website/docs/proxy/db_deadlocks.md
index ef9d31d6232..fd02ce50e83 100644
--- a/docs/my-website/docs/proxy/db_deadlocks.md
+++ b/docs/my-website/docs/proxy/db_deadlocks.md
@@ -4,6 +4,12 @@ import TabItem from '@theme/TabItem';
 
 # High Availability Setup (Resolve DB Deadlocks)
 
+:::tip Essential for Production
+
+This configuration is **required** for production deployments handling 1000+ requests per second. Without Redis configured, you may experience PostgreSQL connection exhaustion (`FATAL: sorry, too many clients already`).
+
+:::
+
 Resolve any Database Deadlocks you see in high traffic by using this setup
 
 ## What causes the problem?
diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md
index 9b4bc6822c1..5686e9fd835 100644
--- a/docs/my-website/docs/proxy/deploy.md
+++ b/docs/my-website/docs/proxy/deploy.md
@@ -359,6 +359,26 @@ LiteLLM is compatible with several SDKs - including OpenAI SDK, Anthropic SDK, M
 ### Deploy with Database
 ##### Docker, Kubernetes, Helm Chart
 
+:::warning High Traffic Deployments (1000+ RPS)
+
+If you expect high traffic (1000+ requests per second), **Redis is required** to prevent database connection exhaustion and deadlocks.
+
+Add this to your config:
+```yaml
+general_settings:
+  use_redis_transaction_buffer: true
+
+litellm_settings:
+  cache: true
+  cache_params:
+    type: redis
+    host: your-redis-host
+```
+
+See [Resolve DB Deadlocks](/docs/proxy/db_deadlocks) for details.
+
+:::
+
 Requirements:
 - Need a postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), etc) Set `DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname>` in your env 
 - Set a `LITELLM_MASTER_KEY`, this is your Proxy Admin key - you can use this to create other keys (🚨 must start with `sk-`)
diff --git a/docs/my-website/docs/proxy/endpoint_activity.md b/docs/my-website/docs/proxy/endpoint_activity.md
new file mode 100644
index 00000000000..a66c0f7a5e5
--- /dev/null
+++ b/docs/my-website/docs/proxy/endpoint_activity.md
@@ -0,0 +1,117 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Endpoint Activity
+
+Track and visualize API endpoint usage directly in the dashboard. Monitor endpoint-level activity analytics, spend breakdowns, and performance metrics to understand which endpoints are receiving the most traffic and how they're performing.
+
+## Overview
+
+Endpoint Activity enables you to track spend and usage for individual API endpoints automatically. Every time you call an endpoint through the LiteLLM proxy, activity is automatically tracked and aggregated. This allows you to:
+
+- Track spend per endpoint automatically
+- View endpoint-level usage analytics in the Admin UI
+- Monitor token consumption by endpoint
+- Analyze success and failure rates per endpoint
+- Identify which endpoints are getting the most activity
+- View trend data showing endpoint usage over time
+
+<Image img={require('../../img/ui_endpoint_activity.png')} />
+
+## How Endpoint Activity Works
+
+Endpoint activity is **automatically tracked** whenever you make API calls through the LiteLLM proxy. No additional configuration is required - simply call your endpoints as usual and activity will be tracked.
+
+### Example API Call
+
+When you make a request to any endpoint, activity is automatically recorded:
+
+```bash showLineNumbers title="Endpoint activity is automatically tracked"
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \ # 👈 ENDPOINT AUTOMATICALLY TRACKED
+  --header 'Content-Type: application/json' \
+  --header 'Authorization: Bearer sk-1234' \ # 👈 YOUR PROXY KEY
+  --data '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+      {
+        "role": "user",
+        "content": "What is the capital of France?"
+      }
+    ]
+  }'
+```
+
+The endpoint (`/chat/completions`) will be automatically tracked with:
+
+- Token counts (prompt tokens, completion tokens, total tokens)
+- Spend for the request
+- Request status (success or failure)
+- Timestamp and other metadata
+
+## How to View Endpoint Activity
+
+### View Activity in Admin UI
+
+Navigate to the Endpoint Activity tab in the Admin UI to view endpoint-level analytics:
+
+#### 1. Access Endpoint Activity
+
+Go to the Usage page in the Admin UI (`PROXY_BASE_URL/ui/?login=success&page=new_usage`) and click on the **Endpoint Activity** tab.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-10/67601fc0-8415-49b4-8e55-0673d37540c2/ascreenshot_f609a506dfe745c5aadccd332681c32d_text_export.jpeg)
+
+#### 2. View Endpoint Analytics
+
+The Endpoint Activity dashboard provides:
+
+- **Endpoint usage table**: View all endpoints with aggregated metrics including:
+  - Total requests (successful and failed)
+  - Success rate percentage
+  - Total tokens consumed
+  - Total spend per endpoint
+- **Success vs Failed requests chart**: Visualize request success and failure rates by endpoint
+- **Usage trends**: See how endpoint activity changes over time with daily trend data
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-10/41b2b158-3ab3-4154-a0d0-7233451d3f2b/ascreenshot_ff46db6e09b54ea9bf34ae9028aff58a_text_export.jpeg)
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-10/bce32f99-f0ba-4502-8a3a-76257ff5e47a/ascreenshot_2273d3a94acd42e983ad7d6436722c2a_text_export.jpeg)
+
+#### 3. Understand Endpoint Metrics
+
+Each endpoint displays the following metrics:
+
+- **Successful Requests**: Number of requests that completed successfully
+- **Failed Requests**: Number of requests that encountered errors
+- **Total Requests**: Sum of successful and failed requests
+- **Success Rate**: Percentage of successful requests
+- **Total Tokens**: Sum of prompt and completion tokens
+- **Spend**: Total cost for all requests to that endpoint
+
+## Use Cases
+
+### Performance Monitoring
+
+Monitor endpoint health and performance:
+
+- Identify endpoints with high failure rates
+- Track which endpoints are receiving the most traffic
+- Monitor token consumption patterns by endpoint
+- Detect anomalies in endpoint usage
+
+### Cost Optimization
+
+Understand spend distribution across endpoints:
+
+- Identify high-cost endpoints
+- Optimize expensive endpoints
+- Allocate budget based on endpoint usage
+- Track cost trends over time
+
+---
+
+## Related Features
+
+- [Customer Usage](./customer_usage.md) - Track spend and usage for individual customers
+- [Cost Tracking](./cost_tracking.md) - Comprehensive cost tracking and analytics
+- [Spend Logs](./spend_logs.md) - Detailed request-level spend logs
diff --git a/docs/my-website/docs/proxy/guardrails/qualifire.md b/docs/my-website/docs/proxy/guardrails/qualifire.md
index 66961c92d9d..850af37e47f 100644
--- a/docs/my-website/docs/proxy/guardrails/qualifire.md
+++ b/docs/my-website/docs/proxy/guardrails/qualifire.md
@@ -8,13 +8,7 @@ Use [Qualifire](https://qualifire.ai) to evaluate LLM outputs for quality, safet
 
 ## Quick Start
 
-### 1. Install the Qualifire SDK
-
-```bash
-pip install qualifire
-```
-
-### 2. Define Guardrails on your LiteLLM config.yaml
+### 1. Define Guardrails on your LiteLLM config.yaml
 
 Define your guardrails under the `guardrails` section:
 
@@ -61,13 +55,13 @@ guardrails:
 - `post_call` Run **after** LLM call, on **input & output**
 - `during_call` Run **during** LLM call, on **input**. Same as `pre_call` but runs in parallel as LLM call. Response not returned until guardrail check completes
 
-### 3. Start LiteLLM Gateway
+### 2. Start LiteLLM Gateway
 
 ```shell
 litellm --config config.yaml --detailed_debug
 ```
 
-### 4. Test request
+### 3. Test request
 
 **[Langchain, OpenAI SDK Usage Examples](../proxy/user_keys#request-format)**
 
@@ -142,7 +136,7 @@ guardrails:
       evaluation_id: eval_abc123 # Your evaluation ID from Qualifire dashboard
 ```
 
-When `evaluation_id` is provided, LiteLLM will use `invoke_evaluation()` instead of `evaluate()`, running the pre-configured evaluation from your dashboard.
+When `evaluation_id` is provided, LiteLLM will use the invoke evaluation API endpoint instead of the evaluate endpoint, running the pre-configured evaluation from your dashboard.
 
 ## Available Checks
 
@@ -213,19 +207,19 @@ guardrails:
 
 ### Parameter Reference
 
-| Parameter                      | Type        | Default                     | Description                                              |
-| ------------------------------ | ----------- | --------------------------- | -------------------------------------------------------- |
-| `api_key`                      | `str`       | `QUALIFIRE_API_KEY` env var | Your Qualifire API key                                   |
-| `api_base`                     | `str`       | `None`                      | Custom API base URL (optional)                           |
-| `evaluation_id`                | `str`       | `None`                      | Pre-configured evaluation ID from Qualifire dashboard    |
-| `prompt_injections`            | `bool`      | `true` (if no other checks) | Enable prompt injection detection                        |
-| `hallucinations_check`         | `bool`      | `None`                      | Enable hallucination detection                           |
-| `grounding_check`              | `bool`      | `None`                      | Enable grounding verification                            |
-| `pii_check`                    | `bool`      | `None`                      | Enable PII detection                                     |
-| `content_moderation_check`     | `bool`      | `None`                      | Enable content moderation                                |
-| `tool_selection_quality_check` | `bool`      | `None`                      | Enable tool selection quality check                      |
-| `assertions`                   | `List[str]` | `None`                      | Custom assertions to validate                            |
-| `on_flagged`                   | `str`       | `"block"`                   | Action when content is flagged: `"block"` or `"monitor"` |
+| Parameter                      | Type        | Default                      | Description                                              |
+| ------------------------------ | ----------- | ---------------------------- | -------------------------------------------------------- |
+| `api_key`                      | `str`       | `QUALIFIRE_API_KEY` env var  | Your Qualifire API key                                   |
+| `api_base`                     | `str`       | `https://proxy.qualifire.ai` | Custom API base URL (optional)                           |
+| `evaluation_id`                | `str`       | `None`                       | Pre-configured evaluation ID from Qualifire dashboard    |
+| `prompt_injections`            | `bool`      | `true` (if no other checks)  | Enable prompt injection detection                        |
+| `hallucinations_check`         | `bool`      | `None`                       | Enable hallucination detection                           |
+| `grounding_check`              | `bool`      | `None`                       | Enable grounding verification                            |
+| `pii_check`                    | `bool`      | `None`                       | Enable PII detection                                     |
+| `content_moderation_check`     | `bool`      | `None`                       | Enable content moderation                                |
+| `tool_selection_quality_check` | `bool`      | `None`                       | Enable tool selection quality check                      |
+| `assertions`                   | `List[str]` | `None`                       | Custom assertions to validate                            |
+| `on_flagged`                   | `str`       | `"block"`                    | Action when content is flagged: `"block"` or `"monitor"` |
 
 ### Default Behavior
 
@@ -261,4 +255,3 @@ This evaluates whether the LLM selected the appropriate tools and provided corre
 
 - [Qualifire Documentation](https://docs.qualifire.ai)
 - [Qualifire Dashboard](https://app.qualifire.ai)
-- [Qualifire Python SDK](https://github.com/qualifire-dev/qualifire-python-sdk)
diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md
index 4cff7e5d041..42f6ef1aa51 100644
--- a/docs/my-website/docs/proxy/load_balancing.md
+++ b/docs/my-website/docs/proxy/load_balancing.md
@@ -264,8 +264,15 @@ model_list:
       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
 ```
 
+:::important
+The `order` parameter requires `enable_pre_call_checks: true` in `router_settings`.
+:::
+
 If `order=1` deployment is unavailable (e.g., rate-limited), the router falls back to `order=2` deployments.
 
 ### When You'll See Load Balancing in Action
diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md
index 5fe8f17d7b0..a27b6dcf083 100644
--- a/docs/my-website/docs/proxy/logging.md
+++ b/docs/my-website/docs/proxy/logging.md
@@ -67,7 +67,7 @@ Set `litellm.turn_off_message_logging=True` This will prevent the messages and r
 
 <TabItem value="global" label="Global">
 
-**1. Setup config.yaml **
+**1. Setup config.yaml**
 ```yaml
 model_list:
  - model_name: gpt-3.5-turbo
diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md
index 03454004b8c..cf8168764b8 100644
--- a/docs/my-website/docs/proxy/pass_through.md
+++ b/docs/my-website/docs/proxy/pass_through.md
@@ -165,6 +165,7 @@ general_settings:
       target: string                  # Target URL for forwarding
       auth: boolean                   # Enable LiteLLM authentication (Enterprise)
       forward_headers: boolean        # Forward all incoming headers
+      include_subpath: boolean        # If true, forwards requests to sub-paths (default: false)
       headers:                        # Custom headers to add
         Authorization: string         # Auth header for target API
         content-type: string         # Request content type
@@ -181,6 +182,23 @@ general_settings:
 - **LANGFUSE_PUBLIC_KEY/SECRET_KEY**: For Langfuse integration
 - **Custom headers**: Any additional key-value pairs
 
+### Sub-path Routing
+
+By default, pass-through endpoints only match the **exact path** specified. To forward requests to sub-paths, set `include_subpath: true`:
+
+```yaml
+general_settings:
+  pass_through_endpoints:
+    - path: "/custom-api"                    # Any path prefix you choose
+      target: "https://api.example.com"
+      include_subpath: true  # Forward /custom-api/*, not just /custom-api
+```
+
+| Setting | Behavior |
+|---------|----------|
+| `include_subpath: false` (default) | Only `/custom-api` is forwarded |
+| `include_subpath: true` | `/custom-api`, `/custom-api/v1/chat`, `/custom-api/anything` are all forwarded |
+
 ---
 
 ## Advanced: Custom Adapters
diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md
index 2539f70d5bc..8ac56463d5e 100644
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@@ -861,9 +861,13 @@ model_list = [
     },
 ]
 
-router = Router(model_list=model_list)
+router = Router(model_list=model_list, enable_pre_call_checks=True)  # 👈 Required for 'order' to work
 ```
 
+:::important
+The `order` parameter requires `enable_pre_call_checks=True` to be set on the Router.
+:::
+
 </TabItem>
 <TabItem value="proxy" label="PROXY">
 
@@ -880,6 +884,9 @@ model_list:
       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
 ```
 
 </TabItem>
diff --git a/docs/my-website/img/ui_endpoint_activity.png b/docs/my-website/img/ui_endpoint_activity.png
new file mode 100644
index 00000000000..e2550066a07
Binary files /dev/null and b/docs/my-website/img/ui_endpoint_activity.png differ
diff --git a/docs/my-website/release_notes/v1.80.11-stable/index.md b/docs/my-website/release_notes/v1.80.11-stable/index.md
index b671b795602..ea0e6083dfc 100644
--- a/docs/my-website/release_notes/v1.80.11-stable/index.md
+++ b/docs/my-website/release_notes/v1.80.11-stable/index.md
@@ -1,5 +1,5 @@
 ---
-title: "[Preview] v1.80.11 - Google Interactions API"
+title: "v1.80.11 - Google Interactions API"
 slug: "v1-80-11"
 date: 2025-12-20T10:00:00
 authors:
@@ -27,7 +27,7 @@ import TabItem from '@theme/TabItem';
 docker run \
 -e STORE_MODEL_IN_DB=True \
 -p 4000:4000 \
-docker.litellm.ai/berriai/litellm:v1.80.11.rc.1
+docker.litellm.ai/berriai/litellm:v1.80.11-stable
 ```
 
 </TabItem>
diff --git a/docs/my-website/release_notes/v1.80.15/index.md b/docs/my-website/release_notes/v1.80.15/index.md
new file mode 100644
index 00000000000..c8364e5a49c
--- /dev/null
+++ b/docs/my-website/release_notes/v1.80.15/index.md
@@ -0,0 +1,643 @@
+---
+title: "v1.80.15 - Manus API Support"
+slug: "v1-80-15"
+date: 2026-01-10T10:00:00
+authors:
+  - 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
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+<Tabs>
+<TabItem value="docker" label="Docker">
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+docker.litellm.ai/berriai/litellm:v1.80.15.rc.1
+```
+
+</TabItem>
+
+<TabItem value="pip" label="Pip">
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.80.15
+```
+
+</TabItem>
+</Tabs>
+
+---
+
+## Key Highlights
+
+- **Manus API Support** - [New provider support for Manus API on /responses and GET /responses endpoints](../../docs/providers/manus)
+- **MiniMax Provider** - [Full support for MiniMax chat completions, TTS, and Anthropic native endpoint](../../docs/providers/minimax)
+- **AWS Polly TTS** - [New TTS provider using AWS Polly API](../../docs/providers/aws_polly)
+- **SSO Role Mapping** - Configure role mappings for SSO providers directly in the UI
+- **Cost Estimator** - New UI tool for estimating costs across multiple models and requests
+- **MCP Global Mode** - [Configure MCP servers globally with visibility controls](../../docs/mcp)
+- **Interactions API Bridge** - [Use all LiteLLM providers with the Interactions API](../../docs/interactions)
+- **RAG Query Endpoint** - [New RAG Search/Query endpoint for retrieval-augmented generation](../../docs/search/index)
+- **UI Usage - Endpoint Activity** - [Users can now see Endpoint Activity Metrics in the UI](../../docs/proxy/endpoint_activity.md)
+- **50% Overhead Reduction** - LiteLLM now sends 2.5× more requests to LLM providers
+
+
+---
+
+## Performance - 50% Overhead Reduction
+
+LiteLLM now sends 2.5× more requests to LLM providers by replacing sequential if/elif chains with O(1) dictionary lookups for provider configuration resolution (92.7% faster). This optimization has a high impact because it runs inside the client decorator, which is invoked on every HTTP request made to the proxy server.
+
+### Before
+
+> **Note:** Worse-looking provider metrics are a good sign here—they indicate requests spend less time inside LiteLLM.
+
+```
+============================================================
+Fake LLM Provider Stats (When called by LiteLLM)
+============================================================
+Total Time:            0.56s
+Requests/Second:       10746.68
+
+Latency Statistics (seconds):
+   Mean:               0.2039s
+   Median (p50):       0.2310s
+   Min:                0.0323s
+   Max:                0.3928s
+   Std Dev:            0.1166s
+   p95:                0.3574s
+   p99:                0.3748s
+
+Status Codes:
+   200: 6000
+```
+
+### After
+
+```
+============================================================
+Fake LLM Provider Stats (When called by LiteLLM)
+============================================================
+Total Time:            1.42s
+Requests/Second:       4224.49
+
+Latency Statistics (seconds):
+   Mean:               0.5300s
+   Median (p50):       0.5871s
+   Min:                0.0885s
+   Max:                1.0482s
+   Std Dev:            0.3065s
+   p95:                0.9750s
+   p99:                1.0444s
+
+Status Codes:
+   200: 6000
+```
+
+> The benchmarks run LiteLLM locally with a lightweight LLM provider to eliminate network latency, isolating internal overhead and bottlenecks so we can focus on reducing pure LiteLLM overhead on a single instance.
+
+---
+
+### UI Usage - Endpoint Activity
+
+<Image
+img={require('../../img/ui_endpoint_activity.png')}
+style={{width: '100%', display: 'block', margin: '2rem auto'}}
+/>
+
+Users can now see Endpoint Activity Metrics in the UI.
+
+---
+
+## New Providers and Endpoints
+
+### New Providers (11 new providers)
+
+| Provider | Supported LiteLLM Endpoints | Description |
+| -------- | ------------------- | ----------- |
+| [Manus](../../docs/providers/manus) | `/responses` | Manus API for agentic workflows |
+| [Manus](../../docs/providers/manus) | `GET /responses` | Manus API for retrieving responses |
+| [Manus](../../docs/providers/manus) | `/files` | Manus API for file management |
+| [MiniMax](../../docs/providers/minimax) | `/chat/completions` | MiniMax chat completions |
+| [MiniMax](../../docs/providers/minimax) | `/audio/speech` | MiniMax text-to-speech |
+| [AWS Polly](../../docs/providers/aws_polly) | `/audio/speech` | AWS Polly text-to-speech API |
+| [GigaChat](../../docs/providers/gigachat) | `/chat/completions` | GigaChat provider for Russian language AI |
+| [LlamaGate](../../docs/providers/llamagate) | `/chat/completions` | LlamaGate chat completions |
+| [LlamaGate](../../docs/providers/llamagate) | `/embeddings` | LlamaGate embeddings |
+| [Abliteration AI](../../docs/providers/abliteration) | `/chat/completions` | Abliteration.ai provider support |
+| [Bedrock](../../docs/providers/bedrock) | `/v1/messages/count_tokens` | Bedrock as new provider for token counting |
+
+### New LLM API Endpoints (3 new endpoints)
+
+| Endpoint | Method | Description | Documentation |
+| -------- | ------ | ----------- | ------------- |
+| `/responses/compact` | POST | Compact responses API endpoint | [Docs](../../docs/response_api) |
+| `/rag/query` | POST | RAG Search/Query endpoint | [Docs](../../docs/search/index) |
+| `/containers/{id}/files` | POST | Upload files to containers | [Docs](../../docs/container_files) |
+
+---
+
+## New Models / Updated Models
+
+#### New Model Support (100+ new models)
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| Azure | `azure/gpt-5.2` | 400K | $1.75 | $14.00 | Reasoning, vision, caching |
+| Azure | `azure/gpt-5.2-chat` | 128K | $1.75 | $14.00 | Reasoning, vision |
+| Azure | `azure/gpt-5.2-pro` | 400K | $21.00 | $168.00 | Reasoning, vision, web search |
+| Azure | `azure/gpt-image-1.5` | - | Token-based | Token-based | Image generation/editing |
+| Azure AI | `azure_ai/gpt-oss-120b` | 131K | $0.15 | $0.60 | Function calling |
+| Azure AI | `azure_ai/flux.2-pro` | - | - | $0.04/image | Image generation |
+| Azure AI | `azure_ai/deepseek-v3.2` | 164K | $0.58 | $1.68 | Reasoning, function calling |
+| Bedrock | `amazon.nova-2-multimodal-embeddings-v1:0` | 8K | $0.135 | - | Multimodal embeddings |
+| Bedrock | `writer.palmyra-x4-v1:0` | 128K | $2.50 | $10.00 | Function calling, PDF |
+| Bedrock | `writer.palmyra-x5-v1:0` | 1M | $0.60 | $6.00 | Function calling, PDF |
+| Bedrock | `moonshot.kimi-k2-v1:0` | - | - | - | Kimi K2 model |
+| Cerebras | `cerebras/zai-glm-4.6` | 128K | $2.25 | $2.75 | Reasoning, function calling |
+| GigaChat | `gigachat/GigaChat-2-Lite` | - | - | - | Chat completions |
+| GigaChat | `gigachat/GigaChat-2-Max` | - | - | - | Chat completions |
+| GigaChat | `gigachat/GigaChat-2-Pro` | - | - | - | Chat completions |
+| Gemini | `gemini/veo-3.1-generate-001` | - | - | - | Video generation |
+| Gemini | `gemini/veo-3.1-fast-generate-001` | - | - | - | Video generation |
+| GitHub Copilot | 25+ models | Various | - | - | Chat completions |
+| LlamaGate | 15+ models | Various | - | - | Chat, vision, embeddings |
+| MiniMax | `minimax/abab7-chat-preview` | - | - | - | Chat completions |
+| Novita | 80+ models | Various | Various | Various | Chat, vision, embeddings |
+| OpenRouter | `openrouter/google/gemini-3-flash-preview` | - | - | - | Chat completions |
+| Together AI | Multiple models | Various | Various | Various | Response schema support |
+| Vertex AI | `vertex_ai/zai-glm-4.7` | - | - | - | GLM 4.7 support |
+
+#### Features
+
+- **[Gemini](../../docs/providers/gemini)**
+    - Add image tokens in chat completion - [PR #18327](https://github.com/BerriAI/litellm/pull/18327)
+    - Add usage object in image generation - [PR #18328](https://github.com/BerriAI/litellm/pull/18328)
+    - Add thought signature support via tool call id - [PR #18374](https://github.com/BerriAI/litellm/pull/18374)
+    - Add thought signature for non tool call requests - [PR #18581](https://github.com/BerriAI/litellm/pull/18581)
+    - Preserve system instructions - [PR #18585](https://github.com/BerriAI/litellm/pull/18585)
+    - Fix Gemini 3 images in tool response - [PR #18190](https://github.com/BerriAI/litellm/pull/18190)
+    - Support snake_case for google_search tool parameters - [PR #18451](https://github.com/BerriAI/litellm/pull/18451)
+    - Google GenAI adapter inline data support - [PR #18477](https://github.com/BerriAI/litellm/pull/18477)
+    - Add deprecation_date for discontinued Google models - [PR #18550](https://github.com/BerriAI/litellm/pull/18550)
+- **[Vertex AI](../../docs/providers/vertex)**
+    - Add centralized get_vertex_base_url() helper for global location support - [PR #18410](https://github.com/BerriAI/litellm/pull/18410)
+    - Convert image URLs to base64 for Vertex AI Anthropic - [PR #18497](https://github.com/BerriAI/litellm/pull/18497)
+    - Separate Tool objects for each tool type per API spec - [PR #18514](https://github.com/BerriAI/litellm/pull/18514)
+    - Add thought_signatures to VertexGeminiConfig - [PR #18853](https://github.com/BerriAI/litellm/pull/18853)
+    - Add support for Vertex AI API keys - [PR #18806](https://github.com/BerriAI/litellm/pull/18806)
+    - Add zai glm-4.7 model support - [PR #18782](https://github.com/BerriAI/litellm/pull/18782)
+- **[Azure](../../docs/providers/azure/azure)**
+    - Add Azure gpt-image-1.5 pricing to cost map - [PR #18347](https://github.com/BerriAI/litellm/pull/18347)
+    - Add azure/gpt-5.2-chat model - [PR #18361](https://github.com/BerriAI/litellm/pull/18361)
+    - Add support for image generation via Azure AD token - [PR #18413](https://github.com/BerriAI/litellm/pull/18413)
+    - Add logprobs support for Azure OpenAI GPT-5.2 model - [PR #18856](https://github.com/BerriAI/litellm/pull/18856)
+    - Add Azure BFL Flux 2 models for image generation and editing - [PR #18764](https://github.com/BerriAI/litellm/pull/18764), [PR #18766](https://github.com/BerriAI/litellm/pull/18766)
+- **[Bedrock](../../docs/providers/bedrock)**
+    - Add Bedrock Kimi K2 model support - [PR #18797](https://github.com/BerriAI/litellm/pull/18797)
+    - Add support for model id in bedrock passthrough - [PR #18800](https://github.com/BerriAI/litellm/pull/18800)
+    - Fix Nova model detection for Bedrock provider - [PR #18250](https://github.com/BerriAI/litellm/pull/18250)
+    - Ensure toolUse.input is always a dict when converting from OpenAI format - [PR #18414](https://github.com/BerriAI/litellm/pull/18414)
+- **[Databricks](../../docs/providers/databricks)**
+    - Add enhanced authentication, security features, and custom user-agent support - [PR #18349](https://github.com/BerriAI/litellm/pull/18349)
+- **[MiniMax](../../docs/providers/minimax)**
+    - Add MiniMax chat completion support - [PR #18380](https://github.com/BerriAI/litellm/pull/18380)
+    - Add Anthropic native endpoint support for MiniMax - [PR #18377](https://github.com/BerriAI/litellm/pull/18377)
+    - Add support for MiniMax TTS - [PR #18334](https://github.com/BerriAI/litellm/pull/18334)
+    - Add MiniMax provider support to UI dashboard - [PR #18496](https://github.com/BerriAI/litellm/pull/18496)
+- **[Together AI](../../docs/providers/togetherai)**
+    - Add supports_response_schema to all supported Together AI models - [PR #18368](https://github.com/BerriAI/litellm/pull/18368)
+- **[OpenRouter](../../docs/providers/openrouter)**
+    - Add OpenRouter embeddings API support - [PR #18391](https://github.com/BerriAI/litellm/pull/18391)
+- **[Anthropic](../../docs/providers/anthropic)**
+    - Pass server_tool_use and tool_search_tool_result blocks - [PR #18770](https://github.com/BerriAI/litellm/pull/18770)
+    - Add Anthropic cache control option to image tool call results - [PR #18674](https://github.com/BerriAI/litellm/pull/18674)
+- **[Ollama](../../docs/providers/ollama)**
+    - Add dimensions for ollama embedding - [PR #18536](https://github.com/BerriAI/litellm/pull/18536)
+    - Extract pure base64 data from data URLs for Ollama - [PR #18465](https://github.com/BerriAI/litellm/pull/18465)
+- **[Watsonx](../../docs/providers/watsonx/index)**
+    - Add Watsonx fields support - [PR #18569](https://github.com/BerriAI/litellm/pull/18569)
+    - Fix Watsonx Audio Transcription - filter model field - [PR #18810](https://github.com/BerriAI/litellm/pull/18810)
+- **[SAP](../../docs/providers/sap)**
+    - Add SAP creds for list in proxy UI - [PR #18375](https://github.com/BerriAI/litellm/pull/18375)
+    - Pass through extra params from allowed_openai_params - [PR #18432](https://github.com/BerriAI/litellm/pull/18432)
+    - Add client header for SAP AI Core Tracking - [PR #18714](https://github.com/BerriAI/litellm/pull/18714)
+- **[Fireworks AI](../../docs/providers/fireworks_ai)**
+    - Correct deepseek-v3p2 pricing - [PR #18483](https://github.com/BerriAI/litellm/pull/18483)
+- **[ZAI](../../docs/providers/zai)**
+    - Add GLM-4.7 model with reasoning support - [PR #18476](https://github.com/BerriAI/litellm/pull/18476)
+- **[Codestral](../../docs/providers/codestral)**
+    - Correctly route codestral chat and FIM endpoints - [PR #18467](https://github.com/BerriAI/litellm/pull/18467)
+- **[Azure AI](../../docs/providers/azure_ai)**
+    - Fix authentication errors at messages API via azure_ai - [PR #18500](https://github.com/BerriAI/litellm/pull/18500)
+
+#### New Provider Support
+
+- **[AWS Polly](../../docs/providers/aws_polly)** - Add AWS Polly API for TTS - [PR #18326](https://github.com/BerriAI/litellm/pull/18326)
+- **[GigaChat](../../docs/providers/gigachat)** - Add GigaChat provider support - [PR #18564](https://github.com/BerriAI/litellm/pull/18564)
+- **[LlamaGate](../../docs/providers/llamagate)** - Add LlamaGate as a new provider - [PR #18673](https://github.com/BerriAI/litellm/pull/18673)
+- **[Abliteration AI](../../docs/providers/abliteration)** - Add abliteration.ai provider - [PR #18678](https://github.com/BerriAI/litellm/pull/18678)
+- **[Manus](../../docs/providers/manus)** - Add Manus API support on /responses, GET /responses - [PR #18804](https://github.com/BerriAI/litellm/pull/18804)
+- **5 AI Providers via openai_like** - Add 5 AI providers using openai_like - [PR #18362](https://github.com/BerriAI/litellm/pull/18362)
+
+### Bug Fixes
+
+- **[Gemini](../../docs/providers/gemini)**
+    - Properly catch context window exceeded errors - [PR #18283](https://github.com/BerriAI/litellm/pull/18283)
+    - Remove prompt caching headers as support has been removed - [PR #18579](https://github.com/BerriAI/litellm/pull/18579)
+    - Fix generate content request with audio file id - [PR #18745](https://github.com/BerriAI/litellm/pull/18745)
+    - Fix google_genai streaming adapter provider handling - [PR #18845](https://github.com/BerriAI/litellm/pull/18845)
+- **[Groq](../../docs/providers/groq)**
+    - Remove deprecated Groq models and update model registry - [PR #18062](https://github.com/BerriAI/litellm/pull/18062)
+- **[Vertex AI](../../docs/providers/vertex)**
+    - Handle unsupported region for Vertex AI count tokens endpoint - [PR #18665](https://github.com/BerriAI/litellm/pull/18665)
+- **General**
+    - Fix request body for image embedding request - [PR #18336](https://github.com/BerriAI/litellm/pull/18336)
+    - Fix lost tool_calls when streaming has both text and tool_calls - [PR #18316](https://github.com/BerriAI/litellm/pull/18316)
+    - Add all resolution for gpt-image-1.5 - [PR #18586](https://github.com/BerriAI/litellm/pull/18586)
+    - Fix gpt-image-1 cost calculation using token-based pricing - [PR #17906](https://github.com/BerriAI/litellm/pull/17906)
+    - Fix response_format leaking into extra_body - [PR #18859](https://github.com/BerriAI/litellm/pull/18859)
+    - Align max_tokens with max_output_tokens for consistency - [PR #18820](https://github.com/BerriAI/litellm/pull/18820)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **[Responses API](../../docs/response_api)**
+    - Add new compact endpoint (v1/responses/compact) - [PR #18697](https://github.com/BerriAI/litellm/pull/18697)
+    - Support more streaming callback hooks - [PR #18513](https://github.com/BerriAI/litellm/pull/18513)
+    - Add mapping for reasoning effort to summary param - [PR #18635](https://github.com/BerriAI/litellm/pull/18635)
+    - Add output_text property to ResponsesAPIResponse - [PR #18491](https://github.com/BerriAI/litellm/pull/18491)
+    - Add annotations to completions responses API bridge - [PR #18754](https://github.com/BerriAI/litellm/pull/18754)
+- **[Interactions API](../../docs/interactions)**
+    - Allow using all LiteLLM providers (interactions -> responses API bridge) - [PR #18373](https://github.com/BerriAI/litellm/pull/18373)
+- **[RAG Search API](../../docs/search/index)**
+    - Add RAG Search/Query endpoint - [PR #18376](https://github.com/BerriAI/litellm/pull/18376)
+- **[CountTokens API](../../docs/anthropic_count_tokens)**
+    - Add Bedrock as a new provider for `/v1/messages/count_tokens` - [PR #18858](https://github.com/BerriAI/litellm/pull/18858)
+- **[Generate Content](../../docs/providers/gemini)**
+    - Add generate content in LLM route - [PR #18405](https://github.com/BerriAI/litellm/pull/18405)
+- **General**
+    - Enable async_post_call_failure_hook to transform error responses - [PR #18348](https://github.com/BerriAI/litellm/pull/18348)
+    - Calculate total_tokens manually if missing and can be calculated - [PR #18445](https://github.com/BerriAI/litellm/pull/18445)
+    - Add custom llm provider to get_llm_provider when sent via UI - [PR #18638](https://github.com/BerriAI/litellm/pull/18638)
+
+#### Bugs
+
+- **General**
+    - Handle empty error objects in response conversion - [PR #18493](https://github.com/BerriAI/litellm/pull/18493)
+    - Preserve client error status codes in streaming mode - [PR #18698](https://github.com/BerriAI/litellm/pull/18698)
+    - Return json error response instead of SSE format for initial streaming errors - [PR #18757](https://github.com/BerriAI/litellm/pull/18757)
+    - Fix auth header for custom api base in generateContent request - [PR #18637](https://github.com/BerriAI/litellm/pull/18637)
+    - Tool content should be string for Deepinfra - [PR #18739](https://github.com/BerriAI/litellm/pull/18739)
+    - Fix incomplete usage in response object passed - [PR #18799](https://github.com/BerriAI/litellm/pull/18799)
+    - Unify model names to provider-defined names - [PR #18573](https://github.com/BerriAI/litellm/pull/18573)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **SSO Configuration**
+    - Add SSO Role Mapping feature - [PR #18090](https://github.com/BerriAI/litellm/pull/18090)
+    - Add SSO Settings Page - [PR #18600](https://github.com/BerriAI/litellm/pull/18600)
+    - Allow adding role mappings for SSO - [PR #18593](https://github.com/BerriAI/litellm/pull/18593)
+    - SSO Settings Page Add Role Mappings - [PR #18677](https://github.com/BerriAI/litellm/pull/18677)
+    - SSO Settings Loading State + Deprecate Previous SSO Flow - [PR #18617](https://github.com/BerriAI/litellm/pull/18617)
+- **Virtual Keys**
+    - Allow deleting key expiry - [PR #18278](https://github.com/BerriAI/litellm/pull/18278)
+    - Add optional query param "expand" to /key/list - [PR #18502](https://github.com/BerriAI/litellm/pull/18502)
+    - Key Table Loading Skeleton - [PR #18527](https://github.com/BerriAI/litellm/pull/18527)
+    - Allow column resizing on Keys Table - [PR #18424](https://github.com/BerriAI/litellm/pull/18424)
+    - Virtual Keys Table Loading State Between Pages - [PR #18619](https://github.com/BerriAI/litellm/pull/18619)
+    - Key and Team Router Setting - [PR #18790](https://github.com/BerriAI/litellm/pull/18790)
+    - Allow router_settings on Keys and Teams - [PR #18675](https://github.com/BerriAI/litellm/pull/18675)
+    - Use timedelta to calculate key expiry on generate - [PR #18666](https://github.com/BerriAI/litellm/pull/18666)
+- **Models + Endpoints**
+    - Add Model Clearer Flow For Team Admins - [PR #18532](https://github.com/BerriAI/litellm/pull/18532)
+    - Model Page Loading State - [PR #18574](https://github.com/BerriAI/litellm/pull/18574)
+    - Model Page Model Provider Select Performance - [PR #18425](https://github.com/BerriAI/litellm/pull/18425)
+    - Model Page Sorting Sorts Entire Set - [PR #18420](https://github.com/BerriAI/litellm/pull/18420)
+    - Refactor Model Hub Page - [PR #18568](https://github.com/BerriAI/litellm/pull/18568)
+    - Add request provider form on UI - [PR #18704](https://github.com/BerriAI/litellm/pull/18704)
+- **Organizations & Teams**
+    - Allow Organization Admins to See Organization Tab - [PR #18400](https://github.com/BerriAI/litellm/pull/18400)
+    - Resolve Organization Alias on Team Table - [PR #18401](https://github.com/BerriAI/litellm/pull/18401)
+    - Resolve Team Alias in Organization Info View - [PR #18404](https://github.com/BerriAI/litellm/pull/18404)
+    - Allow Organization Admins to View Their Organization Info - [PR #18417](https://github.com/BerriAI/litellm/pull/18417)
+    - Allow editing team_member_budget_duration in /team/update - [PR #18735](https://github.com/BerriAI/litellm/pull/18735)
+    - Reusable Duration Select + Team Update Member Budget Duration - [PR #18736](https://github.com/BerriAI/litellm/pull/18736)
+- **Usage & Spend**
+    - Add Error Code Filtering on Spend Logs - [PR #18359](https://github.com/BerriAI/litellm/pull/18359)
+    - Add Error Code Filtering on UI - [PR #18366](https://github.com/BerriAI/litellm/pull/18366)
+    - Usage Page User Max Budget fix - [PR #18555](https://github.com/BerriAI/litellm/pull/18555)
+    - Add endpoint to Daily Activity Tables - [PR #18729](https://github.com/BerriAI/litellm/pull/18729)
+    - Endpoint Activity in Usage - [PR #18798](https://github.com/BerriAI/litellm/pull/18798)
+- **Cost Estimator**
+    - Add Cost Estimator for AI Gateway - [PR #18643](https://github.com/BerriAI/litellm/pull/18643)
+    - Add view for estimating costs across requests - [PR #18645](https://github.com/BerriAI/litellm/pull/18645)
+    - Allow selecting many models for cost estimator - [PR #18653](https://github.com/BerriAI/litellm/pull/18653)
+- **CloudZero**
+    - Improve Create and Delete Path for CloudZero - [PR #18263](https://github.com/BerriAI/litellm/pull/18263)
+    - Add CloudZero UI Docs - [PR #18350](https://github.com/BerriAI/litellm/pull/18350)
+- **Playground**
+    - Add MCP test support to completions on Playground - [PR #18440](https://github.com/BerriAI/litellm/pull/18440)
+    - Add selectable MCP servers to the playground - [PR #18578](https://github.com/BerriAI/litellm/pull/18578)
+    - Add custom proxy base URL support to Playground - [PR #18661](https://github.com/BerriAI/litellm/pull/18661)
+- **General UI**
+    - UI styling improvements and fixes - [PR #18310](https://github.com/BerriAI/litellm/pull/18310)
+    - Add reusable "New" badge component for feature highlights - [PR #18537](https://github.com/BerriAI/litellm/pull/18537)
+    - Hide New Badges - [PR #18547](https://github.com/BerriAI/litellm/pull/18547)
+    - Change Budget page to Have Tabs - [PR #18576](https://github.com/BerriAI/litellm/pull/18576)
+    - Clicking on Logo Directs to Correct URL - [PR #18575](https://github.com/BerriAI/litellm/pull/18575)
+    - Add UI support for configuring meta URLs - [PR #18580](https://github.com/BerriAI/litellm/pull/18580)
+    - Expire Previous UI Session Tokens on Login - [PR #18557](https://github.com/BerriAI/litellm/pull/18557)
+    - Add license endpoint - [PR #18311](https://github.com/BerriAI/litellm/pull/18311)
+    - Router Fields Endpoint + React Query for Router Fields - [PR #18880](https://github.com/BerriAI/litellm/pull/18880)
+
+#### Bugs
+
+- **UI Fixes**
+    - Fix Key Creation MCP Settings Submit Form Unintentionally - [PR #18355](https://github.com/BerriAI/litellm/pull/18355)
+    - Fix UI Disappears in Development Environments - [PR #18399](https://github.com/BerriAI/litellm/pull/18399)
+    - Fix Disable Admin UI Flag - [PR #18397](https://github.com/BerriAI/litellm/pull/18397)
+    - Remove Model Analytics From Model Page - [PR #18552](https://github.com/BerriAI/litellm/pull/18552)
+    - Useful Links Remove Modal on Adding Links - [PR #18602](https://github.com/BerriAI/litellm/pull/18602)
+    - SSO Edit Modal Clear Role Mapping Values on Provider Change - [PR #18680](https://github.com/BerriAI/litellm/pull/18680)
+    - UI Login Case Sensitivity fix - [PR #18877](https://github.com/BerriAI/litellm/pull/18877)
+- **API Fixes**
+    - Fix User Invite & Key Generation Email Notification Logic - [PR #18524](https://github.com/BerriAI/litellm/pull/18524)
+    - Normalize Proxy Config Callback - [PR #18775](https://github.com/BerriAI/litellm/pull/18775)
+    - Return empty data array instead of 500 when no models configured - [PR #18556](https://github.com/BerriAI/litellm/pull/18556)
+    - Enforce org level max budget - [PR #18813](https://github.com/BerriAI/litellm/pull/18813)
+
+---
+
+## AI Integrations
+
+### New Integrations (4 new integrations)
+
+| Integration | Type | Description |
+| ----------- | ---- | ----------- |
+| [Focus](../../docs/observability/focus) | Logging | Focus export support for observability - [PR #18802](https://github.com/BerriAI/litellm/pull/18802) |
+| [SigNoz](../../docs/observability/signoz) | Logging | SigNoz integration for observability - [PR #18726](https://github.com/BerriAI/litellm/pull/18726) |
+| [Qualifire](../../docs/proxy/guardrails/qualifire) | Guardrails | Qualifire guardrails and eval webhook - [PR #18594](https://github.com/BerriAI/litellm/pull/18594) |
+| [Levo AI](../../docs/observability/levo_integration) | Guardrails | Levo AI integration for security - [PR #18529](https://github.com/BerriAI/litellm/pull/18529) |
+
+### Logging
+
+- **[DataDog](../../docs/proxy/logging#datadog)**
+    - Fix span kind fallback when parent_id missing - [PR #18418](https://github.com/BerriAI/litellm/pull/18418)
+- **[Langfuse](../../docs/proxy/logging#langfuse)**
+    - Map Gemini cached_tokens to Langfuse cache_read_input_tokens - [PR #18614](https://github.com/BerriAI/litellm/pull/18614)
+- **[Prometheus](../../docs/proxy/logging#prometheus)**
+    - Align prometheus metric names with DEFINED_PROMETHEUS_METRICS - [PR #18463](https://github.com/BerriAI/litellm/pull/18463)
+    - Add Prometheus metrics for request queue time and guardrails - [PR #17973](https://github.com/BerriAI/litellm/pull/17973)
+    - Add caching metrics for cache hits, misses, and tokens - [PR #18755](https://github.com/BerriAI/litellm/pull/18755)
+    - Skip metrics for invalid API key requests - [PR #18788](https://github.com/BerriAI/litellm/pull/18788)
+- **[Braintrust](../../docs/proxy/logging#braintrust)**
+    - Pass span_attributes in async logging and skip tags on non-root spans - [PR #18409](https://github.com/BerriAI/litellm/pull/18409)
+- **[CloudZero](../../docs/proxy/logging#cloudzero)**
+    - Add user email to CloudZero - [PR #18584](https://github.com/BerriAI/litellm/pull/18584)
+- **[OpenTelemetry](../../docs/proxy/logging#opentelemetry)**
+    - Use already configured opentelemetry providers - [PR #18279](https://github.com/BerriAI/litellm/pull/18279)
+    - Prevent LiteLLM from closing external OTEL spans - [PR #18553](https://github.com/BerriAI/litellm/pull/18553)
+    - Allow configuring arize project name for OpenTelemetry service name - [PR #18738](https://github.com/BerriAI/litellm/pull/18738)
+- **[LangSmith](../../docs/proxy/logging#langsmith)**
+    - Add support for LangSmith organization-scoped API keys with tenant ID - [PR #18623](https://github.com/BerriAI/litellm/pull/18623)
+- **[Generic API Logger](../../docs/proxy/logging#generic-api-logger)**
+    - Add log_format option to GenericAPILogger - [PR #18587](https://github.com/BerriAI/litellm/pull/18587)
+
+### Guardrails
+
+- **[Content Filter](../../docs/proxy/guardrails/litellm_content_filter)**
+    - Add content filter logs page - [PR #18335](https://github.com/BerriAI/litellm/pull/18335)
+    - Log actual event type for guardrails - [PR #18489](https://github.com/BerriAI/litellm/pull/18489)
+- **[Qualifire](../../docs/proxy/guardrails/qualifire)**
+    - Add Qualifire eval webhook - [PR #18836](https://github.com/BerriAI/litellm/pull/18836)
+- **[Lasso Security](../../docs/proxy/guardrails/lasso_security)**
+    - Add Lasso guardrail API docs - [PR #18652](https://github.com/BerriAI/litellm/pull/18652)
+- **[Noma Security](../../docs/proxy/guardrails/noma_security)**
+    - Add MCP guardrail support for Noma - [PR #18668](https://github.com/BerriAI/litellm/pull/18668)
+- **[Bedrock Guardrails](../../docs/proxy/guardrails/bedrock)**
+    - Remove redundant Bedrock guardrail block handling - [PR #18634](https://github.com/BerriAI/litellm/pull/18634)
+- **General**
+    - Generic guardrail API update - [PR #18647](https://github.com/BerriAI/litellm/pull/18647)
+    - Prevent proxy startup failures from case-sensitive tool permission guardrail validation - [PR #18662](https://github.com/BerriAI/litellm/pull/18662)
+    - Extend case normalization to ALL guardrail types - [PR #18664](https://github.com/BerriAI/litellm/pull/18664)
+    - Fix MCP handling in unified guardrail - [PR #18630](https://github.com/BerriAI/litellm/pull/18630)
+    - Fix embeddings calltype for guardrail precallhook - [PR #18740](https://github.com/BerriAI/litellm/pull/18740)
+
+---
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- **Platform Fee / Margins** - Add support for Platform Fee / Margins - [PR #18427](https://github.com/BerriAI/litellm/pull/18427)
+- **Negative Budget Validation** - Add validation for negative budget - [PR #18583](https://github.com/BerriAI/litellm/pull/18583)
+- **Cost Calculation Fixes**
+    - Correct cost calculation when reasoning_tokens are without text_tokens - [PR #18607](https://github.com/BerriAI/litellm/pull/18607)
+    - Fix background cost tracking tests - [PR #18588](https://github.com/BerriAI/litellm/pull/18588)
+- **Tag Routing** - Support toggling tag matching between ANY and ALL - [PR #18776](https://github.com/BerriAI/litellm/pull/18776)
+
+---
+
+## MCP Gateway
+
+- **MCP Global Mode** - Add MCP global mode - [PR #18639](https://github.com/BerriAI/litellm/pull/18639)
+- **MCP Server Visibility** - Add configurable MCP server visibility - [PR #18681](https://github.com/BerriAI/litellm/pull/18681)
+- **MCP Registry** - Add MCP registry - [PR #18850](https://github.com/BerriAI/litellm/pull/18850)
+- **MCP Stdio Header** - Support MCP stdio header env overrides - [PR #18324](https://github.com/BerriAI/litellm/pull/18324)
+- **Parallel Tool Fetching** - Parallelize tool fetching from multiple MCP servers - [PR #18627](https://github.com/BerriAI/litellm/pull/18627)
+- **Optimize MCP Server Listing** - Separate health checks for optimized listing - [PR #18530](https://github.com/BerriAI/litellm/pull/18530)
+- **Auth Improvements**
+    - Require auth for MCP connection test endpoint - [PR #18290](https://github.com/BerriAI/litellm/pull/18290)
+    - Fix MCP gateway OAuth2 auth issues and ClosedResourceError - [PR #18281](https://github.com/BerriAI/litellm/pull/18281)
+- **Bug Fixes**
+    - Fix MCP server health status reporting - [PR #18443](https://github.com/BerriAI/litellm/pull/18443)
+    - Fix OpenAPI to MCP tool conversion - [PR #18597](https://github.com/BerriAI/litellm/pull/18597)
+    - Remove exec() usage and handle invalid OpenAPI parameter names for security - [PR #18480](https://github.com/BerriAI/litellm/pull/18480)
+    - Fix MCP error when using multiple servers simultaneously - [PR #18855](https://github.com/BerriAI/litellm/pull/18855)
+- **Migrate MCP Fetching Logic to React Query** - [PR #18352](https://github.com/BerriAI/litellm/pull/18352)
+
+---
+
+## Performance / Loadbalancing / Reliability improvements
+
+- **92.7% Faster Provider Config Lookup** - LiteLLM now stresses LLM providers 2.5x more - [PR #18867](https://github.com/BerriAI/litellm/pull/18867)
+- **Lazy Loading Improvements**
+    - Consolidate lazy import handlers with registry pattern - [PR #18389](https://github.com/BerriAI/litellm/pull/18389)
+    - Complete lazy loading migration for all 180+ LLM config classes - [PR #18392](https://github.com/BerriAI/litellm/pull/18392)
+    - Lazy load additional components (types, callbacks, utilities) - [PR #18396](https://github.com/BerriAI/litellm/pull/18396)
+    - Add lazy loading for get_llm_provider - [PR #18591](https://github.com/BerriAI/litellm/pull/18591)
+    - Lazy-load heavy audio library and loggers - [PR #18592](https://github.com/BerriAI/litellm/pull/18592)
+    - Lazy load 9 heavy imports in litellm/utils.py - [PR #18595](https://github.com/BerriAI/litellm/pull/18595)
+    - Lazy load heavy imports to improve import time and memory usage - [PR #18610](https://github.com/BerriAI/litellm/pull/18610)
+    - Implement lazy loading for provider configs, model info classes, streaming handlers - [PR #18611](https://github.com/BerriAI/litellm/pull/18611)
+    - Lazy load 15 additional imports - [PR #18613](https://github.com/BerriAI/litellm/pull/18613)
+    - Lazy load 15+ unused imports - [PR #18616](https://github.com/BerriAI/litellm/pull/18616)
+    - Lazy load DatadogLLMObsInitParams - [PR #18658](https://github.com/BerriAI/litellm/pull/18658)
+    - Migrate utils.py lazy imports to registry pattern - [PR #18657](https://github.com/BerriAI/litellm/pull/18657)
+    - Lazy load get_llm_provider and remove_index_from_tool_calls - [PR #18608](https://github.com/BerriAI/litellm/pull/18608)
+- **Router Improvements**
+    - Validate routing_strategy at startup to fail fast with helpful error - [PR #18624](https://github.com/BerriAI/litellm/pull/18624)
+    - Correct num_retries tracking in retry logic - [PR #18712](https://github.com/BerriAI/litellm/pull/18712)
+    - Improve error messages and validation for wildcard routing with multiple credentials - [PR #18629](https://github.com/BerriAI/litellm/pull/18629)
+- **Memory Improvements**
+    - Add memory pattern detection test and fix bad memory patterns - [PR #18589](https://github.com/BerriAI/litellm/pull/18589)
+    - Add unbounded data structure detection to memory test - [PR #18590](https://github.com/BerriAI/litellm/pull/18590)
+    - Add memory leak detection tests with CI integration - [PR #18881](https://github.com/BerriAI/litellm/pull/18881)
+- **Database**
+    - Add idx on LOWER(user_email) for faster duplicate email checks - [PR #18828](https://github.com/BerriAI/litellm/pull/18828)
+    - Proactive RDS IAM token refresh to prevent 15-min connection failed - [PR #18795](https://github.com/BerriAI/litellm/pull/18795)
+    - Clarify database_connection_pool_limit applies per worker - [PR #18780](https://github.com/BerriAI/litellm/pull/18780)
+    - Make base_connection_pool_limit default value the same - [PR #18721](https://github.com/BerriAI/litellm/pull/18721)
+- **Docker**
+    - Add libsndfile to database Docker image for audio processing - [PR #18612](https://github.com/BerriAI/litellm/pull/18612)
+    - Add line_profiler support for performance analysis and fix Windows CRLF issues - [PR #18773](https://github.com/BerriAI/litellm/pull/18773)
+- **Helm**
+    - Add lifecycle support to Helm charts - [PR #18517](https://github.com/BerriAI/litellm/pull/18517)
+- **Authentication**
+    - Add Kubernetes ServiceAccount JWT authentication support - [PR #18055](https://github.com/BerriAI/litellm/pull/18055)
+    - Use async anthropic client to prevent event loop blocking - [PR #18435](https://github.com/BerriAI/litellm/pull/18435)
+- **Logging Worker**
+    - Handle event loop changes in multiprocessing - [PR #18423](https://github.com/BerriAI/litellm/pull/18423)
+- **Security**
+    - Prevent expired key plaintext leak in error response - [PR #18860](https://github.com/BerriAI/litellm/pull/18860)
+    - Mask extra header secrets in model info - [PR #18822](https://github.com/BerriAI/litellm/pull/18822)
+    - Prevent duplicate User-Agent tags in request_tags - [PR #18723](https://github.com/BerriAI/litellm/pull/18723)
+    - Properly use litellm api keys - [PR #18832](https://github.com/BerriAI/litellm/pull/18832)
+- **Misc**
+    - Remove double imports in main.py - [PR #18406](https://github.com/BerriAI/litellm/pull/18406)
+    - Add LITELLM_DISABLE_LAZY_LOADING env var to fix VCR cassette creation issue - [PR #18725](https://github.com/BerriAI/litellm/pull/18725)
+    - Add xiaomi_mimo to LlmProviders enum to fix router support - [PR #18819](https://github.com/BerriAI/litellm/pull/18819)
+    - Allow installation with current grpcio on old Python - [PR #18473](https://github.com/BerriAI/litellm/pull/18473)
+    - Add Custom CA certificates to boto3 clients - [PR #18852](https://github.com/BerriAI/litellm/pull/18852)
+    - Fix bedrock_cache, metadata and max_model_budget - [PR #18872](https://github.com/BerriAI/litellm/pull/18872)
+    - Fix LiteLLM SDK embedding headers missing field - [PR #18844](https://github.com/BerriAI/litellm/pull/18844)
+    - Put automatic reasoning summary inclusion behind feat flag - [PR #18688](https://github.com/BerriAI/litellm/pull/18688)
+    - turn_off_message_logging Does Not Redact Request Messages in proxy_server_request Field - [PR #18897](https://github.com/BerriAI/litellm/pull/18897)
+
+---
+
+## Documentation Updates
+
+- **Provider Documentation**
+    - Update MiniMax docs to be in proper format - [PR #18403](https://github.com/BerriAI/litellm/pull/18403)
+    - Add docs for 5 AI providers - [PR #18388](https://github.com/BerriAI/litellm/pull/18388)
+    - Fix gpt-5-mini reasoning_effort supported values - [PR #18346](https://github.com/BerriAI/litellm/pull/18346)
+    - Fix PDF documentation inconsistency in Anthropic page - [PR #18816](https://github.com/BerriAI/litellm/pull/18816)
+    - Update OpenRouter docs to include embedding support - [PR #18874](https://github.com/BerriAI/litellm/pull/18874)
+    - Add LITELLM_REASONING_AUTO_SUMMARY in doc - [PR #18705](https://github.com/BerriAI/litellm/pull/18705)
+- **MCP Documentation**
+    - Agentcore MCP server docs - [PR #18603](https://github.com/BerriAI/litellm/pull/18603)
+    - Mention MCP prompt/resources types in overview - [PR #18669](https://github.com/BerriAI/litellm/pull/18669)
+    - Add Focus docs - [PR #18837](https://github.com/BerriAI/litellm/pull/18837)
+- **Guardrails Documentation**
+    - Qualifire docs hotfix - [PR #18724](https://github.com/BerriAI/litellm/pull/18724)
+- **Infrastructure Documentation**
+    - IAM Roles Anywhere docs - [PR #18559](https://github.com/BerriAI/litellm/pull/18559)
+    - Fix formatting in proxy configs documentation - [PR #18498](https://github.com/BerriAI/litellm/pull/18498)
+    - Fix GCS cache docs missing for proxy mode - [PR #13328](https://github.com/BerriAI/litellm/pull/13328)
+    - Fix how to execute cloudzero sql - [PR #18841](https://github.com/BerriAI/litellm/pull/18841)
+- **General**
+    - LiteLLM adopters section - [PR #18605](https://github.com/BerriAI/litellm/pull/18605)
+    - Remove redundant comments about setting litellm.callbacks - [PR #18711](https://github.com/BerriAI/litellm/pull/18711)
+    - Update header to be markdown bold by removing space - [PR #18846](https://github.com/BerriAI/litellm/pull/18846)
+    - Manus docs - new provider - [PR #18817](https://github.com/BerriAI/litellm/pull/18817)
+
+---
+
+## New Contributors
+
+* @prasadkona made their first contribution in [PR #18349](https://github.com/BerriAI/litellm/pull/18349)
+* @lucasrothman made their first contribution in [PR #18283](https://github.com/BerriAI/litellm/pull/18283)
+* @aggeentik made their first contribution in [PR #18317](https://github.com/BerriAI/litellm/pull/18317)
+* @mihidumh made their first contribution in [PR #18361](https://github.com/BerriAI/litellm/pull/18361)
+* @Prazeina made their first contribution in [PR #18498](https://github.com/BerriAI/litellm/pull/18498)
+* @systec-dk made their first contribution in [PR #18500](https://github.com/BerriAI/litellm/pull/18500)
+* @xuan07t2 made their first contribution in [PR #18514](https://github.com/BerriAI/litellm/pull/18514)
+* @RensDimmendaal made their first contribution in [PR #18190](https://github.com/BerriAI/litellm/pull/18190)
+* @yurekami made their first contribution in [PR #18483](https://github.com/BerriAI/litellm/pull/18483)
+* @agertz7 made their first contribution in [PR #18556](https://github.com/BerriAI/litellm/pull/18556)
+* @yudelevi made their first contribution in [PR #18550](https://github.com/BerriAI/litellm/pull/18550)
+* @smallp made their first contribution in [PR #18536](https://github.com/BerriAI/litellm/pull/18536)
+* @kevinpauer made their first contribution in [PR #18569](https://github.com/BerriAI/litellm/pull/18569)
+* @cansakiroglu made their first contribution in [PR #18517](https://github.com/BerriAI/litellm/pull/18517)
+* @dee-walia20 made their first contribution in [PR #18432](https://github.com/BerriAI/litellm/pull/18432)
+* @luxinfeng made their first contribution in [PR #18477](https://github.com/BerriAI/litellm/pull/18477)
+* @cantalupo555 made their first contribution in [PR #18476](https://github.com/BerriAI/litellm/pull/18476)
+* @andersk made their first contribution in [PR #18473](https://github.com/BerriAI/litellm/pull/18473)
+* @majiayu000 made their first contribution in [PR #18467](https://github.com/BerriAI/litellm/pull/18467)
+* @amangupta-20 made their first contribution in [PR #18529](https://github.com/BerriAI/litellm/pull/18529)
+* @hamzaq453 made their first contribution in [PR #18480](https://github.com/BerriAI/litellm/pull/18480)
+* @ktsaou made their first contribution in [PR #18627](https://github.com/BerriAI/litellm/pull/18627)
+* @FlibbertyGibbitz made their first contribution in [PR #18624](https://github.com/BerriAI/litellm/pull/18624)
+* @drorIvry made their first contribution in [PR #18594](https://github.com/BerriAI/litellm/pull/18594)
+* @urainshah made their first contribution in [PR #18524](https://github.com/BerriAI/litellm/pull/18524)
+* @mangabits made their first contribution in [PR #18279](https://github.com/BerriAI/litellm/pull/18279)
+* @0717376 made their first contribution in [PR #18564](https://github.com/BerriAI/litellm/pull/18564)
+* @nmgarza5 made their first contribution in [PR #17330](https://github.com/BerriAI/litellm/pull/17330)
+* @wileykestner made their first contribution in [PR #18445](https://github.com/BerriAI/litellm/pull/18445)
+* @minijeong-log made their first contribution in [PR #14440](https://github.com/BerriAI/litellm/pull/14440)
+* @Isaac4real made their first contribution in [PR #18710](https://github.com/BerriAI/litellm/pull/18710)
+* @marukaz made their first contribution in [PR #18711](https://github.com/BerriAI/litellm/pull/18711)
+* @rohitravirane made their first contribution in [PR #18712](https://github.com/BerriAI/litellm/pull/18712)
+* @lizzzcai made their first contribution in [PR #18714](https://github.com/BerriAI/litellm/pull/18714)
+* @hkd987 made their first contribution in [PR #18673](https://github.com/BerriAI/litellm/pull/18673)
+* @Mr-Pepe made their first contribution in [PR #18674](https://github.com/BerriAI/litellm/pull/18674)
+* @gkarthi-signoz made their first contribution in [PR #18726](https://github.com/BerriAI/litellm/pull/18726)
+* @Tianduo16 made their first contribution in [PR #18723](https://github.com/BerriAI/litellm/pull/18723)
+* @wilsonjr made their first contribution in [PR #18721](https://github.com/BerriAI/litellm/pull/18721)
+* @abliteration-ai made their first contribution in [PR #18678](https://github.com/BerriAI/litellm/pull/18678)
+* @danialkhan02 made their first contribution in [PR #18770](https://github.com/BerriAI/litellm/pull/18770)
+* @ihower made their first contribution in [PR #18409](https://github.com/BerriAI/litellm/pull/18409)
+* @elkkhan made their first contribution in [PR #18391](https://github.com/BerriAI/litellm/pull/18391)
+* @runixer made their first contribution in [PR #18435](https://github.com/BerriAI/litellm/pull/18435)
+* @choby-shun made their first contribution in [PR #18776](https://github.com/BerriAI/litellm/pull/18776)
+* @jutaz made their first contribution in [PR #18853](https://github.com/BerriAI/litellm/pull/18853)
+* @sjmatta made their first contribution in [PR #18250](https://github.com/BerriAI/litellm/pull/18250)
+* @andres-ortizl made their first contribution in [PR #18856](https://github.com/BerriAI/litellm/pull/18856)
+* @gauthiermartin made their first contribution in [PR #18844](https://github.com/BerriAI/litellm/pull/18844)
+* @mel2oo made their first contribution in [PR #18845](https://github.com/BerriAI/litellm/pull/18845)
+* @DominikHallab made their first contribution in [PR #18846](https://github.com/BerriAI/litellm/pull/18846)
+* @ji-chuan-che made their first contribution in [PR #18540](https://github.com/BerriAI/litellm/pull/18540)
+* @raghav-stripe made their first contribution in [PR #18858](https://github.com/BerriAI/litellm/pull/18858)
+* @akraines made their first contribution in [PR #18629](https://github.com/BerriAI/litellm/pull/18629)
+* @otaviofbrito made their first contribution in [PR #18665](https://github.com/BerriAI/litellm/pull/18665)
+* @chetanchoudhary-sumo made their first contribution in [PR #18587](https://github.com/BerriAI/litellm/pull/18587)
+* @pascalwhoop made their first contribution in [PR #13328](https://github.com/BerriAI/litellm/pull/13328)
+* @orgersh92 made their first contribution in [PR #18652](https://github.com/BerriAI/litellm/pull/18652)
+* @DevajMody made their first contribution in [PR #18497](https://github.com/BerriAI/litellm/pull/18497)
+* @matt-greathouse made their first contribution in [PR #18247](https://github.com/BerriAI/litellm/pull/18247)
+* @emerzon made their first contribution in [PR #18290](https://github.com/BerriAI/litellm/pull/18290)
+* @Eric84626 made their first contribution in [PR #18281](https://github.com/BerriAI/litellm/pull/18281)
+* @LukasdeBoer made their first contribution in [PR #18055](https://github.com/BerriAI/litellm/pull/18055)
+* @LingXuanYin made their first contribution in [PR #18513](https://github.com/BerriAI/litellm/pull/18513)
+* @krisxia0506 made their first contribution in [PR #18698](https://github.com/BerriAI/litellm/pull/18698)
+* @LouisShark made their first contribution in [PR #18414](https://github.com/BerriAI/litellm/pull/18414)
+
+---
+
+## Full Changelog
+
+**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/compare/v1.80.11.rc.1...v1.80.14.rc.1)**
+
+
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 482d855082e..2e8ea07ab75 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -55,6 +55,7 @@ const sidebars = {
         "proxy/guardrails/test_playground",
         "proxy/guardrails/litellm_content_filter",
         ...[
+          "proxy/guardrails/qualifire",
           "proxy/guardrails/aim_security",
           "proxy/guardrails/onyx_security",
           "proxy/guardrails/aporia_api",
@@ -653,12 +654,13 @@ const sidebars = {
             "providers/bedrock_writer",
             "providers/bedrock_batches",
             "providers/aws_polly",
-            "providers/bedrock_vector_store",
-          ]
-        },
-        "providers/litellm_proxy",
-        "providers/ai21",
-        "providers/aiml",
+        "providers/bedrock_vector_store",
+      ]
+    },
+    "providers/litellm_proxy",
+    "providers/abliteration",
+    "providers/ai21",
+    "providers/aiml",
         "providers/aleph_alpha",
         "providers/amazon_nova",
         "providers/anyscale",
@@ -710,6 +712,7 @@ const sidebars = {
         "providers/llamafile",
         "providers/llamagate",
         "providers/lm_studio",
+        "providers/manus",
         "providers/meta_llama",
         "providers/milvus_vector_stores",
         "providers/mistral",
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.21-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.21-py3-none-any.whl
new file mode 100644
index 00000000000..650b89963e5
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.21-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.21.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.21.tar.gz
new file mode 100644
index 00000000000..d06f5a75976
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.21.tar.gz differ
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260108_add_user_email_lower_idx/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260108_add_user_email_lower_idx/migration.sql
new file mode 100644
index 00000000000..add80b39e7f
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260108_add_user_email_lower_idx/migration.sql
@@ -0,0 +1,9 @@
+-- CreateIndex
+-- Fixes performance issue in _check_duplicate_user_email function
+-- by enabling fast case-insensitive email lookups.
+-- 
+-- Without this index, queries with mode: "insensitive" cause full table scans.
+-- With this index, PostgreSQL can use an Index Scan for O(log n) performance.
+--
+-- Related: GitHub Issue #18411
+CREATE INDEX "LiteLLM_UserTable_user_email_lower_idx" ON "LiteLLM_UserTable"(LOWER("user_email"));
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 7eccab254e3..2952aa6c979 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm-proxy-extras"
-version = "0.4.20"
+version = "0.4.21"
 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 = "0.4.20"
+version = "0.4.21"
 version_files = [
     "pyproject.toml:version",
     "../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index 1bc690e561f..2b7d26de129 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -9,7 +9,7 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*
 warnings.filterwarnings(
     "ignore", message=".*Accessing the.*attribute on the instance is deprecated.*"
 )
-### INIT VARIABLES #######################
+### INIT VARIABLES ########################
 import threading
 import os
 from typing import (
@@ -134,6 +134,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
     "bitbucket",
     "gitlab",
     "cloudzero",
+    "focus",
     "posthog",
     "levo",
 ]
@@ -486,6 +487,7 @@ vertex_mistral_models: Set = set()
 vertex_openai_models: Set = set()
 vertex_minimax_models: Set = set()
 vertex_moonshot_models: Set = set()
+vertex_zai_models: Set = set()
 ai21_models: Set = set()
 ai21_chat_models: Set = set()
 nlp_cloud_models: Set = set()
@@ -664,6 +666,9 @@ def add_known_models():
         elif value.get("litellm_provider") == "vertex_ai-moonshot_models":
             key = key.replace("vertex_ai/", "")
             vertex_moonshot_models.add(key)
+        elif value.get("litellm_provider") == "vertex_ai-zai_models":
+            key = key.replace("vertex_ai/", "")
+            vertex_zai_models.add(key)
         elif value.get("litellm_provider") == "ai21":
             if value.get("mode") == "chat":
                 ai21_chat_models.add(key)
@@ -950,7 +955,8 @@ models_by_provider: dict = {
     | vertex_language_models
     | vertex_deepseek_models
     | vertex_minimax_models
-    | vertex_moonshot_models,
+    | vertex_moonshot_models
+    | vertex_zai_models,
     "ai21": ai21_models,
     "bedrock": bedrock_models | bedrock_converse_models,
     "petals": petals_models,
@@ -1338,6 +1344,7 @@ if TYPE_CHECKING:
     from .llms.bedrock.chat.invoke_transformations.amazon_llama_transformation import AmazonLlamaConfig as AmazonLlamaConfig
     from .llms.bedrock.chat.invoke_transformations.amazon_deepseek_transformation import AmazonDeepSeekR1Config as AmazonDeepSeekR1Config
     from .llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation import AmazonMistralConfig as AmazonMistralConfig
+    from .llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import AmazonMoonshotConfig as AmazonMoonshotConfig
     from .llms.bedrock.chat.invoke_transformations.amazon_titan_transformation import AmazonTitanConfig as AmazonTitanConfig
     from .llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation import AmazonTwelveLabsPegasusConfig as AmazonTwelveLabsPegasusConfig
     from .llms.bedrock.chat.invoke_transformations.base_invoke_transformation import AmazonInvokeConfig as AmazonInvokeConfig
@@ -1367,6 +1374,7 @@ if TYPE_CHECKING:
     from .llms.azure.responses.o_series_transformation import AzureOpenAIOSeriesResponsesAPIConfig as AzureOpenAIOSeriesResponsesAPIConfig
     from .llms.xai.responses.transformation import XAIResponsesAPIConfig as XAIResponsesAPIConfig
     from .llms.litellm_proxy.responses.transformation import LiteLLMProxyResponsesAPIConfig as LiteLLMProxyResponsesAPIConfig
+    from .llms.manus.responses.transformation import ManusResponsesAPIConfig as ManusResponsesAPIConfig
     from .llms.gemini.interactions.transformation import GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig
     from .llms.openai.chat.o_series_transformation import OpenAIOSeriesConfig as OpenAIOSeriesConfig, OpenAIOSeriesConfig as OpenAIO1Config
     from .llms.anthropic.skills.transformation import AnthropicSkillsConfig as AnthropicSkillsConfig
diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py
index 26133ebc222..f37c4dc6d04 100644
--- a/litellm/_lazy_imports_registry.py
+++ b/litellm/_lazy_imports_registry.py
@@ -165,6 +165,7 @@ LLM_CONFIG_NAMES = (
     "AmazonLlamaConfig",
     "AmazonDeepSeekR1Config",
     "AmazonMistralConfig",
+    "AmazonMoonshotConfig",
     "AmazonTitanConfig",
     "AmazonTwelveLabsPegasusConfig",
     "AmazonInvokeConfig",
@@ -252,6 +253,7 @@ LLM_CONFIG_NAMES = (
     "IBMWatsonXAudioTranscriptionConfig",
     "GithubCopilotConfig",
     "GithubCopilotResponsesAPIConfig",
+    "ManusResponsesAPIConfig",
     "GithubCopilotEmbeddingConfig",
     "NebiusConfig",
     "WandbConfig",
@@ -556,6 +558,7 @@ _LLM_CONFIGS_IMPORT_MAP = {
     "AmazonLlamaConfig": (".llms.bedrock.chat.invoke_transformations.amazon_llama_transformation", "AmazonLlamaConfig"),
     "AmazonDeepSeekR1Config": (".llms.bedrock.chat.invoke_transformations.amazon_deepseek_transformation", "AmazonDeepSeekR1Config"),
     "AmazonMistralConfig": (".llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation", "AmazonMistralConfig"),
+    "AmazonMoonshotConfig": (".llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation", "AmazonMoonshotConfig"),
     "AmazonTitanConfig": (".llms.bedrock.chat.invoke_transformations.amazon_titan_transformation", "AmazonTitanConfig"),
     "AmazonTwelveLabsPegasusConfig": (".llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation", "AmazonTwelveLabsPegasusConfig"),
     "AmazonInvokeConfig": (".llms.bedrock.chat.invoke_transformations.base_invoke_transformation", "AmazonInvokeConfig"),
@@ -588,6 +591,7 @@ _LLM_CONFIGS_IMPORT_MAP = {
     "AzureOpenAIOSeriesResponsesAPIConfig": (".llms.azure.responses.o_series_transformation", "AzureOpenAIOSeriesResponsesAPIConfig"),
     "XAIResponsesAPIConfig": (".llms.xai.responses.transformation", "XAIResponsesAPIConfig"),
     "LiteLLMProxyResponsesAPIConfig": (".llms.litellm_proxy.responses.transformation", "LiteLLMProxyResponsesAPIConfig"),
+    "ManusResponsesAPIConfig": (".llms.manus.responses.transformation", "ManusResponsesAPIConfig"),
     "GoogleAIStudioInteractionsConfig": (".llms.gemini.interactions.transformation", "GoogleAIStudioInteractionsConfig"),
     "OpenAIOSeriesConfig": (".llms.openai.chat.o_series_transformation", "OpenAIOSeriesConfig"),
     "AnthropicSkillsConfig": (".llms.anthropic.skills.transformation", "AnthropicSkillsConfig"),
diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py
index a89efc4e82b..af8185aa215 100644
--- a/litellm/completion_extras/litellm_responses_transformation/transformation.py
+++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py
@@ -31,6 +31,7 @@ from litellm.llms.base_llm.bridges.completion_transformation import (
     CompletionTransformationBridge,
 )
 from litellm.types.llms.openai import (
+    ChatCompletionAnnotation,
     ChatCompletionToolParamFunctionChunk,
     Reasoning,
     ResponsesAPIOptionalRequestParams,
@@ -90,9 +91,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                     content_type = content_item.get("type")
                     if content_type == "output_text":
                         response_text = content_item.get("text", "")
+                        # Extract annotations from content if present
+                        annotations = LiteLLMResponsesTransformationHandler._convert_annotations_to_chat_format(
+                            content_item.get("annotations", None)
+                        )
                         msg = Message(
                             role=item.get("role", "assistant"),
                             content=response_text if response_text else "",
+                            annotations=annotations,
                         )
                         choice = Choices(message=msg, finish_reason="stop", index=index)
                         return choice, index + 1
@@ -364,10 +370,16 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
             elif isinstance(item, ResponseOutputMessage):
                 for content in item.content:
                     response_text = getattr(content, "text", "")
+                    # Extract annotations from content if present
+                    raw_annotations = getattr(content, "annotations", None)
+                    annotations = LiteLLMResponsesTransformationHandler._convert_annotations_to_chat_format(
+                        raw_annotations
+                    )
                     msg = Message(
                         role=item.role,
                         content=response_text if response_text else "",
                         reasoning_content=reasoning_content,
+                        annotations=annotations,
                     )
 
                     choices.append(
@@ -763,6 +775,42 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                 return {"format": {"type": "text"}}
 
         return None
+    
+    @staticmethod
+    def _convert_annotations_to_chat_format(
+        annotations: Optional[List[Any]],
+    ) -> Optional[List["ChatCompletionAnnotation"]]:
+        """
+        Convert annotations from Responses API to Chat Completions format.
+        
+        Annotations are already in compatible format between both APIs,
+        so we just need to convert Pydantic models to dicts.
+        """
+        if not annotations:
+            return None
+
+        result: List[ChatCompletionAnnotation] = []
+        for annotation in annotations:
+            try:
+                # Convert Pydantic models to dicts (handles both v1 and v2)
+                if hasattr(annotation, "model_dump"):
+                    annotation_dict = annotation.model_dump()
+                elif hasattr(annotation, "dict"):
+                    annotation_dict = annotation.dict()
+                elif isinstance(annotation, dict):
+                    annotation_dict = annotation
+                else:
+                    # Skip unsupported annotation types
+                    verbose_logger.debug(f"Skipping unsupported annotation type: {type(annotation)}")
+                    continue
+
+                result.append(annotation_dict)  # type: ignore
+            except Exception as e:
+                # Skip malformed annotations
+                verbose_logger.debug(f"Skipping malformed annotation: {annotation}, error: {e}")
+                continue
+
+        return result if result else None
 
     def _map_responses_status_to_finish_reason(self, status: Optional[str]) -> str:
         """Map responses API status to chat completion finish_reason"""
diff --git a/litellm/constants.py b/litellm/constants.py
index db9d0114118..e24186d567e 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -909,6 +909,7 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
     "twelvelabs",
     "openai",
     "stability",
+    "moonshot",
 ]
 
 BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[
diff --git a/litellm/files/main.py b/litellm/files/main.py
index a7c82290c29..913ec84626d 100644
--- a/litellm/files/main.py
+++ b/litellm/files/main.py
@@ -8,6 +8,7 @@ https://platform.openai.com/docs/api-reference/files
 import asyncio
 import contextvars
 import os
+import time
 from functools import partial
 from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast
 
@@ -60,7 +61,7 @@ async def acreate_file(
     file: FileTypes,
     purpose: Literal["assistants", "batch", "fine-tune"],
     expires_after: Optional[FileExpiresAfter] = None,
-    custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "manus"] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -105,7 +106,7 @@ def create_file(
     file: FileTypes,
     purpose: Literal["assistants", "batch", "fine-tune"],
     expires_after: Optional[FileExpiresAfter] = None,
-    custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"]] = None,
+    custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "manus"]] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -274,7 +275,7 @@ def create_file(
             )
         else:
             raise litellm.exceptions.BadRequestError(
-                message="LiteLLM doesn't support {} for 'create_file'. Only ['openai', 'azure', 'vertex_ai'] are supported.".format(
+                message="LiteLLM doesn't support {} for 'create_file'. Only ['openai', 'azure', 'vertex_ai', 'manus'] are supported.".format(
                     custom_llm_provider
                 ),
                 model="n/a",
@@ -293,7 +294,7 @@ def create_file(
 @client
 async def afile_retrieve(
     file_id: str,
-    custom_llm_provider: Literal["openai", "azure", "hosted_vllm"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "manus"] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -334,7 +335,7 @@ async def afile_retrieve(
 @client
 def file_retrieve(
     file_id: str,
-    custom_llm_provider: Literal["openai", "azure", "hosted_vllm"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "manus"] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -428,18 +429,60 @@ def file_retrieve(
                 file_id=file_id,
             )
         else:
-            raise litellm.exceptions.BadRequestError(
-                message="LiteLLM doesn't support {} for 'file_retrieve'. Only 'openai' and 'azure' are supported.".format(
-                    custom_llm_provider
-                ),
-                model="n/a",
-                llm_provider=custom_llm_provider,
-                response=httpx.Response(
-                    status_code=400,
-                    content="Unsupported provider",
-                    request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"),  # type: ignore
-                ),
+            # Try using provider config pattern (for Manus, Bedrock, etc.)
+            provider_config = ProviderConfigManager.get_provider_files_config(
+                model="",
+                provider=LlmProviders(custom_llm_provider),
             )
+            if provider_config is not None:
+                litellm_params_dict = get_litellm_params(**kwargs)
+                litellm_params_dict["api_key"] = optional_params.api_key
+                litellm_params_dict["api_base"] = optional_params.api_base
+                
+                logging_obj = kwargs.get("litellm_logging_obj")
+                if logging_obj is None:
+                    from litellm.litellm_core_utils.litellm_logging import (
+                        Logging as LiteLLMLoggingObj,
+                    )
+                    logging_obj = LiteLLMLoggingObj(
+                        model="",
+                        messages=[],
+                        stream=False,
+                        call_type="afile_retrieve" if _is_async else "file_retrieve",
+                        start_time=time.time(),
+                        litellm_call_id=kwargs.get("litellm_call_id", str(uuid.uuid4())),
+                        function_id=str(kwargs.get("id") or ""),
+                    )
+                
+                client = kwargs.get("client")
+                response = base_llm_http_handler.retrieve_file(
+                    file_id=file_id,
+                    provider_config=provider_config,
+                    litellm_params=litellm_params_dict,
+                    headers=extra_headers or {},
+                    logging_obj=logging_obj,
+                    _is_async=_is_async,
+                    client=(
+                        client
+                        if client is not None
+                        and isinstance(client, (HTTPHandler, AsyncHTTPHandler))
+                        else None
+                    ),
+                    timeout=timeout,
+                )
+            else:
+                raise litellm.exceptions.BadRequestError(
+                    message="LiteLLM doesn't support {} for 'file_retrieve'. Only 'openai', 'azure', and 'manus' are supported.".format(
+                        custom_llm_provider
+                    ),
+                    model="n/a",
+                    llm_provider=custom_llm_provider,
+                    response=httpx.Response(
+                        status_code=400,
+                        content="Unsupported provider",
+                        request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"),  # type: ignore
+                    ),
+                )
 
         return cast(FileObject, response)
     except Exception as e:
@@ -450,7 +493,7 @@ def file_retrieve(
 @client
 async def afile_delete(
     file_id: str,
-    custom_llm_provider: Literal["openai", "azure"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "manus"] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -494,7 +537,7 @@ async def afile_delete(
 def file_delete(
     file_id: str,
     model: Optional[str] = None,
-    custom_llm_provider: Union[Literal["openai", "azure"], str] = "openai",
+    custom_llm_provider: Union[Literal["openai", "azure", "manus"], str] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -596,18 +639,58 @@ def file_delete(
                 litellm_params=litellm_params_dict,
             )
         else:
-            raise litellm.exceptions.BadRequestError(
-                message="LiteLLM doesn't support {} for 'delete_batch'. Only 'openai' is supported.".format(
-                    custom_llm_provider
-                ),
-                model="n/a",
-                llm_provider=custom_llm_provider,
-                response=httpx.Response(
-                    status_code=400,
-                    content="Unsupported provider",
-                    request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"),  # type: ignore
-                ),
+            # Try using provider config pattern (for Manus, Bedrock, etc.)
+            provider_config = ProviderConfigManager.get_provider_files_config(
+                model="",
+                provider=LlmProviders(custom_llm_provider),
             )
+            if provider_config is not None:
+                litellm_params_dict["api_key"] = optional_params.api_key
+                litellm_params_dict["api_base"] = optional_params.api_base
+                
+                logging_obj = kwargs.get("litellm_logging_obj")
+                if logging_obj is None:
+                    from litellm.litellm_core_utils.litellm_logging import (
+                        Logging as LiteLLMLoggingObj,
+                    )
+                    logging_obj = LiteLLMLoggingObj(
+                        model="",
+                        messages=[],
+                        stream=False,
+                        call_type="afile_delete" if _is_async else "file_delete",
+                        start_time=time.time(),
+                        litellm_call_id=kwargs.get("litellm_call_id", str(uuid.uuid4())),
+                        function_id=str(kwargs.get("id") or ""),
+                    )
+                
+                response = base_llm_http_handler.delete_file(
+                    file_id=file_id,
+                    provider_config=provider_config,
+                    litellm_params=litellm_params_dict,
+                    headers=extra_headers or {},
+                    logging_obj=logging_obj,
+                    _is_async=_is_async,
+                    client=(
+                        client
+                        if client is not None
+                        and isinstance(client, (HTTPHandler, AsyncHTTPHandler))
+                        else None
+                    ),
+                    timeout=timeout,
+                )
+            else:
+                raise litellm.exceptions.BadRequestError(
+                    message="LiteLLM doesn't support {} for 'file_delete'. Only 'openai', 'azure', and 'manus' are supported.".format(
+                        custom_llm_provider
+                    ),
+                    model="n/a",
+                    llm_provider=custom_llm_provider,
+                    response=httpx.Response(
+                        status_code=400,
+                        content="Unsupported provider",
+                        request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"),  # type: ignore
+                    ),
+                )
         return cast(FileDeleted, response)
     except Exception as e:
         raise e
@@ -616,7 +699,7 @@ def file_delete(
 # List files
 @client
 async def afile_list(
-    custom_llm_provider: Literal["openai", "azure"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "manus"] = "openai",
     purpose: Optional[str] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
@@ -657,7 +740,7 @@ async def afile_list(
 
 @client
 def file_list(
-    custom_llm_provider: Literal["openai", "azure"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "manus"] = "openai",
     purpose: Optional[str] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
@@ -687,7 +770,50 @@ def file_list(
             timeout = 600.0
 
         _is_async = kwargs.pop("is_async", False) is True
-        if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS:
+        
+        # Check if provider has a custom files config (e.g., Manus, Bedrock, Vertex AI)
+        provider_config = ProviderConfigManager.get_provider_files_config(
+            model="",
+            provider=LlmProviders(custom_llm_provider),
+        )
+        if provider_config is not None:
+            litellm_params_dict = get_litellm_params(**kwargs)
+            litellm_params_dict["api_key"] = optional_params.api_key
+            litellm_params_dict["api_base"] = optional_params.api_base
+            
+            logging_obj = kwargs.get("litellm_logging_obj")
+            if logging_obj is None:
+                from litellm.litellm_core_utils.litellm_logging import (
+                    Logging as LiteLLMLoggingObj,
+                )
+                logging_obj = LiteLLMLoggingObj(
+                    model="",
+                    messages=[],
+                    stream=False,
+                    call_type="afile_list" if _is_async else "file_list",
+                    start_time=time.time(),
+                    litellm_call_id=kwargs.get("litellm_call_id", str(uuid.uuid4())),
+                    function_id=str(kwargs.get("id", "")),
+                )
+            
+            client = kwargs.get("client")
+            response = base_llm_http_handler.list_files(
+                purpose=purpose,
+                provider_config=provider_config,
+                litellm_params=litellm_params_dict,
+                headers=extra_headers or {},
+                logging_obj=logging_obj,
+                _is_async=_is_async,
+                client=(
+                    client
+                    if client is not None
+                    and isinstance(client, (HTTPHandler, AsyncHTTPHandler))
+                    else None
+                ),
+                timeout=timeout,
+            )
+            return response
+        elif custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS:
             # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
             api_base = (
                 optional_params.api_base
@@ -752,7 +878,7 @@ def file_list(
             )
         else:
             raise litellm.exceptions.BadRequestError(
-                message="LiteLLM doesn't support {} for 'file_list'. Only 'openai' and 'azure' are supported.".format(
+                message="LiteLLM doesn't support {} for 'file_list'. Only 'openai', 'azure', and 'manus' are supported.".format(
                     custom_llm_provider
                 ),
                 model="n/a",
@@ -771,7 +897,7 @@ def file_list(
 @client
 async def afile_content(
     file_id: str,
-    custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic"] = "openai",
+    custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic", "manus"] = "openai",
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
@@ -816,7 +942,7 @@ def file_content(
     file_id: str,
     model: Optional[str] = None,
     custom_llm_provider: Optional[
-        Union[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic"], str]
+        Union[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic", "manus"], str]
     ] = None,
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
@@ -977,7 +1103,7 @@ def file_content(
             )
         else:
             raise litellm.exceptions.BadRequestError(
-                message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai', 'bedrock'.".format(
+                message="LiteLLM doesn't support {} for 'file_content'. Supported providers are 'openai', 'azure', 'vertex_ai', 'bedrock', 'manus'.".format(
                     custom_llm_provider
                 ),
                 model="n/a",
diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py
index 575c36b946a..209e03d2bda 100644
--- a/litellm/google_genai/adapters/handler.py
+++ b/litellm/google_genai/adapters/handler.py
@@ -37,9 +37,14 @@ class GenerateContentToCompletionHandler:
 
         completion_kwargs: Dict[str, Any] = dict(completion_request)
 
-        # feed metadata for custom callback
-        if extra_kwargs is not None and "metadata" in extra_kwargs:
-            completion_kwargs["metadata"] = extra_kwargs["metadata"]
+        # Forward extra_kwargs that should be passed to completion call
+        if extra_kwargs is not None:
+            # Forward metadata for custom callback
+            if "metadata" in extra_kwargs:
+                completion_kwargs["metadata"] = extra_kwargs["metadata"]
+            # Forward extra_headers for providers that require custom headers (e.g., github_copilot)
+            if "extra_headers" in extra_kwargs:
+                completion_kwargs["extra_headers"] = extra_kwargs["extra_headers"]
 
         if stream:
             completion_kwargs["stream"] = stream
diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py
index b7523ef8c16..9ec56c37170 100644
--- a/litellm/google_genai/main.py
+++ b/litellm/google_genai/main.py
@@ -130,6 +130,9 @@ class GenerateContentHelper:
             api_key=litellm_params.api_key,
         )
 
+        if litellm_params.custom_llm_provider is None:
+            litellm_params.custom_llm_provider = custom_llm_provider
+
         # get provider config
         generate_content_provider_config: Optional[
             BaseGoogleGenAIGenerateContentConfig
@@ -327,6 +330,7 @@ def generate_content(
                 tools=tools,
                 _is_async=_is_async,
                 litellm_params=setup_result.litellm_params,
+                extra_headers=extra_headers,
                 **kwargs,
             )
 
@@ -407,6 +411,9 @@ async def agenerate_content_stream(
 
         # Check if we should use the adapter (when provider config is None)
         if setup_result.generate_content_provider_config is None:
+            if "stream" in kwargs:
+                kwargs.pop("stream", None)
+
             # Use the adapter to convert to completion format
             return (
                 await GenerateContentToCompletionHandler.async_generate_content_handler(
@@ -416,6 +423,7 @@ async def agenerate_content_stream(
                     litellm_params=setup_result.litellm_params,
                     tools=tools,
                     stream=True,
+                    extra_headers=extra_headers,
                     **kwargs,
                 )
             )
@@ -490,6 +498,9 @@ def generate_content_stream(
 
         # Check if we should use the adapter (when provider config is None)
         if setup_result.generate_content_provider_config is None:
+            if "stream" in kwargs:
+                kwargs.pop("stream", None)
+
             # Use the adapter to convert to completion format
             return GenerateContentToCompletionHandler.generate_content_handler(
                 model=model,
@@ -498,6 +509,7 @@ def generate_content_stream(
                 _is_async=_is_async,
                 litellm_params=setup_result.litellm_params,
                 stream=True,
+                extra_headers=extra_headers,
                 **kwargs,
             )
 
diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py
index 364fa3f5def..585de510e8b 100644
--- a/litellm/integrations/braintrust_logging.py
+++ b/litellm/integrations/braintrust_logging.py
@@ -225,10 +225,13 @@ class BraintrustLogger(CustomLogger):
                 "id": litellm_call_id,
                 "input": prompt["messages"],
                 "metadata": standard_logging_object,
-                "tags": tags,
                 "span_attributes": {"name": span_name, "type": "llm"},
             }
-            
+
+            # Braintrust cannot specify 'tags' for non-root spans 
+            if dynamic_metadata.get("root_span_id") is None:
+                request_data["tags"] = tags
+
             # Only add those that are not None (or falsy)
             for key, value in span_attributes.items():
                 if value:
@@ -351,14 +354,37 @@ class BraintrustLogger(CustomLogger):
             # Allow metadata override for span name
             span_name = dynamic_metadata.get("span_name", "Chat Completion")
 
+            # Span parents is a special case
+            span_parents = dynamic_metadata.get("span_parents")
+
+            # Convert comma-separated string to list if present
+            if span_parents:
+                span_parents = [s.strip() for s in span_parents.split(",") if s.strip()]
+
+            # Add optional span attributes only if present
+            span_attributes = {
+                "span_id": dynamic_metadata.get("span_id"),
+                "root_span_id": dynamic_metadata.get("root_span_id"),
+                "span_parents": span_parents,
+            }
+
             request_data = {
                 "id": litellm_call_id,
                 "input": prompt["messages"],
                 "output": output,
                 "metadata": standard_logging_object,
-                "tags": tags,
                 "span_attributes": {"name": span_name, "type": "llm"},
             }
+
+            # Braintrust cannot specify 'tags' for non-root spans 
+            if dynamic_metadata.get("root_span_id") is None:
+                request_data["tags"] = tags
+
+            # Only add those that are not None (or falsy)
+            for key, value in span_attributes.items():
+                if value:
+                    request_data[key] = value
+
             if choices is not None:
                 request_data["output"] = [choice.dict() for choice in choices]
             else:
@@ -367,9 +393,6 @@ class BraintrustLogger(CustomLogger):
             if metrics is not None:
                 request_data["metrics"] = metrics
 
-            if metrics is not None:
-                request_data["metrics"] = metrics
-
             try:
                 await self.global_braintrust_http_handler.post(
                     url=f"{self.api_base}/project_logs/{project_id}/insert",
diff --git a/litellm/integrations/cloudzero/database.py b/litellm/integrations/cloudzero/database.py
index 2128b55bf83..71929398103 100644
--- a/litellm/integrations/cloudzero/database.py
+++ b/litellm/integrations/cloudzero/database.py
@@ -19,7 +19,7 @@
 """Database connection and data extraction for LiteLLM."""
 
 from datetime import datetime
-from typing import Any, Dict, Optional
+from typing import Any, Optional, List
 
 import polars as pl
 
@@ -46,19 +46,9 @@ class LiteLLMDatabase:
         """Retrieve usage data from LiteLLM daily user spend table."""
         client = self._ensure_prisma_client()
 
-        # Build WHERE clause for time filtering
-        where_conditions = []
-        if start_time_utc:
-            where_conditions.append(f"dus.updated_at >= '{start_time_utc.isoformat()}'")
-        if end_time_utc:
-            where_conditions.append(f"dus.updated_at <= '{end_time_utc.isoformat()}'")
-
-        where_clause = ""
-        if where_conditions:
-            where_clause = "WHERE " + " AND ".join(where_conditions)
-
-        # Query to get user spend data with team information
-        query = f"""
+        # Query to get user spend data with team information. Use parameter binding to
+        # avoid SQL injection from user-supplied timestamps or limits.
+        query = """
         SELECT
             dus.id,
             dus.date,
@@ -85,163 +75,27 @@ class LiteLLMDatabase:
         LEFT JOIN "LiteLLM_VerificationToken" vt ON dus.api_key = vt.token
         LEFT JOIN "LiteLLM_TeamTable" tt ON vt.team_id = tt.team_id
         LEFT JOIN "LiteLLM_UserTable" ut ON dus.user_id = ut.user_id
-        {where_clause}
+        WHERE ($1::timestamptz IS NULL OR dus.updated_at >= $1::timestamptz)
+          AND ($2::timestamptz IS NULL OR dus.updated_at <= $2::timestamptz)
         ORDER BY dus.date DESC, dus.created_at DESC
         """
 
-        if limit:
-            query += f" LIMIT {limit}"
+        params: List[Any] = [
+            start_time_utc,
+            end_time_utc,
+        ]
+
+        if limit is not None:
+            try:
+                params.append(int(limit))
+            except (TypeError, ValueError):
+                raise ValueError("limit must be an integer")
+            query += " LIMIT $3"
 
         try:
-            db_response = await client.db.query_raw(query)
+            db_response = await client.db.query_raw(query, *params)
             # Convert the response to polars DataFrame with full schema inference
             # This prevents schema mismatch errors when data types vary across rows
             return pl.DataFrame(db_response, infer_schema_length=None)
         except Exception as e:
             raise Exception(f"Error retrieving usage data: {str(e)}")
-
-    async def get_table_info(self) -> Dict[str, Any]:
-        """Get information about the daily user spend table."""
-        client = self._ensure_prisma_client()
-
-        try:
-            # Get row count from user spend table
-            user_count = await self._get_table_row_count("LiteLLM_DailyUserSpend")
-
-            # Get column structure from user spend table
-            query = """
-            SELECT column_name, data_type, is_nullable
-            FROM information_schema.columns
-            WHERE table_name = 'LiteLLM_DailyUserSpend'
-            ORDER BY ordinal_position;
-            """
-            columns_response = await client.db.query_raw(query)
-
-            return {
-                "columns": columns_response,
-                "row_count": user_count,
-                "table_name": "LiteLLM_DailyUserSpend",
-            }
-        except Exception as e:
-            raise Exception(f"Error getting table info: {str(e)}")
-
-    async def _get_table_row_count(self, table_name: str) -> int:
-        """Get row count from specified table."""
-        client = self._ensure_prisma_client()
-
-        try:
-            query = f'SELECT COUNT(*) as count FROM "{table_name}"'
-            response = await client.db.query_raw(query)
-
-            if response and len(response) > 0:
-                return response[0].get("count", 0)
-            return 0
-        except Exception:
-            return 0
-
-    async def discover_all_tables(self) -> Dict[str, Any]:
-        """Discover all tables in the LiteLLM database and their schemas."""
-        client = self._ensure_prisma_client()
-
-        try:
-            # Get all LiteLLM tables
-            litellm_tables_query = """
-            SELECT table_name 
-            FROM information_schema.tables 
-            WHERE table_schema = 'public' 
-            AND table_name LIKE 'LiteLLM_%'
-            ORDER BY table_name;
-            """
-            tables_response = await client.db.query_raw(litellm_tables_query)
-            table_names = [row["table_name"] for row in tables_response]
-
-            # Get detailed schema for each table
-            tables_info = {}
-            for table_name in table_names:
-                # Get column information
-                columns_query = """
-                SELECT 
-                    column_name,
-                    data_type,
-                    is_nullable,
-                    column_default,
-                    character_maximum_length,
-                    numeric_precision,
-                    numeric_scale,
-                    ordinal_position
-                FROM information_schema.columns 
-                WHERE table_name = $1
-                AND table_schema = 'public'
-                ORDER BY ordinal_position;
-                """
-                columns_response = await client.db.query_raw(columns_query, table_name)
-
-                # Get primary key information
-                pk_query = """
-                SELECT a.attname
-                FROM pg_index i
-                JOIN pg_attribute a ON a.attrelid = i.indrelid AND a.attnum = ANY(i.indkey)
-                WHERE i.indrelid = $1::regclass AND i.indisprimary;
-                """
-                pk_response = await client.db.query_raw(pk_query, f'"{table_name}"')
-                primary_keys = (
-                    [row["attname"] for row in pk_response] if pk_response else []
-                )
-
-                # Get foreign key information
-                fk_query = """
-                SELECT
-                    tc.constraint_name,
-                    kcu.column_name,
-                    ccu.table_name AS foreign_table_name,
-                    ccu.column_name AS foreign_column_name
-                FROM information_schema.table_constraints AS tc
-                JOIN information_schema.key_column_usage AS kcu
-                    ON tc.constraint_name = kcu.constraint_name
-                JOIN information_schema.constraint_column_usage AS ccu
-                    ON ccu.constraint_name = tc.constraint_name
-                WHERE tc.constraint_type = 'FOREIGN KEY'
-                AND tc.table_name = $1;
-                """
-                fk_response = await client.db.query_raw(fk_query, table_name)
-                foreign_keys = fk_response if fk_response else []
-
-                # Get indexes
-                indexes_query = """
-                SELECT
-                    i.relname AS index_name,
-                    array_agg(a.attname ORDER BY a.attnum) AS column_names,
-                    ix.indisunique AS is_unique
-                FROM pg_class t
-                JOIN pg_index ix ON t.oid = ix.indrelid
-                JOIN pg_class i ON i.oid = ix.indexrelid
-                JOIN pg_attribute a ON a.attrelid = t.oid AND a.attnum = ANY(ix.indkey)
-                WHERE t.relname = $1
-                AND t.relkind = 'r'
-                GROUP BY i.relname, ix.indisunique
-                ORDER BY i.relname;
-                """
-                indexes_response = await client.db.query_raw(indexes_query, table_name)
-                indexes = indexes_response if indexes_response else []
-
-                # Get row count
-                try:
-                    row_count = await self._get_table_row_count(table_name)
-                except Exception:
-                    row_count = 0
-
-                tables_info[table_name] = {
-                    "columns": columns_response,
-                    "primary_keys": primary_keys,
-                    "foreign_keys": foreign_keys,
-                    "indexes": indexes,
-                    "row_count": row_count,
-                }
-
-            return {
-                "tables": tables_info,
-                "table_count": len(table_names),
-                "table_names": table_names,
-            }
-        except Exception as e:
-            raise Exception(f"Error discovering tables: {str(e)}")
diff --git a/litellm/integrations/focus/__init__.py b/litellm/integrations/focus/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/integrations/focus/database.py b/litellm/integrations/focus/database.py
new file mode 100644
index 00000000000..298254670eb
--- /dev/null
+++ b/litellm/integrations/focus/database.py
@@ -0,0 +1,113 @@
+"""Database access helpers for Focus export."""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any, Dict, Optional
+
+import polars as pl
+
+
+class FocusLiteLLMDatabase:
+    """Retrieves LiteLLM usage data for Focus export workflows."""
+
+    def _ensure_prisma_client(self):
+        from litellm.proxy.proxy_server import prisma_client
+
+        if prisma_client is None:
+            raise RuntimeError(
+                "Database not connected. Connect a database to your proxy - "
+                "https://docs.litellm.ai/docs/simple_proxy#managing-auth---virtual-keys"
+            )
+        return prisma_client
+
+    async def get_usage_data(
+        self,
+        *,
+        limit: Optional[int] = None,
+        start_time_utc: Optional[datetime] = None,
+        end_time_utc: Optional[datetime] = None,
+    ) -> pl.DataFrame:
+        """Return usage data for the requested window."""
+        client = self._ensure_prisma_client()
+
+        where_clauses: list[str] = []
+        query_params: list[Any] = []
+        placeholder_index = 1
+        if start_time_utc:
+            where_clauses.append(f"dus.updated_at >= ${placeholder_index}::timestamptz")
+            query_params.append(start_time_utc)
+            placeholder_index += 1
+        if end_time_utc:
+            where_clauses.append(f"dus.updated_at <= ${placeholder_index}::timestamptz")
+            query_params.append(end_time_utc)
+            placeholder_index += 1
+
+        where_clause = ""
+        if where_clauses:
+            where_clause = "WHERE " + " AND ".join(where_clauses)
+
+        limit_clause = ""
+        if limit is not None:
+            try:
+                limit_value = int(limit)
+            except (TypeError, ValueError) as exc:  # pragma: no cover - defensive guard
+                raise ValueError("limit must be an integer") from exc
+            if limit_value < 0:
+                raise ValueError("limit must be non-negative")
+            limit_clause = f" LIMIT ${placeholder_index}"
+            query_params.append(limit_value)
+
+        query = f"""
+        SELECT
+            dus.id,
+            dus.date,
+            dus.user_id,
+            dus.api_key,
+            dus.model,
+            dus.model_group,
+            dus.custom_llm_provider,
+            dus.prompt_tokens,
+            dus.completion_tokens,
+            dus.spend,
+            dus.api_requests,
+            dus.successful_requests,
+            dus.failed_requests,
+            dus.cache_creation_input_tokens,
+            dus.cache_read_input_tokens,
+            dus.created_at,
+            dus.updated_at,
+            vt.team_id,
+            vt.key_alias as api_key_alias,
+            tt.team_alias,
+            ut.user_email as user_email
+        FROM "LiteLLM_DailyUserSpend" dus
+        LEFT JOIN "LiteLLM_VerificationToken" vt ON dus.api_key = vt.token
+        LEFT JOIN "LiteLLM_TeamTable" tt ON vt.team_id = tt.team_id
+        LEFT JOIN "LiteLLM_UserTable" ut ON dus.user_id = ut.user_id
+        {where_clause}
+        ORDER BY dus.date DESC, dus.created_at DESC
+        {limit_clause}
+        """
+
+        try:
+            db_response = await client.db.query_raw(query, *query_params)
+            return pl.DataFrame(db_response, infer_schema_length=None)
+        except Exception as exc:
+            raise RuntimeError(f"Error retrieving usage data: {exc}") from exc
+
+    async def get_table_info(self) -> Dict[str, Any]:
+        """Return metadata about the spend table for diagnostics."""
+        client = self._ensure_prisma_client()
+
+        info_query = """
+        SELECT column_name, data_type, is_nullable
+        FROM information_schema.columns
+        WHERE table_name = 'LiteLLM_DailyUserSpend'
+        ORDER BY ordinal_position;
+        """
+        try:
+            columns_response = await client.db.query_raw(info_query)
+            return {"columns": columns_response, "table_name": "LiteLLM_DailyUserSpend"}
+        except Exception as exc:
+            raise RuntimeError(f"Error getting table info: {exc}") from exc
diff --git a/litellm/integrations/focus/destinations/__init__.py b/litellm/integrations/focus/destinations/__init__.py
new file mode 100644
index 00000000000..233f1da0c9b
--- /dev/null
+++ b/litellm/integrations/focus/destinations/__init__.py
@@ -0,0 +1,12 @@
+"""Destination implementations for Focus export."""
+
+from .base import FocusDestination, FocusTimeWindow
+from .factory import FocusDestinationFactory
+from .s3_destination import FocusS3Destination
+
+__all__ = [
+    "FocusDestination",
+    "FocusDestinationFactory",
+    "FocusTimeWindow",
+    "FocusS3Destination",
+]
diff --git a/litellm/integrations/focus/destinations/base.py b/litellm/integrations/focus/destinations/base.py
new file mode 100644
index 00000000000..8042a7e23b9
--- /dev/null
+++ b/litellm/integrations/focus/destinations/base.py
@@ -0,0 +1,30 @@
+"""Abstract destination interfaces for Focus export."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Protocol
+
+
+@dataclass(frozen=True)
+class FocusTimeWindow:
+    """Represents the span of data exported in a single batch."""
+
+    start_time: datetime
+    end_time: datetime
+    frequency: str
+
+
+class FocusDestination(Protocol):
+    """Protocol for anything that can receive Focus export files."""
+
+    async def deliver(
+        self,
+        *,
+        content: bytes,
+        time_window: FocusTimeWindow,
+        filename: str,
+    ) -> None:
+        """Persist the serialized export for the provided time window."""
+        ...
diff --git a/litellm/integrations/focus/destinations/factory.py b/litellm/integrations/focus/destinations/factory.py
new file mode 100644
index 00000000000..cb7696a11de
--- /dev/null
+++ b/litellm/integrations/focus/destinations/factory.py
@@ -0,0 +1,59 @@
+"""Factory helpers for Focus export destinations."""
+
+from __future__ import annotations
+
+import os
+from typing import Any, Dict, Optional
+
+from .base import FocusDestination
+from .s3_destination import FocusS3Destination
+
+
+class FocusDestinationFactory:
+    """Builds destination instances based on provider/config settings."""
+
+    @staticmethod
+    def create(
+        *,
+        provider: str,
+        prefix: str,
+        config: Optional[Dict[str, Any]] = None,
+    ) -> FocusDestination:
+        """Return a destination implementation for the requested provider."""
+        provider_lower = provider.lower()
+        normalized_config = FocusDestinationFactory._resolve_config(
+            provider=provider_lower, overrides=config or {}
+        )
+        if provider_lower == "s3":
+            return FocusS3Destination(prefix=prefix, config=normalized_config)
+        raise NotImplementedError(
+            f"Provider '{provider}' not supported for Focus export"
+        )
+
+    @staticmethod
+    def _resolve_config(
+        *,
+        provider: str,
+        overrides: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        if provider == "s3":
+            resolved = {
+                "bucket_name": overrides.get("bucket_name")
+                or os.getenv("FOCUS_S3_BUCKET_NAME"),
+                "region_name": overrides.get("region_name")
+                or os.getenv("FOCUS_S3_REGION_NAME"),
+                "endpoint_url": overrides.get("endpoint_url")
+                or os.getenv("FOCUS_S3_ENDPOINT_URL"),
+                "aws_access_key_id": overrides.get("aws_access_key_id")
+                or os.getenv("FOCUS_S3_ACCESS_KEY"),
+                "aws_secret_access_key": overrides.get("aws_secret_access_key")
+                or os.getenv("FOCUS_S3_SECRET_KEY"),
+                "aws_session_token": overrides.get("aws_session_token")
+                or os.getenv("FOCUS_S3_SESSION_TOKEN"),
+            }
+            if not resolved.get("bucket_name"):
+                raise ValueError("FOCUS_S3_BUCKET_NAME must be provided for S3 exports")
+            return {k: v for k, v in resolved.items() if v is not None}
+        raise NotImplementedError(
+            f"Provider '{provider}' not supported for Focus export configuration"
+        )
diff --git a/litellm/integrations/focus/destinations/s3_destination.py b/litellm/integrations/focus/destinations/s3_destination.py
new file mode 100644
index 00000000000..c6d5554b438
--- /dev/null
+++ b/litellm/integrations/focus/destinations/s3_destination.py
@@ -0,0 +1,74 @@
+"""S3 destination implementation for Focus export."""
+
+from __future__ import annotations
+
+import asyncio
+from datetime import timezone
+from typing import Any, Optional
+
+import boto3
+
+from .base import FocusDestination, FocusTimeWindow
+
+
+class FocusS3Destination(FocusDestination):
+    """Handles uploading serialized exports to S3 buckets."""
+
+    def __init__(
+        self,
+        *,
+        prefix: str,
+        config: Optional[dict[str, Any]] = None,
+    ) -> None:
+        config = config or {}
+        bucket_name = config.get("bucket_name")
+        if not bucket_name:
+            raise ValueError("bucket_name must be provided for S3 destination")
+        self.bucket_name = bucket_name
+        self.prefix = prefix.rstrip("/")
+        self.config = config
+
+    async def deliver(
+        self,
+        *,
+        content: bytes,
+        time_window: FocusTimeWindow,
+        filename: str,
+    ) -> None:
+        object_key = self._build_object_key(time_window=time_window, filename=filename)
+        await asyncio.to_thread(self._upload, content, object_key)
+
+    def _build_object_key(self, *, time_window: FocusTimeWindow, filename: str) -> str:
+        start_utc = time_window.start_time.astimezone(timezone.utc)
+        date_component = f"date={start_utc.strftime('%Y-%m-%d')}"
+        parts = [self.prefix, date_component]
+        if time_window.frequency == "hourly":
+            parts.append(f"hour={start_utc.strftime('%H')}")
+        key_prefix = "/".join(filter(None, parts))
+        return f"{key_prefix}/{filename}" if key_prefix else filename
+
+    def _upload(self, content: bytes, object_key: str) -> None:
+        client_kwargs: dict[str, Any] = {}
+        region_name = self.config.get("region_name")
+        if region_name:
+            client_kwargs["region_name"] = region_name
+        endpoint_url = self.config.get("endpoint_url")
+        if endpoint_url:
+            client_kwargs["endpoint_url"] = endpoint_url
+
+        session_kwargs: dict[str, Any] = {}
+        for key in (
+            "aws_access_key_id",
+            "aws_secret_access_key",
+            "aws_session_token",
+        ):
+            if self.config.get(key):
+                session_kwargs[key] = self.config[key]
+
+        s3_client = boto3.client("s3", **client_kwargs, **session_kwargs)
+        s3_client.put_object(
+            Bucket=self.bucket_name,
+            Key=object_key,
+            Body=content,
+            ContentType="application/octet-stream",
+        )
diff --git a/litellm/integrations/focus/export_engine.py b/litellm/integrations/focus/export_engine.py
new file mode 100644
index 00000000000..22ebce2a168
--- /dev/null
+++ b/litellm/integrations/focus/export_engine.py
@@ -0,0 +1,124 @@
+"""Core export engine for Focus integrations (heavy dependencies)."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+import polars as pl
+
+from litellm._logging import verbose_logger
+
+from .database import FocusLiteLLMDatabase
+from .destinations import FocusDestinationFactory, FocusTimeWindow
+from .serializers import FocusParquetSerializer, FocusSerializer
+from .transformer import FocusTransformer
+
+
+class FocusExportEngine:
+    """Engine that fetches, normalizes, and uploads Focus exports."""
+
+    def __init__(
+        self,
+        *,
+        provider: str,
+        export_format: str,
+        prefix: str,
+        destination_config: Optional[dict[str, Any]] = None,
+    ) -> None:
+        self.provider = provider
+        self.export_format = export_format
+        self.prefix = prefix
+        self._destination = FocusDestinationFactory.create(
+            provider=self.provider,
+            prefix=self.prefix,
+            config=destination_config,
+        )
+        self._serializer = self._init_serializer()
+        self._transformer = FocusTransformer()
+        self._database = FocusLiteLLMDatabase()
+
+    def _init_serializer(self) -> FocusSerializer:
+        if self.export_format != "parquet":
+            raise NotImplementedError("Only parquet export supported currently")
+        return FocusParquetSerializer()
+
+    async def dry_run_export_usage_data(self, limit: Optional[int]) -> Dict[str, Any]:
+        data = await self._database.get_usage_data(limit=limit)
+        normalized = self._transformer.transform(data)
+
+        usage_sample = data.head(min(50, len(data))).to_dicts()
+        normalized_sample = normalized.head(min(50, len(normalized))).to_dicts()
+
+        summary = {
+            "total_records": len(normalized),
+            "total_spend": self._sum_column(normalized, "spend"),
+            "total_tokens": self._sum_column(normalized, "total_tokens"),
+            "unique_teams": self._count_unique(normalized, "team_id"),
+            "unique_models": self._count_unique(normalized, "model"),
+        }
+
+        return {
+            "usage_data": usage_sample,
+            "normalized_data": normalized_sample,
+            "summary": summary,
+        }
+
+    async def export_window(
+        self,
+        *,
+        window: FocusTimeWindow,
+        limit: Optional[int],
+    ) -> None:
+        data = await self._database.get_usage_data(
+            limit=limit,
+            start_time_utc=window.start_time,
+            end_time_utc=window.end_time,
+        )
+        if data.is_empty():
+            verbose_logger.debug("Focus export: no usage data for window %s", window)
+            return
+
+        normalized = self._transformer.transform(data)
+        if normalized.is_empty():
+            verbose_logger.debug(
+                "Focus export: normalized data empty for window %s", window
+            )
+            return
+
+        await self._serialize_and_upload(normalized, window)
+
+    async def _serialize_and_upload(
+        self, frame: pl.DataFrame, window: FocusTimeWindow
+    ) -> None:
+        payload = self._serializer.serialize(frame)
+        if not payload:
+            verbose_logger.debug("Focus export: serializer returned empty payload")
+            return
+        await self._destination.deliver(
+            content=payload,
+            time_window=window,
+            filename=self._build_filename(),
+        )
+
+    def _build_filename(self) -> str:
+        if not self._serializer.extension:
+            raise ValueError("Serializer must declare a file extension")
+        return f"usage.{self._serializer.extension}"
+
+    @staticmethod
+    def _sum_column(frame: pl.DataFrame, column: str) -> float:
+        if frame.is_empty() or column not in frame.columns:
+            return 0.0
+        value = frame.select(pl.col(column).sum().alias("sum")).row(0)[0]
+        if value is None:
+            return 0.0
+        return float(value)
+
+    @staticmethod
+    def _count_unique(frame: pl.DataFrame, column: str) -> int:
+        if frame.is_empty() or column not in frame.columns:
+            return 0
+        value = frame.select(pl.col(column).n_unique().alias("unique")).row(0)[0]
+        if value is None:
+            return 0
+        return int(value)
diff --git a/litellm/integrations/focus/focus_logger.py b/litellm/integrations/focus/focus_logger.py
new file mode 100644
index 00000000000..ade1cf861b1
--- /dev/null
+++ b/litellm/integrations/focus/focus_logger.py
@@ -0,0 +1,211 @@
+"""Focus export logger orchestrating DB pull/transform/upload."""
+
+from __future__ import annotations
+
+import os
+from datetime import datetime, timedelta, timezone
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, cast
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+
+from .destinations import FocusTimeWindow
+
+if TYPE_CHECKING:
+    from apscheduler.schedulers.asyncio import AsyncIOScheduler
+    from .export_engine import FocusExportEngine
+else:
+    AsyncIOScheduler = Any
+
+FOCUS_USAGE_DATA_JOB_NAME = "focus_export_usage_data"
+DEFAULT_DRY_RUN_LIMIT = 500
+
+
+class FocusLogger(CustomLogger):
+    """Coordinates Focus export jobs across transformer/serializer/destination layers."""
+
+    def __init__(
+        self,
+        *,
+        provider: Optional[str] = None,
+        export_format: Optional[str] = None,
+        frequency: Optional[str] = None,
+        cron_offset_minute: Optional[int] = None,
+        interval_seconds: Optional[int] = None,
+        prefix: Optional[str] = None,
+        destination_config: Optional[dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.provider = (provider or os.getenv("FOCUS_PROVIDER") or "s3").lower()
+        self.export_format = (
+            export_format or os.getenv("FOCUS_FORMAT") or "parquet"
+        ).lower()
+        self.frequency = (frequency or os.getenv("FOCUS_FREQUENCY") or "hourly").lower()
+        self.cron_offset_minute = (
+            cron_offset_minute
+            if cron_offset_minute is not None
+            else int(os.getenv("FOCUS_CRON_OFFSET", "5"))
+        )
+        raw_interval = (
+            interval_seconds
+            if interval_seconds is not None
+            else os.getenv("FOCUS_INTERVAL_SECONDS")
+        )
+        self.interval_seconds = int(raw_interval) if raw_interval is not None else None
+        env_prefix = os.getenv("FOCUS_PREFIX")
+        self.prefix: str = (
+            prefix if prefix is not None else (env_prefix if env_prefix else "focus_exports")
+        )
+
+        self._destination_config = destination_config
+        self._engine: Optional["FocusExportEngine"] = None
+
+    def _ensure_engine(self) -> "FocusExportEngine":
+        """Instantiate the heavy export engine lazily."""
+        if self._engine is None:
+            from .export_engine import FocusExportEngine
+
+            self._engine = FocusExportEngine(
+                provider=self.provider,
+                export_format=self.export_format,
+                prefix=self.prefix,
+                destination_config=self._destination_config,
+            )
+        return self._engine
+
+    async def export_usage_data(
+        self,
+        *,
+        limit: Optional[int] = None,
+        start_time_utc: Optional[datetime] = None,
+        end_time_utc: Optional[datetime] = None,
+    ) -> None:
+        """Public hook to trigger export immediately."""
+        if bool(start_time_utc) ^ bool(end_time_utc):
+            raise ValueError(
+                "start_time_utc and end_time_utc must be provided together"
+            )
+
+        if start_time_utc and end_time_utc:
+            window = FocusTimeWindow(
+                start_time=start_time_utc,
+                end_time=end_time_utc,
+                frequency=self.frequency,
+            )
+        else:
+            window = self._compute_time_window(datetime.now(timezone.utc))
+        await self._export_window(window=window, limit=limit)
+
+    async def dry_run_export_usage_data(
+        self, limit: Optional[int] = DEFAULT_DRY_RUN_LIMIT
+    ) -> dict[str, Any]:
+        """Return transformed data without uploading."""
+        engine = self._ensure_engine()
+        return await engine.dry_run_export_usage_data(limit=limit)
+
+    async def initialize_focus_export_job(self) -> None:
+        """Entry point for scheduler jobs to run export cycle with locking."""
+        from litellm.proxy.proxy_server import proxy_logging_obj
+
+        pod_lock_manager = None
+        if proxy_logging_obj is not None:
+            writer = getattr(proxy_logging_obj, "db_spend_update_writer", None)
+            if writer is not None:
+                pod_lock_manager = getattr(writer, "pod_lock_manager", None)
+
+        if pod_lock_manager and pod_lock_manager.redis_cache:
+            acquired = await pod_lock_manager.acquire_lock(
+                cronjob_id=FOCUS_USAGE_DATA_JOB_NAME
+            )
+            if not acquired:
+                verbose_logger.debug("Focus export: unable to acquire pod lock")
+                return
+            try:
+                await self._run_scheduled_export()
+            finally:
+                await pod_lock_manager.release_lock(
+                    cronjob_id=FOCUS_USAGE_DATA_JOB_NAME
+                )
+        else:
+            await self._run_scheduled_export()
+
+    @staticmethod
+    async def init_focus_export_background_job(
+        scheduler: AsyncIOScheduler,
+    ) -> None:
+        """Register the export cron/interval job with the provided scheduler."""
+
+        focus_loggers: List[
+            CustomLogger
+        ] = litellm.logging_callback_manager.get_custom_loggers_for_type(
+            callback_type=FocusLogger
+        )
+        if not focus_loggers:
+            verbose_logger.debug(
+                "No Focus export logger registered; skipping scheduler"
+            )
+            return
+
+        focus_logger = cast(FocusLogger, focus_loggers[0])
+        trigger_kwargs = focus_logger._build_scheduler_trigger()
+        scheduler.add_job(
+            focus_logger.initialize_focus_export_job,
+            **trigger_kwargs,
+        )
+
+    def _build_scheduler_trigger(self) -> Dict[str, Any]:
+        """Return scheduler configuration for the selected frequency."""
+        if self.frequency == "interval":
+            seconds = self.interval_seconds or 60
+            return {"trigger": "interval", "seconds": seconds}
+
+        if self.frequency == "hourly":
+            minute = max(0, min(59, self.cron_offset_minute))
+            return {"trigger": "cron", "minute": minute, "second": 0}
+
+        if self.frequency == "daily":
+            total_minutes = max(0, self.cron_offset_minute)
+            hour = min(23, total_minutes // 60)
+            minute = min(59, total_minutes % 60)
+            return {"trigger": "cron", "hour": hour, "minute": minute, "second": 0}
+
+        raise ValueError(f"Unsupported frequency: {self.frequency}")
+
+    async def _run_scheduled_export(self) -> None:
+        """Execute the scheduled export for the configured window."""
+        window = self._compute_time_window(datetime.now(timezone.utc))
+        await self._export_window(window=window, limit=None)
+
+    async def _export_window(
+        self,
+        *,
+        window: FocusTimeWindow,
+        limit: Optional[int],
+    ) -> None:
+        engine = self._ensure_engine()
+        await engine.export_window(window=window, limit=limit)
+
+    def _compute_time_window(self, now: datetime) -> FocusTimeWindow:
+        """Derive the time window to export based on configured frequency."""
+        now_utc = now.astimezone(timezone.utc)
+        if self.frequency == "hourly":
+            end_time = now_utc.replace(minute=0, second=0, microsecond=0)
+            start_time = end_time - timedelta(hours=1)
+        elif self.frequency == "daily":
+            end_time = now_utc.replace(hour=0, minute=0, second=0, microsecond=0)
+            start_time = end_time - timedelta(days=1)
+        elif self.frequency == "interval":
+            interval = timedelta(seconds=self.interval_seconds or 60)
+            end_time = now_utc
+            start_time = end_time - interval
+        else:
+            raise ValueError(f"Unsupported frequency: {self.frequency}")
+        return FocusTimeWindow(
+            start_time=start_time,
+            end_time=end_time,
+            frequency=self.frequency,
+        )
+
+__all__ = ["FocusLogger"]
diff --git a/litellm/integrations/focus/schema.py b/litellm/integrations/focus/schema.py
new file mode 100644
index 00000000000..ac2f33dad0a
--- /dev/null
+++ b/litellm/integrations/focus/schema.py
@@ -0,0 +1,50 @@
+"""Schema definitions for Focus export data."""
+
+from __future__ import annotations
+
+import polars as pl
+
+# see: https://focus.finops.org/focus-specification/v1-2/
+FOCUS_NORMALIZED_SCHEMA = pl.Schema(
+    [
+        ("BilledCost", pl.Decimal(18, 6)),
+        ("BillingAccountId", pl.String),
+        ("BillingAccountName", pl.String),
+        ("BillingCurrency", pl.String),
+        ("BillingPeriodStart", pl.Datetime(time_unit="us")),
+        ("BillingPeriodEnd", pl.Datetime(time_unit="us")),
+        ("ChargeCategory", pl.String),
+        ("ChargeClass", pl.String),
+        ("ChargeDescription", pl.String),
+        ("ChargeFrequency", pl.String),
+        ("ChargePeriodStart", pl.Datetime(time_unit="us")),
+        ("ChargePeriodEnd", pl.Datetime(time_unit="us")),
+        ("ConsumedQuantity", pl.Decimal(18, 6)),
+        ("ConsumedUnit", pl.String),
+        ("ContractedCost", pl.Decimal(18, 6)),
+        ("ContractedUnitPrice", pl.Decimal(18, 6)),
+        ("EffectiveCost", pl.Decimal(18, 6)),
+        ("InvoiceIssuerName", pl.String),
+        ("ListCost", pl.Decimal(18, 6)),
+        ("ListUnitPrice", pl.Decimal(18, 6)),
+        ("PricingCategory", pl.String),
+        ("PricingQuantity", pl.Decimal(18, 6)),
+        ("PricingUnit", pl.String),
+        ("ProviderName", pl.String),
+        ("PublisherName", pl.String),
+        ("RegionId", pl.String),
+        ("RegionName", pl.String),
+        ("ResourceId", pl.String),
+        ("ResourceName", pl.String),
+        ("ResourceType", pl.String),
+        ("ServiceCategory", pl.String),
+        ("ServiceSubcategory", pl.String),
+        ("ServiceName", pl.String),
+        ("SubAccountId", pl.String),
+        ("SubAccountName", pl.String),
+        ("SubAccountType", pl.String),
+        ("Tags", pl.Object),
+    ]
+)
+
+__all__ = ["FOCUS_NORMALIZED_SCHEMA"]
diff --git a/litellm/integrations/focus/serializers/__init__.py b/litellm/integrations/focus/serializers/__init__.py
new file mode 100644
index 00000000000..18187bf73e5
--- /dev/null
+++ b/litellm/integrations/focus/serializers/__init__.py
@@ -0,0 +1,6 @@
+"""Serializer package exports for Focus integration."""
+
+from .base import FocusSerializer
+from .parquet import FocusParquetSerializer
+
+__all__ = ["FocusSerializer", "FocusParquetSerializer"]
diff --git a/litellm/integrations/focus/serializers/base.py b/litellm/integrations/focus/serializers/base.py
new file mode 100644
index 00000000000..6da080dae81
--- /dev/null
+++ b/litellm/integrations/focus/serializers/base.py
@@ -0,0 +1,18 @@
+"""Serializer abstractions for Focus export."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+import polars as pl
+
+
+class FocusSerializer(ABC):
+    """Base serializer turning Focus frames into bytes."""
+
+    extension: str = ""
+
+    @abstractmethod
+    def serialize(self, frame: pl.DataFrame) -> bytes:
+        """Convert the normalized Focus frame into the chosen format."""
+        raise NotImplementedError
diff --git a/litellm/integrations/focus/serializers/parquet.py b/litellm/integrations/focus/serializers/parquet.py
new file mode 100644
index 00000000000..6b3dde5903d
--- /dev/null
+++ b/litellm/integrations/focus/serializers/parquet.py
@@ -0,0 +1,22 @@
+"""Parquet serializer for Focus export."""
+
+from __future__ import annotations
+
+import io
+
+import polars as pl
+
+from .base import FocusSerializer
+
+
+class FocusParquetSerializer(FocusSerializer):
+    """Serialize normalized Focus frames to Parquet bytes."""
+
+    extension = "parquet"
+
+    def serialize(self, frame: pl.DataFrame) -> bytes:
+        """Encode the provided frame as a parquet payload."""
+        target = frame if not frame.is_empty() else pl.DataFrame(schema=frame.schema)
+        buffer = io.BytesIO()
+        target.write_parquet(buffer, compression="snappy")
+        return buffer.getvalue()
diff --git a/litellm/integrations/focus/transformer.py b/litellm/integrations/focus/transformer.py
new file mode 100644
index 00000000000..cac12b7be14
--- /dev/null
+++ b/litellm/integrations/focus/transformer.py
@@ -0,0 +1,90 @@
+"""Focus export data transformer."""
+
+from __future__ import annotations
+
+from datetime import timedelta
+
+import polars as pl
+
+from .schema import FOCUS_NORMALIZED_SCHEMA
+
+
+class FocusTransformer:
+    """Transforms LiteLLM DB rows into Focus-compatible schema."""
+
+    schema = FOCUS_NORMALIZED_SCHEMA
+
+    def transform(self, frame: pl.DataFrame) -> pl.DataFrame:
+        """Return a normalized frame expected by downstream serializers."""
+        if frame.is_empty():
+            return pl.DataFrame(schema=self.schema)
+
+        # derive period start/end from usage date
+        frame = frame.with_columns(
+            pl.col("date")
+            .cast(pl.Utf8)
+            .str.strptime(pl.Datetime(time_unit="us"), format="%Y-%m-%d", strict=False)
+            .alias("usage_date"),
+        )
+        frame = frame.with_columns(
+            pl.col("usage_date").alias("ChargePeriodStart"),
+            (pl.col("usage_date") + timedelta(days=1)).alias("ChargePeriodEnd"),
+        )
+
+        def fmt(col):
+            return col.dt.strftime("%Y-%m-%dT%H:%M:%SZ")
+
+        DEC = pl.Decimal(18, 6)
+
+        def dec(col):
+            return col.cast(DEC)
+
+        none_str = pl.lit(None, dtype=pl.Utf8)
+        none_dec = pl.lit(None, dtype=pl.Decimal(18, 6))
+
+        return frame.select(
+            dec(pl.col("spend").fill_null(0.0)).alias("BilledCost"),
+            pl.col("api_key").cast(pl.String).alias("BillingAccountId"),
+            pl.col("api_key_alias").cast(pl.String).alias("BillingAccountName"),
+            pl.lit("API Key").alias("BillingAccountType"),
+            pl.lit("USD").alias("BillingCurrency"),
+            fmt(pl.col("ChargePeriodEnd")).alias("BillingPeriodEnd"),
+            fmt(pl.col("ChargePeriodStart")).alias("BillingPeriodStart"),
+            pl.lit("Usage").alias("ChargeCategory"),
+            none_str.alias("ChargeClass"),
+            pl.col("model").cast(pl.String).alias("ChargeDescription"),
+            pl.lit("Usage-Based").alias("ChargeFrequency"),
+            fmt(pl.col("ChargePeriodEnd")).alias("ChargePeriodEnd"),
+            fmt(pl.col("ChargePeriodStart")).alias("ChargePeriodStart"),
+            dec(pl.lit(1.0)).alias("ConsumedQuantity"),
+            pl.lit("Requests").alias("ConsumedUnit"),
+            dec(pl.col("spend").fill_null(0.0)).alias("ContractedCost"),
+            none_str.alias("ContractedUnitPrice"),
+            dec(pl.col("spend").fill_null(0.0)).alias("EffectiveCost"),
+            pl.col("custom_llm_provider").cast(pl.String).alias("InvoiceIssuerName"),
+            none_str.alias("InvoiceId"),
+            dec(pl.col("spend").fill_null(0.0)).alias("ListCost"),
+            none_dec.alias("ListUnitPrice"),
+            none_str.alias("AvailabilityZone"),
+            pl.lit("USD").alias("PricingCurrency"),
+            none_str.alias("PricingCategory"),
+            dec(pl.lit(1.0)).alias("PricingQuantity"),
+            none_dec.alias("PricingCurrencyContractedUnitPrice"),
+            dec(pl.col("spend").fill_null(0.0)).alias("PricingCurrencyEffectiveCost"),
+            none_dec.alias("PricingCurrencyListUnitPrice"),
+            pl.lit("Requests").alias("PricingUnit"),
+            pl.col("custom_llm_provider").cast(pl.String).alias("ProviderName"),
+            pl.col("custom_llm_provider").cast(pl.String).alias("PublisherName"),
+            none_str.alias("RegionId"),
+            none_str.alias("RegionName"),
+            pl.col("model").cast(pl.String).alias("ResourceId"),
+            pl.col("model").cast(pl.String).alias("ResourceName"),
+            pl.col("model").cast(pl.String).alias("ResourceType"),
+            pl.lit("AI and Machine Learning").alias("ServiceCategory"),
+            pl.lit("Generative AI").alias("ServiceSubcategory"),
+            pl.col("model_group").cast(pl.String).alias("ServiceName"),
+            pl.col("team_id").cast(pl.String).alias("SubAccountId"),
+            pl.col("team_alias").cast(pl.String).alias("SubAccountName"),
+            none_str.alias("SubAccountType"),
+            none_str.alias("Tags"),
+        )
diff --git a/litellm/integrations/generic_api/generic_api_compatible_callbacks.json b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json
index 12dc4ae643c..13fe79ae671 100644
--- a/litellm/integrations/generic_api/generic_api_compatible_callbacks.json
+++ b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json
@@ -1,28 +1,37 @@
 {
-    "sample_callback": {
-        "event_types": ["llm_api_success", "llm_api_failure"],
-        "endpoint": "{{environment_variables.SAMPLE_CALLBACK_URL}}",
-        "headers": {
-            "Content-Type": "application/json",
-            "Authorization": "Bearer {{environment_variables.SAMPLE_CALLBACK_API_KEY}}"
-        },
-        "environment_variables": ["SAMPLE_CALLBACK_URL", "SAMPLE_CALLBACK_API_KEY"]
+  "sample_callback": {
+    "event_types": ["llm_api_success", "llm_api_failure"],
+    "endpoint": "{{environment_variables.SAMPLE_CALLBACK_URL}}",
+    "headers": {
+      "Content-Type": "application/json",
+      "Authorization": "Bearer {{environment_variables.SAMPLE_CALLBACK_API_KEY}}"
     },
-    "rubrik": {
-        "event_types": ["llm_api_success"],
-        "endpoint": "{{environment_variables.RUBRIK_WEBHOOK_URL}}",
-        "headers": {
-            "Content-Type": "application/json",
-            "Authorization": "Bearer {{environment_variables.RUBRIK_API_KEY}}"
-        },
-        "environment_variables": ["RUBRIK_API_KEY", "RUBRIK_WEBHOOK_URL"]
+    "environment_variables": ["SAMPLE_CALLBACK_URL", "SAMPLE_CALLBACK_API_KEY"]
+  },
+  "rubrik": {
+    "event_types": ["llm_api_success"],
+    "endpoint": "{{environment_variables.RUBRIK_WEBHOOK_URL}}",
+    "headers": {
+      "Content-Type": "application/json",
+      "Authorization": "Bearer {{environment_variables.RUBRIK_API_KEY}}"
     },
-    "sumologic": {
-        "endpoint": "{{environment_variables.SUMOLOGIC_WEBHOOK_URL}}",
-        "headers": {
-            "Content-Type": "application/json"
-        },
-        "environment_variables": ["SUMOLOGIC_WEBHOOK_URL"],
-        "log_format": "ndjson"
-    }
-}
\ No newline at end of file
+    "environment_variables": ["RUBRIK_API_KEY", "RUBRIK_WEBHOOK_URL"]
+  },
+  "sumologic": {
+    "endpoint": "{{environment_variables.SUMOLOGIC_WEBHOOK_URL}}",
+    "headers": {
+      "Content-Type": "application/json"
+    },
+    "environment_variables": ["SUMOLOGIC_WEBHOOK_URL"],
+    "log_format": "ndjson"
+  },
+  "qualifire_eval": {
+    "event_types": ["llm_api_success"],
+    "endpoint": "{{environment_variables.QUALIFIRE_WEBHOOK_URL}}",
+    "headers": {
+      "Content-Type": "application/json",
+      "X-Qualifire-API-Key": "{{environment_variables.QUALIFIRE_API_KEY}}"
+    },
+    "environment_variables": ["QUALIFIRE_API_KEY", "QUALIFIRE_WEBHOOK_URL"]
+  }
+}
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index c01f7481277..2385edc5297 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -14,6 +14,7 @@ from typing import (
     Literal,
     Optional,
     Tuple,
+    Union,
     cast,
 )
 
@@ -44,6 +45,7 @@ def _get_cached_end_user_id_for_cost_tracking():
     global _get_end_user_id_for_cost_tracking
     if _get_end_user_id_for_cost_tracking is None:
         from litellm.utils import get_end_user_id_for_cost_tracking
+
         _get_end_user_id_for_cost_tracking = get_end_user_id_for_cost_tracking
     return _get_end_user_id_for_cost_tracking
 
@@ -237,6 +239,36 @@ class PrometheusLogger(CustomLogger):
                 ),
                 buckets=LATENCY_BUCKETS,
             )
+
+            # Request queue time metric
+            self.litellm_request_queue_time_metric = self._histogram_factory(
+                "litellm_request_queue_time_seconds",
+                "Time spent in request queue before processing starts (seconds)",
+                labelnames=self.get_labels_for_metric(
+                    "litellm_request_queue_time_seconds"
+                ),
+                buckets=LATENCY_BUCKETS,
+            )
+
+            # Guardrail metrics
+            self.litellm_guardrail_latency_metric = self._histogram_factory(
+                "litellm_guardrail_latency_seconds",
+                "Latency (seconds) for guardrail execution",
+                labelnames=["guardrail_name", "status", "error_type", "hook_type"],
+                buckets=LATENCY_BUCKETS,
+            )
+
+            self.litellm_guardrail_errors_total = self._counter_factory(
+                "litellm_guardrail_errors_total",
+                "Total number of errors encountered during guardrail execution",
+                labelnames=["guardrail_name", "error_type", "hook_type"],
+            )
+
+            self.litellm_guardrail_requests_total = self._counter_factory(
+                "litellm_guardrail_requests_total",
+                "Total number of guardrail invocations",
+                labelnames=["guardrail_name", "status", "hook_type"],
+            )
             # llm api provider budget metrics
             self.litellm_provider_remaining_budget_metric = self._gauge_factory(
                 "litellm_provider_remaining_budget_metric",
@@ -329,6 +361,25 @@ class PrometheusLogger(CustomLogger):
                 labelnames=self.get_labels_for_metric("litellm_requests_metric"),
             )
 
+            # Cache metrics
+            self.litellm_cache_hits_metric = self._counter_factory(
+                name="litellm_cache_hits_metric",
+                documentation="Total number of LiteLLM cache hits",
+                labelnames=self.get_labels_for_metric("litellm_cache_hits_metric"),
+            )
+
+            self.litellm_cache_misses_metric = self._counter_factory(
+                name="litellm_cache_misses_metric",
+                documentation="Total number of LiteLLM cache misses",
+                labelnames=self.get_labels_for_metric("litellm_cache_misses_metric"),
+            )
+
+            self.litellm_cached_tokens_metric = self._counter_factory(
+                name="litellm_cached_tokens_metric",
+                documentation="Total tokens served from LiteLLM cache",
+                labelnames=self.get_labels_for_metric("litellm_cached_tokens_metric"),
+            )
+
         except Exception as e:
             print_verbose(f"Got exception on init prometheus client {str(e)}")
             raise e
@@ -791,11 +842,16 @@ class PrometheusLogger(CustomLogger):
                 f"standard_logging_object is required, got={standard_logging_payload}"
             )
 
+        if self._should_skip_metrics_for_invalid_key(
+            kwargs=kwargs, standard_logging_payload=standard_logging_payload
+        ):
+            return
+
         model = kwargs.get("model", "")
         litellm_params = kwargs.get("litellm_params", {}) or {}
         _metadata = litellm_params.get("metadata", {})
         get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking()
-        
+
         end_user_id = get_end_user_id_for_cost_tracking(
             litellm_params, service_type="prometheus"
         )
@@ -815,20 +871,8 @@ class PrometheusLogger(CustomLogger):
         user_api_key_auth_metadata: Optional[dict] = standard_logging_payload[
             "metadata"
         ].get("user_api_key_auth_metadata")
-        
-        # Include top-level metadata fields (excluding nested dictionaries)
-        # This allows accessing fields like requester_ip_address from top-level metadata
-        top_level_metadata = standard_logging_payload.get("metadata", {})
-        top_level_fields: Dict[str, Any] = {}
-        if isinstance(top_level_metadata, dict):
-            top_level_fields = {
-                k: v
-                for k, v in top_level_metadata.items()
-                if not isinstance(v, dict)  # Exclude nested dicts to avoid conflicts
-            }
-        
+
         combined_metadata: Dict[str, Any] = {
-            **top_level_fields,  # Include top-level fields first
             **(_requester_metadata if _requester_metadata else {}),
             **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}),
         }
@@ -945,6 +989,12 @@ class PrometheusLogger(CustomLogger):
             kwargs, start_time, end_time, enum_values, output_tokens
         )
 
+        # cache metrics
+        self._increment_cache_metrics(
+            standard_logging_payload=standard_logging_payload,  # type: ignore
+            enum_values=enum_values,
+        )
+
         if (
             standard_logging_payload["stream"] is True
         ):  # log successful streaming requests from logging event hook.
@@ -1014,6 +1064,54 @@ class PrometheusLogger(CustomLogger):
             standard_logging_payload["completion_tokens"]
         )
 
+    def _increment_cache_metrics(
+        self,
+        standard_logging_payload: StandardLoggingPayload,
+        enum_values: UserAPIKeyLabelValues,
+    ):
+        """
+        Increment cache-related Prometheus metrics based on cache hit/miss status.
+
+        Args:
+            standard_logging_payload: Contains cache_hit field (True/False/None)
+            enum_values: Label values for Prometheus metrics
+        """
+        cache_hit = standard_logging_payload.get("cache_hit")
+
+        # Only track if cache_hit has a definite value (True or False)
+        if cache_hit is None:
+            return
+
+        if cache_hit is True:
+            # Increment cache hits counter
+            _labels = prometheus_label_factory(
+                supported_enum_labels=self.get_labels_for_metric(
+                    metric_name="litellm_cache_hits_metric"
+                ),
+                enum_values=enum_values,
+            )
+            self.litellm_cache_hits_metric.labels(**_labels).inc()
+
+            # Increment cached tokens counter
+            total_tokens = standard_logging_payload.get("total_tokens", 0)
+            if total_tokens > 0:
+                _labels = prometheus_label_factory(
+                    supported_enum_labels=self.get_labels_for_metric(
+                        metric_name="litellm_cached_tokens_metric"
+                    ),
+                    enum_values=enum_values,
+                )
+                self.litellm_cached_tokens_metric.labels(**_labels).inc(total_tokens)
+        else:
+            # cache_hit is False - increment cache misses counter
+            _labels = prometheus_label_factory(
+                supported_enum_labels=self.get_labels_for_metric(
+                    metric_name="litellm_cache_misses_metric"
+                ),
+                enum_values=enum_values,
+            )
+            self.litellm_cache_misses_metric.labels(**_labels).inc()
+
     async def _increment_remaining_budget_metrics(
         self,
         user_api_team: Optional[str],
@@ -1182,6 +1280,22 @@ class PrometheusLogger(CustomLogger):
                 total_time_seconds
             )
 
+        # request queue time (time from arrival to processing start)
+        _litellm_params = kwargs.get("litellm_params", {}) or {}
+        queue_time_seconds = _litellm_params.get("metadata", {}).get(
+            "queue_time_seconds"
+        )
+        if queue_time_seconds is not None and queue_time_seconds >= 0:
+            _labels = prometheus_label_factory(
+                supported_enum_labels=self.get_labels_for_metric(
+                    metric_name="litellm_request_queue_time_seconds"
+                ),
+                enum_values=enum_values,
+            )
+            self.litellm_request_queue_time_metric.labels(**_labels).observe(
+                queue_time_seconds
+            )
+
     async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
         from litellm.types.utils import StandardLoggingPayload
 
@@ -1189,14 +1303,20 @@ class PrometheusLogger(CustomLogger):
             f"prometheus Logging - Enters failure logging function for kwargs {kwargs}"
         )
 
-        # unpack kwargs
-        model = kwargs.get("model", "")
         standard_logging_payload: StandardLoggingPayload = kwargs.get(
             "standard_logging_object", {}
         )
+        
+        if self._should_skip_metrics_for_invalid_key(
+            kwargs=kwargs, standard_logging_payload=standard_logging_payload
+        ):
+            return
+        
+        model = kwargs.get("model", "")
+        
         litellm_params = kwargs.get("litellm_params", {}) or {}
         get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking()
-        
+
         end_user_id = get_end_user_id_for_cost_tracking(
             litellm_params, service_type="prometheus"
         )
@@ -1207,7 +1327,6 @@ class PrometheusLogger(CustomLogger):
         user_api_team_alias = standard_logging_payload["metadata"][
             "user_api_key_team_alias"
         ]
-        kwargs.get("exception", None)
 
         try:
             self.litellm_llm_api_failed_requests_metric.labels(
@@ -1227,6 +1346,139 @@ class PrometheusLogger(CustomLogger):
             pass
         pass
 
+    def _extract_status_code(
+        self,
+        kwargs: Optional[dict] = None,
+        enum_values: Optional[Any] = None,
+        exception: Optional[Exception] = None,
+    ) -> Optional[int]:
+        """
+        Extract HTTP status code from various input formats for validation.
+        
+        This is a centralized helper to extract status code from different
+        callback function signatures. Handles both ProxyException (uses 'code')
+        and standard exceptions (uses 'status_code').
+        
+        Args:
+            kwargs: Dictionary potentially containing 'exception' key
+            enum_values: Object with 'status_code' attribute
+            exception: Exception object to extract status code from directly
+            
+        Returns:
+            Status code as integer if found, None otherwise
+        """
+        status_code = None
+        
+        # Try from enum_values first (most common in our callbacks)
+        if enum_values and hasattr(enum_values, "status_code") and enum_values.status_code:
+            try:
+                status_code = int(enum_values.status_code)
+            except (ValueError, TypeError):
+                pass
+        
+        if not status_code and exception:
+            # ProxyException uses 'code' attribute, other exceptions may use 'status_code'
+            status_code = getattr(exception, "status_code", None) or getattr(exception, "code", None)
+            if status_code is not None:
+                try:
+                    status_code = int(status_code)
+                except (ValueError, TypeError):
+                    status_code = None
+        
+        if not status_code and kwargs:
+            exception_in_kwargs = kwargs.get("exception")
+            if exception_in_kwargs:
+                status_code = getattr(exception_in_kwargs, "status_code", None) or getattr(exception_in_kwargs, "code", None)
+                if status_code is not None:
+                    try:
+                        status_code = int(status_code)
+                    except (ValueError, TypeError):
+                        status_code = None
+        
+        return status_code
+    
+    def _is_invalid_api_key_request(
+        self,
+        status_code: Optional[int],
+        exception: Optional[Exception] = None,
+    ) -> bool:
+        """
+        Determine if a request has an invalid API key based on status code and exception.
+        
+        This method prevents invalid authentication attempts from being recorded in
+        Prometheus metrics. A 401 status code is the definitive indicator of authentication
+        failure. Additionally, we check exception messages for authentication error patterns
+        to catch cases where the exception hasn't been converted to a ProxyException yet.
+        
+        Args:
+            status_code: HTTP status code (401 indicates authentication error)
+            exception: Exception object to check for auth-related error messages
+            
+        Returns:
+            True if the request has an invalid API key and metrics should be skipped,
+            False otherwise
+        """
+        if status_code == 401:
+            return True
+        
+        # Handle cases where AssertionError is raised before conversion to ProxyException
+        if exception is not None:
+            exception_str = str(exception).lower()
+            auth_error_patterns = [
+                "virtual key expected",
+                "expected to start with 'sk-'",
+                "authentication error",
+                "invalid api key",
+                "api key not valid",
+            ]
+            if any(pattern in exception_str for pattern in auth_error_patterns):
+                return True
+        
+        return False
+    
+    def _should_skip_metrics_for_invalid_key(
+        self,
+        kwargs: Optional[dict] = None,
+        user_api_key_dict: Optional[Any] = None,
+        enum_values: Optional[Any] = None,
+        standard_logging_payload: Optional[Union[dict, StandardLoggingPayload]] = None,
+        exception: Optional[Exception] = None,
+    ) -> bool:
+        """
+        Determine if Prometheus metrics should be skipped for invalid API key requests.
+        
+        This is a centralized validation method that extracts status code and exception
+        information from various callback function signatures and determines if the request
+        represents an invalid API key attempt that should be filtered from metrics.
+        
+        Args:
+            kwargs: Dictionary potentially containing exception and other data
+            user_api_key_dict: User API key authentication object (currently unused)
+            enum_values: Object with status_code attribute
+            standard_logging_payload: Standard logging payload dictionary
+            exception: Exception object to check directly
+            
+        Returns:
+            True if metrics should be skipped (invalid key detected), False otherwise
+        """
+        status_code = self._extract_status_code(
+            kwargs=kwargs,
+            enum_values=enum_values,
+            exception=exception,
+        )
+        
+        if exception is None and kwargs:
+            exception = kwargs.get("exception")
+        
+        if self._is_invalid_api_key_request(status_code, exception=exception):
+            verbose_logger.debug(
+                "Skipping Prometheus metrics for invalid API key request: "
+                f"status_code={status_code}, exception={type(exception).__name__ if exception else None}"
+            )
+            return True
+        
+        return False
+
     async def async_post_call_failure_hook(
         self,
         request_data: dict,
@@ -1252,6 +1504,14 @@ class PrometheusLogger(CustomLogger):
             StandardLoggingPayloadSetup,
         )
 
+        if self._should_skip_metrics_for_invalid_key(
+            user_api_key_dict=user_api_key_dict,
+            exception=original_exception,
+        ):
+            return
+
+        status_code = self._extract_status_code(exception=original_exception)
+
         try:
             _tags = StandardLoggingPayloadSetup._get_request_tags(
                 litellm_params=request_data,
@@ -1266,8 +1526,8 @@ class PrometheusLogger(CustomLogger):
                 team=user_api_key_dict.team_id,
                 team_alias=user_api_key_dict.team_alias,
                 requested_model=request_data.get("model", ""),
-                status_code=str(getattr(original_exception, "status_code", None)),
-                exception_status=str(getattr(original_exception, "status_code", None)),
+                status_code=str(status_code),
+                exception_status=str(status_code),
                 exception_class=self._get_exception_class_name(original_exception),
                 tags=_tags,
                 route=user_api_key_dict.request_route,
@@ -1305,6 +1565,11 @@ class PrometheusLogger(CustomLogger):
                 StandardLoggingPayloadSetup,
             )
 
+            if self._should_skip_metrics_for_invalid_key(
+                user_api_key_dict=user_api_key_dict
+            ):
+                return
+
             enum_values = UserAPIKeyLabelValues(
                 end_user=user_api_key_dict.end_user_id,
                 hashed_api_key=user_api_key_dict.api_key,
@@ -1360,6 +1625,15 @@ class PrometheusLogger(CustomLogger):
             exception = request_kwargs.get("exception", None)
 
             llm_provider = _litellm_params.get("custom_llm_provider", None)
+            
+            if self._should_skip_metrics_for_invalid_key(
+                kwargs=request_kwargs,
+                standard_logging_payload=standard_logging_payload,
+            ):
+                return
+            hashed_api_key = standard_logging_payload.get("metadata", {}).get(
+                "user_api_key_hash"
+            )
 
             # Create enum_values for the label factory (always create for use in different metrics)
             enum_values = UserAPIKeyLabelValues(
@@ -1374,9 +1648,7 @@ class PrometheusLogger(CustomLogger):
                     self._get_exception_class_name(exception) if exception else None
                 ),
                 requested_model=model_group,
-                hashed_api_key=standard_logging_payload["metadata"][
-                    "user_api_key_hash"
-                ],
+                hashed_api_key=hashed_api_key,
                 api_key_alias=standard_logging_payload["metadata"][
                     "user_api_key_alias"
                 ],
@@ -1398,7 +1670,6 @@ class PrometheusLogger(CustomLogger):
                 api_provider=llm_provider or "",
             )
             if exception is not None:
-
                 _labels = prometheus_label_factory(
                     supported_enum_labels=self.get_labels_for_metric(
                         metric_name="litellm_deployment_failure_responses"
@@ -1431,16 +1702,23 @@ class PrometheusLogger(CustomLogger):
         enum_values: UserAPIKeyLabelValues,
         output_tokens: float = 1.0,
     ):
-
         try:
             verbose_logger.debug("setting remaining tokens requests metric")
-            standard_logging_payload: Optional[StandardLoggingPayload] = (
-                request_kwargs.get("standard_logging_object")
-            )
+            standard_logging_payload: Optional[
+                StandardLoggingPayload
+            ] = request_kwargs.get("standard_logging_object")
 
             if standard_logging_payload is None:
                 return
 
+            # Skip recording metrics for invalid API key requests
+            if self._should_skip_metrics_for_invalid_key(
+                kwargs=request_kwargs,
+                enum_values=enum_values,
+                standard_logging_payload=standard_logging_payload,
+            ):
+                return
+
             api_base = standard_logging_payload["api_base"]
             _litellm_params = request_kwargs.get("litellm_params", {}) or {}
             _metadata = _litellm_params.get("metadata", {})
@@ -1571,6 +1849,50 @@ class PrometheusLogger(CustomLogger):
             )
             return
 
+    def _record_guardrail_metrics(
+        self,
+        guardrail_name: str,
+        latency_seconds: float,
+        status: str,
+        error_type: Optional[str],
+        hook_type: str,
+    ):
+        """
+        Record guardrail metrics for prometheus.
+
+        Args:
+            guardrail_name: Name of the guardrail
+            latency_seconds: Execution latency in seconds
+            status: "success" or "error"
+            error_type: Type of error if any, None otherwise
+            hook_type: "pre_call", "during_call", or "post_call"
+        """
+        try:
+            # Record latency
+            self.litellm_guardrail_latency_metric.labels(
+                guardrail_name=guardrail_name,
+                status=status,
+                error_type=error_type or "none",
+                hook_type=hook_type,
+            ).observe(latency_seconds)
+
+            # Record request count
+            self.litellm_guardrail_requests_total.labels(
+                guardrail_name=guardrail_name,
+                status=status,
+                hook_type=hook_type,
+            ).inc()
+
+            # Record error count if there was an error
+            if status == "error" and error_type:
+                self.litellm_guardrail_errors_total.labels(
+                    guardrail_name=guardrail_name,
+                    error_type=error_type,
+                    hook_type=hook_type,
+                ).inc()
+        except Exception as e:
+            verbose_logger.debug(f"Error recording guardrail metrics: {str(e)}")
+
     @staticmethod
     def _get_exception_class_name(exception: Exception) -> str:
         exception_class_name = ""
@@ -2208,10 +2530,10 @@ class PrometheusLogger(CustomLogger):
         from litellm.constants import PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES
         from litellm.integrations.custom_logger import CustomLogger
 
-        prometheus_loggers: List[CustomLogger] = (
-            litellm.logging_callback_manager.get_custom_loggers_for_type(
-                callback_type=PrometheusLogger
-            )
+        prometheus_loggers: List[
+            CustomLogger
+        ] = litellm.logging_callback_manager.get_custom_loggers_for_type(
+            callback_type=PrometheusLogger
         )
         # we need to get the initialized prometheus logger instance(s) and call logger.initialize_remaining_budget_metrics() on them
         verbose_logger.debug("found %s prometheus loggers", len(prometheus_loggers))
@@ -2283,7 +2605,7 @@ def prometheus_label_factory(
 
     if UserAPIKeyLabelNames.END_USER.value in filtered_labels:
         get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking()
-        
+
         filtered_labels["end_user"] = get_end_user_id_for_cost_tracking(
             litellm_params={"user_api_key_end_user_id": enum_values.end_user},
             service_type="prometheus",
diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py
index 47cbcb8aec9..a3c25ab65e9 100644
--- a/litellm/litellm_core_utils/custom_logger_registry.py
+++ b/litellm/litellm_core_utils/custom_logger_registry.py
@@ -18,6 +18,7 @@ from litellm.integrations.azure_storage.azure_storage import AzureBlobStorageLog
 from litellm.integrations.bitbucket import BitBucketPromptManager
 from litellm.integrations.braintrust_logging import BraintrustLogger
 from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger
+from litellm.integrations.focus.focus_logger import FocusLogger
 from litellm.integrations.datadog.datadog import DataDogLogger
 from litellm.integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger
 from litellm.integrations.deepeval import DeepEvalLogger
@@ -93,6 +94,7 @@ class CustomLoggerRegistry:
         "bitbucket": BitBucketPromptManager,
         "gitlab": GitLabPromptManager,
         "cloudzero": CloudZeroLogger,
+        "focus": FocusLogger,
         "posthog": PostHogLogger,
     }
 
diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py
index b753e9fa8b5..21d69177336 100644
--- a/litellm/litellm_core_utils/get_llm_provider_logic.py
+++ b/litellm/litellm_core_utils/get_llm_provider_logic.py
@@ -913,6 +913,14 @@ def _get_openai_compatible_provider_info(  # noqa: PLR0915
             or "http://localhost:2024"
         )
         dynamic_api_key = api_key or get_secret_str("LANGGRAPH_API_KEY")
+    elif custom_llm_provider == "manus":
+        # Manus is OpenAI compatible for responses API
+        api_base = (
+            api_base
+            or get_secret_str("MANUS_API_BASE")
+            or "https://api.manus.im"
+        )
+        dynamic_api_key = api_key or get_secret_str("MANUS_API_KEY")
 
     if api_base is not None and not isinstance(api_base, str):
         raise Exception("api base needs to be a string. api_base={}".format(api_base))
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 5448fe7c771..e0a799d8e5c 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -3756,6 +3756,15 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
             cloudzero_logger = CloudZeroLogger()
             _in_memory_loggers.append(cloudzero_logger)
             return cloudzero_logger  # type: ignore
+        elif logging_integration == "focus":
+            from litellm.integrations.focus.focus_logger import FocusLogger
+
+            for callback in _in_memory_loggers:
+                if isinstance(callback, FocusLogger):
+                    return callback  # type: ignore
+            focus_logger = FocusLogger()
+            _in_memory_loggers.append(focus_logger)
+            return focus_logger  # type: ignore
         elif logging_integration == "deepeval":
             for callback in _in_memory_loggers:
                 if isinstance(callback, DeepEvalLogger):
@@ -4076,6 +4085,12 @@ def get_custom_logger_compatible_class(  # noqa: PLR0915
             for callback in _in_memory_loggers:
                 if isinstance(callback, CloudZeroLogger):
                     return callback
+        elif logging_integration == "focus":
+            from litellm.integrations.focus.focus_logger import FocusLogger
+
+            for callback in _in_memory_loggers:
+                if isinstance(callback, FocusLogger):
+                    return callback
         elif logging_integration == "deepeval":
             for callback in _in_memory_loggers:
                 if isinstance(callback, DeepEvalLogger):
@@ -4800,7 +4815,7 @@ class StandardLoggingPayloadSetup:
         """
         Extract additional header tags for spend tracking based on config.
         """
-        extra_headers: List[str] = litellm.extra_spend_tag_headers or []
+        extra_headers: List[str] = getattr(litellm, "extra_spend_tag_headers", None) or []
         if not extra_headers:
             return None
 
@@ -4824,9 +4839,9 @@ class StandardLoggingPayloadSetup:
         metadata = litellm_params.get("metadata") or {}
         litellm_metadata = litellm_params.get("litellm_metadata") or {}
         if metadata.get("tags", []):
-            request_tags = metadata.get("tags", [])
+            request_tags = metadata.get("tags", []).copy()
         elif litellm_metadata.get("tags", []):
-            request_tags = litellm_metadata.get("tags", [])
+            request_tags = litellm_metadata.get("tags", []).copy()
         else:
             request_tags = []
         user_agent_tags = StandardLoggingPayloadSetup._get_user_agent_tags(
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index cbc0763382c..65e77f014a3 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -485,9 +485,14 @@ def _calculate_input_cost(
         model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"]
     )
 
-    ### IMAGE TOKEN COST (for gpt-image-1 and similar models)
+    ### IMAGE TOKEN COST
+    # For image token costs:
+    # First check if input_cost_per_image_token is available. If not, default to generic input_cost_per_token.
+    image_token_cost_key = "input_cost_per_image_token"
+    if model_info.get(image_token_cost_key) is None:
+        image_token_cost_key = "input_cost_per_token"
     prompt_cost += calculate_cost_component(
-        model_info, "input_cost_per_image_token", prompt_tokens_details["image_tokens"]
+        model_info, image_token_cost_key, prompt_tokens_details["image_tokens"]
     )
 
     ### CACHE WRITING COST - Now uses tiered pricing
@@ -521,7 +526,7 @@ def _calculate_input_cost(
     return prompt_cost
 
 
-def generic_cost_per_token(
+def generic_cost_per_token(  # noqa: PLR0915
     model: str,
     usage: Usage,
     custom_llm_provider: str,
diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index b100b9b516b..a8b8b207de4 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -6,6 +6,7 @@ import io
 import mimetypes
 import re
 from os import PathLike
+from pathlib import Path
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -94,7 +95,9 @@ def handle_messages_with_content_list_to_str_conversion(
     return messages
 
 
-def strip_name_from_message(message: AllMessageValues, allowed_name_roles: List[str] = ["user"]) -> AllMessageValues:
+def strip_name_from_message(
+    message: AllMessageValues, allowed_name_roles: List[str] = ["user"]
+) -> AllMessageValues:
     """
     Removes 'name' from message
     """
@@ -103,6 +106,7 @@ def strip_name_from_message(message: AllMessageValues, allowed_name_roles: List[
         msg_copy.pop("name", None)  # type: ignore
     return msg_copy
 
+
 def strip_name_from_messages(
     messages: List[AllMessageValues], allowed_name_roles: List[str] = ["user"]
 ) -> List[AllMessageValues]:
@@ -443,7 +447,7 @@ def update_responses_input_with_model_file_ids(
     """
     Updates responses API input with provider-specific file IDs.
     File IDs are always inside the content array, not as direct input_file items.
-    
+
     For managed files (unified file IDs), decodes the base64-encoded unified file ID
     and extracts the llm_output_file_id directly.
     """
@@ -451,25 +455,28 @@ def update_responses_input_with_model_file_ids(
         _is_base64_encoded_unified_file_id,
         convert_b64_uid_to_unified_uid,
     )
-    
+
     if isinstance(input, str):
         return input
-    
+
     if not isinstance(input, list):
         return input
-    
+
     updated_input = []
     for item in input:
         if not isinstance(item, dict):
             updated_input.append(item)
             continue
-        
+
         updated_item = item.copy()
         content = item.get("content")
         if isinstance(content, list):
             updated_content = []
             for content_item in content:
-                if isinstance(content_item, dict) and content_item.get("type") == "input_file":
+                if (
+                    isinstance(content_item, dict)
+                    and content_item.get("type") == "input_file"
+                ):
                     file_id = content_item.get("file_id")
                     if file_id:
                         # Check if this is a managed file ID (base64-encoded unified file ID)
@@ -477,7 +484,9 @@ def update_responses_input_with_model_file_ids(
                         if is_unified_file_id:
                             unified_file_id = convert_b64_uid_to_unified_uid(file_id)
                             if "llm_output_file_id," in unified_file_id:
-                                provider_file_id = unified_file_id.split("llm_output_file_id,")[1].split(";")[0]
+                                provider_file_id = unified_file_id.split(
+                                    "llm_output_file_id,"
+                                )[1].split(";")[0]
                             else:
                                 # Fallback: keep original if we can't extract
                                 provider_file_id = file_id
@@ -491,9 +500,9 @@ def update_responses_input_with_model_file_ids(
                 else:
                     updated_content.append(content_item)
             updated_item["content"] = updated_content
-        
+
         updated_input.append(updated_item)
-    
+
     return updated_input
 
 
@@ -533,6 +542,12 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData:
     # Convert content to bytes
     if isinstance(file_content, (str, PathLike)):
         # If it's a path, open and read the file
+        # Extract filename from path if not already set
+        if filename is None:
+            if isinstance(file_content, PathLike):
+                filename = Path(file_content).name
+            else:
+                filename = Path(str(file_content)).name
         with open(file_content, "rb") as f:
             content = f.read()
     elif isinstance(file_content, io.IOBase):
@@ -550,11 +565,11 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData:
 
     # Use provided content type or guess based on filename
     if not content_type:
-        content_type = (
-            mimetypes.guess_type(filename)[0]
-            if filename
-            else "application/octet-stream"
-        )
+        if filename:
+            guessed_type = mimetypes.guess_type(filename)[0]
+            content_type = guessed_type if guessed_type else "application/octet-stream"
+        else:
+            content_type = "application/octet-stream"
 
     return ExtractedFileData(
         filename=filename,
@@ -690,9 +705,9 @@ def _get_image_mime_type_from_url(url: str) -> Optional[str]:
     video/flv
     """
     from urllib.parse import urlparse
-    
+
     url = url.lower()
-    
+
     # Parse URL to extract path without query parameters
     # This handles URLs like: https://example.com/image.jpg?signature=...
     parsed = urlparse(url)
@@ -737,28 +752,28 @@ def infer_content_type_from_url_and_content(
 ) -> str:
     """
     Infer content type from URL extension and binary content when content-type header is missing or generic.
-    
+
     This helper implements a fallback strategy for determining MIME types when HTTP headers
     are missing or provide generic values (like binary/octet-stream). It's commonly used
     when processing images and documents from various sources (S3, URLs, etc.).
-    
+
     Fallback Strategy:
     1. If current_content_type is valid (not None and not generic octet-stream), return it
     2. Try to infer from URL extension (handles query parameters)
     3. Try to detect from binary content signature (magic bytes)
     4. Raise ValueError if all methods fail
-    
+
     Args:
         url: The URL of the content (used to extract file extension)
         content: The binary content (first ~100 bytes are sufficient for detection)
         current_content_type: The current content-type from headers (may be None or generic)
-    
+
     Returns:
         str: The inferred MIME type (e.g., "image/png", "application/pdf")
-        
+
     Raises:
         ValueError: If content type cannot be determined by any method
-        
+
     Example:
         >>> content_type = infer_content_type_from_url_and_content(
         ...     url="https://s3.amazonaws.com/bucket/image.png?AWSAccessKeyId=123",
@@ -769,14 +784,14 @@ def infer_content_type_from_url_and_content(
         "image/png"
     """
     from litellm.litellm_core_utils.token_counter import get_image_type
-    
+
     # If we have a valid content type that's not generic, use it
     if current_content_type and current_content_type not in [
         "binary/octet-stream",
         "application/octet-stream",
     ]:
         return current_content_type
-    
+
     # Extension to MIME type mapping
     # Supports images, documents, and other common file types
     extension_to_mime = {
@@ -797,14 +812,14 @@ def infer_content_type_from_url_and_content(
         "txt": "text/plain",
         "md": "text/markdown",
     }
-    
+
     # Try to infer from URL extension
     if url:
         extension = url.split(".")[-1].lower().split("?")[0]  # Remove query params
         inferred_type = extension_to_mime.get(extension)
         if inferred_type:
             return inferred_type
-    
+
     # Try to detect from binary content signature (magic bytes)
     if content:
         detected_type = get_image_type(content[:100])
@@ -818,7 +833,7 @@ def infer_content_type_from_url_and_content(
             }
             if detected_type in type_to_mime:
                 return type_to_mime[detected_type]
-    
+
     # If all fallbacks failed, raise error
     raise ValueError(
         f"Unable to determine content type from URL: {url}. "
@@ -1078,7 +1093,9 @@ def _parse_content_for_reasoning(
         return None, message_text
 
     reasoning_match = re.match(
-        r"<(?:think|thinking|budget:thinking)>(.*?)</(?:think|thinking|budget:thinking)>(.*)", message_text, re.DOTALL
+        r"<(?:think|thinking|budget:thinking)>(.*?)</(?:think|thinking|budget:thinking)>(.*)",
+        message_text,
+        re.DOTALL,
     )
 
     if reasoning_match:
@@ -1128,3 +1145,47 @@ def extract_images_from_message(message: AllMessageValues) -> List[str]:
                 elif isinstance(image_url, dict) and "url" in image_url:
                     images.append(_extract_base64_data(image_url["url"]))
     return images
+
+
+def parse_tool_call_arguments(
+    arguments: Optional[str],
+    tool_name: Optional[str] = None,
+    context: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Parse tool call arguments from a JSON string.
+
+    This function handles malformed JSON gracefully by raising a ValueError
+    with context about what failed and what the problematic input was.
+
+    Args:
+        arguments: The JSON string containing tool arguments, or None.
+        tool_name: Optional name of the tool (for error messages).
+        context: Optional context string (e.g., "Anthropic Messages API").
+
+    Returns:
+        Parsed arguments as a dictionary. Returns empty dict if arguments is None or empty.
+
+    Raises:
+        ValueError: If the arguments string is not valid JSON.
+    """
+    import json
+
+    if not arguments:
+        return {}
+
+    try:
+        return json.loads(arguments)
+    except json.JSONDecodeError as e:
+        error_parts = ["Failed to parse tool call arguments"]
+
+        if tool_name:
+            error_parts.append(f"for tool '{tool_name}'")
+        if context:
+            error_parts.append(f"({context})")
+
+        error_message = (
+            " ".join(error_parts) + f". Error: {str(e)}. Arguments: {arguments}"
+        )
+
+        raise ValueError(error_message) from e
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 0c331e43038..4320f756454 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -44,6 +44,7 @@ from .common_utils import (
     convert_content_list_to_str,
     infer_content_type_from_url_and_content,
     is_non_content_values_set,
+    parse_tool_call_arguments,
 )
 from .image_handling import convert_url_to_base64
 
@@ -911,13 +912,13 @@ def convert_to_anthropic_image_obj(
 
 
 def create_anthropic_image_param(
-    image_url_input: Union[str, dict], 
+    image_url_input: Union[str, dict],
     format: Optional[str] = None,
-    is_bedrock_invoke: bool = False
+    is_bedrock_invoke: bool = False,
 ) -> AnthropicMessagesImageParam:
     """
     Create an AnthropicMessagesImageParam from an image URL input.
-    
+
     Supports both URL references (for HTTP/HTTPS URLs) and base64 encoding.
     """
     # Extract URL and format from input
@@ -927,7 +928,7 @@ def create_anthropic_image_param(
         image_url = image_url_input.get("url", "")
         if format is None:
             format = image_url_input.get("format")
-    
+
     # Check if the image URL is an HTTP/HTTPS URL
     if image_url.startswith("http://") or image_url.startswith("https://"):
         # For Bedrock invoke and Vertex AI Anthropic, always convert URLs to base64
@@ -1031,9 +1032,11 @@ def convert_to_anthropic_tool_invoke_xml(tool_calls: list) -> str:
         tool_function = get_attribute_or_key(tool, "function")
         tool_name = get_attribute_or_key(tool_function, "name")
         tool_arguments = get_attribute_or_key(tool_function, "arguments")
+        parsed_args = parse_tool_call_arguments(
+            tool_arguments, tool_name=tool_name, context="Anthropic XML tool invoke"
+        )
         parameters = "".join(
-            f"<{param}>{val}</{param}>\n"
-            for param, val in json.loads(tool_arguments).items()
+            f"<{param}>{val}</{param}>\n" for param, val in parsed_args.items()
         )
         invokes += (
             "<invoke>\n"
@@ -1071,8 +1074,14 @@ def anthropic_messages_pt_xml(messages: list):
             if isinstance(messages[msg_i]["content"], list):
                 for m in messages[msg_i]["content"]:
                     if m.get("type", "") == "image_url":
-                        format = m["image_url"].get("format") if isinstance(m["image_url"], dict) else None
-                        image_param = create_anthropic_image_param(m["image_url"], format=format)
+                        format = (
+                            m["image_url"].get("format")
+                            if isinstance(m["image_url"], dict)
+                            else None
+                        )
+                        image_param = create_anthropic_image_param(
+                            m["image_url"], format=format
+                        )
                         # Convert to dict format for XML version
                         source = image_param["source"]
                         if isinstance(source, dict) and source.get("type") == "url":
@@ -1381,10 +1390,10 @@ def convert_to_gemini_tool_call_invoke(
         if tool_calls is not None:
             for idx, tool in enumerate(tool_calls):
                 if "function" in tool:
-                    gemini_function_call: Optional[
-                        VertexFunctionCall
-                    ] = _gemini_tool_call_invoke_helper(
-                        function_call_params=tool["function"]
+                    gemini_function_call: Optional[VertexFunctionCall] = (
+                        _gemini_tool_call_invoke_helper(
+                            function_call_params=tool["function"]
+                        )
                     )
                     if gemini_function_call is not None:
                         part_dict: VertexPartType = {
@@ -1484,10 +1493,10 @@ def convert_to_gemini_tool_call_result(
     }
     """
     from litellm.types.llms.vertex_ai import BlobType
-    
+
     content_str: str = ""
     inline_data: Optional[BlobType] = None
-    
+
     if "content" in message:
         if isinstance(message["content"], str):
             content_str = message["content"]
@@ -1500,15 +1509,21 @@ def convert_to_gemini_tool_call_result(
                 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", "")
-                    image_url = image_url_data.get("url", "") if isinstance(image_url_data, dict) else image_url_data
-                    
+                    image_url = (
+                        image_url_data.get("url", "")
+                        if isinstance(image_url_data, dict)
+                        else image_url_data
+                    )
+
                     if image_url:
                         # Convert image to base64 blob format for Gemini
                         try:
-                            image_obj = convert_to_anthropic_image_obj(image_url, format=None)
+                            image_obj = convert_to_anthropic_image_obj(
+                                image_url, format=None
+                            )
                             inline_data = BlobType(
                                 data=image_obj["data"],
-                                mime_type=image_obj["media_type"]
+                                mime_type=image_obj["media_type"],
                             )
                         except Exception as e:
                             verbose_logger.warning(
@@ -1541,6 +1556,7 @@ def convert_to_gemini_tool_call_result(
     response_data: dict
     try:
         import json
+
         if content_str.strip().startswith("{") or content_str.strip().startswith("["):
             # Try to parse as JSON (for Computer Use structured responses)
             parsed = json.loads(content_str)
@@ -1553,7 +1569,7 @@ def convert_to_gemini_tool_call_result(
     except (json.JSONDecodeError, ValueError):
         # Not valid JSON, wrap in content field
         response_data = {"content": content_str}
-    
+
     # We can't determine from openai message format whether it's a successful or
     # error call result so default to the successful result template
     _function_response = VertexFunctionResponse(
@@ -1562,7 +1578,7 @@ def convert_to_gemini_tool_call_result(
 
     # 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:
     # - One part with function_response
     # - One part with inline_data
@@ -1570,19 +1586,19 @@ def convert_to_gemini_tool_call_result(
     if inline_data:
         image_part: VertexPartType = {"inline_data": inline_data}
         return [_part, image_part]
-    
+
     return _part
 
 
 def _sanitize_anthropic_tool_use_id(tool_use_id: str) -> str:
     """
     Sanitize tool_use_id to match Anthropic's required pattern: ^[a-zA-Z0-9_-]+$
-    
+
     Anthropic requires tool_use_id to only contain alphanumeric characters, underscores, and hyphens.
     This function replaces any invalid characters with underscores.
     """
     # Replace any character that's not alphanumeric, underscore, or hyphen with underscore
-    sanitized = re.sub(r'[^a-zA-Z0-9_-]', '_', tool_use_id)
+    sanitized = re.sub(r"[^a-zA-Z0-9_-]", "_", tool_use_id)
     # Ensure it's not empty (fallback to a default if needed)
     if not sanitized:
         sanitized = "tool_use_id"
@@ -1644,10 +1660,19 @@ def convert_to_anthropic_tool_result(
                     )
                 )
             elif content["type"] == "image_url":
-                format = content["image_url"].get("format") if isinstance(content["image_url"], dict) else None
-                anthropic_content_list.append(
-                    create_anthropic_image_param(content["image_url"], format=format)
+                format = (
+                    content["image_url"].get("format")
+                    if isinstance(content["image_url"], dict)
+                    else None
                 )
+                _anthropic_image_param = create_anthropic_image_param(
+                    content["image_url"], format=format
+                )
+                _anthropic_image_param = add_cache_control_to_content(
+                    anthropic_content_element=_anthropic_image_param,
+                    original_content_element=content,
+                )
+                anthropic_content_list.append(_anthropic_image_param)
 
         anthropic_content = anthropic_content_list
     anthropic_tool_result: Optional[AnthropicMessagesToolResultParam] = None
@@ -1662,7 +1687,9 @@ def convert_to_anthropic_tool_result(
         # We can't determine from openai message format whether it's a successful or
         # error call result so default to the successful result template
         anthropic_tool_result = AnthropicMessagesToolResultParam(
-            type="tool_result", tool_use_id=sanitized_tool_use_id, content=anthropic_content
+            type="tool_result",
+            tool_use_id=sanitized_tool_use_id,
+            content=anthropic_content,
         )
 
     if message["role"] == "function":
@@ -1671,7 +1698,9 @@ def convert_to_anthropic_tool_result(
         # Sanitize tool_use_id to match Anthropic's pattern requirement: ^[a-zA-Z0-9_-]+$
         sanitized_tool_use_id = _sanitize_anthropic_tool_use_id(tool_call_id)
         anthropic_tool_result = AnthropicMessagesToolResultParam(
-            type="tool_result", tool_use_id=sanitized_tool_use_id, content=anthropic_content
+            type="tool_result",
+            tool_use_id=sanitized_tool_use_id,
+            content=anthropic_content,
         )
 
     if anthropic_tool_result is None:
@@ -1687,12 +1716,17 @@ def convert_function_to_anthropic_tool_invoke(
     try:
         _name = get_attribute_or_key(function_call, "name") or ""
         _arguments = get_attribute_or_key(function_call, "arguments")
+
+        tool_input = parse_tool_call_arguments(
+            _arguments, tool_name=_name, context="Anthropic function to tool invoke"
+        )
+
         anthropic_tool_invoke = [
             AnthropicMessagesToolUseParam(
                 type="tool_use",
                 id=str(uuid.uuid4()),
                 name=_name,
-                input=json.loads(_arguments) if _arguments else {},
+                input=tool_input,
             )
         ]
         return anthropic_tool_invoke
@@ -1746,7 +1780,9 @@ def convert_to_anthropic_tool_invoke(
 
     Fixes: https://github.com/BerriAI/litellm/issues/17737
     """
-    anthropic_tool_invoke: List[Union[AnthropicMessagesToolUseParam, Dict[str, Any]]] = []
+    anthropic_tool_invoke: List[
+        Union[AnthropicMessagesToolUseParam, Dict[str, Any]]
+    ] = []
 
     for tool in tool_calls:
         if not get_attribute_or_key(tool, "type") == "function":
@@ -1757,10 +1793,10 @@ def convert_to_anthropic_tool_invoke(
             str,
             get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"),
         )
-        tool_input = json.loads(
-            get_attribute_or_key(
-                get_attribute_or_key(tool, "function"), "arguments"
-            )
+        tool_input = parse_tool_call_arguments(
+            get_attribute_or_key(get_attribute_or_key(tool, "function"), "arguments"),
+            tool_name=tool_name,
+            context="Anthropic tool invoke",
         )
 
         # Check if this is a server-side tool (web_search, tool_search, etc.)
@@ -2012,11 +2048,17 @@ def anthropic_messages_pt(  # noqa: PLR0915
                     for m in user_message_types_block["content"]:
                         if m.get("type", "") == "image_url":
                             m = cast(ChatCompletionImageObject, m)
-                            format = m["image_url"].get("format") if isinstance(m["image_url"], dict) else None
+                            format = (
+                                m["image_url"].get("format")
+                                if isinstance(m["image_url"], dict)
+                                else None
+                            )
                             # Convert ChatCompletionImageUrlObject to dict if needed
                             image_url_value = m["image_url"]
                             if isinstance(image_url_value, str):
-                                image_url_input: Union[str, dict[str, Any]] = image_url_value
+                                image_url_input: Union[str, dict[str, Any]] = (
+                                    image_url_value
+                                )
                             else:
                                 # ChatCompletionImageUrlObject or dict case - convert to dict
                                 image_url_input = {
@@ -2026,20 +2068,26 @@ def anthropic_messages_pt(  # noqa: PLR0915
                             # Bedrock invoke models have format: invoke/...
                             # Vertex AI Anthropic also doesn't support URL sources for images
                             is_bedrock_invoke = model.lower().startswith("invoke/")
-                            is_vertex_ai = llm_provider.startswith("vertex_ai") if llm_provider else False
+                            is_vertex_ai = (
+                                llm_provider.startswith("vertex_ai")
+                                if llm_provider
+                                else False
+                            )
                             force_base64 = is_bedrock_invoke or is_vertex_ai
                             _anthropic_content_element = create_anthropic_image_param(
-                                image_url_input, format=format, is_bedrock_invoke=force_base64
-                            ) 
+                                image_url_input,
+                                format=format,
+                                is_bedrock_invoke=force_base64,
+                            )
                             _content_element = add_cache_control_to_content(
                                 anthropic_content_element=_anthropic_content_element,
                                 original_content_element=dict(m),
                             )
 
                             if "cache_control" in _content_element:
-                                _anthropic_content_element[
-                                    "cache_control"
-                                ] = _content_element["cache_control"]
+                                _anthropic_content_element["cache_control"] = (
+                                    _content_element["cache_control"]
+                                )
                             user_content.append(_anthropic_content_element)
                         elif m.get("type", "") == "text":
                             m = cast(ChatCompletionTextObject, m)
@@ -2077,9 +2125,9 @@ def anthropic_messages_pt(  # noqa: PLR0915
                     )
 
                     if "cache_control" in _content_element:
-                        _anthropic_content_text_element[
-                            "cache_control"
-                        ] = _content_element["cache_control"]
+                        _anthropic_content_text_element["cache_control"] = (
+                            _content_element["cache_control"]
+                        )
 
                     user_content.append(_anthropic_content_text_element)
 
@@ -2175,18 +2223,27 @@ def anthropic_messages_pt(  # noqa: PLR0915
             ):  # support assistant tool invoke conversion
                 # Get web_search_results from provider_specific_fields for server_tool_use reconstruction
                 # Fixes: https://github.com/BerriAI/litellm/issues/17737
-                _provider_specific_fields_raw = assistant_content_block.get("provider_specific_fields")
+                _provider_specific_fields_raw = assistant_content_block.get(
+                    "provider_specific_fields"
+                )
                 _provider_specific_fields: Dict[str, Any] = {}
                 if isinstance(_provider_specific_fields_raw, dict):
-                    _provider_specific_fields = cast(Dict[str, Any], _provider_specific_fields_raw)
-                _web_search_results = _provider_specific_fields.get("web_search_results")
+                    _provider_specific_fields = cast(
+                        Dict[str, Any], _provider_specific_fields_raw
+                    )
+                _web_search_results = _provider_specific_fields.get(
+                    "web_search_results"
+                )
                 tool_invoke_results = convert_to_anthropic_tool_invoke(
                     assistant_tool_calls,
                     web_search_results=_web_search_results,
                 )
                 # AnthropicMessagesAssistantMessageValues includes AnthropicMessagesToolUseParam
                 assistant_content.extend(
-                    cast(List[AnthropicMessagesAssistantMessageValues], tool_invoke_results)
+                    cast(
+                        List[AnthropicMessagesAssistantMessageValues],
+                        tool_invoke_results,
+                    )
                 )
 
             assistant_function_call = assistant_content_block.get("function_call")
@@ -3249,14 +3306,18 @@ def _convert_to_bedrock_tool_call_result(
     """
     - 
     """
-    tool_result_content_blocks:List[BedrockToolResultContentBlock] = []
+    tool_result_content_blocks: List[BedrockToolResultContentBlock] = []
     if isinstance(message["content"], str):
-        tool_result_content_blocks.append(BedrockToolResultContentBlock(text=message["content"]))
+        tool_result_content_blocks.append(
+            BedrockToolResultContentBlock(text=message["content"])
+        )
     elif isinstance(message["content"], List):
         content_list = message["content"]
         for content in content_list:
             if content["type"] == "text":
-                tool_result_content_blocks.append(BedrockToolResultContentBlock(text=content["text"]))
+                tool_result_content_blocks.append(
+                    BedrockToolResultContentBlock(text=content["text"])
+                )
             elif content["type"] == "image_url":
                 format: Optional[str] = None
                 if isinstance(content["image_url"], dict):
@@ -3264,12 +3325,14 @@ def _convert_to_bedrock_tool_call_result(
                     format = content["image_url"].get("format")
                 else:
                     image_url = content["image_url"]
-                _block:BedrockContentBlock = BedrockImageProcessor.process_image_sync(
+                _block: BedrockContentBlock = BedrockImageProcessor.process_image_sync(
                     image_url=image_url,
                     format=format,
                 )
                 if "image" in _block:
-                    tool_result_content_blocks.append(BedrockToolResultContentBlock(image=_block["image"]))
+                    tool_result_content_blocks.append(
+                        BedrockToolResultContentBlock(image=_block["image"])
+                    )
 
     message.get("name", "")
     id = str(message.get("tool_call_id", str(uuid.uuid4())))
diff --git a/litellm/litellm_core_utils/sensitive_data_masker.py b/litellm/litellm_core_utils/sensitive_data_masker.py
index 206810943ca..8b6ae744637 100644
--- a/litellm/litellm_core_utils/sensitive_data_masker.py
+++ b/litellm/litellm_core_utils/sensitive_data_masker.py
@@ -1,4 +1,5 @@
-from typing import Any, Dict, Optional, Set
+from collections.abc import Mapping
+from typing import Any, Dict, List, Optional, Set
 
 from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER
 
@@ -17,6 +18,7 @@ class SensitiveDataMasker:
             "key",
             "token",
             "auth",
+            "authorization",
             "credential",
             "access",
             "private",
@@ -42,22 +44,52 @@ class SensitiveDataMasker:
         else:
             return f"{value_str[:self.visible_prefix]}{self.mask_char * masked_length}{value_str[-self.visible_suffix:]}"
 
-    def is_sensitive_key(self, key: str, excluded_keys: Optional[Set[str]] = None) -> bool:
+    def is_sensitive_key(
+        self, key: str, excluded_keys: Optional[Set[str]] = None
+    ) -> bool:
         # Check if key is in excluded_keys first (exact match)
         if excluded_keys and key in excluded_keys:
             return False
-        
+
         key_lower = str(key).lower()
-        # Split on underscores and check if any segment matches the pattern
+        # Split on underscores/hyphens and check if any segment matches the pattern
         # This avoids false positives like "max_tokens" matching "token"
         # but still catches "api_key", "access_token", etc.
-        key_segments = key_lower.replace('-', '_').split('_')
-        result = any(
-            pattern in key_segments
-            for pattern in self.sensitive_patterns
-        )
+        key_segments = key_lower.replace("-", "_").split("_")
+        result = any(pattern in key_segments for pattern in self.sensitive_patterns)
         return result
 
+    def _mask_sequence(
+        self,
+        values: List[Any],
+        depth: int,
+        max_depth: int,
+        excluded_keys: Optional[Set[str]],
+        key_is_sensitive: bool,
+    ) -> List[Any]:
+        masked_items: List[Any] = []
+        if depth >= max_depth:
+            return values
+
+        for item in values:
+            if isinstance(item, Mapping):
+                masked_items.append(
+                    self.mask_dict(dict(item), depth + 1, max_depth, excluded_keys)
+                )
+            elif isinstance(item, list):
+                masked_items.append(
+                    self._mask_sequence(
+                        item, depth + 1, max_depth, excluded_keys, key_is_sensitive
+                    )
+                )
+            elif key_is_sensitive and isinstance(item, str):
+                masked_items.append(self._mask_value(item))
+            else:
+                masked_items.append(
+                    item if isinstance(item, (int, float, bool, str, list)) else str(item)
+                )
+        return masked_items
+
     def mask_dict(
         self,
         data: Dict[str, Any],
@@ -71,11 +103,20 @@ class SensitiveDataMasker:
         masked_data: Dict[str, Any] = {}
         for k, v in data.items():
             try:
-                if isinstance(v, dict):
-                    masked_data[k] = self.mask_dict(v, depth + 1, max_depth, excluded_keys)
+                key_is_sensitive = self.is_sensitive_key(k, excluded_keys)
+                if isinstance(v, Mapping):
+                    masked_data[k] = self.mask_dict(
+                        dict(v), depth + 1, max_depth, excluded_keys
+                    )
+                elif isinstance(v, list):
+                    masked_data[k] = self._mask_sequence(
+                        v, depth + 1, max_depth, excluded_keys, key_is_sensitive
+                    )
                 elif hasattr(v, "__dict__") and not isinstance(v, type):
-                    masked_data[k] = self.mask_dict(vars(v), depth + 1, max_depth, excluded_keys)
-                elif self.is_sensitive_key(k, excluded_keys):
+                    masked_data[k] = self.mask_dict(
+                        vars(v), depth + 1, max_depth, excluded_keys
+                    )
+                elif key_is_sensitive:
                     str_value = str(v) if v is not None else ""
                     masked_data[k] = self._mask_value(str_value)
                 else:
diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py
index 26e6016095e..2ad9a6cdf83 100644
--- a/litellm/llms/anthropic/chat/handler.py
+++ b/litellm/llms/anthropic/chat/handler.py
@@ -732,6 +732,19 @@ class ModelResponseIterator:
                     provider_specific_fields["web_search_results"] = (
                         self.web_search_results
                     )
+                elif (
+                    content_block_start["content_block"]["type"]
+                    == "web_fetch_tool_result"
+                ):
+                    # Capture web_fetch_tool_result for multi-turn reconstruction
+                    # The full content comes in content_block_start, not in deltas
+                    # Fixes: https://github.com/BerriAI/litellm/issues/18137
+                    self.web_search_results.append(
+                        content_block_start["content_block"]
+                    )
+                    provider_specific_fields["web_search_results"] = (
+                        self.web_search_results
+                    )
             elif type_chunk == "content_block_stop":
                 ContentBlockStop(**chunk)  # type: ignore
                 # check if tool call content block - only for tool_use and server_tool_use blocks
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index c71edcdc2d1..da50db145a9 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -62,6 +62,7 @@ from litellm.utils import (
     ModelResponse,
     Usage,
     add_dummy_tool,
+    any_assistant_message_has_thinking_blocks,
     get_max_tokens,
     has_tool_call_blocks,
     last_assistant_with_tool_calls_has_no_thinking_blocks,
@@ -1013,10 +1014,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
 
         # Drop thinking param if thinking is enabled but thinking_blocks are missing
         # This prevents the error: "Expected thinking or redacted_thinking, but found tool_use"
+        #
+        # IMPORTANT: Only drop thinking if NO assistant messages have thinking_blocks.
+        # If any message has thinking_blocks, we must keep thinking enabled, otherwise
+        # Anthropic errors with: "When thinking is disabled, an assistant message cannot contain thinking"
+        # Related issue: https://github.com/BerriAI/litellm/issues/18926
         if (
             optional_params.get("thinking") is not None
             and messages is not None
             and last_assistant_with_tool_calls_has_no_thinking_blocks(messages)
+            and not any_assistant_message_has_thinking_blocks(messages)
         ):
             if litellm.modify_params:
                 optional_params.pop("thinking", None)
@@ -1162,6 +1169,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 if web_search_results is None:
                     web_search_results = []
                 web_search_results.append(content)
+            ## WEB FETCH TOOL RESULT - preserve web fetch results for multi-turn conversations
+            ## Fixes: https://github.com/BerriAI/litellm/issues/18137
+            elif content["type"] == "web_fetch_tool_result":
+                if web_search_results is None:
+                    web_search_results = []
+                web_search_results.append(content)
             elif content.get("thinking", None) is not None:
                 if thinking_blocks is None:
                     thinking_blocks = []
@@ -1265,14 +1278,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             cache_creation_tokens=cache_creation_input_tokens,
             cache_creation_token_details=cache_creation_token_details,
         )
-        completion_token_details = (
-            CompletionTokensDetailsWrapper(
-                reasoning_tokens=token_counter(
-                    text=reasoning_content, count_response_tokens=True
-                )
-            )
+        # Always populate completion_token_details, not just when there's reasoning_content
+        reasoning_tokens = (
+            token_counter(text=reasoning_content, count_response_tokens=True)
             if reasoning_content
-            else None
+            else 0
+        )
+        completion_token_details = CompletionTokensDetailsWrapper(
+            reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
+            text_tokens=completion_tokens - reasoning_tokens if reasoning_tokens > 0 else completion_tokens,
         )
         total_tokens = prompt_tokens + completion_tokens
 
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index 8868fabdcef..06092755b17 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 (
 
 from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice
 
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    parse_tool_call_arguments,
+)
+
 from litellm.types.llms.anthropic import (
     AllAnthropicToolsValues,
     AnthopicMessagesAssistantMessageParam,
@@ -425,15 +429,15 @@ class LiteLLMAnthropicMessagesAdapter:
     ) -> Optional[str]:
         """
         Translate Anthropic's thinking parameter to OpenAI's reasoning_effort.
-        
+
         Anthropic thinking format: {'type': 'enabled'|'disabled', 'budget_tokens': int}
         OpenAI reasoning_effort: 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' | 'default'
         """
         if not isinstance(thinking, dict):
             return None
-        
+
         thinking_type = thinking.get("type", "disabled")
-        
+
         if thinking_type == "disabled":
             return None
         elif thinking_type == "enabled":
@@ -446,7 +450,7 @@ class LiteLLMAnthropicMessagesAdapter:
                 return "low"
             else:
                 return "minimal"
-        
+
         return None
 
     def translate_anthropic_tool_choice_to_openai(
@@ -676,10 +680,10 @@ class LiteLLMAnthropicMessagesAdapter:
                         type="tool_use",
                         id=tool_call.id,
                         name=tool_call.function.name or "",
-                        input=(
-                            json.loads(tool_call.function.arguments)
-                            if tool_call.function.arguments
-                            else {}
+                        input=parse_tool_call_arguments(
+                            tool_call.function.arguments,
+                            tool_name=tool_call.function.name,
+                            context="Anthropic pass-through adapter",
                         ),
                     )
                     # Add provider_specific_fields if signature is present
diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py
index 87f81d117f0..506b7fdfe5e 100644
--- a/litellm/llms/azure/chat/gpt_5_transformation.py
+++ b/litellm/llms/azure/chat/gpt_5_transformation.py
@@ -25,7 +25,24 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
         return "gpt-5" in model or "gpt5_series" in model
 
     def get_supported_openai_params(self, model: str) -> List[str]:
-        return OpenAIGPT5Config.get_supported_openai_params(self, model=model)
+        """Get supported parameters for Azure OpenAI GPT-5 models.
+
+        Azure OpenAI GPT-5.2 models support logprobs, unlike OpenAI's GPT-5.
+        This overrides the parent class to add logprobs support back for gpt-5.2.
+
+        Reference:
+        - Tested with Azure OpenAI GPT-5.2 (api-version: 2025-01-01-preview)
+        - Azure returns logprobs successfully despite Microsoft's general
+          documentation stating reasoning models don't support it.
+        """
+        params = OpenAIGPT5Config.get_supported_openai_params(self, model=model)
+
+        # Only gpt-5.2 has been verified to support logprobs on Azure
+        if self.is_model_gpt_5_2_model(model):
+            azure_supported_params = ["logprobs", "top_logprobs"]
+            params.extend(azure_supported_params)
+
+        return params
 
     def map_openai_params(
         self,
diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py
index b592c23846d..41a1797cebe 100644
--- a/litellm/llms/base_llm/chat/transformation.py
+++ b/litellm/llms/base_llm/chat/transformation.py
@@ -132,10 +132,10 @@ class BaseConfig(ABC):
 
         Checks 'non_default_params' for 'thinking' and 'max_tokens'
 
-        if 'thinking' is enabled and 'max_tokens' is not specified, set 'max_tokens' to the thinking token budget + DEFAULT_MAX_TOKENS
+        if 'thinking' is enabled and 'max_tokens' or 'max_completion_tokens' is not specified, set 'max_tokens' to the thinking token budget + DEFAULT_MAX_TOKENS
         """
         is_thinking_enabled = self.is_thinking_enabled(optional_params)
-        if is_thinking_enabled and "max_tokens" not in non_default_params:
+        if is_thinking_enabled and ("max_tokens" not in non_default_params and "max_completion_tokens" not in non_default_params):
             thinking_token_budget = cast(dict, optional_params["thinking"]).get(
                 "budget_tokens", None
             )
diff --git a/litellm/llms/base_llm/files/transformation.py b/litellm/llms/base_llm/files/transformation.py
index 35b76479cdc..7b0a1868f19 100644
--- a/litellm/llms/base_llm/files/transformation.py
+++ b/litellm/llms/base_llm/files/transformation.py
@@ -2,11 +2,14 @@ from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
 import httpx
+from openai.types.file_deleted import FileDeleted
 
 from litellm.proxy._types import UserAPIKeyAuth
+from litellm.types.files import TwoStepFileUploadConfig
 from litellm.types.llms.openai import (
     AllMessageValues,
     CreateFileRequest,
+    FileContentRequest,
     OpenAICreateFileRequestOptionalParams,
     OpenAIFileObject,
     OpenAIFilesPurpose,
@@ -75,7 +78,15 @@ class BaseFilesConfig(BaseConfig):
         create_file_data: CreateFileRequest,
         optional_params: dict,
         litellm_params: dict,
-    ) -> Union[dict, str, bytes]:
+    ) -> Union[dict, str, bytes, "TwoStepFileUploadConfig"]:
+        """
+        Transform OpenAI-style file creation request into provider-specific format.
+        
+        Returns:
+            - dict: For pre-signed single-step uploads (e.g., Bedrock S3)
+            - str/bytes: For traditional file uploads
+            - TwoStepFileUploadConfig: For two-step upload process (e.g., Manus, GCS)
+        """
         pass
 
     @abstractmethod
@@ -88,6 +99,86 @@ class BaseFilesConfig(BaseConfig):
     ) -> OpenAIFileObject:
         pass
 
+    @abstractmethod
+    def transform_retrieve_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Transform file retrieve request into provider-specific format."""
+        pass
+
+    @abstractmethod
+    def transform_retrieve_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> OpenAIFileObject:
+        """Transform file retrieve response into OpenAI format."""
+        pass
+
+    @abstractmethod
+    def transform_delete_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Transform file delete request into provider-specific format."""
+        pass
+
+    @abstractmethod
+    def transform_delete_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> "FileDeleted":
+        """Transform file delete response into OpenAI format."""
+        pass
+
+    @abstractmethod
+    def transform_list_files_request(
+        self,
+        purpose: Optional[str],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Transform file list request into provider-specific format."""
+        pass
+
+    @abstractmethod
+    def transform_list_files_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> List[OpenAIFileObject]:
+        """Transform file list response into OpenAI format."""
+        pass
+
+    @abstractmethod
+    def transform_file_content_request(
+        self,
+        file_content_request: "FileContentRequest",
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Transform file content request into provider-specific format."""
+        pass
+
+    @abstractmethod
+    def transform_file_content_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> "HttpxBinaryResponseContent":
+        """Transform file content response into OpenAI format."""
+        pass
+
     def transform_request(
         self,
         model: str,
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 71d21001cc3..18e9deb53b0 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -74,6 +74,41 @@ class BaseAWSLLM:
             "aws_external_id",
         ]
 
+    def _get_ssl_verify(self):
+        """
+        Get SSL verification setting for boto3 clients.
+        
+        This ensures that custom CA certificates are properly used for all AWS API calls,
+        including STS and Bedrock services.
+        
+        Returns:
+            Union[bool, str]: SSL verification setting - False to disable, True to enable,
+                            or a string path to a CA bundle file
+        """
+        import litellm
+        from litellm.secret_managers.main import str_to_bool
+        
+        # Check environment variable first (highest priority)
+        ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify)
+        
+        # Convert string "False"/"True" to boolean
+        if isinstance(ssl_verify, str):
+            # Check if it's a file path
+            if os.path.exists(ssl_verify):
+                return ssl_verify
+            # Otherwise try to convert to boolean
+            ssl_verify_bool = str_to_bool(ssl_verify)
+            if ssl_verify_bool is not None:
+                ssl_verify = ssl_verify_bool
+        
+        # Check SSL_CERT_FILE environment variable for custom CA bundle
+        if ssl_verify is True or ssl_verify == "True":
+            ssl_cert_file = os.getenv("SSL_CERT_FILE")
+            if ssl_cert_file and os.path.exists(ssl_cert_file):
+                return ssl_cert_file
+        
+        return ssl_verify
+
     def get_cache_key(self, credential_args: Dict[str, Optional[str]]) -> str:
         """
         Generate a unique cache key based on the credential arguments.
@@ -314,6 +349,12 @@ class BaseAWSLLM:
         if model.startswith("invoke/"):
             model = model.replace("invoke/", "", 1)
 
+        # Special case: Check for "nova" in model name first (before "amazon")
+        # This handles amazon.nova-* models which would otherwise match "amazon" (Titan)
+        if "nova" in model.lower():
+            if "nova" in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
+                return cast(BEDROCK_INVOKE_PROVIDERS_LITERAL, "nova")
+
         _split_model = model.split(".")[0]
         if _split_model in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
             return cast(BEDROCK_INVOKE_PROVIDERS_LITERAL, _split_model)
@@ -323,13 +364,9 @@ class BaseAWSLLM:
         if provider is not None:
             return provider
 
-        # check if provider == "nova"
-        if "nova" in model:
-            return "nova"
-        else:
-            for provider in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
-                if provider in model:
-                    return provider
+        for provider in get_args(BEDROCK_INVOKE_PROVIDERS_LITERAL):
+            if provider in model:
+                return provider
         return None
 
     @staticmethod
@@ -364,11 +401,15 @@ class BaseAWSLLM:
         elif provider == "qwen3" and "qwen3/" in model_id:
             model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
                 model_id, spec="qwen3"
-            ) 
+            )
         elif provider == "stability" and "stability/" in model_id:
             model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
                 model_id, spec="stability"
             )
+        elif provider == "moonshot" and "moonshot/" in model_id:
+            model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
+                model_id, spec="moonshot"
+            )
         return model_id
 
     @staticmethod
@@ -412,7 +453,7 @@ class BaseAWSLLM:
         if "nova" in model.lower():
             if "nova" in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL):
                 return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, "nova")
-        
+
         # Handle regional models like us.twelvelabs.marengo-embed-2-7-v1:0
         if "." in model:
             parts = model.split(".")
@@ -563,6 +604,7 @@ class BaseAWSLLM:
                 "sts",
                 region_name=aws_region_name,
                 endpoint_url=sts_endpoint,
+                verify=self._get_ssl_verify(),
             )
 
         # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
@@ -619,7 +661,7 @@ class BaseAWSLLM:
 
         # Create an STS client without credentials
         with tracer.trace("boto3.client(sts) for manual IRSA"):
-            sts_client = boto3.client("sts", region_name=region)
+            sts_client = boto3.client("sts", region_name=region, verify=self._get_ssl_verify())
 
         # Manually assume the IRSA role with the session name
         verbose_logger.debug(
@@ -642,6 +684,7 @@ class BaseAWSLLM:
                 aws_access_key_id=irsa_creds["AccessKeyId"],
                 aws_secret_access_key=irsa_creds["SecretAccessKey"],
                 aws_session_token=irsa_creds["SessionToken"],
+                verify=self._get_ssl_verify(),
             )
 
         # Get current caller identity for debugging
@@ -680,7 +723,7 @@ class BaseAWSLLM:
 
         verbose_logger.debug("Same account role assumption, using automatic IRSA")
         with tracer.trace("boto3.client(sts) with automatic IRSA"):
-            sts_client = boto3.client("sts", region_name=region)
+            sts_client = boto3.client("sts", region_name=region, verify=self._get_ssl_verify())
 
         # Get current caller identity for debugging
         try:
@@ -803,7 +846,7 @@ class BaseAWSLLM:
         # This allows the web identity token to work automatically
         if aws_access_key_id is None and aws_secret_access_key is None:
             with tracer.trace("boto3.client(sts)"):
-                sts_client = boto3.client("sts")
+                sts_client = boto3.client("sts", verify=self._get_ssl_verify())
         else:
             with tracer.trace("boto3.client(sts)"):
                 sts_client = boto3.client(
@@ -811,6 +854,7 @@ class BaseAWSLLM:
                     aws_access_key_id=aws_access_key_id,
                     aws_secret_access_key=aws_secret_access_key,
                     aws_session_token=aws_session_token,
+                    verify=self._get_ssl_verify(),
                 )
 
         assume_role_params = {
@@ -958,7 +1002,9 @@ class BaseAWSLLM:
         return endpoint_url, proxy_endpoint_url
 
     def _select_default_endpoint_url(
-        self, endpoint_type: Optional[Literal["runtime", "agent", "agentcore"]], aws_region_name: str
+        self,
+        endpoint_type: Optional[Literal["runtime", "agent", "agentcore"]],
+        aws_region_name: str,
     ) -> str:
         """
         Select the default endpoint url based on the endpoint type
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index 13dbec3952a..59590e464fc 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -339,6 +339,55 @@ class AmazonConverseConfig(BaseConfig):
             }
         }
 
+    def _handle_reasoning_effort_parameter(
+        self, model: str, reasoning_effort: str, optional_params: dict
+    ) -> None:
+        """
+        Handle the reasoning_effort parameter based on the model type.
+
+        Different model families handle reasoning effort differently:
+        - GPT-OSS models: Keep reasoning_effort as-is (passed to additionalModelRequestFields)
+        - Nova Lite 2 models: Transform to reasoningConfig structure
+        - Other models (Anthropic, etc.): Convert to thinking parameter
+
+        Args:
+            model: The model identifier
+            reasoning_effort: The reasoning effort value
+            optional_params: Dictionary of optional parameters to update in-place
+
+        Examples:
+            >>> config = AmazonConverseConfig()
+            >>> params = {}
+            >>> config._handle_reasoning_effort_parameter("gpt-oss-model", "high", params)
+            >>> params
+            {'reasoning_effort': 'high'}
+
+            >>> params = {}
+            >>> config._handle_reasoning_effort_parameter("amazon.nova-2-lite-v1:0", "high", params)
+            >>> params
+            {'reasoningConfig': {'type': 'enabled', 'maxReasoningEffort': 'high'}}
+
+            >>> params = {}
+            >>> config._handle_reasoning_effort_parameter("anthropic.claude-3", "high", params)
+            >>> params
+            {'thinking': {'type': 'enabled', 'budget_tokens': 10000}}
+        """
+        if "gpt-oss" in model:
+            # GPT-OSS models: keep reasoning_effort as-is
+            # It will be passed through to additionalModelRequestFields
+            optional_params["reasoning_effort"] = reasoning_effort
+        elif self._is_nova_lite_2_model(model):
+            # Nova Lite 2 models: transform to reasoningConfig
+            reasoning_config = self._transform_reasoning_effort_to_reasoning_config(
+                reasoning_effort
+            )
+            optional_params.update(reasoning_config)
+        else:
+            # Anthropic and other models: convert to thinking parameter
+            optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
+                reasoning_effort
+            )
+
     def get_supported_openai_params(self, model: str) -> List[str]:
         from litellm.utils import supports_function_calling
 
@@ -353,6 +402,7 @@ class AmazonConverseConfig(BaseConfig):
             "extra_headers",
             "response_format",
             "requestMetadata",
+            "service_tier",
         ]
 
         if (
@@ -657,25 +707,22 @@ class AmazonConverseConfig(BaseConfig):
             if param == "thinking":
                 optional_params["thinking"] = value
             elif param == "reasoning_effort" and isinstance(value, str):
-                if "gpt-oss" in model:
-                    # GPT-OSS models: keep reasoning_effort as-is
-                    # It will be passed through to additionalModelRequestFields
-                    optional_params["reasoning_effort"] = value
-                elif self._is_nova_lite_2_model(model):
-                    # Nova Lite 2 models: transform to reasoningConfig
-                    reasoning_config = (
-                        self._transform_reasoning_effort_to_reasoning_config(value)
-                    )
-                    optional_params.update(reasoning_config)
-                else:
-                    # Anthropic and other models: convert to thinking parameter
-                    optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
-                        value
-                    )
+                self._handle_reasoning_effort_parameter(
+                    model=model, reasoning_effort=value, optional_params=optional_params
+                )
             if param == "requestMetadata":
                 if value is not None and isinstance(value, dict):
                     self._validate_request_metadata(value)  # type: ignore
                     optional_params["requestMetadata"] = value
+            if param == "service_tier" and isinstance(value, str):
+                # Map OpenAI service_tier (string) to Bedrock serviceTier (object)
+                # OpenAI values: "auto", "default", "flex", "priority"
+                # Bedrock values: "default", "flex", "priority" (no "auto")
+                bedrock_tier = value
+                if value == "auto":
+                    bedrock_tier = "default"  # Bedrock doesn't support "auto"
+                if bedrock_tier in ("default", "flex", "priority"):
+                    optional_params["serviceTier"] = {"type": bedrock_tier}
 
         # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models
         # Nova Lite 2 handles token budgeting differently through reasoningConfig
@@ -685,10 +732,7 @@ class AmazonConverseConfig(BaseConfig):
             )
 
         final_is_thinking_enabled = self.is_thinking_enabled(optional_params)
-        if (
-            final_is_thinking_enabled
-            and "tool_choice" in optional_params
-        ):
+        if final_is_thinking_enabled and "tool_choice" in optional_params:
             tool_choice_block = optional_params["tool_choice"]
             if isinstance(tool_choice_block, dict):
                 if "any" in tool_choice_block or "tool" in tool_choice_block:
@@ -912,20 +956,22 @@ class AmazonConverseConfig(BaseConfig):
         inference_params = {
             k: v for k, v in inference_params.items() if k in total_supported_params
         }
-        
+
         # Only set the topK value in for models that support it
         additional_request_params.update(
             self._handle_top_k_value(model, inference_params)
         )
-        
+
         # Filter out internal/MCP-related parameters that shouldn't be sent to the API
         # These are LiteLLM internal parameters, not API parameters
         additional_request_params = filter_internal_params(additional_request_params)
-        
+
         # Filter out non-serializable objects (exceptions, callables, logging objects, etc.)
         # from additional_request_params to prevent JSON serialization errors
         # This filters: Exception objects, callable objects (functions), Logging objects, etc.
-        additional_request_params = filter_exceptions_from_params(additional_request_params)
+        additional_request_params = filter_exceptions_from_params(
+            additional_request_params
+        )
 
         return inference_params, additional_request_params, request_metadata
 
@@ -950,7 +996,10 @@ class AmazonConverseConfig(BaseConfig):
         if original_tools:
             for tool in original_tools:
                 tool_type = tool.get("type", "")
-                if tool_type in ("tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119"):
+                if tool_type in (
+                    "tool_search_tool_regex_20251119",
+                    "tool_search_tool_bm25_20251119",
+                ):
                     # Tool search not supported in Converse API - skip it
                     continue
                 filtered_tools.append(tool)
@@ -1535,6 +1584,13 @@ class AmazonConverseConfig(BaseConfig):
         if "trace" in completion_response:
             setattr(model_response, "trace", completion_response["trace"])
 
+        # Add service_tier if present in Bedrock response
+        # Map Bedrock serviceTier (object) to OpenAI service_tier (string)
+        if "serviceTier" in completion_response:
+            service_tier_block = completion_response["serviceTier"]
+            if isinstance(service_tier_block, dict) and "type" in service_tier_block:
+                setattr(model_response, "service_tier", service_tier_block["type"])
+
         return model_response
 
     def get_error_class(
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py
new file mode 100644
index 00000000000..e53410760dd
--- /dev/null
+++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py
@@ -0,0 +1,256 @@
+"""
+Transformation for Bedrock Moonshot AI (Kimi K2) models.
+
+Supports the Kimi K2 Thinking model available on Amazon Bedrock.
+Model format: bedrock/moonshot.kimi-k2-thinking-v1:0
+
+Reference: https://aws.amazon.com/about-aws/whats-new/2025/12/amazon-bedrock-fully-managed-open-weight-models/
+"""
+
+from typing import TYPE_CHECKING, Any, List, Optional, Union
+import re
+
+import httpx
+
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+from litellm.llms.bedrock.common_utils import BedrockError
+from litellm.llms.moonshot.chat.transformation import MoonshotChatConfig
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import Choices
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+    from litellm.types.utils import ModelResponse
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class AmazonMoonshotConfig(AmazonInvokeConfig, MoonshotChatConfig):
+    """
+    Configuration for Bedrock Moonshot AI (Kimi K2) models.
+    
+    Reference:
+        https://aws.amazon.com/about-aws/whats-new/2025/12/amazon-bedrock-fully-managed-open-weight-models/
+        https://platform.moonshot.ai/docs/api/chat
+    
+    Supported Params for the Amazon / Moonshot models:
+    - `max_tokens` (integer) max tokens
+    - `temperature` (float) temperature for model (0-1 for Moonshot)
+    - `top_p` (float) top p for model
+    - `stream` (bool) whether to stream responses
+    - `tools` (list) tool definitions (supported on kimi-k2-thinking)
+    - `tool_choice` (str|dict) tool choice specification (supported on kimi-k2-thinking)
+    
+    NOT Supported on Bedrock:
+    - `stop` sequences (Bedrock doesn't support stopSequences field for this model)
+    
+    Note: The kimi-k2-thinking model DOES support tool calls, unlike kimi-thinking-preview.
+    """
+
+    def __init__(self, **kwargs):
+        AmazonInvokeConfig.__init__(self, **kwargs)
+        MoonshotChatConfig.__init__(self, **kwargs)
+
+    @property
+    def custom_llm_provider(self) -> Optional[str]:
+        return "bedrock"
+
+    def _get_model_id(self, model: str) -> str:
+        """
+        Extract the actual model ID from the LiteLLM model name.
+        
+        Removes routing prefixes like:
+        - bedrock/invoke/moonshot.kimi-k2-thinking -> moonshot.kimi-k2-thinking
+        - invoke/moonshot.kimi-k2-thinking -> moonshot.kimi-k2-thinking
+        - moonshot.kimi-k2-thinking -> moonshot.kimi-k2-thinking
+        """
+        # Remove bedrock/ prefix if present
+        if model.startswith("bedrock/"):
+            model = model[8:]
+        
+        # Remove invoke/ prefix if present
+        if model.startswith("invoke/"):
+            model = model[7:]
+        
+        # Remove any provider prefix (e.g., moonshot/)
+        if "/" in model and not model.startswith("arn:"):
+            parts = model.split("/", 1)
+            if len(parts) == 2:
+                model = parts[1]
+        
+        return model
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        """
+        Get the supported OpenAI params for Moonshot AI models on Bedrock.
+        
+        Bedrock-specific limitations:
+        - stopSequences field is not supported on Bedrock (unlike native Moonshot API)
+        - functions parameter is not supported (use tools instead)
+        - tool_choice doesn't support "required" value
+        
+        Note: kimi-k2-thinking DOES support tool calls (unlike kimi-thinking-preview)
+        The parent MoonshotChatConfig class handles the kimi-thinking-preview exclusion.
+        """
+        excluded_params: List[str] = ["functions", "stop"]  # Bedrock doesn't support stopSequences
+        
+        base_openai_params = super(MoonshotChatConfig, self).get_supported_openai_params(model=model)
+        final_params: List[str] = []
+        for param in base_openai_params:
+            if param not in excluded_params:
+                final_params.append(param)
+        
+        return final_params
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        Map OpenAI parameters to Moonshot AI parameters for Bedrock.
+        
+        Handles Moonshot AI specific limitations:
+        - tool_choice doesn't support "required" value
+        - Temperature <0.3 limitation for n>1
+        - Temperature range is [0, 1] (not [0, 2] like OpenAI)
+        """
+        return MoonshotChatConfig.map_openai_params(
+            self,
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Transform the request for Bedrock Moonshot AI models.
+        
+        Uses the Moonshot transformation logic which handles:
+        - Converting content lists to strings (Moonshot doesn't support list format)
+        - Adding tool_choice="required" message if needed
+        - Temperature and parameter validation
+
+        """
+        # Filter out AWS credentials using the existing method from BaseAWSLLM
+        self._get_boto_credentials_from_optional_params(optional_params, model)
+        
+        # Strip routing prefixes to get the actual model ID
+        clean_model_id = self._get_model_id(model)
+        
+        # Use Moonshot's transform_request which handles message transformation
+        # and tool_choice="required" workaround
+        return MoonshotChatConfig.transform_request(
+            self,
+            model=clean_model_id,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+    def _extract_reasoning_from_content(self, content: str) -> tuple[Optional[str], str]:
+        """
+        Extract reasoning content from <reasoning> tags in the response.
+        
+        Moonshot AI's Kimi K2 Thinking model returns reasoning in <reasoning> tags.
+        This method extracts that content and returns it separately.
+        
+        Args:
+            content: The full content string from the API response
+            
+        Returns:
+            tuple: (reasoning_content, main_content)
+        """
+        if not content:
+            return None, content
+        
+        # Match <reasoning>...</reasoning> tags
+        reasoning_match = re.match(
+            r"<reasoning>(.*?)</reasoning>\s*(.*)", 
+            content, 
+            re.DOTALL
+        )
+        
+        if reasoning_match:
+            reasoning_content = reasoning_match.group(1).strip()
+            main_content = reasoning_match.group(2).strip()
+            return reasoning_content, main_content
+        
+        return None, content
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: "ModelResponse",
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> "ModelResponse":
+        """
+        Transform the response from Bedrock Moonshot AI models.
+        
+        Moonshot AI uses OpenAI-compatible response format, but returns reasoning
+        content in <reasoning> tags. This method:
+        1. Calls parent class transformation
+        2. Extracts reasoning content from <reasoning> tags
+        3. Sets reasoning_content on the message object
+        """
+        # First, get the standard transformation
+        model_response = MoonshotChatConfig.transform_response(
+            self,
+            model=model,
+            raw_response=raw_response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            request_data=request_data,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            encoding=encoding,
+            api_key=api_key,
+            json_mode=json_mode,
+        )
+        
+        # Extract reasoning content from <reasoning> tags
+        if model_response.choices and len(model_response.choices) > 0:
+            for choice in model_response.choices:
+                # Only process Choices (not StreamingChoices) which have message attribute
+                if isinstance(choice, Choices) and choice.message and choice.message.content:
+                    reasoning_content, main_content = self._extract_reasoning_from_content(
+                        choice.message.content
+                    )
+                    
+                    if reasoning_content:
+                        # Set the reasoning_content field
+                        choice.message.reasoning_content = reasoning_content
+                        # Update the main content without reasoning tags
+                        choice.message.content = main_content
+        
+        return model_response
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BedrockError:
+        """Return the appropriate error class for Bedrock."""
+        return BedrockError(status_code=status_code, message=error_message)
diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py
index c602b71fe05..cf8aee6954b 100644
--- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py
+++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py
@@ -524,6 +524,12 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
         if model.startswith("invoke/"):
             model = model.replace("invoke/", "", 1)
 
+        # Special case: Check for "nova" in model name first (before "amazon")
+        # This handles amazon.nova-* models which would otherwise match "amazon" (Titan)
+        if "nova" in model.lower():
+            if "nova" in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
+                return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, "nova")
+
         _split_model = model.split(".")[0]
         if _split_model in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
             return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, _split_model)
@@ -533,10 +539,6 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
         if provider is not None:
             return provider
 
-        # check if provider == "nova"
-        if "nova" in model:
-            return "nova"
-
         for provider in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
             if provider in model:
                 return provider
diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py
index 21a78c30343..f4b5de8f7c0 100644
--- a/litellm/llms/bedrock/common_utils.py
+++ b/litellm/llms/bedrock/common_utils.py
@@ -15,7 +15,7 @@ import litellm
 from litellm.llms.base_llm.anthropic_messages.transformation import (
     BaseAnthropicMessagesConfig,
 )
-from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
+from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.secret_managers.main import get_secret
 
@@ -132,6 +132,38 @@ def add_custom_header(headers):
     return callback
 
 
+def _get_bedrock_client_ssl_verify() -> Union[bool, str]:
+    """
+    Get SSL verification setting for Bedrock client.
+    
+    Returns the SSL verification setting which can be:
+    - True: Use default SSL verification
+    - False: Disable SSL verification
+    - str: Path to a custom CA bundle file
+    """
+    from litellm.secret_managers.main import str_to_bool
+    
+    ssl_verify: Union[bool, str, None] = os.getenv("SSL_VERIFY", litellm.ssl_verify)
+    
+    # Convert string "False"/"True" to boolean
+    if isinstance(ssl_verify, str):
+        # Check if it's a file path
+        if os.path.exists(ssl_verify):
+            return ssl_verify  # Keep the file path
+        # Otherwise try to convert to boolean
+        ssl_verify_bool = str_to_bool(ssl_verify)
+        if ssl_verify_bool is not None:
+            ssl_verify = ssl_verify_bool
+    
+    # Check SSL_CERT_FILE environment variable for custom CA bundle
+    if ssl_verify is True or ssl_verify == "True":
+        ssl_cert_file = os.getenv("SSL_CERT_FILE")
+        if ssl_cert_file and os.path.exists(ssl_cert_file):
+            return ssl_cert_file
+    
+    return ssl_verify if ssl_verify is not None else True
+
+
 def init_bedrock_client(
     region_name=None,
     aws_access_key_id: Optional[str] = None,
@@ -177,8 +209,7 @@ def init_bedrock_client(
         aws_web_identity_token,
     ) = params_to_check
 
-    # SSL certificates (a.k.a CA bundle) used to verify the identity of requested hosts.
-    ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify)
+    ssl_verify = _get_bedrock_client_ssl_verify()
 
     ### SET REGION NAME
     if region_name:
@@ -229,7 +260,7 @@ def init_bedrock_client(
                 status_code=401,
             )
 
-        sts_client = boto3.client("sts")
+        sts_client = boto3.client("sts", verify=ssl_verify)
 
         # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
         # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
@@ -359,6 +390,70 @@ def get_bedrock_tool_name(response_tool_name: str) -> str:
     return response_tool_name
 
 
+# Cache the global regions list at module level
+_BEDROCK_GLOBAL_REGIONS: Optional[List[str]] = None
+
+
+def _get_all_bedrock_regions() -> List[str]:
+    """Get all Bedrock regions, cached at module level."""
+    global _BEDROCK_GLOBAL_REGIONS
+    if _BEDROCK_GLOBAL_REGIONS is None:
+        _BEDROCK_GLOBAL_REGIONS = AmazonBedrockGlobalConfig().get_all_regions()
+    return _BEDROCK_GLOBAL_REGIONS
+
+
+def get_bedrock_cross_region_inference_regions() -> List[str]:
+    """Abbreviations of regions AWS Bedrock supports for cross region inference."""
+    return ["global", "us", "eu", "apac", "jp", "au", "us-gov"]
+
+
+def extract_model_name_from_bedrock_arn(model: str) -> str:
+    """
+    Extract the model name from an AWS Bedrock ARN.
+    Returns the string after the last '/' if 'arn' is in the input string.
+    """
+    if "arn" in model.lower():
+        return model.split("/")[-1]
+    return model
+
+
+def strip_bedrock_routing_prefix(model: str) -> str:
+    """Strip LiteLLM routing prefixes from model name."""
+    for prefix in ["bedrock/", "converse/", "invoke/", "openai/"]:
+        if model.startswith(prefix):
+            model = model.split("/", 1)[1]
+    return model
+
+
+def get_bedrock_base_model(model: str) -> str:
+    """
+    Get the base model from the given model name.
+
+    Handle model names like:
+    - "us.meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
+    - "bedrock/converse/model" -> "model"
+    """
+    model = strip_bedrock_routing_prefix(model)
+    model = extract_model_name_from_bedrock_arn(model)
+
+    potential_region = model.split(".", 1)[0]
+    alt_potential_region = model.split("/", 1)[0]
+
+    if potential_region in get_bedrock_cross_region_inference_regions():
+        return model.split(".", 1)[1]
+    elif (
+        alt_potential_region in _get_all_bedrock_regions()
+        and len(model.split("/", 1)) > 1
+    ):
+        return model.split("/", 1)[1]
+
+    return model
+
+
+# Import after standalone functions to avoid circular imports
+from litellm.llms.bedrock.count_tokens.bedrock_token_counter import BedrockTokenCounter
+
+
 class BedrockModelInfo(BaseLLMModelInfo):
     global_config = AmazonBedrockGlobalConfig()
     all_global_regions = global_config.get_all_regions()
@@ -394,76 +489,34 @@ class BedrockModelInfo(BaseLLMModelInfo):
     ) -> List[str]:
         return []
 
-    @staticmethod
-    def extract_model_name_from_arn(model: str) -> str:
+    def get_token_counter(self) -> Optional[BaseTokenCounter]:
         """
-        Extract the model name from an AWS Bedrock ARN.
-        Returns the string after the last '/' if 'arn' is in the input string.
-
-        Args:
-            arn (str): The ARN string to parse
+        Factory method to create a Bedrock token counter.
 
         Returns:
-            str: The extracted model name if 'arn' is in the string,
-                otherwise returns the original string
+            BedrockTokenCounter instance for this provider.
         """
-        if "arn" in model.lower():
-            return model.split("/")[-1]
-        return model
+        return BedrockTokenCounter()
+
+    @staticmethod
+    def extract_model_name_from_arn(model: str) -> str:
+        """Wrapper for standalone function. See extract_model_name_from_bedrock_arn()."""
+        return extract_model_name_from_bedrock_arn(model)
 
     @staticmethod
     def get_non_litellm_routing_model_name(model: str) -> str:
-        if model.startswith("bedrock/"):
-            model = model.split("/", 1)[1]
-
-        if model.startswith("converse/"):
-            model = model.split("/", 1)[1]
-
-        if model.startswith("invoke/"):
-            model = model.split("/", 1)[1]
-
-        if model.startswith("openai/"):
-            model = model.split("/", 1)[1]
-
-        return model
+        """Wrapper for standalone function. See strip_bedrock_routing_prefix()."""
+        return strip_bedrock_routing_prefix(model)
 
     @staticmethod
     def get_base_model(model: str) -> str:
-        """
-        Get the base model from the given model name.
-
-        Handle model names like - "us.meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
-        AND "meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
-        """
-
-        model = BedrockModelInfo.get_non_litellm_routing_model_name(model=model)
-        model = BedrockModelInfo.extract_model_name_from_arn(model)
-
-        potential_region = model.split(".", 1)[0]
-
-        alt_potential_region = model.split("/", 1)[
-            0
-        ]  # in model cost map we store regional information like `/us-west-2/bedrock-model`
-
-        if (
-            potential_region
-            in BedrockModelInfo._supported_cross_region_inference_region()
-        ):
-            return model.split(".", 1)[1]
-        elif (
-            alt_potential_region in BedrockModelInfo.all_global_regions
-            and len(model.split("/", 1)) > 1
-        ):
-            return model.split("/", 1)[1]
-
-        return model
+        """Wrapper for standalone function. See get_bedrock_base_model()."""
+        return get_bedrock_base_model(model)
 
     @staticmethod
     def _supported_cross_region_inference_region() -> List[str]:
-        """
-        Abbreviations of regions AWS Bedrock supports for cross region inference
-        """
-        return ["global", "us", "eu", "apac", "jp", "au", "us-gov"]
+        """Wrapper for standalone function. See get_bedrock_cross_region_inference_regions()."""
+        return get_bedrock_cross_region_inference_regions()
 
     @staticmethod
     def get_bedrock_route(
@@ -629,6 +682,8 @@ def get_bedrock_chat_config(model: str):
         return litellm.AmazonCohereConfig()
     elif bedrock_invoke_provider == "mistral":
         return litellm.AmazonMistralConfig()
+    elif bedrock_invoke_provider == "moonshot":
+        return litellm.AmazonMoonshotConfig()
     elif bedrock_invoke_provider == "deepseek_r1":
         return litellm.AmazonDeepSeekR1Config()
     elif bedrock_invoke_provider == "nova":
diff --git a/litellm/llms/bedrock/count_tokens/bedrock_token_counter.py b/litellm/llms/bedrock/count_tokens/bedrock_token_counter.py
new file mode 100644
index 00000000000..b680bd046ef
--- /dev/null
+++ b/litellm/llms/bedrock/count_tokens/bedrock_token_counter.py
@@ -0,0 +1,87 @@
+"""
+Bedrock Token Counter implementation using the CountTokens API.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from litellm._logging import verbose_logger
+from litellm.llms.base_llm.base_utils import BaseTokenCounter
+from litellm.llms.bedrock.common_utils import get_bedrock_base_model
+from litellm.llms.bedrock.count_tokens.handler import BedrockCountTokensHandler
+from litellm.types.utils import LlmProviders, TokenCountResponse
+
+
+class BedrockTokenCounter(BaseTokenCounter):
+    """Token counter implementation for AWS Bedrock provider using the CountTokens API."""
+
+    def should_use_token_counting_api(
+        self,
+        custom_llm_provider: Optional[str] = None,
+    ) -> bool:
+        """
+        Returns True if we should use the Bedrock CountTokens API for token counting.
+        """
+        return custom_llm_provider == LlmProviders.BEDROCK.value
+
+    async def count_tokens(
+        self,
+        model_to_use: str,
+        messages: Optional[List[Dict[str, Any]]],
+        contents: Optional[List[Dict[str, Any]]],
+        deployment: Optional[Dict[str, Any]] = None,
+        request_model: str = "",
+    ) -> Optional[TokenCountResponse]:
+        """
+        Count tokens using AWS Bedrock's CountTokens API.
+
+        This method calls the existing BedrockCountTokensHandler to make an API call
+        to Bedrock's token counting endpoint, bypassing the local tiktoken-based counting.
+
+        Args:
+            model_to_use: The model identifier
+            messages: The messages to count tokens for
+            contents: Alternative content format (not used for Bedrock)
+            deployment: Deployment configuration containing litellm_params
+            request_model: The original request model name
+
+        Returns:
+            TokenCountResponse with token count, or None if counting fails
+        """
+        if not messages:
+            return None
+
+        deployment = deployment or {}
+        litellm_params = deployment.get("litellm_params", {})
+
+        # Build request data in the format expected by BedrockCountTokensHandler
+        request_data = {
+            "model": model_to_use,
+            "messages": messages,
+        }
+
+        # Get the resolved model (strip prefixes like bedrock/, converse/, etc.)
+        resolved_model = get_bedrock_base_model(model_to_use)
+
+        try:
+            handler = BedrockCountTokensHandler()
+            result = await handler.handle_count_tokens_request(
+                request_data=request_data,
+                litellm_params=litellm_params,
+                resolved_model=resolved_model,
+            )
+
+            # Transform response to TokenCountResponse
+            if result is not None:
+                return TokenCountResponse(
+                    total_tokens=result.get("input_tokens", 0),
+                    request_model=request_model,
+                    model_used=model_to_use,
+                    tokenizer_type="bedrock_api",
+                    original_response=result,
+                )
+        except Exception as e:
+            verbose_logger.warning(
+                f"Error calling Bedrock CountTokens API: {e}, falling back to default tokenizer"
+            )
+
+        return None
diff --git a/litellm/llms/bedrock/count_tokens/handler.py b/litellm/llms/bedrock/count_tokens/handler.py
index d4355c0c360..e8366165b65 100644
--- a/litellm/llms/bedrock/count_tokens/handler.py
+++ b/litellm/llms/bedrock/count_tokens/handler.py
@@ -6,10 +6,9 @@ Simplified handler leveraging existing LiteLLM Bedrock infrastructure.
 
 from typing import Any, Dict
 
-from fastapi import HTTPException
-
 import litellm
 from litellm._logging import verbose_logger
+from litellm.llms.bedrock.common_utils import BedrockError
 from litellm.llms.bedrock.count_tokens.transformation import BedrockCountTokensConfig
 from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
 
@@ -70,6 +69,8 @@ class BedrockCountTokensHandler(BedrockCountTokensConfig):
             verbose_logger.debug(f"Making request to: {endpoint_url}")
 
             # Use existing _sign_request method from BaseAWSLLM
+            # Extract api_key for bearer token auth if provided
+            api_key = litellm_params.get("api_key", None)
             headers = {"Content-Type": "application/json"}
             signed_headers, signed_body = self._sign_request(
                 service_name="bedrock",
@@ -78,6 +79,7 @@ class BedrockCountTokensHandler(BedrockCountTokensConfig):
                 request_data=bedrock_request,
                 api_base=endpoint_url,
                 model=resolved_model,
+                api_key=api_key,
             )
 
             async_client = get_async_httpx_client(llm_provider=litellm.LlmProviders.BEDROCK)
@@ -94,9 +96,9 @@ class BedrockCountTokensHandler(BedrockCountTokensConfig):
             if response.status_code != 200:
                 error_text = response.text
                 verbose_logger.error(f"AWS Bedrock error: {error_text}")
-                raise HTTPException(
-                    status_code=400,
-                    detail={"error": f"AWS Bedrock error: {error_text}"},
+                raise BedrockError(
+                    status_code=response.status_code,
+                    message=f"AWS Bedrock error: {error_text}",
                 )
 
             bedrock_response = response.json()
@@ -112,12 +114,12 @@ class BedrockCountTokensHandler(BedrockCountTokensConfig):
 
             return final_response
 
-        except HTTPException:
-            # Re-raise HTTP exceptions as-is
+        except BedrockError:
+            # Re-raise Bedrock exceptions as-is
             raise
         except Exception as e:
             verbose_logger.error(f"Error in CountTokens handler: {str(e)}")
-            raise HTTPException(
+            raise BedrockError(
                 status_code=500,
-                detail={"error": f"CountTokens processing error: {str(e)}"},
+                message=f"CountTokens processing error: {str(e)}",
             )
diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py
index d46ed3aa452..b313cc9df3c 100644
--- a/litellm/llms/bedrock/count_tokens/transformation.py
+++ b/litellm/llms/bedrock/count_tokens/transformation.py
@@ -8,7 +8,7 @@ to AWS Bedrock's CountTokens API format and vice versa.
 from typing import Any, Dict, List
 
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
-from litellm.llms.bedrock.common_utils import BedrockModelInfo
+from litellm.llms.bedrock.common_utils import get_bedrock_base_model
 
 
 class BedrockCountTokensConfig(BaseAWSLLM):
@@ -141,7 +141,7 @@ class BedrockCountTokensConfig(BaseAWSLLM):
             Complete endpoint URL for CountTokens API
         """
         # Use existing LiteLLM function to get the base model ID (removes region prefix)
-        model_id = BedrockModelInfo.get_base_model(model)
+        model_id = get_bedrock_base_model(model)
 
         # Remove bedrock/ prefix if present
         if model_id.startswith("bedrock/"):
diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py
index d6177e090d5..0350271dc44 100644
--- a/litellm/llms/bedrock/files/handler.py
+++ b/litellm/llms/bedrock/files/handler.py
@@ -142,6 +142,7 @@ class BedrockFilesHandler(BaseAWSLLM):
             aws_secret_access_key=credentials.secret_key,
             aws_session_token=credentials.token,
             region_name=aws_region_name,
+            verify=self._get_ssl_verify(),
         )
         
         # Download file from S3
diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py
index 0a95cf9168f..fdcbe1a8242 100644
--- a/litellm/llms/bedrock/files/transformation.py
+++ b/litellm/llms/bedrock/files/transformation.py
@@ -1,12 +1,14 @@
 import json
 import os
 import time
-from litellm._uuid import uuid
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import httpx
 from httpx import Headers, Response
+from openai.types.file_deleted import FileDeleted
 
 from litellm._logging import verbose_logger
+from litellm._uuid import uuid
 from litellm.files.utils import FilesAPIUtils
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
@@ -18,6 +20,7 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     CreateFileRequest,
     FileTypes,
+    HttpxBinaryResponseContent,
     OpenAICreateFileRequestOptionalParams,
     OpenAIFileObject,
     PathLike,
@@ -539,6 +542,70 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig):
             status_code=status_code, message=error_message, headers=headers
         )
 
+    def transform_retrieve_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("BedrockFilesConfig does not support file retrieval")
+
+    def transform_retrieve_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> OpenAIFileObject:
+        raise NotImplementedError("BedrockFilesConfig does not support file retrieval")
+
+    def transform_delete_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("BedrockFilesConfig does not support file deletion")
+
+    def transform_delete_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> FileDeleted:
+        raise NotImplementedError("BedrockFilesConfig does not support file deletion")
+
+    def transform_list_files_request(
+        self,
+        purpose: Optional[str],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("BedrockFilesConfig does not support file listing")
+
+    def transform_list_files_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> List[OpenAIFileObject]:
+        raise NotImplementedError("BedrockFilesConfig does not support file listing")
+
+    def transform_file_content_request(
+        self,
+        file_content_request,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("BedrockFilesConfig does not support file content retrieval")
+
+    def transform_file_content_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> HttpxBinaryResponseContent:
+        raise NotImplementedError("BedrockFilesConfig does not support file content retrieval")
+
 
 class BedrockJsonlFilesTransformation:
     """
diff --git a/litellm/llms/bedrock/passthrough/transformation.py b/litellm/llms/bedrock/passthrough/transformation.py
index 5791bfb8013..71c418757bf 100644
--- a/litellm/llms/bedrock/passthrough/transformation.py
+++ b/litellm/llms/bedrock/passthrough/transformation.py
@@ -24,6 +24,37 @@ class BedrockPassthroughConfig(
     def is_streaming_request(self, endpoint: str, request_data: dict) -> bool:
         return "stream" in endpoint
 
+    def _encode_model_id_for_endpoint(self, model_id: str) -> str:
+        """
+        Encode model_id (especially ARNs) for use in Bedrock endpoints.
+        
+        ARNs contain special characters like colons and slashes that need to be
+        properly URL-encoded when used in HTTP request paths. For example:
+        arn:aws:bedrock:us-east-1:123:application-inference-profile/abc123
+        becomes:
+        arn:aws:bedrock:us-east-1:123:application-inference-profile%2Fabc123
+        
+        Args:
+            model_id: The model ID or ARN to encode
+            
+        Returns:
+            The encoded model_id suitable for use in endpoint URLs
+        """
+        from litellm.passthrough.utils import CommonUtils
+        import re
+        
+        # Create a temporary endpoint with the model_id to check if encoding is needed
+        temp_endpoint = f"/model/{model_id}/converse"
+        encoded_temp_endpoint = CommonUtils.encode_bedrock_runtime_modelid_arn(temp_endpoint)
+        
+        # Extract the encoded model_id from the temporary endpoint
+        encoded_model_id_match = re.search(r'/model/([^/]+)/', encoded_temp_endpoint)
+        if encoded_model_id_match:
+            return encoded_model_id_match.group(1)
+        else:
+            # Fallback to original model_id if extraction fails
+            return model_id
+
     def get_complete_url(
         self,
         api_base: Optional[str],
@@ -34,11 +65,12 @@ class BedrockPassthroughConfig(
         litellm_params: dict,
     ) -> Tuple["URL", str]:
         optional_params = litellm_params.copy()
+        model_id = optional_params.get("model_id", None)
 
         aws_region_name = self._get_aws_region_name(
             optional_params=optional_params,
             model=model,
-            model_id=None,
+            model_id=model_id,
         )
 
         aws_bedrock_runtime_endpoint = optional_params.get("aws_bedrock_runtime_endpoint")
@@ -49,6 +81,16 @@ class BedrockPassthroughConfig(
             endpoint_type="runtime",
         )
 
+        # If model_id is provided (e.g., Application Inference Profile ARN), use it in the endpoint
+        # instead of the translated model name
+        if model_id is not None:
+            import re
+            
+            # Encode the model_id if it's an ARN to properly handle special characters
+            encoded_model_id = self._encode_model_id_for_endpoint(model_id)
+            
+            # Replace the model name in the endpoint with the encoded model_id
+            endpoint = re.sub(r'model/[^/]+/', f'model/{encoded_model_id}/', endpoint)
         return self.format_url(endpoint, endpoint_url, request_query_params or {}), endpoint_url
 
     def sign_request(
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index ea740400664..1da6e61252f 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -14,6 +14,7 @@ from typing import (
 )
 
 import httpx  # type: ignore
+from openai.types.file_deleted import FileDeleted
 
 import litellm
 import litellm.litellm_core_utils
@@ -71,6 +72,7 @@ from litellm.types.containers.main import (
     ContainerObject,
     DeleteContainerResult,
 )
+from litellm.types.files import TwoStepFileUploadConfig
 from litellm.types.llms.anthropic_messages.anthropic_response import (
     AnthropicMessagesResponse,
 )
@@ -82,6 +84,7 @@ from litellm.types.llms.anthropic_skills import (
 from litellm.types.llms.openai import (
     CreateBatchRequest,
     CreateFileRequest,
+    FileContentRequest,
     HttpxBinaryResponseContent,
     OpenAIFileObject,
     ResponseInputParam,
@@ -2782,6 +2785,38 @@ class BaseLLMHTTPHandler:
             logging_obj=logging_obj,
         )
 
+    def _extract_upload_url_from_response(
+        self,
+        response: httpx.Response,
+        upload_url_location: str,
+        upload_url_key: str = "upload_url",
+    ) -> tuple[Optional[str], Optional[dict]]:
+        """
+        Extract upload URL from initial file creation response.
+        
+        Args:
+            response: HTTP response from initial file creation request
+            upload_url_location: Where to find URL ('headers' or 'body')
+            upload_url_key: Key name for URL in response body (default: 'upload_url')
+            
+        Returns:
+            Tuple of (upload_url, response_data)
+            - upload_url: The extracted upload URL, or None if not found
+            - response_data: Parsed response body (for 'body' location), or None
+        """
+        if upload_url_location == "headers":
+            # Google Cloud Storage style - URL in X-Goog-Upload-URL header
+            upload_url = response.headers.get("X-Goog-Upload-URL")
+            return upload_url, None
+        else:
+            # Response body style (e.g., Manus, S3 presigned URLs)
+            try:
+                response_data = response.json()
+                upload_url = response_data.get(upload_url_key)
+                return upload_url, response_data if upload_url else None
+            except Exception:
+                return None, None
+
     def create_file(
         self,
         create_file_data: CreateFileRequest,
@@ -2844,14 +2879,58 @@ class BaseLLMHTTPHandler:
         else:
             sync_httpx_client = client
 
-        if isinstance(transformed_request, dict) and "method" in transformed_request:
+        if isinstance(transformed_request, dict) and "initial_request" in transformed_request:
+            # Handle two-step uploads (TwoStepFileUploadConfig)
+            # Used by providers like Manus, Google Cloud Storage
+            try:
+                # Step 1: Initial request to get upload URL
+                initial_response = sync_httpx_client.post(
+                    url=api_base,
+                    headers={
+                        **headers,
+                        **transformed_request["initial_request"]["headers"],
+                    },
+                    data=json.dumps(transformed_request["initial_request"]["data"]),
+                    timeout=timeout,
+                )
+
+                # Extract upload URL from response
+                upload_url, initial_response_data = self._extract_upload_url_from_response(
+                    response=initial_response,
+                    upload_url_location=transformed_request.get("upload_url_location", "headers"),
+                    upload_url_key=transformed_request.get("upload_url_key", "upload_url"),
+                )
+
+                if not upload_url:
+                    raise ValueError("Failed to get upload URL from initial request")
+
+                # Step 2: Upload the actual file
+                upload_method = transformed_request["upload_request"].get("method", "POST").lower()
+                upload_response = getattr(sync_httpx_client, upload_method)(
+                    url=upload_url,
+                    headers=transformed_request["upload_request"]["headers"],
+                    data=transformed_request["upload_request"]["data"],
+                    timeout=timeout,
+                )
+                
+                # Store initial response for transformation
+                if initial_response_data:
+                    litellm_params["initial_file_response"] = initial_response_data
+            except Exception as e:
+                raise self._handle_error(
+                    e=e,
+                    provider_config=provider_config,
+                )
+        elif isinstance(transformed_request, dict) and "method" in transformed_request and "initial_request" not in transformed_request:
             # Handle pre-signed requests (e.g., from Bedrock S3 uploads)
+            # Type narrowing: this is a plain dict, not TwoStepFileUploadConfig
+            presigned_request = cast(Dict[str, Any], transformed_request)
             upload_response = getattr(
-                sync_httpx_client, transformed_request["method"].lower()
+                sync_httpx_client, presigned_request["method"].lower()
             )(
-                url=transformed_request["url"],
-                headers=transformed_request["headers"],
-                data=transformed_request["data"],
+                url=presigned_request["url"],
+                headers=presigned_request["headers"],
+                data=presigned_request["data"],
                 timeout=timeout,
             )
         elif isinstance(transformed_request, str) or isinstance(
@@ -2879,36 +2958,7 @@ class BaseLLMHTTPHandler:
                     timeout=timeout,
                 )
         else:
-            try:
-                # Step 1: Initial request to get upload URL
-                initial_response = sync_httpx_client.post(
-                    url=api_base,
-                    headers={
-                        **headers,
-                        **transformed_request["initial_request"]["headers"],
-                    },
-                    data=json.dumps(transformed_request["initial_request"]["data"]),
-                    timeout=timeout,
-                )
-
-                # Extract upload URL from response headers
-                upload_url = initial_response.headers.get("X-Goog-Upload-URL")
-
-                if not upload_url:
-                    raise ValueError("Failed to get upload URL from initial request")
-
-                # Step 2: Upload the actual file
-                upload_response = sync_httpx_client.post(
-                    url=upload_url,
-                    headers=transformed_request["upload_request"]["headers"],
-                    data=transformed_request["upload_request"]["data"],
-                    timeout=timeout,
-                )
-            except Exception as e:
-                raise self._handle_error(
-                    e=e,
-                    provider_config=provider_config,
-                )
+            raise ValueError(f"Unsupported transformed_request type: {type(transformed_request)}")
 
         # Store the upload URL in litellm_params for the transformation method
         litellm_params_with_url = dict(litellm_params)
@@ -2923,7 +2973,7 @@ class BaseLLMHTTPHandler:
 
     async def async_create_file(
         self,
-        transformed_request: Union[bytes, str, dict],
+        transformed_request: Union[bytes, str, dict, "TwoStepFileUploadConfig"],
         litellm_params: dict,
         provider_config: BaseFilesConfig,
         headers: dict,
@@ -2955,14 +3005,59 @@ class BaseLLMHTTPHandler:
             },
         )
 
-        if isinstance(transformed_request, dict) and "method" in transformed_request:
+        if isinstance(transformed_request, dict) and "initial_request" in transformed_request:
+            # Handle two-step uploads (TwoStepFileUploadConfig)
+            # Used by providers like Manus, Google Cloud Storage
+            try:
+                # Step 1: Initial request to get upload URL
+                initial_response = await async_httpx_client.post(
+                    url=api_base,
+                    headers={
+                        **headers,
+                        **transformed_request["initial_request"]["headers"],
+                    },
+                    data=json.dumps(transformed_request["initial_request"]["data"]),
+                    timeout=timeout,
+                )
+
+                # Extract upload URL from response
+                upload_url, initial_response_data = self._extract_upload_url_from_response(
+                    response=initial_response,
+                    upload_url_location=transformed_request.get("upload_url_location", "headers"),
+                    upload_url_key=transformed_request.get("upload_url_key", "upload_url"),
+                )
+
+                if not upload_url:
+                    raise ValueError("Failed to get upload URL from initial request")
+
+                # Step 2: Upload the actual file
+                upload_method = transformed_request["upload_request"].get("method", "POST").lower()
+                upload_response = await getattr(async_httpx_client, upload_method)(
+                    url=upload_url,
+                    headers=transformed_request["upload_request"]["headers"],
+                    data=transformed_request["upload_request"]["data"],
+                    timeout=timeout,
+                )
+                
+                # Store initial response for transformation
+                if initial_response_data:
+                    litellm_params["initial_file_response"] = initial_response_data
+            except Exception as e:
+                verbose_logger.exception(f"Error creating file: {e}")
+                raise self._handle_error(
+                    e=e,
+                    provider_config=provider_config,
+                )
+        elif isinstance(transformed_request, dict) and "method" in transformed_request and "initial_request" not in transformed_request:
             # Handle pre-signed requests (e.g., from Bedrock S3 uploads)
+            # Type narrowing: this is a plain dict, not TwoStepFileUploadConfig
+            presigned_request = cast(Dict[str, Any], transformed_request)
             upload_response = await getattr(
-                async_httpx_client, transformed_request["method"].lower()
+                async_httpx_client, presigned_request["method"].lower()
             )(
-                url=transformed_request["url"],
-                headers=transformed_request["headers"],
-                data=transformed_request["data"],
+                url=presigned_request["url"],
+                headers=presigned_request["headers"],
+                data=presigned_request["data"],
                 timeout=timeout,
             )
         elif isinstance(transformed_request, str) or isinstance(
@@ -2990,37 +3085,7 @@ class BaseLLMHTTPHandler:
                     timeout=timeout,
                 )
         else:
-            try:
-                # Step 1: Initial request to get upload URL
-                initial_response = await async_httpx_client.post(
-                    url=api_base,
-                    headers={
-                        **headers,
-                        **transformed_request["initial_request"]["headers"],
-                    },
-                    data=json.dumps(transformed_request["initial_request"]["data"]),
-                    timeout=timeout,
-                )
-
-                # Extract upload URL from response headers
-                upload_url = initial_response.headers.get("X-Goog-Upload-URL")
-
-                if not upload_url:
-                    raise ValueError("Failed to get upload URL from initial request")
-
-                # Step 2: Upload the actual file
-                upload_response = await async_httpx_client.post(
-                    url=upload_url,
-                    headers=transformed_request["upload_request"]["headers"],
-                    data=transformed_request["upload_request"]["data"],
-                    timeout=timeout,
-                )
-            except Exception as e:
-                verbose_logger.exception(f"Error creating file: {e}")
-                raise self._handle_error(
-                    e=e,
-                    provider_config=provider_config,
-                )
+            raise ValueError(f"Unsupported transformed_request type: {type(transformed_request)}")
 
         return provider_config.transform_create_file_response(
             model=None,
@@ -3734,29 +3799,525 @@ class BaseLLMHTTPHandler:
             logging_obj=logging_obj,
         )
 
-    def list_files(self):
+    def retrieve_file(
+        self,
+        file_id: str,
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        _is_async: bool = False,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> Union[OpenAIFileObject, Coroutine[Any, Any, OpenAIFileObject]]:
         """
-        Lists all files
+        Retrieve file metadata by ID
         """
-        pass
+        if _is_async:
+            return self.async_retrieve_file(
+                file_id=file_id,
+                provider_config=provider_config,
+                litellm_params=litellm_params,
+                headers=headers,
+                logging_obj=logging_obj,
+                client=client,
+                timeout=timeout,
+            )
 
-    def delete_file(self):
-        """
-        Deletes a file
-        """
-        pass
+        if client is None or not isinstance(client, HTTPHandler):
+            sync_httpx_client = _get_httpx_client()
+        else:
+            sync_httpx_client = client
 
-    def retrieve_file(self):
-        """
-        Returns the metadata of the file
-        """
-        pass
+        # Get URL and params from provider config
+        url, params = provider_config.transform_retrieve_file_request(
+            file_id=file_id,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
 
-    def retrieve_file_content(self):
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "file_id": file_id,
+            },
+        )
+
+        try:
+            response = sync_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_retrieve_file_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    async def async_retrieve_file(
+        self,
+        file_id: str,
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> OpenAIFileObject:
         """
-        Returns the content of the file
+        Async retrieve file metadata by ID
         """
-        pass
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            async_httpx_client = get_async_httpx_client(
+                llm_provider=provider_config.custom_llm_provider
+            )
+        else:
+            async_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_retrieve_file_request(
+            file_id=file_id,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "file_id": file_id,
+            },
+        )
+
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_retrieve_file_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    def delete_file(
+        self,
+        file_id: str,
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        _is_async: bool = False,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> Union["FileDeleted", Coroutine[Any, Any, "FileDeleted"]]:
+        """
+        Delete a file by ID
+        """
+        if _is_async:
+            return self.async_delete_file(
+                file_id=file_id,
+                provider_config=provider_config,
+                litellm_params=litellm_params,
+                headers=headers,
+                logging_obj=logging_obj,
+                client=client,
+                timeout=timeout,
+            )
+
+        if client is None or not isinstance(client, HTTPHandler):
+            sync_httpx_client = _get_httpx_client()
+        else:
+            sync_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_delete_file_request(
+            file_id=file_id,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "file_id": file_id,
+            },
+        )
+
+        try:
+            response = sync_httpx_client.delete(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_delete_file_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    async def async_delete_file(
+        self,
+        file_id: str,
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> "FileDeleted":
+        """
+        Async delete a file by ID
+        """
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            async_httpx_client = get_async_httpx_client(
+                llm_provider=provider_config.custom_llm_provider
+            )
+        else:
+            async_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_delete_file_request(
+            file_id=file_id,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "file_id": file_id,
+            },
+        )
+
+        try:
+            response = await async_httpx_client.delete(
+                url=url, headers=headers, params=params, timeout=timeout
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_delete_file_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    def list_files(
+        self,
+        purpose: Optional[str],
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        _is_async: bool = False,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> Union[List[OpenAIFileObject], Coroutine[Any, Any, List[OpenAIFileObject]]]:
+        """
+        List all files
+        """
+        if _is_async:
+            return self.async_list_files(
+                purpose=purpose,
+                provider_config=provider_config,
+                litellm_params=litellm_params,
+                headers=headers,
+                logging_obj=logging_obj,
+                client=client,
+                timeout=timeout,
+            )
+
+        if client is None or not isinstance(client, HTTPHandler):
+            sync_httpx_client = _get_httpx_client()
+        else:
+            sync_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_list_files_request(
+            purpose=purpose,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "purpose": purpose,
+            },
+        )
+
+        try:
+            response = sync_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_list_files_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    async def async_list_files(
+        self,
+        purpose: Optional[str],
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> List[OpenAIFileObject]:
+        """
+        Async list all files
+        """
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            async_httpx_client = get_async_httpx_client(
+                llm_provider=provider_config.custom_llm_provider
+            )
+        else:
+            async_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_list_files_request(
+            purpose=purpose,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "purpose": purpose,
+            },
+        )
+
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_list_files_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    def retrieve_file_content(
+        self,
+        file_content_request: "FileContentRequest",
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        _is_async: bool = False,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> Union["HttpxBinaryResponseContent", Coroutine[Any, Any, "HttpxBinaryResponseContent"]]:
+        """
+        Retrieve file content by ID
+        """
+        if _is_async:
+            return self.async_retrieve_file_content(
+                file_content_request=file_content_request,
+                provider_config=provider_config,
+                litellm_params=litellm_params,
+                headers=headers,
+                logging_obj=logging_obj,
+                client=client,
+                timeout=timeout,
+            )
+
+        if client is None or not isinstance(client, HTTPHandler):
+            sync_httpx_client = _get_httpx_client()
+        else:
+            sync_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_file_content_request(
+            file_content_request=file_content_request,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "file_id": file_content_request.get("file_id"),
+            },
+        )
+
+        try:
+            response = sync_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_file_content_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    async def async_retrieve_file_content(
+        self,
+        file_content_request: "FileContentRequest",
+        provider_config: BaseFilesConfig,
+        litellm_params: dict,
+        headers: dict,
+        logging_obj: LiteLLMLoggingObj,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+    ) -> "HttpxBinaryResponseContent":
+        """
+        Async retrieve file content by ID
+        """
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            async_httpx_client = get_async_httpx_client(
+                llm_provider=provider_config.custom_llm_provider
+            )
+        else:
+            async_httpx_client = client
+
+        # Get URL and params from provider config
+        url, params = provider_config.transform_file_content_request(
+            file_content_request=file_content_request,
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        # Validate environment and get headers
+        headers = provider_config.validate_environment(
+            api_key=litellm_params.get("api_key"),
+            headers=headers,
+            model="",
+            messages=[],
+            optional_params={},
+            litellm_params=litellm_params,
+        )
+
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "api_base": url,
+                "headers": headers,
+                "file_id": file_content_request.get("file_id"),
+            },
+        )
+
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=params
+            )
+        except Exception as e:
+            raise self._handle_error(e=e, provider_config=provider_config)
+
+        return provider_config.transform_file_content_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
 
     def _prepare_fake_stream_request(
         self,
diff --git a/litellm/llms/deepinfra/chat/transformation.py b/litellm/llms/deepinfra/chat/transformation.py
index 09cdabcdd82..5198260a24b 100644
--- a/litellm/llms/deepinfra/chat/transformation.py
+++ b/litellm/llms/deepinfra/chat/transformation.py
@@ -1,9 +1,11 @@
-from typing import Optional, Tuple, Union
+import json
+from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload
 
 import litellm
 from litellm.constants import MIN_NON_ZERO_TEMPERATURE
 from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import AllMessageValues
 
 
 class DeepInfraConfig(OpenAIGPTConfig):
@@ -117,6 +119,79 @@ class DeepInfraConfig(OpenAIGPTConfig):
                     optional_params[param] = value
         return optional_params
 
+    def _transform_tool_message_content(self, messages: List[AllMessageValues]) -> List[AllMessageValues]:
+        """
+        Transform tool message content from array to string format for DeepInfra compatibility.
+        
+        DeepInfra requires tool message content to be a string, not an array.
+        This method converts tool message content from array format to string format.
+        
+        Example transformation:
+        - Input:  {"role": "tool", "content": [{"type": "text", "text": "20"}]}
+        - Output: {"role": "tool", "content": "20"}
+        
+        Or if content is complex:
+        - Input:  {"role": "tool", "content": [{"type": "text", "text": "result"}]}
+        - Output: {"role": "tool", "content": "[{\"type\": \"text\", \"text\": \"result\"}]"}
+        """
+        for message in messages:
+            if message.get("role") == "tool":
+                content = message.get("content")
+                
+                # If content is a list/array, convert it to string
+                if isinstance(content, list):
+                    # Check if it's a simple single text item
+                    if (
+                        len(content) == 1 
+                        and isinstance(content[0], dict) 
+                        and content[0].get("type") == "text"
+                        and "text" in content[0]
+                    ):
+                        # Extract just the text value for simple cases
+                        message["content"] = content[0]["text"]
+                    else:
+                        # For complex content, serialize the entire array as JSON string
+                        message["content"] = json.dumps(content)
+        
+        return messages
+
+    @overload
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str, is_async: Literal[True]
+    ) -> Coroutine[Any, Any, List[AllMessageValues]]:
+        ...
+
+    @overload
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str, is_async: Literal[False] = False
+    ) -> List[AllMessageValues]:
+        ...
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str, is_async: bool = False
+    ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]:
+        """
+        Transform messages for DeepInfra compatibility.
+        Handles both sync and async transformations.
+        """
+        if is_async:
+            # For async case, create an async function that awaits parent and applies our transformation
+            async def _async_transform():
+                # Call parent with is_async=True (literal) for async case
+                parent_result = super(DeepInfraConfig, self)._transform_messages(
+                    messages=messages, model=model, is_async=cast(Literal[True], True)
+                )
+                transformed_messages = await parent_result
+                return self._transform_tool_message_content(transformed_messages)
+            return _async_transform()
+        else:
+            # Call parent with is_async=False (literal) for sync case
+            parent_result = super()._transform_messages(
+                messages=messages, model=model, is_async=cast(Literal[False], False)
+            )
+            # For sync case, parent_result is already the transformed messages
+            return self._transform_tool_message_content(parent_result)
+
     def _get_openai_compatible_provider_info(
         self, api_base: Optional[str], api_key: Optional[str]
     ) -> Tuple[Optional[str], Optional[str]]:
diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py
index 62897fe6ecb..f6d075392b2 100644
--- a/litellm/llms/gemini/chat/transformation.py
+++ b/litellm/llms/gemini/chat/transformation.py
@@ -87,6 +87,7 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
             "stop",
             "logprobs",
             "frequency_penalty",
+            "presence_penalty",
             "modalities",
             "parallel_tool_calls",
             "web_search_options",
diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py
index e98e76dabc8..d9ebf69a97a 100644
--- a/litellm/llms/gemini/files/transformation.py
+++ b/litellm/llms/gemini/files/transformation.py
@@ -7,6 +7,7 @@ import time
 from typing import List, Optional
 
 import httpx
+from openai.types.file_deleted import FileDeleted
 
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
@@ -17,6 +18,7 @@ from litellm.llms.base_llm.files.transformation import (
 from litellm.types.llms.gemini import GeminiCreateFilesResponseObject
 from litellm.types.llms.openai import (
     CreateFileRequest,
+    HttpxBinaryResponseContent,
     OpenAICreateFileRequestOptionalParams,
     OpenAIFileObject,
 )
@@ -171,3 +173,67 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
         except Exception as e:
             verbose_logger.exception(f"Error parsing file upload response: {str(e)}")
             raise ValueError(f"Error parsing file upload response: {str(e)}")
+
+    def transform_retrieve_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file retrieval")
+
+    def transform_retrieve_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> OpenAIFileObject:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file retrieval")
+
+    def transform_delete_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file deletion")
+
+    def transform_delete_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> FileDeleted:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file deletion")
+
+    def transform_list_files_request(
+        self,
+        purpose: Optional[str],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file listing")
+
+    def transform_list_files_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> List[OpenAIFileObject]:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file listing")
+
+    def transform_file_content_request(
+        self,
+        file_content_request,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file content retrieval")
+
+    def transform_file_content_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> HttpxBinaryResponseContent:
+        raise NotImplementedError("GoogleAIStudioFilesHandler does not support file content retrieval")
diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py
index 3474c8abe34..48046dd9dfa 100644
--- a/litellm/llms/gemini/google_genai/transformation.py
+++ b/litellm/llms/gemini/google_genai/transformation.py
@@ -89,6 +89,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
             "audio_timestamp",
             "automatic_function_calling",
             "thinking_config",
+            "image_config",
         ]
 
     def map_generate_content_optional_params(
diff --git a/litellm/llms/manus/__init__.py b/litellm/llms/manus/__init__.py
new file mode 100644
index 00000000000..81eef025461
--- /dev/null
+++ b/litellm/llms/manus/__init__.py
@@ -0,0 +1,2 @@
+# Manus provider implementation
+
diff --git a/litellm/llms/manus/files/__init__.py b/litellm/llms/manus/files/__init__.py
new file mode 100644
index 00000000000..66d23ca0340
--- /dev/null
+++ b/litellm/llms/manus/files/__init__.py
@@ -0,0 +1,2 @@
+# Manus Files API implementation
+
diff --git a/litellm/llms/manus/files/transformation.py b/litellm/llms/manus/files/transformation.py
new file mode 100644
index 00000000000..a7965011969
--- /dev/null
+++ b/litellm/llms/manus/files/transformation.py
@@ -0,0 +1,439 @@
+"""
+Manus Files API implementation.
+
+Manus has an OpenAI-compatible Files API with some differences:
+- Uses API_KEY header instead of Authorization: Bearer
+- File upload is a two-step process:
+  1. Create file record to get upload URL
+  2. Upload file content to the upload URL
+
+Reference: https://open.manus.im/docs/openai-compatibility#file-management
+"""
+
+import time
+from typing import Any, Dict, List, Optional, Union
+
+import httpx
+from openai.types.file_deleted import FileDeleted
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.files.transformation import (
+    BaseFilesConfig,
+    LiteLLMLoggingObj,
+)
+from litellm.llms.openai.common_utils import OpenAIError
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.files import TwoStepFileUploadConfig, TwoStepFileUploadRequest
+from litellm.types.llms.openai import (
+    CreateFileRequest,
+    FileContentRequest,
+    HttpxBinaryResponseContent,
+    OpenAICreateFileRequestOptionalParams,
+    OpenAIFileObject,
+)
+from litellm.types.utils import LlmProviders
+
+MANUS_API_BASE = "https://api.manus.im"
+
+
+class ManusFilesConfig(BaseFilesConfig):
+    """
+    Configuration for Manus Files API.
+
+    Manus uses:
+    - API_KEY header for authentication (not Authorization: Bearer)
+    - Two-step file upload process
+    - Content-Type: application/json for all requests
+
+    Reference: https://open.manus.im/docs/openai-compatibility#file-management
+    """
+
+    def __init__(self):
+        pass
+
+    @property
+    def custom_llm_provider(self) -> LlmProviders:
+        return LlmProviders.MANUS
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: list,
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        """
+        Validate environment and set up headers for Manus API.
+
+        Manus uses API_KEY header instead of Authorization: Bearer.
+        For file uploads, don't set Content-Type - httpx will set it for multipart.
+        """
+        api_key = (
+            api_key
+            or litellm.api_key
+            or get_secret_str("MANUS_API_KEY")
+        )
+
+        if not api_key:
+            raise ValueError(
+                "Manus API key is required. Set MANUS_API_KEY environment variable or pass api_key parameter."
+            )
+
+        # Manus uses API_KEY header, not Authorization: Bearer
+        # Manus requires Content-Type: application/json for all requests (even GET)
+        headers.update(
+            {
+                "API_KEY": api_key,
+                "Content-Type": "application/json",
+            }
+        )
+        return headers
+
+    def get_supported_openai_params(
+        self, model: str
+    ) -> List[OpenAICreateFileRequestOptionalParams]:
+        """
+        Return supported OpenAI file creation parameters for Manus.
+        Manus supports the standard 'purpose' parameter.
+        """
+        return ["purpose"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        Map OpenAI parameters to Manus-specific parameters.
+        Manus is OpenAI-compatible, so no special mapping needed.
+        """
+        return optional_params
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        """
+        Get the complete URL for Manus Files API endpoint.
+
+        Returns:
+            str: The full URL for the Manus /v1/files endpoint
+        """
+        api_base = (
+            api_base
+            or litellm.api_base
+            or get_secret_str("MANUS_API_BASE")
+            or MANUS_API_BASE
+        )
+
+        # Remove trailing slashes
+        api_base = api_base.rstrip("/")
+
+        # Manus API uses /v1/files endpoint
+        if api_base.endswith("/v1"):
+            return f"{api_base}/files"
+        return f"{api_base}/v1/files"
+
+    def get_error_class(
+        self,
+        error_message: str,
+        status_code: int,
+        headers: Union[dict, httpx.Headers],
+    ) -> BaseLLMException:
+        """
+        Return the appropriate error class for Manus API errors.
+        Uses OpenAIError since Manus is OpenAI-compatible.
+        """
+        return OpenAIError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
+
+    def transform_create_file_request(
+        self,
+        model: str,
+        create_file_data: CreateFileRequest,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> TwoStepFileUploadConfig:
+        """
+        Transform OpenAI-style file creation request into Manus's two-step format.
+
+        Manus API spec (https://open.manus.im/docs/openai-compatibility#file-management):
+        1. POST /v1/files with JSON {"filename": "..."} → returns {"id": "...", "upload_url": "..."}
+        2. PUT to upload_url with raw file content
+        """
+        # Extract file data
+        file_data = create_file_data.get("file")
+        if file_data is None:
+            raise ValueError("File data is required")
+
+        extracted_data = extract_file_data(file_data)
+        filename = extracted_data["filename"] or f"file_{int(time.time())}"
+        content = extracted_data["content"]
+
+        # Get API base URL
+        api_base = self.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            api_key=litellm_params.get("api_key"),
+            model=model,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        
+        # Get API key
+        api_key = (
+            litellm_params.get("api_key")
+            or litellm.api_key
+            or get_secret_str("MANUS_API_KEY")
+        )
+        
+        if not api_key:
+            raise ValueError(
+                "Manus API key is required. Set MANUS_API_KEY environment variable or pass api_key parameter."
+            )
+
+        # Build typed two-step upload config
+        return TwoStepFileUploadConfig(
+            initial_request=TwoStepFileUploadRequest(
+                method="POST",
+                url=api_base,
+                headers={
+                    "API_KEY": api_key,
+                    "Content-Type": "application/json",
+                },
+                data={"filename": filename},
+            ),
+            upload_request=TwoStepFileUploadRequest(
+                method="PUT",
+                url="",  # Will be populated from initial_request response
+                headers={},
+                data=content,
+            ),
+            upload_url_location="body",
+            upload_url_key="upload_url",
+        )
+
+    def transform_create_file_response(
+        self,
+        model: Optional[str],
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> OpenAIFileObject:
+        """
+        Transform Manus's file upload response into OpenAI-style FileObject.
+
+        For two-step uploads, the handler stores the initial response in litellm_params.
+        We need to return the file object from the initial POST, not the final PUT.
+
+        Manus initial response format:
+        {
+            "id": "file-abc123xyz",
+            "object": "file",
+            "filename": "document.pdf",
+            "status": "pending",
+            "upload_url": "https://...",
+            "upload_expires_at": "...",
+            "created_at": "..."
+        }
+        """
+        try:
+            # For two-step uploads, get the initial response from litellm_params
+            initial_response_data = litellm_params.get("initial_file_response")
+            if initial_response_data:
+                response_json = initial_response_data
+            else:
+                # Log raw response for debugging
+                verbose_logger.debug(f"Manus raw response text: {raw_response.text}")
+                response_json = raw_response.json()
+
+            verbose_logger.debug(f"Manus file response: {response_json}")
+
+            # Parse created_at timestamp
+            created_at_str = response_json.get("created_at", "")
+            if created_at_str:
+                try:
+                    # Try parsing ISO format
+                    created_at = int(
+                        time.mktime(
+                            time.strptime(
+                                created_at_str.replace("Z", "+00:00")[:19],
+                                "%Y-%m-%dT%H:%M:%S",
+                            )
+                        )
+                    )
+                except (ValueError, TypeError):
+                    created_at = int(time.time())
+            else:
+                created_at = int(time.time())
+
+            return OpenAIFileObject(
+                id=response_json.get("id", ""),
+                bytes=response_json.get("bytes", 0),
+                created_at=created_at,
+                filename=response_json.get("filename", ""),
+                object="file",
+                purpose=response_json.get("purpose", "assistants"),
+                status="uploaded",  # After successful upload, status is uploaded
+                status_details=response_json.get("status_details"),
+            )
+        except Exception as e:
+            verbose_logger.exception(f"Error parsing Manus file response: {str(e)}")
+            raise ValueError(f"Error parsing Manus file response: {str(e)}")
+
+    def transform_retrieve_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Get URL and params for retrieving a file."""
+        api_base = self.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            api_key=litellm_params.get("api_key"),
+            model="",
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        return f"{api_base}/{file_id}", {}
+
+    def transform_retrieve_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> OpenAIFileObject:
+        """Transform retrieve file response."""
+        return self.transform_create_file_response(
+            model=None,
+            raw_response=raw_response,
+            logging_obj=logging_obj,
+            litellm_params=litellm_params,
+        )
+
+    def transform_delete_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Get URL and params for deleting a file."""
+        api_base = self.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            api_key=litellm_params.get("api_key"),
+            model="",
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        return f"{api_base}/{file_id}", {}
+
+    def transform_delete_file_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> FileDeleted:
+        """Transform delete file response."""
+        response_json = raw_response.json()
+        return FileDeleted(**response_json)
+
+    def transform_list_files_request(
+        self,
+        purpose: Optional[str],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Get URL and params for listing files."""
+        api_base = self.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            api_key=litellm_params.get("api_key"),
+            model="",
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        params = {}
+        if purpose:
+            params["purpose"] = purpose
+        return api_base, params
+
+    def transform_list_files_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> List[OpenAIFileObject]:
+        """Transform list files response."""
+        response_json = raw_response.json()
+        files_data = response_json.get("data", [])
+        return [self._parse_file_dict(f) for f in files_data]
+
+    def _parse_file_dict(self, file_dict: Dict[str, Any]) -> OpenAIFileObject:
+        """Parse a file dict into OpenAIFileObject."""
+        created_at_str = file_dict.get("created_at", "")
+        if created_at_str:
+            try:
+                created_at = int(
+                    time.mktime(
+                        time.strptime(
+                            created_at_str.replace("Z", "+00:00")[:19],
+                            "%Y-%m-%dT%H:%M:%S",
+                        )
+                    )
+                )
+            except (ValueError, TypeError):
+                created_at = int(time.time())
+        else:
+            created_at = int(time.time())
+
+        return OpenAIFileObject(
+            id=file_dict.get("id", ""),
+            bytes=file_dict.get("bytes", 0),
+            created_at=created_at,
+            filename=file_dict.get("filename", ""),
+            object="file",
+            purpose=file_dict.get("purpose", "assistants"),
+            status=file_dict.get("status", "uploaded"),
+            status_details=file_dict.get("status_details"),
+        )
+
+    def transform_file_content_request(
+        self,
+        file_content_request: FileContentRequest,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        """Get URL and params for retrieving file content."""
+        file_id = file_content_request.get("file_id")
+        api_base = self.get_complete_url(
+            api_base=litellm_params.get("api_base"),
+            api_key=litellm_params.get("api_key"),
+            model="",
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        return f"{api_base}/{file_id}/content", {}
+
+    def transform_file_content_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> HttpxBinaryResponseContent:
+        """Transform file content response."""
+        return HttpxBinaryResponseContent(response=raw_response)
+
diff --git a/litellm/llms/manus/responses/__init__.py b/litellm/llms/manus/responses/__init__.py
new file mode 100644
index 00000000000..e8cabc54266
--- /dev/null
+++ b/litellm/llms/manus/responses/__init__.py
@@ -0,0 +1,2 @@
+# Manus Responses API implementation
+
diff --git a/litellm/llms/manus/responses/transformation.py b/litellm/llms/manus/responses/transformation.py
new file mode 100644
index 00000000000..fbbed19f8d4
--- /dev/null
+++ b/litellm/llms/manus/responses/transformation.py
@@ -0,0 +1,340 @@
+import uuid
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+
+import httpx
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.core_helpers import process_response_headers
+from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
+    _safe_convert_created_field,
+)
+from litellm.llms.openai.common_utils import OpenAIError
+from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import (
+    ResponseAPIUsage,
+    ResponseInputParam,
+    ResponsesAPIResponse,
+)
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import LlmProviders
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+MANUS_API_BASE = "https://api.manus.im"
+
+
+class ManusResponsesAPIConfig(OpenAIResponsesAPIConfig):
+    """
+    Configuration for Manus API's Responses API.
+    
+    Manus API is OpenAI-compatible but has some differences:
+    - API key passed via `API_KEY` header (not `Authorization: Bearer`)
+    - Model format: `manus/{agent_profile}` (e.g., `manus/manus-1.6`)
+    - Requires `extra_body` with `task_mode: "agent"` and `agent_profile`
+    
+    Reference: https://open.manus.im/docs/openai-compatibility
+    """
+
+    @property
+    def custom_llm_provider(self) -> LlmProviders:
+        return LlmProviders.MANUS
+
+    def should_fake_stream(
+        self,
+        model: Optional[str],
+        stream: Optional[bool],
+        custom_llm_provider: Optional[str] = None,
+    ) -> bool:
+        """
+        Manus API doesn't support real-time streaming.
+        It returns a task that runs asynchronously.
+        We fake streaming by converting the response into streaming events.
+        """
+        return stream is True
+
+    def _extract_agent_profile(self, model: str) -> str:
+        """
+        Extract agent profile from model name.
+        
+        Model format: `manus/{agent_profile}`
+        Examples: `manus/manus-1.6`, `manus/manus-1.6-lite`, `manus/manus-1.6-max`
+        
+        Returns:
+            str: The agent profile (e.g., "manus-1.6")
+        """
+        if "/" in model:
+            return model.split("/", 1)[1]
+        # If no slash, assume the model name itself is the agent profile
+        return model
+
+    def validate_environment(
+        self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams]
+    ) -> dict:
+        """
+        Validate environment and set up headers for Manus API.
+        
+        Manus uses `API_KEY` header instead of `Authorization: Bearer`.
+        """
+        litellm_params = litellm_params or GenericLiteLLMParams()
+        api_key = (
+            litellm_params.api_key
+            or litellm.api_key
+            or get_secret_str("MANUS_API_KEY")
+        )
+        
+        if not api_key:
+            raise ValueError(
+                "Manus API key is required. Set MANUS_API_KEY environment variable or pass api_key parameter."
+            )
+        
+        # Manus uses API_KEY header, not Authorization: Bearer
+        # Content-Type is required for all requests (including GET)
+        headers.update(
+            {
+                "API_KEY": api_key,
+                "Content-Type": "application/json",
+            }
+        )
+        return headers
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        litellm_params: dict,
+    ) -> str:
+        """
+        Get the complete URL for Manus Responses API endpoint.
+        
+        Returns:
+            str: The full URL for the Manus /v1/responses endpoint
+        """
+        api_base = (
+            api_base
+            or litellm.api_base
+            or get_secret_str("MANUS_API_BASE")
+            or MANUS_API_BASE
+        )
+        
+        # Remove trailing slashes
+        api_base = api_base.rstrip("/")
+        
+        # Manus API uses /v1/responses endpoint (OpenAI-compatible)
+        if api_base.endswith("/v1"):
+            return f"{api_base}/responses"
+        return f"{api_base}/v1/responses"
+
+    def transform_responses_api_request(
+        self,
+        model: str,
+        input: Union[str, ResponseInputParam],
+        response_api_optional_request_params: Dict,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Dict:
+        """
+        Transform the request for Manus API.
+        
+        Manus requires:
+        - `task_mode: "agent"` in the request body
+        - `agent_profile` extracted from model name in the request body
+        """
+        # First, get the base OpenAI request
+        base_request = super().transform_responses_api_request(
+            model=model,
+            input=input,
+            response_api_optional_request_params=response_api_optional_request_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+        
+        # Extract agent profile from model name
+        agent_profile = self._extract_agent_profile(model=model)
+        
+        # Add Manus-specific parameters directly to the request body
+        # These will be sent as part of the request
+        base_request["task_mode"] = "agent"
+        base_request["agent_profile"] = agent_profile
+        
+        # Merge any existing extra_body into the request
+        extra_body = response_api_optional_request_params.get("extra_body", {}) or {}
+        if extra_body:
+            base_request.update(extra_body)
+        
+        verbose_logger.debug(
+            f"Manus: Using agent_profile={agent_profile}, task_mode=agent"
+        )
+        
+        return base_request
+
+    def transform_response_api_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> ResponsesAPIResponse:
+        """
+        Transform Manus API response to OpenAI-compatible format.
+        
+        Manus uses camelCase (createdAt) instead of snake_case (created_at).
+        """
+        try:
+            logging_obj.post_call(
+                original_response=raw_response.text,
+                additional_args={"complete_input_dict": {}},
+            )
+            raw_response_json = raw_response.json()
+            
+            # Manus uses camelCase "createdAt" instead of snake_case "created_at"
+            if "createdAt" in raw_response_json and "created_at" not in raw_response_json:
+                raw_response_json["created_at"] = _safe_convert_created_field(
+                    raw_response_json["createdAt"]
+                )
+            
+            # Ensure created_at is set
+            if "created_at" in raw_response_json:
+                raw_response_json["created_at"] = _safe_convert_created_field(
+                    raw_response_json["created_at"]
+                )
+        except Exception:
+            raise OpenAIError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        
+        raw_response_headers = dict(raw_response.headers)
+        processed_headers = process_response_headers(raw_response_headers)
+        
+        # Ensure reasoning is an empty dict if not present, OpenAI SDK does not allow None
+        if "reasoning" not in raw_response_json or raw_response_json.get("reasoning") is None:
+            raw_response_json["reasoning"] = {}
+        
+        if "text" not in raw_response_json or raw_response_json.get("text") is None:
+            raw_response_json["text"] = {}
+        
+        if "output" not in raw_response_json or raw_response_json.get("output") is None:
+            raw_response_json["output"] = []
+        
+        # Ensure usage is present with default values if not provided
+        if "usage" not in raw_response_json or raw_response_json.get("usage") is None:
+            raw_response_json["usage"] = ResponseAPIUsage(
+                input_tokens=0,
+                output_tokens=0,
+                total_tokens=0,
+            )
+        
+        # Ensure id is present - failed responses may not include it
+        if "id" not in raw_response_json or raw_response_json.get("id") is None:
+            # Generate a placeholder id for failed responses
+            # This allows the response object to be created even when the API doesn't return an id
+            raw_response_json["id"] = f"unknown-{uuid.uuid4().hex[:8]}"
+        
+        try:
+            response = ResponsesAPIResponse(**raw_response_json)
+        except Exception:
+            verbose_logger.debug(
+                f"Error constructing ResponsesAPIResponse: {raw_response_json}, using model_construct"
+            )
+            response = ResponsesAPIResponse.model_construct(**raw_response_json)
+        
+        # Store processed headers in additional_headers so they get returned to the client
+        response._hidden_params["additional_headers"] = processed_headers
+        response._hidden_params["headers"] = raw_response_headers
+        return response
+
+    def transform_get_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the get response API request into a URL and data.
+        
+        Manus API follows OpenAI-compatible format:
+        - GET /v1/responses/{response_id}
+        
+        Reference: https://open.manus.im/docs/openai-compatibility
+        """
+        url = f"{api_base}/{response_id}"
+        data: Dict = {}
+        return url, data
+
+    def transform_get_response_api_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> ResponsesAPIResponse:
+        """
+        Transform Manus API GET response to OpenAI-compatible format.
+        
+        Manus uses camelCase (createdAt) instead of snake_case (created_at).
+        Same transformation as transform_response_api_response.
+        """
+        try:
+            logging_obj.post_call(
+                original_response=raw_response.text,
+                additional_args={"complete_input_dict": {}},
+            )
+            raw_response_json = raw_response.json()
+            
+            # Manus uses camelCase "createdAt" instead of snake_case "created_at"
+            if "createdAt" in raw_response_json and "created_at" not in raw_response_json:
+                raw_response_json["created_at"] = _safe_convert_created_field(
+                    raw_response_json["createdAt"]
+                )
+            
+            # Ensure created_at is set
+            if "created_at" in raw_response_json:
+                raw_response_json["created_at"] = _safe_convert_created_field(
+                    raw_response_json["created_at"]
+                )
+        except Exception:
+            raise OpenAIError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        
+        raw_response_headers = dict(raw_response.headers)
+        processed_headers = process_response_headers(raw_response_headers)
+        
+        # Ensure reasoning, text, output, and usage are present with defaults
+        if "reasoning" not in raw_response_json or raw_response_json.get("reasoning") is None:
+            raw_response_json["reasoning"] = {}
+        
+        if "text" not in raw_response_json or raw_response_json.get("text") is None:
+            raw_response_json["text"] = {}
+        
+        if "output" not in raw_response_json or raw_response_json.get("output") is None:
+            raw_response_json["output"] = []
+        
+        if "usage" not in raw_response_json or raw_response_json.get("usage") is None:
+            raw_response_json["usage"] = ResponseAPIUsage(
+                input_tokens=0,
+                output_tokens=0,
+                total_tokens=0,
+            )
+        
+        # Ensure id is present - failed responses may not include it
+        if "id" not in raw_response_json or raw_response_json.get("id") is None:
+            # Generate a placeholder id for failed responses
+            raw_response_json["id"] = f"unknown-{uuid.uuid4().hex[:8]}"
+        
+        try:
+            response = ResponsesAPIResponse(**raw_response_json)
+        except Exception:
+            verbose_logger.debug(
+                f"Error constructing ResponsesAPIResponse: {raw_response_json}, using model_construct"
+            )
+            response = ResponsesAPIResponse.model_construct(**raw_response_json)
+        
+        # Store processed headers in additional_headers so they get returned to the client
+        response._hidden_params["additional_headers"] = processed_headers
+        response._hidden_params["headers"] = raw_response_headers
+        return response
+
diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py
index 038895a39e5..7af7be2094a 100644
--- a/litellm/llms/oci/chat/transformation.py
+++ b/litellm/llms/oci/chat/transformation.py
@@ -1124,8 +1124,11 @@ def adapt_messages_to_generic_oci_standard_content_message(
 
         elif type == "image_url":
             image_url = content_item.get("image_url")
+            # Handle both OpenAI format (object with url) and string format
+            if isinstance(image_url, dict):
+                image_url = image_url.get("url")
             if not isinstance(image_url, str):
-                raise Exception("Prop `image_url` is not a string")
+                raise Exception("Prop `image_url` must be a string or an object with a `url` property")
             new_content.append(OCIImageContentPart(imageUrl=image_url))
 
     return OCIMessage(
diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py
index 034ccae94ad..04a10bd7fbe 100644
--- a/litellm/llms/openai/chat/gpt_transformation.py
+++ b/litellm/llms/openai/chat/gpt_transformation.py
@@ -771,9 +771,9 @@ class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator):
             return ModelResponseStream(
                 id=chunk["id"],
                 object="chat.completion.chunk",
-                created=chunk["created"],
-                model=chunk["model"],
-                choices=chunk["choices"],
+                created=chunk.get("created"),
+                model=chunk.get("model"),
+                choices=chunk.get("choices", []),
             )
         except Exception as e:
             raise e
diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index 6c573894f69..e2cf9f46104 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -83,6 +83,10 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
                 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:
+                inputs["tools"] = tools
 
             guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
                 inputs=inputs,
diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json
index 206aee1359d..bda3684a8a8 100644
--- a/litellm/llms/openai_like/providers.json
+++ b/litellm/llms/openai_like/providers.json
@@ -61,6 +61,10 @@
       "max_completion_tokens": "max_tokens"
     }
   },
+  "abliteration": {
+    "base_url": "https://api.abliteration.ai/v1",
+    "api_key_env": "ABLITERATION_API_KEY"
+  },
   "llamagate": {
     "base_url": "https://api.llamagate.dev/v1",
     "api_key_env": "LLAMAGATE_API_KEY",
diff --git a/litellm/llms/openrouter/embedding/transformation.py b/litellm/llms/openrouter/embedding/transformation.py
new file mode 100644
index 00000000000..d1d0e911d16
--- /dev/null
+++ b/litellm/llms/openrouter/embedding/transformation.py
@@ -0,0 +1,182 @@
+"""
+OpenRouter Embedding API Configuration.
+
+This module provides the configuration for OpenRouter's Embedding API.
+OpenRouter is OpenAI-compatible and supports embeddings via the /v1/embeddings endpoint.
+
+Docs: https://openrouter.ai/docs
+"""
+from typing import TYPE_CHECKING, Any, Optional
+
+import httpx
+
+from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
+from litellm.types.llms.openai import AllEmbeddingInputValues
+from litellm.types.utils import EmbeddingResponse
+from litellm.utils import convert_to_model_response_object
+
+from ..common_utils import OpenRouterException
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class OpenrouterEmbeddingConfig(BaseEmbeddingConfig):
+    """
+    Configuration for OpenRouter's Embedding API.
+
+    Reference: https://openrouter.ai/docs
+    """
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: list,
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        """
+        Validate environment and set up headers for OpenRouter API.
+
+        OpenRouter requires:
+        - Authorization header with Bearer token
+        - HTTP-Referer header (site URL)
+        - X-Title header (app name)
+        """
+        from litellm import get_secret
+
+        # Get OpenRouter-specific headers
+        openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai"
+        openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM"
+
+        openrouter_headers = {
+            "HTTP-Referer": openrouter_site_url,
+            "X-Title": openrouter_app_name,
+            "Content-Type": "application/json",
+        }
+
+        # Add Authorization header if api_key is provided
+        if api_key:
+            openrouter_headers["Authorization"] = f"Bearer {api_key}"
+
+        # Merge with existing headers (user's extra_headers take priority)
+        merged_headers = {**openrouter_headers, **headers}
+
+        return merged_headers
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        """
+        Get the complete URL for OpenRouter Embedding API endpoint.
+        """
+        # api_base is already set to https://openrouter.ai/api/v1 in main.py
+        # Remove trailing slashes
+        if api_base:
+            api_base = api_base.rstrip("/")
+        else:
+            api_base = "https://openrouter.ai/api/v1"
+
+        # Return the embeddings endpoint
+        return f"{api_base}/embeddings"
+
+    def transform_embedding_request(
+        self,
+        model: str,
+        input: AllEmbeddingInputValues,
+        optional_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Transform embedding request to OpenRouter format (OpenAI-compatible).
+        """
+        # Ensure input is a list
+        if isinstance(input, str):
+            input = [input]
+
+        # OpenRouter expects the full model name (e.g., google/gemini-embedding-001)
+        # Strip 'openrouter/' prefix if present
+        if model.startswith("openrouter/"):
+            model = model.replace("openrouter/", "", 1)
+
+        return {
+            "model": model,
+            "input": input,
+            **optional_params,
+        }
+
+    def transform_embedding_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: EmbeddingResponse,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str],
+        request_data: dict,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> EmbeddingResponse:
+        """
+        Transform embedding response from OpenRouter format (OpenAI-compatible).
+        """
+        logging_obj.post_call(original_response=raw_response.text)
+
+        # OpenRouter returns standard OpenAI-compatible embedding response
+        response_json = raw_response.json()
+
+        return convert_to_model_response_object(
+            response_object=response_json,
+            model_response_object=model_response,
+            response_type="embedding",
+        )
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get list of supported OpenAI parameters for OpenRouter embeddings.
+        """
+        return [
+            "timeout",
+            "dimensions",
+            "encoding_format",
+            "user",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        Map OpenAI parameters to OpenRouter format.
+        """
+        for param, value in non_default_params.items():
+            if param in self.get_supported_openai_params(model):
+                optional_params[param] = value
+        return optional_params
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Any
+    ) -> Any:
+        """
+        Get the error class for OpenRouter errors.
+        """
+        return OpenRouterException(
+            message=error_message,
+            status_code=status_code,
+            headers=headers,
+        )
diff --git a/litellm/llms/replicate/chat/handler.py b/litellm/llms/replicate/chat/handler.py
index e4bb64fed71..4c75db5abc6 100644
--- a/litellm/llms/replicate/chat/handler.py
+++ b/litellm/llms/replicate/chat/handler.py
@@ -213,7 +213,7 @@ def completion(
             response = httpx_client.get(url=prediction_url, headers=headers)
             if (
                 response.status_code == 200
-                and response.json().get("status") == "processing"
+                and response.json().get("status") in ["processing", "starting"]
             ):
                 continue
             return litellm.ReplicateConfig().transform_response(
@@ -284,7 +284,7 @@ async def async_completion(
         response = await async_handler.get(url=prediction_url, headers=headers)
         if (
             response.status_code == 200
-            and response.json().get("status") == "processing"
+            and response.json().get("status") in ["processing", "starting"]
         ):
             continue
         return litellm.ReplicateConfig().transform_response(
diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py
index 01f6c86fd4d..b3612113ec2 100644
--- a/litellm/llms/vertex_ai/files/transformation.py
+++ b/litellm/llms/vertex_ai/files/transformation.py
@@ -1,11 +1,12 @@
 import json
 import os
 import time
-from litellm._uuid import uuid
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from httpx import Headers, Response
+from openai.types.file_deleted import FileDeleted
 
+from litellm._uuid import uuid
 from litellm.files.utils import FilesAPIUtils
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
@@ -24,6 +25,7 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     CreateFileRequest,
     FileTypes,
+    HttpxBinaryResponseContent,
     OpenAICreateFileRequestOptionalParams,
     OpenAIFileObject,
     PathLike,
@@ -333,6 +335,70 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
             status_code=status_code, message=error_message, headers=headers
         )
 
+    def transform_retrieve_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("VertexAIFilesConfig does not support file retrieval")
+
+    def transform_retrieve_file_response(
+        self,
+        raw_response: Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> OpenAIFileObject:
+        raise NotImplementedError("VertexAIFilesConfig does not support file retrieval")
+
+    def transform_delete_file_request(
+        self,
+        file_id: str,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("VertexAIFilesConfig does not support file deletion")
+
+    def transform_delete_file_response(
+        self,
+        raw_response: Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> FileDeleted:
+        raise NotImplementedError("VertexAIFilesConfig does not support file deletion")
+
+    def transform_list_files_request(
+        self,
+        purpose: Optional[str],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("VertexAIFilesConfig does not support file listing")
+
+    def transform_list_files_response(
+        self,
+        raw_response: Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> List[OpenAIFileObject]:
+        raise NotImplementedError("VertexAIFilesConfig does not support file listing")
+
+    def transform_file_content_request(
+        self,
+        file_content_request,
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> tuple[str, dict]:
+        raise NotImplementedError("VertexAIFilesConfig does not support file content retrieval")
+
+    def transform_file_content_response(
+        self,
+        raw_response: Response,
+        logging_obj: LiteLLMLoggingObj,
+        litellm_params: dict,
+    ) -> HttpxBinaryResponseContent:
+        raise NotImplementedError("VertexAIFilesConfig does not support file content retrieval")
+
 
 class VertexAIJsonlFilesTransformation(VertexGeminiConfig):
     """
diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py
index 2bbdfa17cde..22042f7d641 100644
--- a/litellm/llms/vertex_ai/gemini/transformation.py
+++ b/litellm/llms/vertex_ai/gemini/transformation.py
@@ -115,7 +115,7 @@ def _process_gemini_image(
             and (image_type := format or _get_image_mime_type_from_url(image_url))
             is not None
         ):
-            file_data = FileDataType(file_uri=image_url, mime_type=image_type)
+            file_data = FileDataType(mime_type=image_type, file_uri=image_url)
             part = {"file_data": file_data}
             
             if media_resolution_enum is not None and model is not None:
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 ba1788a217f..7a37812bcff 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
@@ -310,9 +310,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         """
         return Tools(googleSearch={})
 
-    def _transform_computer_use_config(
-        self, computer_use_config: dict
-    ) -> dict:
+    def _transform_computer_use_config(self, computer_use_config: dict) -> dict:
         """
         Transform Computer Use configuration to Gemini API format.
 
@@ -323,7 +321,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             Transformed computer use configuration for Gemini API
         """
         transformed_config = {}
-        
+
         # Transform environment values if needed
         if "environment" in computer_use_config:
             env_value = computer_use_config["environment"]
@@ -339,13 +337,17 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     f"Invalid environment value for computer_use: {env_value}. "
                     f"Supported: 'browser', 'unspecified', 'ENVIRONMENT_BROWSER', 'ENVIRONMENT_UNSPECIFIED'"
                 )
-        
+
         # Transform excluded_predefined_functions to camelCase
         if "excluded_predefined_functions" in computer_use_config:
-            transformed_config["excludedPredefinedFunctions"] = computer_use_config["excluded_predefined_functions"]
+            transformed_config["excludedPredefinedFunctions"] = computer_use_config[
+                "excluded_predefined_functions"
+            ]
         elif "excludedPredefinedFunctions" in computer_use_config:
-            transformed_config["excludedPredefinedFunctions"] = computer_use_config["excludedPredefinedFunctions"]
-        
+            transformed_config["excludedPredefinedFunctions"] = computer_use_config[
+                "excludedPredefinedFunctions"
+            ]
+
         return transformed_config
 
     def _extract_google_maps_retrieval_config(
@@ -446,9 +448,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         value = _remove_strict_from_schema(value)
 
         for tool in value:
-            openai_function_object: Optional[
-                ChatCompletionToolParamFunctionChunk
-            ] = None
+            openai_function_object: Optional[ChatCompletionToolParamFunctionChunk] = (
+                None
+            )
             if "function" in tool:  # tools list
                 _openai_function_object = ChatCompletionToolParamFunctionChunk(  # type: ignore
                     **tool["function"]
@@ -480,20 +482,21 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 or tool_name == VertexToolName.CODE_EXECUTION.value
             ):  # code_execution maintained for backwards compatibility
                 code_execution = self.get_tool_value(tool, "codeExecution")
-            elif tool_name and tool_name == VertexToolName.GOOGLE_SEARCH.value:
-                googleSearch = self.get_tool_value(
-                    tool, VertexToolName.GOOGLE_SEARCH.value
-                )
-            elif (
-                tool_name and tool_name == VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value
+            elif tool_name and (
+                tool_name == VertexToolName.GOOGLE_SEARCH.value
+                or tool_name == "google_search"
             ):
-                googleSearchRetrieval = self.get_tool_value(
-                    tool, VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value
-                )
-            elif tool_name and tool_name == VertexToolName.ENTERPRISE_WEB_SEARCH.value:
-                enterpriseWebSearch = self.get_tool_value(
-                    tool, VertexToolName.ENTERPRISE_WEB_SEARCH.value
-                )
+                googleSearch = self.get_tool_value(tool, tool_name)
+            elif tool_name and (
+                tool_name == VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value
+                or tool_name == "google_search_retrieval"
+            ):
+                googleSearchRetrieval = self.get_tool_value(tool, tool_name)
+            elif tool_name and (
+                tool_name == VertexToolName.ENTERPRISE_WEB_SEARCH.value
+                or tool_name == "enterprise_web_search"
+            ):
+                enterpriseWebSearch = self.get_tool_value(tool, tool_name)
             elif tool_name and (
                 tool_name == VertexToolName.URL_CONTEXT.value
                 or tool_name == "urlContext"
@@ -552,7 +555,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     "Invalid tool={}. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request."
                 )
 
-# Build list of Tool objects - each Tool should contain exactly one type
+        # Build list of Tool objects - each Tool should contain exactly one type
         # per Vertex AI API spec: "A Tool object should contain exactly one type of Tool"
         _tools_list: List[Tools] = []
 
@@ -569,11 +572,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             _tools_list.append(search_tool)
         if googleSearchRetrieval is not None:
             retrieval_tool = Tools()
-            retrieval_tool[VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value] = googleSearchRetrieval
+            retrieval_tool[VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value] = (
+                googleSearchRetrieval
+            )
             _tools_list.append(retrieval_tool)
         if enterpriseWebSearch is not None:
             enterprise_tool = Tools()
-            enterprise_tool[VertexToolName.ENTERPRISE_WEB_SEARCH.value] = enterpriseWebSearch
+            enterprise_tool[VertexToolName.ENTERPRISE_WEB_SEARCH.value] = (
+                enterpriseWebSearch
+            )
             _tools_list.append(enterprise_tool)
         if code_execution is not None:
             code_tool = Tools()
@@ -592,7 +599,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             computer_tool[VertexToolName.COMPUTER_USE.value] = computerUse
             _tools_list.append(computer_tool)
 
-
         # Add retrieval config to toolConfig if googleMaps has location data
         if google_maps_retrieval_config is not None:
             if "toolConfig" not in optional_params:
@@ -709,8 +715,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             GeminiThinkingConfig with thinkingLevel and includeThoughts
         """
         # Check if this is gemini-3-flash which supports MINIMAL thinking level
-        is_gemini3flash= model and (
-            "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
+        is_gemini3flash = model and (
+            "gemini-3-flash-preview" in model.lower()
+            or "gemini-3-flash" in model.lower()
         )
         if reasoning_effort == "minimal":
             if is_gemini3flash:
@@ -798,7 +805,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         thinking_budget = thinking_param.get("budget_tokens")
 
         params: GeminiThinkingConfig = {}
-        
+
         # For Gemini 3+ models, use thinkingLevel instead of thinkingBudget
         if model and VertexGeminiConfig._is_gemini_3_or_newer(model):
             if thinking_enabled:
@@ -807,11 +814,21 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 else:
                     params["includeThoughts"] = True
                     if thinking_budget >= 10000:
-                        is_gemini3flash = "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
-                        params["thinkingLevel"] = "minimal" if is_gemini3flash else "low"
+                        is_gemini3flash = (
+                            "gemini-3-flash-preview" in model.lower()
+                            or "gemini-3-flash" in model.lower()
+                        )
+                        params["thinkingLevel"] = (
+                            "minimal" if is_gemini3flash else "low"
+                        )
                     else:
-                        is_gemini3flash = "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
-                        params["thinkingLevel"] = "minimal" if is_gemini3flash else "low"
+                        is_gemini3flash = (
+                            "gemini-3-flash-preview" in model.lower()
+                            or "gemini-3-flash" in model.lower()
+                        )
+                        params["thinkingLevel"] = (
+                            "minimal" if is_gemini3flash else "low"
+                        )
             else:
                 # Thinking disabled
                 params["includeThoughts"] = False
@@ -823,7 +840,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 params["includeThoughts"] = True
             if thinking_budget is not None and isinstance(thinking_budget, int):
                 params["thinkingBudget"] = thinking_budget
-        
+
         return params
 
     def map_response_modalities(self, value: list) -> list:
@@ -979,16 +996,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     param_description="thinking_budget",
                 )
                 if VertexGeminiConfig._is_gemini_3_or_newer(model):
-                    optional_params[
-                        "thinkingConfig"
-                    ] = VertexGeminiConfig._map_reasoning_effort_to_thinking_level(
-                        value, model
+                    optional_params["thinkingConfig"] = (
+                        VertexGeminiConfig._map_reasoning_effort_to_thinking_level(
+                            value, model
+                        )
                     )
                 else:
-                    optional_params[
-                        "thinkingConfig"
-                    ] = VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(
-                        value, model
+                    optional_params["thinkingConfig"] = (
+                        VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(
+                            value, model
+                        )
                     )
             elif param == "thinking":
                 # Validate no conflict with thinking_level
@@ -997,11 +1014,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     param_name="thinking",
                     param_description="thinking_budget",
                 )
-                optional_params[
-                    "thinkingConfig"
-                ] = VertexGeminiConfig._map_thinking_param(
-                    cast(AnthropicThinkingParam, value),
-                    model=model,
+                optional_params["thinkingConfig"] = (
+                    VertexGeminiConfig._map_thinking_param(
+                        cast(AnthropicThinkingParam, value),
+                        model=model,
+                    )
                 )
             elif param == "modalities" and isinstance(value, list):
                 response_modalities = self.map_response_modalities(value)
@@ -1035,8 +1052,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 ):
                     # For gemini-3-flash-preview, default to "minimal" to match Gemini 2.5 Flash behavior
                     # For other Gemini 3 models, default to "low"
-                    is_gemini3flash = "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
-                    thinking_config["thinkingLevel"] = "minimal" if is_gemini3flash else "low"
+                    is_gemini3flash = (
+                        "gemini-3-flash-preview" in model.lower()
+                        or "gemini-3-flash" in model.lower()
+                    )
+                    thinking_config["thinkingLevel"] = (
+                        "minimal" if is_gemini3flash else "low"
+                    )
                     optional_params["thinkingConfig"] = thinking_config
 
         return optional_params
@@ -1225,7 +1247,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 block: ChatCompletionThinkingBlock = {
                     "type": "thinking",
                     "thinking": thinking_text,
-                }                
+                }
                 signature = part.get("thoughtSignature")
                 if signature is not None:
                     block["signature"] = signature
@@ -1359,10 +1381,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                         _tool_response_chunk["provider_specific_fields"] = {  # type: ignore
                             "thought_signature": thought_signature
                         }
-                        _tool_response_chunk[
-                            "id"
-                        ] = _encode_tool_call_id_with_signature(
-                            _tool_response_chunk["id"] or "", thought_signature
+                        _tool_response_chunk["id"] = (
+                            _encode_tool_call_id_with_signature(
+                                _tool_response_chunk["id"] or "", thought_signature
+                            )
                         )
                     _tools.append(_tool_response_chunk)
                 cumulative_tool_call_idx += 1
@@ -1514,8 +1536,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             )
         cached_tokens: Optional[int] = None
         audio_tokens: Optional[int] = None
-        text_tokens: Optional[int] = None
         image_tokens: Optional[int] = None
+        text_tokens: Optional[int] = None
         prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
         reasoning_tokens: Optional[int] = None
         response_tokens: Optional[int] = None
@@ -1534,6 +1556,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     response_tokens_details.text_tokens = detail.get("tokenCount", 0)
                 elif detail["modality"] == "AUDIO":
                     response_tokens_details.audio_tokens = detail.get("tokenCount", 0)
+
         #########################################################
 
         ## CANDIDATES TOKEN DETAILS (e.g., for image generation models) ##
@@ -1550,16 +1573,22 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 elif modality == "IMAGE":
                     response_tokens_details.image_tokens = token_count
 
-            # Calculate text_tokens if not explicitly provided in candidatesTokensDetails
-            # candidatesTokenCount includes all modalities, so: text = total - (image + audio)
+        # Calculate text_tokens if not explicitly provided in candidatesTokensDetails
+        # candidatesTokenCount includes all modalities, so: text = total - (image + audio)
+        candidates_token_count = usage_metadata.get("candidatesTokenCount", 0)
+        if candidates_token_count > 0:
+            if response_tokens_details is None:
+                response_tokens_details = CompletionTokensDetailsWrapper()
             if response_tokens_details.text_tokens is None:
-                candidates_token_count = usage_metadata.get("candidatesTokenCount", 0)
                 image_tokens = response_tokens_details.image_tokens or 0
                 audio_tokens_candidate = response_tokens_details.audio_tokens or 0
-                calculated_text_tokens = candidates_token_count - image_tokens - audio_tokens_candidate
+                calculated_text_tokens = (
+                    candidates_token_count - image_tokens - audio_tokens_candidate
+                )
                 response_tokens_details.text_tokens = calculated_text_tokens
         #########################################################
 
+        ## 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":
@@ -1568,6 +1597,32 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     text_tokens = detail.get("tokenCount", 0)
                 elif detail["modality"] == "IMAGE":
                     image_tokens = detail.get("tokenCount", 0)
+
+        ## Parse cacheTokensDetails (breakdown of cached tokens by modality)
+        ## When explicit caching is used, Gemini provides this field to show which modalities were cached
+        cached_text_tokens: Optional[int] = None
+        cached_audio_tokens: Optional[int] = None
+        cached_image_tokens: Optional[int] = None
+
+        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)
+
+        ## Calculate non-cached tokens by subtracting cached from total (per modality)
+        ## This is necessary because promptTokensDetails includes both cached and non-cached tokens
+        ## See: https://github.com/BerriAI/litellm/issues/18750
+        if cached_text_tokens is not None and text_tokens is not None:
+            text_tokens = text_tokens - cached_text_tokens
+        if cached_audio_tokens is not None and audio_tokens is not None:
+            audio_tokens = audio_tokens - cached_audio_tokens
+        if cached_image_tokens is not None and image_tokens is not None:
+            image_tokens = image_tokens - cached_image_tokens
+
         if "thoughtsTokenCount" in usage_metadata:
             reasoning_tokens = usage_metadata["thoughtsTokenCount"]
             # Also add reasoning tokens to response_tokens_details
@@ -1575,15 +1630,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 response_tokens_details = CompletionTokensDetailsWrapper()
             response_tokens_details.reasoning_tokens = reasoning_tokens
 
-        ## adjust 'text_tokens' to subtract cached tokens
-        if (
-            (audio_tokens is None or audio_tokens == 0)
-            and text_tokens is not None
-            and text_tokens > 0
-            and cached_tokens is not None
-        ):
-            text_tokens = text_tokens - cached_tokens
-
         prompt_tokens_details = PromptTokensDetailsWrapper(
             cached_tokens=cached_tokens,
             audio_tokens=audio_tokens,
@@ -1605,6 +1651,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             completion_tokens=completion_tokens,
             total_tokens=usage_metadata.get("totalTokenCount", 0),
             prompt_tokens_details=prompt_tokens_details,
+            cache_read_input_tokens=cached_tokens,
             reasoning_tokens=reasoning_tokens,
             completion_tokens_details=response_tokens_details,
         )
@@ -1811,6 +1858,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         functions: Optional[ChatCompletionToolCallFunctionChunk] = None
         thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
         reasoning_content: Optional[str] = None
+        thought_signatures: Optional[Any] = None
 
         for idx, candidate in enumerate(_candidates):
             if "content" not in candidate:
@@ -2074,28 +2122,28 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             ## ADD METADATA TO RESPONSE ##
 
             setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)
-            model_response._hidden_params[
-                "vertex_ai_grounding_metadata"
-            ] = grounding_metadata
+            model_response._hidden_params["vertex_ai_grounding_metadata"] = (
+                grounding_metadata
+            )
 
             setattr(
                 model_response, "vertex_ai_url_context_metadata", url_context_metadata
             )
 
-            model_response._hidden_params[
-                "vertex_ai_url_context_metadata"
-            ] = url_context_metadata
+            model_response._hidden_params["vertex_ai_url_context_metadata"] = (
+                url_context_metadata
+            )
 
             setattr(model_response, "vertex_ai_safety_results", safety_ratings)
-            model_response._hidden_params[
-                "vertex_ai_safety_results"
-            ] = safety_ratings  # older approach - maintaining to prevent regressions
+            model_response._hidden_params["vertex_ai_safety_results"] = (
+                safety_ratings  # older approach - maintaining to prevent regressions
+            )
 
             ## ADD CITATION METADATA ##
             setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)
-            model_response._hidden_params[
-                "vertex_ai_citation_metadata"
-            ] = citation_metadata  # older approach - maintaining to prevent regressions
+            model_response._hidden_params["vertex_ai_citation_metadata"] = (
+                citation_metadata  # older approach - maintaining to prevent regressions
+            )
 
         except Exception as e:
             raise VertexAIError(
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
index 712a06dece1..123d925f7c1 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
@@ -40,6 +40,7 @@ class PartnerModelPrefixes(str, Enum):
     GPT_OSS_PREFIX = "openai/gpt-oss-"
     MINIMAX_PREFIX = "minimaxai/"
     MOONSHOT_PREFIX = "moonshotai/"
+    ZAI_PREFIX = "zai-org/"
 
 
 class VertexAIPartnerModels(VertexBase):
@@ -66,6 +67,7 @@ class VertexAIPartnerModels(VertexBase):
             or model.startswith(PartnerModelPrefixes.GPT_OSS_PREFIX)
             or model.startswith(PartnerModelPrefixes.MINIMAX_PREFIX)
             or model.startswith(PartnerModelPrefixes.MOONSHOT_PREFIX)
+            or model.startswith(PartnerModelPrefixes.ZAI_PREFIX)
         ):
             return True
         return False
@@ -79,6 +81,7 @@ class VertexAIPartnerModels(VertexBase):
             PartnerModelPrefixes.GPT_OSS_PREFIX,
             PartnerModelPrefixes.MINIMAX_PREFIX,
             PartnerModelPrefixes.MOONSHOT_PREFIX,
+            PartnerModelPrefixes.ZAI_PREFIX,
         ]
         if any(provider in model for provider in OPENAI_LIKE_VERTEX_PROVIDERS):
             return True
diff --git a/litellm/llms/watsonx/audio_transcription/transformation.py b/litellm/llms/watsonx/audio_transcription/transformation.py
index 186d858321a..c7e6a77b96f 100644
--- a/litellm/llms/watsonx/audio_transcription/transformation.py
+++ b/litellm/llms/watsonx/audio_transcription/transformation.py
@@ -7,13 +7,14 @@ WatsonX follows the OpenAI spec for audio transcription.
 from typing import Any, Dict, List, Optional
 
 import litellm
+from httpx import Response
 from litellm.litellm_core_utils.audio_utils.utils import process_audio_file
 from litellm.types.llms.openai import (
     AllMessageValues,
     OpenAIAudioTranscriptionOptionalParams,
 )
 from litellm.types.llms.watsonx import WatsonXAudioTranscriptionRequestBody
-from litellm.types.utils import FileTypes
+from litellm.types.utils import FileTypes, TranscriptionResponse
 
 from ...base_llm.audio_transcription.transformation import (
     AudioTranscriptionRequestData,
@@ -156,3 +157,48 @@ class IBMWatsonXAudioTranscriptionConfig(
         url = f"{url}?version={api_version}"
 
         return url
+
+    def transform_audio_transcription_response(
+        self,
+        raw_response: Response,
+    ) -> TranscriptionResponse:
+        """
+        Transform the audio transcription response from WatsonX.
+        
+        WatsonX may include a 'model' field in the response, which needs to be
+        removed before creating the TranscriptionResponse object.
+        """
+        try:
+            raw_response_json = raw_response.json()
+        except Exception as e:
+            raise ValueError(
+                f"Error transforming response to json: {str(e)}\nResponse: {raw_response.text}"
+            )
+
+        # Extract only valid fields for TranscriptionResponse.__init__()
+        # TranscriptionResponse only accepts 'text' and 'usage' in __init__()
+        text = raw_response_json.get("text")
+        usage = raw_response_json.get("usage")
+        
+        # Create response with only valid fields
+        response_kwargs = {}
+        if text is not None:
+            response_kwargs["text"] = text
+        if usage is not None:
+            response_kwargs["usage"] = usage
+        
+        if not response_kwargs:
+            raise ValueError(
+                "Invalid response format. Received response does not match the expected format. Got: ",
+                raw_response_json,
+            )
+        
+        response = TranscriptionResponse(**response_kwargs)
+        
+        # Add other fields using dictionary-style assignment (like duration, task, etc.)
+        # Skip fields that TranscriptionResponse doesn't accept in __init__()
+        for key, value in raw_response_json.items():
+            if key not in ["text", "usage", "model"]:  # text/usage already set, model should be excluded
+                response[key] = value
+        
+        return response
diff --git a/litellm/main.py b/litellm/main.py
index e8a8b504d96..a1992752bdb 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -4587,8 +4587,8 @@ def embedding(  # noqa: PLR0915
                 or get_secret_str("OPENAI_API_KEY")
             )
 
-            if extra_headers is not None:
-                optional_params["extra_headers"] = extra_headers
+            if headers is not None and headers != {}:
+                optional_params["extra_headers"] = headers
             
             if encoding_format is not None:
                 optional_params["encoding_format"] = encoding_format
@@ -4656,8 +4656,8 @@ def embedding(  # noqa: PLR0915
                     or get_secret_str("OPENAI_LIKE_API_KEY")
                 )
 
-            if extra_headers is not None:
-                optional_params["extra_headers"] = extra_headers
+            if headers is not None and headers != {}:
+                optional_params["extra_headers"] = headers
 
             ## EMBEDDING CALL
             response = openai_like_embedding.embedding(
@@ -4681,9 +4681,9 @@ def embedding(  # noqa: PLR0915
                 or litellm.api_key
             )
 
-            if extra_headers is not None and isinstance(extra_headers, dict):
-                headers = extra_headers
-            else:
+            # Use the merged headers variable (already merged at the top of the function)
+            # Don't overwrite it with just extra_headers
+            if headers is None:
                 headers = {}
 
             response = base_llm_http_handler.embedding(
@@ -4701,6 +4701,51 @@ def embedding(  # noqa: PLR0915
                 litellm_params=litellm_params_dict,
                 headers=headers,
             )
+        elif custom_llm_provider == "openrouter":
+            api_base = (
+                api_base
+                or litellm.api_base
+                or get_secret_str("OPENROUTER_API_BASE")
+                or "https://openrouter.ai/api/v1"
+            )
+
+            api_key = (
+                api_key
+                or litellm.api_key
+                or litellm.openrouter_key
+                or get_secret("OPENROUTER_API_KEY")
+                or get_secret("OR_API_KEY")
+            )
+
+            openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai"
+            openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM"
+
+            openrouter_headers = {
+                "HTTP-Referer": openrouter_site_url,
+                "X-Title": openrouter_app_name,
+            }
+
+            _headers = headers or litellm.headers
+            if _headers:
+                openrouter_headers.update(_headers)
+
+            headers = openrouter_headers
+
+            response = base_llm_http_handler.embedding(
+                model=model,
+                input=input,
+                custom_llm_provider=custom_llm_provider,
+                api_base=api_base,
+                api_key=api_key,
+                logging_obj=logging,
+                timeout=timeout,
+                model_response=EmbeddingResponse(),
+                optional_params=optional_params,
+                client=client,
+                aembedding=aembedding,
+                litellm_params=litellm_params_dict,
+                headers=headers,
+            )
         elif custom_llm_provider == "huggingface":
             api_key = (
                 api_key
@@ -5602,11 +5647,9 @@ def text_completion(  # noqa: PLR0915
         )
         and isinstance(prompt, list)
         and len(prompt) > 0
-        and isinstance(prompt[0], list)
+        and (isinstance(prompt[0], list) or isinstance(prompt[0], int))
     ):
-        verbose_logger.warning(
-            msg="List of lists being passed. If this is for tokens, then it might not work across all models."
-        )
+        # Support for token IDs as prompt (list of integers or list of lists of integers)
         messages = [{"role": "user", "content": prompt}]  # type: ignore
     else:
         raise Exception(
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index fb00f636409..ed218d50af7 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -410,8 +410,8 @@
         "max_input_tokens": 8172,
         "max_tokens": 8172,
         "mode": "embedding",
-        "input_cost_per_token": 1.35e-7,
-        "input_cost_per_image": 6e-5,
+        "input_cost_per_token": 1.35e-07,
+        "input_cost_per_image": 6e-05,
         "input_cost_per_video_per_second": 0.0007,
         "input_cost_per_audio_per_second": 0.00014,
         "output_cost_per_token": 0.0,
@@ -1398,8 +1398,8 @@
         "mode": "chat"
     },
     "azure_ai/gpt-oss-120b": {
-        "input_cost_per_token": 1.5e-7,
-        "output_cost_per_token": 6e-7,
+        "input_cost_per_token": 1.5e-07,
+        "output_cost_per_token": 6e-07,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 131072,
         "max_output_tokens": 131072,
@@ -2077,7 +2077,7 @@
         "litellm_provider": "azure",
         "max_input_tokens": 4097,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_function_calling": true,
@@ -2090,7 +2090,7 @@
         "litellm_provider": "azure",
         "max_input_tokens": 4097,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_function_calling": true,
@@ -2869,7 +2869,7 @@
             "/v1/audio/transcriptions"
         ]
     },
-   "azure/gpt-5.1-2025-11-13": {
+    "azure/gpt-5.1-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.5e-07,
         "input_cost_per_token": 1.25e-06,
@@ -2905,7 +2905,7 @@
         "supports_service_tier": true,
         "supports_vision": true
     },
-   "azure/gpt-5.1-chat-2025-11-13": {
+    "azure/gpt-5.1-chat-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.5e-07,
         "input_cost_per_token": 1.25e-06,
@@ -2940,7 +2940,7 @@
         "supports_tool_choice": false,
         "supports_vision": true
     },
-   "azure/gpt-5.1-codex-2025-11-13": {
+    "azure/gpt-5.1-codex-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.5e-07,
         "input_cost_per_token": 1.25e-06,
@@ -3298,7 +3298,7 @@
         "litellm_provider": "azure",
         "max_input_tokens": 272000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "mode": "responses",
         "output_cost_per_token": 0.00012,
         "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?pivots=azure-openai&tabs=global-standard-aoai%2Cstandard-chat-completions%2Cglobal-standard#gpt-5",
@@ -3623,7 +3623,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "responses",
-        "output_cost_per_token": 1.68e-04,
+        "output_cost_per_token": 0.000168,
         "supported_endpoints": [
             "/v1/batch",
             "/v1/responses"
@@ -3654,7 +3654,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "responses",
-        "output_cost_per_token": 1.68e-04,
+        "output_cost_per_token": 0.000168,
         "supported_endpoints": [
             "/v1/batch",
             "/v1/responses"
@@ -4297,13 +4297,13 @@
         "output_cost_per_token": 0.0
     },
     "azure/speech/azure-tts": {
-        "input_cost_per_character": 15e-06,
+        "input_cost_per_character": 1.5e-05,
         "litellm_provider": "azure",
         "mode": "audio_speech",
         "source": "https://azure.microsoft.com/en-us/pricing/calculator/"
     },
     "azure/speech/azure-tts-hd": {
-        "input_cost_per_character": 30e-06,
+        "input_cost_per_character": 3e-05,
         "litellm_provider": "azure",
         "mode": "audio_speech",
         "source": "https://azure.microsoft.com/en-us/pricing/calculator/"
@@ -5197,7 +5197,7 @@
     },
     "azure_ai/mistral-document-ai-2505": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 3e-3,
+        "ocr_cost_per_page": 0.003,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5206,7 +5206,7 @@
     },
     "azure_ai/doc-intelligence/prebuilt-read": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 1.5e-3,
+        "ocr_cost_per_page": 0.0015,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5215,7 +5215,7 @@
     },
     "azure_ai/doc-intelligence/prebuilt-layout": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 1e-2,
+        "ocr_cost_per_page": 0.01,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5224,7 +5224,7 @@
     },
     "azure_ai/doc-intelligence/prebuilt-document": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 1e-2,
+        "ocr_cost_per_page": 0.01,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5298,12 +5298,12 @@
         "mode": "rerank",
         "output_cost_per_token": 0.0
     },
-        "azure_ai/deepseek-v3.2": {
+    "azure_ai/deepseek-v3.2": {
         "input_cost_per_token": 5.8e-07,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 163840,
         "max_output_tokens": 163840,
-        "max_tokens": 8192,
+        "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
         "supports_assistant_prefill": true,
@@ -5317,7 +5317,7 @@
         "litellm_provider": "azure_ai",
         "max_input_tokens": 163840,
         "max_output_tokens": 163840,
-        "max_tokens": 8192,
+        "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
         "supports_assistant_prefill": true,
@@ -5452,7 +5452,7 @@
         "supports_web_search": true
     },
     "azure_ai/grok-4-fast-non-reasoning": {
-        "input_cost_per_token": 0.43e-06,
+        "input_cost_per_token": 4.3e-07,
         "output_cost_per_token": 1.73e-06,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 131072,
@@ -5465,7 +5465,7 @@
         "supports_web_search": true
     },
     "azure_ai/grok-4-fast-reasoning": {
-        "input_cost_per_token": 0.43e-06,
+        "input_cost_per_token": 4.3e-07,
         "output_cost_per_token": 1.73e-06,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 131072,
@@ -5623,7 +5623,7 @@
         "litellm_provider": "text-completion-openai",
         "max_input_tokens": 16384,
         "max_output_tokens": 4096,
-        "max_tokens": 16384,
+        "max_tokens": 4096,
         "mode": "completion",
         "output_cost_per_token": 4e-07
     },
@@ -7038,7 +7038,7 @@
         "litellm_provider": "anthropic",
         "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
-        "max_tokens": 1000000,
+        "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
         "output_cost_per_token_above_200k_tokens": 2.25e-05,
@@ -7800,7 +7800,7 @@
         "litellm_provider": "deepseek",
         "max_input_tokens": 131072,
         "max_output_tokens": 8192,
-        "max_tokens": 131072,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.7e-06,
         "source": "https://api-docs.deepseek.com/quick_start/pricing",
@@ -7821,7 +7821,7 @@
         "litellm_provider": "deepseek",
         "max_input_tokens": 131072,
         "max_output_tokens": 65536,
-        "max_tokens": 131072,
+        "max_tokens": 65536,
         "mode": "chat",
         "output_cost_per_token": 1.7e-06,
         "source": "https://api-docs.deepseek.com/quick_start/pricing",
@@ -7842,7 +7842,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 16384,
-        "max_tokens": 1000000,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7854,7 +7854,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -7883,7 +7883,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -7913,7 +7913,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 30720,
         "max_output_tokens": 8192,
-        "max_tokens": 32768,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 6.4e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7926,7 +7926,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7939,7 +7939,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 8192,
-        "max_tokens": 131072,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7952,7 +7952,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 4e-06,
         "output_cost_per_token": 1.2e-06,
@@ -7966,7 +7966,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 4e-06,
         "output_cost_per_token": 1.2e-06,
@@ -7979,7 +7979,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8010,7 +8010,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8041,7 +8041,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8073,7 +8073,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 5e-07,
         "output_cost_per_token": 2e-07,
@@ -8087,7 +8087,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192,
-        "max_tokens": 1000000,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 2e-07,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -8100,7 +8100,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 16384,
-        "max_tokens": 1000000,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 5e-07,
         "output_cost_per_token": 2e-07,
@@ -8114,7 +8114,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 16384,
-        "max_tokens": 1000000,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 5e-07,
         "output_cost_per_token": 2e-07,
@@ -8127,7 +8127,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8138,7 +8138,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8187,7 +8187,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8232,7 +8232,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8281,7 +8281,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8326,7 +8326,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 258048,
         "max_output_tokens": 65536,
-        "max_tokens": 262144,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8364,7 +8364,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 98304,
         "max_output_tokens": 8192,
-        "max_tokens": 131072,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 2.4e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -8393,7 +8393,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 128000,
-        "max_tokens": 200000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8412,7 +8412,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8431,7 +8431,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 32000,
-        "max_tokens": 200000,
+        "max_tokens": 32000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8450,7 +8450,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 32000,
-        "max_tokens": 200000,
+        "max_tokens": 32000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8469,7 +8469,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8488,7 +8488,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8507,7 +8507,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8526,7 +8526,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8545,7 +8545,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 1048576,
         "max_output_tokens": 65535,
-        "max_tokens": 1048576,
+        "max_tokens": 65535,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8562,7 +8562,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 1048576,
         "max_output_tokens": 65536,
-        "max_tokens": 1048576,
+        "max_tokens": 65536,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8579,7 +8579,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 128000,
         "max_output_tokens": 32000,
-        "max_tokens": 128000,
+        "max_tokens": 32000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8594,7 +8594,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8609,7 +8609,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8624,7 +8624,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8639,7 +8639,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8747,7 +8747,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 128000,
-        "max_tokens": 200000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8846,7 +8846,7 @@
         "litellm_provider": "text-completion-openai",
         "max_input_tokens": 16384,
         "max_output_tokens": 4096,
-        "max_tokens": 16384,
+        "max_tokens": 4096,
         "mode": "completion",
         "output_cost_per_token": 2e-06
     },
@@ -10107,7 +10107,7 @@
         "litellm_provider": "deepseek",
         "max_input_tokens": 163840,
         "max_output_tokens": 163840,
-        "max_tokens": 8192,
+        "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 4e-07,
         "supports_assistant_prefill": true,
@@ -10121,7 +10121,7 @@
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 163840,
         "max_output_tokens": 81920,
-        "max_tokens": 163840,
+        "max_tokens": 81920,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
         "supports_function_calling": true,
@@ -10202,14 +10202,14 @@
         "mode": "search",
         "tiered_pricing": [
             {
-                "input_cost_per_query": 5e-03,
+                "input_cost_per_query": 0.005,
                 "max_results_range": [
                     0,
                     25
                 ]
             },
             {
-                "input_cost_per_query": 25e-03,
+                "input_cost_per_query": 0.025,
                 "max_results_range": [
                     26,
                     100
@@ -10222,70 +10222,70 @@
         "mode": "search",
         "tiered_pricing": [
             {
-                "input_cost_per_query": 1.66e-03,
+                "input_cost_per_query": 0.00166,
                 "max_results_range": [
                     1,
                     10
                 ]
             },
             {
-                "input_cost_per_query": 3.32e-03,
+                "input_cost_per_query": 0.00332,
                 "max_results_range": [
                     11,
                     20
                 ]
             },
             {
-                "input_cost_per_query": 4.98e-03,
+                "input_cost_per_query": 0.00498,
                 "max_results_range": [
                     21,
                     30
                 ]
             },
             {
-                "input_cost_per_query": 6.64e-03,
+                "input_cost_per_query": 0.00664,
                 "max_results_range": [
                     31,
                     40
                 ]
             },
             {
-                "input_cost_per_query": 8.3e-03,
+                "input_cost_per_query": 0.0083,
                 "max_results_range": [
                     41,
                     50
                 ]
             },
             {
-                "input_cost_per_query": 9.96e-03,
+                "input_cost_per_query": 0.00996,
                 "max_results_range": [
                     51,
                     60
                 ]
             },
             {
-                "input_cost_per_query": 11.62e-03,
+                "input_cost_per_query": 0.01162,
                 "max_results_range": [
                     61,
                     70
                 ]
             },
             {
-                "input_cost_per_query": 13.28e-03,
+                "input_cost_per_query": 0.01328,
                 "max_results_range": [
                     71,
                     80
                 ]
             },
             {
-                "input_cost_per_query": 14.94e-03,
+                "input_cost_per_query": 0.01494,
                 "max_results_range": [
                     81,
                     90
                 ]
             },
             {
-                "input_cost_per_query": 16.6e-03,
+                "input_cost_per_query": 0.0166,
                 "max_results_range": [
                     91,
                     100
@@ -10297,7 +10297,7 @@
         }
     },
     "perplexity/search": {
-        "input_cost_per_query": 5e-03,
+        "input_cost_per_query": 0.005,
         "litellm_provider": "perplexity",
         "mode": "search"
     },
@@ -10395,7 +10395,7 @@
         "supports_embedding_image_input": true
     },
     "embed-multilingual-light-v3.0": {
-        "input_cost_per_token": 1e-04,
+        "input_cost_per_token": 0.0001,
         "litellm_provider": "cohere",
         "max_input_tokens": 1024,
         "max_tokens": 1024,
@@ -10689,7 +10689,7 @@
         "litellm_provider": "bedrock",
         "max_input_tokens": 128000,
         "max_output_tokens": 4096,
-        "max_tokens": 128000,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.3e-07,
         "supports_function_calling": true,
@@ -10700,7 +10700,7 @@
         "litellm_provider": "bedrock",
         "max_input_tokens": 128000,
         "max_output_tokens": 4096,
-        "max_tokens": 128000,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.9e-07,
         "supports_function_calling": true,
@@ -10711,7 +10711,7 @@
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 128000,
         "max_output_tokens": 4096,
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@@ -25204,12 +25544,12 @@
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@@ -25925,7 +26265,7 @@
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+        "mode": "chat",
+        "input_cost_per_token": 5e-04,
+        "output_cost_per_token": 5e-04,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/gryphe/mythomax-l2-13b": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 7.2e-04,
+        "output_cost_per_token": 7.2e-04,
+        "max_input_tokens": 4096,
+        "max_output_tokens": 3200,
+        "max_tokens": 3200,
+        "supports_system_messages": true
+    },
+    "novita/baidu/ernie-4.5-vl-28b-a3b-thinking": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 3.9e-03,
+        "output_cost_per_token": 3.9e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-vl-8b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 6.4e-04,
+        "output_cost_per_token": 4e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/zai-org/glm-4.5-air": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.04e-03,
+        "output_cost_per_token": 6.8e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 98304,
+        "max_tokens": 98304,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-vl-30b-a3b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.6e-03,
+        "output_cost_per_token": 5.6e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/qwen/qwen3-vl-30b-a3b-thinking": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.6e-03,
+        "output_cost_per_token": 8e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/qwen/qwen-mt-plus": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2e-03,
+        "output_cost_per_token": 6e-03,
+        "max_input_tokens": 16384,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "supports_system_messages": true
+    },
+    "novita/baidu/ernie-4.5-vl-28b-a3b": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.12e-03,
+        "output_cost_per_token": 4.48e-03,
+        "max_input_tokens": 30000,
+        "max_output_tokens": 8000,
+        "max_tokens": 8000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/baidu/ernie-4.5-21B-a3b": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 2.24e-03,
+        "max_input_tokens": 120000,
+        "max_output_tokens": 8000,
+        "max_tokens": 8000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/qwen/qwen3-8b-fp8": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2.8e-04,
+        "output_cost_per_token": 1.104e-03,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 20000,
+        "max_tokens": 20000,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-4b-fp8": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2.4e-04,
+        "output_cost_per_token": 2.4e-04,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 20000,
+        "max_tokens": 20000,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen2.5-7b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 5.6e-04,
+        "max_input_tokens": 32000,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/meta-llama/llama-3.2-3b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2.4e-04,
+        "output_cost_per_token": 4e-04,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/sao10k/l31-70b-euryale-v2.2": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.48e-02,
+        "output_cost_per_token": 1.48e-02,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/qwen/qwen3-embedding-0.6b": {
+        "litellm_provider": "novita",
+        "mode": "embedding",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 0,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768
+    },
+    "novita/qwen/qwen3-embedding-8b": {
+        "litellm_provider": "novita",
+        "mode": "embedding",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 0,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096
+    },
+    "novita/baai/bge-m3": {
+        "litellm_provider": "novita",
+        "mode": "embedding",
+        "input_cost_per_token": 1e-04,
+        "output_cost_per_token": 1e-04,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 96000,
+        "max_tokens": 96000
+    },
+    "novita/qwen/qwen3-reranker-8b": {
+        "litellm_provider": "novita",
+        "mode": "rerank",
+        "input_cost_per_token": 4e-04,
+        "output_cost_per_token": 4e-04,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096
+    },
+    "novita/baai/bge-reranker-v2-m3": {
+        "litellm_provider": "novita",
+        "mode": "rerank",
+        "input_cost_per_token": 1e-04,
+        "output_cost_per_token": 1e-04,
+        "max_input_tokens": 8000,
+        "max_output_tokens": 8000,
+        "max_tokens": 8000
+    },
     "llamagate/llama-3.1-8b": {
         "max_tokens": 8192,
         "max_input_tokens": 131072,
@@ -32354,4 +33801,3 @@
         "mode": "embedding"
     }
 }
-
diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py
index 3df3037ed58..df4737cec85 100644
--- a/litellm/passthrough/main.py
+++ b/litellm/passthrough/main.py
@@ -216,6 +216,11 @@ def llm_passthrough_route(
     )
 
     litellm_params_dict = get_litellm_params(**kwargs)
+    
+    # Add model_id to litellm_params if present in kwargs (for Bedrock Application Inference Profiles)
+    if "model_id" in kwargs:
+        litellm_params_dict["model_id"] = kwargs["model_id"]
+    
     litellm_logging_obj.update_environment_variables(
         model=model,
         litellm_params=litellm_params_dict,
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index 3a548e203c5..1029f2241a1 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -551,6 +551,7 @@ class MCPServerManager:
             allowed_tools=getattr(mcp_server, "allowed_tools", None),
             disallowed_tools=getattr(mcp_server, "disallowed_tools", None),
             allow_all_keys=mcp_server.allow_all_keys,
+            updated_at=getattr(mcp_server, "updated_at", None),
         )
         return new_server
 
@@ -697,9 +698,7 @@ class MCPServerManager:
         results = await asyncio.gather(*tasks)
 
         # Flatten results into single list
-        list_tools_result: List[MCPTool] = [
-            tool for tools in results for tool in tools
-        ]
+        list_tools_result: List[MCPTool] = [tool for tools in results for tool in tools]
 
         verbose_logger.info(
             f"Successfully fetched {len(list_tools_result)} tools total from all servers"
@@ -2059,7 +2058,8 @@ class MCPServerManager:
 
         return None
 
-    async def _add_mcp_servers_from_db_to_in_memory_registry(self):
+    async def reload_servers_from_database(self):
+        """Re-synchronize the in-memory MCP server registry with the database."""
         from litellm.proxy._experimental.mcp_server.db import get_all_mcp_servers
         from litellm.proxy.management_endpoints.mcp_management_endpoints import (
             get_prisma_client_or_throw,
@@ -2074,15 +2074,34 @@ class MCPServerManager:
         db_mcp_servers = await get_all_mcp_servers(prisma_client)
         verbose_logger.info(f"Found {len(db_mcp_servers)} MCP servers in database")
 
-        # ensure the global_mcp_server_manager is up to date with the db
+        previous_registry = self.registry
+        new_registry: Dict[str, MCPServer] = {}
+
         for server in db_mcp_servers:
+            existing_server = previous_registry.get(server.server_id)
+
+            if (
+                existing_server is not None
+                and existing_server.updated_at is not None
+                and server.updated_at is not None
+                and existing_server.updated_at == server.updated_at
+            ):
+                # Re-use existing server instance to avoid re-running build_mcp_server_from_table()
+                # which can perform network discovery for OAuth2 servers.
+                new_registry[server.server_id] = existing_server
+                continue
+
             verbose_logger.debug(
-                f"Adding server to registry: {server.server_id} ({server.server_name})"
+                f"Building server from DB: {server.server_id} ({server.server_name})"
             )
-            await self.add_server(server)
+            new_registry[server.server_id] = await self.build_mcp_server_from_table(
+                server
+            )
+
+        self.registry = new_registry
 
         verbose_logger.debug(
-            f"Registry now contains {len(self.get_registry())} servers"
+            "MCP registry refreshed (%s servers in registry)", len(new_registry)
         )
 
     def get_mcp_servers_from_ids(self, server_ids: List[str]) -> List[MCPServer]:
@@ -2369,13 +2388,6 @@ class MCPServerManager:
             servers.append(self._build_mcp_server_table(server))
         return servers
 
-    async def reload_servers_from_database(self):
-        """
-        Public method to reload all MCP servers from database into registry.
-        This can be called from management endpoints to ensure registry is up to date.
-        """
-        await self._add_mcp_servers_from_db_to_in_memory_registry()
-
     async def get_all_mcp_servers_with_health_unfiltered(
         self, server_ids: Optional[List[str]] = None
     ) -> List[LiteLLM_MCPServerTable]:
diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
index 4c947b99ba3..642cb0cec2d 100644
--- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
@@ -218,10 +218,10 @@ if MCP_AVAILABLE:
         from fastapi import HTTPException
 
         from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException
-        from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config
         from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
             MCPRequestHandler,
         )
+        from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config
 
         try:
             data = await request.json()
@@ -252,7 +252,12 @@ if MCP_AVAILABLE:
             if mcp_server_auth_headers:
                 data["mcp_server_auth_headers"] = mcp_server_auth_headers
             data["raw_headers"] = raw_headers_from_request
-
+            
+            # Extract user_api_key_auth from metadata and add to top level
+            # call_mcp_tool expects user_api_key_auth as a top-level parameter
+            if "metadata" in data and "user_api_key_auth" in data["metadata"]:
+                data["user_api_key_auth"] = data["metadata"]["user_api_key_auth"]
+            
             result = await call_mcp_tool(**data)
             return result
         except BlockedPiiEntityError as e:
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1108-909e2cc499a05c96.js b/litellm/proxy/_experimental/out/_next/static/chunks/1108-909e2cc499a05c96.js
new file mode 100644
index 00000000000..e0f05f91e88
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/1108-909e2cc499a05c96.js
@@ -0,0 +1 @@
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deleted file mode 100644
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similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/137-cbbf776473e39926.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1713-ce16d8a0e658a15d.js b/litellm/proxy/_experimental/out/_next/static/chunks/1713-ce16d8a0e658a15d.js
deleted file mode 100644
index 6aae7a1e1e9..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1713-ce16d8a0e658a15d.js
+++ /dev/null
@@ -1 +0,0 @@
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index 68196561bb4..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1909-3c9de76bfc43e12c.js b/litellm/proxy/_experimental/out/_next/static/chunks/1909-3c9de76bfc43e12c.js
new file mode 100644
index 00000000000..83918eda483
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/1909-3c9de76bfc43e12c.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2202-f9c2c9a967498fa2.js b/litellm/proxy/_experimental/out/_next/static/chunks/2202-8048c018718c1d43.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/2202-f9c2c9a967498fa2.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/2202-8048c018718c1d43.js
index 191e7a40b6c..2e8353debb2 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/2202-f9c2c9a967498fa2.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2202-8048c018718c1d43.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3897-548448f3542aa392.js b/litellm/proxy/_experimental/out/_next/static/chunks/3242-6e6ec7e18f5d698d.js
similarity index 81%
rename from litellm/proxy/_experimental/out/_next/static/chunks/3897-548448f3542aa392.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/3242-6e6ec7e18f5d698d.js
index 465500a9bcc..ca70e6ca4e7 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/3897-548448f3542aa392.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/3242-6e6ec7e18f5d698d.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2843-eda3a290faa906b3.js b/litellm/proxy/_experimental/out/_next/static/chunks/3295-07f20db51db68705.js
similarity index 68%
rename from litellm/proxy/_experimental/out/_next/static/chunks/2843-eda3a290faa906b3.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/3295-07f20db51db68705.js
index d53877ceeb0..13d037d6fa8 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/2843-eda3a290faa906b3.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/3295-07f20db51db68705.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/3801-d4b8e60d32adbf31.js b/litellm/proxy/_experimental/out/_next/static/chunks/3801-d4b8e60d32adbf31.js
deleted file mode 100644
index 4ed4195b60b..00000000000
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deleted file mode 100644
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+++ /dev/null
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deleted file mode 100644
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deleted file mode 100644
index 6f6992dc7fb..00000000000
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+++ /dev/null
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deleted file mode 100644
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index c84c0e6b2d0..00000000000
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+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5333-e9bc197d1822e3ad.js b/litellm/proxy/_experimental/out/_next/static/chunks/5333-1540faf81c7d7006.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/5333-e9bc197d1822e3ad.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/5333-1540faf81c7d7006.js
index 5e4051ca903..2a03ddfa5f9 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5333-e9bc197d1822e3ad.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/5333-1540faf81c7d7006.js
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deleted file mode 100644
index b630c727571..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5830-0662e9fc2ba82b07.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6399-565ef7c239265f07.js b/litellm/proxy/_experimental/out/_next/static/chunks/6399-b337c562adf0ec71.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6399-565ef7c239265f07.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6399-b337c562adf0ec71.js
index 92874eef0da..4d7b607b01f 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/6399-565ef7c239265f07.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/6399-b337c562adf0ec71.js
@@ -1 +1 @@
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deleted file mode 100644
index 67fca90ed6b..00000000000
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deleted file mode 100644
index 59d227f6d62..00000000000
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new file mode 100644
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t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:l,selectedTags:o,selectedVectorStores:c,selectedGuardrails:d,selectedMCPServers:m,mcpServers:p,mcpServerToolRestrictions:u,selectedVoice:g,endpointType:x,selectedModel:h,selectedSdk:f,proxySettings:_}=e,b="session"===n?s:r,v=window.location.origin,j=null==_?void 0:_.LITELLM_UI_API_DOC_BASE_URL;j&&j.trim()?v=j:(null==_?void 0:_.PROXY_BASE_URL)&&(v=_.PROXY_BASE_URL);let y=i||"Your prompt here",N=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),w=l.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),A={};o.length>0&&(A.tags=o),c.length>0&&(A.vector_stores=c),d.length>0&&(A.guardrails=d);let S=h||"your-model-name",C="azure"===f?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(b||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(v,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(b||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(v,'"\n)');switch(x){case a.KP.CHAT:{let e=Object.keys(A).length>0,n="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=w.length>0?w:[{role:"user",content:y}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(S,'",\n    messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(S,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(N,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(A).length>0,n="";if(e){let e=JSON.stringify({metadata:A},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let a=w.length>0?w:[{role:"user",content:y}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(S,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(S,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(N,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===f?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(S,'",\n	prompt="').concat(i,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(N,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(S,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===f?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(N,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(S,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(N,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(S,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7526-fb065faf5cf04772.js b/litellm/proxy/_experimental/out/_next/static/chunks/7526-fb065faf5cf04772.js
deleted file mode 100644
index 4ca4283d6e4..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/7526-fb065faf5cf04772.js
+++ /dev/null
@@ -1 +0,0 @@
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here",v=j.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),y=i.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),N={};o.length>0&&(N.tags=o),c.length>0&&(N.vector_stores=c),d.length>0&&(N.guardrails=d);let w=g||"your-model-name",A="azure"===x?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(f||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(_,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(f||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(_,'"\n)');switch(u){case s.KP.CHAT:{let e=Object.keys(N).length>0,n="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let s=y.length>0?y:[{role:"user",content:j}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(w,'",\n    messages=').concat(JSON.stringify(s,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(w,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(v,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case s.KP.RESPONSES:{let e=Object.keys(N).length>0,n="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n    extra_body=".concat(e)}let s=y.length>0?y:[{role:"user",content:j}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(w,'",\n    input=').concat(JSON.stringify(s,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(w,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(v,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case s.KP.IMAGE:t="azure"===x?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(w,'",\n	prompt="').concat(l,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(v,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case s.KP.IMAGE_EDITS:t="azure"===x?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(v,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(v,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case s.KP.EMBEDDINGS:t='\nresponse = client.embeddings.create(\n	input="'.concat(l||"Your string here",'",\n	model="').concat(w,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case s.KP.TRANSCRIPTION:t='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(w,'",\n	file=audio_file').concat(l?',\n	prompt="'.concat(l.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case s.KP.SPEECH:t='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(w,'",\n	input="').concat(l||"Your text to convert to speech here",'",\n	voice="').concat(p,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7641-c24fc7cf92d8a6c5.js b/litellm/proxy/_experimental/out/_next/static/chunks/7641-c24fc7cf92d8a6c5.js
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index 38af3c21b89..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7906-1b1cdd8da2773bb2.js b/litellm/proxy/_experimental/out/_next/static/chunks/7906-ff470037aad6df61.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/7906-1b1cdd8da2773bb2.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/7906-ff470037aad6df61.js
index 7b5d0fdca13..342acb0398a 100644
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t(e){e.languages.sqf=e.languages.extend("clike",{string:{pattern:/"(?:(?:"")?[^"])*"(?!")|'(?:[^'])*'/,greedy:!0},keyword:/\b(?:breakOut|breakTo|call|case|catch|default|do|echo|else|execFSM|execVM|exitWith|for|forEach|forEachMember|forEachMemberAgent|forEachMemberTeam|from|goto|if|nil|preprocessFile|preprocessFileLineNumbers|private|scopeName|spawn|step|switch|then|throw|to|try|while|with)\b/i,boolean:/\b(?:false|true)\b/i,function:/\b(?:abs|accTime|acos|action|actionIDs|actionKeys|actionKeysImages|actionKeysNames|actionKeysNamesArray|actionName|actionParams|activateAddons|activatedAddons|activateKey|add3DENConnection|add3DENEventHandler|add3DENLayer|addAction|addBackpack|addBackpackCargo|addBackpackCargoGlobal|addBackpackGlobal|addCamShake|addCuratorAddons|addCuratorCameraArea|addCuratorEditableObjects|addCuratorEditingArea|addCuratorPoints|addEditorObject|addEventHandler|addForce|addForceGeneratorRTD|addGoggles|addGroupIcon|addHandgunItem|addHeadgear|addItem|addItemCargo|addItemCargoGlobal|addItemPool|addItemToBackpack|addItemToUniform|addItemToVest|addLiveStats|addMagazine|addMagazineAmmoCargo|addMagazineCargo|addMagazineCargoGlobal|addMagazineGlobal|addMagazinePool|addMagazines|addMagazineTurret|addMenu|addMenuItem|addMissionEventHandler|addMPEventHandler|addMusicEventHandler|addOwnedMine|addPlayerScores|addPrimaryWeaponItem|addPublicVariableEventHandler|addRating|addResources|addScore|addScoreSide|addSecondaryWeaponItem|addSwitchableUnit|addTeamMember|addToRemainsCollector|addTorque|addUniform|addVehicle|addVest|addWaypoint|addWeapon|addWeaponCargo|addWeaponCargoGlobal|addWeaponGlobal|addWeaponItem|addWeaponPool|addWeaponTurret|admin|agent|agents|AGLToASL|aimedAtTarget|aimPos|airDensityCurveRTD|airDensityRTD|airplaneThrottle|airportSide|AISFinishHeal|alive|all3DENEntities|allAirports|allControls|allCurators|allCutLayers|allDead|allDeadMen|allDisplays|allGroups|allMapMarkers|allMines|allMissionObjects|allow3DMode|allowCrewInImmobile|allowCuratorLogicIgnoreAreas|allowDamage|allowDammage|allowFileOperations|allowFleeing|allowGetIn|allowSprint|allPlayers|allSimpleObjects|allSites|allTurrets|allUnits|allUnitsUAV|allVariables|ammo|ammoOnPylon|animate|animateBay|animateDoor|animatePylon|animateSource|animationNames|animationPhase|animationSourcePhase|animationState|append|apply|armoryPoints|arrayIntersect|asin|ASLToAGL|ASLToATL|assert|assignAsCargo|assignAsCargoIndex|assignAsCommander|assignAsDriver|assignAsGunner|assignAsTurret|assignCurator|assignedCargo|assignedCommander|assignedDriver|assignedGunner|assignedItems|assignedTarget|assignedTeam|assignedVehicle|assignedVehicleRole|assignItem|assignTeam|assignToAirport|atan|atan2|atg|ATLToASL|attachedObject|attachedObjects|attachedTo|attachObject|attachTo|attackEnabled|backpack|backpackCargo|backpackContainer|backpackItems|backpackMagazines|backpackSpaceFor|behaviour|benchmark|binocular|blufor|boundingBox|boundingBoxReal|boundingCenter|briefingName|buildingExit|buildingPos|buldozer_EnableRoadDiag|buldozer_IsEnabledRoadDiag|buldozer_LoadNewRoads|buldozer_reloadOperMap|buttonAction|buttonSetAction|cadetMode|callExtension|camCommand|camCommit|camCommitPrepared|camCommitted|camConstuctionSetParams|camCreate|camDestroy|cameraEffect|cameraEffectEnableHUD|cameraInterest|cameraOn|cameraView|campaignConfigFile|camPreload|camPreloaded|camPrepareBank|camPrepareDir|camPrepareDive|camPrepareFocus|camPrepareFov|camPrepareFovRange|camPreparePos|camPrepareRelPos|camPrepareTarget|camSetBank|camSetDir|camSetDive|camSetFocus|camSetFov|camSetFovRange|camSetPos|camSetRelPos|camSetTarget|camTarget|camUseNVG|canAdd|canAddItemToBackpack|canAddItemToUniform|canAddItemToVest|cancelSimpleTaskDestination|canFire|canMove|canSlingLoad|canStand|canSuspend|canTriggerDynamicSimulation|canUnloadInCombat|canVehicleCargo|captive|captiveNum|cbChecked|cbSetChecked|ceil|channelEnabled|cheatsEnabled|checkAIFeature|checkVisibility|civilian|className|clear3DENAttribute|clear3DENInventory|clearAllItemsFromBackpack|clearBackpackCargo|clearBackpackCargoGlobal|clearForcesRTD|clearGroupIcons|clearItemCargo|clearItemCargoGlobal|clearItemPool|clearMagazineCargo|clearMagazineCargoGlobal|clearMagazinePool|clearOverlay|clearRadio|clearVehicleInit|clearWeaponCargo|clearWeaponCargoGlobal|clearWeaponPool|clientOwner|closeDialog|closeDisplay|closeOverlay|collapseObjectTree|collect3DENHistory|collectiveRTD|combatMode|commandArtilleryFire|commandChat|commander|commandFire|commandFollow|commandFSM|commandGetOut|commandingMenu|commandMove|commandRadio|commandStop|commandSuppressiveFire|commandTarget|commandWatch|comment|commitOverlay|compile|compileFinal|completedFSM|composeText|configClasses|configFile|configHierarchy|configName|configNull|configProperties|configSourceAddonList|configSourceMod|configSourceModList|confirmSensorTarget|connectTerminalToUAV|controlNull|controlsGroupCtrl|copyFromClipboard|copyToClipboard|copyWaypoints|cos|count|countEnemy|countFriendly|countSide|countType|countUnknown|create3DENComposition|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t(e){e.languages.sqf=e.languages.extend("clike",{string:{pattern:/"(?:(?:"")?[^"])*"(?!")|'(?:[^'])*'/,greedy:!0},keyword:/\b(?:breakOut|breakTo|call|case|catch|default|do|echo|else|execFSM|execVM|exitWith|for|forEach|forEachMember|forEachMemberAgent|forEachMemberTeam|from|goto|if|nil|preprocessFile|preprocessFileLineNumbers|private|scopeName|spawn|step|switch|then|throw|to|try|while|with)\b/i,boolean:/\b(?:false|true)\b/i,function:/\b(?:abs|accTime|acos|action|actionIDs|actionKeys|actionKeysImages|actionKeysNames|actionKeysNamesArray|actionName|actionParams|activateAddons|activatedAddons|activateKey|add3DENConnection|add3DENEventHandler|add3DENLayer|addAction|addBackpack|addBackpackCargo|addBackpackCargoGlobal|addBackpackGlobal|addCamShake|addCuratorAddons|addCuratorCameraArea|addCuratorEditableObjects|addCuratorEditingArea|addCuratorPoints|addEditorObject|addEventHandler|addForce|addForceGeneratorRTD|addGoggles|addGroupIcon|addHandgunItem|addHeadgear|addItem|addItemCargo|addItemCargoGlobal|addItemPool|addItemToBackpack|addItemToUniform|addItemToVest|addLiveStats|addMagazine|addMagazineAmmoCargo|addMagazineCargo|addMagazineCargoGlobal|addMagazineGlobal|addMagazinePool|addMagazines|addMagazineTurret|addMenu|addMenuItem|addMissionEventHandler|addMPEventHandler|addMusicEventHandler|addOwnedMine|addPlayerScores|addPrimaryWeaponItem|addPublicVariableEventHandler|addRating|addResources|addScore|addScoreSide|addSecondaryWeaponItem|addSwitchableUnit|addTeamMember|addToRemainsCollector|addTorque|addUniform|addVehicle|addVest|addWaypoint|addWeapon|addWeaponCargo|addWeaponCargoGlobal|addWeaponGlobal|addWeaponItem|addWeaponPool|addWeaponTurret|admin|agent|agents|AGLToASL|aimedAtTarget|aimPos|airDensityCurveRTD|airDensityRTD|airplaneThrottle|airportSide|AISFinishHeal|alive|all3DENEntities|allAirports|allControls|allCurators|allCutLayers|allDead|allDeadMen|allDisplays|allGroups|allMapMarkers|allMines|allMissionObjects|allow3DMode|allowCrewInImmobile|allowCuratorLogicIgnoreAreas|allowDamage|allowDammage|allowFileOperations|allowFleeing|allowGetIn|allowSprint|allPlayers|allSimpleObjects|allSites|allTurrets|allUnits|allUnitsUAV|allVariables|ammo|ammoOnPylon|animate|animateBay|animateDoor|animatePylon|animateSource|animationNames|animationPhase|animationSourcePhase|animationState|append|apply|armoryPoints|arrayIntersect|asin|ASLToAGL|ASLToATL|assert|assignAsCargo|assignAsCargoIndex|assignAsCommander|assignAsDriver|assignAsGunner|assignAsTurret|assignCurator|assignedCargo|assignedCommander|assignedDriver|assignedGunner|assignedItems|assignedTarget|assignedTeam|assignedVehicle|assignedVehicleRole|assignItem|assignTeam|assignToAirport|atan|atan2|atg|ATLToASL|attachedObject|attachedObjects|attachedTo|attachObject|attachTo|attackEnabled|backpack|backpackCargo|backpackContainer|backpackItems|backpackMagazines|backpackSpaceFor|behaviour|benchmark|binocular|blufor|boundingBox|boundingBoxReal|boundingCenter|briefingName|buildingExit|buildingPos|buldozer_EnableRoadDiag|buldozer_IsEnabledRoadDiag|buldozer_LoadNewRoads|buldozer_reloadOperMap|buttonAction|buttonSetAction|cadetMode|callExtension|camCommand|camCommit|camCommitPrepared|camCommitted|camConstuctionSetParams|camCreate|camDestroy|cameraEffect|cameraEffectEnableHUD|cameraInterest|cameraOn|cameraView|campaignConfigFile|camPreload|camPreloaded|camPrepareBank|camPrepareDir|camPrepareDive|camPrepareFocus|camPrepareFov|camPrepareFovRange|camPreparePos|camPrepareRelPos|camPrepareTarget|camSetBank|camSetDir|camSetDive|camSetFocus|camSetFov|camSetFovRange|camSetPos|camSetRelPos|camSetTarget|camTarget|camUseNVG|canAdd|canAddItemToBackpack|canAddItemToUniform|canAddItemToVest|cancelSimpleTaskDestination|canFire|canMove|canSlingLoad|canStand|canSuspend|canTriggerDynamicSimulation|canUnloadInCombat|canVehicleCargo|captive|captiveNum|cbChecked|cbSetChecked|ceil|channelEnabled|cheatsEnabled|checkAIFeature|checkVisibility|civilian|className|clear3DENAttribute|clear3DENInventory|clearAllItemsFromBackpack|clearBackpackCargo|clearBackpackCargoGlobal|clearForcesRTD|clearGroupIcons|clearItemCargo|clearItemCargoGlobal|clearItemPool|clearMagazineCargo|clearMagazineCargoGlobal|clearMagazinePool|clearOverlay|clearRadio|clearVehicleInit|clearWeaponCargo|clearWeaponCargoGlobal|clearWeaponPool|clientOwner|closeDialog|closeDisplay|closeOverlay|collapseObjectTree|collect3DENHistory|collectiveRTD|combatMode|commandArtilleryFire|commandChat|commander|commandFire|commandFollow|commandFSM|commandGetOut|commandingMenu|commandMove|commandRadio|commandStop|commandSuppressiveFire|commandTarget|commandWatch|comment|commitOverlay|compile|compileFinal|completedFSM|composeText|configClasses|configFile|configHierarchy|configName|configNull|configProperties|configSourceAddonList|configSourceMod|configSourceModList|confirmSensorTarget|connectTerminalToUAV|controlNull|controlsGroupCtrl|copyFromClipboard|copyToClipboard|copyWaypoints|cos|count|countEnemy|countFriendly|countSide|countType|countUnknown|create3DENComposition|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deleted file mode 100644
index 92b376b506d..00000000000
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+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8143-3f24c5ca9a8b2457.js b/litellm/proxy/_experimental/out/_next/static/chunks/8143-011a548bbfa47ec5.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/8143-3f24c5ca9a8b2457.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/8143-011a548bbfa47ec5.js
index 4399298f84e..55f63d05887 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8143-3f24c5ca9a8b2457.js
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deleted file mode 100644
index 792eb6c80cf..00000000000
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+++ /dev/null
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 00000000000..d6638966352
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/budgets/page-8591009dfdcbf46b.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/budgets/page-8591009dfdcbf46b.js
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index 832828ccee9..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/playground/page-a23e22313e3f1a56.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/playground/page-a23e22313e3f1a56.js
new file mode 100644
index 00000000000..85346198926
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I=r||"Your prompt here",x=I.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),w=s.filter(e=>!e.isImage).map(e=>{let{role:n,content:t}=e;return{role:n,content:t}}),S={};l.length>0&&(S.tags=l),p.length>0&&(S.vector_stores=p),u.length>0&&(S.guardrails=u);let A=f||"your-model-name",O="azure"===h?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(v,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(v,'"\n)');switch(_){case i.KP.CHAT:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=w.length>0?w:[{role:"user",content:I}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(A,'",\n    messages=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(A,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(x,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case i.KP.RESPONSES:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=w.length>0?w:[{role:"user",content:I}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(A,'",\n    input=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(A,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(x,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case i.KP.IMAGE:n="azure"===h?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(A,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(x,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.IMAGE_EDITS:n="azure"===h?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(x,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(x,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(A,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case i.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(A,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case i.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(A,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(g,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(A,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(O,"\n").concat(n)}},8443:function(e,n,t){"use strict";var i,a,o,r;t.d(n,{KP:function(){return a},vf:function(){return l}}),(o=i||(i={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=a||(a={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return a}});var i=t(19250);let a=async e=>{try{let n=await (0,i.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},97415:function(e,n,t){"use strict";var i=t(57437),a=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:u=!1}=e,[c,m]=(0,a.useState)([]),[d,g]=(0,a.useState)(!1);return(0,a.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&m(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,i.jsx)("div",{children:(0,i.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,options:c.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:u})})}},91624:function(e,n,t){"use strict";t.d(n,{C:function(){return a}});var i=t(19250);let a=async e=>{if(!e)return null;try{return await (0,i.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}}}},function(e){e.O(0,[9028,9409,1713,4865,337,8135,1442,2926,2409,3709,353,1994,8565,5319,7906,816,7271,8205,4333,8049,5992,9039,2971,2117,1744],function(){return e(e.s=19235)}),_N_E=e.O()}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/playground/page-ee95ec3fb92cdf8c.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/playground/page-ee95ec3fb92cdf8c.js
deleted file mode 100644
index 76b21911d46..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/playground/page-ee95ec3fb92cdf8c.js
+++ /dev/null
@@ -1 +0,0 @@
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or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(w,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(E,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(x).length>0,t="";if(e){let e=JSON.stringify({metadata:x},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=I.length>0?I:[{role:"user",content:v}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(w,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(w,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(E,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===_?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(w,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(E,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===_?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(E,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(E,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(w,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(w,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(w,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(c,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(w,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(S,"\n").concat(n)}},8443:function(e,n,t){"use strict";var a,i,o,r;t.d(n,{KP:function(){return i},vf:function(){return l}}),(o=a||(a={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=i||(i={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return i}});var a=t(19250);let i=async e=>{try{let n=await (0,a.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},97415:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:u=!1}=e,[m,c]=(0,i.useState)([]),[d,g]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&c(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,options:m.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:u})})}},91624:function(e,n,t){"use strict";t.d(n,{C:function(){return i}});var a=t(19250);let i=async e=>{if(!e)return null;try{return await (0,a.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}}}},function(e){e.O(0,[9028,9409,4865,337,8135,1442,2926,2409,353,1994,8565,3709,5319,7906,4804,7271,8205,507,8049,1253,9039,2971,2117,1744],function(){return e(e.s=19235)}),_N_E=e.O()}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/admin-settings/page-47f52cb50166848e.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/admin-settings/page-47f52cb50166848e.js
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index 3c5d2b57d4f..00000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/admin-settings/page-84498a4ff4f7d566.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/admin-settings/page-84498a4ff4f7d566.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-36bd1b362fe7168f.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-36bd1b362fe7168f.js
deleted file mode 100644
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or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(w,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(I,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(x).length>0,t="";if(e){let e=JSON.stringify({metadata:x},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let a=v.length>0?v:[{role:"user",content:E}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(w,'",\n    input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(w,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(I,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===_?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(w,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===_?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(I,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(w,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case a.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(w,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case a.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(w,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case a.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(w,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(c,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(w,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(S,"\n").concat(n)}},8443:function(e,n,t){"use strict";var a,i,o,r;t.d(n,{KP:function(){return i},vf:function(){return l}}),(o=a||(a={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=i||(i={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return i}});var a=t(19250);let i=async e=>{try{let n=await (0,a.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},97415:function(e,n,t){"use strict";var a=t(57437),i=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:u=!1}=e,[m,c]=(0,i.useState)([]),[d,g]=(0,i.useState)(!1);return(0,i.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&c(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,a.jsx)("div",{children:(0,a.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,options:m.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:u})})}},91624:function(e,n,t){"use strict";t.d(n,{C:function(){return i}});var a=t(19250);let i=async e=>{if(!e)return null;try{return await (0,a.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}}}},function(e){e.O(0,[9028,9409,4865,337,8135,2409,353,1994,8565,3709,5319,7906,4804,7271,8205,3792,8049,1253,2971,2117,1744],function(){return e(e.s=22859)}),_N_E=e.O()}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-4bc7bb18a051c1c8.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-4bc7bb18a051c1c8.js
new file mode 100644
index 00000000000..76fcb2d3f2c
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/test-key/page-4bc7bb18a051c1c8.js
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i=t(57437),a=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,disabled:p}=e,[u,c]=(0,a.useState)([]),[m,d]=(0,a.useState)(!1);return(0,a.useEffect)(()=>{(async()=>{if(l){d(!0);try{let e=await (0,r.getGuardrailsList)(l);console.log("Guardrails response:",e),e.guardrails&&(console.log("Guardrails data:",e.guardrails),c(e.guardrails))}catch(e){console.error("Error fetching guardrails:",e)}finally{d(!1)}}})()},[l]),(0,i.jsx)("div",{children:(0,i.jsx)(o.default,{mode:"multiple",disabled:p,placeholder:p?"Setting guardrails is a premium feature.":"Select guardrails",onChange:e=>{console.log("Selected guardrails:",e),n(e)},value:t,loading:m,className:s,options:u.map(e=>(console.log("Mapping guardrail:",e),{label:"".concat(e.guardrail_name),value:e.guardrail_name})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"}})})}},82971:function(e,n,t){"use strict";t.d(n,{L:function(){return a}});var i=t(8443);let a=e=>{let n;let{apiKeySource:t,accessToken:a,apiKey:o,inputMessage:r,chatHistory:s,selectedTags:l,selectedVectorStores:p,selectedGuardrails:u,selectedMCPServers:c,mcpServers:m,mcpServerToolRestrictions:d,selectedVoice:g,endpointType:_,selectedModel:f,selectedSdk:h,proxySettings:b}=e,y="session"===t?a:o,v=window.location.origin,E=null==b?void 0:b.LITELLM_UI_API_DOC_BASE_URL;E&&E.trim()?v=E:(null==b?void 0:b.PROXY_BASE_URL)&&(v=b.PROXY_BASE_URL);let I=r||"Your prompt here",x=I.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),w=s.filter(e=>!e.isImage).map(e=>{let{role:n,content:t}=e;return{role:n,content:t}}),S={};l.length>0&&(S.tags=l),p.length>0&&(S.vector_stores=p),u.length>0&&(S.guardrails=u);let A=f||"your-model-name",O="azure"===h?'import openai\n\nclient = openai.AzureOpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	azure_endpoint="').concat(v,'",\n	api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n	api_key="'.concat(y||"YOUR_LITELLM_API_KEY",'",\n	base_url="').concat(v,'"\n)');switch(_){case i.KP.CHAT:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=w.length>0?w:[{role:"user",content:I}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n    model="'.concat(A,'",\n    messages=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.chat.completions.create(\n#     model="').concat(A,'",\n#     messages=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {\n#                     "type": "text",\n#                     "text": "').concat(x,'"\n#                 },\n#                 {\n#                     "type": "image_url",\n#                     "image_url": {\n#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}\n#                     }\n#                 }\n#             ]\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case i.KP.RESPONSES:{let e=Object.keys(S).length>0,t="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n    extra_body=".concat(e)}let i=w.length>0?w:[{role:"user",content:I}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n    with open(image_path, "rb") as image_file:\n        return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n    model="'.concat(A,'",\n    input=').concat(JSON.stringify(i,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf\n# response_with_file = client.responses.create(\n#     model="').concat(A,'",\n#     input=[\n#         {\n#             "role": "user",\n#             "content": [\n#                 {"type": "input_text", "text": "').concat(x,'"},\n#                 {\n#                     "type": "input_image",\n#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}\n#                 },\n#             ],\n#         }\n#     ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case i.KP.IMAGE:n="azure"===h?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n	model=\"".concat(A,'",\n	prompt="').concat(r,'",\n	n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n	os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n	# Retrieve the generated image\n	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n			image_url = json_response["data"][0]["url"]\n			generated_image = requests.get(image_url).content\n			with open(image_path, "wb") as image_file:\n					image_file.write(generated_image)\n\n			print(f"Image saved to {image_path}")\n			# Display the image\n			image = Image.open(image_path)\n			image.show()\n	else:\n			print("Could not find image URL in response.")\n			print("Full response:", json_response)\nexcept Exception as e:\n	print(f"An error occurred: {e}")\n	print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(x,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.IMAGE_EDITS:n="azure"===h?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, "rb") as image_file:\n			return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(x,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n	with open(image_path, \"rb\") as image_file:\n			return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n	# In a real implementation, this would upload the file to OpenAI\n	# For this example, we'll just return a placeholder ID\n	return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(x,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n	model="').concat(A,'",\n	input=[\n			{\n					"role": "user",\n					"content": [\n							{"type": "input_text", "text": prompt},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image1}",\n							},\n							{\n									"type": "input_image",\n									"image_url": f"data:image/jpeg;base64,{base64_image2}",\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id1,\n							},\n							{\n									"type": "input_image",\n									"file_id": file_id2,\n							}\n					],\n			}\n	],\n	tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n	output\n	for output in response.output\n	if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n	image_base64 = image_data[0]\n	image_filename = f"edited_image_{int(time.time())}.png"\n	with open(image_filename, "wb") as f:\n			f.write(base64.b64decode(image_base64))\n	print(f"Image saved to {image_filename}")\nelse:\n	# If no image is generated, there might be a text response with an explanation\n	text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n	if text_response:\n			print("No image generated. Model response:")\n			print("\\n".join(text_response))\n	else:\n			print("No image data found in response.")\n	print("Full response for debugging:")\n	print(response)\n');break;case i.KP.EMBEDDINGS:n='\nresponse = client.embeddings.create(\n	input="'.concat(r||"Your string here",'",\n	model="').concat(A,'",\n	encoding_format="base64" # or "float"\n)\n\nprint(response.data[0].embedding)\n');break;case i.KP.TRANSCRIPTION:n='\n# Open the audio file\naudio_file = open("path/to/your/audio/file.mp3", "rb")\n\n# Make the transcription request\nresponse = client.audio.transcriptions.create(\n	model="'.concat(A,'",\n	file=audio_file').concat(r?',\n	prompt="'.concat(r.replace(/"/g,'\\"'),'"'):"","\n)\n\nprint(response.text)\n");break;case i.KP.SPEECH:n='\n# Make the text-to-speech request\nresponse = client.audio.speech.create(\n	model="'.concat(A,'",\n	input="').concat(r||"Your text to convert to speech here",'",\n	voice="').concat(g,'"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer\n)\n\n# Save the audio to a file\noutput_filename = "output_speech.mp3"\nresponse.stream_to_file(output_filename)\nprint(f"Audio saved to {output_filename}")\n\n# Optional: Customize response format and speed\n# response = client.audio.speech.create(\n#     model="').concat(A,'",\n#     input="').concat(r||"Your text to convert to speech here",'",\n#     voice="alloy",\n#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm\n#     speed=1.0  # Range: 0.25 to 4.0\n# )\n# response.stream_to_file("output_speech.mp3")\n');break;default:n="\n# Code generation for this endpoint is not implemented yet."}return"".concat(O,"\n").concat(n)}},8443:function(e,n,t){"use strict";var i,a,o,r;t.d(n,{KP:function(){return a},vf:function(){return l}}),(o=i||(i={})).AUDIO_SPEECH="audio_speech",o.AUDIO_TRANSCRIPTION="audio_transcription",o.IMAGE_GENERATION="image_generation",o.VIDEO_GENERATION="video_generation",o.CHAT="chat",o.RESPONSES="responses",o.IMAGE_EDITS="image_edits",o.ANTHROPIC_MESSAGES="anthropic_messages",o.EMBEDDING="embedding",(r=a||(a={})).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents";let s={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"},l=e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let n=s[e];return console.log("endpointType:",n),n}return"chat"}},10703:function(e,n,t){"use strict";t.d(n,{p:function(){return a}});var i=t(19250);let a=async e=>{try{let n=await (0,i.modelHubCall)(e);if(console.log("model_info:",n),(null==n?void 0:n.data.length)>0){let e=n.data.map(e=>({model_group:e.model_group,mode:null==e?void 0:e.mode}));return e.sort((e,n)=>e.model_group.localeCompare(n.model_group)),e}return[]}catch(e){throw console.error("Error fetching model info:",e),e}}},97415:function(e,n,t){"use strict";var i=t(57437),a=t(2265),o=t(37592),r=t(19250);n.Z=e=>{let{onChange:n,value:t,className:s,accessToken:l,placeholder:p="Select vector stores",disabled:u=!1}=e,[c,m]=(0,a.useState)([]),[d,g]=(0,a.useState)(!1);return(0,a.useEffect)(()=>{(async()=>{if(l){g(!0);try{let e=await (0,r.vectorStoreListCall)(l);e.data&&m(e.data)}catch(e){console.error("Error fetching vector stores:",e)}finally{g(!1)}}})()},[l]),(0,i.jsx)("div",{children:(0,i.jsx)(o.default,{mode:"multiple",placeholder:p,onChange:n,value:t,loading:d,className:s,options:c.map(e=>({label:"".concat(e.vector_store_name||e.vector_store_id," (").concat(e.vector_store_id,")"),value:e.vector_store_id,title:e.vector_store_description||e.vector_store_id})),optionFilterProp:"label",showSearch:!0,style:{width:"100%"},disabled:u})})}},91624:function(e,n,t){"use strict";t.d(n,{C:function(){return a}});var i=t(19250);let a=async e=>{if(!e)return null;try{return await (0,i.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}}}},function(e){e.O(0,[9028,9409,1713,4865,337,8135,2409,3709,353,1994,8565,5319,7906,816,7271,8205,9709,8049,5992,2971,2117,1744],function(){return e(e.s=22859)}),_N_E=e.O()}]);
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deleted file mode 100644
index a0ae07e2707..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/tools/mcp-servers/page-75cbf1f7cdaead36.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-a67e608f0d011c37.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-29dff15d61beb001.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-a67e608f0d011c37.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-29dff15d61beb001.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/page-0da481042ef13e97.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/page-0da481042ef13e97.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/css/bc4973c9b8cf5a2a.css b/litellm/proxy/_experimental/out/_next/static/css/bc4973c9b8cf5a2a.css
new file mode 100644
index 00000000000..19b72a188ba
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/css/bc4973c9b8cf5a2a.css
@@ -0,0 +1,3 @@
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diff --git a/litellm/proxy/_experimental/out/experimental/old-usage.txt b/litellm/proxy/_experimental/out/experimental/old-usage.txt
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diff --git a/litellm/proxy/_experimental/out/models-and-endpoints.txt b/litellm/proxy/_experimental/out/models-and-endpoints.txt
index 2888613d7f2..d804a8c2244 100644
--- a/litellm/proxy/_experimental/out/models-and-endpoints.txt
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 1:null
diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py
index 4140273ea25..8c0a2e6ae2a 100644
--- a/litellm/proxy/_types.py
+++ b/litellm/proxy/_types.py
@@ -993,7 +993,9 @@ class LiteLLM_ProxyModelTable(LiteLLMPydanticObjectBase):
     model_name: str
     litellm_params: dict
     model_info: dict
+    created_at: Optional[datetime] = None
     created_by: str
+    updated_at: Optional[datetime] = None
     updated_by: str
 
     @model_validator(mode="before")
@@ -1942,7 +1944,7 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase):
         description="connect to a postgres db - needed for generating temporary keys + tracking spend / key",
     )
     database_connection_pool_limit: Optional[int] = Field(
-        100,
+        10,
         description="default connection pool for prisma client connecting to postgres db",
     )
     database_connection_timeout: Optional[float] = Field(
@@ -2104,6 +2106,7 @@ class LiteLLM_VerificationToken(LiteLLMPydanticObjectBase):
     rotation_interval: Optional[str] = None  # How often to rotate (e.g., "30d", "90d")
     last_rotation_at: Optional[datetime] = None  # When this key was last rotated
     key_rotation_at: Optional[datetime] = None  # When this key should next be rotated
+    router_settings: Optional[Dict] = None  # Router settings for this key (Key > Team > Global precedence)
 
     model_config = ConfigDict(protected_namespaces=())
 
diff --git a/litellm/proxy/anthropic_endpoints/endpoints.py b/litellm/proxy/anthropic_endpoints/endpoints.py
index 334362a0271..7de6b7fccfc 100644
--- a/litellm/proxy/anthropic_endpoints/endpoints.py
+++ b/litellm/proxy/anthropic_endpoints/endpoints.py
@@ -11,7 +11,7 @@ from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.integrations.custom_guardrail import ModifyResponseException
 from litellm.proxy.common_request_processing import (
     ProxyBaseLLMRequestProcessing,
-    create_streaming_response,
+    create_response,
 )
 from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
 from litellm.types.utils import TokenCountResponse
@@ -106,7 +106,7 @@ async def anthropic_response(  # noqa: PLR0915
                 )
             )
 
-            return await create_streaming_response(
+            return await create_response(
                 generator=selected_data_generator,
                 media_type="text/event-stream",
                 headers={},
diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py
index de4973ecc69..a741869e5fc 100644
--- a/litellm/proxy/auth/auth_checks.py
+++ b/litellm/proxy/auth/auth_checks.py
@@ -147,6 +147,7 @@ async def common_checks(
     # 3.1. If organization is in budget
     await _organization_max_budget_check(
         valid_token=valid_token,
+        team_object=team_object,
         prisma_client=prisma_client,
         user_api_key_cache=user_api_key_cache,
         proxy_logging_obj=proxy_logging_obj,
@@ -1793,11 +1794,20 @@ async def get_org_object(
     user_api_key_cache: DualCache,
     parent_otel_span: Optional[Span] = None,
     proxy_logging_obj: Optional[ProxyLogging] = None,
+    include_budget_table: bool = False,
 ) -> Optional[LiteLLM_OrganizationTable]:
     """
     - Check if org id in proxy Org Table
     - if valid, return LiteLLM_OrganizationTable object
     - if not, then raise an error
+    
+    Args:
+        org_id: Organization ID to look up
+        prisma_client: Database client
+        user_api_key_cache: Cache for storing results
+        parent_otel_span: Optional OpenTelemetry span
+        proxy_logging_obj: Optional proxy logging object
+        include_budget_table: If True, includes litellm_budget_table in the query
     """
     if prisma_client is None:
         raise Exception(
@@ -1806,8 +1816,13 @@ async def get_org_object(
     if not isinstance(org_id, str):
         return None
 
+    # Use different cache key if budget table is included
+    cache_key = "org_id:{}".format(org_id)
+    if include_budget_table:
+        cache_key = "org_id:{}:with_budget".format(org_id)
+    
     # check if in cache
-    cached_org_obj = user_api_key_cache.async_get_cache(key="org_id:{}".format(org_id))
+    cached_org_obj = user_api_key_cache.async_get_cache(key=cache_key)
     if cached_org_obj is not None:
         if isinstance(cached_org_obj, dict):
             return LiteLLM_OrganizationTable(**cached_org_obj)
@@ -1815,13 +1830,24 @@ async def get_org_object(
             return cached_org_obj
     # else, check db
     try:
+        query_kwargs: Dict[str, Any] = {"where": {"organization_id": org_id}}
+        if include_budget_table:
+            query_kwargs["include"] = {"litellm_budget_table": True}
+        
         response = await prisma_client.db.litellm_organizationtable.find_unique(
-            where={"organization_id": org_id}
+            **query_kwargs
         )
 
         if response is None:
             raise Exception
 
+        # Cache the result
+        await user_api_key_cache.async_set_cache(
+            key=cache_key,
+            value=response.model_dump() if hasattr(response, "model_dump") else response,
+            ttl=DEFAULT_IN_MEMORY_TTL,
+        )
+
         return response
     except Exception:
         raise Exception(
@@ -2310,61 +2336,89 @@ async def _team_max_budget_check(
 
 async def _organization_max_budget_check(
     valid_token: Optional[UserAPIKeyAuth],
+    team_object: Optional[LiteLLM_TeamTable],
     prisma_client: Optional[PrismaClient],
     user_api_key_cache: DualCache,
     proxy_logging_obj: ProxyLogging,
 ):
     """
     Check if the organization is over its max budget.
+    
+    This function checks the organization budget using:
+    1. First, tries to use valid_token.org_id (if key has organization_id set)
+    2. Falls back to team_object.organization_id (if key doesn't have org_id but team does)
+    
+    This ensures organization budget checks work even when keys don't have organization_id
+    set directly, as long as their team belongs to an organization.
 
     Raises:
         BudgetExceededError if the organization is over its max budget.
         Triggers a budget alert if the organization is over its max budget.
     """
-    # Only check if token has organization info and organization_max_budget is set
-    if (
-        valid_token is None
-        or valid_token.org_id is None
-        or valid_token.organization_max_budget is None
-        or valid_token.organization_max_budget <= 0
-    ):
+    if valid_token is None or prisma_client is None:
         return
 
-    # Get organization object to check current spend
-    if prisma_client is not None:
+    # Determine organization_id: first try from token, then fallback to team
+    org_id: Optional[str] = None
+    if valid_token.org_id is not None:
+        org_id = valid_token.org_id
+    elif team_object is not None and team_object.organization_id is not None:
+        org_id = team_object.organization_id
+    
+    # If no organization_id found, skip the check
+    if org_id is None:
+        return
+
+    # Get organization object with budget table - use get_org_object so it can be mocked in tests
+    try:
         org_table = await get_org_object(
-            org_id=valid_token.org_id,
+            org_id=org_id,
             prisma_client=prisma_client,
             user_api_key_cache=user_api_key_cache,
+            proxy_logging_obj=proxy_logging_obj,
+            include_budget_table=True,
+        )
+    except Exception:
+        # If organization lookup fails, skip the check
+        return
+
+    if org_table is None:
+        return
+
+    # Get max_budget from organization's budget table
+    org_max_budget: Optional[float] = None
+    if org_table.litellm_budget_table is not None:
+        org_max_budget = org_table.litellm_budget_table.max_budget
+
+    # Only check if organization has a valid max_budget set
+    if org_max_budget is None or org_max_budget <= 0:
+        return
+
+    # Check if organization spend exceeds max budget
+    if org_table.spend >= org_max_budget:
+        # Trigger budget alert
+        call_info = CallInfo(
+            token=valid_token.token,
+            spend=org_table.spend,
+            max_budget=org_max_budget,
+            user_id=valid_token.user_id,
+            team_id=valid_token.team_id,
+            team_alias=valid_token.team_alias,
+            organization_id=org_id,
+            event_group=Litellm_EntityType.ORGANIZATION,
+        )
+        asyncio.create_task(
+            proxy_logging_obj.budget_alerts(
+                type="organization_budget",
+                user_info=call_info,
+            )
         )
 
-        if (
-            org_table is not None
-            and org_table.spend >= valid_token.organization_max_budget
-        ):
-            # Trigger budget alert
-            call_info = CallInfo(
-                token=valid_token.token,
-                spend=org_table.spend,
-                max_budget=valid_token.organization_max_budget,
-                user_id=valid_token.user_id,
-                team_id=valid_token.team_id,
-                team_alias=valid_token.team_alias,
-                organization_id=valid_token.org_id,
-                event_group=Litellm_EntityType.ORGANIZATION,
-            )
-            asyncio.create_task(
-                proxy_logging_obj.budget_alerts(
-                    type="organization_budget",
-                    user_info=call_info,
-                )
-            )
-
-            raise litellm.BudgetExceededError(
-                current_cost=org_table.spend,
-                max_budget=valid_token.organization_max_budget,
-                message=f"Budget has been exceeded! Organization={valid_token.org_id} Current cost: {org_table.spend}, Max budget: {valid_token.organization_max_budget}",
-            )
+        raise litellm.BudgetExceededError(
+            current_cost=org_table.spend,
+            max_budget=org_max_budget,
+            message=f"Budget has been exceeded! Organization={org_id} Current cost: {org_table.spend}, Max budget: {org_max_budget}",
+        )
 
 
 async def _tag_max_budget_check(
@@ -2601,4 +2655,4 @@ def _can_object_call_vector_stores(
                 code=status.HTTP_401_UNAUTHORIZED,
             )
 
-    return True
+    return True
\ No newline at end of file
diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py
index 7a71af1da5c..797540deaa4 100644
--- a/litellm/proxy/auth/auth_utils.py
+++ b/litellm/proxy/auth/auth_utils.py
@@ -426,38 +426,65 @@ def get_key_model_rpm_limit(
     user_api_key_dict: UserAPIKeyAuth,
 ) -> Optional[Dict[str, int]]:
     """
-    Get the model rpm limit for a given api key
-    - check key metadata
-    - check key model max budget
-    - check team metadata
+    Get the model rpm limit for a given api key.
+
+    Priority order (returns first found):
+    1. Key metadata (model_rpm_limit)
+    2. Key model_max_budget (rpm_limit per model)
+    3. Team metadata (model_rpm_limit)
     """
+    # 1. Check key metadata first (takes priority)
     if user_api_key_dict.metadata:
-        if "model_rpm_limit" in user_api_key_dict.metadata:
-            return user_api_key_dict.metadata["model_rpm_limit"]
-    elif user_api_key_dict.model_max_budget:
+        result = user_api_key_dict.metadata.get("model_rpm_limit")
+        if result:
+            return result
+
+    # 2. Check model_max_budget
+    if user_api_key_dict.model_max_budget:
         model_rpm_limit: Dict[str, Any] = {}
         for model, budget in user_api_key_dict.model_max_budget.items():
-            if "rpm_limit" in budget and budget["rpm_limit"] is not None:
+            if isinstance(budget, dict) and budget.get("rpm_limit") is not None:
                 model_rpm_limit[model] = budget["rpm_limit"]
-        return model_rpm_limit
-    elif user_api_key_dict.team_metadata:
-        if "model_rpm_limit" in user_api_key_dict.team_metadata:
-            return user_api_key_dict.team_metadata["model_rpm_limit"]
+        if model_rpm_limit:
+            return model_rpm_limit
+
+    # 3. Fallback to team metadata
+    if user_api_key_dict.team_metadata:
+        return user_api_key_dict.team_metadata.get("model_rpm_limit")
+
     return None
 
 
 def get_key_model_tpm_limit(
     user_api_key_dict: UserAPIKeyAuth,
 ) -> Optional[Dict[str, int]]:
+    """
+    Get the model tpm limit for a given api key.
+
+    Priority order (returns first found):
+    1. Key metadata (model_tpm_limit)
+    2. Key model_max_budget (tpm_limit per model)
+    3. Team metadata (model_tpm_limit)
+    """
+    # 1. Check key metadata first (takes priority)
     if user_api_key_dict.metadata:
-        if "model_tpm_limit" in user_api_key_dict.metadata:
-            return user_api_key_dict.metadata["model_tpm_limit"]
-    elif user_api_key_dict.model_max_budget:
-        if "tpm_limit" in user_api_key_dict.model_max_budget:
-            return user_api_key_dict.model_max_budget["tpm_limit"]
-    elif user_api_key_dict.team_metadata:
-        if "model_tpm_limit" in user_api_key_dict.team_metadata:
-            return user_api_key_dict.team_metadata["model_tpm_limit"]
+        result = user_api_key_dict.metadata.get("model_tpm_limit")
+        if result:
+            return result
+
+    # 2. Check model_max_budget (iterate per-model like RPM does)
+    if user_api_key_dict.model_max_budget:
+        model_tpm_limit: Dict[str, Any] = {}
+        for model, budget in user_api_key_dict.model_max_budget.items():
+            if isinstance(budget, dict) and budget.get("tpm_limit") is not None:
+                model_tpm_limit[model] = budget["tpm_limit"]
+        if model_tpm_limit:
+            return model_tpm_limit
+
+    # 3. Fallback to team metadata
+    if user_api_key_dict.team_metadata:
+        return user_api_key_dict.team_metadata.get("model_tpm_limit")
+
     return None
 
 
@@ -469,7 +496,8 @@ def get_model_rate_limit_from_metadata(
     if getattr(user_api_key_dict, metadata_accessor_key):
         return getattr(user_api_key_dict, metadata_accessor_key).get(rate_limit_key)
     return None
-  
+
+
 def get_team_model_rpm_limit(
     user_api_key_dict: UserAPIKeyAuth,
 ) -> Optional[Dict[str, int]]:
diff --git a/litellm/proxy/auth/login_utils.py b/litellm/proxy/auth/login_utils.py
index 8cc33ce6cdd..5be44f479b8 100644
--- a/litellm/proxy/auth/login_utils.py
+++ b/litellm/proxy/auth/login_utils.py
@@ -188,7 +188,7 @@ async def authenticate_user(  # noqa: PLR0915
         _user_row = cast(
             Optional[LiteLLM_UserTable],
             await prisma_client.db.litellm_usertable.find_first(
-                where={"user_email": {"equals": username}}
+                where={"user_email": {"equals": username, "mode": "insensitive"}}
             ),
         )
 
diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py
index 9b53d9a3a80..401aa7fd443 100644
--- a/litellm/proxy/auth/user_api_key_auth.py
+++ b/litellm/proxy/auth/user_api_key_auth.py
@@ -138,7 +138,7 @@ def _apply_budget_limits_to_end_user_params(
 ) -> None:
     """
     Helper function to apply budget limits to end user parameters.
-    
+
     Args:
         end_user_params: Dictionary to update with budget parameters
         budget_info: Budget table object containing limits
@@ -146,16 +146,14 @@ def _apply_budget_limits_to_end_user_params(
     """
     if budget_info.tpm_limit is not None:
         end_user_params["end_user_tpm_limit"] = budget_info.tpm_limit
-    
+
     if budget_info.rpm_limit is not None:
         end_user_params["end_user_rpm_limit"] = budget_info.rpm_limit
-    
+
     if budget_info.max_budget is not None:
         end_user_params["end_user_max_budget"] = budget_info.max_budget
-    
-    verbose_proxy_logger.debug(
-        f"Applied budget limits to end user {end_user_id}"
-    )
+
+    verbose_proxy_logger.debug(f"Applied budget limits to end user {end_user_id}")
 
 
 async def user_api_key_auth_websocket(websocket: WebSocket):
@@ -170,12 +168,10 @@ async def user_api_key_auth_websocket(websocket: WebSocket):
 
     model = query_params.get("model")
 
-    
     async def return_body():
         return _realtime_request_body(model)
-    
-    request.body = return_body  # type: ignore
 
+    request.body = return_body  # type: ignore
 
     authorization = websocket.headers.get("authorization")
     # If no Authorization header, try the api-key header
@@ -586,7 +582,9 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                         if team_membership is not None
                         else None
                     ),
-                    team_metadata=team_object.metadata if team_object is not None else None,
+                    team_metadata=team_object.metadata
+                    if team_object is not None
+                    else None,
                 )
                 # run through common checks
                 _ = await common_checks(
@@ -669,9 +667,9 @@ 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,
@@ -753,7 +751,7 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                         message=f"Authentication Error - Expired Key. Key Expiry time {expiry_time} and current time {current_time}",
                         type=ProxyErrorTypes.expired_key,
                         code=400,
-                        param=api_key,
+                        param=abbreviate_api_key(api_key=api_key),
                     )
             valid_token = update_valid_token_with_end_user_params(
                 valid_token=valid_token, end_user_params=end_user_params
@@ -994,7 +992,6 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
 
             # Check 3. Check if user is in their team budget
             if valid_token.team_member_spend is not None:
-
                 if prisma_client is not None:
                     _cache_key = f"{valid_token.team_id}_{valid_token.user_id}"
 
@@ -1055,7 +1052,7 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
                         message=f"Authentication Error - Expired Key. Key Expiry time {expiry_time} and current time {current_time}",
                         type=ProxyErrorTypes.expired_key,
                         code=400,
-                        param=api_key,
+                        param=abbreviate_api_key(api_key=api_key),
                     )
 
             # Check 4. Token Spend is under budget
@@ -1216,8 +1213,6 @@ async def _user_api_key_auth_builder(  # noqa: PLR0915
         )
 
 
-
-
 @tracer.wrap()
 async def user_api_key_auth(
     request: Request,
diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py
index 537b48f06ed..61bffde3aca 100644
--- a/litellm/proxy/common_request_processing.py
+++ b/litellm/proxy/common_request_processing.py
@@ -17,7 +17,7 @@ from typing import (
 import httpx
 import orjson
 from fastapi import HTTPException, Request, status
-from fastapi.responses import Response, StreamingResponse
+from fastapi.responses import JSONResponse, Response, StreamingResponse
 
 import litellm
 from litellm._logging import verbose_proxy_logger
@@ -96,16 +96,55 @@ async def _parse_event_data_for_error(event_line: Union[str, bytes]) -> Optional
     return None
 
 
-async def create_streaming_response(
+def _extract_error_from_sse_chunk(event_line: Union[str, bytes]) -> dict:
+    """
+    Extract error dictionary from SSE format chunk.
+
+    Args:
+        event_line: SSE format event line, e.g. "data: {"error": {...}}\n\n"
+
+    Returns:
+        Error dictionary in OpenAI API format
+    """
+    event_line = (
+        event_line.decode("utf-8") if isinstance(event_line, bytes) else event_line
+    )
+
+    # Default error format
+    default_error = {
+        "message": "Unknown error",
+        "type": "internal_server_error",
+        "param": None,
+        "code": "500",
+    }
+
+    if event_line.startswith("data: "):
+        json_str = event_line[len("data: ") :].strip()
+        if not json_str or json_str == "[DONE]":
+            return default_error
+
+        try:
+            data = orjson.loads(json_str)
+            if isinstance(data, dict) and "error" in data:
+                error_obj = data["error"]
+                if isinstance(error_obj, dict):
+                    return error_obj
+        except (orjson.JSONDecodeError, json.JSONDecodeError):
+            pass
+
+    return default_error
+
+
+async def create_response(
     generator: AsyncGenerator[str, None],
     media_type: str,
     headers: dict,
     default_status_code: int = status.HTTP_200_OK,
-) -> StreamingResponse:
+) -> Union[StreamingResponse, JSONResponse]:
     """
-    Creates a StreamingResponse by inspecting the first chunk for an error code.
-    The entire original generator content is streamed, but the HTTP status code
-    of the response is set based on the first chunk if it's a recognized error.
+    Create streaming response, checking if the first chunk is an error.
+    If the first chunk is an error, return a standard JSON error response.
+    Otherwise, return StreamingResponse and stream all content.
     """
     first_chunk_value: Optional[str] = None
     final_status_code = default_status_code
@@ -124,9 +163,27 @@ async def create_streaming_response(
                     first_chunk_value
                 )
                 if error_code_from_chunk is not None:
+                    # First chunk is an error, stream hasn't really started yet
+                    # Should return standard JSON error response instead of SSE format
                     final_status_code = error_code_from_chunk
                     verbose_proxy_logger.debug(
-                        f"Error detected in first stream chunk. Status code set to: {final_status_code}"
+                        f"Error detected in first stream chunk. Returning JSON error response with status code: {final_status_code}"
+                    )
+
+                    # Parse error content
+                    error_dict = _extract_error_from_sse_chunk(first_chunk_value)
+
+                    # Consume and close generator (avoid resource leak)
+                    try:
+                        await generator.aclose()
+                    except Exception:
+                        pass
+
+                    # Return JSON format error response
+                    return JSONResponse(
+                        status_code=final_status_code,
+                        content={"error": error_dict},
+                        headers=headers,
                     )
             except Exception as e:
                 verbose_proxy_logger.debug(f"Error parsing first chunk value: {e}")
@@ -237,7 +294,11 @@ class ProxyBaseLLMRequestProcessing:
         if response_cost is not None:
             try:
                 # Convert response_cost to float if it's a string
-                cost_value = float(response_cost) if isinstance(response_cost, str) else response_cost
+                cost_value = (
+                    float(response_cost)
+                    if isinstance(response_cost, str)
+                    else response_cost
+                )
                 if cost_value > 0:
                     updated_spend = current_spend + cost_value
             except (ValueError, TypeError):
@@ -376,6 +437,16 @@ class ProxyBaseLLMRequestProcessing:
     ) -> Tuple[dict, LiteLLMLoggingObj]:
         start_time = datetime.now()  # start before calling guardrail hooks
 
+        # Calculate request queue time if arrival_time is available
+        # Use start_time.timestamp() to avoid extra time.time() call for better performance
+        proxy_server_request = self.data.get("proxy_server_request", {})
+        arrival_time = proxy_server_request.get("arrival_time")
+        queue_time_seconds = None
+        if arrival_time is not None:
+            # Convert start_time (datetime) to timestamp for calculation
+            processing_start_time = start_time.timestamp()
+            queue_time_seconds = processing_start_time - arrival_time
+
         self.data = await add_litellm_data_to_request(
             data=self.data,
             request=request,
@@ -385,6 +456,19 @@ class ProxyBaseLLMRequestProcessing:
             proxy_config=proxy_config,
         )
 
+        # Store queue time in metadata after add_litellm_data_to_request to ensure it's preserved
+        if queue_time_seconds is not None:
+            from litellm.proxy.litellm_pre_call_utils import _get_metadata_variable_name
+
+            _metadata_variable_name = _get_metadata_variable_name(request)
+            if _metadata_variable_name not in self.data:
+                self.data[_metadata_variable_name] = {}
+            if not isinstance(self.data[_metadata_variable_name], dict):
+                self.data[_metadata_variable_name] = {}
+            self.data[_metadata_variable_name][
+                "queue_time_seconds"
+            ] = queue_time_seconds
+
         self.data["model"] = (
             general_settings.get("completion_model", None)  # server default
             or user_model  # model name passed via cli args
@@ -440,6 +524,29 @@ class ProxyBaseLLMRequestProcessing:
             user_api_key_dict=user_api_key_dict, data=self.data, call_type=route_type  # type: ignore
         )
 
+        # Apply hierarchical router_settings (Key > Team > Global)
+        if llm_router is not None and proxy_config is not None:
+            from litellm.proxy.proxy_server import prisma_client
+            
+            router_settings = await proxy_config._get_hierarchical_router_settings(
+                user_api_key_dict=user_api_key_dict,
+                prisma_client=prisma_client,
+            )
+            
+            # If router_settings found (from key, team, or global), apply them
+            # This ensures key/team settings override global settings
+            if router_settings is not None and router_settings:
+                # Get model_list from current router
+                model_list = llm_router.get_model_list()
+                if model_list is not None:
+                    # Create user_config with model_list and router_settings
+                    # This creates a per-request router with the hierarchical settings
+                    user_config = {
+                        "model_list": model_list,
+                        **router_settings
+                    }
+                    self.data["user_config"] = user_config
+
         if "messages" in self.data and self.data["messages"]:
             logging_obj.update_messages(self.data["messages"])
 
@@ -647,7 +754,7 @@ class ProxyBaseLLMRequestProcessing:
                         proxy_logging_obj=proxy_logging_obj,
                     )
                 )
-                return await create_streaming_response(
+                return await create_response(
                     generator=selected_data_generator,
                     media_type="text/event-stream",
                     headers=custom_headers,
@@ -658,7 +765,7 @@ class ProxyBaseLLMRequestProcessing:
                     user_api_key_dict=user_api_key_dict,
                     request_data=self.data,
                 )
-                return await create_streaming_response(
+                return await create_response(
                     generator=selected_data_generator,
                     media_type="text/event-stream",
                     headers=custom_headers,
@@ -900,11 +1007,11 @@ class ProxyBaseLLMRequestProcessing:
     @staticmethod
     def _get_pre_call_type(
         route_type: Literal["acompletion", "aembedding", "aresponses", "allm_passthrough_route"],
-    ) -> Literal["completion", "embeddings", "responses", "allm_passthrough_route"]:
+    ) -> Literal["completion", "embedding", "responses", "allm_passthrough_route"]:
         if route_type == "acompletion":
             return "completion"
         elif route_type == "aembedding":
-            return "embeddings"
+            return "embedding"
         elif route_type == "aresponses":
             return "responses"
         elif route_type == "allm_passthrough_route":
@@ -1155,9 +1262,9 @@ class ProxyBaseLLMRequestProcessing:
 
             # Add cache-related fields to **params (handled by Usage.__init__)
             if cache_creation_input_tokens is not None:
-                usage_kwargs["cache_creation_input_tokens"] = (
-                    cache_creation_input_tokens
-                )
+                usage_kwargs[
+                    "cache_creation_input_tokens"
+                ] = cache_creation_input_tokens
             if cache_read_input_tokens is not None:
                 usage_kwargs["cache_read_input_tokens"] = cache_read_input_tokens
 
diff --git a/litellm/proxy/db/prisma_client.py b/litellm/proxy/db/prisma_client.py
index 406ddceabf5..c9c0cfe8f68 100644
--- a/litellm/proxy/db/prisma_client.py
+++ b/litellm/proxy/db/prisma_client.py
@@ -17,50 +17,141 @@ from litellm.secret_managers.main import str_to_bool
 
 
 class PrismaWrapper:
+    """
+    Wrapper around Prisma client that handles RDS IAM token authentication.
+
+    When iam_token_db_auth is enabled, this wrapper:
+    1. Proactively refreshes IAM tokens before they expire (background task)
+    2. Falls back to synchronous refresh if a token is found expired
+    3. Uses proper locking to prevent race conditions during reconnection
+
+    RDS IAM tokens are valid for 15 minutes. This wrapper refreshes them
+    3 minutes before expiration to ensure uninterrupted database connectivity.
+    """
+
+    # Buffer time in seconds before token expiration to trigger refresh
+    # Refresh 3 minutes (180 seconds) before the token expires
+    TOKEN_REFRESH_BUFFER_SECONDS = 180
+
+    # Fallback refresh interval if token parsing fails (10 minutes)
+    FALLBACK_REFRESH_INTERVAL_SECONDS = 600
+
     def __init__(self, original_prisma: Any, iam_token_db_auth: bool):
         self._original_prisma = original_prisma
         self.iam_token_db_auth = iam_token_db_auth
 
+        # Background token refresh task management
+        self._token_refresh_task: Optional[asyncio.Task] = None
+        self._reconnection_lock = asyncio.Lock()
+        self._last_refresh_time: Optional[datetime] = None
+
+    def _extract_token_from_db_url(self, db_url: Optional[str]) -> Optional[str]:
+        """
+        Extract the token (password) from the DATABASE_URL.
+
+        The token contains the AWS signature with X-Amz-Date and X-Amz-Expires parameters.
+
+        Important: We must parse the URL while it's still encoded to preserve structure,
+        then decode the password portion. Otherwise the '?' in the token breaks URL parsing.
+        """
+        if db_url is None:
+            return None
+        try:
+            # Parse URL while still encoded to preserve structure
+            parsed = urllib.parse.urlparse(db_url)
+            if parsed.password:
+                # Now decode just the password/token
+                return urllib.parse.unquote(parsed.password)
+            return None
+        except Exception:
+            return None
+
+    def _parse_token_expiration(self, token: Optional[str]) -> Optional[datetime]:
+        """
+        Parse the token to extract its expiration time.
+
+        Returns the datetime when the token expires, or None if parsing fails.
+        """
+        if token is None:
+            return None
+
+        try:
+            # Token format: ...?X-Amz-Date=YYYYMMDDTHHMMSSZ&X-Amz-Expires=900&...
+            if "?" not in token:
+                return None
+
+            query_string = token.split("?", 1)[1]
+            params = urllib.parse.parse_qs(query_string)
+
+            expires_str = params.get("X-Amz-Expires", [None])[0]
+            date_str = params.get("X-Amz-Date", [None])[0]
+
+            if not expires_str or not date_str:
+                return None
+
+            token_created = datetime.strptime(date_str, "%Y%m%dT%H%M%SZ")
+            expires_in = int(expires_str)
+
+            return token_created + timedelta(seconds=expires_in)
+        except Exception as e:
+            verbose_proxy_logger.debug(f"Failed to parse token expiration: {e}")
+            return None
+
+    def _calculate_seconds_until_refresh(self) -> float:
+        """
+        Calculate exactly how many seconds until we need to refresh the token.
+
+        Uses precise timing: sleeps until (token_expiration - buffer_seconds).
+        For a 15-minute (900s) token with 180s buffer, this returns ~720s (12 min).
+
+        Returns:
+            Number of seconds to sleep before the next refresh.
+            Returns 0 if token should be refreshed immediately.
+            Returns FALLBACK_REFRESH_INTERVAL_SECONDS if parsing fails.
+        """
+        db_url = os.getenv("DATABASE_URL")
+        token = self._extract_token_from_db_url(db_url)
+        expiration_time = self._parse_token_expiration(token)
+
+        if expiration_time is None:
+            # If we can't parse the token, use fallback interval
+            verbose_proxy_logger.debug(
+                f"Could not parse token expiration, using fallback interval of "
+                f"{self.FALLBACK_REFRESH_INTERVAL_SECONDS}s"
+            )
+            return self.FALLBACK_REFRESH_INTERVAL_SECONDS
+
+        # Calculate when we should refresh (expiration - buffer)
+        refresh_at = expiration_time - timedelta(
+            seconds=self.TOKEN_REFRESH_BUFFER_SECONDS
+        )
+
+        # How long until refresh time?
+        now = datetime.utcnow()
+        seconds_until_refresh = (refresh_at - now).total_seconds()
+
+        # If already past refresh time, return 0 (refresh immediately)
+        return max(0, seconds_until_refresh)
+
     def is_token_expired(self, token_url: Optional[str]) -> bool:
+        """Check if the token in the given URL is expired."""
         if token_url is None:
             return True
-        # Decode the token URL to handle URL-encoded characters
-        decoded_url = urllib.parse.unquote(token_url)
 
-        # Parse the token URL
-        parsed_url = urllib.parse.urlparse(decoded_url)
+        token = self._extract_token_from_db_url(token_url)
+        expiration_time = self._parse_token_expiration(token)
 
-        # Parse the query parameters from the path component (if they exist there)
-        query_params = urllib.parse.parse_qs(parsed_url.query)
+        if expiration_time is None:
+            # If we can't parse the token, assume it's expired to trigger refresh
+            verbose_proxy_logger.debug(
+                "Could not parse token expiration, treating as expired"
+            )
+            return True
 
-        # Get expiration time from the query parameters
-        expires = query_params.get("X-Amz-Expires", [None])[0]
-        if expires is None:
-            raise ValueError("X-Amz-Expires parameter is missing or invalid.")
-
-        expires_int = int(expires)
-
-        # Get the token's creation time from the X-Amz-Date parameter
-        token_time_str = query_params.get("X-Amz-Date", [""])[0]
-        if not token_time_str:
-            raise ValueError("X-Amz-Date parameter is missing or invalid.")
-
-        # Ensure the token time string is parsed correctly
-        try:
-            token_time = datetime.strptime(token_time_str, "%Y%m%dT%H%M%SZ")
-        except ValueError as e:
-            raise ValueError(f"Invalid X-Amz-Date format: {e}")
-
-        # Calculate the expiration time
-        expiration_time = token_time + timedelta(seconds=expires_int)
-
-        # Current time in UTC
-        current_time = datetime.utcnow()
-
-        # Check if the token is expired
-        return current_time > expiration_time
+        return datetime.utcnow() > expiration_time
 
     def get_rds_iam_token(self) -> Optional[str]:
+        """Generate a new RDS IAM token and update DATABASE_URL."""
         if self.iam_token_db_auth:
             from litellm.proxy.auth.rds_iam_token import generate_iam_auth_token
 
@@ -74,7 +165,6 @@ class PrismaWrapper:
                 db_host=db_host, db_port=db_port, db_user=db_user
             )
 
-            # print(f"token: {token}")
             _db_url = f"postgresql://{db_user}:{token}@{db_host}:{db_port}/{db_name}"
             if db_schema:
                 _db_url += f"?schema={db_schema}"
@@ -86,6 +176,7 @@ class PrismaWrapper:
     async def recreate_prisma_client(
         self, new_db_url: str, http_client: Optional[Any] = None
     ):
+        """Disconnect and reconnect the Prisma client with a new database URL."""
         from prisma import Prisma  # type: ignore
 
         try:
@@ -100,21 +191,159 @@ class PrismaWrapper:
 
         await self._original_prisma.connect()
 
+    async def start_token_refresh_task(self) -> None:
+        """
+        Start the background token refresh task.
+
+        This task proactively refreshes RDS IAM tokens before they expire,
+        preventing connection failures. Should be called after the initial
+        Prisma client connection is established.
+        """
+        if not self.iam_token_db_auth:
+            verbose_proxy_logger.debug(
+                "IAM token auth not enabled, skipping token refresh task"
+            )
+            return
+
+        if self._token_refresh_task is not None:
+            verbose_proxy_logger.debug("Token refresh task already running")
+            return
+
+        self._token_refresh_task = asyncio.create_task(self._token_refresh_loop())
+        verbose_proxy_logger.info(
+            "Started RDS IAM token proactive refresh background task"
+        )
+
+    async def stop_token_refresh_task(self) -> None:
+        """
+        Stop the background token refresh task gracefully.
+
+        Should be called during application shutdown to clean up resources.
+        """
+        if self._token_refresh_task is None:
+            return
+
+        self._token_refresh_task.cancel()
+        try:
+            await self._token_refresh_task
+        except asyncio.CancelledError:
+            pass
+        self._token_refresh_task = None
+        verbose_proxy_logger.info("Stopped RDS IAM token refresh background task")
+
+    async def _token_refresh_loop(self) -> None:
+        """
+        Background loop that proactively refreshes RDS IAM tokens before expiration.
+
+        Uses precise timing: calculates the exact sleep duration until the token
+        needs to be refreshed (expiration - 3 minute buffer), then refreshes.
+        This is more efficient than polling, requiring only 1 wake-up per token cycle.
+        """
+        verbose_proxy_logger.info(
+            f"RDS IAM token refresh loop started. "
+            f"Tokens will be refreshed {self.TOKEN_REFRESH_BUFFER_SECONDS}s before expiration."
+        )
+
+        while True:
+            try:
+                # Calculate exactly how long to sleep until next refresh
+                sleep_seconds = self._calculate_seconds_until_refresh()
+
+                if sleep_seconds > 0:
+                    verbose_proxy_logger.info(
+                        f"RDS IAM token refresh scheduled in {sleep_seconds:.0f} seconds "
+                        f"({sleep_seconds / 60:.1f} minutes)"
+                    )
+                    await asyncio.sleep(sleep_seconds)
+
+                # Refresh the token
+                verbose_proxy_logger.info("Proactively refreshing RDS IAM token...")
+                await self._safe_refresh_token()
+
+            except asyncio.CancelledError:
+                verbose_proxy_logger.info("RDS IAM token refresh loop cancelled")
+                break
+            except Exception as e:
+                verbose_proxy_logger.error(
+                    f"Error in RDS IAM token refresh loop: {e}. "
+                    f"Retrying in {self.FALLBACK_REFRESH_INTERVAL_SECONDS}s..."
+                )
+                # On error, wait before retrying to avoid tight error loops
+                try:
+                    await asyncio.sleep(self.FALLBACK_REFRESH_INTERVAL_SECONDS)
+                except asyncio.CancelledError:
+                    break
+
+    async def _safe_refresh_token(self) -> None:
+        """
+        Refresh the RDS IAM token with proper locking to prevent race conditions.
+
+        Uses an asyncio lock to ensure only one refresh operation happens at a time,
+        preventing multiple concurrent reconnection attempts.
+        """
+        async with self._reconnection_lock:
+            new_db_url = self.get_rds_iam_token()
+            if new_db_url:
+                await self.recreate_prisma_client(new_db_url)
+                self._last_refresh_time = datetime.utcnow()
+                verbose_proxy_logger.info(
+                    "RDS IAM token refreshed successfully. New token valid for ~15 minutes."
+                )
+            else:
+                verbose_proxy_logger.error(
+                    "Failed to generate new RDS IAM token during proactive refresh"
+                )
+
     def __getattr__(self, name: str):
+        """
+        Proxy attribute access to the underlying Prisma client.
+
+        If IAM token auth is enabled and the token is expired, this method
+        provides a synchronous fallback to refresh the token. However, this
+        should rarely be needed since the background task proactively refreshes
+        tokens before they expire.
+
+        FIXED: Now properly waits for reconnection to complete before returning,
+        instead of the previous fire-and-forget pattern that caused the bug.
+        """
         original_attr = getattr(self._original_prisma, name)
+
         if self.iam_token_db_auth:
             db_url = os.getenv("DATABASE_URL")
-            if self.is_token_expired(db_url):
-                db_url = self.get_rds_iam_token()
-                loop = asyncio.get_event_loop()
 
-                if db_url:
+            # Check if token is expired (should be rare if background task is running)
+            if self.is_token_expired(db_url):
+                verbose_proxy_logger.warning(
+                    "RDS IAM token expired in __getattr__ - proactive refresh may have failed. "
+                    "Triggering synchronous fallback refresh..."
+                )
+
+                new_db_url = self.get_rds_iam_token()
+                if new_db_url:
+                    loop = asyncio.get_event_loop()
+
                     if loop.is_running():
-                        asyncio.run_coroutine_threadsafe(
-                            self.recreate_prisma_client(db_url), loop
+                        # FIXED: Actually wait for the reconnection to complete!
+                        # The previous code used fire-and-forget which caused the bug.
+                        future = asyncio.run_coroutine_threadsafe(
+                            self.recreate_prisma_client(new_db_url), loop
                         )
+                        try:
+                            # Wait up to 30 seconds for reconnection
+                            future.result(timeout=30)
+                            verbose_proxy_logger.info(
+                                "Synchronous token refresh completed successfully"
+                            )
+                        except Exception as e:
+                            verbose_proxy_logger.error(
+                                f"Failed to refresh token synchronously: {e}"
+                            )
+                            raise
                     else:
-                        asyncio.run(self.recreate_prisma_client(db_url))
+                        asyncio.run(self.recreate_prisma_client(new_db_url))
+
+                    # Get the NEW attribute after reconnection
+                    original_attr = getattr(self._original_prisma, name)
                 else:
                     raise ValueError("Failed to get RDS IAM token")
 
diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py
index 569634ee140..bba5b87024b 100644
--- a/litellm/proxy/google_endpoints/endpoints.py
+++ b/litellm/proxy/google_endpoints/endpoints.py
@@ -37,6 +37,11 @@ async def google_generate_content(
     if "model" not in data:
         data["model"] = model_name
     
+    # Extract generationConfig and pass it as config parameter
+    generation_config = data.pop("generationConfig", None)
+    if generation_config:
+        data["config"] = generation_config
+    
     # Add user authentication metadata for cost tracking
     data = await add_litellm_data_to_request(
         data=data,
@@ -83,6 +88,11 @@ async def google_stream_generate_content(
 
     data["stream"] = True  # enforce streaming for this endpoint
 
+    # Extract generationConfig and pass it as config parameter
+    generation_config = data.pop("generationConfig", None)
+    if generation_config:
+        data["config"] = generation_config
+
     # Add user authentication metadata for cost tracking
     data = await add_litellm_data_to_request(
         data=data,
diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py
index 0dd00bfe55d..41e0a23b9aa 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py
@@ -223,9 +223,10 @@ class GenericGuardrailAPI(CustomGuardrail):
 
         try:
             # Make the API request
+            # Use mode="json" to ensure all iterables are converted to lists
             response = await self.async_handler.post(
                 url=self.api_base,
-                json=guardrail_request.model_dump(),
+                json=guardrail_request.model_dump(mode="json"),
                 headers=headers,
             )
 
diff --git a/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py b/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py
index aad2a69ca52..852cf01bc09 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/openai/moderations.py
@@ -178,6 +178,33 @@ class OpenAIModerationGuardrail(OpenAIGuardrailBase, CustomGuardrail):
                 },
             )
 
+    def _extract_user_content_from_data(self, data: Dict[str, Any]) -> Optional[str]:
+        """
+        Extract user content from request data, supporting both Chat Completions and Responses API.
+        
+        For Chat Completions: extracts from 'messages' field
+        For Responses API: extracts from 'input' field
+        
+        Returns:
+            The extracted user content string, or None if no content found
+        """
+        # Try to get messages first (Chat Completions API)
+        messages: Optional[List["AllMessageValues"]] = data.get("messages")
+        if messages is not None:
+            return self.get_user_prompt(messages)
+        
+        # Try to get input (Responses API)
+        input_data = data.get("input")
+        if input_data is not None:
+            # input can be a string or a list of message-like objects
+            if isinstance(input_data, str):
+                return input_data
+            elif isinstance(input_data, list):
+                # Treat input as messages and extract user content
+                return self.get_user_prompt(input_data)
+        
+        return None
+
     @log_guardrail_information
     async def async_pre_call_hook(
         self,
@@ -210,14 +237,14 @@ class OpenAIModerationGuardrail(OpenAIGuardrailBase, CustomGuardrail):
         if call_type == "moderation":
             return data
 
-        new_messages: Optional[List["AllMessageValues"]] = data.get("messages")
-        if new_messages is None:
+        user_prompt = self._extract_user_content_from_data(data)
+        
+        if user_prompt is None:
             verbose_proxy_logger.warning(
-                "OpenAI Moderation: not running guardrail. No messages in data"
+                "OpenAI Moderation: not running guardrail. No messages or input in data"
             )
             return data
 
-        user_prompt = self.get_user_prompt(new_messages)
         if user_prompt:
             verbose_proxy_logger.debug(
                 f"OpenAI Moderation: User prompt: {user_prompt[:100]}..."  # Log first 100 chars for debugging
@@ -265,14 +292,15 @@ class OpenAIModerationGuardrail(OpenAIGuardrailBase, CustomGuardrail):
         if call_type == "moderation":
             return data
 
-        new_messages: Optional[List["AllMessageValues"]] = data.get("messages")
-        if new_messages is None:
+        # Extract user content from either messages or input field
+        user_prompt = self._extract_user_content_from_data(data)
+        
+        if user_prompt is None:
             verbose_proxy_logger.warning(
-                "OpenAI Moderation: not running guardrail. No messages in data"
+                "OpenAI Moderation: not running guardrail. No messages or input in data"
             )
             return data
 
-        user_prompt = self.get_user_prompt(new_messages)
         if user_prompt:
             moderation_response = await self.async_make_request(
                 input_text=user_prompt,
diff --git a/litellm/proxy/guardrails/guardrail_hooks/pangea/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/pangea/__init__.py
index 181633065e4..210d9ac7879 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/pangea/__init__.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/pangea/__init__.py
@@ -21,6 +21,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"
         pangea_output_recipe=litellm_params.pangea_output_recipe,
         api_base=litellm_params.api_base,
         api_key=litellm_params.api_key,
+        event_hook=litellm_params.mode,
         default_on=litellm_params.default_on,
     )
     litellm.logging_callback_manager.add_litellm_callback(_pangea_callback)
diff --git a/litellm/proxy/guardrails/guardrail_hooks/pangea/pangea.py b/litellm/proxy/guardrails/guardrail_hooks/pangea/pangea.py
index 619323e9073..ca3e6a90801 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/pangea/pangea.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/pangea/pangea.py
@@ -95,8 +95,17 @@ class PangeaHandler(CustomGuardrail):
         self.pangea_input_recipe = pangea_input_recipe
         self.pangea_output_recipe = pangea_output_recipe
 
+        supported_event_hooks = [
+            GuardrailEventHooks.pre_call,
+            GuardrailEventHooks.post_call,
+        ]
+
         # Pass relevant kwargs to the parent class
-        super().__init__(guardrail_name=guardrail_name, **kwargs)
+        super().__init__(
+            guardrail_name=guardrail_name,
+            supported_event_hooks=supported_event_hooks,
+            **kwargs
+        )
         verbose_proxy_logger.debug(
             f"Initialized Pangea Guardrail: name={guardrail_name}, recipe={pangea_input_recipe}, api_base={self.api_base}"
         )
diff --git a/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py b/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py
index a6971b49f3b..87da11efad0 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/qualifire/qualifire.py
@@ -5,6 +5,7 @@
 # +-------------------------------------------------------------+
 #  Qualifire - Evaluate LLM outputs for quality, safety, and reliability
 
+import json
 import os
 from typing import Any, Dict, List, Literal, Optional, Type
 
@@ -15,12 +16,17 @@ from litellm.integrations.custom_guardrail import CustomGuardrail
 from litellm.litellm_core_utils.litellm_logging import (
     Logging as LiteLLMLoggingObj,
 )
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel
 from litellm.types.utils import GenericGuardrailAPIInputs
 
 GUARDRAIL_NAME = "qualifire"
+DEFAULT_QUALIFIRE_API_BASE = "https://proxy.qualifire.ai"
 
 
 class QualifireGuardrail(CustomGuardrail):
@@ -44,7 +50,7 @@ class QualifireGuardrail(CustomGuardrail):
 
         Args:
             api_key: API key for Qualifire (or use QUALIFIRE_API_KEY env var)
-            api_base: Optional custom API base URL
+            api_base: Optional custom API base URL (defaults to https://api.qualifire.ai)
             evaluation_id: Pre-configured evaluation ID from Qualifire dashboard
             prompt_injections: Enable prompt injection detection (default if no other checks)
             hallucinations_check: Enable hallucination detection
@@ -64,6 +70,7 @@ class QualifireGuardrail(CustomGuardrail):
             api_base
             or get_secret_str("QUALIFIRE_BASE_URL")
             or os.environ.get("QUALIFIRE_BASE_URL")
+            or DEFAULT_QUALIFIRE_API_BASE
         )
         self.evaluation_id = evaluation_id
         self.prompt_injections = prompt_injections
@@ -79,7 +86,11 @@ class QualifireGuardrail(CustomGuardrail):
         if not self._has_any_check_enabled() and not self.evaluation_id:
             self.prompt_injections = True
 
-        self._client = None
+        # Initialize async HTTP client for direct API calls
+        self.async_handler = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.GuardrailCallback
+        )
+
         super().__init__(**kwargs)
 
     def _has_any_check_enabled(self) -> bool:
@@ -96,43 +107,22 @@ class QualifireGuardrail(CustomGuardrail):
             ]
         )
 
-    def _get_client(self):
-        """Lazy initialization of Qualifire client."""
-        if self._client is None:
-            try:
-                from qualifire.client import Client
-            except ImportError:
-                raise ImportError(
-                    "qualifire package is required for QualifireGuardrail. "
-                    "Install it with: pip install qualifire"
-                )
-
-            client_kwargs: Dict[str, Any] = {}
-            if self.qualifire_api_key:
-                client_kwargs["api_key"] = self.qualifire_api_key
-            if self.qualifire_api_base:
-                client_kwargs["base_url"] = self.qualifire_api_base
-
-            self._client = Client(**client_kwargs)
-
-        return self._client
-
-    def _convert_messages_to_qualifire_format(
+    def _convert_messages_to_api_format(
         self, messages: List[AllMessageValues]
-    ) -> List[Any]:
+    ) -> List[Dict[str, Any]]:
         """
-        Convert LiteLLM messages to Qualifire's LLMMessage format.
+        Convert LiteLLM messages to Qualifire API format.
         Supports tool calls for tool_selection_quality_check.
-        """
-        try:
-            from qualifire.types import LLMMessage, LLMToolCall
-        except ImportError:
-            raise ImportError(
-                "qualifire package is required for QualifireGuardrail. "
-                "Install it with: pip install qualifire"
-            )
 
-        qualifire_messages = []
+        Returns a list of dicts matching the API's ModelInvocationCanonicalMessage schema:
+        {
+            "role": "user" | "assistant" | "system" | "tool",
+            "content": "...",
+            "tool_call_id": "...",  # optional
+            "tool_calls": [{"id": "...", "name": "...", "arguments": {...}}]  # optional
+        }
+        """
+        api_messages = []
         for msg in messages:
             role = msg.get("role", "user")
             content = msg.get("content", "")
@@ -147,42 +137,86 @@ class QualifireGuardrail(CustomGuardrail):
                         text_parts.append(part)
                 content = "\n".join(text_parts)
 
-            llm_message_kwargs: Dict[str, Any] = {
+            api_message: Dict[str, Any] = {
                 "role": role,
                 "content": content if isinstance(content, str) else str(content),
             }
 
+            # Handle tool_call_id for tool response messages
+            tool_call_id = msg.get("tool_call_id")
+            if tool_call_id:
+                api_message["tool_call_id"] = tool_call_id
+
             # Handle tool calls if present
             tool_calls = msg.get("tool_calls")
             if tool_calls and isinstance(tool_calls, list):
-                qualifire_tool_calls = []
+                api_tool_calls = []
                 for tc in tool_calls:
                     if isinstance(tc, dict):
                         function_info = tc.get("function", {})
                         # Arguments can be a string (JSON) or dict
                         args = function_info.get("arguments", {})
                         if isinstance(args, str):
-                            import json
-
                             try:
                                 args = json.loads(args)
                             except json.JSONDecodeError:
                                 args = {}
-                        qualifire_tool_calls.append(
-                            LLMToolCall(
-                                id=tc.get("id") or "",
-                                name=function_info.get("name") or "",
-                                arguments=args if isinstance(args, dict) else {},
-                            )
+                        api_tool_calls.append(
+                            {
+                                "id": tc.get("id") or "",
+                                "name": function_info.get("name") or "",
+                                "arguments": args if isinstance(args, dict) else {},
+                            }
                         )
-                if qualifire_tool_calls:
-                    llm_message_kwargs["tool_calls"] = qualifire_tool_calls
+                if api_tool_calls:
+                    api_message["tool_calls"] = api_tool_calls
 
-            qualifire_messages.append(LLMMessage(**llm_message_kwargs))
+            api_messages.append(api_message)
 
-        return qualifire_messages
+        return api_messages
 
-    def _check_if_flagged(self, result: Any) -> bool:
+    def _convert_tools_to_api_format(
+        self, tools: Optional[List[Any]]
+    ) -> Optional[List[Dict[str, Any]]]:
+        """
+        Convert OpenAI-format tools to Qualifire API format.
+
+        Returns a list of dicts matching the API's ModelInvocationToolDefinition schema:
+        {
+            "name": "...",
+            "description": "...",
+            "parameters": {...}
+        }
+        """
+        if not tools:
+            return None
+
+        api_tools = []
+        for tool in tools:
+            if isinstance(tool, dict):
+                # Handle OpenAI function tool format
+                if tool.get("type") == "function":
+                    function_def = tool.get("function", {})
+                    api_tools.append(
+                        {
+                            "name": function_def.get("name", ""),
+                            "description": function_def.get("description", ""),
+                            "parameters": function_def.get("parameters", {}),
+                        }
+                    )
+                # Handle direct tool format
+                elif "name" in tool:
+                    api_tools.append(
+                        {
+                            "name": tool.get("name", ""),
+                            "description": tool.get("description", ""),
+                            "parameters": tool.get("parameters", {}),
+                        }
+                    )
+
+        return api_tools if api_tools else None
+
+    def _check_if_flagged(self, result: Dict[str, Any]) -> bool:
         """
         Check if the Qualifire evaluation result indicates flagged content.
 
@@ -190,65 +224,53 @@ class QualifireGuardrail(CustomGuardrail):
         A high score (close to 100) indicates GOOD content, low score indicates problems.
         """
         # Check evaluation results for any flagged items
-        evaluation_results = getattr(result, "evaluationResults", None) or []
-        if isinstance(result, dict):
-            evaluation_results = result.get("evaluationResults", []) or []
+        evaluation_results = result.get("evaluationResults", []) or []
 
         for eval_result in evaluation_results:
-            results: List[Any] = []
-            if isinstance(eval_result, dict):
-                results = eval_result.get("results", []) or []
-            else:
-                results = getattr(eval_result, "results", []) or []
-
+            results = eval_result.get("results", []) or []
             for r in results:
-                flagged = (
-                    r.get("flagged")
-                    if isinstance(r, dict)
-                    else getattr(r, "flagged", False)
-                )
-                if flagged:
+                if r.get("flagged"):
                     return True
 
         return False
 
-    def _build_evaluate_kwargs(
+    def _build_evaluate_payload(
         self,
-        qualifire_messages: List[Any],
+        api_messages: List[Dict[str, Any]],
         output: Optional[str],
         assertions: Optional[List[str]],
-        available_tools: Optional[List[Any]],
+        available_tools: Optional[List[Dict[str, Any]]],
     ) -> Dict[str, Any]:
-        """Build kwargs dictionary for the evaluate call."""
-        kwargs: Dict[str, Any] = {"messages": qualifire_messages}
+        """Build payload dictionary for the /api/evaluation/evaluate endpoint."""
+        payload: Dict[str, Any] = {"messages": api_messages}
 
         if output is not None:
-            kwargs["output"] = output
+            payload["output"] = output
 
         # Add enabled checks
         if self.prompt_injections:
-            kwargs["prompt_injections"] = True
+            payload["prompt_injections"] = True
         if self.hallucinations_check:
-            kwargs["hallucinations_check"] = True
+            payload["hallucinations_check"] = True
         if self.grounding_check:
-            kwargs["grounding_check"] = True
+            payload["grounding_check"] = True
         if self.pii_check:
-            kwargs["pii_check"] = True
+            payload["pii_check"] = True
         if self.content_moderation_check:
-            kwargs["content_moderation_check"] = True
+            payload["content_moderation_check"] = True
         if self.tool_selection_quality_check:
             # Only enable tool_selection_quality_check if available_tools is provided
             if available_tools:
-                kwargs["tool_selection_quality_check"] = True
-                kwargs["available_tools"] = available_tools
+                payload["tool_selection_quality_check"] = True
+                payload["available_tools"] = available_tools
             else:
                 verbose_proxy_logger.debug(
                     "Qualifire Guardrail: tool_selection_quality_check enabled but no available_tools provided, skipping this check"
                 )
         if assertions:
-            kwargs["assertions"] = assertions
+            payload["assertions"] = assertions
 
-        return kwargs
+        return payload
 
     async def _run_qualifire_check(
         self,
@@ -274,11 +296,17 @@ class QualifireGuardrail(CustomGuardrail):
         assertions = dynamic_params.get("assertions") or self.assertions
         on_flagged = dynamic_params.get("on_flagged") or self.on_flagged
 
-        try:
-            client = self._get_client()
-            qualifire_messages = self._convert_messages_to_qualifire_format(messages)
+        # Prepare headers
+        headers = {
+            "X-Qualifire-API-Key": self.qualifire_api_key or "",
+            "Content-Type": "application/json",
+        }
 
-            # Use invoke_evaluation if evaluation_id is provided
+        try:
+            # Convert messages to API format
+            api_messages = self._convert_messages_to_api_format(messages)
+
+            # Use invoke endpoint if evaluation_id is provided
             if evaluation_id:
                 # For invoke_evaluation, we need to extract input/output
                 input_text = ""
@@ -291,25 +319,47 @@ class QualifireGuardrail(CustomGuardrail):
                             input_text = content
                         break
 
-                result = client.invoke_evaluation(
-                    evaluation_id=evaluation_id,
-                    input=input_text,
-                    output=output or "",
-                )
+                payload = {
+                    "evaluation_id": evaluation_id,
+                    "input": input_text,
+                    "output": output or "",
+                    "messages": api_messages,
+                }
+
+                # Convert tools if provided
+                api_tools = self._convert_tools_to_api_format(available_tools)
+                if api_tools:
+                    payload["available_tools"] = api_tools
+
+                url = f"{self.qualifire_api_base}/api/evaluation/invoke"
             else:
-                # Use evaluate with individual checks
-                kwargs = self._build_evaluate_kwargs(
-                    qualifire_messages=qualifire_messages,
+                # Use evaluate endpoint with individual checks
+                api_tools = self._convert_tools_to_api_format(available_tools)
+                payload = self._build_evaluate_payload(
+                    api_messages=api_messages,
                     output=output,
                     assertions=assertions,
-                    available_tools=available_tools,
+                    available_tools=api_tools,
                 )
-                result = client.evaluate(**kwargs)
+                url = f"{self.qualifire_api_base}/api/evaluation/evaluate"
 
-            # Convert result to dict for logging
+            verbose_proxy_logger.debug(
+                f"Qualifire Guardrail: Making request to {url}"
+            )
+
+            # Make the API request
+            response = await self.async_handler.post(
+                url=url,
+                headers=headers,
+                json=payload,
+            )
+            response.raise_for_status()
+            result = response.json()
+
+            # Extract response info for logging
             qualifire_response = {
-                "score": getattr(result, "score", None),
-                "status": getattr(result, "status", None),
+                "score": result.get("score"),
+                "status": result.get("status"),
             }
 
             verbose_proxy_logger.debug(
diff --git a/litellm/proxy/hooks/litellm_skills/main.py b/litellm/proxy/hooks/litellm_skills/main.py
index 26d4cbe1de7..c2ad1e29447 100644
--- a/litellm/proxy/hooks/litellm_skills/main.py
+++ b/litellm/proxy/hooks/litellm_skills/main.py
@@ -336,8 +336,8 @@ class SkillsInjectionHook(CustomLogger):
         )
 
         # Check if code execution is enabled for this request
-        litellm_metadata = request_data.get("litellm_metadata", {})
-        metadata = request_data.get("metadata", {})
+        litellm_metadata = request_data.get("litellm_metadata") or {}
+        metadata = request_data.get("metadata") or {}
         
         code_exec_enabled = (
             litellm_metadata.get("_litellm_code_execution_enabled") or
diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py
index b47343427f7..222798a7546 100644
--- a/litellm/proxy/hooks/parallel_request_limiter_v3.py
+++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py
@@ -1704,25 +1704,55 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
     def _get_total_tokens_from_usage(
         self, usage: Any | None, rate_limit_type: Literal["output", "input", "total"]
     ) -> int:
-        # Get total tokens from response
+        """
+        Get total tokens from response usage for rate limiting.
+
+        For 'input' and 'total' rate limit types, cached tokens are excluded
+        because providers like AWS Bedrock don't count cached tokens toward
+        rate limits. This aligns LiteLLM's TPM calculation with provider behavior.
+        """
         total_tokens = 0
-        # spot fix for /responses api
+        cached_tokens = 0
+
         if usage:
             if isinstance(usage, Usage):
                 if rate_limit_type == "output":
-                    total_tokens = usage.completion_tokens
+                    total_tokens = usage.completion_tokens or 0
                 elif rate_limit_type == "input":
-                    total_tokens = usage.prompt_tokens
+                    total_tokens = usage.prompt_tokens or 0
                 elif rate_limit_type == "total":
-                    total_tokens = usage.total_tokens
+                    total_tokens = usage.total_tokens or 0
+
+                # Get cached tokens to exclude from input/total
+                if rate_limit_type in ("input", "total"):
+                    if (
+                        hasattr(usage, "prompt_tokens_details")
+                        and usage.prompt_tokens_details is not None
+                    ):
+                        cached_tokens = (
+                            getattr(usage.prompt_tokens_details, "cached_tokens", 0)
+                            or 0
+                        )
+
             elif isinstance(usage, dict):
-                # Responses API usage comes as a dict in ResponsesAPIResponse
+                # Responses API usage comes as a dict
                 if rate_limit_type == "output":
-                    total_tokens = usage.get("completion_tokens", 0)
+                    total_tokens = usage.get("completion_tokens", 0) or 0
                 elif rate_limit_type == "input":
-                    total_tokens = usage.get("prompt_tokens", 0)
+                    total_tokens = usage.get("prompt_tokens", 0) or 0
                 elif rate_limit_type == "total":
-                    total_tokens = usage.get("total_tokens", 0)
+                    total_tokens = usage.get("total_tokens", 0) or 0
+
+                # Get cached tokens from dict
+                if rate_limit_type in ("input", "total"):
+                    prompt_details = usage.get("prompt_tokens_details") or {}
+                    if isinstance(prompt_details, dict):
+                        cached_tokens = prompt_details.get("cached_tokens", 0) or 0
+
+        # Subtract cached tokens for input/total (providers don't count them)
+        if cached_tokens > 0:
+            total_tokens = max(0, total_tokens - cached_tokens)
+
         return total_tokens
 
     async def _execute_token_increment_script(
diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py
index 5b5723efc3d..3f844f21eb0 100644
--- a/litellm/proxy/litellm_pre_call_utils.py
+++ b/litellm/proxy/litellm_pre_call_utils.py
@@ -161,7 +161,6 @@ class KeyAndTeamLoggingSettings:
 
     @staticmethod
     def get_team_dynamic_logging_settings(user_api_key_dict: UserAPIKeyAuth):
-
         if (
             user_api_key_dict.team_metadata is not None
             and "logging" in user_api_key_dict.team_metadata
@@ -174,12 +173,12 @@ def _get_dynamic_logging_metadata(
     user_api_key_dict: UserAPIKeyAuth, proxy_config: ProxyConfig
 ) -> Optional[TeamCallbackMetadata]:
     callback_settings_obj: Optional[TeamCallbackMetadata] = None
-    key_dynamic_logging_settings: Optional[dict] = (
-        KeyAndTeamLoggingSettings.get_key_dynamic_logging_settings(user_api_key_dict)
-    )
-    team_dynamic_logging_settings: Optional[dict] = (
-        KeyAndTeamLoggingSettings.get_team_dynamic_logging_settings(user_api_key_dict)
-    )
+    key_dynamic_logging_settings: Optional[
+        dict
+    ] = KeyAndTeamLoggingSettings.get_key_dynamic_logging_settings(user_api_key_dict)
+    team_dynamic_logging_settings: Optional[
+        dict
+    ] = KeyAndTeamLoggingSettings.get_team_dynamic_logging_settings(user_api_key_dict)
     #########################################################################################
     # Key-based callbacks
     #########################################################################################
@@ -462,7 +461,6 @@ class LiteLLMProxyRequestSetup:
                 team_id=user_api_key_dict.team_id,
             )  # handles aliases, wildcards, etc.
         ):
-
             _headers = LiteLLMProxyRequestSetup.add_headers_to_llm_call(
                 headers, user_api_key_dict
             )
@@ -663,11 +661,11 @@ class LiteLLMProxyRequestSetup:
 
         ## KEY-LEVEL SPEND LOGS / TAGS
         if "tags" in key_metadata and key_metadata["tags"] is not None:
-            data[_metadata_variable_name]["tags"] = (
-                LiteLLMProxyRequestSetup._merge_tags(
-                    request_tags=data[_metadata_variable_name].get("tags"),
-                    tags_to_add=key_metadata["tags"],
-                )
+            data[_metadata_variable_name][
+                "tags"
+            ] = LiteLLMProxyRequestSetup._merge_tags(
+                request_tags=data[_metadata_variable_name].get("tags"),
+                tags_to_add=key_metadata["tags"],
             )
         if "disable_global_guardrails" in key_metadata and isinstance(
             key_metadata["disable_global_guardrails"], bool
@@ -815,11 +813,14 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
     # Init - Proxy Server Request
     # we do this as soon as entering so we track the original request
     ##########################################################
+    # Track arrival time for queue time metric
+    arrival_time = time.time()
     data["proxy_server_request"] = {
         "url": str(request.url),
         "method": request.method,
         "headers": _headers,
         "body": copy.copy(data),  # use copy instead of deepcopy
+        "arrival_time": arrival_time,  # Track when request arrived at proxy
     }
 
     safe_add_api_version_from_query_params(data, request)
@@ -930,9 +931,9 @@ async def add_litellm_data_to_request(  # noqa: PLR0915
     data[_metadata_variable_name]["litellm_api_version"] = version
 
     if general_settings is not None:
-        data[_metadata_variable_name]["global_max_parallel_requests"] = (
-            general_settings.get("global_max_parallel_requests", None)
-        )
+        data[_metadata_variable_name][
+            "global_max_parallel_requests"
+        ] = general_settings.get("global_max_parallel_requests", None)
 
     ### KEY-LEVEL Controls
     key_metadata = user_api_key_dict.metadata
diff --git a/litellm/proxy/management_endpoints/mcp_management_endpoints.py b/litellm/proxy/management_endpoints/mcp_management_endpoints.py
index 47793c8fc8e..d8816df010a 100644
--- a/litellm/proxy/management_endpoints/mcp_management_endpoints.py
+++ b/litellm/proxy/management_endpoints/mcp_management_endpoints.py
@@ -36,12 +36,19 @@ from litellm._logging import verbose_logger, verbose_proxy_logger
 from litellm._uuid import uuid
 from litellm.constants import LITELLM_PROXY_ADMIN_NAME
 from litellm.proxy._experimental.mcp_server.utils import (
+    get_server_prefix,
     validate_and_normalize_mcp_server_payload,
 )
 
 router = APIRouter(prefix="/v1/mcp", tags=["mcp"])
+
 MCP_AVAILABLE: bool = True
+
 TEMPORARY_MCP_SERVER_TTL_SECONDS = 300
+DEFAULT_MCP_REGISTRY_VERSION = "1.0.0"
+LITELLM_MCP_SERVER_NAME = "litellm-mcp-server"
+LITELLM_MCP_SERVER_DESCRIPTION = "MCP Server for LiteLLM"
+
 try:
     importlib.import_module("mcp")
 except ImportError as e:
@@ -57,6 +64,7 @@ if MCP_AVAILABLE:
         update_mcp_server,
     )
     from litellm.proxy._experimental.mcp_server.discoverable_endpoints import (
+        get_request_base_url,
         authorize_with_server,
         exchange_token_with_server,
         register_client_with_server,
@@ -89,6 +97,66 @@ if MCP_AVAILABLE:
         server: MCPServer
         expires_at: datetime
 
+    def _is_public_registry_enabled() -> bool:
+        from litellm.proxy.proxy_server import (
+            general_settings as proxy_general_settings,
+        )
+
+        return bool(proxy_general_settings.get("enable_mcp_registry"))
+
+    def _build_registry_remote_url(base_url: str, path: str) -> str:
+        normalized_base = base_url.rstrip("/")
+        normalized_path = path if path.startswith("/") else f"/{path}"
+        return f"{normalized_base}{normalized_path}"
+
+    def _build_mcp_registry_server_name(server: MCPServer) -> str:
+        if server.alias:
+            return server.alias
+        if server.server_name:
+            return server.server_name
+        return server.server_id
+
+    def _build_mcp_registry_entry_for_server(
+        server: MCPServer, base_url: str
+    ) -> Dict[str, Any]:
+        server_name = _build_mcp_registry_server_name(server)
+        title = server_name
+        description = server_name
+        version = DEFAULT_MCP_REGISTRY_VERSION
+
+        server_prefix = get_server_prefix(server)
+        if not server_prefix:
+            raise ValueError("MCP server prefix is missing")
+        remote_url = _build_registry_remote_url(base_url, f"/{server_prefix}/mcp")
+
+        return {
+            "name": server_name,
+            "title": title,
+            "description": description,
+            "version": version,
+            "remotes": [
+                {
+                    "type": "streamable-http",
+                    "url": remote_url,
+                }
+            ],
+        }
+
+    def _build_builtin_registry_entry(base_url: str) -> Dict[str, Any]:
+        remote_url = _build_registry_remote_url(base_url, "/mcp")
+        return {
+            "name": LITELLM_MCP_SERVER_NAME,
+            "title": LITELLM_MCP_SERVER_NAME,
+            "description": LITELLM_MCP_SERVER_DESCRIPTION,
+            "version": DEFAULT_MCP_REGISTRY_VERSION,
+            "remotes": [
+                {
+                    "type": "streamable-http",
+                    "url": remote_url,
+                }
+            ],
+        }
+
     _temporary_mcp_servers: Dict[str, _TemporaryMCPServerEntry] = {}
 
     def _prune_expired_temporary_mcp_servers() -> None:
@@ -302,15 +370,42 @@ if MCP_AVAILABLE:
         access_groups_list = sorted(list(access_groups))
         return {"access_groups": access_groups_list}
 
+    @router.get(
+        "/registry.json",
+        tags=["mcp"],
+        description="MCP registry endpoint. Spec: https://github.com/modelcontextprotocol/registry",
+    )
+    async def get_mcp_registry(request: Request):
+        if not _is_public_registry_enabled():
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="MCP registry is not enabled",
+            )
+
+        base_url = get_request_base_url(request)
+        registry_servers: List[Dict[str, Any]] = []
+        registry_servers.append({"server": _build_builtin_registry_entry(base_url)})
+
+        registered_servers = list(global_mcp_server_manager.get_registry().values())
+        registered_servers.sort(key=_build_mcp_registry_server_name)
+
+        for server in registered_servers:
+            try:
+                entry = _build_mcp_registry_entry_for_server(server, base_url)
+            except Exception as e:
+                verbose_proxy_logger.debug(
+                    f"Skipping MCP server {getattr(server, 'server_id', 'unknown')} in registry: {e}"
+                )
+                continue
+            registry_servers.append({"server": entry})
+
+        return {"servers": registry_servers}
+
     ## FastAPI Routes
     def _get_user_mcp_management_mode() -> UserMCPManagementMode:
-        proxy_general_settings: dict = {}
-        try:
-            from litellm.proxy.proxy_server import (
-                general_settings as proxy_general_settings,
-            )
-        except Exception:
-            pass
+        from litellm.proxy.proxy_server import (
+            general_settings as proxy_general_settings,
+        )
 
         mode = proxy_general_settings.get("user_mcp_management_mode")
         if mode == "view_all":
diff --git a/litellm/proxy/management_endpoints/router_settings_endpoints.py b/litellm/proxy/management_endpoints/router_settings_endpoints.py
index 167160c72d1..4d4c41a3dc0 100644
--- a/litellm/proxy/management_endpoints/router_settings_endpoints.py
+++ b/litellm/proxy/management_endpoints/router_settings_endpoints.py
@@ -4,6 +4,7 @@ ROUTER SETTINGS MANAGEMENT
 Endpoints for accessing router configuration and metadata
 
 GET /router/settings - Get router configuration including available routing strategies
+GET /router/fields - Get router settings field definitions without values (for UI rendering)
 """
 
 import inspect
@@ -37,6 +38,15 @@ class RouterSettingsResponse(BaseModel):
     )
 
 
+class RouterFieldsResponse(BaseModel):
+    fields: List[RouterSettingsField] = Field(
+        description="List of all configurable router settings with metadata (without field values)"
+    )
+    routing_strategy_descriptions: Dict[str, str] = Field(
+        description="Descriptions for each routing strategy option"
+    )
+
+
 def _get_routing_strategies_from_router_class() -> List[str]:
     """
     Dynamically extract routing strategies from the Router class __init__ method.
@@ -120,3 +130,53 @@ async def get_router_settings(
         )
         raise
 
+
+@router.get(
+    "/router/fields",
+    tags=["Router Settings"],
+    dependencies=[Depends(user_api_key_auth)],
+    response_model=RouterFieldsResponse,
+)
+async def get_router_fields(
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Get router settings field definitions without values.
+    
+    Returns only the field metadata (type, description, default, options) without
+    populating field_value. This is useful for UI components that need to know
+    what fields to render, but will get the actual values from a different endpoint.
+    
+    Returns:
+    - fields: List of all configurable router settings with their metadata (type, description, default, options)
+              The routing_strategy field includes available options extracted from the Router class
+              Note: field_value will be None for all fields
+    - routing_strategy_descriptions: Descriptions for each routing strategy option
+    """
+    try:
+        # Get available routing strategies dynamically from Router class
+        available_routing_strategies = _get_routing_strategies_from_router_class()
+        
+        # Get router settings fields from types file
+        router_fields = [field.model_copy(deep=True) for field in ROUTER_SETTINGS_FIELDS]
+        
+        # Populate routing_strategy field with available options
+        for field in router_fields:
+            if field.field_name == "routing_strategy":
+                field.options = available_routing_strategies
+                break
+        
+        # Ensure field_value is None for all fields (don't populate values)
+        for field in router_fields:
+            field.field_value = None
+        
+        return RouterFieldsResponse(
+            fields=router_fields,
+            routing_strategy_descriptions=ROUTING_STRATEGY_DESCRIPTIONS,
+        )
+    except Exception as e:
+        verbose_proxy_logger.error(
+            f"Error fetching router fields: {str(e)}"
+        )
+        raise
+
diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
index 84550092d2e..d9798dae690 100644
--- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
+++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py
@@ -776,6 +776,7 @@ async def handle_bedrock_count_tokens(
     - /v1/messages/count_tokens
     - /v1/messages/count-tokens
     """
+    from litellm.llms.bedrock.common_utils import BedrockError
     from litellm.llms.bedrock.count_tokens.handler import BedrockCountTokensHandler
     from litellm.proxy.proxy_server import llm_router
 
@@ -822,6 +823,12 @@ async def handle_bedrock_count_tokens(
 
         return result
 
+    except BedrockError as e:
+        # Convert BedrockError to HTTPException for FastAPI
+        verbose_proxy_logger.error(f"BedrockError in handle_bedrock_count_tokens: {str(e)}")
+        raise HTTPException(
+            status_code=e.status_code, detail={"error": e.message}
+        )
     except HTTPException:
         # Re-raise HTTP exceptions as-is
         raise
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 06525e39133..2e99a2f6824 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -229,7 +229,7 @@ from litellm.proxy.batches_endpoints.endpoints import router as batches_router
 from litellm.proxy.caching_routes import router as caching_router
 from litellm.proxy.common_request_processing import (
     ProxyBaseLLMRequestProcessing,
-    create_streaming_response,
+    create_response,
 )
 from litellm.proxy.common_utils.callback_utils import initialize_callbacks_on_proxy
 from litellm.proxy.common_utils.debug_utils import init_verbose_loggers
@@ -533,9 +533,9 @@ except ImportError:
 server_root_path = os.getenv("SERVER_ROOT_PATH", "")
 _license_check = LicenseCheck()
 premium_user: bool = _license_check.is_premium()
-premium_user_data: Optional["EnterpriseLicenseData"] = (
-    _license_check.airgapped_license_data
-)
+premium_user_data: Optional[
+    "EnterpriseLicenseData"
+] = _license_check.airgapped_license_data
 global_max_parallel_request_retries_env: Optional[str] = os.getenv(
     "LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRIES"
 )
@@ -658,7 +658,7 @@ async def _initialize_shared_aiohttp_session():
 
 
 @asynccontextmanager
-async def proxy_startup_event(app: FastAPI):
+async def proxy_startup_event(app: FastAPI):  # noqa: PLR0915
     global prisma_client, master_key, use_background_health_checks, llm_router, llm_model_list, general_settings, proxy_budget_rescheduler_min_time, proxy_budget_rescheduler_max_time, litellm_proxy_admin_name, db_writer_client, store_model_in_db, premium_user, _license_check, proxy_batch_polling_interval, shared_aiohttp_session
     import json
 
@@ -788,6 +788,17 @@ async def proxy_startup_event(app: FastAPI):
         except Exception as e:
             verbose_proxy_logger.error(f"Error closing shared aiohttp session: {e}")
 
+    # Shutdown event - stop RDS IAM token refresh background task
+    if (
+        prisma_client is not None
+        and hasattr(prisma_client, "db")
+        and hasattr(prisma_client.db, "stop_token_refresh_task")
+    ):
+        try:
+            await prisma_client.db.stop_token_refresh_task()
+        except Exception as e:
+            verbose_proxy_logger.error(f"Error stopping token refresh task: {e}")
+
     await proxy_shutdown_event()  # type: ignore[reportGeneralTypeIssues]
 
 
@@ -1083,9 +1094,7 @@ try:
     # In non-root Docker, we restructure in /var/lib/litellm/ui.
     try:
         _restructure_ui_html_files(ui_path)
-        verbose_proxy_logger.info(
-            f"Restructured UI directory: {ui_path}"
-        )
+        verbose_proxy_logger.info(f"Restructured UI directory: {ui_path}")
     except PermissionError as e:
         verbose_proxy_logger.exception(
             f"Permission error while restructuring UI directory {ui_path}: {e}"
@@ -1171,9 +1180,9 @@ master_key: Optional[str] = None
 config_agents: Optional[List[AgentConfig]] = None
 otel_logging = False
 prisma_client: Optional[PrismaClient] = None
-shared_aiohttp_session: Optional["ClientSession"] = (
-    None  # Global shared session for connection reuse
-)
+shared_aiohttp_session: Optional[
+    "ClientSession"
+] = None  # Global shared session for connection reuse
 user_api_key_cache = DualCache(
     default_in_memory_ttl=UserAPIKeyCacheTTLEnum.in_memory_cache_ttl.value
 )
@@ -1181,9 +1190,9 @@ model_max_budget_limiter = _PROXY_VirtualKeyModelMaxBudgetLimiter(
     dual_cache=user_api_key_cache
 )
 litellm.logging_callback_manager.add_litellm_callback(model_max_budget_limiter)
-redis_usage_cache: Optional[RedisCache] = (
-    None  # redis cache used for tracking spend, tpm/rpm limits
-)
+redis_usage_cache: Optional[
+    RedisCache
+] = None  # redis cache used for tracking spend, tpm/rpm limits
 polling_via_cache_enabled: Union[Literal["all"], List[str], bool] = False
 polling_cache_ttl: int = 3600  # Default 1 hour TTL for polling cache
 user_custom_auth = None
@@ -1522,9 +1531,9 @@ async def update_cache(  # noqa: PLR0915
         _id = "team_id:{}".format(team_id)
         try:
             # Fetch the existing cost for the given user
-            existing_spend_obj: Optional[LiteLLM_TeamTable] = (
-                await user_api_key_cache.async_get_cache(key=_id)
-            )
+            existing_spend_obj: Optional[
+                LiteLLM_TeamTable
+            ] = await user_api_key_cache.async_get_cache(key=_id)
             if existing_spend_obj is None:
                 # do nothing if team not in api key cache
                 return
@@ -1876,7 +1885,6 @@ class ProxyConfig:
                 "environment_variables" in config_to_save
                 and config_to_save["environment_variables"]
             ):
-
                 # decrypt the environment_variables - in case a caller function has already encrypted the environment_variables
                 decrypted_env_vars = self._decrypt_and_set_db_env_variables(
                     environment_variables=config_to_save["environment_variables"],
@@ -2794,21 +2802,21 @@ class ProxyConfig:
         verbose_proxy_logger.debug(f"_alerting_callbacks: {general_settings}")
         if _alerting_callbacks is None:
             return
-        
+
         # Ensure proxy_logging_obj.alerting is set for all alerting types
         _alerting_value = general_settings.get("alerting", None)
-        verbose_proxy_logger.debug(f"_load_alerting_settings: Calling update_values with alerting={_alerting_value}")
+        verbose_proxy_logger.debug(
+            f"_load_alerting_settings: Calling update_values with alerting={_alerting_value}"
+        )
         proxy_logging_obj.update_values(
             alerting=_alerting_value,
             alerting_threshold=general_settings.get("alerting_threshold", 600),
             alert_types=general_settings.get("alert_types", None),
-            alert_to_webhook_url=general_settings.get(
-                "alert_to_webhook_url", None
-            ),
+            alert_to_webhook_url=general_settings.get("alert_to_webhook_url", None),
             alerting_args=general_settings.get("alerting_args", None),
             redis_cache=redis_usage_cache,
         )
-        
+
         for _alert in _alerting_callbacks:
             if _alert == "slack":
                 # [OLD] v0 implementation - already handled by update_values above
@@ -3222,6 +3230,84 @@ class ProxyConfig:
             decrypted_variables[k] = decrypted_value
         return decrypted_variables
 
+    async def _get_hierarchical_router_settings(
+        self,
+        user_api_key_dict: Optional["UserAPIKeyAuth"],
+        prisma_client: Optional[PrismaClient],
+    ) -> Optional[dict]:
+        """
+        Get router_settings in priority order: Key > Team > Global
+        
+        Returns:
+            dict: Combined router_settings, or None if no settings found
+        """
+        if prisma_client is None:
+            return None
+        
+        import json
+        import yaml
+        
+        # 1. Try key-level router_settings
+        if user_api_key_dict is not None:
+            # Check if router_settings is available on the key object
+            key_router_settings_value = getattr(user_api_key_dict, "router_settings", None)
+            if key_router_settings_value is not None:
+                key_router_settings = None
+                if isinstance(key_router_settings_value, str):
+                    try:
+                        key_router_settings = yaml.safe_load(key_router_settings_value)
+                    except (yaml.YAMLError, json.JSONDecodeError):
+                        try:
+                            key_router_settings = json.loads(key_router_settings_value)
+                        except json.JSONDecodeError:
+                            pass
+                elif isinstance(key_router_settings_value, dict):
+                    key_router_settings = key_router_settings_value
+                
+                # If key has router_settings (non-empty dict), use it
+                if key_router_settings is not None and isinstance(key_router_settings, dict) and key_router_settings:
+                    return key_router_settings
+        
+        # 2. Try team-level router_settings
+        if user_api_key_dict is not None and user_api_key_dict.team_id is not None:
+            try:
+                team_obj = await prisma_client.db.litellm_teamtable.find_unique(
+                    where={"team_id": user_api_key_dict.team_id}
+                )
+                if team_obj is not None:
+                    team_router_settings_value = getattr(team_obj, "router_settings", None)
+                    if team_router_settings_value is not None:
+                        team_router_settings = None
+                        if isinstance(team_router_settings_value, str):
+                            try:
+                                team_router_settings = yaml.safe_load(team_router_settings_value)
+                            except (yaml.YAMLError, json.JSONDecodeError):
+                                try:
+                                    team_router_settings = json.loads(team_router_settings_value)
+                                except json.JSONDecodeError:
+                                    pass
+                        elif isinstance(team_router_settings_value, dict):
+                            team_router_settings = team_router_settings_value
+                        
+                        # If team has router_settings (non-empty dict), use it
+                        if team_router_settings is not None and isinstance(team_router_settings, dict) and team_router_settings:
+                            return team_router_settings
+            except Exception:
+                # If team lookup fails, continue to global settings
+                pass
+        
+        # 3. Try global router_settings
+        try:
+            db_router_settings = await prisma_client.db.litellm_config.find_first(
+                where={"param_name": "router_settings"}
+            )
+            if db_router_settings is not None and isinstance(db_router_settings.param_value, dict) and db_router_settings.param_value:
+                return db_router_settings.param_value
+        except Exception:
+            pass
+        
+        return None
+
     async def _add_router_settings_from_db_config(
         self,
         config_data: dict,
@@ -3279,7 +3365,7 @@ class ProxyConfig:
             proxy_logging_obj: ProxyLogging
         """
         _general_settings = config_data.get("general_settings", {})
-    
+
         if _general_settings is not None and "alerting" in _general_settings:
             if (
                 general_settings is not None
@@ -3294,7 +3380,8 @@ class ProxyConfig:
                 _merged_alerting = list(_yaml_alerting.union(_db_alerting))
                 # Preserve order: YAML values first, then DB values
                 _merged_alerting = list(general_settings["alerting"]) + [
-                    item for item in _general_settings["alerting"] 
+                    item
+                    for item in _general_settings["alerting"]
                     if item not in general_settings["alerting"]
                 ]
                 verbose_proxy_logger.debug(
@@ -3402,8 +3489,8 @@ class ProxyConfig:
 
         def _deep_merge_dicts(dst: dict, src: dict) -> None:
             """
-            Deep-merge src into dst, skipping None values from src.
-            On conflicts, src (DB) wins.
+            Deep-merge src into dst, skipping None values and empty lists from src.
+            On conflicts, src (DB) wins, but empty lists are treated as "no value" and don't overwrite.
             """
             stack = [(dst, src)]
             while stack:
@@ -3412,6 +3499,9 @@ class ProxyConfig:
                     if v is None:
                         # Preserve existing config when DB value is None (matches prior behavior)
                         continue
+                    # Skip empty lists - treat them as "no value" to preserve file config
+                    if isinstance(v, list) and len(v) == 0:
+                        continue
                     if isinstance(v, dict) and isinstance(d.get(k), dict):
                         stack.append((d[k], v))
                     else:
@@ -3605,7 +3695,6 @@ class ProxyConfig:
             await self._init_vector_stores_in_db(prisma_client=prisma_client)
 
         if self._should_load_db_object(object_type="vector_store_indexes"):
-
             await self._init_vector_store_indexes_in_db(prisma_client=prisma_client)
 
         if self._should_load_db_object(object_type="mcp"):
@@ -3804,10 +3893,10 @@ class ProxyConfig:
         )
 
         try:
-            guardrails_in_db: List[Guardrail] = (
-                await GuardrailRegistry.get_all_guardrails_from_db(
-                    prisma_client=prisma_client
-                )
+            guardrails_in_db: List[
+                Guardrail
+            ] = await GuardrailRegistry.get_all_guardrails_from_db(
+                prisma_client=prisma_client
             )
             verbose_proxy_logger.debug(
                 "guardrails from the DB %s", str(guardrails_in_db)
@@ -3894,7 +3983,7 @@ class ProxyConfig:
         )
 
         try:
-            await global_mcp_server_manager._add_mcp_servers_from_db_to_in_memory_registry()
+            await global_mcp_server_manager.reload_servers_from_database()
         except Exception as e:
             verbose_proxy_logger.exception(
                 "litellm.proxy.proxy_server.py::ProxyConfig:_init_mcp_servers_in_db - {}".format(
@@ -4134,9 +4223,9 @@ async def initialize(  # noqa: PLR0915
         user_api_base = api_base
         dynamic_config[user_model]["api_base"] = api_base
     if api_version:
-        os.environ["AZURE_API_VERSION"] = (
-            api_version  # set this for azure - litellm can read this from the env
-        )
+        os.environ[
+            "AZURE_API_VERSION"
+        ] = api_version  # set this for azure - litellm can read this from the env
     if max_tokens:  # model-specific param
         dynamic_config[user_model]["max_tokens"] = max_tokens
     if temperature:  # model-specific param
@@ -4568,6 +4657,7 @@ class ProxyStartupEvent:
                 misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME,
             )
             await proxy_config.get_credentials(prisma_client=prisma_client)
+
         await cls._initialize_slack_alerting_jobs(
             scheduler=scheduler,
             general_settings=general_settings,
@@ -4654,10 +4744,14 @@ class ProxyStartupEvent:
                     replace_existing=True,
                     misfire_grace_time=APSCHEDULER_MISFIRE_GRACE_TIME,
                 )
-                verbose_proxy_logger.info("Responses cost check job scheduled successfully")
+                verbose_proxy_logger.info(
+                    "Responses cost check job scheduled successfully"
+                )
 
             except Exception as e:
-                verbose_proxy_logger.debug(f"Failed to setup responses cost checking: {e}")
+                verbose_proxy_logger.debug(
+                    f"Failed to setup responses cost checking: {e}"
+                )
                 verbose_proxy_logger.debug(
                     "Checking responses cost for LiteLLM Managed Files is an Enterprise Feature. Skipping..."
                 )
@@ -4681,8 +4775,9 @@ class ProxyStartupEvent:
         """
         Initialize the spend tracking and other background jobs
         1. CloudZero Background Job
-        2. Prometheus Background Job
-        3. Key Rotation Background Job
+        2. Focus Background Job
+        3. Prometheus Background Job
+        4. Key Rotation Background Job
 
         Args:
             scheduler: The scheduler to add the background jobs to
@@ -4691,11 +4786,17 @@ class ProxyStartupEvent:
         # CloudZero Background Job
         ########################################################
         from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger
+        from litellm.integrations.focus.focus_logger import FocusLogger
         from litellm.proxy.spend_tracking.cloudzero_endpoints import is_cloudzero_setup
 
         if await is_cloudzero_setup():
             await CloudZeroLogger.init_cloudzero_background_job(scheduler=scheduler)
 
+        ########################################################
+        # Focus Background Job
+        ########################################################
+        await FocusLogger.init_focus_export_background_job(scheduler=scheduler)
+
         ########################################################
         # Prometheus Background Job
         ########################################################
@@ -4826,6 +4927,14 @@ class ProxyStartupEvent:
 
                 await prisma_client.connect()
 
+                ## Start RDS IAM token refresh background task if enabled ##
+                # This proactively refreshes IAM tokens before they expire,
+                # preventing the 15-minute connection failure bug (#16220)
+                if hasattr(prisma_client, "db") and hasattr(
+                    prisma_client.db, "start_token_refresh_task"
+                ):
+                    await prisma_client.db.start_token_refresh_task()
+
                 ## Add necessary views to proxy ##
                 asyncio.create_task(
                     prisma_client.check_view_exists()
@@ -5937,7 +6046,6 @@ async def realtime_websocket_endpoint(
     ),
     user_api_key_dict=Depends(user_api_key_auth_websocket),
 ):
-
     await websocket.accept()
 
     # Only use explicit parameters, not all query params
@@ -6736,7 +6844,7 @@ async def run_thread(
         if (
             "stream" in data and data["stream"] is True
         ):  # use generate_responses to stream responses
-            return await create_streaming_response(
+            return await create_response(
                 generator=async_assistants_data_generator(
                     user_api_key_dict=user_api_key_dict,
                     response=response,
@@ -9525,9 +9633,9 @@ async def get_config_list(
                             hasattr(sub_field_info, "description")
                             and sub_field_info.description is not None
                         ):
-                            nested_fields[idx].field_description = (
-                                sub_field_info.description
-                            )
+                            nested_fields[
+                                idx
+                            ].field_description = sub_field_info.description
                         idx += 1
 
                     _stored_in_db = None
@@ -9762,6 +9870,18 @@ async def get_config():  # noqa: PLR0915
         _failure_callbacks = _litellm_settings.get("failure_callback", [])
         _success_and_failure_callbacks = _litellm_settings.get("callbacks", [])
 
+        # Normalize string callbacks to lists
+        def normalize_callback(callback):
+            if isinstance(callback, str):
+                return [callback]
+            elif callback is None:
+                return []
+            return callback
+
+        _success_callbacks = normalize_callback(_success_callbacks)
+        _failure_callbacks = normalize_callback(_failure_callbacks)
+        _success_and_failure_callbacks = normalize_callback(_success_and_failure_callbacks)
+
         _data_to_return = []
         """
         [
diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py
index 5d2e13a78b9..4b3a65737d4 100644
--- a/litellm/proxy/route_llm_request.py
+++ b/litellm/proxy/route_llm_request.py
@@ -181,24 +181,26 @@ async def route_request(
         else:
             return getattr(litellm, f"{route_type}")(**data)
 
-    elif "user_config" in data:
-        router_config = data.pop("user_config")
-        user_router = litellm.Router(**router_config)
-        ret_val = getattr(user_router, f"{route_type}")(**data)
-        user_router.discard()
-        return ret_val
-
     elif (
         route_type == "acompletion"
         and data.get("model", "") is not None
         and "," in data.get("model", "")
         and llm_router is not None
     ):
+        # Handle batch completions with comma-separated models BEFORE user_config check
+        # This ensures batch completion logic is applied even when user_config is set
         if data.get("fastest_response", False):
             return llm_router.abatch_completion_fastest_response(**data)
         else:
             models = [model.strip() for model in data.pop("model").split(",")]
             return llm_router.abatch_completion(models=models, **data)
+
+    elif "user_config" in data:
+        router_config = data.pop("user_config")
+        user_router = litellm.Router(**router_config)
+        ret_val = getattr(user_router, f"{route_type}")(**data)
+        user_router.discard()
+        return ret_val
     elif llm_router is not None:
         # Skip model-based routing for container operations
         if route_type in [
diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py
index 1c457d7bf4c..5d348d35cb2 100644
--- a/litellm/proxy/spend_tracking/spend_tracking_utils.py
+++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py
@@ -1,3 +1,4 @@
+import copy
 import hashlib
 import json
 import secrets
@@ -433,9 +434,11 @@ def get_logging_payload(  # noqa: PLR0915
             messages=_get_messages_for_spend_logs_payload(
                 standard_logging_payload=standard_logging_payload, metadata=metadata
             ),
-            response=_get_response_for_spend_logs_payload(standard_logging_payload),
+            response=_get_response_for_spend_logs_payload(
+                payload=standard_logging_payload, kwargs=kwargs
+            ),
             proxy_server_request=_get_proxy_server_request_for_spend_logs_payload(
-                metadata=metadata, litellm_params=litellm_params
+                metadata=metadata, litellm_params=litellm_params, kwargs=kwargs
             ),
             session_id=_get_session_id_for_spend_log(
                 kwargs=kwargs,
@@ -642,9 +645,12 @@ def _sanitize_request_body_for_spend_logs_payload(
 def _get_proxy_server_request_for_spend_logs_payload(
     metadata: dict,
     litellm_params: dict,
+    kwargs: Optional[dict] = None,
 ) -> str:
     """
     Only store if _should_store_prompts_and_responses_in_spend_logs() is True
+    
+    If turn_off_message_logging is enabled, redact messages in the request body.
     """
     if _should_store_prompts_and_responses_in_spend_logs():
         _proxy_server_request = cast(
@@ -652,6 +658,27 @@ def _get_proxy_server_request_for_spend_logs_payload(
         )
         if _proxy_server_request is not None:
             _request_body = _proxy_server_request.get("body", {}) or {}
+            
+            # Apply message redaction if turn_off_message_logging is enabled
+            if kwargs is not None:
+                from litellm.litellm_core_utils.redact_messages import (
+                    perform_redaction,
+                    should_redact_message_logging,
+                )
+
+                # Build model_call_details dict to check redaction settings
+                model_call_details = {
+                    "litellm_params": litellm_params,
+                    "standard_callback_dynamic_params": kwargs.get(
+                        "standard_callback_dynamic_params"
+                    ),
+                }
+                
+                # If redaction is enabled, deep copy request body before redacting
+                if should_redact_message_logging(model_call_details=model_call_details):
+                    _request_body = copy.deepcopy(_request_body)
+                    perform_redaction(model_call_details=_request_body, result=None)
+            
             _request_body = _sanitize_request_body_for_spend_logs_payload(_request_body)
             _request_body_json_str = json.dumps(_request_body, default=str)
             return _request_body_json_str
@@ -685,6 +712,7 @@ def _get_vector_store_request_for_spend_logs_payload(
 
 def _get_response_for_spend_logs_payload(
     payload: Optional[StandardLoggingPayload],
+    kwargs: Optional[dict] = None,
 ) -> str:
     if payload is None:
         return "{}"
@@ -692,6 +720,26 @@ def _get_response_for_spend_logs_payload(
         response_obj: Any = payload.get("response")
         if response_obj is None:
             return "{}"
+        
+        # Apply message redaction if turn_off_message_logging is enabled
+        if kwargs is not None:
+            from litellm.litellm_core_utils.redact_messages import (
+                perform_redaction,
+                should_redact_message_logging,
+            )
+            
+            litellm_params = kwargs.get("litellm_params", {})
+            model_call_details = {
+                "litellm_params": litellm_params,
+                "standard_callback_dynamic_params": kwargs.get(
+                    "standard_callback_dynamic_params"
+                ),
+            }
+            
+            # If redaction is enabled, deep copy response before redacting
+            if should_redact_message_logging(model_call_details=model_call_details):
+                response_obj = copy.deepcopy(response_obj)
+                response_obj = perform_redaction(model_call_details={}, result=response_obj)
 
         sanitized_wrapper = _sanitize_request_body_for_spend_logs_payload(
             {"response": response_obj}
diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py
index d1a78534dae..9ea2ea7d5c9 100644
--- a/litellm/proxy/utils.py
+++ b/litellm/proxy/utils.py
@@ -131,6 +131,7 @@ else:
 
 unified_guardrail = UnifiedLLMGuardrails()
 
+_anthropic_async_clients = {}
 
 def print_verbose(print_statement):
     """
@@ -961,6 +962,7 @@ class ProxyLogging:
             Updated data dictionary if guardrail passes, None if guardrail should be skipped
         """
         from litellm.types.guardrails import GuardrailEventHooks
+        from litellm.integrations.prometheus import PrometheusLogger
 
         # Determine the event type based on call type
         event_type = GuardrailEventHooks.pre_call
@@ -973,30 +975,62 @@ class ProxyLogging:
 
         guardrail_name = callback.guardrail_name
 
-        # Check if load balancing should be used
-        if guardrail_name and self._should_use_guardrail_load_balancing(guardrail_name):
-            response = await self._execute_guardrail_with_load_balancing(
-                guardrail_name=guardrail_name,
-                hook_type="pre_call",
-                data=data,
-                user_api_key_dict=user_api_key_dict,
-                call_type=call_type,
-            )
-        else:
-            # Single guardrail - execute directly
-            response = await self._execute_guardrail_hook(
-                callback=callback,
-                hook_type="pre_call",
-                data=data,
-                user_api_key_dict=user_api_key_dict,
-                call_type=call_type,
-            )
+        # Track timing and errors for prometheus metrics
+        # Use time.perf_counter() for more accurate duration measurements
+        guardrail_start_time = time.perf_counter()
+        status = "success"
+        error_type = None
 
-        # Process the response if one was returned
-        if response is not None:
-            data = await self.process_pre_call_hook_response(
-                response=response, data=data, call_type=call_type
-            )
+        try:
+            # Check if load balancing should be used
+            if guardrail_name and self._should_use_guardrail_load_balancing(guardrail_name):
+                response = await self._execute_guardrail_with_load_balancing(
+                    guardrail_name=guardrail_name,
+                    hook_type="pre_call",
+                    data=data,
+                    user_api_key_dict=user_api_key_dict,
+                    call_type=call_type,
+                )
+            else:
+                # Single guardrail - execute directly
+                response = await self._execute_guardrail_hook(
+                    callback=callback,
+                    hook_type="pre_call",
+                    data=data,
+                    user_api_key_dict=user_api_key_dict,
+                    call_type=call_type,
+                )
+
+            # Process the response if one was returned
+            if response is not None:
+                data = await self.process_pre_call_hook_response(
+                    response=response, data=data, call_type=call_type
+                )
+
+        except Exception as e:
+            status = "error"
+            error_type = type(e).__name__
+            # Re-raise the exception to maintain existing behavior
+            raise
+        finally:
+            # Record prometheus metrics
+            guardrail_end_time = time.perf_counter()
+            latency_seconds = guardrail_end_time - guardrail_start_time
+
+            # Get guardrail name for metrics (fallback if not set)
+            metrics_guardrail_name = guardrail_name or getattr(callback, "guardrail_name", callback.__class__.__name__) or "unknown"
+
+            # Find PrometheusLogger in callbacks and record metrics
+            for prom_callback in litellm.callbacks:
+                if isinstance(prom_callback, PrometheusLogger):
+                    prom_callback._record_guardrail_metrics(
+                        guardrail_name=metrics_guardrail_name,
+                        latency_seconds=latency_seconds,
+                        status=status,
+                        error_type=error_type,
+                        hook_type="pre_call",
+                    )
+                    break
 
         return data
 
@@ -1195,7 +1229,7 @@ class ProxyLogging:
                     and _callback.__class__.async_pre_call_hook
                     != CustomLogger.async_pre_call_hook
                 ):
-                    if call_type == "mcp_call" and user_api_key_dict is None:
+                    if call_type == "call_mcp_tool" and user_api_key_dict is None:
                         continue
 
                     response = await _callback.async_pre_call_hook(
@@ -4254,11 +4288,16 @@ async def count_tokens_with_anthropic_api(
 
         if anthropic_api_key and messages:
             # Call Anthropic API directly for more accurate token counting
-            client = anthropic.Anthropic(api_key=anthropic_api_key)
+            
+            # Use cached client if available to avoid socket exhaustion
+            if anthropic_api_key not in _anthropic_async_clients:
+                _anthropic_async_clients[anthropic_api_key] = anthropic.AsyncAnthropic(api_key=anthropic_api_key)
+            
+            client = _anthropic_async_clients[anthropic_api_key]
 
             # Call with explicit parameters to satisfy type checking
             # Type ignore for now since messages come from generic dict input
-            response = client.beta.messages.count_tokens(
+            response = await client.beta.messages.count_tokens(
                 model=model_to_use,
                 messages=messages,  # type: ignore
                 betas=["token-counting-2024-11-01"],
@@ -4440,21 +4479,35 @@ def validate_model_access(
 ) -> None:
     """
     Validate that a model is accessible to the user.
+    Supports batch requests with comma-separated model IDs.
 
     Args:
-        model_id: The model ID to validate
+        model_id: The model ID to validate (can be comma-separated for batch requests)
         available_models: List of models available to the user
 
     Raises:
         HTTPException: If the model is not accessible
     """
-    if model_id not in available_models:
-        raise HTTPException(
-            status_code=404,
-            detail="The model `{}` does not exist or is not accessible".format(
-                model_id
-            ),
-        )
+    # Handle batch requests with comma-separated models
+    if "," in model_id:
+        models = [m.strip() for m in model_id.split(",")]
+        inaccessible_models = [m for m in models if m not in available_models]
+        if inaccessible_models:
+            raise HTTPException(
+                status_code=404,
+                detail="The following model(s) do not exist or are not accessible: {}".format(
+                    ", ".join(inaccessible_models)
+                ),
+            )
+    else:
+        # Single model validation
+        if model_id not in available_models:
+            raise HTTPException(
+                status_code=404,
+                detail="The model `{}` does not exist or is not accessible".format(
+                    model_id
+                ),
+            )
 
 
 def _path_matches_pattern(path: str, pattern: str) -> bool:
diff --git a/litellm/responses/main.py b/litellm/responses/main.py
index 8177b177fe6..07fc3cb02cc 100644
--- a/litellm/responses/main.py
+++ b/litellm/responses/main.py
@@ -577,7 +577,13 @@ def responses(
             api_base=litellm_params.api_base,
             api_key=litellm_params.api_key,
         )
-        
+
+        # 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
+
         #########################################################
         # Update input with provider-specific file IDs if managed files are used
         #########################################################
@@ -1483,6 +1489,12 @@ def compact_responses(
             api_key=litellm_params.api_key,
         )
 
+        # 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
+
         if custom_llm_provider is None:
             raise ValueError("custom_llm_provider is required but passed as None")
 
diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py
index a92b5d25a37..49b8e123fe2 100644
--- a/litellm/responses/utils.py
+++ b/litellm/responses/utils.py
@@ -198,11 +198,14 @@ class ResponsesAPIRequestUtils:
         model_id = model_info.get("id")
 
         # access the response id based on the object type
-        response_id = (
-            responses_api_response["id"]
-            if isinstance(responses_api_response, dict)
-            else responses_api_response.id
-        )
+        if isinstance(responses_api_response, dict):
+            response_id = responses_api_response.get("id")
+        else:
+            response_id = getattr(responses_api_response, "id", None)
+        
+        # If no response_id, return the response as-is (likely an error response)
+        if response_id is None:
+            return responses_api_response
 
         updated_id = ResponsesAPIRequestUtils._build_responses_api_response_id(
             model_id=model_id,
@@ -443,11 +446,18 @@ class ResponseAPILoggingUtils:
                 completion_tokens=0,
                 total_tokens=0,
             )
-        response_api_usage: ResponseAPIUsage = (
-            ResponseAPIUsage(**usage_input)
-            if isinstance(usage_input, dict)
-            else usage_input
-        )
+        response_api_usage: ResponseAPIUsage
+        if isinstance(usage_input, dict):
+            total_tokens = usage_input.get("total_tokens")
+            if total_tokens is None:
+                input_tokens = usage_input.get("input_tokens")
+                output_tokens = usage_input.get("output_tokens")
+                if input_tokens is not None and output_tokens is not None:
+                    total_tokens = input_tokens + output_tokens
+                    usage_input["total_tokens"] = total_tokens
+            response_api_usage = ResponseAPIUsage(**usage_input)
+        else:
+            response_api_usage = usage_input
         prompt_tokens: int = response_api_usage.input_tokens or 0
         completion_tokens: int = response_api_usage.output_tokens or 0
         prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
diff --git a/litellm/router.py b/litellm/router.py
index 98ccf41c96d..638df49ac05 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -255,6 +255,7 @@ class Router:
         ] = {},
         enable_pre_call_checks: bool = False,
         enable_tag_filtering: bool = False,
+        tag_filtering_match_any: bool = True,
         retry_after: int = 0,  # min time to wait before retrying a failed request
         retry_policy: Optional[
             Union[RetryPolicy, dict]
@@ -363,6 +364,7 @@ class Router:
         self.debug_level = debug_level
         self.enable_pre_call_checks = enable_pre_call_checks
         self.enable_tag_filtering = enable_tag_filtering
+        self.tag_filtering_match_any = tag_filtering_match_any
         from litellm._service_logger import ServiceLogging
 
         self.service_logger_obj: ServiceLogging = ServiceLogging()
@@ -384,9 +386,9 @@ class Router:
         )  # names of models under litellm_params. ex. azure/chatgpt-v-2
         self.deployment_latency_map = {}
         ### CACHING ###
-        cache_type: Literal["local", "redis", "redis-semantic", "s3", "disk"] = (
-            "local"  # default to an in-memory cache
-        )
+        cache_type: Literal[
+            "local", "redis", "redis-semantic", "s3", "disk"
+        ] = "local"  # default to an in-memory cache
         redis_cache = None
         cache_config: Dict[str, Any] = {}
 
@@ -428,9 +430,9 @@ class Router:
         self.default_max_parallel_requests = default_max_parallel_requests
         self.provider_default_deployment_ids: List[str] = []
         self.pattern_router = PatternMatchRouter()
-        self.team_pattern_routers: Dict[str, PatternMatchRouter] = (
-            {}
-        )  # {"TEAM_ID": PatternMatchRouter}
+        self.team_pattern_routers: Dict[
+            str, PatternMatchRouter
+        ] = {}  # {"TEAM_ID": PatternMatchRouter}
         self.auto_routers: Dict[str, "AutoRouter"] = {}
 
         # Initialize model_group_alias early since it's used in set_model_list
@@ -611,9 +613,9 @@ class Router:
                 )
             )
 
-        self.model_group_retry_policy: Optional[Dict[str, RetryPolicy]] = (
-            model_group_retry_policy
-        )
+        self.model_group_retry_policy: Optional[
+            Dict[str, RetryPolicy]
+        ] = model_group_retry_policy
 
         self.allowed_fails_policy: Optional[AllowedFailsPolicy] = None
         if allowed_fails_policy is not None:
@@ -720,7 +722,10 @@ class Router:
         valid_strategy_strings = ["simple-shuffle"] + [s.value for s in RoutingStrategy]
 
         if routing_strategy is not None:
-            is_valid_string = isinstance(routing_strategy, str) and routing_strategy in valid_strategy_strings
+            is_valid_string = (
+                isinstance(routing_strategy, str)
+                and routing_strategy in valid_strategy_strings
+            )
             is_valid_enum = isinstance(routing_strategy, RoutingStrategy)
             if not is_valid_string and not is_valid_enum:
                 raise ValueError(
@@ -1069,7 +1074,7 @@ class Router:
         self.delete_container = self.factory_function(
             delete_container, call_type="delete_container"
         )
-        
+
         # Auto-register JSON-generated container file endpoints
         for name, func in container_file_endpoints.items():
             setattr(self, name, self.factory_function(func, call_type=name))  # type: ignore[arg-type]
@@ -1498,10 +1503,7 @@ class Router:
 
     async def _acompletion(
         self, model: str, messages: List[Dict[str, str]], **kwargs
-    ) -> Union[
-        ModelResponse,
-        CustomStreamWrapper,
-    ]:
+    ) -> Union[ModelResponse, CustomStreamWrapper,]:
         """
         - Get an available deployment
         - call it with a semaphore over the call
@@ -3019,7 +3021,9 @@ class Router:
         kwargs["original_generic_function"] = original_function
         kwargs["original_function"] = self._aguardrail_helper
         self._update_kwargs_before_fallbacks(
-            model=guardrail_name, kwargs=kwargs, metadata_variable_name="litellm_metadata"
+            model=guardrail_name,
+            kwargs=kwargs,
+            metadata_variable_name="litellm_metadata",
         )
         verbose_router_logger.debug(
             f"Inside aguardrail() - guardrail_name: {guardrail_name}; kwargs: {kwargs}"
@@ -3312,8 +3316,7 @@ class Router:
             kwargs["model"] = model
             kwargs["input"] = input
             kwargs["original_function"] = self._embedding
-            kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
-            kwargs.setdefault("metadata", {}).update({"model_group": model})
+            self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
             response = self.function_with_fallbacks(**kwargs)
             return response
         except Exception as e:
@@ -3615,9 +3618,9 @@ class Router:
                 healthy_deployments=healthy_deployments, responses=responses
             )
             returned_response = cast(OpenAIFileObject, responses[0])
-            returned_response._hidden_params["model_file_id_mapping"] = (
-                model_file_id_mapping
-            )
+            returned_response._hidden_params[
+                "model_file_id_mapping"
+            ] = model_file_id_mapping
             return returned_response
         except Exception as e:
             verbose_router_logger.exception(
@@ -4364,11 +4367,11 @@ class Router:
 
             if isinstance(e, litellm.ContextWindowExceededError):
                 if context_window_fallbacks is not None:
-                    context_window_fallback_model_group: Optional[List[str]] = (
-                        self._get_fallback_model_group_from_fallbacks(
-                            fallbacks=context_window_fallbacks,
-                            model_group=model_group,
-                        )
+                    context_window_fallback_model_group: Optional[
+                        List[str]
+                    ] = self._get_fallback_model_group_from_fallbacks(
+                        fallbacks=context_window_fallbacks,
+                        model_group=model_group,
                     )
                     if context_window_fallback_model_group is None:
                         raise original_exception
@@ -4400,11 +4403,11 @@ class Router:
                     e.message += "\n{}".format(error_message)
             elif isinstance(e, litellm.ContentPolicyViolationError):
                 if content_policy_fallbacks is not None:
-                    content_policy_fallback_model_group: Optional[List[str]] = (
-                        self._get_fallback_model_group_from_fallbacks(
-                            fallbacks=content_policy_fallbacks,
-                            model_group=model_group,
-                        )
+                    content_policy_fallback_model_group: Optional[
+                        List[str]
+                    ] = self._get_fallback_model_group_from_fallbacks(
+                        fallbacks=content_policy_fallbacks,
+                        model_group=model_group,
                     )
                     if content_policy_fallback_model_group is None:
                         raise original_exception
@@ -5667,26 +5670,26 @@ class Router:
         """
         from litellm.router_strategy.auto_router.auto_router import AutoRouter
 
-        auto_router_config_path: Optional[str] = (
-            deployment.litellm_params.auto_router_config_path
-        )
+        auto_router_config_path: Optional[
+            str
+        ] = deployment.litellm_params.auto_router_config_path
         auto_router_config: Optional[str] = deployment.litellm_params.auto_router_config
         if auto_router_config_path is None and auto_router_config is None:
             raise ValueError(
                 "auto_router_config_path or auto_router_config is required for auto-router deployments. Please set it in the litellm_params"
             )
 
-        default_model: Optional[str] = (
-            deployment.litellm_params.auto_router_default_model
-        )
+        default_model: Optional[
+            str
+        ] = deployment.litellm_params.auto_router_default_model
         if default_model is None:
             raise ValueError(
                 "auto_router_default_model is required for auto-router deployments. Please set it in the litellm_params"
             )
 
-        embedding_model: Optional[str] = (
-            deployment.litellm_params.auto_router_embedding_model
-        )
+        embedding_model: Optional[
+            str
+        ] = deployment.litellm_params.auto_router_embedding_model
         if embedding_model is None:
             raise ValueError(
                 "auto_router_embedding_model is required for auto-router deployments. Please set it in the litellm_params"
@@ -6233,9 +6236,9 @@ class Router:
 
         # Add custom_llm_provider
         if deployment.litellm_params.custom_llm_provider:
-            credentials["custom_llm_provider"] = (
-                deployment.litellm_params.custom_llm_provider
-            )
+            credentials[
+                "custom_llm_provider"
+            ] = deployment.litellm_params.custom_llm_provider
         elif "/" in deployment.litellm_params.model:
             # Extract provider from "provider/model" format
             credentials["custom_llm_provider"] = deployment.litellm_params.model.split(
@@ -6929,42 +6932,44 @@ class Router:
         """
         return candidate_id in self.model_id_to_deployment_index_map
 
-    def resolve_model_name_from_model_id(self, model_id: Optional[str]) -> Optional[str]:
+    def resolve_model_name_from_model_id(
+        self, model_id: Optional[str]
+    ) -> Optional[str]:
         """
         Resolve model_name from model_id.
-        
+
         This method attempts to find the correct model_name to use with the router
         so that litellm_params can be automatically injected from the model config.
-        
+
         Strategy:
         1. First, check if model_id directly matches a model_name or deployment ID
         2. If not, search through router's model_list to find a match by litellm_params.model
         3. Return the model_name if found, None otherwise
-        
+
         Args:
             model_id: The model_id extracted from decoded video_id
                      (could be model_name or litellm_params.model value)
-        
+
         Returns:
             model_name if found, None otherwise. If None, the request will fall through
             to normal flow using environment variables.
         """
         if not model_id:
             return None
-        
+
         # Strategy 1: Check if model_id directly matches a model_name or deployment ID
         if model_id in self.model_names or self.has_model_id(model_id):
             return model_id
-        
+
         # Strategy 2: Search through router's model_list to find by litellm_params.model
         all_models = self.get_model_list(model_name=None)
         if not all_models:
             return None
-        
+
         for deployment in all_models:
             litellm_params = deployment.get("litellm_params", {})
             actual_model = litellm_params.get("model")
-            
+
             # Match by exact match or by checking if actual_model ends with /model_id or :model_id
             # e.g., model_id="veo-2.0-generate-001" matches actual_model="vertex_ai/veo-2.0-generate-001"
             matches = (
@@ -6972,12 +6977,12 @@ class Router:
                 or (actual_model and actual_model.endswith(f"/{model_id}"))
                 or (actual_model and actual_model.endswith(f":{model_id}"))
             )
-            
+
             if matches:
                 model_name = deployment.get("model_name")
                 if model_name:
                     return model_name
-        
+
         # No match found
         return None
 
@@ -7767,14 +7772,18 @@ class Router:
             request_kwargs=request_kwargs,
         )
 
-        verbose_router_logger.debug(f"healthy_deployments after team filter: {healthy_deployments}")
+        verbose_router_logger.debug(
+            f"healthy_deployments after team filter: {healthy_deployments}"
+        )
 
         healthy_deployments = filter_web_search_deployments(
             healthy_deployments=healthy_deployments,
             request_kwargs=request_kwargs,
         )
 
-        verbose_router_logger.debug(f"healthy_deployments after web search filter: {healthy_deployments}")
+        verbose_router_logger.debug(
+            f"healthy_deployments after web search filter: {healthy_deployments}"
+        )
 
         if isinstance(healthy_deployments, dict):
             return healthy_deployments
diff --git a/litellm/router_strategy/tag_based_routing.py b/litellm/router_strategy/tag_based_routing.py
index b25c20eb281..e960e00a68f 100644
--- a/litellm/router_strategy/tag_based_routing.py
+++ b/litellm/router_strategy/tag_based_routing.py
@@ -20,17 +20,28 @@ else:
 
 
 def is_valid_deployment_tag(
-    deployment_tags: List[str], request_tags: List[str]
+    deployment_tags: List[str], request_tags: List[str], match_any: bool = True
 ) -> bool:
     """
-    Check if a tag is valid
+    Check if a tag is valid, the matching can be either any or all based on `match_any` flag
     """
+    if not request_tags:
+        return False
 
-    if any(tag in deployment_tags for tag in request_tags):
+    dep_set = set(deployment_tags)
+    req_set = set(request_tags)
+
+    if match_any:
+        is_valid_deployment = bool(dep_set & req_set)
+    else:
+        is_valid_deployment = req_set.issubset(dep_set)
+
+    if is_valid_deployment:
         verbose_logger.debug(
-            "adding deployment with tags: %s, request tags: %s",
+            "adding deployment with tags: %s, request tags: %s for match_any=%s",
             deployment_tags,
             request_tags,
+            match_any,
         )
         return True
     return False
@@ -68,6 +79,7 @@ async def get_deployments_for_tag(
     if metadata_variable_name in request_kwargs:
         metadata = request_kwargs[metadata_variable_name]
         request_tags = metadata.get("tags")
+        match_any = llm_router_instance.tag_filtering_match_any
 
         new_healthy_deployments = []
         default_deployments = []
@@ -76,7 +88,6 @@ async def get_deployments_for_tag(
                 "get_deployments_for_tag routing: router_keys: %s", request_tags
             )
             # example this can be router_keys=["free", "custom"]
-            # get all deployments that have a superset of these router keys
             for deployment in healthy_deployments:
                 deployment_litellm_params = deployment.get("litellm_params")
                 deployment_tags = deployment_litellm_params.get("tags")
@@ -90,7 +101,7 @@ async def get_deployments_for_tag(
                 if deployment_tags is None:
                     continue
 
-                if is_valid_deployment_tag(deployment_tags, request_tags):
+                if is_valid_deployment_tag(deployment_tags, request_tags, match_any):
                     new_healthy_deployments.append(deployment)
 
                 if "default" in deployment_tags:
diff --git a/litellm/types/files.py b/litellm/types/files.py
index 600ad806e22..8b87b33cd1b 100644
--- a/litellm/types/files.py
+++ b/litellm/types/files.py
@@ -1,6 +1,8 @@
 from enum import Enum
 from types import MappingProxyType
-from typing import List, Set, Mapping
+from typing import Any, Dict, List, Literal, Mapping, Set, Union
+
+from typing_extensions import Required, TypedDict
 
 """
 Base Enums/Consts
@@ -281,3 +283,41 @@ GEMINI_1_5_ACCEPTED_FILE_TYPES: Set[FileType] = {
 
 def is_gemini_1_5_accepted_file_type(file_type: FileType) -> bool:
     return file_type in GEMINI_1_5_ACCEPTED_FILE_TYPES
+
+
+"""
+Two-Step File Upload Types
+"""
+
+
+class TwoStepFileUploadRequest(TypedDict):
+    """
+    Request structure for two-step file upload process.
+    
+    Step 1: Initial request to get upload URL
+    Step 2: Upload file content to the upload URL
+    
+    Used by providers like Manus and Google Cloud Storage.
+    """
+
+    method: Required[str]
+    url: Required[str]
+    headers: Required[Dict[str, str]]
+    data: Required[Union[str, bytes, Dict[str, Any]]]
+
+
+class TwoStepFileUploadConfig(TypedDict, total=False):
+    """
+    Configuration for two-step file upload process.
+    
+    Properties:
+        initial_request: Request to create file record and get upload URL
+        upload_request: Request to upload actual file content
+        upload_url_location: Where to find upload URL ('headers' or 'body')
+        upload_url_key: Key name for upload URL in response (default: 'upload_url')
+    """
+
+    initial_request: Required[TwoStepFileUploadRequest]
+    upload_request: Required[TwoStepFileUploadRequest]
+    upload_url_location: Required[Literal["headers", "body"]]
+    upload_url_key: str
diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py
index 6a254fc8252..88dee19ae5a 100644
--- a/litellm/types/integrations/prometheus.py
+++ b/litellm/types/integrations/prometheus.py
@@ -1,7 +1,7 @@
 import re
 from dataclasses import dataclass
 from enum import Enum
-from typing import Dict, List, Literal, Optional, Tuple, Union
+from typing import Dict, List, Literal, Optional, Tuple
 
 from pydantic import BaseModel, Field
 from typing_extensions import Annotated
@@ -185,6 +185,14 @@ DEFINED_PROMETHEUS_METRICS = Literal[
     "litellm_redis_daily_spend_update_queue_size",
     "litellm_in_memory_spend_update_queue_size",
     "litellm_redis_spend_update_queue_size",
+    "litellm_request_queue_time_seconds",
+    "litellm_guardrail_latency_seconds",
+    "litellm_guardrail_errors_total",
+    "litellm_guardrail_requests_total",
+    # Cache metrics
+    "litellm_cache_hits_metric",
+    "litellm_cache_misses_metric",
+    "litellm_cached_tokens_metric",
 ]
 
 
@@ -219,6 +227,23 @@ class PrometheusMetricLabels:
         UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
     ]
 
+    litellm_request_queue_time_seconds = [
+        UserAPIKeyLabelNames.END_USER.value,
+        UserAPIKeyLabelNames.API_KEY_HASH.value,
+        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
+        UserAPIKeyLabelNames.REQUESTED_MODEL.value,
+        UserAPIKeyLabelNames.TEAM.value,
+        UserAPIKeyLabelNames.TEAM_ALIAS.value,
+        UserAPIKeyLabelNames.USER.value,
+        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
+    ]
+
+    # Guardrail metrics - these use custom labels (guardrail_name, status, error_type, hook_type)
+    # which are not part of UserAPIKeyLabelNames
+    litellm_guardrail_latency_seconds: List[str] = []
+    litellm_guardrail_errors_total: List[str] = []
+    litellm_guardrail_requests_total: List[str] = []
+
     litellm_proxy_total_requests_metric = [
         UserAPIKeyLabelNames.END_USER.value,
         UserAPIKeyLabelNames.API_KEY_HASH.value,
@@ -436,6 +461,21 @@ class PrometheusMetricLabels:
 
     litellm_redis_spend_update_queue_size: List[str] = []
 
+    # Cache metrics - track cache hits, misses, and tokens served from cache
+    _cache_metric_labels = [
+        UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value,
+        UserAPIKeyLabelNames.API_KEY_HASH.value,
+        UserAPIKeyLabelNames.API_KEY_ALIAS.value,
+        UserAPIKeyLabelNames.TEAM.value,
+        UserAPIKeyLabelNames.TEAM_ALIAS.value,
+        UserAPIKeyLabelNames.END_USER.value,
+        UserAPIKeyLabelNames.USER.value,
+    ]
+
+    litellm_cache_hits_metric = _cache_metric_labels
+    litellm_cache_misses_metric = _cache_metric_labels
+    litellm_cached_tokens_metric = _cache_metric_labels
+
     @staticmethod
     def get_labels(label_name: DEFINED_PROMETHEUS_METRICS) -> List[str]:
         default_labels = getattr(PrometheusMetricLabels, label_name)
@@ -460,11 +500,6 @@ class PrometheusMetricLabels:
         return default_labels + custom_labels
 
 
-from typing import List, Optional
-
-from pydantic import BaseModel, Field
-
-
 class UserAPIKeyLabelValues(BaseModel):
     end_user: Annotated[
         Optional[str], Field(..., alias=UserAPIKeyLabelNames.END_USER.value)
diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py
index 74853956e60..ef2f1ba4d5e 100644
--- a/litellm/types/llms/bedrock.py
+++ b/litellm/types/llms/bedrock.py
@@ -128,14 +128,19 @@ class ConverseTokenUsageBlock(TypedDict):
     cacheWriteInputTokens: int
 
 
-class ConverseResponseBlock(TypedDict):
+class ServiceTierBlock(TypedDict):
+    type: Literal["priority", "default", "flex"]
+
+
+class ConverseResponseBlock(TypedDict, total=False):
     additionalModelResponseFields: dict
     metrics: ConverseMetricsBlock
-    output: ConverseResponseOutputBlock
-    stopReason: (
-        str  # end_turn | tool_use | max_tokens | stop_sequence | content_filtered
-    )
-    usage: ConverseTokenUsageBlock
+    output: Required[ConverseResponseOutputBlock]
+    stopReason: Required[
+        str
+    ]  # end_turn | tool_use | max_tokens | stop_sequence | content_filtered
+    usage: Required[ConverseTokenUsageBlock]
+    serviceTier: ServiceTierBlock  # Optional - only present when serviceTier was sent in request
 
 
 class ToolJsonSchemaBlock(TypedDict, total=False):
@@ -216,10 +221,6 @@ class PerformanceConfigBlock(TypedDict):
     latency: Literal["optimized", "throughput"]
 
 
-class ServiceTierBlock(TypedDict):
-    type: Literal["priority", "default", "flex"]
-
-
 class CommonRequestObject(
     TypedDict, total=False
 ):  # common request object across sync + async flows
diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py
index 381d91de762..74d6339887b 100644
--- a/litellm/types/llms/vertex_ai.py
+++ b/litellm/types/llms/vertex_ai.py
@@ -260,6 +260,7 @@ class UsageMetadata(TypedDict, total=False):
     responseTokenCount: int
     cachedContentTokenCount: int
     promptTokensDetails: List[PromptTokensDetails]
+    cacheTokensDetails: List[PromptTokensDetails]
     thoughtsTokenCount: int
     responseTokensDetails: List[PromptTokensDetails]
     candidatesTokensDetails: List[PromptTokensDetails]  # Alternative key name used in some responses
diff --git a/litellm/types/management_endpoints/router_settings_endpoints.py b/litellm/types/management_endpoints/router_settings_endpoints.py
index 9e3002ecf45..8b05c1483e8 100644
--- a/litellm/types/management_endpoints/router_settings_endpoints.py
+++ b/litellm/types/management_endpoints/router_settings_endpoints.py
@@ -184,6 +184,14 @@ ROUTER_SETTINGS_FIELDS: List[RouterSettingsField] = [
         field_default=False,
         ui_field_name="Enable Tag Filtering",
         link="https://docs.litellm.ai/docs/proxy/tag_routing",
+    ),    
+    RouterSettingsField(
+        field_name="tag_filtering_match_any",
+        field_type="Boolean",
+        field_value=None,
+        field_description="Match any tag instead of all tags for tag-based routing",
+        field_default=True,
+        ui_field_name="Tag Filtering Match Any",
     ),
     RouterSettingsField(
         field_name="disable_cooldowns",
diff --git a/litellm/types/mcp_server/mcp_server_manager.py b/litellm/types/mcp_server/mcp_server_manager.py
index 96fd79f466b..94f33ffb297 100644
--- a/litellm/types/mcp_server/mcp_server_manager.py
+++ b/litellm/types/mcp_server/mcp_server_manager.py
@@ -1,3 +1,4 @@
+from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, ConfigDict
@@ -50,4 +51,5 @@ class MCPServer(BaseModel):
     env: Optional[Dict[str, str]] = None
     access_groups: Optional[List[str]] = None
     allow_all_keys: bool = False
+    updated_at: Optional[datetime] = None
     model_config = ConfigDict(arbitrary_types_allowed=True)
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 3817f46c3e2..891826787ef 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -3016,6 +3016,7 @@ class LlmProviders(str, Enum):
     AUTO_ROUTER = "auto_router"
     VERCEL_AI_GATEWAY = "vercel_ai_gateway"
     DOTPROMPT = "dotprompt"
+    MANUS = "manus"
     WANDB = "wandb"
     OVHCLOUD = "ovhcloud"
     LEMONADE = "lemonade"
@@ -3028,6 +3029,7 @@ class LlmProviders(str, Enum):
     NANOGPT = "nano-gpt"
     POE = "poe"
     CHUTES = "chutes"
+    XIAOMI_MIMO = "xiaomi_mimo"
 
 
 
diff --git a/litellm/utils.py b/litellm/utils.py
index fbbaa94f7a1..fc8982f382f 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -47,8 +47,8 @@ from tiktoken import Encoding
 from tokenizers import Tokenizer
 
 import litellm
-
 import litellm.litellm_core_utils
+
 # audio_utils.utils is lazy-loaded - only imported when needed for transcription calls
 import litellm.litellm_core_utils.json_validation_rule
 from litellm._lazy_imports import (
@@ -72,8 +72,6 @@ from litellm.constants import (
     TOOL_CHOICE_OBJECT_TOKEN_COUNT,
 )
 
-
-
 _CachingHandlerResponse = None
 _LLMCachingHandler = None
 _CustomGuardrail = None
@@ -2895,6 +2893,7 @@ def get_optional_params_image_gen(
                     litellm.drop_params is True or drop_params is True
                 ) and k not in supported_params:  # drop the unsupported non-default values
                     non_default_params.pop(k, None)
+                    passed_params.pop(k, None)
                 elif k not in supported_params:
                     raise UnsupportedParamsError(
                         status_code=500,
@@ -7196,6 +7195,29 @@ def has_tool_call_blocks(messages: List[AllMessageValues]) -> bool:
     return False
 
 
+def any_assistant_message_has_thinking_blocks(
+    messages: List[AllMessageValues],
+) -> bool:
+    """
+    Returns true if ANY assistant message has thinking_blocks.
+
+    This is used to prevent dropping the thinking param when some messages
+    in the conversation already contain thinking blocks. Dropping thinking
+    when thinking blocks exist causes Anthropic error:
+    "When thinking is disabled, an assistant message cannot contain thinking"
+
+    Related issue: https://github.com/BerriAI/litellm/issues/18926
+    """
+    for message in messages:
+        if message.get("role") == "assistant":
+            thinking_blocks = message.get("thinking_blocks")
+            if thinking_blocks is not None and (
+                not hasattr(thinking_blocks, "__len__") or len(thinking_blocks) > 0
+            ):
+                return True
+    return False
+
+
 def last_assistant_with_tool_calls_has_no_thinking_blocks(
     messages: List[AllMessageValues],
 ) -> bool:
@@ -7208,6 +7230,10 @@ def last_assistant_with_tool_calls_has_no_thinking_blocks(
     When thinking is enabled, assistant messages with tool_calls must include thinking_blocks.
     If the client didn't preserve thinking_blocks, we need to drop the thinking param.
 
+    IMPORTANT: This should only be used in conjunction with
+    any_assistant_message_has_thinking_blocks() to ensure we don't drop thinking
+    when other messages in the conversation contain thinking blocks.
+
     Related issues: https://github.com/BerriAI/litellm/issues/14194, https://github.com/BerriAI/litellm/issues/9020
     """
     # Find the last assistant message with tool_calls
@@ -7410,15 +7436,184 @@ def validate_chat_completion_tool_choice(
 
 
 class ProviderConfigManager:
+    # Dictionary mapping for O(1) provider lookup
+    # Stores tuples of (factory_function, needs_model_parameter)
+    # This is initialized lazily on first access to avoid circular imports
+    _PROVIDER_CONFIG_MAP: Optional[dict[LlmProviders, tuple[Callable, bool]]] = None
+
+    @staticmethod
+    def _build_provider_config_map() -> dict[LlmProviders, tuple[Callable, bool]]:
+        """Build the provider-to-config mapping dictionary.
+        
+        Returns a dict mapping provider to (factory_function, needs_model_parameter).
+        This avoids expensive inspect.signature() calls at runtime.
+        """
+        return {
+            # Most common providers first for readability
+            # Format: (factory_function, needs_model_parameter: bool)
+            LlmProviders.OPENAI: (lambda: litellm.OpenAIGPTConfig(), False),
+            LlmProviders.ANTHROPIC: (lambda: litellm.AnthropicConfig(), False),
+            LlmProviders.AZURE: (lambda model: ProviderConfigManager._get_azure_config(model), True),
+            LlmProviders.AZURE_AI: (lambda model: ProviderConfigManager._get_azure_ai_config(model), True),
+            LlmProviders.VERTEX_AI: (lambda model: ProviderConfigManager._get_vertex_ai_config(model), True),
+            LlmProviders.BEDROCK: (lambda model: ProviderConfigManager._get_bedrock_config(model), True),
+            LlmProviders.COHERE: (lambda model: ProviderConfigManager._get_cohere_config(model), True),
+            LlmProviders.COHERE_CHAT: (lambda model: ProviderConfigManager._get_cohere_config(model), True),
+            # Simple provider mappings (no model parameter needed)
+            LlmProviders.DEEPSEEK: (lambda: litellm.DeepSeekChatConfig(), False),
+            LlmProviders.GROQ: (lambda: litellm.GroqChatConfig(), False),
+            LlmProviders.BYTEZ: (lambda: litellm.BytezChatConfig(), False),
+            LlmProviders.DATABRICKS: (lambda: litellm.DatabricksConfig(), False),
+            LlmProviders.XAI: (lambda: litellm.XAIChatConfig(), False),
+            LlmProviders.ZAI: (lambda: litellm.ZAIChatConfig(), False),
+            LlmProviders.LAMBDA_AI: (lambda: litellm.LambdaAIChatConfig(), False),
+            LlmProviders.LLAMA: (lambda: litellm.LlamaAPIConfig(), False),
+            LlmProviders.TEXT_COMPLETION_OPENAI: (lambda: litellm.OpenAITextCompletionConfig(), False),
+            LlmProviders.SNOWFLAKE: (lambda: litellm.SnowflakeConfig(), False),
+            LlmProviders.CLARIFAI: (lambda: litellm.ClarifaiConfig(), False),
+            LlmProviders.ANTHROPIC_TEXT: (lambda: litellm.AnthropicTextConfig(), False),
+            LlmProviders.VERTEX_AI_BETA: (lambda: litellm.VertexGeminiConfig(), False),
+            LlmProviders.CLOUDFLARE: (lambda: litellm.CloudflareChatConfig(), False),
+            LlmProviders.SAGEMAKER_CHAT: (lambda: litellm.SagemakerChatConfig(), False),
+            LlmProviders.SAGEMAKER: (lambda: litellm.SagemakerConfig(), False),
+            LlmProviders.FIREWORKS_AI: (lambda: litellm.FireworksAIConfig(), False),
+            LlmProviders.FRIENDLIAI: (lambda: litellm.FriendliaiChatConfig(), False),
+            LlmProviders.WATSONX: (lambda: litellm.IBMWatsonXChatConfig(), False),
+            LlmProviders.WATSONX_TEXT: (lambda: litellm.IBMWatsonXAIConfig(), False),
+            LlmProviders.EMPOWER: (lambda: litellm.EmpowerChatConfig(), False),
+            LlmProviders.MINIMAX: (lambda: litellm.MinimaxChatConfig(), False),
+            LlmProviders.GITHUB: (lambda: litellm.GithubChatConfig(), False),
+            LlmProviders.COMPACTIFAI: (lambda: litellm.CompactifAIChatConfig(), False),
+            LlmProviders.GITHUB_COPILOT: (lambda: litellm.GithubCopilotConfig(), False),
+            LlmProviders.GIGACHAT: (lambda: litellm.GigaChatConfig(), False),
+            LlmProviders.RAGFLOW: (lambda: litellm.RAGFlowConfig(), False),
+            LlmProviders.CUSTOM: (lambda: litellm.OpenAILikeChatConfig(), False),
+            LlmProviders.CUSTOM_OPENAI: (lambda: litellm.OpenAILikeChatConfig(), False),
+            LlmProviders.OPENAI_LIKE: (lambda: litellm.OpenAILikeChatConfig(), False),
+            LlmProviders.AIOHTTP_OPENAI: (lambda: litellm.AiohttpOpenAIChatConfig(), False),
+            LlmProviders.HOSTED_VLLM: (lambda: litellm.HostedVLLMChatConfig(), False),
+            LlmProviders.LLAMAFILE: (lambda: litellm.LlamafileChatConfig(), False),
+            LlmProviders.LM_STUDIO: (lambda: litellm.LMStudioChatConfig(), False),
+            LlmProviders.GALADRIEL: (lambda: litellm.GaladrielChatConfig(), False),
+            LlmProviders.REPLICATE: (lambda: litellm.ReplicateConfig(), False),
+            LlmProviders.HUGGINGFACE: (lambda: litellm.HuggingFaceChatConfig(), False),
+            LlmProviders.TOGETHER_AI: (lambda: litellm.TogetherAIConfig(), False),
+            LlmProviders.OPENROUTER: (lambda: litellm.OpenrouterConfig(), False),
+            LlmProviders.VERCEL_AI_GATEWAY: (lambda: litellm.VercelAIGatewayConfig(), False),
+            LlmProviders.COMETAPI: (lambda: litellm.CometAPIConfig(), False),
+            LlmProviders.DATAROBOT: (lambda: litellm.DataRobotConfig(), False),
+            LlmProviders.GEMINI: (lambda: litellm.GoogleAIStudioGeminiConfig(), False),
+            LlmProviders.AI21: (lambda: litellm.AI21ChatConfig(), False),
+            LlmProviders.AI21_CHAT: (lambda: litellm.AI21ChatConfig(), False),
+            LlmProviders.AZURE_TEXT: (lambda: litellm.AzureOpenAITextConfig(), False),
+            LlmProviders.NLP_CLOUD: (lambda: litellm.NLPCloudConfig(), False),
+            LlmProviders.OOBABOOGA: (lambda: litellm.OobaboogaConfig(), False),
+            LlmProviders.OLLAMA_CHAT: (lambda: litellm.OllamaChatConfig(), False),
+            LlmProviders.DEEPINFRA: (lambda: litellm.DeepInfraConfig(), False),
+            LlmProviders.PERPLEXITY: (lambda: litellm.PerplexityChatConfig(), False),
+            LlmProviders.MISTRAL: (lambda: litellm.MistralConfig(), False),
+            LlmProviders.CODESTRAL: (lambda: litellm.MistralConfig(), False),
+            LlmProviders.NVIDIA_NIM: (lambda: litellm.NvidiaNimConfig(), False),
+            LlmProviders.CEREBRAS: (lambda: litellm.CerebrasConfig(), False),
+            LlmProviders.BASETEN: (lambda: litellm.BasetenConfig(), False),
+            LlmProviders.VOLCENGINE: (lambda: litellm.VolcEngineConfig(), False),
+            LlmProviders.TEXT_COMPLETION_CODESTRAL: (lambda: litellm.CodestralTextCompletionConfig(), False),
+            LlmProviders.SAMBANOVA: (lambda: litellm.SambanovaConfig(), False),
+            LlmProviders.MARITALK: (lambda: litellm.MaritalkConfig(), False),
+            LlmProviders.VLLM: (lambda: litellm.VLLMConfig(), False),
+            LlmProviders.OLLAMA: (lambda: litellm.OllamaConfig(), False),
+            LlmProviders.PREDIBASE: (lambda: litellm.PredibaseConfig(), False),
+            LlmProviders.TRITON: (lambda: litellm.TritonConfig(), False),
+            LlmProviders.PETALS: (lambda: litellm.PetalsConfig(), False),
+            LlmProviders.SAP_GENERATIVE_AI_HUB: (lambda: litellm.GenAIHubOrchestrationConfig(), False),
+            LlmProviders.FEATHERLESS_AI: (lambda: litellm.FeatherlessAIConfig(), False),
+            LlmProviders.NOVITA: (lambda: litellm.NovitaConfig(), False),
+            LlmProviders.NEBIUS: (lambda: litellm.NebiusConfig(), False),
+            LlmProviders.WANDB: (lambda: litellm.WandbConfig(), False),
+            LlmProviders.DASHSCOPE: (lambda: litellm.DashScopeChatConfig(), False),
+            LlmProviders.MOONSHOT: (lambda: litellm.MoonshotChatConfig(), False),
+            LlmProviders.DOCKER_MODEL_RUNNER: (lambda: litellm.DockerModelRunnerChatConfig(), False),
+            LlmProviders.V0: (lambda: litellm.V0ChatConfig(), False),
+            LlmProviders.MORPH: (lambda: litellm.MorphChatConfig(), False),
+            LlmProviders.LITELLM_PROXY: (lambda: litellm.LiteLLMProxyChatConfig(), False),
+            LlmProviders.GRADIENT_AI: (lambda: litellm.GradientAIConfig(), False),
+            LlmProviders.NSCALE: (lambda: litellm.NscaleConfig(), False),
+            LlmProviders.HEROKU: (lambda: litellm.HerokuChatConfig(), False),
+            LlmProviders.OCI: (lambda: litellm.OCIChatConfig(), False),
+            LlmProviders.HYPERBOLIC: (lambda: litellm.HyperbolicChatConfig(), False),
+            LlmProviders.OVHCLOUD: (lambda: litellm.OVHCloudChatConfig(), False),
+            LlmProviders.AMAZON_NOVA: (lambda: litellm.AmazonNovaChatConfig(), False),
+            LlmProviders.LANGGRAPH: (lambda: ProviderConfigManager._get_langgraph_config(), False),
+        }
+
+    @staticmethod
+    def _get_azure_config(model: str) -> BaseConfig:
+        """Get Azure config based on model type."""
+        if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
+            return litellm.AzureOpenAIO1Config()
+        if litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model):
+            return litellm.AzureOpenAIGPT5Config()
+        return litellm.AzureOpenAIConfig()
+
+    @staticmethod
+    def _get_azure_ai_config(model: str) -> BaseConfig:
+        """Get Azure AI config based on model type."""
+        if "claude" in model.lower():
+            return litellm.AzureAnthropicConfig()
+        return litellm.AzureAIStudioConfig()
+
+    @staticmethod
+    def _get_vertex_ai_config(model: str) -> BaseConfig:
+        """Get Vertex AI config based on model type."""
+        if "gemini" in model:
+            return litellm.VertexGeminiConfig()
+        elif "claude" in model:
+            return litellm.VertexAIAnthropicConfig()
+        elif "gpt-oss" in model:
+            from litellm.llms.vertex_ai.vertex_ai_partner_models.gpt_oss.transformation import (
+                VertexAIGPTOSSTransformation,
+            )
+            return VertexAIGPTOSSTransformation()
+        elif model in litellm.vertex_mistral_models:
+            if "codestral" in model:
+                return litellm.CodestralTextCompletionConfig()
+            return litellm.MistralConfig()
+        elif model in litellm.vertex_ai_ai21_models:
+            return litellm.VertexAIAi21Config()
+        else:
+            return litellm.VertexAILlama3Config()
+
+    @staticmethod
+    def _get_bedrock_config(model: str) -> BaseConfig:
+        """Get Bedrock config based on model."""
+        from litellm.llms.bedrock.common_utils import get_bedrock_chat_config
+        return get_bedrock_chat_config(model=model)
+
+    @staticmethod
+    def _get_cohere_config(model: str) -> BaseConfig:
+        """Get Cohere config based on route."""
+        CohereModelInfo = getattr(sys.modules[__name__], 'CohereModelInfo')
+        route = CohereModelInfo.get_cohere_route(model)
+        if route == "v2":
+            return litellm.CohereV2ChatConfig()
+        return litellm.CohereChatConfig()
+
+    @staticmethod
+    def _get_langgraph_config() -> BaseConfig:
+        """Get LangGraph config."""
+        from litellm.llms.langgraph.chat.transformation import LangGraphConfig
+        return LangGraphConfig()
+
     @staticmethod
     def get_provider_chat_config(  # noqa: PLR0915
         model: str, provider: LlmProviders
     ) -> Optional[BaseConfig]:
         """
         Returns the provider config for a given provider.
+        
+        Uses O(1) dictionary lookup for fast provider resolution.
         """
-
-        # Check JSON providers FIRST
+        # Check JSON providers FIRST (these override standard mappings)
         from litellm.llms.openai_like.dynamic_config import create_config_class
         from litellm.llms.openai_like.json_loader import JSONProviderRegistry
 
@@ -7428,244 +7623,29 @@ class ProviderConfigManager:
                 raise ValueError(f"Provider {provider.value} not found")
             return create_config_class(provider_config)()
 
-        if (
-            provider == LlmProviders.OPENAI
-            and litellm.openaiOSeriesConfig.is_model_o_series_model(model=model)
-        ):
-            return litellm.openaiOSeriesConfig
-        elif (
-            provider == LlmProviders.OPENAI
-            and litellm.OpenAIGPT5Config.is_model_gpt_5_model(model=model)
-        ):
-            return litellm.OpenAIGPT5Config()
-        elif litellm.LlmProviders.DEEPSEEK == provider:
-            return litellm.DeepSeekChatConfig()
-        elif litellm.LlmProviders.GROQ == provider:
-            return litellm.GroqChatConfig()
-        elif litellm.LlmProviders.BYTEZ == provider:
-            return litellm.BytezChatConfig()
-        elif litellm.LlmProviders.DATABRICKS == provider:
-            return litellm.DatabricksConfig()
-        elif litellm.LlmProviders.XAI == provider:
-            return litellm.XAIChatConfig()
-        elif litellm.LlmProviders.ZAI == provider:
-            return litellm.ZAIChatConfig()
-        elif litellm.LlmProviders.LAMBDA_AI == provider:
-            return litellm.LambdaAIChatConfig()
-        elif litellm.LlmProviders.LLAMA == provider:
-            return litellm.LlamaAPIConfig()
-        elif litellm.LlmProviders.TEXT_COMPLETION_OPENAI == provider:
-            return litellm.OpenAITextCompletionConfig()
-        elif (
-            litellm.LlmProviders.COHERE_CHAT == provider
-            or litellm.LlmProviders.COHERE == provider
-        ):
-            CohereModelInfo = getattr(sys.modules[__name__], 'CohereModelInfo')
-            route = CohereModelInfo.get_cohere_route(model)
-            if route == "v2":
-                return litellm.CohereV2ChatConfig()
-            else:
+        # Handle OpenAI special cases (O-series and GPT-5 models)
+        if provider == LlmProviders.OPENAI:
+            if litellm.openaiOSeriesConfig.is_model_o_series_model(model=model):
+                return litellm.openaiOSeriesConfig
+            if litellm.OpenAIGPT5Config.is_model_gpt_5_model(model=model):
+                return litellm.OpenAIGPT5Config()
 
-                return litellm.CohereChatConfig()
-        elif litellm.LlmProviders.SNOWFLAKE == provider:
-            return litellm.SnowflakeConfig()
-        elif litellm.LlmProviders.CLARIFAI == provider:
-            return litellm.ClarifaiConfig()
-        elif litellm.LlmProviders.ANTHROPIC == provider:
-            return litellm.AnthropicConfig()
-        elif litellm.LlmProviders.ANTHROPIC_TEXT == provider:
-            return litellm.AnthropicTextConfig()
-        elif litellm.LlmProviders.VERTEX_AI_BETA == provider:
-            return litellm.VertexGeminiConfig()
-        elif litellm.LlmProviders.VERTEX_AI == provider:
-            if "gemini" in model:
-                return litellm.VertexGeminiConfig()
-            elif "claude" in model:
-                return litellm.VertexAIAnthropicConfig()
-            elif "gpt-oss" in model:
-                from litellm.llms.vertex_ai.vertex_ai_partner_models.gpt_oss.transformation import (
-                    VertexAIGPTOSSTransformation,
-                )
+        # Initialize provider config map lazily (avoids circular imports)
+        if ProviderConfigManager._PROVIDER_CONFIG_MAP is None:
+            ProviderConfigManager._PROVIDER_CONFIG_MAP = ProviderConfigManager._build_provider_config_map()
 
-                return VertexAIGPTOSSTransformation()
-            elif model in litellm.vertex_mistral_models:
-                if "codestral" in model:
-                    return litellm.CodestralTextCompletionConfig()
-                else:
-                    return litellm.MistralConfig()
-            elif model in litellm.vertex_ai_ai21_models:
-                return litellm.VertexAIAi21Config()
-            else:  # use generic openai-like param mapping
-                return litellm.VertexAILlama3Config()
-        elif litellm.LlmProviders.CLOUDFLARE == provider:
-            return litellm.CloudflareChatConfig()
-        elif litellm.LlmProviders.SAGEMAKER_CHAT == provider:
-            return litellm.SagemakerChatConfig()
-        elif litellm.LlmProviders.SAGEMAKER == provider:
-            return litellm.SagemakerConfig()
-        elif litellm.LlmProviders.FIREWORKS_AI == provider:
-            return litellm.FireworksAIConfig()
-        elif litellm.LlmProviders.FRIENDLIAI == provider:
-            return litellm.FriendliaiChatConfig()
-        elif litellm.LlmProviders.WATSONX == provider:
-            return litellm.IBMWatsonXChatConfig()
-        elif litellm.LlmProviders.WATSONX_TEXT == provider:
-            return litellm.IBMWatsonXAIConfig()
-        elif litellm.LlmProviders.EMPOWER == provider:
-            return litellm.EmpowerChatConfig()
-        elif litellm.LlmProviders.MINIMAX == provider:            
-            return litellm.MinimaxChatConfig()
-        elif litellm.LlmProviders.GITHUB == provider:
-            return litellm.GithubChatConfig()
-        elif litellm.LlmProviders.COMPACTIFAI == provider:
-            return litellm.CompactifAIChatConfig()
-        elif litellm.LlmProviders.GITHUB_COPILOT == provider:
-            return litellm.GithubCopilotConfig()
-        elif litellm.LlmProviders.GIGACHAT == provider:
-            return litellm.GigaChatConfig()
-        elif litellm.LlmProviders.RAGFLOW == provider:
-            return litellm.RAGFlowConfig()
-        elif (
-            litellm.LlmProviders.CUSTOM == provider
-            or litellm.LlmProviders.CUSTOM_OPENAI == provider
-            or litellm.LlmProviders.OPENAI_LIKE == provider
-        ):
-            return litellm.OpenAILikeChatConfig()
-        elif litellm.LlmProviders.AIOHTTP_OPENAI == provider:
-            return litellm.AiohttpOpenAIChatConfig()
-        elif litellm.LlmProviders.HOSTED_VLLM == provider:
-            return litellm.HostedVLLMChatConfig()
-        elif litellm.LlmProviders.LLAMAFILE == provider:
-            return litellm.LlamafileChatConfig()
-        elif litellm.LlmProviders.LM_STUDIO == provider:
-            return litellm.LMStudioChatConfig()
-        elif litellm.LlmProviders.GALADRIEL == provider:
-            return litellm.GaladrielChatConfig()
-        elif litellm.LlmProviders.REPLICATE == provider:
-            return litellm.ReplicateConfig()
-        elif litellm.LlmProviders.HUGGINGFACE == provider:
-            return litellm.HuggingFaceChatConfig()
-        elif litellm.LlmProviders.TOGETHER_AI == provider:
-            return litellm.TogetherAIConfig()
-        elif litellm.LlmProviders.OPENROUTER == provider:
-            return litellm.OpenrouterConfig()
-        elif litellm.LlmProviders.VERCEL_AI_GATEWAY == provider:
-            return litellm.VercelAIGatewayConfig()
-        elif litellm.LlmProviders.COMETAPI == provider:
-            return litellm.CometAPIConfig()
-        elif litellm.LlmProviders.DATAROBOT == provider:
-            return litellm.DataRobotConfig()
-        elif litellm.LlmProviders.GEMINI == provider:
-            return litellm.GoogleAIStudioGeminiConfig()
-        elif (
-            litellm.LlmProviders.AI21 == provider
-            or litellm.LlmProviders.AI21_CHAT == provider
-        ):
-            return litellm.AI21ChatConfig()
-        elif litellm.LlmProviders.AZURE == provider:
-            if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
-                return litellm.AzureOpenAIO1Config()
-            if litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=model):
-                return litellm.AzureOpenAIGPT5Config()
-            return litellm.AzureOpenAIConfig()
-        elif litellm.LlmProviders.AZURE_AI == provider:
-            if "claude" in model.lower():
-                return litellm.AzureAnthropicConfig()
-            return litellm.AzureAIStudioConfig()
-        elif litellm.LlmProviders.AZURE_TEXT == provider:
-            return litellm.AzureOpenAITextConfig()
-        elif litellm.LlmProviders.HOSTED_VLLM == provider:
-            return litellm.HostedVLLMChatConfig()
-        elif litellm.LlmProviders.NLP_CLOUD == provider:
-            return litellm.NLPCloudConfig()
-        elif litellm.LlmProviders.OOBABOOGA == provider:
-            return litellm.OobaboogaConfig()
-        elif litellm.LlmProviders.OLLAMA_CHAT == provider:
-            return litellm.OllamaChatConfig()
-        elif litellm.LlmProviders.DEEPINFRA == provider:
-            return litellm.DeepInfraConfig()
-        elif litellm.LlmProviders.PERPLEXITY == provider:
-            return litellm.PerplexityChatConfig()
-        elif (
-            litellm.LlmProviders.MISTRAL == provider
-            or litellm.LlmProviders.CODESTRAL == provider
-        ):
-            return litellm.MistralConfig()
-        elif litellm.LlmProviders.NVIDIA_NIM == provider:
-            return litellm.NvidiaNimConfig()
-        elif litellm.LlmProviders.CEREBRAS == provider:
-            return litellm.CerebrasConfig()
-        elif litellm.LlmProviders.BASETEN == provider:
-            return litellm.BasetenConfig()
-        elif litellm.LlmProviders.VOLCENGINE == provider:
-            return litellm.VolcEngineConfig()
-        elif litellm.LlmProviders.TEXT_COMPLETION_CODESTRAL == provider:
-            return litellm.CodestralTextCompletionConfig()
-        elif litellm.LlmProviders.SAMBANOVA == provider:
-            return litellm.SambanovaConfig()
-        elif litellm.LlmProviders.MARITALK == provider:
-            return litellm.MaritalkConfig()
-        elif litellm.LlmProviders.CLOUDFLARE == provider:
-            return litellm.CloudflareChatConfig()
-        elif litellm.LlmProviders.ANTHROPIC_TEXT == provider:
-            return litellm.AnthropicTextConfig()
-        elif litellm.LlmProviders.VLLM == provider:
-            return litellm.VLLMConfig()
-        elif litellm.LlmProviders.OLLAMA == provider:
-            return litellm.OllamaConfig()
-        elif litellm.LlmProviders.PREDIBASE == provider:
-            return litellm.PredibaseConfig()
-        elif litellm.LlmProviders.TRITON == provider:
-            return litellm.TritonConfig()
-        elif litellm.LlmProviders.PETALS == provider:
-            return litellm.PetalsConfig()
-        elif litellm.LlmProviders.SAP_GENERATIVE_AI_HUB == provider:
-            return litellm.GenAIHubOrchestrationConfig()
-        elif litellm.LlmProviders.FEATHERLESS_AI == provider:
-            return litellm.FeatherlessAIConfig()
-        elif litellm.LlmProviders.NOVITA == provider:
-            return litellm.NovitaConfig()
-        elif litellm.LlmProviders.NEBIUS == provider:
-            return litellm.NebiusConfig()
-        elif litellm.LlmProviders.WANDB == provider:
-            return litellm.WandbConfig()
-        elif litellm.LlmProviders.DASHSCOPE == provider:
-            return litellm.DashScopeChatConfig()
-        elif litellm.LlmProviders.MOONSHOT == provider:
-            return litellm.MoonshotChatConfig()
-        elif litellm.LlmProviders.DOCKER_MODEL_RUNNER == provider:
-            return litellm.DockerModelRunnerChatConfig()
-        elif litellm.LlmProviders.V0 == provider:
-            return litellm.V0ChatConfig()
-        elif litellm.LlmProviders.MORPH == provider:
-            return litellm.MorphChatConfig()
-        elif litellm.LlmProviders.BEDROCK == provider:
-            from litellm.llms.bedrock.common_utils import get_bedrock_chat_config
+        # O(1) dictionary lookup
+        config_entry = ProviderConfigManager._PROVIDER_CONFIG_MAP.get(provider)
+        if config_entry is None:
+            return None
 
-            return get_bedrock_chat_config(model=model)
-        elif litellm.LlmProviders.LITELLM_PROXY == provider:
-            return litellm.LiteLLMProxyChatConfig()
-        elif litellm.LlmProviders.OPENAI == provider:
-            return litellm.OpenAIGPTConfig()
-        elif litellm.LlmProviders.GRADIENT_AI == provider:
-            return litellm.GradientAIConfig()
-        elif litellm.LlmProviders.NSCALE == provider:
-            return litellm.NscaleConfig()
-        elif litellm.LlmProviders.HEROKU == provider:
-            return litellm.HerokuChatConfig()
-        elif litellm.LlmProviders.OCI == provider:
-            return litellm.OCIChatConfig()
-        elif litellm.LlmProviders.HYPERBOLIC == provider:
-            return litellm.HyperbolicChatConfig()
-        elif litellm.LlmProviders.OVHCLOUD == provider:
-            return litellm.OVHCloudChatConfig()
-        elif litellm.LlmProviders.AMAZON_NOVA == provider:
-            return litellm.AmazonNovaChatConfig()
-        elif litellm.LlmProviders.LANGGRAPH == provider:
-            from litellm.llms.langgraph.chat.transformation import LangGraphConfig
-
-            return LangGraphConfig()
-        return None
+        # Unpack factory function and whether it needs model parameter
+        # This avoids expensive inspect.signature() calls at runtime
+        config_factory, needs_model = config_entry
+        if needs_model:
+            return config_factory(model)  # type: ignore
+        else:
+            return config_factory()  # type: ignore
 
     @staticmethod
     def get_provider_embedding_config(
@@ -7718,6 +7698,11 @@ class ProviderConfigManager:
             return litellm.CometAPIEmbeddingConfig()
         elif litellm.LlmProviders.GITHUB_COPILOT == provider:
             return litellm.GithubCopilotEmbeddingConfig()
+        elif litellm.LlmProviders.OPENROUTER == provider:
+            from litellm.llms.openrouter.embedding.transformation import (
+                OpenrouterEmbeddingConfig,
+            )
+            return OpenrouterEmbeddingConfig()
         elif litellm.LlmProviders.GIGACHAT == provider:
             return litellm.GigaChatEmbeddingConfig()
         elif litellm.LlmProviders.SAGEMAKER == provider:
@@ -7871,6 +7856,8 @@ class ProviderConfigManager:
             return litellm.GithubCopilotResponsesAPIConfig()
         elif litellm.LlmProviders.LITELLM_PROXY == provider:
             return litellm.LiteLLMProxyResponsesAPIConfig()
+        elif litellm.LlmProviders.MANUS == provider:
+            return litellm.ManusResponsesAPIConfig()
         return None
 
     @staticmethod
@@ -7939,6 +7926,8 @@ class ProviderConfigManager:
             return litellm.LemonadeChatConfig()
         elif LlmProviders.CLARIFAI == provider:
             return litellm.ClarifaiConfig()
+        elif LlmProviders.BEDROCK == provider:
+            return litellm.llms.bedrock.common_utils.BedrockModelInfo()
         return None
 
     @staticmethod
@@ -7996,6 +7985,10 @@ class ProviderConfigManager:
             from litellm.llms.bedrock.files.transformation import BedrockFilesConfig
 
             return BedrockFilesConfig()
+        elif LlmProviders.MANUS == provider:
+            from litellm.llms.manus.files.transformation import ManusFilesConfig
+
+            return ManusFilesConfig()
         return None
 
     @staticmethod
diff --git a/mcp_servers.json b/mcp_servers.json
index 3e1afe0916f..3dd21c0d65f 100644
--- a/mcp_servers.json
+++ b/mcp_servers.json
@@ -14,5 +14,10 @@
     "linear": {
         "sse_url": "https://mcp.linear.app/sse",
         "description": "The Linear MCP server provides programmatic access to Linear's issue tracking capabilities."
+    },
+    "scrapegraph": {
+        "http_url": "https://smithery.ai/api/mcp/scrapegraph-mcp",
+        "sse_url": "https://smithery.ai/api/mcp/scrapegraph-mcp/sse",
+        "description": "The ScrapeGraph MCP server provides programmatic access to ScrapeGraph AI's web scraping capabilities, including smart scraping, web crawling, search scraping, and agentic scraping workflows."
     }
-}
\ No newline at end of file
+}
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index fb00f636409..ed218d50af7 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -410,8 +410,8 @@
         "max_input_tokens": 8172,
         "max_tokens": 8172,
         "mode": "embedding",
-        "input_cost_per_token": 1.35e-7,
-        "input_cost_per_image": 6e-5,
+        "input_cost_per_token": 1.35e-07,
+        "input_cost_per_image": 6e-05,
         "input_cost_per_video_per_second": 0.0007,
         "input_cost_per_audio_per_second": 0.00014,
         "output_cost_per_token": 0.0,
@@ -1398,8 +1398,8 @@
         "mode": "chat"
     },
     "azure_ai/gpt-oss-120b": {
-        "input_cost_per_token": 1.5e-7,
-        "output_cost_per_token": 6e-7,
+        "input_cost_per_token": 1.5e-07,
+        "output_cost_per_token": 6e-07,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 131072,
         "max_output_tokens": 131072,
@@ -2077,7 +2077,7 @@
         "litellm_provider": "azure",
         "max_input_tokens": 4097,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_function_calling": true,
@@ -2090,7 +2090,7 @@
         "litellm_provider": "azure",
         "max_input_tokens": 4097,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_function_calling": true,
@@ -2869,7 +2869,7 @@
             "/v1/audio/transcriptions"
         ]
     },
-   "azure/gpt-5.1-2025-11-13": {
+    "azure/gpt-5.1-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.5e-07,
         "input_cost_per_token": 1.25e-06,
@@ -2905,7 +2905,7 @@
         "supports_service_tier": true,
         "supports_vision": true
     },
-   "azure/gpt-5.1-chat-2025-11-13": {
+    "azure/gpt-5.1-chat-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.5e-07,
         "input_cost_per_token": 1.25e-06,
@@ -2940,7 +2940,7 @@
         "supports_tool_choice": false,
         "supports_vision": true
     },
-   "azure/gpt-5.1-codex-2025-11-13": {
+    "azure/gpt-5.1-codex-2025-11-13": {
         "cache_read_input_token_cost": 1.25e-07,
         "cache_read_input_token_cost_priority": 2.5e-07,
         "input_cost_per_token": 1.25e-06,
@@ -3298,7 +3298,7 @@
         "litellm_provider": "azure",
         "max_input_tokens": 272000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "mode": "responses",
         "output_cost_per_token": 0.00012,
         "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?pivots=azure-openai&tabs=global-standard-aoai%2Cstandard-chat-completions%2Cglobal-standard#gpt-5",
@@ -3623,7 +3623,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "responses",
-        "output_cost_per_token": 1.68e-04,
+        "output_cost_per_token": 0.000168,
         "supported_endpoints": [
             "/v1/batch",
             "/v1/responses"
@@ -3654,7 +3654,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "responses",
-        "output_cost_per_token": 1.68e-04,
+        "output_cost_per_token": 0.000168,
         "supported_endpoints": [
             "/v1/batch",
             "/v1/responses"
@@ -4297,13 +4297,13 @@
         "output_cost_per_token": 0.0
     },
     "azure/speech/azure-tts": {
-        "input_cost_per_character": 15e-06,
+        "input_cost_per_character": 1.5e-05,
         "litellm_provider": "azure",
         "mode": "audio_speech",
         "source": "https://azure.microsoft.com/en-us/pricing/calculator/"
     },
     "azure/speech/azure-tts-hd": {
-        "input_cost_per_character": 30e-06,
+        "input_cost_per_character": 3e-05,
         "litellm_provider": "azure",
         "mode": "audio_speech",
         "source": "https://azure.microsoft.com/en-us/pricing/calculator/"
@@ -5197,7 +5197,7 @@
     },
     "azure_ai/mistral-document-ai-2505": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 3e-3,
+        "ocr_cost_per_page": 0.003,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5206,7 +5206,7 @@
     },
     "azure_ai/doc-intelligence/prebuilt-read": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 1.5e-3,
+        "ocr_cost_per_page": 0.0015,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5215,7 +5215,7 @@
     },
     "azure_ai/doc-intelligence/prebuilt-layout": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 1e-2,
+        "ocr_cost_per_page": 0.01,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5224,7 +5224,7 @@
     },
     "azure_ai/doc-intelligence/prebuilt-document": {
         "litellm_provider": "azure_ai",
-        "ocr_cost_per_page": 1e-2,
+        "ocr_cost_per_page": 0.01,
         "mode": "ocr",
         "supported_endpoints": [
             "/v1/ocr"
@@ -5298,12 +5298,12 @@
         "mode": "rerank",
         "output_cost_per_token": 0.0
     },
-        "azure_ai/deepseek-v3.2": {
+    "azure_ai/deepseek-v3.2": {
         "input_cost_per_token": 5.8e-07,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 163840,
         "max_output_tokens": 163840,
-        "max_tokens": 8192,
+        "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
         "supports_assistant_prefill": true,
@@ -5317,7 +5317,7 @@
         "litellm_provider": "azure_ai",
         "max_input_tokens": 163840,
         "max_output_tokens": 163840,
-        "max_tokens": 8192,
+        "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
         "supports_assistant_prefill": true,
@@ -5452,7 +5452,7 @@
         "supports_web_search": true
     },
     "azure_ai/grok-4-fast-non-reasoning": {
-        "input_cost_per_token": 0.43e-06,
+        "input_cost_per_token": 4.3e-07,
         "output_cost_per_token": 1.73e-06,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 131072,
@@ -5465,7 +5465,7 @@
         "supports_web_search": true
     },
     "azure_ai/grok-4-fast-reasoning": {
-        "input_cost_per_token": 0.43e-06,
+        "input_cost_per_token": 4.3e-07,
         "output_cost_per_token": 1.73e-06,
         "litellm_provider": "azure_ai",
         "max_input_tokens": 131072,
@@ -5623,7 +5623,7 @@
         "litellm_provider": "text-completion-openai",
         "max_input_tokens": 16384,
         "max_output_tokens": 4096,
-        "max_tokens": 16384,
+        "max_tokens": 4096,
         "mode": "completion",
         "output_cost_per_token": 4e-07
     },
@@ -7038,7 +7038,7 @@
         "litellm_provider": "anthropic",
         "max_input_tokens": 1000000,
         "max_output_tokens": 64000,
-        "max_tokens": 1000000,
+        "max_tokens": 64000,
         "mode": "chat",
         "output_cost_per_token": 1.5e-05,
         "output_cost_per_token_above_200k_tokens": 2.25e-05,
@@ -7800,7 +7800,7 @@
         "litellm_provider": "deepseek",
         "max_input_tokens": 131072,
         "max_output_tokens": 8192,
-        "max_tokens": 131072,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.7e-06,
         "source": "https://api-docs.deepseek.com/quick_start/pricing",
@@ -7821,7 +7821,7 @@
         "litellm_provider": "deepseek",
         "max_input_tokens": 131072,
         "max_output_tokens": 65536,
-        "max_tokens": 131072,
+        "max_tokens": 65536,
         "mode": "chat",
         "output_cost_per_token": 1.7e-06,
         "source": "https://api-docs.deepseek.com/quick_start/pricing",
@@ -7842,7 +7842,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 16384,
-        "max_tokens": 1000000,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7854,7 +7854,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -7883,7 +7883,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -7913,7 +7913,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 30720,
         "max_output_tokens": 8192,
-        "max_tokens": 32768,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 6.4e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7926,7 +7926,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7939,7 +7939,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 8192,
-        "max_tokens": 131072,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -7952,7 +7952,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 4e-06,
         "output_cost_per_token": 1.2e-06,
@@ -7966,7 +7966,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 4e-06,
         "output_cost_per_token": 1.2e-06,
@@ -7979,7 +7979,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8010,7 +8010,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8041,7 +8041,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 32768,
-        "max_tokens": 1000000,
+        "max_tokens": 32768,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8073,7 +8073,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 5e-07,
         "output_cost_per_token": 2e-07,
@@ -8087,7 +8087,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192,
-        "max_tokens": 1000000,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 2e-07,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -8100,7 +8100,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 16384,
-        "max_tokens": 1000000,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 5e-07,
         "output_cost_per_token": 2e-07,
@@ -8114,7 +8114,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 1000000,
         "max_output_tokens": 16384,
-        "max_tokens": 1000000,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_reasoning_token": 5e-07,
         "output_cost_per_token": 2e-07,
@@ -8127,7 +8127,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 129024,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8138,7 +8138,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8187,7 +8187,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8232,7 +8232,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8281,7 +8281,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 997952,
         "max_output_tokens": 65536,
-        "max_tokens": 1000000,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8326,7 +8326,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 258048,
         "max_output_tokens": 65536,
-        "max_tokens": 262144,
+        "max_tokens": 65536,
         "mode": "chat",
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
         "supports_function_calling": true,
@@ -8364,7 +8364,7 @@
         "litellm_provider": "dashscope",
         "max_input_tokens": 98304,
         "max_output_tokens": 8192,
-        "max_tokens": 131072,
+        "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 2.4e-06,
         "source": "https://www.alibabacloud.com/help/en/model-studio/models",
@@ -8393,7 +8393,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 128000,
-        "max_tokens": 200000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8412,7 +8412,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8431,7 +8431,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 32000,
-        "max_tokens": 200000,
+        "max_tokens": 32000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8450,7 +8450,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 32000,
-        "max_tokens": 200000,
+        "max_tokens": 32000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8469,7 +8469,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8488,7 +8488,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8507,7 +8507,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8526,7 +8526,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 64000,
-        "max_tokens": 200000,
+        "max_tokens": 64000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8545,7 +8545,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 1048576,
         "max_output_tokens": 65535,
-        "max_tokens": 1048576,
+        "max_tokens": 65535,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8562,7 +8562,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 1048576,
         "max_output_tokens": 65536,
-        "max_tokens": 1048576,
+        "max_tokens": 65536,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8579,7 +8579,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 128000,
         "max_output_tokens": 32000,
-        "max_tokens": 128000,
+        "max_tokens": 32000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8594,7 +8594,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8609,7 +8609,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8624,7 +8624,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8639,7 +8639,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 400000,
         "max_output_tokens": 128000,
-        "max_tokens": 400000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8747,7 +8747,7 @@
         "litellm_provider": "databricks",
         "max_input_tokens": 200000,
         "max_output_tokens": 128000,
-        "max_tokens": 200000,
+        "max_tokens": 128000,
         "metadata": {
             "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
         },
@@ -8846,7 +8846,7 @@
         "litellm_provider": "text-completion-openai",
         "max_input_tokens": 16384,
         "max_output_tokens": 4096,
-        "max_tokens": 16384,
+        "max_tokens": 4096,
         "mode": "completion",
         "output_cost_per_token": 2e-06
     },
@@ -10107,7 +10107,7 @@
         "litellm_provider": "deepseek",
         "max_input_tokens": 163840,
         "max_output_tokens": 163840,
-        "max_tokens": 8192,
+        "max_tokens": 163840,
         "mode": "chat",
         "output_cost_per_token": 4e-07,
         "supports_assistant_prefill": true,
@@ -10121,7 +10121,7 @@
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 163840,
         "max_output_tokens": 81920,
-        "max_tokens": 163840,
+        "max_tokens": 81920,
         "mode": "chat",
         "output_cost_per_token": 1.68e-06,
         "supports_function_calling": true,
@@ -10202,14 +10202,14 @@
         "mode": "search",
         "tiered_pricing": [
             {
-                "input_cost_per_query": 5e-03,
+                "input_cost_per_query": 0.005,
                 "max_results_range": [
                     0,
                     25
                 ]
             },
             {
-                "input_cost_per_query": 25e-03,
+                "input_cost_per_query": 0.025,
                 "max_results_range": [
                     26,
                     100
@@ -10222,70 +10222,70 @@
         "mode": "search",
         "tiered_pricing": [
             {
-                "input_cost_per_query": 1.66e-03,
+                "input_cost_per_query": 0.00166,
                 "max_results_range": [
                     1,
                     10
                 ]
             },
             {
-                "input_cost_per_query": 3.32e-03,
+                "input_cost_per_query": 0.00332,
                 "max_results_range": [
                     11,
                     20
                 ]
             },
             {
-                "input_cost_per_query": 4.98e-03,
+                "input_cost_per_query": 0.00498,
                 "max_results_range": [
                     21,
                     30
                 ]
             },
             {
-                "input_cost_per_query": 6.64e-03,
+                "input_cost_per_query": 0.00664,
                 "max_results_range": [
                     31,
                     40
                 ]
             },
             {
-                "input_cost_per_query": 8.3e-03,
+                "input_cost_per_query": 0.0083,
                 "max_results_range": [
                     41,
                     50
                 ]
             },
             {
-                "input_cost_per_query": 9.96e-03,
+                "input_cost_per_query": 0.00996,
                 "max_results_range": [
                     51,
                     60
                 ]
             },
             {
-                "input_cost_per_query": 11.62e-03,
+                "input_cost_per_query": 0.01162,
                 "max_results_range": [
                     61,
                     70
                 ]
             },
             {
-                "input_cost_per_query": 13.28e-03,
+                "input_cost_per_query": 0.01328,
                 "max_results_range": [
                     71,
                     80
                 ]
             },
             {
-                "input_cost_per_query": 14.94e-03,
+                "input_cost_per_query": 0.01494,
                 "max_results_range": [
                     81,
                     90
                 ]
             },
             {
-                "input_cost_per_query": 16.6e-03,
+                "input_cost_per_query": 0.0166,
                 "max_results_range": [
                     91,
                     100
@@ -10297,7 +10297,7 @@
         }
     },
     "perplexity/search": {
-        "input_cost_per_query": 5e-03,
+        "input_cost_per_query": 0.005,
         "litellm_provider": "perplexity",
         "mode": "search"
     },
@@ -10395,7 +10395,7 @@
         "supports_embedding_image_input": true
     },
     "embed-multilingual-light-v3.0": {
-        "input_cost_per_token": 1e-04,
+        "input_cost_per_token": 0.0001,
         "litellm_provider": "cohere",
         "max_input_tokens": 1024,
         "max_tokens": 1024,
@@ -10689,7 +10689,7 @@
         "litellm_provider": "bedrock",
         "max_input_tokens": 128000,
         "max_output_tokens": 4096,
-        "max_tokens": 128000,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.3e-07,
         "supports_function_calling": true,
@@ -10700,7 +10700,7 @@
         "litellm_provider": "bedrock",
         "max_input_tokens": 128000,
         "max_output_tokens": 4096,
-        "max_tokens": 128000,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.9e-07,
         "supports_function_calling": true,
@@ -10711,7 +10711,7 @@
         "litellm_provider": "bedrock_converse",
         "max_input_tokens": 128000,
         "max_output_tokens": 4096,
-        "max_tokens": 128000,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 6e-06,
         "supports_function_calling": true,
@@ -10817,14 +10817,14 @@
         "litellm_provider": "featherless_ai",
         "max_input_tokens": 32768,
         "max_output_tokens": 4096,
-        "max_tokens": 32768,
+        "max_tokens": 4096,
         "mode": "chat"
     },
     "featherless_ai/featherless-ai/Qwerky-QwQ-32B": {
         "litellm_provider": "featherless_ai",
         "max_input_tokens": 32768,
         "max_output_tokens": 4096,
-        "max_tokens": 32768,
+        "max_tokens": 4096,
         "mode": "chat"
     },
     "fireworks-ai-4.1b-to-16b": {
@@ -11031,7 +11031,7 @@
         "supports_tool_choice": true
     },
     "fireworks_ai/accounts/fireworks/models/glm-4p6": {
-        "input_cost_per_token": 0.55e-06,
+        "input_cost_per_token": 5.5e-07,
         "output_cost_per_token": 2.19e-06,
         "litellm_provider": "fireworks_ai",
         "max_input_tokens": 202800,
@@ -11077,7 +11077,7 @@
         "litellm_provider": "fireworks_ai",
         "max_input_tokens": 131072,
         "max_output_tokens": 16384,
-        "max_tokens": 131072,
+        "max_tokens": 16384,
         "mode": "chat",
         "output_cost_per_token": 2.5e-06,
         "source": "https://fireworks.ai/models/fireworks/kimi-k2-instruct",
@@ -11090,7 +11090,7 @@
         "litellm_provider": "fireworks_ai",
         "max_input_tokens": 262144,
         "max_output_tokens": 32768,
-        "max_tokens": 262144,
+        "max_tokens": 32768,
         "mode": "chat",
         "output_cost_per_token": 2.5e-06,
         "source": "https://app.fireworks.ai/models/fireworks/kimi-k2-instruct-0905",
@@ -11337,7 +11337,7 @@
         "litellm_provider": "text-completion-openai",
         "max_input_tokens": 16384,
         "max_output_tokens": 4096,
-        "max_tokens": 16384,
+        "max_tokens": 4096,
         "mode": "completion",
         "output_cost_per_token": 1.6e-06,
         "output_cost_per_token_batches": 2e-07
@@ -11348,7 +11348,7 @@
         "litellm_provider": "text-completion-openai",
         "max_input_tokens": 16384,
         "max_output_tokens": 4096,
-        "max_tokens": 16384,
+        "max_tokens": 4096,
         "mode": "completion",
         "output_cost_per_token": 1.2e-05,
         "output_cost_per_token_batches": 1e-06
@@ -11638,7 +11638,7 @@
         "litellm_provider": "vertex_ai-language-models",
         "max_input_tokens": 8192,
         "max_output_tokens": 2048,
-        "max_tokens": 8192,
+        "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_character": 3.75e-07,
         "output_cost_per_token": 1.5e-06,
@@ -11655,7 +11655,7 @@
         "litellm_provider": "vertex_ai-language-models",
         "max_input_tokens": 8192,
         "max_output_tokens": 2048,
-        "max_tokens": 8192,
+        "max_tokens": 2048,
         "mode": "chat",
         "output_cost_per_character": 3.75e-07,
         "output_cost_per_token": 1.5e-06,
@@ -12532,6 +12532,7 @@
         "deprecation_date": "2026-01-15",
         "cache_read_input_token_cost": 7.5e-08,
         "input_cost_per_audio_token": 1e-06,
+        "input_cost_per_image_token": 3e-07,
         "input_cost_per_token": 3e-07,
         "litellm_provider": "vertex_ai-language-models",
         "max_audio_length_hours": 8.4,
@@ -12585,10 +12586,10 @@
         "litellm_provider": "vertex_ai-language-models",
         "max_input_tokens": 65536,
         "max_output_tokens": 32768,
-        "max_tokens": 65536,
+        "max_tokens": 32768,
         "mode": "image_generation",
         "output_cost_per_image": 0.134,
-        "output_cost_per_image_token": 1.2e-04,
+        "output_cost_per_image_token": 0.00012,
         "output_cost_per_token": 1.2e-05,
         "output_cost_per_token_batches": 6e-06,
         "source": "https://ai.google.dev/gemini-api/docs/pricing",
@@ -14112,7 +14113,7 @@
         "max_input_tokens": 1048576,
         "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 8192,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -14162,7 +14163,7 @@
         "max_input_tokens": 1048576,
         "max_output_tokens": 65536,
         "max_pdf_size_mb": 30,
-        "max_tokens": 8192,
+        "max_tokens": 65536,
         "max_video_length": 1,
         "max_videos_per_prompt": 10,
         "mode": "chat",
@@ -14388,10 +14389,10 @@
         "litellm_provider": "gemini",
         "max_input_tokens": 65536,
         "max_output_tokens": 32768,
-        "max_tokens": 65536,
+        "max_tokens": 32768,
         "mode": "image_generation",
         "output_cost_per_image": 0.134,
-        "output_cost_per_image_token": 1.2e-04,
+        "output_cost_per_image_token": 0.00012,
         "output_cost_per_token": 1.2e-05,
         "rpm": 1000,
         "tpm": 4000000,
@@ -15524,7 +15525,7 @@
         "max_input_tokens": 1024,
         "max_tokens": 1024,
         "mode": "video_generation",
-        "output_cost_per_second": 0.40,
+        "output_cost_per_second": 0.4,
         "source": "https://ai.google.dev/gemini-api/docs/video",
         "supported_modalities": [
             "text"
@@ -15552,7 +15553,7 @@
         "max_input_tokens": 1024,
         "max_tokens": 1024,
         "mode": "video_generation",
-        "output_cost_per_second": 0.40,
+        "output_cost_per_second": 0.4,
         "source": "https://ai.google.dev/gemini-api/docs/video",
         "supported_modalities": [
             "text"
@@ -16056,11 +16057,11 @@
         "supports_vision": true
     },
     "gpt-3.5-turbo": {
-        "input_cost_per_token": 0.5e-06,
+        "input_cost_per_token": 5e-07,
         "litellm_provider": "openai",
         "max_input_tokens": 16385,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "supports_function_calling": true,
@@ -16073,7 +16074,7 @@
         "litellm_provider": "openai",
         "max_input_tokens": 16385,
         "max_output_tokens": 4096,
-        "max_tokens": 16385,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "supports_function_calling": true,
@@ -16087,7 +16088,7 @@
         "litellm_provider": "openai",
         "max_input_tokens": 4097,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_prompt_caching": true,
@@ -16099,7 +16100,7 @@
         "litellm_provider": "openai",
         "max_input_tokens": 4097,
         "max_output_tokens": 4096,
-        "max_tokens": 4097,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_function_calling": true,
@@ -16113,7 +16114,7 @@
         "litellm_provider": "openai",
         "max_input_tokens": 16385,
         "max_output_tokens": 4096,
-        "max_tokens": 16385,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 2e-06,
         "supports_function_calling": true,
@@ -16127,7 +16128,7 @@
         "litellm_provider": "openai",
         "max_input_tokens": 16385,
         "max_output_tokens": 4096,
-        "max_tokens": 16385,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 4e-06,
         "supports_prompt_caching": true,
@@ -16139,7 +16140,7 @@
         "litellm_provider": "openai",
         "max_input_tokens": 16385,
         "max_output_tokens": 4096,
-        "max_tokens": 16385,
+        "max_tokens": 4096,
         "mode": "chat",
         "output_cost_per_token": 4e-06,
         "supports_prompt_caching": true,
@@ -17191,7 +17192,7 @@
         "supports_pdf_input": true
     },
     "high/1024-x-1536/gpt-image-1.5": {
-        "input_cost_per_image": 0.20,
+        "input_cost_per_image": 0.2,
         "litellm_provider": "openai",
         "mode": "image_generation",
         "supported_endpoints": [
@@ -17202,7 +17203,7 @@
         "supports_pdf_input": true
     },
     "high/1536-x-1024/gpt-image-1.5": {
-        "input_cost_per_image": 0.20,
+        "input_cost_per_image": 0.2,
         "litellm_provider": "openai",
         "mode": "image_generation",
         "supported_endpoints": [
@@ -17356,7 +17357,7 @@
         "supports_pdf_input": true
     },
     "high/1024-x-1536/gpt-image-1.5-2025-12-16": {
-        "input_cost_per_image": 0.20,
+        "input_cost_per_image": 0.2,
         "litellm_provider": "openai",
         "mode": "image_generation",
         "supported_endpoints": [
@@ -17367,7 +17368,7 @@
         "supports_pdf_input": true
     },
     "high/1536-x-1024/gpt-image-1.5-2025-12-16": {
-        "input_cost_per_image": 0.20,
+        "input_cost_per_image": 0.2,
         "litellm_provider": "openai",
         "mode": "image_generation",
         "supported_endpoints": [
@@ -17704,7 +17705,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "responses",
-        "output_cost_per_token": 1.68e-04,
+        "output_cost_per_token": 0.000168,
         "supported_endpoints": [
             "/v1/batch",
             "/v1/responses"
@@ -17735,7 +17736,7 @@
         "max_output_tokens": 128000,
         "max_tokens": 128000,
         "mode": "responses",
-        "output_cost_per_token": 1.68e-04,
+        "output_cost_per_token": 0.000168,
         "supported_endpoints": [
             "/v1/batch",
             "/v1/responses"
@@ -17767,7 +17768,7 @@
         "max_output_tokens": 272000,
         "max_tokens": 272000,
         "mode": "responses",
-        "output_cost_per_token": 1.2e-04,
+        "output_cost_per_token": 0.00012,
         "output_cost_per_token_batches": 6e-05,
         "supported_endpoints": [
             "/v1/batch",
@@ -17800,7 +17801,7 @@
         "max_output_tokens": 272000,
         "max_tokens": 272000,
         "mode": "responses",
-        "output_cost_per_token": 1.2e-04,
+        "output_cost_per_token": 0.00012,
         "output_cost_per_token_batches": 6e-05,
         "supported_endpoints": [
             "/v1/batch",
@@ -18503,7 +18504,7 @@
     "lemonade/Qwen3-Coder-30B-A3B-Instruct-GGUF": {
         "input_cost_per_token": 0,
         "litellm_provider": "lemonade",
-        "max_tokens": 262144,
+        "max_tokens": 32768,
         "max_input_tokens": 262144,
         "max_output_tokens": 32768,
         "mode": "chat",
@@ -18515,7 +18516,7 @@
     "lemonade/gpt-oss-20b-mxfp4-GGUF": {
         "input_cost_per_token": 0,
         "litellm_provider": "lemonade",
-        "max_tokens": 131072,
+        "max_tokens": 32768,
         "max_input_tokens": 131072,
         "max_output_tokens": 32768,
         "mode": "chat",
@@ -18527,7 +18528,7 @@
     "lemonade/gpt-oss-120b-mxfp-GGUF": {
         "input_cost_per_token": 0,
         "litellm_provider": "lemonade",
-        "max_tokens": 131072,
+        "max_tokens": 32768,
         "max_input_tokens": 131072,
         "max_output_tokens": 32768,
         "mode": "chat",
@@ -18539,7 +18540,7 @@
     "lemonade/Gemma-3-4b-it-GGUF": {
         "input_cost_per_token": 0,
         "litellm_provider": "lemonade",
-        "max_tokens": 128000,
+        "max_tokens": 8192,
         "max_input_tokens": 128000,
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@@ -25204,12 +25544,12 @@
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@@ -25925,7 +26265,7 @@
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+        "max_tokens": 131072,
+        "supports_tool_choice": true,
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+        "supports_response_schema": true,
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+    "novita/thudm/glm-4.1v-9b-thinking": {
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+        "supports_vision": true,
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+    "novita/baidu/ernie-4.5-vl-424b-a47b": {
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+        "supports_reasoning": true
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+    "novita/baidu/ernie-4.5-300b-a47b-paddle": {
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+        "mode": "chat",
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+        "output_cost_per_token": 8.8e-03,
+        "max_input_tokens": 123000,
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+        "max_tokens": 12000,
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+        "supports_response_schema": true
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+    "novita/deepseek/deepseek-prover-v2-671b": {
+        "litellm_provider": "novita",
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+        "supports_system_messages": true
+    },
+    "novita/qwen/qwen3-32b-fp8": {
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+        "mode": "chat",
+        "input_cost_per_token": 8e-04,
+        "output_cost_per_token": 3.6e-03,
+        "max_input_tokens": 40960,
+        "max_output_tokens": 20000,
+        "max_tokens": 20000,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-30b-a3b-fp8": {
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+        "mode": "chat",
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+        "output_cost_per_token": 3.6e-03,
+        "max_input_tokens": 40960,
+        "max_output_tokens": 20000,
+        "max_tokens": 20000,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/google/gemma-3-27b-it": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 9.52e-04,
+        "output_cost_per_token": 1.6e-03,
+        "max_input_tokens": 98304,
+        "max_output_tokens": 16384,
+        "max_tokens": 16384,
+        "supports_vision": true,
+        "supports_system_messages": true
+    },
+    "novita/deepseek/deepseek-v3-turbo": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 3.2e-03,
+        "output_cost_per_token": 1.04e-02,
+        "max_input_tokens": 64000,
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+        "max_tokens": 16000,
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+        "supports_system_messages": true
+    },
+    "novita/deepseek/deepseek-r1-turbo": {
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+        "mode": "chat",
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+        "output_cost_per_token": 2e-02,
+        "max_input_tokens": 64000,
+        "max_output_tokens": 16000,
+        "max_tokens": 16000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/Sao10K/L3-8B-Stheno-v3.2": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 5e-04,
+        "output_cost_per_token": 5e-04,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/gryphe/mythomax-l2-13b": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 7.2e-04,
+        "output_cost_per_token": 7.2e-04,
+        "max_input_tokens": 4096,
+        "max_output_tokens": 3200,
+        "max_tokens": 3200,
+        "supports_system_messages": true
+    },
+    "novita/baidu/ernie-4.5-vl-28b-a3b-thinking": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 3.9e-03,
+        "output_cost_per_token": 3.9e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "supports_function_calling": true,
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+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-vl-8b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 6.4e-04,
+        "output_cost_per_token": 4e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/zai-org/glm-4.5-air": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.04e-03,
+        "output_cost_per_token": 6.8e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 98304,
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+        "supports_function_calling": true,
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+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-vl-30b-a3b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.6e-03,
+        "output_cost_per_token": 5.6e-03,
+        "max_input_tokens": 131072,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/qwen/qwen3-vl-30b-a3b-thinking": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.6e-03,
+        "output_cost_per_token": 8e-03,
+        "max_input_tokens": 131072,
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+        "max_tokens": 32768,
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+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/qwen/qwen-mt-plus": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2e-03,
+        "output_cost_per_token": 6e-03,
+        "max_input_tokens": 16384,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "supports_system_messages": true
+    },
+    "novita/baidu/ernie-4.5-vl-28b-a3b": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.12e-03,
+        "output_cost_per_token": 4.48e-03,
+        "max_input_tokens": 30000,
+        "max_output_tokens": 8000,
+        "max_tokens": 8000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/baidu/ernie-4.5-21B-a3b": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 2.24e-03,
+        "max_input_tokens": 120000,
+        "max_output_tokens": 8000,
+        "max_tokens": 8000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/qwen/qwen3-8b-fp8": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2.8e-04,
+        "output_cost_per_token": 1.104e-03,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 20000,
+        "max_tokens": 20000,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen3-4b-fp8": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2.4e-04,
+        "output_cost_per_token": 2.4e-04,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 20000,
+        "max_tokens": 20000,
+        "supports_system_messages": true,
+        "supports_reasoning": true
+    },
+    "novita/qwen/qwen2.5-7b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 5.6e-04,
+        "max_input_tokens": 32000,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true,
+        "supports_response_schema": true
+    },
+    "novita/meta-llama/llama-3.2-3b-instruct": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 2.4e-04,
+        "output_cost_per_token": 4e-04,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/sao10k/l31-70b-euryale-v2.2": {
+        "litellm_provider": "novita",
+        "mode": "chat",
+        "input_cost_per_token": 1.48e-02,
+        "output_cost_per_token": 1.48e-02,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_system_messages": true
+    },
+    "novita/qwen/qwen3-embedding-0.6b": {
+        "litellm_provider": "novita",
+        "mode": "embedding",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 0,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 32768,
+        "max_tokens": 32768
+    },
+    "novita/qwen/qwen3-embedding-8b": {
+        "litellm_provider": "novita",
+        "mode": "embedding",
+        "input_cost_per_token": 5.6e-04,
+        "output_cost_per_token": 0,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096
+    },
+    "novita/baai/bge-m3": {
+        "litellm_provider": "novita",
+        "mode": "embedding",
+        "input_cost_per_token": 1e-04,
+        "output_cost_per_token": 1e-04,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 96000,
+        "max_tokens": 96000
+    },
+    "novita/qwen/qwen3-reranker-8b": {
+        "litellm_provider": "novita",
+        "mode": "rerank",
+        "input_cost_per_token": 4e-04,
+        "output_cost_per_token": 4e-04,
+        "max_input_tokens": 32768,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096
+    },
+    "novita/baai/bge-reranker-v2-m3": {
+        "litellm_provider": "novita",
+        "mode": "rerank",
+        "input_cost_per_token": 1e-04,
+        "output_cost_per_token": 1e-04,
+        "max_input_tokens": 8000,
+        "max_output_tokens": 8000,
+        "max_tokens": 8000
+    },
     "llamagate/llama-3.1-8b": {
         "max_tokens": 8192,
         "max_input_tokens": 131072,
@@ -32354,4 +33801,3 @@
         "mode": "embedding"
     }
 }
-
diff --git a/poetry.lock b/poetry.lock
index 0a4ef10d09f..3bafdb157ca 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3081,15 +3081,15 @@ files = [
 
 [[package]]
 name = "litellm-proxy-extras"
-version = "0.4.18"
+version = "0.4.21"
 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-0.4.18-py3-none-any.whl", hash = "sha256:c3edee68bf8eb073c6158dcf7df05727dfc829e63c03a617fcb48853d11490df"},
-    {file = "litellm_proxy_extras-0.4.18.tar.gz", hash = "sha256:898b28e3e74acdc29142906b84787ab05a90e30aa3c0c8aee849915e3a16adb3"},
+    {file = "litellm_proxy_extras-0.4.21-py3-none-any.whl", hash = "sha256:83a1734e9773610945230606012e602bbcbfba1c60fde836d51102c1a296f166"},
+    {file = "litellm_proxy_extras-0.4.21.tar.gz", hash = "sha256:fa0e012984aa8e5114f88f4bad53d6abb589e5ca3eab445f74f8ddeceb62d848"},
 ]
 
 [[package]]
@@ -7981,4 +7981,4 @@ utils = ["numpydoc"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "e9fd12b5ccc703ec156d98877452417083e3ac18b5970cb3a58c3bde09d267bb"
+content-hash = "ea62b77c662ab9fc486e421c576f0868bcde16d62a24703ee1f4916a0465ffb2"
diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json
index f671409175a..db11ec8a0de 100644
--- a/provider_endpoints_support.json
+++ b/provider_endpoints_support.json
@@ -32,6 +32,23 @@
     }
   },
   "providers": {
+    "abliteration": {
+      "display_name": "Abliteration (`abliteration`)",
+      "url": "https://docs.litellm.ai/docs/providers/abliteration",
+      "endpoints": {
+        "chat_completions": true,
+        "messages": false,
+        "responses": false,
+        "embeddings": false,
+        "image_generations": false,
+        "audio_transcriptions": false,
+        "audio_speech": false,
+        "moderations": false,
+        "batches": false,
+        "rerank": false,
+        "a2a": false
+      }
+    },
     "aiml": {
       "display_name": "AI/ML API (`aiml`)",
       "url": "https://docs.litellm.ai/docs/providers/aiml",
@@ -1559,7 +1576,7 @@
         "chat_completions": true,
         "messages": true,
         "responses": true,
-        "embeddings": false,
+        "embeddings": true,
         "image_generations": false,
         "audio_transcriptions": false,
         "audio_speech": false,
@@ -2304,6 +2321,25 @@
         "messages": true,
         "responses": true
       }
+    },
+    "manus": {
+      "display_name": "Manus (`manus`)",
+      "url": "https://docs.litellm.ai/docs/providers/manus",
+      "endpoints": {
+        "chat_completions": true,
+        "messages": true,
+        "responses": true,
+        "embeddings": false,
+        "image_generations": false,
+        "audio_transcriptions": false,
+        "audio_speech": false,
+        "moderations": false,
+        "batches": false,
+        "files": true,
+        "rerank": false,
+        "a2a": true,
+        "interactions": true
+      }
     }
   },
   "endpoints": {
diff --git a/pyproject.toml b/pyproject.toml
index 51ef8650d0c..36305ad0ed3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.80.12"
+version = "1.80.15"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -59,7 +59,7 @@ websockets = {version = "^15.0.1", optional = true}
 boto3 = {version = "1.36.0", optional = true}
 redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
 mcp = {version = "^1.21.2", optional = true, python = ">=3.10"}
-litellm-proxy-extras = {version = "0.4.20", optional = true}
+litellm-proxy-extras = {version = "0.4.21", optional = true}
 rich = {version = "13.7.1", optional = true}
 litellm-enterprise = {version = "0.1.27", optional = true}
 diskcache = {version = "^5.6.1", optional = true}
@@ -167,7 +167,7 @@ requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.80.12"
+version = "1.80.15"
 version_files = [
     "pyproject.toml:^version"
 ]
@@ -179,4 +179,14 @@ plugins = "pydantic.mypy"
 asyncio_mode = "auto"
 markers = [
     "asyncio: mark test as an asyncio test",
+    "limit_leaks: mark test with memory limit for leak detection (e.g., '40 MB')",
+    "no_parallel: mark test to run sequentially (not in parallel) - typically for memory measurement tests",
+]
+filterwarnings = [
+    # Suppress Pydantic serializer warnings from mock server responses (non-critical for memory tests)
+    # These occur because the mock server returns a simplified response format
+    "ignore:Pydantic serializer warnings:UserWarning",
+    "ignore::UserWarning:pydantic.main",
+    # Suppress pytest-asyncio event loop deprecation warning (handled automatically by pytest-asyncio)
+    "ignore::DeprecationWarning:pytest_asyncio.plugin",
 ]
diff --git a/requirements.txt b/requirements.txt
index ceafa23a22f..5f00a269a7c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -47,7 +47,7 @@ sentry_sdk==2.21.0 # for sentry error handling
 detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests
 cryptography==44.0.1
 tzdata==2025.1 # IANA time zone database
-litellm-proxy-extras==0.4.20 # for proxy extras - e.g. prisma migrations
+litellm-proxy-extras==0.4.21 # 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/test_generic_guardrail_config.yaml b/test_generic_guardrail_config.yaml
new file mode 100644
index 00000000000..d6cb505f7ed
--- /dev/null
+++ b/test_generic_guardrail_config.yaml
@@ -0,0 +1,29 @@
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: openai/gpt-4
+      api_key: os.environ/OPENAI_API_KEY
+
+  - model_name: gpt-4o
+    litellm_params:
+      model: openai/gpt-4o
+      api_key: os.environ/OPENAI_API_KEY
+
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: openai/gpt-3.5-turbo
+      api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+  - guardrail_name: thisispillar
+    litellm_params:
+      guardrail: generic_guardrail_api
+      mode: [pre_call, post_call]
+      api_base: os.environ/PILLAR_API_BASE
+      api_key: os.environ/PILLAR_API_KEY
+      default_on: true
+      additional_provider_specific_params:
+        plr_evidence: true
+
+general_settings:
+  master_key: sk-1234
diff --git a/tests/batches_tests/test_manus_files_all_methods.py b/tests/batches_tests/test_manus_files_all_methods.py
new file mode 100644
index 00000000000..49322bdcb17
--- /dev/null
+++ b/tests/batches_tests/test_manus_files_all_methods.py
@@ -0,0 +1,72 @@
+"""
+E2E test for all Manus Files API methods.
+"""
+
+import os
+import pytest
+import litellm
+
+
+@pytest.mark.asyncio
+async def test_manus_files_api_e2e_all_methods():
+    """
+    E2E test for Manus Files API: create, retrieve, list, delete.
+    """
+    litellm._turn_on_debug()
+
+    api_key = os.getenv("MANUS_API_KEY")
+    if api_key is None:
+        pytest.skip("MANUS_API_KEY not set")
+
+    # Create a simple test file content
+    test_content = b"This is a test file for Manus Files API - all methods test."
+    test_filename = "test_file_all_methods.txt"
+
+    # Step 1: Create file
+    print("Step 1: Creating file...")
+    created_file = await litellm.acreate_file(
+        file=(test_filename, test_content),
+        purpose="assistants",
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Created file: {created_file}")
+    assert created_file.filename == test_filename
+    assert created_file.status == "uploaded"
+    # Note: Manus doesn't return bytes in initial response
+    file_id = created_file.id
+
+    # Step 2: Retrieve file
+    print(f"\nStep 2: Retrieving file {file_id}...")
+    retrieved_file = await litellm.afile_retrieve(
+        file_id=file_id,
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Retrieved file: {retrieved_file}")
+    assert retrieved_file.id == file_id
+    assert retrieved_file.filename == test_filename
+
+    # Step 3: List files
+    print("\nStep 3: Listing files...")
+    files_list = await litellm.afile_list(
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Files list: {files_list}")
+    assert isinstance(files_list, list)
+    assert any(f.id == file_id for f in files_list)
+
+    # Step 4: Delete file
+    print(f"\nStep 4: Deleting file {file_id}...")
+    deleted_file = await litellm.afile_delete(
+        file_id=file_id,
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Deleted file: {deleted_file}")
+    assert deleted_file.id == file_id
+    assert deleted_file.deleted is True
+
+    print("\n✅ All Manus Files API methods working!")
+
diff --git a/tests/code_coverage_tests/recursive_detector.py b/tests/code_coverage_tests/recursive_detector.py
index 20e6a381e5b..2a460f621b2 100644
--- a/tests/code_coverage_tests/recursive_detector.py
+++ b/tests/code_coverage_tests/recursive_detector.py
@@ -35,6 +35,7 @@ IGNORE_FUNCTIONS = [
     "_fix_enum_types",  # max depth set.
     "_collect_argument_paths",  # max depth set.
     "_split_text",  # max depth set.
+    "_mask_sequence",  # max depth set.
     "_delete_nested_value_custom",  # max depth set (bounded by number of path segments).
     "filter_exceptions_from_params",  # max depth set (default 20) to prevent infinite recursion.
     "__getattr__",  # lazy loading pattern in litellm/__init__.py with proper caching to prevent infinite recursion.
diff --git a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
index 2f92afb3824..e8fe4dd3393 100644
--- a/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
+++ b/tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
@@ -1124,124 +1124,6 @@ def test_get_custom_labels_from_metadata_tags(monkeypatch):
     assert get_custom_labels_from_metadata(metadata) == {}
 
 
-def test_get_custom_labels_from_top_level_metadata(monkeypatch):
-    """
-    Test that get_custom_labels_from_metadata can extract fields from top-level metadata,
-    such as requester_ip_address, not just from nested dictionaries like requester_metadata.
-    """
-    monkeypatch.setattr(
-        "litellm.custom_prometheus_metadata_labels",
-        ["requester_ip_address", "user_api_key_alias"],
-    )
-    # Simulate metadata structure with top-level fields
-    metadata = {
-        "requester_ip_address": "10.48.203.20",  # Top-level field
-        "user_api_key_alias": "TestAlias",  # Top-level field
-        "requester_metadata": {"nested_field": "nested_value"},  # Nested dict (excluded)
-        "user_api_key_auth_metadata": {"another_nested": "value"},  # Nested dict (excluded)
-    }
-    result = get_custom_labels_from_metadata(metadata)
-    assert result == {
-        "requester_ip_address": "10.48.203.20",
-        "user_api_key_alias": "TestAlias",
-    }
-
-
-def test_get_custom_labels_from_top_level_and_nested_metadata(monkeypatch):
-    """
-    Test that get_custom_labels_from_metadata can extract fields from both top-level
-    and nested metadata (requester_metadata, user_api_key_auth_metadata).
-    """
-    monkeypatch.setattr(
-        "litellm.custom_prometheus_metadata_labels",
-        [
-            "requester_ip_address",  # Top-level
-            "metadata.foo",  # From requester_metadata
-            "metadata.bar",  # From user_api_key_auth_metadata
-        ],
-    )
-    # Simulate combined_metadata structure as it would appear after merging
-    # This is what gets passed to get_custom_labels_from_metadata
-    combined_metadata = {
-        "requester_ip_address": "10.48.203.20",  # Top-level field
-        "foo": "bar_value",  # From requester_metadata (spread)
-        "bar": "baz_value",  # From user_api_key_auth_metadata (spread)
-    }
-    result = get_custom_labels_from_metadata(combined_metadata)
-    assert result == {
-        "requester_ip_address": "10.48.203.20",
-        "metadata_foo": "bar_value",
-        "metadata_bar": "baz_value",
-    }
-
-
-async def test_async_log_success_event_with_top_level_metadata(prometheus_logger, monkeypatch):
-    """
-    Test that async_log_success_event correctly extracts custom labels from top-level metadata
-    fields like requester_ip_address, not just from nested dictionaries.
-    """
-    # Configure custom metadata labels to extract requester_ip_address
-    monkeypatch.setattr(
-        "litellm.custom_prometheus_metadata_labels", ["requester_ip_address"]
-    )
-
-    # Create standard logging payload with requester_ip_address at top-level metadata
-    standard_logging_object = create_standard_logging_payload()
-    standard_logging_object["metadata"]["requester_ip_address"] = "10.48.203.20"
-    standard_logging_object["metadata"]["requester_metadata"] = {}  # Empty nested dict
-    standard_logging_object["metadata"]["user_api_key_auth_metadata"] = {}  # Empty nested dict
-
-    kwargs = {
-        "model": "gpt-3.5-turbo",
-        "stream": True,
-        "litellm_params": {
-            "metadata": {
-                "user_api_key": "test_key",
-                "user_api_key_user_id": "test_user",
-                "user_api_key_team_id": "test_team",
-                "user_api_key_end_user_id": "test_end_user",
-            }
-        },
-        "start_time": datetime.now(),
-        "completion_start_time": datetime.now(),
-        "api_call_start_time": datetime.now(),
-        "end_time": datetime.now() + timedelta(seconds=1),
-        "standard_logging_object": standard_logging_object,
-    }
-    response_obj = MagicMock()
-
-    # Mock the prometheus client methods
-    prometheus_logger.litellm_requests_metric = MagicMock()
-    prometheus_logger.litellm_spend_metric = MagicMock()
-    prometheus_logger.litellm_tokens_metric = MagicMock()
-    prometheus_logger.litellm_input_tokens_metric = MagicMock()
-    prometheus_logger.litellm_output_tokens_metric = MagicMock()
-    prometheus_logger.litellm_remaining_team_budget_metric = MagicMock()
-    prometheus_logger.litellm_remaining_api_key_budget_metric = MagicMock()
-    prometheus_logger.litellm_remaining_api_key_requests_for_model = MagicMock()
-    prometheus_logger.litellm_remaining_api_key_tokens_for_model = MagicMock()
-    prometheus_logger.litellm_llm_api_time_to_first_token_metric = MagicMock()
-    prometheus_logger.litellm_llm_api_latency_metric = MagicMock()
-    prometheus_logger.litellm_request_total_latency_metric = MagicMock()
-
-    await prometheus_logger.async_log_success_event(
-        kwargs, response_obj, kwargs["start_time"], kwargs["end_time"]
-    )
-
-    # Verify that the metrics were called with labels including requester_ip_address
-    # Check that labels() was called - the actual labels dict should include requester_ip_address
-    assert prometheus_logger.litellm_requests_metric.labels.called
-    assert prometheus_logger.litellm_spend_metric.labels.called
-
-    # Get the actual call arguments to verify requester_ip_address is included
-    # The custom labels should be extracted and included in the label factory
-    call_args = prometheus_logger.litellm_requests_metric.labels.call_args
-    assert call_args is not None
-    # The labels() method receives a dict with label names and values
-    # We can't easily assert the exact values without checking the internal implementation,
-    # but we've verified the function is called, which means the extraction happened
-
-
 def test_get_custom_labels_from_tags(monkeypatch):
     from litellm.integrations.prometheus import get_custom_labels_from_tags
 
diff --git a/tests/image_gen_tests/test_image_edits.py b/tests/image_gen_tests/test_image_edits.py
index 810bd80a5b0..393b4cb67a1 100644
--- a/tests/image_gen_tests/test_image_edits.py
+++ b/tests/image_gen_tests/test_image_edits.py
@@ -154,8 +154,8 @@ class TestAzureAIFlux2ImageEdit(BaseLLMImageEditTest):
         return {
             "model": "azure_ai/flux.2-pro",
             "image": SINGLE_TEST_IMAGE,
-            "api_base": os.getenv("AZURE_AI_API_BASE", "https://litellm-ci-cd-prod.services.ai.azure.com"),
-            "api_key": os.getenv("AZURE_AI_API_KEY"),
+            "api_base": "https://litellm-ci-cd-prod.services.ai.azure.com",
+            "api_key": os.getenv("AZURE_API_KEY"),
             "api_version": "preview",
         }
 
diff --git a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
index c23f4501a2c..f706a025a09 100644
--- a/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
+++ b/tests/litellm/llms/oci/chat/test_oci_chat_transformation.py
@@ -197,3 +197,90 @@ class TestOCIGetCompleteUrl:
 
         assert "eu-frankfurt-1" in url
         assert "inference.generativeai" in url
+
+
+class TestOCIImageUrlTransformation:
+    """Tests for OCI image_url format handling in multimodal messages.
+
+    Fixes: https://github.com/BerriAI/litellm/issues/18270
+    """
+
+    def test_image_url_as_string(self):
+        """Test that image_url as a plain string works."""
+        from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is in this image?"},
+                    {"type": "image_url", "image_url": "https://example.com/image.png"},
+                ],
+            }
+        ]
+
+        result = adapt_messages_to_generic_oci_standard(messages)
+
+        assert len(result) == 1
+        assert result[0].role == "USER"
+        assert len(result[0].content) == 2
+        assert result[0].content[1].imageUrl == "https://example.com/image.png"
+
+    def test_image_url_as_openai_object(self):
+        """Test that image_url as OpenAI-style object {"url": "..."} works."""
+        from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is in this image?"},
+                    {"type": "image_url", "image_url": {"url": "https://example.com/image.png"}},
+                ],
+            }
+        ]
+
+        result = adapt_messages_to_generic_oci_standard(messages)
+
+        assert len(result) == 1
+        assert result[0].role == "USER"
+        assert len(result[0].content) == 2
+        assert result[0].content[1].imageUrl == "https://example.com/image.png"
+
+    def test_image_url_invalid_type_raises_error(self):
+        """Test that invalid image_url type raises an error."""
+        from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is in this image?"},
+                    {"type": "image_url", "image_url": 12345},  # Invalid type
+                ],
+            }
+        ]
+
+        with pytest.raises(Exception) as exc_info:
+            adapt_messages_to_generic_oci_standard(messages)
+
+        assert "image_url" in str(exc_info.value)
+
+    def test_image_url_object_missing_url_raises_error(self):
+        """Test that object without 'url' property raises an error."""
+        from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What is in this image?"},
+                    {"type": "image_url", "image_url": {"detail": "high"}},  # Missing 'url'
+                ],
+            }
+        ]
+
+        with pytest.raises(Exception) as exc_info:
+            adapt_messages_to_generic_oci_standard(messages)
+
+        assert "image_url" in str(exc_info.value)
diff --git a/tests/litellm/llms/openai_like/test_abliteration_provider.py b/tests/litellm/llms/openai_like/test_abliteration_provider.py
new file mode 100644
index 00000000000..8b8d443fc44
--- /dev/null
+++ b/tests/litellm/llms/openai_like/test_abliteration_provider.py
@@ -0,0 +1,50 @@
+"""
+Unit tests for the Abliteration OpenAI-like provider.
+"""
+
+import os
+import sys
+
+sys.path.insert(
+    0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../.."))
+)
+
+from litellm.llms.openai_like.dynamic_config import create_config_class
+from litellm.llms.openai_like.json_loader import JSONProviderRegistry
+
+ABLITERATION_BASE_URL = "https://api.abliteration.ai/v1"
+
+
+def _get_config():
+    provider = JSONProviderRegistry.get("abliteration")
+    assert provider is not None
+    config_class = create_config_class(provider)
+    return config_class()
+
+
+def test_abliteration_provider_registered():
+    provider = JSONProviderRegistry.get("abliteration")
+    assert provider is not None
+    assert provider.base_url == ABLITERATION_BASE_URL
+    assert provider.api_key_env == "ABLITERATION_API_KEY"
+
+
+def test_abliteration_resolves_env_api_key(monkeypatch):
+    config = _get_config()
+    monkeypatch.setenv("ABLITERATION_API_KEY", "test-key")
+    api_base, api_key = config._get_openai_compatible_provider_info(None, None)
+    assert api_base == ABLITERATION_BASE_URL
+    assert api_key == "test-key"
+
+
+def test_abliteration_complete_url_appends_endpoint():
+    config = _get_config()
+    url = config.get_complete_url(
+        api_base=ABLITERATION_BASE_URL,
+        api_key="test-key",
+        model="abliteration/abliterated-model",
+        optional_params={},
+        litellm_params={},
+        stream=False,
+    )
+    assert url == f"{ABLITERATION_BASE_URL}/chat/completions"
diff --git a/tests/litellm/llms/vertex_ai/gemini/test_transformation.py b/tests/litellm/llms/vertex_ai/gemini/test_transformation.py
index 6d005af28ac..20f48b6f393 100644
--- a/tests/litellm/llms/vertex_ai/gemini/test_transformation.py
+++ b/tests/litellm/llms/vertex_ai/gemini/test_transformation.py
@@ -7,6 +7,7 @@ sys.path.insert(
     0, os.path.abspath("../../../../..")
 )  # Adds the parent directory to the system path
 from litellm.llms.vertex_ai.gemini import transformation
+from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig
 from litellm.types.llms import openai
 from litellm.types import completion
 from litellm.types.llms.vertex_ai import RequestBody
@@ -225,4 +226,65 @@ async def test__transform_request_body_image_config_with_image_size():
     assert "generationConfig" in rb
     assert "imageConfig" in rb["generationConfig"]
     assert rb["generationConfig"]["imageConfig"]["aspectRatio"] == "16:9"
-    assert rb["generationConfig"]["imageConfig"]["imageSize"] == "4K"
\ No newline at end of file
+    assert rb["generationConfig"]["imageConfig"]["imageSize"] == "4K"
+
+
+def test_map_function_google_search_snake_case():
+    """
+    Test that google_search tool (snake_case) is properly mapped to googleSearch.
+    Fixes issue where tools=[{"google_search": {}}] was being stripped.
+    """
+    config = VertexGeminiConfig()
+    optional_params = {}
+
+    # Test snake_case google_search
+    tools = [{"google_search": {}}]
+    result = config._map_function(tools, optional_params)
+
+    assert len(result) == 1
+    assert "googleSearch" in result[0]
+    assert result[0]["googleSearch"] == {}
+
+
+def test_map_function_google_search_camel_case():
+    """
+    Test that googleSearch tool (camelCase) still works.
+    """
+    config = VertexGeminiConfig()
+    optional_params = {}
+
+    # Test camelCase googleSearch
+    tools = [{"googleSearch": {}}]
+    result = config._map_function(tools, optional_params)
+
+    assert len(result) == 1
+    assert "googleSearch" in result[0]
+    assert result[0]["googleSearch"] == {}
+
+
+def test_map_function_google_search_retrieval_snake_case():
+    """
+    Test that google_search_retrieval tool (snake_case) is properly mapped.
+    """
+    config = VertexGeminiConfig()
+    optional_params = {}
+
+    tools = [{"google_search_retrieval": {"dynamic_retrieval_config": {"mode": "MODE_DYNAMIC"}}}]
+    result = config._map_function(tools, optional_params)
+
+    assert len(result) == 1
+    assert "googleSearchRetrieval" in result[0]
+
+
+def test_map_function_enterprise_web_search_snake_case():
+    """
+    Test that enterprise_web_search tool (snake_case) is properly mapped.
+    """
+    config = VertexGeminiConfig()
+    optional_params = {}
+
+    tools = [{"enterprise_web_search": {}}]
+    result = config._map_function(tools, optional_params)
+
+    assert len(result) == 1
+    assert "enterpriseWebSearch" in result[0]
\ No newline at end of file
diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py
index f68b373feae..37ed1a9b08c 100644
--- a/tests/llm_responses_api_testing/base_responses_api.py
+++ b/tests/llm_responses_api_testing/base_responses_api.py
@@ -54,9 +54,10 @@ def validate_responses_api_response(response, final_chunk: bool = False):
     assert "created_at" in response and isinstance(
         response["created_at"], int
     ), "Response should have an integer 'created_at' field"
-    assert "output" in response and isinstance(
-        response["output"], list
-    ), "Response should have a list 'output' field"
+    if response.get("status") == "completed":
+        assert "output" in response and isinstance(
+            response["output"], list
+        ), "Response should have a list 'output' field"
 
     # Optional fields with their expected types
     optional_fields = {
@@ -91,7 +92,7 @@ def validate_responses_api_response(response, final_chunk: bool = False):
             ), f"Field '{field}' should be of type {expected_type}, but got {type(response[field])}"
 
     # Check if output has at least one item
-    if final_chunk is True:
+    if final_chunk is True and response.get("status") == "completed":
         assert (
             len(response["output"]) > 0
         ), "Response 'output' field should have at least one item"
@@ -170,48 +171,57 @@ class BaseResponsesAPITest(ABC):
                 elif event.type == "response.completed":
                     response_completed_event = event
 
-        # assert the delta chunks content had len(collected_content_string) > 0
-        # this content is typically rendered on chat ui's
-        assert len(collected_content_string) > 0
-
         # assert the response completed event is not None
         assert response_completed_event is not None
 
         # assert the response completed event has a response
         assert response_completed_event.response is not None
 
-        # assert the response completed event includes the usage
-        assert response_completed_event.response.usage is not None
+        # For async agent APIs (like Manus), the response may be in 'running' state
+        # without content yet - this is valid behavior
+        response_status = response_completed_event.response.status
+        if response_status in ["running", "pending"]:
+            # Running/pending state is acceptable - task started successfully
+            print(f"Response is in '{response_status}' state - async agent API behavior")
+            assert response_completed_event.response.id is not None
+        else:
+            # For completed responses, validate content and usage
+            # assert the delta chunks content had len(collected_content_string) > 0
+            # this content is typically rendered on chat ui's
+            assert len(collected_content_string) > 0
 
-        # basic test assert the usage seems reasonable
-        print(
-            "response_completed_event.response.usage=",
-            response_completed_event.response.usage,
-        )
-        assert (
-            response_completed_event.response.usage.input_tokens > 0
-            and response_completed_event.response.usage.input_tokens < 100
-        )
-        assert (
-            response_completed_event.response.usage.output_tokens > 0
-            and response_completed_event.response.usage.output_tokens < 2000
-        )
-        assert (
-            response_completed_event.response.usage.total_tokens > 0
-            and response_completed_event.response.usage.total_tokens < 2000
-        )
+            # assert the response completed event includes the usage
+            assert response_completed_event.response.usage is not None
 
-        # total tokens should be the sum of input and output tokens
-        assert (
-            response_completed_event.response.usage.total_tokens
-            == response_completed_event.response.usage.input_tokens
-            + response_completed_event.response.usage.output_tokens
-        )
+            # basic test assert the usage seems reasonable
+            print(
+                "response_completed_event.response.usage=",
+                response_completed_event.response.usage,
+            )
+            assert (
+                response_completed_event.response.usage.input_tokens > 0
+                and response_completed_event.response.usage.input_tokens < 100
+            )
+            assert (
+                response_completed_event.response.usage.output_tokens > 0
+                and response_completed_event.response.usage.output_tokens < 2000
+            )
+            assert (
+                response_completed_event.response.usage.total_tokens > 0
+                and response_completed_event.response.usage.total_tokens < 2000
+            )
 
-        # assert the response completed event includes cost when include_cost_in_streaming_usage is True
-        assert hasattr(response_completed_event.response.usage, "cost"), "Cost should be included in streaming responses API usage object"
-        assert response_completed_event.response.usage.cost > 0, "Cost should be greater than 0"
-        print(f"Cost found in streaming response: {response_completed_event.response.usage.cost}")
+            # total tokens should be the sum of input and output tokens
+            assert (
+                response_completed_event.response.usage.total_tokens
+                == response_completed_event.response.usage.input_tokens
+                + response_completed_event.response.usage.output_tokens
+            )
+
+            # assert the response completed event includes cost when include_cost_in_streaming_usage is True
+            assert hasattr(response_completed_event.response.usage, "cost"), "Cost should be included in streaming responses API usage object"
+            assert response_completed_event.response.usage.cost > 0, "Cost should be greater than 0"
+            print(f"Cost found in streaming response: {response_completed_event.response.usage.cost}")
         
         # Reset the setting
         litellm.include_cost_in_streaming_usage = False
@@ -450,7 +460,13 @@ class BaseResponsesAPITest(ABC):
         # Additional assertions specific to tool calls
         assert response is not None
         assert "output" in response
-        assert len(response["output"]) > 0
+        # For async agent APIs (like Manus), the response may be in 'running' state
+        # without output yet - this is valid behavior
+        if response.get("status") in ["running", "pending"]:
+            print(f"Response is in '{response.get('status')}' state - async agent API behavior")
+            assert response.get("id") is not None
+        else:
+            assert len(response["output"]) > 0
 
     @pytest.mark.asyncio
     async def test_responses_api_multi_turn_with_reasoning_and_structured_output(self):
diff --git a/tests/llm_responses_api_testing/test_manus_files_all_methods.py b/tests/llm_responses_api_testing/test_manus_files_all_methods.py
new file mode 100644
index 00000000000..39311441f59
--- /dev/null
+++ b/tests/llm_responses_api_testing/test_manus_files_all_methods.py
@@ -0,0 +1,71 @@
+"""
+E2E test for all Manus Files API methods.
+"""
+
+import os
+import pytest
+import litellm
+
+
+@pytest.mark.asyncio
+async def test_manus_files_api_e2e_all_methods():
+    """
+    E2E test for Manus Files API: create, retrieve, list, delete.
+    """
+    litellm._turn_on_debug()
+
+    api_key = os.getenv("MANUS_API_KEY")
+    if api_key is None:
+        pytest.skip("MANUS_API_KEY not set")
+
+    # Create a simple test file content
+    test_content = b"This is a test file for Manus Files API - all methods test."
+    test_filename = "test_file_all_methods.txt"
+
+    # Step 1: Create file
+    print("Step 1: Creating file...")
+    created_file = await litellm.acreate_file(
+        file=(test_filename, test_content),
+        purpose="assistants",
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Created file: {created_file}")
+    assert created_file.filename == test_filename
+    assert created_file.status == "uploaded"
+    # Note: Manus doesn't return bytes in initial response
+    file_id = created_file.id
+
+    # Step 2: Retrieve file
+    print(f"\nStep 2: Retrieving file {file_id}...")
+    retrieved_file = await litellm.afile_retrieve(
+        file_id=file_id,
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Retrieved file: {retrieved_file}")
+    assert retrieved_file.id == file_id
+    assert retrieved_file.filename == test_filename
+
+    # Step 3: List files
+    print("\nStep 3: Listing files...")
+    files_list = await litellm.afile_list(
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Files list: {files_list}")
+    assert isinstance(files_list, list)
+    assert any(f.id == file_id for f in files_list)
+
+    # Step 4: Delete file
+    print(f"\nStep 4: Deleting file {file_id}...")
+    deleted_file = await litellm.afile_delete(
+        file_id=file_id,
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Deleted file: {deleted_file}")
+    assert deleted_file.id == file_id
+    assert deleted_file.deleted is True
+
+    print("\n✅ All Manus Files API methods working!")
diff --git a/tests/llm_responses_api_testing/test_manus_responses_api.py b/tests/llm_responses_api_testing/test_manus_responses_api.py
new file mode 100644
index 00000000000..6a2aed3812d
--- /dev/null
+++ b/tests/llm_responses_api_testing/test_manus_responses_api.py
@@ -0,0 +1,127 @@
+import os
+import sys
+import pytest
+import asyncio
+from typing import Optional
+from unittest.mock import patch, AsyncMock
+
+sys.path.insert(0, os.path.abspath("../.."))
+import litellm
+from litellm.integrations.custom_logger import CustomLogger
+import json
+from litellm.types.utils import StandardLoggingPayload
+from litellm.types.llms.openai import (
+    ResponseCompletedEvent,
+    ResponsesAPIResponse,
+    ResponseAPIUsage,
+    IncompleteDetails,
+)
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from base_responses_api import BaseResponsesAPITest
+
+
+@pytest.mark.asyncio
+async def test_manus_responses_api_with_agent_profile():
+    """
+    Test that Manus API correctly extracts agent profile from model name
+    and includes task_mode and agent_profile in the request.
+    """
+    litellm._turn_on_debug()
+    
+    response = await litellm.aresponses(
+        model="manus/manus-1.6-lite",
+        input="What's the color of the sky?",
+        api_key=os.getenv("MANUS_API_KEY"),
+        max_output_tokens=50,
+    )
+    
+    print("Manus response=", json.dumps(response, indent=4, default=str))
+
+    ## Get the status of the response
+    got_response = await litellm.aget_responses(
+        response_id=response.id,
+        custom_llm_provider="manus",
+        api_key=os.getenv("MANUS_API_KEY"),
+    )
+    print("GET API MANUS RESPONSE=", json.dumps(got_response, indent=4, default=str))
+    if got_response.status == "completed":
+        assert got_response.output is not None
+        assert len(got_response.output) > 0
+
+
+
+@pytest.mark.asyncio
+async def test_manus_responses_api_with_file_upload():
+    """
+    Test that uploads a file via Files API and then passes it to Responses API.
+    """
+    litellm._turn_on_debug()
+    
+    api_key = os.getenv("MANUS_API_KEY")
+    if api_key is None:
+        pytest.skip("MANUS_API_KEY not set")
+    
+    # Step 1: Upload a file
+    test_content = b"Warren Buffett's 2023 Letter to Shareholders\n\nKey Points:\n1. Long-term value creation\n2. Capital allocation strategy\n3. Market volatility perspective"
+    test_filename = "buffett_letter_summary.txt"
+    
+    print("Step 1: Uploading file...")
+    uploaded_file = await litellm.acreate_file(
+        file=(test_filename, test_content),
+        purpose="assistants",
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Uploaded file: {uploaded_file}")
+    assert uploaded_file.id is not None
+    file_id = uploaded_file.id
+    
+    # Step 2: Create a response with the uploaded file
+    print(f"\nStep 2: Creating response with file {file_id}...")
+    response = await litellm.aresponses(
+        model="manus/manus-1.6-lite",
+        input=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_text",
+                        "text": "Summarize the key points from this letter.",
+                    },
+                    {
+                        "type": "input_file",
+                        "file_id": file_id,
+                    },
+                ],
+            },
+        ],
+        api_key=api_key,
+        max_output_tokens=100,
+    )
+    
+    print(f"Response created: {response}")
+    print(f"Response type: {type(response)}")
+    print(f"Response has id: {hasattr(response, 'id')}")
+    
+    # Handle both dict and ResponsesAPIResponse object
+    if isinstance(response, dict):
+        response_id = response.get("id")
+    else:
+        response_id = getattr(response, "id", None)
+    
+    assert response_id is not None, f"Response ID is None. Response: {response}"
+
+    
+    # Step 3: Clean up - delete the file
+    print(f"\nStep 4: Cleaning up - deleting file {file_id}...")
+    deleted_file = await litellm.afile_delete(
+        file_id=file_id,
+        custom_llm_provider="manus",
+        api_key=api_key,
+    )
+    print(f"Deleted file: {deleted_file}")
+    assert deleted_file.deleted is True
+    
+    print("\n✅ File upload and responses API integration test passed!")
+
+
diff --git a/tests/llm_translation/test_bedrock_common_utils.py b/tests/llm_translation/test_bedrock_common_utils.py
new file mode 100644
index 00000000000..7b6a05b6988
--- /dev/null
+++ b/tests/llm_translation/test_bedrock_common_utils.py
@@ -0,0 +1,182 @@
+"""
+Unit tests for litellm/llms/bedrock/common_utils.py
+
+Tests the standalone model name utility functions and BedrockTokenCounter.
+"""
+
+import pytest
+
+from litellm.llms.bedrock.common_utils import (
+    BedrockModelInfo,
+    extract_model_name_from_bedrock_arn,
+    get_bedrock_base_model,
+    get_bedrock_cross_region_inference_regions,
+    strip_bedrock_routing_prefix,
+)
+from litellm.llms.bedrock.count_tokens.bedrock_token_counter import BedrockTokenCounter
+
+
+class TestStripBedrockRoutingPrefix:
+    """Tests for strip_bedrock_routing_prefix function."""
+
+    def test_strips_bedrock_prefix(self):
+        assert strip_bedrock_routing_prefix("bedrock/claude-3-sonnet") == "claude-3-sonnet"
+
+    def test_strips_converse_prefix(self):
+        assert strip_bedrock_routing_prefix("converse/claude-3-sonnet") == "claude-3-sonnet"
+
+    def test_strips_invoke_prefix(self):
+        assert strip_bedrock_routing_prefix("invoke/claude-3-sonnet") == "claude-3-sonnet"
+
+    def test_strips_openai_prefix(self):
+        assert strip_bedrock_routing_prefix("openai/gpt-4") == "gpt-4"
+
+    def test_strips_all_known_prefixes(self):
+        # Function strips all known prefixes iteratively
+        # bedrock/converse/model -> converse/model -> model
+        assert strip_bedrock_routing_prefix("bedrock/converse/claude-3") == "claude-3"
+
+    def test_no_prefix_unchanged(self):
+        assert strip_bedrock_routing_prefix("claude-3-sonnet") == "claude-3-sonnet"
+
+    def test_model_with_dots_unchanged(self):
+        assert (
+            strip_bedrock_routing_prefix("anthropic.claude-3-sonnet-20240229-v1:0")
+            == "anthropic.claude-3-sonnet-20240229-v1:0"
+        )
+
+
+class TestExtractModelNameFromBedrockArn:
+    """Tests for extract_model_name_from_bedrock_arn function."""
+
+    def test_extracts_from_provisioned_model_arn(self):
+        arn = "arn:aws:bedrock:us-east-1:123456789012:provisioned-model/my-model-id"
+        assert extract_model_name_from_bedrock_arn(arn) == "my-model-id"
+
+    def test_extracts_from_foundation_model_arn(self):
+        arn = "arn:aws:bedrock:us-west-2:123456789012:foundation-model/anthropic.claude-v2"
+        assert extract_model_name_from_bedrock_arn(arn) == "anthropic.claude-v2"
+
+    def test_non_arn_unchanged(self):
+        model = "anthropic.claude-3-sonnet-20240229-v1:0"
+        assert extract_model_name_from_bedrock_arn(model) == model
+
+    def test_case_insensitive_arn_detection(self):
+        arn = "ARN:aws:bedrock:us-east-1:123456789012:model/my-model"
+        assert extract_model_name_from_bedrock_arn(arn) == "my-model"
+
+
+class TestGetBedrockCrossRegionInferenceRegions:
+    """Tests for get_bedrock_cross_region_inference_regions function."""
+
+    def test_returns_expected_regions(self):
+        regions = get_bedrock_cross_region_inference_regions()
+        assert "us" in regions
+        assert "eu" in regions
+        assert "global" in regions
+        assert "apac" in regions
+
+    def test_returns_list(self):
+        regions = get_bedrock_cross_region_inference_regions()
+        assert isinstance(regions, list)
+
+
+class TestGetBedrockBaseModel:
+    """Tests for get_bedrock_base_model function."""
+
+    def test_strips_bedrock_prefix(self):
+        assert get_bedrock_base_model("bedrock/claude-3-sonnet") == "claude-3-sonnet"
+
+    def test_strips_converse_prefix(self):
+        assert get_bedrock_base_model("bedrock/converse/claude-3-sonnet") == "claude-3-sonnet"
+
+    def test_strips_us_region_prefix(self):
+        # us.anthropic.model -> anthropic.model
+        assert (
+            get_bedrock_base_model("us.anthropic.claude-3-sonnet-20240229-v1:0")
+            == "anthropic.claude-3-sonnet-20240229-v1:0"
+        )
+
+    def test_strips_eu_region_prefix(self):
+        assert (
+            get_bedrock_base_model("eu.anthropic.claude-3-sonnet-20240229-v1:0")
+            == "anthropic.claude-3-sonnet-20240229-v1:0"
+        )
+
+    def test_extracts_from_arn(self):
+        arn = "arn:aws:bedrock:us-east-1:123456789012:provisioned-model/my-model"
+        assert get_bedrock_base_model(arn) == "my-model"
+
+    def test_model_without_prefix_unchanged(self):
+        model = "anthropic.claude-3-sonnet-20240229-v1:0"
+        assert get_bedrock_base_model(model) == model
+
+    def test_combined_bedrock_and_region_prefix(self):
+        # bedrock/us.anthropic.model -> anthropic.model
+        assert (
+            get_bedrock_base_model("bedrock/us.anthropic.claude-3-sonnet-20240229-v1:0")
+            == "anthropic.claude-3-sonnet-20240229-v1:0"
+        )
+
+
+class TestBedrockModelInfoWrappers:
+    """Tests that BedrockModelInfo methods correctly wrap standalone functions."""
+
+    def test_get_base_model_matches_standalone(self):
+        test_cases = [
+            "bedrock/claude-3-sonnet",
+            "us.anthropic.claude-3-sonnet-20240229-v1:0",
+            "arn:aws:bedrock:us-east-1:123:model/my-model",
+        ]
+        for model in test_cases:
+            assert BedrockModelInfo.get_base_model(model) == get_bedrock_base_model(model)
+
+    def test_extract_model_name_from_arn_matches_standalone(self):
+        arn = "arn:aws:bedrock:us-east-1:123456789012:provisioned-model/my-model"
+        assert (
+            BedrockModelInfo.extract_model_name_from_arn(arn)
+            == extract_model_name_from_bedrock_arn(arn)
+        )
+
+    def test_get_non_litellm_routing_model_name_matches_standalone(self):
+        model = "bedrock/converse/claude-3"
+        assert (
+            BedrockModelInfo.get_non_litellm_routing_model_name(model)
+            == strip_bedrock_routing_prefix(model)
+        )
+
+
+class TestBedrockTokenCounter:
+    """Tests for BedrockTokenCounter class."""
+
+    def test_should_use_token_counting_api_for_bedrock(self):
+        counter = BedrockTokenCounter()
+        assert counter.should_use_token_counting_api("bedrock") is True
+
+    def test_should_not_use_token_counting_api_for_other_providers(self):
+        counter = BedrockTokenCounter()
+        assert counter.should_use_token_counting_api("openai") is False
+        assert counter.should_use_token_counting_api("anthropic") is False
+        assert counter.should_use_token_counting_api(None) is False
+
+    def test_get_token_counter_returns_bedrock_token_counter(self):
+        model_info = BedrockModelInfo()
+        token_counter = model_info.get_token_counter()
+        assert isinstance(token_counter, BedrockTokenCounter)
+
+    @pytest.mark.asyncio
+    async def test_count_tokens_returns_none_for_empty_messages(self):
+        counter = BedrockTokenCounter()
+        result = await counter.count_tokens(
+            model_to_use="anthropic.claude-3-sonnet",
+            messages=None,
+            contents=None,
+        )
+        assert result is None
+
+        result = await counter.count_tokens(
+            model_to_use="anthropic.claude-3-sonnet",
+            messages=[],
+            contents=None,
+        )
+        assert result is None
diff --git a/tests/llm_translation/test_bedrock_completion.py b/tests/llm_translation/test_bedrock_completion.py
index ec510b8f953..29c2d26981c 100644
--- a/tests/llm_translation/test_bedrock_completion.py
+++ b/tests/llm_translation/test_bedrock_completion.py
@@ -2835,6 +2835,34 @@ def test_bedrock_invoke_provider():
         )
         == "nova"
     )
+    assert (
+        litellm.AmazonInvokeConfig().get_bedrock_invoke_provider("amazon.nova-pro-v1:0")
+        == "nova"
+    )
+    assert (
+        litellm.AmazonInvokeConfig().get_bedrock_invoke_provider(
+            "amazon.nova-lite-v1:0"
+        )
+        == "nova"
+    )
+    assert (
+        litellm.AmazonInvokeConfig().get_bedrock_invoke_provider(
+            "amazon.nova-micro-v1:0"
+        )
+        == "nova"
+    )
+    assert (
+        litellm.AmazonInvokeConfig().get_bedrock_invoke_provider(
+            "amazon.nova-premier-v1:0"
+        )
+        == "nova"
+    )
+    assert (
+        litellm.AmazonInvokeConfig().get_bedrock_invoke_provider(
+            "amazon.nova-2-lite-v1:0"
+        )
+        == "nova"
+    )
 
 
 def test_bedrock_description_param():
@@ -3488,7 +3516,9 @@ def test_bedrock_openai_imported_model():
         url = mock_post.call_args.kwargs["url"]
         print(f"URL: {url}")
         assert "bedrock-runtime.us-east-1.amazonaws.com" in url
-        assert "arn:aws:bedrock:us-east-1:117159858402:imported-model/m4gc1mrfuddy" in url
+        assert (
+            "arn:aws:bedrock:us-east-1:117159858402:imported-model/m4gc1mrfuddy" in url
+        )
         assert "/invoke" in url
 
         # Validate request body follows OpenAI format
@@ -3517,7 +3547,9 @@ def test_bedrock_openai_imported_model():
         # Check image_url content
         assert user_msg["content"][1]["type"] == "image_url"
         assert "image_url" in user_msg["content"][1]
-        assert user_msg["content"][1]["image_url"]["url"].startswith("data:image/jpeg;base64,")
+        assert user_msg["content"][1]["image_url"]["url"].startswith(
+            "data:image/jpeg;base64,"
+        )
 
         assert user_msg["content"][2]["type"] == "image_url"
         assert "image_url" in user_msg["content"][2]
@@ -3526,21 +3558,67 @@ def test_bedrock_openai_imported_model():
         assert request_body["max_tokens"] == 300
         assert request_body["temperature"] == 0.5
 
+
+def test_bedrock_nova_provider_detection():
+    """
+    Test that Nova models are correctly detected even when prefixed with "amazon."
+    Regression test for issue #17910 where models like "amazon.nova-pro-v1:0"
+    were incorrectly identified as "amazon" (Titan) instead of "nova".
+    """
+    from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+
+    # Test various Nova model formats
+    nova_test_cases = [
+        ("us.amazon.nova-pro-v1:0", "nova"),
+        ("us.amazon.nova-lite-v1:0", "nova"),
+        ("us.amazon.nova-micro-v1:0", "nova"),
+        ("amazon.nova-pro-v1:0", "nova"),
+        ("amazon.nova-lite-v1:0", "nova"),
+        ("amazon.nova-micro-v1:0", "nova"),
+        ("amazon.nova-premier-v1:0", "nova"),
+        ("amazon.nova-2-lite-v1:0", "nova"),
+        ("bedrock/amazon.nova-pro-v1:0", "nova"),
+        ("bedrock/invoke/amazon.nova-pro-v1:0", "nova"),
+        ("amazon.Nova-pro-v1:0", "nova"),
+        ("amazon.NOVA-pro-v1:0", "nova"),
+    ]
+
+    for model, expected in nova_test_cases:
+        provider = BaseAWSLLM.get_bedrock_invoke_provider(model)
+        assert (
+            provider == expected
+        ), f"Failed for model: {model}, expected: {expected}, got: {provider}"
+
+    # Verify that Amazon Titan models still return "amazon"
+    titan_test_cases = [
+        ("amazon.titan-text-express-v1", "amazon"),
+        ("us.amazon.titan-text-lite-v1", "amazon"),
+    ]
+
+    for model, expected in titan_test_cases:
+        provider = BaseAWSLLM.get_bedrock_invoke_provider(model)
+        assert (
+            provider == expected
+        ), f"Failed for model: {model}, expected: {expected}, got: {provider}"
+
+
 def test_bedrock_openai_provider_detection():
     """
     Test that the OpenAI provider is correctly detected from model strings.
     """
     from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
-    
+
     # Test various OpenAI model formats
     test_cases = [
         "openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/abc123",
         "bedrock/openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/xyz789",
     ]
-    
+
     for model in test_cases:
         provider = BaseAWSLLM.get_bedrock_invoke_provider(model)
-        assert provider == "openai", f"Failed for model: {model}, got provider: {provider}"
+        assert (
+            provider == "openai"
+        ), f"Failed for model: {model}, got provider: {provider}"
         print(f"✓ Provider detection works for: {model}")
 
 
@@ -3549,16 +3627,16 @@ def test_bedrock_openai_model_id_extraction():
     Test that the model ID (ARN) is correctly extracted and encoded for OpenAI models.
     """
     from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
-    
-    model = "openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/test-model-123"
-    provider = BaseAWSLLM.get_bedrock_invoke_provider(model)
-    
-    model_id = BaseAWSLLM.get_bedrock_model_id(
-        model=model,
-        provider=provider,
-        optional_params={}
+
+    model = (
+        "openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/test-model-123"
     )
-    
+    provider = BaseAWSLLM.get_bedrock_invoke_provider(model)
+
+    model_id = BaseAWSLLM.get_bedrock_model_id(
+        model=model, provider=provider, optional_params={}
+    )
+
     # The ARN should be double URL encoded
     assert "arn" in model_id
     assert "imported-model" in model_id
@@ -3570,20 +3648,17 @@ def test_bedrock_openai_convert_messages_to_prompt():
     Test that convert_messages_to_prompt returns empty string for OpenAI models.
     """
     from litellm.llms.bedrock.chat.invoke_handler import BedrockLLM
-    
+
     bedrock_llm = BedrockLLM()
     messages = [
         {"role": "system", "content": "You are helpful"},
-        {"role": "user", "content": "Hello"}
+        {"role": "user", "content": "Hello"},
     ]
-    
+
     prompt, chat_history = bedrock_llm.convert_messages_to_prompt(
-        model="test-model",
-        messages=messages,
-        provider="openai",
-        custom_prompt_dict={}
+        model="test-model", messages=messages, provider="openai", custom_prompt_dict={}
     )
-    
+
     # OpenAI models use messages directly, no prompt conversion
     assert prompt == ""
     assert chat_history is None
@@ -3598,37 +3673,33 @@ def test_bedrock_openai_response_parsing():
     from litellm import ModelResponse
     from unittest.mock import Mock
     import json
-    
+
     bedrock_llm = BedrockLLM()
-    
+
     # Mock OpenAI-style response
     openai_response = {
         "choices": [
             {
                 "message": {
                     "content": "The capital of France is Paris.",
-                    "role": "assistant"
+                    "role": "assistant",
                 },
                 "finish_reason": "stop",
-                "index": 0
+                "index": 0,
             }
         ],
-        "usage": {
-            "prompt_tokens": 10,
-            "completion_tokens": 8,
-            "total_tokens": 18
-        }
+        "usage": {"prompt_tokens": 10, "completion_tokens": 8, "total_tokens": 18},
     }
-    
+
     mock_response = Mock()
     mock_response.json.return_value = openai_response
     mock_response.text = json.dumps(openai_response)
     mock_response.status_code = 200
     mock_response.headers = {}
-    
+
     model_response = ModelResponse()
     mock_logging = Mock()
-    
+
     result = bedrock_llm.process_response(
         model="openai/arn:aws:bedrock:us-east-1:123:imported-model/test",
         response=mock_response,
@@ -3640,18 +3711,18 @@ def test_bedrock_openai_response_parsing():
         data={},
         messages=[{"role": "user", "content": "What is the capital of France?"}],
         print_verbose=lambda x: None,
-        encoding=None
+        encoding=None,
     )
-    
+
     # Verify response content
     assert result.choices[0].message.content == "The capital of France is Paris."
     assert result.choices[0].finish_reason == "stop"
-    
+
     # Verify usage
     assert result.usage.prompt_tokens == 10
     assert result.usage.completion_tokens == 8
     assert result.usage.total_tokens == 18
-    
+
     print("✓ OpenAI response parsing works correctly")
 
 
@@ -3659,45 +3730,47 @@ def test_bedrock_openai_request_transformation():
     """
     Test that the request is correctly transformed for OpenAI models.
     """
-    from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import AmazonInvokeConfig
-    
+    from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+        AmazonInvokeConfig,
+    )
+
     config = AmazonInvokeConfig()
-    
+
     model = "openai/arn:aws:bedrock:us-east-1:123:imported-model/test"
     messages = [
         {"role": "system", "content": "You are helpful"},
-        {"role": "user", "content": "Hello"}
+        {"role": "user", "content": "Hello"},
     ]
-    
+
     optional_params = {
         "max_tokens": 100,
         "temperature": 0.7,
         "top_p": 0.9,
-        "stream": False
+        "stream": False,
     }
-    
+
     litellm_params = {}
     headers = {}
-    
-    with patch.object(config, 'get_bedrock_invoke_provider', return_value="openai"):
+
+    with patch.object(config, "get_bedrock_invoke_provider", return_value="openai"):
         result = config.transform_request(
             model=model,
             messages=messages,
             optional_params=optional_params.copy(),
             litellm_params=litellm_params,
-            headers=headers
+            headers=headers,
         )
-    
+
     # Verify the request uses messages format (not prompt)
     assert "messages" in result
     assert len(result["messages"]) == 2
     assert result["messages"][0]["role"] == "system"
     assert result["messages"][1]["role"] == "user"
-    
+
     # Verify parameters are included
     assert "max_tokens" in result
     assert "temperature" in result
-    
+
     print("✓ Request transformation works correctly")
 
 
@@ -3705,20 +3778,22 @@ def test_bedrock_openai_parameter_filtering():
     """
     Test that only supported OpenAI parameters are included in the request.
     """
-    from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import AmazonBedrockOpenAIConfig
-    
+    from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import (
+        AmazonBedrockOpenAIConfig,
+    )
+
     config = AmazonBedrockOpenAIConfig()
     model = "test-model"
-    
+
     supported_params = config.get_supported_openai_params(model=model)
-    
+
     # Verify common OpenAI parameters are supported
     assert "max_tokens" in supported_params
     assert "temperature" in supported_params
     assert "top_p" in supported_params
     assert "stream" in supported_params
     assert "stop" in supported_params
-    
+
     print(f"✓ Parameter filtering supports: {len(supported_params)} parameters")
     print(f"  Supported params: {supported_params}")
 
@@ -3728,12 +3803,12 @@ def test_bedrock_openai_route_detection():
     Test that the OpenAI route is correctly detected.
     """
     from litellm.llms.bedrock.common_utils import BedrockModelInfo
-    
+
     test_cases = [
         ("openai/arn:aws:bedrock:us-east-1:123:imported-model/test", "openai"),
         ("bedrock/openai/arn:aws:bedrock:us-east-1:123:imported-model/test", "openai"),
     ]
-    
+
     for model, expected_route in test_cases:
         route = BedrockModelInfo.get_bedrock_route(model)
         assert route == expected_route, f"Failed for model: {model}, got route: {route}"
@@ -3745,15 +3820,30 @@ def test_bedrock_openai_explicit_route_check():
     Test the explicit OpenAI route checker helper method.
     """
     from litellm.llms.bedrock.common_utils import BedrockModelInfo
-    
+
     # Test with openai/ prefix
-    assert BedrockModelInfo._explicit_openai_route("openai/arn:aws:bedrock:us-east-1:123:imported-model/test") is True
-    assert BedrockModelInfo._explicit_openai_route("bedrock/openai/arn:aws:bedrock:us-east-1:123:imported-model/test") is True
-    
+    assert (
+        BedrockModelInfo._explicit_openai_route(
+            "openai/arn:aws:bedrock:us-east-1:123:imported-model/test"
+        )
+        is True
+    )
+    assert (
+        BedrockModelInfo._explicit_openai_route(
+            "bedrock/openai/arn:aws:bedrock:us-east-1:123:imported-model/test"
+        )
+        is True
+    )
+
     # Test without openai/ prefix
     assert BedrockModelInfo._explicit_openai_route("anthropic.claude-3-sonnet") is False
-    assert BedrockModelInfo._explicit_openai_route("arn:aws:bedrock:us-east-1:123:imported-model/test") is False
-    
+    assert (
+        BedrockModelInfo._explicit_openai_route(
+            "arn:aws:bedrock:us-east-1:123:imported-model/test"
+        )
+        is False
+    )
+
     print("✓ Explicit route check works correctly")
 
 
@@ -3761,16 +3851,18 @@ def test_bedrock_openai_config_initialization():
     """
     Test that AmazonBedrockOpenAIConfig can be properly initialized.
     """
-    from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import AmazonBedrockOpenAIConfig
-    
+    from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import (
+        AmazonBedrockOpenAIConfig,
+    )
+
     config = AmazonBedrockOpenAIConfig()
-    
+
     # Verify it has the necessary methods
-    assert hasattr(config, 'get_supported_openai_params')
-    assert hasattr(config, 'transform_request')
-    assert hasattr(config, 'transform_response')
-    assert hasattr(config, 'map_openai_params')
-    
+    assert hasattr(config, "get_supported_openai_params")
+    assert hasattr(config, "transform_request")
+    assert hasattr(config, "transform_response")
+    assert hasattr(config, "map_openai_params")
+
     print("✓ AmazonBedrockOpenAIConfig initializes correctly")
 
 
@@ -3779,9 +3871,9 @@ def test_bedrock_openai_multiple_message_types():
     Test that various message content types are handled correctly.
     """
     from litellm.llms.custom_httpx.http_handler import HTTPHandler
-    
+
     client = HTTPHandler()
-    
+
     # Test with mixed content types
     messages = [
         {"role": "system", "content": "You are helpful"},
@@ -3790,11 +3882,14 @@ def test_bedrock_openai_multiple_message_types():
             "role": "user",
             "content": [
                 {"type": "text", "text": "Complex message with text"},
-                {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,iVBORw0KGg"}}
-            ]
-        }
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "data:image/jpeg;base64,iVBORw0KGg"},
+                },
+            ],
+        },
     ]
-    
+
     with patch.object(client, "post") as mock_post:
         try:
             response = completion(
@@ -3805,18 +3900,18 @@ def test_bedrock_openai_multiple_message_types():
             )
         except Exception as e:
             pass
-        
+
         # Verify the request was made
         if mock_post.called:
             request_body = json.loads(mock_post.call_args.kwargs["data"])
-            
+
             # Verify messages are preserved
             assert "messages" in request_body
             assert len(request_body["messages"]) == 3
-            
+
             # Verify mixed content is handled
             assert isinstance(request_body["messages"][2]["content"], list)
-            
+
             print("✓ Multiple message types handled correctly")
 
 
@@ -3829,18 +3924,18 @@ def test_bedrock_openai_error_handling():
     from litellm.llms.bedrock.common_utils import BedrockError
     from unittest.mock import Mock
     import json
-    
+
     bedrock_llm = BedrockLLM()
-    
+
     # Mock error response
     mock_response = Mock()
     mock_response.json.side_effect = Exception("Invalid JSON")
     mock_response.text = "Invalid response"
     mock_response.status_code = 422
-    
+
     model_response = ModelResponse()
     mock_logging = Mock()
-    
+
     with pytest.raises(BedrockError) as exc_info:
         bedrock_llm.process_response(
             model="openai/arn:aws:bedrock:us-east-1:123:imported-model/test",
@@ -3853,8 +3948,8 @@ def test_bedrock_openai_error_handling():
             data={},
             messages=[],
             print_verbose=lambda x: None,
-            encoding=None
+            encoding=None,
         )
-    
+
     assert exc_info.value.status_code == 422
     print("✓ Error handling works correctly")
diff --git a/tests/llm_translation/test_bedrock_moonshot.py b/tests/llm_translation/test_bedrock_moonshot.py
new file mode 100644
index 00000000000..c6066c7db42
--- /dev/null
+++ b/tests/llm_translation/test_bedrock_moonshot.py
@@ -0,0 +1,290 @@
+"""
+Tests for Bedrock Moonshot (Kimi K2) integration.
+
+This test suite verifies:
+1. Basic completion functionality
+2. Streaming responses
+3. System message support
+4. Temperature parameter handling
+5. Reasoning content extraction from <reasoning> tags
+6. Tool calling support (including tool response handling)
+7. Parameter validation (e.g., stop sequences not supported)
+"""
+
+from base_llm_unit_tests import BaseLLMChatTest
+import pytest
+import sys
+import os
+import json
+
+sys.path.insert(0, os.path.abspath("../.."))
+import litellm
+from litellm.llms.bedrock.common_utils import get_bedrock_chat_config
+
+
+class TestBedrockMoonshotInvoke(BaseLLMChatTest):
+    """
+    Test suite for Bedrock Moonshot via invoke route.
+    Inherits all standard LLM tests from BaseLLMChatTest.
+    """
+    
+    def get_base_completion_call_args(self) -> dict:
+        litellm._turn_on_debug()
+        return {
+            "model": "bedrock/invoke/moonshot.kimi-k2-thinking",
+        }
+    
+    def test_tool_call_no_arguments(self, tool_call_no_arguments):
+        """Test that tool calls with no arguments is translated correctly."""
+        pass
+
+
+class TestBedrockMoonshotBasic:
+    """Unit tests for Bedrock Moonshot configuration and transformations."""
+
+    def test_provider_detection_invoke(self):
+        """Test that Bedrock Moonshot invoke models are correctly detected."""
+        config = get_bedrock_chat_config("bedrock/invoke/moonshot.kimi-k2-thinking")
+        assert config is not None
+        assert config.__class__.__name__ == "AmazonMoonshotConfig"
+    
+    def test_provider_detection_converse(self):
+        """Test that Bedrock Moonshot converse models are correctly detected."""
+        config = get_bedrock_chat_config("bedrock/moonshot.kimi-k2-thinking")
+        assert config is not None
+
+    def test_config_initialization(self):
+        """Test that AmazonMoonshotConfig initializes correctly."""
+        config = get_bedrock_chat_config("invoke/moonshot.kimi-k2-thinking")
+        assert config is not None
+        assert config.custom_llm_provider == "bedrock"
+
+    def test_supported_params(self):
+        """Test that supported OpenAI params are correctly defined."""
+        config = get_bedrock_chat_config("invoke/moonshot.kimi-k2-thinking")
+        supported_params = config.get_supported_openai_params("moonshot.kimi-k2-thinking")
+        
+        # Should support these params
+        assert "temperature" in supported_params
+        assert "max_tokens" in supported_params
+        assert "top_p" in supported_params
+        assert "stream" in supported_params
+        assert "tools" in supported_params
+        assert "tool_choice" in supported_params
+        
+        # Should NOT support stop sequences on Bedrock
+        assert "stop" not in supported_params
+        
+        # Should NOT support functions (use tools instead)
+        assert "functions" not in supported_params
+
+    def test_transform_request_strips_model_prefix(self):
+        """Test that model ID prefixes are correctly stripped in transform_request."""
+        from litellm.llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
+            AmazonMoonshotConfig,
+        )
+        
+        config = AmazonMoonshotConfig()
+        
+        messages = [{"role": "user", "content": "Hello"}]
+        
+        # Test that bedrock/invoke/ prefix is stripped
+        transformed = config.transform_request(
+            model="bedrock/invoke/moonshot.kimi-k2-thinking",
+            messages=messages,
+            optional_params={},
+            litellm_params={},
+            headers={}
+        )
+        
+        # The model ID in the request body should be stripped
+        assert transformed["model"] == "moonshot.kimi-k2-thinking"
+
+
+class TestBedrockMoonshotReasoningContent:
+    """Tests for reasoning content extraction."""
+
+    def test_reasoning_content_extraction(self):
+        """Test that reasoning content is extracted from <reasoning> tags."""
+        from litellm.llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
+            AmazonMoonshotConfig,
+        )
+        
+        config = AmazonMoonshotConfig()
+        
+        # Test with reasoning tags
+        content_with_reasoning = "<reasoning>This is my thought process</reasoning>This is the answer"
+        reasoning, content = config._extract_reasoning_from_content(content_with_reasoning)
+        
+        assert reasoning == "This is my thought process"
+        assert content == "This is the answer"
+        assert "<reasoning>" not in content
+        
+        # Test without reasoning tags
+        content_without_reasoning = "This is just a regular answer"
+        reasoning, content = config._extract_reasoning_from_content(content_without_reasoning)
+        
+        assert reasoning is None
+        assert content == "This is just a regular answer"
+
+
+class TestBedrockMoonshotToolCalling:
+    """Unit tests for tool calling functionality."""
+
+    def test_tool_calling_supported(self):
+        """Test that tool calling is supported for Kimi K2 Thinking model."""
+        config = get_bedrock_chat_config("invoke/moonshot.kimi-k2-thinking")
+        supported_params = config.get_supported_openai_params("moonshot.kimi-k2-thinking")
+        
+        # Kimi K2 Thinking DOES support tool calls (unlike kimi-thinking-preview)
+        assert "tools" in supported_params
+        assert "tool_choice" in supported_params
+
+    def test_tool_call_request_format(self):
+        """Test that tool call requests are formatted correctly."""
+        from litellm.llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
+            AmazonMoonshotConfig,
+        )
+        
+        config = AmazonMoonshotConfig()
+        
+        messages = [
+            {"role": "user", "content": "What's the weather in San Francisco?"}
+        ]
+        
+        optional_params = {
+            "tools": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "description": "Get the current weather",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "location": {"type": "string"}
+                            },
+                            "required": ["location"]
+                        }
+                    }
+                }
+            ]
+        }
+        
+        transformed = config.transform_request(
+            model="bedrock/invoke/moonshot.kimi-k2-thinking",
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params={},
+            headers={}
+        )
+        
+        # Verify model ID is stripped
+        assert transformed["model"] == "moonshot.kimi-k2-thinking"
+        
+        # Verify tools are included
+        assert "tools" in transformed
+        assert len(transformed["tools"]) == 1
+        assert transformed["tools"][0]["function"]["name"] == "get_weather"
+
+    def test_tool_response_message_format(self):
+        """Test that tool response messages are formatted correctly."""
+        # This tests the proper format for sending tool responses back
+        tool_response_message = {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "content": json.dumps({"temperature": 72, "condition": "sunny"})
+        }
+        
+        # Verify the message structure
+        assert tool_response_message["role"] == "tool"
+        assert "tool_call_id" in tool_response_message
+        assert "content" in tool_response_message
+
+
+class TestBedrockMoonshotParameterValidation:
+    """Tests for parameter validation and edge cases."""
+
+    def test_stop_sequences_not_supported(self):
+        """Test that stop sequences are correctly excluded from supported params."""
+        config = get_bedrock_chat_config("invoke/moonshot.kimi-k2-thinking")
+        supported_params = config.get_supported_openai_params("moonshot.kimi-k2-thinking")
+        
+        # Bedrock Moonshot doesn't support stopSequences field
+        assert "stop" not in supported_params
+
+    def test_temperature_range(self):
+        """Test that temperature parameter is handled correctly."""
+        # Moonshot models support temperature 0-1
+        # This is handled by the parent MoonshotChatConfig class
+        config = get_bedrock_chat_config("invoke/moonshot.kimi-k2-thinking")
+        
+        # Verify config exists and can handle temperature
+        assert config is not None
+        supported_params = config.get_supported_openai_params("moonshot.kimi-k2-thinking")
+        assert "temperature" in supported_params
+
+
+class TestBedrockMoonshotTransformations:
+    """Tests for request/response transformations."""
+
+    def test_transform_request_basic(self):
+        """Test basic request transformation."""
+        from litellm.llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
+            AmazonMoonshotConfig,
+        )
+        
+        config = AmazonMoonshotConfig()
+        
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"}
+        ]
+        
+        optional_params = {
+            "temperature": 0.7,
+            "max_tokens": 100
+        }
+        
+        transformed = config.transform_request(
+            model="bedrock/invoke/moonshot.kimi-k2-thinking",
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params={},
+            headers={}
+        )
+        
+        # Verify model ID is stripped
+        assert transformed["model"] == "moonshot.kimi-k2-thinking"
+        
+        # Verify messages are included
+        assert "messages" in transformed
+        assert len(transformed["messages"]) >= 1
+        
+        # Verify optional params are included
+        assert transformed["temperature"] == 0.7
+        assert transformed["max_tokens"] == 100
+
+    def test_transform_request_with_system_message(self):
+        """Test request transformation with system message."""
+        from litellm.llms.bedrock.chat.invoke_transformations.amazon_moonshot_transformation import (
+            AmazonMoonshotConfig,
+        )
+        
+        config = AmazonMoonshotConfig()
+        
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"}
+        ]
+        
+        transformed = config.transform_request(
+            model="moonshot.kimi-k2-thinking",
+            messages=messages,
+            optional_params={},
+            litellm_params={},
+            headers={}
+        )
+        
+        # System messages should be supported
+        assert "messages" in transformed
diff --git a/tests/llm_translation/test_openrouter.py b/tests/llm_translation/test_openrouter.py
index 839d08e12bf..105b05d3449 100644
--- a/tests/llm_translation/test_openrouter.py
+++ b/tests/llm_translation/test_openrouter.py
@@ -32,3 +32,18 @@ def test_completion_openrouter_image_generation():
         .message.images[0]["image_url"]["url"]
         .startswith("data:image/png;base64,")
     )
+
+
+def test_openrouter_embedding():
+    """Test OpenRouter embeddings support."""
+    litellm._turn_on_debug()
+    resp = litellm.embedding(
+        model="openrouter/openai/text-embedding-3-small",
+        input=["Hello world", "How are you?"],
+    )
+    print(resp)
+    assert resp is not None
+    assert len(resp.data) == 2
+    assert resp.data[0]["embedding"] is not None
+    assert isinstance(resp.data[0]["embedding"], list)
+    assert len(resp.data[0]["embedding"]) > 0
diff --git a/tests/llm_translation/test_prompt_factory.py b/tests/llm_translation/test_prompt_factory.py
index d0c0c12ba7c..8974632631d 100644
--- a/tests/llm_translation/test_prompt_factory.py
+++ b/tests/llm_translation/test_prompt_factory.py
@@ -7,11 +7,10 @@ import pytest
 
 sys.path.insert(0, os.path.abspath("../.."))
 
-from typing import Union, List
+from typing import List
 
 # from litellm.litellm_core_utils.prompt_templates.factory import prompt_factory
 import litellm
-from litellm import completion
 from litellm.litellm_core_utils.prompt_templates.factory import (
     _bedrock_tools_pt,
     anthropic_messages_pt,
@@ -31,7 +30,7 @@ from litellm.llms.vertex_ai.gemini.transformation import (
     _gemini_convert_messages_with_history,
 )
 from litellm.types.llms.openai import AllMessageValues
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
 
 def test_llama_3_prompt():
@@ -129,10 +128,6 @@ def test_anthropic_pt_formatting():
 
 
 def test_anthropic_messages_nested_pt():
-    from litellm.types.llms.anthropic import (
-        AnthopicMessagesAssistantMessageParam,
-        AnthropicMessagesUserMessageParam,
-    )
 
     messages = [
         {"content": [{"text": "here is a task", "type": "text"}], "role": "user"},
@@ -214,7 +209,7 @@ def test_create_anthropic_image_param_with_http_url():
     image_param = create_anthropic_image_param(
         "https://example.com/image.jpg", format=None
     )
-    
+
     assert image_param["type"] == "image"
     assert image_param["source"]["type"] == "url"
     assert image_param["source"]["url"] == "https://example.com/image.jpg"
@@ -225,7 +220,7 @@ def test_create_anthropic_image_param_with_https_url():
     image_param = create_anthropic_image_param(
         "https://example.com/image.png", format=None
     )
-    
+
     assert image_param["type"] == "image"
     assert image_param["source"]["type"] == "url"
     assert image_param["source"]["url"] == "https://example.com/image.png"
@@ -236,7 +231,7 @@ def test_create_anthropic_image_param_with_dict_input():
     image_param = create_anthropic_image_param(
         {"url": "https://example.com/image.jpg", "format": "image/jpeg"}, format=None
     )
-    
+
     assert image_param["type"] == "image"
     assert image_param["source"]["type"] == "url"
     assert image_param["source"]["url"] == "https://example.com/image.jpg"
@@ -247,7 +242,7 @@ def test_create_anthropic_image_param_with_base64_data_uri():
     image_param = create_anthropic_image_param(
         "data:image/jpeg;base64,/9j/4AAQSkZJRg==", format=None
     )
-    
+
     assert image_param["type"] == "image"
     assert image_param["source"]["type"] == "base64"
     assert image_param["source"]["media_type"] == "image/jpeg"
@@ -259,7 +254,7 @@ def test_create_anthropic_image_param_with_format_override():
     image_param = create_anthropic_image_param(
         "data:image/jpeg;base64,1234", format="image/png"
     )
-    
+
     assert image_param["type"] == "image"
     assert image_param["source"]["type"] == "base64"
     assert image_param["source"]["media_type"] == "image/png"
@@ -279,19 +274,19 @@ def test_anthropic_messages_pt_with_url_image():
             ],
         }
     ]
-    
+
     result = anthropic_messages_pt(
         messages=messages, model="claude-3-5-sonnet", llm_provider="anthropic"
     )
-    
+
     assert len(result) == 1
     assert result[0]["role"] == "user"
     assert isinstance(result[0]["content"], list)
     assert len(result[0]["content"]) == 2
-    
+
     # Check text content
     assert result[0]["content"][0]["type"] == "text"
-    
+
     # Check image content - should be URL reference, not base64
     assert result[0]["content"][1]["type"] == "image"
     assert result[0]["content"][1]["source"]["type"] == "url"
@@ -312,16 +307,16 @@ def test_anthropic_messages_pt_with_base64_image():
             ],
         }
     ]
-    
+
     result = anthropic_messages_pt(
         messages=messages, model="claude-3-5-sonnet", llm_provider="anthropic"
     )
-    
+
     assert len(result) == 1
     assert result[0]["role"] == "user"
     assert isinstance(result[0]["content"], list)
     assert len(result[0]["content"]) == 2
-    
+
     # Check image content - should be base64, not URL
     assert result[0]["content"][1]["type"] == "image"
     assert result[0]["content"][1]["source"]["type"] == "base64"
@@ -568,7 +563,9 @@ def test_vertex_only_image_user_message():
         },
     ]
 
-    response = _gemini_convert_messages_with_history(messages=messages, model="gemini-1.5-pro")
+    response = _gemini_convert_messages_with_history(
+        messages=messages, model="gemini-1.5-pro"
+    )
 
     expected_response = [
         {
@@ -962,8 +959,8 @@ def test_convert_to_anthropic_tool_invoke_regular_tool():
             "type": "function",
             "function": {
                 "name": "get_weather",
-                "arguments": '{"location": "San Francisco"}'
-            }
+                "arguments": '{"location": "San Francisco"}',
+            },
         }
     ]
 
@@ -979,7 +976,7 @@ def test_convert_to_anthropic_tool_invoke_regular_tool():
 def test_convert_to_anthropic_tool_invoke_server_tool():
     """
     Test that server_tool_use (srvtoolu_) is reconstructed as server_tool_use.
-    
+
     Fixes: https://github.com/BerriAI/litellm/issues/17737
     """
     tool_calls = [
@@ -988,8 +985,8 @@ def test_convert_to_anthropic_tool_invoke_server_tool():
             "type": "function",
             "function": {
                 "name": "web_search",
-                "arguments": '{"query": "elephant weight"}'
-            }
+                "arguments": '{"query": "elephant weight"}',
+            },
         }
     ]
 
@@ -1005,7 +1002,7 @@ def test_convert_to_anthropic_tool_invoke_server_tool():
 def test_convert_to_anthropic_tool_invoke_with_web_search_results():
     """
     Test that web_search_tool_result is included after server_tool_use.
-    
+
     Fixes: https://github.com/BerriAI/litellm/issues/17737
     """
     tool_calls = [
@@ -1014,8 +1011,8 @@ def test_convert_to_anthropic_tool_invoke_with_web_search_results():
             "type": "function",
             "function": {
                 "name": "web_search",
-                "arguments": '{"query": "elephant weight"}'
-            }
+                "arguments": '{"query": "elephant weight"}',
+            },
         }
     ]
 
@@ -1028,13 +1025,15 @@ def test_convert_to_anthropic_tool_invoke_with_web_search_results():
                     "type": "web_search_result",
                     "url": "https://example.com",
                     "title": "Elephant Facts",
-                    "snippet": "Elephants weigh 5000 kg"
+                    "snippet": "Elephants weigh 5000 kg",
                 }
-            ]
+            ],
         }
     ]
 
-    result = convert_to_anthropic_tool_invoke(tool_calls, web_search_results=web_search_results)
+    result = convert_to_anthropic_tool_invoke(
+        tool_calls, web_search_results=web_search_results
+    )
 
     assert len(result) == 2
     # First: server_tool_use
@@ -1048,7 +1047,7 @@ def test_convert_to_anthropic_tool_invoke_with_web_search_results():
 def test_convert_to_anthropic_tool_invoke_mixed_tools():
     """
     Test that mixed server and regular tools are reconstructed correctly.
-    
+
     Fixes: https://github.com/BerriAI/litellm/issues/17737
     """
     tool_calls = [
@@ -1057,28 +1056,27 @@ def test_convert_to_anthropic_tool_invoke_mixed_tools():
             "type": "function",
             "function": {
                 "name": "web_search",
-                "arguments": '{"query": "elephant weight"}'
-            }
+                "arguments": '{"query": "elephant weight"}',
+            },
         },
         {
             "id": "toolu_01XYZ789",
             "type": "function",
-            "function": {
-                "name": "add_numbers",
-                "arguments": '{"a": 5000, "b": 100}'
-            }
-        }
+            "function": {"name": "add_numbers", "arguments": '{"a": 5000, "b": 100}'},
+        },
     ]
 
     web_search_results = [
         {
             "type": "web_search_tool_result",
             "tool_use_id": "srvtoolu_01ABC123",
-            "content": [{"url": "https://example.com", "title": "Test"}]
+            "content": [{"url": "https://example.com", "title": "Test"}],
         }
     ]
 
-    result = convert_to_anthropic_tool_invoke(tool_calls, web_search_results=web_search_results)
+    result = convert_to_anthropic_tool_invoke(
+        tool_calls, web_search_results=web_search_results
+    )
 
     assert len(result) == 3
     # First: server_tool_use
@@ -1094,7 +1092,7 @@ def test_convert_to_anthropic_tool_invoke_mixed_tools():
 def test_anthropic_messages_pt_with_server_tool_use():
     """
     Test that anthropic_messages_pt correctly reconstructs server_tool_use from provider_specific_fields.
-    
+
     Fixes: https://github.com/BerriAI/litellm/issues/17737
     """
     messages = [
@@ -1108,36 +1106,40 @@ def test_anthropic_messages_pt_with_server_tool_use():
                     "type": "function",
                     "function": {
                         "name": "web_search",
-                        "arguments": '{"query": "elephant weight"}'
-                    }
+                        "arguments": '{"query": "elephant weight"}',
+                    },
                 },
                 {
                     "id": "toolu_01XYZ789",
                     "type": "function",
                     "function": {
                         "name": "add_numbers",
-                        "arguments": '{"a": 5000, "b": 100}'
-                    }
-                }
+                        "arguments": '{"a": 5000, "b": 100}',
+                    },
+                },
             ],
             "provider_specific_fields": {
                 "web_search_results": [
                     {
                         "type": "web_search_tool_result",
                         "tool_use_id": "srvtoolu_01ABC123",
-                        "content": [{"url": "https://example.com", "title": "Test", "snippet": "5000 kg"}]
+                        "content": [
+                            {
+                                "url": "https://example.com",
+                                "title": "Test",
+                                "snippet": "5000 kg",
+                            }
+                        ],
                     }
                 ]
-            }
+            },
         },
-        {
-            "role": "tool",
-            "tool_call_id": "toolu_01XYZ789",
-            "content": "5100"
-        }
+        {"role": "tool", "tool_call_id": "toolu_01XYZ789", "content": "5100"},
     ]
 
-    result = anthropic_messages_pt(messages, model="claude-sonnet-4-5", llm_provider="anthropic")
+    result = anthropic_messages_pt(
+        messages, model="claude-sonnet-4-5", llm_provider="anthropic"
+    )
 
     # Find the assistant message
     assistant_msg = next(m for m in result if m["role"] == "assistant")
@@ -1162,3 +1164,73 @@ def test_anthropic_messages_pt_with_server_tool_use():
     # Verify regular tool_use
     tool_use = next(c for c in content if c.get("type") == "tool_use")
     assert tool_use["id"] == "toolu_01XYZ789"
+
+
+# ============ parse_tool_call_arguments Tests ============
+# Tests for the shared utility that parses tool call JSON arguments
+
+
+def test_parse_tool_call_arguments_valid_json():
+    """Test that valid JSON is parsed correctly."""
+    from litellm.litellm_core_utils.prompt_templates.common_utils import (
+        parse_tool_call_arguments,
+    )
+
+    result = parse_tool_call_arguments('{"city": "Paris", "units": "celsius"}')
+    assert result == {"city": "Paris", "units": "celsius"}
+
+
+def test_parse_tool_call_arguments_empty_input():
+    """Test that None/empty input returns empty dict."""
+    from litellm.litellm_core_utils.prompt_templates.common_utils import (
+        parse_tool_call_arguments,
+    )
+
+    assert parse_tool_call_arguments(None) == {}
+    assert parse_tool_call_arguments("") == {}
+
+
+def test_parse_tool_call_arguments_malformed_json():
+    """Test that malformed JSON raises ValueError with context."""
+    from litellm.litellm_core_utils.prompt_templates.common_utils import (
+        parse_tool_call_arguments,
+    )
+
+    with pytest.raises(ValueError) as exc_info:
+        parse_tool_call_arguments(
+            '{"skill_name": "pptx',
+            tool_name="load_skill",
+            context="Anthropic tool invoke",
+        )
+
+    error_msg = str(exc_info.value)
+    assert "load_skill" in error_msg
+    assert "Anthropic tool invoke" in error_msg
+    assert '{"skill_name": "pptx' in error_msg
+    assert "Unterminated string" in error_msg
+
+
+def test_convert_to_anthropic_tool_invoke_malformed_json():
+    """
+    Test that convert_to_anthropic_tool_invoke raises ValueError with context
+    when tool arguments contain malformed JSON.
+
+    Fixes: https://github.com/BerriAI/litellm/issues/18920
+    """
+    tool_calls = [
+        {
+            "id": "toolu_01_invalid",
+            "type": "function",
+            "function": {
+                "name": "bad_tool",
+                "arguments": '{"truncated',  # Malformed JSON
+            },
+        }
+    ]
+
+    with pytest.raises(ValueError) as exc_info:
+        convert_to_anthropic_tool_invoke(tool_calls)
+
+    error_msg = str(exc_info.value)
+    assert "bad_tool" in error_msg
+    assert '{"truncated' in error_msg
diff --git a/tests/llm_translation/test_replicate.py b/tests/llm_translation/test_replicate.py
new file mode 100644
index 00000000000..dee4f5969e6
--- /dev/null
+++ b/tests/llm_translation/test_replicate.py
@@ -0,0 +1,289 @@
+"""
+Unit tests for Replicate provider, particularly testing DeepSeek models
+"""
+
+import asyncio
+import json
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock, Mock, patch
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import litellm
+from litellm import completion
+from litellm.llms.replicate.chat.handler import (
+    async_completion,
+    completion as replicate_completion,
+)
+
+
+class TestReplicateStartingStatus:
+    """Test that Replicate handler correctly handles 'starting' status for DeepSeek models"""
+
+    @pytest.mark.asyncio
+    @patch("litellm.llms.replicate.chat.handler.get_async_httpx_client")
+    async def test_async_completion_handles_starting_status(
+        self, mock_get_client
+    ):
+        """Test that async completion polls correctly when status is 'starting'"""
+        # Mock the async HTTP client
+        mock_client = AsyncMock()
+        mock_get_client.return_value = mock_client
+
+        # Mock the initial POST response (creates prediction)
+        post_response = Mock()
+        post_response.json.return_value = {
+            "id": "test-prediction-id",
+            "urls": {
+                "get": "https://api.replicate.com/v1/predictions/test-id",
+                "cancel": "https://api.replicate.com/v1/predictions/test-id/cancel",
+            },
+        }
+        mock_client.post = AsyncMock(return_value=post_response)
+
+        # Mock GET responses - first 'starting', then 'processing', then 'succeeded'
+        get_response_starting = Mock()
+        get_response_starting.status_code = 200
+        get_response_starting.json.return_value = {
+            "id": "test-prediction-id",
+            "status": "starting",
+            "output": None,
+        }
+
+        get_response_processing = Mock()
+        get_response_processing.status_code = 200
+        get_response_processing.json.return_value = {
+            "id": "test-prediction-id",
+            "status": "processing",
+            "output": None,
+        }
+
+        get_response_succeeded = Mock()
+        get_response_succeeded.status_code = 200
+        get_response_succeeded.json.return_value = {
+            "id": "test-prediction-id",
+            "status": "succeeded",
+            "output": ["Hello", " from", " DeepSeek!"],
+        }
+        get_response_succeeded.text = json.dumps(
+            get_response_succeeded.json.return_value
+        )
+        get_response_succeeded.headers = {}
+
+        # Configure mock to return different responses on successive calls
+        mock_client.get = AsyncMock(
+            side_effect=[
+                get_response_starting,
+                get_response_processing,
+                get_response_succeeded,
+            ]
+        )
+
+        # Create mock model response
+        model_response = litellm.ModelResponse()
+        model_response.choices = [litellm.Choices()]
+        model_response.choices[0].message = litellm.Message(content="")
+
+        # Create mock logging object
+        mock_logging = Mock()
+        mock_logging.post_call = Mock()
+
+        # Call async_completion
+        result = await async_completion(
+            model_response=model_response,
+            model="deepseek-ai/deepseek-v3",
+            messages=[{"role": "user", "content": "Hi"}],
+            encoding=None,
+            optional_params={},
+            litellm_params={},
+            version_id="deepseek-ai/deepseek-v3",
+            input_data={"input": {"prompt": "test"}},
+            api_key="test-key",
+            api_base="https://api.replicate.com",
+            logging_obj=mock_logging,
+            print_verbose=print,
+            headers={"Authorization": "Token test-key"},
+        )
+
+        # Assert that we got responses
+        assert result is not None
+        assert result.choices[0].message.content == "Hello from DeepSeek!"
+        
+        # Verify that GET was called 3 times (starting, processing, succeeded)
+        assert mock_client.get.call_count == 3
+
+    @patch("litellm.llms.replicate.chat.handler._get_httpx_client")
+    def test_sync_completion_handles_starting_status(self, mock_get_client):
+        """Test that sync completion polls correctly when status is 'starting'"""
+        # Mock the sync HTTP client
+        mock_client = Mock()
+        mock_get_client.return_value = mock_client
+
+        # Mock the initial POST response
+        post_response = Mock()
+        post_response.json.return_value = {
+            "id": "test-prediction-id",
+            "urls": {
+                "get": "https://api.replicate.com/v1/predictions/test-id",
+                "cancel": "https://api.replicate.com/v1/predictions/test-id/cancel",
+            },
+        }
+        mock_client.post.return_value = post_response
+
+        # Mock GET responses
+        get_response_starting = Mock()
+        get_response_starting.status_code = 200
+        get_response_starting.json.return_value = {
+            "id": "test-prediction-id",
+            "status": "starting",
+            "output": None,
+        }
+
+        get_response_succeeded = Mock()
+        get_response_succeeded.status_code = 200
+        get_response_succeeded.json.return_value = {
+            "id": "test-prediction-id",
+            "status": "succeeded",
+            "output": ["Hello", " DeepSeek!"],
+        }
+        get_response_succeeded.text = json.dumps(
+            get_response_succeeded.json.return_value
+        )
+        get_response_succeeded.headers = {}
+
+        # Configure mock to return different responses
+        mock_client.get.side_effect = [get_response_starting, get_response_succeeded]
+
+        # Create mock objects
+        model_response = litellm.ModelResponse()
+        model_response.choices = [litellm.Choices()]
+        model_response.choices[0].message = litellm.Message(content="")
+
+        mock_logging = Mock()
+        mock_logging.post_call = Mock()
+
+        # Call completion with mock_response to avoid actual API call
+        with patch("time.sleep"):  # Skip sleep delays in test
+            result = replicate_completion(
+                model="deepseek-ai/deepseek-v3",
+                messages=[{"role": "user", "content": "Hi"}],
+                api_base="https://api.replicate.com",
+                model_response=model_response,
+                print_verbose=print,
+                optional_params={},
+                litellm_params={},
+                logging_obj=mock_logging,
+                api_key="test-key",
+                encoding=None,
+                headers={},
+            )
+
+        # Assert results
+        assert result is not None
+        assert result.choices[0].message.content == "Hello DeepSeek!"
+        
+        # Verify GET was called multiple times
+        assert mock_client.get.call_count >= 1
+
+
+class TestReplicateOutputFormats:
+    """Test that Replicate handler handles different output formats from models"""
+
+    def test_transform_response_list_output(self):
+        """Test standard list output format"""
+        from litellm.llms.replicate.chat.transformation import ReplicateConfig
+
+        config = ReplicateConfig()
+        
+        # Mock response with list output
+        mock_response = Mock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = {
+            "status": "succeeded",
+            "output": ["Hello", " ", "world"],
+        }
+        mock_response.text = json.dumps(mock_response.json.return_value)
+        mock_response.headers = {}
+
+        model_response = litellm.ModelResponse()
+        model_response.choices = [litellm.Choices()]
+        model_response.choices[0].message = litellm.Message(content="")
+
+        mock_logging = Mock()
+        mock_logging.post_call = Mock()
+
+        result = config.transform_response(
+            model="meta/llama-2-70b-chat",
+            raw_response=mock_response,
+            model_response=model_response,
+            logging_obj=mock_logging,
+            request_data={"input": {"prompt": "test"}},
+            messages=[{"role": "user", "content": "Hi"}],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+            api_key="test-key",
+        )
+
+        assert result.choices[0].message.content == "Hello world"
+
+    def test_transform_response_string_output(self):
+        """Test string output format (as used by some DeepSeek models)"""
+        from litellm.llms.replicate.chat.transformation import ReplicateConfig
+
+        config = ReplicateConfig()
+        
+        # Mock response with string output
+        mock_response = Mock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = {
+            "status": "succeeded",
+            "output": "Hello from DeepSeek",
+        }
+        mock_response.text = json.dumps(mock_response.json.return_value)
+        mock_response.headers = {}
+
+        model_response = litellm.ModelResponse()
+        model_response.choices = [litellm.Choices()]
+        model_response.choices[0].message = litellm.Message(content="")
+
+        mock_logging = Mock()
+        mock_logging.post_call = Mock()
+
+        result = config.transform_response(
+            model="deepseek-ai/deepseek-v3",
+            raw_response=mock_response,
+            model_response=model_response,
+            logging_obj=mock_logging,
+            request_data={"input": {"prompt": "test"}},
+            messages=[{"role": "user", "content": "Hi"}],
+            optional_params={},
+            litellm_params={},
+            encoding=None,
+            api_key="test-key",
+        )
+
+        assert result.choices[0].message.content == "Hello from DeepSeek"
+
+
+# Integration test (requires actual API key - skip in CI)
+@pytest.mark.skip(reason="Requires REPLICATE_API_KEY environment variable")
+def test_replicate_deepseek_integration():
+    """Integration test with actual DeepSeek model on Replicate"""
+    try:
+        response = completion(
+            model="replicate/deepseek-ai/deepseek-v3",
+            messages=[{"role": "user", "content": "Say 'Hello World' and nothing else"}],
+            max_tokens=20,
+        )
+        
+        assert response is not None
+        assert response.choices[0].message.content is not None
+        assert len(response.choices[0].message.content) > 0
+        print(f"Response: {response.choices[0].message.content}")
+        
+    except Exception as e:
+        pytest.fail(f"Integration test failed: {e}")
diff --git a/tests/load_tests/memory_leak_utils.py b/tests/load_tests/memory_leak_utils.py
new file mode 100644
index 00000000000..160a67fa184
--- /dev/null
+++ b/tests/load_tests/memory_leak_utils.py
@@ -0,0 +1,314 @@
+"""
+Memory Leak Testing Utilities
+
+This module provides reusable utilities, fixtures, and helpers for memory leak
+and OOM (Out of Memory) detection tests. It includes:
+- Mock server setup for local testing
+- Memory tracking fixtures
+- Router fixtures configured for testing
+- Helper functions for running memory baseline tests
+
+Usage:
+    from tests.load_tests.memory_leak_utils import (
+        mock_server,
+        limit_memory,
+        test_router,
+        run_memory_baseline_test,
+    )
+"""
+
+import gc
+import os
+import socket
+import sys
+import time
+from threading import Thread
+
+# Add parent directory to path to import litellm (same pattern as other tests)
+filepath = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, os.path.abspath(os.path.join(filepath, "../..")))
+
+import httpx
+import pytest
+import psutil
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+from litellm.router import Router
+
+# Test Configuration Constants
+TEST_API_KEY = "sk-1234"
+TEST_MODEL_NAME = "gpt-3.5-turbo"
+
+# Timing Constants (seconds)
+GC_STABILIZATION_DELAY = 0.05
+
+
+# Mock OpenAI-compatible server
+def create_mock_server():
+    """Create a simple FastAPI mock server that mimics OpenAI API responses."""
+    app = FastAPI()
+    
+    @app.post("/v1/chat/completions")
+    @app.post("/chat/completions")
+    async def chat_completions(request: Request):
+        """Mock OpenAI chat completions endpoint."""
+        request_data = await request.json()
+        # Return a simple mock response
+        return JSONResponse({
+            "id": "chatcmpl-mock",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": request_data.get("model", TEST_MODEL_NAME),
+            "choices": [{
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": "Mock response"
+                },
+                "finish_reason": "stop"
+            }],
+            "usage": {
+                "prompt_tokens": 10,
+                "completion_tokens": 5,
+                "total_tokens": 15
+            }
+        })
+    
+    # Catch-all route to see what URLs are being requested
+    @app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"])
+    async def catch_all(request: Request, path: str):
+        """Catch-all route to debug what URLs are being requested."""
+        print(f"[Mock Server] Received request: {request.method} {request.url.path}")
+        # For non-chat-completions, return 404
+        return JSONResponse({"detail": "Not Found"}, status_code=404)
+    
+    return app
+
+
+def run_server(app, port):
+    """Run uvicorn server in a thread."""
+    import uvicorn
+    # Use uvicorn.run which blocks - this is fine in a daemon thread
+    uvicorn.run(app, host="127.0.0.1", port=port, log_level="error", access_log=False)
+
+
+@pytest.fixture(scope="session")
+def mock_server():
+    """Start a mock server in a separate thread for the test session.
+    
+    Yields the server URL (with trailing slash) for use in router configuration.
+    """
+    app = create_mock_server()
+    port = 18888
+    
+    # Check if port is already in use
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        sock.bind(("127.0.0.1", port))
+        sock.close()
+    except OSError:
+        # Port already in use, try next port
+        port = 18889
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        try:
+            sock.bind(("127.0.0.1", port))
+            sock.close()
+        except OSError:
+            pytest.fail(f"Could not find available port for mock server (tried 18888, 18889)")
+    
+    # Start server in background thread
+    thread = Thread(target=lambda: run_server(app, port), daemon=True)
+    thread.start()
+    
+    # Wait for server to start and verify it's accessible
+    # Ensure api_base has trailing slash (LiteLLM appends /v1/chat/completions)
+    server_url = f"http://127.0.0.1:{port}/"
+    max_attempts = 20  # More attempts to ensure server is ready
+    server_ready = False
+    for attempt in range(max_attempts):
+        time.sleep(0.3)  # Longer wait between attempts
+        try:
+            # Test the actual endpoint we'll use (LiteLLM appends /v1/chat/completions to api_base)
+            response = httpx.post(
+                f"{server_url}v1/chat/completions",
+                json={"model": TEST_MODEL_NAME, "messages": [{"role": "user", "content": "test"}]},
+                timeout=2.0
+            )
+            if response.status_code == 200:
+                server_ready = True
+                print(f"[Mock Server] Server ready at {server_url}")
+                break
+        except (httpx.ConnectError, httpx.TimeoutException, httpx.NetworkError) as e:
+            # Server not ready yet, continue waiting
+            if attempt == max_attempts - 1:
+                pytest.fail(
+                    f"Mock server failed to start on {server_url} after {max_attempts} attempts. "
+                    f"Could not connect to /v1/chat/completions endpoint. Error: {e}"
+                )
+            continue
+        except Exception as e:
+            # Other errors might indicate server is up but endpoint has issues
+            # If we get a response (even error), server is running
+            print(f"[Mock Server] Server responded with error (but is running): {e}")
+            server_ready = True
+            break
+    
+    if not server_ready:
+        pytest.fail(f"Mock server not accessible at {server_url} after {max_attempts} attempts")
+    
+    yield server_url
+    
+    # Server will be cleaned up when thread dies (daemon=True)
+
+
+@pytest.fixture
+def limit_memory(request):
+    """Fixture to track memory usage and enforce limits via @pytest.mark.limit_leaks marker.
+    
+    Usage:
+        @pytest.mark.limit_leaks("40 MB")
+        def test_something(limit_memory):
+            # Test code here
+            # Memory will be measured and test will fail if increase exceeds limit
+    """
+    marker = request.node.get_closest_marker("limit_leaks")
+    if marker:
+        # Parse limit from marker (e.g., "30 MB" -> 30)
+        limit_str = marker.args[0] if marker.args else "100 MB"
+        limit_mb = float(limit_str.split()[0])
+        limit_bytes = limit_mb * 1024 * 1024
+        
+        # Measure baseline memory (router will be fresh from fixture)
+        process = psutil.Process(os.getpid())
+        baseline_memory = process.memory_info().rss
+        
+        yield
+        
+        # Force GC before measuring final memory
+        gc.collect()
+        # Small delay for memory to stabilize
+        time.sleep(GC_STABILIZATION_DELAY)
+        
+        # Measure final memory after test
+        final_memory = process.memory_info().rss
+        memory_increase = final_memory - baseline_memory
+        memory_increase_mb = memory_increase / 1024 / 1024
+        
+        # Print memory stats
+        print(f"\n[Memory Limit Test] Memory usage:")
+        print(f"  Baseline: {baseline_memory / 1024 / 1024:.2f} MB")
+        print(f"  Final: {final_memory / 1024 / 1024:.2f} MB")
+        print(f"  Increase: {memory_increase_mb:+.2f} MB")
+        print(f"  Limit: {limit_mb:.2f} MB")
+        
+        # Fail if memory increase exceeds limit
+        if memory_increase > limit_bytes:
+            pytest.fail(
+                f"Memory limit exceeded: {memory_increase_mb:.2f} MB increase > {limit_mb:.2f} MB limit. "
+                f"Baseline: {baseline_memory / 1024 / 1024:.2f} MB, Final: {final_memory / 1024 / 1024:.2f} MB"
+            )
+    else:
+        yield
+
+
+@pytest.fixture
+def test_router(mock_server):
+    """Fixture to create a fresh router instance for each test.
+    
+    Uses the mock server fixture to avoid external API calls.
+    Disables cooldowns to prevent deployments from being marked unavailable.
+    
+    Usage:
+        def test_something(test_router, limit_memory):
+            # Use test_router for making requests
+            response = await test_router.acompletion(...)
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": TEST_MODEL_NAME,
+                "litellm_params": {
+                    "model": f"openai/{TEST_MODEL_NAME}",
+                    "api_base": mock_server,
+                    "api_key": TEST_API_KEY,
+                },
+            },
+        ],
+        disable_cooldowns=True,  # Disable cooldowns for testing
+        allowed_fails=1000,  # Allow many failures before cooldown (effectively disabled)
+    )
+    yield router
+    # Cleanup after test
+    try:
+        router.discard()
+    except Exception:
+        pass  # Ignore cleanup errors
+
+
+async def run_memory_baseline_test(num_requests: int, router: Router, limit_memory):
+    """Helper function to run memory baseline test with specified number of requests.
+    
+    Makes requests concurrently in batches for speed, with proper error handling
+    that doesn't fail the test on individual request failures.
+    
+    Args:
+        num_requests: Number of requests to make.
+        router: Router instance to use for requests.
+        limit_memory: Pytest fixture for memory tracking (reference to suppress linter warning).
+    
+    Example:
+        @pytest.mark.asyncio
+        @pytest.mark.limit_leaks("40 MB")
+        async def test_memory(test_router, limit_memory):
+            await run_memory_baseline_test(1000, test_router, limit_memory)
+    """
+    # Fixture is used automatically by pytest - reference it to suppress linter warning
+    _ = limit_memory
+    
+    # Make requests concurrently in batches for speed
+    # Batch size of 20 provides good balance between speed and memory pressure
+    BATCH_SIZE = 20
+    
+    for batch_start in range(0, num_requests, BATCH_SIZE):
+        batch_end = min(batch_start + BATCH_SIZE, num_requests)
+        # Create concurrent tasks for this batch
+        tasks = [
+            router.acompletion(
+                model=TEST_MODEL_NAME,
+                messages=[{"role": "user", "content": f"Test request {i}"}],
+            )
+            for i in range(batch_start, batch_end)
+        ]
+        # Execute batch concurrently
+        # Note: return_exceptions=True allows test to continue even if some requests fail
+        import asyncio
+        responses = await asyncio.gather(*tasks, return_exceptions=True)
+        # Filter out failed requests but continue with test
+        valid_responses = []
+        failed_count = 0
+        for i, response in enumerate(responses):
+            if isinstance(response, Exception):
+                failed_count += 1
+                # Log exception but continue
+                print(f"  Warning: Request {batch_start + i} failed: {type(response).__name__}: {response}")
+            elif response is None:
+                failed_count += 1
+                print(f"  Warning: Request {batch_start + i} returned None")
+            else:
+                valid_responses.append(response)
+        
+        # Continue with valid responses - don't fail the test
+        # If all failed, that's logged but test continues (might indicate bigger issue)
+        if failed_count > 0:
+            print(f"  Note: {failed_count}/{len(responses)} requests failed in batch {batch_start}-{batch_end}, continuing with {len(valid_responses)} valid responses")
+        
+        # Use valid_responses for cleanup
+        responses = valid_responses
+        # Clean up batch
+        del responses
+        del tasks
+        del valid_responses
+        # GC after each batch to prevent accumulation
+        gc.collect()
+    
+    print(f"[Simple Memory Test] Completed {num_requests} requests")
diff --git a/tests/load_tests/test_linear_memory_growth.py b/tests/load_tests/test_linear_memory_growth.py
new file mode 100644
index 00000000000..3b7b8041b90
--- /dev/null
+++ b/tests/load_tests/test_linear_memory_growth.py
@@ -0,0 +1,121 @@
+"""
+Memory Leak Detection Tests - Linear Memory Growth
+
+Tests that check for linear/progressive memory growth by running different numbers
+of requests (1k, 2k, 4k, 10k, 30k) with the same memory limit. If lower request
+count tests pass but higher ones fail, it indicates linear memory growth per request.
+
+These tests will fail if memory leaks are detected, helping catch OOM issues before production.
+
+IMPORTANT: These tests should be run INDIVIDUALLY, not all together. Running them
+together causes memory baseline drift between tests, making it difficult to detect
+linear growth accurately. Each test should be run in isolation:
+
+    pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_1k -v
+    pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_2k -v
+    # etc.
+
+NOTE: Not recommended for accurate results:
+pytest tests/load_tests/test_linear_memory_growth.py -v
+"""
+
+import pytest
+
+from tests.load_tests.memory_leak_utils import (
+    limit_memory,  # noqa: F401  # pytest fixture used via dependency injection
+    mock_server,  # noqa: F401  # pytest fixture used via dependency injection
+    run_memory_baseline_test,
+    test_router,  # noqa: F401  # pytest fixture used via dependency injection
+)
+
+# Memory limit for all linear memory growth tests
+MEMORY_LIMIT = "40 MB"
+
+
+@pytest.mark.asyncio
+@pytest.mark.limit_leaks(MEMORY_LIMIT)
+@pytest.mark.no_parallel  # Must run sequentially - measures process memory
+async def test_memory_baseline_1k(test_router, limit_memory):
+    """
+    Memory baseline test with 1,000 requests.
+    Uses @pytest.mark.limit_leaks("40 MB") to enforce memory limit.
+    If this passes but higher request count tests fail, indicates progressive memory leak.
+    
+    NOTE: This test should be run INDIVIDUALLY, not with other tests in this file.
+    Running multiple tests together causes memory baseline drift, making it difficult
+    to accurately detect linear memory growth. Run with:
+        pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_1k -v
+    """
+    await run_memory_baseline_test(1000, test_router, limit_memory)
+
+
+@pytest.mark.asyncio
+@pytest.mark.limit_leaks(MEMORY_LIMIT)
+@pytest.mark.no_parallel  # Must run sequentially - measures process memory
+async def test_memory_baseline_2k(test_router, limit_memory):
+    """
+    Memory baseline test with 2,000 requests.
+    Uses @pytest.mark.limit_leaks("40 MB") to enforce memory limit.
+    If this passes but test_memory_baseline_4k fails, indicates progressive memory leak.
+    
+    NOTE: This test should be run INDIVIDUALLY, not with other tests in this file.
+    Running multiple tests together causes memory baseline drift, making it difficult
+    to accurately detect linear memory growth. Run with:
+        pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_2k -v
+    """
+    await run_memory_baseline_test(2000, test_router, limit_memory)
+
+
+@pytest.mark.asyncio
+@pytest.mark.limit_leaks(MEMORY_LIMIT)
+@pytest.mark.no_parallel  # Must run sequentially - measures process memory
+async def test_memory_baseline_4k(test_router, limit_memory):
+    """
+    Memory baseline test with 4,000 requests.
+    Uses @pytest.mark.limit_leaks("40 MB") to enforce memory limit.
+    If test_memory_baseline_1k and test_memory_baseline_2k pass but this fails,
+    it's a clear sign of sequential/progressive memory growth.
+    
+    NOTE: This test should be run INDIVIDUALLY, not with other tests in this file.
+    Running multiple tests together causes memory baseline drift, making it difficult
+    to accurately detect linear memory growth. Run with:
+        pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_4k -v
+    """
+    await run_memory_baseline_test(4000, test_router, limit_memory)
+
+
+
+@pytest.mark.asyncio
+@pytest.mark.limit_leaks(MEMORY_LIMIT)
+@pytest.mark.no_parallel  # Must run sequentially - measures process memory
+async def test_memory_baseline_10k(test_router, limit_memory):
+    """
+    Memory baseline test with 10,000 requests.
+    Uses @pytest.mark.limit_leaks("40 MB") to enforce memory limit.
+    If test_memory_baseline_1k and test_memory_baseline_2k pass but this fails,
+    it's a clear sign of sequential/progressive memory growth.
+    
+    NOTE: This test should be run INDIVIDUALLY, not with other tests in this file.
+    Running multiple tests together causes memory baseline drift, making it difficult
+    to accurately detect linear memory growth. Run with:
+        pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_10k -v
+    """
+    await run_memory_baseline_test(10000, test_router, limit_memory)
+
+
+@pytest.mark.asyncio
+@pytest.mark.limit_leaks(MEMORY_LIMIT)
+@pytest.mark.no_parallel  # Must run sequentially - measures process memory
+async def test_memory_baseline_30k(test_router, limit_memory):
+    """
+    Memory baseline test with 30,000 requests.
+    Uses @pytest.mark.limit_leaks("40 MB") to enforce memory limit.
+    If test_memory_baseline_1k and test_memory_baseline_2k pass but this fails,
+    it's a clear sign of sequential/progressive memory growth.
+    
+    NOTE: This test should be run INDIVIDUALLY, not with other tests in this file.
+    Running multiple tests together causes memory baseline drift, making it difficult
+    to accurately detect linear memory growth. Run with:
+        pytest tests/load_tests/test_linear_memory_growth.py::test_memory_baseline_30k -v
+    """
+    await run_memory_baseline_test(30000, test_router, limit_memory)
diff --git a/tests/local_testing/test_openai_moderations_hook.py b/tests/local_testing/test_openai_moderations_hook.py
index 5d2cab38b33..3acd36f32f0 100644
--- a/tests/local_testing/test_openai_moderations_hook.py
+++ b/tests/local_testing/test_openai_moderations_hook.py
@@ -85,3 +85,101 @@ async def test_openai_moderation_error_raising(monkeypatch):
         print("Got exception: ", e)
         assert "Violated content safety policy" in str(e)
         pass
+
+
+@pytest.mark.asyncio
+async def test_openai_moderation_responses_api_input_field():
+    """
+    Tests that OpenAI Moderation works with Responses API input field.
+    
+    This test verifies the fix for the issue where moderation was skipped
+    for Responses API because it only checked for 'messages' field but
+    Responses API uses 'input' field instead.
+    """
+    from unittest.mock import AsyncMock, MagicMock, patch
+    from litellm.types.llms.openai import (
+        OpenAIModerationResponse,
+        OpenAIModerationResult,
+    )
+    from litellm.proxy.guardrails.guardrail_hooks.openai.moderations import (
+        OpenAIModerationGuardrail,
+    )
+    
+    # Initialize the open-source OpenAI Moderation guardrail
+    openai_mod = OpenAIModerationGuardrail(
+        guardrail_name="openai-moderation-test",
+        api_key="fake-key-for-testing",
+        model="omni-moderation-latest",
+    )
+    
+    _api_key = "sk-12345"
+    _api_key = hash_token("sk-12345")
+    user_api_key_dict = UserAPIKeyAuth(api_key=_api_key)
+    
+    # Mock the async_make_request to return a flagged response
+    mock_moderation_response = OpenAIModerationResponse(
+        id="modr-123",
+        model="omni-moderation-latest",
+        results=[
+            OpenAIModerationResult(
+                flagged=True,
+                categories={"violence": True, "hate": False},
+                category_scores={"violence": 0.95, "hate": 0.1},
+                category_applied_input_types=None,
+            )
+        ],
+    )
+    
+    with patch.object(
+        openai_mod, "async_make_request", return_value=mock_moderation_response
+    ):
+        # Test 1: Responses API with input as string
+        try:
+            await openai_mod.async_moderation_hook(
+                data={
+                    "model": "gpt-4o",
+                    "input": "I want to hurt people",
+                },
+                user_api_key_dict=user_api_key_dict,
+                call_type="responses",
+            )
+            pytest.fail("Should have raised HTTPException for flagged content")
+        except Exception as e:
+            print("Got exception for string input: ", e)
+            assert "Violated OpenAI moderation policy" in str(e)
+        
+        # Test 2: Responses API with input as list of messages
+        try:
+            await openai_mod.async_moderation_hook(
+                data={
+                    "model": "gpt-4o",
+                    "input": [
+                        {"role": "user", "content": "I want to hurt people"}
+                    ],
+                },
+                user_api_key_dict=user_api_key_dict,
+                call_type="responses",
+            )
+            pytest.fail("Should have raised HTTPException for flagged content")
+        except Exception as e:
+            print("Got exception for list input: ", e)
+            assert "Violated OpenAI moderation policy" in str(e)
+        
+        # Test 3: Verify it still works with messages field (Chat Completions)
+        try:
+            await openai_mod.async_moderation_hook(
+                data={
+                    "model": "gpt-4o",
+                    "messages": [
+                        {"role": "user", "content": "I want to hurt people"}
+                    ],
+                },
+                user_api_key_dict=user_api_key_dict,
+                call_type="completion",
+            )
+            pytest.fail("Should have raised HTTPException for flagged content")
+        except Exception as e:
+            print("Got exception for messages field: ", e)
+            assert "Violated OpenAI moderation policy" in str(e)
+    
+    print("✓ All Responses API moderation tests passed!")
diff --git a/tests/ocr_tests/base_ocr_unit_tests.py b/tests/ocr_tests/base_ocr_unit_tests.py
index 88d6caf1435..3a77a8af3ff 100644
--- a/tests/ocr_tests/base_ocr_unit_tests.py
+++ b/tests/ocr_tests/base_ocr_unit_tests.py
@@ -27,28 +27,6 @@ class BaseOCRTest(ABC):
         """Must return the base OCR call args for the specific provider"""
         pass
 
-    @pytest.fixture(autouse=True)
-    def _handle_rate_limits(self):
-        """Fixture to handle rate limit errors for all test methods"""
-        try:
-            yield
-        except litellm.RateLimitError as e:
-            # Check if it's a quota exceeded error
-            error_msg = str(e)
-            if "Quota exceeded" in error_msg or "RESOURCE_EXHAUSTED" in error_msg:
-                pytest.skip(f"Quota exceeded - {error_msg}")
-            else:
-                pytest.skip(f"Rate limit exceeded - {error_msg}")
-        except litellm.InternalServerError:
-            pytest.skip("Model is overloaded")
-        except litellm.BadRequestError as e:
-            # Handle URL rejection errors from Vertex AI
-            error_msg = str(e)
-            if "URL_REJECTED" in error_msg or "Cannot fetch content from the provided URL" in error_msg:
-                pytest.skip(f"URL rejected by provider - {error_msg}")
-            else:
-                raise
-
     @pytest.mark.parametrize("sync_mode", [True, False])
     @pytest.mark.asyncio
     async def test_basic_ocr_with_url(self, sync_mode):
@@ -124,9 +102,20 @@ class BaseOCRTest(ABC):
             assert response_cost > 0, "Response cost should be greater than 0"
             print("response_cost=", response_cost)
             
-        except (litellm.RateLimitError, litellm.InternalServerError):
-            # Re-raise these errors so the fixture can handle them
-            raise
+        except litellm.RateLimitError as e:
+            error_msg = str(e)
+            if "Quota exceeded" in error_msg or "RESOURCE_EXHAUSTED" in error_msg:
+                pytest.skip(f"Quota exceeded - {error_msg}")
+            else:
+                pytest.skip(f"Rate limit exceeded - {error_msg}")
+        except litellm.InternalServerError:
+            pytest.skip("Model is overloaded")
+        except litellm.BadRequestError as e:
+            error_msg = str(e)
+            if "URL_REJECTED" in error_msg or "Cannot fetch content from the provided URL" in error_msg:
+                pytest.skip(f"URL rejected by provider - {error_msg}")
+            else:
+                pytest.fail(f"OCR call failed: {str(e)}")
         except Exception as e:
             pytest.fail(f"OCR call failed: {str(e)}")
 
@@ -170,9 +159,20 @@ class BaseOCRTest(ABC):
                 print(f"  - pages_processed: {response.usage_info.pages_processed}")
                 print(f"  - doc_size_bytes: {response.usage_info.doc_size_bytes}")
         
-        except (litellm.RateLimitError, litellm.InternalServerError):
-            # Re-raise these errors so the fixture can handle them
-            raise
+        except litellm.RateLimitError as e:
+            error_msg = str(e)
+            if "Quota exceeded" in error_msg or "RESOURCE_EXHAUSTED" in error_msg:
+                pytest.skip(f"Quota exceeded - {error_msg}")
+            else:
+                pytest.skip(f"Rate limit exceeded - {error_msg}")
+        except litellm.InternalServerError:
+            pytest.skip("Model is overloaded")
+        except litellm.BadRequestError as e:
+            error_msg = str(e)
+            if "URL_REJECTED" in error_msg or "Cannot fetch content from the provided URL" in error_msg:
+                pytest.skip(f"URL rejected by provider - {error_msg}")
+            else:
+                pytest.fail(f"OCR response structure test failed: {str(e)}")
         except Exception as e:
             pytest.fail(f"OCR response structure test failed: {str(e)}")
 
diff --git a/tests/proxy_unit_tests/test_proxy_custom_logger.py b/tests/proxy_unit_tests/test_proxy_custom_logger.py
index 28de522a4c7..b7e8f6cf499 100644
--- a/tests/proxy_unit_tests/test_proxy_custom_logger.py
+++ b/tests/proxy_unit_tests/test_proxy_custom_logger.py
@@ -91,7 +91,9 @@ def test_embedding(client):
         assert "headers" in metadata
         proxy_server_request = litellm_params.get("proxy_server_request")
         model_info = litellm_params.get("model_info")
-        assert proxy_server_request == {
+        # Remove arrival_time from comparison as it's dynamic
+        proxy_server_request_copy = {k: v for k, v in proxy_server_request.items() if k != "arrival_time"}
+        assert proxy_server_request_copy == {
             "url": "http://testserver/embeddings",
             "method": "POST",
             "headers": {
@@ -188,7 +190,9 @@ def test_chat_completion(client):
         }
 
         assert "authorization" not in proxy_server_request_object["headers"]
-        assert proxy_server_request_object == {
+        # Remove arrival_time from comparison as it's dynamic
+        proxy_server_request_copy = {k: v for k, v in proxy_server_request_object.items() if k != "arrival_time"}
+        assert proxy_server_request_copy == {
             "url": "http://testserver/chat/completions",
             "method": "POST",
             "headers": {
diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py
index 8d3b0fee48f..0db1240544a 100644
--- a/tests/proxy_unit_tests/test_proxy_server.py
+++ b/tests/proxy_unit_tests/test_proxy_server.py
@@ -642,8 +642,8 @@ def test_embedding(mock_aembedding, client_no_auth):
 
         during_call_kwargs = mock_during_hook.await_args_list[0].kwargs
         assert (
-            during_call_kwargs.get("call_type") == "embeddings"
-        ), f"expected during_call_hook to receive call_type='embeddings', got {during_call_kwargs.get('call_type')}"
+            during_call_kwargs.get("call_type") == "embedding"
+        ), f"expected during_call_hook to receive call_type='embedding', got {during_call_kwargs.get('call_type')}"
     except Exception as e:
         pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}")
 
@@ -2185,6 +2185,10 @@ async def test_proxy_server_prisma_setup():
         mock_client._set_spend_logs_row_count_in_proxy_state = (
             AsyncMock()
         )  # Mock the _set_spend_logs_row_count_in_proxy_state method
+        # Mock the db attribute with start_token_refresh_task for RDS IAM token refresh
+        mock_db = MagicMock()
+        mock_db.start_token_refresh_task = AsyncMock()
+        mock_client.db = mock_db
 
         await ProxyStartupEvent._setup_prisma_client(
             database_url=os.getenv("DATABASE_URL"),
diff --git a/tests/proxy_unit_tests/test_proxy_utils.py b/tests/proxy_unit_tests/test_proxy_utils.py
index 5c3c3948920..64f1ec24234 100644
--- a/tests/proxy_unit_tests/test_proxy_utils.py
+++ b/tests/proxy_unit_tests/test_proxy_utils.py
@@ -1574,6 +1574,10 @@ async def test_health_check_not_called_when_disabled(monkeypatch):
     mock_prisma.health_check = AsyncMock()
     mock_prisma.check_view_exists = AsyncMock()
     mock_prisma._set_spend_logs_row_count_in_proxy_state = AsyncMock()
+    # Mock the db attribute with start_token_refresh_task for RDS IAM token refresh
+    mock_db = MagicMock()
+    mock_db.start_token_refresh_task = AsyncMock()
+    mock_prisma.db = mock_db
     # Mock PrismaClient constructor
     monkeypatch.setattr(
         "litellm.proxy.proxy_server.PrismaClient", lambda **kwargs: mock_prisma
diff --git a/tests/router_unit_tests/test_router_embedding_headers.py b/tests/router_unit_tests/test_router_embedding_headers.py
new file mode 100644
index 00000000000..6d480792b7c
--- /dev/null
+++ b/tests/router_unit_tests/test_router_embedding_headers.py
@@ -0,0 +1,372 @@
+"""
+Test suite for router embedding method header propagation.
+
+This tests the fix for the issue where the embedding method was not
+propagating proxy model configuration headers to the LLM API calls.
+
+The fix ensures that router.embedding() calls _update_kwargs_before_fallbacks()
+just like router.completion() does, which properly sets up metadata and allows
+default_litellm_params (including headers) to be propagated.
+"""
+import os
+import sys
+from unittest.mock import MagicMock, patch, AsyncMock
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm import Router
+
+
+class TestRouterEmbeddingHeaders:
+    """Test that embedding methods properly propagate headers from router configuration."""
+
+    def test_embedding_calls_update_kwargs_before_fallbacks(self):
+        """
+        Test that router.embedding() calls _update_kwargs_before_fallbacks.
+
+        This ensures that metadata is properly set up before the fallback mechanism,
+        which is necessary for header propagation to work correctly.
+        """
+        model_list = [
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        router = Router(model_list=model_list)
+
+        # Mock the _update_kwargs_before_fallbacks method to verify it's called
+        with patch.object(
+            router,
+            "_update_kwargs_before_fallbacks",
+            wraps=router._update_kwargs_before_fallbacks,
+        ) as mock_update:
+            with patch("litellm.embedding") as mock_litellm_embedding:
+                mock_litellm_embedding.return_value = MagicMock(
+                    data=[{"embedding": [0.1, 0.2, 0.3]}]
+                )
+
+                router.embedding(model="text-embedding-ada-002", input=["test input"])
+
+                # Verify _update_kwargs_before_fallbacks was called
+                mock_update.assert_called_once()
+                call_kwargs = mock_update.call_args[1]
+                assert call_kwargs["model"] == "text-embedding-ada-002"
+                assert "kwargs" in call_kwargs
+
+    @pytest.mark.asyncio
+    async def test_aembedding_calls_update_kwargs_before_fallbacks(self):
+        """
+        Test that router.aembedding() calls _update_kwargs_before_fallbacks.
+
+        This ensures consistency between sync and async embedding methods.
+        """
+        model_list = [
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        router = Router(model_list=model_list)
+
+        # Mock the _update_kwargs_before_fallbacks method to verify it's called
+        with patch.object(
+            router,
+            "_update_kwargs_before_fallbacks",
+            wraps=router._update_kwargs_before_fallbacks,
+        ) as mock_update:
+            with patch(
+                "litellm.aembedding", new_callable=AsyncMock
+            ) as mock_litellm_aembedding:
+                mock_litellm_aembedding.return_value = MagicMock(
+                    data=[{"embedding": [0.1, 0.2, 0.3]}]
+                )
+
+                await router.aembedding(
+                    model="text-embedding-ada-002", input=["test input"]
+                )
+
+                # Verify _update_kwargs_before_fallbacks was called
+                mock_update.assert_called_once()
+                call_kwargs = mock_update.call_args[1]
+                assert call_kwargs["model"] == "text-embedding-ada-002"
+                assert "kwargs" in call_kwargs
+
+    def test_embedding_propagates_default_litellm_params(self):
+        """
+        Test that embedding calls properly propagate default_litellm_params including headers.
+
+        This is the main fix - ensuring that headers set in default_litellm_params
+        are included in the embedding request.
+        """
+        custom_headers = {"X-Custom-Header": "test-value", "X-API-Version": "v2"}
+
+        model_list = [
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        # Create router with default_litellm_params containing headers
+        router = Router(
+            model_list=model_list,
+            default_litellm_params={
+                "headers": custom_headers,
+                "metadata": {"test_key": "test_value"},
+            },
+        )
+
+        with patch("litellm.embedding") as mock_litellm_embedding:
+            mock_litellm_embedding.return_value = MagicMock(
+                data=[{"embedding": [0.1, 0.2, 0.3]}]
+            )
+
+            router.embedding(model="text-embedding-ada-002", input=["test input"])
+
+            # Verify that litellm.embedding was called with the headers
+            mock_litellm_embedding.assert_called_once()
+            call_kwargs = mock_litellm_embedding.call_args[1]
+
+            # Check that headers were included
+            assert "headers" in call_kwargs
+            assert call_kwargs["headers"] == custom_headers
+
+            # Check that metadata was properly set up
+            assert "metadata" in call_kwargs
+            assert "model_group" in call_kwargs["metadata"]
+            assert call_kwargs["metadata"]["model_group"] == "text-embedding-ada-002"
+
+    @pytest.mark.asyncio
+    async def test_aembedding_propagates_default_litellm_params(self):
+        """
+        Test that async embedding calls properly propagate default_litellm_params including headers.
+        """
+        custom_headers = {"X-Custom-Header": "test-value", "X-API-Version": "v2"}
+
+        model_list = [
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        # Create router with default_litellm_params containing headers
+        router = Router(
+            model_list=model_list,
+            default_litellm_params={
+                "headers": custom_headers,
+                "metadata": {"test_key": "test_value"},
+            },
+        )
+
+        with patch(
+            "litellm.aembedding", new_callable=AsyncMock
+        ) as mock_litellm_aembedding:
+            mock_litellm_aembedding.return_value = MagicMock(
+                data=[{"embedding": [0.1, 0.2, 0.3]}]
+            )
+
+            await router.aembedding(
+                model="text-embedding-ada-002", input=["test input"]
+            )
+
+            # Verify that litellm.aembedding was called with the headers
+            mock_litellm_aembedding.assert_called_once()
+            call_kwargs = mock_litellm_aembedding.call_args[1]
+
+            # Check that headers were included
+            assert "headers" in call_kwargs
+            assert call_kwargs["headers"] == custom_headers
+
+            # Check that metadata was properly set up
+            assert "metadata" in call_kwargs
+            assert "model_group" in call_kwargs["metadata"]
+            assert call_kwargs["metadata"]["model_group"] == "text-embedding-ada-002"
+
+    def test_embedding_metadata_includes_model_group(self):
+        """
+        Test that embedding calls include model_group in metadata.
+
+        The _update_kwargs_before_fallbacks method should set this up.
+        """
+        model_list = [
+            {
+                "model_name": "test-embedding-model",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        router = Router(model_list=model_list)
+
+        with patch("litellm.embedding") as mock_litellm_embedding:
+            mock_litellm_embedding.return_value = MagicMock(
+                data=[{"embedding": [0.1, 0.2, 0.3]}]
+            )
+
+            router.embedding(model="test-embedding-model", input=["test input"])
+
+            call_kwargs = mock_litellm_embedding.call_args[1]
+
+            # Verify metadata contains model_group
+            assert "metadata" in call_kwargs
+            assert "model_group" in call_kwargs["metadata"]
+            assert call_kwargs["metadata"]["model_group"] == "test-embedding-model"
+
+    def test_embedding_sets_num_retries_from_router(self):
+        """
+        Test that embedding calls inherit num_retries from router configuration.
+
+        This is set by _update_kwargs_before_fallbacks.
+        """
+        model_list = [
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        # Create router with num_retries set
+        router = Router(model_list=model_list, num_retries=3)
+
+        with patch("litellm.embedding") as mock_litellm_embedding:
+            mock_litellm_embedding.return_value = MagicMock(
+                data=[{"embedding": [0.1, 0.2, 0.3]}]
+            )
+
+            router.embedding(model="text-embedding-ada-002", input=["test input"])
+
+            # Verify num_retries was not set in the call (it's handled by function_with_fallbacks)
+            # The important thing is that it was set in kwargs before being passed to function_with_fallbacks
+            # We verify this indirectly by checking that _update_kwargs_before_fallbacks was called
+            mock_litellm_embedding.assert_called_once()
+
+    def test_embedding_sets_litellm_trace_id(self):
+        """
+        Test that embedding calls include a litellm_trace_id.
+
+        This is generated and set by _update_kwargs_before_fallbacks.
+        """
+        model_list = [
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            }
+        ]
+
+        router = Router(model_list=model_list)
+
+        with patch("litellm.embedding") as mock_litellm_embedding:
+            mock_litellm_embedding.return_value = MagicMock(
+                data=[{"embedding": [0.1, 0.2, 0.3]}]
+            )
+
+            router.embedding(model="text-embedding-ada-002", input=["test input"])
+
+            call_kwargs = mock_litellm_embedding.call_args[1]
+
+            # Verify litellm_trace_id was set
+            assert "litellm_trace_id" in call_kwargs
+            assert isinstance(call_kwargs["litellm_trace_id"], str)
+            assert len(call_kwargs["litellm_trace_id"]) > 0
+
+    def test_embedding_consistency_with_completion(self):
+        """
+        Test that embedding and completion methods handle kwargs similarly.
+
+        Both should call _update_kwargs_before_fallbacks to ensure consistent behavior.
+        """
+        custom_headers = {"X-Test": "value"}
+
+        model_list = [
+            {
+                "model_name": "gpt-3.5-turbo",
+                "litellm_params": {
+                    "model": "gpt-3.5-turbo",
+                    "api_key": "fake-key",
+                },
+            },
+            {
+                "model_name": "text-embedding-ada-002",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fake-key",
+                },
+            },
+        ]
+
+        router = Router(
+            model_list=model_list, default_litellm_params={"headers": custom_headers}
+        )
+
+        # Test completion
+        with patch("litellm.completion") as mock_completion:
+            mock_completion.return_value = MagicMock()
+
+            router.completion(
+                model="gpt-3.5-turbo", messages=[{"role": "user", "content": "test"}]
+            )
+
+            completion_kwargs = mock_completion.call_args[1]
+
+        # Test embedding
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(
+                data=[{"embedding": [0.1, 0.2, 0.3]}]
+            )
+
+            router.embedding(model="text-embedding-ada-002", input=["test input"])
+
+            embedding_kwargs = mock_embedding.call_args[1]
+
+        # Both should have headers from default_litellm_params
+        assert "headers" in completion_kwargs
+        assert "headers" in embedding_kwargs
+        assert completion_kwargs["headers"] == custom_headers
+        assert embedding_kwargs["headers"] == custom_headers
+
+        # Both should have metadata with model_group
+        assert "metadata" in completion_kwargs
+        assert "metadata" in embedding_kwargs
+        assert "model_group" in completion_kwargs["metadata"]
+        assert "model_group" in embedding_kwargs["metadata"]
+
+        # Both should have litellm_trace_id
+        assert "litellm_trace_id" in completion_kwargs
+        assert "litellm_trace_id" in embedding_kwargs
+
+
+if __name__ == "__main__":
+    # Run a simple test
+    test = TestRouterEmbeddingHeaders()
+    test.test_embedding_calls_update_kwargs_before_fallbacks()
+    test.test_embedding_propagates_default_litellm_params()
+    test.test_embedding_metadata_includes_model_group()
+    test.test_embedding_sets_litellm_trace_id()
+    test.test_embedding_consistency_with_completion()
+    print("All tests passed!")  # noqa: T201
diff --git a/tests/router_unit_tests/test_router_embedding_integration.py b/tests/router_unit_tests/test_router_embedding_integration.py
new file mode 100644
index 00000000000..ab2071714a9
--- /dev/null
+++ b/tests/router_unit_tests/test_router_embedding_integration.py
@@ -0,0 +1,355 @@
+"""
+Integration tests for router embedding method with various configurations.
+
+These tests simulate real-world scenarios where headers and configuration
+need to be properly propagated through the router to the LLM API.
+"""
+import os
+import sys
+from unittest.mock import MagicMock, patch, AsyncMock
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm import Router
+
+
+class TestRouterEmbeddingIntegration:
+    """Integration tests for embedding with router configuration."""
+
+    def test_embedding_with_deployment_specific_headers(self):
+        """
+        Test that deployment-specific headers are propagated.
+
+        This simulates a scenario where different deployments have
+        different header requirements (e.g., different API versions).
+        """
+        model_list = [
+            {
+                "model_name": "embedding-deployment-1",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "key-1",
+                    "headers": {"X-Deployment": "deployment-1"},
+                },
+            },
+            {
+                "model_name": "embedding-deployment-2",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "key-2",
+                    "headers": {"X-Deployment": "deployment-2"},
+                },
+            },
+        ]
+
+        router = Router(model_list=model_list)
+
+        # Test first deployment
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            router.embedding(model="embedding-deployment-1", input=["test"])
+
+            call_kwargs = mock_embedding.call_args[1]
+            assert call_kwargs["api_key"] == "key-1"
+
+        # Test second deployment
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            router.embedding(model="embedding-deployment-2", input=["test"])
+
+            call_kwargs = mock_embedding.call_args[1]
+            assert call_kwargs["api_key"] == "key-2"
+
+    def test_embedding_with_router_and_deployment_headers_merge(self):
+        """
+        Test that router-level headers are propagated.
+
+        When no request headers are provided, router default headers should be used.
+        """
+        model_list = [
+            {
+                "model_name": "test-embedding",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "test-key",
+                },
+            }
+        ]
+
+        router = Router(
+            model_list=model_list,
+            default_litellm_params={
+                "headers": {
+                    "X-Router-Header": "router-value",
+                    "X-Common-Header": "router-common",
+                }
+            },
+        )
+
+        # Test: No request headers - router headers should be used
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            router.embedding(
+                model="test-embedding",
+                input=["test"],
+            )
+
+            call_kwargs = mock_embedding.call_args[1]
+
+            # Router headers should be present
+            assert "headers" in call_kwargs
+            assert call_kwargs["headers"]["X-Router-Header"] == "router-value"
+            assert call_kwargs["headers"]["X-Common-Header"] == "router-common"
+
+    def test_embedding_metadata_propagation(self):
+        """
+        Test that metadata is properly set up and propagated.
+
+        This is important for logging, tracking, and debugging.
+        """
+        model_list = [
+            {
+                "model_name": "test-embedding",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "test-key",
+                },
+            }
+        ]
+
+        router = Router(
+            model_list=model_list,
+            default_litellm_params={
+                "metadata": {"environment": "test", "service": "embedding-service"}
+            },
+        )
+
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            router.embedding(
+                model="test-embedding",
+                input=["test"],
+                metadata={"request_id": "req-123"},  # Additional metadata from request
+            )
+
+            call_kwargs = mock_embedding.call_args[1]
+
+            # Check metadata contains all expected fields
+            assert "metadata" in call_kwargs
+            metadata = call_kwargs["metadata"]
+
+            # From _update_kwargs_before_fallbacks
+            assert "model_group" in metadata
+            assert metadata["model_group"] == "test-embedding"
+
+            # From default_litellm_params
+            assert "environment" in metadata
+            assert metadata["environment"] == "test"
+            assert "service" in metadata
+            assert metadata["service"] == "embedding-service"
+
+            # From request
+            assert "request_id" in metadata
+            assert metadata["request_id"] == "req-123"
+
+    @pytest.mark.asyncio
+    async def test_async_embedding_with_multiple_retries(self):
+        """
+        Test that async embedding properly uses num_retries from router config.
+
+        This ensures the fix works with the retry mechanism.
+        """
+        model_list = [
+            {
+                "model_name": "test-embedding",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "test-key",
+                },
+            }
+        ]
+
+        router = Router(model_list=model_list, num_retries=2)
+
+        with patch("litellm.aembedding", new_callable=AsyncMock) as mock_aembedding:
+            mock_aembedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            await router.aembedding(model="test-embedding", input=["test"])
+
+            # The call should succeed
+            mock_aembedding.assert_called_once()
+
+    def test_embedding_with_timeout_from_router(self):
+        """
+        Test that timeout settings from router config are propagated.
+        """
+        model_list = [
+            {
+                "model_name": "test-embedding",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "test-key",
+                },
+            }
+        ]
+
+        router = Router(model_list=model_list, timeout=30.0)
+
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            router.embedding(model="test-embedding", input=["test"])
+
+            call_kwargs = mock_embedding.call_args[1]
+
+            # Timeout should be set from router config
+            assert "timeout" in call_kwargs
+            assert call_kwargs["timeout"] == 30.0
+
+    def test_embedding_with_multiple_deployments_load_balancing(self):
+        """
+        Test that headers are correctly propagated when router load balances
+        between multiple deployments.
+        """
+        model_list = [
+            {
+                "model_name": "shared-embedding-model",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "key-1",
+                },
+            },
+            {
+                "model_name": "shared-embedding-model",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "key-2",
+                },
+            },
+        ]
+
+        router = Router(
+            model_list=model_list,
+            default_litellm_params={"headers": {"X-Shared-Header": "shared-value"}},
+        )
+
+        # Make multiple calls and verify headers are always present
+        for i in range(5):
+            with patch("litellm.embedding") as mock_embedding:
+                mock_embedding.return_value = MagicMock(
+                    data=[{"embedding": [0.1, 0.2]}]
+                )
+
+                router.embedding(model="shared-embedding-model", input=[f"test {i}"])
+
+                call_kwargs = mock_embedding.call_args[1]
+
+                # Headers should always be present regardless of which deployment is chosen
+                assert "headers" in call_kwargs
+                assert call_kwargs["headers"]["X-Shared-Header"] == "shared-value"
+
+    @pytest.mark.asyncio
+    async def test_embedding_with_fallback_configuration(self):
+        """
+        Test that headers are propagated correctly when using fallback models.
+        """
+        model_list = [
+            {
+                "model_name": "primary-embedding",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "primary-key",
+                },
+            },
+            {
+                "model_name": "fallback-embedding",
+                "litellm_params": {
+                    "model": "text-embedding-ada-002",
+                    "api_key": "fallback-key",
+                },
+            },
+        ]
+
+        router = Router(
+            model_list=model_list,
+            fallbacks=[{"primary-embedding": ["fallback-embedding"]}],
+            default_litellm_params={"headers": {"X-Fallback-Test": "test-value"}},
+        )
+
+        # Simulate primary failing, fallback succeeding
+        with patch("litellm.aembedding", new_callable=AsyncMock) as mock_aembedding:
+            call_count = 0
+
+            async def side_effect(*args, **kwargs):
+                nonlocal call_count
+                call_count += 1
+                if call_count == 1:
+                    # First call (primary) fails
+                    raise Exception("Primary failed")
+                else:
+                    # Second call (fallback) succeeds
+                    return MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            mock_aembedding.side_effect = side_effect
+
+            await router.aembedding(model="primary-embedding", input=["test"])
+
+            # Both calls should have headers
+            assert mock_aembedding.call_count == 2
+
+            # Check that both calls had headers
+            for call_obj in mock_aembedding.call_args_list:
+                call_kwargs = call_obj[1]
+                assert "headers" in call_kwargs
+                assert call_kwargs["headers"]["X-Fallback-Test"] == "test-value"
+
+    def test_embedding_with_custom_provider_headers(self):
+        """
+        Test that provider-specific headers are correctly propagated.
+
+        Some providers require specific headers for API versioning, features, etc.
+        """
+        model_list = [
+            {
+                "model_name": "azure-embedding",
+                "litellm_params": {
+                    "model": "azure/text-embedding-ada-002",
+                    "api_key": "azure-key",
+                    "api_base": "https://example.openai.azure.com",
+                    "api_version": "2024-02-01",
+                },
+            }
+        ]
+
+        router = Router(
+            model_list=model_list,
+            default_litellm_params={
+                "headers": {"X-Custom-Azure-Header": "azure-value"}
+            },
+        )
+
+        with patch("litellm.embedding") as mock_embedding:
+            mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
+
+            router.embedding(model="azure-embedding", input=["test"])
+
+            call_kwargs = mock_embedding.call_args[1]
+
+            # Verify Azure-specific params are present
+            assert call_kwargs["api_base"] == "https://example.openai.azure.com"
+            assert call_kwargs["api_version"] == "2024-02-01"
+
+            # Verify custom headers are present
+            assert "headers" in call_kwargs
+            assert call_kwargs["headers"]["X-Custom-Azure-Header"] == "azure-value"
+
+
+if __name__ == "__main__":
+    # Run tests
+    pytest.main([__file__, "-v"])
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 596398e639f..f8a082ee30c 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
@@ -1095,3 +1095,186 @@ def test_map_reasoning_effort_adds_summary_detailed():
             os.environ["LITELLM_REASONING_AUTO_SUMMARY"] = original_env
         elif "LITELLM_REASONING_AUTO_SUMMARY" in os.environ:
             del os.environ["LITELLM_REASONING_AUTO_SUMMARY"]
+
+
+def test_transform_response_preserves_annotations():
+    """
+    Test that annotations from Responses API are preserved when transforming to Chat Completions format.
+    
+    This is a regression test for the bug where annotations (like url_citation) were being
+    dropped during the transformation from ResponsesAPIResponse to ModelResponse.
+    
+    The fix ensures annotations are extracted from ResponseOutputText content items and
+    passed through to the Message object in the Chat Completions response.
+    """
+    from unittest.mock import Mock
+
+    from openai.types.responses import ResponseOutputMessage, ResponseOutputText
+
+    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()
+
+    # Create annotations similar to what OpenAI Responses API returns
+    annotations = [
+        {
+            "type": "url_citation",
+            "start_index": 0,
+            "end_index": 100,
+            "title": "Example Article",
+            "url": "https://example.com/article",
+        },
+        {
+            "type": "url_citation",
+            "start_index": 101,
+            "end_index": 200,
+            "title": "Another Source",
+            "url": "https://example.com/source",
+        },
+    ]
+
+    # Create output text with annotations
+    output_text = ResponseOutputText(
+        annotations=annotations,
+        text="Here is some information with citations.",
+        type="output_text",
+        logprobs=[],
+    )
+
+    # Create output message
+    output_message = ResponseOutputMessage(
+        id="msg_test123",
+        content=[output_text],
+        role="assistant",
+        status="completed",
+        type="message",
+    )
+
+    # Create usage information
+    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,
+    )
+
+    # Create the full ResponsesAPIResponse
+    raw_response = ResponsesAPIResponse(
+        id="resp_test123",
+        created_at=1234567890,
+        error=None,
+        incomplete_details=None,
+        instructions=None,
+        metadata={},
+        model="gpt-5.1",
+        object="response",
+        output=[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=None,
+        status="completed",
+        text={"format": {"type": "text"}, "verbosity": "medium"},
+        truncation="disabled",
+        usage=usage,
+        user=None,
+        store=True,
+        background=False,
+        billing={"payer": "openai"},
+        max_tool_calls=None,
+        prompt_cache_key=None,
+        safety_identifier=None,
+        service_tier="default",
+        top_logprobs=0,
+    )
+
+    # Create empty model_response
+    model_response = ModelResponse(
+        id="chatcmpl-test123",
+        created=1234567890,
+        model=None,
+        object="chat.completion",
+        system_fingerprint=None,
+        choices=[],
+        usage=Usage(completion_tokens=0, prompt_tokens=0, total_tokens=0),
+    )
+
+    # Create mock objects for required parameters
+    logging_obj = Mock()
+    messages = [{"role": "user", "content": "Tell me about AI"}]
+    request_data = {"model": "gpt-5.1"}
+    optional_params = {}
+    litellm_params = {"acompletion": False, "api_key": None}
+    encoding = Mock()
+
+    # Call transform_response
+    result = handler.transform_response(
+        model="gpt-5.1",
+        raw_response=raw_response,
+        model_response=model_response,
+        logging_obj=logging_obj,
+        request_data=request_data,
+        messages=messages,
+        optional_params=optional_params,
+        litellm_params=litellm_params,
+        encoding=encoding,
+        api_key=None,
+        json_mode=None,
+    )
+
+    # Assertions
+    assert result.model == "gpt-5.1"
+    assert len(result.choices) == 1
+
+    # Check the choice
+    choice = result.choices[0]
+    assert choice.finish_reason == "stop"
+    assert choice.index == 0
+    assert choice.message.role == "assistant"
+    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 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)}"
+
+    # Verify annotation content
+    annotation1 = choice.message.annotations[0]
+    assert annotation1["type"] == "url_citation"
+    assert annotation1["title"] == "Example Article"
+    assert annotation1["url"] == "https://example.com/article"
+    assert annotation1["start_index"] == 0
+    assert annotation1["end_index"] == 100
+
+    annotation2 = choice.message.annotations[1]
+    assert annotation2["type"] == "url_citation"
+    assert annotation2["title"] == "Another Source"
+    assert annotation2["url"] == "https://example.com/source"
+    assert annotation2["start_index"] == 101
+    assert annotation2["end_index"] == 200
+
+    # Check usage
+    assert result.usage.prompt_tokens == 10
+    assert result.usage.completion_tokens == 20
+    assert result.usage.total_tokens == 30
+
+    print("✓ Annotations from Responses API are correctly preserved in Chat Completions format")
diff --git a/tests/test_litellm/google_genai/test_google_genai_adapter.py b/tests/test_litellm/google_genai/test_google_genai_adapter.py
index 135881ad209..884e06fdbc0 100644
--- a/tests/test_litellm/google_genai/test_google_genai_adapter.py
+++ b/tests/test_litellm/google_genai/test_google_genai_adapter.py
@@ -1120,12 +1120,13 @@ async def test_google_generate_content_with_openai():
         
         # Print the response for verification
         print(f"Response: {response}")
-        ######################################################### 
+        #########################################################
         # validate only expected fields were sent to litellm.completion
         passed_fields = set(call_kwargs.keys())
         # remove any GenericLiteLLMParams fields
         passed_fields = passed_fields - set(GenericLiteLLMParams.model_fields.keys())
-        assert passed_fields == set(["model", "messages"]), f"Expected only model and messages to be passed through, got {passed_fields}"
+        # extra_headers is now explicitly passed through for providers that need custom headers
+        assert passed_fields == set(["model", "messages", "extra_headers"]), f"Expected model, messages, and extra_headers to be passed through, got {passed_fields}"
 @pytest.mark.asyncio
 async def test_agenerate_content_x_goog_api_key_header():
     """
diff --git a/tests/test_litellm/google_genai/test_google_genai_adapter_fixes.py b/tests/test_litellm/google_genai/test_google_genai_adapter_fixes.py
index d4d0ba9d44c..b45064003c6 100644
--- a/tests/test_litellm/google_genai/test_google_genai_adapter_fixes.py
+++ b/tests/test_litellm/google_genai/test_google_genai_adapter_fixes.py
@@ -252,7 +252,7 @@ def test_stream_transformation_error_handling():
 def test_non_stream_response_when_stream_requested():
     """Test handling of non-stream responses when streaming was requested"""
     from litellm.types.utils import Choices
-    
+
     # Mock a non-stream response (ModelResponse with valid choices)
     mock_response = ModelResponse(
         id="test-123",
@@ -270,13 +270,13 @@ def test_non_stream_response_when_stream_requested():
         model="gpt-3.5-turbo",
         object="chat.completion"
     )
-    
+
     # Create an instance of the adapter
     adapter = GoogleGenAIAdapter()
-    
+
     # Test the adapter's translate_completion_to_generate_content method directly
     result = adapter.translate_completion_to_generate_content(mock_response)
-    
+
     # Verify the result is a valid Google GenAI format response
     assert "candidates" in result
     assert isinstance(result["candidates"], list)
@@ -287,4 +287,70 @@ def test_non_stream_response_when_stream_requested():
     assert isinstance(candidate["content"]["parts"], list)
     assert len(candidate["content"]["parts"]) > 0
     assert "text" in candidate["content"]["parts"][0]
-    assert candidate["content"]["parts"][0]["text"] == "Hello, world!"
\ No newline at end of file
+    assert candidate["content"]["parts"][0]["text"] == "Hello, world!"
+
+
+def test_extra_headers_forwarding():
+    """Test that extra_headers is correctly forwarded to completion call.
+
+    This is important for providers like github_copilot that require custom
+    headers (e.g., Editor-Version) for authentication.
+    """
+    # Test that extra_headers is included in completion kwargs
+    model = "gpt-3.5-turbo"
+    contents = {"role": "user", "parts": [{"text": "Test"}]}
+    config = {"temperature": 0.7}
+
+    extra_kwargs = {
+        "extra_headers": {
+            "Editor-Version": "vscode/1.95.0",
+            "Editor-Plugin-Version": "copilot-chat/0.22.4",
+            "Custom-Header": "custom-value"
+        },
+        "metadata": {"user_id": "test-user"}
+    }
+
+    completion_kwargs = GenerateContentToCompletionHandler._prepare_completion_kwargs(
+        model=model,
+        contents=contents,
+        config=config,
+        stream=False,
+        extra_kwargs=extra_kwargs
+    )
+
+    # Verify extra_headers is forwarded
+    assert "extra_headers" in completion_kwargs, "extra_headers should be forwarded to completion call"
+    assert completion_kwargs["extra_headers"]["Editor-Version"] == "vscode/1.95.0"
+    assert completion_kwargs["extra_headers"]["Editor-Plugin-Version"] == "copilot-chat/0.22.4"
+    assert completion_kwargs["extra_headers"]["Custom-Header"] == "custom-value"
+
+    # Verify metadata is also forwarded (existing behavior)
+    assert "metadata" in completion_kwargs
+    assert completion_kwargs["metadata"]["user_id"] == "test-user"
+
+
+def test_extra_headers_not_present():
+    """Test that missing extra_headers doesn't cause issues."""
+    model = "gpt-3.5-turbo"
+    contents = {"role": "user", "parts": [{"text": "Test"}]}
+    config = {"temperature": 0.7}
+
+    # extra_kwargs without extra_headers
+    extra_kwargs = {
+        "metadata": {"user_id": "test-user"}
+    }
+
+    completion_kwargs = GenerateContentToCompletionHandler._prepare_completion_kwargs(
+        model=model,
+        contents=contents,
+        config=config,
+        stream=False,
+        extra_kwargs=extra_kwargs
+    )
+
+    # Verify extra_headers is not present (no error)
+    assert "extra_headers" not in completion_kwargs
+
+    # Verify metadata is still forwarded
+    assert "metadata" in completion_kwargs
+    assert completion_kwargs["metadata"]["user_id"] == "test-user"
\ No newline at end of file
diff --git a/tests/test_litellm/integrations/cloudzero/test_cloudzero.py b/tests/test_litellm/integrations/cloudzero/test_cloudzero.py
index 31a2f6cbf51..b0aac17e7d9 100644
--- a/tests/test_litellm/integrations/cloudzero/test_cloudzero.py
+++ b/tests/test_litellm/integrations/cloudzero/test_cloudzero.py
@@ -66,16 +66,77 @@ class TestCloudZeroHourlyExport:
             fake_client = MagicMock()
             fake_db = MagicMock()
 
-            async def query_raw_mock(query: str):
-                sql_context = pl.SQLContext(
-                    LiteLLM_DailyUserSpend=spend_mock_data,
-                    LiteLLM_VerificationToken=verification_mock_data,
-                    LiteLLM_TeamTable=team_mock_data,
-                    LiteLLM_UserTable=user_mock_data,
-                )
-                result = sql_context.execute(query).collect()
+            async def query_raw_mock(query: str, *params):
+                start_time_utc = params[0] if len(params) > 0 else None
+                end_time_utc = params[1] if len(params) > 1 else None
+                limit = params[2] if len(params) > 2 else None
 
-                return result
+                spend_df = spend_mock_data.collect()
+                verification_df = verification_mock_data.collect().rename(
+                    {"key_alias": "api_key_alias"}
+                )
+                team_df = team_mock_data.collect()
+                user_df = user_mock_data.collect()
+
+                joined = (
+                    spend_df.join(
+                        verification_df, left_on="api_key", right_on="token", how="left"
+                    )
+                    .join(
+                        team_df,
+                        left_on="team_id",
+                        right_on="team_id",
+                        how="left",
+                        suffix="_team",
+                    )
+                    .join(
+                        user_df,
+                        left_on="user_id",
+                        right_on="user_id",
+                        how="left",
+                        suffix="_user",
+                    )
+                )
+
+                for duplicate_column in ("team_id_team", "user_id_user"):
+                    if duplicate_column in joined.columns:
+                        joined = joined.drop(duplicate_column)
+
+                if start_time_utc is not None:
+                    joined = joined.filter(pl.col("updated_at") >= start_time_utc)
+                if end_time_utc is not None:
+                    joined = joined.filter(pl.col("updated_at") <= end_time_utc)
+
+                joined = joined.select(
+                    [
+                        "id",
+                        "date",
+                        "user_id",
+                        "api_key",
+                        "model",
+                        "model_group",
+                        "custom_llm_provider",
+                        "prompt_tokens",
+                        "completion_tokens",
+                        "spend",
+                        "api_requests",
+                        "successful_requests",
+                        "failed_requests",
+                        "cache_creation_input_tokens",
+                        "cache_read_input_tokens",
+                        "created_at",
+                        "updated_at",
+                        "team_id",
+                        "api_key_alias",
+                        "team_alias",
+                        "user_email",
+                    ]
+                ).sort(["date", "created_at"], descending=[True, True])
+
+                if limit is not None:
+                    joined = joined.head(int(limit))
+
+                return joined
 
             fake_db.query_raw = AsyncMock(side_effect=query_raw_mock)
             fake_client.db = fake_db
diff --git a/tests/test_litellm/integrations/cloudzero/test_cloudzero_database.py b/tests/test_litellm/integrations/cloudzero/test_cloudzero_database.py
new file mode 100644
index 00000000000..89a5028011c
--- /dev/null
+++ b/tests/test_litellm/integrations/cloudzero/test_cloudzero_database.py
@@ -0,0 +1,57 @@
+"""Tests for LiteLLM CloudZero database helper."""
+
+from datetime import datetime, timezone
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+
+from litellm.integrations.cloudzero.database import LiteLLMDatabase
+
+
+def _setup_db(monkeypatch: pytest.MonkeyPatch, query_return):
+    """Return a database instance with prisma client mocked out."""
+    query_mock = AsyncMock(return_value=query_return)
+    mock_client = SimpleNamespace(db=SimpleNamespace(query_raw=query_mock))
+    db = LiteLLMDatabase()
+    monkeypatch.setattr(db, "_ensure_prisma_client", lambda: mock_client)
+    return db, query_mock
+
+
+@pytest.mark.asyncio
+async def test_get_usage_data_parameterized(monkeypatch: pytest.MonkeyPatch):
+    """Start/end filters and limit should be parameterized via placeholders."""
+    start = datetime(2024, 5, 1, tzinfo=timezone.utc)
+    end = datetime(2024, 5, 2, tzinfo=timezone.utc)
+    db, query_mock = _setup_db(monkeypatch, [])
+
+    await db.get_usage_data(limit=10, start_time_utc=start, end_time_utc=end)
+
+    query_text, *params = query_mock.await_args.args
+    assert "dus.updated_at >= $1::timestamptz" in query_text
+    assert "dus.updated_at <= $2::timestamptz" in query_text
+    assert "LIMIT $3" in query_text
+    assert params == [start, end, 10]
+
+
+@pytest.mark.asyncio
+async def test_get_usage_data_handles_missing_filters(monkeypatch: pytest.MonkeyPatch):
+    """When no filters provided the params should be None placeholders."""
+    db, query_mock = _setup_db(monkeypatch, [])
+
+    await db.get_usage_data()
+
+    query_text, *params = query_mock.await_args.args
+    assert "LIMIT $3" not in query_text
+    assert params == [None, None]
+
+
+@pytest.mark.asyncio
+async def test_get_usage_data_rejects_invalid_limit(monkeypatch: pytest.MonkeyPatch):
+    """limit must coerce to int or raise ValueError before hitting the DB."""
+    db, query_mock = _setup_db(monkeypatch, [])
+
+    with pytest.raises(ValueError):
+        await db.get_usage_data(limit="invalid")
+
+    assert query_mock.await_count == 0
diff --git a/tests/test_litellm/integrations/focus/test_focus_database.py b/tests/test_litellm/integrations/focus/test_focus_database.py
new file mode 100644
index 00000000000..5ee98cc9dd0
--- /dev/null
+++ b/tests/test_litellm/integrations/focus/test_focus_database.py
@@ -0,0 +1,74 @@
+"""Tests for FocusLiteLLMDatabase query construction."""
+
+from datetime import datetime, timezone
+from types import SimpleNamespace
+from unittest.mock import AsyncMock
+
+import pytest
+
+from litellm.integrations.focus.database import FocusLiteLLMDatabase
+
+
+def _setup_db(monkeypatch: pytest.MonkeyPatch, query_return):
+    """Create a database instance with a stubbed prisma client."""
+    query_mock = AsyncMock(return_value=query_return)
+    mock_client = SimpleNamespace(db=SimpleNamespace(query_raw=query_mock))
+    db = FocusLiteLLMDatabase()
+    monkeypatch.setattr(db, "_ensure_prisma_client", lambda: mock_client)
+    return db, query_mock
+
+
+@pytest.mark.asyncio
+async def test_should_parameterize_filters_and_limit(monkeypatch: pytest.MonkeyPatch):
+    start = datetime(2024, 1, 1, tzinfo=timezone.utc)
+    end = datetime(2024, 1, 2, tzinfo=timezone.utc)
+    db, query_mock = _setup_db(monkeypatch, [])
+
+    await db.get_usage_data(limit=25, start_time_utc=start, end_time_utc=end)
+
+    query_text, *params = query_mock.await_args.args
+    assert "dus.updated_at >= $1::timestamptz" in query_text
+    assert "dus.updated_at <= $2::timestamptz" in query_text
+    assert "LIMIT $3" in query_text
+    assert params == [start, end, 25]
+
+
+@pytest.mark.asyncio
+async def test_should_execute_without_filters(monkeypatch: pytest.MonkeyPatch):
+    row = {
+        "id": 1,
+        "user_id": "user",
+        "date": datetime(2024, 1, 1, tzinfo=timezone.utc),
+    }
+    db, query_mock = _setup_db(monkeypatch, [row])
+
+    result = await db.get_usage_data()
+
+    query_text, *params = query_mock.await_args.args
+    assert "WHERE" not in query_text
+    assert "LIMIT $" not in query_text
+    assert params == []
+    assert result.height == 1
+    assert result["id"][0] == 1
+
+
+@pytest.mark.asyncio
+async def test_should_accept_string_timestamps(monkeypatch: pytest.MonkeyPatch):
+    db, query_mock = _setup_db(monkeypatch, [])
+
+    start = "2024-02-01T00:00:00+00:00"
+    end = "2024-02-02T00:00:00+00:00"
+    await db.get_usage_data(start_time_utc=start, end_time_utc=end)
+
+    _, *params = query_mock.await_args.args
+    assert params == [start, end]
+
+
+@pytest.mark.asyncio
+async def test_should_reject_invalid_limit(monkeypatch: pytest.MonkeyPatch):
+    db, query_mock = _setup_db(monkeypatch, [])
+
+    with pytest.raises(ValueError):
+        await db.get_usage_data(limit="invalid")
+
+    assert query_mock.await_count == 0
diff --git a/tests/test_litellm/integrations/focus/test_s3_destination.py b/tests/test_litellm/integrations/focus/test_s3_destination.py
new file mode 100644
index 00000000000..f915b2c56a3
--- /dev/null
+++ b/tests/test_litellm/integrations/focus/test_s3_destination.py
@@ -0,0 +1,100 @@
+"""Tests for FocusS3Destination behavior."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from types import SimpleNamespace
+from typing import Any, Dict
+
+import pytest
+
+import litellm.integrations.focus.destinations.s3_destination as s3_module
+from litellm.integrations.focus.destinations.base import FocusTimeWindow
+from litellm.integrations.focus.destinations.s3_destination import FocusS3Destination
+
+
+def _window(freq: str = "hourly", hour: int = 5) -> FocusTimeWindow:
+    start = datetime(2024, 1, 2, hour, tzinfo=timezone.utc)
+    end = start.replace(hour=hour + 1)
+    return FocusTimeWindow(start_time=start, end_time=end, frequency=freq)
+
+
+def test_should_require_bucket_name():
+    with pytest.raises(ValueError):
+        FocusS3Destination(prefix="focus", config={})
+
+
+def test_should_build_hourly_object_key():
+    dest = FocusS3Destination(prefix="exports/", config={"bucket_name": "bucket"})
+    key = dest._build_object_key(
+        time_window=_window(freq="hourly", hour=3), filename="data.snappy"
+    )
+    assert key == "exports/date=2024-01-02/hour=03/data.snappy"
+
+
+def test_should_build_daily_key_without_hour_segment():
+    dest = FocusS3Destination(prefix="", config={"bucket_name": "bucket"})
+    key = dest._build_object_key(
+        time_window=_window(freq="daily", hour=0), filename="daily.parquet"
+    )
+    assert key == "date=2024-01-02/daily.parquet"
+
+
+@pytest.mark.asyncio
+async def test_should_dispatch_upload_via_thread(monkeypatch: pytest.MonkeyPatch):
+    dest = FocusS3Destination(prefix="focus", config={"bucket_name": "bucket"})
+    captured: Dict[str, Any] = {}
+
+    async def fake_to_thread(func, *args, **kwargs):  # type: ignore[override]
+        captured["func"] = func
+        captured["args"] = args
+        captured["kwargs"] = kwargs
+
+    monkeypatch.setattr(s3_module.asyncio, "to_thread", fake_to_thread)
+
+    window = _window(freq="hourly", hour=1)
+    await dest.deliver(content=b"payload", time_window=window, filename="file.bin")
+
+    assert captured["func"] == dest._upload
+    assert captured["args"][0] == b"payload"
+    assert captured["args"][1].endswith("/file.bin")
+
+
+def test_should_upload_with_configured_client(monkeypatch: pytest.MonkeyPatch):
+    config = {
+        "bucket_name": "bucket",
+        "region_name": "us-east-2",
+        "endpoint_url": "http://localhost:4566",
+        "aws_access_key_id": "key",
+        "aws_secret_access_key": "secret",
+        "aws_session_token": "token",
+    }
+    dest = FocusS3Destination(prefix="focus", config=config)
+    captured: Dict[str, Any] = {}
+
+    def fake_client(service: str, **kwargs):
+        assert service == "s3"
+        captured["client_kwargs"] = kwargs
+
+        def put_object(**put_kwargs):
+            captured["put_kwargs"] = put_kwargs
+
+        return SimpleNamespace(put_object=put_object)
+
+    monkeypatch.setattr(s3_module.boto3, "client", fake_client)
+
+    dest._upload(content=b"payload", object_key="path/file.bin")
+
+    assert captured["client_kwargs"] == {
+        "region_name": "us-east-2",
+        "endpoint_url": "http://localhost:4566",
+        "aws_access_key_id": "key",
+        "aws_secret_access_key": "secret",
+        "aws_session_token": "token",
+    }
+    assert captured["put_kwargs"] == {
+        "Bucket": "bucket",
+        "Key": "path/file.bin",
+        "Body": b"payload",
+        "ContentType": "application/octet-stream",
+    }
diff --git a/tests/test_litellm/integrations/test_custom_guardrail.py b/tests/test_litellm/integrations/test_custom_guardrail.py
index a322dfe9a2b..d7d7720ff43 100644
--- a/tests/test_litellm/integrations/test_custom_guardrail.py
+++ b/tests/test_litellm/integrations/test_custom_guardrail.py
@@ -530,7 +530,7 @@ class TestPassthroughCallTypeHandling:
         )
         assert (
             ProxyBaseLLMRequestProcessing._get_pre_call_type(route_type="aembedding")
-            == "embeddings"
+            == "embedding"
         )
         assert (
             ProxyBaseLLMRequestProcessing._get_pre_call_type(route_type="aresponses")
diff --git a/tests/test_litellm/integrations/test_prometheus_cache_metrics.py b/tests/test_litellm/integrations/test_prometheus_cache_metrics.py
new file mode 100644
index 00000000000..660757673f6
--- /dev/null
+++ b/tests/test_litellm/integrations/test_prometheus_cache_metrics.py
@@ -0,0 +1,211 @@
+"""
+Unit tests for cache Prometheus metrics.
+
+Run with: poetry run pytest tests/test_litellm/integrations/test_prometheus_cache_metrics.py -v
+"""
+import pytest
+from unittest.mock import MagicMock, patch
+from litellm.types.integrations.prometheus import UserAPIKeyLabelValues
+
+
+class TestPrometheusCacheMetrics:
+    """Tests for cache-related Prometheus metrics"""
+
+    @pytest.fixture
+    def sample_enum_values(self):
+        """Create sample enum values for labels"""
+        return UserAPIKeyLabelValues(
+            end_user="test-end-user",
+            hashed_api_key="test-key-hash",
+            api_key_alias="test-key-alias",
+            team="test-team",
+            team_alias="test-team-alias",
+            user="test-user",
+            model="gpt-3.5-turbo",
+        )
+
+    def test_cache_metrics_defined_in_types(self):
+        """Test that cache metrics are defined in DEFINED_PROMETHEUS_METRICS"""
+        from litellm.types.integrations.prometheus import DEFINED_PROMETHEUS_METRICS
+        from typing import get_args
+
+        defined_metrics = get_args(DEFINED_PROMETHEUS_METRICS)
+
+        assert "litellm_cache_hits_metric" in defined_metrics
+        assert "litellm_cache_misses_metric" in defined_metrics
+        assert "litellm_cached_tokens_metric" in defined_metrics
+
+    def test_cache_metric_labels_defined(self):
+        """Test that cache metric labels are properly defined"""
+        from litellm.types.integrations.prometheus import PrometheusMetricLabels
+
+        # Verify labels are defined for each cache metric
+        assert hasattr(PrometheusMetricLabels, "litellm_cache_hits_metric")
+        assert hasattr(PrometheusMetricLabels, "litellm_cache_misses_metric")
+        assert hasattr(PrometheusMetricLabels, "litellm_cached_tokens_metric")
+
+        # Verify labels include expected keys
+        expected_labels = [
+            "model",
+            "hashed_api_key",
+            "api_key_alias",
+            "team",
+            "team_alias",
+            "end_user",
+            "user",
+        ]
+        for label in expected_labels:
+            assert label in PrometheusMetricLabels.litellm_cache_hits_metric
+            assert label in PrometheusMetricLabels.litellm_cache_misses_metric
+            assert label in PrometheusMetricLabels.litellm_cached_tokens_metric
+
+    def test_increment_cache_metrics_on_cache_hit(self, sample_enum_values):
+        """Test that cache hit increments the correct metrics"""
+        # Create mock for PrometheusLogger instance
+        mock_logger = MagicMock()
+
+        # Import the method directly and bind it to our mock
+        from litellm.integrations.prometheus import PrometheusLogger
+
+        # Create a mock standard logging payload with cache_hit=True
+        standard_logging_payload = {
+            "cache_hit": True,
+            "total_tokens": 100,
+            "prompt_tokens": 50,
+            "completion_tokens": 50,
+            "model_group": "openai",
+            "request_tags": [],
+        }
+
+        # Create mock metrics
+        mock_logger.litellm_cache_hits_metric = MagicMock()
+        mock_logger.litellm_cache_misses_metric = MagicMock()
+        mock_logger.litellm_cached_tokens_metric = MagicMock()
+        mock_logger.get_labels_for_metric = MagicMock(
+            return_value=[
+                "model",
+                "hashed_api_key",
+                "api_key_alias",
+                "team",
+                "team_alias",
+                "end_user",
+                "user",
+            ]
+        )
+
+        # Call the method using unbound method approach
+        PrometheusLogger._increment_cache_metrics(
+            mock_logger,
+            standard_logging_payload=standard_logging_payload,
+            enum_values=sample_enum_values,
+        )
+
+        # Verify cache hits metric was incremented
+        mock_logger.litellm_cache_hits_metric.labels.assert_called()
+        mock_logger.litellm_cache_hits_metric.labels().inc.assert_called_once()
+
+        # Verify cached tokens metric was incremented with total_tokens
+        mock_logger.litellm_cached_tokens_metric.labels.assert_called()
+        mock_logger.litellm_cached_tokens_metric.labels().inc.assert_called_once_with(
+            100
+        )
+
+        # Verify cache misses metric was NOT called
+        mock_logger.litellm_cache_misses_metric.labels.assert_not_called()
+
+    def test_increment_cache_metrics_on_cache_miss(self, sample_enum_values):
+        """Test that cache miss increments the correct metrics"""
+        # Create mock for PrometheusLogger instance
+        mock_logger = MagicMock()
+
+        from litellm.integrations.prometheus import PrometheusLogger
+
+        # Create a mock standard logging payload with cache_hit=False
+        standard_logging_payload = {
+            "cache_hit": False,
+            "total_tokens": 100,
+            "prompt_tokens": 50,
+            "completion_tokens": 50,
+            "model_group": "openai",
+            "request_tags": [],
+        }
+
+        # Create mock metrics
+        mock_logger.litellm_cache_hits_metric = MagicMock()
+        mock_logger.litellm_cache_misses_metric = MagicMock()
+        mock_logger.litellm_cached_tokens_metric = MagicMock()
+        mock_logger.get_labels_for_metric = MagicMock(
+            return_value=[
+                "model",
+                "hashed_api_key",
+                "api_key_alias",
+                "team",
+                "team_alias",
+                "end_user",
+                "user",
+            ]
+        )
+
+        # Call the method
+        PrometheusLogger._increment_cache_metrics(
+            mock_logger,
+            standard_logging_payload=standard_logging_payload,
+            enum_values=sample_enum_values,
+        )
+
+        # Verify cache misses metric was incremented
+        mock_logger.litellm_cache_misses_metric.labels.assert_called()
+        mock_logger.litellm_cache_misses_metric.labels().inc.assert_called_once()
+
+        # Verify cache hits and cached tokens metrics were NOT called
+        mock_logger.litellm_cache_hits_metric.labels.assert_not_called()
+        mock_logger.litellm_cached_tokens_metric.labels.assert_not_called()
+
+    def test_increment_cache_metrics_when_cache_hit_is_none(self, sample_enum_values):
+        """Test that no metrics are incremented when cache_hit is None"""
+        # Create mock for PrometheusLogger instance
+        mock_logger = MagicMock()
+
+        from litellm.integrations.prometheus import PrometheusLogger
+
+        # Create a mock standard logging payload with cache_hit=None
+        standard_logging_payload = {
+            "cache_hit": None,
+            "total_tokens": 100,
+            "prompt_tokens": 50,
+            "completion_tokens": 50,
+            "model_group": "openai",
+            "request_tags": [],
+        }
+
+        # Create mock metrics
+        mock_logger.litellm_cache_hits_metric = MagicMock()
+        mock_logger.litellm_cache_misses_metric = MagicMock()
+        mock_logger.litellm_cached_tokens_metric = MagicMock()
+        mock_logger.get_labels_for_metric = MagicMock(
+            return_value=[
+                "model",
+                "hashed_api_key",
+                "api_key_alias",
+                "team",
+                "team_alias",
+                "end_user",
+                "user",
+            ]
+        )
+
+        # Call the method
+        PrometheusLogger._increment_cache_metrics(
+            mock_logger,
+            standard_logging_payload=standard_logging_payload,
+            enum_values=sample_enum_values,
+        )
+
+        # Verify NO metrics were called
+        mock_logger.litellm_cache_hits_metric.labels.assert_not_called()
+        mock_logger.litellm_cache_misses_metric.labels.assert_not_called()
+        mock_logger.litellm_cached_tokens_metric.labels.assert_not_called()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
diff --git a/tests/test_litellm/integrations/test_prometheus_invalid_key_filtering.py b/tests/test_litellm/integrations/test_prometheus_invalid_key_filtering.py
new file mode 100644
index 00000000000..ff433480d5e
--- /dev/null
+++ b/tests/test_litellm/integrations/test_prometheus_invalid_key_filtering.py
@@ -0,0 +1,161 @@
+"""
+Unit tests for Prometheus invalid API key request filtering.
+
+Tests functionality that prevents invalid API key requests (401 status codes)
+from being recorded in Prometheus metrics.
+"""
+
+import os
+import sys
+from unittest.mock import Mock, patch
+
+import pytest
+from prometheus_client import REGISTRY
+
+sys.path.insert(0, os.path.abspath("../../.."))
+
+from litellm.integrations.prometheus import PrometheusLogger
+from litellm.proxy._types import UserAPIKeyAuth
+
+
+@pytest.fixture(scope="function")
+def prometheus_logger():
+    """Create a PrometheusLogger instance for testing."""
+    collectors = list(REGISTRY._collector_to_names.keys())
+    for collector in collectors:
+        REGISTRY.unregister(collector)
+    return PrometheusLogger()
+
+
+class ExceptionWithCode:
+    """Exception-like object with 'code' attribute (ProxyException pattern)."""
+    def __init__(self, code):
+        self.code = code
+
+
+class ExceptionWithStatusCode:
+    """Exception-like object with 'status_code' attribute."""
+    def __init__(self, status_code):
+        self.status_code = status_code
+
+
+class TestExtractStatusCode:
+    """Test status code extraction from various sources."""
+
+    @pytest.mark.parametrize("exception_class,code_value,expected", [
+        (ExceptionWithCode, "401", 401),
+        (ExceptionWithStatusCode, 401, 401),
+    ])
+    def test_extract_from_exception(self, prometheus_logger, exception_class, code_value, expected):
+        exception = exception_class(code_value)
+        assert prometheus_logger._extract_status_code(exception=exception) == expected
+
+    def test_extract_from_kwargs(self, prometheus_logger):
+        exception = ExceptionWithCode("401")
+        assert prometheus_logger._extract_status_code(kwargs={"exception": exception}) == 401
+
+    def test_extract_from_enum_values(self, prometheus_logger):
+        enum_values = Mock(status_code="401")
+        assert prometheus_logger._extract_status_code(enum_values=enum_values) == 401
+
+
+class TestInvalidAPIKeyDetection:
+    """Test invalid API key request detection logic."""
+
+    @pytest.mark.parametrize("status_code,expected", [
+        (401, True),
+        (200, False),
+        (500, False),
+        (None, False),
+    ])
+    def test_status_code_detection(self, prometheus_logger, status_code, expected):
+        assert prometheus_logger._is_invalid_api_key_request(status_code=status_code) == expected
+
+    def test_auth_error_message_detection(self, prometheus_logger):
+        exception = AssertionError("LiteLLM Virtual Key expected. Received=invalid-key-12345, expected to start with 'sk-'.")
+        assert prometheus_logger._is_invalid_api_key_request(status_code=None, exception=exception) is True
+
+    def test_non_auth_exception_not_detected(self, prometheus_logger):
+        exception = ValueError("Some other error")
+        assert prometheus_logger._is_invalid_api_key_request(status_code=None, exception=exception) is False
+
+
+class TestSkipMetricsValidation:
+    """Test high-level validation method that orchestrates detection and extraction."""
+
+    def test_skip_for_401_exception(self, prometheus_logger):
+        """Test full flow: extraction -> detection -> skip decision."""
+        exception = ExceptionWithCode("401")
+        assert prometheus_logger._should_skip_metrics_for_invalid_key(exception=exception) is True
+
+    def test_skip_for_auth_error_message(self, prometheus_logger):
+        """Test full flow: exception message -> detection -> skip decision."""
+        exception = AssertionError("expected to start with 'sk-'")
+        assert prometheus_logger._should_skip_metrics_for_invalid_key(exception=exception) is True
+
+    def test_no_skip_for_valid_request(self, prometheus_logger):
+        assert prometheus_logger._should_skip_metrics_for_invalid_key() is False
+
+
+class TestAsyncHooks:
+    """Test async hook methods skip metrics for invalid API keys."""
+
+    @pytest.fixture
+    def mock_user_api_key(self):
+        """Create a mock UserAPIKeyAuth object."""
+        user_key = Mock(spec=UserAPIKeyAuth)
+        user_key.api_key = "test-key"
+        user_key.end_user_id = None
+        user_key.user_id = None
+        user_key.user_email = None
+        user_key.key_alias = None
+        user_key.team_id = None
+        user_key.team_alias = None
+        user_key.request_route = "/test"
+        return user_key
+
+    @pytest.mark.asyncio
+    async def test_post_call_failure_hook_skips_401(self, prometheus_logger, mock_user_api_key):
+        exception = ExceptionWithCode("401")
+        exception.__class__.__name__ = "ProxyException"
+
+        with patch.object(prometheus_logger, 'litellm_proxy_failed_requests_metric') as mock_failed, \
+             patch.object(prometheus_logger, 'litellm_proxy_total_requests_metric') as mock_total:
+
+            await prometheus_logger.async_post_call_failure_hook(
+                request_data={"model": "test-model"},
+                original_exception=exception,
+                user_api_key_dict=mock_user_api_key
+            )
+
+            mock_failed.labels.assert_not_called()
+            mock_total.labels.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_log_failure_event_skips_401(self, prometheus_logger):
+        exception = ExceptionWithCode("401")
+        kwargs = {
+            "model": "test-model",
+            "standard_logging_object": {
+                "metadata": {
+                    "user_api_key_hash": "test-key",
+                    "user_api_key_user_id": "test-user",
+                },
+                "model_group": "test-model",
+            },
+            "exception": exception,
+            "litellm_params": {},
+        }
+
+        with patch.object(prometheus_logger, 'litellm_llm_api_failed_requests_metric') as mock_failed, \
+             patch.object(prometheus_logger, 'set_llm_deployment_failure_metrics') as mock_deployment:
+
+            await prometheus_logger.async_log_failure_event(
+                kwargs=kwargs,
+                response_obj=None,
+                start_time=None,
+                end_time=None
+            )
+
+            mock_failed.labels.assert_not_called()
+            mock_deployment.assert_not_called()
diff --git a/tests/test_litellm/integrations/test_prometheus_queue_guardrail_metrics.py b/tests/test_litellm/integrations/test_prometheus_queue_guardrail_metrics.py
new file mode 100644
index 00000000000..0743a9c7ba2
--- /dev/null
+++ b/tests/test_litellm/integrations/test_prometheus_queue_guardrail_metrics.py
@@ -0,0 +1,424 @@
+"""
+Unit tests for prometheus queue time and guardrail metrics
+"""
+from datetime import datetime
+from unittest.mock import MagicMock
+
+import pytest
+from prometheus_client import REGISTRY
+
+from litellm.integrations.prometheus import PrometheusLogger
+from litellm.types.integrations.prometheus import UserAPIKeyLabelValues
+
+
+@pytest.fixture(autouse=True)
+def cleanup_prometheus_registry():
+    """Clean up prometheus registry between tests"""
+    # Clear the registry before each test
+    collectors = list(REGISTRY._collector_to_names.keys())
+    for collector in collectors:
+        REGISTRY.unregister(collector)
+    yield
+    # Clean up after test
+    collectors = list(REGISTRY._collector_to_names.keys())
+    for collector in collectors:
+        REGISTRY.unregister(collector)
+
+
+class TestPrometheusQueueTimeMetric:
+    """Test request queue time metric recording"""
+
+    def test_queue_time_metric_recorded_in_set_latency_metrics(self):
+        """Test that queue time metric is recorded when queue_time_seconds is present in metadata"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock the metric
+        mock_metric = MagicMock()
+        mock_labeled_metric = MagicMock()
+        mock_metric.labels.return_value = mock_labeled_metric
+        prometheus_logger.litellm_request_queue_time_metric = mock_metric
+
+        # Create mock kwargs with queue_time_seconds in metadata
+        queue_time_seconds = 0.5
+
+        kwargs = {
+            "litellm_params": {"metadata": {"queue_time_seconds": queue_time_seconds}},
+            "model": "gpt-3.5-turbo",
+            "start_time": datetime.now(),
+            "end_time": datetime.now(),
+        }
+
+        enum_values = UserAPIKeyLabelValues(
+            end_user=None,
+            hashed_api_key="test-key",
+            api_key_alias="test-alias",
+            requested_model="gpt-3.5-turbo",
+            model_group="gpt-3.5-turbo",
+            team=None,
+            team_alias=None,
+            user=None,
+            user_email=None,
+            status_code="200",
+            model="gpt-3.5-turbo",
+            litellm_model_name="gpt-3.5-turbo",
+            tags=[],
+            model_id="gpt-3.5-turbo",
+            api_base="https://api.openai.com",
+            api_provider="openai",
+            exception_status=None,
+            exception_class=None,
+            custom_metadata_labels={},
+            route=None,
+        )
+
+        # Act
+        prometheus_logger._set_latency_metrics(
+            kwargs=kwargs,
+            model="gpt-3.5-turbo",
+            user_api_key="test-key",
+            user_api_key_alias="test-alias",
+            user_api_team=None,
+            user_api_team_alias=None,
+            enum_values=enum_values,
+        )
+
+        # Assert - queue time metric should be called
+        mock_metric.labels.assert_called()
+        # Check that observe was called on the queue time metric
+        assert mock_labeled_metric.observe.called
+        # Verify the observed value
+        observed_value = None
+        for call in mock_labeled_metric.observe.call_args_list:
+            if len(call[0]) > 0:
+                observed_value = call[0][0]
+                if observed_value == queue_time_seconds:
+                    break
+        assert observed_value == queue_time_seconds
+        assert observed_value >= 0
+
+    def test_queue_time_metric_not_recorded_when_missing(self):
+        """Test that queue time metric is not recorded when queue_time_seconds is missing"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock the metric
+        mock_metric = MagicMock()
+        mock_labeled_metric = MagicMock()
+        mock_metric.labels.return_value = mock_labeled_metric
+        prometheus_logger.litellm_request_queue_time_metric = mock_metric
+
+        # Create mock kwargs without queue_time_seconds
+        kwargs = {
+            "litellm_params": {"metadata": {}},
+            "model": "gpt-3.5-turbo",
+            "start_time": datetime.now(),
+            "end_time": datetime.now(),
+        }
+
+        enum_values = UserAPIKeyLabelValues(
+            end_user=None,
+            hashed_api_key="test-key",
+            api_key_alias="test-alias",
+            requested_model="gpt-3.5-turbo",
+            model_group="gpt-3.5-turbo",
+            team=None,
+            team_alias=None,
+            user=None,
+            user_email=None,
+            status_code="200",
+            model="gpt-3.5-turbo",
+            litellm_model_name="gpt-3.5-turbo",
+            tags=[],
+            model_id="gpt-3.5-turbo",
+            api_base="https://api.openai.com",
+            api_provider="openai",
+            exception_status=None,
+            exception_class=None,
+            custom_metadata_labels={},
+            route=None,
+        )
+
+        # Act
+        prometheus_logger._set_latency_metrics(
+            kwargs=kwargs,
+            model="gpt-3.5-turbo",
+            user_api_key="test-key",
+            user_api_key_alias="test-alias",
+            user_api_team=None,
+            user_api_team_alias=None,
+            enum_values=enum_values,
+        )
+
+        # Assert - queue time metric should not be called (queue_time_seconds is None)
+        # We check that observe was not called with queue_time_seconds
+        queue_time_called = False
+        for call in mock_labeled_metric.observe.call_args_list:
+            if len(call[0]) > 0 and call[0][0] == 0.5:  # Our test queue time value
+                queue_time_called = True
+                break
+        assert (
+            not queue_time_called
+        ), "Queue time metric should not be recorded when queue_time_seconds is missing"
+
+    def test_queue_time_metric_not_recorded_when_negative(self):
+        """Test that queue time metric is not recorded when queue_time_seconds is negative"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock the metric
+        mock_metric = MagicMock()
+        mock_labeled_metric = MagicMock()
+        mock_metric.labels.return_value = mock_labeled_metric
+        prometheus_logger.litellm_request_queue_time_metric = mock_metric
+
+        # Create mock kwargs with negative queue_time_seconds
+        kwargs = {
+            "litellm_params": {
+                "metadata": {"queue_time_seconds": -0.1}  # Negative value
+            },
+            "model": "gpt-3.5-turbo",
+            "start_time": datetime.now(),
+            "end_time": datetime.now(),
+        }
+
+        enum_values = UserAPIKeyLabelValues(
+            end_user=None,
+            hashed_api_key="test-key",
+            api_key_alias="test-alias",
+            requested_model="gpt-3.5-turbo",
+            model_group="gpt-3.5-turbo",
+            team=None,
+            team_alias=None,
+            user=None,
+            user_email=None,
+            status_code="200",
+            model="gpt-3.5-turbo",
+            litellm_model_name="gpt-3.5-turbo",
+            tags=[],
+            model_id="gpt-3.5-turbo",
+            api_base="https://api.openai.com",
+            api_provider="openai",
+            exception_status=None,
+            exception_class=None,
+            custom_metadata_labels={},
+            route=None,
+        )
+
+        # Act
+        prometheus_logger._set_latency_metrics(
+            kwargs=kwargs,
+            model="gpt-3.5-turbo",
+            user_api_key="test-key",
+            user_api_key_alias="test-alias",
+            user_api_team=None,
+            user_api_team_alias=None,
+            enum_values=enum_values,
+        )
+
+        # Assert - queue time metric should not be called for negative values
+        # We check that observe was not called with the negative value
+        negative_value_called = False
+        for call in mock_labeled_metric.observe.call_args_list:
+            if len(call[0]) > 0 and call[0][0] == -0.1:
+                negative_value_called = True
+                break
+        assert (
+            not negative_value_called
+        ), "Queue time metric should not be recorded for negative values"
+
+
+class TestPrometheusGuardrailMetrics:
+    """Test guardrail metrics recording"""
+
+    def test_record_guardrail_metrics_success(self):
+        """Test recording guardrail metrics for successful execution"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock metrics
+        mock_latency_metric = MagicMock()
+        mock_requests_metric = MagicMock()
+        mock_errors_metric = MagicMock()
+
+        prometheus_logger.litellm_guardrail_latency_metric = mock_latency_metric
+        prometheus_logger.litellm_guardrail_requests_total = mock_requests_metric
+        prometheus_logger.litellm_guardrail_errors_total = mock_errors_metric
+
+        guardrail_name = "test_guardrail"
+        latency_seconds = 0.15
+        status = "success"
+        error_type = None
+        hook_type = "pre_call"
+
+        # Act
+        prometheus_logger._record_guardrail_metrics(
+            guardrail_name=guardrail_name,
+            latency_seconds=latency_seconds,
+            status=status,
+            error_type=error_type,
+            hook_type=hook_type,
+        )
+
+        # Assert - latency metric should be recorded
+        mock_latency_metric.labels.assert_called_once_with(
+            guardrail_name=guardrail_name,
+            status=status,
+            error_type="none",
+            hook_type=hook_type,
+        )
+        mock_latency_metric.labels.return_value.observe.assert_called_once_with(
+            latency_seconds
+        )
+
+        # Assert - requests metric should be incremented
+        mock_requests_metric.labels.assert_called_once_with(
+            guardrail_name=guardrail_name,
+            status=status,
+            hook_type=hook_type,
+        )
+        mock_requests_metric.labels.return_value.inc.assert_called_once()
+
+        # Assert - errors metric should NOT be called for success
+        mock_errors_metric.labels.assert_not_called()
+
+    def test_record_guardrail_metrics_error(self):
+        """Test recording guardrail metrics for failed execution"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock metrics
+        mock_latency_metric = MagicMock()
+        mock_requests_metric = MagicMock()
+        mock_errors_metric = MagicMock()
+
+        prometheus_logger.litellm_guardrail_latency_metric = mock_latency_metric
+        prometheus_logger.litellm_guardrail_requests_total = mock_requests_metric
+        prometheus_logger.litellm_guardrail_errors_total = mock_errors_metric
+
+        guardrail_name = "test_guardrail"
+        latency_seconds = 0.2
+        status = "error"
+        error_type = "ValueError"
+        hook_type = "pre_call"
+
+        # Act
+        prometheus_logger._record_guardrail_metrics(
+            guardrail_name=guardrail_name,
+            latency_seconds=latency_seconds,
+            status=status,
+            error_type=error_type,
+            hook_type=hook_type,
+        )
+
+        # Assert - latency metric should be recorded
+        mock_latency_metric.labels.assert_called_once_with(
+            guardrail_name=guardrail_name,
+            status=status,
+            error_type=error_type,
+            hook_type=hook_type,
+        )
+        mock_latency_metric.labels.return_value.observe.assert_called_once_with(
+            latency_seconds
+        )
+
+        # Assert - requests metric should be incremented
+        mock_requests_metric.labels.assert_called_once_with(
+            guardrail_name=guardrail_name,
+            status=status,
+            hook_type=hook_type,
+        )
+        mock_requests_metric.labels.return_value.inc.assert_called_once()
+
+        # Assert - errors metric should be incremented
+        mock_errors_metric.labels.assert_called_once_with(
+            guardrail_name=guardrail_name,
+            error_type=error_type,
+            hook_type=hook_type,
+        )
+        mock_errors_metric.labels.return_value.inc.assert_called_once()
+
+    def test_record_guardrail_metrics_during_call_hook(self):
+        """Test recording guardrail metrics for during_call hook"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock metrics
+        mock_latency_metric = MagicMock()
+        mock_requests_metric = MagicMock()
+
+        prometheus_logger.litellm_guardrail_latency_metric = mock_latency_metric
+        prometheus_logger.litellm_guardrail_requests_total = mock_requests_metric
+
+        guardrail_name = "moderation_guardrail"
+        latency_seconds = 0.1
+        status = "success"
+        hook_type = "during_call"
+
+        # Act
+        prometheus_logger._record_guardrail_metrics(
+            guardrail_name=guardrail_name,
+            latency_seconds=latency_seconds,
+            status=status,
+            error_type=None,
+            hook_type=hook_type,
+        )
+
+        # Assert - hook_type should be "during_call"
+        mock_latency_metric.labels.assert_called_once()
+        call_kwargs = mock_latency_metric.labels.call_args[1]
+        assert call_kwargs["hook_type"] == "during_call"
+
+    def test_record_guardrail_metrics_handles_exception(self):
+        """Test that _record_guardrail_metrics handles exceptions gracefully"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock metric to raise exception
+        mock_metric = MagicMock()
+        mock_metric.labels.side_effect = Exception("Test error")
+        prometheus_logger.litellm_guardrail_latency_metric = mock_metric
+        prometheus_logger.litellm_guardrail_requests_total = MagicMock()
+
+        # Act & Assert - should not raise exception
+        try:
+            prometheus_logger._record_guardrail_metrics(
+                guardrail_name="test",
+                latency_seconds=0.1,
+                status="success",
+                error_type=None,
+                hook_type="pre_call",
+            )
+        except Exception:
+            pytest.fail("_record_guardrail_metrics should handle exceptions gracefully")
+
+    def test_record_guardrail_metrics_with_guardrail_name_attribute(self):
+        """Test that guardrail name is extracted from guardrail_name attribute if available"""
+        # Arrange
+        prometheus_logger = PrometheusLogger()
+
+        # Mock metrics
+        mock_latency_metric = MagicMock()
+        mock_requests_metric = MagicMock()
+
+        prometheus_logger.litellm_guardrail_latency_metric = mock_latency_metric
+        prometheus_logger.litellm_guardrail_requests_total = mock_requests_metric
+
+        guardrail_name = "custom_guardrail_name"
+        latency_seconds = 0.1
+        status = "success"
+        hook_type = "pre_call"
+
+        # Act
+        prometheus_logger._record_guardrail_metrics(
+            guardrail_name=guardrail_name,
+            latency_seconds=latency_seconds,
+            status=status,
+            error_type=None,
+            hook_type=hook_type,
+        )
+
+        # Assert - guardrail_name should be used
+        mock_latency_metric.labels.assert_called_once()
+        call_kwargs = mock_latency_metric.labels.call_args[1]
+        assert call_kwargs["guardrail_name"] == guardrail_name
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 4914ec0bfb7..42a2b5d0971 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
@@ -620,26 +620,26 @@ def test_bedrock_tools_unpack_defs():
 
 def test_bedrock_image_processor_content_type_fallback_url_extension():
     """
-    Test that _post_call_image_processing falls back to URL extension 
+    Test that _post_call_image_processing falls back to URL extension
     when content-type is binary/octet-stream or application/octet-stream
     """
     import base64
-    
+
     # Create mock response with binary/octet-stream content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "binary/octet-stream"
-    
+
     # Create a simple PNG header (magic bytes)
     png_header = b"\x89\x50\x4e\x47\x0d\x0a\x1a\x0a"
     png_content = png_header + b"\x00" * 100  # Add some padding
     mock_response.content = png_content
-    
+
     # Test with .png URL
     image_url = "https://example.com/test-image.png"
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, image_url
     )
-    
+
     assert content_type == "image/png"
     assert base64_bytes == base64.b64encode(png_content).decode("utf-8")
 
@@ -650,22 +650,22 @@ def test_bedrock_image_processor_content_type_fallback_binary_detection():
     when content-type is missing and URL extension is not recognized
     """
     import base64
-    
+
     # Create mock response with no content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = None
-    
+
     # Create a JPEG header (magic bytes)
     jpeg_header = b"\xff\xd8\xff"
     jpeg_content = jpeg_header + b"\x00" * 100  # Add some padding
     mock_response.content = jpeg_content
-    
+
     # Test with URL without extension
     image_url = "https://example.com/test-image-without-extension"
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, image_url
     )
-    
+
     assert content_type == "image/jpeg"
     assert base64_bytes == base64.b64encode(jpeg_content).decode("utf-8")
 
@@ -675,22 +675,22 @@ def test_bedrock_image_processor_content_type_fallback_application_octet_stream(
     Test that _post_call_image_processing handles application/octet-stream correctly
     """
     import base64
-    
+
     # Create mock response with application/octet-stream content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "application/octet-stream"
-    
+
     # Create a GIF header (magic bytes)
     gif_header = b"GIF8" + b"\x00" + b"a"
     gif_content = gif_header + b"\x00" * 100  # Add some padding
     mock_response.content = gif_content
-    
+
     # Test with .gif URL
     image_url = "https://s3.amazonaws.com/bucket/image.gif"
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, image_url
     )
-    
+
     assert content_type == "image/gif"
     assert base64_bytes == base64.b64encode(gif_content).decode("utf-8")
 
@@ -700,22 +700,22 @@ def test_bedrock_image_processor_content_type_with_query_params():
     Test that _post_call_image_processing correctly extracts extension from URL with query parameters
     """
     import base64
-    
+
     # Create mock response with binary/octet-stream content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "binary/octet-stream"
-    
+
     # Create a WebP header (magic bytes)
     webp_header = b"RIFF" + b"\x00\x00\x00\x00" + b"WEBP"
     webp_content = webp_header + b"\x00" * 100  # Add some padding
     mock_response.content = webp_content
-    
+
     # Test with URL containing query parameters (common in S3 signed URLs)
     image_url = "https://s3.amazonaws.com/bucket/image.webp?AWSAccessKeyId=123&Expires=456&Signature=789"
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, image_url
     )
-    
+
     assert content_type == "image/webp"
     assert base64_bytes == base64.b64encode(webp_content).decode("utf-8")
 
@@ -725,21 +725,21 @@ def test_bedrock_image_processor_content_type_normal_header():
     Test that _post_call_image_processing works normally when content-type is correctly set
     """
     import base64
-    
+
     # Create mock response with correct content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "image/png"
-    
+
     # Create a PNG header
     png_header = b"\x89\x50\x4e\x47\x0d\x0a\x1a\x0a"
     png_content = png_header + b"\x00" * 100
     mock_response.content = png_content
-    
+
     image_url = "https://example.com/test-image.png"
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, image_url
     )
-    
+
     assert content_type == "image/png"
     assert base64_bytes == base64.b64encode(png_content).decode("utf-8")
 
@@ -751,16 +751,16 @@ def test_bedrock_image_processor_content_type_fallback_failure():
     # Create mock response with binary/octet-stream content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "binary/octet-stream"
-    
+
     # Create content with unrecognizable image format
     mock_response.content = b"\x00" * 100
-    
+
     # Test with URL without recognizable extension
     image_url = "https://example.com/unknown-file"
-    
+
     with pytest.raises(ValueError) as excinfo:
         BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
-    
+
     assert "Unable to determine content type" in str(excinfo.value)
 
 
@@ -771,18 +771,18 @@ def test_bedrock_image_processor_content_type_jpeg_variants():
     # Create mock response with binary/octet-stream
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "binary/octet-stream"
-    
+
     jpeg_header = b"\xff\xd8\xff"
     jpeg_content = jpeg_header + b"\x00" * 100
     mock_response.content = jpeg_content
-    
+
     # Test with .jpg extension
     image_url_jpg = "https://example.com/photo.jpg"
     _, content_type_jpg = BedrockImageProcessor._post_call_image_processing(
         mock_response, image_url_jpg
     )
     assert content_type_jpg == "image/jpeg"
-    
+
     # Test with .jpeg extension
     image_url_jpeg = "https://example.com/photo.jpeg"
     _, content_type_jpeg = BedrockImageProcessor._post_call_image_processing(
@@ -797,22 +797,22 @@ def test_bedrock_image_processor_content_type_pdf_document():
     when content-type is binary/octet-stream
     """
     import base64
-    
+
     # Create mock response with binary/octet-stream content-type
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "binary/octet-stream"
-    
+
     # Create a PDF header (magic bytes: %PDF)
     pdf_header = b"%PDF-1.4"
     pdf_content = pdf_header + b"\x00" * 100
     mock_response.content = pdf_content
-    
+
     # Test with .pdf URL
     pdf_url = "https://s3.amazonaws.com/bucket/document.pdf"
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, pdf_url
     )
-    
+
     assert content_type == "application/pdf"
     assert base64_bytes == base64.b64encode(pdf_content).decode("utf-8")
 
@@ -822,12 +822,12 @@ def test_bedrock_image_processor_content_type_document_formats():
     Test that _post_call_image_processing handles various document formats
     """
     import base64
-    
+
     # Create mock response
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "application/octet-stream"
     mock_response.content = b"\x00" * 100
-    
+
     # Test various document formats
     test_cases = [
         ("https://example.com/doc.pdf", "application/pdf"),
@@ -837,7 +837,7 @@ def test_bedrock_image_processor_content_type_document_formats():
         ("https://example.com/page.html", "text/html"),
         ("https://example.com/readme.txt", "text/plain"),
     ]
-    
+
     for url, expected_mime in test_cases:
         _, content_type = BedrockImageProcessor._post_call_image_processing(
             mock_response, url
@@ -850,21 +850,21 @@ def test_bedrock_image_processor_content_type_s3_pdf_with_query():
     Test that _post_call_image_processing handles S3 PDF with query parameters
     """
     import base64
-    
+
     # Create mock response
     mock_response = MagicMock()
     mock_response.headers.get.return_value = "binary/octet-stream"
-    
+
     pdf_content = b"%PDF-1.4" + b"\x00" * 100
     mock_response.content = pdf_content
-    
+
     # S3 signed URL with query parameters
     s3_url = "https://my-bucket.s3.us-east-1.amazonaws.com/documents/report.pdf?AWSAccessKeyId=AKIAIOSFODNN7EXAMPLE&Expires=1234567890&Signature=abcdef123456"
-    
+
     base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
         mock_response, s3_url
     )
-    
+
     assert content_type == "application/pdf"
     assert base64_bytes == base64.b64encode(pdf_content).decode("utf-8")
 
@@ -1139,6 +1139,170 @@ def test_bedrock_create_bedrock_block_different_document_formats():
         assert block["document"]["format"] == format_type
 
 
+def test_convert_to_anthropic_tool_result_image_with_cache_control():
+    """
+    Test that cache_control is properly applied to image content in tool results.
+    This tests the functionality added in the uncommitted changes where
+    add_cache_control_to_content is called for image_url content types.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_anthropic_tool_result,
+    )
+
+    # Test with base64 image data URI
+    message = {
+        "role": "tool",
+        "tool_call_id": "call_test_123",
+        "content": [
+            {
+                "type": "text",
+                "text": "Here is the image you requested:",
+            },
+            {
+                "type": "image_url",
+                "image_url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQ",
+                "cache_control": {"type": "ephemeral"},
+            },
+        ],
+    }
+
+    result = convert_to_anthropic_tool_result(message)
+
+    # Verify the result structure
+    assert result["type"] == "tool_result"
+    assert result["tool_use_id"] == "call_test_123"
+    assert isinstance(result["content"], list)
+    assert len(result["content"]) == 2
+
+    # Verify text content
+    assert result["content"][0]["type"] == "text"
+    assert result["content"][0]["text"] == "Here is the image you requested:"
+
+    # Verify image content with cache_control
+    assert result["content"][1]["type"] == "image"
+    assert result["content"][1]["source"]["type"] == "base64"
+    assert result["content"][1]["source"]["media_type"] == "image/jpeg"
+    assert "cache_control" in result["content"][1]
+    assert result["content"][1]["cache_control"]["type"] == "ephemeral"
+
+
+def test_convert_to_anthropic_tool_result_image_without_cache_control():
+    """
+    Test that images without cache_control in tool results work correctly.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_anthropic_tool_result,
+    )
+
+    message = {
+        "role": "tool",
+        "tool_call_id": "call_test_456",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUA",
+            },
+        ],
+    }
+
+    result = convert_to_anthropic_tool_result(message)
+
+    # Verify the result structure
+    assert result["type"] == "tool_result"
+    assert result["tool_use_id"] == "call_test_456"
+    assert isinstance(result["content"], list)
+    assert len(result["content"]) == 1
+
+    # Verify image content without cache_control (cache_control will be None if not set)
+    assert result["content"][0]["type"] == "image"
+    assert result["content"][0]["source"]["type"] == "base64"
+    assert result["content"][0]["source"]["media_type"] == "image/png"
+    assert result["content"][0].get("cache_control") is None
+
+
+def test_convert_to_anthropic_tool_result_mixed_content_with_cache_control():
+    """
+    Test tool results with mixed content types (text and image) where only some have cache_control.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_anthropic_tool_result,
+    )
+
+    message = {
+        "role": "tool",
+        "tool_call_id": "call_test_789",
+        "content": [
+            {
+                "type": "text",
+                "text": "First image:",
+                "cache_control": {"type": "ephemeral"},
+            },
+            {
+                "type": "image_url",
+                "image_url": "data:image/jpeg;base64,/9j/4AAQSkZJRg",
+                "cache_control": {"type": "ephemeral"},
+            },
+            {
+                "type": "text",
+                "text": "Second image (no cache):",
+            },
+            {
+                "type": "image_url",
+                "image_url": "data:image/png;base64,iVBORw0KGgo",
+            },
+        ],
+    }
+
+    result = convert_to_anthropic_tool_result(message)
+
+    assert result["type"] == "tool_result"
+    assert isinstance(result["content"], list)
+    assert len(result["content"]) == 4
+
+    # First text with cache_control
+    assert result["content"][0]["type"] == "text"
+    assert result["content"][0]["cache_control"]["type"] == "ephemeral"
+
+    # First image with cache_control
+    assert result["content"][1]["type"] == "image"
+    assert result["content"][1]["cache_control"]["type"] == "ephemeral"
+
+    # Second text without cache_control (cache_control will be None if not set)
+    assert result["content"][2]["type"] == "text"
+    assert result["content"][2].get("cache_control") is None
+
+    # Second image without cache_control (cache_control will be None if not set)
+    assert result["content"][3]["type"] == "image"
+    assert result["content"][3].get("cache_control") is None
+
+
+def test_convert_to_anthropic_tool_result_image_url_as_http():
+    """
+    Test that HTTP/HTTPS URLs with cache_control are handled correctly.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        convert_to_anthropic_tool_result,
+    )
+
+    message = {
+        "role": "tool",
+        "tool_call_id": "call_http_001",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": "https://example.com/image.jpg",
+                "cache_control": {"type": "ephemeral"},
+            },
+        ],
+    }
+
+    result = convert_to_anthropic_tool_result(message)
+
+    # Verify image is passed as URL reference with cache_control
+    assert result["content"][0]["type"] == "image"
+    assert result["content"][0]["source"]["type"] == "url"
+    assert result["content"][0]["source"]["url"] == "https://example.com/image.jpg"
+    assert result["content"][0]["cache_control"]["type"] == "ephemeral"
 def test_anthropic_messages_pt_server_tool_use_passthrough():
     """
     Test that anthropic_messages_pt passes through server_tool_use and 
diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
index 9b150fd89f4..bae0e5bbb4f 100644
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -393,6 +393,63 @@ def test_get_request_tags_from_metadata_and_litellm_metadata():
     assert "User-Agent: litellm/1.0.0" in tags
 
 
+def test_get_request_tags_does_not_mutate_original_tags():
+    """
+    Test that _get_request_tags does not mutate the original tags list in metadata.
+
+    This is a regression test for a bug where calling _get_request_tags multiple times
+    would cause User-Agent tags to be duplicated because the function was mutating
+    the original tags list instead of creating a copy.
+    """
+    from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup
+
+    # Create metadata with original tags
+    original_tags = ["custom-tag-1", "custom-tag-2"]
+    metadata = {"tags": original_tags}
+    litellm_params = {"metadata": metadata}
+    proxy_server_request = {
+        "headers": {
+            "user-agent": "AsyncOpenAI/Python 1.99.9",
+        }
+    }
+
+    # Call _get_request_tags multiple times (simulating multiple callbacks)
+    tags1 = StandardLoggingPayloadSetup._get_request_tags(
+        litellm_params=litellm_params,
+        proxy_server_request=proxy_server_request,
+    )
+    tags2 = StandardLoggingPayloadSetup._get_request_tags(
+        litellm_params=litellm_params,
+        proxy_server_request=proxy_server_request,
+    )
+    tags3 = StandardLoggingPayloadSetup._get_request_tags(
+        litellm_params=litellm_params,
+        proxy_server_request=proxy_server_request,
+    )
+
+    # Verify the original tags list was NOT mutated
+    assert original_tags == ["custom-tag-1", "custom-tag-2"], (
+        f"Original tags list was mutated: {original_tags}"
+    )
+    assert metadata["tags"] == ["custom-tag-1", "custom-tag-2"], (
+        f"metadata['tags'] was mutated: {metadata['tags']}"
+    )
+
+    # Verify each returned list has exactly 2 User-Agent tags (not duplicated)
+    user_agent_count_1 = len([t for t in tags1 if t.startswith("User-Agent:")])
+    user_agent_count_2 = len([t for t in tags2 if t.startswith("User-Agent:")])
+    user_agent_count_3 = len([t for t in tags3 if t.startswith("User-Agent:")])
+
+    assert user_agent_count_1 == 2, f"Expected 2 User-Agent tags, got {user_agent_count_1}"
+    assert user_agent_count_2 == 2, f"Expected 2 User-Agent tags, got {user_agent_count_2}"
+    assert user_agent_count_3 == 2, f"Expected 2 User-Agent tags, got {user_agent_count_3}"
+
+    # Verify all returned lists are independent (different objects)
+    assert tags1 is not tags2
+    assert tags2 is not tags3
+    assert tags1 is not original_tags
+
+
 def test_get_extra_header_tags():
     """Test the _get_extra_header_tags method with various scenarios."""
     import litellm
diff --git a/tests/test_litellm/litellm_core_utils/test_sensitive_data_masker.py b/tests/test_litellm/litellm_core_utils/test_sensitive_data_masker.py
index 2836398228a..b9f17aa4c7f 100644
--- a/tests/test_litellm/litellm_core_utils/test_sensitive_data_masker.py
+++ b/tests/test_litellm/litellm_core_utils/test_sensitive_data_masker.py
@@ -75,3 +75,49 @@ def test_excluded_keys_exact_match():
     assert masked["api_key"] == "sk-1234567890abcdef"  # Should NOT be masked
     assert masked["access_token"] != "token-12345"  # Should still be masked
     assert "*" in masked["access_token"]
+
+
+def test_extra_headers_are_masked_recursively():
+    """
+    Ensure nested dictionaries (like extra_headers) are masked.
+    """
+    masker = SensitiveDataMasker()
+
+    data = {
+        "litellm_params": {
+            "model": "openai/gpt-4",
+            "extra_headers": {
+                "rits_api_key": "sk-secret-12345-very-sensitive",
+                "Authorization": "Bearer token123",
+            },
+        }
+    }
+
+    masked = masker.mask_dict(data)
+    extra_headers = masked["litellm_params"]["extra_headers"]
+
+    assert extra_headers["rits_api_key"] != "sk-secret-12345-very-sensitive"
+    assert "*" in extra_headers["rits_api_key"]
+    assert extra_headers["Authorization"] != "Bearer token123"
+    assert "*" in extra_headers["Authorization"]
+
+
+def test_lists_with_sensitive_keys_are_masked():
+    """
+    Lists belonging to sensitive keys should have their values masked.
+    """
+    masker = SensitiveDataMasker()
+    data = {
+        "api_key": ["sk-1234567890abcdef", "sk-9876543210fedcba"],
+        "tags": ["prod", "test"],
+    }
+
+    masked = masker.mask_dict(data)
+    # sensitive key list entries should be masked
+    assert masked["api_key"][0] != "sk-1234567890abcdef"
+    assert "*" in masked["api_key"][0]
+    assert masked["api_key"][1] != "sk-9876543210fedcba"
+    assert "*" in masked["api_key"][1]
+
+    # non-sensitive list should remain unchanged
+    assert masked["tags"] == ["prod", "test"]
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
index 41febd4920a..d9f513d8d1d 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
@@ -473,7 +473,6 @@ def test_partial_json_chunk_accumulation():
         streaming_response=MagicMock(), sync_stream=True, json_mode=False
     )
 
-    # Simulate a complete JSON chunk being split into two parts
     partial_chunk_1 = '{"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hel'
     partial_chunk_2 = 'lo"}}'
 
@@ -781,6 +780,169 @@ def test_web_search_tool_result_captured_in_provider_specific_fields():
     ), "First result title should match"
 
 
+def test_web_fetch_tool_result_captured_in_provider_specific_fields():
+    """
+    Test that web_fetch_tool_result content is captured in provider_specific_fields.
+
+    This tests the fix for https://github.com/BerriAI/litellm/issues/18137
+    where streaming with Anthropic web fetch wasn't capturing web_fetch_tool_result
+    blocks, causing multi-turn conversations to fail.
+
+    The web_fetch_tool_result content comes ALL AT ONCE in content_block_start,
+    not in deltas, so we need to capture it there.
+    """
+    iterator = ModelResponseIterator(
+        streaming_response=MagicMock(), sync_stream=True, json_mode=False
+    )
+
+    # Simulate the streaming sequence with web_fetch_tool_result
+    chunks = [
+        # 1. message_start
+        {
+            "type": "message_start",
+            "message": {
+                "id": "msg_123",
+                "type": "message",
+                "role": "assistant",
+                "content": [],
+                "usage": {"input_tokens": 10, "output_tokens": 1},
+            },
+        },
+        # 2. server_tool_use block starts (web_fetch)
+        {
+            "type": "content_block_start",
+            "index": 0,
+            "content_block": {
+                "type": "server_tool_use",
+                "id": "srvtoolu_01ABC123",
+                "name": "web_fetch",
+            },
+        },
+        # 3. input_json_delta with the url
+        {
+            "type": "content_block_delta",
+            "index": 0,
+            "delta": {"type": "input_json_delta", "partial_json": '{"url": "https://example.com"}'},
+        },
+        # 4. content_block_stop for server_tool_use
+        {"type": "content_block_stop", "index": 0},
+        # 5. web_fetch_tool_result block starts - THIS IS WHERE THE RESULTS ARE
+        {
+            "type": "content_block_start",
+            "index": 1,
+            "content_block": {
+                "type": "web_fetch_tool_result",
+                "tool_use_id": "srvtoolu_01ABC123",
+                "content": {
+                    "type": "web_fetch_result",
+                    "url": "https://example.com",
+                    "retrieved_at": "2025-12-16T19:28:29.758000+00:00",
+                    "content": {
+                        "type": "document",
+                        "source": {
+                            "type": "text",
+                            "media_type": "text/plain",
+                            "data": "Hello World",
+                        },
+                        "title": "Example Page",
+                    },
+                },
+            },
+        },
+        # 6. content_block_stop for web_fetch_tool_result
+        {"type": "content_block_stop", "index": 1},
+    ]
+
+    web_search_results = None
+    for chunk in chunks:
+        parsed = iterator.chunk_parser(chunk)
+        if (
+            parsed.choices
+            and parsed.choices[0].delta.provider_specific_fields
+            and "web_search_results" in parsed.choices[0].delta.provider_specific_fields
+        ):
+            web_search_results = parsed.choices[0].delta.provider_specific_fields[
+                "web_search_results"
+            ]
+
+    # Verify web_fetch_tool_result was captured (stored in web_search_results list)
+    assert web_search_results is not None, "web_search_results should be captured"
+    assert len(web_search_results) == 1, "Should have 1 web_fetch_tool_result block"
+    assert (
+        web_search_results[0]["type"] == "web_fetch_tool_result"
+    ), "Block type should be web_fetch_tool_result"
+    assert (
+        web_search_results[0]["tool_use_id"] == "srvtoolu_01ABC123"
+    ), "tool_use_id should match"
+    assert (
+        web_search_results[0]["content"]["url"] == "https://example.com"
+    ), "URL should match"
+    assert (
+        web_search_results[0]["content"]["content"]["title"] == "Example Page"
+    ), "Title should match"
+
+
+def test_web_fetch_tool_result_no_extra_tool_calls():
+    """
+    Test that web_fetch_tool_result blocks don't emit tool call chunks.
+
+    This tests the fix for https://github.com/BerriAI/litellm/issues/18137
+    where streaming with Anthropic web fetch was causing issues with tool call arguments.
+
+    The issue was that web_fetch_tool_result blocks have input_json_delta events with {}
+    that were incorrectly being converted to tool calls.
+    """
+    iterator = ModelResponseIterator(
+        streaming_response=MagicMock(), sync_stream=True, json_mode=False
+    )
+
+    # to verify it doesn't emit tool calls
+    chunks = [
+        # 1. web_fetch_tool_result block starts
+        {
+            "type": "content_block_start",
+            "index": 1,
+            "content_block": {
+                "type": "web_fetch_tool_result",
+                "tool_use_id": "srvtoolu_01ABC123",
+                "content": {
+                    "type": "web_fetch_result",
+                    "url": "https://example.com",
+                    "retrieved_at": "2025-12-16T19:28:29.758000+00:00",
+                    "content": {
+                        "type": "document",
+                        "source": {
+                            "type": "text",
+                            "media_type": "text/plain",
+                            "data": "Hello World",
+                        },
+                        "title": "Example Page",
+                    },
+                },
+            },
+        },
+        # 2. input_json_delta with {} - this should NOT emit a tool call
+        {
+            "type": "content_block_delta",
+            "index": 1,
+            "delta": {"type": "input_json_delta", "partial_json": "{}"},
+        },
+        # 3. content_block_stop for web_fetch_tool_result
+        {"type": "content_block_stop", "index": 1},
+    ]
+
+    tool_call_count = 0
+    for chunk in chunks:
+        parsed = iterator.chunk_parser(chunk)
+        if parsed.choices and parsed.choices[0].delta.tool_calls:
+            tool_call_count += 1
+
+    # Should have 0 tool calls - web_fetch_tool_result should not emit tool calls
+    assert (
+        tool_call_count == 0
+    ), f"Expected 0 tool calls, got {tool_call_count}. web_fetch_tool_result should not emit tool calls"
+
+
 def test_container_in_provider_specific_fields_streaming():
     """
     Test that container is captured in provider_specific_fields for streaming responses.
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
index 9b6d1c6e178..5e58601d589 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
@@ -1754,3 +1754,61 @@ def test_transform_request_respects_user_max_tokens():
     )
 
     assert result["max_tokens"] == 1000
+
+
+def test_calculate_usage_completion_tokens_details_always_populated():
+    """
+    Test that completion_tokens_details is always populated in Usage object,
+    not just when there's reasoning_content.
+    
+    Fixes: https://github.com/BerriAI/litellm/issues/18772
+    Bug: completion_tokens_details was None for regular Claude responses without reasoning
+    """
+    config = AnthropicConfig()
+
+    # Test without reasoning_content - completion_tokens_details should still be populated
+    usage_object = {
+        "input_tokens": 37,
+        "output_tokens": 248,
+    }
+    usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None)
+    
+    # completion_tokens_details should NOT be None
+    assert usage.completion_tokens_details is not None
+    assert usage.completion_tokens_details.reasoning_tokens is None
+    assert usage.completion_tokens_details.text_tokens == 248
+    assert usage.completion_tokens == 248
+    assert usage.prompt_tokens == 37
+    assert usage.total_tokens == 285
+
+
+def test_calculate_usage_completion_tokens_details_with_reasoning():
+    """
+    Test that completion_tokens_details correctly splits text_tokens and reasoning_tokens
+    when reasoning_content is present.
+    
+    Fixes: https://github.com/BerriAI/litellm/issues/18772
+    """
+    config = AnthropicConfig()
+
+    # Test with reasoning_content - should split tokens correctly
+    usage_object = {
+        "input_tokens": 100,
+        "output_tokens": 500,
+    }
+    # Simulating reasoning content that would count as ~50 tokens
+    reasoning_content = "Let me think about this step by step. " * 10  # Roughly 50 tokens
+    
+    usage = config.calculate_usage(
+        usage_object=usage_object, 
+        reasoning_content=reasoning_content
+    )
+    
+    # completion_tokens_details should be populated with both reasoning and text tokens
+    assert usage.completion_tokens_details is not None
+    assert usage.completion_tokens_details.reasoning_tokens is not None
+    assert usage.completion_tokens_details.reasoning_tokens > 0
+    # text_tokens should be total minus reasoning
+    expected_text_tokens = 500 - usage.completion_tokens_details.reasoning_tokens
+    assert usage.completion_tokens_details.text_tokens == expected_text_tokens
+    assert usage.completion_tokens == 500
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 91d664c3216..199a16d8590 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
@@ -204,3 +204,62 @@ def test_azure_gpt5_reasoning_effort_none_dropped(config: AzureOpenAIGPT5Config)
     )
     assert "reasoning_effort" not in params or params.get("reasoning_effort") != "none"
 
+
+# Logprobs support tests for Azure GPT-5.2
+def test_azure_gpt5_2_supports_logprobs(config: AzureOpenAIGPT5Config):
+    """Test that Azure GPT-5.2 models support logprobs parameters.
+
+    Only Azure OpenAI GPT-5.2 supports logprobs, unlike OpenAI's GPT-5 or Azure's gpt-5/gpt-5.1.
+    Tested with gpt-5.2 on api-version 2025-01-01-preview.
+    """
+    supported_params = config.get_supported_openai_params(model="gpt-5.2")
+    assert "logprobs" in supported_params
+    assert "top_logprobs" in supported_params
+
+
+def test_azure_gpt5_2_with_prefix_supports_logprobs(config: AzureOpenAIGPT5Config):
+    """Test that Azure GPT-5.2 with azure/ prefix supports logprobs parameters."""
+    supported_params = config.get_supported_openai_params(model="azure/gpt-5.2")
+    assert "logprobs" in supported_params
+    assert "top_logprobs" in supported_params
+
+
+def test_azure_gpt5_2_series_supports_logprobs(config: AzureOpenAIGPT5Config):
+    """Test that Azure GPT-5.2 with gpt5_series prefix supports logprobs."""
+    supported_params = config.get_supported_openai_params(model="gpt5_series/gpt-5.2")
+    assert "logprobs" in supported_params
+    assert "top_logprobs" in supported_params
+
+
+def test_azure_gpt5_2_logprobs_params_passed_through(config: AzureOpenAIGPT5Config):
+    """Test that logprobs parameters are correctly passed through to the API for gpt-5.2."""
+    params = config.map_openai_params(
+        non_default_params={"logprobs": True, "top_logprobs": 5},
+        optional_params={},
+        model="azure/gpt-5.2",
+        drop_params=False,
+        api_version="2025-01-01-preview",
+    )
+    assert params["logprobs"] is True
+    assert params["top_logprobs"] == 5
+
+
+def test_azure_gpt5_base_does_not_support_logprobs(config: AzureOpenAIGPT5Config):
+    """Test that Azure GPT-5 (non-5.2) does not support logprobs parameters.
+
+    Only gpt-5.2 has been verified to support logprobs on Azure.
+    """
+    supported_params = config.get_supported_openai_params(model="gpt-5")
+    assert "logprobs" not in supported_params
+    assert "top_logprobs" not in supported_params
+
+
+def test_azure_gpt5_1_does_not_support_logprobs(config: AzureOpenAIGPT5Config):
+    """Test that Azure GPT-5.1 does not support logprobs parameters.
+
+    Only gpt-5.2 has been verified to support logprobs on Azure.
+    """
+    supported_params = config.get_supported_openai_params(model="gpt-5.1")
+    assert "logprobs" not in supported_params
+    assert "top_logprobs" not in supported_params
+
diff --git a/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py b/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py
index 998510efcd9..987eb5bf998 100644
--- a/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py
+++ b/tests/test_litellm/llms/azure/image_generation/test_azure_image_generation_init.py
@@ -164,3 +164,90 @@ def test_azure_image_generation_headers_without_api_key():
     # Verify api-key is added when api_key is valid
     assert "api-key" in default_headers_with_key
     assert default_headers_with_key["api-key"] == "valid-key-123"
+
+
+def test_azure_image_generation_drop_params_response_format():
+    """
+    Test that unsupported params like response_format are dropped when drop_params=True.
+    
+    Azure gpt-image-1.5 doesn't support response_format parameter. When drop_params=True,
+    this parameter should be completely removed and not appear in the final request body,
+    including not being added to extra_body.
+    
+    This test verifies the fix where:
+    1. Unsupported params are removed from non_default_params in _check_valid_arg
+    2. Unsupported params are also removed from passed_params to prevent them from
+       being re-added via extra_body in add_provider_specific_params_to_optional_params
+    
+    Without the fix, response_format would be added to extra_body and cause Azure to
+    return a 400 Bad Request error due to strict schema validation.
+    """
+    from litellm.llms.openai.image_generation.gpt_transformation import (
+        GPTImageGenerationConfig,
+    )
+
+    # Test with gpt-image-1.5 which doesn't support response_format
+    config = GPTImageGenerationConfig()
+    supported_params = config.get_supported_openai_params(model="gpt-image-1.5")
+    
+    # Verify response_format is NOT in supported params for gpt-image-1.5
+    assert "response_format" not in supported_params
+    assert "n" in supported_params
+    assert "size" in supported_params
+    
+    # Test get_optional_params_image_gen with drop_params=True
+    optional_params = get_optional_params_image_gen(
+        model="gpt-image-1.5",
+        n=1,
+        size="1024x1024",
+        response_format="b64_json",  # This should be dropped
+        custom_llm_provider="azure",
+        provider_config=config,
+        drop_params=True,
+    )
+    
+    # Verify response_format is NOT in optional_params
+    assert "response_format" not in optional_params, (
+        "response_format should be dropped from optional_params"
+    )
+    
+    # Verify response_format is NOT in extra_body either
+    if "extra_body" in optional_params:
+        assert "response_format" not in optional_params["extra_body"], (
+            "response_format should not be in extra_body"
+        )
+    
+    # Verify supported params ARE in optional_params
+    assert "n" in optional_params
+    assert optional_params["n"] == 1
+    assert "size" in optional_params
+    assert optional_params["size"] == "1024x1024"
+
+
+def test_azure_image_generation_drop_params_false_raises_error():
+    """
+    Test that unsupported params raise an error when drop_params=False.
+    
+    This verifies that the error handling still works correctly when drop_params
+    is not enabled.
+    """
+    from litellm.exceptions import UnsupportedParamsError
+    from litellm.llms.openai.image_generation.gpt_transformation import (
+        GPTImageGenerationConfig,
+    )
+
+    config = GPTImageGenerationConfig()
+    
+    # Test that passing unsupported param with drop_params=False raises error
+    with pytest.raises(UnsupportedParamsError) as exc_info:
+        optional_params = get_optional_params_image_gen(
+            model="gpt-image-1.5",
+            n=1,
+            response_format="b64_json",  # Unsupported param
+            custom_llm_provider="azure",
+            provider_config=config,
+            drop_params=False,
+        )
+    
+    # Verify the error message mentions the unsupported parameter
+    assert "response_format" in str(exc_info.value)
diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
index e603f94ab87..692866f8552 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
@@ -2735,3 +2735,75 @@ def test_is_nova_lite_2_model():
     assert config._is_nova_lite_2_model("anthropic.claude-3-5-sonnet-20240620-v1:0") is False
     assert config._is_nova_lite_2_model("meta.llama3-70b-instruct-v1:0") is False
     assert config._is_nova_lite_2_model("mistral.mistral-7b-instruct-v0:2") is False
+
+
+def test_thinking_with_max_completion_tokens():
+    """Test that thinking respects max_completion_tokens parameter."""
+    config = AmazonConverseConfig()
+    
+    # Test case 1: max_completion_tokens is specified - should NOT set maxTokens automatically
+    non_default_params_with_max_completion = {
+        "thinking": {"type": "enabled", "budget_tokens": 5000},
+        "max_completion_tokens": 10000,
+    }
+    optional_params = {}
+    
+    result = config.map_openai_params(
+        non_default_params=non_default_params_with_max_completion,
+        optional_params=optional_params,
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        drop_params=False,
+    )
+    
+    # Should have maxTokens set to max_completion_tokens value
+    assert "maxTokens" in result
+    assert result["maxTokens"] == 10000
+    # Should have thinking config
+    assert "thinking" in result
+    assert result["thinking"]["type"] == "enabled"
+    assert result["thinking"]["budget_tokens"] == 5000
+    
+    # Test case 2: max_tokens is specified - should NOT set maxTokens automatically
+    non_default_params_with_max_tokens = {
+        "thinking": {"type": "enabled", "budget_tokens": 5000},
+        "max_tokens": 8000,
+    }
+    optional_params = {}
+    
+    result = config.map_openai_params(
+        non_default_params=non_default_params_with_max_tokens,
+        optional_params=optional_params,
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        drop_params=False,
+    )
+    
+    # Should have maxTokens set to max_tokens value
+    assert "maxTokens" in result
+    assert result["maxTokens"] == 8000
+    # Should have thinking config
+    assert "thinking" in result
+    assert result["thinking"]["type"] == "enabled"
+    assert result["thinking"]["budget_tokens"] == 5000
+    
+    # Test case 3: Neither max_tokens nor max_completion_tokens specified - should set maxTokens automatically
+    from litellm.constants import DEFAULT_MAX_TOKENS
+    
+    non_default_params_without_max = {
+        "thinking": {"type": "enabled", "budget_tokens": 5000},
+    }
+    optional_params = {}
+    
+    result = config.map_openai_params(
+        non_default_params=non_default_params_without_max,
+        optional_params=optional_params,
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        drop_params=False,
+    )
+    
+    # Should have maxTokens set to budget_tokens + DEFAULT_MAX_TOKENS
+    assert "maxTokens" in result
+    assert result["maxTokens"] == 5000 + DEFAULT_MAX_TOKENS
+    # Should have thinking config
+    assert "thinking" in result
+    assert result["thinking"]["type"] == "enabled"
+    assert result["thinking"]["budget_tokens"] == 5000
diff --git a/tests/test_litellm/llms/bedrock/chat/test_service_tier.py b/tests/test_litellm/llms/bedrock/chat/test_service_tier.py
index f9fedadaaed..a625aae23df 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_service_tier.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_service_tier.py
@@ -147,3 +147,280 @@ def test_service_tier_with_other_config_blocks():
     assert result["serviceTier"]["type"] == "priority"
     assert "performanceConfig" in result
     assert result["performanceConfig"]["latency"] == "optimized"
+
+
+# Tests for OpenAI-compatible service_tier parameter translation
+
+
+def test_service_tier_in_supported_openai_params():
+    """Test that service_tier is in the list of supported OpenAI params."""
+    config = AmazonConverseConfig()
+    supported_params = config.get_supported_openai_params(
+        model="anthropic.claude-3-sonnet-20240229-v1:0"
+    )
+    assert "service_tier" in supported_params
+
+
+def test_map_openai_service_tier_priority():
+    """Test that OpenAI service_tier='priority' maps to Bedrock serviceTier."""
+    config = AmazonConverseConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"service_tier": "priority"},
+        optional_params={},
+        model="anthropic.claude-3-sonnet-20240229-v1:0",
+        drop_params=False,
+    )
+
+    assert "serviceTier" in result
+    assert result["serviceTier"] == {"type": "priority"}
+
+
+def test_map_openai_service_tier_default():
+    """Test that OpenAI service_tier='default' maps to Bedrock serviceTier."""
+    config = AmazonConverseConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"service_tier": "default"},
+        optional_params={},
+        model="anthropic.claude-3-sonnet-20240229-v1:0",
+        drop_params=False,
+    )
+
+    assert "serviceTier" in result
+    assert result["serviceTier"] == {"type": "default"}
+
+
+def test_map_openai_service_tier_flex():
+    """Test that OpenAI service_tier='flex' maps to Bedrock serviceTier."""
+    config = AmazonConverseConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"service_tier": "flex"},
+        optional_params={},
+        model="anthropic.claude-3-sonnet-20240229-v1:0",
+        drop_params=False,
+    )
+
+    assert "serviceTier" in result
+    assert result["serviceTier"] == {"type": "flex"}
+
+
+def test_map_openai_service_tier_auto_maps_to_default():
+    """Test that OpenAI service_tier='auto' maps to Bedrock serviceTier='default'.
+
+    Bedrock doesn't support 'auto', so we map it to 'default'.
+    """
+    config = AmazonConverseConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"service_tier": "auto"},
+        optional_params={},
+        model="anthropic.claude-3-sonnet-20240229-v1:0",
+        drop_params=False,
+    )
+
+    assert "serviceTier" in result
+    assert result["serviceTier"] == {"type": "default"}
+
+
+# Tests for service_tier in response
+
+
+def test_transform_response_with_service_tier():
+    """Test that serviceTier from Bedrock response is mapped to service_tier in OpenAI format."""
+    from unittest.mock import Mock
+
+    import httpx
+
+    from litellm.types.utils import ModelResponse
+
+    config = AmazonConverseConfig()
+
+    # Mock Bedrock response with serviceTier
+    mock_response_data = {
+        "output": {
+            "message": {
+                "role": "assistant",
+                "content": [{"text": "Hello! How can I assist you today?"}],
+            }
+        },
+        "stopReason": "end_turn",
+        "usage": {
+            "inputTokens": 10,
+            "outputTokens": 20,
+            "totalTokens": 30,
+        },
+        "serviceTier": {"type": "priority"},  # This should be mapped to service_tier
+    }
+
+    mock_response = Mock(spec=httpx.Response)
+    mock_response.json.return_value = mock_response_data
+    mock_response.text = json.dumps(mock_response_data)
+
+    model_response = ModelResponse()
+    messages = [{"role": "user", "content": "Hello"}]
+
+    result = config.transform_response(
+        model="bedrock/converse/anthropic.claude-3-sonnet-20240229-v1:0",
+        raw_response=mock_response,
+        model_response=model_response,
+        logging_obj=None,
+        request_data={},
+        messages=messages,
+        optional_params={},
+        litellm_params={},
+        encoding=None,
+    )
+
+    # Verify service_tier is present in the response
+    assert hasattr(result, "service_tier")
+    assert result.service_tier == "priority"
+
+
+def test_transform_response_with_service_tier_default():
+    """Test that serviceTier='default' is correctly mapped."""
+    from unittest.mock import Mock
+
+    import httpx
+
+    from litellm.types.utils import ModelResponse
+
+    config = AmazonConverseConfig()
+
+    mock_response_data = {
+        "output": {
+            "message": {
+                "role": "assistant",
+                "content": [{"text": "Response text"}],
+            }
+        },
+        "stopReason": "end_turn",
+        "usage": {
+            "inputTokens": 10,
+            "outputTokens": 20,
+            "totalTokens": 30,
+        },
+        "serviceTier": {"type": "default"},
+    }
+
+    mock_response = Mock(spec=httpx.Response)
+    mock_response.json.return_value = mock_response_data
+    mock_response.text = json.dumps(mock_response_data)
+
+    model_response = ModelResponse()
+    messages = [{"role": "user", "content": "Hello"}]
+
+    result = config.transform_response(
+        model="bedrock/converse/anthropic.claude-3-sonnet-20240229-v1:0",
+        raw_response=mock_response,
+        model_response=model_response,
+        logging_obj=None,
+        request_data={},
+        messages=messages,
+        optional_params={},
+        litellm_params={},
+        encoding=None,
+    )
+
+    assert hasattr(result, "service_tier")
+    assert result.service_tier == "default"
+
+
+def test_transform_response_with_service_tier_flex():
+    """Test that serviceTier='flex' is correctly mapped."""
+    from unittest.mock import Mock
+
+    import httpx
+
+    from litellm.types.utils import ModelResponse
+
+    config = AmazonConverseConfig()
+
+    mock_response_data = {
+        "output": {
+            "message": {
+                "role": "assistant",
+                "content": [{"text": "Response text"}],
+            }
+        },
+        "stopReason": "end_turn",
+        "usage": {
+            "inputTokens": 10,
+            "outputTokens": 20,
+            "totalTokens": 30,
+        },
+        "serviceTier": {"type": "flex"},
+    }
+
+    mock_response = Mock(spec=httpx.Response)
+    mock_response.json.return_value = mock_response_data
+    mock_response.text = json.dumps(mock_response_data)
+
+    model_response = ModelResponse()
+    messages = [{"role": "user", "content": "Hello"}]
+
+    result = config.transform_response(
+        model="bedrock/converse/anthropic.claude-3-sonnet-20240229-v1:0",
+        raw_response=mock_response,
+        model_response=model_response,
+        logging_obj=None,
+        request_data={},
+        messages=messages,
+        optional_params={},
+        litellm_params={},
+        encoding=None,
+    )
+
+    assert hasattr(result, "service_tier")
+    assert result.service_tier == "flex"
+
+
+def test_transform_response_without_service_tier():
+    """Test that responses without serviceTier don't have service_tier attribute."""
+    from unittest.mock import Mock
+
+    import httpx
+
+    from litellm.types.utils import ModelResponse
+
+    config = AmazonConverseConfig()
+
+    # Mock Bedrock response WITHOUT serviceTier
+    mock_response_data = {
+        "output": {
+            "message": {
+                "role": "assistant",
+                "content": [{"text": "Hello! How can I assist you today?"}],
+            }
+        },
+        "stopReason": "end_turn",
+        "usage": {
+            "inputTokens": 10,
+            "outputTokens": 20,
+            "totalTokens": 30,
+        },
+        # No serviceTier field
+    }
+
+    mock_response = Mock(spec=httpx.Response)
+    mock_response.json.return_value = mock_response_data
+    mock_response.text = json.dumps(mock_response_data)
+
+    model_response = ModelResponse()
+    messages = [{"role": "user", "content": "Hello"}]
+
+    result = config.transform_response(
+        model="bedrock/converse/anthropic.claude-3-sonnet-20240229-v1:0",
+        raw_response=mock_response,
+        model_response=model_response,
+        logging_obj=None,
+        request_data={},
+        messages=messages,
+        optional_params={},
+        litellm_params={},
+        encoding=None,
+    )
+
+    # service_tier should not be present if not in Bedrock response
+    assert not hasattr(result, "service_tier")
diff --git a/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py b/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py
index 7cb1ee2b54a..76fe0d7568a 100644
--- a/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py
+++ b/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py
@@ -175,3 +175,257 @@ def test_format_url_handles_trailing_slash_normalization():
     assert str(result_with_slash) == "http://proxy.com/bedrockproxy/model/test/invoke"
 
 
+def test_bedrock_passthrough_with_application_inference_profile():
+    """
+    Test get_complete_url with Application Inference Profile ARN as model_id.
+    
+    This test verifies the fix for GitHub issue #18761 where Bedrock passthrough
+    was not working with Application Inference Profiles. The model_id (ARN) should
+    replace the translated model name in the endpoint URL and be properly encoded.
+    """
+    config = BedrockPassthroughConfig()
+    
+    model = "anthropic.claude-sonnet-4-20250514-v1:0"
+    model_id = "arn:aws:bedrock:eu-west-1:123456789:application-inference-profile/abcdefgh1234"
+    endpoint = f"model/{model}/invoke"
+    
+    with patch.object(config, '_get_aws_region_name', return_value="eu-west-1"), \
+         patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.eu-west-1.amazonaws.com",
+             "https://bedrock-runtime.eu-west-1.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={"model_id": model_id, "aws_region_name": "eu-west-1"}
+        )
+        
+        # Verify that the URL contains the encoded model_id (ARN) instead of the model name
+        url_str = str(url)
+        # The ARN slash should be encoded as %2F
+        assert "application-inference-profile%2F" in url_str, f"Expected encoded ARN in URL, but got: {url_str}"
+        assert model not in url_str, f"Model name should be replaced by model_id, but got: {url_str}"
+        assert "/invoke" in url_str, "Expected /invoke action in URL"
+        
+        # Verify the complete URL structure with encoded ARN
+        encoded_model_id = "arn:aws:bedrock:eu-west-1:123456789:application-inference-profile%2Fabcdefgh1234"
+        expected_url = f"https://bedrock-runtime.eu-west-1.amazonaws.com/model/{encoded_model_id}/invoke"
+        assert url_str == expected_url, f"Expected {expected_url}, but got: {url_str}"
+
+
+def test_bedrock_passthrough_with_inference_profile_converse_endpoint():
+    """Test Application Inference Profile with converse endpoint and proper ARN encoding"""
+    config = BedrockPassthroughConfig()
+    
+    model = "anthropic.claude-sonnet-4-20250514-v1:0"
+    model_id = "arn:aws:bedrock:us-east-1:123456789:application-inference-profile/xyz123"
+    endpoint = f"model/{model}/converse"
+    
+    with patch.object(config, '_get_aws_region_name', return_value="us-east-1"), \
+         patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.us-east-1.amazonaws.com",
+             "https://bedrock-runtime.us-east-1.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={"model_id": model_id}
+        )
+        
+        url_str = str(url)
+        # The ARN should be encoded with %2F
+        assert "application-inference-profile%2F" in url_str
+        assert "/converse" in url_str
+        assert model not in url_str
+
+
+def test_bedrock_passthrough_without_model_id_backward_compatibility():
+    """
+    Test that passthrough still works without model_id (backward compatibility).
+    
+    When model_id is not provided, the system should use the model name as before.
+    """
+    config = BedrockPassthroughConfig()
+    
+    model = "anthropic.claude-3-sonnet"
+    endpoint = f"model/{model}/invoke"
+    
+    with patch.object(config, '_get_aws_region_name', return_value="us-east-1"), \
+         patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.us-east-1.amazonaws.com",
+             "https://bedrock-runtime.us-east-1.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={}  # No model_id provided
+        )
+        
+        # Verify that the URL contains the model name (not replaced)
+        url_str = str(url)
+        assert model in url_str, f"Expected model name in URL when model_id not provided, but got: {url_str}"
+        expected_url = f"https://bedrock-runtime.us-east-1.amazonaws.com/model/{model}/invoke"
+        assert url_str == expected_url
+
+
+def test_bedrock_passthrough_region_extraction_from_inference_profile_arn():
+    """Test that AWS region is correctly extracted from Application Inference Profile ARN"""
+    config = BedrockPassthroughConfig()
+    
+    model = "anthropic.claude-sonnet-4-20250514-v1:0"
+    # ARN contains us-west-2 region
+    model_id = "arn:aws:bedrock:us-west-2:123456789:application-inference-profile/test123"
+    endpoint = f"model/{model}/invoke"
+    
+    # Don't provide aws_region_name in litellm_params to test ARN extraction
+    with patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.us-west-2.amazonaws.com",
+             "https://bedrock-runtime.us-west-2.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={"model_id": model_id}  # Region should be extracted from ARN
+        )
+        
+        # Verify that the region from ARN is used in the base URL
+        assert "us-west-2" in api_base, f"Expected region 'us-west-2' from ARN in base URL, but got: {api_base}"
+
+
+def test_bedrock_passthrough_model_id_arn_encoding():
+    """
+    Test that model_id ARNs are properly URL-encoded when used in endpoints.
+    
+    This is the critical fix for the issue where ARNs with slashes need to be encoded
+    so they're treated as a single path component rather than multiple path segments.
+    
+    For example:
+    arn:aws:bedrock:us-east-1:590183661440:application-inference-profile/b943q2qbl3m7
+    should become:
+    arn:aws:bedrock:us-east-1:590183661440:application-inference-profile%2Fb943q2qbl3m7
+    """
+    config = BedrockPassthroughConfig()
+    
+    model = "bedrock-claude-4-5-sonnet"
+    # ARN with a slash that needs encoding
+    model_id = "arn:aws:bedrock:us-east-1:590183661440:application-inference-profile/b943q2qbl3m7"
+    endpoint = f"/model/{model}/converse"
+    
+    with patch.object(config, '_get_aws_region_name', return_value="us-east-1"), \
+         patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.us-east-1.amazonaws.com",
+             "https://bedrock-runtime.us-east-1.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={"model_id": model_id}
+        )
+        
+        url_str = str(url)
+        
+        # The slash in the ARN after application-inference-profile should be encoded as %2F
+        assert "application-inference-profile%2F" in url_str, \
+            f"Expected encoded ARN with %2F in URL, but got: {url_str}"
+        
+        # The unencoded version should NOT be in the URL
+        assert "application-inference-profile/" not in url_str, \
+            f"ARN slash should be encoded, but found unencoded version in: {url_str}"
+        
+        # Verify the complete expected URL structure
+        expected_encoded_model_id = "arn:aws:bedrock:us-east-1:590183661440:application-inference-profile%2Fb943q2qbl3m7"
+        expected_url = f"https://bedrock-runtime.us-east-1.amazonaws.com/model/{expected_encoded_model_id}/converse"
+        assert url_str == expected_url, f"Expected {expected_url}, but got: {url_str}"
+
+
+def test_bedrock_passthrough_model_id_arn_encoding_invoke_endpoint():
+    """
+    Test ARN encoding with /invoke endpoint (not just /converse).
+    """
+    config = BedrockPassthroughConfig()
+    
+    model = "anthropic.claude-sonnet-4-5-20250929-v1:0"
+    model_id = "arn:aws:bedrock:us-east-1:123456789:application-inference-profile/xyz789"
+    endpoint = f"/model/{model}/invoke"
+    
+    with patch.object(config, '_get_aws_region_name', return_value="us-east-1"), \
+         patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.us-east-1.amazonaws.com",
+             "https://bedrock-runtime.us-east-1.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={"model_id": model_id}
+        )
+        
+        url_str = str(url)
+        
+        # Verify encoding
+        assert "application-inference-profile%2F" in url_str
+        assert "/invoke" in url_str
+        
+        expected_encoded_model_id = "arn:aws:bedrock:us-east-1:123456789:application-inference-profile%2Fxyz789"
+        expected_url = f"https://bedrock-runtime.us-east-1.amazonaws.com/model/{expected_encoded_model_id}/invoke"
+        assert url_str == expected_url
+
+
+def test_bedrock_passthrough_model_id_without_arn():
+    """
+    Test that non-ARN model_ids (regular model IDs) are not affected by encoding logic.
+    """
+    config = BedrockPassthroughConfig()
+    
+    model = "my-model"
+    # Regular model ID (not an ARN)
+    model_id = "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
+    endpoint = f"/model/{model}/converse"
+    
+    with patch.object(config, '_get_aws_region_name', return_value="us-east-1"), \
+         patch.object(config, 'get_runtime_endpoint', return_value=(
+             "https://bedrock-runtime.us-east-1.amazonaws.com",
+             "https://bedrock-runtime.us-east-1.amazonaws.com"
+         )):
+        
+        url, api_base = config.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model=model,
+            endpoint=endpoint,
+            request_query_params=None,
+            litellm_params={"model_id": model_id}
+        )
+        
+        url_str = str(url)
+        
+        # Regular model ID should be used as-is (no encoding needed)
+        assert model_id in url_str
+        assert "%2F" not in url_str, "Non-ARN model IDs should not be encoded"
+        
+        expected_url = f"https://bedrock-runtime.us-east-1.amazonaws.com/model/{model_id}/converse"
+        assert url_str == expected_url
+
diff --git a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py
index f5856cd12d6..77eda432513 100644
--- a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py
+++ b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py
@@ -582,7 +582,8 @@ def test_eks_irsa_ambient_credentials_used():
         )
         
         # Should create STS client without explicit credentials (using ambient credentials)
-        mock_boto3_client.assert_called_once_with("sts")
+        # Note: verify parameter is passed for SSL verification
+        mock_boto3_client.assert_called_once_with("sts", verify=True)
         
         # Should call assume_role
         mock_sts_client.assume_role.assert_called_once_with(
@@ -637,11 +638,13 @@ def test_explicit_credentials_used_when_provided():
         )
         
         # Should create STS client with explicit credentials
+        # Note: verify parameter is passed for SSL verification
         mock_boto3_client.assert_called_once_with(
             "sts",
             aws_access_key_id="explicit-access-key",
             aws_secret_access_key="explicit-secret-key",
             aws_session_token="assumed-session-token",
+            verify=True,
         )
         
         # Should call assume_role
@@ -701,6 +704,7 @@ def test_partial_credentials_still_use_ambient():
             aws_access_key_id="AKIAEXAMPLE",
             aws_secret_access_key=None,
             aws_session_token=None,
+            verify=True,
         )
         
         # Should still call assume_role
@@ -748,7 +752,7 @@ def test_cross_account_role_assumption():
         )
         
         # Should use ambient credentials
-        mock_boto3_client.assert_called_once_with("sts")
+        mock_boto3_client.assert_called_once_with("sts", verify=True)
         
         # Should call assume_role with cross-account role
         mock_sts_client.assume_role.assert_called_once_with(
diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_ssl_verify.py b/tests/test_litellm/llms/bedrock/test_bedrock_ssl_verify.py
new file mode 100644
index 00000000000..9142de295ea
--- /dev/null
+++ b/tests/test_litellm/llms/bedrock/test_bedrock_ssl_verify.py
@@ -0,0 +1,349 @@
+"""
+Test SSL verification for AWS Bedrock boto3 clients.
+
+This test ensures that custom CA certificates are properly passed to all boto3 clients
+(STS and Bedrock services) to support internal certificate authorities.
+
+Issue: https://github.com/BerriAI/litellm/issues/XXXX
+User reported that SSL_CERT_FILE environment variable and ssl_verify config were not
+being applied to boto3 clients, causing "certificate verify failed" errors.
+"""
+
+import os
+import sys
+import tempfile
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import litellm
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+from litellm.llms.bedrock.common_utils import init_bedrock_client
+
+
+class TestBedrockSSLVerify:
+    """Test suite for SSL verification in Bedrock boto3 clients."""
+
+    def test_base_aws_llm_get_ssl_verify_default(self):
+        """Test that _get_ssl_verify returns default value when no custom config is set."""
+        base_aws = BaseAWSLLM()
+        
+        # Clear any environment variables
+        os.environ.pop("SSL_VERIFY", None)
+        os.environ.pop("SSL_CERT_FILE", None)
+        
+        # Reset litellm.ssl_verify to default
+        litellm.ssl_verify = True
+        
+        ssl_verify = base_aws._get_ssl_verify()
+        assert ssl_verify is True
+
+    def test_base_aws_llm_get_ssl_verify_false(self):
+        """Test that _get_ssl_verify returns False when SSL verification is disabled."""
+        base_aws = BaseAWSLLM()
+        
+        # Set SSL_VERIFY to False via environment
+        os.environ["SSL_VERIFY"] = "False"
+        
+        ssl_verify = base_aws._get_ssl_verify()
+        assert ssl_verify is False
+        
+        # Clean up
+        os.environ.pop("SSL_VERIFY", None)
+
+    def test_base_aws_llm_get_ssl_verify_custom_ca_bundle(self):
+        """Test that _get_ssl_verify returns custom CA bundle path when SSL_CERT_FILE is set."""
+        base_aws = BaseAWSLLM()
+        
+        # Create a temporary CA bundle file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".pem", delete=False) as f:
+            f.write("-----BEGIN CERTIFICATE-----\n")
+            f.write("FAKE CERTIFICATE FOR TESTING\n")
+            f.write("-----END CERTIFICATE-----\n")
+            ca_bundle_path = f.name
+        
+        try:
+            # Set SSL_CERT_FILE environment variable
+            os.environ["SSL_CERT_FILE"] = ca_bundle_path
+            os.environ.pop("SSL_VERIFY", None)
+            litellm.ssl_verify = True
+            
+            ssl_verify = base_aws._get_ssl_verify()
+            assert ssl_verify == ca_bundle_path
+        finally:
+            # Clean up
+            os.environ.pop("SSL_CERT_FILE", None)
+            os.unlink(ca_bundle_path)
+
+    def test_base_aws_llm_get_ssl_verify_litellm_config(self):
+        """Test that _get_ssl_verify uses litellm.ssl_verify when set."""
+        base_aws = BaseAWSLLM()
+        
+        # Clear environment variables
+        os.environ.pop("SSL_VERIFY", None)
+        os.environ.pop("SSL_CERT_FILE", None)
+        
+        # Create a temporary CA bundle file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".pem", delete=False) as f:
+            f.write("-----BEGIN CERTIFICATE-----\n")
+            f.write("FAKE CERTIFICATE FOR TESTING\n")
+            f.write("-----END CERTIFICATE-----\n")
+            ca_bundle_path = f.name
+        
+        try:
+            # Set litellm.ssl_verify to custom CA bundle
+            litellm.ssl_verify = ca_bundle_path
+            
+            ssl_verify = base_aws._get_ssl_verify()
+            # When ssl_verify is a path, it should be returned directly
+            assert ssl_verify == ca_bundle_path
+        finally:
+            # Clean up
+            litellm.ssl_verify = True
+            os.unlink(ca_bundle_path)
+
+    @patch("boto3.client")
+    def test_init_bedrock_client_passes_ssl_verify_to_sts(self, mock_boto3_client):
+        """Test that init_bedrock_client passes ssl_verify to STS client."""
+        # Create a temporary CA bundle file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".pem", delete=False) as f:
+            f.write("-----BEGIN CERTIFICATE-----\n")
+            f.write("FAKE CERTIFICATE FOR TESTING\n")
+            f.write("-----END CERTIFICATE-----\n")
+            ca_bundle_path = f.name
+        
+        try:
+            # Set SSL_CERT_FILE environment variable
+            os.environ["SSL_CERT_FILE"] = ca_bundle_path
+            litellm.ssl_verify = True
+            
+            # Mock the STS client and Bedrock client
+            mock_sts_client = MagicMock()
+            mock_sts_response = {
+                "Credentials": {
+                    "AccessKeyId": "test_access_key",
+                    "SecretAccessKey": "test_secret_key",
+                    "SessionToken": "test_session_token",
+                }
+            }
+            mock_sts_client.assume_role.return_value = mock_sts_response
+            
+            mock_bedrock_client = MagicMock()
+            
+            # Configure mock to return different clients based on service name
+            def side_effect(service_name=None, **kwargs):
+                if service_name == "sts":
+                    return mock_sts_client
+                elif service_name == "bedrock-runtime":
+                    return mock_bedrock_client
+                return MagicMock()
+            
+            mock_boto3_client.side_effect = side_effect
+            
+            # Call init_bedrock_client with role assumption
+            client = init_bedrock_client(
+                aws_region_name="us-west-2",
+                aws_access_key_id="test_key",
+                aws_secret_access_key="test_secret",
+                aws_role_name="arn:aws:iam::123456789012:role/test-role",
+                aws_session_name="test-session",
+            )
+            
+            # Verify that boto3.client was called with verify parameter for STS
+            sts_calls = [
+                call for call in mock_boto3_client.call_args_list
+                if (len(call[0]) > 0 and call[0][0] == "sts") or 
+                   ("service_name" not in call[1])  # STS calls don't use service_name kwarg
+            ]
+            
+            assert len(sts_calls) > 0, "STS client should have been created"
+            
+            # Check that verify parameter was passed to STS client
+            sts_call = sts_calls[0]
+            assert "verify" in sts_call[1], "verify parameter should be passed to STS client"
+            assert sts_call[1]["verify"] == ca_bundle_path, f"verify should be set to CA bundle path, got {sts_call[1]['verify']}"
+            
+            # Verify that boto3.client was called with verify parameter for Bedrock
+            bedrock_calls = [
+                call for call in mock_boto3_client.call_args_list
+                if "service_name" in call[1] and call[1]["service_name"] == "bedrock-runtime"
+            ]
+            
+            assert len(bedrock_calls) > 0, "Bedrock client should have been created"
+            
+            bedrock_call = bedrock_calls[0]
+            assert "verify" in bedrock_call[1], "verify parameter should be passed to Bedrock client"
+            assert bedrock_call[1]["verify"] == ca_bundle_path, f"verify should be set to CA bundle path, got {bedrock_call[1]['verify']}"
+            
+        finally:
+            # Clean up
+            os.environ.pop("SSL_CERT_FILE", None)
+            os.unlink(ca_bundle_path)
+
+    @patch("boto3.client")
+    def test_base_aws_llm_auth_with_role_passes_ssl_verify(self, mock_boto3_client):
+        """Test that _auth_with_aws_role passes ssl_verify to STS client."""
+        base_aws = BaseAWSLLM()
+        
+        # Create a temporary CA bundle file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".pem", delete=False) as f:
+            f.write("-----BEGIN CERTIFICATE-----\n")
+            f.write("FAKE CERTIFICATE FOR TESTING\n")
+            f.write("-----END CERTIFICATE-----\n")
+            ca_bundle_path = f.name
+        
+        try:
+            # Set SSL_CERT_FILE environment variable
+            os.environ["SSL_CERT_FILE"] = ca_bundle_path
+            litellm.ssl_verify = True
+            
+            # Mock the STS client
+            mock_sts_client = MagicMock()
+            mock_sts_response = {
+                "Credentials": {
+                    "AccessKeyId": "test_access_key",
+                    "SecretAccessKey": "test_secret_key",
+                    "SessionToken": "test_session_token",
+                    "Expiration": "2025-01-10T00:00:00Z",
+                }
+            }
+            
+            # Convert Expiration to datetime
+            from datetime import datetime, timezone
+            mock_sts_response["Credentials"]["Expiration"] = datetime.now(timezone.utc)
+            
+            mock_sts_client.assume_role.return_value = mock_sts_response
+            mock_boto3_client.return_value = mock_sts_client
+            
+            # Call _auth_with_aws_role
+            credentials, ttl = base_aws._auth_with_aws_role(
+                aws_access_key_id="test_key",
+                aws_secret_access_key="test_secret",
+                aws_session_token=None,
+                aws_role_name="arn:aws:iam::123456789012:role/test-role",
+                aws_session_name="test-session",
+            )
+            
+            # Verify that boto3.client was called with verify parameter
+            assert mock_boto3_client.called, "boto3.client should have been called"
+            
+            call_kwargs = mock_boto3_client.call_args[1]
+            assert "verify" in call_kwargs, "verify parameter should be passed to STS client"
+            assert call_kwargs["verify"] == ca_bundle_path, f"verify should be set to CA bundle path, got {call_kwargs['verify']}"
+            
+        finally:
+            # Clean up
+            os.environ.pop("SSL_CERT_FILE", None)
+            os.unlink(ca_bundle_path)
+
+    @patch("litellm.llms.bedrock.base_aws_llm.get_secret")
+    @patch("boto3.client")
+    def test_base_aws_llm_auth_with_web_identity_passes_ssl_verify(self, mock_boto3_client, mock_get_secret):
+        """Test that _auth_with_web_identity_token passes ssl_verify to STS client."""
+        base_aws = BaseAWSLLM()
+        
+        # Create a temporary CA bundle file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".pem", delete=False) as f:
+            f.write("-----BEGIN CERTIFICATE-----\n")
+            f.write("FAKE CERTIFICATE FOR TESTING\n")
+            f.write("-----END CERTIFICATE-----\n")
+            ca_bundle_path = f.name
+        
+        try:
+            # Set SSL_CERT_FILE environment variable
+            os.environ["SSL_CERT_FILE"] = ca_bundle_path
+            litellm.ssl_verify = True
+            
+            # Mock get_secret to return the token
+            mock_get_secret.return_value = "mocked_oidc_token"
+            
+            # Mock the STS client
+            mock_sts_client = MagicMock()
+            mock_sts_response = {
+                "Credentials": {
+                    "AccessKeyId": "test_access_key",
+                    "SecretAccessKey": "test_secret_key",
+                    "SessionToken": "test_session_token",
+                },
+                "PackedPolicySize": 100,
+            }
+            
+            mock_sts_client.assume_role_with_web_identity.return_value = mock_sts_response
+            
+            # Mock boto3.Session
+            mock_session = MagicMock()
+            mock_credentials = MagicMock()
+            mock_session.get_credentials.return_value = mock_credentials
+            
+            mock_boto3_client.return_value = mock_sts_client
+            
+            with patch("boto3.Session", return_value=mock_session):
+                # Call _auth_with_web_identity_token
+                credentials, ttl = base_aws._auth_with_web_identity_token(
+                    aws_web_identity_token="test_token",
+                    aws_role_name="arn:aws:iam::123456789012:role/test-role",
+                    aws_session_name="test-session",
+                    aws_region_name="us-west-2",
+                    aws_sts_endpoint=None,
+                )
+            
+            # Verify that boto3.client was called with verify parameter
+            assert mock_boto3_client.called, "boto3.client should have been called"
+            
+            call_kwargs = mock_boto3_client.call_args[1]
+            assert "verify" in call_kwargs, "verify parameter should be passed to STS client"
+            assert call_kwargs["verify"] == ca_bundle_path, f"verify should be set to CA bundle path, got {call_kwargs['verify']}"
+            
+        finally:
+            # Clean up
+            os.environ.pop("SSL_CERT_FILE", None)
+            os.unlink(ca_bundle_path)
+
+    def test_ssl_verify_priority_env_over_litellm_config(self):
+        """Test that SSL_VERIFY environment variable takes priority over litellm.ssl_verify."""
+        base_aws = BaseAWSLLM()
+        
+        # Set litellm.ssl_verify to True
+        litellm.ssl_verify = True
+        
+        # Set SSL_VERIFY environment variable to False
+        os.environ["SSL_VERIFY"] = "False"
+        
+        try:
+            ssl_verify = base_aws._get_ssl_verify()
+            assert ssl_verify is False, "Environment variable should take priority"
+        finally:
+            # Clean up
+            os.environ.pop("SSL_VERIFY", None)
+            litellm.ssl_verify = True
+
+    def test_ssl_cert_file_priority_over_default(self):
+        """Test that SSL_CERT_FILE takes priority when ssl_verify is True."""
+        base_aws = BaseAWSLLM()
+        
+        # Create a temporary CA bundle file
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".pem", delete=False) as f:
+            f.write("-----BEGIN CERTIFICATE-----\n")
+            f.write("FAKE CERTIFICATE FOR TESTING\n")
+            f.write("-----END CERTIFICATE-----\n")
+            ca_bundle_path = f.name
+        
+        try:
+            # Set SSL_CERT_FILE environment variable
+            os.environ["SSL_CERT_FILE"] = ca_bundle_path
+            os.environ.pop("SSL_VERIFY", None)
+            litellm.ssl_verify = True
+            
+            ssl_verify = base_aws._get_ssl_verify()
+            assert ssl_verify == ca_bundle_path, "SSL_CERT_FILE should be used when ssl_verify is True"
+        finally:
+            # Clean up
+            os.environ.pop("SSL_CERT_FILE", None)
+            os.unlink(ca_bundle_path)
+
+
+if __name__ == "__main__":
+    # Run tests
+    pytest.main([__file__, "-v", "-s"])
diff --git a/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py b/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py
index fc8cf6dc60f..49d55f920b5 100644
--- a/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py
+++ b/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py
@@ -24,3 +24,194 @@ def test_deepseek_supported_openai_params():
     supported_openai_params = DeepInfraConfig().get_supported_openai_params(model="deepinfra/deepseek-ai/DeepSeek-V3.1")
     print(supported_openai_params)
     assert "reasoning_effort" in supported_openai_params
+
+
+def test_deepinfra_tool_message_content_transformation():
+    """
+    Test that DeepInfra transforms tool message content from array to string.
+    
+    This fixes the issue where LibreChat sends tool messages with content as an array:
+    {"role": "tool", "content": [{"type": "text", "text": "20"}]}
+    
+    DeepInfra requires content to be a string, so we transform it to:
+    {"role": "tool", "content": "20"}
+    
+    Related to issue #13982
+    """
+    from litellm.llms.deepinfra.chat.transformation import DeepInfraConfig
+
+    config = DeepInfraConfig()
+    
+    # Test case 1: Simple single text item in array (common case from LibreChat)
+    messages_with_array_content = [
+        {
+            "role": "user",
+            "content": "Calculate 10 + 10"
+        },
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "id": "call_123",
+                    "type": "function",
+                    "function": {
+                        "name": "calculator",
+                        "arguments": '{"input": "10 + 10"}'
+                    }
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "name": "calculator",
+            "content": [{"type": "text", "text": "20"}]  # Array format from LibreChat
+        }
+    ]
+    
+    transformed_messages = config._transform_messages(
+        messages=messages_with_array_content,
+        model="deepinfra/Qwen/Qwen3-235B-A22B"
+    )
+    
+    # Verify the tool message content was converted to string
+    tool_message = transformed_messages[2]
+    assert tool_message["role"] == "tool"
+    assert isinstance(tool_message["content"], str)
+    assert tool_message["content"] == "20"
+    print(f"✓ Test case 1 passed: {tool_message['content']}")
+    
+    # Test case 2: Complex content array (multiple items)
+    messages_with_complex_content = [
+        {
+            "role": "user",
+            "content": "Test"
+        },
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": "call_456",
+                    "type": "function",
+                    "function": {"name": "test", "arguments": "{}"}
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_456",
+            "content": [
+                {"type": "text", "text": "Result 1"},
+                {"type": "text", "text": "Result 2"}
+            ]
+        }
+    ]
+    
+    transformed_messages_complex = config._transform_messages(
+        messages=messages_with_complex_content,
+        model="deepinfra/Qwen/Qwen3-235B-A22B"
+    )
+    
+    tool_message_complex = transformed_messages_complex[2]
+    assert tool_message_complex["role"] == "tool"
+    assert isinstance(tool_message_complex["content"], str)
+    # For complex content, it should be JSON stringified
+    parsed_content = json.loads(tool_message_complex["content"])
+    assert len(parsed_content) == 2
+    assert parsed_content[0]["text"] == "Result 1"
+    print(f"✓ Test case 2 passed: {tool_message_complex['content']}")
+    
+    # Test case 3: Tool message with string content (should remain unchanged)
+    messages_with_string_content = [
+        {
+            "role": "user",
+            "content": "Test"
+        },
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": "call_789",
+                    "type": "function",
+                    "function": {"name": "test", "arguments": "{}"}
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_789",
+            "content": "Simple string result"  # Already a string
+        }
+    ]
+    
+    transformed_messages_string = config._transform_messages(
+        messages=messages_with_string_content,
+        model="deepinfra/Qwen/Qwen3-235B-A22B"
+    )
+    
+    tool_message_string = transformed_messages_string[2]
+    assert tool_message_string["role"] == "tool"
+    assert isinstance(tool_message_string["content"], str)
+    assert tool_message_string["content"] == "Simple string result"
+    print(f"✓ Test case 3 passed: {tool_message_string['content']}")
+    
+    print("\n✅ All DeepInfra tool message transformation tests passed!")
+
+
+@pytest.mark.asyncio
+async def test_deepinfra_tool_message_content_transformation_async():
+    """
+    Test that DeepInfra transforms tool message content from array to string in async mode.
+    
+    This ensures the async path works correctly when is_async=True.
+    
+    Related to issue #13982
+    """
+    from litellm.llms.deepinfra.chat.transformation import DeepInfraConfig
+
+    config = DeepInfraConfig()
+    
+    # Test async transformation with tool message containing array content
+    messages_with_array_content = [
+        {
+            "role": "user",
+            "content": "Calculate 10 + 10"
+        },
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [
+                {
+                    "id": "call_123",
+                    "type": "function",
+                    "function": {
+                        "name": "calculator",
+                        "arguments": '{"input": "10 + 10"}'
+                    }
+                }
+            ]
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "name": "calculator",
+            "content": [{"type": "text", "text": "20"}]  # Array format from LibreChat
+        }
+    ]
+    
+    # Call with is_async=True
+    transformed_messages = await config._transform_messages(
+        messages=messages_with_array_content,
+        model="deepinfra/Qwen/Qwen3-235B-A22B",
+        is_async=True
+    )
+    
+    # Verify the tool message content was converted to string
+    tool_message = transformed_messages[2]
+    assert tool_message["role"] == "tool"
+    assert isinstance(tool_message["content"], str)
+    assert tool_message["content"] == "20"
+    print(f"✓ Async test passed: {tool_message['content']}")
+    
+    print("\n✅ DeepInfra async tool message transformation test passed!")
diff --git a/tests/test_litellm/llms/manus/__init__.py b/tests/test_litellm/llms/manus/__init__.py
new file mode 100644
index 00000000000..d4037b65199
--- /dev/null
+++ b/tests/test_litellm/llms/manus/__init__.py
@@ -0,0 +1,2 @@
+# Manus provider tests
+
diff --git a/tests/test_litellm/llms/manus/responses/__init__.py b/tests/test_litellm/llms/manus/responses/__init__.py
new file mode 100644
index 00000000000..a7131749c5c
--- /dev/null
+++ b/tests/test_litellm/llms/manus/responses/__init__.py
@@ -0,0 +1,2 @@
+# Manus Responses API tests
+
diff --git a/tests/test_litellm/llms/manus/responses/test_manus_responses_transformation.py b/tests/test_litellm/llms/manus/responses/test_manus_responses_transformation.py
new file mode 100644
index 00000000000..b47ed77156d
--- /dev/null
+++ b/tests/test_litellm/llms/manus/responses/test_manus_responses_transformation.py
@@ -0,0 +1,60 @@
+"""
+Tests for Manus Responses API transformation
+
+Tests the ManusResponsesAPIConfig class that handles Manus-specific
+transformations for the Responses API.
+
+Source: litellm/llms/manus/responses/transformation.py
+"""
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm.llms.manus.responses.transformation import ManusResponsesAPIConfig
+from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
+from litellm.types.router import GenericLiteLLMParams
+
+
+def test_extract_agent_profile():
+    """Test that agent profile is correctly extracted from model name"""
+    config = ManusResponsesAPIConfig()
+    
+    assert config._extract_agent_profile("manus/manus-1.6") == "manus-1.6"
+    assert config._extract_agent_profile("manus/manus-1.6-lite") == "manus-1.6-lite"
+    assert config._extract_agent_profile("manus/manus-1.6-max") == "manus-1.6-max"
+
+
+def test_transform_responses_api_request_adds_manus_params():
+    """Test that transform_responses_api_request adds task_mode and agent_profile"""
+    config = ManusResponsesAPIConfig()
+    
+    input_param = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "input_text",
+                    "text": "What's the color of the sky?",
+                }
+            ],
+        }
+    ]
+    
+    optional_params = ResponsesAPIOptionalRequestParams()
+    litellm_params = GenericLiteLLMParams()
+    headers = {}
+    
+    result = config.transform_responses_api_request(
+        model="manus/manus-1.6",
+        input=input_param,
+        response_api_optional_request_params=dict(optional_params),
+        litellm_params=litellm_params,
+        headers=headers,
+    )
+    
+    assert result["task_mode"] == "agent"
+    assert result["agent_profile"] == "manus-1.6"
+    assert "input" in result
+    assert "model" in result
+
diff --git a/tests/test_litellm/llms/openai/chat/guardrail_translation/test_handler.py b/tests/test_litellm/llms/openai/chat/guardrail_translation/test_handler.py
index e94f40838ca..6c0195d2831 100644
--- a/tests/test_litellm/llms/openai/chat/guardrail_translation/test_handler.py
+++ b/tests/test_litellm/llms/openai/chat/guardrail_translation/test_handler.py
@@ -84,6 +84,158 @@ class MockGuardrail(CustomGuardrail):
         return result
 
 
+class TestOpenAIChatCompletionsHandlerToolsInput:
+    """Test input processing with tools (function definitions)"""
+
+    @pytest.mark.asyncio
+    async def test_tools_passed_to_guardrail(self):
+        """Test that tools (function definitions) are passed to the guardrail"""
+        handler = OpenAIChatCompletionsHandler()
+        guardrail = MockGuardrail()
+
+        # Create input data with tools (function definitions)
+        data = {
+            "messages": [
+                {"role": "user", "content": "What's the weather in Boston?"},
+            ],
+            "tools": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "description": "Get the current weather in a location",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "location": {
+                                    "type": "string",
+                                    "description": "City name",
+                                },
+                                "unit": {
+                                    "type": "string",
+                                    "enum": ["celsius", "fahrenheit"],
+                                },
+                            },
+                            "required": ["location"],
+                        },
+                    },
+                }
+            ],
+        }
+
+        # Process the input
+        await handler.process_input_messages(data, guardrail)
+
+        # Verify tools were passed to guardrail
+        assert guardrail.last_inputs is not None
+        assert "tools" in guardrail.last_inputs
+        assert len(guardrail.last_inputs["tools"]) == 1
+
+        tool = guardrail.last_inputs["tools"][0]
+        assert tool["type"] == "function"
+        assert tool["function"]["name"] == "get_weather"
+        assert tool["function"]["description"] == "Get the current weather in a location"
+        assert "parameters" in tool["function"]
+
+    @pytest.mark.asyncio
+    async def test_multiple_tools_passed_to_guardrail(self):
+        """Test that multiple tools are passed to the guardrail"""
+        handler = OpenAIChatCompletionsHandler()
+        guardrail = MockGuardrail()
+
+        data = {
+            "messages": [
+                {"role": "user", "content": "What's the weather and time?"},
+            ],
+            "tools": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "parameters": {"type": "object", "properties": {}},
+                    },
+                },
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_time",
+                        "parameters": {"type": "object", "properties": {}},
+                    },
+                },
+            ],
+        }
+
+        await handler.process_input_messages(data, guardrail)
+
+        assert guardrail.last_inputs is not None
+        assert "tools" in guardrail.last_inputs
+        assert len(guardrail.last_inputs["tools"]) == 2
+        assert guardrail.last_inputs["tools"][0]["function"]["name"] == "get_weather"
+        assert guardrail.last_inputs["tools"][1]["function"]["name"] == "get_time"
+
+    @pytest.mark.asyncio
+    async def test_no_tools_in_request(self):
+        """Test that requests without tools work correctly"""
+        handler = OpenAIChatCompletionsHandler()
+        guardrail = MockGuardrail()
+
+        data = {
+            "messages": [
+                {"role": "user", "content": "Hello"},
+            ],
+        }
+
+        await handler.process_input_messages(data, guardrail)
+
+        assert guardrail.last_inputs is not None
+        # tools should not be in inputs if not provided
+        assert "tools" not in guardrail.last_inputs or guardrail.last_inputs.get("tools") is None
+
+    @pytest.mark.asyncio
+    async def test_tools_and_tool_calls_both_passed(self):
+        """Test that both tools (definitions) and tool_calls (invocations) are passed"""
+        handler = OpenAIChatCompletionsHandler()
+        guardrail = MockGuardrail()
+
+        data = {
+            "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": "Boston"}',
+                            },
+                        }
+                    ],
+                },
+            ],
+            "tools": [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "get_weather",
+                        "parameters": {"type": "object", "properties": {"location": {"type": "string"}}},
+                    },
+                }
+            ],
+        }
+
+        await handler.process_input_messages(data, guardrail)
+
+        assert guardrail.last_inputs is not None
+        # Both should be present
+        assert "tools" in guardrail.last_inputs
+        assert "tool_calls" in guardrail.last_inputs
+        assert len(guardrail.last_inputs["tools"]) == 1
+        assert len(guardrail.last_inputs["tool_calls"]) == 1
+
+
 class TestOpenAIChatCompletionsHandlerToolCallsInput:
     """Test input processing with tool calls"""
 
diff --git a/tests/test_litellm/llms/openai/completion/test_text_completion_token_ids.py b/tests/test_litellm/llms/openai/completion/test_text_completion_token_ids.py
new file mode 100644
index 00000000000..b88cda42b69
--- /dev/null
+++ b/tests/test_litellm/llms/openai/completion/test_text_completion_token_ids.py
@@ -0,0 +1,115 @@
+"""
+Unit tests for text_completion with token IDs (list of integers) as prompt.
+Tests the fix for https://github.com/BerriAI/litellm/issues/17118
+"""
+
+import os
+import sys
+
+import pytest
+import respx
+from httpx import Response
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+import litellm
+from litellm import text_completion
+
+
+@pytest.fixture(autouse=True)
+def setup_env(monkeypatch):
+    """Set up test environment variables."""
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-test-fake-key")
+
+
+@pytest.fixture
+def text_completion_response():
+    """Mock response for text completion API."""
+    return {
+        "id": "cmpl-test123",
+        "object": "text_completion",
+        "created": 1677652288,
+        "model": "gpt-3.5-turbo-instruct",
+        "choices": [
+            {
+                "text": " is a greeting",
+                "index": 0,
+                "logprobs": None,
+                "finish_reason": "stop",
+            }
+        ],
+        "usage": {"prompt_tokens": 2, "completion_tokens": 4, "total_tokens": 6},
+    }
+
+
+class TestTextCompletionTokenIds:
+    """Test text_completion with token IDs as prompt."""
+
+    @respx.mock
+    def test_completion_prompt_token_ids(
+        self, text_completion_response, monkeypatch
+    ):
+        """
+        Test text_completion with a list of token IDs (integers).
+        This tests the fix for https://github.com/BerriAI/litellm/issues/17118
+        """
+        # Token IDs for "Hello world" in GPT tokenizer
+        token_ids = [15496, 995]
+
+        # Mock the OpenAI completions endpoint
+        respx.post("https://api.openai.com/v1/completions").mock(
+            return_value=Response(200, json=text_completion_response)
+        )
+
+        response = text_completion(
+            model="gpt-3.5-turbo-instruct",
+            prompt=token_ids,
+            max_tokens=5,
+        )
+
+        assert response is not None
+        assert response.choices[0].text is not None
+        assert response.usage.prompt_tokens == 2
+
+    @respx.mock
+    def test_completion_prompt_token_ids_batch(
+        self, text_completion_response, monkeypatch
+    ):
+        """
+        Test text_completion with multiple prompts as token IDs.
+        """
+        # Multiple token ID lists (batch)
+        token_ids_batch = [[15496, 995], [9906, 0]]
+
+        # Update mock response for batch
+        batch_response = {
+            **text_completion_response,
+            "choices": [
+                {
+                    "text": " is a greeting",
+                    "index": 0,
+                    "logprobs": None,
+                    "finish_reason": "stop",
+                },
+                {
+                    "text": " is another",
+                    "index": 1,
+                    "logprobs": None,
+                    "finish_reason": "stop",
+                },
+            ],
+            "usage": {"prompt_tokens": 4, "completion_tokens": 6, "total_tokens": 10},
+        }
+
+        respx.post("https://api.openai.com/v1/completions").mock(
+            return_value=Response(200, json=batch_response)
+        )
+
+        response = text_completion(
+            model="gpt-3.5-turbo-instruct",
+            prompt=token_ids_batch,
+            max_tokens=5,
+        )
+
+        assert response is not None
+        assert len(response.choices) == 2
diff --git a/tests/test_litellm/llms/openai_like/test_xiaomi_mimo.py b/tests/test_litellm/llms/openai_like/test_xiaomi_mimo.py
new file mode 100644
index 00000000000..d025c716a4a
--- /dev/null
+++ b/tests/test_litellm/llms/openai_like/test_xiaomi_mimo.py
@@ -0,0 +1,150 @@
+"""
+Tests for Xiaomi MiMo provider configuration and integration.
+Related to issue #18794
+"""
+
+import os
+import sys
+from unittest.mock import MagicMock, patch
+
+try:
+    import pytest
+except ImportError:
+    pytest = None
+
+# Add workspace to path
+workspace_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../.."))
+sys.path.insert(0, workspace_path)
+
+import litellm
+
+
+class TestXiaomiMiMoProviderConfig:
+    """Test Xiaomi MiMo provider configuration"""
+
+    def test_xiaomi_mimo_in_provider_list(self):
+        """Test that xiaomi_mimo is in the provider list (fixes #18794)"""
+        from litellm import LlmProviders
+
+        # Verify xiaomi_mimo is in the enum
+        assert hasattr(LlmProviders, 'XIAOMI_MIMO')
+        assert LlmProviders.XIAOMI_MIMO.value == 'xiaomi_mimo'
+
+        # Verify it's in the provider list
+        assert 'xiaomi_mimo' in litellm.provider_list
+
+    def test_xiaomi_mimo_json_config_exists(self):
+        """Test that xiaomi_mimo is configured in providers.json"""
+        from litellm.llms.openai_like.json_loader import JSONProviderRegistry
+
+        # Verify xiaomi_mimo is loaded
+        assert JSONProviderRegistry.exists("xiaomi_mimo")
+
+        # Get xiaomi_mimo config
+        xiaomi_mimo = JSONProviderRegistry.get("xiaomi_mimo")
+        assert xiaomi_mimo is not None
+        assert xiaomi_mimo.base_url == "https://api.xiaomimimo.com/v1"
+        assert xiaomi_mimo.api_key_env == "XIAOMI_MIMO_API_KEY"
+        assert xiaomi_mimo.param_mappings.get("max_completion_tokens") == "max_tokens"
+
+    def test_xiaomi_mimo_provider_resolution(self):
+        """Test that provider resolution finds xiaomi_mimo"""
+        from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
+
+        model, provider, api_key, api_base = get_llm_provider(
+            model="xiaomi_mimo/mimo-v2-flash",
+            custom_llm_provider=None,
+            api_base=None,
+            api_key=None,
+        )
+
+        assert model == "mimo-v2-flash"
+        assert provider == "xiaomi_mimo"
+        assert api_base == "https://api.xiaomimimo.com/v1"
+
+    def test_xiaomi_mimo_router_config(self):
+        """Test that xiaomi_mimo can be used in Router configuration (fixes #18794)"""
+        from litellm import Router
+
+        # This should not raise "Unsupported provider - xiaomi_mimo"
+        router = Router(
+            model_list=[
+                {
+                    "model_name": "mimo-v2-flash",
+                    "litellm_params": {
+                        "model": "xiaomi_mimo/mimo-v2-flash",
+                        "api_key": "test-key",
+                    },
+                }
+            ]
+        )
+
+        # Verify the deployment was created successfully
+        assert len(router.model_list) == 1
+        assert router.model_list[0]["model_name"] == "mimo-v2-flash"
+
+
+class TestXiaomiMiMoIntegration:
+    """Integration tests for Xiaomi MiMo provider"""
+
+    def test_xiaomi_mimo_completion_basic(self):
+        """Test basic completion call to Xiaomi MiMo"""
+        # Skip test if API key not set in environment
+        if not os.environ.get("XIAOMI_MIMO_API_KEY"):
+            if pytest:
+                pytest.skip("XIAOMI_MIMO_API_KEY not set")
+            return
+
+        try:
+            response = litellm.completion(
+                model="xiaomi_mimo/mimo-v2-flash",
+                messages=[{"role": "user", "content": "Say 'test successful' and nothing else"}],
+                max_tokens=10,
+            )
+
+            # Verify response structure
+            assert response is not None
+            assert hasattr(response, "choices")
+            assert len(response.choices) > 0
+            assert hasattr(response.choices[0], "message")
+            assert hasattr(response.choices[0].message, "content")
+            assert response.choices[0].message.content is not None
+
+            # Check that we got a response
+            content = response.choices[0].message.content.lower()
+            assert len(content) > 0
+
+            print(f"✓ Xiaomi MiMo completion successful: {response.choices[0].message.content}")
+
+        except Exception as e:
+            if pytest:
+                pytest.fail(f"Xiaomi MiMo completion failed: {str(e)}")
+            else:
+                raise
+
+
+if __name__ == "__main__":
+    # Run basic tests
+    print("Testing Xiaomi MiMo Provider...")
+
+    test_config = TestXiaomiMiMoProviderConfig()
+
+    print("\n1. Testing provider in list...")
+    test_config.test_xiaomi_mimo_in_provider_list()
+    print("   ✓ xiaomi_mimo in provider list")
+
+    print("\n2. Testing JSON config...")
+    test_config.test_xiaomi_mimo_json_config_exists()
+    print("   ✓ xiaomi_mimo JSON config loaded")
+
+    print("\n3. Testing provider resolution...")
+    test_config.test_xiaomi_mimo_provider_resolution()
+    print("   ✓ Provider resolution works")
+
+    print("\n4. Testing router configuration...")
+    test_config.test_xiaomi_mimo_router_config()
+    print("   ✓ Router configuration works (issue #18794 fixed)")
+
+    print("\n" + "="*50)
+    print("✓ All configuration tests passed!")
+    print("="*50)
diff --git a/tests/test_litellm/llms/openrouter/test_openrouter_embedding_transformation.py b/tests/test_litellm/llms/openrouter/test_openrouter_embedding_transformation.py
new file mode 100644
index 00000000000..714adc346db
--- /dev/null
+++ b/tests/test_litellm/llms/openrouter/test_openrouter_embedding_transformation.py
@@ -0,0 +1,132 @@
+"""
+Unit tests for OpenRouter embedding transformation logic.
+"""
+from litellm.llms.openrouter.embedding.transformation import (
+    OpenrouterEmbeddingConfig,
+)
+
+
+def test_openrouter_embedding_supported_params():
+    """Test that supported OpenAI params are correctly defined."""
+    config = OpenrouterEmbeddingConfig()
+    supported = config.get_supported_openai_params("test-model")
+
+    assert "timeout" in supported
+    assert "dimensions" in supported
+    assert "encoding_format" in supported
+    assert "user" in supported
+
+
+def test_openrouter_embedding_transform_request():
+    """Test request transformation logic."""
+    config = OpenrouterEmbeddingConfig()
+
+    # Test with string input
+    result = config.transform_embedding_request(
+        model="openrouter/google/text-embedding-004",
+        input="Hello world",
+        optional_params={},
+        headers={},
+    )
+
+    assert result["model"] == "google/text-embedding-004"
+    assert result["input"] == ["Hello world"]
+
+    # Test with list input
+    result = config.transform_embedding_request(
+        model="google/text-embedding-004",
+        input=["Hello", "World"],
+        optional_params={"dimensions": 512},
+        headers={},
+    )
+
+    assert result["model"] == "google/text-embedding-004"
+    assert result["input"] == ["Hello", "World"]
+    assert result["dimensions"] == 512
+
+
+def test_openrouter_embedding_validate_environment():
+    """Test environment validation and header setup."""
+    config = OpenrouterEmbeddingConfig()
+
+    # Test with API key
+    headers = config.validate_environment(
+        headers={"Custom-Header": "value"},
+        model="test-model",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key="test-api-key",
+    )
+
+    # Should include OpenRouter-specific headers
+    assert "HTTP-Referer" in headers
+    assert "X-Title" in headers
+    # Should include Content-Type header
+    assert "Content-Type" in headers
+    assert headers["Content-Type"] == "application/json"
+    # Should include Authorization header
+    assert "Authorization" in headers
+    assert headers["Authorization"] == "Bearer test-api-key"
+    # Should preserve custom headers
+    assert headers["Custom-Header"] == "value"
+
+    # Test without API key
+    headers_no_key = config.validate_environment(
+        headers={},
+        model="test-model",
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        api_key=None,
+    )
+
+    # Should still include OpenRouter headers but not Authorization
+    assert "HTTP-Referer" in headers_no_key
+    assert "X-Title" in headers_no_key
+    assert "Content-Type" in headers_no_key
+    assert "Authorization" not in headers_no_key
+
+
+def test_openrouter_embedding_get_complete_url():
+    """Test URL construction."""
+    config = OpenrouterEmbeddingConfig()
+
+    url = config.get_complete_url(
+        api_base="https://openrouter.ai/api/v1",
+        api_key="test-key",
+        model="test-model",
+        optional_params={},
+        litellm_params={},
+    )
+
+    assert url == "https://openrouter.ai/api/v1/embeddings"
+
+    # Test with trailing slash
+    url = config.get_complete_url(
+        api_base="https://openrouter.ai/api/v1/",
+        api_key="test-key",
+        model="test-model",
+        optional_params={},
+        litellm_params={},
+    )
+
+    assert url == "https://openrouter.ai/api/v1/embeddings"
+
+
+def test_openrouter_embedding_map_params():
+    """Test parameter mapping."""
+    config = OpenrouterEmbeddingConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"dimensions": 512, "timeout": 30, "unsupported": "value"},
+        optional_params={},
+        model="test-model",
+        drop_params=False,
+    )
+
+    # Supported params should be included
+    assert result["dimensions"] == 512
+    assert result["timeout"] == 30
+    # Unsupported params should not be included
+    assert "unsupported" not in result
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
index 3c3d68e8be8..70e6e9452e5 100644
--- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py
@@ -721,3 +721,189 @@ def test_convert_tool_response_text_only():
     
     # Check inline_data does NOT exist (no image provided)
     assert "inline_data" not in result
+
+
+def test_file_data_field_order():
+    """
+    Test that file_data fields are in the correct order (mime_type before file_uri).
+    
+    The Gemini API is sensitive to field order in the file_data object.
+    This test verifies that mime_type comes before file_uri in both:
+    1. Dictionary key order
+    2. JSON serialization
+    
+    Related issue: Gemini API returns 400 INVALID_ARGUMENT when fields are in wrong order.
+    """
+    import json
+    from litellm.llms.vertex_ai.gemini.transformation import _process_gemini_image
+    
+    # Test with HTTPS URL and explicit format (audio file)
+    file_url = "https://generativelanguage.googleapis.com/v1beta/files/test123"
+    format = "audio/mpeg"
+    
+    result = _process_gemini_image(image_url=file_url, format=format)
+    
+    # Verify the result has file_data
+    assert "file_data" in result
+    file_data = result["file_data"]
+    
+    # Verify both fields are present
+    assert "mime_type" in file_data
+    assert "file_uri" in file_data
+    assert file_data["mime_type"] == "audio/mpeg"
+    assert file_data["file_uri"] == file_url
+    
+    # Verify field order by checking dictionary keys
+    # In Python 3.7+, dict maintains insertion order
+    file_data_keys = list(file_data.keys())
+    assert file_data_keys.index("mime_type") < file_data_keys.index("file_uri"), \
+        "mime_type must come before file_uri in the file_data dict"
+    
+    # Also verify by serializing to JSON string
+    json_str = json.dumps(file_data)
+    mime_type_pos = json_str.find('"mime_type"')
+    file_uri_pos = json_str.find('"file_uri"')
+    assert mime_type_pos < file_uri_pos, \
+        "mime_type must appear before file_uri in JSON serialization"
+
+
+def test_file_data_field_order_gcs_urls():
+    """Test that GCS URLs also maintain correct field order."""
+    import json
+    from litellm.llms.vertex_ai.gemini.transformation import _process_gemini_image
+    
+    # Test with GCS URL
+    gcs_url = "gs://bucket/audio.mp3"
+    
+    result = _process_gemini_image(image_url=gcs_url)
+    
+    # Verify the result has file_data
+    assert "file_data" in result
+    file_data = result["file_data"]
+    
+    # Verify both fields are present
+    assert "mime_type" in file_data
+    assert "file_uri" in file_data
+    
+    # Verify field order
+    file_data_keys = list(file_data.keys())
+    assert file_data_keys.index("mime_type") < file_data_keys.index("file_uri"), \
+        "mime_type must come before file_uri in the file_data dict"
+
+
+def test_extract_file_data_with_path_object():
+    """
+    Test that filename is correctly extracted from Path objects for MIME type detection.
+    
+    When uploading files using Path objects (e.g., Path("speech.mp3")), the filename
+    must be extracted to enable proper MIME type detection. Without this, files get
+    uploaded with 'application/octet-stream' instead of the correct MIME type.
+    
+    Related issue: Files uploaded with wrong MIME type cause Gemini API to reject
+    requests where the specified format doesn't match the uploaded file's MIME type.
+    """
+    from pathlib import Path
+    from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+    import tempfile
+    import os
+    
+    # Create a temporary MP3 file
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp:
+        tmp.write(b"fake mp3 content")
+        tmp_path = tmp.name
+    
+    try:
+        # Test with Path object
+        path_obj = Path(tmp_path)
+        extracted = extract_file_data(path_obj)
+        
+        # Verify filename was extracted
+        assert extracted["filename"] is not None
+        assert extracted["filename"].endswith(".mp3")
+        
+        # Verify MIME type was correctly detected
+        assert extracted["content_type"] == "audio/mpeg", \
+            f"Expected 'audio/mpeg' but got '{extracted['content_type']}'"
+        
+        # Verify content was read
+        assert extracted["content"] == b"fake mp3 content"
+        
+    finally:
+        # Clean up temporary file
+        os.unlink(tmp_path)
+
+
+def test_extract_file_data_with_string_path():
+    """Test that filename is correctly extracted from string paths."""
+    from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+    import tempfile
+    import os
+    
+    # Create a temporary WAV file
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp.write(b"fake wav content")
+        tmp_path = tmp.name
+    
+    try:
+        # Test with string path
+        extracted = extract_file_data(tmp_path)
+        
+        # Verify filename was extracted
+        assert extracted["filename"] is not None
+        assert extracted["filename"].endswith(".wav")
+        
+        # Verify MIME type was correctly detected (can be audio/wav or audio/x-wav depending on system)
+        assert extracted["content_type"] in ["audio/wav", "audio/x-wav"], \
+            f"Expected 'audio/wav' or 'audio/x-wav' but got '{extracted['content_type']}'"
+        
+        # Verify content was read
+        assert extracted["content"] == b"fake wav content"
+        
+    finally:
+        # Clean up temporary file
+        os.unlink(tmp_path)
+
+
+def test_extract_file_data_with_tuple_format():
+    """Test that tuple format (with explicit content_type) still works correctly."""
+    from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+    
+    # Test with tuple format: (filename, content, content_type)
+    filename = "test_audio.mp3"
+    content = b"test audio content"
+    content_type = "audio/mpeg"
+    
+    extracted = extract_file_data((filename, content, content_type))
+    
+    # Verify all fields are correct
+    assert extracted["filename"] == filename
+    assert extracted["content"] == content
+    assert extracted["content_type"] == content_type
+
+
+def test_extract_file_data_fallback_to_octet_stream():
+    """Test that unknown file types fall back to application/octet-stream."""
+    from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+    import tempfile
+    import os
+    
+    # Create a temporary file with unknown extension
+    with tempfile.NamedTemporaryFile(suffix=".xyz123", delete=False) as tmp:
+        tmp.write(b"unknown content")
+        tmp_path = tmp.name
+    
+    try:
+        # Test with unknown file type
+        extracted = extract_file_data(tmp_path)
+        
+        # Verify filename was extracted
+        assert extracted["filename"] is not None
+        assert extracted["filename"].endswith(".xyz123")
+        
+        # Verify MIME type falls back to octet-stream
+        assert extracted["content_type"] == "application/octet-stream", \
+            f"Expected 'application/octet-stream' for unknown type, got '{extracted['content_type']}'"
+        
+    finally:
+        # Clean up temporary file
+        os.unlink(tmp_path)
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 d09de3a0f26..c0b80ae7716 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
@@ -14,6 +14,7 @@ from litellm.llms.vertex_ai.common_utils import VertexAIError
 from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
     VertexGeminiConfig,
 )
+from litellm.llms.gemini.chat.transformation import GoogleAIStudioGeminiConfig
 from litellm.types.llms.vertex_ai import UsageMetadata
 from litellm.types.utils import ChoiceLogprobs, Usage
 from litellm.utils import CustomStreamWrapper
@@ -518,19 +519,20 @@ def test_vertex_ai_usage_metadata_response_token_count():
 
     v = VertexGeminiConfig()
     usage_metadata = {
-        "promptTokenCount": 57,
+        "promptTokenCount": 66,
         "responseTokenCount": 74,
         "totalTokenCount": 131,
-        "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 57}],
+        "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 57}, {"modality": "IMAGE", "tokenCount": 9}],
         "responseTokensDetails": [{"modality": "TEXT", "tokenCount": 74}],
     }
     usage_metadata = UsageMetadata(**usage_metadata)
     result = v._calculate_usage(completion_response={"usageMetadata": usage_metadata})
     print("result", result)
-    assert result.prompt_tokens == 57
+    assert result.prompt_tokens == 66
     assert result.completion_tokens == 74
     assert result.total_tokens == 131
     assert result.prompt_tokens_details.text_tokens == 57
+    assert result.prompt_tokens_details.image_tokens == 9
     assert result.prompt_tokens_details.audio_tokens is None
     assert result.prompt_tokens_details.cached_tokens is None
     assert result.completion_tokens_details.text_tokens == 74
@@ -2315,6 +2317,17 @@ def test_partial_json_chunk_on_first_chunk():
     assert iterator.chunk_type == "accumulated_json", "Should switch to accumulated_json mode"
 
 
+
+def test_google_ai_studio_presence_penalty_supported():
+    """
+    Test that presence_penalty is supported for Google AI Studio Gemini.
+
+    Regression test for https://github.com/BerriAI/litellm/issues/14753
+    """
+    config = GoogleAIStudioGeminiConfig()
+    supported_params = config.get_supported_openai_params(model="gemini-2.0-flash")
+
+    assert "presence_penalty" in supported_params
 # ==================== Tool Type Separation Tests ====================
 # These tests verify that each Tool object contains exactly one type per Vertex AI API spec
 # Ref: https://cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1beta1/Tool
@@ -2505,3 +2518,72 @@ def test_vertex_ai_multiple_function_declarations_grouped():
     func_names = [f["name"] for f in tools[0]["function_declarations"]]
     assert "func1" in func_names
     assert "func2" in func_names
+
+
+def test_gemini_3_flash_preview_token_usage_fallback():
+    """Test fallback logic when candidatesTokensDetails is missing (e.g. Gemini 3 Flash Preview)."""
+    v = VertexGeminiConfig()
+
+    usage_metadata_dict = {
+        "promptTokenCount": 2145,
+        "candidatesTokenCount": 509,
+        "totalTokenCount": 2654,
+        # candidatesTokensDetails intentionally omitted
+    }
+
+    completion_response = {"usageMetadata": usage_metadata_dict}
+    result = v._calculate_usage(completion_response=completion_response)
+
+    assert result.completion_tokens == 509
+    assert result.prompt_tokens == 2145
+    assert result.total_tokens == 2654
+
+    # Text tokens should be derived from candidatesTokenCount
+    assert result.completion_tokens_details is not None
+    assert result.completion_tokens_details.text_tokens == 509
+    assert result.completion_tokens_details.image_tokens is None
+    assert result.completion_tokens_details.audio_tokens is None
+
+
+def test_gemini_no_reasoning_fallback():
+    """Test fallback when reasoning_effort is absent and details are missing."""
+    v = VertexGeminiConfig()
+
+    usage_metadata_dict = {
+        "promptTokenCount": 100,
+        "candidatesTokenCount": 264,
+        "totalTokenCount": 364,
+    }
+
+    completion_response = {"usageMetadata": usage_metadata_dict}
+    result = v._calculate_usage(completion_response=completion_response)
+
+    assert result.completion_tokens == 264
+    assert result.completion_tokens_details is not None
+    assert result.completion_tokens_details.text_tokens == 264
+    assert (
+        result.completion_tokens_details.reasoning_tokens is None
+        or result.completion_tokens_details.reasoning_tokens == 0
+    )
+
+
+def test_gemini_token_usage_standard_response():
+    """Verify that standard responses with details are computed correctly and not overwritten."""
+    v = VertexGeminiConfig()
+
+    usage_metadata_dict = {
+        "promptTokenCount": 100,
+        "candidatesTokenCount": 50,
+        "totalTokenCount": 150,
+        "candidatesTokensDetails": [
+            {"modality": "TEXT", "tokenCount": 40},
+            {"modality": "IMAGE", "tokenCount": 10},
+        ],
+    }
+
+    completion_response = {"usageMetadata": usage_metadata_dict}
+    result = v._calculate_usage(completion_response=completion_response)
+
+    assert result.completion_tokens == 50
+    assert result.completion_tokens_details.text_tokens == 40
+    assert result.completion_tokens_details.image_tokens == 10
diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_gemini_unbound_local_error.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_gemini_unbound_local_error.py
new file mode 100644
index 00000000000..0a1ac7e2a54
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_gemini_unbound_local_error.py
@@ -0,0 +1,38 @@
+import pytest
+from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig
+from litellm import ModelResponse
+
+def test_process_candidates_unbound_local_error_fix():
+    # Setup
+    candidates = [
+        {
+            "content": {
+                "role": "model"
+                # "parts" is missing intentionally to trigger the issue
+            },
+            "finishReason": "STOP"
+        }
+    ]
+    model_response = ModelResponse()
+    
+    # Execution
+    try:
+        VertexGeminiConfig._process_candidates(
+            _candidates=candidates,
+            model_response=model_response,
+            standard_optional_params={},
+            cumulative_tool_call_index=0
+        )
+    except UnboundLocalError as e:
+        pytest.fail(f"UnboundLocalError raised: {e}")
+    except Exception as e:
+        # Other exceptions might be okay if they are not UnboundLocalError, 
+        # but ideally it should pass without error or raise a specific error if parts are required.
+        # However, the goal is to verify thought_signatures doesn't crash.
+        pass
+
+    # Verify that we didn't crash with UnboundLocalError
+
+if __name__ == "__main__":
+    test_process_candidates_unbound_local_error_fix()
+    print("Test passed!")
diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
index 591b33911dc..b5637db3e52 100644
--- a/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
+++ b/tests/test_litellm/llms/vertex_ai/test_vertex_ai_common_utils.py
@@ -1175,6 +1175,30 @@ def test_vertex_ai_moonshot_uses_openai_handler():
     )
 
 
+def test_vertex_ai_zai_uses_openai_handler():
+    """
+    Ensure ZAI partner models re-use the OpenAI-format handler.
+    """
+    from litellm.llms.vertex_ai.vertex_ai_partner_models.main import (
+        VertexAIPartnerModels,
+    )
+
+    assert VertexAIPartnerModels.should_use_openai_handler(
+        "zai-org/glm-4.7-maas"
+    )
+
+
+def test_vertex_ai_zai_is_partner_model():
+    """
+    Ensure ZAI models are detected as Vertex AI partner models.
+    """
+    from litellm.llms.vertex_ai.vertex_ai_partner_models.main import (
+        VertexAIPartnerModels,
+    )
+
+    assert VertexAIPartnerModels.is_vertex_partner_model("zai-org/glm-4.7-maas")
+
+
 def test_build_vertex_schema_empty_properties():
     """
     Test _build_vertex_schema handles empty properties objects correctly.
diff --git a/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py b/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py
index e36a494998b..fd5f8f3eff8 100644
--- a/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py
+++ b/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py
@@ -14,6 +14,10 @@ import pytest
 sys.path.insert(0, os.path.abspath("../../../../.."))
 
 import litellm
+from litellm.llms.watsonx.audio_transcription.transformation import (
+    IBMWatsonXAudioTranscriptionConfig,
+)
+from litellm.types.utils import TranscriptionResponse
 
 
 class TestWatsonXAudioTranscription:
@@ -189,3 +193,72 @@ class TestWatsonXAudioTranscription:
         # Verify file is sent separately
         files = captured_request.get("files", {})
         assert "file" in files
+
+    def test_transform_audio_transcription_response_removes_model_field(self):
+        """
+        Test that transform_audio_transcription_response removes the 'model' field
+        from WatsonX response before creating TranscriptionResponse.
+        
+        This test ensures that when WatsonX returns a response with a 'model' field,
+        it is removed before creating the TranscriptionResponse object, since
+        TranscriptionResponse doesn't accept a 'model' parameter.
+        """
+        handler = IBMWatsonXAudioTranscriptionConfig()
+
+        # Mock response with 'model' field (as WatsonX may return)
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "text": "Hello, this is a test transcription.",
+            "model": "whisper-large-v3-turbo",  # This field should be removed
+            "duration": 5.5,
+        }
+        mock_response.text = '{"text": "Hello, this is a test transcription.", "model": "whisper-large-v3-turbo", "duration": 5.5}'
+
+        # This should not raise a TypeError - model field should be removed
+        result = handler.transform_audio_transcription_response(mock_response)
+
+        # Verify the result is a TranscriptionResponse
+        assert isinstance(result, TranscriptionResponse)
+        
+        # Verify the text is correct
+        assert result.text == "Hello, this is a test transcription."
+        
+        # Verify duration is set via dictionary assignment
+        assert result["duration"] == 5.5
+        
+        # Verify the model field is NOT in the serialized result
+        # Check via model_dump() or dict() to ensure it's not in the output
+        try:
+            result_dict = result.model_dump()
+        except AttributeError:
+            # Fallback for pydantic v1
+            result_dict = result.dict()
+        
+        # The 'model' field should not be in the result
+        assert "model" not in result_dict, "Model field should be removed from response"
+
+    def test_transform_audio_transcription_response_without_model_field(self):
+        """
+        Test that transform_audio_transcription_response works correctly
+        when WatsonX response doesn't include a 'model' field.
+        """
+        handler = IBMWatsonXAudioTranscriptionConfig()
+
+        # Mock response without 'model' field
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "text": "Hello, this is a test transcription.",
+            "duration": 5.5,
+        }
+        mock_response.text = '{"text": "Hello, this is a test transcription.", "duration": 5.5}'
+
+        result = handler.transform_audio_transcription_response(mock_response)
+
+        # Verify the result is a TranscriptionResponse
+        assert isinstance(result, TranscriptionResponse)
+        
+        # Verify the text is correct
+        assert result.text == "Hello, this is a test transcription."
+        
+        # Verify duration is set via dictionary assignment
+        assert result["duration"] == 5.5
diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py
index 8062243dfdd..f1558ac5791 100644
--- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py
+++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py
@@ -1,4 +1,5 @@
 import asyncio
+from datetime import datetime, timedelta
 from typing import Optional
 from unittest.mock import AsyncMock, MagicMock, patch
 
@@ -7,7 +8,12 @@ from fastapi import HTTPException
 from mcp import ReadResourceResult, Resource
 from mcp.types import Prompt, ResourceTemplate, TextResourceContents
 
-from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy._types import (
+    LiteLLM_MCPServerTable,
+    MCPTransport,
+    UserAPIKeyAuth,
+)
+from litellm.types.mcp_server.mcp_server_manager import MCPServer
 
 
 @pytest.mark.asyncio
@@ -1688,3 +1694,99 @@ def test_filter_tools_by_allowed_tools():
     assert len(filtered_tools) == 2
     assert filtered_tools[0].name == "my_api_mcp-getpetbyid"
     assert filtered_tools[1].name == "my_api_mcp-findpetsbystatus"
+
+
+def _make_db_mcp_server(server_id: str, updated_at: datetime) -> LiteLLM_MCPServerTable:
+    return LiteLLM_MCPServerTable(
+        server_id=server_id,
+        server_name="server",
+        alias="server",
+        url="https://example.com",
+        transport=MCPTransport.http,
+        created_at=updated_at,
+        updated_at=updated_at,
+        mcp_info={},
+    )
+
+
+class TestMCPServerManagerReload:
+    @pytest.mark.asyncio
+    async def test_reuses_existing_server_when_updated_at_matches(self):
+        try:
+            from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+                MCPServerManager,
+            )
+        except ImportError:
+            pytest.skip("MCP server not available")
+
+        manager = MCPServerManager()
+        timestamp = datetime.utcnow()
+        existing_server = MCPServer(
+            server_id="server-1",
+            name="server",
+            transport=MCPTransport.http,
+            updated_at=timestamp,
+        )
+        manager.registry = {existing_server.server_id: existing_server}
+
+        db_row = _make_db_mcp_server("server-1", timestamp)
+
+        with patch(
+            "litellm.proxy._experimental.mcp_server.db.get_all_mcp_servers",
+            new=AsyncMock(return_value=[db_row]),
+        ) as mock_get_all, patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw",
+            return_value=object(),
+        ), patch.object(
+            manager, "build_mcp_server_from_table", AsyncMock()
+        ) as mock_build:
+            await manager.reload_servers_from_database()
+
+        mock_get_all.assert_awaited_once()
+        mock_build.assert_not_awaited()
+        assert manager.registry["server-1"] is existing_server
+
+    @pytest.mark.asyncio
+    async def test_rebuilds_server_when_updated_at_changes(self):
+        try:
+            from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+                MCPServerManager,
+            )
+        except ImportError:
+            pytest.skip("MCP server not available")
+
+        manager = MCPServerManager()
+        timestamp = datetime.utcnow()
+        existing_server = MCPServer(
+            server_id="server-1",
+            name="server",
+            transport=MCPTransport.http,
+            updated_at=timestamp,
+        )
+        manager.registry = {existing_server.server_id: existing_server}
+
+        new_timestamp = timestamp + timedelta(minutes=5)
+        db_row = _make_db_mcp_server("server-1", new_timestamp)
+        rebuilt_server = MCPServer(
+            server_id="server-1",
+            name="server",
+            transport=MCPTransport.http,
+            updated_at=new_timestamp,
+        )
+
+        with patch(
+            "litellm.proxy._experimental.mcp_server.db.get_all_mcp_servers",
+            new=AsyncMock(return_value=[db_row]),
+        ) as mock_get_all, patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw",
+            return_value=object(),
+        ), patch.object(
+            manager,
+            "build_mcp_server_from_table",
+            AsyncMock(return_value=rebuilt_server),
+        ) as mock_build:
+            await manager.reload_servers_from_database()
+
+        mock_get_all.assert_awaited_once()
+        mock_build.assert_awaited_once_with(db_row)
+        assert manager.registry["server-1"] is rebuilt_server
diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py
new file mode 100644
index 00000000000..b1bef63933e
--- /dev/null
+++ b/tests/test_litellm/proxy/auth/test_auth_utils.py
@@ -0,0 +1,131 @@
+"""
+Unit tests for auth_utils functions related to rate limiting.
+"""
+
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.auth.auth_utils import (
+    get_key_model_rpm_limit,
+    get_key_model_tpm_limit,
+)
+
+
+class TestGetKeyModelRpmLimit:
+    """Tests for get_key_model_rpm_limit function."""
+
+    def test_returns_key_metadata_when_present(self):
+        """Key metadata takes priority over team metadata."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            metadata={"model_rpm_limit": {"gpt-4": 100}},
+            team_metadata={"model_rpm_limit": {"gpt-4": 50}},
+        )
+        result = get_key_model_rpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 100}
+
+    def test_falls_back_to_team_metadata_when_key_has_other_metadata(self):
+        """Should fall back to team metadata when key metadata exists but has no model_rpm_limit."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            metadata={
+                "some_other_key": "value"
+            },  # Has metadata, but not model_rpm_limit
+            team_metadata={"model_rpm_limit": {"gpt-4": 50}},
+        )
+        result = get_key_model_rpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 50}
+
+    def test_extracts_from_model_max_budget(self):
+        """Should extract rpm_limit from model_max_budget when metadata is empty."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            model_max_budget={
+                "gpt-4": {"rpm_limit": 100, "tpm_limit": 1000},
+                "gpt-3.5-turbo": {"rpm_limit": 200},
+            },
+        )
+        result = get_key_model_rpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 100, "gpt-3.5-turbo": 200}
+
+    def test_skips_models_without_rpm_limit(self):
+        """Should skip models that don't have rpm_limit in model_max_budget."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            model_max_budget={
+                "gpt-4": {"rpm_limit": 100},
+                "gpt-3.5-turbo": {"tpm_limit": 1000},  # No rpm_limit
+            },
+        )
+        result = get_key_model_rpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 100}
+
+    def test_returns_none_when_no_limits_configured(self):
+        """Should return None when no rate limits are configured."""
+        user_api_key_dict = UserAPIKeyAuth(api_key="sk-123")
+        result = get_key_model_rpm_limit(user_api_key_dict)
+        assert result is None
+
+
+class TestGetKeyModelTpmLimit:
+    """Tests for get_key_model_tpm_limit function."""
+
+    def test_returns_key_metadata_when_present(self):
+        """Key metadata takes priority over team metadata."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            metadata={"model_tpm_limit": {"gpt-4": 10000}},
+            team_metadata={"model_tpm_limit": {"gpt-4": 5000}},
+        )
+        result = get_key_model_tpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 10000}
+
+    def test_falls_back_to_team_metadata_when_key_has_other_metadata(self):
+        """Should fall back to team metadata when key metadata exists but has no model_tpm_limit."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            metadata={
+                "some_other_key": "value"
+            },  # Has metadata, but not model_tpm_limit
+            team_metadata={"model_tpm_limit": {"gpt-4": 5000}},
+        )
+        result = get_key_model_tpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 5000}
+
+    def test_extracts_from_model_max_budget(self):
+        """Should extract tpm_limit from model_max_budget when metadata is empty."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            model_max_budget={
+                "gpt-4": {"tpm_limit": 10000, "rpm_limit": 100},
+                "gpt-3.5-turbo": {"tpm_limit": 20000},
+            },
+        )
+        result = get_key_model_tpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 10000, "gpt-3.5-turbo": 20000}
+
+    def test_skips_models_without_tpm_limit(self):
+        """Should skip models that don't have tpm_limit in model_max_budget."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            model_max_budget={
+                "gpt-4": {"tpm_limit": 10000},
+                "gpt-3.5-turbo": {"rpm_limit": 100},  # No tpm_limit
+            },
+        )
+        result = get_key_model_tpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 10000}
+
+    def test_returns_none_when_no_limits_configured(self):
+        """Should return None when no rate limits are configured."""
+        user_api_key_dict = UserAPIKeyAuth(api_key="sk-123")
+        result = get_key_model_tpm_limit(user_api_key_dict)
+        assert result is None
+
+    def test_model_max_budget_priority_over_team(self):
+        """model_max_budget should take priority over team_metadata."""
+        user_api_key_dict = UserAPIKeyAuth(
+            api_key="sk-123",
+            model_max_budget={"gpt-4": {"tpm_limit": 10000}},
+            team_metadata={"model_tpm_limit": {"gpt-4": 5000}},
+        )
+        result = get_key_model_tpm_limit(user_api_key_dict)
+        assert result == {"gpt-4": 10000}
diff --git a/tests/test_litellm/proxy/auth/test_login_utils.py b/tests/test_litellm/proxy/auth/test_login_utils.py
index c04b8114939..e7b27908c14 100644
--- a/tests/test_litellm/proxy/auth/test_login_utils.py
+++ b/tests/test_litellm/proxy/auth/test_login_utils.py
@@ -248,6 +248,83 @@ async def test_authenticate_user_wrong_password():
         assert "Invalid credentials" in exc_info.value.message
 
 
+@pytest.mark.asyncio
+async def test_authenticate_user_email_case_insensitive_login():
+    """Test that email lookup is case-insensitive during login"""
+    master_key = "sk-1234"
+    stored_email = "testemail@test.com"
+    login_email_mixed_case = "testEmail@test.com"
+    correct_password = "correct-password"
+    hashed_password = hash_token(token=correct_password)
+
+    # `LiteLLM_UserTable` does not define a `password` field, but `authenticate_user()`
+    # expects `user_row.password` to exist (invite-link login). Use a simple object.
+    mock_user = MagicMock()
+    mock_user.user_id = "test-user-123"
+    mock_user.user_email = stored_email
+    mock_user.password = hashed_password
+    mock_user.user_role = LitellmUserRoles.INTERNAL_USER
+
+    def mock_find_first(**kwargs):
+        where = kwargs.get("where", {})
+        user_email = where.get("user_email", {})
+        if user_email.get("mode") != "insensitive":
+            return None
+        if str(user_email.get("equals", "")).lower() == stored_email.lower():
+            return mock_user
+        return None
+
+    mock_prisma_client = MagicMock()
+    mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(
+        side_effect=mock_find_first
+    )
+
+    with patch.dict(
+        os.environ,
+        {
+            "DATABASE_URL": "postgresql://test:test@localhost/test",
+            "UI_USERNAME": "admin",
+            "UI_PASSWORD": "admin-password",
+        },
+    ):
+        with patch(
+            "litellm.proxy.auth.login_utils.expire_previous_ui_session_tokens",
+            new_callable=AsyncMock,
+            return_value=None,
+        ):
+            with patch(
+                "litellm.proxy.auth.login_utils.generate_key_helper_fn",
+                new_callable=AsyncMock,
+            ) as mock_generate_key:
+                mock_generate_key.side_effect = [
+                    {"token": "token-1"},
+                    {"token": "token-2"},
+                ]
+
+                result_mixed = await authenticate_user(
+                    username=login_email_mixed_case,
+                    password=correct_password,
+                    master_key=master_key,
+                    prisma_client=mock_prisma_client,
+                )
+                result_lower = await authenticate_user(
+                    username=stored_email,
+                    password=correct_password,
+                    master_key=master_key,
+                    prisma_client=mock_prisma_client,
+                )
+
+    assert result_mixed.user_id == result_lower.user_id == "test-user-123"
+    assert result_mixed.user_email == result_lower.user_email == stored_email
+
+    calls = mock_prisma_client.db.litellm_usertable.find_first.await_args_list
+    assert len(calls) == 2
+    for call, expected_username in zip(calls, [login_email_mixed_case, stored_email]):
+        where = call.kwargs["where"]
+        assert where["user_email"]["equals"] == expected_username
+        assert where["user_email"]["mode"] == "insensitive"
+
+
 @pytest.mark.asyncio
 async def test_authenticate_user_database_required_for_admin():
     """Test that database is required for admin login"""
diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py
index fcc8c1f0f2e..5f49db66089 100644
--- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py
+++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py
@@ -338,6 +338,17 @@ async def test_proxy_admin_expired_key_from_cache():
                 f"Exception message should mention 'Expired Key', got: {exc_info.value.message}"
             )
             
+            # Verify that the param field does NOT leak the full API key (Issue #18731)
+            # The param should be abbreviated like "sk-...XXXX" not the full plaintext key
+            assert exc_info.value.param is not None, "Exception should have 'param' attribute"
+            assert exc_info.value.param != api_key, (
+                f"SECURITY: Full API key should NOT be in param field! "
+                f"Got: {exc_info.value.param}, Expected abbreviated format like 'sk-...XXXX'"
+            )
+            assert exc_info.value.param.startswith("sk-..."), (
+                f"Param should be abbreviated to 'sk-...XXXX' format. Got: {exc_info.value.param}"
+            )
+            
             # Verify that cache deletion was called
             mock_delete_cache.assert_called_once()
             call_args = mock_delete_cache.call_args
@@ -347,3 +358,4 @@ async def test_proxy_admin_expired_key_from_cache():
         finally:
             # Clean up - restore original values if needed
             pass
+
diff --git a/tests/test_litellm/proxy/db/test_rds_iam_token_expiry.py b/tests/test_litellm/proxy/db/test_rds_iam_token_expiry.py
new file mode 100644
index 00000000000..1492acb0794
--- /dev/null
+++ b/tests/test_litellm/proxy/db/test_rds_iam_token_expiry.py
@@ -0,0 +1,275 @@
+"""
+Tests for the RDS IAM token proactive refresh implementation.
+
+Tests for GitHub Issue #16220: RDS IAM authentication connection failures after 15 minutes.
+
+The fix implements:
+1. Proactive background token refresh (refreshes 3 min before expiration)
+2. Precise sleep timing (1 wake-up per token cycle instead of polling)
+3. Proper locking during reconnection
+4. Fixed __getattr__ fallback that now waits for reconnection
+
+Run these tests:
+    poetry run pytest tests/test_litellm/proxy/db/test_rds_iam_token_expiry.py -v -s
+"""
+
+import asyncio
+import os
+import urllib.parse
+from datetime import datetime, timedelta
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+class TestPrismaWrapperTokenRefresh:
+    """Tests for the PrismaWrapper RDS IAM token refresh implementation."""
+
+    @pytest.fixture
+    def setup_env(self):
+        """Setup environment variables for testing."""
+        os.environ["DATABASE_HOST"] = "test-host.rds.amazonaws.com"
+        os.environ["DATABASE_PORT"] = "5432"
+        os.environ["DATABASE_USER"] = "test_user"
+        os.environ["DATABASE_NAME"] = "test_db"
+        os.environ["IAM_TOKEN_DB_AUTH"] = "True"
+        yield
+        # Cleanup
+        for key in [
+            "DATABASE_HOST",
+            "DATABASE_PORT",
+            "DATABASE_USER",
+            "DATABASE_NAME",
+            "DATABASE_URL",
+            "IAM_TOKEN_DB_AUTH",
+            "DATABASE_SCHEMA",
+        ]:
+            os.environ.pop(key, None)
+
+    def _generate_mock_token(self, expires_in_seconds: int = 900) -> str:
+        """Generate a mock IAM token with expiration info."""
+        now = datetime.utcnow()
+        date_str = now.strftime("%Y%m%dT%H%M%SZ")
+        # Build the token like AWS does
+        token = f"mock-token?X-Amz-Date={date_str}&X-Amz-Expires={expires_in_seconds}&X-Amz-Signature=abc123"
+        return urllib.parse.quote(token, safe="")
+
+    def _set_database_url_with_token(self, expires_in_seconds: int = 900):
+        """Set DATABASE_URL with a mock token."""
+        token = self._generate_mock_token(expires_in_seconds)
+        os.environ[
+            "DATABASE_URL"
+        ] = f"postgresql://test_user:{token}@test-host:5432/test_db"
+
+    @pytest.mark.asyncio
+    async def test_is_token_expired_fresh(self, setup_env):
+        """Test that fresh token is not detected as expired."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        self._set_database_url_with_token(expires_in_seconds=900)
+        db_url = os.getenv("DATABASE_URL")
+
+        assert wrapper.is_token_expired(db_url) is False
+
+    @pytest.mark.asyncio
+    async def test_is_token_expired_old(self, setup_env):
+        """Test that old token is detected as expired."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        # Create an expired token
+        old_date = datetime.utcnow() - timedelta(seconds=901)
+        date_str = old_date.strftime("%Y%m%dT%H%M%SZ")
+        token = (
+            f"mock-token?X-Amz-Date={date_str}&X-Amz-Expires=900&X-Amz-Signature=abc"
+        )
+        encoded_token = urllib.parse.quote(token, safe="")
+        db_url = f"postgresql://test_user:{encoded_token}@test-host:5432/test_db"
+
+        assert wrapper.is_token_expired(db_url) is True
+
+    @pytest.mark.asyncio
+    async def test_start_stop_token_refresh_task(self, setup_env):
+        """Test that token refresh task starts and stops correctly."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        # Set a valid token
+        self._set_database_url_with_token(expires_in_seconds=900)
+
+        # Start the task
+        await wrapper.start_token_refresh_task()
+        assert wrapper._token_refresh_task is not None
+        assert not wrapper._token_refresh_task.done()
+
+        # Stop the task
+        await wrapper.stop_token_refresh_task()
+        assert wrapper._token_refresh_task is None
+
+    @pytest.mark.asyncio
+    async def test_start_task_not_enabled(self, setup_env):
+        """Test that task doesn't start when IAM auth is not enabled."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        # IAM auth disabled
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=False)
+
+        await wrapper.start_token_refresh_task()
+        assert wrapper._token_refresh_task is None
+
+    @pytest.mark.asyncio
+    async def test_is_token_expired_null(self, setup_env):
+        """Test that None token is treated as expired."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        assert wrapper.is_token_expired(None) is True
+
+
+class TestTokenExpirationParsing:
+    """Tests for token expiration parsing utilities."""
+
+    def test_parse_token_expiration_valid(self):
+        """Test parsing expiration from a valid token."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        # Create a token with known expiration
+        token = "mock-token?X-Amz-Date=20240101T120000Z&X-Amz-Expires=900&X-Amz-Signature=abc"
+
+        expiration = wrapper._parse_token_expiration(token)
+
+        assert expiration is not None
+        expected = datetime(2024, 1, 1, 12, 0, 0) + timedelta(seconds=900)
+        assert expiration == expected
+
+    def test_parse_token_expiration_invalid(self):
+        """Test that invalid token returns None."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        # Invalid tokens
+        assert wrapper._parse_token_expiration(None) is None
+        assert wrapper._parse_token_expiration("no-query-params") is None
+        assert wrapper._parse_token_expiration("?missing=params") is None
+
+
+class TestBackgroundRefreshLoop:
+    """Tests for the background refresh loop timing."""
+
+    @pytest.fixture
+    def setup_env(self):
+        """Setup environment variables for testing."""
+        os.environ["DATABASE_HOST"] = "test-host.rds.amazonaws.com"
+        os.environ["DATABASE_PORT"] = "5432"
+        os.environ["DATABASE_USER"] = "test_user"
+        os.environ["DATABASE_NAME"] = "test_db"
+        yield
+        # Cleanup
+        for key in [
+            "DATABASE_HOST",
+            "DATABASE_PORT",
+            "DATABASE_USER",
+            "DATABASE_NAME",
+            "DATABASE_URL",
+        ]:
+            os.environ.pop(key, None)
+
+    @pytest.mark.asyncio
+    async def test_calculate_seconds_fallback_when_no_url(self, setup_env):
+        """Test that fallback is used when DATABASE_URL is not set."""
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+
+        mock_prisma = MagicMock()
+        wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+        # Don't set DATABASE_URL
+        seconds = wrapper._calculate_seconds_until_refresh()
+
+        # Should return fallback interval
+        assert seconds == wrapper.FALLBACK_REFRESH_INTERVAL_SECONDS
+
+
+# ============================================================================
+# DEMONSTRATION SCRIPT
+# ============================================================================
+
+
+async def demonstrate_fix():
+    """
+    Demonstrates the fix for the RDS IAM token expiration bug.
+
+    Shows how the proactive refresh prevents the 15-minute connection failure.
+    """
+    # Import the actual implementation
+    try:
+        from litellm.proxy.db.prisma_client import PrismaWrapper
+    except ImportError:
+        return
+
+    # Setup mock environment
+    os.environ["DATABASE_HOST"] = "mock-rds.region.rds.amazonaws.com"
+    os.environ["DATABASE_PORT"] = "5432"
+    os.environ["DATABASE_USER"] = "iam_user"
+    os.environ["DATABASE_NAME"] = "litellm"
+
+    # Create initial token (expires in 10 seconds for demo)
+    now = datetime.utcnow()
+    date_str = now.strftime("%Y%m%dT%H%M%SZ")
+    token = f"mock-token?X-Amz-Date={date_str}&X-Amz-Expires=10&X-Amz-Signature=abc123"
+    encoded_token = urllib.parse.quote(token, safe="")
+    os.environ[
+        "DATABASE_URL"
+    ] = f"postgresql://iam_user:{encoded_token}@mock-rds:5432/litellm"
+
+    # Create mock prisma client
+    mock_prisma = MagicMock()
+
+    wrapper = PrismaWrapper(original_prisma=mock_prisma, iam_token_db_auth=True)
+
+    # Override buffer for faster demo
+    wrapper.TOKEN_REFRESH_BUFFER_SECONDS = 3
+    wrapper.FALLBACK_REFRESH_INTERVAL_SECONDS = 5
+    _ = wrapper._calculate_seconds_until_refresh()  # Verify calculation works
+    db_url = os.getenv("DATABASE_URL")
+    is_expired = wrapper.is_token_expired(db_url)
+    assert is_expired is False, "Fresh token should not be expired!"
+
+    # Mock the _token_refresh_loop to prevent it from actually running
+    async def mock_loop():
+        try:
+            await asyncio.sleep(1000)
+        except asyncio.CancelledError:
+            pass
+
+    with patch.object(wrapper, "_token_refresh_loop", side_effect=mock_loop):
+        await wrapper.start_token_refresh_task()
+        await wrapper.stop_token_refresh_task()
+
+    # Cleanup
+    for key in [
+        "DATABASE_HOST",
+        "DATABASE_PORT",
+        "DATABASE_USER",
+        "DATABASE_NAME",
+        "DATABASE_URL",
+    ]:
+        os.environ.pop(key, None)
+
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_fix())
diff --git a/tests/test_litellm/proxy/google_endpoints/test_google_api_endpoints.py b/tests/test_litellm/proxy/google_endpoints/test_google_api_endpoints.py
index 11e34cbbea4..26c60b2c303 100644
--- a/tests/test_litellm/proxy/google_endpoints/test_google_api_endpoints.py
+++ b/tests/test_litellm/proxy/google_endpoints/test_google_api_endpoints.py
@@ -306,6 +306,88 @@ def test_google_generate_content_with_system_instruction():
         assert called_data["systemInstruction"] == system_instruction
         assert called_data["systemInstruction"]["parts"][0]["text"] == "Your name is Doodle."
         
+        # Verify contents are also present
+        assert "contents" in called_data
+        assert len(called_data["contents"]) == 1
+        assert called_data["contents"][0]["role"] == "user"
+
+
+def test_google_generate_content_with_image_config():
+    """
+    Test that imageConfig is correctly passed through from generationConfig to the router.
+    
+    This test verifies that imageConfig parameters (aspectRatio, imageSize) are preserved
+    when forwarding requests to Google GenAI through the endpoint.
+    """
+    try:
+        from fastapi import FastAPI
+        from fastapi.testclient import TestClient
+
+        from litellm.proxy.google_endpoints.endpoints import router as google_router
+    except ImportError as e:
+        pytest.skip(f"Skipping test due to missing dependency: {e}")
+    
+    # Create a FastAPI app and include the router
+    app = FastAPI()
+    app.include_router(google_router)
+    
+    # Create a test client
+    client = TestClient(app)
+    
+    # Mock all required proxy server dependencies
+    with patch("litellm.proxy.proxy_server.llm_router") as mock_router, \
+         patch("litellm.proxy.proxy_server.general_settings", {}), \
+         patch("litellm.proxy.proxy_server.proxy_config") as mock_proxy_config, \
+         patch("litellm.proxy.proxy_server.version", "1.0.0"), \
+         patch("litellm.proxy.litellm_pre_call_utils.add_litellm_data_to_request") as mock_add_data:
+        
+        mock_router.agenerate_content = AsyncMock(return_value={"test": "response"})
+        
+        # Mock add_litellm_data_to_request to pass through data unchanged
+        async def mock_add_litellm_data(data, request, user_api_key_dict, proxy_config, general_settings, version):
+            return data
+        
+        mock_add_data.side_effect = mock_add_litellm_data
+        
+        # Send a request with generationConfig containing imageConfig
+        response = client.post(
+            "/v1beta/models/gemini-3-pro-image-preview:generateContent",
+            json={
+                "contents": [{
+                    "role": "user",
+                    "parts": [{"text": "Create a vibrant infographic about photosynthesis"}]
+                }],
+                "generationConfig": {
+                    "responseModalities": ["TEXT", "IMAGE"],
+                    "imageConfig": {
+                        "aspectRatio": "9:16",
+                        "imageSize": "4K"
+                    }
+                }
+            },
+            headers={"Authorization": "Bearer sk-test-key"}
+        )
+        
+        # Verify the response
+        assert response.status_code == 200
+        
+        # Verify that agenerate_content was called
+        mock_router.agenerate_content.assert_called_once()
+        call_args = mock_router.agenerate_content.call_args
+        called_data = call_args[1]
+        
+        # Verify that config is present in the call arguments
+        assert "config" in called_data
+        
+        # Verify that imageConfig is preserved in the config
+        assert "imageConfig" in called_data["config"]
+        assert called_data["config"]["imageConfig"]["aspectRatio"] == "9:16"
+        assert called_data["config"]["imageConfig"]["imageSize"] == "4K"
+        
+        # Verify that responseModalities is also preserved
+        assert "responseModalities" in called_data["config"]
+        assert called_data["config"]["responseModalities"] == ["TEXT", "IMAGE"]
+        
         # Verify contents are also present
         assert "contents" in called_data
         assert len(called_data["contents"]) == 1
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
index b65065be366..8bf84b251d5 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py
@@ -589,3 +589,131 @@ class TestErrorHandling:
                 )
 
             assert "Generic Guardrail API failed" in str(exc_info.value)
+
+
+class TestMultimodalSupport:
+    """Test multimodal (image) message handling and serialization"""
+
+    @pytest.mark.asyncio
+    async def test_multimodal_message_serialization(self):
+        """
+        Test that multimodal messages with images are properly serialized.
+
+        This tests the fix for SerializationIterator error when messages contain
+        image_url content that includes Iterable types.
+        """
+        guardrail = GenericGuardrailAPI(
+            api_base="https://api.test.guardrail.com",
+            guardrail_name="test-multimodal-guardrail",
+        )
+
+        # Create multimodal request data with image content
+        request_data = {
+            "model": "gpt-4o",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": "What's in this image?"},
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": "https://example.com/image.jpg"},
+                        },
+                    ],
+                }
+            ],
+            "metadata": {
+                "user_api_key_user_id": "test-user",
+            },
+        }
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "action": "NONE",
+            "texts": ["What's in this image?"],
+            "images": ["https://example.com/image.jpg"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        with patch.object(
+            guardrail.async_handler, "post", return_value=mock_response
+        ) as mock_post:
+            # This should not raise SerializationIterator error
+            result = await guardrail.apply_guardrail(
+                inputs={
+                    "texts": ["What's in this image?"],
+                    "images": ["https://example.com/image.jpg"],
+                    "structured_messages": request_data["messages"],
+                },
+                request_data=request_data,
+                input_type="request",
+            )
+
+            # Verify API was called successfully
+            mock_post.assert_called_once()
+
+            # Verify the request was properly serialized (no SerializationIterator)
+            call_args = mock_post.call_args
+            json_payload = call_args.kwargs["json"]
+
+            # Verify structured_messages is a proper list, not an iterator
+            assert isinstance(json_payload["structured_messages"], list)
+            assert json_payload["images"] == ["https://example.com/image.jpg"]
+            assert json_payload["texts"] == ["What's in this image?"]
+
+    @pytest.mark.asyncio
+    async def test_iterable_content_serialization(self):
+        """
+        Test that Iterable content types are properly converted to lists.
+
+        The ChatCompletionAssistantMessage type allows content to be an Iterable,
+        which caused SerializationIterator errors before the fix.
+        """
+        guardrail = GenericGuardrailAPI(
+            api_base="https://api.test.guardrail.com",
+            guardrail_name="test-iterable-guardrail",
+        )
+
+        # Simulate a message with content that could be an iterable
+        def content_generator():
+            yield {"type": "text", "text": "Hello"}
+            yield {"type": "text", "text": "World"}
+
+        # Create request with generator-based content (simulating Iterable type)
+        messages_with_iterable = [
+            {
+                "role": "user",
+                "content": list(content_generator()),  # Convert to list for test
+            }
+        ]
+
+        request_data = {
+            "model": "gpt-4",
+            "messages": messages_with_iterable,
+        }
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "action": "NONE",
+            "texts": ["Hello", "World"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        with patch.object(
+            guardrail.async_handler, "post", return_value=mock_response
+        ) as mock_post:
+            result = await guardrail.apply_guardrail(
+                inputs={
+                    "texts": ["Hello", "World"],
+                    "structured_messages": messages_with_iterable,
+                },
+                request_data=request_data,
+                input_type="request",
+            )
+
+            mock_post.assert_called_once()
+
+            # Verify serialization succeeded
+            call_args = mock_post.call_args
+            json_payload = call_args.kwargs["json"]
+            assert isinstance(json_payload["structured_messages"], list)
\ No newline at end of file
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_pangea.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_pangea.py
index 9d5d6fd54c4..d770efee1c9 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_pangea.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_pangea.py
@@ -4,11 +4,13 @@ import httpx
 import pytest
 from fastapi import HTTPException
 
+from litellm.proxy.guardrails.guardrail_hooks.pangea import initialize_guardrail
 from litellm.proxy.guardrails.guardrail_hooks.pangea.pangea import (
     PangeaGuardrailMissingSecrets,
     PangeaHandler,
 )
 from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2
+from litellm.types.guardrails import GuardrailEventHooks, LitellmParams
 from litellm.types.utils import Choices, Message, ModelResponse
 
 
@@ -44,6 +46,28 @@ def test_pangea_guardrail_config():
     )
 
 
+def test_initialize_guardrail_sets_event_hook():
+    litellm_params = LitellmParams(
+        guardrail="pangea",
+        mode=GuardrailEventHooks.post_call,
+        api_key="pts_pangeatokenid",
+        pangea_input_recipe="guard_llm_request",
+        pangea_output_recipe="guard_llm_response",
+    )
+
+    guardrail = {"guardrail_name": "pangea-ai-guard"}
+
+    with patch(
+        "litellm.logging_callback_manager.add_litellm_callback"
+    ) as mock_add_callback:
+        callback = initialize_guardrail(
+            litellm_params=litellm_params, guardrail=guardrail
+        )
+
+    assert callback.event_hook == GuardrailEventHooks.post_call
+    mock_add_callback.assert_called_once_with(callback)
+
+
 def test_pangea_guardrail_config_no_api_key():
     with pytest.raises(PangeaGuardrailMissingSecrets):
         init_guardrails_v2(
diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_qualifire.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_qualifire.py
index 35ed49a84ed..fd72185d1e7 100644
--- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_qualifire.py
+++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_qualifire.py
@@ -2,7 +2,6 @@
 Unit tests for Qualifire guardrail integration.
 """
 
-import sys
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -75,9 +74,37 @@ class TestQualifireGuardrailInit:
 
         assert guardrail.on_flagged == "monitor"
 
+    def test_init_with_default_api_base(self):
+        """Test that default API base is set when not provided."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            DEFAULT_QUALIFIRE_API_BASE,
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            guardrail_name="test_guardrail",
+        )
+
+        assert guardrail.qualifire_api_base == DEFAULT_QUALIFIRE_API_BASE
+
+    def test_init_with_custom_api_base(self):
+        """Test initialization with custom API base URL."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            api_base="https://custom.qualifire.ai",
+            guardrail_name="test_guardrail",
+        )
+
+        assert guardrail.qualifire_api_base == "https://custom.qualifire.ai"
+
 
 class TestQualifireGuardrailMessageConversion:
-    """Tests for message conversion to Qualifire format."""
+    """Tests for message conversion to API format."""
 
     def test_convert_simple_messages(self):
         """Test conversion of simple text messages."""
@@ -95,15 +122,13 @@ class TestQualifireGuardrailMessageConversion:
             {"role": "assistant", "content": "Hi there!"},
         ]
 
-        # Create mock LLMMessage class
-        mock_llm_message = MagicMock()
+        result = guardrail._convert_messages_to_api_format(messages)
 
-        with patch(
-            "litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire.QualifireGuardrail._convert_messages_to_qualifire_format"
-        ) as mock_convert:
-            mock_convert.return_value = [mock_llm_message, mock_llm_message]
-            result = guardrail._convert_messages_to_qualifire_format(messages)
-            assert len(result) == 2
+        assert len(result) == 2
+        assert result[0]["role"] == "user"
+        assert result[0]["content"] == "Hello, world!"
+        assert result[1]["role"] == "assistant"
+        assert result[1]["content"] == "Hi there!"
 
     def test_convert_multimodal_messages(self):
         """Test conversion of multimodal messages with text parts."""
@@ -126,112 +151,258 @@ class TestQualifireGuardrailMessageConversion:
             },
         ]
 
-        with patch(
-            "litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire.QualifireGuardrail._convert_messages_to_qualifire_format"
-        ) as mock_convert:
-            mock_convert.return_value = [MagicMock()]
-            result = guardrail._convert_messages_to_qualifire_format(messages)
-            assert len(result) == 1
+        result = guardrail._convert_messages_to_api_format(messages)
+
+        assert len(result) == 1
+        assert result[0]["role"] == "user"
+        assert result[0]["content"] == "First part\nSecond part"
+
+    def test_convert_messages_with_tool_calls(self):
+        """Test conversion of messages with tool calls."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            guardrail_name="test_guardrail",
+        )
+
+        messages = [
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "id": "call_123",
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": '{"location": "NYC"}',
+                        },
+                    }
+                ],
+            },
+        ]
+
+        result = guardrail._convert_messages_to_api_format(messages)
+
+        assert len(result) == 1
+        assert result[0]["role"] == "assistant"
+        assert "tool_calls" in result[0]
+        assert len(result[0]["tool_calls"]) == 1
+        assert result[0]["tool_calls"][0]["id"] == "call_123"
+        assert result[0]["tool_calls"][0]["name"] == "get_weather"
+        assert result[0]["tool_calls"][0]["arguments"] == {"location": "NYC"}
 
 
-class TestQualifireGuardrailEvaluateKwargs:
-    """Tests for evaluate kwargs passed to Qualifire client."""
+class TestQualifireGuardrailToolConversion:
+    """Tests for tool definition conversion."""
+
+    def test_convert_openai_function_tools(self):
+        """Test conversion of OpenAI function tool format."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            guardrail_name="test_guardrail",
+        )
+
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get weather for a location",
+                    "parameters": {"type": "object", "properties": {}},
+                },
+            }
+        ]
+
+        result = guardrail._convert_tools_to_api_format(tools)
+
+        assert result is not None
+        assert len(result) == 1
+        assert result[0]["name"] == "get_weather"
+        assert result[0]["description"] == "Get weather for a location"
+
+    def test_convert_empty_tools(self):
+        """Test that empty tools returns None."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            guardrail_name="test_guardrail",
+        )
+
+        result = guardrail._convert_tools_to_api_format(None)
+        assert result is None
+
+        result = guardrail._convert_tools_to_api_format([])
+        assert result is None
+
+
+class TestQualifireGuardrailAPICall:
+    """Tests for API call with httpx client."""
 
     @pytest.mark.asyncio
     async def test_evaluate_called_with_prompt_injections(self):
-        """Test that evaluate is called with prompt_injections enabled."""
-        # Mock the qualifire module and its types
-        mock_qualifire_types = MagicMock()
-        mock_llm_message = MagicMock()
-        mock_llm_tool_call = MagicMock()
-        mock_message_instance = MagicMock()
-        mock_llm_message.return_value = mock_message_instance
-        
-        mock_qualifire_types.LLMMessage = mock_llm_message
-        mock_qualifire_types.LLMToolCall = mock_llm_tool_call
-        
-        with patch.dict('sys.modules', {'qualifire': MagicMock(), 'qualifire.types': mock_qualifire_types}):
-            from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
-                QualifireGuardrail,
-            )
+        """Test that evaluate endpoint is called with prompt_injections enabled."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
 
-            guardrail = QualifireGuardrail(
-                api_key="test_key",
-                prompt_injections=True,
-                guardrail_name="test_guardrail",
-            )
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            prompt_injections=True,
+            guardrail_name="test_guardrail",
+        )
 
-            # Mock the client
-            mock_client = MagicMock()
-            mock_result = MagicMock()
-            mock_result.score = 100
-            mock_result.status = "completed"
-            mock_result.evaluationResults = []
-            mock_client.evaluate.return_value = mock_result
-            guardrail._client = mock_client
+        # Mock the async HTTP handler
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "score": 100,
+            "status": "completed",
+            "evaluationResults": [],
+        }
+        mock_response.raise_for_status = MagicMock()
+        guardrail.async_handler.post = AsyncMock(return_value=mock_response)
 
-            messages = [{"role": "user", "content": "Hello, world!"}]
+        messages = [{"role": "user", "content": "Hello, world!"}]
 
-            await guardrail._run_qualifire_check(
-                messages=messages, output=None, dynamic_params={}
-            )
+        await guardrail._run_qualifire_check(
+            messages=messages, output=None, dynamic_params={}
+        )
 
-            # Verify evaluate was called with correct kwargs
-            mock_client.evaluate.assert_called_once()
-            call_kwargs = mock_client.evaluate.call_args[1]
-            assert call_kwargs["prompt_injections"] is True
-            assert "messages" in call_kwargs
+        # Verify the API was called
+        guardrail.async_handler.post.assert_called_once()
+        call_kwargs = guardrail.async_handler.post.call_args[1]
+
+        assert "json" in call_kwargs
+        payload = call_kwargs["json"]
+        assert payload["prompt_injections"] is True
+        assert "messages" in payload
+        assert call_kwargs["url"].endswith("/api/evaluation/evaluate")
 
     @pytest.mark.asyncio
     async def test_evaluate_called_with_multiple_checks(self):
         """Test that evaluate is called with multiple checks enabled."""
-        # Mock the qualifire module and its types
-        mock_qualifire_types = MagicMock()
-        mock_llm_message = MagicMock()
-        mock_llm_tool_call = MagicMock()
-        mock_message_instance = MagicMock()
-        mock_llm_message.return_value = mock_message_instance
-        
-        mock_qualifire_types.LLMMessage = mock_llm_message
-        mock_qualifire_types.LLMToolCall = mock_llm_tool_call
-        
-        with patch.dict('sys.modules', {'qualifire': MagicMock(), 'qualifire.types': mock_qualifire_types}):
-            from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
-                QualifireGuardrail,
-            )
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
 
-            guardrail = QualifireGuardrail(
-                api_key="test_key",
-                prompt_injections=True,
-                pii_check=True,
-                hallucinations_check=True,
-                assertions=["Output must be valid JSON"],
-                guardrail_name="test_guardrail",
-            )
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            prompt_injections=True,
+            pii_check=True,
+            hallucinations_check=True,
+            assertions=["Output must be valid JSON"],
+            guardrail_name="test_guardrail",
+        )
 
-            # Mock the client
-            mock_client = MagicMock()
-            mock_result = MagicMock()
-            mock_result.score = 100
-            mock_result.status = "completed"
-            mock_result.evaluationResults = []
-            mock_client.evaluate.return_value = mock_result
-            guardrail._client = mock_client
+        # Mock the async HTTP handler
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "score": 100,
+            "status": "completed",
+            "evaluationResults": [],
+        }
+        mock_response.raise_for_status = MagicMock()
+        guardrail.async_handler.post = AsyncMock(return_value=mock_response)
 
-            messages = [{"role": "user", "content": "Hello, world!"}]
+        messages = [{"role": "user", "content": "Hello, world!"}]
 
-            await guardrail._run_qualifire_check(
-                messages=messages, output="Test output", dynamic_params={}
-            )
+        await guardrail._run_qualifire_check(
+            messages=messages, output="Test output", dynamic_params={}
+        )
 
-            # Verify evaluate was called with correct kwargs
-            mock_client.evaluate.assert_called_once()
-            call_kwargs = mock_client.evaluate.call_args[1]
-            assert call_kwargs["prompt_injections"] is True
-            assert call_kwargs["pii_check"] is True
-            assert call_kwargs["hallucinations_check"] is True
-            assert call_kwargs["assertions"] == ["Output must be valid JSON"]
-            assert call_kwargs["output"] == "Test output"
+        # Verify the API was called with correct payload
+        guardrail.async_handler.post.assert_called_once()
+        call_kwargs = guardrail.async_handler.post.call_args[1]
+
+        payload = call_kwargs["json"]
+        assert payload["prompt_injections"] is True
+        assert payload["pii_check"] is True
+        assert payload["hallucinations_check"] is True
+        assert payload["assertions"] == ["Output must be valid JSON"]
+        assert payload["output"] == "Test output"
+
+    @pytest.mark.asyncio
+    async def test_invoke_endpoint_used_with_evaluation_id(self):
+        """Test that invoke endpoint is used when evaluation_id is provided."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="test_key",
+            evaluation_id="eval_123",
+            guardrail_name="test_guardrail",
+        )
+
+        # Mock the async HTTP handler
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "score": 100,
+            "status": "completed",
+            "evaluationResults": [],
+        }
+        mock_response.raise_for_status = MagicMock()
+        guardrail.async_handler.post = AsyncMock(return_value=mock_response)
+
+        messages = [{"role": "user", "content": "Hello, world!"}]
+
+        await guardrail._run_qualifire_check(
+            messages=messages, output="Test output", dynamic_params={}
+        )
+
+        # Verify the invoke endpoint was called
+        guardrail.async_handler.post.assert_called_once()
+        call_kwargs = guardrail.async_handler.post.call_args[1]
+
+        assert call_kwargs["url"].endswith("/api/evaluation/invoke")
+        payload = call_kwargs["json"]
+        assert payload["evaluation_id"] == "eval_123"
+        assert payload["input"] == "Hello, world!"
+        assert payload["output"] == "Test output"
+
+    @pytest.mark.asyncio
+    async def test_correct_headers_sent(self):
+        """Test that correct headers are sent with the API request."""
+        from litellm.proxy.guardrails.guardrail_hooks.qualifire.qualifire import (
+            QualifireGuardrail,
+        )
+
+        guardrail = QualifireGuardrail(
+            api_key="my_api_key",
+            guardrail_name="test_guardrail",
+        )
+
+        # Mock the async HTTP handler
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "score": 100,
+            "status": "completed",
+            "evaluationResults": [],
+        }
+        mock_response.raise_for_status = MagicMock()
+        guardrail.async_handler.post = AsyncMock(return_value=mock_response)
+
+        messages = [{"role": "user", "content": "Hello!"}]
+
+        await guardrail._run_qualifire_check(
+            messages=messages, output=None, dynamic_params={}
+        )
+
+        call_kwargs = guardrail.async_handler.post.call_args[1]
+        headers = call_kwargs["headers"]
+
+        assert headers["X-Qualifire-API-Key"] == "my_api_key"
+        assert headers["Content-Type"] == "application/json"
 
 
 class TestQualifireGuardrailCheckIfFlagged:
@@ -248,12 +419,14 @@ class TestQualifireGuardrailCheckIfFlagged:
             guardrail_name="test_guardrail",
         )
 
-        # Mock result with completed status and no flagged items
-        mock_result = MagicMock()
-        mock_result.status = "completed"
-        mock_result.evaluationResults = []
+        # Result with completed status and no flagged items (dict format)
+        result = {
+            "status": "completed",
+            "score": 100,
+            "evaluationResults": [],
+        }
 
-        assert guardrail._check_if_flagged(mock_result) is False
+        assert guardrail._check_if_flagged(result) is False
 
     def test_check_if_flagged_returns_true_for_flagged_content(self):
         """Test that _check_if_flagged returns True when content is flagged."""
@@ -266,18 +439,25 @@ class TestQualifireGuardrailCheckIfFlagged:
             guardrail_name="test_guardrail",
         )
 
-        # Mock result with flagged item
-        mock_inner_result = MagicMock()
-        mock_inner_result.flagged = True
+        # Result with flagged item (dict format matching API response)
+        result = {
+            "status": "completed",
+            "score": 15,
+            "evaluationResults": [
+                {
+                    "type": "prompt_injection",
+                    "results": [
+                        {
+                            "flagged": True,
+                            "score": 0.15,
+                            "reason": "Prompt injection detected",
+                        }
+                    ],
+                }
+            ],
+        }
 
-        mock_eval_result = MagicMock()
-        mock_eval_result.results = [mock_inner_result]
-
-        mock_result = MagicMock()
-        mock_result.status = "completed"
-        mock_result.evaluationResults = [mock_eval_result]
-
-        assert guardrail._check_if_flagged(mock_result) is True
+        assert guardrail._check_if_flagged(result) is True
 
     def test_check_if_flagged_returns_false_when_no_flagged_items(self):
         """Test that _check_if_flagged returns False when no items are flagged."""
@@ -291,17 +471,24 @@ class TestQualifireGuardrailCheckIfFlagged:
         )
 
         # Result with evaluation results but nothing flagged
-        mock_inner_result = MagicMock()
-        mock_inner_result.flagged = False
+        result = {
+            "status": "completed",
+            "score": 95,
+            "evaluationResults": [
+                {
+                    "type": "prompt_injection",
+                    "results": [
+                        {
+                            "flagged": False,
+                            "score": 0.95,
+                            "reason": "No issues detected",
+                        }
+                    ],
+                }
+            ],
+        }
 
-        mock_eval_result = MagicMock()
-        mock_eval_result.results = [mock_inner_result]
-
-        mock_result = MagicMock()
-        mock_result.status = "success"
-        mock_result.evaluationResults = [mock_eval_result]
-
-        assert guardrail._check_if_flagged(mock_result) is False
+        assert guardrail._check_if_flagged(result) is False
 
 
 class TestQualifireGuardrailShouldRun:
diff --git a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py
index b76957dbf39..134fc84965f 100644
--- a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py
+++ b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py
@@ -247,7 +247,9 @@ async def test_rate_limiter_script_return_values_v3(monkeypatch, time_controller
 )
 @pytest.mark.flaky(reruns=3)
 @pytest.mark.asyncio
-async def test_normal_router_call_tpm_v3(monkeypatch, rate_limit_object, time_controller):
+async def test_normal_router_call_tpm_v3(
+    monkeypatch, rate_limit_object, time_controller
+):
     """
     Test normal router call with parallel request limiter v3 for TPM rate limiting
     """
@@ -394,8 +396,10 @@ async def test_normal_router_call_tpm_v3(monkeypatch, rate_limit_object, time_co
 
     # Manually increment the token counter to simulate token usage from previous call
     # This simulates what would happen after a successful call
-    await local_cache.async_increment_cache(key=counter_key, value=15, ttl=2)  # Use up most of our 10 token limit
-    
+    await local_cache.async_increment_cache(
+        key=counter_key, value=15, ttl=2
+    )  # Use up most of our 10 token limit
+
     # Make another request to test rate limiting - this should fail as we've consumed tokens
     with pytest.raises(HTTPException) as exc_info:
         await parallel_request_handler.async_pre_call_hook(
@@ -535,7 +539,9 @@ async def test_async_log_failure_event_v3():
     )
 
     # Mock kwargs with user_api_key via standard_logging_object
-    mock_kwargs = {"standard_logging_object": {"metadata": {"user_api_key_hash": _api_key}}}
+    mock_kwargs = {
+        "standard_logging_object": {"metadata": {"user_api_key_hash": _api_key}}
+    }
 
     # Capture pipeline operations
     captured_ops = []
@@ -785,7 +791,7 @@ async def test_tpm_api_key_rate_limits_v3():
         tpm_limit_per_model=tpms,
         models=[],
     )
-    
+
     user_api_key_dict.metadata["model_tpm_limit"] = tpms
     user_api_key_dict.metadata["model_rpm_limit"] = rpms
 
@@ -804,32 +810,45 @@ async def test_tpm_api_key_rate_limits_v3():
         # Return Error response to ensure HTTPException
         return {
             "overall_code": "OVER_LIMIT",
-            "statuses": [{'code': 'OK', 'current_limit': 2, 'limit_remaining': 1, 'rate_limit_type': 'requests', 'descriptor_key': 'model_per_key'},
-                         {'code': 'OVER_LIMIT', 'current_limit': 2, 'limit_remaining': -18, 'rate_limit_type': 'tokens', 'descriptor_key': 'model_per_key'}]
+            "statuses": [
+                {
+                    "code": "OK",
+                    "current_limit": 2,
+                    "limit_remaining": 1,
+                    "rate_limit_type": "requests",
+                    "descriptor_key": "model_per_key",
+                },
+                {
+                    "code": "OVER_LIMIT",
+                    "current_limit": 2,
+                    "limit_remaining": -18,
+                    "rate_limit_type": "tokens",
+                    "descriptor_key": "model_per_key",
+                },
+            ],
         }
-        
+
     parallel_request_handler.should_rate_limit = mock_should_rate_limit
-    
+
     # Test the pre-call hook
     error = None
     try:
-       await parallel_request_handler.async_pre_call_hook(
+        await parallel_request_handler.async_pre_call_hook(
             user_api_key_dict=user_api_key_dict,
             cache=local_cache,
             data={"model": model},
             call_type="",
         )
     except HTTPException as e:
-        error=e
+        error = e
         assert e.status_code == 429
         assert "rate_limit_type" in e.headers
         assert e.headers.get("rate_limit_type") == "tokens"
         assert "retry-after" in e.headers
-        
-    
+
     assert error is not None, "An Exception must be thrown"
     assert captured_descriptors is not None, "Rate limit descriptors should be captured"
-    
+
     model_per_key_descriptor = None
     for descriptor in captured_descriptors:
         if descriptor["key"] == "model_per_key":
@@ -837,9 +856,15 @@ async def test_tpm_api_key_rate_limits_v3():
             break
 
     assert model_per_key_descriptor is not None, "Api-Key descriptor should be present"
-    assert model_per_key_descriptor["value"] == f"{_api_key_hash}:{model}", "Api-Key value should combine api_key and model"
-    assert model_per_key_descriptor["rate_limit"]["requests_per_unit"] == rpm_limit, "Api-Key RPM limit should be set"
-    assert model_per_key_descriptor["rate_limit"]["tokens_per_unit"] == tpm_limit, "Api-Key TPM limit should be set"
+    assert (
+        model_per_key_descriptor["value"] == f"{_api_key_hash}:{model}"
+    ), "Api-Key value should combine api_key and model"
+    assert (
+        model_per_key_descriptor["rate_limit"]["requests_per_unit"] == rpm_limit
+    ), "Api-Key RPM limit should be set"
+    assert (
+        model_per_key_descriptor["rate_limit"]["tokens_per_unit"] == tpm_limit
+    ), "Api-Key TPM limit should be set"
 
 
 @pytest.mark.asyncio
@@ -861,7 +886,7 @@ async def test_rpm_api_key_rate_limits_v3():
         tpm_limit_per_model=tpms,
         models=[],
     )
-    
+
     user_api_key_dict.metadata["model_tpm_limit"] = tpms
     user_api_key_dict.metadata["model_rpm_limit"] = rpms
 
@@ -880,31 +905,45 @@ async def test_rpm_api_key_rate_limits_v3():
         # Return Error response to ensure HTTPException
         return {
             "overall_code": "OVER_LIMIT",
-            "statuses": [{'code': 'OVER_LIMIT', 'current_limit': 2, 'limit_remaining': -2, 'rate_limit_type': 'requests', 'descriptor_key': 'model_per_key'},
-                         {'code': 'OK', 'current_limit': 2, 'limit_remaining': 2, 'rate_limit_type': 'tokens', 'descriptor_key': 'model_per_key'}]
+            "statuses": [
+                {
+                    "code": "OVER_LIMIT",
+                    "current_limit": 2,
+                    "limit_remaining": -2,
+                    "rate_limit_type": "requests",
+                    "descriptor_key": "model_per_key",
+                },
+                {
+                    "code": "OK",
+                    "current_limit": 2,
+                    "limit_remaining": 2,
+                    "rate_limit_type": "tokens",
+                    "descriptor_key": "model_per_key",
+                },
+            ],
         }
-        
+
     parallel_request_handler.should_rate_limit = mock_should_rate_limit
-    
+
     # Test the pre-call hook
     error = None
     try:
-       await parallel_request_handler.async_pre_call_hook(
+        await parallel_request_handler.async_pre_call_hook(
             user_api_key_dict=user_api_key_dict,
             cache=local_cache,
             data={"model": model},
             call_type="",
         )
     except HTTPException as e:
-        error=e
+        error = e
         assert e.status_code == 429
         assert "rate_limit_type" in e.headers
         assert e.headers.get("rate_limit_type") == "requests"
         assert "retry-after" in e.headers
-    
+
     assert error is not None, "An Exception must be thrown"
     assert captured_descriptors is not None, "Rate limit descriptors should be captured"
-    
+
     model_per_key_descriptor = None
     for descriptor in captured_descriptors:
         if descriptor["key"] == "model_per_key":
@@ -912,9 +951,16 @@ async def test_rpm_api_key_rate_limits_v3():
             break
 
     assert model_per_key_descriptor is not None, "Api-Key descriptor should be present"
-    assert model_per_key_descriptor["value"] == f"{_api_key_hash}:{model}", "Api-Key value should combine api_key and model"
-    assert model_per_key_descriptor["rate_limit"]["requests_per_unit"] == rpm_limit, "Api-Key RPM limit should be set"
-    assert model_per_key_descriptor["rate_limit"]["tokens_per_unit"] == tpm_limit, "Api-Key TPM limit should be set"
+    assert (
+        model_per_key_descriptor["value"] == f"{_api_key_hash}:{model}"
+    ), "Api-Key value should combine api_key and model"
+    assert (
+        model_per_key_descriptor["rate_limit"]["requests_per_unit"] == rpm_limit
+    ), "Api-Key RPM limit should be set"
+    assert (
+        model_per_key_descriptor["rate_limit"]["tokens_per_unit"] == tpm_limit
+    ), "Api-Key TPM limit should be set"
+
 
 @pytest.mark.asyncio
 async def test_team_member_rate_limits_v3():
@@ -925,7 +971,7 @@ async def test_team_member_rate_limits_v3():
     _api_key = hash_token(_api_key)
     _team_id = "team_123"
     _user_id = "user_456"
-    
+
     user_api_key_dict = UserAPIKeyAuth(
         api_key=_api_key,
         team_id=_team_id,
@@ -933,7 +979,7 @@ async def test_team_member_rate_limits_v3():
         team_member_rpm_limit=10,
         team_member_tpm_limit=1000,
     )
-    
+
     local_cache = DualCache()
     parallel_request_handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
@@ -947,15 +993,12 @@ async def test_team_member_rate_limits_v3():
         nonlocal captured_descriptors
         captured_descriptors = descriptors
         # Return OK response to avoid HTTPException
-        return {
-            "overall_code": "OK",
-            "statuses": []
-        }
+        return {"overall_code": "OK", "statuses": []}
 
     parallel_request_handler.should_rate_limit = mock_should_rate_limit
 
     # Test the pre-call hook
-    
+
     await parallel_request_handler.async_pre_call_hook(
         user_api_key_dict=user_api_key_dict,
         cache=local_cache,
@@ -965,24 +1008,32 @@ async def test_team_member_rate_limits_v3():
 
     # Verify team member descriptor was created
     assert captured_descriptors is not None, "Rate limit descriptors should be captured"
-    
+
     team_member_descriptor = None
     for descriptor in captured_descriptors:
         if descriptor["key"] == "team_member":
             team_member_descriptor = descriptor
             break
-    
-    assert team_member_descriptor is not None, "Team member descriptor should be present"
-    assert team_member_descriptor["value"] == f"{_team_id}:{_user_id}", "Team member value should combine team_id and user_id"
-    assert team_member_descriptor["rate_limit"]["requests_per_unit"] == 10, "Team member RPM limit should be set"
-    assert team_member_descriptor["rate_limit"]["tokens_per_unit"] == 1000, "Team member TPM limit should be set"
+
+    assert (
+        team_member_descriptor is not None
+    ), "Team member descriptor should be present"
+    assert (
+        team_member_descriptor["value"] == f"{_team_id}:{_user_id}"
+    ), "Team member value should combine team_id and user_id"
+    assert (
+        team_member_descriptor["rate_limit"]["requests_per_unit"] == 10
+    ), "Team member RPM limit should be set"
+    assert (
+        team_member_descriptor["rate_limit"]["tokens_per_unit"] == 1000
+    ), "Team member TPM limit should be set"
 
 
 @pytest.mark.asyncio
 async def test_dynamic_rate_limiting_v3():
     """
     Test that dynamic rate limiting only enforces limits when model has failures.
-    
+
     When rpm_limit_type is set to "dynamic":
     - If model has no failures, rate limits should NOT be enforced (allow exceeding)
     - If model has failures above threshold, rate limits SHOULD be enforced
@@ -990,75 +1041,75 @@ async def test_dynamic_rate_limiting_v3():
     _api_key = "sk-12345"
     _api_key_hash = hash_token(_api_key)
     model = "gpt-3.5-turbo"
-    
+
     # Set a low RPM limit to make testing easier
     user_api_key_dict = UserAPIKeyAuth(
         api_key=_api_key_hash,
         rpm_limit=2,
         metadata={"rpm_limit_type": "dynamic"},
     )
-    
+
     local_cache = DualCache()
     parallel_request_handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Mock should_rate_limit to track if limits are enforced
     captured_descriptors = []
-    
+
     async def mock_should_rate_limit(descriptors, **kwargs):
         captured_descriptors.clear()
         captured_descriptors.extend(descriptors)
         return {"overall_code": "OK", "statuses": []}
-    
+
     parallel_request_handler.should_rate_limit = mock_should_rate_limit
-    
+
     # Test 1: No failures - rate limits should NOT be enforced (rpm_limit should be None)
     async def mock_check_no_failures(*args, **kwargs):
         return False
-    
+
     parallel_request_handler._check_model_has_recent_failures = mock_check_no_failures
-    
+
     await parallel_request_handler.async_pre_call_hook(
         user_api_key_dict=user_api_key_dict,
         cache=local_cache,
         data={"model": model},
         call_type="",
     )
-    
+
     # Find the API key descriptor
     api_key_descriptor = None
     for descriptor in captured_descriptors:
         if descriptor["key"] == "api_key":
             api_key_descriptor = descriptor
             break
-    
+
     assert api_key_descriptor is not None, "API key descriptor should be present"
     assert (
         api_key_descriptor["rate_limit"]["requests_per_unit"] is None
     ), "RPM limit should be None when dynamic mode and no failures"
-    
+
     # Test 2: With failures - rate limits SHOULD be enforced (rpm_limit should be set)
     async def mock_check_with_failures(*args, **kwargs):
         return True
-    
+
     parallel_request_handler._check_model_has_recent_failures = mock_check_with_failures
     captured_descriptors.clear()
-    
+
     await parallel_request_handler.async_pre_call_hook(
         user_api_key_dict=user_api_key_dict,
         cache=local_cache,
         data={"model": model},
         call_type="",
     )
-    
+
     # Find the API key descriptor again
     api_key_descriptor = None
     for descriptor in captured_descriptors:
         if descriptor["key"] == "api_key":
             api_key_descriptor = descriptor
             break
-    
+
     assert api_key_descriptor is not None, "API key descriptor should be present"
     assert (
         api_key_descriptor["rate_limit"]["requests_per_unit"] == 2
@@ -1069,17 +1120,17 @@ async def test_dynamic_rate_limiting_v3():
 async def test_async_increment_tokens_with_ttl_preservation():
     """
     Test TTL preservation functionality for token increment operations.
-    
+
     This test verifies that:
     1. Keys are created with proper TTL on first increment
     2. TTL is preserved on subsequent increments (not reset)
     3. Both TTL and non-TTL operations work correctly in the same call
-    
+
     Environment variables required:
     - REDIS_HOST: Redis server hostname
     - REDIS_PORT: Redis server port
     - REDIS_PASSWORD: Redis password (optional)
-    
+
     Test scenario:
     1. First call: Create keys with TTL=60s and TTL=None
     2. Wait 2 seconds
@@ -1094,38 +1145,40 @@ async def test_async_increment_tokens_with_ttl_preservation():
 
     # Skip test if Redis environment variables are not set
     redis_host = os.getenv("REDIS_HOST")
-    redis_port = os.getenv("REDIS_PORT") 
+    redis_port = os.getenv("REDIS_PORT")
     redis_password = os.getenv("REDIS_PASSWORD")
-    
+
     if not redis_host or not redis_port:
         pytest.skip("Redis environment variables (REDIS_HOST, REDIS_PORT) not set")
-    
+
     # Setup Redis cache
     redis_cache = RedisCache(
         host=redis_host,
         port=int(redis_port),
         password=redis_password,
     )
-    
+
     local_cache = DualCache(redis_cache=redis_cache)
     parallel_request_handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Verify Redis connection is working
     try:
         await redis_cache.ping()
     except Exception as e:
         pytest.skip(f"Redis connection failed: {str(e)}")
-    
+
     # Verify the TTL preservation script is registered
     if parallel_request_handler.token_increment_script is None:
-        pytest.skip("Token increment script not available - Redis Lua scripting may not be supported")
-    
+        pytest.skip(
+            "Token increment script not available - Redis Lua scripting may not be supported"
+        )
+
     # Test keys - use hash tags to ensure they map to same Redis cluster slot
     test_key_with_ttl = "{test_ttl}:with_ttl"
     test_key_without_ttl = "{test_ttl}:without_ttl"
-    
+
     try:
         # Clean up any existing test keys
         try:
@@ -1134,88 +1187,108 @@ async def test_async_increment_tokens_with_ttl_preservation():
         except Exception:
             # Keys might not exist, ignore cleanup errors
             pass
-        
+
         # First increment: Create operations with mixed TTL scenarios
         pipeline_operations_first = [
             RedisPipelineIncrementOperation(
-                key=test_key_with_ttl,
-                increment_value=10.0,
-                ttl=60
+                key=test_key_with_ttl, increment_value=10.0, ttl=60
             ),
             RedisPipelineIncrementOperation(
-                key=test_key_without_ttl,
-                increment_value=5.0,
-                ttl=None  # No TTL
-            )
+                key=test_key_without_ttl, increment_value=5.0, ttl=None  # No TTL
+            ),
         ]
-        
+
         # Execute first increment
         await parallel_request_handler.async_increment_tokens_with_ttl_preservation(
             pipeline_operations=pipeline_operations_first
         )
-        
+
         # Small delay to ensure Redis has processed the commands
         await asyncio.sleep(0.1)
-        
+
         # Verify keys exist and check initial TTL
         ttl_after_first = await redis_cache.async_get_ttl(test_key_with_ttl)
-        value_after_first_with_ttl = await redis_cache.async_get_cache(test_key_with_ttl)
-        value_after_first_without_ttl = await redis_cache.async_get_cache(test_key_without_ttl)
-        
-        assert value_after_first_with_ttl == 10.0, f"First increment should set value to 10.0, got {value_after_first_with_ttl}"
-        assert value_after_first_without_ttl == 5.0, "First increment should set value to 5.0"
-        assert ttl_after_first is not None and ttl_after_first > 0, "Key with TTL should have positive TTL after first increment"
+        value_after_first_with_ttl = await redis_cache.async_get_cache(
+            test_key_with_ttl
+        )
+        value_after_first_without_ttl = await redis_cache.async_get_cache(
+            test_key_without_ttl
+        )
+
+        assert (
+            value_after_first_with_ttl == 10.0
+        ), f"First increment should set value to 10.0, got {value_after_first_with_ttl}"
+        assert (
+            value_after_first_without_ttl == 5.0
+        ), "First increment should set value to 5.0"
+        assert (
+            ttl_after_first is not None and ttl_after_first > 0
+        ), "Key with TTL should have positive TTL after first increment"
         assert ttl_after_first <= 60, "TTL should not exceed the set value"
-        
+
         # Check TTL for key without TTL (should be None, meaning no expiry)
         ttl_no_ttl_key = await redis_cache.async_get_ttl(test_key_without_ttl)
-        assert ttl_no_ttl_key is None, "Key without TTL should have no expiry (None from async_get_ttl)"
-        
+        assert (
+            ttl_no_ttl_key is None
+        ), "Key without TTL should have no expiry (None from async_get_ttl)"
+
         # Wait a moment to ensure TTL decreases
         await asyncio.sleep(2)
-        
+
         # Second increment: Same operations to test TTL preservation
         pipeline_operations_second = [
             RedisPipelineIncrementOperation(
-                key=test_key_with_ttl,
-                increment_value=15.0,
-                ttl=60  # Same TTL value
+                key=test_key_with_ttl, increment_value=15.0, ttl=60  # Same TTL value
             ),
             RedisPipelineIncrementOperation(
-                key=test_key_without_ttl,
-                increment_value=7.0,
-                ttl=None  # No TTL
-            )
+                key=test_key_without_ttl, increment_value=7.0, ttl=None  # No TTL
+            ),
         ]
-        
+
         # Execute second increment
         await parallel_request_handler.async_increment_tokens_with_ttl_preservation(
             pipeline_operations=pipeline_operations_second
         )
-        
+
         # Small delay to ensure Redis has processed the commands
         await asyncio.sleep(0.1)
-        
+
         # Verify TTL preservation and value updates
         ttl_after_second = await redis_cache.async_get_ttl(test_key_with_ttl)
-        value_after_second_with_ttl = await redis_cache.async_get_cache(test_key_with_ttl)
-        value_after_second_without_ttl = await redis_cache.async_get_cache(test_key_without_ttl)
-        
-        assert value_after_second_with_ttl == 25.0, "Second increment should update value to 25.0"
-        assert value_after_second_without_ttl == 12.0, "Second increment should update value to 12.0"
-        
+        value_after_second_with_ttl = await redis_cache.async_get_cache(
+            test_key_with_ttl
+        )
+        value_after_second_without_ttl = await redis_cache.async_get_cache(
+            test_key_without_ttl
+        )
+
+        assert (
+            value_after_second_with_ttl == 25.0
+        ), "Second increment should update value to 25.0"
+        assert (
+            value_after_second_without_ttl == 12.0
+        ), "Second increment should update value to 12.0"
+
         # Critical test: TTL should be preserved (not reset to 60)
         assert ttl_after_second is not None, "TTL should still exist"
-        assert ttl_after_second < ttl_after_first, "TTL should have decreased (not been reset)"
+        assert (
+            ttl_after_second < ttl_after_first
+        ), "TTL should have decreased (not been reset)"
         assert ttl_after_second > 0, "TTL should still be positive"
-        
+
         # TTL should not be close to the original 60 seconds (proving it wasn't reset)
-        assert ttl_after_second < 59, "TTL should be significantly less than original, proving preservation"
-        
+        assert (
+            ttl_after_second < 59
+        ), "TTL should be significantly less than original, proving preservation"
+
         # Key without TTL should still have no expiry
-        ttl_no_ttl_key_after_second = await redis_cache.async_get_ttl(test_key_without_ttl)
-        assert ttl_no_ttl_key_after_second is None, "Key without TTL should still have no expiry"
-        
+        ttl_no_ttl_key_after_second = await redis_cache.async_get_ttl(
+            test_key_without_ttl
+        )
+        assert (
+            ttl_no_ttl_key_after_second is None
+        ), "Key without TTL should still have no expiry"
+
     finally:
         # Clean up test keys
         try:
@@ -1224,7 +1297,7 @@ async def test_async_increment_tokens_with_ttl_preservation():
         except Exception:
             # Ignore cleanup errors
             pass
-        
+
         # Properly close Redis connections to prevent warnings
         try:
             await redis_cache.disconnect()
@@ -1239,115 +1312,125 @@ async def test_async_increment_tokens_fallback_behavior():
     Test fallback behavior when Lua script is not available.
     """
     from litellm.types.caching import RedisPipelineIncrementOperation
-    
+
     local_cache = DualCache()
     parallel_request_handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Mock the token_increment_script to None to simulate unavailable script
     parallel_request_handler.token_increment_script = None
-    
+
     # Mock the fallback method
     fallback_called = False
-    original_method = parallel_request_handler.internal_usage_cache.dual_cache.async_increment_cache_pipeline
-    
+    original_method = (
+        parallel_request_handler.internal_usage_cache.dual_cache.async_increment_cache_pipeline
+    )
+
     async def mock_fallback(*args, **kwargs):
         nonlocal fallback_called
         fallback_called = True
         return await original_method(*args, **kwargs)
-    
-    parallel_request_handler.internal_usage_cache.dual_cache.async_increment_cache_pipeline = mock_fallback
-    
+
+    parallel_request_handler.internal_usage_cache.dual_cache.async_increment_cache_pipeline = (
+        mock_fallback
+    )
+
     # Test operations
     pipeline_operations = [
         RedisPipelineIncrementOperation(
-            key="test_fallback_key",
-            increment_value=10.0,
-            ttl=60
+            key="test_fallback_key", increment_value=10.0, ttl=60
         )
     ]
-    
+
     # Execute increment
     await parallel_request_handler.async_increment_tokens_with_ttl_preservation(
         pipeline_operations=pipeline_operations
     )
-    
+
     # Verify fallback was called
-    assert fallback_called, "Fallback method should be called when Lua script is not available"
+    assert (
+        fallback_called
+    ), "Fallback method should be called when Lua script is not available"
 
 
 # Redis Cluster Compatibility Tests
 def test_group_keys_by_hash_tag_regular_redis():
     """
     Test that keys are correctly grouped for regular Redis (non-cluster).
-    
+
     For regular Redis, all keys should be grouped together under a single group.
     """
     local_cache = DualCache()
     handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Test keys with different hash tags
     test_keys = [
         "{api_key:sk-123}:window",
-        "{api_key:sk-123}:requests", 
+        "{api_key:sk-123}:requests",
         "{api_key:sk-123}:tokens",
         "{user:user-456}:window",
         "{user:user-456}:requests",
         "{team:team-789}:window",
         "{team:team-789}:tokens",
-        "no_hash_tag_key"
+        "no_hash_tag_key",
     ]
-    
+
     # Group the keys (should be single group for regular Redis)
     groups = handler._group_keys_by_hash_tag(test_keys)
-    
+
     # Verify all keys are in single group for regular Redis
     assert len(groups) == 1, f"Expected 1 group for regular Redis, got {len(groups)}"
     assert "all_keys" in groups, "Expected 'all_keys' group for regular Redis"
-    assert set(groups["all_keys"]) == set(test_keys), "All keys should be in single group"
+    assert set(groups["all_keys"]) == set(
+        test_keys
+    ), "All keys should be in single group"
 
 
 def test_group_keys_by_hash_tag_redis_cluster():
     """
     Test that keys are correctly grouped by Redis cluster slots when using Redis cluster.
-    
+
     This ensures that keys are grouped by their slot number for cluster compatibility.
     """
     from unittest.mock import patch
-    
+
     local_cache = DualCache()
     handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Mock _is_redis_cluster to return True
-    with patch.object(handler, '_is_redis_cluster', return_value=True):
+    with patch.object(handler, "_is_redis_cluster", return_value=True):
         # Test keys with different hash tags
         test_keys = [
             "{api_key:sk-123}:window",
-            "{api_key:sk-123}:requests", 
+            "{api_key:sk-123}:requests",
             "{user:user-456}:window",
             "{user:user-456}:requests",
         ]
-        
+
         # Group the keys (should be grouped by slot for Redis cluster)
         groups = handler._group_keys_by_hash_tag(test_keys)
-        
+
         # Verify keys are grouped by slot
         assert len(groups) >= 1, "Should have at least 1 slot group"
-        
+
         # All group keys should start with "slot_"
         for group_key in groups.keys():
-            assert group_key.startswith("slot_"), f"Group key {group_key} should start with 'slot_'"
-        
+            assert group_key.startswith(
+                "slot_"
+            ), f"Group key {group_key} should start with 'slot_'"
+
         # Verify all original keys are present across groups
         all_grouped_keys = []
         for group_keys in groups.values():
             all_grouped_keys.extend(group_keys)
-        assert set(all_grouped_keys) == set(test_keys), "All keys should be present in groups"
+        assert set(all_grouped_keys) == set(
+            test_keys
+        ), "All keys should be present in groups"
 
 
 def test_keyslot_for_redis_cluster():
@@ -1358,16 +1441,16 @@ def test_keyslot_for_redis_cluster():
     handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Test basic key
     slot1 = handler.keyslot_for_redis_cluster("user:1000")
     assert 0 <= slot1 < 16384, "Slot should be in valid range"
-    
+
     # Test key with hash tag
     slot2 = handler.keyslot_for_redis_cluster("foo{bar}baz")
     slot3 = handler.keyslot_for_redis_cluster("{bar}")
     assert slot2 == slot3, "Keys with same hash tag should have same slot"
-    
+
     # Test keys with same hash tag should have same slot
     slot4 = handler.keyslot_for_redis_cluster("{api_key:sk-123}:requests")
     slot5 = handler.keyslot_for_redis_cluster("{api_key:sk-123}:window")
@@ -1379,67 +1462,70 @@ async def test_execute_redis_batch_rate_limiter_script_cluster_compatibility():
     """
     Test that the Redis batch rate limiter script execution handles cluster compatibility
     by grouping keys and falling back gracefully on errors.
-    
+
     This simulates the Redis cluster error scenario and verifies fallback behavior.
     """
     from unittest.mock import AsyncMock, patch
-    
+
     local_cache = DualCache()
     handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Mock _is_redis_cluster to return True for this test
-    with patch.object(handler, '_is_redis_cluster', return_value=True):
+    with patch.object(handler, "_is_redis_cluster", return_value=True):
         # Mock script that simulates Redis cluster slot conflict
         mock_script = AsyncMock()
         mock_script.side_effect = [
-            Exception("EVALSHA - all keys must map to the same key slot"),  # First group fails
-            [1234, 1, 1234, 2]  # Second group succeeds
+            Exception(
+                "EVALSHA - all keys must map to the same key slot"
+            ),  # First group fails
+            [1234, 1, 1234, 2],  # Second group succeeds
         ]
         handler.batch_rate_limiter_script = mock_script
-        
+
         # Mock in-memory fallback (returns 2 values for 2 keys: window_start, counter)
         handler.in_memory_cache_sliding_window = AsyncMock(return_value=[1234, 1])
-        
+
         # Test keys from different hash tags (would fail in cluster without grouping)
         test_keys = [
             "{api_key:sk-123}:window",
             "{api_key:sk-123}:requests",
-            "{user:user-456}:window", 
-            "{user:user-456}:requests"
+            "{user:user-456}:window",
+            "{user:user-456}:requests",
         ]
-        
+
         # Execute the method
         results = await handler._execute_redis_batch_rate_limiter_script(
-            keys_to_fetch=test_keys,
-            now_int=1234
+            keys_to_fetch=test_keys, now_int=1234
         )
-        
+
         # Verify results: 2 from fallback + 4 from successful script = 6 total
         assert len(results) == 6, f"Expected 6 results, got {len(results)}"
-        
+
         # Verify script was called twice (once per slot group)
         assert mock_script.call_count == 2
-        
+
         # Verify fallback was called for the failed group
         handler.in_memory_cache_sliding_window.assert_called_once()
-        
+
         # Verify the calls were made with grouped keys
         call_args_list = mock_script.call_args_list
-        
+
         # Both calls should have keys, but we can't predict exact grouping without knowing slots
         # Just verify that keys were grouped and calls were made
         assert len(call_args_list) == 2, "Should have made 2 script calls"
-        
+
         # Verify all keys were processed
         all_processed_keys = []
         for call_args in call_args_list:
-            all_processed_keys.extend(call_args[1]['keys'])
-        
+            all_processed_keys.extend(call_args[1]["keys"])
+
         # Should have processed all keys (some might be duplicated due to fallback)
         unique_processed_keys = set(all_processed_keys)
-        assert len(unique_processed_keys) >= 2, "Should have processed at least some keys"
+        assert (
+            len(unique_processed_keys) >= 2
+        ), "Should have processed at least some keys"
 
 
 @pytest.mark.asyncio
@@ -1485,23 +1571,23 @@ async def test_multiple_rate_limits_per_descriptor():
                     "current_limit": 2,
                     "limit_remaining": 1,
                     "rate_limit_type": "requests",
-                    "descriptor_key": "api_key"
+                    "descriptor_key": "api_key",
                 },
                 {
                     "code": "OK",
                     "current_limit": 10,
                     "limit_remaining": 8,
                     "rate_limit_type": "tokens",
-                    "descriptor_key": "api_key"
+                    "descriptor_key": "api_key",
                 },
                 {
                     "code": "OVER_LIMIT",
                     "current_limit": 1,
                     "limit_remaining": -1,
                     "rate_limit_type": "max_parallel_requests",
-                    "descriptor_key": "api_key"
-                }
-            ]
+                    "descriptor_key": "api_key",
+                },
+            ],
         }
 
     parallel_request_handler.should_rate_limit = mock_should_rate_limit
@@ -1560,9 +1646,9 @@ async def test_missing_descriptor_fallback():
                     "current_limit": 2,
                     "limit_remaining": -1,
                     "rate_limit_type": "requests",
-                    "descriptor_key": "nonexistent_key"  # This won't match any descriptor
+                    "descriptor_key": "nonexistent_key",  # This won't match any descriptor
                 }
-            ]
+            ],
         }
 
     parallel_request_handler.should_rate_limit = mock_should_rate_limit
@@ -1597,14 +1683,17 @@ async def test_get_rate_limit_type_default_is_total(monkeypatch):
 
     # Mock general_settings to return empty dict (no token_rate_limit_type set)
     import litellm.proxy.proxy_server as proxy_server
-    original_settings = getattr(proxy_server, 'general_settings', {})
-    monkeypatch.setattr(proxy_server, 'general_settings', {})
+
+    original_settings = getattr(proxy_server, "general_settings", {})
+    monkeypatch.setattr(proxy_server, "general_settings", {})
 
     try:
         result = parallel_request_handler.get_rate_limit_type()
-        assert result == "total", f"Default rate limit type should be 'total', got '{result}'"
+        assert (
+            result == "total"
+        ), f"Default rate limit type should be 'total', got '{result}'"
     finally:
-        monkeypatch.setattr(proxy_server, 'general_settings', original_settings)
+        monkeypatch.setattr(proxy_server, "general_settings", original_settings)
 
 
 @pytest.mark.asyncio
@@ -1619,14 +1708,19 @@ async def test_get_rate_limit_type_invalid_falls_back_to_total(monkeypatch):
 
     # Mock general_settings to return an invalid token_rate_limit_type
     import litellm.proxy.proxy_server as proxy_server
-    original_settings = getattr(proxy_server, 'general_settings', {})
-    monkeypatch.setattr(proxy_server, 'general_settings', {'token_rate_limit_type': 'invalid_type'})
+
+    original_settings = getattr(proxy_server, "general_settings", {})
+    monkeypatch.setattr(
+        proxy_server, "general_settings", {"token_rate_limit_type": "invalid_type"}
+    )
 
     try:
         result = parallel_request_handler.get_rate_limit_type()
-        assert result == "total", f"Invalid rate limit type should fall back to 'total', got '{result}'"
+        assert (
+            result == "total"
+        ), f"Invalid rate limit type should fall back to 'total', got '{result}'"
     finally:
-        monkeypatch.setattr(proxy_server, 'general_settings', original_settings)
+        monkeypatch.setattr(proxy_server, "general_settings", original_settings)
 
 
 @pytest.mark.parametrize(
@@ -1638,7 +1732,9 @@ async def test_get_rate_limit_type_invalid_falls_back_to_total(monkeypatch):
     ],
 )
 @pytest.mark.asyncio
-async def test_async_log_success_event_with_dict_usage(monkeypatch, token_rate_limit_type, expected_field):
+async def test_async_log_success_event_with_dict_usage(
+    monkeypatch, token_rate_limit_type, expected_field
+):
     """
     Test that async_log_success_event correctly handles usage as a dict (Responses API format).
 
@@ -1664,13 +1760,13 @@ async def test_async_log_success_event_with_dict_usage(monkeypatch, token_rate_l
 
     # Create a mock response object with usage as a dict (Responses API format)
     from litellm.types.utils import BaseLiteLLMOpenAIResponseObject
-    
+
     # Use spec to make isinstance checks work correctly with MagicMock
     mock_response = MagicMock(spec=BaseLiteLLMOpenAIResponseObject)
     mock_response.usage = {
         "prompt_tokens": 25,
         "completion_tokens": 35,
-        "total_tokens": 60
+        "total_tokens": 60,
     }
 
     # Create mock kwargs for the success event
@@ -1760,7 +1856,10 @@ async def test_async_log_success_event_with_dict_usage_missing_fields(monkeypatc
         # total_tokens is missing
     }
     from litellm.types.utils import BaseLiteLLMOpenAIResponseObject
-    mock_response.__class__ = type('MockResponse', (BaseLiteLLMOpenAIResponseObject,), {})
+
+    mock_response.__class__ = type(
+        "MockResponse", (BaseLiteLLMOpenAIResponseObject,), {}
+    )
 
     # Create mock kwargs for the success event
     mock_kwargs = {
@@ -1805,7 +1904,9 @@ async def test_async_log_success_event_with_dict_usage_missing_fields(monkeypatc
 
     assert tpm_operation is not None, "Should have a TPM increment operation"
     # Should default to 0 when field is missing
-    assert tpm_operation["increment_value"] == 0, "Should default to 0 when completion_tokens is missing"
+    assert (
+        tpm_operation["increment_value"] == 0
+    ), "Should default to 0 when completion_tokens is missing"
 
 
 @pytest.mark.asyncio
@@ -1813,68 +1914,154 @@ async def test_execute_token_increment_script_cluster_compatibility():
     """
     Test that token increment script execution handles Redis cluster compatibility
     by grouping operations by slot.
-    
+
     This ensures token increments work correctly in cluster environments.
     """
     from typing import List
     from unittest.mock import AsyncMock, patch
 
     from litellm.types.caching import RedisPipelineIncrementOperation
-    
+
     local_cache = DualCache()
     handler = _PROXY_MaxParallelRequestsHandler(
         internal_usage_cache=InternalUsageCache(local_cache)
     )
-    
+
     # Mock _is_redis_cluster to return True for this test
-    with patch.object(handler, '_is_redis_cluster', return_value=True):
+    with patch.object(handler, "_is_redis_cluster", return_value=True):
         # Mock script
         mock_script = AsyncMock()
         handler.token_increment_script = mock_script
-        
+
         # Create pipeline operations with different hash tags
         pipeline_operations: List[RedisPipelineIncrementOperation] = [
+            {"key": "{api_key:sk-123}:tokens", "increment_value": 100, "ttl": 60},
             {
-                "key": "{api_key:sk-123}:tokens",
-                "increment_value": 100,
-                "ttl": 60
-            },
-            {
-                "key": "{api_key:sk-123}:max_parallel_requests", 
+                "key": "{api_key:sk-123}:max_parallel_requests",
                 "increment_value": -1,
-                "ttl": 60
+                "ttl": 60,
             },
-            {
-                "key": "{user:user-456}:tokens",
-                "increment_value": 50,
-                "ttl": 60
-            }
+            {"key": "{user:user-456}:tokens", "increment_value": 50, "ttl": 60},
         ]
-        
+
         # Execute the method
         await handler._execute_token_increment_script(pipeline_operations)
-        
+
         # Verify script was called (at least once, possibly more depending on slot grouping)
         assert mock_script.call_count >= 1, "Script should be called at least once"
-        
+
         call_args_list = mock_script.call_args_list
-        
+
         # Verify all operations were processed
         all_processed_keys = []
         for call_args in call_args_list:
-            all_processed_keys.extend(call_args[1]['keys'])
-        
+            all_processed_keys.extend(call_args[1]["keys"])
+
         # Should have processed all 3 keys
         expected_keys = {
             "{api_key:sk-123}:tokens",
             "{api_key:sk-123}:max_parallel_requests",
-            "{user:user-456}:tokens"
+            "{user:user-456}:tokens",
         }
-        assert set(all_processed_keys) == expected_keys, "All operation keys should be processed"
-        
+        assert (
+            set(all_processed_keys) == expected_keys
+        ), "All operation keys should be processed"
+
         # Verify args structure is correct for each call
         for call_args in call_args_list:
-            keys = call_args[1]['keys']
-            args = call_args[1]['args']
+            keys = call_args[1]["keys"]
+            args = call_args[1]["args"]
             # Each key should have 2 args (increment_value, ttl)
-            assert len(args) == len(keys) * 2, f"Each key should have 2 args, got {len(args)} args for {len(keys)} keys"
+            assert (
+                len(args) == len(keys) * 2
+            ), f"Each key should have 2 args, got {len(args)} args for {len(keys)} keys"
+
+
+class TestGetTotalTokensFromUsageCacheExclusion:
+    """
+    Tests for _get_total_tokens_from_usage cache token exclusion.
+
+    Issue: AWS Bedrock and similar providers exclude cache tokens from TPM calculation,
+    but LiteLLM was including them, causing up to 10x difference in rate limiting.
+    """
+
+    @pytest.fixture
+    def handler(self):
+        """Create a handler instance for testing."""
+        local_cache = DualCache()
+        return _PROXY_MaxParallelRequestsHandler(
+            internal_usage_cache=InternalUsageCache(local_cache),
+        )
+
+    def test_excludes_cached_tokens_from_total(self, handler):
+        """Cached tokens should be excluded from total token count."""
+        from litellm.types.utils import PromptTokensDetailsWrapper
+
+        usage = Usage(
+            prompt_tokens=1000,
+            completion_tokens=500,
+            total_tokens=1500,
+            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=800),
+        )
+
+        # Total should be 1500 - 800 = 700
+        result = handler._get_total_tokens_from_usage(usage, "total")
+        assert result == 700, f"Expected 700 (1500 - 800 cached), got {result}"
+
+    def test_excludes_cached_tokens_from_input(self, handler):
+        """Cached tokens should be excluded from input token count."""
+        from litellm.types.utils import PromptTokensDetailsWrapper
+
+        usage = Usage(
+            prompt_tokens=1000,
+            completion_tokens=500,
+            total_tokens=1500,
+            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=800),
+        )
+
+        # Input should be 1000 - 800 = 200
+        result = handler._get_total_tokens_from_usage(usage, "input")
+        assert result == 200, f"Expected 200 (1000 - 800 cached), got {result}"
+
+    def test_does_not_exclude_cached_tokens_from_output(self, handler):
+        """Cached tokens should NOT affect output token count."""
+        from litellm.types.utils import PromptTokensDetailsWrapper
+
+        usage = Usage(
+            prompt_tokens=1000,
+            completion_tokens=500,
+            total_tokens=1500,
+            prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=800),
+        )
+
+        # Output tokens should be unchanged
+        result = handler._get_total_tokens_from_usage(usage, "output")
+        assert result == 500, f"Expected 500 (no change for output), got {result}"
+
+    def test_handles_no_cached_tokens(self, handler):
+        """Should work correctly when no cached tokens present."""
+        usage = Usage(
+            prompt_tokens=1000,
+            completion_tokens=500,
+            total_tokens=1500,
+        )
+
+        result = handler._get_total_tokens_from_usage(usage, "total")
+        assert result == 1500, f"Expected 1500 (no cache), got {result}"
+
+    def test_handles_dict_usage_with_cached_tokens(self, handler):
+        """Should handle dict usage format (Responses API) with cached tokens."""
+        usage = {
+            "prompt_tokens": 1000,
+            "completion_tokens": 500,
+            "total_tokens": 1500,
+            "prompt_tokens_details": {"cached_tokens": 600},
+        }
+
+        result = handler._get_total_tokens_from_usage(usage, "total")
+        assert result == 900, f"Expected 900 (1500 - 600 cached), got {result}"
+
+    def test_handles_none_usage(self, handler):
+        """Should handle None usage gracefully."""
+        result = handler._get_total_tokens_from_usage(None, "total")
+        assert result == 0, f"Expected 0 for None usage, got {result}"
diff --git a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py
index f2bae2cb14a..bc223d15d5f 100644
--- a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py
+++ b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py
@@ -1,20 +1,20 @@
 import json
 import os
 import sys
-from litellm._uuid import uuid
+import types
 from datetime import datetime, timedelta
-from typing import List
+from typing import List, Optional
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
+from fastapi import FastAPI
 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
 
-from typing import Optional
-
 from litellm.proxy._types import (
     LiteLLM_MCPServerTable,
     LitellmUserRoles,
@@ -118,6 +118,22 @@ def setup_mock_prisma_client(
     return mock_prisma_client
 
 
+def create_mcp_router_test_client() -> TestClient:
+    from litellm.proxy.management_endpoints.mcp_management_endpoints import router
+
+    app = FastAPI()
+    app.include_router(router)
+    return TestClient(app)
+
+
+def patch_proxy_general_settings(settings: dict):
+    fake_proxy_server_module = types.SimpleNamespace(general_settings=settings)
+    return patch.dict(
+        sys.modules,
+        {"litellm.proxy.proxy_server": fake_proxy_server_module},
+    )
+
+
 class TestListMCPServers:
     """Test suite for list MCP servers functionality"""
 
@@ -1082,6 +1098,55 @@ class TestHealthCheckServers:
             assert result[1]["server_id"] == "server-2"
             assert result[1]["status"] == "unhealthy"
 
+
+class TestMCPRegistryEndpoint:
+    def test_registry_returns_404_when_flag_missing(self):
+        client = create_mcp_router_test_client()
+
+        with patch_proxy_general_settings({}):
+            response = client.get("/v1/mcp/registry.json")
+
+        assert response.status_code == 404
+
+    def test_registry_returns_404_when_flag_false(self):
+        client = create_mcp_router_test_client()
+
+        with patch_proxy_general_settings({"enable_mcp_registry": False}):
+            response = client.get("/v1/mcp/registry.json")
+
+        assert response.status_code == 404
+
+    def test_registry_returns_entries_when_enabled(self):
+        client = create_mcp_router_test_client()
+
+        mock_server = generate_mock_mcp_server_config_record(
+            server_id="server-123",
+            name="zapier",
+            url="https://zapier.example.com/mcp",
+            transport="http",
+        )
+
+        mock_manager = MagicMock()
+        mock_manager.get_registry.return_value = {mock_server.server_id: mock_server}
+
+        with patch_proxy_general_settings({"enable_mcp_registry": True}), patch(
+            "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager",
+            mock_manager,
+        ):
+            response = client.get("/v1/mcp/registry.json")
+
+        assert response.status_code == 200
+        data = response.json()
+        assert len(data["servers"]) == 2  # built-in + custom server
+
+        builtin_entry = data["servers"][0]["server"]
+        assert builtin_entry["name"] == "litellm-mcp-server"
+        assert builtin_entry["remotes"][0]["url"].endswith("/mcp")
+
+        custom_entry = data["servers"][1]["server"]
+        assert custom_entry["name"] == "zapier"
+        assert custom_entry["remotes"][0]["url"].endswith("/zapier/mcp")
+
     @pytest.mark.asyncio
     async def test_health_check_specific_servers(self):
         """
diff --git a/tests/test_litellm/proxy/management_endpoints/test_router_settings_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_router_settings_endpoints.py
new file mode 100644
index 00000000000..1f5473e75d4
--- /dev/null
+++ b/tests/test_litellm/proxy/management_endpoints/test_router_settings_endpoints.py
@@ -0,0 +1,71 @@
+"""
+Tests for router settings management endpoints.
+
+Tests the GET endpoints for router settings and router fields.
+"""
+import os
+import sys
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+
+sys.path.insert(
+    0, os.path.abspath("../../../..")
+)
+
+from litellm.proxy.proxy_server import app
+
+client = TestClient(app)
+
+
+class TestRouterSettingsEndpoints:
+    """Test suite for router settings endpoints"""
+
+    @pytest.mark.asyncio
+    async def test_get_router_fields_success(self):
+        """
+        Test GET /router/fields endpoint successfully returns field definitions without values.
+        """
+        # Make request to router fields endpoint
+        response = client.get(
+            "/router/fields",
+            headers={"Authorization": "Bearer sk-1234"}
+        )
+
+        # Verify response
+        assert response.status_code == 200
+        
+        response_data = response.json()
+        
+        # Verify response structure
+        assert "fields" in response_data
+        assert "routing_strategy_descriptions" in response_data
+        
+        # Verify fields is a list
+        assert isinstance(response_data["fields"], list)
+        assert len(response_data["fields"]) > 0
+        
+        # Verify each field has required properties and field_value is None
+        for field in response_data["fields"]:
+            assert "field_name" in field
+            assert "field_type" in field
+            assert "field_description" in field
+            assert "field_default" in field
+            assert "ui_field_name" in field
+            assert "field_value" in field
+            assert field["field_value"] is None  # Ensure field_value is None
+        
+        # Verify routing_strategy_descriptions is a dict
+        assert isinstance(response_data["routing_strategy_descriptions"], dict)
+        assert len(response_data["routing_strategy_descriptions"]) > 0
+        
+        # Verify routing_strategy field has options populated
+        routing_strategy_field = next(
+            (f for f in response_data["fields"] if f["field_name"] == "routing_strategy"),
+            None
+        )
+        assert routing_strategy_field is not None
+        assert "options" in routing_strategy_field
+        assert isinstance(routing_strategy_field["options"], list)
+        assert len(routing_strategy_field["options"]) > 0
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 56bba39e6c3..57ec019e420 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
@@ -1242,7 +1242,7 @@ class TestSpendLogsPayload:
                     "model": "claude-3-7-sonnet-20250219",
                     "user": "",
                     "team_id": "",
-                    "metadata": '{"applied_guardrails": [], "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "guardrail_information": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-3-7-sonnet-20250219", "model_map_value": {"key": "claude-3-7-sonnet-20250219", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
+                    "metadata": '{"applied_guardrails": [], "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "guardrail_information": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-3-7-sonnet-20250219", "model_map_value": {"key": "claude-3-7-sonnet-20250219", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
                     "cache_key": "Cache OFF",
                     "spend": 0.01383,
                     "total_tokens": 2598,
@@ -1334,7 +1334,7 @@ class TestSpendLogsPayload:
                     "model": "claude-3-7-sonnet-20250219",
                     "user": "",
                     "team_id": "",
-                    "metadata": '{"applied_guardrails": [], "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "guardrail_information": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-3-7-sonnet-20250219", "model_map_value": {"key": "claude-3-7-sonnet-20250219", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
+                    "metadata": '{"applied_guardrails": [], "batch_models": null, "mcp_tool_call_metadata": null, "vector_store_request_metadata": null, "guardrail_information": null, "usage_object": {"completion_tokens": 503, "prompt_tokens": 2095, "total_tokens": 2598, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}, "model_map_information": {"model_map_key": "claude-3-7-sonnet-20250219", "model_map_value": {"key": "claude-3-7-sonnet-20250219", "max_tokens": 128000, "max_input_tokens": 200000, "max_output_tokens": 128000, "input_cost_per_token": 3e-06, "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_token_above_200k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "input_cost_per_token_batches": null, "output_cost_per_token_batches": null, "output_cost_per_token": 1.5e-05, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_token_above_200k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "anthropic", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": true, "supports_embedding_image_input": false, "supports_native_streaming": null, "supports_web_search": false, "supports_reasoning": true, "search_context_cost_per_query": null, "tpm": null, "rpm": null, "supported_openai_params": ["stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", "thinking"]}}, "additional_usage_values": {"completion_tokens_details": {"accepted_prediction_tokens": null, "audio_tokens": null, "reasoning_tokens": null, "rejected_prediction_tokens": null, "text_tokens": 503, "image_tokens": null}, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": 0, "text_tokens": null, "image_tokens": null}, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0}}',
                     "cache_key": "Cache OFF",
                     "spend": 0.01383,
                     "total_tokens": 2598,
diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
index 69b7e504184..dd4cbfa2e7f 100644
--- a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
+++ b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py
@@ -19,6 +19,7 @@ import litellm
 from litellm.constants import LITELLM_TRUNCATED_PAYLOAD_FIELD, REDACTED_BY_LITELM_STRING
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.proxy.spend_tracking.spend_tracking_utils import (
+    _get_proxy_server_request_for_spend_logs_payload,
     _get_response_for_spend_logs_payload,
     _get_vector_store_request_for_spend_logs_payload,
     _sanitize_request_body_for_spend_logs_payload,
@@ -850,3 +851,63 @@ def test_get_logging_payload_handles_missing_overhead_gracefully():
         metadata.get("litellm_overhead_time_ms") is None
     ), "litellm_overhead_time_ms should be None when overhead is not provided"
 
+
+@patch(
+    "litellm.proxy.spend_tracking.spend_tracking_utils._should_store_prompts_and_responses_in_spend_logs"
+)
+def test_spend_logs_redacts_request_and_response_when_turn_off_message_logging_enabled(
+    mock_should_store,
+):
+    """
+    Test that both request body and response are redacted when turn_off_message_logging is enabled.
+    """
+    mock_should_store.return_value = True
+
+    # Test request redaction
+    litellm_params = {
+        "proxy_server_request": {
+            "body": {
+                "messages": [{"role": "user", "content": "secret message"}],
+                "model": "gpt-4",
+            }
+        }
+    }
+    metadata = {}
+    kwargs = {
+        "litellm_params": litellm_params,
+        "standard_callback_dynamic_params": {
+            "turn_off_message_logging": True,
+        },
+    }
+
+    request_result = _get_proxy_server_request_for_spend_logs_payload(
+        metadata=metadata, litellm_params=litellm_params, kwargs=kwargs
+    )
+
+    parsed_request = json.loads(request_result)
+    assert parsed_request["messages"] == [{"role": "user", "content": "redacted-by-litellm"}]
+    assert parsed_request["model"] == "gpt-4"
+
+    # Test response redaction - use dict response to verify redaction
+    response_dict = {
+        "id": "test-id",
+        "choices": [
+            {
+                "index": 0,
+                "message": {"role": "assistant", "content": "secret response"},
+            }
+        ],
+        "model": "gpt-4",
+    }
+    payload = cast(
+        StandardLoggingPayload,
+        {"response": response_dict},
+    )
+
+    response_result = _get_response_for_spend_logs_payload(payload=payload, kwargs=kwargs)
+
+    # When redaction is enabled and response is a dict (not ModelResponse),
+    # perform_redaction returns {"text": "redacted-by-litellm"}
+    parsed_response = json.loads(response_result)
+    assert parsed_response == {"text": "redacted-by-litellm"}
+
diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py
index b5d44385698..3d1e9aece41 100644
--- a/tests/test_litellm/proxy/test_common_request_processing.py
+++ b/tests/test_litellm/proxy/test_common_request_processing.py
@@ -3,7 +3,7 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 from fastapi import Request, status
-from fastapi.responses import StreamingResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 
 import litellm
 from litellm._uuid import uuid
@@ -11,9 +11,10 @@ from litellm.integrations.opentelemetry import UserAPIKeyAuth
 from litellm.proxy.common_request_processing import (
     ProxyBaseLLMRequestProcessing,
     ProxyConfig,
+    _extract_error_from_sse_chunk,
     _get_cost_breakdown_from_logging_obj,
     _parse_event_data_for_error,
-    create_streaming_response,
+    create_response,
 )
 from litellm.proxy.utils import ProxyLogging
 
@@ -75,6 +76,84 @@ class TestProxyBaseLLMRequestProcessing:
             pytest.fail("litellm_call_id is not a valid UUID")
         assert data_passed["litellm_call_id"] == returned_data["litellm_call_id"]
 
+    @pytest.mark.asyncio
+    async def test_should_apply_hierarchical_router_settings_to_user_config(
+        self, monkeypatch
+    ):
+        processing_obj = ProxyBaseLLMRequestProcessing(data={})
+        mock_request = MagicMock(spec=Request)
+        mock_request.headers = {}
+
+        async def mock_add_litellm_data_to_request(*args, **kwargs):
+            return {}
+
+        async def mock_common_processing_pre_call_logic(
+            user_api_key_dict, data, call_type
+        ):
+            data_copy = copy.deepcopy(data)
+            return data_copy
+
+        mock_proxy_logging_obj = MagicMock(spec=ProxyLogging)
+        mock_proxy_logging_obj.pre_call_hook = AsyncMock(
+            side_effect=mock_common_processing_pre_call_logic
+        )
+        monkeypatch.setattr(
+            litellm.proxy.common_request_processing,
+            "add_litellm_data_to_request",
+            mock_add_litellm_data_to_request,
+        )
+
+        mock_general_settings = {}
+        mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
+        mock_proxy_config = MagicMock(spec=ProxyConfig)
+        
+        mock_router_settings = {
+            "routing_strategy": "least-busy",
+            "timeout": 30.0,
+            "num_retries": 3,
+        }
+        mock_proxy_config._get_hierarchical_router_settings = AsyncMock(
+            return_value=mock_router_settings
+        )
+
+        mock_model_list = [
+            {"model_name": "gpt-3.5-turbo", "litellm_params": {"model": "gpt-3.5-turbo"}},
+            {"model_name": "gpt-4", "litellm_params": {"model": "gpt-4"}},
+        ]
+        mock_llm_router = MagicMock()
+        mock_llm_router.get_model_list = MagicMock(return_value=mock_model_list)
+
+        mock_prisma_client = MagicMock()
+        monkeypatch.setattr(
+            "litellm.proxy.proxy_server.prisma_client",
+            mock_prisma_client,
+        )
+
+        route_type = "acompletion"
+
+        returned_data, logging_obj = await processing_obj.common_processing_pre_call_logic(
+            request=mock_request,
+            general_settings=mock_general_settings,
+            user_api_key_dict=mock_user_api_key_dict,
+            proxy_logging_obj=mock_proxy_logging_obj,
+            proxy_config=mock_proxy_config,
+            route_type=route_type,
+            llm_router=mock_llm_router,
+        )
+
+        mock_proxy_config._get_hierarchical_router_settings.assert_called_once_with(
+            user_api_key_dict=mock_user_api_key_dict,
+            prisma_client=mock_prisma_client,
+        )
+        mock_llm_router.get_model_list.assert_called_once()
+
+        assert "user_config" in returned_data
+        user_config = returned_data["user_config"]
+        assert user_config["model_list"] == mock_model_list
+        assert user_config["routing_strategy"] == "least-busy"
+        assert user_config["timeout"] == 30.0
+        assert user_config["num_retries"] == 3
+
     @pytest.mark.asyncio
     async def test_stream_timeout_header_processing(self):
         """
@@ -602,21 +681,27 @@ class TestCommonRequestProcessingHelpers:
         assert await _parse_event_data_for_error(event_line) == expected_code
 
     async def test_create_streaming_response_first_chunk_is_error(self):
+        """
+        Test that when the first chunk is an error, a JSON error response is returned
+        instead of an SSE streaming response
+        """
         async def mock_generator():
             yield 'data: {"error": {"code": 403, "message": "forbidden"}}\n\n'
             yield 'data: {"content": "more data"}\n\n'
             yield "data: [DONE]\n\n"
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", {}
         )
+        # Should return JSONResponse instead of StreamingResponse
+        assert isinstance(response, JSONResponse)
         assert response.status_code == status.HTTP_403_FORBIDDEN
-        content = await self.consume_stream(response)
-        assert content == [
-            'data: {"error": {"code": 403, "message": "forbidden"}}\n\n',
-            'data: {"content": "more data"}\n\n',
-            "data: [DONE]\n\n",
-        ]
+        # Verify the response is in standard JSON error format
+        import json
+        body = json.loads(response.body.decode())
+        assert "error" in body
+        assert body["error"]["code"] == 403
+        assert body["error"]["message"] == "forbidden"
 
     async def test_create_streaming_response_first_chunk_not_error(self):
         async def mock_generator():
@@ -624,7 +709,7 @@ class TestCommonRequestProcessingHelpers:
             yield 'data: {"content": "second part"}\n\n'
             yield "data: [DONE]\n\n"
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", {}
         )
         assert response.status_code == status.HTTP_200_OK
@@ -641,7 +726,7 @@ class TestCommonRequestProcessingHelpers:
                 yield
             # Implicitly raises StopAsyncIteration
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", {}
         )
         assert response.status_code == status.HTTP_200_OK
@@ -654,7 +739,7 @@ class TestCommonRequestProcessingHelpers:
         mock_gen = AsyncMock()
         mock_gen.__anext__.side_effect = StopAsyncIteration
 
-        response = await create_streaming_response(mock_gen, "text/event-stream", {})
+        response = await create_response(mock_gen, "text/event-stream", {})
         assert response.status_code == status.HTTP_200_OK
         content = await self.consume_stream(response)
         assert content == []
@@ -665,7 +750,7 @@ class TestCommonRequestProcessingHelpers:
         mock_gen = AsyncMock()
         mock_gen.__anext__.side_effect = ValueError("Test error from generator")
 
-        response = await create_streaming_response(mock_gen, "text/event-stream", {})
+        response = await create_response(mock_gen, "text/event-stream", {})
         assert response.status_code == status.HTTP_500_INTERNAL_SERVER_ERROR
         content = await self.consume_stream(response)
         expected_error_data = {
@@ -682,19 +767,24 @@ class TestCommonRequestProcessingHelpers:
         assert content[1] == "data: [DONE]\n\n"
 
     async def test_create_streaming_response_first_chunk_error_string_code(self):
+        """
+        Test that when the first chunk contains a string error code, a JSON error response is returned
+        """
         async def mock_generator():
             yield 'data: {"error": {"code": "429", "message": "too many requests"}}\n\n'
             yield "data: [DONE]\n\n"
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", {}
         )
+        assert isinstance(response, JSONResponse)
         assert response.status_code == status.HTTP_429_TOO_MANY_REQUESTS
-        content = await self.consume_stream(response)
-        assert content == [
-            'data: {"error": {"code": "429", "message": "too many requests"}}\n\n',
-            "data: [DONE]\n\n",
-        ]
+        # Verify the response is in standard JSON error format
+        import json
+        body = json.loads(response.body.decode())
+        assert "error" in body
+        assert body["error"]["code"] == "429"
+        assert body["error"]["message"] == "too many requests"
 
     async def test_create_streaming_response_custom_headers(self):
         async def mock_generator():
@@ -702,7 +792,7 @@ class TestCommonRequestProcessingHelpers:
             yield "data: [DONE]\n\n"
 
         custom_headers = {"X-Custom-Header": "TestValue"}
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", custom_headers
         )
         assert response.headers["x-custom-header"] == "TestValue"
@@ -712,7 +802,7 @@ class TestCommonRequestProcessingHelpers:
             yield 'data: {"content": "data"}\n\n'
             yield "data: [DONE]\n\n"
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(),
             "text/event-stream",
             {},
@@ -729,7 +819,7 @@ class TestCommonRequestProcessingHelpers:
         async def mock_generator():
             yield "data: [DONE]\n\n"
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", {}
         )
         assert response.status_code == status.HTTP_200_OK  # Default status
@@ -742,7 +832,7 @@ class TestCommonRequestProcessingHelpers:
             yield 'data: {"content": "actual data"}\n\n'
             yield "data: [DONE]\n\n"
 
-        response = await create_streaming_response(
+        response = await create_response(
             mock_generator(), "text/event-stream", {}
         )
         assert response.status_code == status.HTTP_200_OK  # Default status
@@ -773,7 +863,7 @@ class TestCommonRequestProcessingHelpers:
 
         # Patch the tracer in the common_request_processing module
         with patch("litellm.proxy.common_request_processing.tracer", mock_tracer):
-            response = await create_streaming_response(
+            response = await create_response(
                 mock_generator(), "text/event-stream", {}
             )
 
@@ -810,7 +900,10 @@ class TestCommonRequestProcessingHelpers:
                 ), f"Call {i} should have operation name 'streaming.chunk.yield', got {args[0]}"
 
     async def test_create_streaming_response_dd_trace_with_error_chunk(self):
-        """Test that dd trace is applied even when the first chunk contains an error"""
+        """
+        Test that when the first chunk contains an error, JSONResponse is returned
+        and tracing is not triggered (since it's not a streaming response)
+        """
         from unittest.mock import patch
 
         # Create a mock tracer
@@ -827,28 +920,107 @@ class TestCommonRequestProcessingHelpers:
 
         # Patch the tracer in the common_request_processing module
         with patch("litellm.proxy.common_request_processing.tracer", mock_tracer):
-            response = await create_streaming_response(
+            response = await create_response(
                 mock_generator(), "text/event-stream", {}
             )
 
-            # Even with error, status should be set to error code but tracing should still work
+            # Should return JSONResponse instead of StreamingResponse
+            assert isinstance(response, JSONResponse)
             assert response.status_code == 400
 
-            # Consume the stream to trigger the tracer calls
-            content = await self.consume_stream(response)
+            # Verify the response is in standard JSON error format
+            import json
+            body = json.loads(response.body.decode())
+            assert "error" in body
+            assert body["error"]["code"] == 400
+            assert body["error"]["message"] == "bad request"
 
-            # Verify all chunks are present
-            assert len(content) == 3
+            # Since JSONResponse is returned instead of StreamingResponse, streaming tracing should not be triggered
+            # tracer.trace should not be called
+            assert mock_tracer.trace.call_count == 0
 
-            # Verify that tracer.trace was called for each chunk
-            assert mock_tracer.trace.call_count == 3
 
-            # Verify that each call was made with the correct operation name
-            actual_calls = mock_tracer.trace.call_args_list
-            assert len(actual_calls) == 3
+class TestExtractErrorFromSSEChunk:
+    """Tests for _extract_error_from_sse_chunk function"""
+
+    def test_extract_error_from_sse_chunk_with_valid_error(self):
+        """Test extracting error information from a standard SSE chunk"""
+        chunk = 'data: {"error": {"code": 403, "message": "forbidden", "type": "auth_error", "param": "api_key"}}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["code"] == 403
+        assert error["message"] == "forbidden"
+        assert error["type"] == "auth_error"
+        assert error["param"] == "api_key"
+
+    def test_extract_error_from_sse_chunk_with_string_code(self):
+        """Test error code as string type"""
+        chunk = 'data: {"error": {"code": "429", "message": "too many requests"}}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["code"] == "429"
+        assert error["message"] == "too many requests"
+
+    def test_extract_error_from_sse_chunk_with_bytes(self):
+        """Test input as bytes type"""
+        chunk = b'data: {"error": {"code": 500, "message": "internal error"}}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["code"] == 500
+        assert error["message"] == "internal error"
+
+    def test_extract_error_from_sse_chunk_with_done(self):
+        """Test [DONE] marker should return default error"""
+        chunk = "data: [DONE]\n\n"
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["message"] == "Unknown error"
+        assert error["type"] == "internal_server_error"
+        assert error["code"] == "500"
+        assert error["param"] is None
+
+    def test_extract_error_from_sse_chunk_without_error_field(self):
+        """Test missing error field should return default error"""
+        chunk = 'data: {"content": "some content"}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["message"] == "Unknown error"
+        assert error["type"] == "internal_server_error"
+        assert error["code"] == "500"
+
+    def test_extract_error_from_sse_chunk_with_invalid_json(self):
+        """Test invalid JSON should return default error"""
+        chunk = 'data: {invalid json}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["message"] == "Unknown error"
+        assert error["type"] == "internal_server_error"
+        assert error["code"] == "500"
+
+    def test_extract_error_from_sse_chunk_without_data_prefix(self):
+        """Test missing 'data:' prefix should return default error"""
+        chunk = '{"error": {"code": 400, "message": "bad request"}}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["message"] == "Unknown error"
+        assert error["type"] == "internal_server_error"
+        assert error["code"] == "500"
+
+    def test_extract_error_from_sse_chunk_with_empty_string(self):
+        """Test empty string should return default error"""
+        chunk = ""
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["message"] == "Unknown error"
+        assert error["type"] == "internal_server_error"
+        assert error["code"] == "500"
+
+    def test_extract_error_from_sse_chunk_with_minimal_error(self):
+        """Test minimal error object"""
+        chunk = 'data: {"error": {"message": "error occurred"}}\n\n'
+        error = _extract_error_from_sse_chunk(chunk)
+
+        assert error["message"] == "error occurred"
+        # Other fields should be obtained from the original error object (if exists)
+
 
-            for i, call in enumerate(actual_calls):
-                args, kwargs = call
-                assert (
-                    args[0] == "streaming.chunk.yield"
-                ), f"Call {i} should have operation name 'streaming.chunk.yield', got {args[0]}"
diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
index fd39b308a7a..deaa47d9da7 100644
--- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
+++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py
@@ -1297,3 +1297,145 @@ def test_update_model_if_key_alias_exists():
     original_model = data["model"]
     _update_model_if_key_alias_exists(data=data, user_api_key_dict=user_api_key_dict)
     assert data["model"] == original_model  # Should remain unchanged
+
+
+@pytest.mark.asyncio
+async def test_embedding_header_forwarding_with_model_group():
+    """
+    Test that headers are properly forwarded for embedding requests when
+    forward_client_headers_to_llm_api is configured for the model group.
+
+    This test verifies the fix for embedding endpoints not forwarding headers
+    similar to how chat completion endpoints do.
+    """
+    import litellm
+
+    # Setup mock request for embeddings
+    request_mock = MagicMock(spec=Request)
+    request_mock.url.path = "/v1/embeddings"
+    request_mock.url = MagicMock()
+    request_mock.url.__str__.return_value = "http://localhost/v1/embeddings"
+    request_mock.method = "POST"
+    request_mock.query_params = {}
+    request_mock.headers = {
+        "Content-Type": "application/json",
+        "X-Custom-Header": "custom-value",
+        "X-Request-ID": "test-request-123",
+        "Authorization": "Bearer sk-test-key",
+    }
+    request_mock.client = MagicMock()
+    request_mock.client.host = "127.0.0.1"
+
+    # Setup embedding request data
+    data = {
+        "model": "local-openai/text-embedding-3-small",
+        "input": ["Text to embed"],
+    }
+
+    # Setup user API key
+    user_api_key_dict = UserAPIKeyAuth(
+        api_key="test-key",
+        user_id="test-user",
+        org_id="test-org",
+    )
+
+    # Mock model_group_settings to enable header forwarding for the model
+    mock_settings = MagicMock(forward_client_headers_to_llm_api=["local-openai/*"])
+    original_model_group_settings = getattr(litellm, "model_group_settings", None)
+    litellm.model_group_settings = mock_settings
+
+    try:
+        # Call add_litellm_data_to_request which includes header forwarding logic
+        updated_data = await add_litellm_data_to_request(
+            data=data,
+            request=request_mock,
+            user_api_key_dict=user_api_key_dict,
+            proxy_config=MagicMock(),
+            general_settings={},
+            version="test-version",
+        )
+
+        # Verify that headers were added to the request data
+        assert "headers" in updated_data, "Headers should be added to embedding request"
+        
+        # Verify that only x- prefixed headers (except x-stainless) were forwarded
+        forwarded_headers = updated_data["headers"]
+        assert "X-Custom-Header" in forwarded_headers, "X-Custom-Header should be forwarded"
+        assert forwarded_headers["X-Custom-Header"] == "custom-value"
+        assert "X-Request-ID" in forwarded_headers, "X-Request-ID should be forwarded"
+        assert forwarded_headers["X-Request-ID"] == "test-request-123"
+        
+        # Verify that authorization header was NOT forwarded (sensitive header)
+        assert "Authorization" not in forwarded_headers, "Authorization header should not be forwarded"
+        
+        # Verify that Content-Type was NOT forwarded (doesn't start with x-)
+        assert "Content-Type" not in forwarded_headers, "Content-Type should not be forwarded"
+
+        # Verify original data fields are preserved
+        assert updated_data["model"] == "local-openai/text-embedding-3-small"
+        assert updated_data["input"] == ["Text to embed"]
+
+    finally:
+        # Restore original model_group_settings
+        litellm.model_group_settings = original_model_group_settings
+
+
+@pytest.mark.asyncio
+async def test_embedding_header_forwarding_without_model_group_config():
+    """
+    Test that headers are NOT forwarded for embedding requests when
+    the model is not in the forward_client_headers_to_llm_api list.
+    """
+    import litellm
+
+    # Setup mock request for embeddings
+    request_mock = MagicMock(spec=Request)
+    request_mock.url.path = "/v1/embeddings"
+    request_mock.url = MagicMock()
+    request_mock.url.__str__.return_value = "http://localhost/v1/embeddings"
+    request_mock.method = "POST"
+    request_mock.query_params = {}
+    request_mock.headers = {
+        "Content-Type": "application/json",
+        "X-Custom-Header": "custom-value",
+    }
+    request_mock.client = MagicMock()
+    request_mock.client.host = "127.0.0.1"
+
+    # Setup embedding request data with a model NOT in the forward list
+    data = {
+        "model": "text-embedding-ada-002",
+        "input": ["Text to embed"],
+    }
+
+    user_api_key_dict = UserAPIKeyAuth(
+        api_key="test-key",
+        user_id="test-user",
+    )
+
+    # Mock model_group_settings with a different model in the forward list
+    mock_settings = MagicMock(forward_client_headers_to_llm_api=["gpt-4", "claude-*"])
+    original_model_group_settings = getattr(litellm, "model_group_settings", None)
+    litellm.model_group_settings = mock_settings
+
+    try:
+        updated_data = await add_litellm_data_to_request(
+            data=data,
+            request=request_mock,
+            user_api_key_dict=user_api_key_dict,
+            proxy_config=MagicMock(),
+            general_settings={},
+            version="test-version",
+        )
+
+        # Verify that headers were NOT added since model is not in forward list
+        assert "headers" not in updated_data or updated_data.get("headers") is None, \
+            "Headers should not be forwarded for models not in forward_client_headers_to_llm_api list"
+
+        # Verify original data fields are preserved
+        assert updated_data["model"] == "text-embedding-ada-002"
+        assert updated_data["input"] == ["Text to embed"]
+
+    finally:
+        # Restore original model_group_settings
+        litellm.model_group_settings = original_model_group_settings
diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py
index 5c7ece04513..751a9033871 100644
--- a/tests/test_litellm/proxy/test_proxy_server.py
+++ b/tests/test_litellm/proxy/test_proxy_server.py
@@ -3036,3 +3036,183 @@ def test_get_image_root_case_uses_current_dir(monkeypatch):
 
         # Verify FileResponse was called
         assert mock_file_response.called, "FileResponse should be called"
+
+
+def test_get_config_normalizes_string_callbacks(monkeypatch):
+    """
+    Test that /get/config/callbacks normalizes string callbacks to lists.
+    """
+    from litellm.proxy.proxy_server import app, proxy_config, user_api_key_auth
+
+    config_data = {
+        "litellm_settings": {
+            "success_callback": "langfuse",
+            "failure_callback": None,
+            "callbacks": ["prometheus", "datadog"],
+        },
+        "general_settings": {},
+        "environment_variables": {},
+    }
+
+    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)
+    )
+
+    original_overrides = app.dependency_overrides.copy()
+    app.dependency_overrides[user_api_key_auth] = lambda: MagicMock()
+
+    client = TestClient(app)
+    try:
+        response = client.get("/get/config/callbacks")
+    finally:
+        app.dependency_overrides = original_overrides
+
+    assert response.status_code == 200
+    callbacks = response.json()["callbacks"]
+
+    success_callbacks = [cb["name"] for cb in callbacks if cb.get("type") == "success"]
+    failure_callbacks = [cb["name"] for cb in callbacks if cb.get("type") == "failure"]
+    success_and_failure_callbacks = [
+        cb["name"] for cb in callbacks if cb.get("type") == "success_and_failure"
+    ]
+
+    assert "langfuse" in success_callbacks
+    assert len(failure_callbacks) == 0
+    assert "prometheus" in success_and_failure_callbacks
+    assert "datadog" in success_and_failure_callbacks
+
+
+def test_deep_merge_dicts_skips_none_and_empty_lists(monkeypatch):
+    """
+    Test that _update_config_fields deep merge skips None values and empty lists.
+    """
+    from litellm.proxy.proxy_server import ProxyConfig
+
+    proxy_config = ProxyConfig()
+
+    current_config = {
+        "general_settings": {
+            "max_parallel_requests": 10,
+            "allowed_models": ["gpt-3.5-turbo", "gpt-4"],
+            "nested": {
+                "key1": "value1",
+                "key2": "value2",
+            },
+        }
+    }
+
+    db_param_value = {
+        "max_parallel_requests": None,
+        "allowed_models": [],
+        "new_key": "new_value",
+        "nested": {
+            "key1": "updated_value1",
+            "key3": "value3",
+        },
+    }
+
+    result = proxy_config._update_config_fields(
+        current_config, "general_settings", db_param_value
+    )
+
+    assert result["general_settings"]["max_parallel_requests"] == 10
+    assert result["general_settings"]["allowed_models"] == ["gpt-3.5-turbo", "gpt-4"]
+    assert result["general_settings"]["new_key"] == "new_value"
+    assert result["general_settings"]["nested"]["key1"] == "updated_value1"
+    assert result["general_settings"]["nested"]["key2"] == "value2"
+    assert result["general_settings"]["nested"]["key3"] == "value3"
+
+
+@pytest.mark.asyncio
+async def test_get_hierarchical_router_settings():
+    """
+    Test _get_hierarchical_router_settings method's priority order: Key > Team > Global
+    """
+    from unittest.mock import AsyncMock, MagicMock
+
+    from litellm.proxy._types import UserAPIKeyAuth
+    from litellm.proxy.proxy_server import ProxyConfig
+
+    proxy_config = ProxyConfig()
+
+    # Test Case 1: Returns None when prisma_client is None
+    result = await proxy_config._get_hierarchical_router_settings(
+        user_api_key_dict=None,
+        prisma_client=None,
+    )
+    assert result is None
+
+    # Test Case 2: Returns key-level router_settings when available (as dict)
+    mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
+    mock_user_api_key_dict.router_settings = {"routing_strategy": "key-level", "timeout": 10}
+    mock_user_api_key_dict.team_id = None
+
+    mock_prisma_client = MagicMock()
+
+    result = await proxy_config._get_hierarchical_router_settings(
+        user_api_key_dict=mock_user_api_key_dict,
+        prisma_client=mock_prisma_client,
+    )
+    assert result == {"routing_strategy": "key-level", "timeout": 10}
+
+    # Test Case 3: Returns key-level router_settings when available (as YAML string)
+    mock_user_api_key_dict.router_settings = "routing_strategy: key-yaml\ntimeout: 20"
+    result = await proxy_config._get_hierarchical_router_settings(
+        user_api_key_dict=mock_user_api_key_dict,
+        prisma_client=mock_prisma_client,
+    )
+    assert result == {"routing_strategy": "key-yaml", "timeout": 20}
+
+    # Test Case 4: Falls back to team-level router_settings when key-level is not available
+    mock_user_api_key_dict.router_settings = None
+    mock_user_api_key_dict.team_id = "team-123"
+
+    mock_team_obj = MagicMock()
+    mock_team_obj.router_settings = {"routing_strategy": "team-level", "timeout": 30}
+
+    mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock(
+        return_value=mock_team_obj
+    )
+
+    result = await proxy_config._get_hierarchical_router_settings(
+        user_api_key_dict=mock_user_api_key_dict,
+        prisma_client=mock_prisma_client,
+    )
+    assert result == {"routing_strategy": "team-level", "timeout": 30}
+    mock_prisma_client.db.litellm_teamtable.find_unique.assert_called_once_with(
+        where={"team_id": "team-123"}
+    )
+
+    # Test Case 5: Falls back to global router_settings when neither key nor team settings are available
+    mock_user_api_key_dict.router_settings = None
+    mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=None)
+
+    mock_db_config = MagicMock()
+    mock_db_config.param_value = {"routing_strategy": "global-level", "timeout": 40}
+
+    mock_prisma_client.db.litellm_config.find_first = AsyncMock(
+        return_value=mock_db_config
+    )
+
+    result = await proxy_config._get_hierarchical_router_settings(
+        user_api_key_dict=mock_user_api_key_dict,
+        prisma_client=mock_prisma_client,
+    )
+    assert result == {"routing_strategy": "global-level", "timeout": 40}
+    mock_prisma_client.db.litellm_config.find_first.assert_called_once_with(
+        where={"param_name": "router_settings"}
+    )
+
+    # Test Case 6: Returns None when no settings are found
+    mock_user_api_key_dict.router_settings = None
+    mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=None)
+    mock_prisma_client.db.litellm_config.find_first = AsyncMock(return_value=None)
+
+    result = await proxy_config._get_hierarchical_router_settings(
+        user_api_key_dict=mock_user_api_key_dict,
+        prisma_client=mock_prisma_client,
+    )
+    assert result is None
diff --git a/tests/test_litellm/responses/test_responses_utils.py b/tests/test_litellm/responses/test_responses_utils.py
index 96ac2e2c345..09628cd4a76 100644
--- a/tests/test_litellm/responses/test_responses_utils.py
+++ b/tests/test_litellm/responses/test_responses_utils.py
@@ -203,3 +203,22 @@ class TestResponseAPILoggingUtils:
         assert result.prompt_tokens == 0
         assert result.completion_tokens == 20
         assert result.total_tokens == 20
+
+    def test_transform_response_api_usage_calculates_total_from_input_and_output_tokens_if_available(self):
+        """Test transformation calculates total_tokens when it's None and input / output tokens are present"""
+        # Setup
+        usage = {
+            "input_tokens": 15,
+            "output_tokens": 25,
+            "total_tokens": None,
+        }
+
+        # Execute
+        result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(
+            usage
+        )
+
+        # Assert
+        assert result.prompt_tokens == 15
+        assert result.completion_tokens == 25
+        assert result.total_tokens == 40  # 15 + 25
diff --git a/tests/test_litellm/responses/test_text_format_conversion.py b/tests/test_litellm/responses/test_text_format_conversion.py
index 645f0f2e148..c7a79d9c461 100644
--- a/tests/test_litellm/responses/test_text_format_conversion.py
+++ b/tests/test_litellm/responses/test_text_format_conversion.py
@@ -34,7 +34,7 @@ class TestTextFormatConversion:
         Test that when text_format parameter is passed to litellm.aresponses,
         it gets converted to text parameter in the raw API call to OpenAI.
         """
-        from unittest.mock import AsyncMock, patch
+        from unittest.mock import AsyncMock, MagicMock, patch
 
         class TestResponse(BaseModel):
             """Test Pydantic model for structured output"""
@@ -42,20 +42,8 @@ class TestTextFormatConversion:
             answer: str
             confidence: float
 
-        class MockResponse:
-            """Mock response class for testing"""
-
-            def __init__(self, json_data, status_code):
-                self._json_data = json_data
-                self.status_code = status_code
-                self.text = json.dumps(json_data)
-                self.headers = {}
-
-            def json(self):
-                return self._json_data
-
         # Mock response from OpenAI
-        mock_response = {
+        mock_response_data = {
             "id": "resp_123",
             "object": "response",
             "created_at": 1741476542,
@@ -101,13 +89,74 @@ class TestTextFormatConversion:
 
         base_completion_call_args = self.get_base_completion_call_args()
 
-        with patch(
-            "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
-            new_callable=AsyncMock,
-        ) as mock_post:
-            # Configure the mock to return our response
-            mock_post.return_value = MockResponse(mock_response, 200)
+        # Mock the response_api_handler function to capture the request
+        captured_request = {}
 
+        def mock_handler(
+            model,
+            input,
+            responses_api_provider_config,
+            response_api_optional_request_params,
+            custom_llm_provider,
+            litellm_params,
+            logging_obj,
+            extra_headers=None,
+            extra_body=None,
+            timeout=None,
+            client=None,
+            fake_stream=False,
+            litellm_metadata=None,
+            shared_session=None,
+            _is_async=False,
+        ):
+            # Capture the request parameters
+            captured_request["model"] = model
+            captured_request["input"] = input
+            captured_request["params"] = response_api_optional_request_params
+            
+            # Return a mock ResponsesAPIResponse wrapped in a coroutine if async
+            async def async_response():
+                return ResponsesAPIResponse(
+                    id="resp_123",
+                    object="response",
+                    created_at=1741476542,
+                    status="completed",
+                    model="gpt-4o",
+                    output=mock_response_data["output"],
+                    usage=ResponseAPIUsage(
+                        input_tokens=10,
+                        output_tokens=20,
+                        total_tokens=30,
+                    ),
+                    text=mock_response_data.get("text"),
+                    error=None,
+                    incomplete_details=None,
+                )
+            
+            if _is_async:
+                return async_response()
+            else:
+                return ResponsesAPIResponse(
+                    id="resp_123",
+                    object="response",
+                    created_at=1741476542,
+                    status="completed",
+                    model="gpt-4o",
+                    output=mock_response_data["output"],
+                    usage=ResponseAPIUsage(
+                        input_tokens=10,
+                        output_tokens=20,
+                        total_tokens=30,
+                    ),
+                    text=mock_response_data.get("text"),
+                    error=None,
+                    incomplete_details=None,
+                )
+
+        with patch(
+            "litellm.responses.main.base_llm_http_handler.response_api_handler",
+            new=mock_handler,
+        ):
             litellm._turn_on_debug()
             litellm.set_verbose = True
 
@@ -118,21 +167,19 @@ class TestTextFormatConversion:
                 **base_completion_call_args,
             )
 
-            # Verify the request was made correctly
-            mock_post.assert_called_once()
-            request_body = mock_post.call_args.kwargs["json"]
-            print("Request body:", json.dumps(request_body, indent=4))
+            # Verify the captured request
+            print("Captured request:", json.dumps(captured_request, indent=4, default=str))
 
             # Validate that text_format was converted to text parameter
             assert (
-                "text" in request_body
-            ), "text parameter should be present in request body"
+                "text" in captured_request["params"]
+            ), "text parameter should be present in request params"
             assert (
-                "text_format" not in request_body
-            ), "text_format should not be in request body"
+                "text_format" not in captured_request["params"]
+            ), "text_format should not be in request params"
 
             # Validate the text parameter structure
-            text_param = request_body["text"]
+            text_param = captured_request["params"]["text"]
             assert "format" in text_param, "text parameter should have format field"
             assert (
                 text_param["format"]["type"] == "json_schema"
@@ -156,7 +203,7 @@ class TestTextFormatConversion:
             ), "schema should have confidence property"
 
             # Validate other request parameters
-            assert request_body["input"] == "What is the capital of France?"
+            assert captured_request["input"] == "What is the capital of France?"
 
             # Validate the response
             print("Response:", json.dumps(response, indent=4, default=str))
diff --git a/tests/test_litellm/router_strategy/test_router_tag_routing.py b/tests/test_litellm/router_strategy/test_router_tag_routing.py
index a3e722eeb85..1fdd3dad4da 100644
--- a/tests/test_litellm/router_strategy/test_router_tag_routing.py
+++ b/tests/test_litellm/router_strategy/test_router_tag_routing.py
@@ -313,17 +313,31 @@ async def test_error_from_tag_routing():
 
 def test_tag_routing_with_list_of_tags():
     """
-    Test that the router can handle a list of tags
+    Test that the router can handle a list of tags with match_any behavior
     """
     from litellm.router_strategy.tag_based_routing import is_valid_deployment_tag
 
     assert is_valid_deployment_tag(["teamA", "teamB"], ["teamA"])
     assert is_valid_deployment_tag(["teamA", "teamB"], ["teamA", "teamB"])
     assert is_valid_deployment_tag(["teamA", "teamB"], ["teamA", "teamC"])
+    assert is_valid_deployment_tag(["teamA"], ["teamA", "teamB"])
     assert not is_valid_deployment_tag(["teamA", "teamB"], ["teamC"])
     assert not is_valid_deployment_tag(["teamA", "teamB"], [])
     assert not is_valid_deployment_tag(["default"], ["teamA"])
 
+def test_tag_routing_with_list_of_tags_match_all():
+    """
+    Test that the router can handle a list of tags with match_all behavior
+    """
+    from litellm.router_strategy.tag_based_routing import is_valid_deployment_tag
+
+    assert is_valid_deployment_tag(["teamA", "teamB"], ["teamA"], match_any=False)
+    assert is_valid_deployment_tag(["teamA", "teamB"], ["teamA", "teamB"], match_any=False)
+    assert not is_valid_deployment_tag(["teamA", "teamB", "teamC"], ["teamA", "teamD"], match_any=False)
+    assert not is_valid_deployment_tag(["teamA"], ["teamA", "teamB"], match_any=False)
+    assert not is_valid_deployment_tag(["teamA", "teamB"], ["teamA", "teamC"], match_any=False)
+    assert not is_valid_deployment_tag(["teamA", "teamB"], [], match_any=False)
+    assert not is_valid_deployment_tag(["default"], ["teamA"], match_any=False)
 
 @pytest.mark.asyncio()
 async def test_router_free_paid_tier_with_responses_api():
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index 7036a953b83..0f618aeadbd 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -41,17 +41,17 @@ def test_cost_calculator_with_response_cost_in_additional_headers():
     assert result == 1000
 
 
-def test_cost_calculator_with_usage():
+def test_cost_calculator_with_usage(monkeypatch):
     from litellm import get_model_info
 
     os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
     litellm.model_cost = litellm.get_model_cost_map(url="")
 
     usage = Usage(
-        prompt_tokens=100,
+        prompt_tokens=120,
         completion_tokens=100,
         prompt_tokens_details=PromptTokensDetailsWrapper(
-            text_tokens=10, audio_tokens=90
+            text_tokens=10, audio_tokens=90, image_tokens=20,
         ),
     )
     mr = ModelResponse(usage=usage, model="gemini-2.0-flash-001")
@@ -68,15 +68,53 @@ def test_cost_calculator_with_usage():
 
     model_info = litellm.model_cost["gemini-2.0-flash-001"]
 
+    # Step 1: Test a model where input_cost_per_image_token is not set.
+    # In this case the calculation should use input_cost_per_token as fallback.
+    assert model_info.get("input_cost_per_image_token") is None, "Test case expects that input_cost_per_image_token is not set"
+
     expected_cost = (
         usage.prompt_tokens_details.audio_tokens
         * model_info["input_cost_per_audio_token"]
         + usage.prompt_tokens_details.text_tokens * model_info["input_cost_per_token"]
+        + usage.prompt_tokens_details.image_tokens * model_info["input_cost_per_token"]
         + usage.completion_tokens * model_info["output_cost_per_token"]
     )
 
     assert result == expected_cost, f"Got {result}, Expected {expected_cost}"
 
+    # Step 2: Set input_cost_per_image_token.
+    # In this case the explicit cost information should be used.
+    temp_model_info_object = dict(model_info)
+    temp_model_info_object["input_cost_per_image_token"] = 0.5
+
+    monkeypatch.setattr(
+        litellm,
+        "model_cost",
+        {
+            "gemini-2.0-flash-001": temp_model_info_object
+        },
+    )
+
+    result = response_cost_calculator(
+        response_object=mr,
+        model="",
+        custom_llm_provider="vertex_ai",
+        call_type="acompletion",
+        optional_params={},
+        cache_hit=None,
+        base_model=None,
+    )
+
+    expected_cost = (
+        usage.prompt_tokens_details.audio_tokens
+        * temp_model_info_object["input_cost_per_audio_token"]
+        + usage.prompt_tokens_details.text_tokens * temp_model_info_object["input_cost_per_token"]
+        + usage.prompt_tokens_details.image_tokens * temp_model_info_object["input_cost_per_image_token"]
+        + usage.completion_tokens * temp_model_info_object["output_cost_per_token"]
+    )
+
+    assert result == expected_cost, f"Got {result}, Expected {expected_cost}"
+
 
 def test_transcription_cost_uses_token_pricing():
     from litellm import completion_cost
@@ -1377,3 +1415,92 @@ def test_completion_cost_service_tier_priority():
     
     # Costs should be similar (all using flex)
     assert abs(cost_from_params - cost_from_usage) < 1e-6, "Costs from params and usage should be similar (both flex)"
+
+
+def test_gemini_cache_tokens_details_no_negative_values():
+    """
+    Test for Issue #18750: Negative text_tokens with Gemini caching
+    
+    When using Gemini with explicit caching, the response includes cacheTokensDetails
+    which breaks down cached tokens by modality. This test ensures that:
+    1. text_tokens is never negative
+    2. We correctly subtract cached tokens per modality (not total)
+    """
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    # Scenario from issue #18750: Image + text with explicit caching
+    # Real Gemini response structure when using cached content
+    completion_response = {
+        "usageMetadata": {
+            "promptTokenCount": 9660,
+            "candidatesTokenCount": 7,
+            "totalTokenCount": 9667,
+            "cachedContentTokenCount": 9651,
+            # Total tokens by modality (includes cached + non-cached)
+            "promptTokensDetails": [
+                {"modality": "TEXT", "tokenCount": 9402},
+                {"modality": "IMAGE", "tokenCount": 258}
+            ],
+            # Breakdown of cached tokens by modality
+            "cacheTokensDetails": [
+                {"modality": "TEXT", "tokenCount": 9393},
+                {"modality": "IMAGE", "tokenCount": 258}
+            ]
+        }
+    }
+
+    usage = VertexGeminiConfig._calculate_usage(completion_response)
+
+    # Text tokens should be non-cached text only: 9402 - 9393 = 9
+    assert usage.prompt_tokens_details.text_tokens == 9, \
+        f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}"
+
+    # Image tokens should be non-cached image only: 258 - 258 = 0
+    assert usage.prompt_tokens_details.image_tokens == 0, \
+        f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}"
+
+    # Total cached should match
+    assert usage.prompt_tokens_details.cached_tokens == 9651, \
+        f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}"
+
+    # MOST IMPORTANT: text_tokens should NEVER be negative
+    assert usage.prompt_tokens_details.text_tokens >= 0, \
+        f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750"
+
+    print("✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative")
+
+
+def test_gemini_without_cache_tokens_details():
+    """
+    Test Gemini response without cacheTokensDetails (implicit caching or no cache)
+    
+    When cacheTokensDetails is not present, we should use promptTokensDetails as-is
+    without subtracting anything.
+    """
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    completion_response = {
+        "usageMetadata": {
+            "promptTokenCount": 264,
+            "candidatesTokenCount": 15,
+            "totalTokenCount": 279,
+            "promptTokensDetails": [
+                {"modality": "TEXT", "tokenCount": 6},
+                {"modality": "IMAGE", "tokenCount": 258}
+            ]
+            # No cacheTokensDetails
+        }
+    }
+
+    usage = VertexGeminiConfig._calculate_usage(completion_response)
+
+    # Should use promptTokensDetails values directly
+    assert usage.prompt_tokens_details.text_tokens == 6
+    assert usage.prompt_tokens_details.image_tokens == 258
+    assert usage.prompt_tokens_details.text_tokens >= 0
+
+    print("✅ Gemini without cacheTokensDetails works correctly")
diff --git a/tests/test_litellm/test_lazy_imports.py b/tests/test_litellm/test_lazy_imports.py
index 660933efac5..48d78c0b01b 100644
--- a/tests/test_litellm/test_lazy_imports.py
+++ b/tests/test_litellm/test_lazy_imports.py
@@ -42,34 +42,45 @@ from litellm._lazy_imports import (
 
 def _clear_names_from_globals(names: tuple):
     """Clear all names from litellm globals."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
     for name in names:
-        if name in litellm.__dict__:
-            del litellm.__dict__[name]
+        if name in litellm_globals:
+            del litellm_globals[name]
 
 
 def _clear_names_from_utils_globals(names: tuple):
     """Clear all names from litellm.utils globals."""
+    # Get the actual globals dict, not a copy
+    utils_globals = sys.modules["litellm.utils"].__dict__
     for name in names:
-        if name in litellm.utils.__dict__:
-            del litellm.utils.__dict__[name]
+        if name in utils_globals:
+            del utils_globals[name]
 
 
 def _verify_only_requested_name_imported(name: str, all_names: tuple):
     """Verify that only the requested name is in globals, not the others."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
     for other_name in all_names:
         if other_name != name:
-            assert other_name not in litellm.__dict__, f"{other_name} should not be imported when importing {name}"
+            assert other_name not in litellm_globals, f"{other_name} should not be imported when importing {name}"
 
 
 def _verify_only_requested_name_imported_in_utils(name: str, all_names: tuple):
     """Verify that only the requested name is in utils globals, not the others."""
+    # Get the actual globals dict, not a copy
+    utils_globals = sys.modules["litellm.utils"].__dict__
     for other_name in all_names:
         if other_name != name:
-            assert other_name not in litellm.utils.__dict__, f"{other_name} should not be imported when importing {name}"
+            assert other_name not in utils_globals, f"{other_name} should not be imported when importing {name}"
 
 
 def test_cost_calculator_lazy_imports():
     """Test that all cost calculator functions can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     # Test each name individually - only that name should be imported
     for name in COST_CALCULATOR_NAMES:
         # Clear all names before importing just one
@@ -78,7 +89,7 @@ def test_cost_calculator_lazy_imports():
         func = _lazy_import_cost_calculator(name)
         assert func is not None
         assert callable(func)
-        assert name in litellm.__dict__
+        assert name in litellm_globals
         
         # Verify only the requested name is in globals, not the others
         _verify_only_requested_name_imported(name, COST_CALCULATOR_NAMES)
@@ -86,6 +97,9 @@ def test_cost_calculator_lazy_imports():
 
 def test_litellm_logging_lazy_imports():
     """Test that all litellm_logging items can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     # Test each name individually - only that name should be imported
     for name in LITELLM_LOGGING_NAMES:
         # Clear all names before importing just one
@@ -93,7 +107,7 @@ def test_litellm_logging_lazy_imports():
         
         item = _lazy_import_litellm_logging(name)
         assert item is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
         
         # Verify only the requested name is in globals, not the others
         _verify_only_requested_name_imported(name, LITELLM_LOGGING_NAMES)
@@ -101,6 +115,9 @@ def test_litellm_logging_lazy_imports():
 
 def test_utils_lazy_imports():
     """Test that all utils functions can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     # Test each name individually - only that name should be imported
     for name in UTILS_NAMES:
         # Clear all names before importing just one
@@ -108,7 +125,7 @@ def test_utils_lazy_imports():
         
         attr = _lazy_import_utils(name)
         assert attr is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
         
         # Verify only the requested name is in globals, not the others
         _verify_only_requested_name_imported(name, UTILS_NAMES)
@@ -116,6 +133,9 @@ def test_utils_lazy_imports():
 
 def test_caching_lazy_imports():
     """Test that all caching classes can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     # Test each name individually - only that name should be imported
     for name in CACHING_NAMES:
         # Clear all names before importing just one
@@ -123,7 +143,7 @@ def test_caching_lazy_imports():
         
         cls = _lazy_import_caching(name)
         assert cls is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
         
         # Verify only the requested name is in globals, not the others
         _verify_only_requested_name_imported(name, CACHING_NAMES)
@@ -131,71 +151,89 @@ def test_caching_lazy_imports():
 
 def test_token_counter_lazy_imports():
     """Test that token counter utilities can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in TOKEN_COUNTER_NAMES:
         _clear_names_from_globals(TOKEN_COUNTER_NAMES)
 
         func = _lazy_import_token_counter(name)
         assert func is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         _verify_only_requested_name_imported(name, TOKEN_COUNTER_NAMES)
 
 
 def test_bedrock_types_lazy_imports():
     """Test that Bedrock type aliases can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in BEDROCK_TYPES_NAMES:
         _clear_names_from_globals(BEDROCK_TYPES_NAMES)
 
         alias = _lazy_import_bedrock_types(name)
         assert alias is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         _verify_only_requested_name_imported(name, BEDROCK_TYPES_NAMES)
 
 
 def test_types_utils_lazy_imports():
     """Test that common types.utils symbols can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in TYPES_UTILS_NAMES:
         _clear_names_from_globals(TYPES_UTILS_NAMES)
 
         obj = _lazy_import_types_utils(name)
         assert obj is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         _verify_only_requested_name_imported(name, TYPES_UTILS_NAMES)
 
 
 def test_llm_client_cache_lazy_imports():
     """Test that LLM client cache class and singleton can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in LLM_CLIENT_CACHE_NAMES:
         _clear_names_from_globals(LLM_CLIENT_CACHE_NAMES)
 
         obj = _lazy_import_llm_client_cache(name)
         assert obj is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         _verify_only_requested_name_imported(name, LLM_CLIENT_CACHE_NAMES)
 
 
 def test_http_handler_lazy_imports():
     """Test that HTTP handler singletons can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in HTTP_HANDLER_NAMES:
         _clear_names_from_globals(HTTP_HANDLER_NAMES)
 
         handler = _lazy_import_http_handlers(name)
         assert handler is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         _verify_only_requested_name_imported(name, HTTP_HANDLER_NAMES)
 
 
 def test_dotprompt_lazy_imports():
     """Test that dotprompt globals can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in DOTPROMPT_NAMES:
         _clear_names_from_globals(DOTPROMPT_NAMES)
 
         obj = _lazy_import_dotprompt(name)
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         # Only the setter must be callable; others may be None by default
         if name == "set_global_prompt_directory":
@@ -245,12 +283,15 @@ def test_unknown_attribute_raises_error():
 
 def test_llm_config_lazy_imports():
     """Test that LLM config classes can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in LLM_CONFIG_NAMES:
         _clear_names_from_globals(LLM_CONFIG_NAMES)
 
         obj = _lazy_import_llm_configs(name)
         assert obj is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
         # Config classes should be classes/types
         assert isinstance(obj, type), f"{name} should be a class"
 
@@ -259,12 +300,15 @@ def test_llm_config_lazy_imports():
 
 def test_types_lazy_imports():
     """Test that type classes can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in TYPES_NAMES:
         _clear_names_from_globals(TYPES_NAMES)
 
         obj = _lazy_import_types(name)
         assert obj is not None
-        assert name in litellm.__dict__
+        assert name in litellm_globals
         # Type classes should be classes/types
         assert isinstance(obj, type), f"{name} should be a class"
 
@@ -273,25 +317,31 @@ def test_types_lazy_imports():
 
 def test_llm_provider_logic_lazy_imports():
     """Test that LLM provider logic functions can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    litellm_globals = sys.modules["litellm"].__dict__
+    
     for name in LLM_PROVIDER_LOGIC_NAMES:
         _clear_names_from_globals(LLM_PROVIDER_LOGIC_NAMES)
 
         func = _lazy_import_llm_provider_logic(name)
         assert func is not None
         assert callable(func)
-        assert name in litellm.__dict__
+        assert name in litellm_globals
 
         _verify_only_requested_name_imported(name, LLM_PROVIDER_LOGIC_NAMES)
 
 
 def test_utils_module_lazy_imports():
     """Test that utils module attributes can be lazy imported."""
+    # Get the actual globals dict, not a copy
+    utils_globals = sys.modules["litellm.utils"].__dict__
+    
     for name in UTILS_MODULE_NAMES:
         _clear_names_from_utils_globals(UTILS_MODULE_NAMES)
 
         obj = _lazy_import_utils_module(name)
         assert obj is not None
-        assert name in litellm.utils.__dict__
+        assert name in utils_globals
 
         _verify_only_requested_name_imported_in_utils(name, UTILS_MODULE_NAMES)
 
diff --git a/tests/test_litellm/test_responses_id_security.py b/tests/test_litellm/test_responses_id_security.py
index 6b04479326e..2addf504f7c 100644
--- a/tests/test_litellm/test_responses_id_security.py
+++ b/tests/test_litellm/test_responses_id_security.py
@@ -42,8 +42,11 @@ class TestIsEncryptedResponseId:
 
     def test_is_encrypted_response_id_valid(self, responses_id_security):
         """Test that a properly encrypted response ID is identified correctly"""
-        with patch(
-            "litellm.proxy.hooks.responses_id_security.decrypt_value_helper"
+        # Patch at the module level where it's imported
+        import litellm.proxy.hooks.responses_id_security as responses_module
+        
+        with patch.object(
+            responses_module, "decrypt_value_helper"
         ) as mock_decrypt:
             mock_decrypt.return_value = f"{SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value}response_id:resp_123;user_id:user-456"
 
@@ -56,8 +59,11 @@ class TestIsEncryptedResponseId:
 
     def test_is_encrypted_response_id_invalid(self, responses_id_security):
         """Test that an unencrypted response ID returns False"""
-        with patch(
-            "litellm.proxy.hooks.responses_id_security.decrypt_value_helper"
+        # Patch at the module level where it's imported
+        import litellm.proxy.hooks.responses_id_security as responses_module
+        
+        with patch.object(
+            responses_module, "decrypt_value_helper"
         ) as mock_decrypt:
             mock_decrypt.return_value = None
 
@@ -71,8 +77,11 @@ class TestDecryptResponseId:
 
     def test_decrypt_response_id_valid(self, responses_id_security):
         """Test decrypting a valid encrypted response ID"""
-        with patch(
-            "litellm.proxy.hooks.responses_id_security.decrypt_value_helper"
+        # Patch at the module level where it's imported
+        import litellm.proxy.hooks.responses_id_security as responses_module
+        
+        with patch.object(
+            responses_module, "decrypt_value_helper"
         ) as mock_decrypt:
             mock_decrypt.return_value = f"{SpecialEnums.LITELM_MANAGED_FILE_ID_PREFIX.value}response_id:resp_original_123;user_id:user-456;team_id:team-789"
 
@@ -86,8 +95,11 @@ class TestDecryptResponseId:
 
     def test_decrypt_response_id_no_encryption(self, responses_id_security):
         """Test decrypting a non-encrypted response ID"""
-        with patch(
-            "litellm.proxy.hooks.responses_id_security.decrypt_value_helper"
+        # Patch at the module level where it's imported
+        import litellm.proxy.hooks.responses_id_security as responses_module
+        
+        with patch.object(
+            responses_module, "decrypt_value_helper"
         ) as mock_decrypt:
             mock_decrypt.return_value = None
 
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index cd76c438ded..da52b7bcb39 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -520,6 +520,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
                 "input_cost_per_audio_per_second": {"type": "number"},
                 "input_cost_per_audio_per_second_above_128k_tokens": {"type": "number"},
                 "input_cost_per_audio_token": {"type": "number"},
+                "input_cost_per_image_token": {"type": "number"},
                 "input_cost_per_character": {"type": "number"},
                 "input_cost_per_character_above_128k_tokens": {"type": "number"},
                 "input_cost_per_image": {"type": "number"},
@@ -749,6 +750,57 @@ def test_aaamodel_prices_and_context_window_json_is_valid():
         raise AssertionError(error_message)
 
 
+def test_max_tokens_consistency():
+    """
+    Test that max_tokens == max_output_tokens for all models.
+
+    According to the spec in model_prices_and_context_window.json:
+    - max_tokens is a LEGACY parameter
+    - It should be set to max_output_tokens if the provider specifies it
+
+    This test ensures consistency across all model definitions.
+    """
+    import json
+    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:
+        models = json.load(f)
+
+    inconsistencies = []
+
+    for model_name, config in models.items():
+        # Skip the sample_spec
+        if model_name == "sample_spec":
+            continue
+
+        # 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')
+
+            # 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
+                    })
+
+    if inconsistencies:
+        error_msg = f"\n\n❌ Found {len(inconsistencies)} models with max_tokens != max_output_tokens:\n\n"
+        for item in inconsistencies[:10]:  # Show first 10
+            error_msg += f"  {item['model']}: max_tokens={item['max_tokens']}, max_output_tokens={item['max_output_tokens']}\n"
+
+        if len(inconsistencies) > 10:
+            error_msg += f"\n  ... and {len(inconsistencies) - 10} more\n"
+
+        error_msg += "\nTo fix these inconsistencies, run: poetry run python fix_max_tokens_inconsistencies.py"
+        raise AssertionError(error_msg)
+
+
 def test_get_model_info_gemini():
     """
     Tests if ALL gemini models have 'tpm' and 'rpm' in the model info
@@ -2754,3 +2806,117 @@ def test_azure_ai_claude_provider_config():
         provider=LlmProviders.AZURE_AI,
     )
     assert isinstance(config, AzureAIStudioConfig)
+
+
+# Tests for thinking blocks helper functions
+# Related to issue: https://github.com/BerriAI/litellm/issues/18926
+
+
+def test_any_assistant_message_has_thinking_blocks_with_thinking():
+    """Test that function returns True when any assistant message has thinking_blocks."""
+    from litellm.utils import any_assistant_message_has_thinking_blocks
+
+    messages = [
+        {"role": "user", "content": "Hello"},
+        {
+            "role": "assistant",
+            "thinking_blocks": [{"type": "thinking", "thinking": "Let me think..."}],
+            "tool_calls": [{"id": "123", "function": {"name": "test"}}],
+        },
+        {"role": "tool", "tool_call_id": "123", "content": "result"},
+        {
+            "role": "assistant",
+            "tool_calls": [{"id": "456", "function": {"name": "test2"}}],
+            # No thinking_blocks here - Claude sometimes doesn't include them
+        },
+    ]
+
+    assert any_assistant_message_has_thinking_blocks(messages) is True
+
+
+def test_any_assistant_message_has_thinking_blocks_without_thinking():
+    """Test that function returns False when no assistant message has thinking_blocks."""
+    from litellm.utils import any_assistant_message_has_thinking_blocks
+
+    messages = [
+        {"role": "user", "content": "Hello"},
+        {
+            "role": "assistant",
+            "tool_calls": [{"id": "123", "function": {"name": "test"}}],
+        },
+        {"role": "tool", "tool_call_id": "123", "content": "result"},
+    ]
+
+    assert any_assistant_message_has_thinking_blocks(messages) is False
+
+
+def test_any_assistant_message_has_thinking_blocks_empty_list():
+    """Test that function returns False when thinking_blocks is an empty list."""
+    from litellm.utils import any_assistant_message_has_thinking_blocks
+
+    messages = [
+        {"role": "user", "content": "Hello"},
+        {
+            "role": "assistant",
+            "thinking_blocks": [],  # Empty list
+            "tool_calls": [{"id": "123", "function": {"name": "test"}}],
+        },
+    ]
+
+    assert any_assistant_message_has_thinking_blocks(messages) is False
+
+
+def test_last_assistant_with_tool_calls_has_no_thinking_blocks_issue_18926():
+    """
+    Test the scenario from issue #18926 where:
+    - First assistant message HAS thinking_blocks
+    - Second assistant message has NO thinking_blocks
+
+    The old logic would drop thinking because the LAST tool_call message
+    has no thinking_blocks, but this breaks because the first message
+    still has thinking blocks in the conversation.
+    """
+    from litellm.utils import (
+        any_assistant_message_has_thinking_blocks,
+        last_assistant_with_tool_calls_has_no_thinking_blocks,
+    )
+
+    messages = [
+        {"role": "user", "content": "Build a feature"},
+        {
+            "role": "assistant",
+            "thinking_blocks": [
+                {"type": "thinking", "thinking": "Let me analyze the requirements..."}
+            ],
+            "tool_calls": [
+                {"id": "toolu_1", "function": {"name": "file_editor", "arguments": "{}"}}
+            ],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "toolu_1",
+            "content": "File contents here...",
+        },
+        {
+            "role": "assistant",
+            # 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": "{}"}}
+            ],
+        },
+    ]
+
+    # Last assistant with tool_calls has no thinking_blocks
+    assert last_assistant_with_tool_calls_has_no_thinking_blocks(messages) is True
+
+    # But ANY assistant message has thinking_blocks
+    assert any_assistant_message_has_thinking_blocks(messages) is True
+
+    # 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)
+    )
+    assert should_drop_thinking is False
diff --git a/tests/test_litellm/test_utils_custom.py b/tests/test_litellm/test_utils_custom.py
new file mode 100644
index 00000000000..3e924e9c719
--- /dev/null
+++ b/tests/test_litellm/test_utils_custom.py
@@ -0,0 +1,45 @@
+import pytest
+import sys
+from unittest.mock import MagicMock, patch, AsyncMock
+from litellm.proxy.utils import count_tokens_with_anthropic_api, _anthropic_async_clients
+
+@pytest.mark.asyncio
+async def test_count_tokens_caching():
+    """
+    Test that count_tokens_with_anthropic_api caches the client.
+    """
+    # Clear cache
+    _anthropic_async_clients.clear()
+    
+    api_key = "sk-ant-test-key"
+    messages = [{"role": "user", "content": "hello"}]
+    model = "claude-3-opus-20240229"
+    
+    # Create a mock anthropic module
+    mock_anthropic = MagicMock()
+    mock_client = MagicMock()
+    mock_anthropic.AsyncAnthropic.return_value = mock_client
+    
+    # Mock response
+    mock_response = MagicMock()
+    mock_response.input_tokens = 10
+    
+    # Setup async return for count_tokens
+    mock_client.beta.messages.count_tokens = AsyncMock(return_value=mock_response)
+    
+    # Patch sys.modules to ensure our mock is used when anthropic is imported
+    with patch.dict(sys.modules, {"anthropic": mock_anthropic}):
+        # First call
+        with patch.dict("os.environ", {"ANTHROPIC_API_KEY": api_key}):
+            await count_tokens_with_anthropic_api(model, messages)
+            
+        assert api_key in _anthropic_async_clients
+        assert _anthropic_async_clients[api_key] == mock_client
+        mock_anthropic.AsyncAnthropic.assert_called_once() # Should be called once
+        
+        # Second call
+        with patch.dict("os.environ", {"ANTHROPIC_API_KEY": api_key}):
+            await count_tokens_with_anthropic_api(model, messages)
+            
+        # Should still be called once (cached)
+        mock_anthropic.AsyncAnthropic.assert_called_once()
diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py
index 87012f05155..73bfa71d20b 100644
--- a/tests/test_litellm/test_video_generation.py
+++ b/tests/test_litellm/test_video_generation.py
@@ -2,7 +2,7 @@ import asyncio
 import json
 import os
 import sys
-from unittest.mock import MagicMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
@@ -18,6 +18,7 @@ from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
 from litellm.llms.gemini.videos.transformation import GeminiVideoConfig
 from litellm.llms.openai.videos.transformation import OpenAIVideoConfig
 from litellm.types.videos.main import VideoObject, VideoResponse
+from litellm.videos import main as videos_main
 from litellm.videos.main import (
     avideo_generation,
     avideo_status,
@@ -31,32 +32,29 @@ class TestVideoGeneration:
 
     def test_video_generation_basic(self):
         """Test basic video generation functionality."""
-        # Mock the video generation response
-        mock_response = VideoObject(
-            id="video_123",
-            object="video",
-            status="queued",
-            created_at=1712697600,
+        # Use mock_response parameter for reliable testing
+        response = video_generation(
+            prompt="Show them running around the room",
             model="sora-2",
+            seconds="8",
             size="720x1280",
-            seconds="8"
+            mock_response={
+                "id": "video_123",
+                "object": "video",
+                "status": "queued",
+                "created_at": 1712697600,
+                "model": "sora-2",
+                "size": "720x1280",
+                "seconds": "8"
+            }
         )
         
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_generation_handler.return_value = mock_response
-            
-            response = video_generation(
-                prompt="Show them running around the room",
-                model="sora-2",
-                seconds="8",
-                size="720x1280"
-            )
-            
-            assert isinstance(response, VideoObject)
-            assert response.id == "video_123"
-            assert response.model == "sora-2"
-            assert response.size == "720x1280"
-            assert response.seconds == "8"
+        assert isinstance(response, VideoObject)
+        assert response.id == "video_123"
+        assert response.status == "queued"
+        assert response.model == "sora-2"
+        assert response.size == "720x1280"
+        assert response.seconds == "8"
 
     def test_video_generation_with_mock_response(self):
         """Test video generation with mock response."""
@@ -97,26 +95,27 @@ class TestVideoGeneration:
             progress=50
         )
         
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_generation_handler.return_value = mock_response
-            
-            import asyncio
-            
-            async def test_async():
-                response = await avideo_generation(
-                    prompt="A cat playing with a ball",
-                    model="sora-2",
-                    seconds="5",
-                    size="720x1280"
-                )
-                return response
-            
-            response = asyncio.run(test_async())
-            
-            assert isinstance(response, VideoObject)
-            assert response.id == "video_async_123"
-            assert response.status == "processing"
-            assert response.progress == 50
+        # Mock the async_video_generation_handler to return the mock_response
+        async_mock = AsyncMock(return_value=mock_response)
+        with patch.object(videos_main.base_llm_http_handler, 'async_video_generation_handler', async_mock):
+            with patch.object(videos_main.base_llm_http_handler, 'video_generation_handler', side_effect=lambda **kwargs: async_mock(**kwargs)):
+                import asyncio
+                
+                async def test_async():
+                    response = await avideo_generation(
+                        prompt="A cat playing with a ball",
+                        model="sora-2",
+                        seconds="5",
+                        size="720x1280"
+                    )
+                    return response
+                
+                response = asyncio.run(test_async())
+                
+                assert isinstance(response, VideoObject)
+                assert response.id == "video_async_123"
+                assert response.status == "processing"
+                assert response.progress == 50
 
     def test_video_generation_parameter_validation(self):
         """Test video generation parameter validation."""
@@ -132,9 +131,7 @@ class TestVideoGeneration:
 
     def test_video_generation_error_handling(self):
         """Test video generation error handling."""
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_generation_handler.side_effect = Exception("API Error")
-            
+        with patch.object(videos_main.base_llm_http_handler, 'video_generation_handler', side_effect=Exception("API Error")):
             with pytest.raises(Exception):
                 video_generation(
                     prompt="Test video",
@@ -443,32 +440,28 @@ class TestVideoGeneration:
 
     def test_video_status_basic(self):
         """Test basic video status functionality."""
-        # Mock the video status response
-        mock_response = VideoObject(
-            id="video_123",
-            object="video",
-            status="completed",
-            created_at=1712697600,
-            completed_at=1712697660,
+        # Use mock_response parameter for reliable testing
+        response = video_status(
+            video_id="video_123",
             model="sora-2",
-            progress=100,
-            size="720x1280",
-            seconds="8"
+            mock_response={
+                "id": "video_123",
+                "object": "video",
+                "status": "completed",
+                "created_at": 1712697600,
+                "completed_at": 1712697660,
+                "model": "sora-2",
+                "progress": 100,
+                "size": "720x1280",
+                "seconds": "8"
+            }
         )
         
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_status_handler.return_value = mock_response
-            
-            response = video_status(
-                video_id="video_123",
-                model="sora-2"
-            )
-            
-            assert isinstance(response, VideoObject)
-            assert response.id == "video_123"
-            assert response.status == "completed"
-            assert response.progress == 100
-            assert response.model == "sora-2"
+        assert isinstance(response, VideoObject)
+        assert response.id == "video_123"
+        assert response.status == "completed"
+        assert response.progress == 100
+        assert response.model == "sora-2"
 
     def test_video_status_with_mock_response(self):
         """Test video status with mock response."""
@@ -506,24 +499,25 @@ class TestVideoGeneration:
             progress=0
         )
         
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_status_handler.return_value = mock_response
-            
-            import asyncio
-            
-            async def test_async():
-                response = await avideo_status(
-                    video_id="video_async_123",
-                    model="sora-2"
-                )
-                return response
-            
-            response = asyncio.run(test_async())
-            
-            assert isinstance(response, VideoObject)
-            assert response.id == "video_async_123"
-            assert response.status == "queued"
-            assert response.progress == 0
+        # Mock the async_video_status_handler to return the mock_response
+        async_mock = AsyncMock(return_value=mock_response)
+        with patch.object(videos_main.base_llm_http_handler, 'async_video_status_handler', async_mock):
+            with patch.object(videos_main.base_llm_http_handler, 'video_status_handler', side_effect=lambda **kwargs: async_mock(**kwargs)):
+                import asyncio
+                
+                async def test_async():
+                    response = await avideo_status(
+                        video_id="video_async_123",
+                        model="sora-2"
+                    )
+                    return response
+                
+                response = asyncio.run(test_async())
+                
+                assert isinstance(response, VideoObject)
+                assert response.id == "video_async_123"
+                assert response.status == "queued"
+                assert response.progress == 0
 
     def test_video_status_parameter_validation(self):
         """Test video status parameter validation."""
@@ -539,9 +533,7 @@ class TestVideoGeneration:
 
     def test_video_status_error_handling(self):
         """Test video status error handling."""
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_status_handler.side_effect = Exception("API Error")
-            
+        with patch.object(videos_main.base_llm_http_handler, 'video_status_handler', side_effect=Exception("API Error")):
             with pytest.raises(Exception):
                 video_status(
                     video_id="test_video_id",
@@ -672,33 +664,30 @@ class TestVideoGeneration:
 
     def test_video_status_async_inside_async_function(self):
         """Test that sync video_status works inside async functions (no asyncio.run issues)."""
-        mock_response = VideoObject(
-            id="video_sync_in_async",
-            object="video",
-            status="completed",
-            created_at=1712697600,
-            model="sora-2",
-            progress=100
-        )
+        import asyncio
         
-        with patch('litellm.videos.main.base_llm_http_handler') as mock_handler:
-            mock_handler.video_status_handler.return_value = mock_response
-            
-            import asyncio
-            
-            async def test_sync_in_async():
-                # This should work without asyncio.run() issues
-                response = video_status(
-                    video_id="video_sync_in_async",
-                    model="sora-2"
-                )
-                return response
-            
-            response = asyncio.run(test_sync_in_async())
-            
-            assert isinstance(response, VideoObject)
-            assert response.id == "video_sync_in_async"
-            assert response.status == "completed"
+        async def test_sync_in_async():
+            # This should work without asyncio.run() issues
+            # Use mock_response parameter for reliable testing
+            response = video_status(
+                video_id="video_sync_in_async",
+                model="sora-2",
+                mock_response={
+                    "id": "video_sync_in_async",
+                    "object": "video",
+                    "status": "completed",
+                    "created_at": 1712697600,
+                    "model": "sora-2",
+                    "progress": 100
+                }
+            )
+            return response
+        
+        response = asyncio.run(test_sync_in_async())
+        
+        assert isinstance(response, VideoObject)
+        assert response.id == "video_sync_in_async"
+        assert response.status == "completed"
 
     def test_video_status_url_construction(self):
         """Test video status URL construction."""
diff --git a/ui/litellm-dashboard/e2e_tests/fixtures/menuMappings.ts b/ui/litellm-dashboard/e2e_tests/fixtures/menuMappings.ts
new file mode 100644
index 00000000000..4a4bb64c8ed
--- /dev/null
+++ b/ui/litellm-dashboard/e2e_tests/fixtures/menuMappings.ts
@@ -0,0 +1,38 @@
+import { Page } from "./pages";
+
+/**
+ * Maps sidebar menu item labels to their corresponding page enum values.
+ * This mapping is for the admin role.
+ */
+export const menuLabelToPage: Record<string, Page> = {
+  "Virtual Keys": Page.ApiKeys,
+  Playground: Page.LlmPlayground,
+  Models: Page.Models,
+  "Models + Endpoints": Page.Models,
+  Usage: Page.NewUsage,
+  Teams: Page.Teams,
+  "Internal Users": Page.Users,
+  "Internal User": Page.Users, // Legacy label support
+  Organizations: Page.Organizations,
+  "API Reference": Page.ApiRef,
+  "AI Hub": Page.ModelHubTable,
+  "Model Hub": Page.ModelHubTable,
+  Logs: Page.Logs,
+  Guardrails: Page.Guardrails,
+  // Settings submenu items
+  "Router Settings": Page.RouterSettings,
+  "Logging & Alerts": Page.LoggingAndAlerts,
+  "Admin Settings": Page.AdminPanel,
+  "Cost Tracking": Page.CostTracking,
+  "UI Theme": Page.UiTheme,
+  // Experimental submenu items
+  Caching: Page.Caching,
+  Prompts: Page.Prompts,
+  Budgets: Page.Budgets,
+  "API Playground": Page.TransformRequest,
+  "Tag Management": Page.TagManagement,
+  "Old Usage": Page.Usage,
+  // Tools submenu items
+  "MCP Servers": Page.McpServers,
+  "Vector Stores": Page.VectorStores,
+};
diff --git a/ui/litellm-dashboard/e2e_tests/fixtures/pages.ts b/ui/litellm-dashboard/e2e_tests/fixtures/pages.ts
new file mode 100644
index 00000000000..3ea37718ab5
--- /dev/null
+++ b/ui/litellm-dashboard/e2e_tests/fixtures/pages.ts
@@ -0,0 +1,33 @@
+/**
+ * Enum for all page query parameters supported in the app.
+ * These values correspond to the `page` query parameter used in the URL.
+ */
+export enum Page {
+  ApiKeys = "api-keys",
+  Models = "models",
+  LlmPlayground = "llm-playground",
+  Users = "users",
+  Teams = "teams",
+  Organizations = "organizations",
+  AdminPanel = "admin-panel",
+  ApiRef = "api_ref",
+  LoggingAndAlerts = "logging-and-alerts",
+  Budgets = "budgets",
+  Guardrails = "guardrails",
+  Agents = "agents",
+  Prompts = "prompts",
+  TransformRequest = "transform-request",
+  RouterSettings = "router-settings",
+  UiTheme = "ui-theme",
+  CostTracking = "cost-tracking",
+  ModelHubTable = "model-hub-table",
+  Caching = "caching",
+  PassThroughSettings = "pass-through-settings",
+  Logs = "logs",
+  McpServers = "mcp-servers",
+  SearchTools = "search-tools",
+  TagManagement = "tag-management",
+  VectorStores = "vector-stores",
+  NewUsage = "new_usage",
+  Usage = "usage",
+}
diff --git a/ui/litellm-dashboard/e2e_tests/helpers/navigation.ts b/ui/litellm-dashboard/e2e_tests/helpers/navigation.ts
new file mode 100644
index 00000000000..919e516b35b
--- /dev/null
+++ b/ui/litellm-dashboard/e2e_tests/helpers/navigation.ts
@@ -0,0 +1,12 @@
+import { Page } from "../fixtures/pages";
+import { Page as PlaywrightPage } from "@playwright/test";
+
+/**
+ * Navigates to a specific page using the page query parameter.
+ * Uses relative path which will be resolved against the baseURL configured in playwright.config.ts
+ * @param page - The Playwright page object
+ * @param pageEnum - The page enum value to navigate to
+ */
+export async function navigateToPage(page: PlaywrightPage, pageEnum: Page): Promise<void> {
+  await page.goto(`/ui?page=${pageEnum}`);
+}
diff --git a/ui/litellm-dashboard/e2e_tests/tests/modelsPage/addModel.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/modelsPage/addModel.spec.ts
index c0619cfa845..2ab782d5678 100644
--- a/ui/litellm-dashboard/e2e_tests/tests/modelsPage/addModel.spec.ts
+++ b/ui/litellm-dashboard/e2e_tests/tests/modelsPage/addModel.spec.ts
@@ -14,7 +14,7 @@ test.describe("Add Model", () => {
     await providerInputDropdown.fill("Anthropic");
     await page.waitForTimeout(1000);
     await providerInputDropdown.press("Enter");
-    await page.waitForTimeout(1000);
+    await page.waitForTimeout(2000);
 
     const providerModelsDropdown = page.locator(".ant-select-selection-overflow").first();
     await providerModelsDropdown.click();
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 c90be698ae1..ce07cc2b83d 100644
--- a/ui/litellm-dashboard/e2e_tests/tests/navigation/sidebar.spec.ts
+++ b/ui/litellm-dashboard/e2e_tests/tests/navigation/sidebar.spec.ts
@@ -1,6 +1,9 @@
 import test, { expect } from "@playwright/test";
 import { Role } from "../../fixtures/roles";
 import { ADMIN_STORAGE_PATH } from "../../constants";
+import { Page } from "../../fixtures/pages";
+import { menuLabelToPage } from "../../fixtures/menuMappings";
+import { navigateToPage } from "../../helpers/navigation";
 
 const sidebarButtons = {
   [Role.ProxyAdmin]: [
@@ -9,9 +12,7 @@ const sidebarButtons = {
     "Models",
     "Usage",
     "Teams",
-    "Internal User",
-    "Settings",
-    "Experimental",
+    "Internal Users",
     "API Reference",
     "AI Hub",
   ],
@@ -23,13 +24,36 @@ for (const { role, storage } of roles) {
   test.describe(`${role} sidebar`, () => {
     test.use({ storageState: storage });
 
-    test("can see and navigate all sidebar buttons", async ({ page }) => {
+    test("should navigate to correct URL when clicking sidebar menu items from homepage", async ({ page }) => {
       await page.goto("/ui");
-      for (const button of sidebarButtons[role as keyof typeof sidebarButtons]) {
-        const tab = page.getByRole("menuitem", { name: button });
+
+      for (const buttonLabel of sidebarButtons[role as keyof typeof sidebarButtons]) {
+        const expectedPage = menuLabelToPage[buttonLabel];
+
+        if (!expectedPage) {
+          throw new Error(`No page mapping found for menu label: ${buttonLabel}`);
+        }
+
+        const tab = page.getByRole("menuitem", { name: buttonLabel });
         await expect(tab).toBeVisible();
+
         await tab.click();
+
+        // Verify URL contains the correct page query parameter
+        await expect(page).toHaveURL(new RegExp(`[?&]page=${expectedPage}(&|$)`));
       }
     });
+
+    test("should navigate directly to page using navigation helper", async ({ page }) => {
+      // Test direct navigation to verify the helper function works
+      await navigateToPage(page, Page.ApiKeys);
+      await expect(page).toHaveURL(new RegExp(`[?&]page=${Page.ApiKeys}(&|$)`));
+
+      await navigateToPage(page, Page.Models);
+      await expect(page).toHaveURL(new RegExp(`[?&]page=${Page.Models}(&|$)`));
+
+      await navigateToPage(page, Page.LlmPlayground);
+      await expect(page).toHaveURL(new RegExp(`[?&]page=${Page.LlmPlayground}(&|$)`));
+    });
   });
 }
diff --git a/ui/litellm-dashboard/e2e_tests/tests/users/searchUsers.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/users/searchUsers.spec.ts
index 01c1e68f1ee..a9b0e329a2b 100644
--- a/ui/litellm-dashboard/e2e_tests/tests/users/searchUsers.spec.ts
+++ b/ui/litellm-dashboard/e2e_tests/tests/users/searchUsers.spec.ts
@@ -1,6 +1,6 @@
 import { test, expect, Page } from "@playwright/test";
 import { ADMIN_STORAGE_PATH } from "../../constants";
-test.describe("Internal Users Search", () => {
+test.skip("Internal Users Search", () => {
   test.use({ storageState: ADMIN_STORAGE_PATH });
 
   async function goToInternalUsers(page: Page) {
diff --git a/ui/litellm-dashboard/e2e_tests/tests/users/viewInternalUsers.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/users/viewInternalUsers.spec.ts
index 4dfd79c9dff..ea61c238c02 100644
--- a/ui/litellm-dashboard/e2e_tests/tests/users/viewInternalUsers.spec.ts
+++ b/ui/litellm-dashboard/e2e_tests/tests/users/viewInternalUsers.spec.ts
@@ -1,7 +1,7 @@
 import { test, expect, Page } from "@playwright/test";
 import { ADMIN_STORAGE_PATH } from "../../constants";
 
-test.describe("Internal Users Page", () => {
+test.skip("Internal Users Page", () => {
   test.use({ storageState: ADMIN_STORAGE_PATH });
 
   async function goToInternalUsers(page: Page) {
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/router/useRouterFields.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/router/useRouterFields.test.ts
new file mode 100644
index 00000000000..fe4680cedeb
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/router/useRouterFields.test.ts
@@ -0,0 +1,387 @@
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import { renderHook, waitFor } from "@testing-library/react";
+import React, { ReactNode } from "react";
+import { beforeEach, describe, expect, it, vi } from "vitest";
+import { RouterFieldsResponse, useRouterFields } from "./useRouterFields";
+
+// Mock the networking module
+vi.mock("@/components/networking", () => ({
+  proxyBaseUrl: null,
+}));
+
+// Mock useAuthorized hook
+const mockUseAuthorized = vi.fn();
+vi.mock("../useAuthorized", () => ({
+  default: () => mockUseAuthorized(),
+}));
+
+// Mock global fetch
+const mockFetch = vi.fn();
+global.fetch = mockFetch;
+
+// Mock console methods to avoid noise in tests
+vi.spyOn(console, "log").mockImplementation(() => {});
+vi.spyOn(console, "error").mockImplementation(() => {});
+
+// Mock data
+const mockRouterFieldsResponse: RouterFieldsResponse = {
+  fields: [
+    {
+      field_name: "routing_strategy",
+      field_type: "String",
+      field_description: "Routing strategy to use for load balancing across deployments",
+      field_default: "simple-shuffle",
+      options: ["simple-shuffle", "least-busy", "latency-based-routing"],
+      ui_field_name: "Routing Strategy",
+      link: null,
+    },
+    {
+      field_name: "num_retries",
+      field_type: "Integer",
+      field_description: "Number of retries for failed requests",
+      field_default: 0,
+      options: null,
+      ui_field_name: "Number of Retries",
+      link: null,
+    },
+  ],
+  routing_strategy_descriptions: {
+    "simple-shuffle": "Randomly picks a deployment from the list. Simple and fast.",
+    "least-busy": "Routes to the deployment with the lowest number of ongoing requests.",
+    "latency-based-routing": "Routes to the deployment with the lowest latency over a sliding window.",
+  },
+};
+
+describe("useRouterFields", () => {
+  let queryClient: QueryClient;
+
+  beforeEach(() => {
+    queryClient = new QueryClient({
+      defaultOptions: {
+        queries: {
+          retry: false,
+        },
+      },
+    });
+
+    // Reset all mocks
+    vi.clearAllMocks();
+
+    // Set default mock for useAuthorized (enabled state)
+    mockUseAuthorized.mockReturnValue({
+      accessToken: "test-access-token",
+      userRole: "Admin",
+      userId: "test-user-id",
+      token: "test-token",
+      userEmail: "test@example.com",
+      premiumUser: false,
+      disabledPersonalKeyCreation: null,
+      showSSOBanner: false,
+    });
+  });
+
+  const wrapper = ({ children }: { children: ReactNode }) =>
+    React.createElement(QueryClientProvider, { client: queryClient }, children);
+
+  it("should render", () => {
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => mockRouterFieldsResponse,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    expect(result.current).toBeDefined();
+  });
+
+  it("should return router fields data when query is successful", async () => {
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => mockRouterFieldsResponse,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    // Initially loading
+    expect(result.current.isLoading).toBe(true);
+    expect(result.current.data).toBeUndefined();
+
+    // Wait for success
+    await waitFor(() => {
+      expect(result.current.isLoading).toBe(false);
+      expect(result.current.isSuccess).toBe(true);
+    });
+
+    expect(result.current.data).toEqual(mockRouterFieldsResponse);
+    expect(result.current.error).toBeNull();
+    expect(mockFetch).toHaveBeenCalledTimes(1);
+    expect(mockFetch).toHaveBeenCalledWith("/router/fields", {
+      method: "GET",
+      headers: {
+        Authorization: "Bearer test-access-token",
+        "Content-Type": "application/json",
+      },
+    });
+  });
+
+  it("should handle error when fetch fails", async () => {
+    const errorMessage = "Failed to fetch router fields";
+    const errorResponse = { error: errorMessage };
+
+    mockFetch.mockResolvedValueOnce({
+      ok: false,
+      json: async () => errorResponse,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    // Initially loading
+    expect(result.current.isLoading).toBe(true);
+
+    // Wait for error
+    await waitFor(() => {
+      expect(result.current.isLoading).toBe(false);
+      expect(result.current.isError).toBe(true);
+    });
+
+    expect(result.current.error).toBeDefined();
+    expect(result.current.data).toBeUndefined();
+    expect(mockFetch).toHaveBeenCalledTimes(1);
+  });
+
+  it("should not execute query when accessToken is missing", () => {
+    mockUseAuthorized.mockReturnValue({
+      accessToken: null,
+      userRole: "Admin",
+      userId: "test-user-id",
+      token: null,
+      userEmail: "test@example.com",
+      premiumUser: false,
+      disabledPersonalKeyCreation: null,
+      showSSOBanner: false,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    // Query should not execute
+    expect(result.current.isLoading).toBe(false);
+    expect(result.current.data).toBeUndefined();
+    expect(result.current.isFetched).toBe(false);
+
+    // API should not be called
+    expect(mockFetch).not.toHaveBeenCalled();
+  });
+
+  it("should not execute query when userId is missing", () => {
+    mockUseAuthorized.mockReturnValue({
+      accessToken: "test-access-token",
+      userRole: "Admin",
+      userId: null,
+      token: "test-token",
+      userEmail: "test@example.com",
+      premiumUser: false,
+      disabledPersonalKeyCreation: null,
+      showSSOBanner: false,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    // Query should not execute
+    expect(result.current.isLoading).toBe(false);
+    expect(result.current.data).toBeUndefined();
+    expect(result.current.isFetched).toBe(false);
+
+    // API should not be called
+    expect(mockFetch).not.toHaveBeenCalled();
+  });
+
+  it("should not execute query when userRole is missing", () => {
+    mockUseAuthorized.mockReturnValue({
+      accessToken: "test-access-token",
+      userRole: null,
+      userId: "test-user-id",
+      token: "test-token",
+      userEmail: "test@example.com",
+      premiumUser: false,
+      disabledPersonalKeyCreation: null,
+      showSSOBanner: false,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    // Query should not execute
+    expect(result.current.isLoading).toBe(false);
+    expect(result.current.data).toBeUndefined();
+    expect(result.current.isFetched).toBe(false);
+
+    // API should not be called
+    expect(mockFetch).not.toHaveBeenCalled();
+  });
+
+  it("should handle network error", async () => {
+    const networkError = new Error("Network error");
+    mockFetch.mockRejectedValueOnce(networkError);
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    // Wait for error
+    await waitFor(() => {
+      expect(result.current.isError).toBe(true);
+    });
+
+    expect(result.current.error).toBeDefined();
+    expect(result.current.data).toBeUndefined();
+  });
+
+  it("should use relative URL when proxyBaseUrl is null", async () => {
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => mockRouterFieldsResponse,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    await waitFor(() => {
+      expect(result.current.isSuccess).toBe(true);
+    });
+
+    // When proxyBaseUrl is null, should use relative URL
+    expect(mockFetch).toHaveBeenCalledWith("/router/fields", {
+      method: "GET",
+      headers: {
+        Authorization: "Bearer test-access-token",
+        "Content-Type": "application/json",
+      },
+    });
+  });
+
+  it("should handle error response with different error formats", async () => {
+    const errorFormats = [
+      { error: { message: "Error message" } },
+      { message: "Error message" },
+      { detail: "Error detail" },
+      { error: "Error string" },
+      { unknown: "format" },
+    ];
+
+    for (const errorFormat of errorFormats) {
+      vi.clearAllMocks();
+      mockFetch.mockResolvedValueOnce({
+        ok: false,
+        json: async () => errorFormat,
+      });
+
+      const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+      await waitFor(() => {
+        expect(result.current.isError).toBe(true);
+      });
+
+      expect(result.current.error).toBeDefined();
+    }
+  });
+
+  it("should return empty fields array when API returns empty fields", async () => {
+    const emptyResponse: RouterFieldsResponse = {
+      fields: [],
+      routing_strategy_descriptions: {},
+    };
+
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => emptyResponse,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    await waitFor(() => {
+      expect(result.current.isSuccess).toBe(true);
+    });
+
+    expect(result.current.data?.fields).toEqual([]);
+    expect(result.current.data?.routing_strategy_descriptions).toEqual({});
+  });
+
+  it("should have correct query configuration", async () => {
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => mockRouterFieldsResponse,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    await waitFor(() => {
+      expect(result.current.isSuccess).toBe(true);
+    });
+
+    // Verify the query was called
+    expect(mockFetch).toHaveBeenCalledTimes(1);
+
+    // The hook should have the expected properties from useQuery
+    expect(result.current).toHaveProperty("data");
+    expect(result.current).toHaveProperty("isLoading");
+    expect(result.current).toHaveProperty("isError");
+    expect(result.current).toHaveProperty("isSuccess");
+    expect(result.current).toHaveProperty("error");
+  });
+
+  it("should handle fields with null options", async () => {
+    const responseWithNullOptions: RouterFieldsResponse = {
+      fields: [
+        {
+          field_name: "timeout",
+          field_type: "Float",
+          field_description: "Timeout for requests in seconds",
+          field_default: null,
+          options: null,
+          ui_field_name: "Timeout",
+          link: null,
+        },
+      ],
+      routing_strategy_descriptions: {},
+    };
+
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => responseWithNullOptions,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    await waitFor(() => {
+      expect(result.current.isSuccess).toBe(true);
+    });
+
+    expect(result.current.data?.fields[0].options).toBeNull();
+  });
+
+  it("should handle fields with link property", async () => {
+    const responseWithLink: RouterFieldsResponse = {
+      fields: [
+        {
+          field_name: "enable_tag_filtering",
+          field_type: "Boolean",
+          field_description: "Enable tag-based routing",
+          field_default: false,
+          options: null,
+          ui_field_name: "Enable Tag Filtering",
+          link: "https://docs.litellm.ai/docs/proxy/tag_routing",
+        },
+      ],
+      routing_strategy_descriptions: {},
+    };
+
+    mockFetch.mockResolvedValueOnce({
+      ok: true,
+      json: async () => responseWithLink,
+    });
+
+    const { result } = renderHook(() => useRouterFields(), { wrapper });
+
+    await waitFor(() => {
+      expect(result.current.isSuccess).toBe(true);
+    });
+
+    expect(result.current.data?.fields[0].link).toBe("https://docs.litellm.ai/docs/proxy/tag_routing");
+  });
+});
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/router/useRouterFields.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/router/useRouterFields.ts
new file mode 100644
index 00000000000..589508c5ddb
--- /dev/null
+++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/router/useRouterFields.ts
@@ -0,0 +1,69 @@
+import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized";
+import { useQuery, UseQueryResult } from "@tanstack/react-query";
+import { createQueryKeys } from "../common/queryKeysFactory";
+import { proxyBaseUrl } from "@/components/networking";
+
+export interface RouterSettingsField {
+  field_name: string;
+  field_type: string;
+  field_description: string;
+  field_default: any;
+  options: string[] | null;
+  ui_field_name: string;
+  link: string | null;
+}
+
+export interface RouterFieldsResponse {
+  fields: RouterSettingsField[];
+  routing_strategy_descriptions: Record<string, string>;
+}
+
+const routerFieldsKeys = createQueryKeys("routerFields");
+
+const deriveErrorMessage = (errorData: any): string => {
+  return (
+    (errorData?.error && (errorData.error.message || errorData.error)) ||
+    errorData?.message ||
+    errorData?.detail ||
+    errorData?.error ||
+    JSON.stringify(errorData)
+  );
+};
+
+const getRouterFields = async (accessToken: string): Promise<RouterFieldsResponse> => {
+  try {
+    const url = proxyBaseUrl ? `${proxyBaseUrl}/router/fields` : `/router/fields`;
+
+    console.log("Fetching router fields from:", url);
+
+    const response = await fetch(url, {
+      method: "GET",
+      headers: {
+        Authorization: `Bearer ${accessToken}`,
+        "Content-Type": "application/json",
+      },
+    });
+
+    if (!response.ok) {
+      const errorData = await response.json();
+      const errorMessage = deriveErrorMessage(errorData);
+      throw new Error(errorMessage);
+    }
+
+    const data: RouterFieldsResponse = await response.json();
+    console.log("Fetched router fields:", data);
+    return data;
+  } catch (error) {
+    console.error("Failed to fetch router fields:", error);
+    throw error;
+  }
+};
+
+export const useRouterFields = (): UseQueryResult<RouterFieldsResponse> => {
+  const { accessToken, userId, userRole } = useAuthorized();
+  return useQuery<RouterFieldsResponse>({
+    queryKey: routerFieldsKeys.detail("fields"),
+    queryFn: async () => await getRouterFields(accessToken!),
+    enabled: Boolean(accessToken && userId && userRole),
+  });
+};
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/EndpointUsage.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/EndpointUsage.test.tsx
new file mode 100644
index 00000000000..69987d1481f
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/EndpointUsage.test.tsx
@@ -0,0 +1,25 @@
+import { render, screen } from "@testing-library/react";
+import { describe, expect, it, vi } from "vitest";
+import EndpointUsage from "./EndpointUsage";
+
+vi.mock("./components/EndpointUsageBarChart", () => ({
+  default: () => <div>Endpoint Usage Bar Chart</div>,
+}));
+
+vi.mock("./components/EndpointUsageLineChart", () => ({
+  default: () => <div>Endpoint Usage Line Chart</div>,
+}));
+
+vi.mock("./components/EndpointUsageTable", () => ({
+  default: () => <div>Endpoint Usage Table</div>,
+}));
+
+describe("EndpointUsage", () => {
+  it("should render", () => {
+    render(<EndpointUsage />);
+
+    expect(screen.getByText("Endpoint Usage Table")).toBeInTheDocument();
+    expect(screen.getByText("Endpoint Usage Bar Chart")).toBeInTheDocument();
+    expect(screen.getByText("Endpoint Usage Line Chart")).toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/EndpointUsage.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/EndpointUsage.tsx
new file mode 100644
index 00000000000..68959405a8f
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/EndpointUsage.tsx
@@ -0,0 +1,67 @@
+import React, { useMemo } from "react";
+import EndpointUsageBarChart from "./components/EndpointUsageBarChart";
+import EndpointUsageLineChart from "./components/EndpointUsageLineChart";
+import EndpointUsageTable from "./components/EndpointUsageTable";
+import { DailyData, MetricWithMetadata } from "../../types";
+
+interface EndpointUsageProps {
+  userSpendData?: {
+    results: DailyData[];
+    metadata: any;
+  };
+}
+
+const EndpointUsage: React.FC<EndpointUsageProps> = ({ userSpendData }) => {
+  // Aggregate endpoints data from all days
+  const endpointData = useMemo(() => {
+    const aggregatedEndpoints: Record<string, MetricWithMetadata> = {};
+
+    if (!userSpendData?.results) {
+      return aggregatedEndpoints;
+    }
+
+    userSpendData.results.forEach((day) => {
+      Object.entries(day.breakdown.endpoints || {}).forEach(([endpoint, metrics]) => {
+        if (!aggregatedEndpoints[endpoint]) {
+          aggregatedEndpoints[endpoint] = {
+            metrics: {
+              spend: 0,
+              prompt_tokens: 0,
+              completion_tokens: 0,
+              total_tokens: 0,
+              api_requests: 0,
+              successful_requests: 0,
+              failed_requests: 0,
+              cache_read_input_tokens: 0,
+              cache_creation_input_tokens: 0,
+            },
+            metadata: metrics.metadata || {},
+            api_key_breakdown: {},
+          };
+        }
+        aggregatedEndpoints[endpoint].metrics.spend += metrics.metrics.spend;
+        aggregatedEndpoints[endpoint].metrics.prompt_tokens += metrics.metrics.prompt_tokens;
+        aggregatedEndpoints[endpoint].metrics.completion_tokens += metrics.metrics.completion_tokens;
+        aggregatedEndpoints[endpoint].metrics.total_tokens += metrics.metrics.total_tokens;
+        aggregatedEndpoints[endpoint].metrics.api_requests += metrics.metrics.api_requests;
+        aggregatedEndpoints[endpoint].metrics.successful_requests += metrics.metrics.successful_requests || 0;
+        aggregatedEndpoints[endpoint].metrics.failed_requests += metrics.metrics.failed_requests || 0;
+        aggregatedEndpoints[endpoint].metrics.cache_read_input_tokens += metrics.metrics.cache_read_input_tokens || 0;
+        aggregatedEndpoints[endpoint].metrics.cache_creation_input_tokens +=
+          metrics.metrics.cache_creation_input_tokens || 0;
+      });
+    });
+
+    return aggregatedEndpoints;
+  }, [userSpendData]);
+
+  return (
+    <div className="space-y-4">
+      <EndpointUsageTable endpointData={endpointData} />
+      <EndpointUsageBarChart endpointData={endpointData} />
+      <EndpointUsageLineChart dailyData={userSpendData} endpointData={endpointData} />
+    </div>
+  );
+};
+
+export default EndpointUsage;
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageBarChart.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageBarChart.test.tsx
new file mode 100644
index 00000000000..96b66cbb3d2
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageBarChart.test.tsx
@@ -0,0 +1,41 @@
+import { render, screen } from "@testing-library/react";
+import { describe, expect, it, vi } from "vitest";
+import EndpointUsageBarChart from "./EndpointUsageBarChart";
+
+vi.mock("@tremor/react", async () => {
+  const React = await import("react");
+
+  function Card({ children }: any) {
+    return React.createElement("div", { "data-testid": "tremor-card" }, children);
+  }
+  (Card as any).displayName = "Card";
+
+  function Title({ children }: any) {
+    return React.createElement("h2", { "data-testid": "tremor-title" }, children);
+  }
+  (Title as any).displayName = "Title";
+
+  function BarChart(_props: any) {
+    return React.createElement("div", { "data-testid": "tremor-bar-chart" }, "Bar Chart");
+  }
+  (BarChart as any).displayName = "BarChart";
+
+  return { Card, Title, BarChart };
+});
+
+vi.mock("@/components/common_components/chartUtils", () => ({
+  CustomLegend: ({ categories }: any) => (
+    <div data-testid="custom-legend">{categories.join(", ")}</div>
+  ),
+  CustomTooltip: () => <div data-testid="custom-tooltip">Tooltip</div>,
+}));
+
+describe("EndpointUsageBarChart", () => {
+  it("should render", () => {
+    render(<EndpointUsageBarChart />);
+
+    expect(screen.getByTestId("tremor-card")).toBeInTheDocument();
+    expect(screen.getByText("Success vs Failed Requests by Endpoint")).toBeInTheDocument();
+    expect(screen.getByTestId("tremor-bar-chart")).toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageBarChart.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageBarChart.tsx
new file mode 100644
index 00000000000..dd712fdb232
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageBarChart.tsx
@@ -0,0 +1,53 @@
+import React from "react";
+import { BarChart, Card, Title } from "@tremor/react";
+import { CustomLegend, CustomTooltip } from "@/components/common_components/chartUtils";
+import { MetricWithMetadata } from "../../../types";
+
+interface EndpointUsageBarChartProps {
+  endpointData?: Record<string, MetricWithMetadata>;
+}
+
+const EndpointUsageBarChart: React.FC<EndpointUsageBarChartProps> = ({ endpointData }) => {
+  const dataToUse = endpointData || {};
+
+  // Transform endpoint data into chart format
+  const chartData = React.useMemo(() => {
+    return Object.entries(dataToUse).map(([endpoint, data]) => ({
+      endpoint,
+      "metrics.successful_requests": data.metrics.successful_requests,
+      "metrics.failed_requests": data.metrics.failed_requests,
+      metrics: {
+        successful_requests: data.metrics.successful_requests,
+        failed_requests: data.metrics.failed_requests,
+      },
+    }));
+  }, [dataToUse]);
+
+  const valueFormatter = (value: number) => value.toLocaleString();
+
+  return (
+    <Card>
+      <div className="flex justify-between items-center">
+        <Title>Success vs Failed Requests by Endpoint</Title>
+        <CustomLegend
+          categories={["metrics.successful_requests", "metrics.failed_requests"]}
+          colors={["green", "red"]}
+        />
+      </div>
+      <BarChart
+        className="mt-4"
+        data={chartData}
+        index="endpoint"
+        categories={["metrics.successful_requests", "metrics.failed_requests"]}
+        colors={["green", "red"]}
+        valueFormatter={valueFormatter}
+        customTooltip={CustomTooltip}
+        showLegend={false}
+        stack={true}
+        yAxisWidth={60}
+      />
+    </Card>
+  );
+};
+
+export default EndpointUsageBarChart;
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageLineChart.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageLineChart.test.tsx
new file mode 100644
index 00000000000..ec99825289c
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageLineChart.test.tsx
@@ -0,0 +1,34 @@
+import { render, screen } from "@testing-library/react";
+import { describe, expect, it, vi } from "vitest";
+import EndpointUsageLineChart from "./EndpointUsageLineChart";
+
+vi.mock("@tremor/react", async () => {
+  const React = await import("react");
+
+  function Card({ children }: any) {
+    return React.createElement("div", { "data-testid": "tremor-card" }, children);
+  }
+  (Card as any).displayName = "Card";
+
+  function Title({ children }: any) {
+    return React.createElement("h2", { "data-testid": "tremor-title" }, children);
+  }
+  (Title as any).displayName = "Title";
+
+  function LineChart(_props: any) {
+    return React.createElement("div", { "data-testid": "tremor-line-chart" }, "Line Chart");
+  }
+  (LineChart as any).displayName = "LineChart";
+
+  return { Card, Title, LineChart };
+});
+
+describe("EndpointUsageLineChart", () => {
+  it("should render", () => {
+    render(<EndpointUsageLineChart />);
+
+    expect(screen.getByTestId("tremor-card")).toBeInTheDocument();
+    expect(screen.getByText("Endpoint Usage Trends")).toBeInTheDocument();
+    expect(screen.getByTestId("tremor-line-chart")).toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageLineChart.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageLineChart.tsx
new file mode 100644
index 00000000000..564eaa1fe85
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageLineChart.tsx
@@ -0,0 +1,86 @@
+import { Card, LineChart, Title } from "@tremor/react";
+import { useMemo } from "react";
+import { DailyData } from "../../../types";
+
+interface EndpointUsageLineChartProps {
+  dailyData?: { results: DailyData[] };
+  endpointData?: Record<string, any>;
+}
+
+// Transform daily data into chart format
+function transformDailyDataToChart(dailyData: DailyData[]): Array<Record<string, string | number>> {
+  const chartData: Array<Record<string, string | number>> = [];
+
+  // Get all unique endpoint names
+  const endpointNames = new Set<string>();
+  dailyData.forEach((day) => {
+    if (day.breakdown.endpoints) {
+      Object.keys(day.breakdown.endpoints).forEach((name) => endpointNames.add(name));
+    }
+  });
+
+  dailyData.forEach((day) => {
+    const date = new Date(day.date);
+    const dateStr = date.toLocaleDateString("en-US", {
+      month: "short",
+      day: "numeric",
+    });
+
+    const dataPoint: Record<string, string | number> = {
+      date: dateStr,
+    };
+
+    endpointNames.forEach((endpointName) => {
+      const endpoint = day.breakdown.endpoints?.[endpointName];
+      dataPoint[endpointName] = endpoint?.metrics.api_requests || 0;
+    });
+
+    chartData.push(dataPoint);
+  });
+
+  // Reverse the array so most recent dates appear on the right
+  return chartData.reverse();
+}
+
+export function EndpointUsageLineChart({ dailyData, endpointData }: EndpointUsageLineChartProps) {
+  const chartData = useMemo(() => {
+    if (!dailyData?.results || dailyData.results.length === 0) {
+      return [];
+    }
+
+    return transformDailyDataToChart(dailyData.results);
+  }, [dailyData]);
+
+  // Get endpoint names from chart data
+  const categories = useMemo(() => {
+    if (chartData.length === 0) return [];
+    const keys = Object.keys(chartData[0]).filter((key) => key !== "date");
+    return keys;
+  }, [chartData]);
+
+  // Tremor color palette for multiple lines
+  const colors = ["blue", "cyan", "indigo", "violet", "purple", "fuchsia", "pink", "rose", "red", "orange"];
+
+  return (
+    <Card className="mb-6">
+      <div className="flex items-center justify-between mb-4">
+        <Title>Endpoint Usage Trends</Title>
+      </div>
+      <LineChart
+        className="h-80"
+        data={chartData}
+        index="date"
+        categories={categories}
+        colors={colors.slice(0, categories.length)}
+        valueFormatter={(value) => value.toLocaleString()}
+        showLegend={true}
+        showGridLines={true}
+        yAxisWidth={60}
+        connectNulls={true}
+        curveType="natural"
+      />
+    </Card>
+  );
+}
+
+export default EndpointUsageLineChart;
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageTable.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageTable.test.tsx
new file mode 100644
index 00000000000..793e4c6e3cf
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageTable.test.tsx
@@ -0,0 +1,70 @@
+import { render, screen } from "@testing-library/react";
+import { describe, expect, it, vi } from "vitest";
+import EndpointUsageTable from "./EndpointUsageTable";
+
+vi.mock("antd", async () => {
+  const React = await import("react");
+
+  function Table({ columns, dataSource }: any) {
+    return React.createElement(
+      "div",
+      { "data-testid": "antd-table" },
+      columns?.map((col: any) =>
+        React.createElement("div", { key: col.key, "data-testid": `column-${col.key}` }, col.title),
+      ),
+      dataSource?.map((row: any) =>
+        React.createElement(
+          "div",
+          { key: row.key, "data-testid": `row-${row.key}` },
+          React.createElement("div", null, row.endpoint),
+        ),
+      ),
+    );
+  }
+  (Table as any).displayName = "Table";
+
+  function Progress({ percent }: any) {
+    return React.createElement("div", { "data-testid": "antd-progress", "data-percent": percent });
+  }
+  (Progress as any).displayName = "Progress";
+
+  return { Table, Progress };
+});
+
+vi.mock("@/utils/dataUtils", () => ({
+  formatNumberWithCommas: (value: number, decimals?: number) => {
+    return value.toFixed(decimals || 0);
+  },
+}));
+
+describe("EndpointUsageTable", () => {
+  it("should render", () => {
+    const mockEndpointData = {
+      "endpoint-1": {
+        metrics: {
+          spend: 100.5,
+          prompt_tokens: 5000,
+          completion_tokens: 3000,
+          total_tokens: 8000,
+          api_requests: 100,
+          successful_requests: 95,
+          failed_requests: 5,
+          cache_read_input_tokens: 0,
+          cache_creation_input_tokens: 0,
+        },
+        metadata: {},
+        api_key_breakdown: {},
+      },
+    };
+
+    render(<EndpointUsageTable endpointData={mockEndpointData} />);
+
+    expect(screen.getByTestId("antd-table")).toBeInTheDocument();
+    expect(screen.getByTestId("column-endpoint")).toBeInTheDocument();
+    expect(screen.getByTestId("column-requests")).toBeInTheDocument();
+    expect(screen.getByTestId("column-api_requests")).toBeInTheDocument();
+    expect(screen.getByTestId("column-successRate")).toBeInTheDocument();
+    expect(screen.getByTestId("column-total_tokens")).toBeInTheDocument();
+    expect(screen.getByTestId("column-spend")).toBeInTheDocument();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageTable.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageTable.tsx
new file mode 100644
index 00000000000..f5cfb553370
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EndpointUsage/components/EndpointUsageTable.tsx
@@ -0,0 +1,122 @@
+import React from "react";
+import { Table, Progress } from "antd";
+import type { ColumnsType } from "antd/es/table";
+import { formatNumberWithCommas } from "@/utils/dataUtils";
+import { MetricWithMetadata } from "../../../types";
+
+interface EndpointUsageTableProps {
+  endpointData: Record<string, MetricWithMetadata>;
+}
+
+interface EndpointRow {
+  key: string;
+  endpoint: string;
+  successful_requests: number;
+  failed_requests: number;
+  api_requests: number;
+  total_tokens: number;
+  spend: number;
+  successRate: number;
+}
+
+const EndpointUsageTable: React.FC<EndpointUsageTableProps> = ({ endpointData }) => {
+  const calculateSuccessRate = (successful: number, total: number): number => {
+    if (total === 0) return 0;
+    return (successful / total) * 100;
+  };
+
+  const dataSource: EndpointRow[] = Object.entries(endpointData).map(([endpoint, data]) => ({
+    key: endpoint,
+    endpoint,
+    successful_requests: data.metrics.successful_requests,
+    failed_requests: data.metrics.failed_requests,
+    api_requests: data.metrics.api_requests,
+    total_tokens: data.metrics.total_tokens,
+    spend: data.metrics.spend,
+    successRate: calculateSuccessRate(data.metrics.successful_requests, data.metrics.api_requests),
+  }));
+
+  const columns: ColumnsType<EndpointRow> = [
+    {
+      title: "Endpoint",
+      dataIndex: "endpoint",
+      key: "endpoint",
+      render: (text: string) => <span className="font-medium">{text}</span>,
+    },
+    {
+      title: "Successful / Failed",
+      key: "requests",
+      render: (_: any, record: EndpointRow) => {
+        const successPercentage =
+          record.api_requests > 0 ? (record.successful_requests / record.api_requests) * 100 : 0;
+        const failurePercentage = record.api_requests > 0 ? (record.failed_requests / record.api_requests) * 100 : 0;
+        const totalPercentage = successPercentage + failurePercentage;
+
+        const strokeColorConfig: Record<string, string> = {
+          "0%": "#22c55e",
+        };
+        if (successPercentage > 0 && successPercentage < 100) {
+          strokeColorConfig[`${successPercentage}%`] = "#22c55e";
+          strokeColorConfig[`${successPercentage + 0.01}%`] = "#ef4444";
+        }
+        strokeColorConfig["100%"] = failurePercentage > 0 ? "#ef4444" : "#22c55e";
+
+        return (
+          <div className="flex items-center space-x-3">
+            <div className="flex-1 relative">
+              <Progress percent={totalPercentage} size="small" strokeColor={strokeColorConfig} showInfo={false} />
+            </div>
+            <div className="flex items-center space-x-2 text-sm min-w-[100px]">
+              <span className="text-green-600 font-medium">{record.successful_requests.toLocaleString()}</span>
+              <span className="text-gray-400">/</span>
+              <span className="text-red-600 font-medium">{record.failed_requests.toLocaleString()}</span>
+            </div>
+          </div>
+        );
+      },
+    },
+    {
+      title: "Total Request",
+      dataIndex: "api_requests",
+      key: "api_requests",
+      render: (value: number) => value.toLocaleString(),
+    },
+    {
+      title: "Success Rate",
+      dataIndex: "successRate",
+      key: "successRate",
+      render: (value: number) => {
+        const successRateStr = value.toFixed(2);
+        return (
+          <span
+            className={
+              value >= 95
+                ? "text-green-600 font-medium"
+                : value >= 80
+                  ? "text-yellow-600 font-medium"
+                  : "text-red-600 font-medium"
+            }
+          >
+            {successRateStr}%
+          </span>
+        );
+      },
+    },
+    {
+      title: "Total Tokens",
+      dataIndex: "total_tokens",
+      key: "total_tokens",
+      render: (value: number) => value.toLocaleString(),
+    },
+    {
+      title: "Spend",
+      dataIndex: "spend",
+      key: "spend",
+      render: (value: number) => `$${formatNumberWithCommas(value, 2)}`,
+    },
+  ];
+
+  return <Table columns={columns} dataSource={dataSource} pagination={false} />;
+};
+
+export default EndpointUsageTable;
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/EntityUsage.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/EntityUsage.tsx
index 75cf8c9292c..52e75005ccc 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/EntityUsage.tsx
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/EntityUsage.tsx
@@ -38,6 +38,8 @@ import { valueFormatterSpend } from "../../utils/value_formatters";
 import TopKeyView from "./TopKeyView";
 import TopModelView from "./TopModelView";
 import useTeams from "@/app/(dashboard)/hooks/useTeams";
+import EndpointUsage from "../EndpointUsage/EndpointUsage";
+import NewBadge from "../../../common_components/NewBadge";
 
 interface EntityMetrics {
   metrics: {
@@ -399,11 +401,14 @@ const EntityUsage: React.FC<EntityUsageProps> = ({
         filterOptions={getAllTags() || undefined}
       />
       <TabGroup>
-        <TabList variant="solid" className="mt-1">
-          <Tab>Cost</Tab>
-          <Tab>{entityType === "agent" ? "Request / Token Consumption" : "Model Activity"}</Tab>
-          <Tab>Key Activity</Tab>
-        </TabList>
+        <NewBadge>
+          <TabList variant="solid" className="mt-1">
+            <Tab>Cost</Tab>
+            <Tab>{entityType === "agent" ? "Request / Token Consumption" : "Model Activity"}</Tab>
+            <Tab>Key Activity</Tab>
+            <Tab>Endpoint Activity</Tab>
+          </TabList>
+        </NewBadge>
         <TabPanels>
           <TabPanel>
             <Grid numItems={2} className="gap-2 w-full">
@@ -682,6 +687,9 @@ const EntityUsage: React.FC<EntityUsageProps> = ({
           <TabPanel>
             <ActivityMetrics modelMetrics={keyMetrics} hidePromptCachingMetrics={entityType === "agent"} />
           </TabPanel>
+          <TabPanel>
+            <EndpointUsage userSpendData={spendData} />
+          </TabPanel>
         </TabPanels>
       </TabGroup>
     </div>
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx
index 09415e87443..69a1bb4e305 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx
@@ -70,8 +70,9 @@ vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({
   default: vi.fn(),
 }));
 
-vi.mock("antd", async () => {
+vi.mock("antd", async (importOriginal) => {
   const React = await import("react");
+  const actual = await importOriginal<typeof import("antd")>();
 
   function Select(props: any) {
     const { value, onChange, options, ...rest } = props;
@@ -111,7 +112,34 @@ vi.mock("antd", async () => {
   }
   (Badge as any).displayName = "AntdBadge";
 
-  return { Select, Alert, Badge };
+  function Table({ columns, dataSource, ...rest }: any) {
+    return React.createElement(
+      "div",
+      { ...rest, "data-testid": "antd-table" },
+      columns?.map((col: any) =>
+        React.createElement("div", { key: col.key, "data-testid": `column-${col.key}` }, col.title),
+      ),
+      dataSource?.map((row: any) =>
+        React.createElement(
+          "div",
+          { key: row.key, "data-testid": `row-${row.key}` },
+          columns?.map((col: any) => {
+            const value = col.render ? col.render(row[col.dataIndex], row) : row[col.dataIndex];
+            return React.createElement("div", { key: col.key }, value);
+          }),
+        ),
+      ),
+    );
+  }
+  (Table as any).displayName = "Table";
+
+  return {
+    ...actual,
+    Select,
+    Alert,
+    Badge,
+    Table,
+  };
 });
 
 vi.mock("@ant-design/icons", async () => {
@@ -341,9 +369,11 @@ describe("NewUsage", () => {
     });
 
     // Check that key metrics are displayed
-    expect(screen.getByText("Total Requests")).toBeInTheDocument();
+    const totalRequestElements = screen.getAllByText("Total Requests");
+    expect(totalRequestElements.length).toBeGreaterThan(0);
     expect(screen.getByText("1,500")).toBeInTheDocument();
-    expect(screen.getByText("Successful Requests")).toBeInTheDocument();
+    const successfulRequestLabelElements = screen.getAllByText("Successful Requests");
+    expect(successfulRequestLabelElements.length).toBeGreaterThan(0);
     // Use getAllByText since this value appears in multiple places (metrics card + table)
     const successfulRequestElements = screen.getAllByText("1,450");
     expect(successfulRequestElements.length).toBeGreaterThan(0);
@@ -357,10 +387,14 @@ describe("NewUsage", () => {
     });
 
     // Check for usage metrics cards
-    expect(screen.getByText("Total Requests")).toBeInTheDocument();
-    expect(screen.getByText("Successful Requests")).toBeInTheDocument();
-    expect(screen.getByText("Failed Requests")).toBeInTheDocument();
-    expect(screen.getByText("Total Tokens")).toBeInTheDocument();
+    const totalRequestElements = screen.getAllByText("Total Requests");
+    expect(totalRequestElements.length).toBeGreaterThan(0);
+    const successfulRequestElements = screen.getAllByText("Successful Requests");
+    expect(successfulRequestElements.length).toBeGreaterThan(0);
+    const failedRequestElements = screen.getAllByText("Failed Requests");
+    expect(failedRequestElements.length).toBeGreaterThan(0);
+    const totalTokensElements = screen.getAllByText("Total Tokens");
+    expect(totalTokensElements.length).toBeGreaterThan(0);
 
     // Check for chart titles
     expect(screen.getByText("Daily Spend")).toBeInTheDocument();
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx
index a9c86c064ef..d891fcf604c 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx
@@ -53,6 +53,8 @@ import EntityUsage, { EntityList } from "./EntityUsage/EntityUsage";
 import TopKeyView from "./EntityUsage/TopKeyView";
 import { UsageOption, UsageViewSelect } from "./UsageViewSelect/UsageViewSelect";
 import { useCurrentUser } from "@/app/(dashboard)/hooks/users/useCurrentUser";
+import EndpointUsage from "./EndpointUsage/EndpointUsage";
+import NewBadge from "../../common_components/NewBadge";
 
 interface UsagePageProps {
   teams: Team[];
@@ -434,12 +436,15 @@ const UsagePage: React.FC<UsagePageProps> = ({ teams, organizations }) => {
           {usageView === "global" && (
             <TabGroup>
               <div className="flex justify-between items-center">
-                <TabList variant="solid" className="mt-1">
-                  <Tab>Cost</Tab>
-                  <Tab>Model Activity</Tab>
-                  <Tab>Key Activity</Tab>
-                  <Tab>MCP Server Activity</Tab>
-                </TabList>
+                <NewBadge>
+                  <TabList variant="solid" className="mt-1">
+                    <Tab>Cost</Tab>
+                    <Tab>Model Activity</Tab>
+                    <Tab>Key Activity</Tab>
+                    <Tab>MCP Server Activity</Tab>
+                    <Tab>Endpoint Activity</Tab>
+                  </TabList>
+                </NewBadge>
                 <Button
                   onClick={() => setIsGlobalExportModalOpen(true)}
                   icon={() => (
@@ -728,6 +733,9 @@ const UsagePage: React.FC<UsagePageProps> = ({ teams, organizations }) => {
                 <TabPanel>
                   <ActivityMetrics modelMetrics={mcpServerMetrics} />
                 </TabPanel>
+                <TabPanel>
+                  <EndpointUsage userSpendData={userSpendData} />
+                </TabPanel>
               </TabPanels>
             </TabGroup>
           )}
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.test.tsx
index ed837cf038c..a63fd96fc16 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.test.tsx
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.test.tsx
@@ -109,13 +109,4 @@ describe("UsageViewSelect", () => {
 
     expect(mockOnChange).toHaveBeenCalledWith("team");
   });
-
-  it("should render badge when option has badgeText", () => {
-    render(<UsageViewSelect value="agent" onChange={mockOnChange} isAdmin={true} />);
-
-    const badge = screen.getByTestId("antd-badge");
-    expect(badge).toBeInTheDocument();
-    expect(badge).toHaveAttribute("data-color", "blue");
-    expect(screen.getByTestId("antd-badge-count")).toHaveTextContent("New");
-  });
 });
diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.tsx
index 6b8347b6fbd..3d456de5a65 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.tsx
+++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsageViewSelect/UsageViewSelect.tsx
@@ -78,7 +78,6 @@ const OPTIONS: OptionConfig[] = [
     description: "View usage by AI agents",
     icon: <RobotOutlined style={{ fontSize: "16px" }} />,
     adminOnly: true,
-    badgeText: "New",
   },
   {
     value: "user-agent-activity",
diff --git a/ui/litellm-dashboard/src/components/UsagePage/types.ts b/ui/litellm-dashboard/src/components/UsagePage/types.ts
index f55b0dc8e28..2539dfaec16 100644
--- a/ui/litellm-dashboard/src/components/UsagePage/types.ts
+++ b/ui/litellm-dashboard/src/components/UsagePage/types.ts
@@ -23,6 +23,7 @@ export interface BreakdownMetrics {
   providers: { [key: string]: MetricWithMetadata };
   api_keys: { [key: string]: KeyMetricWithMetadata };
   entities: { [key: string]: MetricWithMetadata };
+  endpoints?: { [key: string]: MetricWithMetadata };
 }
 
 export interface MetricWithMetadata {
diff --git a/ui/litellm-dashboard/src/components/leftnav.tsx b/ui/litellm-dashboard/src/components/leftnav.tsx
index fc248ee049a..69374528e99 100644
--- a/ui/litellm-dashboard/src/components/leftnav.tsx
+++ b/ui/litellm-dashboard/src/components/leftnav.tsx
@@ -152,7 +152,11 @@ const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapse
           page: "new_usage",
           icon: <BarChartOutlined />,
           roles: [...all_admin_roles, ...internalUserRoles],
-          label: <span className="flex items-center gap-4">Usage</span>,
+          label: (
+            <span className="flex items-center gap-4">
+              Usage <NewBadge />
+            </span>
+          ),
         },
         {
           key: "logs",
@@ -260,11 +264,7 @@ const Sidebar: React.FC<SidebarProps> = ({ setPage, defaultSelectedKey, collapse
         {
           key: "settings",
           page: "settings",
-          label: (
-            <span className="flex items-center gap-4">
-              Settings <NewBadge />
-            </span>
-          ),
+          label: <span className="flex items-center gap-4">Settings</span>,
           icon: <SettingOutlined />,
           roles: all_admin_roles,
           children: [
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatImageUtils.test.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatImageUtils.test.tsx
new file mode 100644
index 00000000000..ecc4914b0ab
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatImageUtils.test.tsx
@@ -0,0 +1,187 @@
+import { describe, expect, it, vi, beforeEach } from "vitest";
+import {
+  convertImageToBase64,
+  createChatMultimodalMessage,
+  createChatDisplayMessage,
+  shouldShowChatAttachedImage,
+} from "./ChatImageUtils";
+import { MessageType } from "./types";
+
+describe("ChatImageUtils", () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  describe("convertImageToBase64", () => {
+    it("should convert file to base64 data URI", async () => {
+      const file = new File(["test content"], "test.png", { type: "image/png" });
+      const result = await convertImageToBase64(file);
+      expect(result).toMatch(/^data:image\/png;base64,/);
+    });
+
+    it("should handle different file types", async () => {
+      const jpegFile = new File(["jpeg content"], "test.jpg", { type: "image/jpeg" });
+      const result = await convertImageToBase64(jpegFile);
+      expect(result).toMatch(/^data:image\/jpeg;base64,/);
+    });
+
+    it("should reject on file read error", async () => {
+      const file = new File(["test"], "test.png", { type: "image/png" });
+      const originalReadAsDataURL = FileReader.prototype.readAsDataURL;
+
+      FileReader.prototype.readAsDataURL = vi.fn(function (this: FileReader) {
+        setTimeout(() => {
+          if (this.onerror) {
+            this.onerror(new Error("Read error") as any);
+          }
+        }, 0);
+      });
+
+      await expect(convertImageToBase64(file)).rejects.toThrow();
+
+      FileReader.prototype.readAsDataURL = originalReadAsDataURL;
+    });
+  });
+
+  describe("createChatMultimodalMessage", () => {
+    it("should create multimodal message with text and image", async () => {
+      const file = new File(["test content"], "test.png", { type: "image/png" });
+      const inputMessage = "What is in this image?";
+
+      const result = await createChatMultimodalMessage(inputMessage, file);
+
+      expect(result.role).toBe("user");
+      expect(result.content).toHaveLength(2);
+      expect(result.content[0]).toEqual({ type: "text", text: inputMessage });
+      expect(result.content[1]).toMatchObject({
+        type: "image_url",
+        image_url: {
+          url: expect.stringMatching(/^data:image\/png;base64,/),
+        },
+      });
+    });
+
+    it("should include base64 data URI in image_url", async () => {
+      const file = new File(["test content"], "test.png", { type: "image/png" });
+      const result = await createChatMultimodalMessage("test", file);
+
+      const imageContent = result.content[1];
+      expect(imageContent.type).toBe("image_url");
+      if ("image_url" in imageContent && imageContent.image_url) {
+        expect(imageContent.image_url.url).toMatch(/^data:/);
+      }
+    });
+  });
+
+  describe("createChatDisplayMessage", () => {
+    it("should create display message without file", () => {
+      const result = createChatDisplayMessage("Hello world", false);
+
+      expect(result.role).toBe("user");
+      expect(result.content).toBe("Hello world");
+      expect(result.imagePreviewUrl).toBeUndefined();
+    });
+
+    it("should create display message with PDF file", () => {
+      const filePreviewUrl = "blob:test-url";
+      const result = createChatDisplayMessage("Read this", true, filePreviewUrl, "document.pdf");
+
+      expect(result.content).toBe("Read this [PDF attached]");
+      expect(result.imagePreviewUrl).toBe(filePreviewUrl);
+    });
+
+    it("should create display message with image file", () => {
+      const filePreviewUrl = "blob:test-url";
+      const result = createChatDisplayMessage("Look at this", true, filePreviewUrl, "photo.jpg");
+
+      expect(result.content).toBe("Look at this [Image attached]");
+      expect(result.imagePreviewUrl).toBe(filePreviewUrl);
+    });
+
+    it("should create display message with file but no fileName", () => {
+      const filePreviewUrl = "blob:test-url";
+      const result = createChatDisplayMessage("Check this", true, filePreviewUrl);
+
+      expect(result.content).toBe("Check this ");
+      expect(result.imagePreviewUrl).toBe(filePreviewUrl);
+    });
+
+    it("should create display message with file but no preview URL", () => {
+      const result = createChatDisplayMessage("See this", true, undefined, "image.png");
+
+      expect(result.content).toBe("See this [Image attached]");
+      expect(result.imagePreviewUrl).toBeUndefined();
+    });
+  });
+
+  describe("shouldShowChatAttachedImage", () => {
+    it("should return true for user message with image attachment", () => {
+      const message: MessageType = {
+        role: "user",
+        content: "Check this [Image attached]",
+        imagePreviewUrl: "blob:test-url",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(true);
+    });
+
+    it("should return true for user message with PDF attachment", () => {
+      const message: MessageType = {
+        role: "user",
+        content: "Read this [PDF attached]",
+        imagePreviewUrl: "blob:test-url",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(true);
+    });
+
+    it("should return false for assistant message", () => {
+      const message: MessageType = {
+        role: "assistant",
+        content: "Here is the image [Image attached]",
+        imagePreviewUrl: "blob:test-url",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(false);
+    });
+
+    it("should return false when content is not a string", () => {
+      const message: MessageType = {
+        role: "user",
+        content: [{ type: "input_text", text: "test" }],
+        imagePreviewUrl: "blob:test-url",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(false);
+    });
+
+    it("should return false when content does not include attachment marker", () => {
+      const message: MessageType = {
+        role: "user",
+        content: "Just regular text",
+        imagePreviewUrl: "blob:test-url",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(false);
+    });
+
+    it("should return false when imagePreviewUrl is missing", () => {
+      const message: MessageType = {
+        role: "user",
+        content: "Check this [Image attached]",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(false);
+    });
+
+    it("should return false when imagePreviewUrl is empty string", () => {
+      const message: MessageType = {
+        role: "user",
+        content: "Check this [Image attached]",
+        imagePreviewUrl: "",
+      };
+
+      expect(shouldShowChatAttachedImage(message)).toBe(false);
+    });
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx
index cee9db74276..f747b5b2599 100644
--- a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx
@@ -84,10 +84,7 @@ interface ChatUIProps {
   };
 }
 
-const MCP_SUPPORTED_ENDPOINTS = new Set<EndpointType>([
-  EndpointType.CHAT,
-  EndpointType.RESPONSES,
-]);
+const MCP_SUPPORTED_ENDPOINTS = new Set<EndpointType>([EndpointType.CHAT, EndpointType.RESPONSES]);
 
 const ChatUI: React.FC<ChatUIProps> = ({
   accessToken,
@@ -130,6 +127,9 @@ const ChatUI: React.FC<ChatUIProps> = ({
     return disabledPersonalKeyCreation ? "custom" : "session";
   });
   const [apiKey, setApiKey] = useState<string>(() => sessionStorage.getItem("apiKey") || "");
+  const [customProxyBaseUrl, setCustomProxyBaseUrl] = useState<string>(
+    () => sessionStorage.getItem("customProxyBaseUrl") || "",
+  );
   const [inputMessage, setInputMessage] = useState("");
   const [chatHistory, setChatHistory] = useState<MessageType[]>(() => {
     try {
@@ -212,7 +212,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
   const [temperature, setTemperature] = useState<number>(1.0);
   const [maxTokens, setMaxTokens] = useState<number>(2048);
   const [useAdvancedParams, setUseAdvancedParams] = useState<boolean>(false);
-  
+
   // Code Interpreter state (using custom hook)
   const codeInterpreter = useCodeInterpreter();
 
@@ -241,16 +241,15 @@ const ChatUI: React.FC<ChatUIProps> = ({
 
     try {
       const response = await listMCPTools(userApiKey, serverId);
-      setServerToolsMap(prev => ({
+      setServerToolsMap((prev) => ({
         ...prev,
-        [serverId]: response.tools || []
+        [serverId]: response.tools || [],
       }));
     } catch (error) {
       console.error(`Error fetching tools for server ${serverId}:`, error);
     }
   };
 
-
   useEffect(() => {
     if (isGetCodeModalVisible) {
       const code = generateCodeSnippet({
@@ -392,7 +391,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
 
     const loadAgents = async () => {
       try {
-        const agents = await fetchAvailableAgents(userApiKey);
+        const agents = await fetchAvailableAgents(userApiKey, customProxyBaseUrl || undefined);
         setAgentInfo(agents);
         // Clear selection if current agent not in list
         if (selectedAgent && !agents.some((a) => a.agent_name === selectedAgent)) {
@@ -404,7 +403,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
     };
 
     loadAgents();
-  }, [accessToken, apiKeySource, apiKey, endpointType]);
+  }, [accessToken, apiKeySource, apiKey, endpointType, customProxyBaseUrl, selectedAgent]);
 
   useEffect(() => {
     // Scroll to the bottom of the chat whenever chatHistory updates
@@ -900,6 +899,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
             useAdvancedParams ? temperature : undefined,
             useAdvancedParams ? maxTokens : undefined,
             updateTotalLatency,
+            customProxyBaseUrl || undefined,
             mcpServers,
             mcpServerToolRestrictions,
           );
@@ -912,6 +912,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
             effectiveApiKey,
             selectedTags,
             signal,
+            customProxyBaseUrl || undefined,
           );
         } else if (endpointType === EndpointType.SPEECH) {
           // For audio speech
@@ -923,6 +924,9 @@ const ChatUI: React.FC<ChatUIProps> = ({
             effectiveApiKey,
             selectedTags,
             signal,
+            undefined, // responseFormat
+            undefined, // speed
+            customProxyBaseUrl || undefined,
           );
         } else if (endpointType === EndpointType.IMAGE_EDITS) {
           // For image edits
@@ -935,6 +939,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
               effectiveApiKey,
               selectedTags,
               signal,
+              customProxyBaseUrl || undefined,
             );
           }
         } else if (endpointType === EndpointType.RESPONSES) {
@@ -973,6 +978,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
             handleMCPEvent, // Pass MCP event handler
             codeInterpreter.enabled, // Enable Code Interpreter tool
             codeInterpreter.setResult, // Handle code interpreter output
+            customProxyBaseUrl || undefined,
             mcpServers,
             mcpServerToolRestrictions,
           );
@@ -997,6 +1003,8 @@ const ChatUI: React.FC<ChatUIProps> = ({
             traceId,
             selectedVectorStores.length > 0 ? selectedVectorStores : undefined,
             selectedGuardrails.length > 0 ? selectedGuardrails : undefined,
+            selectedMCPServers, // Pass the selected tools array
+            customProxyBaseUrl || undefined,
           );
         } else if (endpointType === EndpointType.EMBEDDINGS) {
           await makeOpenAIEmbeddingsRequest(
@@ -1005,6 +1013,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
             selectedModel,
             effectiveApiKey,
             selectedTags,
+            customProxyBaseUrl || undefined,
           );
         } else if (endpointType === EndpointType.TRANSCRIPTION) {
           // For audio transcriptions
@@ -1016,6 +1025,11 @@ const ChatUI: React.FC<ChatUIProps> = ({
               effectiveApiKey,
               selectedTags,
               signal,
+              undefined, // language
+              undefined, // prompt
+              undefined, // responseFormat
+              undefined, // temperature
+              customProxyBaseUrl || undefined,
             );
           }
         }
@@ -1032,6 +1046,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
           updateTimingData,
           updateTotalLatency,
           updateA2AMetadata,
+          customProxyBaseUrl || undefined,
         );
       }
     } catch (error) {
@@ -1156,6 +1171,40 @@ const ChatUI: React.FC<ChatUIProps> = ({
                 )}
               </div>
 
+              <div>
+                <div className="flex items-center justify-between mb-2">
+                  <Text className="font-medium text-gray-700 flex items-center">
+                    <SettingOutlined className="mr-2" /> Custom Proxy Base URL
+                  </Text>
+                  {customProxyBaseUrl && (
+                    <Button
+                      type="link"
+                      size="small"
+                      icon={<ClearOutlined />}
+                      onClick={() => {
+                        setCustomProxyBaseUrl("");
+                        sessionStorage.removeItem("customProxyBaseUrl");
+                      }}
+                      className="text-gray-500 hover:text-gray-700"
+                    >
+                      Clear
+                    </Button>
+                  )}
+                </div>
+                <TextInput
+                  placeholder="Optional: Enter custom proxy URL (e.g., http://localhost:5000)"
+                  onValueChange={(value) => {
+                    setCustomProxyBaseUrl(value);
+                    sessionStorage.setItem("customProxyBaseUrl", value);
+                  }}
+                  value={customProxyBaseUrl}
+                  icon={ApiOutlined}
+                />
+                {customProxyBaseUrl && (
+                  <Text className="text-xs text-gray-500 mt-1">API calls will be sent to: {customProxyBaseUrl}</Text>
+                )}
+              </div>
+
               <div>
                 <Text className="font-medium block mb-2 text-gray-700 flex items-center">
                   <ApiOutlined className="mr-2" /> Endpoint Type
@@ -1327,7 +1376,11 @@ const ChatUI: React.FC<ChatUIProps> = ({
                     optionLabelProp="label"
                   >
                     {agentInfo.map((agent) => (
-                      <Select.Option key={agent.agent_id} value={agent.agent_name} label={agent.agent_name || agent.agent_id}>
+                      <Select.Option
+                        key={agent.agent_id}
+                        value={agent.agent_name}
+                        label={agent.agent_name || agent.agent_id}
+                      >
                         <div className="flex flex-col py-1">
                           <span className="font-medium">{agent.agent_name || agent.agent_id}</span>
                           {agent.agent_card_params?.description && (
@@ -1361,10 +1414,7 @@ const ChatUI: React.FC<ChatUIProps> = ({
               <div>
                 <Text className="font-medium block mb-2 text-gray-700 flex items-center">
                   <ToolOutlined className="mr-2" /> MCP Servers
-                  <Tooltip
-                    className="ml-1"
-                    title="Select MCP servers to use in your conversation."
-                  >
+                  <Tooltip className="ml-1" title="Select MCP servers to use in your conversation.">
                     <InfoCircleOutlined />
                   </Tooltip>
                 </Text>
@@ -1380,15 +1430,15 @@ const ChatUI: React.FC<ChatUIProps> = ({
                     } else {
                       setSelectedMCPServers(value);
                       // Clean up tool restrictions for removed servers
-                      setMCPServerToolRestrictions(prev => {
+                      setMCPServerToolRestrictions((prev) => {
                         const updated = { ...prev };
-                        Object.keys(updated).forEach(serverId => {
+                        Object.keys(updated).forEach((serverId) => {
                           if (!value.includes(serverId)) delete updated[serverId];
                         });
                         return updated;
                       });
                       // Load tools for newly selected servers
-                      value.forEach(serverId => {
+                      value.forEach((serverId) => {
                         if (!serverToolsMap[serverId]) {
                           loadServerTools(serverId);
                         }
@@ -1419,12 +1469,8 @@ const ChatUI: React.FC<ChatUIProps> = ({
                       disabled={selectedMCPServers.includes("__all__")}
                     >
                       <div className="flex flex-col py-1">
-                        <span className="font-medium">
-                          {server.alias || server.server_name || server.server_id}
-                        </span>
-                        {server.description && (
-                          <span className="text-xs text-gray-500 mt-1">{server.description}</span>
-                        )}
+                        <span className="font-medium">{server.alias || server.server_name || server.server_id}</span>
+                        {server.description && <span className="text-xs text-gray-500 mt-1">{server.description}</span>}
                       </div>
                     </Select.Option>
                   ))}
@@ -1434,40 +1480,40 @@ const ChatUI: React.FC<ChatUIProps> = ({
                 {selectedMCPServers.length > 0 &&
                   !selectedMCPServers.includes("__all__") &&
                   MCP_SUPPORTED_ENDPOINTS.has(endpointType as EndpointType) && (
-                  <div className="mt-3 space-y-2">
-                    {selectedMCPServers.map(serverId => {
-                      const server = mcpServers.find(s => s.server_id === serverId);
-                      const tools = serverToolsMap[serverId] || [];
-                      if (tools.length === 0) return null;
+                    <div className="mt-3 space-y-2">
+                      {selectedMCPServers.map((serverId) => {
+                        const server = mcpServers.find((s) => s.server_id === serverId);
+                        const tools = serverToolsMap[serverId] || [];
+                        if (tools.length === 0) return null;
 
-                      return (
-                        <div key={serverId} className="border rounded p-2">
-                          <Text className="text-xs text-gray-600 mb-1">
-                            Limit tools for {server?.alias || server?.server_name || serverId}:
-                          </Text>
-                          <Select
-                            mode="multiple"
-                            size="small"
-                            style={{ width: "100%" }}
-                            placeholder="All tools (default)"
-                            value={mcpServerToolRestrictions[serverId] || []}
-                            onChange={(selectedTools) => {
-                              setMCPServerToolRestrictions(prev => ({
-                                ...prev,
-                                [serverId]: selectedTools
-                              }));
-                            }}
-                            options={tools.map(tool => ({
-                              value: tool.name,
-                              label: tool.name
-                            }))}
-                            maxTagCount={2}
-                          />
-                        </div>
-                      );
-                    })}
-                  </div>
-                )}
+                        return (
+                          <div key={serverId} className="border rounded p-2">
+                            <Text className="text-xs text-gray-600 mb-1">
+                              Limit tools for {server?.alias || server?.server_name || serverId}:
+                            </Text>
+                            <Select
+                              mode="multiple"
+                              size="small"
+                              style={{ width: "100%" }}
+                              placeholder="All tools (default)"
+                              value={mcpServerToolRestrictions[serverId] || []}
+                              onChange={(selectedTools) => {
+                                setMCPServerToolRestrictions((prev) => ({
+                                  ...prev,
+                                  [serverId]: selectedTools,
+                                }));
+                              }}
+                              options={tools.map((tool) => ({
+                                value: tool.name,
+                                label: tool.name,
+                              }))}
+                              maxTagCount={2}
+                            />
+                          </div>
+                        );
+                      })}
+                    </div>
+                  )}
               </div>
 
               <div>
@@ -2018,7 +2064,13 @@ const ChatUI: React.FC<ChatUIProps> = ({
                     )}
                     {/* Quick Code Interpreter toggle for Responses */}
                     {endpointType === EndpointType.RESPONSES && (
-                      <Tooltip title={codeInterpreter.enabled ? "Code Interpreter enabled (click to disable)" : "Enable Code Interpreter"}>
+                      <Tooltip
+                        title={
+                          codeInterpreter.enabled
+                            ? "Code Interpreter enabled (click to disable)"
+                            : "Enable Code Interpreter"
+                        }
+                      >
                         <button
                           className={`p-1.5 rounded-md transition-colors ${
                             codeInterpreter.enabled
diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/CodeInterpreterOutput.test.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/CodeInterpreterOutput.test.tsx
new file mode 100644
index 00000000000..d87a74f4641
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/playground/chat_ui/CodeInterpreterOutput.test.tsx
@@ -0,0 +1,326 @@
+import { render, screen, waitFor } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import { describe, expect, it, vi, beforeEach, afterEach } from "vitest";
+import CodeInterpreterOutput from "./CodeInterpreterOutput";
+
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => "https://example.com"),
+}));
+
+global.fetch = vi.fn();
+
+describe("CodeInterpreterOutput", () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+    URL.createObjectURL = vi.fn((blob) => `blob:${blob}`);
+    URL.revokeObjectURL = vi.fn();
+  });
+
+  afterEach(() => {
+    vi.restoreAllMocks();
+  });
+
+  it("should render", () => {
+    render(<CodeInterpreterOutput code="print('hello')" accessToken="test-token" />);
+
+    expect(screen.getByText("Python Code Executed")).toBeInTheDocument();
+  });
+
+  it("should display code in syntax highlighter", async () => {
+    const user = userEvent.setup();
+    const code = "print('hello world')";
+    const { container } = render(<CodeInterpreterOutput code={code} accessToken="test-token" />);
+
+    expect(screen.getByText("Python Code Executed")).toBeInTheDocument();
+
+    const collapseHeader = screen.getByRole("button");
+    await user.click(collapseHeader);
+
+    await waitFor(() => {
+      const codeElement = container.querySelector("code.language-python");
+      expect(codeElement).toBeInTheDocument();
+      expect(codeElement?.textContent).toContain(code);
+    });
+  });
+
+  it("should fetch and display images from annotations", async () => {
+    const mockBlob = new Blob(["image data"], { type: "image/png" });
+    const mockResponse = {
+      ok: true,
+      blob: vi.fn().mockResolvedValue(mockBlob),
+    };
+
+    (global.fetch as any).mockResolvedValue(mockResponse);
+
+    const annotations = [
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-1",
+        filename: "chart.png",
+        start_index: 0,
+        end_index: 10,
+      },
+    ];
+
+    render(
+      <CodeInterpreterOutput
+        code="import matplotlib.pyplot as plt"
+        annotations={annotations}
+        accessToken="test-token"
+      />,
+    );
+
+    await waitFor(() => {
+      expect(global.fetch).toHaveBeenCalledWith(
+        "https://example.com/v1/containers/container-1/files/file-1/content",
+        expect.objectContaining({
+          headers: {
+            Authorization: "Bearer test-token",
+          },
+        }),
+      );
+    });
+
+    await waitFor(() => {
+      expect(screen.getByText("chart.png")).toBeInTheDocument();
+    });
+  });
+
+  it("should show loading state while fetching images", async () => {
+    const mockBlob = new Blob(["image data"], { type: "image/png" });
+    let resolveBlob: (value: Blob) => void;
+    const blobPromise = new Promise<Blob>((resolve) => {
+      resolveBlob = resolve;
+    });
+
+    const mockResponse = {
+      ok: true,
+      blob: vi.fn().mockReturnValue(blobPromise),
+    };
+
+    (global.fetch as any).mockResolvedValue(mockResponse);
+
+    const annotations = [
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-1",
+        filename: "chart.png",
+        start_index: 0,
+        end_index: 10,
+      },
+    ];
+
+    render(
+      <CodeInterpreterOutput
+        code="import matplotlib.pyplot as plt"
+        annotations={annotations}
+        accessToken="test-token"
+      />,
+    );
+
+    await waitFor(() => {
+      expect(screen.getByText("Loading image...")).toBeInTheDocument();
+    });
+
+    resolveBlob!(mockBlob);
+    await waitFor(() => {
+      expect(screen.queryByText("Loading image...")).not.toBeInTheDocument();
+    });
+  });
+
+  it("should handle download for image files", async () => {
+    const user = userEvent.setup();
+    const mockBlob = new Blob(["image data"], { type: "image/png" });
+    const mockResponse = {
+      ok: true,
+      blob: vi.fn().mockResolvedValue(mockBlob),
+    };
+
+    (global.fetch as any).mockResolvedValue(mockResponse);
+
+    const annotations = [
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-1",
+        filename: "chart.png",
+        start_index: 0,
+        end_index: 10,
+      },
+    ];
+
+    const createElementSpy = vi.spyOn(document, "createElement");
+    const appendChildSpy = vi.spyOn(document.body, "appendChild");
+    const removeChildSpy = vi.spyOn(document.body, "removeChild");
+
+    render(
+      <CodeInterpreterOutput
+        code="import matplotlib.pyplot as plt"
+        annotations={annotations}
+        accessToken="test-token"
+      />,
+    );
+
+    await waitFor(() => {
+      expect(screen.getByText("chart.png")).toBeInTheDocument();
+    });
+
+    const downloadButton = screen.getByText("Download");
+    await user.click(downloadButton);
+
+    await waitFor(() => {
+      expect(global.fetch).toHaveBeenCalledWith(
+        "https://example.com/v1/containers/container-1/files/file-1/content",
+        expect.objectContaining({
+          headers: {
+            Authorization: "Bearer test-token",
+          },
+        }),
+      );
+    });
+
+    createElementSpy.mockRestore();
+    appendChildSpy.mockRestore();
+    removeChildSpy.mockRestore();
+  });
+
+  it("should handle download for non-image files", async () => {
+    const user = userEvent.setup();
+    const mockBlob = new Blob(["file data"], { type: "text/plain" });
+    const mockResponse = {
+      ok: true,
+      blob: vi.fn().mockResolvedValue(mockBlob),
+    };
+
+    (global.fetch as any).mockResolvedValue(mockResponse);
+
+    const annotations = [
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-1",
+        filename: "data.csv",
+        start_index: 0,
+        end_index: 10,
+      },
+    ];
+
+    render(<CodeInterpreterOutput code="import pandas as pd" annotations={annotations} accessToken="test-token" />);
+
+    await waitFor(() => {
+      expect(screen.getByText("data.csv")).toBeInTheDocument();
+    });
+
+    const downloadButton = screen.getByText("data.csv").closest("button");
+    expect(downloadButton).toBeInTheDocument();
+    if (downloadButton) {
+      await user.click(downloadButton);
+    }
+
+    await waitFor(() => {
+      expect(global.fetch).toHaveBeenCalledWith(
+        "https://example.com/v1/containers/container-1/files/file-1/content",
+        expect.objectContaining({
+          headers: {
+            Authorization: "Bearer test-token",
+          },
+        }),
+      );
+    });
+  });
+
+  it("should return null when no code and no annotations", () => {
+    const { container } = render(<CodeInterpreterOutput accessToken="test-token" />);
+
+    expect(container.firstChild).toBeNull();
+  });
+
+  it("should handle multiple image formats", async () => {
+    const mockBlob = new Blob(["image data"], { type: "image/png" });
+    const mockResponse = {
+      ok: true,
+      blob: vi.fn().mockResolvedValue(mockBlob),
+    };
+
+    (global.fetch as any).mockResolvedValue(mockResponse);
+
+    const annotations = [
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-1",
+        filename: "image.png",
+        start_index: 0,
+        end_index: 10,
+      },
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-2",
+        filename: "image.jpg",
+        start_index: 0,
+        end_index: 10,
+      },
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-3",
+        filename: "image.jpeg",
+        start_index: 0,
+        end_index: 10,
+      },
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-4",
+        filename: "image.gif",
+        start_index: 0,
+        end_index: 10,
+      },
+    ];
+
+    render(
+      <CodeInterpreterOutput
+        code="import matplotlib.pyplot as plt"
+        annotations={annotations}
+        accessToken="test-token"
+      />,
+    );
+
+    await waitFor(() => {
+      expect(global.fetch).toHaveBeenCalledTimes(4);
+    });
+  });
+
+  it("should handle fetch errors gracefully", async () => {
+    const consoleErrorSpy = vi.spyOn(console, "error").mockImplementation(() => {});
+    (global.fetch as any).mockRejectedValue(new Error("Network error"));
+
+    const annotations = [
+      {
+        type: "container_file_citation" as const,
+        container_id: "container-1",
+        file_id: "file-1",
+        filename: "chart.png",
+        start_index: 0,
+        end_index: 10,
+      },
+    ];
+
+    render(
+      <CodeInterpreterOutput
+        code="import matplotlib.pyplot as plt"
+        annotations={annotations}
+        accessToken="test-token"
+      />,
+    );
+
+    await waitFor(() => {
+      expect(consoleErrorSpy).toHaveBeenCalled();
+    });
+
+    consoleErrorSpy.mockRestore();
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx b/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx
index ff35188bbb5..92bcb21c4b5 100644
--- a/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx
+++ b/ui/litellm-dashboard/src/components/playground/compareUI/CompareUI.tsx
@@ -106,6 +106,9 @@ export default function CompareUI({ accessToken, disabledPersonalKeyCreation }:
   );
   const [customApiKey, setCustomApiKey] = useState("");
   const [debouncedCustomApiKey, setDebouncedCustomApiKey] = useState("");
+  const [customProxyBaseUrl] = useState<string>(
+    () => sessionStorage.getItem("customProxyBaseUrl") || ""
+  );
   useEffect(() => {
     const timer = setTimeout(() => {
       setDebouncedCustomApiKey(customApiKey);
@@ -171,7 +174,7 @@ export default function CompareUI({ accessToken, disabledPersonalKeyCreation }:
       }
       setIsLoadingAgents(true);
       try {
-        const agents = await fetchAvailableAgents(effectiveApiKey);
+        const agents = await fetchAvailableAgents(effectiveApiKey, customProxyBaseUrl || undefined);
         if (!active) return;
         setAgentOptions(agents);
       } catch (error) {
@@ -598,6 +601,8 @@ export default function CompareUI({ accessToken, disabledPersonalKeyCreation }:
             undefined,
             (time) => updateTimingDataForComparison(prepared.id, time),
             (latency) => updateTotalLatencyForComparison(prepared.id, latency),
+            undefined, // onA2AMetadata
+            customProxyBaseUrl || undefined,
           )
         : makeOpenAIChatCompletionRequest(
             prepared.apiChatHistory,
@@ -618,6 +623,7 @@ export default function CompareUI({ accessToken, disabledPersonalKeyCreation }:
             useAdvancedParams ? prepared.temperature : undefined,
             useAdvancedParams ? prepared.maxTokens : undefined,
             (latency) => updateTotalLatencyForComparison(prepared.id, latency),
+            customProxyBaseUrl || undefined,
           );
 
       requestPromise
diff --git a/ui/litellm-dashboard/src/components/playground/compareUI/components/UnifiedSelector.test.tsx b/ui/litellm-dashboard/src/components/playground/compareUI/components/UnifiedSelector.test.tsx
new file mode 100644
index 00000000000..1c6951fc421
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/playground/compareUI/components/UnifiedSelector.test.tsx
@@ -0,0 +1,161 @@
+import { render, screen, waitFor } from "@testing-library/react";
+import userEvent from "@testing-library/user-event";
+import { describe, expect, it, vi } from "vitest";
+import { UnifiedSelector } from "./UnifiedSelector";
+import { EndpointId, ENDPOINT_CONFIGS } from "../endpoint_config";
+
+describe("UnifiedSelector", () => {
+  it("should render", () => {
+    const onChange = vi.fn();
+    const options = [
+      { value: "option1", label: "Option 1" },
+      { value: "option2", label: "Option 2" },
+    ];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    render(<UnifiedSelector value="" options={options} loading={false} config={config} onChange={onChange} />);
+
+    const select = screen.getByRole("combobox");
+    expect(select).toBeInTheDocument();
+  });
+
+  it("should display placeholder when not loading", () => {
+    const onChange = vi.fn();
+    const options = [{ value: "option1", label: "Option 1" }];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    const { container } = render(
+      <UnifiedSelector value="" options={options} loading={false} config={config} onChange={onChange} />,
+    );
+
+    const placeholder = container.querySelector(".ant-select-selection-placeholder");
+    expect(placeholder).toHaveTextContent(config.selectorPlaceholder);
+  });
+
+  it("should display loading placeholder when loading", () => {
+    const onChange = vi.fn();
+    const options = [{ value: "option1", label: "Option 1" }];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    const { container } = render(
+      <UnifiedSelector value="" options={options} loading={true} config={config} onChange={onChange} />,
+    );
+
+    const placeholder = container.querySelector(".ant-select-selection-placeholder");
+    expect(placeholder).toHaveTextContent(`Loading ${config.selectorLabel.toLowerCase()}s...`);
+  });
+
+  it("should call onChange when option is selected", async () => {
+    const user = userEvent.setup();
+    const onChange = vi.fn();
+    const options = [
+      { value: "option1", label: "Option 1" },
+      { value: "option2", label: "Option 2" },
+    ];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    render(<UnifiedSelector value="" options={options} loading={false} config={config} onChange={onChange} />);
+
+    const select = screen.getByRole("combobox");
+    await user.click(select);
+
+    await waitFor(() => {
+      const option = screen.getByText("Option 1");
+      expect(option).toBeInTheDocument();
+    });
+
+    const option = screen.getByText("Option 1");
+    await user.click(option);
+
+    await waitFor(() => {
+      expect(onChange).toHaveBeenCalled();
+    });
+    const callArgs = onChange.mock.calls[0];
+    expect(callArgs[0]).toBe("option1");
+  });
+
+  it("should display selected value", () => {
+    const onChange = vi.fn();
+    const options = [
+      { value: "option1", label: "Option 1" },
+      { value: "option2", label: "Option 2" },
+    ];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    const { container } = render(
+      <UnifiedSelector value="option1" options={options} loading={false} config={config} onChange={onChange} />,
+    );
+
+    const selectedValue = container.querySelector(".ant-select-selection-item");
+    expect(selectedValue).toHaveTextContent("Option 1");
+  });
+
+  it("should filter options by search input", async () => {
+    const user = userEvent.setup();
+    const onChange = vi.fn();
+    const options = [
+      { value: "option1", label: "Option One" },
+      { value: "option2", label: "Option Two" },
+      { value: "option3", label: "Different" },
+    ];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    render(<UnifiedSelector value="" options={options} loading={false} config={config} onChange={onChange} />);
+
+    const select = screen.getByRole("combobox");
+    await user.click(select);
+    await user.type(select, "One");
+
+    await waitFor(() => {
+      expect(screen.getByText("Option One")).toBeInTheDocument();
+      expect(screen.queryByText("Option Two")).not.toBeInTheDocument();
+      expect(screen.queryByText("Different")).not.toBeInTheDocument();
+    });
+  });
+
+  it("should show loading spinner in notFoundContent when loading", async () => {
+    const user = userEvent.setup();
+    const onChange = vi.fn();
+    const options: { value: string; label: string }[] = [];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    render(<UnifiedSelector value="" options={options} loading={true} config={config} onChange={onChange} />);
+
+    const select = screen.getByRole("combobox");
+    await user.click(select);
+
+    await waitFor(() => {
+      const spin = document.querySelector(".ant-spin");
+      expect(spin).toBeInTheDocument();
+    });
+  });
+
+  it("should show no options message when not loading and no options", async () => {
+    const user = userEvent.setup();
+    const onChange = vi.fn();
+    const options: { value: string; label: string }[] = [];
+    const config = ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS];
+
+    render(<UnifiedSelector value="" options={options} loading={false} config={config} onChange={onChange} />);
+
+    const select = screen.getByRole("combobox");
+    await user.click(select);
+
+    await waitFor(() => {
+      expect(screen.getByText(`No ${config.selectorLabel.toLowerCase()}s available`)).toBeInTheDocument();
+    });
+  });
+
+  it("should work with agent endpoint config", () => {
+    const onChange = vi.fn();
+    const options = [{ value: "agent1", label: "Agent One" }];
+    const config = ENDPOINT_CONFIGS[EndpointId.A2A_AGENTS];
+
+    const { container } = render(
+      <UnifiedSelector value="" options={options} loading={false} config={config} onChange={onChange} />,
+    );
+
+    const placeholder = container.querySelector(".ant-select-selection-placeholder");
+    expect(placeholder).toHaveTextContent(config.selectorPlaceholder);
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/playground/compareUI/endpoint_config.test.ts b/ui/litellm-dashboard/src/components/playground/compareUI/endpoint_config.test.ts
new file mode 100644
index 00000000000..67ecf32fc2a
--- /dev/null
+++ b/ui/litellm-dashboard/src/components/playground/compareUI/endpoint_config.test.ts
@@ -0,0 +1,104 @@
+import { describe, expect, it } from "vitest";
+import {
+  EndpointId,
+  ENDPOINT_CONFIGS,
+  getAvailableEndpoints,
+  getEndpointConfig,
+  isAgentEndpoint,
+  isModelEndpoint,
+  modelOptionsToSelectorOptions,
+  agentOptionsToSelectorOptions,
+  getSelectionFieldName,
+  getComparisonSelection,
+  hasValidSelection,
+} from "./endpoint_config";
+import { Agent } from "../llm_calls/fetch_agents";
+
+describe("endpoint_config", () => {
+  it("should export EndpointId constants", () => {
+    expect(EndpointId.CHAT_COMPLETIONS).toBe("/v1/chat/completions");
+    expect(EndpointId.A2A_AGENTS).toBe("/a2a");
+  });
+
+  it("should have endpoint configs for all endpoint IDs", () => {
+    expect(ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS]).toBeDefined();
+    expect(ENDPOINT_CONFIGS[EndpointId.A2A_AGENTS]).toBeDefined();
+    expect(ENDPOINT_CONFIGS[EndpointId.CHAT_COMPLETIONS].selectorType).toBe("model");
+    expect(ENDPOINT_CONFIGS[EndpointId.A2A_AGENTS].selectorType).toBe("agent");
+  });
+
+  it("should get available endpoints", () => {
+    const endpoints = getAvailableEndpoints();
+    expect(endpoints).toHaveLength(2);
+    expect(endpoints).toContainEqual({
+      value: EndpointId.CHAT_COMPLETIONS,
+      label: "/v1/chat/completions",
+    });
+    expect(endpoints).toContainEqual({
+      value: EndpointId.A2A_AGENTS,
+      label: "/a2a (Agents)",
+    });
+  });
+
+  it("should get endpoint config by ID", () => {
+    const config = getEndpointConfig(EndpointId.CHAT_COMPLETIONS);
+    expect(config.id).toBe(EndpointId.CHAT_COMPLETIONS);
+    expect(config.selectorType).toBe("model");
+    expect(config.selectorLabel).toBe("Model");
+  });
+
+  it("should check if endpoint is agent endpoint", () => {
+    expect(isAgentEndpoint(EndpointId.A2A_AGENTS)).toBe(true);
+    expect(isAgentEndpoint(EndpointId.CHAT_COMPLETIONS)).toBe(false);
+  });
+
+  it("should check if endpoint is model endpoint", () => {
+    expect(isModelEndpoint(EndpointId.CHAT_COMPLETIONS)).toBe(true);
+    expect(isModelEndpoint(EndpointId.A2A_AGENTS)).toBe(false);
+  });
+
+  it("should convert model options to selector options", () => {
+    const models = ["gpt-4", "gpt-3.5-turbo", "claude-3"];
+    const options = modelOptionsToSelectorOptions(models);
+    expect(options).toHaveLength(3);
+    expect(options[0]).toEqual({ value: "gpt-4", label: "gpt-4" });
+    expect(options[1]).toEqual({ value: "gpt-3.5-turbo", label: "gpt-3.5-turbo" });
+    expect(options[2]).toEqual({ value: "claude-3", label: "claude-3" });
+  });
+
+  it("should convert agent options to selector options", () => {
+    const agents: Agent[] = [
+      { agent_id: "agent-1", agent_name: "Agent One" },
+      { agent_id: "agent-2", agent_name: "Agent Two" },
+      { agent_id: "agent-3", agent_name: undefined as any },
+    ];
+    const options = agentOptionsToSelectorOptions(agents);
+    expect(options).toHaveLength(3);
+    expect(options[0]).toEqual({ value: "Agent One", label: "Agent One" });
+    expect(options[1]).toEqual({ value: "Agent Two", label: "Agent Two" });
+    expect(options[2]).toEqual({ value: undefined, label: "agent-3" });
+  });
+
+  it("should get selection field name based on endpoint", () => {
+    expect(getSelectionFieldName(EndpointId.CHAT_COMPLETIONS)).toBe("model");
+    expect(getSelectionFieldName(EndpointId.A2A_AGENTS)).toBe("agent");
+  });
+
+  it("should get comparison selection based on endpoint", () => {
+    const comparison = { model: "gpt-4", agent: "agent-1" };
+    expect(getComparisonSelection(comparison, EndpointId.CHAT_COMPLETIONS)).toBe("gpt-4");
+    expect(getComparisonSelection(comparison, EndpointId.A2A_AGENTS)).toBe("agent-1");
+  });
+
+  it("should check if comparison has valid selection", () => {
+    const comparisonWithModel = { model: "gpt-4", agent: "" };
+    const comparisonWithAgent = { model: "", agent: "agent-1" };
+    const comparisonEmpty = { model: "", agent: "" };
+    const comparisonWhitespace = { model: "   ", agent: "" };
+
+    expect(hasValidSelection(comparisonWithModel, EndpointId.CHAT_COMPLETIONS)).toBe(true);
+    expect(hasValidSelection(comparisonWithAgent, EndpointId.A2A_AGENTS)).toBe(true);
+    expect(hasValidSelection(comparisonEmpty, EndpointId.CHAT_COMPLETIONS)).toBe(false);
+    expect(hasValidSelection(comparisonWhitespace, EndpointId.CHAT_COMPLETIONS)).toBe(false);
+  });
+});
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/a2a_send_message.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/a2a_send_message.tsx
index 94206937f02..0654db32920 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/a2a_send_message.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/a2a_send_message.tsx
@@ -113,8 +113,9 @@ export const makeA2ASendMessageRequest = async (
   onTimingData?: (timeToFirstToken: number) => void,
   onTotalLatency?: (totalLatency: number) => void,
   onA2AMetadata?: (metadata: A2ATaskMetadata) => void,
+  customBaseUrl?: string,
 ): Promise<void> => {
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   const url = proxyBaseUrl
     ? `${proxyBaseUrl}/a2a/${agentId}/message/send`
     : `/a2a/${agentId}/message/send`;
@@ -242,8 +243,9 @@ export const makeA2AStreamMessageRequest = async (
   onTimingData?: (timeToFirstToken: number) => void,
   onTotalLatency?: (totalLatency: number) => void,
   onA2AMetadata?: (metadata: A2ATaskMetadata) => void,
+  customBaseUrl?: string,
 ): Promise<void> => {
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   const url = proxyBaseUrl
     ? `${proxyBaseUrl}/a2a/${agentId}`
     : `/a2a/${agentId}`;
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx
index 3aef2f2d5ef..2e8b0be88bb 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx
@@ -17,6 +17,8 @@ export async function makeAnthropicMessagesRequest(
   traceId?: string,
   vector_store_ids?: string[],
   guardrails?: string[],
+  selectedMCPTools?: string[],
+  customBaseUrl?: string,
 ) {
   if (!accessToken) {
     throw new Error("Virtual Key is required");
@@ -27,7 +29,7 @@ export async function makeAnthropicMessagesRequest(
     console.log = function () {};
   }
 
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
 
   // Prepare headers with tags and trace ID
   const headers: Record<string, string> = {};
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/audio_speech.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/audio_speech.tsx
index de2bb761012..c5d4ae4d686 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/audio_speech.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/audio_speech.tsx
@@ -13,6 +13,7 @@ export async function makeOpenAIAudioSpeechRequest(
   signal?: AbortSignal,
   responseFormat?: string,
   speed?: number,
+  customBaseUrl?: string,
 ) {
   // base url should be the current base_url
   const isLocal = process.env.NODE_ENV === "development";
@@ -20,7 +21,7 @@ export async function makeOpenAIAudioSpeechRequest(
     console.log = function () {};
   }
   console.log("isLocal:", isLocal);
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   const client = new openai.OpenAI({
     apiKey: accessToken,
     baseURL: proxyBaseUrl,
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/audio_transcriptions.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/audio_transcriptions.tsx
index 61460951ec4..cdc512ba2f7 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/audio_transcriptions.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/audio_transcriptions.tsx
@@ -13,6 +13,7 @@ export async function makeOpenAIAudioTranscriptionRequest(
   prompt?: string,
   responseFormat?: string,
   temperature?: number,
+  customBaseUrl?: string,
 ) {
   // base url should be the current base_url
   const isLocal = process.env.NODE_ENV === "development";
@@ -20,7 +21,7 @@ export async function makeOpenAIAudioTranscriptionRequest(
     console.log = function () {};
   }
   console.log("isLocal:", isLocal);
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
 
   const client = new openai.OpenAI({
     apiKey: accessToken,
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx
index f86c051951e..dd23e0591ea 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.test.tsx
@@ -119,8 +119,25 @@ describe("chat_completion", () => {
   it("should configure MCP tools per server with restrictions", async () => {
     const selectedMCPServers = ["server-1", "server-2"];
     const mcpServers = [
-      { server_id: "server-1", alias: "alpha", server_name: "Alpha" },
-      { server_id: "server-2", server_name: "Beta" },
+      {
+        server_id: "server-1",
+        alias: "alpha",
+        server_name: "Alpha",
+        url: "http://example.com",
+        created_at: "2024-01-01",
+        created_by: "test",
+        updated_at: "2024-01-01",
+        updated_by: "test",
+      },
+      {
+        server_id: "server-2",
+        server_name: "Beta",
+        url: "http://example.com",
+        created_at: "2024-01-01",
+        created_by: "test",
+        updated_at: "2024-01-01",
+        updated_by: "test",
+      },
     ];
     const mcpServerToolRestrictions = {
       "server-1": ["toolA", "toolB"],
@@ -132,41 +149,43 @@ describe("chat_completion", () => {
       mockUpdateUI,
       "gpt-4",
       "test-token",
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
+      undefined, // tags
+      undefined, // signal
+      undefined, // onReasoningContent
+      undefined, // onTimingData
+      undefined, // onUsageData
+      undefined, // traceId
+      undefined, // vector_store_ids
+      undefined, // guardrails
       selectedMCPServers,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
-      undefined,
+      undefined, // onImageGenerated
+      undefined, // onSearchResults
+      undefined, // temperature
+      undefined, // max_tokens
+      undefined, // onTotalLatency
+      undefined, // customBaseUrl
       mcpServers,
       mcpServerToolRestrictions,
     );
 
     const callArgs = mockCreate.mock.calls[0][0];
     expect(callArgs.tool_choice).toBe("auto");
-    expect(callArgs.tools).toEqual([
-      {
-        type: "mcp",
-        server_label: "litellm",
-        server_url: "litellm_proxy/mcp/alpha",
-        require_approval: "never",
-        allowed_tools: ["toolA", "toolB"],
-      },
-      {
-        type: "mcp",
-        server_label: "litellm",
-        server_url: "litellm_proxy/mcp/Beta",
-        require_approval: "never",
-        allowed_tools: ["toolC"],
-      },
-    ]);
+    expect(callArgs.tools).toHaveLength(2);
+
+    // Check first tool
+    const firstTool = callArgs.tools[0];
+    expect(firstTool.type).toBe("mcp");
+    expect(firstTool.server_label).toBe("litellm");
+    expect(firstTool.server_url).toBe("litellm_proxy/mcp/alpha");
+    expect(firstTool.require_approval).toBe("never");
+    expect(firstTool.allowed_tools).toEqual(["toolA", "toolB"]);
+
+    // Check second tool
+    const secondTool = callArgs.tools[1];
+    expect(secondTool.type).toBe("mcp");
+    expect(secondTool.server_label).toBe("litellm");
+    expect(secondTool.server_url).toBe("litellm_proxy/mcp/Beta");
+    expect(secondTool.require_approval).toBe("never");
+    expect(secondTool.allowed_tools).toEqual(["toolC"]);
   });
 });
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.tsx
index 906c72245fd..24112ca1666 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/chat_completion.tsx
@@ -24,6 +24,7 @@ export async function makeOpenAIChatCompletionRequest(
   temperature?: number,
   max_tokens?: number,
   onTotalLatency?: (latency: number) => void,
+  customBaseUrl?: string,
   mcpServers?: MCPServer[],
   mcpServerToolRestrictions?: Record<string, string[]>,
 ) {
@@ -33,7 +34,7 @@ export async function makeOpenAIChatCompletionRequest(
     console.log = function () {};
   }
   console.log("isLocal:", isLocal);
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   // Prepare headers with tags and trace ID
   const headers: Record<string, string> = {};
   if (tags && tags.length > 0) {
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx
index d0939c00437..84192a1d866 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx
@@ -7,6 +7,7 @@ export async function makeOpenAIEmbeddingsRequest(
   selectedModel: string,
   accessToken: string,
   tags?: string[],
+  customBaseUrl?: string,
 ) {
   if (!accessToken) {
     throw new Error("Virtual Key is required");
@@ -18,7 +19,7 @@ export async function makeOpenAIEmbeddingsRequest(
     console.log = function () {};
   }
 
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   // Prepare headers with tags and trace ID
   const headers: Record<string, string> = {};
   if (tags && tags.length > 0) {
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/fetch_agents.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/fetch_agents.tsx
index 054e2216b63..889b012c5f7 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/fetch_agents.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/fetch_agents.tsx
@@ -16,9 +16,12 @@ export interface Agent {
 /**
  * Fetches available A2A agents from /v1/agents endpoint.
  */
-export const fetchAvailableAgents = async (accessToken: string): Promise<Agent[]> => {
+export const fetchAvailableAgents = async (
+  accessToken: string,
+  customBaseUrl?: string,
+): Promise<Agent[]> => {
   try {
-    const proxyBaseUrl = getProxyBaseUrl();
+    const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
     const url = proxyBaseUrl ? `${proxyBaseUrl}/v1/agents` : `/v1/agents`;
 
     const response = await fetch(url, {
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/image_edits.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/image_edits.tsx
index 504798ae8b2..233f4201b17 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/image_edits.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/image_edits.tsx
@@ -10,6 +10,7 @@ export async function makeOpenAIImageEditsRequest(
   accessToken: string,
   tags?: string[],
   signal?: AbortSignal,
+  customBaseUrl?: string,
 ) {
   // base url should be the current base_url
   const isLocal = process.env.NODE_ENV === "development";
@@ -17,7 +18,7 @@ export async function makeOpenAIImageEditsRequest(
     console.log = function () {};
   }
   console.log("isLocal:", isLocal);
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
 
   const client = new openai.OpenAI({
     apiKey: accessToken,
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/image_generation.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/image_generation.tsx
index 4eb8ea1b551..102b26c6d0d 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/image_generation.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/image_generation.tsx
@@ -9,6 +9,7 @@ export async function makeOpenAIImageGenerationRequest(
   accessToken: string,
   tags?: string[],
   signal?: AbortSignal,
+  customBaseUrl?: string,
 ) {
   // base url should be the current base_url
   const isLocal = process.env.NODE_ENV === "development";
@@ -16,7 +17,7 @@ export async function makeOpenAIImageGenerationRequest(
     console.log = function () {};
   }
   console.log("isLocal:", isLocal);
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   const client = new openai.OpenAI({
     apiKey: accessToken,
     baseURL: proxyBaseUrl,
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx
index 40c8e41ffb3..569c9878243 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.test.tsx
@@ -72,8 +72,25 @@ describe("responses_api", () => {
   it("should configure MCP tools per server with restrictions", async () => {
     const selectedMCPServers = ["server-1", "server-2"];
     const mcpServers = [
-      { server_id: "server-1", alias: "alpha", server_name: "Alpha" },
-      { server_id: "server-2", server_name: "Beta" },
+      {
+        server_id: "server-1",
+        alias: "alpha",
+        server_name: "Alpha",
+        url: "http://example.com",
+        created_at: "2024-01-01",
+        created_by: "test",
+        updated_at: "2024-01-01",
+        updated_by: "test",
+      },
+      {
+        server_id: "server-2",
+        server_name: "Beta",
+        url: "http://example.com",
+        created_at: "2024-01-01",
+        created_by: "test",
+        updated_at: "2024-01-01",
+        updated_by: "test",
+      },
     ];
     const mcpServerToolRestrictions: Record<string, string[]> = {
       "server-1": ["toolA"],
@@ -99,6 +116,7 @@ describe("responses_api", () => {
       undefined,
       undefined,
       undefined,
+      undefined,
       mcpServers,
       mcpServerToolRestrictions,
     );
diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx
index e30c14111fd..b658610a21e 100644
--- a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx
+++ b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx
@@ -33,6 +33,7 @@ export async function makeOpenAIResponsesRequest(
   onMCPEvent?: (event: MCPEvent) => void,
   codeInterpreterEnabled?: boolean,
   onCodeInterpreterResult?: (result: CodeInterpreterResult) => void,
+  customBaseUrl?: string,
   mcpServers?: MCPServer[],
   mcpServerToolRestrictions?: Record<string, string[]>,
 ) {
@@ -50,7 +51,7 @@ export async function makeOpenAIResponsesRequest(
     console.log = function () {};
   }
 
-  const proxyBaseUrl = getProxyBaseUrl();
+  const proxyBaseUrl = customBaseUrl || getProxyBaseUrl();
   // Prepare headers with tags and trace ID
   const headers: Record<string, string> = {};
   if (tags && tags.length > 0) {
diff --git a/ui/litellm-dashboard/src/components/settings.tsx b/ui/litellm-dashboard/src/components/settings.tsx
index 735ee6ee75c..ed45d95222d 100644
--- a/ui/litellm-dashboard/src/components/settings.tsx
+++ b/ui/litellm-dashboard/src/components/settings.tsx
@@ -28,6 +28,8 @@ const { Title, Paragraph } = Typography;
 
 import FormItem from "antd/es/form/FormItem";
 import AlertingSettings from "./alerting/alerting_settings";
+import CloudZeroCostTracking from "./CloudZeroCostTracking/CloudZeroCostTracking";
+import DeleteResourceModal from "./common_components/DeleteResourceModal";
 import {
   deleteCallback,
   getCallbackConfigsCall,
@@ -38,9 +40,6 @@ import {
 import { LoggingCallbacksTable } from "./Settings/LoggingAndAlerts/LoggingCallbacks/LoggingCallbacksTable";
 import { AlertingObject } from "./Settings/LoggingAndAlerts/LoggingCallbacks/types";
 import { parseErrorMessage } from "./shared/errorUtils";
-import DeleteResourceModal from "./common_components/DeleteResourceModal";
-import CloudZeroCostTracking from "./CloudZeroCostTracking/CloudZeroCostTracking";
-import NewBadge from "./common_components/NewBadge";
 interface SettingsPageProps {
   accessToken: string | null;
   userRole: string | null;
@@ -570,9 +569,7 @@ const Settings: React.FC<SettingsPageProps> = ({ accessToken, userRole, userID,
         <TabGroup>
           <TabList variant="line" defaultValue="1">
             <Tab value="1">Logging Callbacks</Tab>
-            <Tab value="2">
-              CloudZero Cost Tracking <NewBadge />
-            </Tab>
+            <Tab value="2">CloudZero Cost Tracking</Tab>
             <Tab value="2">Alerting Types</Tab>
             <Tab value="3">Alerting Settings</Tab>
             <Tab value="4">Email Alerts</Tab>