Merge remote-tracking branch 'origin/main' into fix-sap-creds

.circleci/config.yml

@@ -44,8 +44,8 @@ commands:
             pip install "pytest-asyncio==0.21.1"
             pip install "respx==0.22.0"
             pip install "hypercorn==0.17.3"
-            pip install "pydantic==2.10.2"
-            pip install "mcp==1.10.1"
+            pip install "pydantic==2.11.0"
+            pip install "mcp==1.25.0"
             pip install "requests-mock>=1.12.1"
             pip install "responses==0.25.7"
             pip install "pytest-xdist==3.6.1"
@@ -112,14 +112,14 @@ jobs:
             python -m mypy .
             cd ..
           no_output_timeout: 10m
-  local_testing:
+  local_testing_part1:
     docker:
       - image: cimg/python:3.12
         auth:
           username: ${DOCKERHUB_USERNAME}
           password: ${DOCKERHUB_PASSWORD}
     working_directory: ~/project
-
+    parallelism: 4
     steps:
       - checkout
       - setup_google_dns
@@ -144,8 +144,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install lunary==0.2.5
             pip install "azure-identity==1.16.1"
@@ -205,20 +205,32 @@ jobs:
 
       # Run pytest and generate JUnit XML report
       - run:
-          name: Run tests
+          name: Run tests (Part 1 - A-M)
           command: |
-            pwd
-            ls
-            # Add --timeout to kill hanging tests after 300s (5 min)
-            # Add -v to show test names as they run for debugging
-            # Add --tb=short for shorter tracebacks
-            python -m pytest -vv tests/local_testing --cov=litellm --cov-report=xml --junitxml=test-results/junit.xml --durations=20 -k "not test_python_38.py and not test_basic_python_version.py and not router and not assistants and not langfuse and not caching and not cache" -n 4 --timeout=300 --timeout_method=thread
+            mkdir test-results
+
+            # Discover test files (A-M)
+            TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_[a-mA-M]*.py")
+
+            echo "$TEST_FILES" | circleci tests run \
+              --split-by=timings \
+              --verbose \
+              --command="xargs python -m pytest \
+                -vv \
+                --cov=litellm \
+                --cov-report=xml \
+                --junitxml=test-results/junit.xml \
+                --durations=20 \
+                -k \"not test_python_38.py and not test_basic_python_version.py and not router and not assistants and not langfuse and not caching and not cache\" \
+                -n 4 \
+                --timeout=300 \
+                --timeout_method=thread"
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
           command: |
-            mv coverage.xml local_testing_coverage.xml
-            mv .coverage local_testing_coverage
+            mv coverage.xml local_testing_part1_coverage.xml
+            mv .coverage local_testing_part1_coverage
 
       # Store test results
       - store_test_results:
@@ -226,8 +238,136 @@ jobs:
       - persist_to_workspace:
           root: .
           paths:
-            - local_testing_coverage.xml
-            - local_testing_coverage
+            - local_testing_part1_coverage.xml
+            - local_testing_part1_coverage
+  local_testing_part2:
+    docker:
+      - image: cimg/python:3.12
+        auth:
+          username: ${DOCKERHUB_USERNAME}
+          password: ${DOCKERHUB_PASSWORD}
+    working_directory: ~/project
+    parallelism: 4
+    steps:
+      - checkout
+      - setup_google_dns
+      - run:
+          name: Show git commit hash
+          command: |
+            echo "Git commit hash: $CIRCLE_SHA1"
+
+      - restore_cache:
+          keys:
+            - v1-dependencies-{{ checksum ".circleci/requirements.txt" }}
+      - run:
+          name: Install Dependencies
+          command: |
+            python -m pip install --upgrade pip
+            python -m pip install -r .circleci/requirements.txt
+            pip install "pytest==7.3.1"
+            pip install "pytest-retry==1.6.3"
+            pip install "pytest-asyncio==0.21.1"
+            pip install "pytest-cov==5.0.0"
+            pip install "mypy==1.18.2"
+            pip install "google-generativeai==0.3.2"
+            pip install "google-cloud-aiplatform==1.43.0"
+            pip install pyarrow
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
+            pip install langchain
+            pip install lunary==0.2.5
+            pip install "azure-identity==1.16.1"
+            pip install "langfuse==2.59.7"
+            pip install "logfire==0.29.0"
+            pip install numpydoc
+            pip install traceloop-sdk==0.21.1
+            pip install opentelemetry-api==1.25.0
+            pip install opentelemetry-sdk==1.25.0
+            pip install opentelemetry-exporter-otlp==1.25.0
+            pip install openai==1.100.1
+            pip install prisma==0.11.0
+            pip install "detect_secrets==1.5.0"
+            pip install "httpx==0.24.1"
+            pip install "respx==0.22.0"
+            pip install fastapi
+            pip install "gunicorn==21.2.0"
+            pip install "anyio==4.2.0"
+            pip install "aiodynamo==23.10.1"
+            pip install "asyncio==3.4.3"
+            pip install "apscheduler==3.10.4"
+            pip install "PyGithub==1.59.1"
+            pip install argon2-cffi
+            pip install "pytest-mock==3.12.0"
+            pip install python-multipart
+            pip install google-cloud-aiplatform
+            pip install prometheus-client==0.20.0
+            pip install "pydantic==2.10.2"
+            pip install "diskcache==5.6.1"
+            pip install "Pillow==10.3.0"
+            pip install "jsonschema==4.22.0"
+            pip install "pytest-xdist==3.6.1"
+            pip install "pytest-timeout==2.2.0"
+            pip install "websockets==13.1.0"
+            pip install semantic_router --no-deps
+            pip install aurelio_sdk --no-deps
+            pip uninstall posthog -y
+      - setup_litellm_enterprise_pip
+      - save_cache:
+          paths:
+            - ./venv
+          key: v1-dependencies-{{ checksum ".circleci/requirements.txt" }}
+      - run:
+          name: Run prisma ./docker/entrypoint.sh
+          command: |
+            set +e
+            chmod +x docker/entrypoint.sh
+            ./docker/entrypoint.sh
+            set -e
+      - run:
+          name: Black Formatting
+          command: |
+            cd litellm
+            python -m pip install black
+            python -m black .
+            cd ..
+
+      # Run pytest and generate JUnit XML report
+      - run:
+          name: Run tests (Part 2 - N-Z)
+          command: |
+            mkdir test-results
+
+            # Discover test files (N-Z)
+            TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_[n-zN-Z]*.py")
+
+            echo "$TEST_FILES" | circleci tests run \
+              --split-by=timings \
+              --verbose \
+              --command="xargs python -m pytest \
+                -vv \
+                --cov=litellm \
+                --cov-report=xml \
+                --junitxml=test-results/junit.xml \
+                --durations=20 \
+                -k \"not test_python_38.py and not test_basic_python_version.py and not router and not assistants and not langfuse and not caching and not cache\" \
+                -n 4 \
+                --timeout=300 \
+                --timeout_method=thread"
+          no_output_timeout: 120m
+      - run:
+          name: Rename the coverage files
+          command: |
+            mv coverage.xml local_testing_part2_coverage.xml
+            mv .coverage local_testing_part2_coverage
+
+      # Store test results
+      - store_test_results:
+          path: test-results
+      - persist_to_workspace:
+          root: .
+          paths:
+            - local_testing_part2_coverage.xml
+            - local_testing_part2_coverage
   langfuse_logging_unit_tests:
     docker:
       - image: cimg/python:3.11
@@ -260,8 +400,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install lunary==0.2.5
             pip install "azure-identity==1.16.1"
@@ -367,8 +507,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install lunary==0.2.5
             pip install "azure-identity==1.16.1"
@@ -499,7 +639,6 @@ jobs:
           username: ${DOCKERHUB_USERNAME}
           password: ${DOCKERHUB_PASSWORD}
     working_directory: ~/project
-
     steps:
       - checkout
       - setup_google_dns
@@ -513,6 +652,7 @@ jobs:
             pip install "pytest-cov==5.0.0"
             pip install "pytest-retry==1.6.3"
             pip install "pytest-asyncio==0.21.1"
+            pip install "pytest-xdist==3.6.1"
             pip install semantic_router --no-deps
             pip install aurelio_sdk --no-deps
       # Run pytest and generate JUnit XML report
@@ -575,8 +715,8 @@ jobs:
       - run:
           name: Rename the coverage files
           command: |
-            mv coverage.xml litellm_router_coverage.xml
-            mv .coverage litellm_router_coverage
+            mv coverage.xml litellm_router_unit_coverage.xml
+            mv .coverage litellm_router_unit_coverage
       # Store test results
       - store_test_results:
           path: test-results
@@ -584,8 +724,8 @@ jobs:
       - persist_to_workspace:
           root: .
           paths:
-            - litellm_router_coverage.xml
-            - litellm_router_coverage
+            - litellm_router_unit_coverage.xml
+            - litellm_router_unit_coverage
   litellm_security_tests:
     machine:
       image: ubuntu-2204:2023.10.1
@@ -637,8 +777,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install "langfuse>=2.0.0"
             pip install "logfire==0.29.0"
@@ -759,8 +899,8 @@ jobs:
             pip install "google-cloud-aiplatform==1.43.0"
             pip install "google-genai==1.22.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install lunary==0.2.5
             pip install "azure-identity==1.16.1"
@@ -865,8 +1005,8 @@ jobs:
             pip install "google-cloud-aiplatform==1.43.0"
             pip install "google-genai==1.22.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install lunary==0.2.5
             pip install "azure-identity==1.16.1"
@@ -972,8 +1112,8 @@ jobs:
             pip install "google-cloud-aiplatform==1.43.0"
             pip install "google-genai==1.22.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install lunary==0.2.5
             pip install "azure-identity==1.16.1"
@@ -1152,8 +1292,8 @@ jobs:
             pip install "pytest-cov==5.0.0"
             pip install "pytest-asyncio==0.21.1"
             pip install "respx==0.22.0"
-            pip install "pydantic==2.10.2"
-            pip install "mcp==1.10.1"
+            pip install "pydantic==2.11.0"
+            pip install "mcp==1.25.0"
       # Run pytest and generate JUnit XML report
       - run:
           name: Run tests
@@ -1198,7 +1338,7 @@ jobs:
             pip install "pytest-asyncio==0.21.1"
             pip install "respx==0.22.0"
             pip install "pydantic==2.10.2"
-            pip install "boto3==1.40.61"
+            pip install "boto3==1.36.0"
       # Run pytest and generate JUnit XML report
       - run:
           name: Run tests
@@ -1556,8 +1696,8 @@ jobs:
             pip install "pytest-asyncio==0.21.1"
             pip install "respx==0.22.0"
             pip install "hypercorn==0.17.3"
-            pip install "pydantic==2.10.2"
-            pip install "mcp==1.10.1"
+            pip install "pydantic==2.11.0"
+            pip install "mcp==1.25.0"
             pip install "requests-mock>=1.12.1"
             pip install "responses==0.25.7"
             pip install "pytest-xdist==3.6.1"
@@ -1743,13 +1883,14 @@ jobs:
             pip install "pytest-cov==5.0.0"
             pip install "pytest-asyncio==0.21.1"
             pip install "respx==0.22.0"
+            pip install "pytest-xdist==3.6.1"
       # Run pytest and generate JUnit XML report
       - run:
           name: Run tests
           command: |
             pwd
             ls
-            python -m pytest -vv tests/image_gen_tests --cov=litellm --cov-report=xml -x -v --junitxml=test-results/junit.xml --durations=5
+            python -m pytest -vv tests/image_gen_tests -n 4 --cov=litellm --cov-report=xml -x -v --junitxml=test-results/junit.xml --durations=5
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
@@ -1792,6 +1933,7 @@ jobs:
             pip install "mlflow==2.17.2"
             pip install "anthropic==0.52.0"
             pip install "blockbuster==1.5.24"
+            pip install "pytest-xdist==3.6.1"
       # Run pytest and generate JUnit XML report
       - setup_litellm_enterprise_pip
       - run:
@@ -1799,7 +1941,7 @@ jobs:
           command: |
             pwd
             ls
-            python -m pytest -vv tests/logging_callback_tests --cov=litellm --cov-report=xml -s -v --junitxml=test-results/junit.xml --durations=5
+            python -m pytest -vv tests/logging_callback_tests --cov=litellm -n 4 --cov-report=xml -s -v --junitxml=test-results/junit.xml --durations=5
           no_output_timeout: 120m
       - run:
           name: Rename the coverage files
@@ -1879,7 +2021,7 @@ jobs:
             pip install aiohttp
             pip install openai
             pip install click
-            pip install "boto3==1.40.61"
+            pip install "boto3==1.36.0"
             pip install jinja2
             pip install "tokenizers==0.20.0"
             pip install "uvloop==0.21.0"
@@ -1915,7 +2057,7 @@ jobs:
             pip install "pytest-asyncio==0.21.1"
             pip install "pytest-cov==5.0.0"
             pip install "tomli==2.2.1"
-            pip install "mcp==1.10.1"
+            pip install "mcp==1.25.0"
       - run:
           name: Run tests
           command: |
@@ -2176,8 +2318,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install "langfuse>=2.0.0"
             pip install "logfire==0.29.0"
@@ -2192,6 +2334,8 @@ jobs:
             pip install "asyncio==3.4.3"
             pip install "PyGithub==1.59.1"
             pip install "openai==1.100.1"
+            pip install "litellm[proxy]"
+            pip install "pytest-xdist==3.6.1"
       - run:
           name: Install dockerize
           command: |
@@ -2268,7 +2412,7 @@ jobs:
           command: |
             pwd
             ls
-            python -m pytest -s -vv tests/*.py -x --junitxml=test-results/junit.xml --durations=5 --ignore=tests/otel_tests --ignore=tests/spend_tracking_tests --ignore=tests/pass_through_tests --ignore=tests/proxy_admin_ui_tests --ignore=tests/load_tests --ignore=tests/llm_translation --ignore=tests/llm_responses_api_testing --ignore=tests/mcp_tests --ignore=tests/guardrails_tests --ignore=tests/image_gen_tests --ignore=tests/pass_through_unit_tests
+            python -m pytest -s -vv tests/*.py -x --junitxml=test-results/junit.xml -n 4 --durations=5 --ignore=tests/otel_tests --ignore=tests/spend_tracking_tests --ignore=tests/pass_through_tests --ignore=tests/proxy_admin_ui_tests --ignore=tests/load_tests --ignore=tests/llm_translation --ignore=tests/llm_responses_api_testing --ignore=tests/mcp_tests --ignore=tests/guardrails_tests --ignore=tests/image_gen_tests --ignore=tests/pass_through_unit_tests
           no_output_timeout: 120m
 
       # Store test results
@@ -2316,8 +2460,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install "langchain_mcp_adapters==0.0.5"
             pip install "langfuse>=2.0.0"
@@ -2462,8 +2606,8 @@ jobs:
             pip install "google-generativeai==0.3.2"
             pip install "google-cloud-aiplatform==1.43.0"
             pip install pyarrow
-            pip install "boto3==1.40.61"
-            pip install "aioboto3==15.5.0"
+            pip install "boto3==1.36.0"
+            pip install "aioboto3==13.4.0"
             pip install langchain
             pip install "langfuse>=2.0.0"
             pip install "logfire==0.29.0"
@@ -3118,7 +3262,7 @@ jobs:
             pip install "pytest==7.3.1"
             pip install "pytest-mock==3.12.0"
             pip install "pytest-asyncio==0.21.1"
-            pip install "boto3==1.40.61"
+            pip install "boto3==1.36.0"
             pip install "mypy==1.18.2"
             pip install pyarrow
             pip install numpydoc
@@ -3263,6 +3407,110 @@ jobs:
       - store_test_results:
           path: test-results
 
+  proxy_e2e_anthropic_messages_tests:
+    machine:
+      image: ubuntu-2204:2023.10.1
+    resource_class: xlarge
+    working_directory: ~/project
+    steps:
+      - checkout
+      - setup_google_dns
+      - run:
+          name: Install Docker CLI (In case it's not already installed)
+          command: |
+            curl -fsSL https://get.docker.com | sh
+            sudo usermod -aG docker $USER
+            docker version
+      - run:
+          name: Install Python 3.10
+          command: |
+            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --output miniconda.sh
+            bash miniconda.sh -b -p $HOME/miniconda
+            export PATH="$HOME/miniconda/bin:$PATH"
+            conda init bash
+            source ~/.bashrc
+            conda create -n myenv python=3.10 -y
+            conda activate myenv
+            python --version
+      - run:
+          name: Install Dependencies
+          command: |
+            export PATH="$HOME/miniconda/bin:$PATH"
+            source $HOME/miniconda/etc/profile.d/conda.sh
+            conda activate myenv
+            pip install "pytest==7.3.1"
+            pip install "pytest-asyncio==0.21.1"
+            pip install "boto3==1.36.0"
+            pip install "httpx==0.27.0"
+            pip install "claude-agent-sdk"
+            pip install -r requirements.txt
+      - run:
+          name: Install dockerize
+          command: |
+            wget https://github.com/jwilder/dockerize/releases/download/v0.6.1/dockerize-linux-amd64-v0.6.1.tar.gz
+            sudo tar -C /usr/local/bin -xzvf dockerize-linux-amd64-v0.6.1.tar.gz
+            rm dockerize-linux-amd64-v0.6.1.tar.gz
+      - run:
+          name: Start PostgreSQL Database
+          command: |
+            docker run -d \
+              --name postgres-db \
+              -e POSTGRES_USER=postgres \
+              -e POSTGRES_PASSWORD=postgres \
+              -e POSTGRES_DB=circle_test \
+              -p 5432:5432 \
+              postgres:14
+      - run:
+          name: Wait for PostgreSQL to be ready
+          command: dockerize -wait tcp://localhost:5432 -timeout 1m
+      - attach_workspace:
+          at: ~/project
+      - run:
+          name: Load Docker Database Image
+          command: |
+            gunzip -c litellm-docker-database.tar.gz | docker load
+            docker images | grep litellm-docker-database
+      - run:
+          name: Run Docker container with test config
+          command: |
+            docker run -d \
+              -p 4000:4000 \
+              -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \
+              -e LITELLM_MASTER_KEY="sk-1234" \
+              -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
+              -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
+              -e AWS_REGION_NAME="us-east-1" \
+              --add-host host.docker.internal:host-gateway \
+              --name my-app \
+              -v $(pwd)/tests/proxy_e2e_anthropic_messages_tests/test_config.yaml:/app/config.yaml \
+              litellm-docker-database:ci \
+              --config /app/config.yaml \
+              --port 4000 \
+              --detailed_debug
+      - run:
+          name: Start outputting logs
+          command: docker logs -f my-app
+          background: true
+      - run:
+          name: Wait for app to be ready
+          command: dockerize -wait http://localhost:4000 -timeout 5m
+      - run:
+          name: Run Claude Agent SDK E2E Tests
+          command: |
+            export PATH="$HOME/miniconda/bin:$PATH"
+            source $HOME/miniconda/etc/profile.d/conda.sh
+            conda activate myenv
+            export LITELLM_PROXY_URL="http://localhost:4000"
+            export LITELLM_API_KEY="sk-1234"
+            pwd
+            ls
+            python -m pytest -vv tests/proxy_e2e_anthropic_messages_tests/ -x -s --junitxml=test-results/junit.xml --durations=5
+          no_output_timeout: 120m
+
+      # Store test results
+      - store_test_results:
+          path: test-results
+
   upload-coverage:
     docker:
       - image: cimg/python:3.9
@@ -3284,7 +3532,7 @@ jobs:
             python -m venv venv
             . venv/bin/activate
             pip install coverage
-            coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage local_testing_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage
+            coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage
             coverage xml
       - codecov/upload:
           file: ./coverage.xml
@@ -3334,8 +3582,22 @@ jobs:
                 ls dist/
                 twine upload --verbose dist/*
             else
-                echo "Version ${VERSION} of package is already published on PyPI. Skipping PyPI publish."
-                circleci step halt
+                echo "Version ${VERSION} of package is already published on PyPI."
+                
+                # Check if corresponding Docker nightly image exists
+                NIGHTLY_TAG="v${VERSION}-nightly"
+                echo "Checking for Docker nightly image: litellm/litellm:${NIGHTLY_TAG}"
+                
+                # Check Docker Hub for the nightly image
+                if curl -s "https://hub.docker.com/v2/repositories/litellm/litellm/tags/${NIGHTLY_TAG}" | grep -q "name"; then
+                    echo "Docker nightly image ${NIGHTLY_TAG} exists. This release was already completed successfully."
+                    echo "Skipping PyPI publish and continuing to ensure Docker images are up to date."
+                    circleci step halt
+                else
+                    echo "ERROR: PyPI package ${VERSION} exists but Docker nightly image ${NIGHTLY_TAG} does not exist!"
+                    echo "This indicates an incomplete release. Please investigate."
+                    exit 1
+                fi
             fi
       - run:
           name: Trigger Github Action for new Docker Container + Trigger Load Testing
@@ -3344,11 +3606,21 @@ jobs:
             python3 -m pip install toml
             VERSION=$(python3 -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])")
             echo "LiteLLM Version ${VERSION}"
+
+            # Determine which branch to use for Docker build
+            if [[ "$CIRCLE_BRANCH" =~ ^litellm_release_day_.* ]]; then
+              BUILD_BRANCH="$CIRCLE_BRANCH"
+              echo "Using release branch: $BUILD_BRANCH"
+            else
+              BUILD_BRANCH="main"
+              echo "Using default branch: $BUILD_BRANCH"
+            fi
+
             curl -X POST \
               -H "Accept: application/vnd.github.v3+json" \
               -H "Authorization: Bearer $GITHUB_TOKEN" \
               "https://api.github.com/repos/BerriAI/litellm/actions/workflows/ghcr_deploy.yml/dispatches" \
-              -d "{\"ref\":\"main\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}"
+              -d "{\"ref\":\"${BUILD_BRANCH}\", \"inputs\":{\"tag\":\"v${VERSION}-nightly\", \"commit_hash\":\"$CIRCLE_SHA1\"}}"
             echo "triggering load testing server for version ${VERSION} and commit ${CIRCLE_SHA1}"
             curl -X POST "https://proxyloadtester-production.up.railway.app/start/load/test?version=${VERSION}&commit_hash=${CIRCLE_SHA1}&release_type=nightly"
 
@@ -3482,6 +3754,9 @@ jobs:
 
             cd ui/litellm-dashboard
 
+            # Remove node_modules and package-lock to ensure clean install (fixes dependency resolution issues)
+            rm -rf node_modules package-lock.json
+
             # Install dependencies first
             npm install
 
@@ -3739,7 +4014,13 @@ workflows:
               only:
                 - main
                 - /litellm_.*/
-      - local_testing:
+      - local_testing_part1:
+          filters:
+            branches:
+              only:
+                - main
+                - /litellm_.*/
+      - local_testing_part2:
           filters:
             branches:
               only:
@@ -3901,6 +4182,14 @@ workflows:
               only:
                 - main
                 - /litellm_.*/
+      - proxy_e2e_anthropic_messages_tests:
+          requires:
+            - build_docker_database_image
+          filters:
+            branches:
+              only:
+                - main
+                - /litellm_.*/
       - llm_translation_testing:
           filters:
             branches:
@@ -4044,7 +4333,8 @@ workflows:
             - litellm_proxy_unit_testing_part2
             - litellm_security_tests
             - langfuse_logging_unit_tests
-            - local_testing
+            - local_testing_part1
+            - local_testing_part2
             - litellm_assistants_api_testing
             - auth_ui_unit_tests
       - db_migration_disable_update_check:
@@ -4084,10 +4374,12 @@ workflows:
             branches:
               only:
                 - main
+                - /litellm_release_day_.*/
       - publish_to_pypi:
           requires:
             - mypy_linting
-            - local_testing
+            - local_testing_part1
+            - local_testing_part2
             - build_and_test
             - e2e_openai_endpoints
             - test_bad_database_url

.circleci/requirements.txt

@@ -8,12 +8,13 @@ redis==5.2.1
 redisvl==0.4.1
 anthropic
 orjson==3.10.12 # fast /embedding responses
-pydantic==2.10.2
+pydantic==2.11.0
 google-cloud-aiplatform==1.43.0
 google-cloud-iam==2.19.1
 fastapi-sso==0.16.0 
 uvloop==0.21.0
-mcp==1.10.1    # for MCP server  
+mcp==1.25.0    # for MCP server  
 semantic_router==0.1.10 # for auto-routing with litellm
 fastuuid==0.12.0
-responses==0.25.7  # for proxy client tests
+responses==0.25.7  # for proxy client tests
+pytest-retry==1.6.3  # for automatic test retries

.github/workflows/create_daily_staging_branch.yml

@@ -2,7 +2,7 @@ name: Create Daily Staging Branch
 
 on:
   schedule:
-    - cron: '0 0 * * *'  # Runs daily at midnight UTC
+    - cron: '0 0,12 * * *'  # Runs every 12 hours at midnight and noon UTC
   workflow_dispatch:  # Allow manual trigger
 
 jobs:
@@ -24,7 +24,7 @@ jobs:
           git config user.email "github-actions[bot]@users.noreply.github.com"
 
           # Generate branch name with MM_DD_YYYY format
-          BRANCH_NAME="litellm_staging_$(date +'%m_%d_%Y')"
+          BRANCH_NAME="litellm_oss_staging_$(date +'%m_%d_%Y')"
           echo "Creating branch: $BRANCH_NAME"
 
           # Fetch all branches

.github/workflows/ghcr_deploy.yml

@@ -320,72 +320,36 @@ jobs:
         run: |
           echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV}
     
-      - name: Get LiteLLM Latest Tag
-        id: current_app_tag
-        shell: bash
-        run: |
-          LATEST_TAG=$(git describe --tags --exclude "*dev*" --abbrev=0)
-          if [ -z "${LATEST_TAG}" ]; then
-            echo "latest_tag=latest" | tee -a $GITHUB_OUTPUT
-          else
-            echo "latest_tag=${LATEST_TAG}" | tee -a $GITHUB_OUTPUT
-          fi
-
-      - name: Get last published chart version
-        id: current_version
-        shell: bash
-        run: |
-          CHART_LIST=$(helm show chart oci://${{ env.REGISTRY }}/${{ env.REPO_OWNER }}/${{ env.CHART_NAME }} 2>/dev/null || true)
-          if [ -z "${CHART_LIST}" ]; then
-            echo "current-version=1.0.0" | tee -a $GITHUB_OUTPUT
-          else
-            # Extract version and strip any prerelease suffix (e.g., 1.0.5-latest -> 1.0.5)
-            VERSION=$(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
-        env:
-          HELM_EXPERIMENTAL_OCI: '1'
-
-      # Automatically update the helm chart version one "patch" level
-      - name: Bump release version
-        id: bump_version
-        uses: christian-draeger/increment-semantic-version@1.1.0
-        with:
-          current-version: ${{ steps.current_version.outputs.current-version || '1.0.0' }}
-          version-fragment: 'bug'
-
-      # Add suffix for non-stable releases (semantic versioning)
+      # Sync Helm chart version with LiteLLM release version (1-1 versioning)
+      # This allows users to easily map Helm chart versions to LiteLLM versions
+      # See: https://codefresh.io/docs/docs/ci-cd-guides/helm-best-practices/
       - name: Calculate chart and app versions
         id: chart_version
         shell: bash
         run: |
-          BASE_VERSION="${{ steps.bump_version.outputs.next-version || '1.0.0' }}"
-          RELEASE_TYPE="${{ github.event.inputs.release_type }}"
           INPUT_TAG="${{ github.event.inputs.tag }}"
+          RELEASE_TYPE="${{ github.event.inputs.release_type }}"
 
-          # 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 $GITHUB_OUTPUT
+          # Chart version = LiteLLM version without 'v' prefix (Helm semver convention)
+          # v1.81.0 -> 1.81.0, v1.81.0.rc.1 -> 1.81.0.rc.1
+          CHART_VERSION="${INPUT_TAG#v}"
+
+          # Add suffix for 'latest' releases (rc already has suffix in tag)
+          if [ "$RELEASE_TYPE" = "latest" ]; then
+            CHART_VERSION="${CHART_VERSION}-latest"
           fi
 
-          # App version (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
+          # App version = Docker tag (keeps 'v' prefix to match Docker image tags)
+          APP_VERSION="${INPUT_TAG}"
 
+          echo "version=${CHART_VERSION}" | tee -a $GITHUB_OUTPUT
           echo "app_version=${APP_VERSION}" | tee -a $GITHUB_OUTPUT
 
       - uses: ./.github/actions/helm-oci-chart-releaser
         with:
           name: ${{ env.CHART_NAME }}
           repository: ${{ env.REPO_OWNER }}
-          tag: ${{ github.event.inputs.chartVersion || steps.chart_version.outputs.version || '1.0.0' }}
+          tag: ${{ steps.chart_version.outputs.version }}
           app_version: ${{ steps.chart_version.outputs.app_version }}
           path: deploy/charts/${{ env.CHART_NAME }}
           registry: ${{ env.REGISTRY }}

.github/workflows/ghcr_helm_deploy.yml

@@ -1,10 +1,12 @@
-# this workflow is triggered by an API call when there is a new PyPI release of LiteLLM
+# Standalone workflow to publish LiteLLM Helm Chart
+# Note: The main ghcr_deploy.yml workflow also publishes the Helm chart as part of a full release
 name: Build, Publish LiteLLM Helm Chart. New Release
 on:
   workflow_dispatch:
     inputs:
-      chartVersion:
-        description: "Update the helm chart's version to this"
+      tag:
+        description: "LiteLLM version tag (e.g., v1.81.0)"
+        required: true
 
 # Defines two custom environment variables for the workflow. Used for the Container registry domain, and a name for the Docker image that this workflow builds.
 env:
@@ -31,24 +33,22 @@ jobs:
         run: |
           echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV}
 
-      - name: Get LiteLLM Latest Tag
-        id: current_app_tag
-        uses: WyriHaximus/github-action-get-previous-tag@v1.3.0
-
-      - name: Get last published chart version
-        id: current_version
+      # Sync Helm chart version with LiteLLM release version (1-1 versioning)
+      - name: Calculate chart and app versions
+        id: chart_version
         shell: bash
-        run: helm show chart oci://${{ env.REGISTRY }}/${{ env.REPO_OWNER }}/litellm-helm | grep '^version:' | awk 'BEGIN{FS=":"}{print "current-version="$2}' | tr -d " " | tee -a $GITHUB_OUTPUT
-        env:
-          HELM_EXPERIMENTAL_OCI: '1'
+        run: |
+          INPUT_TAG="${{ github.event.inputs.tag }}"
 
-      # Automatically update the helm chart version one "patch" level
-      - name: Bump release version
-        id: bump_version
-        uses: christian-draeger/increment-semantic-version@1.1.0
-        with:
-          current-version: ${{ steps.current_version.outputs.current-version || '0.1.0' }}
-          version-fragment: 'bug'
+          # Chart version = LiteLLM version without 'v' prefix
+          # v1.81.0 -> 1.81.0
+          CHART_VERSION="${INPUT_TAG#v}"
+
+          # App version = Docker tag (keeps 'v' prefix)
+          APP_VERSION="${INPUT_TAG}"
+
+          echo "version=${CHART_VERSION}" | tee -a $GITHUB_OUTPUT
+          echo "app_version=${APP_VERSION}" | tee -a $GITHUB_OUTPUT
 
       - name: Lint helm chart
         run: helm lint deploy/charts/litellm-helm
@@ -57,8 +57,8 @@ jobs:
         with:
           name: litellm-helm
           repository: ${{ env.REPO_OWNER }}
-          tag: ${{ github.event.inputs.chartVersion || steps.bump_version.outputs.next-version || '0.1.0' }}
-          app_version: ${{ steps.current_app_tag.outputs.tag || 'latest' }}
+          tag: ${{ steps.chart_version.outputs.version }}
+          app_version: ${{ steps.chart_version.outputs.app_version }}
           path: deploy/charts/litellm-helm
           registry: ${{ env.REGISTRY }}
           registry_username: ${{ github.actor }}

.github/workflows/label-component.yml

@@ -80,3 +80,37 @@ jobs:
                 break;
               }
             }
+
+            // Check for 'claude code' keyword (can be applied alongside component labels)
+            if (/claude code/i.test(body)) {
+              const claudeLabel = {
+                name: 'claude code',
+                color: '7c3aed',
+                description: 'Issues related to Claude Code usage'
+              };
+
+              try {
+                await github.rest.issues.getLabel({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  name: claudeLabel.name
+                });
+              } catch (error) {
+                if (error.status === 404) {
+                  await github.rest.issues.createLabel({
+                    owner: context.repo.owner,
+                    repo: context.repo.repo,
+                    name: claudeLabel.name,
+                    color: claudeLabel.color,
+                    description: claudeLabel.description
+                  });
+                }
+              }
+
+              await github.rest.issues.addLabels({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                labels: [claudeLabel.name]
+              });
+            }

.github/workflows/test-linting.yml

@@ -73,4 +73,4 @@ jobs:
 
     - name: Check import safety
       run: |
-        poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
+        poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)

.github/workflows/test-litellm.yml

@@ -34,7 +34,7 @@ jobs:
         poetry run pip install "google-genai==1.22.0"
         poetry run pip install "google-cloud-aiplatform>=1.38"
         poetry run pip install "fastapi-offline==1.7.3"
-        poetry run pip install "python-multipart==0.0.18"
+        poetry run pip install "python-multipart==0.0.22"
         poetry run pip install "openapi-core"
     - name: Setup litellm-enterprise as local package
       run: |

.github/workflows/test-mcp.yml

@@ -34,8 +34,8 @@ jobs:
         poetry run pip install "pytest-cov==5.0.0"
         poetry run pip install "pytest-asyncio==0.21.1"
         poetry run pip install "respx==0.22.0"
-        poetry run pip install "pydantic==2.10.2"
-        poetry run pip install "mcp==1.10.1"
+        poetry run pip install "pydantic==2.11.0"
+        poetry run pip install "mcp==1.25.0"
         poetry run pip install pytest-xdist
 
     - name: Setup litellm-enterprise as local package

.github/workflows/test-model-map.yaml

@@ -0,0 +1,15 @@
+name: Validate model_prices_and_context_window.json
+
+on:
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  validate-model-prices-json:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Validate model_prices_and_context_window.json
+        run: |
+          jq empty model_prices_and_context_window.json

.gitignore

@@ -1,5 +1,6 @@
 .python-version
 .venv
+.venv_policy_test
 .env
 .newenv
 newenv/*
@@ -59,10 +60,6 @@ litellm/proxy/_super_secret_config.yaml
 litellm/proxy/myenv/bin/activate
 litellm/proxy/myenv/bin/Activate.ps1
 myenv/*
-litellm/proxy/_experimental/out/_next/
-litellm/proxy/_experimental/out/404/index.html
-litellm/proxy/_experimental/out/model_hub/index.html
-litellm/proxy/_experimental/out/onboarding/index.html
 litellm/tests/log.txt
 litellm/tests/langfuse.log
 litellm/tests/langfuse.log
@@ -75,9 +72,6 @@ tests/local_testing/log.txt
 litellm/proxy/_new_new_secret_config.yaml
 litellm/proxy/custom_guardrail.py
 .mypy_cache/*
-litellm/proxy/_experimental/out/404.html
-litellm/proxy/_experimental/out/404.html
-litellm/proxy/_experimental/out/model_hub.html
 .mypy_cache/*
 litellm/proxy/application.log
 tests/llm_translation/vertex_test_account.json
@@ -99,9 +93,9 @@ litellm_config.yaml
 litellm/proxy/to_delete_loadtest_work/*
 update_model_cost_map.py
 tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py
-litellm/proxy/_experimental/out/guardrails/index.html
 scripts/test_vertex_ai_search.py
 LAZY_LOADING_IMPROVEMENTS.md
+STABILIZATION_TODO.md
 **/test-results
 **/playwright-report
 **/*.storageState.json

.trivyignore

@@ -0,0 +1,12 @@
+# LiteLLM Trivy Ignore File
+# CVEs listed here are temporarily allowlisted pending fixes
+
+# Next.js vulnerabilities in UI dashboard (next@14.2.35)
+# Allowlisted: 2026-01-31, 7-day fix timeline
+# Fix: Upgrade to Next.js 15.5.10+ or 16.1.5+
+
+# HIGH: DoS via request deserialization
+GHSA-h25m-26qc-wcjf
+
+# MEDIUM: Image Optimizer DoS
+CVE-2025-59471

AGENTS.md

@@ -51,12 +51,14 @@ LiteLLM is a unified interface for 100+ LLMs that:
 
 ### MAKING CODE CHANGES FOR THE UI (IGNORE FOR BACKEND)
 
-1. **Use Common Components as much as possible**:
+1. **Tremor is DEPRECATED, do not use Tremor components in new features/changes**
+   - The only exception is the Tremor Table component and its required Tremor Table sub components.
+
+2. **Use Common Components as much as possible**:
    - These are usually defined in the `common_components` directory
    - Use these components as much as possible and avoid building new components unless needed
-   - Tremor components are deprecated; prefer using Ant Design (AntD) as much as possible
 
-2. **Testing**:
+3. **Testing**:
    - The codebase uses **Vitest** and **React Testing Library**
    - **Query Priority Order**: Use query methods in this order: `getByRole`, `getByLabelText`, `getByPlaceholderText`, `getByText`, `getByTestId`
    - **Always use `screen`** instead of destructuring from `render()` (e.g., use `screen.getByText()` not `getByText`)

ARCHITECTURE.md

@@ -28,16 +28,25 @@ sequenceDiagram
     participant Client
     participant ProxyServer as proxy/proxy_server.py
     participant Auth as proxy/auth/user_api_key_auth.py
+    participant Redis as Redis Cache
     participant Hooks as proxy/hooks/
     participant Router as router.py
-    participant Main as main.py
+    participant Main as main.py + utils.py
     participant Handler as llms/custom_httpx/llm_http_handler.py
     participant Transform as llms/{provider}/chat/transformation.py
     participant Provider as LLM Provider API
+    participant CostCalc as cost_calculator.py
+    participant LoggingObj as litellm_logging.py
+    participant DBWriter as db/db_spend_update_writer.py
+    participant Postgres as PostgreSQL
 
+    %% Request Flow
     Client->>ProxyServer: POST /v1/chat/completions
     ProxyServer->>Auth: user_api_key_auth()
+    Auth->>Redis: Check API key cache
+    Redis-->>Auth: Key info + spend limits
     ProxyServer->>Hooks: max_budget_limiter, parallel_request_limiter
+    Hooks->>Redis: Check/increment rate limit counters
     ProxyServer->>Router: route_request()
     Router->>Main: litellm.acompletion()
     Main->>Handler: BaseLLMHTTPHandler.completion()
@@ -45,8 +54,25 @@ sequenceDiagram
     Handler->>Provider: HTTP Request
     Provider-->>Handler: Response
     Handler->>Transform: ProviderConfig.transform_response()
-    Handler-->>Hooks: async_log_success_event()
-    Handler-->>Client: ModelResponse
+    Transform-->>Handler: ModelResponse
+    Handler-->>Main: ModelResponse
+    
+    %% Cost Attribution (in utils.py wrapper)
+    Main->>LoggingObj: update_response_metadata()
+    LoggingObj->>CostCalc: _response_cost_calculator()
+    CostCalc->>CostCalc: completion_cost(tokens × price)
+    CostCalc-->>LoggingObj: response_cost
+    LoggingObj-->>Main: Set response._hidden_params["response_cost"]
+    Main-->>ProxyServer: ModelResponse (with cost in _hidden_params)
+    
+    %% Response Headers + Async Logging
+    ProxyServer->>ProxyServer: Extract cost from hidden_params
+    ProxyServer->>LoggingObj: async_success_handler()
+    LoggingObj->>Hooks: async_log_success_event()
+    Hooks->>DBWriter: update_database(response_cost)
+    DBWriter->>Redis: Queue spend increment
+    DBWriter->>Postgres: Batch write spend logs (async)
+    ProxyServer-->>Client: ModelResponse + x-litellm-response-cost header
 ```
 
 ### Proxy Components
@@ -75,11 +101,19 @@ graph TD
         Main["main.py"]
     end
 
+    subgraph "Infrastructure"
+        DualCache["DualCache<br/>(in-memory + Redis)"]
+        Postgres["PostgreSQL<br/>(keys, teams, spend logs)"]
+    end
+
     Client --> Endpoint
     Endpoint --> Auth
+    Auth --> DualCache
+    DualCache -.->|cache miss| Postgres
     Auth --> PreCall
     PreCall --> RouteRequest
     RouteRequest --> Router
+    Router --> DualCache
     Router --> Main
     Main --> Client
 ```
@@ -119,6 +153,93 @@ graph TD
 
 To add a new proxy hook, implement `CustomLogger` and register in `PROXY_HOOKS`.
 
+### Infrastructure Components
+
+The AI Gateway uses external infrastructure for persistence and caching:
+
+```mermaid
+graph LR
+    subgraph "AI Gateway (proxy/)"
+        Proxy["proxy_server.py"]
+        Auth["auth/user_api_key_auth.py"]
+        DBWriter["db/db_spend_update_writer.py<br/>DBSpendUpdateWriter"]
+        InternalCache["utils.py<br/>InternalUsageCache"]
+        CostCallback["hooks/proxy_track_cost_callback.py<br/>_ProxyDBLogger"]
+        Scheduler["APScheduler<br/>ProxyStartupEvent"]
+    end
+
+    subgraph "SDK (litellm/)"
+        Router["router.py<br/>Router.cache (DualCache)"]
+        LLMCache["caching/caching_handler.py<br/>LLMCachingHandler"]
+        CacheClass["caching/caching.py<br/>Cache"]
+    end
+
+    subgraph "Redis (caching/redis_cache.py)"
+        RateLimit["Rate Limit Counters"]
+        SpendQueue["Spend Increment Queue"]
+        KeyCache["API Key Cache"]
+        TPM_RPM["TPM/RPM Tracking"]
+        Cooldowns["Deployment Cooldowns"]
+        LLMResponseCache["LLM Response Cache"]
+    end
+
+    subgraph "PostgreSQL (proxy/schema.prisma)"
+        Keys["LiteLLM_VerificationToken"]
+        Teams["LiteLLM_TeamTable"]
+        SpendLogs["LiteLLM_SpendLogs"]
+        Users["LiteLLM_UserTable"]
+    end
+
+    Auth --> InternalCache
+    InternalCache --> KeyCache
+    InternalCache -.->|cache miss| Keys
+    InternalCache --> RateLimit
+    Router --> TPM_RPM
+    Router --> Cooldowns
+    LLMCache --> CacheClass
+    CacheClass --> LLMResponseCache
+    CostCallback --> DBWriter
+    DBWriter --> SpendQueue
+    DBWriter --> SpendLogs
+    Scheduler --> SpendLogs
+    Scheduler --> Keys
+```
+
+| Component | Purpose | Key Files/Classes |
+|-----------|---------|-------------------|
+| **Redis** | Rate limiting, API key caching, TPM/RPM tracking, cooldowns, LLM response caching, spend queuing | `caching/redis_cache.py` (`RedisCache`), `caching/dual_cache.py` (`DualCache`) |
+| **PostgreSQL** | API keys, teams, users, spend logs | `proxy/utils.py` (`PrismaClient`), `proxy/schema.prisma` |
+| **InternalUsageCache** | Proxy-level cache for rate limits + API keys (in-memory + Redis) | `proxy/utils.py` (`InternalUsageCache`) |
+| **Router.cache** | TPM/RPM tracking, deployment cooldowns, client caching (in-memory + Redis) | `router.py` (`Router.cache: DualCache`) |
+| **LLMCachingHandler** | SDK-level LLM response/embedding caching | `caching/caching_handler.py` (`LLMCachingHandler`), `caching/caching.py` (`Cache`) |
+| **DBSpendUpdateWriter** | Batches spend updates to reduce DB writes | `proxy/db/db_spend_update_writer.py` (`DBSpendUpdateWriter`) |
+| **Cost Tracking** | Calculates and logs response costs | `proxy/hooks/proxy_track_cost_callback.py` (`_ProxyDBLogger`) |
+
+**Background Jobs** (APScheduler, initialized in `proxy/proxy_server.py` → `ProxyStartupEvent.initialize_scheduled_background_jobs()`):
+
+| Job | Interval | Purpose | Key Files |
+|-----|----------|---------|-----------|
+| `update_spend` | 60s | Batch write spend logs to PostgreSQL | `proxy/db/db_spend_update_writer.py` |
+| `reset_budget` | 10-12min | Reset budgets for keys/users/teams | `proxy/management_helpers/budget_reset_job.py` |
+| `add_deployment` | 10s | Sync new model deployments from DB | `proxy/proxy_server.py` (`ProxyConfig`) |
+| `cleanup_old_spend_logs` | cron/interval | Delete old spend logs | `proxy/management_helpers/spend_log_cleanup.py` |
+| `check_batch_cost` | 30min | Calculate costs for batch jobs | `proxy/management_helpers/check_batch_cost_job.py` |
+| `check_responses_cost` | 30min | Calculate costs for responses API | `proxy/management_helpers/check_responses_cost_job.py` |
+| `process_rotations` | 1hr | Auto-rotate API keys | `proxy/management_helpers/key_rotation_manager.py` |
+| `_run_background_health_check` | continuous | Health check model deployments | `proxy/proxy_server.py` |
+| `send_weekly_spend_report` | weekly | Slack spend alerts | `proxy/utils.py` (`SlackAlerting`) |
+| `send_monthly_spend_report` | monthly | Slack spend alerts | `proxy/utils.py` (`SlackAlerting`) |
+
+**Cost Attribution Flow:**
+1. LLM response returns to `utils.py` wrapper after `litellm.acompletion()` completes
+2. `update_response_metadata()` (`llm_response_utils/response_metadata.py`) is called
+3. `logging_obj._response_cost_calculator()` (`litellm_logging.py`) calculates cost via `litellm.completion_cost()` (`cost_calculator.py`)
+4. Cost is stored in `response._hidden_params["response_cost"]`
+5. `proxy/common_request_processing.py` extracts cost from `hidden_params` and adds to response headers (`x-litellm-response-cost`)
+6. `logging_obj.async_success_handler()` triggers callbacks including `_ProxyDBLogger.async_log_success_event()`
+7. `DBSpendUpdateWriter.update_database()` queues spend increments to Redis
+8. Background job `update_spend` flushes queued spend to PostgreSQL every 60s
+
 ---
 
 ## 2. SDK Request Flow

Dockerfile

@@ -46,8 +46,9 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
 # Ensure runtime stage runs as root
 USER root
 
-# Install runtime dependencies
-RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip
+# Install runtime dependencies (libsndfile needed for audio processing on ARM64)
+RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile && \
+    npm install -g npm@latest tar@latest
 
 WORKDIR /app
 # Copy the current directory contents into the container at /app
@@ -69,8 +70,8 @@ RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \
 # Convert Windows line endings to Unix and make executable
 RUN sed -i 's/\r$//' docker/install_auto_router.sh && chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh
 
-# Generate prisma client
-RUN prisma generate
+# Generate prisma client using the correct schema
+RUN prisma generate --schema=./litellm/proxy/schema.prisma
 # Convert Windows line endings to Unix for entrypoint scripts
 RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh
 RUN sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh

README.md

@@ -258,6 +258,19 @@ LiteLLM Performance: **8ms P95 latency** at 1k RPS (See benchmarks [here](https:
 
 Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+).
 
+## OSS Adopters 
+
+<table>
+  <tr>
+    <td><img height="60" alt="Stripe" src="https://github.com/user-attachments/assets/f7296d4f-9fbd-460d-9d05-e4df31697c4b" /></td>
+    <td><img height="60" alt="Google ADK" src="https://github.com/user-attachments/assets/caf270a2-5aee-45c4-8222-41a2070c4f19" /></td>
+    <td><img height="60" alt="Greptile" src="https://github.com/user-attachments/assets/0be4bd8a-7cfa-48d3-9090-f415fe948280" /></td>
+    <td><img height="60" alt="OpenHands" src="https://github.com/user-attachments/assets/a6150c4c-149e-4cae-888b-8b92be6e003f" /></td>
+    <td><h2>Netflix</h2></td>
+    <td><img height="60" alt="OpenAI Agents SDK" src="https://github.com/user-attachments/assets/c02f7be0-8c2e-4d27-aea7-7c024bfaebc0" /></td>
+  </tr>
+</table>
+
 ## Supported Providers ([Website Supported Models](https://models.litellm.ai/) | [Docs](https://docs.litellm.ai/docs/providers))
 
 | Provider                                                                            | `/chat/completions` | `/messages` | `/responses` | `/embeddings` | `/image/generations` | `/audio/transcriptions` | `/audio/speech` | `/moderations` | `/batches` | `/rerank` |
@@ -374,7 +387,9 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 1. (In root) create virtual environment `python -m venv .venv`
 2. Activate virtual environment `source .venv/bin/activate`
 3. Install dependencies `pip install -e ".[all]"`
-4. Start proxy backend `python litellm/proxy_cli.py`
+4. `pip install prisma`
+5. `prisma generate`
+6. Start proxy backend `python litellm/proxy/proxy_cli.py`
 
 ### Frontend
 1. Navigate to `ui/litellm-dashboard`

ci_cd/security_scans.sh

@@ -81,10 +81,10 @@ run_trivy_scans() {
     echo "Running Trivy scans..."
     
     echo "Scanning LiteLLM Docs..."
-    trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./docs/
+    trivy fs --ignorefile .trivyignore --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./docs/
     
     echo "Scanning LiteLLM UI..."
-    trivy fs --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./ui/
+    trivy fs --ignorefile .trivyignore --scanners vuln --dependency-tree --exit-code 1 --severity HIGH,CRITICAL,MEDIUM ./ui/
     
     echo "Trivy scans completed successfully"
 }
@@ -137,6 +137,24 @@ run_grype_scans() {
         "CVE-2019-1010025" # glibc pthread heap address leak - awaiting patched Wolfi glibc build
         "CVE-2026-22184" # zlib untgz buffer overflow - untgz unused + no fixed Wolfi build yet
         "GHSA-58pv-8j8x-9vj2" # jaraco.context path traversal - setuptools vendored only (v5.3.0), not used in application code (using v6.1.0+)
+        "GHSA-34x7-hfp2-rc4v" # node-tar hardlink path traversal - not applicable, tar CLI not exposed in application code
+        "GHSA-r6q2-hw4h-h46w" # node-tar not used by application runtime, Linux-only container, not affect by macOS APFS-specific exploit
+        "GHSA-8rrh-rw8j-w5fx" # wheel is from chainguard and will be handled by then TODO: Remove this after Chainguard updates the wheel
+        "CVE-2025-59465" # We do not use Node in application runtime, only used for building Admin UI
+        "CVE-2025-55131" # We do not use Node in application runtime, only used for building Admin UI
+        "CVE-2025-59466" # We do not use Node in application runtime, only used for building Admin UI
+        "CVE-2025-55130" # We do not use Node in application runtime, only used for building Admin UI
+        "CVE-2025-59467" # We do not use Node in application runtime, only used for building Admin UI
+        "CVE-2026-21637" # We do not use Node in application runtime, only used for building Admin UI
+        "CVE-2025-15281" # No fix available yet
+        "CVE-2026-0865" # No fix available yet
+        "CVE-2025-15282" # No fix available yet
+        "CVE-2026-0672" # No fix available yet
+        "CVE-2025-15366" # No fix available yet
+        "CVE-2025-15367" # No fix available yet
+        "CVE-2025-12781" # No fix available yet
+        "CVE-2025-11468" # No fix available yet
+        "CVE-2026-1299" # Python 3.13 email module header injection - not applicable, LiteLLM doesn't use BytesGenerator for email serialization
     )
 
     # Build JSON array of allowlisted CVE IDs for jq

cookbook/ai_coding_tool_guides/claude_code_quickstart/guide.md

@@ -97,17 +97,75 @@ export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY"
 
 ## Step 5: Use Claude Code
 
-Start Claude Code and it will automatically use your configured models:
+### Choosing Your Model
+
+You have two options for specifying which model Claude Code uses:
+
+#### Option 1: Command Line / Session Model Selection
+
+Specify the model directly when starting Claude Code or during a session:
 
 ```bash
-# Claude Code will use the models configured in your LiteLLM proxy
-claude
-
-# Or specify a model if you have multiple configured
+# Specify model at startup
 claude --model claude-3-5-sonnet-20241022
-claude --model claude-3-5-haiku-20241022
+
+# Or change model during a session
+/model claude-3-5-haiku-20241022
 ```
 
+This method uses the exact model you specify.
+
+#### Option 2: Environment Variables
+
+Configure default models using environment variables:
+
+```bash
+# Tell Claude Code which models to use by default
+export ANTHROPIC_DEFAULT_SONNET_MODEL=claude-3-5-sonnet-20241022
+export ANTHROPIC_DEFAULT_HAIKU_MODEL=claude-3-5-haiku-20241022
+export ANTHROPIC_DEFAULT_OPUS_MODEL=claude-opus-3-5-20240229
+
+claude  # Will use the models specified above
+```
+
+**Note:** Claude Code may cache the model from a previous session. If environment variables don't take effect, use Option 1 to explicitly set the model.
+
+**Important:** The `model_name` in your LiteLLM config must match what Claude Code requests (either from env vars or command line).
+
+### Using 1M Context Window
+
+Claude Code supports extended context (1 million tokens) using the `[1m]` suffix with Claude 4+ models:
+
+```bash
+# Use Sonnet 4.5 with 1M context (requires quotes for shell)
+claude --model 'claude-sonnet-4-5-20250929[1m]'
+
+# Inside a Claude Code session (no quotes needed)
+/model claude-sonnet-4-5-20250929[1m]
+```
+
+**Important:** When using `--model` with `[1m]` in the shell, you must use quotes to prevent the shell from interpreting the brackets.
+
+Alternatively, set as default with environment variables:
+
+```bash
+export ANTHROPIC_DEFAULT_SONNET_MODEL='claude-sonnet-4-5-20250929[1m]'
+claude
+```
+
+**How it works:**
+- Claude Code strips the `[1m]` suffix before sending to LiteLLM
+- Claude Code automatically adds the header `anthropic-beta: context-1m-2025-08-07`
+- Your LiteLLM config should **NOT** include `[1m]` in model names
+
+**Verify 1M context is active:**
+```bash
+/context
+# Should show: 21k/1000k tokens (2%)
+```
+
+**Pricing:** Models using 1M context have different pricing. Input tokens above 200k are charged at a higher rate.
+
 ## Troubleshooting
 
 Common issues and solutions:
@@ -123,18 +181,25 @@ Common issues and solutions:
 - Ensure the `ANTHROPIC_AUTH_TOKEN` matches your LiteLLM master key
 
 **Model not found:**
-- Ensure the model name in Claude Code matches exactly with your `config.yaml`
-- Check LiteLLM logs for detailed error messages
+- Check what model Claude Code is requesting in LiteLLM logs
+- Ensure your `config.yaml` has a matching `model_name` entry
+- If using environment variables, verify they're set: `echo $ANTHROPIC_DEFAULT_SONNET_MODEL`
+
+**1M context not working (showing 200k instead of 1000k):**
+- Verify you're using the `[1m]` suffix: `/model your-model-name[1m]`
+- Check LiteLLM logs for the header `context-1m-2025-08-07` in the request
+- Ensure your model supports 1M context (only certain Claude models do)
+- Your LiteLLM config should **NOT** include `[1m]` in the `model_name`
 
 ## Using Multiple Models and Providers
 
-Expand your configuration to support multiple providers and models:
+You can configure LiteLLM to route to any supported provider. Here's an example with multiple providers:
 
 ```yaml
 model_list:
   # OpenAI models
   - model_name: codex-mini
-    litellm_params:  
+    litellm_params:
       model: openai/codex-mini
       api_key: os.environ/OPENAI_API_KEY
       api_base: https://api.openai.com/v1
@@ -156,7 +221,7 @@ model_list:
     litellm_params:
       model: anthropic/claude-3-5-sonnet-20241022
       api_key: os.environ/ANTHROPIC_API_KEY
-  
+
   - model_name: claude-3-5-haiku-20241022
     litellm_params:
       model: anthropic/claude-3-5-haiku-20241022
@@ -174,19 +239,54 @@ litellm_settings:
   master_key: os.environ/LITELLM_MASTER_KEY
 ```
 
+**Note:** The `model_name` can be anything you choose. Claude Code will request whatever model you specify (via env vars or command line), and LiteLLM will route to the `model` configured in `litellm_params`.
+
 Switch between models seamlessly:
 
 ```bash
-# Use Claude for complex reasoning
-claude --model claude-3-5-sonnet-20241022
+# Use environment variables to set defaults
+export ANTHROPIC_DEFAULT_SONNET_MODEL=claude-3-5-sonnet-20241022
+export ANTHROPIC_DEFAULT_HAIKU_MODEL=claude-3-5-haiku-20241022
 
-# Use Haiku for fast responses
-claude --model claude-3-5-haiku-20241022
-
-# Use Bedrock deployment
-claude --model claude-bedrock
+# Or specify directly
+claude --model claude-3-5-sonnet-20241022  # Complex reasoning
+claude --model claude-3-5-haiku-20241022    # Fast responses
+claude --model claude-bedrock                # Bedrock deployment
 ```
 
+## Default Models Used by Claude Code
+
+If you **don't** set environment variables, Claude Code uses these default model names:
+
+| Purpose | Default Model Name (v2.1.14) |
+|---------|------------------------------|
+| Main model | `claude-sonnet-4-5-20250929` |
+| Light tasks (subagents, summaries) | `claude-haiku-4-5-20251001` |
+| Planning mode | `claude-opus-4-5-20251101` |
+
+Your LiteLLM config should include these model names if you want Claude Code to work without setting environment variables:
+
+```yaml
+model_list:
+  - model_name: claude-sonnet-4-5-20250929
+    litellm_params:
+      # Can be any provider - Anthropic, Bedrock, Vertex AI, etc.
+      model: anthropic/claude-sonnet-4-5-20250929
+      api_key: os.environ/ANTHROPIC_API_KEY
+
+  - model_name: claude-haiku-4-5-20251001
+    litellm_params:
+      model: anthropic/claude-haiku-4-5-20251001
+      api_key: os.environ/ANTHROPIC_API_KEY
+
+  - model_name: claude-opus-4-5-20251101
+    litellm_params:
+      model: anthropic/claude-opus-4-5-20251101
+      api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+**Warning:** These default model names may change with new Claude Code versions. Check LiteLLM proxy logs for "model not found" errors to identify what Claude Code is requesting.
+
 ## Additional Resources
 
 - [LiteLLM Documentation](https://docs.litellm.ai/)

cookbook/ai_coding_tool_guides/index.json

@@ -95,4 +95,40 @@
         "LiteLLM",
         "Quickstart"
     ]
+},
+{
+    "title": "AI Coding Tool Usage Tracking",
+    "description": "This is a guide to tracking usage for AI coding tools monitor the use of Claude Code , Google Antigravity, OpenAI Codex, Roo Code etc. through LiteLLM.",
+    "url": "https://docs.litellm.ai/docs/tutorials/cost_tracking_coding",
+    "date": "2026-01-17",
+    "version": "1.0.0",
+    "tags": [
+        "Claude Code",
+        "Gemini CLI",
+        "OpenAI Codex",
+        "LiteLLM"
+    ]
+},
+{
+    "title": "Use Web Search with Claude Code (across Bedrock/OpenAI/Gemini/etc.)",
+    "description": "This is a guide for using Web Search with Claude Code via LiteLLM.",
+    "url": "https://docs.litellm.ai/docs/tutorials/claude_code_websearch",
+    "date": "2026-01-17",
+    "version": "1.0.0",
+    "tags": [
+        "Claude Code",
+        "LiteLLM",
+        "Web Search"
+    ]
+},
+{
+    "title": "Track Claude Code Usage per user via Custom Headers",
+    "description": "This is a guide for tracking claude code user usage by passing a customer ID header.",
+    "url": "https://docs.litellm.ai/docs/tutorials/claude_code_customer_tracking",
+    "date": "2026-01-17",
+    "version": "1.0.0",
+    "tags": [
+        "Claude Code",
+        "LiteLLM"
+    ]
 }]

cookbook/anthropic_agent_sdk/README.md

@@ -0,0 +1,144 @@
+# Claude Agent SDK with LiteLLM Gateway
+
+A simple example showing how to use Claude's Agent SDK with LiteLLM as a proxy. This lets you use any LLM provider (OpenAI, Bedrock, Azure, etc.) through the Agent SDK.
+
+## Quick Start
+
+### 1. Install dependencies
+
+```bash
+pip install anthropic claude-agent-sdk litellm
+```
+
+### 2. Start LiteLLM proxy
+
+```bash
+# Simple start with Claude
+litellm --model claude-sonnet-4-20250514
+
+# Or with a config file
+litellm --config config.yaml
+```
+
+### 3. Run the chat
+
+**Basic Agent (no MCP):**
+
+```bash
+python main.py
+```
+
+**Agent with MCP (DeepWiki2 for research):**
+
+```bash
+python agent_with_mcp.py
+```
+
+If MCP connection fails, you can disable it:
+
+```bash
+USE_MCP=false python agent_with_mcp.py
+```
+
+That's it! You can now chat with the agent in your terminal.
+
+### Chat Commands
+
+While chatting, you can use these commands:
+- `models` - List all available models (fetched from your LiteLLM proxy)
+- `model` - Switch to a different model
+- `clear` - Start a new conversation
+- `quit` or `exit` - End the chat
+
+The chat automatically fetches available models from your LiteLLM proxy's `/models` endpoint, so you'll always see what's currently configured.
+
+## Configuration
+
+Set these environment variables if needed:
+
+```bash
+export LITELLM_PROXY_URL="http://localhost:4000"
+export LITELLM_API_KEY="sk-1234"
+export LITELLM_MODEL="bedrock-claude-sonnet-4.5"
+```
+
+Or just use the defaults - it'll connect to `http://localhost:4000` by default.
+
+## Files
+
+- `main.py` - Basic interactive agent without MCP
+- `agent_with_mcp.py` - Agent with MCP server integration (DeepWiki2)
+- `common.py` - Shared utilities and functions
+- `config.example.yaml` - Example LiteLLM configuration
+- `requirements.txt` - Python dependencies
+
+## Example Config File
+
+If you want to use multiple models, create a `config.yaml` (see `config.example.yaml`):
+
+```yaml
+model_list:
+  - model_name: bedrock-claude-sonnet-4
+    litellm_params:
+      model: "bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0"
+      aws_region_name: "us-east-1"
+
+  - model_name: bedrock-claude-sonnet-4.5
+    litellm_params:
+      model: "bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0"
+      aws_region_name: "us-east-1"
+```
+
+Then start LiteLLM with: `litellm --config config.yaml`
+
+## How It Works
+
+The key is pointing the Agent SDK to LiteLLM instead of directly to Anthropic:
+
+```python
+# Point to LiteLLM gateway (not Anthropic)
+os.environ["ANTHROPIC_BASE_URL"] = "http://localhost:4000"
+os.environ["ANTHROPIC_API_KEY"] = "sk-1234"  # Your LiteLLM key
+
+# Use any model configured in LiteLLM
+options = ClaudeAgentOptions(
+    model="bedrock-claude-sonnet-4",  # or gpt-4, or anything else
+    system_prompt="You are a helpful assistant.",
+    max_turns=50,
+)
+```
+
+Note: Don't add `/anthropic` to the base URL - LiteLLM handles the routing automatically.
+
+## Why Use This?
+
+- **Switch providers easily**: Use the same code with OpenAI, Bedrock, Azure, etc.
+- **Cost tracking**: LiteLLM tracks spending across all your agent conversations
+- **Rate limiting**: Set budgets and limits on your agent usage
+- **Load balancing**: Distribute requests across multiple API keys or regions
+- **Fallbacks**: Automatically retry with a different model if one fails
+
+## Troubleshooting
+
+**Connection errors?**
+- Make sure LiteLLM is running: `litellm --model your-model`
+- Check the URL is correct (default: `http://localhost:4000`)
+
+**Authentication errors?**
+- Verify your LiteLLM API key is correct
+- Make sure the model is configured in your LiteLLM setup
+
+**Model not found?**
+- Check the model name matches what's in your LiteLLM config
+- Run `litellm --model your-model` to test it works
+
+**Agent with MCP stuck or failing?**
+- The MCP server might not be available at `http://localhost:4000/mcp/deepwiki2`
+- Try disabling MCP: `USE_MCP=false python agent_with_mcp.py`
+- Or use the basic agent: `python main.py`
+
+## Learn More
+
+- [LiteLLM Docs](https://docs.litellm.ai/)
+- [Claude Agent SDK](https://github.com/anthropics/anthropic-agent-sdk)
+- [LiteLLM Proxy Guide](https://docs.litellm.ai/docs/proxy/quick_start)

cookbook/anthropic_agent_sdk/agent_with_mcp.py

@@ -0,0 +1,140 @@
+"""
+Interactive Claude Agent SDK CLI with MCP Support
+
+This example demonstrates an interactive CLI chat with the Anthropic Agent SDK using LiteLLM as a proxy,
+with MCP (Model Context Protocol) server integration for enhanced capabilities.
+"""
+
+import asyncio
+import os
+from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
+from common import (
+    Config,
+    fetch_available_models,
+    setup_litellm_env,
+    print_header,
+    handle_model_list,
+    handle_model_switch,
+    stream_response,
+)
+
+
+async def interactive_chat_with_mcp():
+    """
+    Interactive CLI chat with the agent and MCP server
+    """
+    config = Config()
+    
+    # Configure Anthropic SDK to point to LiteLLM gateway
+    litellm_base_url = setup_litellm_env(config)
+    
+    # Fetch available models from proxy
+    available_models = await fetch_available_models(litellm_base_url, config.LITELLM_API_KEY)
+    
+    current_model = config.LITELLM_MODEL
+    
+    # MCP server configuration
+    mcp_server_url = f"{litellm_base_url}/mcp/deepwiki2"
+    use_mcp = os.getenv("USE_MCP", "true").lower() == "true"
+    
+    if not use_mcp:
+        print("⚠️  MCP disabled via USE_MCP=false")
+    
+    print_header(litellm_base_url, current_model, has_mcp=use_mcp)
+    
+    while True:
+        # Configure agent options
+        if use_mcp:
+            try:
+                # Try with MCP server (HTTP transport)
+                # Using McpHttpServerConfig format from Agent SDK
+                options = ClaudeAgentOptions(
+                    system_prompt="You are a helpful AI assistant with access to DeepWiki for research. Be concise, accurate, and friendly.",
+                    model=current_model,
+                    max_turns=50,
+                    mcp_servers={
+                        "deepwiki2": {
+                            "type": "http",
+                            "url": mcp_server_url,
+                            "headers": {
+                                "Authorization": f"Bearer {config.LITELLM_API_KEY}"
+                            }
+                        }
+                    },
+                )
+            except Exception as e:
+                print(f"⚠️  Warning: Could not configure MCP server: {e}")
+                print("Continuing without MCP...\n")
+                use_mcp = False
+                options = ClaudeAgentOptions(
+                    system_prompt="You are a helpful AI assistant. Be concise, accurate, and friendly.",
+                    model=current_model,
+                    max_turns=50,
+                )
+        else:
+            # Without MCP
+            options = ClaudeAgentOptions(
+                system_prompt="You are a helpful AI assistant. Be concise, accurate, and friendly.",
+                model=current_model,
+                max_turns=50,
+            )
+        
+        # Create agent client
+        try:
+            async with ClaudeSDKClient(options=options) as client:
+                conversation_active = True
+                
+                while conversation_active:
+                    # Get user input
+                    try:
+                        user_input = input("\n👤 You: ").strip()
+                    except (EOFError, KeyboardInterrupt):
+                        print("\n\n👋 Goodbye!")
+                        return
+                    
+                    # Handle commands
+                    if user_input.lower() in ['quit', 'exit']:
+                        print("\n👋 Goodbye!")
+                        return
+                    
+                    if user_input.lower() == 'clear':
+                        print("\n🔄 Starting new conversation...\n")
+                        conversation_active = False
+                        continue
+                    
+                    if user_input.lower() == 'models':
+                        handle_model_list(available_models, current_model)
+                        continue
+                    
+                    if user_input.lower() == 'model':
+                        new_model, should_restart = handle_model_switch(available_models, current_model)
+                        if should_restart:
+                            current_model = new_model
+                            conversation_active = False
+                        continue
+                    
+                    if not user_input:
+                        continue
+                    
+                    # Stream response from agent
+                    await stream_response(client, user_input)
+        
+        except Exception as e:
+            print(f"\n❌ Error creating agent client: {e}")
+            print("This might be an MCP configuration issue. Try running without MCP:")
+            print("  USE_MCP=false python agent_with_mcp.py")
+            print("\nOr use the basic agent:")
+            print("  python main.py")
+            return
+
+
+def main():
+    """Run interactive chat with MCP"""
+    try:
+        asyncio.run(interactive_chat_with_mcp())
+    except KeyboardInterrupt:
+        print("\n\n👋 Goodbye!")
+
+
+if __name__ == "__main__":
+    main()

cookbook/anthropic_agent_sdk/common.py

@@ -0,0 +1,160 @@
+"""
+Common utilities for Claude Agent SDK examples
+"""
+
+import os
+import httpx
+
+
+class Config:
+    """Configuration for LiteLLM Gateway connection"""
+    
+    # LiteLLM proxy URL (default to local instance)
+    LITELLM_PROXY_URL = os.getenv("LITELLM_PROXY_URL", "http://localhost:4000")
+    
+    # LiteLLM API key (master key or virtual key)
+    LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "sk-1234")
+    
+    # Model name as configured in LiteLLM (e.g., "bedrock-claude-sonnet-4", "gpt-4", etc.)
+    LITELLM_MODEL = os.getenv("LITELLM_MODEL", "bedrock-claude-sonnet-4.5")
+
+
+async def fetch_available_models(base_url: str, api_key: str) -> list[str]:
+    """
+    Fetch available models from LiteLLM proxy /models endpoint
+    """
+    try:
+        async with httpx.AsyncClient() as client:
+            response = await client.get(
+                f"{base_url}/models",
+                headers={"Authorization": f"Bearer {api_key}"},
+                timeout=10.0
+            )
+            response.raise_for_status()
+            data = response.json()
+            return [model["id"] for model in data.get("data", [])]
+    except Exception as e:
+        print(f"⚠️  Warning: Could not fetch models from proxy: {e}")
+        print("Using default model list...")
+        # Fallback to default models
+        return [
+            "bedrock-claude-sonnet-3.5",
+            "bedrock-claude-sonnet-4",
+            "bedrock-claude-sonnet-4.5",
+            "bedrock-claude-opus-4.5",
+            "bedrock-nova-premier",
+        ]
+
+
+def setup_litellm_env(config: Config):
+    """
+    Configure environment variables to point Agent SDK to LiteLLM
+    """
+    litellm_base_url = config.LITELLM_PROXY_URL.rstrip('/')
+    os.environ["ANTHROPIC_BASE_URL"] = litellm_base_url
+    os.environ["ANTHROPIC_API_KEY"] = config.LITELLM_API_KEY
+    return litellm_base_url
+
+
+def print_header(base_url: str, current_model: str, has_mcp: bool = False):
+    """
+    Print the chat header
+    """
+    mcp_indicator = " + MCP" if has_mcp else ""
+    print("=" * 70)
+    print(f"🤖 Claude Agent SDK with LiteLLM Gateway{mcp_indicator} - Interactive Chat")
+    print("=" * 70)
+    print(f"🚀 Connected to: {base_url}")
+    print(f"📦 Current model: {current_model}")
+    if has_mcp:
+        print("🔌 MCP: deepwiki2 enabled")
+    print("\nType your messages below. Commands:")
+    print("  - 'quit' or 'exit' to end the conversation")
+    print("  - 'clear' to start a new conversation")
+    print("  - 'model' to switch models")
+    print("  - 'models' to list available models")
+    print("=" * 70)
+    print()
+
+
+def handle_model_list(available_models: list[str], current_model: str):
+    """
+    Display available models
+    """
+    print("\n📋 Available models:")
+    for i, model in enumerate(available_models, 1):
+        marker = "✓" if model == current_model else " "
+        print(f"  {marker} {i}. {model}")
+
+
+def handle_model_switch(available_models: list[str], current_model: str) -> tuple[str, bool]:
+    """
+    Handle model switching
+    
+    Returns:
+        tuple: (new_model, should_restart_conversation)
+    """
+    print("\n📋 Select a model:")
+    for i, model in enumerate(available_models, 1):
+        marker = "✓" if model == current_model else " "
+        print(f"  {marker} {i}. {model}")
+    
+    try:
+        choice = input("\nEnter number (or press Enter to cancel): ").strip()
+        if choice:
+            idx = int(choice) - 1
+            if 0 <= idx < len(available_models):
+                new_model = available_models[idx]
+                print(f"\n✅ Switched to: {new_model}")
+                print("🔄 Starting new conversation with new model...\n")
+                return new_model, True
+            else:
+                print("❌ Invalid choice")
+    except (ValueError, IndexError):
+        print("❌ Invalid input")
+    
+    return current_model, False
+
+
+async def stream_response(client, user_input: str):
+    """
+    Stream response from the agent
+    """
+    print("\n🤖 Assistant: ", end='', flush=True)
+    
+    try:
+        await client.query(user_input)
+        
+        # Show loading indicator
+        print("⏳ thinking...", end='', flush=True)
+        
+        # Stream the response
+        first_chunk = True
+        async for msg in client.receive_response():
+            # Clear loading indicator on first message
+            if first_chunk:
+                print("\r🤖 Assistant: ", end='', flush=True)
+                first_chunk = False
+            
+            # Handle different message types
+            if hasattr(msg, 'type'):
+                if msg.type == 'content_block_delta':
+                    # Streaming text delta
+                    if hasattr(msg, 'delta') and hasattr(msg.delta, 'text'):
+                        print(msg.delta.text, end='', flush=True)
+                elif msg.type == 'content_block_start':
+                    # Start of content block
+                    if hasattr(msg, 'content_block') and hasattr(msg.content_block, 'text'):
+                        print(msg.content_block.text, end='', flush=True)
+            
+            # Fallback to original content handling
+            if hasattr(msg, 'content'):
+                for content_block in msg.content:
+                    if hasattr(content_block, 'text'):
+                        print(content_block.text, end='', flush=True)
+        
+        print()  # New line after response
+        
+    except Exception as e:
+        print(f"\r\n❌ Error: {e}")
+        print("Please check your LiteLLM gateway is running and configured correctly.")

cookbook/anthropic_agent_sdk/config.example.yaml

@@ -0,0 +1,25 @@
+model_list:
+  - model_name: bedrock-claude-sonnet-3.5
+    litellm_params:
+      model: "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"
+      aws_region_name: "us-east-1"
+
+  - model_name: bedrock-claude-sonnet-4
+    litellm_params:
+      model: "bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0"
+      aws_region_name: "us-east-1"
+
+  - model_name: bedrock-claude-sonnet-4.5
+    litellm_params:
+      model: "bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0"
+      aws_region_name: "us-east-1"
+
+  - model_name: bedrock-claude-opus-4.5
+    litellm_params:
+      model: "bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0"
+      aws_region_name: "us-east-1"
+
+  - model_name: bedrock-nova-premier
+    litellm_params:
+      model: "bedrock/amazon.nova-premier-v1:0"
+      aws_region_name: "us-east-1"

cookbook/anthropic_agent_sdk/main.py

@@ -0,0 +1,95 @@
+"""
+Simple Interactive Claude Agent SDK CLI using LiteLLM Gateway
+
+This example demonstrates an interactive CLI chat with the Anthropic Agent SDK using LiteLLM as a proxy.
+LiteLLM acts as a unified interface, allowing you to use any LLM provider (OpenAI, Azure, Bedrock, etc.)
+through the Claude Agent SDK by pointing it to the LiteLLM gateway.
+"""
+
+import asyncio
+from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
+from common import (
+    Config,
+    fetch_available_models,
+    setup_litellm_env,
+    print_header,
+    handle_model_list,
+    handle_model_switch,
+    stream_response,
+)
+
+
+async def interactive_chat():
+    """
+    Interactive CLI chat with the agent
+    """
+    config = Config()
+    
+    # Configure Anthropic SDK to point to LiteLLM gateway
+    litellm_base_url = setup_litellm_env(config)
+    
+    # Fetch available models from proxy
+    available_models = await fetch_available_models(litellm_base_url, config.LITELLM_API_KEY)
+    
+    current_model = config.LITELLM_MODEL
+    
+    print_header(litellm_base_url, current_model)
+    
+    while True:
+        # Configure agent options for each conversation
+        options = ClaudeAgentOptions(
+            system_prompt="You are a helpful AI assistant. Be concise, accurate, and friendly.",
+            model=current_model,
+            max_turns=50,
+        )
+        
+        # Create agent client
+        async with ClaudeSDKClient(options=options) as client:
+            conversation_active = True
+            
+            while conversation_active:
+                # Get user input
+                try:
+                    user_input = input("\n👤 You: ").strip()
+                except (EOFError, KeyboardInterrupt):
+                    print("\n\n👋 Goodbye!")
+                    return
+                
+                # Handle commands
+                if user_input.lower() in ['quit', 'exit']:
+                    print("\n👋 Goodbye!")
+                    return
+                
+                if user_input.lower() == 'clear':
+                    print("\n🔄 Starting new conversation...\n")
+                    conversation_active = False
+                    continue
+                
+                if user_input.lower() == 'models':
+                    handle_model_list(available_models, current_model)
+                    continue
+                
+                if user_input.lower() == 'model':
+                    new_model, should_restart = handle_model_switch(available_models, current_model)
+                    if should_restart:
+                        current_model = new_model
+                        conversation_active = False
+                    continue
+                
+                if not user_input:
+                    continue
+                
+                # Stream response from agent
+                await stream_response(client, user_input)
+
+
+def main():
+    """Run interactive chat"""
+    try:
+        asyncio.run(interactive_chat())
+    except KeyboardInterrupt:
+        print("\n\n👋 Goodbye!")
+
+
+if __name__ == "__main__":
+    main()

cookbook/anthropic_agent_sdk/requirements.txt

@@ -0,0 +1,2 @@
+claude-agent-sdk
+httpx>=0.27.0

cookbook/nova_sonic_realtime.py

@@ -0,0 +1,284 @@
+"""
+Client script to test Nova Sonic realtime API through LiteLLM proxy.
+
+This script connects to LiteLLM proxy's realtime endpoint and enables
+speech-to-speech conversation with Bedrock Nova Sonic.
+
+Prerequisites:
+- LiteLLM proxy running with Bedrock configured
+- pyaudio installed: pip install pyaudio
+- websockets installed: pip install websockets
+
+Usage:
+    python nova_sonic_realtime.py
+"""
+
+import asyncio
+import base64
+import json
+import pyaudio
+import websockets
+from typing import Optional
+
+# Audio configuration (matching Nova Sonic requirements)
+INPUT_SAMPLE_RATE = 16000  # Nova Sonic expects 16kHz input
+OUTPUT_SAMPLE_RATE = 24000  # Nova Sonic outputs 24kHz
+CHANNELS = 1
+FORMAT = pyaudio.paInt16
+CHUNK_SIZE = 1024
+
+# LiteLLM proxy configuration
+LITELLM_PROXY_URL = "ws://localhost:4000/v1/realtime?model=bedrock-sonic"
+LITELLM_API_KEY = "sk-12345"  # Your LiteLLM API key
+
+
+class RealtimeClient:
+    """Client for LiteLLM realtime API with audio support."""
+
+    def __init__(self, url: str, api_key: str):
+        self.url = url
+        self.api_key = api_key
+        self.ws: Optional[websockets.WebSocketClientProtocol] = None
+        self.is_active = False
+        self.audio_queue = asyncio.Queue()
+        self.pyaudio = pyaudio.PyAudio()
+        self.input_stream = None
+        self.output_stream = None
+
+    async def connect(self):
+        """Connect to LiteLLM proxy realtime endpoint."""
+        print(f"Connecting to {self.url}...")
+        
+        headers = {}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+        
+        self.ws = await websockets.connect(
+            self.url,
+            additional_headers=headers,
+            max_size=10 * 1024 * 1024,  # 10MB max message size
+        )
+        self.is_active = True
+        print("✓ Connected to LiteLLM proxy")
+
+    async def send_session_update(self):
+        """Send session configuration."""
+        session_update = {
+            "type": "session.update",
+            "session": {
+                "instructions": "You are a friendly assistant. Keep your responses short and conversational.",
+                "voice": "matthew",
+                "temperature": 0.8,
+                "max_response_output_tokens": 1024,
+                "modalities": ["text", "audio"],
+                "input_audio_format": "pcm16",
+                "output_audio_format": "pcm16",
+                "turn_detection": {
+                    "type": "server_vad",
+                    "threshold": 0.5,
+                    "prefix_padding_ms": 300,
+                    "silence_duration_ms": 500,
+                },
+            },
+        }
+        await self.ws.send(json.dumps(session_update))
+        print("✓ Session configuration sent")
+
+    async def receive_messages(self):
+        """Receive and process messages from the server."""
+        try:
+            async for message in self.ws:
+                if not self.is_active:
+                    break
+
+                try:
+                    data = json.loads(message)
+                    event_type = data.get("type")
+
+                    if event_type == "session.created":
+                        print(f"✓ Session created: {data.get('session', {}).get('id')}")
+
+                    elif event_type == "response.created":
+                        print("🤖 Assistant is responding...")
+
+                    elif event_type == "response.text.delta":
+                        # Print text transcription
+                        delta = data.get("delta", "")
+                        print(delta, end="", flush=True)
+
+                    elif event_type == "response.audio.delta":
+                        # Queue audio for playback
+                        audio_b64 = data.get("delta", "")
+                        if audio_b64:
+                            audio_bytes = base64.b64decode(audio_b64)
+                            await self.audio_queue.put(audio_bytes)
+
+                    elif event_type == "response.text.done":
+                        print()  # New line after text
+
+                    elif event_type == "response.done":
+                        print("✓ Response complete")
+
+                    elif event_type == "error":
+                        print(f"❌ Error: {data.get('error', {})}")
+
+                    else:
+                        # Debug: print other event types
+                        print(f"[{event_type}]", end=" ")
+
+                except json.JSONDecodeError:
+                    print(f"Failed to parse message: {message[:100]}")
+
+        except websockets.exceptions.ConnectionClosed:
+            print("\n✗ Connection closed")
+        except Exception as e:
+            print(f"\n✗ Error receiving messages: {e}")
+        finally:
+            self.is_active = False
+
+    async def send_audio_chunk(self, audio_bytes: bytes):
+        """Send audio chunk to server."""
+        if not self.is_active or not self.ws:
+            return
+
+        audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
+        message = {
+            "type": "input_audio_buffer.append",
+            "audio": audio_b64,
+        }
+        await self.ws.send(json.dumps(message))
+
+    async def commit_audio_buffer(self):
+        """Commit the audio buffer to trigger processing."""
+        if not self.is_active or not self.ws:
+            return
+
+        message = {"type": "input_audio_buffer.commit"}
+        await self.ws.send(json.dumps(message))
+
+    async def capture_audio(self):
+        """Capture audio from microphone and send to server."""
+        print("\n🎤 Starting audio capture...")
+        print("Speak into your microphone. Press Ctrl+C to stop.\n")
+
+        self.input_stream = self.pyaudio.open(
+            format=FORMAT,
+            channels=CHANNELS,
+            rate=INPUT_SAMPLE_RATE,
+            input=True,
+            frames_per_buffer=CHUNK_SIZE,
+        )
+
+        try:
+            while self.is_active:
+                audio_data = self.input_stream.read(CHUNK_SIZE, exception_on_overflow=False)
+                await self.send_audio_chunk(audio_data)
+                await asyncio.sleep(0.01)  # Small delay to prevent overwhelming
+        except Exception as e:
+            print(f"Error capturing audio: {e}")
+        finally:
+            if self.input_stream:
+                self.input_stream.stop_stream()
+                self.input_stream.close()
+
+    async def play_audio(self):
+        """Play audio responses from the server."""
+        print("🔊 Starting audio playback...")
+
+        self.output_stream = self.pyaudio.open(
+            format=FORMAT,
+            channels=CHANNELS,
+            rate=OUTPUT_SAMPLE_RATE,
+            output=True,
+            frames_per_buffer=CHUNK_SIZE,
+        )
+
+        try:
+            while self.is_active:
+                try:
+                    audio_data = await asyncio.wait_for(
+                        self.audio_queue.get(), timeout=0.1
+                    )
+                    if audio_data:
+                        self.output_stream.write(audio_data)
+                except asyncio.TimeoutError:
+                    continue
+        except Exception as e:
+            print(f"Error playing audio: {e}")
+        finally:
+            if self.output_stream:
+                self.output_stream.stop_stream()
+                self.output_stream.close()
+
+    async def close(self):
+        """Close the connection and cleanup."""
+        self.is_active = False
+
+        if self.ws:
+            await self.ws.close()
+
+        if self.input_stream:
+            self.input_stream.stop_stream()
+            self.input_stream.close()
+
+        if self.output_stream:
+            self.output_stream.stop_stream()
+            self.output_stream.close()
+
+        self.pyaudio.terminate()
+        print("\n✓ Connection closed")
+
+
+async def main():
+    """Main function to run the realtime client."""
+    print("=" * 80)
+    print("Bedrock Nova Sonic Realtime Client")
+    print("=" * 80)
+    print()
+
+    client = RealtimeClient(LITELLM_PROXY_URL, LITELLM_API_KEY)
+
+    try:
+        # Connect to server
+        await client.connect()
+
+        # Send session configuration
+        await client.send_session_update()
+
+        # Wait a moment for session to be established
+        await asyncio.sleep(0.5)
+
+        # Start tasks
+        receive_task = asyncio.create_task(client.receive_messages())
+        capture_task = asyncio.create_task(client.capture_audio())
+        playback_task = asyncio.create_task(client.play_audio())
+
+        # Wait for user to interrupt
+        await asyncio.gather(
+            receive_task,
+            capture_task,
+            playback_task,
+            return_exceptions=True,
+        )
+
+    except KeyboardInterrupt:
+        print("\n\n⚠ Interrupted by user")
+    except Exception as e:
+        print(f"\n❌ Error: {e}")
+        import traceback
+        traceback.print_exc()
+    finally:
+        await client.close()
+
+
+if __name__ == "__main__":
+    print("\nMake sure:")
+    print("1. LiteLLM proxy is running on port 4000")
+    print("2. Bedrock is configured in proxy_server_config.yaml")
+    print("3. AWS credentials are set")
+    print()
+    
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        print("\n\nGoodbye!")

deploy/charts/litellm-helm/Chart.yaml

@@ -18,7 +18,7 @@ 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: 1.0.0
+version: 1.1.0
 
 # 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

deploy/charts/litellm-helm/templates/deployment.yaml

@@ -10,7 +10,7 @@ metadata:
     {{- toYaml .Values.deploymentLabels | nindent 4 }}
     {{- end }}
 spec:
-  {{- if not .Values.autoscaling.enabled }}
+  {{- if and (not .Values.keda.enabled) (not .Values.autoscaling.enabled) }}
   replicas: {{ .Values.replicaCount }}
   {{- end }}
   selector:
@@ -38,6 +38,10 @@ spec:
       serviceAccountName: {{ include "litellm.serviceAccountName" . }}
       securityContext:
         {{- toYaml .Values.podSecurityContext | nindent 8 }}
+      {{- with .Values.extraInitContainers }}
+      initContainers:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
       containers:
         - name: {{ include "litellm.name" . }}
           securityContext:

deploy/charts/litellm-helm/templates/keda.yaml

@@ -0,0 +1,37 @@
+{{- if and .Values.keda.enabled (not .Values.autoscaling.enabled) }}
+apiVersion: keda.sh/v1alpha1
+kind: ScaledObject
+metadata:
+  name: {{ include "litellm.fullname" . }}
+  labels:
+    {{- include "litellm.labels" . | nindent 4 }}
+  {{- if .Values.keda.scaledObject.annotations }}
+  annotations: {{ toYaml .Values.keda.scaledObject.annotations | nindent 4 }}
+  {{- end }}
+spec:
+  scaleTargetRef:
+    name: {{ include "litellm.fullname" . }}
+  pollingInterval: {{ .Values.keda.pollingInterval }}
+  cooldownPeriod: {{ .Values.keda.cooldownPeriod }}
+  minReplicaCount: {{ .Values.keda.minReplicas }}
+  maxReplicaCount: {{ .Values.keda.maxReplicas }}
+{{- with .Values.keda.fallback }}
+  fallback:
+    failureThreshold: {{ .failureThreshold | default 3 }}
+    replicas: {{ .replicas | default $.Values.keda.maxReplicas }}
+{{- end }}
+  triggers:
+{{- with .Values.keda.triggers }}
+  {{- toYaml . | nindent 2 }}
+{{- end }}
+  advanced:
+    restoreToOriginalReplicaCount: {{ .Values.keda.restoreToOriginalReplicaCount }}
+{{- if .Values.keda.behavior }}
+    horizontalPodAutoscalerConfig:
+      behavior:
+{{- with .Values.keda.behavior }}
+{{- toYaml . | nindent 8 }}
+{{- end }}
+
+{{- end }}
+{{- end }}

deploy/charts/litellm-helm/templates/migrations-job.yaml

@@ -35,6 +35,10 @@ spec:
         {{- toYaml . | nindent 8 }}
       {{- end }}
       serviceAccountName: {{ include "litellm.serviceAccountName" . }}
+      {{- with .Values.migrationJob.extraInitContainers }}
+      initContainers:
+        {{- toYaml . | nindent 8 }}
+      {{- end }} 
       containers:
         - name: prisma-migrations
           image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}"

deploy/charts/litellm-helm/values.yaml

@@ -156,6 +156,40 @@ autoscaling:
   targetCPUUtilizationPercentage: 80
   # targetMemoryUtilizationPercentage: 80
 
+# Autoscaling with keda is mutually exclusive with hpa
+keda:
+  enabled: false
+  minReplicas: 1
+  maxReplicas: 100
+  pollingInterval: 30
+  cooldownPeriod: 300
+  # fallback:
+  #   failureThreshold: 3
+  #   replicas: 11
+  restoreToOriginalReplicaCount: false
+  scaledObject:
+    annotations: {}
+  triggers: []
+  # - type: prometheus
+  #   metadata:
+  #     serverAddress: http://<prometheus-host>:9090
+  #     metricName: http_requests_total
+  #     threshold: '100'
+  #     query: sum(rate(http_requests_total{deployment="my-deployment"}[2m]))
+  behavior: {}
+  # scaleDown:
+  #   stabilizationWindowSeconds: 300
+  #   policies:
+  #   - type: Pods
+  #     value: 1
+  #     periodSeconds: 180
+  # scaleUp:
+  #   stabilizationWindowSeconds: 300
+  #   policies:
+  #   - type: Pods
+  #     value: 2
+  #     periodSeconds: 60
+
 # Additional volumes on the output Deployment definition.
 volumes: []
 # - name: foo
@@ -200,6 +234,14 @@ db:
   #  instance.  See the "postgresql" top level key for additional configuration.
   deployStandalone: true
 
+# Lifecycle hooks for the LiteLLM container
+# Example:
+# lifecycle:
+#   preStop:
+#     exec:
+#       command: ["/bin/sh", "-c", "sleep 10"]
+lifecycle: {}
+
 # Settings for Bitnami postgresql chart (if db.deployStandalone is true, ignored
 #  otherwise)
 postgresql:
@@ -239,6 +281,7 @@ migrationJob:
   #    cpu: 100m
   #    memory: 100Mi
   extraContainers: []
+  extraInitContainers: []
 
   # Hook configuration
   hooks:

docker/Dockerfile.custom_ui

@@ -5,7 +5,8 @@ FROM ghcr.io/berriai/litellm:litellm_fwd_server_root_path-dev
 WORKDIR /app
 
 # Install Node.js and npm (adjust version as needed)
-RUN apt-get update && apt-get install -y nodejs npm
+RUN apt-get update && apt-get install -y nodejs npm && \
+    npm install -g npm@latest tar@latest
 
 # Copy the UI source into the container
 COPY ./ui/litellm-dashboard /app/ui/litellm-dashboard

docker/Dockerfile.database

@@ -49,7 +49,8 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
 USER root
 
 # Install runtime dependencies
-RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile
+RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile && \
+    npm install -g npm@latest tar@latest
 
 WORKDIR /app
 # Copy the current directory contents into the container at /app

docker/Dockerfile.dev

@@ -61,7 +61,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libatomic1 \
     nodejs \
     npm \
-    && rm -rf /var/lib/apt/lists/*
+    && rm -rf /var/lib/apt/lists/* \
+    && npm install -g npm@latest tar@latest
 
 WORKDIR /app
 

docker/Dockerfile.health_check

@@ -0,0 +1,16 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Copy health check script and requirements
+COPY scripts/health_check/health_check_client.py /app/health_check_client.py
+COPY scripts/health_check/health_check_requirements.txt /app/requirements.txt
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make script executable
+RUN chmod +x /app/health_check_client.py
+
+# Set entrypoint
+ENTRYPOINT ["python", "/app/health_check_client.py"]

docker/Dockerfile.non_root

@@ -15,6 +15,7 @@ USER root
 RUN for i in 1 2 3; do \
     apk add --no-cache \
     python3 \
+    python3-dev \
     py3-pip \
     clang \
     llvm \
@@ -103,7 +104,8 @@ RUN for i in 1 2 3; do \
     done \
   && for i in 1 2 3; do \
     apk add --no-cache python3 py3-pip bash openssl tzdata nodejs npm supervisor && break || sleep 5; \
-    done
+    done \
+  && npm install -g npm@latest tar@latest
 
 # Copy artifacts from builder
 COPY --from=builder /app/requirements.txt /app/requirements.txt
@@ -169,12 +171,14 @@ RUN sed -i 's/\r$//' docker/entrypoint.sh && \
     [ -n "$LITELLM_PROXY_EXTRAS_PATH" ] && chmod -R g+w $LITELLM_PROXY_EXTRAS_PATH || true && \
     chmod -R g+rX $PRISMA_PATH && \
     chmod -R g+rX /app/.cache && \
-    mkdir -p /tmp/.npm /nonexistent /.npm && \
-    prisma generate
+    mkdir -p /tmp/.npm /nonexistent /.npm
 
 # Switch to non-root user for runtime
 USER nobody
 
+# Generate Prisma client as nobody user to ensure correct file ownership
+RUN prisma generate
+
 # Prisma runtime knobs for offline containers
 ENV PRISMA_SKIP_POSTINSTALL_GENERATE=1 \
     PRISMA_HIDE_UPDATE_MESSAGE=1 \

docker/prod_entrypoint.sh

@@ -2,6 +2,7 @@
 
 if [ "$SEPARATE_HEALTH_APP" = "1" ]; then
     export LITELLM_ARGS="$@"
+    export SUPERVISORD_STOPWAITSECS="${SUPERVISORD_STOPWAITSECS:-3600}"
     exec supervisord -c /etc/supervisord.conf
 fi
 

docker/supervisord.conf

@@ -1,6 +1,8 @@
 [supervisord]
 nodaemon=true
 loglevel=info
+logfile=/tmp/supervisord.log
+pidfile=/tmp/supervisord.pid
 
 [group:litellm]
 programs=main,health
@@ -14,6 +16,7 @@ priority=1
 exitcodes=0
 stopasgroup=true
 killasgroup=true
+stopwaitsecs=%(ENV_SUPERVISORD_STOPWAITSECS)s
 stdout_logfile=/dev/stdout
 stderr_logfile=/dev/stderr
 stdout_logfile_maxbytes = 0
@@ -29,6 +32,7 @@ priority=2
 exitcodes=0
 stopasgroup=true
 killasgroup=true
+stopwaitsecs=%(ENV_SUPERVISORD_STOPWAITSECS)s
 stdout_logfile=/dev/stdout
 stderr_logfile=/dev/stderr
 stdout_logfile_maxbytes = 0

docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md

@@ -15,6 +15,7 @@ authors:
     title: "CTO, LiteLLM"
     url: https://www.linkedin.com/in/reffajnaahsi/
     image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+description: "Guide to Claude Opus 4.5 and advanced features in LiteLLM: Tool Search, Programmatic Tool Calling, and Effort Parameter."
 tags: [anthropic, claude, tool search, programmatic tool calling, effort, advanced features]
 hide_table_of_contents: false
 ---

docs/my-website/blog/gemini_3/index.md

@@ -15,6 +15,7 @@ authors:
     title: "CTO, LiteLLM"
     url: https://www.linkedin.com/in/reffajnaahsi/
     image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+description: "Common questions and best practices for using gemini-3-pro-preview with LiteLLM Proxy and SDK."
 tags: [gemini, day 0 support, llms]
 hide_table_of_contents: false
 ---

docs/my-website/blog/gemini_3_flash/index.md

@@ -15,6 +15,7 @@ authors:
     title: "CTO, LiteLLM"
     url: https://www.linkedin.com/in/reffajnaahsi/
     image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+description: "Guide to using Gemini 3 Flash on LiteLLM Proxy and SDK with day 0 support."
 tags: [gemini, day 0 support, llms]
 hide_table_of_contents: false
 ---

docs/my-website/blog/sub_millisecond_proxy_overhead/index.md

@@ -0,0 +1,92 @@
+---
+slug: sub-millisecond-proxy-overhead
+title: "Achieving Sub-Millisecond Proxy Overhead"
+date: 2026-02-02T10:00:00
+authors:
+  - name: Alexsander Hamir
+    title: "Performance Engineer, LiteLLM"
+    url: https://www.linkedin.com/in/alexsander-baptista/
+    image_url: https://github.com/AlexsanderHamir.png
+  - name: Krrish Dholakia
+    title: "CEO, LiteLLM"
+    url: https://www.linkedin.com/in/krish-d/
+    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+  - name: Ishaan Jaff
+    title: "CTO, LiteLLM"
+    url: https://www.linkedin.com/in/reffajnaahsi/
+    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+description: "Our Q1 performance target and architectural direction for achieving sub-millisecond proxy overhead on modest hardware."
+tags: [performance, architecture]
+hide_table_of_contents: false
+---
+
+![Sidecar architecture: Python control plane vs. sidecar hot path](https://raw.githubusercontent.com/AlexsanderHamir/assets/main/Screenshot%202026-02-02%20172554.png)
+
+# Achieving Sub-Millisecond Proxy Overhead
+
+## Introduction
+
+Our Q1 performance target is to aggressively move toward sub-millisecond proxy overhead on a single instance with 4 CPUs and 8 GB of RAM, and to continue pushing that boundary over time. Our broader goal is to make LiteLLM inexpensive to deploy, lightweight, and fast. This post outlines the architectural direction behind that effort.
+
+Proxy overhead refers to the latency introduced by LiteLLM itself, independent of the upstream provider.
+
+To measure it, we run the same workload directly against the provider and through LiteLLM at identical QPS (for example, 1,000 QPS) and compare the latency delta. To reduce noise, the load generator, LiteLLM, and a mock LLM endpoint all run on the same machine, ensuring the difference reflects proxy overhead rather than network latency.
+
+---
+
+## Where We're Coming From
+
+Under the same benchmark originally conducted by [TensorZero](https://www.tensorzero.com/docs/gateway/benchmarks), LiteLLM previously failed at around 1,000 QPS.
+
+That is no longer the case. Today, LiteLLM can be stress-tested at 1,000 QPS with no failures and can scale up to 5,000 QPS without failures on a 4-CPU, 8-GB RAM single instance setup.
+
+This establishes a more up to date baseline and provides useful context as we continue working on proxy overhead and overall performance.
+
+---
+
+## Design Choice
+
+Achieving sub-millisecond proxy overhead with a Python-based system requires being deliberate about where work happens.
+
+Python is a strong fit for flexibility and extensibility: provider abstraction, configuration-driven routing, and a rich callback ecosystem. These are areas where development velocity and correctness matter more than raw throughput.
+
+At higher request rates, however, certain classes of work become expensive when executed inside the Python process on every request. Rather than rewriting LiteLLM or introducing complex deployment requirements, we adopt an optional **sidecar architecture**.
+
+This architectural change is how we intend to make LiteLLM **permanently fast**. While it supports our near-term performance targets, it is a long-term investment.
+
+Python continues to own:
+
+- Request validation and normalization
+- Model and provider selection
+- Callbacks and integrations
+
+The sidecar owns **performance-critical execution**, such as:
+
+- Efficient request forwarding
+- Connection reuse and pooling
+- Enforcing timeouts and limits
+- Aggregating high-frequency metrics
+
+This separation allows each component to focus on what it does best: Python acts as the control plane, while the sidecar handles the hot path.
+
+---
+
+### Why the Sidecar Is Optional
+
+The sidecar is intentionally **optional**.
+
+This allows us to ship it incrementally, validate it under real-world workloads, and avoid making it a hard dependency before it is fully battle-tested across all LiteLLM features.
+
+Just as importantly, this ensures that self-hosting LiteLLM remains simple. The sidecar is bundled and started automatically, requires no additional infrastructure, and can be disabled entirely. From a user's perspective, LiteLLM continues to behave like a single service.
+
+As of today, the sidecar is an optimization, not a requirement.
+
+---
+
+## Conclusion
+
+Sub-millisecond proxy overhead is not achieved through a single optimization, but through architectural changes.
+
+By keeping Python focused on orchestration and extensibility, and offloading performance-critical execution to a sidecar, we establish a foundation for making LiteLLM **permanently fast over time**—even on modest hardware such as a 1-CPU, 2-GB RAM instance, while keeping deployment and self-hosting simple.
+
+This work extends beyond Q1, and we will continue sharing benchmarks and updates as the architecture evolves.

docs/my-website/docs/a2a.md

@@ -68,7 +68,7 @@ Follow [this guide, to add your pydantic ai agent to LiteLLM Agent Gateway](./pr
 
 ## Invoking your Agents
 
-Use the [A2A Python SDK](https://pypi.org/project/a2a/) to invoke agents through LiteLLM.
+Use the [A2A Python SDK](https://pypi.org/project/a2a-sdk) to invoke agents through LiteLLM.
 
 This example shows how to:
 1. **List available agents** - Query `/v1/agents` to see which agents your key can access
@@ -193,6 +193,120 @@ The logs show:
   style={{width: '100%', display: 'block', margin: '2rem auto'}}
 />
 
+
+## Forwarding LiteLLM Context Headers
+
+When LiteLLM invokes your A2A agent, it sends special headers that enable:
+- **Trace Grouping**: All LLM calls from the same agent execution appear under one trace
+- **Agent Spend Tracking**: Costs are attributed to the specific agent
+
+| Header | Purpose |
+|--------|---------|
+| `X-LiteLLM-Trace-Id` | Links all LLM calls to the same execution flow |
+| `X-LiteLLM-Agent-Id` | Attributes spend to the correct agent |
+
+
+To enable these features, your A2A server must **forward these headers** to any LLM calls it makes back to LiteLLM.
+
+### Implementation Steps
+
+**Step 1: Extract headers from incoming A2A request**
+```python def get_litellm_headers(request) -> dict:
+    """Extract X-LiteLLM-* headers from incoming A2A request."""
+    all_headers = request.call_context.state.get('headers', {})
+    return {
+        k: v for k, v in all_headers.items() 
+        if k.lower().startswith('x-litellm-')
+    }
+```
+
+**Step 2: Forward headers to your LLM calls**
+Pass the extracted headers when making calls back to LiteLLM:
+<Tabs>
+<TabItem value="openai" label="OpenAI SDK" default>
+
+```python from openai import OpenAI
+
+headers = get_litellm_headers(request)
+
+client = OpenAI(
+    api_key="sk-your-litellm-key",
+    base_url="http://localhost:4000",
+    default_headers=headers,  # Forward headers
+)
+
+response = client.chat.completions.create(
+    model="gpt-4o",
+    messages=[{"role": "user", "content": "Hello"}]
+)
+```
+</TabItem>
+
+<TabItem value="langchain" label="LangChain">
+
+```python
+from langchain_openai import ChatOpenAI
+
+headers = get_litellm_headers(request)
+
+llm = ChatOpenAI(
+    model="gpt-4o",
+    openai_api_key="sk-your-litellm-key",
+    base_url="http://localhost:4000",
+    default_headers=headers,  # Forward headers
+)
+```
+</TabItem>
+<TabItem value="litellm" label="LiteLLM SDK">
+
+```python
+import litellm
+
+headers = get_litellm_headers(request)
+
+response = litellm.completion(
+    model="gpt-4o",
+    messages=[{"role": "user", "content": "Hello"}],
+    api_base="http://localhost:4000",
+    extra_headers=headers,  # Forward headers
+)
+```
+</TabItem>
+<TabItem value="requests" label="HTTP (requests/httpx)">
+
+```python
+import httpx
+
+headers = get_litellm_headers(request)
+headers["Authorization"] = "Bearer sk-your-litellm-key"
+
+response = httpx.post(
+    "http://localhost:4000/v1/chat/completions",
+    headers=headers,
+    json={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}
+)
+```
+</TabItem>
+</Tabs>
+
+### Result
+
+With header forwarding enabled, you'll see:
+
+**Trace Grouping in Langfuse:**
+
+<Image
+  img={require('../img/a2a_trace_grouping.png')}
+  style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
+/>
+
+**Agent Spend Attribution:**
+
+<Image
+  img={require('../img/a2a_agent_spend.png')}
+  style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
+/>
+
 ## API Reference
 
 ### Endpoint

docs/my-website/docs/adding_provider/generic_guardrail_api.md

@@ -237,6 +237,27 @@ litellm_settings:
           language: "en"
 ```
 
+### Example: Pillar Security
+
+[Pillar Security](https://pillar.security) uses the Generic Guardrail API to provide comprehensive AI security scanning including prompt injection protection, PII/PCI detection, secret detection, and content moderation.
+
+```yaml
+guardrails:
+  - guardrail_name: "pillar-security"
+    litellm_params:
+      guardrail: generic_guardrail_api
+      mode: [pre_call, post_call]
+      api_base: https://api.pillar.security/api/v1/integrations/litellm
+      api_key: os.environ/PILLAR_API_KEY
+      default_on: true
+      additional_provider_specific_params:
+        plr_mask: true      # Enable automatic masking of sensitive data
+        plr_evidence: true  # Include detection evidence in response
+        plr_scanners: true  # Include scanner details in response
+```
+
+See the [Pillar Security documentation](../proxy/guardrails/pillar_security.md) for full configuration options.
+
 ## Usage
 
 Users apply your guardrail by name:

docs/my-website/docs/anthropic_unified/structured_output.md

@@ -0,0 +1,294 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Structured Output /v1/messages
+
+Use LiteLLM to call Anthropic's structured output feature via the `/v1/messages` endpoint.
+
+## Supported Providers
+
+| Provider | Supported | Notes |
+|----------|-----------|-------|
+| Anthropic | ✅ | Native support |
+| Azure AI (Anthropic models) | ✅ | Claude models on Azure AI |
+| Bedrock (Converse Anthropic models) | ✅ | Claude models via Bedrock Converse API |
+| Bedrock (Invoke Anthropic models) | ✅ | Claude models via Bedrock Invoke API |
+
+## Usage
+
+### LiteLLM Proxy Server
+
+<Tabs>
+<TabItem value="anthropic" label="Anthropic">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: claude-sonnet
+    litellm_params:
+      model: anthropic/claude-sonnet-4-5-20250514
+      api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://localhost:4000/v1/messages \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -d '{
+    "model": "claude-sonnet",
+    "max_tokens": 1024,
+    "messages": [
+      {
+        "role": "user",
+        "content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
+      }
+    ],
+    "output_format": {
+      "type": "json_schema",
+      "schema": {
+        "type": "object",
+        "properties": {
+          "name": {"type": "string"},
+          "email": {"type": "string"},
+          "plan_interest": {"type": "string"},
+          "demo_requested": {"type": "boolean"}
+        },
+        "required": ["name", "email", "plan_interest", "demo_requested"],
+        "additionalProperties": false
+      }
+    }
+  }'
+```
+
+</TabItem>
+
+<TabItem value="azure_ai" label="Azure AI (Anthropic)">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: azure-claude-sonnet
+    litellm_params:
+      model: azure_ai/claude-sonnet-4-5-20250514
+      api_key: os.environ/AZURE_AI_API_KEY
+      api_base: https://your-endpoint.inference.ai.azure.com
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://localhost:4000/v1/messages \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -d '{
+    "model": "azure-claude-sonnet",
+    "max_tokens": 1024,
+    "messages": [
+      {
+        "role": "user",
+        "content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
+      }
+    ],
+    "output_format": {
+      "type": "json_schema",
+      "schema": {
+        "type": "object",
+        "properties": {
+          "name": {"type": "string"},
+          "email": {"type": "string"},
+          "plan_interest": {"type": "string"},
+          "demo_requested": {"type": "boolean"}
+        },
+        "required": ["name", "email", "plan_interest", "demo_requested"],
+        "additionalProperties": false
+      }
+    }
+  }'
+```
+
+</TabItem>
+
+<TabItem value="bedrock" label="Bedrock (Converse)">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: bedrock-claude-sonnet
+    litellm_params:
+      model: bedrock/global.anthropic.claude-sonnet-4-5-20250929-v1:0
+      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
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://localhost:4000/v1/messages \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -d '{
+    "model": "bedrock-claude-sonnet",
+    "max_tokens": 1024,
+    "messages": [
+      {
+        "role": "user",
+        "content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
+      }
+    ],
+    "output_format": {
+      "type": "json_schema",
+      "schema": {
+        "type": "object",
+        "properties": {
+          "name": {"type": "string"},
+          "email": {"type": "string"},
+          "plan_interest": {"type": "string"},
+          "demo_requested": {"type": "boolean"}
+        },
+        "required": ["name", "email", "plan_interest", "demo_requested"],
+        "additionalProperties": false
+      }
+    }
+  }'
+```
+
+</TabItem>
+
+<TabItem value="bedrock_invoke" label="Bedrock (Invoke)">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: bedrock-claude-invoke
+    litellm_params:
+      model: bedrock/invoke/global.anthropic.claude-sonnet-4-5-20250929-v1:0
+      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
+litellm --config /path/to/config.yaml
+```
+
+3. Test it!
+
+```bash
+curl http://localhost:4000/v1/messages \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -d '{
+    "model": "bedrock-claude-invoke",
+    "max_tokens": 1024,
+    "messages": [
+      {
+        "role": "user",
+        "content": "Extract the key information from this email: John Smith (john@example.com) is interested in our Enterprise plan and wants to schedule a demo for next Tuesday at 2pm."
+      }
+    ],
+    "output_format": {
+      "type": "json_schema",
+      "schema": {
+        "type": "object",
+        "properties": {
+          "name": {"type": "string"},
+          "email": {"type": "string"},
+          "plan_interest": {"type": "string"},
+          "demo_requested": {"type": "boolean"}
+        },
+        "required": ["name", "email", "plan_interest", "demo_requested"],
+        "additionalProperties": false
+      }
+    }
+  }'
+```
+
+
+</TabItem>
+</Tabs>
+
+## Example Response
+
+```json
+{
+  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
+  "type": "message",
+  "role": "assistant",
+  "content": [
+    {
+      "type": "text",
+      "text": "{\"name\":\"John Smith\",\"email\":\"john@example.com\",\"plan_interest\":\"Enterprise\",\"demo_requested\":true}"
+    }
+  ],
+  "model": "claude-sonnet-4-5-20250514",
+  "stop_reason": "end_turn",
+  "stop_sequence": null,
+  "usage": {
+    "input_tokens": 75,
+    "output_tokens": 28
+  }
+}
+```
+
+## Request Format
+
+### output_format
+
+The `output_format` parameter specifies the structured output format.
+
+```json
+{
+  "output_format": {
+    "type": "json_schema",
+    "schema": {
+      "type": "object",
+      "properties": {
+        "field_name": {"type": "string"},
+        "another_field": {"type": "integer"}
+      },
+      "required": ["field_name", "another_field"],
+      "additionalProperties": false
+    }
+  }
+}
+```
+
+#### Fields
+
+- **type** (string): Must be `"json_schema"`
+- **schema** (object): A JSON Schema object defining the expected output structure
+  - **type** (string): The root type, typically `"object"`
+  - **properties** (object): Defines the fields and their types
+  - **required** (array): List of required field names
+  - **additionalProperties** (boolean): Set to `false` to enforce strict schema adherence

docs/my-website/docs/benchmarks.md

@@ -48,6 +48,28 @@ In these tests the baseline latency characteristics are measured against a fake-
 - High-percentile latencies drop significantly: P95 630 ms → 150 ms, P99 1,200 ms → 240 ms.
 - Setting workers equal to CPU count gives optimal performance.
 
+## `/realtime` API Benchmarks
+
+End-to-end latency benchmarks for the `/realtime` endpoint tested against a fake realtime endpoint.
+
+### Performance Metrics
+
+| Metric          | Value      |
+| --------------- | ---------- |
+| Median latency  | 59 ms      |
+| p95 latency     | 67 ms      |
+| p99 latency     | 99 ms      |
+| Average latency | 63 ms      |
+| RPS             | 1,207      |
+
+### Test Setup
+
+| Category | Specification |
+|----------|---------------|
+| **Load Testing** | Locust: 1,000 concurrent users, 500 ramp-up |
+| **System** | 4 vCPUs, 8 GB RAM, 4 workers, 4 instances |
+| **Database** | PostgreSQL (Redis unused) |
+
 ## Machine Spec used for testing
 
 Each machine deploying LiteLLM had the following specs:

docs/my-website/docs/caching/all_caches.md

@@ -105,6 +105,14 @@ Then simply initialize:
 litellm.cache = Cache(type="redis")
 ```
 
+:::info
+Use `REDIS_*` environment variables as the primary mechanism for configuring all Redis client library parameters. This approach automatically maps environment variables to Redis client kwargs and is the suggested way to toggle Redis settings.
+:::
+
+:::warning
+If you need to pass non-string Redis parameters (integers, booleans, complex objects), avoid `REDIS_*` environment variables as they may fail during Redis client initialization. Instead, pass them directly as kwargs to the `Cache()` constructor.
+:::
+
 </TabItem>
 
 <TabItem value="gcs" label="gcs-cache">

docs/my-website/docs/completion/input.md

@@ -199,6 +199,8 @@ messages=[{"role": "user", "content": [
     - `include_usage` *boolean (optional)* - If set, an additional chunk will be streamed before the data: [DONE] message. The usage field on this chunk shows the token usage statistics for the entire request, and the choices field will always be an empty array. All other chunks will also include a usage field, but with a null value. 
 
 - `stop`: *string/ array/ null (optional)* - Up to 4 sequences where the API will stop generating further tokens.
+  
+  **Note**: OpenAI supports a maximum of 4 stop sequences. If you provide more than 4, LiteLLM will automatically truncate the list to the first 4 elements. To disable this automatic truncation, set `litellm.disable_stop_sequence_limit = True`.
 
 - `max_completion_tokens`: *integer (optional)* -  An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
 

docs/my-website/docs/completion/json_mode.md

@@ -341,4 +341,90 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 ```
 
 </TabItem>
-</Tabs>
+</Tabs>
+
+## Gemini - Native JSON Schema Format (Gemini 2.0+)
+
+Gemini 2.0+ models automatically use the native `responseJsonSchema` parameter, which provides better compatibility with standard JSON Schema format.
+
+### Benefits (Gemini 2.0+):
+- Standard JSON Schema format (lowercase types like `string`, `object`)
+- Supports `additionalProperties: false` for stricter validation
+- Better compatibility with Pydantic's `model_json_schema()`
+- No `propertyOrdering` required
+
+### Usage
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import completion
+from pydantic import BaseModel
+
+class UserInfo(BaseModel):
+    name: str
+    age: int
+
+response = completion(
+    model="gemini/gemini-2.0-flash",
+    messages=[{"role": "user", "content": "Extract: John is 25 years old"}],
+    response_format={
+        "type": "json_schema",
+        "json_schema": {
+            "name": "user_info",
+            "schema": {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string"},
+                    "age": {"type": "integer"}
+                },
+                "required": ["name", "age"],
+                "additionalProperties": False  # Supported on Gemini 2.0+
+            }
+        }
+    }
+)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -d '{
+    "model": "gemini-2.0-flash",
+    "messages": [
+        {"role": "user", "content": "Extract: John is 25 years old"}
+    ],
+    "response_format": {
+        "type": "json_schema",
+        "json_schema": {
+            "name": "user_info",
+            "schema": {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string"},
+                    "age": {"type": "integer"}
+                },
+                "required": ["name", "age"],
+                "additionalProperties": false
+            }
+        }
+    }
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+### Model Behavior
+
+| Model | Format Used | `additionalProperties` Support |
+|-------|-------------|-------------------------------|
+| Gemini 2.0+ | `responseJsonSchema` (JSON Schema) | ✅ Yes |
+| Gemini 1.5 | `responseSchema` (OpenAPI) | ❌ No |
+
+LiteLLM automatically selects the appropriate format based on the model version.

docs/my-website/docs/completion/message_sanitization.md

@@ -1,468 +0,0 @@
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# Message Sanitization for Tool Calling for anthropic models
-
-**Automatically fix common message formatting issues when using tool calling with `modify_params=True`**
-
-LiteLLM can automatically sanitize messages to handle common issues that occur during tool calling workflows, especially when using OpenAI-compatible clients with providers that have strict message format requirements (like Anthropic Claude).
-
-## Overview
-
-When `litellm.modify_params = True` is enabled, LiteLLM automatically sanitizes messages to fix three common issues:
-
-1. **Orphaned Tool Calls** - Assistant messages with tool_calls but missing tool results
-2. **Orphaned Tool Results** - Tool messages that reference non-existent tool_call_ids
-3. **Empty Message Content** - Messages with empty or whitespace-only text content
-
-This ensures your tool calling workflows work seamlessly across different LLM providers without manual message validation.
-
-## Why Message Sanitization?
-
-Different LLM providers have varying requirements for message formats, especially during tool calling:
-
-- **Anthropic Claude** requires every tool_call to have a corresponding tool result
-- Some providers reject messages with empty content
-- OpenAI-compatible clients may not always maintain perfect message consistency
-
-Without sanitization, these issues cause API errors that interrupt your workflows. With `modify_params=True`, LiteLLM handles these edge cases automatically.
-
-## Quick Start
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-import litellm
-
-# Enable automatic message sanitization
-litellm.modify_params = True
-
-# This will work even if messages have formatting issues
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=[
-        {"role": "user", "content": "What's the weather in Boston?"},
-        {
-            "role": "assistant",
-            "tool_calls": [
-                {
-                    "id": "call_123",
-                    "type": "function",
-                    "function": {"name": "get_weather", "arguments": '{"city": "Boston"}'}
-                }
-            ]
-            # Missing tool result - LiteLLM will add a dummy result automatically
-        },
-        {"role": "user", "content": "Thanks!"}
-    ],
-    tools=[{
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get weather for a city",
-            "parameters": {
-                "type": "object",
-                "properties": {"city": {"type": "string"}},
-                "required": ["city"]
-            }
-        }
-    }]
-)
-```
-
-</TabItem>
-<TabItem value="proxy" label="PROXY">
-
-```yaml
-litellm_settings:
-  modify_params: true  # Enable automatic message sanitization
-
-model_list:
-  - model_name: claude-3-5-sonnet
-    litellm_params:
-      model: anthropic/claude-3-5-sonnet-20241022
-```
-
-</TabItem>
-</Tabs>
-
-## Sanitization Cases
-
-### Case A: Orphaned Tool Calls (Missing Tool Results)
-
-**Problem:** An assistant message contains `tool_calls`, but no corresponding tool result messages follow.
-
-**Solution:** LiteLLM automatically adds dummy tool result messages for any missing tool results.
-
-**Example:**
-
-```python
-import litellm
-litellm.modify_params = True
-
-# Messages with orphaned tool calls
-messages = [
-    {"role": "user", "content": "Search for Python tutorials"},
-    {
-        "role": "assistant",
-        "tool_calls": [
-            {
-                "id": "call_abc123",
-                "type": "function",
-                "function": {"name": "web_search", "arguments": '{"query": "Python tutorials"}'}
-            }
-        ]
-    },
-    # Missing tool result here!
-    {"role": "user", "content": "What about JavaScript?"}
-]
-
-# LiteLLM automatically adds:
-# {
-#     "role": "tool",
-#     "tool_call_id": "call_abc123",
-#     "content": "[System: Tool execution skipped/interrupted by user. No result provided for tool 'web_search'.]"
-# }
-
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=messages,
-    tools=[...]
-)
-```
-
-**When this happens:**
-- User interrupts tool execution
-- Client loses tool results due to network issues
-- Conversation flow changes before tool completes
-- Multi-turn conversations where tools are optional
-
-### Case B: Orphaned Tool Results (Invalid tool_call_id)
-
-**Problem:** A tool message references a `tool_call_id` that doesn't exist in any previous assistant message.
-
-**Solution:** LiteLLM automatically removes these orphaned tool result messages.
-
-**Example:**
-
-```python
-import litellm
-litellm.modify_params = True
-
-# Messages with orphaned tool result
-messages = [
-    {"role": "user", "content": "Hello"},
-    {"role": "assistant", "content": "Hi! How can I help?"},
-    {
-        "role": "tool",
-        "tool_call_id": "call_nonexistent",  # This tool_call_id doesn't exist!
-        "content": "Some result"
-    }
-]
-
-# LiteLLM automatically removes the orphaned tool message
-
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=messages
-)
-```
-
-**When this happens:**
-- Message history is manually edited
-- Tool results are duplicated or mismatched
-- Conversation state is restored incorrectly
-- Messages are merged from different conversations
-
-### Case C: Empty Message Content
-
-**Problem:** User or assistant messages have empty or whitespace-only content.
-
-**Solution:** LiteLLM replaces empty content with a system placeholder message.
-
-**Example:**
-
-```python
-import litellm
-litellm.modify_params = True
-
-# Messages with empty content
-messages = [
-    {"role": "user", "content": ""},  # Empty content
-    {"role": "assistant", "content": "   "},  # Whitespace only
-]
-
-# LiteLLM automatically replaces with:
-# {"role": "user", "content": "[System: Empty message content sanitised to satisfy protocol]"}
-# {"role": "assistant", "content": "[System: Empty message content sanitised to satisfy protocol]"}
-
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=messages
-)
-```
-
-**When this happens:**
-- UI sends empty messages
-- Content is stripped during preprocessing
-- Placeholder messages in conversation history
-- Edge cases in message construction
-
-## Configuration
-
-### Enable Globally
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-import litellm
-
-# Enable for all completion calls
-litellm.modify_params = True
-```
-
-</TabItem>
-<TabItem value="proxy" label="PROXY">
-
-```yaml
-litellm_settings:
-  modify_params: true
-```
-
-</TabItem>
-<TabItem value="env" label="Environment Variable">
-
-```bash
-export LITELLM_MODIFY_PARAMS=True
-```
-
-</TabItem>
-</Tabs>
-
-### Enable Per-Request
-
-```python
-import litellm
-
-# Enable only for specific requests
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=messages,
-    modify_params=True  # Override global setting
-)
-```
-
-## Supported Providers
-
-Message sanitization works with all LLM providers that support tool calling:
-
-- ✅ Anthropic (Claude)
-- ✅ OpenAI (GPT-4, GPT-3.5)
-- ✅ AWS Bedrock (Claude, Titan)
-- ✅ Google Vertex AI (Claude, Gemini)
-- ✅ Azure OpenAI
-- ✅ And all other providers with tool calling support
-
-## Implementation Details
-
-### How It Works
-
-The message sanitization process runs **before** messages are converted to provider-specific formats:
-
-1. **Input:** OpenAI-format messages with potential issues
-2. **Sanitization:** Three helper functions process the messages:
-   - `_sanitize_empty_text_content()` - Fixes empty content
-   - `_add_missing_tool_results()` - Adds dummy tool results
-   - `_is_orphaned_tool_result()` - Identifies orphaned results
-3. **Output:** Clean, provider-compatible messages
-
-### Code Reference
-
-The sanitization logic is implemented in:
-- `litellm/litellm_core_utils/prompt_templates/factory.py`
-- Function: `sanitize_messages_for_tool_calling()`
-
-### Logging
-
-When sanitization occurs, LiteLLM logs debug messages:
-
-```python
-import litellm
-litellm.set_verbose = True  # Enable debug logging
-
-# You'll see logs like:
-# "_add_missing_tool_results: Found 1 orphaned tool calls. Adding dummy tool results."
-# "_is_orphaned_tool_result: Found orphaned tool result with tool_call_id=call_123"
-# "_sanitize_empty_text_content: Replaced empty text content in user message"
-```
-
-## Best Practices
-
-### 1. Enable for Production Workflows
-
-```python
-# Recommended for production
-litellm.modify_params = True
-
-# Ensures robust handling of edge cases
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=messages,
-    tools=tools
-)
-```
-
-### 2. Preserve Tool Results When Possible
-
-While sanitization handles missing tool results, it's better to provide actual results:
-
-```python
-# Good: Provide actual tool results
-messages = [
-    {"role": "user", "content": "Search for Python"},
-    {"role": "assistant", "tool_calls": [...]},
-    {"role": "tool", "tool_call_id": "call_123", "content": "Actual search results"}
-]
-
-# Fallback: Sanitization adds dummy result if missing
-messages = [
-    {"role": "user", "content": "Search for Python"},
-    {"role": "assistant", "tool_calls": [...]},
-    # Missing tool result - sanitization adds dummy
-]
-```
-
-### 3. Monitor Sanitization Events
-
-Use logging to track when sanitization occurs:
-
-```python
-import litellm
-import logging
-
-# Enable debug logging
-litellm.set_verbose = True
-logging.basicConfig(level=logging.DEBUG)
-
-# Track sanitization events in your application
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=messages
-)
-```
-
-### 4. Test Edge Cases
-
-Ensure your application handles sanitized messages correctly:
-
-```python
-import litellm
-litellm.modify_params = True
-
-# Test orphaned tool calls
-test_messages = [
-    {"role": "user", "content": "Test"},
-    {"role": "assistant", "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "test", "arguments": "{}"}}]},
-    {"role": "user", "content": "Continue"}  # No tool result
-]
-
-response = litellm.completion(
-    model="anthropic/claude-3-5-sonnet-20241022",
-    messages=test_messages,
-    tools=[...]
-)
-
-# Verify the response handles the dummy tool result appropriately
-```
-
-## Related Features
-
-- **[Drop Params](./drop_params.md)** - Drop unsupported parameters for specific providers
-- **[Message Trimming](./message_trimming.md)** - Trim messages to fit token limits
-- **[Function Calling](./function_call.md)** - Complete guide to tool/function calling
-- **[Reasoning Content](../reasoning_content.md)** - Extended thinking with tool calling
-
-## Troubleshooting
-
-### Sanitization Not Working
-
-**Issue:** Messages still cause errors despite `modify_params=True`
-
-**Solution:**
-1. Verify `modify_params` is enabled:
-   ```python
-   import litellm
-   print(litellm.modify_params)  # Should be True
-   ```
-
-2. Check if the issue is provider-specific:
-   ```python
-   litellm.set_verbose = True  # Enable debug logging
-   ```
-
-3. Ensure you're using a recent version of LiteLLM:
-   ```bash
-   pip install --upgrade litellm
-   ```
-
-### Unexpected Dummy Tool Results
-
-**Issue:** Dummy tool results appear when you expect actual results
-
-**Cause:** Tool result messages are missing or have incorrect `tool_call_id`
-
-**Solution:**
-1. Verify tool result messages have correct `tool_call_id`:
-   ```python
-   # Correct
-   {"role": "tool", "tool_call_id": "call_123", "content": "result"}
-   
-   # Incorrect - will be treated as orphaned
-   {"role": "tool", "tool_call_id": "wrong_id", "content": "result"}
-   ```
-
-2. Ensure tool results immediately follow assistant messages with tool_calls
-
-### Performance Impact
-
-**Issue:** Concerned about performance overhead
-
-**Details:** Message sanitization has minimal performance impact:
-- Runs in O(n) time where n = number of messages
-- Only processes messages when `modify_params=True`
-- Typically adds < 1ms to request processing time
-
-## FAQ
-
-**Q: Does sanitization modify my original messages?**
-
-A: No, sanitization creates a new list of messages. Your original messages remain unchanged.
-
-**Q: Can I disable specific sanitization cases?**
-
-A: Currently, all three cases are handled together when `modify_params=True`. To disable sanitization entirely, set `modify_params=False`.
-
-**Q: What happens to the dummy tool results?**
-
-A: Dummy tool results are sent to the LLM provider along with other messages. The model sees them as regular tool results with informative error messages.
-
-**Q: Does this work with streaming?**
-
-A: Yes, message sanitization works with both streaming and non-streaming requests.
-
-**Q: Is this related to `drop_params`?**
-
-A: No, they're separate features:
-- `modify_params` - Modifies/fixes message content and structure
-- `drop_params` - Removes unsupported API parameters
-
-Both can be enabled simultaneously.
-
-## See Also
-
-- [Reasoning Content with Tool Calling](../reasoning_content.md)
-- [Function Calling Guide](./function_call.md)
-- [Bedrock Provider Documentation](../providers/bedrock.md)
-- [Anthropic Provider Documentation](../providers/anthropic.md)

docs/my-website/docs/completion/token_usage.md

@@ -100,7 +100,7 @@ from litellm import cost_per_token
 
 prompt_tokens =  5
 completion_tokens = 10
-prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token(model="gpt-3.5-turbo", prompt_tokens=prompt_tokens, completion_tokens=completion_tokens))
+prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token(model="gpt-3.5-turbo", prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
 
 print(prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar)
 ```
@@ -162,7 +162,7 @@ print(model_cost) # {'gpt-3.5-turbo': {'max_tokens': 4000, 'input_cost_per_token
 
 **Dictionary**
 ```python
-from litellm import register_model
+import litellm
 
 litellm.register_model({
         "gpt-4": {

docs/my-website/docs/contributing.md

@@ -1,45 +1,100 @@
 # Contributing - UI
 
-Here's how to run the LiteLLM UI locally for making changes: 
+Thanks for contributing to the LiteLLM UI! This guide will help you set up your local development environment.
+
+
+## 1. Clone the repo
 
-## 1. Clone the repo 
 ```bash
 git clone https://github.com/BerriAI/litellm.git
+cd litellm
 ```
 
-## 2. Start the UI + Proxy 
+## 2. Start the Proxy
 
-**2.1 Start the proxy on port 4000** 
+Create a config file (e.g., `config.yaml`):
 
-Tell the proxy where the UI is located
-```bash
-DATABASE_URL = "postgresql://<user>:<password>@<host>:<port>/<dbname>"
-LITELLM_MASTER_KEY = "sk-1234"
-STORE_MODEL_IN_DB = "True"
+```yaml
+model_list:
+  - model_name: gpt-4o
+    litellm_params:
+      model: openai/gpt-4o
+
+general_settings:
+  master_key: sk-1234
+  database_url: postgresql://<user>:<password>@<host>:<port>/<dbname>
+  store_model_in_db: true
 ```
 
+Start the proxy on port 4000:
+
 ```bash
-cd litellm/litellm/proxy
-python3 proxy_cli.py --config /path/to/config.yaml --port 4000
+poetry run litellm --config config.yaml --port 4000
 ```
 
-**2.2 Start the UI**
+The UI comes pre-built in the repo. Access it at `http://localhost:4000/ui`
 
-Set the mode as development (this will assume the proxy is running on localhost:4000)
-```bash
-npm install # install dependencies
-```
+## 3. UI Development
+
+There are two options for UI development:
+
+### Option A: Development Mode (Hot Reload)
+
+This runs the UI on port 3000 with hot reload. The proxy runs on port 4000.
 
 ```bash
-cd litellm/ui/litellm-dashboard
-
+cd ui/litellm-dashboard
+npm install
 npm run dev
-
-# starts on http://0.0.0.0:3000
 ```
 
-## 3. Go to local UI 
+**Login flow:**
+1. Go to `http://localhost:3000`
+2. You'll be redirected to `http://localhost:4000/ui` for login
+3. After logging in, manually navigate back to `http://localhost:3000/`
+4. You're now authenticated and can develop with hot reload
+
+:::note
+If you experience redirect loops or authentication issues, clear your browser cookies for localhost or use Build Mode instead.
+:::
+
+### Option B: Build Mode
+
+This builds the UI and copies it to the proxy. Changes require rebuilding.
+
+1. Make your code changes in `ui/litellm-dashboard/src/`
+
+2. Build the UI
+```bash
+cd ui/litellm-dashboard
+npm install
+npm run build
+```
+
+After building, copy the output to the proxy:
 
 ```bash
-http://0.0.0.0:3000
-```
+cp -r out/* ../../litellm/proxy/_experimental/out/
+```
+
+Then restart the proxy and access the UI at `http://localhost:4000/ui`
+
+## 4. Submitting a PR
+
+1. Create a new branch for your changes:
+```bash
+git checkout -b feat/your-feature-name
+```
+
+2. Stage and commit your changes:
+```bash
+git add .
+git commit -m "feat: description of your changes"
+```
+
+3. Push to your fork:
+```bash
+git push origin feat/your-feature-name
+```
+
+4. Create a Pull Request on GitHub following the [PR template](https://github.com/BerriAI/litellm/blob/main/.github/pull_request_template.md)

docs/my-website/docs/enterprise.md

@@ -74,6 +74,18 @@ You can find [supported data regions litellm here](../docs/data_security#support
 
 ## Frequently Asked Questions
 
+### How to set up and verify your Enterprise License
+
+1. Add your license key to the environment:
+
+```env
+LITELLM_LICENSE="eyJ..."
+```
+
+2. Restart LiteLLM Proxy.
+
+3. Open `http://<your-proxy-host>:<port>/` — the Swagger page should show **"Enterprise Edition"** in the description. If it doesn't, check that the key is correct, unexpired, and that the proxy was fully restarted.
+
 ### SLA's + Professional Support
 
 Professional Support can assist with LLM/Provider integrations, deployment, upgrade management, and LLM Provider troubleshooting.  We can’t solve your own infrastructure-related issues but we will guide you to fix them.

docs/my-website/docs/guides/security_settings.md

@@ -187,4 +187,37 @@ export AIOHTTP_TRUST_ENV='True'
 ```
 </TabItem>
 </Tabs>
+## 7. Per-Service SSL Verification
 
+LiteLLM allows you to override SSL verification settings for specific services or provider calls. This is useful when different services (e.g., an internal guardrail vs. a public LLM provider) require different CA certificates.
+
+### Bedrock (SDK)
+You can pass `ssl_verify` directly in the `completion` call.
+
+```python
+import litellm
+
+response = litellm.completion(
+    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+    messages=[{"role": "user", "content": "hi"}],
+    ssl_verify="path/to/bedrock_cert.pem" # Or False to disable
+)
+```
+
+### AIM Guardrail (Proxy)
+You can configure `ssl_verify` per guardrail in your `config.yaml`.
+
+```yaml
+guardrails:
+  - guardrail_name: aim-protected-app
+    litellm_params:
+      guardrail: aim
+      ssl_verify: "/path/to/aim_cert.pem" # Use specific cert for AIM
+```
+
+### Priority Logic
+LiteLLM resolves `ssl_verify` using the following priority:
+1. **Explicit Parameter**: Passed in `completion()` or guardrail config.
+2. **Environment Variable**: `SSL_VERIFY` environment variable.
+3. **Global Setting**: `litellm.ssl_verify` setting.
+4. **System Standard**: `SSL_CERT_FILE` environment variable.

docs/my-website/docs/mcp.md

@@ -21,6 +21,11 @@ LiteLLM Proxy provides an MCP Gateway that allows you to use a fixed endpoint fo
 | Supported MCP Transports | • Streamable HTTP<br/>• SSE<br/>• Standard Input/Output (stdio) |
 | LiteLLM Permission Management | • By Key<br/>• By Team<br/>• By Organization |
 
+:::caution MCP protocol update
+Starting in LiteLLM v1.80.18, the LiteLLM MCP protocol version is `2025-11-25`.<br/> 
+LiteLLM namespaces multiple MCP servers by prefixing each tool name with its MCP server name, so newly created servers now must use names that comply with SEP-986—noncompliant names cannot be added anymore. Existing servers that still violate SEP-986 only emit warnings today, but future MCP-side rollouts may block those names entirely, so we recommend updating any legacy server names proactively before MCP enforcement makes them unusable.
+:::
+
 ## Adding your MCP
 
 ### Prerequisites

docs/my-website/docs/mcp_semantic_filter.md

@@ -0,0 +1,158 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# MCP Semantic Tool Filter
+
+Automatically filter MCP tools by semantic relevance. When you have many MCP tools registered, LiteLLM semantically matches the user's query against tool descriptions and sends only the most relevant tools to the LLM.
+
+## How It Works
+
+Tool search shifts tool selection from a prompt-engineering problem to a retrieval problem. Instead of injecting a large static list of tools into every prompt, the semantic filter:
+
+1. Builds a semantic index of all available MCP tools on startup
+2. On each request, semantically matches the user's query against tool descriptions
+3. Returns only the top-K most relevant tools to the LLM
+
+This approach improves context efficiency, increases reliability by reducing tool confusion, and enables scalability to ecosystems with hundreds or thousands of MCP tools.
+
+```mermaid
+sequenceDiagram
+    participant Client
+    participant LiteLLM as LiteLLM Proxy
+    participant SemanticFilter as Semantic Filter
+    participant MCP as MCP Registry
+    participant LLM as LLM Provider
+
+    Note over LiteLLM,MCP: Startup: Build Semantic Index
+    LiteLLM->>MCP: Fetch all registered MCP tools
+    MCP->>LiteLLM: Return all tools (e.g., 50 tools)
+    LiteLLM->>SemanticFilter: Build semantic router with embeddings
+    SemanticFilter->>LLM: Generate embeddings for tool descriptions
+    LLM->>SemanticFilter: Return embeddings
+    Note over SemanticFilter: Index ready for fast lookup
+
+    Note over Client,LLM: Request: Semantic Tool Filtering
+    Client->>LiteLLM: POST /v1/responses with MCP tools
+    LiteLLM->>SemanticFilter: Expand MCP references (50 tools available)
+    SemanticFilter->>SemanticFilter: Extract user query from request
+    SemanticFilter->>LLM: Generate query embedding
+    LLM->>SemanticFilter: Return query embedding
+    SemanticFilter->>SemanticFilter: Match query against tool embeddings
+    SemanticFilter->>LiteLLM: Return top-K tools (e.g., 3 most relevant)
+    LiteLLM->>LLM: Forward request with filtered tools (3 tools)
+    LLM->>LiteLLM: Return response
+    LiteLLM->>Client: Response with headers<br/>x-litellm-semantic-filter: 50->3<br/>x-litellm-semantic-filter-tools: tool1,tool2,tool3
+```
+
+## Configuration
+
+Enable semantic filtering in your LiteLLM config:
+
+```yaml title="config.yaml" showLineNumbers
+litellm_settings:
+  mcp_semantic_tool_filter:
+    enabled: true
+    embedding_model: "text-embedding-3-small"  # Model for semantic matching
+    top_k: 5                                    # Max tools to return
+    similarity_threshold: 0.3                   # Min similarity score
+```
+
+**Configuration Options:**
+- `enabled` - Enable/disable semantic filtering (default: `false`)
+- `embedding_model` - Model for generating embeddings (default: `"text-embedding-3-small"`)
+- `top_k` - Maximum number of tools to return (default: `10`)
+- `similarity_threshold` - Minimum similarity score for matches (default: `0.3`)
+
+## Usage
+
+Use MCP tools normally with the Responses API or Chat Completions. The semantic filter runs automatically:
+
+<Tabs>
+<TabItem value="responses" label="Responses API">
+
+```bash title="Responses API with Semantic Filtering" showLineNumbers
+curl --location 'http://localhost:4000/v1/responses' \
+--header 'Content-Type: application/json' \
+--header "Authorization: Bearer sk-1234" \
+--data '{
+    "model": "gpt-4o",
+    "input": [
+    {
+      "role": "user",
+      "content": "give me TLDR of what BerriAI/litellm repo is about",
+      "type": "message"
+    }
+  ],
+    "tools": [
+        {
+            "type": "mcp",
+            "server_url": "litellm_proxy",
+            "require_approval": "never"
+        }
+    ],
+    "tool_choice": "required"
+}'
+```
+
+</TabItem>
+<TabItem value="chat" label="Chat Completions">
+
+```bash title="Chat Completions with Semantic Filtering" showLineNumbers
+curl --location 'http://localhost:4000/v1/chat/completions' \
+--header 'Content-Type: application/json' \
+--header "Authorization: Bearer sk-1234" \
+--data '{
+  "model": "gpt-4o",
+  "messages": [
+    {"role": "user", "content": "Search Wikipedia for LiteLLM"}
+  ],
+  "tools": [
+    {
+      "type": "mcp",
+      "server_url": "litellm_proxy"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+## Response Headers
+
+The semantic filter adds diagnostic headers to every response:
+
+```
+x-litellm-semantic-filter: 10->3
+x-litellm-semantic-filter-tools: wikipedia-fetch,github-search,slack-post
+```
+
+- **`x-litellm-semantic-filter`** - Shows before→after tool count (e.g., `10->3` means 10 tools were filtered down to 3)
+- **`x-litellm-semantic-filter-tools`** - CSV list of the filtered tool names (max 150 chars, clipped with `...` if longer)
+
+These headers help you understand which tools were selected for each request and verify the filter is working correctly.
+
+## Example
+
+If you have 50 MCP tools registered and make a request asking about Wikipedia, the semantic filter will:
+
+1. Semantically match your query `"Search Wikipedia for LiteLLM"` against all 50 tool descriptions
+2. Select the top 5 most relevant tools (e.g., `wikipedia-fetch`, `wikipedia-search`, etc.)
+3. Pass only those 5 tools to the LLM
+4. Add headers showing `x-litellm-semantic-filter: 50->5`
+
+This dramatically reduces prompt size while ensuring the LLM has access to the right tools for the task.
+
+## Performance
+
+The semantic filter is optimized for production:
+- Router builds once on startup (no per-request overhead)
+- Semantic matching typically takes under 50ms
+- Fails gracefully - returns all tools if filtering fails
+- No impact on latency for requests without MCP tools
+
+## Related
+
+- [MCP Overview](./mcp.md) - Learn about MCP in LiteLLM
+- [MCP Permission Management](./mcp_control.md) - Control tool access by key/team
+- [Using MCP](./mcp_usage.md) - Complete MCP usage guide

docs/my-website/docs/observability/datadog.md

@@ -7,6 +7,7 @@ import TabItem from '@theme/TabItem';
 LiteLLM Supports logging to the following Datdog Integrations:
 - `datadog` [Datadog Logs](https://docs.datadoghq.com/logs/)
 - `datadog_llm_observability` [Datadog LLM Observability](https://www.datadoghq.com/product/llm-observability/)
+- `datadog_cost_management` [Datadog Cloud Cost Management](#datadog-cloud-cost-management)
 - `ddtrace-run` [Datadog Tracing](#datadog-tracing)
 
 ## Datadog Logs
@@ -73,7 +74,7 @@ Send logs through a local DataDog agent (useful for containerized environments):
 ```shell
 LITELLM_DD_AGENT_HOST="localhost"         # hostname or IP of DataDog agent
 LITELLM_DD_AGENT_PORT="10518"             # [OPTIONAL] port of DataDog agent (default: 10518)
-DD_API_KEY="5f2d0f310***********"         # [OPTIONAL] your datadog API Key (agent handles auth)
+DD_API_KEY="5f2d0f310***********"         # [OPTIONAL] your datadog API Key (Agent handles auth for Logs. REQUIRED for LLM Observability)
 DD_SOURCE="litellm_dev"                   # [OPTIONAL] your datadog source
 ```
 
@@ -84,6 +85,9 @@ When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of direc
 
 **Note:** We use `LITELLM_DD_AGENT_HOST` instead of `DD_AGENT_HOST` to avoid conflicts with `ddtrace` which automatically sets `DD_AGENT_HOST` for APM tracing.
 
+> [!IMPORTANT]
+> **Datadog LLM Observability**: `DD_API_KEY` is **REQUIRED** even when using the Datadog Agent (`LITELLM_DD_AGENT_HOST`). The agent acts as a proxy but the API key header is mandatory for the LLM Observability endpoint.
+
 **Step 3**: Start the proxy, make a test request
 
 Start proxy
@@ -161,6 +165,50 @@ On the Datadog LLM Observability page, you should see that both input messages a
 
 
 
+<Image img={require('../../img/dd_llm_obs.png')} />
+
+
+## Datadog Cloud Cost Management
+
+| Feature | Details |
+|---------|---------|
+| **What is logged** | Aggregated LLM Costs (FOCUS format) |
+| **Events** | Periodic Uploads of Aggregated Cost Data |
+| **Product Link** | [Datadog Cloud Cost Management](https://docs.datadoghq.com/cost_management/) |
+
+We will use the `--config` to set `litellm.callbacks = ["datadog_cost_management"]`. This will periodically upload aggregated LLM cost data to Datadog.
+
+**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
+
+```yaml
+model_list:
+ - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+litellm_settings:
+  callbacks: ["datadog_cost_management"]
+```
+
+**Step 2**: Set Required env variables
+
+```shell
+DD_API_KEY="your-api-key"
+DD_APP_KEY="your-app-key" # REQUIRED for Cost Management
+DD_SITE="us5.datadoghq.com"
+```
+
+**Step 3**: Start the proxy
+
+```shell
+litellm --config config.yaml
+```
+
+**How it works**
+* LiteLLM aggregates costs in-memory by Provider, Model, Date, and Tags.
+* Requires `DD_APP_KEY` for the Custom Costs API.
+* Costs are uploaded periodically (flushed).
+
+
 ### Datadog Tracing
 
 Use `ddtrace-run` to enable [Datadog Tracing](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html) on litellm proxy
@@ -203,5 +251,5 @@ LiteLLM supports customizing the following Datadog environment variables
 | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No |
 
 \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required  
-\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required
+\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required for **Datadog Logs**. (**Note: `DD_API_KEY` IS REQUIRED for Datadog LLM Observability**)
 

docs/my-website/docs/observability/opentelemetry_integration.md

@@ -63,6 +63,8 @@ OTEL_EXPORTER_OTLP_PROTOCOL=grpc
 OTEL_EXPORTER_OTLP_HEADERS="api-key=key,other-config-value=value"
 ```
 
+> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+
 </TabItem>
 
 <TabItem value="laminar" label="Log to Laminar">
@@ -73,6 +75,8 @@ OTEL_ENDPOINT="https://api.lmnr.ai:8443"
 OTEL_HEADERS="authorization=Bearer <project-api-key>"
 ```
 
+> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+
 </TabItem>
 
 </Tabs>
@@ -128,4 +132,4 @@ If you don't see traces landing on your integration, set `OTEL_DEBUG="True"` in
 export OTEL_DEBUG="True"
 ```
 
-This will emit any logging issues to the console.
+This will emit any logging issues to the console.

docs/my-website/docs/observability/phoenix_integration.md

@@ -73,6 +73,8 @@ environment_variables:
     PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/s/<space-name>/v1/traces" # OPTIONAL - For setting the HTTP endpoint
 ```
 
+> Note: If you set the gRPC endpoint, install `grpcio` via `pip install "litellm[grpc]"` (or `grpcio`).
+
 2. Start the proxy
 
 ```bash

docs/my-website/docs/observability/signoz.md

@@ -99,6 +99,8 @@ OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai \
 opentelemetry-instrument <your_run_command>
 ```
 
+> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+
 > 📌 Note: We're using `OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai` in the run command to disable the OpenAI instrumentor for tracing. This avoids conflicts with LiteLLM's native telemetry/instrumentation, ensuring that telemetry is captured exclusively through LiteLLM's built-in instrumentation.
 
 - **`<service_name>`** is the name of your service
@@ -362,6 +364,8 @@ export OTEL_METRICS_EXPORTER="otlp"
 export OTEL_LOGS_EXPORTER="otlp"
 ```
 
+> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+
 - Set the `<region>` to match your SigNoz Cloud [region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)
 - Replace `<your_ingestion_key>` with your SigNoz [ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)
 

docs/my-website/docs/pass_through/openai_passthrough.md

@@ -1,6 +1,6 @@
 # OpenAI Passthrough
 
-Pass-through endpoints for `/openai`
+Pass-through endpoints for direct OpenAI API access
 
 ## Overview
 
@@ -10,12 +10,27 @@ Pass-through endpoints for `/openai`
 | Logging | ✅ | Works across all integrations |
 | Streaming | ✅ | Fully supported |
 
-### When to use this?
+## Available Endpoints
+
+### `/openai_passthrough` - Recommended
+Dedicated passthrough endpoint that guarantees direct routing to OpenAI without conflicts.
+
+**Use this for:**
+- OpenAI Responses API (`/v1/responses`)
+- Any endpoint where you need guaranteed passthrough
+- When `/openai` routes are conflicting with LiteLLM's native implementations
+
+### `/openai` - Legacy
+Standard passthrough endpoint that may conflict with LiteLLM's native implementations.
+
+**Note:** Some endpoints like `/openai/v1/responses` will be routed to LiteLLM's native implementation instead of OpenAI.
+
+## When to use this?
 
 - For 90% of your use cases, you should use the [native LiteLLM OpenAI Integration](https://docs.litellm.ai/docs/providers/openai) (`/chat/completions`, `/embeddings`, `/completions`, `/images`, `/batches`, etc.)
-- Use this passthrough to call less popular or newer OpenAI endpoints that LiteLLM doesn't fully support yet, such as `/assistants`, `/threads`, `/vector_stores`
+- Use `/openai_passthrough` to call less popular or newer OpenAI endpoints that LiteLLM doesn't fully support yet, such as `/assistants`, `/threads`, `/vector_stores`, `/responses`
 
-Simply replace `https://api.openai.com` with `LITELLM_PROXY_BASE_URL/openai`
+Simply replace `https://api.openai.com` with `LITELLM_PROXY_BASE_URL/openai_passthrough`
 
 ## Usage Examples
 
@@ -34,7 +49,7 @@ Make sure you do the following:
 import openai
 
 client = openai.OpenAI(
-    base_url="http://0.0.0.0:4000/openai",  # <your-proxy-url>/openai
+    base_url="http://0.0.0.0:4000/openai_passthrough",  # <your-proxy-url>/openai_passthrough
     api_key="sk-anything"  # <your-proxy-api-key>
 )
 ```

docs/my-website/docs/pass_through/vertex_ai.md

@@ -45,7 +45,7 @@ model_list:
     litellm_params:
       model: vertex_ai/gemini-1.0-pro
       vertex_project: adroit-crow-413218
-      vertex_region: us-central1
+      vertex_location: us-central1
       vertex_credentials: /path/to/credentials.json
       use_in_pass_through: true # 👈 KEY CHANGE
 ```
@@ -57,9 +57,9 @@ model_list:
 <TabItem value="yaml" label="Set in config.yaml">
 
 ```yaml
-default_vertex_config: 
+default_vertex_config:
   vertex_project: adroit-crow-413218
-  vertex_region: us-central1
+  vertex_location: us-central1
   vertex_credentials: /path/to/credentials.json
 ```
 </TabItem>
@@ -461,3 +461,48 @@ generateContent();
 
 </TabItem>
 </Tabs>
+
+### Using Anthropic Beta Features on Vertex AI
+
+When using Anthropic models via Vertex AI passthrough (e.g., Claude on Vertex), you can enable Anthropic beta features like extended context windows.
+
+The `anthropic-beta` header is automatically forwarded to Vertex AI when calling Anthropic models.
+
+```bash
+curl http://localhost:4000/vertex_ai/v1/projects/${PROJECT_ID}/locations/us-east5/publishers/anthropic/models/claude-3-5-sonnet:rawPredict \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -H "anthropic-beta: context-1m-2025-08-07" \
+  -d '{
+    "anthropic_version": "vertex-2023-10-16",
+    "messages": [{"role": "user", "content": "Hello"}],
+    "max_tokens": 500
+  }'
+```
+
+### Forwarding Custom Headers with `x-pass-` Prefix
+
+You can forward any custom header to the provider by prefixing it with `x-pass-`. The prefix is stripped before the header is sent to the provider.
+
+For example:
+- `x-pass-anthropic-beta: value` becomes `anthropic-beta: value`
+- `x-pass-custom-header: value` becomes `custom-header: value`
+
+This is useful when you need to send provider-specific headers that aren't in the default allowlist.
+
+```bash
+curl http://localhost:4000/vertex_ai/v1/projects/${PROJECT_ID}/locations/us-east5/publishers/anthropic/models/claude-3-5-sonnet:rawPredict \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-1234" \
+  -H "x-pass-anthropic-beta: context-1m-2025-08-07" \
+  -H "x-pass-custom-feature: enabled" \
+  -d '{
+    "anthropic_version": "vertex-2023-10-16",
+    "messages": [{"role": "user", "content": "Hello"}],
+    "max_tokens": 500
+  }'
+```
+
+:::info
+The `x-pass-` prefix works for all LLM pass-through endpoints, not just Vertex AI.
+:::

docs/my-website/docs/providers/anthropic_tool_search.md

@@ -1,43 +1,46 @@
-# Anthropic Tool Search
+# Tool Search
 
 Tool search enables Claude to dynamically discover and load tools on-demand from large tool catalogs (10,000+ tools). Instead of loading all tool definitions into the context window upfront, Claude searches your tool catalog and loads only the tools it needs.
 
+## Supported Providers
+
+| Provider | Chat Completions API | Messages API |
+|----------|---------------------|--------------|
+| **Anthropic API** | ✅ | ✅ |
+| **Azure Anthropic** (Microsoft Foundry) | ✅ | ✅ |
+| **Google Cloud Vertex AI** | ✅ | ✅ |
+| **Amazon Bedrock** | ✅ (Invoke API only, Opus 4.5 only) | ✅ (Invoke API only, Opus 4.5 only) |
+
+
 ## Benefits
 
 - **Context efficiency**: Avoid consuming massive portions of your context window with tool definitions
 - **Better tool selection**: Claude's tool selection accuracy degrades with more than 30-50 tools. Tool search maintains accuracy even with thousands of tools
 - **On-demand loading**: Tools are only loaded when Claude needs them
 
-## Supported Models
-
-Tool search is available on:
-- Claude Opus 4.5
-- Claude Sonnet 4.5
-
-## Supported Platforms
-
-- Anthropic API (direct)
-- Azure Anthropic (Microsoft Foundry)
-- Google Cloud Vertex AI
-- Amazon Bedrock (invoke API only, not converse API)
-
 ## Tool Search Variants
 
 LiteLLM supports both tool search variants:
 
 ### 1. Regex Tool Search (`tool_search_tool_regex_20251119`)
 
-Claude constructs regex patterns to search for tools.
+Claude constructs regex patterns to search for tools. Best for exact pattern matching (faster).
 
 ### 2. BM25 Tool Search (`tool_search_tool_bm25_20251119`)
 
-Claude uses natural language queries to search for tools using the BM25 algorithm.
+Claude uses natural language queries to search for tools using the BM25 algorithm. Best for natural language semantic search.
 
-## Quick Start
+**Note**: BM25 variant is not supported on Bedrock.
 
-### Basic Example with Regex Tool Search
+---
 
-```python
+## Chat Completions API
+
+### SDK Usage
+
+#### Basic Example with Regex Tool Search
+
+```python showLineNumbers title="Basic Tool Search Example"
 import litellm
 
 response = litellm.completion(
@@ -70,26 +73,6 @@ response = litellm.completion(
                 }
             },
             "defer_loading": True  # Mark for deferred loading
-        },
-        # Another deferred tool
-        {
-            "type": "function",
-            "function": {
-                "name": "search_files",
-                "description": "Search through files in the workspace",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {"type": "string"},
-                        "file_types": {
-                            "type": "array",
-                            "items": {"type": "string"}
-                        }
-                    },
-                    "required": ["query"]
-                }
-            },
-            "defer_loading": True
         }
     ]
 )
@@ -97,9 +80,9 @@ response = litellm.completion(
 print(response.choices[0].message.content)
 ```
 
-### BM25 Tool Search Example
+#### BM25 Tool Search Example
 
-```python
+```python showLineNumbers title="BM25 Tool Search"
 import litellm
 
 response = litellm.completion(
@@ -134,9 +117,9 @@ response = litellm.completion(
 )
 ```
 
-## Using with Azure Anthropic
+#### Azure Anthropic Example
 
-```python
+```python showLineNumbers title="Azure Anthropic Tool Search"
 import litellm
 
 response = litellm.completion(
@@ -170,9 +153,9 @@ response = litellm.completion(
 )
 ```
 
-## Using with Vertex AI
+#### Vertex AI Example
 
-```python
+```python showLineNumbers title="Vertex AI Tool Search"
 import litellm
 
 response = litellm.completion(
@@ -192,11 +175,9 @@ response = litellm.completion(
 )
 ```
 
-## Streaming Support
+#### Streaming Support
 
-Tool search works with streaming:
-
-```python
+```python showLineNumbers title="Streaming with Tool Search"
 import litellm
 
 response = litellm.completion(
@@ -233,13 +214,13 @@ for chunk in response:
         print(chunk.choices[0].delta.content, end="")
 ```
 
-## LiteLLM Proxy
+### AI Gateway Usage
 
-Tool search works automatically through the LiteLLM proxy:
+Tool search works automatically through the LiteLLM proxy.
 
-### Proxy Config
+#### Proxy Configuration
 
-```yaml
+```yaml showLineNumbers title="config.yaml"
 model_list:
   - model_name: claude-sonnet
     litellm_params:
@@ -247,18 +228,19 @@ model_list:
       api_key: os.environ/ANTHROPIC_API_KEY
 ```
 
-### Client Request
+#### Client Request
 
-```python
-import openai
+```python showLineNumbers title="Client Request via Proxy"
+from anthropic import Anthropic
 
-client = openai.OpenAI(
+client = Anthropic(
     api_key="your-litellm-proxy-key",
     base_url="http://0.0.0.0:4000"
 )
 
-response = client.chat.completions.create(
+response = client.messages.create(
     model="claude-sonnet",
+    max_tokens=1024,
     messages=[
         {"role": "user", "content": "What's the weather?"}
     ],
@@ -268,17 +250,14 @@ response = client.chat.completions.create(
             "name": "tool_search_tool_regex"
         },
         {
-            "type": "function",
-            "function": {
-                "name": "get_weather",
-                "description": "Get weather information",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {"type": "string"}
-                    },
-                    "required": ["location"]
-                }
+            "name": "get_weather",
+            "description": "Get weather information",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"}
+                },
+                "required": ["location"]
             },
             "defer_loading": True
         }
@@ -286,127 +265,278 @@ response = client.chat.completions.create(
 )
 ```
 
-## Important Notes
+---
 
-### Beta Header
+## Messages API
 
-LiteLLM automatically detects tool search tools and adds the appropriate beta header based on your provider:
+The Messages API provides native Anthropic-style tool search support via the `litellm.anthropic.messages` interface.
 
-- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20`
-- **Google Cloud Vertex AI**: `tool-search-tool-2025-10-19`
-- **Amazon Bedrock** (Invoke API, Opus 4.5 only): `tool-search-tool-2025-10-19`
+### SDK Usage
 
-You don't need to manually specify beta headers—LiteLLM handles this automatically.
+#### Basic Example
 
-### Deferred Loading
+```python showLineNumbers title="Messages API - Basic Tool Search"
+import litellm
 
-- Tools with `defer_loading: true` are only loaded when Claude discovers them via search
-- At least one tool must be non-deferred (the tool search tool itself)
-- Keep your 3-5 most frequently used tools as non-deferred for optimal performance
-
-### Tool Descriptions
-
-Write clear, descriptive tool names and descriptions that match how users describe tasks. The search algorithm uses:
-- Tool names
-- Tool descriptions
-- Argument names
-- Argument descriptions
-
-### Usage Tracking
-
-Tool search requests are tracked in the usage object:
-
-```python
-response = litellm.completion(
-    model="anthropic/claude-sonnet-4-5-20250929",
-    messages=[{"role": "user", "content": "Search for tools"}],
-    tools=[...]
+response = await litellm.anthropic.messages.acreate(
+    model="anthropic/claude-sonnet-4-20250514",
+    messages=[
+        {
+            "role": "user",
+            "content": "What's the weather in San Francisco?"
+        }
+    ],
+    tools=[
+        {
+            "type": "tool_search_tool_regex_20251119",
+            "name": "tool_search_tool_regex"
+        },
+        {
+            "name": "get_weather",
+            "description": "Get the current weather for a location",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA"
+                    }
+                },
+                "required": ["location"]
+            },
+            "defer_loading": True
+        }
+    ],
+    max_tokens=1024,
+    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
 )
 
-# Check tool search usage
-if response.usage.server_tool_use:
-    print(f"Tool search requests: {response.usage.server_tool_use.tool_search_requests}")
+print(response)
 ```
 
-## Error Handling
+#### Azure Anthropic Messages Example
 
-### All Tools Deferred
+```python showLineNumbers title="Azure Anthropic Messages API"
+import litellm
 
-```python
-# ❌ This will fail - at least one tool must be non-deferred
-tools = [
-    {
-        "type": "function",
-        "function": {...},
-        "defer_loading": True
-    }
-]
-
-# ✅ Correct - tool search tool is non-deferred
-tools = [
-    {
-        "type": "tool_search_tool_regex_20251119",
-        "name": "tool_search_tool_regex"
-    },
-    {
-        "type": "function",
-        "function": {...},
-        "defer_loading": True
-    }
-]
+response = await litellm.anthropic.messages.acreate(
+    model="azure_anthropic/claude-sonnet-4-20250514",
+    messages=[
+        {
+            "role": "user",
+            "content": "What's the stock price of Apple?"
+        }
+    ],
+    tools=[
+        {
+            "type": "tool_search_tool_regex_20251119",
+            "name": "tool_search_tool_regex"
+        },
+        {
+            "name": "get_stock_price",
+            "description": "Get the current stock price for a ticker symbol",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "ticker": {
+                        "type": "string",
+                        "description": "The stock ticker symbol, e.g. AAPL"
+                    }
+                },
+                "required": ["ticker"]
+            },
+            "defer_loading": True
+        }
+    ],
+    max_tokens=1024,
+    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
+)
 ```
 
-### Missing Tool Definition
+#### Vertex AI Messages Example
 
-If Claude references a tool that isn't in your deferred tools list, you'll get an error. Make sure all tools that might be discovered are included in the tools parameter with `defer_loading: true`.
+```python showLineNumbers title="Vertex AI Messages API"
+import litellm
 
-## Best Practices
+response = await litellm.anthropic.messages.acreate(
+    model="vertex_ai/claude-sonnet-4@20250514",
+    messages=[
+        {
+            "role": "user",
+            "content": "Search the web for information about AI"
+        }
+    ],
+    tools=[
+        {
+            "type": "tool_search_tool_bm25_20251119",
+            "name": "tool_search_tool_bm25"
+        },
+        {
+            "name": "search_web",
+            "description": "Search the web for information",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "query": {
+                        "type": "string",
+                        "description": "The search query"
+                    }
+                },
+                "required": ["query"]
+            },
+            "defer_loading": True
+        }
+    ],
+    max_tokens=1024,
+    extra_headers={"anthropic-beta": "tool-search-tool-2025-10-19"}
+)
+```
 
-1. **Keep frequently used tools non-deferred**: Your 3-5 most common tools should not have `defer_loading: true`
+#### Bedrock Messages Example
 
-2. **Use semantic descriptions**: Tool descriptions should use natural language that matches user queries
+```python showLineNumbers title="Bedrock Messages API (Invoke)"
+import litellm
 
-3. **Choose the right variant**:
-   - Use **regex** for exact pattern matching (faster)
-   - Use **BM25** for natural language semantic search
+response = await litellm.anthropic.messages.acreate(
+    model="bedrock/invoke/anthropic.claude-opus-4-20250514-v1:0",
+    messages=[
+        {
+            "role": "user",
+            "content": "What's the weather?"
+        }
+    ],
+    tools=[
+        {
+            "type": "tool_search_tool_regex_20251119",
+            "name": "tool_search_tool_regex"
+        },
+        {
+            "name": "get_weather",
+            "description": "Get weather information",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"}
+                },
+                "required": ["location"]
+            },
+            "defer_loading": True
+        }
+    ],
+    max_tokens=1024,
+    extra_headers={"anthropic-beta": "tool-search-tool-2025-10-19"}
+)
+```
 
-4. **Monitor usage**: Track `tool_search_requests` in the usage object to understand search patterns
+#### Streaming Support
 
-5. **Optimize tool catalog**: Remove unused tools and consolidate similar functionality
+```python showLineNumbers title="Messages API - Streaming"
+import litellm
+import json
 
-## When to Use Tool Search
+response = await litellm.anthropic.messages.acreate(
+    model="anthropic/claude-sonnet-4-20250514",
+    messages=[
+        {
+            "role": "user",
+            "content": "What's the weather in Tokyo?"
+        }
+    ],
+    tools=[
+        {
+            "type": "tool_search_tool_regex_20251119",
+            "name": "tool_search_tool_regex"
+        },
+        {
+            "name": "get_weather",
+            "description": "Get weather information",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"}
+                },
+                "required": ["location"]
+            },
+            "defer_loading": True
+        }
+    ],
+    max_tokens=1024,
+    stream=True,
+    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
+)
 
-**Good use cases:**
-- 10+ tools available in your system
-- Tool definitions consuming >10K tokens
-- Experiencing tool selection accuracy issues
-- Building systems with multiple tool categories
-- Tool library growing over time
+async for chunk in response:
+    if isinstance(chunk, bytes):
+        chunk_str = chunk.decode("utf-8")
+        for line in chunk_str.split("\n"):
+            if line.startswith("data: "):
+                try:
+                    json_data = json.loads(line[6:])
+                    print(json_data)
+                except json.JSONDecodeError:
+                    pass
+```
 
-**When traditional tool calling is better:**
-- Less than 10 tools total
-- All tools are frequently used
-- Very small tool definitions (\<100 tokens total)
+### AI Gateway Usage
 
-## Limitations
+Configure the proxy to use Messages API endpoints.
 
-- Not compatible with tool use examples
-- Requires Claude Opus 4.5 or Sonnet 4.5
-- On Bedrock, only available via invoke API (not converse API)
-- On Bedrock, only supported for Claude Opus 4.5 (not Sonnet 4.5)
-- BM25 variant (`tool_search_tool_bm25_20251119`) is not supported on Bedrock
-- Maximum 10,000 tools in catalog
-- Returns 3-5 most relevant tools per search
+#### Proxy Configuration
 
-### Bedrock-Specific Notes
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: claude-sonnet-messages
+    litellm_params:
+      model: anthropic/claude-sonnet-4-20250514
+      api_key: os.environ/ANTHROPIC_API_KEY
+```
 
-When using Bedrock's Invoke API:
-- The regex variant (`tool_search_tool_regex_20251119`) is automatically normalized to `tool_search_tool_regex`
-- The BM25 variant (`tool_search_tool_bm25_20251119`) is automatically filtered out as it's not supported
-- Tool search is only available for Claude Opus 4.5 models
+#### Client Request
+
+```python showLineNumbers title="Client Request via Proxy (Messages API)"
+from anthropic import Anthropic
+
+client = Anthropic(
+    api_key="your-litellm-proxy-key",
+    base_url="http://0.0.0.0:4000"
+)
+
+response = client.messages.create(
+    model="claude-sonnet-messages",
+    max_tokens=1024,
+    messages=[
+        {
+            "role": "user",
+            "content": "What's the weather?"
+        }
+    ],
+    tools=[
+        {
+            "type": "tool_search_tool_regex_20251119",
+            "name": "tool_search_tool_regex"
+        },
+        {
+            "name": "get_weather",
+            "description": "Get weather information",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"}
+                },
+                "required": ["location"]
+            },
+            "defer_loading": True
+        }
+    ],
+    extra_headers={"anthropic-beta": "advanced-tool-use-2025-11-20"}
+)
+
+print(response)
+```
+
+---
 
 ## Additional Resources
 
 - [Anthropic Tool Search Documentation](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/tool-search)
 - [LiteLLM Tool Calling Guide](https://docs.litellm.ai/docs/completion/function_call)
-

docs/my-website/docs/providers/azure_ai/azure_model_router.md

@@ -5,19 +5,38 @@ Azure Model Router is a feature in Azure AI Foundry that automatically routes yo
 ## Key Features
 
 - **Automatic Model Selection**: Azure Model Router dynamically selects the best model for your request
-- **Cost Tracking**: LiteLLM automatically tracks costs based on the actual model used (e.g., `gpt-4.1-nano`), not the router endpoint
+- **Cost Tracking**: LiteLLM automatically tracks costs based on the actual model used (e.g., `gpt-4.1-nano`), plus the Model Router infrastructure fee
 - **Streaming Support**: Full support for streaming responses with accurate cost calculation
+- **Simple Configuration**: Easy to set up via UI or config file
+
+## Model Naming Pattern
+
+Use the pattern: `azure_ai/model_router/<deployment-name>`
+
+**Components:**
+- `azure_ai` - The provider identifier
+- `model_router` - Indicates this is a Model Router deployment
+- `<deployment-name>` - Your actual deployment name from Azure AI Foundry (e.g., `azure-model-router`)
+
+**Example:** `azure_ai/model_router/azure-model-router`
+
+**How it works:**
+- LiteLLM automatically strips the `model_router/` prefix when sending requests to Azure
+- Only your deployment name (e.g., `azure-model-router`) is sent to the Azure API
+- The full path is preserved in responses and logs for proper cost tracking
 
 ## LiteLLM Python SDK
 
 ### Basic Usage
 
+Use the pattern `azure_ai/model_router/<deployment-name>` where `<deployment-name>` is your Azure deployment name:
+
 ```python
 import litellm
 import os
 
 response = litellm.completion(
-    model="azure_ai/azure-model-router",
+    model="azure_ai/model_router/azure-model-router",  # Use your deployment name
     messages=[{"role": "user", "content": "Hello!"}],
     api_base="https://your-endpoint.cognitiveservices.azure.com/openai/v1/",
     api_key=os.getenv("AZURE_MODEL_ROUTER_API_KEY"),
@@ -26,6 +45,13 @@ response = litellm.completion(
 print(response)
 ```
 
+**Pattern Explanation:**
+- `azure_ai` - The provider
+- `model_router` - Indicates this is a model router deployment
+- `azure-model-router` - Your actual deployment name from Azure AI Foundry
+
+LiteLLM will automatically strip the `model_router/` prefix when sending the request to Azure, so only `azure-model-router` is sent to the API.
+
 ### Streaming with Usage Tracking
 
 ```python
@@ -33,7 +59,7 @@ import litellm
 import os
 
 response = await litellm.acompletion(
-    model="azure_ai/azure-model-router",
+    model="azure_ai/model_router/azure-model-router",  # Use your deployment name
     messages=[{"role": "user", "content": "hi"}],
     api_base="https://your-endpoint.cognitiveservices.azure.com/openai/v1/",
     api_key=os.getenv("AZURE_MODEL_ROUTER_API_KEY"),
@@ -51,13 +77,15 @@ async for chunk in response:
 
 ```yaml
 model_list:
-  - model_name: azure-model-router
+  - model_name: azure-model-router  # Public name for your users
     litellm_params:
-      model: azure_ai/azure-model-router
+      model: azure_ai/model_router/azure-model-router  # Use your deployment name
       api_base: https://your-endpoint.cognitiveservices.azure.com/openai/v1/
       api_key: os.environ/AZURE_MODEL_ROUTER_API_KEY
 ```
 
+**Note:** Replace `azure-model-router` in the model path with your actual deployment name from Azure AI Foundry.
+
 ### Start Proxy
 
 ```bash
@@ -80,49 +108,42 @@ curl -X POST http://localhost:4000/chat/completions \
 
 This walkthrough shows how to add an Azure Model Router endpoint to LiteLLM using the Admin Dashboard.
 
-### Select Provider
+### Quick Start
+
+1. Navigate to the **Models** page in the LiteLLM UI
+2. Select **"Azure AI Foundry (Studio)"** as the provider
+3. Enter your deployment name (e.g., `azure-model-router`)
+4. LiteLLM will automatically format it as `azure_ai/model_router/azure-model-router`
+5. Add your API base URL and API key
+6. Test and save
+
+### Detailed Walkthrough
+
+#### Step 1: Select Provider
 
 Navigate to the Models page and select "Azure AI Foundry (Studio)" as the provider.
 
-#### Navigate to Models Page
+##### Navigate to Models Page
 
 ![Navigate to Models](./img/azure_model_router_01.jpeg)
 
-#### Click Provider Dropdown
+##### Click Provider Dropdown
 
 ![Click Provider](./img/azure_model_router_02.jpeg)
 
-#### Choose Azure AI Foundry
+##### Choose Azure AI Foundry
 
 ![Select Azure AI Foundry](./img/azure_model_router_03.jpeg)
 
-### Configure Model Name
+#### Step 2: Enter Deployment Name
 
-Set up the model name by entering `azure_ai/` followed by your model router deployment name from Azure.
+**New Simplified Method:** Just enter your deployment name directly in the text field. If your deployment name contains "model-router" or "model_router", LiteLLM will automatically format it as `azure_ai/model_router/<deployment-name>`.
 
-#### Click Model Name Field
+**Example:**
+- Enter: `azure-model-router`
+- LiteLLM creates: `azure_ai/model_router/azure-model-router`
 
-![Click Model Field](./img/azure_model_router_04.jpeg)
-
-#### Select Custom Model Name
-
-![Select Custom Model](./img/azure_model_router_05.jpeg)
-
-#### Enter LiteLLM Model Name
-
-![LiteLLM Model Name](./img/azure_model_router_06.jpeg)
-
-#### Click Custom Model Name Field
-
-![Enter Custom Name Field](./img/azure_model_router_07.jpeg)
-
-#### Type Model Prefix
-
-Type `azure_ai/` as the prefix.
-
-![Type azure_ai prefix](./img/azure_model_router_08.jpeg)
-
-#### Copy Model Name from Azure Portal
+##### Copy Deployment Name from Azure Portal
 
 Switch to Azure AI Foundry and copy your model router deployment name.
 
@@ -130,73 +151,79 @@ Switch to Azure AI Foundry and copy your model router deployment name.
 
 ![Copy Model Name](./img/azure_model_router_10.jpeg)
 
-#### Paste Model Name
+##### Enter Deployment Name in LiteLLM
 
-Paste to get `azure_ai/azure-model-router`.
+Paste your deployment name (e.g., `azure-model-router`) directly into the text field.
 
-![Paste Model Name](./img/azure_model_router_11.jpeg)
+![Enter Deployment Name](./img/azure_model_router_04.jpeg)
 
-### Configure API Base and Key
+**What happens behind the scenes:**
+- You enter: `azure-model-router`
+- LiteLLM automatically detects this is a model router deployment
+- The full model path becomes: `azure_ai/model_router/azure-model-router`
+- When making API calls, only `azure-model-router` is sent to Azure
+
+#### Step 3: Configure API Base and Key
 
 Copy the endpoint URL and API key from Azure portal.
 
-#### Copy API Base URL from Azure
+##### Copy API Base URL from Azure
 
 ![Copy API Base](./img/azure_model_router_12.jpeg)
 
-#### Enter API Base in LiteLLM
+##### Enter API Base in LiteLLM
 
 ![Click API Base Field](./img/azure_model_router_13.jpeg)
 
 ![Paste API Base](./img/azure_model_router_14.jpeg)
 
-#### Copy API Key from Azure
+##### Copy API Key from Azure
 
 ![Copy API Key](./img/azure_model_router_15.jpeg)
 
-#### Enter API Key in LiteLLM
+##### Enter API Key in LiteLLM
 
 ![Enter API Key](./img/azure_model_router_16.jpeg)
 
-### Test and Add Model
+#### Step 4: Test and Add Model
 
 Verify your configuration works and save the model.
 
-#### Test Connection
+##### Test Connection
 
 ![Test Connection](./img/azure_model_router_17.jpeg)
 
-#### Close Test Dialog
+##### Close Test Dialog
 
 ![Close Dialog](./img/azure_model_router_18.jpeg)
 
-#### Add Model
+##### Add Model
 
 ![Add Model](./img/azure_model_router_19.jpeg)
 
-### Verify in Playground
+#### Step 5: Verify in Playground
 
 Test your model and verify cost tracking is working.
 
-#### Open Playground
+##### Open Playground
 
 ![Go to Playground](./img/azure_model_router_20.jpeg)
 
-#### Select Model
+##### Select Model
 
 ![Select Model](./img/azure_model_router_21.jpeg)
 
-#### Send Test Message
+##### Send Test Message
 
 ![Send Message](./img/azure_model_router_22.jpeg)
 
-#### View Logs
+##### View Logs
 
 ![View Logs](./img/azure_model_router_23.jpeg)
 
-#### Verify Cost Tracking
+##### Verify Cost Tracking
 
-Cost is tracked based on the actual model used (e.g., `gpt-4.1-nano`).
+Cost is tracked based on the actual model used (e.g., `gpt-4.1-nano`), plus a flat infrastructure cost of $0.14 per million input tokens for using the Model Router.
 
 ![Verify Cost](./img/azure_model_router_24.jpeg)
 
@@ -205,28 +232,50 @@ Cost is tracked based on the actual model used (e.g., `gpt-4.1-nano`).
 LiteLLM automatically handles cost tracking for Azure Model Router by:
 
 1. **Detecting the actual model**: When Azure Model Router routes your request to a specific model (e.g., `gpt-4.1-nano-2025-04-14`), LiteLLM extracts this from the response
-2. **Calculating accurate costs**: Costs are calculated based on the actual model used, not the router endpoint name
+2. **Calculating accurate costs**: Costs are calculated based on:
+   - The actual model used (e.g., `gpt-4.1-nano` token costs)
+   - Plus a flat infrastructure cost of **$0.14 per million input tokens** for using the Model Router
 3. **Streaming support**: Cost tracking works correctly for both streaming and non-streaming requests
 
+### Cost Breakdown
+
+When you use Azure Model Router, the total cost includes:
+
+- **Model Cost**: Based on the actual model that handled your request (e.g., `gpt-4.1-nano`)
+- **Router Flat Cost**: $0.14 per million input tokens (Azure AI Foundry infrastructure fee)
+
 ### Example Response with Cost
 
 ```python
 import litellm
 
 response = litellm.completion(
-    model="azure_ai/azure-model-router",
+    model="azure_ai/model_router/azure-model-router",
     messages=[{"role": "user", "content": "Hello!"}],
     api_base="https://your-endpoint.cognitiveservices.azure.com/openai/v1/",
     api_key="your-api-key",
 )
 
 # The response will show the actual model used
-print(f"Model used: {response.model}")  # e.g., "gpt-4.1-nano-2025-04-14"
+print(f"Model used: {response.model}")  # e.g., "azure_ai/gpt-4.1-nano-2025-04-14"
 
-# Get cost
+# Get cost (includes both model cost and router flat cost)
 from litellm import completion_cost
 cost = completion_cost(completion_response=response)
-print(f"Cost: ${cost}")
+print(f"Total cost: ${cost}")
+
+# Access detailed cost breakdown
+if hasattr(response, '_hidden_params') and 'response_cost' in response._hidden_params:
+    print(f"Response cost: ${response._hidden_params['response_cost']}")
 ```
 
+### Viewing Cost Breakdown in UI
+
+When viewing logs in the LiteLLM UI, you'll see:
+- **Model Cost**: The cost for the actual model used
+- **Azure Model Router Flat Cost**: The $0.14/M input tokens infrastructure fee
+- **Total Cost**: Sum of both costs
+
+This breakdown helps you understand exactly what you're paying for when using the Model Router.
+
 

docs/my-website/docs/providers/bedrock.md

@@ -9,7 +9,7 @@ ALL Bedrock models (Anthropic, Meta, Deepseek, Mistral, Amazon, etc.) are Suppor
 | 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), [`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` |
+| Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings`, `/images/generations`, `/v1/realtime`|
 | Rerank Endpoint | `/rerank` |
 | Pass-through Endpoint | [Supported](../pass_through/bedrock.md) |
 

docs/my-website/docs/providers/bedrock_realtime_with_audio.md

@@ -0,0 +1,362 @@
+# Bedrock Realtime API
+
+## Overview
+
+Amazon Bedrock's Nova Sonic model supports real-time bidirectional audio streaming for voice conversations. This tutorial shows how to use it through LiteLLM Proxy.
+
+## Setup
+
+### 1. Configure LiteLLM Proxy
+
+Create a `config.yaml` file:
+
+```yaml
+model_list:
+  - model_name: "bedrock-sonic"
+    litellm_params:
+      model: bedrock/amazon.nova-sonic-v1:0
+      aws_region_name: us-east-1  # or your preferred region
+    model_info:
+      mode: realtime
+```
+
+### 2. Start LiteLLM Proxy
+
+```bash
+litellm --config config.yaml
+```
+
+## Basic Text Interaction
+
+```python
+import asyncio
+import websockets
+import json
+
+LITELLM_API_KEY = "sk-1234"  # Your LiteLLM API key
+LITELLM_URL = 'ws://localhost:4000/v1/realtime?model=bedrock-sonic'
+
+async def test_text_conversation():
+    async with websockets.connect(
+        LITELLM_URL,
+        additional_headers={
+            "Authorization": f"Bearer {LITELLM_API_KEY}"
+        }
+    ) as ws:
+        # Wait for session.created
+        response = await ws.recv()
+        print(f"Connected: {json.loads(response)['type']}")
+        
+        # Configure session
+        session_update = {
+            "type": "session.update",
+            "session": {
+                "instructions": "You are a helpful assistant.",
+                "modalities": ["text"],
+                "temperature": 0.8
+            }
+        }
+        await ws.send(json.dumps(session_update))
+        
+        # Send a message
+        message = {
+            "type": "conversation.item.create",
+            "item": {
+                "type": "message",
+                "role": "user",
+                "content": [{"type": "input_text", "text": "Hello!"}]
+            }
+        }
+        await ws.send(json.dumps(message))
+        
+        # Trigger response
+        await ws.send(json.dumps({"type": "response.create"}))
+        
+        # Listen for response
+        while True:
+            response = await ws.recv()
+            event = json.loads(response)
+            
+            if event['type'] == 'response.text.delta':
+                print(event['delta'], end='', flush=True)
+            elif event['type'] == 'response.done':
+                print("\n✓ Complete")
+                break
+
+if __name__ == "__main__":
+    asyncio.run(test_text_conversation())
+```
+
+## Audio Streaming with Voice Conversation
+
+```python
+import asyncio
+import websockets
+import json
+import base64
+import pyaudio
+
+LITELLM_API_KEY = "sk-1234"
+LITELLM_URL = 'ws://localhost:4000/v1/realtime?model=bedrock-sonic'
+
+# Audio configuration
+INPUT_RATE = 16000   # Nova Sonic expects 16kHz input
+OUTPUT_RATE = 24000  # Nova Sonic outputs 24kHz
+CHUNK = 1024
+
+async def audio_conversation():
+    # Initialize PyAudio
+    p = pyaudio.PyAudio()
+    
+    # Input stream (microphone)
+    input_stream = p.open(
+        format=pyaudio.paInt16,
+        channels=1,
+        rate=INPUT_RATE,
+        input=True,
+        frames_per_buffer=CHUNK
+    )
+    
+    # Output stream (speakers)
+    output_stream = p.open(
+        format=pyaudio.paInt16,
+        channels=1,
+        rate=OUTPUT_RATE,
+        output=True,
+        frames_per_buffer=CHUNK
+    )
+    
+    async with websockets.connect(
+        LITELLM_URL,
+        additional_headers={"Authorization": f"Bearer {LITELLM_API_KEY}"}
+    ) as ws:
+        # Wait for session.created
+        await ws.recv()
+        print("✓ Connected")
+        
+        # Configure session with audio
+        session_update = {
+            "type": "session.update",
+            "session": {
+                "instructions": "You are a friendly voice assistant.",
+                "modalities": ["text", "audio"],
+                "voice": "matthew",
+                "input_audio_format": "pcm16",
+                "output_audio_format": "pcm16"
+            }
+        }
+        await ws.send(json.dumps(session_update))
+        print("🎤 Speak into your microphone...")
+        
+        async def send_audio():
+            """Capture and send audio from microphone"""
+            while True:
+                audio_data = input_stream.read(CHUNK, exception_on_overflow=False)
+                audio_b64 = base64.b64encode(audio_data).decode('utf-8')
+                await ws.send(json.dumps({
+                    "type": "input_audio_buffer.append",
+                    "audio": audio_b64
+                }))
+                await asyncio.sleep(0.01)
+        
+        async def receive_audio():
+            """Receive and play audio responses"""
+            while True:
+                response = await ws.recv()
+                event = json.loads(response)
+                
+                if event['type'] == 'response.audio.delta':
+                    audio_b64 = event.get('delta', '')
+                    if audio_b64:
+                        audio_bytes = base64.b64decode(audio_b64)
+                        output_stream.write(audio_bytes)
+                
+                elif event['type'] == 'response.text.delta':
+                    print(event['delta'], end='', flush=True)
+                
+                elif event['type'] == 'response.done':
+                    print("\n✓ Response complete")
+        
+        # Run both tasks concurrently
+        await asyncio.gather(send_audio(), receive_audio())
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(audio_conversation())
+    except KeyboardInterrupt:
+        print("\n\nGoodbye!")
+```
+
+## Using Tools/Function Calling
+
+```python
+import asyncio
+import websockets
+import json
+from datetime import datetime
+
+LITELLM_API_KEY = "sk-1234"
+LITELLM_URL = 'ws://localhost:4000/v1/realtime?model=bedrock-sonic'
+
+# Define tools
+TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get current weather for a location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "City name"
+                    }
+                },
+                "required": ["location"]
+            }
+        }
+    }
+]
+
+def get_weather(location: str) -> dict:
+    """Simulated weather function"""
+    return {
+        "location": location,
+        "temperature": 72,
+        "conditions": "sunny"
+    }
+
+async def conversation_with_tools():
+    async with websockets.connect(
+        LITELLM_URL,
+        additional_headers={"Authorization": f"Bearer {LITELLM_API_KEY}"}
+    ) as ws:
+        # Wait for session.created
+        await ws.recv()
+        
+        # Configure session with tools
+        session_update = {
+            "type": "session.update",
+            "session": {
+                "instructions": "You are a helpful assistant with access to tools.",
+                "modalities": ["text"],
+                "tools": TOOLS
+            }
+        }
+        await ws.send(json.dumps(session_update))
+        
+        # Send a message that requires a tool
+        message = {
+            "type": "conversation.item.create",
+            "item": {
+                "type": "message",
+                "role": "user",
+                "content": [{"type": "input_text", "text": "What's the weather in San Francisco?"}]
+            }
+        }
+        await ws.send(json.dumps(message))
+        await ws.send(json.dumps({"type": "response.create"}))
+        
+        # Handle responses and tool calls
+        while True:
+            response = await ws.recv()
+            event = json.loads(response)
+            
+            if event['type'] == 'response.text.delta':
+                print(event['delta'], end='', flush=True)
+            
+            elif event['type'] == 'response.function_call_arguments.done':
+                # Execute the tool
+                function_name = event['name']
+                arguments = json.loads(event['arguments'])
+                
+                print(f"\n🔧 Calling {function_name}({arguments})")
+                result = get_weather(**arguments)
+                
+                # Send tool result back
+                tool_result = {
+                    "type": "conversation.item.create",
+                    "item": {
+                        "type": "function_call_output",
+                        "call_id": event['call_id'],
+                        "output": json.dumps(result)
+                    }
+                }
+                await ws.send(json.dumps(tool_result))
+                await ws.send(json.dumps({"type": "response.create"}))
+            
+            elif event['type'] == 'response.done':
+                print("\n✓ Complete")
+                break
+
+if __name__ == "__main__":
+    asyncio.run(conversation_with_tools())
+```
+
+## Configuration Options
+
+### Voice Options
+Available voices: `matthew`, `joanna`, `ruth`, `stephen`, `gregory`, `amy`
+
+### Audio Formats
+- **Input**: 16kHz PCM16 (mono)
+- **Output**: 24kHz PCM16 (mono)
+
+### Modalities
+- `["text"]` - Text only
+- `["audio"]` - Audio only  
+- `["text", "audio"]` - Both text and audio
+
+## Example Test Scripts
+
+Complete working examples are available in the LiteLLM repository:
+
+- **Basic audio streaming**: `test_bedrock_realtime_client.py`
+- **Simple text test**: `test_bedrock_realtime_simple.py`
+- **Tool calling**: `test_bedrock_realtime_tools.py`
+
+## Requirements
+
+```bash
+pip install litellm websockets pyaudio
+```
+
+## AWS Configuration
+
+Ensure your AWS credentials are configured:
+
+```bash
+export AWS_ACCESS_KEY_ID=your_access_key
+export AWS_SECRET_ACCESS_KEY=your_secret_key
+export AWS_REGION_NAME=us-east-1
+```
+
+Or use AWS CLI configuration:
+
+```bash
+aws configure
+```
+
+## Troubleshooting
+
+### Connection Issues
+- Ensure LiteLLM proxy is running on the correct port
+- Verify AWS credentials are properly configured
+- Check that the Bedrock model is available in your region
+
+### Audio Issues
+- Verify PyAudio is properly installed
+- Check microphone/speaker permissions
+- Ensure correct sample rates (16kHz input, 24kHz output)
+
+### Tool Calling Issues
+- Ensure tools are properly defined in session.update
+- Verify tool results are sent back with correct call_id
+- Check that response.create is sent after tool result
+
+## Related Resources
+
+- [OpenAI Realtime API Documentation](https://platform.openai.com/docs/guides/realtime)
+- [Amazon Bedrock Nova Sonic Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/nova-sonic.html)
+- [LiteLLM Realtime API Documentation](/docs/realtime)

docs/my-website/docs/providers/chatgpt.md

@@ -0,0 +1,84 @@
+# ChatGPT Subscription
+
+Use ChatGPT Pro/Max subscription models through LiteLLM with OAuth device flow authentication.
+
+| Property | Details |
+|-------|-------|
+| Description | ChatGPT subscription access (Codex + GPT-5.2 family) via ChatGPT backend API |
+| Provider Route on LiteLLM | `chatgpt/` |
+| Supported Endpoints | `/responses`, `/chat/completions` (bridged to Responses for supported models) |
+| API Reference | https://chatgpt.com |
+
+ChatGPT subscription access is native to the Responses API. Chat Completions requests are bridged to Responses for supported models (for example `chatgpt/gpt-5.2`).
+
+Notes:
+- The ChatGPT subscription backend rejects token limit fields (`max_tokens`, `max_output_tokens`, `max_completion_tokens`) and `metadata`. LiteLLM strips these fields for this provider.
+- `/v1/chat/completions` honors `stream`. When `stream` is false (default), LiteLLM aggregates the Responses stream into a single JSON response.
+
+## Authentication
+
+ChatGPT subscription access uses an OAuth device code flow:
+
+1. LiteLLM prints a device code and verification URL
+2. Open the URL, sign in, and enter the code
+3. Tokens are stored locally for reuse
+
+## Usage - LiteLLM Python SDK
+
+### Responses (recommended for Codex models)
+
+```python showLineNumbers title="ChatGPT Responses"
+import litellm
+
+response = litellm.responses(
+    model="chatgpt/gpt-5.2-codex",
+    input="Write a Python hello world"
+)
+
+print(response)
+```
+
+### Chat Completions (bridged to Responses)
+
+```python showLineNumbers title="ChatGPT Chat Completions"
+import litellm
+
+response = litellm.completion(
+    model="chatgpt/gpt-5.2",
+    messages=[{"role": "user", "content": "Write a Python hello world"}]
+)
+
+print(response)
+```
+
+## Usage - LiteLLM Proxy
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: chatgpt/gpt-5.2
+    model_info:
+      mode: responses
+    litellm_params:
+      model: chatgpt/gpt-5.2
+  - model_name: chatgpt/gpt-5.2-codex
+    model_info:
+      mode: responses
+    litellm_params:
+      model: chatgpt/gpt-5.2-codex
+```
+
+```bash showLineNumbers title="Start LiteLLM Proxy"
+litellm --config config.yaml
+```
+
+## Configuration
+
+### Environment Variables
+
+- `CHATGPT_TOKEN_DIR`: Custom token storage directory
+- `CHATGPT_AUTH_FILE`: Auth file name (default: `auth.json`)
+- `CHATGPT_API_BASE`: Override API base (default: `https://chatgpt.com/backend-api/codex`)
+- `OPENAI_CHATGPT_API_BASE`: Alias for `CHATGPT_API_BASE`
+- `CHATGPT_ORIGINATOR`: Override the `originator` header value
+- `CHATGPT_USER_AGENT`: Override the `User-Agent` header value
+- `CHATGPT_USER_AGENT_SUFFIX`: Optional suffix appended to the `User-Agent` header

docs/my-website/docs/providers/gemini.md

@@ -15,6 +15,17 @@ import TabItem from '@theme/TabItem';
 
 <br />
 
+:::tip Gemini API vs Vertex AI
+| Model Format | Provider | Auth Required |
+|-------------|----------|---------------|
+| `gemini/gemini-2.0-flash` | Gemini API | `GEMINI_API_KEY` (simple API key) |
+| `vertex_ai/gemini-2.0-flash` | Vertex AI | GCP credentials + project |
+| `gemini-2.0-flash` (no prefix) | Vertex AI | GCP credentials + project |
+
+**If you just want to use an API key** (like OpenAI), use the `gemini/` prefix.
+
+Models without a prefix default to Vertex AI which requires full GCP authentication.
+:::
 
 ## API Keys
 
@@ -1547,16 +1558,21 @@ LiteLLM Supports the following image types passed in `url`
 - Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg
 - Image in local storage - ./localimage.jpeg
 
-## Image Resolution Control (Gemini 3+)
+## Media Resolution Control (Images & Videos)
 
-For Gemini 3+ models, LiteLLM supports per-part media resolution control using OpenAI's `detail` parameter. This allows you to specify different resolution levels for individual images in your request.
+For Gemini 3+ models, LiteLLM supports per-part media resolution control using OpenAI's `detail` parameter. This allows you to specify different resolution levels for individual images and videos in your request, whether using `image_url` or `file` content types.
 
 **Supported `detail` values:**
 - `"low"` - Maps to `media_resolution: "low"` (280 tokens for images, 70 tokens per frame for videos)
+- `"medium"` - Maps to `media_resolution: "medium"`
 - `"high"` - Maps to `media_resolution: "high"` (1120 tokens for images)
+- `"ultra_high"` - Maps to `media_resolution: "ultra_high"`
 - `"auto"` or `None` - Model decides optimal resolution (no `media_resolution` set)
 
-**Usage Example:**
+**Usage Examples:**
+
+<Tabs>
+<TabItem value="images" label="Images">
 
 ```python
 from litellm import completion
@@ -1593,10 +1609,193 @@ response = completion(
 )
 ```
 
+</TabItem>
+<TabItem value="videos" label="Videos with Files">
+
+```python
+from litellm import completion
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "Analyze this video"
+            },
+            {
+                "type": "file",
+                "file": {
+                    "file_id": "gs://my-bucket/video.mp4",
+                    "format": "video/mp4",
+                    "detail": "high"  # High resolution for detailed video analysis
+                }
+            }
+        ]
+    }
+]
+
+response = completion(
+    model="gemini/gemini-3-pro-preview",
+    messages=messages,
+)
+```
+
+</TabItem>
+</Tabs>
+
 :::info
-**Per-Part Resolution:** Each image in your request can have its own `detail` setting, allowing mixed-resolution requests (e.g., a high-res chart alongside a low-res icon). This feature is only available for Gemini 3+ models.
+**Per-Part Resolution:** Each image or video in your request can have its own `detail` setting, allowing mixed-resolution requests (e.g., a high-res chart alongside a low-res icon). This feature works with both `image_url` and `file` content types, and is only available for Gemini 3+ models.
 :::
 
+## Video Metadata Control
+
+For Gemini 3+ models, LiteLLM supports fine-grained video processing control through the `video_metadata` field. This allows you to specify frame extraction rates and time ranges for video analysis.
+
+**Supported `video_metadata` parameters:**
+
+| Parameter | Type | Description | Example |
+|-----------|------|-------------|---------|
+| `fps` | Number | Frame extraction rate (frames per second) | `5` |
+| `start_offset` | String | Start time for video clip processing | `"10s"` |
+| `end_offset` | String | End time for video clip processing | `"60s"` |
+
+:::note
+**Field Name Conversion:** LiteLLM automatically converts snake_case field names to camelCase for the Gemini API:
+- `start_offset` → `startOffset`
+- `end_offset` → `endOffset`
+- `fps` remains unchanged
+:::
+
+:::warning
+- **Gemini 3+ Only:** This feature is only available for Gemini 3.0 and newer models
+- **Video Files Recommended:** While `video_metadata` is designed for video files, error handling for other media types is delegated to the Vertex AI API
+- **File Formats Supported:** Works with `gs://`, `https://`, and base64-encoded video files
+:::
+
+**Usage Examples:**
+
+<Tabs>
+<TabItem value="basic" label="Basic Video Metadata">
+
+```python
+from litellm import completion
+
+response = completion(
+    model="gemini/gemini-3-pro-preview",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Analyze this video clip"},
+                {
+                    "type": "file",
+                    "file": {
+                        "file_id": "gs://my-bucket/video.mp4",
+                        "format": "video/mp4",
+                        "video_metadata": {
+                            "fps": 5,               # Extract 5 frames per second
+                            "start_offset": "10s",  # Start from 10 seconds
+                            "end_offset": "60s"     # End at 60 seconds
+                        }
+                    }
+                }
+            ]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+<TabItem value="combined" label="Combined with Detail">
+
+```python
+from litellm import completion
+
+response = completion(
+    model="gemini/gemini-3-pro-preview",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Provide detailed analysis of this video segment"},
+                {
+                    "type": "file",
+                    "file": {
+                        "file_id": "https://example.com/presentation.mp4",
+                        "format": "video/mp4",
+                        "detail": "high",  # High resolution for detailed analysis
+                        "video_metadata": {
+                            "fps": 10,              # Extract 10 frames per second
+                            "start_offset": "30s",  # Start from 30 seconds
+                            "end_offset": "90s"     # End at 90 seconds
+                        }
+                    }
+                }
+            ]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: gemini-3-pro
+    litellm_params:
+      model: gemini/gemini-3-pro-preview
+      api_key: os.environ/GEMINI_API_KEY
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Make request
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
+  -d '{
+    "model": "gemini-3-pro",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {"type": "text", "text": "Analyze this video clip"},
+          {
+            "type": "file",
+            "file": {
+              "file_id": "gs://my-bucket/video.mp4",
+              "format": "video/mp4",
+              "detail": "high",
+              "video_metadata": {
+                "fps": 5,
+                "start_offset": "10s",
+                "end_offset": "60s"
+              }
+            }
+          }
+        ]
+      }
+    ]
+  }'
+```
+
+</TabItem>
+</Tabs>
+
 ## Sample Usage
 ```python
 import os
@@ -1641,6 +1840,57 @@ content = response.get('choices', [{}])[0].get('message', {}).get('content')
 print(content)
 ```
 
+## gemini-robotics-er-1.5-preview Usage
+
+```python
+from litellm import api_base
+from openai import OpenAI
+import os
+import base64
+
+client = OpenAI(base_url="http://0.0.0.0:4000", api_key="sk-12345")
+base64_image = base64.b64encode(open("closeup-object-on-table-many-260nw-1216144471.webp", "rb").read()).decode()
+
+import json
+import re
+tools = [{"codeExecution": {}}] 
+response = client.chat.completions.create(
+    model="gemini/gemini-robotics-er-1.5-preview",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "Point to no more than 10 items in the image. The label returned should be an identifying name for the object detected. The answer should follow the json format: [{\"point\": [y, x], \"label\": <label1>}, ...]. The points are in [y, x] format normalized to 0-1000."
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
+                }
+            ]
+        }
+    ],
+    tools=tools
+)
+
+# Extract JSON from markdown code block if present
+content = response.choices[0].message.content
+# Look for triple-backtick JSON block
+match = re.search(r'```json\s*(.*?)\s*```', content, re.DOTALL)
+if match:
+    json_str = match.group(1)
+else:
+    json_str = content
+
+try:
+    data = json.loads(json_str)
+    print(json.dumps(data, indent=2))
+except Exception as e:
+    print("Error parsing response as JSON:", e)
+    print("Response content:", content)
+```
+
 ## Usage - PDF / Videos / etc. Files
 
 ### Inline Data (e.g. audio stream)

docs/my-website/docs/providers/gmi.md

@@ -0,0 +1,140 @@
+# GMI Cloud
+
+## Overview
+
+| Property | Details |
+|-------|-------|
+| Description | GMI Cloud is a GPU cloud infrastructure provider offering access to top AI models including Claude, GPT, DeepSeek, Gemini, and more through OpenAI-compatible APIs. |
+| Provider Route on LiteLLM | `gmi/` |
+| Link to Provider Doc | [GMI Cloud Docs ↗](https://docs.gmicloud.ai) |
+| Base URL | `https://api.gmi-serving.com/v1` |
+| Supported Operations | [`/chat/completions`](#sample-usage), [`/models`](#supported-models) |
+
+<br />
+
+## What is GMI Cloud?
+
+GMI Cloud is a venture-backed digital infrastructure company ($82M+ funding) providing:
+- **Top-tier GPU Access**: NVIDIA H100 GPUs for AI workloads
+- **Multiple AI Models**: Claude, GPT, DeepSeek, Gemini, Kimi, Qwen, and more
+- **OpenAI-Compatible API**: Drop-in replacement for OpenAI SDK
+- **Global Infrastructure**: Data centers in US (Colorado) and APAC (Taiwan)
+
+## Required Variables
+
+```python showLineNumbers title="Environment Variables"
+os.environ["GMI_API_KEY"] = ""  # your GMI Cloud API key
+```
+
+Get your GMI Cloud API key from [console.gmicloud.ai](https://console.gmicloud.ai).
+
+## Usage - LiteLLM Python SDK
+
+### Non-streaming
+
+```python showLineNumbers title="GMI Cloud Non-streaming Completion"
+import os
+import litellm
+from litellm import completion
+
+os.environ["GMI_API_KEY"] = ""  # your GMI Cloud API key
+
+messages = [{"content": "What is the capital of France?", "role": "user"}]
+
+# GMI Cloud call
+response = completion(
+    model="gmi/deepseek-ai/DeepSeek-V3.2",
+    messages=messages
+)
+
+print(response)
+```
+
+### Streaming
+
+```python showLineNumbers title="GMI Cloud Streaming Completion"
+import os
+import litellm
+from litellm import completion
+
+os.environ["GMI_API_KEY"] = ""  # your GMI Cloud API key
+
+messages = [{"content": "Write a short poem about AI", "role": "user"}]
+
+# GMI Cloud call with streaming
+response = completion(
+    model="gmi/anthropic/claude-sonnet-4.5",
+    messages=messages,
+    stream=True
+)
+
+for chunk in response:
+    print(chunk)
+```
+
+## Usage - LiteLLM Proxy Server
+
+### 1. Save key in your environment
+
+```bash
+export GMI_API_KEY=""
+```
+
+### 2. Start the proxy
+
+```yaml
+model_list:
+  - model_name: deepseek-v3
+    litellm_params:
+      model: gmi/deepseek-ai/DeepSeek-V3.2
+      api_key: os.environ/GMI_API_KEY
+  - model_name: claude-sonnet
+    litellm_params:
+      model: gmi/anthropic/claude-sonnet-4.5
+      api_key: os.environ/GMI_API_KEY
+```
+
+## Supported Models
+
+| Model | Model ID | Context Length |
+|-------|----------|----------------|
+| Claude Opus 4.5 | `gmi/anthropic/claude-opus-4.5` | 409K |
+| Claude Sonnet 4.5 | `gmi/anthropic/claude-sonnet-4.5` | 409K |
+| Claude Sonnet 4 | `gmi/anthropic/claude-sonnet-4` | 409K |
+| Claude Opus 4 | `gmi/anthropic/claude-opus-4` | 409K |
+| GPT-5.2 | `gmi/openai/gpt-5.2` | 409K |
+| GPT-5.1 | `gmi/openai/gpt-5.1` | 409K |
+| GPT-5 | `gmi/openai/gpt-5` | 409K |
+| GPT-4o | `gmi/openai/gpt-4o` | 131K |
+| GPT-4o-mini | `gmi/openai/gpt-4o-mini` | 131K |
+| DeepSeek V3.2 | `gmi/deepseek-ai/DeepSeek-V3.2` | 163K |
+| DeepSeek V3 0324 | `gmi/deepseek-ai/DeepSeek-V3-0324` | 163K |
+| Gemini 3 Pro | `gmi/google/gemini-3-pro-preview` | 1M |
+| Gemini 3 Flash | `gmi/google/gemini-3-flash-preview` | 1M |
+| Kimi K2 Thinking | `gmi/moonshotai/Kimi-K2-Thinking` | 262K |
+| MiniMax M2.1 | `gmi/MiniMaxAI/MiniMax-M2.1` | 196K |
+| Qwen3-VL 235B | `gmi/Qwen/Qwen3-VL-235B-A22B-Instruct-FP8` | 262K |
+| GLM-4.7 | `gmi/zai-org/GLM-4.7-FP8` | 202K |
+
+## Supported OpenAI Parameters
+
+GMI Cloud supports all standard OpenAI-compatible parameters:
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `messages` | array | **Required**. Array of message objects with 'role' and 'content' |
+| `model` | string | **Required**. Model ID from available models |
+| `stream` | boolean | Optional. Enable streaming responses |
+| `temperature` | float | Optional. Sampling temperature |
+| `top_p` | float | Optional. Nucleus sampling parameter |
+| `max_tokens` | integer | Optional. Maximum tokens to generate |
+| `frequency_penalty` | float | Optional. Penalize frequent tokens |
+| `presence_penalty` | float | Optional. Penalize tokens based on presence |
+| `stop` | string/array | Optional. Stop sequences |
+| `response_format` | object | Optional. JSON mode with `{"type": "json_object"}` |
+
+## Additional Resources
+
+- [GMI Cloud Website](https://www.gmicloud.ai)
+- [GMI Cloud Documentation](https://docs.gmicloud.ai)
+- [GMI Cloud Console](https://console.gmicloud.ai)

docs/my-website/docs/providers/openai/text_to_speech.md

@@ -46,7 +46,7 @@ os.environ["OPENAI_API_KEY"] = "sk-.."
 
 async def test_async_speech(): 
     speech_file_path = Path(__file__).parent / "speech.mp3"
-    response = await litellm.aspeech(
+    response = await aspeech(
             model="openai/tts-1",
             voice="alloy",
             input="the quick brown fox jumped over the lazy dogs",

docs/my-website/docs/providers/sarvam.md

@@ -0,0 +1,92 @@
+# Sarvam.ai
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+LiteLLM supports all the text models from [Sarvam ai](https://docs.sarvam.ai/api-reference-docs/chat/chat-completions)
+
+## Usage
+
+```python
+import os
+from litellm import completion
+
+# Set your Sarvam API key
+os.environ["SARVAM_API_KEY"] = ""
+
+messages = [{"role": "user", "content": "Hello"}]
+
+response = completion(
+    model="sarvam/sarvam-m",
+    messages=messages,
+)
+print(response)
+```
+
+## Usage with LiteLLM Proxy Server
+
+Here's how to call a Sarvam.ai model with the LiteLLM Proxy Server
+
+1. **Modify the `config.yaml`:**
+
+    ```yaml
+    model_list:
+      - model_name: my-model
+        litellm_params:
+          model: sarvam/<your-model-name>  # add sarvam/ prefix to route as Sarvam provider
+          api_key: api-key                 # api key to send your model
+    ```
+
+2. **Start the proxy:**
+
+    ```bash
+    $ litellm --config /path/to/config.yaml
+    ```
+
+3. **Send a request to LiteLLM Proxy Server:**
+
+    <Tabs>
+
+    <TabItem value="openai" label="OpenAI Python v1.0.0+">
+
+    ```python
+    import openai
+
+    client = openai.OpenAI(
+        api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
+        base_url="http://0.0.0.0:4000" # litellm-proxy-base url
+    )
+
+    response = client.chat.completions.create(
+        model="my-model",
+        messages=[
+            {
+                "role": "user",
+                "content": "what llm are you"
+            }
+        ],
+    )
+
+    print(response)
+    ```
+    </TabItem>
+
+    <TabItem value="curl" label="curl">
+
+    ```shell
+    curl --location 'http://0.0.0.0:4000/chat/completions' \
+        --header 'Authorization: Bearer sk-1234' \
+        --header 'Content-Type: application/json' \
+        --data '{
+        "model": "my-model",
+        "messages": [
+            {
+            "role": "user",
+            "content": "what llm are you"
+            }
+        ]
+    }'
+    ```
+    </TabItem>
+
+    </Tabs>

docs/my-website/docs/providers/stability.md

@@ -173,6 +173,14 @@ Stability AI returns images in base64 format. The response is OpenAI-compatible:
 
 Stability AI supports various image editing operations including inpainting, upscaling, outpainting, background removal, and more.
 
+:::info Optional Parameters
+**Important:** Different Stability models have different parameter requirements:
+- Some models don't require a `prompt` (e.g., upscaling, background removal)
+- The `style-transfer` model uses `init_image` and `style_image` instead of `image`
+- The `outpaint` model requires numeric parameters (`left`, `right`, `up`, `down`)
+LiteLLM automatically handles these differences for you.
+:::
+
 ### Usage - LiteLLM Python SDK
 
 #### Inpainting (Edit with Mask)
@@ -217,11 +225,11 @@ response = image_edit(
     creativity=0.3,  # 0-0.35, higher = more creative
 )
 
-# Fast upscaling - quick upscaling
+# Fast upscaling - quick upscaling (no prompt needed)
 response = image_edit(
     model="stability/stable-fast-upscale-v1:0",
     image=open("low_res_image.png", "rb"),
-    prompt="Quickly upscale this image",
+    # No prompt required for fast upscale
 )
 print(response)
 ```
@@ -259,7 +267,7 @@ os.environ['STABILITY_API_KEY'] = "your-api-key"
 response = image_edit(
     model="stability/stable-image-remove-background-v1:0",
     image=open("portrait.png", "rb"),
-    prompt="Remove the background",
+    # No prompt required for fast upscale
 )
 print(response)
 ```
@@ -329,10 +337,29 @@ response = image_edit(
     model="stability/stable-image-erase-object-v1:0",
     image=open("scene.png", "rb"),
     mask=open("object_mask.png", "rb"),  # Mask the object to erase
-    prompt="Remove the object",
+    # No prompt needed
 )
 print(response)
 ```
+#### Style Transfer
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Transfer style from one image to another
+# Note: Uses init_image (via image param) and style_image
+response = image_edit(
+    model="stability/stable-style-transfer-v1:0",
+    image=open("content_image.png", "rb"),  # Maps to init_image
+    style_image=open("style_reference.png", "rb"),  # Style to apply
+    fidelity=0.5,  # 0-1, balance between content and style
+    # No prompt needed
+)
+
+print(response)
 
 ### Supported Image Edit Models
 
@@ -419,6 +446,23 @@ response = image_edit(
 )
 print(response)
 ```
+# Fast upscale without prompt
+response = image_edit(
+    model="bedrock/stability.stable-fast-upscale-v1:0",
+    image=open("low_res_image.png", "rb"),
+)
+
+# Outpaint with numeric parameters
+response = image_edit(
+    model="bedrock/stability.stable-outpaint-v1:0",
+    image=open("original_image.png", "rb"),
+    left=100,   # Automatically converted to int
+    right=100,
+    up=50,
+    down=50,
+)
+
+print(response)
 
 ### Supported Bedrock Stability Models
 

docs/my-website/docs/providers/vercel_ai_gateway.md

@@ -11,7 +11,7 @@ import TabItem from '@theme/TabItem';
 | Provider Route on LiteLLM | `vercel_ai_gateway/` |
 | Link to Provider Doc | [Vercel AI Gateway Documentation ↗](https://vercel.com/docs/ai-gateway) |
 | Base URL | `https://ai-gateway.vercel.sh/v1` |
-| Supported Operations | `/chat/completions`, `/models` |
+| Supported Operations | `/chat/completions`, `/embeddings`, `/models` |
 
 <br />
 <br />
@@ -73,7 +73,7 @@ messages = [{"content": "Hello, how are you?", "role": "user"}]
 
 # Vercel AI Gateway call with streaming
 response = completion(
-    model="vercel_ai_gateway/openai/gpt-4o", 
+    model="vercel_ai_gateway/openai/gpt-4o",
     messages=messages,
     stream=True
 )
@@ -82,6 +82,33 @@ for chunk in response:
     print(chunk)
 ```
 
+### Embeddings
+
+```python showLineNumbers title="Vercel AI Gateway Embeddings"
+import os
+from litellm import embedding
+
+os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-api-key"
+
+# Vercel AI Gateway embedding call
+response = embedding(
+    model="vercel_ai_gateway/openai/text-embedding-3-small",
+    input="Hello world"
+)
+
+print(response.data[0]["embedding"][:5])  # Print first 5 dimensions
+```
+
+You can also specify the `dimensions` parameter:
+
+```python showLineNumbers title="Vercel AI Gateway Embeddings with Dimensions"
+response = embedding(
+    model="vercel_ai_gateway/openai/text-embedding-3-small",
+    input=["Hello world", "Goodbye world"],
+    dimensions=768
+)
+```
+
 ## Usage - LiteLLM Proxy
 
 Add the following to your LiteLLM Proxy configuration file:
@@ -97,6 +124,11 @@ model_list:
     litellm_params:
       model: vercel_ai_gateway/anthropic/claude-4-sonnet
       api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY
+
+  - model_name: text-embedding-3-small-gateway
+    litellm_params:
+      model: vercel_ai_gateway/openai/text-embedding-3-small
+      api_key: os.environ/VERCEL_AI_GATEWAY_API_KEY
 ```
 
 Start your LiteLLM Proxy server:

docs/my-website/docs/providers/vertex.md

@@ -14,6 +14,17 @@ import TabItem from '@theme/TabItem';
 | Base URL | 1. Regional endpoints<br/>`https://{vertex_location}-aiplatform.googleapis.com/`<br/>2. Global endpoints (limited availability)<br/>`https://aiplatform.googleapis.com/`|
 | Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models), [`/rerank`](#rerank-api) |
 
+:::tip Vertex AI vs Gemini API
+| Model Format | Provider | Auth Required |
+|-------------|----------|---------------|
+| `vertex_ai/gemini-2.0-flash` | Vertex AI | GCP credentials + project |
+| `gemini-2.0-flash` (no prefix) | Vertex AI | GCP credentials + project |
+| `gemini/gemini-2.0-flash` | Gemini API | `GEMINI_API_KEY` (simple API key) |
+
+**If you just want to use an API key** (like OpenAI), use the `gemini/` prefix instead. See [Gemini - Google AI Studio](./gemini.md).
+
+Models without a prefix default to Vertex AI which requires GCP authentication.
+:::
 
 <br />
 <br />
@@ -1390,6 +1401,77 @@ model_list:
 
 
 
+### **Workload Identity Federation**
+
+LiteLLM supports [Google Cloud Workload Identity Federation (WIF)](https://cloud.google.com/iam/docs/workload-identity-federation), which allows you to grant on-premises or multi-cloud workloads access to Google Cloud resources without using a service account key. This is the recommended approach for workloads running in other cloud environments (AWS, Azure, etc.) or on-premises.
+
+To use Workload Identity Federation, pass the path to your WIF credentials configuration file via `vertex_credentials`:
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import completion
+
+response = completion(
+    model="vertex_ai/gemini-1.5-pro",
+    messages=[{"role": "user", "content": "Hello!"}],
+    vertex_credentials="/path/to/wif-credentials.json",  # 👈 WIF credentials file
+    vertex_project="your-gcp-project-id",
+    vertex_location="us-central1"
+)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+```yaml
+model_list:
+  - model_name: gemini-model
+    litellm_params:
+      model: vertex_ai/gemini-1.5-pro
+      vertex_project: your-gcp-project-id
+      vertex_location: us-central1
+      vertex_credentials: /path/to/wif-credentials.json  # 👈 WIF credentials file
+```
+
+Alternatively, you can create credentials in **LLM Credentials** in the LiteLLM UI and use those to authenticate your models:
+
+```yaml
+model_list:
+  - model_name: gemini-model
+    litellm_params:
+      model: vertex_ai/gemini-1.5-pro
+      vertex_project: your-gcp-project-id
+      vertex_location: us-central1
+      litellm_credential_name: my-vertex-wif-credential  # 👈 Reference credential stored in UI
+```
+
+</TabItem>
+</Tabs>
+
+**WIF Credentials File Format**
+
+Your WIF credentials JSON file typically looks like this (for AWS federation):
+
+```json
+{
+  "type": "external_account",
+  "audience": "//iam.googleapis.com/projects/PROJECT_NUMBER/locations/global/workloadIdentityPools/POOL_ID/providers/PROVIDER_ID",
+  "subject_token_type": "urn:ietf:params:aws:token-type:aws4_request",
+  "service_account_impersonation_url": "https://iamcredentials.googleapis.com/v1/projects/-/serviceAccounts/SERVICE_ACCOUNT_EMAIL:generateAccessToken",
+  "token_url": "https://sts.googleapis.com/v1/token",
+  "credential_source": {
+    "environment_id": "aws1",
+    "region_url": "http://169.254.169.254/latest/meta-data/placement/availability-zone",
+    "url": "http://169.254.169.254/latest/meta-data/iam/security-credentials",
+    "regional_cred_verification_url": "https://sts.{region}.amazonaws.com?Action=GetCallerIdentity&Version=2011-06-15"
+  }
+}
+```
+
+For more details on setting up Workload Identity Federation, see [Google Cloud WIF documentation](https://cloud.google.com/iam/docs/workload-identity-federation).
+
 ### **Environment Variables**
 
 You can set:
@@ -1886,6 +1968,244 @@ assert isinstance(
 
 ```
 
+## Media Resolution Control (Images & Videos)
+
+For Gemini 3+ models, LiteLLM supports per-part media resolution control using OpenAI's `detail` parameter. This allows you to specify different resolution levels for individual images and videos in your request, whether using `image_url` or `file` content types.
+
+**Supported `detail` values:**
+- `"low"` - Maps to `media_resolution: "low"` (280 tokens for images, 70 tokens per frame for videos)
+- `"medium"` - Maps to `media_resolution: "medium"`
+- `"high"` - Maps to `media_resolution: "high"` (1120 tokens for images)
+- `"ultra_high"` - Maps to `media_resolution: "ultra_high"`
+- `"auto"` or `None` - Model decides optimal resolution (no `media_resolution` set)
+
+**Usage Examples:**
+
+<Tabs>
+<TabItem value="images" label="Images">
+
+```python
+from litellm import completion
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": "https://example.com/chart.png",
+                    "detail": "high"  # High resolution for detailed chart analysis
+                }
+            },
+            {
+                "type": "text",
+                "text": "Analyze this chart"
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": "https://example.com/icon.png",
+                    "detail": "low"  # Low resolution for simple icon
+                }
+            }
+        ]
+    }
+]
+
+response = completion(
+    model="vertex_ai/gemini-3-pro-preview",
+    messages=messages,
+)
+```
+
+</TabItem>
+<TabItem value="videos" label="Videos with Files">
+
+```python
+from litellm import completion
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "Analyze this video"
+            },
+            {
+                "type": "file",
+                "file": {
+                    "file_id": "gs://my-bucket/video.mp4",
+                    "format": "video/mp4",
+                    "detail": "high"  # High resolution for detailed video analysis
+                }
+            }
+        ]
+    }
+]
+
+response = completion(
+    model="vertex_ai/gemini-3-pro-preview",
+    messages=messages,
+)
+```
+
+</TabItem>
+</Tabs>
+
+:::info
+**Per-Part Resolution:** Each image or video in your request can have its own `detail` setting, allowing mixed-resolution requests (e.g., a high-res chart alongside a low-res icon). This feature works with both `image_url` and `file` content types, and is only available for Gemini 3+ models.
+:::
+
+## Video Metadata Control
+
+For Gemini 3+ models, LiteLLM supports fine-grained video processing control through the `video_metadata` field. This allows you to specify frame extraction rates and time ranges for video analysis.
+
+**Supported `video_metadata` parameters:**
+
+| Parameter | Type | Description | Example |
+|-----------|------|-------------|---------|
+| `fps` | Number | Frame extraction rate (frames per second) | `5` |
+| `start_offset` | String | Start time for video clip processing | `"10s"` |
+| `end_offset` | String | End time for video clip processing | `"60s"` |
+
+:::note
+**Field Name Conversion:** LiteLLM automatically converts snake_case field names to camelCase for the Gemini API:
+- `start_offset` → `startOffset`
+- `end_offset` → `endOffset`
+- `fps` remains unchanged
+:::
+
+:::warning
+- **Gemini 3+ Only:** This feature is only available for Gemini 3.0 and newer models
+- **Video Files Recommended:** While `video_metadata` is designed for video files, error handling for other media types is delegated to the Vertex AI API
+- **File Formats Supported:** Works with `gs://`, `https://`, and base64-encoded video files
+:::
+
+**Usage Examples:**
+
+<Tabs>
+<TabItem value="basic" label="Basic Video Metadata">
+
+```python
+from litellm import completion
+
+response = completion(
+    model="vertex_ai/gemini-3-pro-preview",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Analyze this video clip"},
+                {
+                    "type": "file",
+                    "file": {
+                        "file_id": "gs://my-bucket/video.mp4",
+                        "format": "video/mp4",
+                        "video_metadata": {
+                            "fps": 5,               # Extract 5 frames per second
+                            "start_offset": "10s",  # Start from 10 seconds
+                            "end_offset": "60s"     # End at 60 seconds
+                        }
+                    }
+                }
+            ]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+<TabItem value="combined" label="Combined with Detail">
+
+```python
+from litellm import completion
+
+response = completion(
+    model="vertex_ai/gemini-3-pro-preview",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Provide detailed analysis of this video segment"},
+                {
+                    "type": "file",
+                    "file": {
+                        "file_id": "https://example.com/presentation.mp4",
+                        "format": "video/mp4",
+                        "detail": "high",  # High resolution for detailed analysis
+                        "video_metadata": {
+                            "fps": 10,              # Extract 10 frames per second
+                            "start_offset": "30s",  # Start from 30 seconds
+                            "end_offset": "90s"     # End at 90 seconds
+                        }
+                    }
+                }
+            ]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+1. Setup config.yaml
+
+```yaml
+model_list:
+  - model_name: gemini-3-pro
+    litellm_params:
+      model: vertex_ai/gemini-3-pro-preview
+      vertex_project: your-project
+      vertex_location: us-central1
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Make request
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
+  -d '{
+    "model": "gemini-3-pro",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {"type": "text", "text": "Analyze this video clip"},
+          {
+            "type": "file",
+            "file": {
+              "file_id": "gs://my-bucket/video.mp4",
+              "format": "video/mp4",
+              "detail": "high",
+              "video_metadata": {
+                "fps": 5,
+                "start_offset": "10s",
+                "end_offset": "60s"
+              }
+            }
+          }
+        ]
+      }
+    ]
+  }'
+```
+
+</TabItem>
+</Tabs>
 
 ## Usage - PDF / Videos / Audio etc. Files 
 

docs/my-website/docs/providers/vertex_speech.md

@@ -312,6 +312,7 @@ Gemini models with audio output capabilities using the chat completions API.
 - Only supports `pcm16` audio format
 - Streaming not yet supported
 - Must set `modalities: ["audio"]`
+- When using via LiteLLM Proxy, must include `"allowed_openai_params": ["audio", "modalities"]` in the request body to enable audio parameters
 :::
 
 ### Quick Start
@@ -372,7 +373,8 @@ curl http://0.0.0.0:4000/v1/chat/completions \
     "model": "gemini-tts",
     "messages": [{"role": "user", "content": "Say hello in a friendly voice"}],
     "modalities": ["audio"],
-    "audio": {"voice": "Kore", "format": "pcm16"}
+    "audio": {"voice": "Kore", "format": "pcm16"},
+    "allowed_openai_params": ["audio", "modalities"]
   }'
 ```
 
@@ -389,6 +391,7 @@ response = client.chat.completions.create(
     messages=[{"role": "user", "content": "Say hello in a friendly voice"}],
     modalities=["audio"],
     audio={"voice": "Kore", "format": "pcm16"},
+    extra_body={"allowed_openai_params": ["audio", "modalities"]}
 )
 print(response)
 ```

docs/my-website/docs/proxy/caching.md

@@ -282,6 +282,10 @@ Set either `REDIS_URL` or the `REDIS_HOST` in your os environment, to enable cac
   ```
 
 **Additional kwargs**  
+:::info
+Use `REDIS_*` environment variables to configure all Redis client library parameters. This is the suggested mechanism for toggling Redis settings as it automatically maps environment variables to Redis client kwargs.
+:::
+
 You can pass in any additional redis.Redis arg, by storing the variable + value in your os
 environment, like this:
 
@@ -289,6 +293,17 @@ environment, like this:
 REDIS_<redis-kwarg-name> = ""
 ```
 
+For example:
+```shell
+REDIS_SSL = "True"
+REDIS_SSL_CERT_REQS = "None" 
+REDIS_CONNECTION_POOL_KWARGS = '{"max_connections": 20}'
+```
+
+:::warning
+**Note**: For non-string Redis parameters (like integers, booleans, or complex objects), avoid using `REDIS_*` environment variables as they may fail during Redis client initialization. Instead, use `cache_kwargs` in your router configuration for such parameters.
+:::
+
 [**See how it's read from the environment**](https://github.com/BerriAI/litellm/blob/4d7ff1b33b9991dcf38d821266290631d9bcd2dd/litellm/_redis.py#L40)
 
 #### Step 3: Run proxy with config

docs/my-website/docs/proxy/call_hooks.md

@@ -19,6 +19,7 @@ import Image from '@theme/IdealImage';
 | `async_post_call_success_hook` | Modify outgoing response (non-streaming) | After successful LLM API call, for non-streaming responses |
 | `async_post_call_failure_hook` | Transform error responses sent to clients | After failed LLM API call |
 | `async_post_call_streaming_hook` | Modify outgoing response (streaming) | After successful LLM API call, for streaming responses |
+| `async_post_call_response_headers_hook` | Inject custom HTTP response headers | After LLM API call (both success and failure) |
 
 See a complete example with our [parallel request rate limiter](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py)
 
@@ -115,6 +116,18 @@ class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observabilit
         async for item in response:
             yield item
 
+    async def async_post_call_response_headers_hook(
+        self,
+        data: dict,
+        user_api_key_dict: UserAPIKeyAuth,
+        response: Any,
+        request_headers: Optional[Dict[str, str]] = None,
+    ) -> Optional[Dict[str, str]]:
+        """
+        Inject custom headers into HTTP response (runs for both success and failure).
+        """
+        return {"x-custom-header": "custom-value"}
+
 proxy_handler_instance = MyCustomHandler()
 ```
 
@@ -389,3 +402,31 @@ proxy_handler_instance = MyErrorTransformer()
 ```
 
 **Result:** Clients receive `"Your prompt is too long..."` instead of `"ContextWindowExceededError: Prompt exceeds context window"`.
+
+## Advanced - Inject Custom HTTP Response Headers
+
+Use `async_post_call_response_headers_hook` to inject custom HTTP headers into responses. This hook runs for **both successful and failed** LLM API calls.
+
+```python
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.proxy.proxy_server import UserAPIKeyAuth
+from typing import Any, Dict, Optional
+
+class CustomHeaderLogger(CustomLogger):
+    def __init__(self):
+        super().__init__()
+
+    async def async_post_call_response_headers_hook(
+        self,
+        data: dict,
+        user_api_key_dict: UserAPIKeyAuth,
+        response: Any,
+        request_headers: Optional[Dict[str, str]] = None,
+    ) -> Optional[Dict[str, str]]:
+        """
+        Inject custom headers into all responses (success and failure).
+        """
+        return {"x-custom-header": "custom-value"}
+
+proxy_handler_instance = CustomHeaderLogger()
+```

docs/my-website/docs/proxy/cli_sso.md

@@ -28,6 +28,37 @@ EXPERIMENTAL_UI_LOGIN="True" litellm --config config.yaml
 
 :::
 
+### Configuration
+
+#### JWT Token Expiration
+
+By default, CLI authentication tokens expire after **24 hours**. You can customize this expiration time by setting the `LITELLM_CLI_JWT_EXPIRATION_HOURS` environment variable when starting your LiteLLM Proxy:
+
+```bash
+# Set CLI JWT tokens to expire after 48 hours
+export LITELLM_CLI_JWT_EXPIRATION_HOURS=48
+export EXPERIMENTAL_UI_LOGIN="True"
+litellm --config config.yaml
+```
+
+Or in a single command:
+
+```bash
+LITELLM_CLI_JWT_EXPIRATION_HOURS=48 EXPERIMENTAL_UI_LOGIN="True" litellm --config config.yaml
+```
+
+**Examples:**
+- `LITELLM_CLI_JWT_EXPIRATION_HOURS=12` - Tokens expire after 12 hours
+- `LITELLM_CLI_JWT_EXPIRATION_HOURS=168` - Tokens expire after 7 days (168 hours)
+- `LITELLM_CLI_JWT_EXPIRATION_HOURS=720` - Tokens expire after 30 days (720 hours)
+
+:::tip
+You can check your current token's age and expiration status using:
+```bash
+litellm-proxy whoami
+```
+:::
+
 ### Steps
 
 1. **Install the CLI**

docs/my-website/docs/proxy/config_settings.md

@@ -178,6 +178,7 @@ router_settings:
 | turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data [Proxy Logging](logging) |
 | modify_params | boolean | If true, allows modifying the parameters of the request before it is sent to the LLM provider |
 | enable_preview_features | boolean | If true, enables preview features - e.g. Azure O1 Models with streaming support.|
+| LITELLM_DISABLE_STOP_SEQUENCE_LIMIT | Disable validation for stop sequence limit (default: 4) |  
 | redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) |
 | mcp_aliases | object | Maps friendly aliases to MCP server names for easier tool access. Only the first alias for each server is used. [MCP Aliases](../mcp#mcp-aliases) |
 | langfuse_default_tags | array of strings | Default tags for Langfuse Logging. Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields as tags. [Further docs](./logging#litellm-specific-tags-on-langfuse---cache_hit-cache_key) |
@@ -320,6 +321,7 @@ router_settings:
 | redis_host | string | The host address for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them** |
 | redis_password | string | The password for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them** |
 | redis_port | string | The port number for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them**|
+| redis_db | int | The database number for the Redis server. **Only set this if you have multiple instances of LiteLLM Proxy and want current tpm/rpm tracking to be shared across them**|
 | enable_pre_call_check | boolean | If true, checks if a call is within the model's context window before making the call. [More information here](reliability) |
 | 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) |
@@ -339,7 +341,7 @@ router_settings:
 | stream_timeout | Optional[float] | The default timeout for a streaming request. If not set, the 'timeout' value is used. |
 | debug_level | Literal["DEBUG", "INFO"] | The debug level for the logging library in the router. Defaults to "INFO". |
 | client_ttl | int | Time-to-live for cached clients in seconds. Defaults to 3600. |
-| cache_kwargs | dict | Additional keyword arguments for the cache initialization. |
+| cache_kwargs | dict | Additional keyword arguments for the cache initialization. Use this for non-string Redis parameters that may fail when set via `REDIS_*` environment variables. |
 | routing_strategy_args | dict | Additional keyword arguments for the routing strategy - e.g. lowest latency routing default ttl |
 | model_group_alias | dict | Model group alias mapping. E.g. `{"claude-3-haiku": "claude-3-haiku-20240229"}` |
 | num_retries | int | Number of retries for a request. Defaults to 3. |
@@ -397,6 +399,7 @@ router_settings:
 | AUDIO_SPEECH_CHUNK_SIZE | Chunk size for audio speech processing. Default is 1024
 | ANTHROPIC_API_KEY | API key for Anthropic service
 | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com
+| ANTHROPIC_TOKEN_COUNTING_BETA_VERSION | Beta version header for Anthropic token counting API. Default is `token-counting-2024-11-01`
 | AWS_ACCESS_KEY_ID | Access Key ID for AWS services
 | AWS_BATCH_ROLE_ARN | ARN of the AWS IAM role for batch operations
 | AWS_DEFAULT_REGION | Default AWS region for service interactions when AWS_REGION is not set
@@ -412,6 +415,8 @@ router_settings:
 | AWS_WEB_IDENTITY_TOKEN | Web identity token for AWS
 | AWS_WEB_IDENTITY_TOKEN_FILE | Path to file containing web identity token for AWS
 | AZURE_API_VERSION | Version of the Azure API being used
+| AZURE_AI_API_BASE | Base URL for Azure AI services (e.g., Azure AI Anthropic)
+| AZURE_AI_API_KEY | API key for Azure AI services (e.g., Azure AI Anthropic)
 | AZURE_AUTHORITY_HOST | Azure authority host URL
 | AZURE_CERTIFICATE_PASSWORD | Password for Azure OpenAI certificate
 | AZURE_CLIENT_ID | Client ID for Azure services
@@ -448,9 +453,19 @@ router_settings:
 | BERRISPEND_ACCOUNT_ID | Account ID for BerriSpend service
 | BRAINTRUST_API_KEY | API key for Braintrust integration
 | BRAINTRUST_API_BASE | Base URL for Braintrust API. Default is https://api.braintrustdata.com/v1
+| BRAINTRUST_MOCK | Enable mock mode for Braintrust integration testing. When set to true, intercepts Braintrust API calls and returns mock responses without making actual network calls. Default is false
+| BRAINTRUST_MOCK_LATENCY_MS | Mock latency in milliseconds for Braintrust API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms
 | CACHED_STREAMING_CHUNK_DELAY | Delay in seconds for cached streaming chunks. Default is 0.02
+| CHATGPT_API_BASE | Base URL for ChatGPT API. Default is https://chatgpt.com/backend-api/codex
+| CHATGPT_AUTH_FILE | Filename for ChatGPT authentication data. Default is "auth.json"
+| CHATGPT_DEFAULT_INSTRUCTIONS | Default system instructions for ChatGPT provider
+| CHATGPT_ORIGINATOR | Originator identifier for ChatGPT API requests. Default is "codex_cli_rs"
+| CHATGPT_TOKEN_DIR | Directory to store ChatGPT authentication tokens. Default is "~/.config/litellm/chatgpt"
+| CHATGPT_USER_AGENT | Custom user agent string for ChatGPT API requests
+| CHATGPT_USER_AGENT_SUFFIX | Suffix to append to the ChatGPT user agent string
 | CIRCLE_OIDC_TOKEN | OpenID Connect token for CircleCI
 | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI
+| CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours. Can also be set via LITELLM_CLI_JWT_EXPIRATION_HOURS
 | CLOUDZERO_API_KEY | CloudZero API key for authentication
 | CLOUDZERO_CONNECTION_ID | CloudZero connection ID for data submission
 | CLOUDZERO_EXPORT_INTERVAL_MINUTES | Interval in minutes for CloudZero data export operations
@@ -493,12 +508,15 @@ router_settings:
 | DD_AGENT_HOST | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API
 | DD_AGENT_PORT | Port of DataDog agent for log intake. Default is 10518
 | DD_API_KEY | API key for Datadog integration
+| DD_APP_KEY | Application key for Datadog Cost Management integration. Required along with DD_API_KEY for cost metrics
 | DD_SITE | Site URL for Datadog (e.g., datadoghq.com)
 | DD_SOURCE | Source identifier for Datadog logs
 | DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE | Resource name for Datadog tracing of streaming chunk yields. Default is "streaming.chunk.yield"
 | DD_ENV | Environment identifier for Datadog logs. Only supported for `datadog_llm_observability` callback
 | DD_SERVICE | Service identifier for Datadog logs. Defaults to "litellm-server"
 | DD_VERSION | Version identifier for Datadog logs. Defaults to "unknown"
+| DATADOG_MOCK | Enable mock mode for Datadog integration testing. When set to true, intercepts Datadog API calls and returns mock responses without making actual network calls. Default is false
+| DATADOG_MOCK_LATENCY_MS | Mock latency in milliseconds for Datadog API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms
 | DEBUG_OTEL | Enable debug mode for OpenTelemetry
 | DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3
 | DEFAULT_A2A_AGENT_TIMEOUT | Default timeout in seconds for A2A (Agent-to-Agent) protocol requests. Default is 6000
@@ -527,6 +545,9 @@ router_settings:
 | DEFAULT_MAX_TOKENS | Default maximum tokens for LLM calls. Default is 4096
 | DEFAULT_MAX_TOKENS_FOR_TRITON | Default maximum tokens for Triton models. Default is 2000
 | DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE | Default maximum size for redis batch cache. Default is 1000
+| DEFAULT_MCP_SEMANTIC_FILTER_EMBEDDING_MODEL | Default embedding model for MCP semantic tool filtering. Default is "text-embedding-3-small"
+| DEFAULT_MCP_SEMANTIC_FILTER_SIMILARITY_THRESHOLD | Default similarity threshold for MCP semantic tool filtering. Default is 0.3
+| DEFAULT_MCP_SEMANTIC_FILTER_TOP_K | Default number of top results to return for MCP semantic tool filtering. Default is 10
 | DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT | Default token count for mock response completions. Default is 20
 | DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT | Default token count for mock response prompts. Default is 10
 | DEFAULT_MODEL_CREATED_AT_TIME | Default creation timestamp for models. Default is 1677610602
@@ -600,9 +621,12 @@ router_settings:
 | GALILEO_USERNAME | Username for Galileo authentication
 | GOOGLE_SECRET_MANAGER_PROJECT_ID | Project ID for Google Secret Manager
 | GCS_BUCKET_NAME | Name of the Google Cloud Storage bucket
+| GCS_MOCK | Enable mock mode for GCS integration testing. When set to true, intercepts GCS API calls and returns mock responses without making actual network calls. Default is false
+| GCS_MOCK_LATENCY_MS | Mock latency in milliseconds for GCS API calls when mock mode is enabled. Simulates network round-trip time. Default is 150ms
 | GCS_PATH_SERVICE_ACCOUNT | Path to the Google Cloud service account JSON file
 | GCS_FLUSH_INTERVAL | Flush interval for GCS logging (in seconds). Specify how often you want a log to be sent to GCS. **Default is 20 seconds**
 | GCS_BATCH_SIZE | Batch size for GCS logging. Specify after how many logs you want to flush to GCS. If `BATCH_SIZE` is set to 10, logs are flushed every 10 logs. **Default is 2048**
+| GCS_USE_BATCHED_LOGGING | Enable batched logging for GCS. When enabled (default), multiple log payloads are combined into single GCS object uploads (NDJSON format), dramatically reducing API calls. When disabled, sends each log individually as separate GCS objects (legacy behavior). **Default is true**
 | GCS_PUBSUB_TOPIC_ID | PubSub Topic ID to send LiteLLM SpendLogs to.
 | GCS_PUBSUB_PROJECT_ID | PubSub Project ID to send LiteLLM SpendLogs to.
 | GENERIC_AUTHORIZATION_ENDPOINT | Authorization endpoint for generic OAuth providers
@@ -624,6 +648,10 @@ router_settings:
 | GENERIC_USERINFO_ENDPOINT | Endpoint to fetch user information in generic OAuth
 | GENERIC_LOGGER_ENDPOINT | Endpoint URL for the Generic Logger callback to send logs to
 | GENERIC_LOGGER_HEADERS | JSON string of headers to include in Generic Logger callback requests
+| GENERIC_ROLE_MAPPINGS_DEFAULT_ROLE | Default LiteLLM role to assign when no role mapping matches in generic SSO. Used with GENERIC_ROLE_MAPPINGS_ROLES
+| GENERIC_ROLE_MAPPINGS_GROUP_CLAIM | The claim/attribute name in the SSO token that contains the user's groups. Used for role mapping
+| GENERIC_ROLE_MAPPINGS_ROLES | Python dict string mapping LiteLLM roles to SSO group names. Example: `{"proxy_admin": ["admin-group"], "internal_user": ["users"]}`
+| GENERIC_USER_ROLE_MAPPINGS | Alternative to GENERIC_ROLE_MAPPINGS_ROLES for configuring user role mappings from SSO
 | GEMINI_API_BASE | Base URL for Gemini API. Default is https://generativelanguage.googleapis.com
 | GALILEO_BASE_URL | Base URL for Galileo platform
 | GALILEO_PASSWORD | Password for Galileo authentication
@@ -660,6 +688,8 @@ router_settings:
 | HCP_VAULT_CERT_ROLE | Role for [Hashicorp Vault Secret Manager Auth](../secret.md#hashicorp-vault)
 | HELICONE_API_KEY | API key for Helicone service
 | HELICONE_API_BASE | Base URL for Helicone service, defaults to `https://api.helicone.ai`
+| HELICONE_MOCK | Enable mock mode for Helicone integration testing. When set to true, intercepts Helicone API calls and returns mock responses without making actual network calls. Default is false
+| HELICONE_MOCK_LATENCY_MS | Mock latency in milliseconds for Helicone API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms
 | HOSTNAME | Hostname for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog)
 | HOURS_IN_A_DAY | Hours in a day for calculation purposes. Default is 24
 | HIDDENLAYER_API_BASE | Base URL for HiddenLayer API. Defaults to `https://api.hiddenlayer.ai`
@@ -685,6 +715,8 @@ router_settings:
 | LANGFUSE_FLUSH_INTERVAL | Interval for flushing Langfuse logs
 | LANGFUSE_TRACING_ENVIRONMENT | Environment for Langfuse tracing
 | LANGFUSE_HOST | Host URL for Langfuse service
+| LANGFUSE_MOCK | Enable mock mode for Langfuse integration testing. When set to true, intercepts Langfuse API calls and returns mock responses without making actual network calls. Default is false
+| LANGFUSE_MOCK_LATENCY_MS | Mock latency in milliseconds for Langfuse API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms
 | LANGFUSE_PUBLIC_KEY | Public key for Langfuse authentication
 | LANGFUSE_RELEASE | Release version of Langfuse integration
 | LANGFUSE_SECRET_KEY | Secret key for Langfuse authentication
@@ -696,6 +728,8 @@ router_settings:
 | LANGSMITH_PROJECT | Project name for Langsmith integration
 | LANGSMITH_SAMPLING_RATE | Sampling rate for Langsmith logging
 | LANGSMITH_TENANT_ID | Tenant ID for Langsmith multi-tenant deployments
+| LANGSMITH_MOCK | Enable mock mode for Langsmith integration testing. When set to true, intercepts Langsmith API calls and returns mock responses without making actual network calls. Default is false
+| LANGSMITH_MOCK_LATENCY_MS | Mock latency in milliseconds for Langsmith API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms
 | LANGTRACE_API_KEY | API key for Langtrace service
 | LASSO_API_BASE | Base URL for Lasso API
 | LASSO_API_KEY | API key for Lasso service
@@ -707,8 +741,10 @@ router_settings:
 | LITERAL_API_URL | API URL for Literal service
 | LITERAL_BATCH_SIZE | Batch size for Literal operations
 | LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX | Disable automatic URL suffix appending for Anthropic API base URLs. When set to `true`, prevents LiteLLM from automatically adding `/v1/messages` or `/v1/complete` to custom Anthropic API endpoints
+| LITELLM_CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours
 | LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API
 | LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518
+| LITELLM_DD_LLM_OBS_PORT | Port for Datadog LLM Observability agent. Default is 8126
 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI
 | LITELLM_DROP_PARAMS | Parameters to drop in LiteLLM requests
 | LITELLM_MODIFY_PARAMS | Parameters to modify in LiteLLM requests
@@ -755,6 +791,7 @@ router_settings:
 | LOGGING_WORKER_AGGRESSIVE_CLEAR_COOLDOWN_SECONDS | Cooldown time in seconds before allowing another aggressive clear operation when the queue is full. Default is 0.5 
 | MAX_STRING_LENGTH_PROMPT_IN_DB | Maximum length for strings in spend logs when sanitizing request bodies. Strings longer than this will be truncated. Default is 1000
 | MAX_IN_MEMORY_QUEUE_FLUSH_COUNT | Maximum count for in-memory queue flush operations. Default is 1000
+| MAX_IMAGE_URL_DOWNLOAD_SIZE_MB | Maximum size in MB for downloading images from URLs. Prevents memory issues from downloading very large images. Images exceeding this limit will be rejected before download. Set to 0 to completely disable image URL handling (all image_url requests will be blocked). Default is 50MB (matching [OpenAI's limit](https://platform.openai.com/docs/guides/images-vision?api-mode=chat#image-input-requirements))
 | MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES | Maximum length for the long side of high-resolution images. Default is 2000
 | MAX_REDIS_BUFFER_DEQUEUE_COUNT | Maximum count for Redis buffer dequeue operations. Default is 100
 | MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES | Maximum length for the short side of high-resolution images. Default is 768
@@ -768,6 +805,7 @@ router_settings:
 | MAXIMUM_TRACEBACK_LINES_TO_LOG | Maximum number of lines to log in traceback in LiteLLM Logs UI. Default is 100
 | MAX_RETRY_DELAY | Maximum delay in seconds for retrying requests. Default is 8.0
 | MAX_LANGFUSE_INITIALIZED_CLIENTS | Maximum number of Langfuse clients to initialize on proxy. Default is 50. This is set since langfuse initializes 1 thread everytime a client is initialized. We've had an incident in the past where we reached 100% cpu utilization because Langfuse was initialized several times.
+| MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH | Maximum header length for MCP semantic filter tools. Default is 150
 | MIN_NON_ZERO_TEMPERATURE | Minimum non-zero temperature value. Default is 0.0001
 | MINIMUM_PROMPT_CACHE_TOKEN_COUNT | Minimum token count for caching a prompt. Default is 1024
 | MISTRAL_API_BASE | Base URL for Mistral API. Default is https://api.mistral.ai
@@ -792,6 +830,7 @@ router_settings:
 | OPENAI_BASE_URL | Base URL for OpenAI API
 | OPENAI_API_BASE | Base URL for OpenAI API. Default is https://api.openai.com/
 | OPENAI_API_KEY | API key for OpenAI services
+| OPENAI_CHATGPT_API_BASE | Alternative to CHATGPT_API_BASE. Base URL for ChatGPT API
 | OPENAI_FILE_SEARCH_COST_PER_1K_CALLS | Cost per 1000 calls for OpenAI file search. Default is 0.0025
 | OPENAI_ORGANIZATION | Organization identifier for OpenAI
 | OPENID_BASE_URL | Base URL for OpenID Connect services
@@ -802,6 +841,7 @@ router_settings:
 | OPENMETER_EVENT_TYPE | Type of events sent to OpenMeter
 | ONYX_API_BASE | Base URL for Onyx Security AI Guard service (defaults to https://ai-guard.onyx.security)
 | ONYX_API_KEY | API key for Onyx Security AI Guard service
+| ONYX_TIMEOUT | Timeout in seconds for Onyx Guard server requests. Default is 10
 | OTEL_ENDPOINT | OpenTelemetry endpoint for traces
 | OTEL_EXPORTER_OTLP_ENDPOINT | OpenTelemetry endpoint for traces
 | OTEL_ENVIRONMENT_NAME | Environment name for OpenTelemetry
@@ -825,6 +865,8 @@ router_settings:
 | POD_NAME | Pod name for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog) as `POD_NAME` 
 | POSTHOG_API_KEY | API key for PostHog analytics integration
 | POSTHOG_API_URL | Base URL for PostHog API (defaults to https://us.i.posthog.com)
+| POSTHOG_MOCK | Enable mock mode for PostHog integration testing. When set to true, intercepts PostHog API calls and returns mock responses without making actual network calls. Default is false
+| POSTHOG_MOCK_LATENCY_MS | Mock latency in milliseconds for PostHog API calls when mock mode is enabled. Simulates network round-trip time. Default is 100ms
 | PREDIBASE_API_BASE | Base URL for Predibase API
 | PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service
 | PRESIDIO_ANONYMIZER_API_BASE | Base URL for Presidio Anonymizer service
@@ -862,9 +904,12 @@ router_settings:
 | ROUTER_MAX_FALLBACKS | Maximum number of fallbacks for router. Default is 5
 | RUNWAYML_DEFAULT_API_VERSION | Default API version for RunwayML service. Default is "2024-11-06"
 | RUNWAYML_POLLING_TIMEOUT | Timeout in seconds for RunwayML image generation polling. Default is 600 (10 minutes)
+| S3_VECTORS_DEFAULT_DIMENSION | Default vector dimension for S3 Vectors RAG ingestion. Default is 1024
+| S3_VECTORS_DEFAULT_DISTANCE_METRIC | Default distance metric for S3 Vectors RAG ingestion. Options: "cosine", "euclidean". Default is "cosine"
 | SECRET_MANAGER_REFRESH_INTERVAL | Refresh interval in seconds for secret manager. Default is 86400 (24 hours)
 | SEPARATE_HEALTH_APP | If set to '1', runs health endpoints on a separate ASGI app and port. Default: '0'.
 | SEPARATE_HEALTH_PORT | Port for the separate health endpoints app. Only used if SEPARATE_HEALTH_APP=1. Default: 4001.
+| SUPERVISORD_STOPWAITSECS | Upper bound timeout in seconds for graceful shutdown when SEPARATE_HEALTH_APP=1. Default: 3600 (1 hour).
 | SERVER_ROOT_PATH | Root path for the server application
 | SEND_USER_API_KEY_ALIAS | Flag to send user API key alias to Zscaler AI Guard. Default is False
 | SEND_USER_API_KEY_TEAM_ID | Flag to send user API key team ID to Zscaler AI Guard. Default is False

docs/my-website/docs/proxy/custom_pricing.md

@@ -9,6 +9,7 @@ LiteLLM provides flexible cost tracking and pricing customization for all LLM pr
 - **Custom Pricing** - Override default model costs or set pricing for custom models
 - **Cost Per Token** - Track costs based on input/output tokens (most common)
 - **Cost Per Second** - Track costs based on runtime (e.g., Sagemaker)
+- **Zero-Cost Models** - Bypass budget checks for free/on-premises models by setting costs to 0
 - **[Provider Discounts](./provider_discounts.md)** - Apply percentage-based discounts to specific providers
 - **[Provider Margins](./provider_margins.md)** - Add fees/margins to LLM costs for internal billing
 - **Base Model Mapping** - Ensure accurate cost tracking for Azure deployments
@@ -106,6 +107,51 @@ There are other keys you can use to specify costs for different scenarios and mo
 
 These keys evolve based on how new models handle multimodality. The latest version can be found at [https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
 
+## Zero-Cost Models (Bypass Budget Checks)
+
+**Use Case**: You have on-premises or free models that should be accessible even when users exceed their budget limits.
+
+**Solution** ✅: Set both `input_cost_per_token` and `output_cost_per_token` to `0` (explicitly) to bypass all budget checks for that model.
+
+:::info
+
+When a model is configured with zero cost, LiteLLM will automatically skip ALL budget checks (user, team, team member, end-user, organization, and global proxy budget) for requests to that model.
+
+**Important**: Both costs must be **explicitly set to 0**. If costs are `null` or undefined, the model will be treated as having cost and budget checks will apply.
+
+:::
+
+### Configuration Example
+
+```yaml
+model_list:
+  # On-premises model - free to use
+  - model_name: on-prem-llama
+    litellm_params:
+      model: ollama/llama3
+      api_base: http://localhost:11434
+    model_info:
+      input_cost_per_token: 0   # 👈 Explicitly set to 0
+      output_cost_per_token: 0  # 👈 Explicitly set to 0
+  
+  # Paid cloud model - budget checks apply
+  - model_name: gpt-4
+    litellm_params:
+      model: gpt-4
+      api_key: os.environ/OPENAI_API_KEY
+    # No model_info - uses default pricing from cost map
+```
+
+### Behavior
+
+With the above configuration:
+
+- **User over budget** → Can still use `on-prem-llama` ✅, but blocked from `gpt-4` ❌
+- **Team over budget** → Can still use `on-prem-llama` ✅, but blocked from `gpt-4` ❌
+- **End-user over budget** → Can still use `on-prem-llama` ✅, but blocked from `gpt-4` ❌
+
+This ensures your free/on-premises models remain accessible regardless of budget constraints, while paid models are still properly governed.
+
 ## Set 'base_model' for Cost Tracking (e.g. Azure deployments)
 
 **Problem**: Azure returns `gpt-4` in the response when `azure/gpt-4-1106-preview` is used. This leads to inaccurate cost tracking
@@ -127,6 +173,28 @@ model_list:
       base_model: azure/gpt-4-1106-preview
 ```
 
+### OpenAI Models with Dated Versions
+
+`base_model` is also useful when OpenAI returns a dated model name in the response that differs from your configured model name.
+
+**Example**: You configure custom pricing for `gpt-4o-mini-audio-preview`, but OpenAI returns `gpt-4o-mini-audio-preview-2024-12-17` in the response. Since LiteLLM uses the response model name for pricing lookup, your custom pricing won't be applied.
+
+**Solution** ✅: Set `base_model` to the key you want LiteLLM to use for pricing lookup.
+
+```yaml
+model_list:
+  - model_name: my-audio-model
+    litellm_params:
+      model: openai/gpt-4o-mini-audio-preview
+      api_key: os.environ/OPENAI_API_KEY
+    model_info:
+      base_model: gpt-4o-mini-audio-preview  # 👈 Used for pricing lookup
+      input_cost_per_token: 0.0000006
+      output_cost_per_token: 0.0000024
+      input_cost_per_audio_token: 0.00001
+      output_cost_per_audio_token: 0.00002
+```
+
 
 ## Debugging 
 

docs/my-website/docs/proxy/deleted_keys_teams.md

@@ -0,0 +1,106 @@
+import Image from '@theme/IdealImage';
+
+# Deleted Keys & Teams Audit Logs
+
+<Image img={require('../../img/ui_deleted_keys_table.png')} />
+
+View deleted API keys and teams along with their spend and budget information at the time of deletion for auditing and compliance purposes.
+
+## Overview
+
+The Deleted Keys & Teams feature provides a comprehensive audit trail for deleted entities in your LiteLLM proxy. This feature was implemented to easily allow audits of which key or team was deleted along with the spend/budget at the time of deletion.
+
+When a key or team is deleted, LiteLLM automatically captures:
+
+- **Deletion timestamp** - When the entity was deleted
+- **Deleted by** - Who performed the deletion action
+- **Spend at deletion** - The total spend accumulated at the time of deletion
+- **Original budget** - The budget that was set for the entity before deletion
+- **Entity details** - Key or team identification information
+
+This information is preserved even after deletion, allowing you to maintain accurate financial records and audit trails for compliance purposes.
+
+## Viewing Deleted Keys
+
+### Step 1: Navigate to API Keys Page
+
+Navigate to the API Keys page in the LiteLLM UI:
+
+```
+http://localhost:4000/ui/?login=success&page=api-keys
+```
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/73b97ba9-0ab5-4140-aee2-05fa90463461/ascreenshot_5e6d9f05d452405c83d7a368349d087d_text_export.jpeg)
+
+### Step 2: Access Logs Section
+
+Click on the "Logs" menu item in the navigation.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/73b97ba9-0ab5-4140-aee2-05fa90463461/ascreenshot_8ebab354b1e542e59e1082e519927edd_text_export.jpeg)
+
+### Step 3: View Deleted Keys
+
+Click on "Deleted Keys" to view the table of all deleted API keys.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/00668558-9326-4a6f-8e87-159d54b17a72/ascreenshot_d0e50e49e9aa43d4a22ada6f12a78b12_text_export.jpeg)
+
+### Step 4: Review Deletion Information
+
+The Deleted Keys table includes comprehensive information about each deleted key:
+
+- **When** the key was deleted (timestamp)
+- **Who** deleted the key (user/admin information)
+- **Key identification** details
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/8538f7c4-634e-44c8-8d7d-fafbd6da0b02/ascreenshot_6b73f9c6a52d4e40a2368ef441cf6c8f_text_export.jpeg)
+
+### Step 5: View Financial Information
+
+The table also displays financial information captured at the time of deletion:
+
+- **Spend at deletion** - Total spend accumulated when the key was deleted
+- **Original budget** - The budget limit that was set for the key
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/f8b03850-b17c-490c-a507-c3b0b6c050ab/ascreenshot_070b139f111844bba38fbed8835b097b_text_export.jpeg)
+
+## Viewing Deleted Teams
+
+### Step 1: Access Deleted Teams
+
+From the Logs section, click on "Deleted Teams" to view all deleted teams.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/716ce26f-09af-4a6d-99c5-921d6b6a8555/ascreenshot_d36c16f1cf894340aa8bc20ada5922ac_text_export.jpeg)
+
+### Step 2: Review Team Deletion Information
+
+The Deleted Teams table provides detailed information about each deleted team:
+
+- **When** the team was deleted (timestamp)
+- **Who** deleted the team (user/admin information)
+- **Team identification** details
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/0a3f2d3f-179a-4ad7-916e-b77a13dca01d/ascreenshot_ded5970762d54528ae656421148116c4_text_export.jpeg)
+
+### Step 3: View Team Financial Information
+
+Similar to deleted keys, the Deleted Teams table shows financial information:
+
+- **Spend at deletion** - Total spend accumulated when the team was deleted
+- **Original budget** - The budget limit that was set for the team
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-17/5b24871f-b57e-404d-8fbe-a4b27cb2a6a0/ascreenshot_3121fbafbd6b4abf90993ce6c03c608d_text_export.jpeg)
+
+## Use Cases
+
+This feature is particularly useful for:
+
+- **Financial Auditing** - Track spend and budgets for deleted entities
+- **Compliance** - Maintain records of who deleted what and when
+- **Cost Analysis** - Understand spending patterns before deletion
+- **Accountability** - Identify which admin or user performed deletions
+- **Historical Records** - Preserve financial data even after entity deletion
+
+## Related Features
+
+- [Audit Logs](./multiple_admins.md) - View comprehensive audit logs for all entity changes
+- [UI Logs](./ui_logs.md) - View request logs and spend tracking

docs/my-website/docs/proxy/deploy.md

@@ -4,6 +4,10 @@ import Image from '@theme/IdealImage';
 
 # Docker, Helm, Terraform
 
+:::info No Limits on LiteLLM OSS
+There are **no limits** on the number of users, keys, or teams you can create on LiteLLM OSS.
+:::
+
 You can find the Dockerfile to build litellm proxy [here](https://github.com/BerriAI/litellm/blob/main/Dockerfile)
 
 > Note: Production requires at least 4 CPU cores and 8 GB RAM.
@@ -196,6 +200,7 @@ Example `requirements.txt`
 
 ```shell
 litellm[proxy]==1.57.3 # Specify the litellm version you want to use
+litellm-enterprise
 prometheus_client
 langfuse
 prisma

docs/my-website/docs/proxy/embedding.md

@@ -6,6 +6,16 @@ import TabItem from '@theme/TabItem';
 
 See supported Embedding Providers & Models [here](https://docs.litellm.ai/docs/embedding/supported_embedding)
 
+## Supported Input Formats
+
+The `/v1/embeddings` endpoint follows the [OpenAI embeddings API specification](https://platform.openai.com/docs/api-reference/embeddings/create). The following input formats are supported:
+
+| Format | Example |
+|--------|---------|
+| String | `"input": "Hello"` |
+| Array of strings | `"input": ["Hello", "World"]` |
+| Array of tokens (integers) | `"input": [1234, 5678, 9012]` |
+| Array of token arrays | `"input": [[1234, 5678], [9012, 3456]]` |
 
 ## Quick start
 Here's how to route between GPT-J embedding (sagemaker endpoint), Amazon Titan embedding (Bedrock) and Azure OpenAI embedding on the proxy server: 

docs/my-website/docs/proxy/fallback_management.md

@@ -76,7 +76,7 @@ response = requests.post(
 print(response.json())
 ```
 
-### GET /fallback/{model}
+### GET /fallback/\{model\}
 
 Get fallback configuration for a specific model.
 
@@ -112,7 +112,7 @@ response = requests.get(
 print(response.json())
 ```
 
-### DELETE /fallback/{model}
+### DELETE /fallback/\{model\}
 
 Delete fallback configuration for a specific model.
 
@@ -150,9 +150,6 @@ print(response.json())
 
 ### Test fallback
 
-</TabItem>
-<TabItem value="proxy" label="PROXY">
-
 ```bash
 curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 -H 'Content-Type: application/json' \
@@ -170,9 +167,6 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 '
 ```
 
-</TabItem>
-</Tabs>
-
 
 
 ## Validation

docs/my-website/docs/proxy/guardrails/aim_security.md

@@ -46,6 +46,7 @@ guardrails:
       mode: [pre_call, post_call] # "During_call" is also available
       api_key: os.environ/AIM_API_KEY
       api_base: os.environ/AIM_API_BASE # Optional, use only when using a self-hosted Aim Outpost
+      ssl_verify: False # Optional, set to False to disable SSL verification or a string path to a custom CA bundle
 ```
 
 Under the `api_key`, insert the API key you were issued. The key can be found in the guard's page.

docs/my-website/docs/proxy/guardrails/guardrail_policies.md

@@ -0,0 +1,283 @@
+# [Beta] Guardrail Policies
+
+Use policies to group guardrails and control which ones run for specific teams, keys, or models.
+
+## Why use policies?
+
+- Enable/disable specific guardrails for teams, keys, or models
+- Group guardrails into a single policy
+- Inherit from existing policies and override what you need
+
+## Quick Start
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: openai/gpt-4
+
+# 1. Define your guardrails
+guardrails:
+  - guardrail_name: pii_masking
+    litellm_params:
+      guardrail: presidio
+      mode: pre_call
+
+  - guardrail_name: prompt_injection
+    litellm_params:
+      guardrail: lakera
+      mode: pre_call
+      api_key: os.environ/LAKERA_API_KEY
+
+# 2. Create a policy
+policies:
+  my-policy:
+    guardrails:
+      add:
+        - pii_masking
+        - prompt_injection
+
+# 3. Attach the policy
+policy_attachments:
+  - policy: my-policy
+    scope: "*"  # apply to all requests
+```
+
+Response headers show what ran:
+
+```
+x-litellm-applied-policies: my-policy
+x-litellm-applied-guardrails: pii_masking,prompt_injection
+```
+
+## Add guardrails for a specific team
+
+:::info
+✨ Enterprise only feature for team/key-based policy attachments. [Get a free trial](https://www.litellm.ai/enterprise#trial)
+:::
+
+You have a global baseline, but want to add extra guardrails for a specific team.
+
+```yaml showLineNumbers title="config.yaml"
+policies:
+  global-baseline:
+    guardrails:
+      add:
+        - pii_masking
+
+  finance-team-policy:
+    inherit: global-baseline
+    guardrails:
+      add:
+        - strict_compliance_check
+        - audit_logger
+
+policy_attachments:
+  - policy: global-baseline
+    scope: "*"
+
+  - policy: finance-team-policy
+    teams:
+      - finance  # team alias from /team/new
+```
+
+Now the `finance` team gets `pii_masking` + `strict_compliance_check` + `audit_logger`, while everyone else just gets `pii_masking`.
+
+## Remove guardrails for a specific team
+
+:::info
+✨ Enterprise only feature for team/key-based policy attachments. [Get a free trial](https://www.litellm.ai/enterprise#trial)
+:::
+
+You have guardrails running globally, but want to disable some for a specific team (e.g., internal testing).
+
+```yaml showLineNumbers title="config.yaml"
+policies:
+  global-baseline:
+    guardrails:
+      add:
+        - pii_masking
+        - prompt_injection
+
+  internal-team-policy:
+    inherit: global-baseline
+    guardrails:
+      remove:
+        - pii_masking  # don't need PII masking for internal testing
+
+policy_attachments:
+  - policy: global-baseline
+    scope: "*"
+
+  - policy: internal-team-policy
+    teams:
+      - internal-testing  # team alias from /team/new
+```
+
+Now the `internal-testing` team only gets `prompt_injection`, while everyone else gets both guardrails.
+
+## Inheritance
+
+Start with a base policy and build on it:
+
+```yaml showLineNumbers title="config.yaml"
+policies:
+  base:
+    guardrails:
+      add:
+        - pii_masking
+        - toxicity_filter
+
+  strict:
+    inherit: base
+    guardrails:
+      add:
+        - prompt_injection
+
+  relaxed:
+    inherit: base
+    guardrails:
+      remove:
+        - toxicity_filter
+```
+
+What you get:
+- `base` → `[pii_masking, toxicity_filter]`
+- `strict` → `[pii_masking, toxicity_filter, prompt_injection]`
+- `relaxed` → `[pii_masking]`
+
+## Model Conditions
+
+Run guardrails only for specific models:
+
+```yaml showLineNumbers title="config.yaml"
+policies:
+  gpt4-safety:
+    guardrails:
+      add:
+        - strict_content_filter
+    condition:
+      model: "gpt-4.*"  # regex - matches gpt-4, gpt-4-turbo, gpt-4o
+
+  bedrock-compliance:
+    guardrails:
+      add:
+        - audit_logger
+    condition:
+      model:  # exact match list
+        - bedrock/claude-3
+        - bedrock/claude-2
+```
+
+## Attachments
+
+Policies don't do anything until you attach them. Attachments tell LiteLLM *where* to apply each policy.
+
+**Global** - runs on every request:
+
+```yaml showLineNumbers title="config.yaml"
+policy_attachments:
+  - policy: default
+    scope: "*"
+```
+
+**Team-specific** (uses team alias from `/team/new`):
+
+```yaml showLineNumbers title="config.yaml"
+policy_attachments:
+  - policy: hipaa-compliance
+    teams:
+      - healthcare-team  # team alias
+      - medical-research  # team alias
+```
+
+**Key-specific** (uses key alias from `/key/generate`, wildcards supported):
+
+```yaml showLineNumbers title="config.yaml"
+policy_attachments:
+  - policy: internal-testing
+    keys:
+      - "dev-*"  # key alias pattern
+      - "test-*"  # key alias pattern
+```
+
+## Config Reference
+
+### `policies`
+
+```yaml
+policies:
+  <policy-name>:
+    description: ...
+    inherit: ...
+    guardrails:
+      add: [...]
+      remove: [...]
+    condition:
+      model: ...
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `description` | `string` | Optional. What this policy does. |
+| `inherit` | `string` | Optional. Parent policy to inherit guardrails from. |
+| `guardrails.add` | `list[string]` | Guardrails to enable. |
+| `guardrails.remove` | `list[string]` | Guardrails to disable (useful with inheritance). |
+| `condition.model` | `string` or `list[string]` | Optional. Only apply when model matches. Supports regex. |
+
+### `policy_attachments`
+
+```yaml
+policy_attachments:
+  - policy: ...
+    scope: ...
+    teams: [...]
+    keys: [...]
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `policy` | `string` | **Required.** Name of the policy to attach. |
+| `scope` | `string` | Use `"*"` to apply globally. |
+| `teams` | `list[string]` | Team aliases (from `/team/new`). |
+| `keys` | `list[string]` | Key aliases (from `/key/generate`). Supports `*` wildcard. |
+
+### Response Headers
+
+| Header | Description |
+|--------|-------------|
+| `x-litellm-applied-policies` | Policies that matched this request |
+| `x-litellm-applied-guardrails` | Guardrails that actually ran |
+
+## How it works
+
+Example config:
+
+```yaml showLineNumbers title="config.yaml"
+policies:
+  base:
+    guardrails:
+      add: [pii_masking]
+
+  finance-policy:
+    inherit: base
+    guardrails:
+      add: [audit_logger]
+
+policy_attachments:
+  - policy: base
+    scope: "*"
+  - policy: finance-policy
+    teams: [finance]
+```
+
+```mermaid
+flowchart TD
+    A["Request with team_alias='finance'"] --> B["Matches policies: base, finance-policy"]
+    B --> C["Resolves guardrails: pii_masking, audit_logger"]
+```
+
+1. Request comes in with `team_alias='finance'`
+2. Matches `base` (via `scope: "*"`) and `finance-policy` (via `teams: [finance]`)
+3. Resolves guardrails: `base` adds `pii_masking`, `finance-policy` inherits and adds `audit_logger`
+4. Final guardrails: `pii_masking`, `audit_logger`

docs/my-website/docs/proxy/guardrails/onyx_security.md

@@ -128,6 +128,7 @@ guardrails:
       mode: ["pre_call", "post_call", "during_call"] # Run at multiple stages
       api_key: os.environ/ONYX_API_KEY
       api_base: os.environ/ONYX_API_BASE
+      timeout: 10.0 # Optional, defaults to 10 seconds
 ```
 
 ### Required Parameters
@@ -137,6 +138,7 @@ guardrails:
 ### Optional Parameters
 
 - **`api_base`**: Onyx API base URL (defaults to `https://ai-guard.onyx.security`)
+- **`timeout`**: Request timeout in seconds (defaults to `10.0`)
 
 ## Environment Variables
 
@@ -145,4 +147,5 @@ You can set these environment variables instead of hardcoding values in your con
 ```shell
 export ONYX_API_KEY="your-api-key-here"
 export ONYX_API_BASE="https://ai-guard.onyx.security"   # Optional
+export ONYX_TIMEOUT=10                                  # Optional, timeout in seconds
 ```

docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md

@@ -206,6 +206,7 @@ Expected successful response:
 | `mode` | No | When to run the guardrail | `pre_call` |
 | `fallback_on_error` | No | Action when PANW API is unavailable: `"block"` (fail-closed, default) or `"allow"` (fail-open). Config errors always block. | `block` |
 | `timeout` | No | PANW API call timeout in seconds (1-60) | `10.0` |
+| `violation_message_template` | No | Custom template for error message when request is blocked. Supports `{guardrail_name}`, `{category}`, `{action_type}`, `{default_message}` placeholders. | - |
 
 ### Regional Endpoints
 
@@ -449,6 +450,33 @@ LiteLLM does not alter or configure your PANW security profile. To change what c
 The guardrail is **fail-closed** by default - if the PANW API is unavailable, requests are blocked to ensure no unscanned content reaches your LLM. This provides maximum security.
 :::
 
+### Custom Violation Messages
+
+You can customize the error message returned to the user when a request is blocked by configuring the `violation_message_template` parameter. This is useful for providing user-friendly feedback instead of technical details.
+
+```yaml
+guardrails:
+  - guardrail_name: "panw-custom-message"
+    litellm_params:
+      guardrail: panw_prisma_airs
+      api_key: os.environ/PANW_PRISMA_AIRS_API_KEY
+      # Simple message
+      violation_message_template: "Your request was blocked by our AI Security Policy."
+
+  - guardrail_name: "panw-detailed-message"
+    litellm_params:
+      guardrail: panw_prisma_airs
+      api_key: os.environ/PANW_PRISMA_AIRS_API_KEY
+      # Message with placeholders
+      violation_message_template: "{action_type} blocked due to {category} violation. Please contact support."
+```
+
+**Supported Placeholders:**
+- `{guardrail_name}`: Name of the guardrail (e.g. "panw-custom-message")
+- `{category}`: Violation category (e.g. "malicious", "injection", "dlp")
+- `{action_type}`: "Prompt" or "Response"
+- `{default_message}`: The original technical error message
+
 ### Fail-Open Configuration
 
 By default, the PANW guardrail operates in **fail-closed** mode for maximum security. If the PANW API is unavailable (timeout, rate limit, network error), requests are blocked. You can configure **fail-open** mode for high-availability scenarios where service continuity is critical.

docs/my-website/docs/proxy/guardrails/quick_start.md

@@ -59,6 +59,18 @@ guardrails:
       presidio_score_thresholds:  # minimum confidence scores for keeping detections
         CREDIT_CARD: 0.8
         EMAIL_ADDRESS: 0.6
+
+# Example Pillar Security config via Generic Guardrail API
+  - guardrail_name: "pillar-security"
+    litellm_params:
+      guardrail: generic_guardrail_api
+      mode: [pre_call, post_call]
+      api_base: https://api.pillar.security/api/v1/integrations/litellm
+      api_key: os.environ/PILLAR_API_KEY
+      additional_provider_specific_params:
+        plr_mask: true
+        plr_evidence: true
+        plr_scanners: true
 ```
 
 
@@ -191,8 +203,12 @@ Your response headers will include `x-litellm-applied-guardrails` with the guard
 x-litellm-applied-guardrails: aporia-pre-guard
 ```
 
+### Guardrail Policies
 
-
+Need more control? Use [Guardrail Policies](./guardrail_policies.md) to:
+- Group guardrails into reusable policies
+- Enable/disable guardrails for specific teams, keys, or models
+- Inherit from existing policies and override specific guardrails
 
 ## **Using Guardrails Client Side**
 
@@ -389,14 +405,10 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 
 ## **Proxy Admin Controls**
 
-### ✨ Monitoring Guardrails
+### Monitoring Guardrails
 
 Monitor which guardrails were executed and whether they passed or failed. e.g. guardrail going rogue and failing requests we don't intend to fail
 
-:::info
-
-✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)
-
 :::
 
 #### Setup

docs/my-website/docs/proxy/keys_teams_router_settings.md

@@ -0,0 +1,150 @@
+import Image from '@theme/IdealImage';
+
+# UI - Router Settings for Keys and Teams
+
+Configure router settings at the key and team level to achieve granular control over routing behavior, fallbacks, retries, and other router configurations. This enables you to customize routing behavior for specific keys or teams without affecting global settings.
+
+## Overview
+
+Router Settings for Keys and Teams allows you to configure router behavior at different levels of granularity. Previously, router settings could only be configured globally, applying the same routing strategy, fallbacks, timeouts, and retry policies to all requests across your entire proxy instance.
+
+With key-level and team-level router settings, you can now:
+
+- **Customize routing strategies** per key or team (e.g., use `least-busy` for high-priority keys, `latency-based-routing` for others)
+- **Configure different fallback chains** for different keys or teams
+- **Set key-specific or team-specific timeouts** and retry policies
+- **Apply different reliability settings** (cooldowns, allowed failures) per key or team
+- **Override global settings** when needed for specific use cases
+
+<Image img={require('../../img/ui_granular_router_settings.png')} />
+
+## Summary
+
+Router settings follow a **hierarchical resolution order**: **Keys > Teams > Global**. When a request is made:
+
+1. **Key-level settings** are checked first. If router settings are configured for the API key being used, those settings are applied.
+2. **Team-level settings** are checked next. If the key belongs to a team and that team has router settings configured, those settings are used (unless key-level settings exist).
+3. **Global settings** are used as the final fallback. If neither key nor team settings are found, the global router settings from your proxy configuration are applied.
+
+This hierarchical approach ensures that the most specific settings take precedence, allowing you to fine-tune routing behavior for individual keys or teams while maintaining sensible defaults at the global level.
+
+## How Router Settings Resolution Works
+
+Router settings are resolved in the following priority order:
+
+### Resolution Order: Key > Team > Global
+
+1. **Key-level router settings** (highest priority)
+   - Applied when router settings are configured directly on an API key
+   - Takes precedence over all other settings
+   - Useful for individual key customization
+
+2. **Team-level router settings** (medium priority)
+   - Applied when the API key belongs to a team with router settings configured
+   - Only used if no key-level settings exist
+   - Useful for applying consistent settings across multiple keys in a team
+
+3. **Global router settings** (lowest priority)
+   - Applied from your proxy configuration file or database
+   - Used as the default when no key or team settings are found
+   - Previously, this was the only option available
+
+## How to Configure Router Settings
+
+### Configuring Router Settings for Keys
+
+Follow these steps to configure router settings for an API key:
+
+1. Navigate to [http://localhost:4000/ui/?login=success](http://localhost:4000/ui/?login=success)
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/61889da3-32de-4ebf-9cf3-7dc1db2fc993/ascreenshot_2492cf6d916a4ab98197cc8336e3a371_text_export.jpeg)
+
+2. Click "+ Create New Key" (or edit an existing key)
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/61889da3-32de-4ebf-9cf3-7dc1db2fc993/ascreenshot_5a25380cf5044b4f93c146139d84403a_text_export.jpeg)
+
+3. Click "Optional Settings"
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/e5eb5858-1cc1-4273-90bd-19ad139feebd/ascreenshot_33888989cfb9445bb83660f702ba32e0_text_export.jpeg)
+
+4. Click "Router Settings"
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/d9eeca83-1f76-4fcf-bf61-d89edf3454d3/ascreenshot_825c7993f4b24949aee9b31d4a788d8a_text_export.jpeg)
+
+5. Configure your desired router settings. For example, click "Fallbacks" to configure fallback models:
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/30ff647f-0254-4410-8311-660eef7ec0c4/ascreenshot_16966c8a0160473eb03e0f2c3b5c3afa_text_export.jpeg)
+
+6. Click "Select a model to begin configuring fallbacks" and configure your fallback chain:
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/918f1b5b-c656-4864-98bd-d8c58924b6d9/ascreenshot_79ca6cd93be04033929f080e0c8d040a_text_export.jpeg)
+
+### Configuring Router Settings for Teams
+
+Follow these steps to configure router settings for a team:
+
+1. Navigate to [http://localhost:4000/ui/?login=success](http://localhost:4000/ui/?login=success)
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/60a33a8c-2e48-4788-a1a2-e5bcffa98cca/ascreenshot_9e255ba48f914c72ae57db7d3c1c7cd5_text_export.jpeg)
+
+2. Click "Teams"
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/60a33a8c-2e48-4788-a1a2-e5bcffa98cca/ascreenshot_070934fa9c17453987f21f58117e673b_text_export.jpeg)
+
+3. Click "+ Create New Team" (or edit an existing team)
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/6f964ce2-f458-4719-a070-1af444ad92f5/ascreenshot_10f427f3106a4032a65d1046668880bd_text_export.jpeg)
+
+4. Click "Router Settings"
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/a923c4ae-29f2-42b5-93ae-12f62d442691/ascreenshot_144520f2dd2f419dad79dffb1579ec04_text_export.jpeg)
+
+5. Configure your desired router settings. For example, click "Fallbacks" to configure fallback models:
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/b062ecfa-bf5b-4c99-93a1-84b8b56fdb4c/ascreenshot_ea9acbc4e75448709b64a22addfb4157_text_export.jpeg)
+
+6. Click "Select a model to begin configuring fallbacks" and configure your fallback chain:
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-01-24/67ca2655-4e82-4f93-be9a-7244ad22640f/ascreenshot_4fdbed826cd546d784e8738626be835d_text_export.jpeg)
+
+## Use Cases
+
+### Different Routing Strategies per Key
+
+Configure different routing strategies for different use cases:
+
+- **High-priority production keys**: Use `latency-based-routing` for optimal performance
+- **Development keys**: Use `simple-shuffle` for simplicity
+- **Cost-sensitive keys**: Use `cost-based-routing` to minimize expenses
+
+### Team-Level Consistency
+
+Apply consistent router settings across all keys in a team:
+
+- Set team-wide fallback chains for reliability
+- Configure team-specific timeout policies
+- Apply uniform retry policies across team members
+
+### Override Global Settings
+
+Override global settings for specific scenarios:
+
+- Production keys may need stricter timeout policies than development
+- Certain teams may require different fallback models
+- Individual keys may need custom retry policies for specific use cases
+
+### Gradual Rollout
+
+Test new router settings on specific keys or teams before applying globally:
+
+- Configure new routing strategies on a test key first
+- Validate fallback chains on a small team before global rollout
+- A/B test different timeout values across different keys
+
+## Related Features
+
+- [Router Settings Reference](./config_settings.md#router_settings---reference) - Complete reference of all router settings
+- [Load Balancing](./load_balancing.md) - Learn about routing strategies and load balancing
+- [Reliability](./reliability.md) - Configure fallbacks, retries, and error handling
+- [Keys](./keys.md) - Manage API keys and their settings
+- [Teams](./teams.md) - Organize keys into teams

docs/my-website/docs/proxy/litellm_managed_files.md

@@ -11,7 +11,7 @@ import Image from '@theme/IdealImage';
 
 This is a free LiteLLM Enterprise feature.
 
-Available via the `litellm[proxy]` package or any `litellm` docker image.
+Available via the `litellm` docker image. If you are using the pip package, you must install [`litellm-enterprise`](https://pypi.org/project/litellm-enterprise/).
 
 :::
 

docs/my-website/docs/proxy/load_balancing.md

@@ -69,6 +69,67 @@ router_settings:
   redis_port: 1992
 ```
 
+## Enforce Model Rate Limits
+
+Strictly enforce RPM/TPM limits set on deployments. When limits are exceeded, requests are blocked **before** reaching the LLM provider with a `429 Too Many Requests` error.
+
+:::info
+By default, `rpm` and `tpm` values are only used for **routing decisions** (picking deployments with capacity). With `enforce_model_rate_limits`, they become **hard limits**.
+:::
+
+### Quick Start
+
+```yaml
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: openai/gpt-4
+      api_key: os.environ/OPENAI_API_KEY
+    rpm: 60     # 60 requests per minute
+    tpm: 90000  # 90k tokens per minute
+
+router_settings:
+  optional_pre_call_checks:
+    - enforce_model_rate_limits  # 👈 Enables strict enforcement
+```
+
+### How It Works
+
+| Limit Type | Enforcement | Accuracy |
+|------------|-------------|----------|
+| **RPM** | Hard limit - blocked at exact threshold | 100% accurate |
+| **TPM** | Best-effort - may slightly exceed | Blocked when already over limit |
+
+**Why TPM is best-effort:** Token count is unknown until the LLM responds. TPM is checked before each request (blocks if already over), and tracked after (adds actual tokens used).
+
+### Error Response
+
+```json
+{
+  "error": {
+    "message": "Model rate limit exceeded. RPM limit=60, current usage=60",
+    "type": "rate_limit_error",
+    "code": 429
+  }
+}
+```
+
+Response includes `retry-after: 60` header.
+
+### Multi-Instance Deployment
+
+For multiple LiteLLM proxy instances, add Redis to share rate limit state:
+
+```yaml
+router_settings:
+  optional_pre_call_checks:
+    - enforce_model_rate_limits
+  redis_host: redis.example.com
+  redis_port: 6379
+  redis_password: your-password
+```
+
+
 :::info
 Detailed information about [routing strategies can be found here](../routing)
 :::

docs/my-website/docs/proxy/logging.md

@@ -982,6 +982,8 @@ OTEL_ENDPOINT="http:/0.0.0.0:4317"
 OTEL_HEADERS="x-honeycomb-team=<your-api-key>" # Optional
 ```
 
+> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+
 Add `otel` as a callback on your `litellm_config.yaml`
 
 ```shell

docs/my-website/docs/proxy/prod.md

@@ -277,8 +277,13 @@ Set the following environment variable(s):
 ```bash
 SEPARATE_HEALTH_APP="1" # Default "0" 
 SEPARATE_HEALTH_PORT="8001" # Default "4001", Works only if `SEPARATE_HEALTH_APP` is "1"
+SUPERVISORD_STOPWAITSECS="3600" # Optional: Upper bound timeout in seconds for graceful shutdown. Default: 3600 (1 hour). Only used when SEPARATE_HEALTH_APP=1.
 ```
 
+**Graceful Shutdown:**
+
+Previously, `stopwaitsecs` was not set, defaulting to 10 seconds and causing in-flight requests to fail. `SUPERVISORD_STOPWAITSECS` (default: 3600) provides an upper bound for graceful shutdown, allowing uvicorn to wait for all in-flight requests to complete.
+
 <video controls width="100%" style={{ borderRadius: '8px', marginBottom: '1em' }}>
   <source src="https://cdn.loom.com/sessions/thumbnails/b08be303331246b88fdc053940d03281-1718990992822.mp4" type="video/mp4" />
   Your browser does not support the video tag.

docs/my-website/docs/proxy/prometheus.md

@@ -121,8 +121,8 @@ Use this to track overall LiteLLM Proxy usage.
 
 | Metric Name          | Description                          |
 |----------------------|--------------------------------------|
-| `litellm_proxy_failed_requests_metric`             | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class", "route"`          |
-| `litellm_proxy_total_requests_metric`             | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route"`          |
+| `litellm_proxy_failed_requests_metric`             | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "user_email", "exception_status", "exception_class", "route", "model_id"`          |
+| `litellm_proxy_total_requests_metric`             | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route", "model_id"`          |
 
 ### Callback Logging Metrics
 
@@ -130,7 +130,12 @@ Monitor failures while shipping logs to downstream callbacks like `s3_v3` cold s
 
 | Metric Name          | Description                          |
 |----------------------|--------------------------------------|
-| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`. |
+| `litellm_callback_logging_failures_metric` | Total number of failed attempts to emit logs to a configured callback. Labels: `"callback_name"`. Use this to alert on callback delivery issues such as repeated failures when writing to `s3_v3`, `langfuse`, or `langfuse_otel` and other otel providers |
+
+**Supported Callbacks:**
+- `S3Logger` - S3 v2 cold storage failures
+- `langfuse` - Langfuse logging failures
+- `otel` -  OpenTelemetry logging failures
 
 ## LLM Provider Metrics
 
@@ -191,10 +196,10 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok
 
 | Metric Name          | Description                          |
 |----------------------|--------------------------------------|
-| `litellm_request_total_latency_metric`             | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" |
+| `litellm_request_total_latency_metric`             | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model", "model_id" |
 | `litellm_overhead_latency_metric`             | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias" |
 | `litellm_llm_api_latency_metric`  | Latency (seconds) for just the LLM API call - tracked for labels "model", "hashed_api_key", "api_key_alias", "team", "team_alias", "requested_model", "end_user", "user" |
-| `litellm_llm_api_time_to_first_token_metric`             | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias` [Note: only emitted for streaming requests] |
+| `litellm_llm_api_time_to_first_token_metric`             | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias`, `requested_model`, `end_user`, `user`, `model_id` [Note: only emitted for streaming requests] |
 
 ## Tracking `end_user` on Prometheus
 

docs/my-website/docs/proxy/request_tags.md

@@ -0,0 +1,58 @@
+# Request Tags for Spend Tracking
+
+Add tags to model deployments to track spend by environment, AWS account, or any custom label.
+
+Tags appear in the `request_tags` field of LiteLLM spend logs.
+
+## Config Setup
+
+Set tags on model deployments in `config.yaml`:
+
+```yaml title="config.yaml"
+model_list:
+  - model_name: gpt-4
+    litellm_params:
+      model: azure/gpt-4-prod
+      api_key: os.environ/AZURE_PROD_API_KEY
+      api_base: https://prod.openai.azure.com/
+      tags: ["AWS_IAM_PROD"]  # 👈 Tag for production
+
+  - model_name: gpt-4-dev
+    litellm_params:
+      model: azure/gpt-4-dev
+      api_key: os.environ/AZURE_DEV_API_KEY
+      api_base: https://dev.openai.azure.com/
+      tags: ["AWS_IAM_DEV"]  # 👈 Tag for development
+```
+
+## Make Request
+
+Requests just specify the model - tags are automatically applied:
+
+```bash
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+  -H 'Authorization: Bearer sk-1234' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "gpt-4",
+    "messages": [{"role": "user", "content": "Hello"}]
+  }'
+```
+
+## Spend Logs
+
+The tag from the model config appears in `LiteLLM_SpendLogs`:
+
+```json
+{
+  "request_id": "chatcmpl-abc123",
+  "request_tags": ["AWS_IAM_PROD"],
+  "spend": 0.002,
+  "model": "gpt-4"
+}
+```
+
+## Related
+
+- [Spend Tracking Overview](cost_tracking.md)
+- [Tag Budgets](tag_budgets.md) - Set budget limits per tag

docs/my-website/docs/proxy/ui/page_visibility.md

@@ -0,0 +1,121 @@
+import Image from '@theme/IdealImage';
+
+# Control Page Visibility for Internal Users
+
+Configure which navigation tabs and pages are visible to internal users (non-admin developers) in the LiteLLM UI.
+
+Use this feature to simplify the UI and control which pages your internal users/developers can see when signing in.
+
+## Overview
+
+By default, all pages accessible to internal users are visible in the navigation sidebar. The page visibility control allows admins to restrict which pages internal users can see, creating a more focused and streamlined experience.
+
+
+## Configure Page Visibility
+
+### 1. Navigate to Settings
+
+Click the **Settings** icon in the sidebar.
+
+![Navigate to Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/cbb6f272-ab18-4996-b57d-7ed4aad721ea/ascreenshot_ab80f3175b1a41b0bdabdd2cd3980573_text_export.jpeg)
+
+### 2. Go to Admin Settings
+
+Click **Admin Settings** from the settings menu.
+
+![Go to Admin Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/e2b327bf-1cfd-4519-a9ce-8a6ecb2de53a/ascreenshot_23bb1577b3f84d22be78e0faa58dee3d_text_export.jpeg)
+
+### 3. Select UI Settings
+
+Click **UI Settings** to access the page visibility controls.
+
+![Select UI Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/fff0366a-4944-457a-8f6a-e22018dde108/ascreenshot_0e268e8651654e75bb9fb40d2ed366a9_text_export.jpeg)
+
+### 4. Open Page Visibility Configuration
+
+Click **Configure Page Visibility** to expand the configuration panel.
+
+![Open Configuration](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/3a4761d6-145a-4afd-8abf-d92744b9ac9f/ascreenshot_23c16eb79c32481887b879d961f1f00a_text_export.jpeg)
+
+### 5. Select Pages to Make Visible
+
+Check the boxes for the pages you want internal users to see. Pages are organized by category for easy navigation.
+
+![Select Pages](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/b9c96b54-6c20-484f-8b0b-3a86decb5717/ascreenshot_3347ade01ebe4ea390bc7b57e53db43f_text_export.jpeg)
+
+**Available pages include:**
+- Virtual Keys
+- Playground
+- Models + Endpoints
+- Agents
+- MCP Servers
+- Search Tools
+- Vector Stores
+- Logs
+- Teams
+- Organizations
+- Usage
+- Budgets
+- And more...
+
+### 6. Save Your Configuration
+
+Click **Save Page Visibility Settings** to apply the changes.
+
+![Save Settings](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/8a215378-44f5-4bb8-b984-06fa2aa03903/ascreenshot_44e7aeebe25a477ba92f73a3ed3df644_text_export.jpeg)
+
+### 7. Verify Changes
+
+Internal users will now only see the selected pages in their navigation sidebar.
+
+![Verify Changes](https://colony-recorder.s3.amazonaws.com/files/2026-01-28/493a7718-b276-40b9-970f-5814054932d9/ascreenshot_ad23b8691f824095ba60256f91ad24f8_text_export.jpeg)
+
+## Reset to Default
+
+To restore all pages to internal users:
+
+1. Open the Page Visibility configuration
+2. Click **Reset to Default (All Pages)**
+3. Click **Save Page Visibility Settings**
+
+This will clear the restriction and show all accessible pages to internal users.
+
+## API Configuration
+
+You can also configure page visibility programmatically using the API:
+
+### Get Current Settings
+
+```bash
+curl -X GET 'http://localhost:4000/ui_settings/get' \
+  -H 'Authorization: Bearer <your-admin-key>'
+```
+
+### Update Page Visibility
+
+```bash
+curl -X PATCH 'http://localhost:4000/ui_settings/update' \
+  -H 'Authorization: Bearer <your-admin-key>' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "enabled_ui_pages_internal_users": [
+      "api-keys",
+      "agents",
+      "mcp-servers",
+      "logs",
+      "teams"
+    ]
+  }'
+```
+
+### Clear Page Visibility Restrictions
+
+```bash
+curl -X PATCH 'http://localhost:4000/ui_settings/update' \
+  -H 'Authorization: Bearer <your-admin-key>' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "enabled_ui_pages_internal_users": null
+  }'
+```
+

docs/my-website/docs/proxy/ui_logs.md

@@ -25,7 +25,10 @@ View Spend, Token Usage, Key, Team Name for Each Request to LiteLLM
 
 ## Tracking - Request / Response Content in Logs Page 
 
-If you want to view request and response content on LiteLLM Logs, you need to opt in with this setting
+If you want to view request and response content on LiteLLM Logs, you can enable it in either place:
+
+- **From the UI (no restart):** Use [UI Spend Log Settings](./ui_spend_log_settings.md) — open Logs → Settings → enable "Store Prompts in Spend Logs" → Save. Takes effect immediately and overrides config.
+- **From config:** Add this to your `proxy_config.yaml` (requires restart):
 
 ```yaml
 general_settings:
@@ -34,6 +37,40 @@ general_settings:
 
 <Image img={require('../../img/ui_request_logs_content.png')}/>
 
+## Tracing Tools
+
+View which tools were provided and called in your completion requests.
+
+<Image img={require('../../img/ui_tools.png')}/>
+
+**Example:** Make a completion request with tools:
+
+```bash
+curl -X POST 'http://localhost:4000/chat/completions' \
+  -H 'Authorization: Bearer sk-1234' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "gpt-4",
+    "messages": [{"role": "user", "content": "What is the weather?"}],
+    "tools": [
+      {
+        "type": "function",
+        "function": {
+          "name": "get_weather",
+          "description": "Get the current weather",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "location": {"type": "string"}
+            }
+          }
+        }
+      }
+    ]
+  }'
+```
+
+Check the Logs page to see all tools provided and which ones were called.
 
 ## Stop storing Error Logs in DB
 
@@ -57,7 +94,10 @@ general_settings:
 
 If you're storing spend logs, it might be a good idea to delete them regularly to keep the database fast.
 
-LiteLLM lets you configure this in your `proxy_config.yaml`:
+You can set the retention period in either place:
+
+- **From the UI (no restart):** [UI Spend Log Settings](./ui_spend_log_settings.md) — Logs → Settings → set Retention Period → Save.
+- **From config:** Add the following to your `proxy_config.yaml` (requires restart):
 
 ```yaml
 general_settings: