Merge branch 'main' into litellm_oss_staging_01_29_2026

.circleci/config.yml

@@ -715,8 +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
@@ -724,8 +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
@@ -3407,6 +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
@@ -3428,7 +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_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 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
@@ -3478,8 +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 ${NIGHTLY_TAG} does not exist!"
+                    echo "This indicates an incomplete release. Please investigate."
+                    exit 1
+                fi
             fi
       - run:
           name: Trigger Github Action for new Docker Container + Trigger Load Testing
@@ -3488,11 +3606,21 @@ jobs:
             python3 -m pip install toml
             VERSION=$(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"
 
@@ -4051,6 +4179,14 @@ workflows:
               only:
                 - main
                 - /litellm_.*/
+      - proxy_e2e_anthropic_messages_tests:
+          requires:
+            - build_docker_database_image
+          filters:
+            branches:
+              only:
+                - main
+                - /litellm_.*/
       - llm_translation_testing:
           filters:
             branches:
@@ -4235,6 +4371,7 @@ workflows:
             branches:
               only:
                 - main
+                - /litellm_release_day_.*/
       - publish_to_pypi:
           requires:
             - mypy_linting

.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-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

@@ -60,10 +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
@@ -76,9 +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
@@ -100,7 +93,6 @@ 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
 **/test-results

cookbook/anthropic_agent_sdk/README.md

@@ -0,0 +1,117 @@
+# 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
+
+```bash
+python main.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="claude-sonnet-4-20250514"
+```
+
+Or just use the defaults - it'll connect to `http://localhost:4000` by default.
+
+## 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
+
+## 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/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 +1,196 @@
+"""
+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 os
+import asyncio
+import httpx
+from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
+
+
+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",
+        ]
+
+
+async def interactive_chat():
+    """
+    Interactive CLI chat with the agent
+    """
+    config = Config()
+    
+    # Configure Anthropic SDK to point to LiteLLM gateway
+    # Note: We don't add /anthropic to the base URL - LiteLLM handles routing
+    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
+    
+    # Fetch available models from proxy
+    available_models = await fetch_available_models(litellm_base_url, config.LITELLM_API_KEY)
+    
+    current_model = config.LITELLM_MODEL
+    
+    print("=" * 70)
+    print("🤖 Claude Agent SDK with LiteLLM Gateway - Interactive Chat")
+    print("=" * 70)
+    print(f"🚀 Connected to: {litellm_base_url}")
+    print(f"📦 Current model: {current_model}")
+    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()
+    
+    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':
+                    print("\n📋 Available models:")
+                    for i, model in enumerate(available_models, 1):
+                        marker = "✓" if model == current_model else " "
+                        print(f"  {marker} {i}. {model}")
+                    continue
+                
+                if user_input.lower() == 'model':
+                    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):
+                                current_model = available_models[idx]
+                                print(f"\n✅ Switched to: {current_model}")
+                                print("🔄 Starting new conversation with new model...\n")
+                                conversation_active = False
+                            else:
+                                print("❌ Invalid choice")
+                    except (ValueError, IndexError):
+                        print("❌ Invalid input")
+                    continue
+                
+                if not user_input:
+                    continue
+                
+                # Send query to agent with loading indicator
+                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.")
+
+
+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

docker/Dockerfile.non_root

@@ -170,12 +170,14 @@ 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 \

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

@@ -462,6 +462,7 @@ router_settings:
 | 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
@@ -723,6 +724,7 @@ 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_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI

docs/my-website/docs/tutorials/claude_agent_sdk.md

@@ -0,0 +1,115 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Claude Agent SDK with LiteLLM
+
+Use Anthropic's Claude Agent SDK with any LLM provider through LiteLLM Proxy.
+
+The Claude Agent SDK provides a high-level interface for building AI agents. By pointing it to LiteLLM, you can use the same agent code with OpenAI, Bedrock, Azure, Vertex AI, or any other provider.
+
+## Quick Start
+
+### 1. Install Dependencies
+
+```bash
+pip install claude-agent-sdk
+```
+
+### 2. Start LiteLLM Proxy
+
+```yaml title="config.yaml" showLineNumbers
+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"
+```
+
+```bash
+litellm --config config.yaml
+```
+
+### 3. Point Agent SDK to LiteLLM
+
+| Environment Variable | Value | Description |
+|---------------------|-------|-------------|
+| `ANTHROPIC_BASE_URL` | `http://localhost:4000` | LiteLLM proxy URL |
+| `ANTHROPIC_API_KEY` | `sk-1234` | Your LiteLLM API key (not Anthropic key) |
+
+```python title="agent.py" showLineNumbers
+import os
+from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions
+
+# Point to LiteLLM proxy (not Anthropic)
+os.environ["ANTHROPIC_BASE_URL"] = "http://localhost:4000"
+os.environ["ANTHROPIC_API_KEY"] = "sk-1234"  # Your LiteLLM key
+
+# Configure agent with any model from your config
+options = ClaudeAgentOptions(
+    system_prompt="You are a helpful AI assistant.",
+    model="bedrock-claude-sonnet-4",  # Use any model from config.yaml
+    max_turns=20,
+)
+
+async with ClaudeSDKClient(options=options) as client:
+    await client.query("What is LiteLLM?")
+    
+    async for msg in client.receive_response():
+        if hasattr(msg, 'content'):
+            for content_block in msg.content:
+                if hasattr(content_block, 'text'):
+                    print(content_block.text, end='', flush=True)
+```
+
+
+
+## Why Use LiteLLM with Agent SDK?
+
+| Feature | Benefit |
+|---------|---------|
+| **Multi-Provider** | Use the same agent code with OpenAI, Bedrock, Azure, Vertex AI, etc. |
+| **Cost Tracking** | Track spending across all agent conversations |
+| **Rate Limiting** | Set budgets and limits on agent usage |
+| **Load Balancing** | Distribute requests across multiple API keys or regions |
+| **Fallbacks** | Automatically retry with different models if one fails |
+
+## Complete Example
+
+See our [cookbook example](https://github.com/BerriAI/litellm/tree/main/cookbook/anthropic_agent_sdk) for a complete interactive CLI agent that:
+- Streams responses in real-time
+- Switches between models dynamically
+- Fetches available models from the proxy
+
+```bash
+# Clone and run the example
+git clone https://github.com/BerriAI/litellm.git
+cd litellm/cookbook/anthropic_agent_sdk
+pip install -r requirements.txt
+python main.py
+```
+
+## Related Resources
+
+- [Claude Agent SDK Documentation](https://github.com/anthropics/anthropic-agent-sdk)
+- [LiteLLM Proxy Quick Start](../proxy/quick_start)
+- [Complete Cookbook Example](https://github.com/BerriAI/litellm/tree/main/cookbook/anthropic_agent_sdk)

docs/my-website/sidebars.js

@@ -139,6 +139,20 @@ const sidebars = {
         "tutorials/openai_codex"
       ]
     },
+    {
+      type: "category",
+      label: "Agent SDKs",
+      link: {
+        type: "generated-index",
+        title: "Agent SDKs",
+        description: "Use LiteLLM with agent frameworks and SDKs",
+        slug: "/agent_sdks"
+      },
+      items: [
+        "tutorials/claude_agent_sdk",
+        "tutorials/google_adk",
+      ]
+    },
 
   ],
   // But you can create a sidebar manually
@@ -931,7 +945,6 @@ const sidebars = {
           type: "category",
           label: "LiteLLM Python SDK Tutorials",
           items: [
-            'tutorials/google_adk',
             'tutorials/azure_openai',
             'tutorials/instructor',
             "tutorials/gradio_integration",

litellm-proxy-extras/litellm_proxy_extras/migrations/20251119131227_add_prompt_versioning/migration.sql

@@ -1,12 +1,12 @@
 -- DropIndex
-DROP INDEX "LiteLLM_PromptTable_prompt_id_key";
+DROP INDEX IF EXISTS "LiteLLM_PromptTable_prompt_id_key";
 
 -- AlterTable
-ALTER TABLE "LiteLLM_PromptTable" ADD COLUMN     "version" INTEGER NOT NULL DEFAULT 1;
+ALTER TABLE "LiteLLM_PromptTable"
+ADD COLUMN "version" INTEGER NOT NULL DEFAULT 1;
 
 -- CreateIndex
-CREATE INDEX "LiteLLM_PromptTable_prompt_id_idx" ON "LiteLLM_PromptTable"("prompt_id");
+CREATE INDEX "LiteLLM_PromptTable_prompt_id_idx" ON "LiteLLM_PromptTable" ("prompt_id");
 
 -- CreateIndex
-CREATE UNIQUE INDEX "LiteLLM_PromptTable_prompt_id_version_key" ON "LiteLLM_PromptTable"("prompt_id", "version");
-
+CREATE UNIQUE INDEX "LiteLLM_PromptTable_prompt_id_version_key" ON "LiteLLM_PromptTable" ("prompt_id", "version");

litellm-proxy-extras/litellm_proxy_extras/schema.prisma

@@ -760,6 +760,11 @@ model LiteLLM_ManagedVectorStoresTable {
   updated_at DateTime @updatedAt
   litellm_credential_name String?
   litellm_params Json?
+  team_id String?
+  user_id String?
+  
+  @@index([team_id])
+  @@index([user_id])
 }
 
 // Guardrails table for storing guardrail configurations

litellm/constants.py

@@ -980,6 +980,7 @@ BEDROCK_CONVERSE_MODELS = [
     "meta.llama3-2-90b-instruct-v1:0",
     "amazon.nova-lite-v1:0",
     "amazon.nova-2-lite-v1:0",
+    "amazon.nova-2-pro-preview-20251202-v1:0",
     "amazon.nova-pro-v1:0",
     "writer.palmyra-x4-v1:0",
     "writer.palmyra-x5-v1:0",
@@ -1165,7 +1166,12 @@ LITELLM_CLI_SOURCE_IDENTIFIER = "litellm-cli"
 LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token"
 CLI_SSO_SESSION_CACHE_KEY_PREFIX = "cli_sso_session"
 CLI_JWT_TOKEN_NAME = "cli-jwt-token"
-CLI_JWT_EXPIRATION_HOURS = int(os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS", 24))
+# Support both CLI_JWT_EXPIRATION_HOURS and LITELLM_CLI_JWT_EXPIRATION_HOURS for backwards compatibility
+CLI_JWT_EXPIRATION_HOURS = int(
+    os.getenv("CLI_JWT_EXPIRATION_HOURS") 
+    or os.getenv("LITELLM_CLI_JWT_EXPIRATION_HOURS") 
+    or 24
+)
 
 ########################### DB CRON JOB NAMES ###########################
 DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job"

litellm/litellm_core_utils/litellm_logging.py

@@ -4752,7 +4752,14 @@ class StandardLoggingPayloadSetup:
     ) -> StandardLoggingPayloadErrorInformation:
         from litellm.constants import MAXIMUM_TRACEBACK_LINES_TO_LOG
 
-        error_status: str = str(getattr(original_exception, "status_code", ""))
+        # Check for 'code' first (used by ProxyException), then fall back to 'status_code' (used by LiteLLM exceptions)
+        # Ensure error_code is always a string for Prisma Python JSON field compatibility
+        error_code_attr = getattr(original_exception, "code", None)
+        if error_code_attr is not None and str(error_code_attr) not in ("", "None"):
+            error_status: str = str(error_code_attr)
+        else:
+            status_code_attr = getattr(original_exception, "status_code", None)
+            error_status = str(status_code_attr) if status_code_attr is not None else ""
         error_class: str = (
             str(original_exception.__class__.__name__) if original_exception else ""
         )

litellm/litellm_core_utils/logging_worker.py

@@ -415,6 +415,28 @@ class LoggingWorker:
         """
         Safely log a message during shutdown, suppressing errors if logging is closed.
         """
+        # Check if logger has valid handlers before attempting to log
+        # During shutdown, handlers may be closed, causing ValueError when writing
+        if not hasattr(verbose_logger, 'handlers') or not verbose_logger.handlers:
+            return
+        
+        # Check if any handler has a valid stream
+        has_valid_handler = False
+        for handler in verbose_logger.handlers:
+            try:
+                if hasattr(handler, 'stream') and handler.stream and not handler.stream.closed:
+                    has_valid_handler = True
+                    break
+                elif not hasattr(handler, 'stream'):
+                    # Non-stream handlers (like NullHandler) are always valid
+                    has_valid_handler = True
+                    break
+            except (AttributeError, ValueError):
+                continue
+        
+        if not has_valid_handler:
+            return
+        
         try:
             if level == "debug":
                 verbose_logger.debug(message)

litellm/llms/anthropic/chat/transformation.py

@@ -290,10 +290,19 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         elif tool_choice == "none":
             _tool_choice = AnthropicMessagesToolChoice(type="none")
         elif isinstance(tool_choice, dict):
-            _tool_name = tool_choice.get("function", {}).get("name")
-            _tool_choice = AnthropicMessagesToolChoice(type="tool")
-            if _tool_name is not None:
-                _tool_choice["name"] = _tool_name
+            if "type" in tool_choice and "function" not in tool_choice:
+                tool_type = tool_choice.get("type")
+                if tool_type == "auto":
+                    _tool_choice = AnthropicMessagesToolChoice(type="auto")
+                elif tool_type == "required" or tool_type == "any":
+                    _tool_choice = AnthropicMessagesToolChoice(type="any")
+                elif tool_type == "none":
+                    _tool_choice = AnthropicMessagesToolChoice(type="none")
+            else:
+                _tool_name = tool_choice.get("function", {}).get("name")
+                if _tool_name is not None:
+                    _tool_choice = AnthropicMessagesToolChoice(type="tool")
+                    _tool_choice["name"] = _tool_name
 
         if parallel_tool_use is not None:
             # Anthropic uses 'disable_parallel_tool_use' flag to determine if parallel tool use is allowed

litellm/llms/azure/chat/gpt_5_transformation.py

@@ -22,7 +22,8 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
         Accepts both explicit gpt-5 model names and the ``gpt5_series/`` prefix
         used for manual routing.
         """
-        return "gpt-5" in model or "gpt5_series" in model
+        # gpt-5-chat* is a chat model and shouldn't go through GPT-5 reasoning restrictions.
+        return ("gpt-5" in model and "gpt-5-chat" not in model) or "gpt5_series" in model
 
     def get_supported_openai_params(self, model: str) -> List[str]:
         """Get supported parameters for Azure OpenAI GPT-5 models.
@@ -37,6 +38,11 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
         """
         params = OpenAIGPT5Config.get_supported_openai_params(self, model=model)
 
+        # Azure supports tool_choice for GPT-5 deployments, but the base GPT-5 config
+        # can drop it when the deployment name isn't in the OpenAI model registry.
+        if "tool_choice" not in params:
+            params.append("tool_choice")
+
         # Only gpt-5.2 has been verified to support logprobs on Azure
         if self.is_model_gpt_5_2_model(model):
             azure_supported_params = ["logprobs", "top_logprobs"]

litellm/llms/bedrock/chat/converse_transformation.py

@@ -76,6 +76,13 @@ BEDROCK_COMPUTER_USE_TOOLS = [
     "text_editor_",
 ]
 
+# Beta header patterns that are not supported by Bedrock Converse API
+# These will be filtered out to prevent errors
+UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [
+    "advanced-tool-use",  # Bedrock Converse doesn't support advanced-tool-use beta headers
+    "prompt-caching",  # Prompt caching not supported in Converse API
+]
+
 
 class AmazonConverseConfig(BaseConfig):
     """
@@ -610,6 +617,37 @@ class AmazonConverseConfig(BaseConfig):
 
         return transformed_tools
 
+    def _filter_unsupported_beta_headers_for_bedrock(
+        self, model: str, beta_list: list
+    ) -> list:
+        """
+        Remove beta headers that are not supported on Bedrock Converse API for the given model.
+
+        Extended thinking beta headers are only supported on specific Claude 4+ models.
+        Some beta headers are universally unsupported on Bedrock Converse API.
+
+        Args:
+            model: The model name
+            beta_list: The list of beta headers to filter
+
+        Returns:
+            Filtered list of beta headers
+        """
+        filtered_betas = []
+        
+        # 1. Filter out beta headers that are universally unsupported on Bedrock Converse
+        for beta in beta_list:
+            should_keep = True
+            for unsupported_pattern in UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS:
+                if unsupported_pattern in beta.lower():
+                    should_keep = False
+                    break
+            
+            if should_keep:
+                filtered_betas.append(beta)
+                
+        return filtered_betas
+
     def _separate_computer_use_tools(
         self, tools: List[OpenAIChatCompletionToolParam], model: str
     ) -> Tuple[
@@ -1088,7 +1126,14 @@ class AmazonConverseConfig(BaseConfig):
                 if beta not in seen:
                     unique_betas.append(beta)
                     seen.add(beta)
-            additional_request_params["anthropic_beta"] = unique_betas
+            
+            # Filter out unsupported beta headers for Bedrock Converse API
+            filtered_betas = self._filter_unsupported_beta_headers_for_bedrock(
+                model=model,
+                beta_list=unique_betas,
+            )
+            
+            additional_request_params["anthropic_beta"] = filtered_betas
 
         return bedrock_tools, anthropic_beta_list
 

litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py

@@ -53,13 +53,26 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
         model: str,
         drop_params: bool,
     ) -> dict:
-        return AnthropicConfig.map_openai_params(
+        # Force tool-based structured outputs for Bedrock Invoke
+        # (similar to VertexAI fix in #19201)
+        # Bedrock Invoke doesn't support output_format parameter
+        original_model = model
+        if "response_format" in non_default_params:
+            # Use a model name that forces tool-based approach
+            model = "claude-3-sonnet-20240229"
+        
+        optional_params = AnthropicConfig.map_openai_params(
             self,
             non_default_params,
             optional_params,
             model,
             drop_params,
         )
+        
+        # Restore original model name
+        model = original_model
+        
+        return optional_params
 
 
     def transform_request(
@@ -90,6 +103,8 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
 
         _anthropic_request.pop("model", None)
         _anthropic_request.pop("stream", None)
+        # Bedrock Invoke doesn't support output_format parameter
+        _anthropic_request.pop("output_format", None)
         if "anthropic_version" not in _anthropic_request:
             _anthropic_request["anthropic_version"] = self.anthropic_version
 
@@ -117,6 +132,26 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
             if "opus-4" in model.lower() or "opus_4" in model.lower():
                 beta_set.add("tool-search-tool-2025-10-19")
 
+        # Filter out beta headers that Bedrock Invoke doesn't support
+        # AWS Bedrock only supports a specific whitelist of beta flags
+        # Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html
+        BEDROCK_SUPPORTED_BETAS = {
+            "computer-use-2024-10-22",  # Legacy computer use
+            "computer-use-2025-01-24",  # Current computer use (Claude 3.7 Sonnet)
+            "token-efficient-tools-2025-02-19",  # Tool use (Claude 3.7+ and Claude 4+)
+            "interleaved-thinking-2025-05-14",  # Interleaved thinking (Claude 4+)
+            "output-128k-2025-02-19",  # 128K output tokens (Claude 3.7 Sonnet)
+            "dev-full-thinking-2025-05-14",  # Developer mode for raw thinking (Claude 4+)
+            "context-1m-2025-08-07",  # 1 million tokens (Claude Sonnet 4)
+            "context-management-2025-06-27",  # Context management (Claude Sonnet/Haiku 4.5)
+            "effort-2025-11-24",  # Effort parameter (Claude Opus 4.5)
+            "tool-search-tool-2025-10-19",  # Tool search (Claude Opus 4.5)
+            "tool-examples-2025-10-29",  # Tool use examples (Claude Opus 4.5)
+        }
+        
+        # Only keep beta headers that Bedrock supports
+        beta_set = {beta for beta in beta_set if beta in BEDROCK_SUPPORTED_BETAS}
+
         if beta_set:
             _anthropic_request["anthropic_beta"] = list(beta_set)
 

litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py

@@ -54,6 +54,7 @@ class AmazonAnthropicClaudeMessagesConfig(
     # These will be filtered out to prevent 400 "invalid beta flag" errors
     UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS = [
         "advanced-tool-use",  # Bedrock Invoke doesn't support advanced-tool-use beta headers
+        "prompt-caching-scope"
     ]
 
     def __init__(self, **kwargs):

litellm/llms/gemini/chat/transformation.py

@@ -92,7 +92,7 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
             "parallel_tool_calls",
             "web_search_options",
         ]
-        if supports_reasoning(model):
+        if supports_reasoning(model, custom_llm_provider="gemini"):
             supported_params.append("reasoning_effort")
             supported_params.append("thinking")
         if self.is_model_gemini_audio_model(model):

litellm/llms/openai/chat/gpt_5_transformation.py

@@ -19,7 +19,9 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
 
     @classmethod
     def is_model_gpt_5_model(cls, model: str) -> bool:
-        return "gpt-5" in model
+        # gpt-5-chat* behaves like a regular chat model (supports temperature, etc.)
+        # Don't route it through GPT-5 reasoning-specific parameter restrictions.
+        return "gpt-5" in model and "gpt-5-chat" not in model
 
     @classmethod
     def is_model_gpt_5_codex_model(cls, model: str) -> bool:

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -1657,7 +1657,17 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         ## This is necessary because promptTokensDetails includes both cached and non-cached tokens
         ## See: https://github.com/BerriAI/litellm/issues/18750
         if cached_text_tokens is not None and prompt_text_tokens is not None:
+            # Explicit caching: subtract cached tokens per modality from cacheTokensDetails
             prompt_text_tokens = prompt_text_tokens - cached_text_tokens
+        elif (
+            cached_tokens is not None
+            and prompt_text_tokens is not None
+            and cached_text_tokens is None
+        ):
+            # Implicit caching: only cachedContentTokenCount is provided (no cacheTokensDetails)
+            # Subtract from text tokens since implicit caching is primarily for text content
+            # See: https://github.com/BerriAI/litellm/issues/16341
+            prompt_text_tokens = prompt_text_tokens - cached_tokens
         if cached_audio_tokens is not None and prompt_audio_tokens is not None:
             prompt_audio_tokens = prompt_audio_tokens - cached_audio_tokens
         if cached_image_tokens is not None and prompt_image_tokens is not None:

litellm/main.py

@@ -7347,8 +7347,11 @@ def _get_encoding():
 def __getattr__(name: str) -> Any:
     """Lazy import handler for main module"""
     if name == "encoding":
-        # Lazy load encoding to avoid heavy tiktoken import at module load time
-        _encoding = tiktoken.get_encoding("cl100k_base")
+        # Use _get_default_encoding which properly sets TIKTOKEN_CACHE_DIR
+        # before loading tiktoken, ensuring the local cache is used
+        # instead of downloading from the internet
+        from litellm._lazy_imports import _get_default_encoding
+        _encoding = _get_default_encoding()
         # Cache it in the module's __dict__ for subsequent accesses
         import sys
 

litellm/model_prices_and_context_window_backup.json

@@ -354,6 +354,25 @@
         "supports_video_input": true,
         "supports_vision": true
     },
+    "amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
     "apac.amazon.nova-2-lite-v1:0": {
         "cache_read_input_token_cost": 8.25e-08,
         "input_cost_per_token": 3.3e-07,
@@ -371,6 +390,25 @@
         "supports_video_input": true,
         "supports_vision": true
     },
+    "apac.amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
     "eu.amazon.nova-2-lite-v1:0": {
         "cache_read_input_token_cost": 8.25e-08,
         "input_cost_per_token": 3.3e-07,
@@ -388,6 +426,25 @@
         "supports_video_input": true,
         "supports_vision": true
     },
+    "eu.amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
     "us.amazon.nova-2-lite-v1:0": {
         "cache_read_input_token_cost": 8.25e-08,
         "input_cost_per_token": 3.3e-07,
@@ -405,6 +462,25 @@
         "supports_video_input": true,
         "supports_vision": true
     },
+    "us.amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
     "amazon.nova-2-multimodal-embeddings-v1:0": {
         "litellm_provider": "bedrock",
         "max_input_tokens": 8172,
@@ -3130,7 +3206,7 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_system_messages": true,
-        "supports_tool_choice": false,
+        "supports_tool_choice": true,
         "supports_vision": true
     },
     "azure/gpt-5-chat-latest": {
@@ -3162,7 +3238,7 @@
         "supports_reasoning": true,
         "supports_response_schema": true,
         "supports_system_messages": true,
-        "supports_tool_choice": false,
+        "supports_tool_choice": true,
         "supports_vision": true
     },
     "azure/gpt-5-codex": {
@@ -13521,6 +13597,42 @@
         "supports_vision": true,
         "supports_web_search": true
     },
+    "gemini-robotics-er-1.5-preview": {
+        "cache_read_input_token_cost": 0,
+        "input_cost_per_token": 3e-07,
+        "input_cost_per_audio_token": 1e-06,
+        "litellm_provider": "vertex_ai-language-models",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65535,
+        "max_tokens": 65535,
+        "mode": "chat",
+        "output_cost_per_token": 2.5e-06,
+        "output_cost_per_reasoning_token": 2.5e-06,
+        "source": "https://ai.google.dev/gemini-api/docs/models#gemini-robotics-er-1-5-preview",
+        "supported_endpoints": [
+            "/v1/chat/completions",
+            "/v1/completions"
+        ],
+        "supported_modalities": [
+            "text",
+            "image",
+            "video",
+            "audio"
+        ],
+        "supported_output_modalities": [
+            "text"
+        ],
+        "supports_audio_output": false,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_prompt_caching": false,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_url_context": true,
+        "supports_vision": true
+    },
     "gemini/gemini-robotics-er-1.5-preview": {
         "cache_read_input_token_cost": 0,
         "input_cost_per_token": 3e-07,
@@ -24255,6 +24367,72 @@
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
+    "openrouter/xiaomi/mimo-v2-flash": {
+            "input_cost_per_token": 9e-08,
+            "output_cost_per_token": 2.9e-07,
+            "cache_creation_input_token_cost": 0.0,
+            "cache_read_input_token_cost": 0.0,
+            "litellm_provider": "openrouter",
+            "max_input_tokens": 262144,
+            "max_output_tokens": 16384,
+            "max_tokens": 16384,
+            "mode": "chat",
+            "supports_function_calling": true,
+            "supports_tool_choice": true,
+            "supports_reasoning": true,
+            "supports_vision": false,
+            "supports_prompt_caching": false
+        },
+    "openrouter/z-ai/glm-4.7": {
+        "input_cost_per_token": 4e-07,
+        "output_cost_per_token": 1.5e-06,
+        "cache_creation_input_token_cost": 0.0,
+        "cache_read_input_token_cost": 0.0,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 202752,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_reasoning": true,
+        "supports_vision": true,
+        "supports_prompt_caching": false,
+        "supports_assistant_prefill": true
+    },
+    "openrouter/z-ai/glm-4.7-flash": {
+        "input_cost_per_token": 7e-08,
+        "output_cost_per_token": 4e-07,
+        "cache_creation_input_token_cost": 0.0,
+        "cache_read_input_token_cost": 0.0,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 200000,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_reasoning": true,
+        "supports_vision": true,
+        "supports_prompt_caching": false
+    },
+    "openrouter/minimax/minimax-m2.1": {
+        "input_cost_per_token": 2.7e-07,
+        "output_cost_per_token": 1.2e-06,
+        "cache_creation_input_token_cost": 0.0,
+        "cache_read_input_token_cost": 0.0,
+        "litellm_provider": "openrouter",
+        "max_input_tokens": 204000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_reasoning": true,
+        "supports_vision": true,
+        "supports_prompt_caching": false,
+        "supports_computer_use": false
+    },
     "ovhcloud/DeepSeek-R1-Distill-Llama-70B": {
         "input_cost_per_token": 6.7e-07,
         "litellm_provider": "ovhcloud",
@@ -34554,4 +34732,4 @@
         "output_cost_per_token": 0,
         "supports_reasoning": true
     }
-}
+}

litellm/proxy/_experimental/out/_next/static/8YepvLrDdt6e_FwiLneCs/_buildManifest.js

@@ -0,0 +1 @@
+self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-cf5ca766ac8f493f.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();

litellm/proxy/_experimental/out/_next/static/8YepvLrDdt6e_FwiLneCs/_ssgManifest.js

@@ -0,0 +1 @@
+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()