The Azure Responses API uses a different schema (flattened) for tools compared to the standard OpenAI/Azure Chat Completions API (nested). This caused a `BadRequestError` when users passed standard tool definitions.
Changes:
- Implemented tool flattening logic in `AzureOpenAIResponsesAPIConfig.transform_responses_api_request`.
- Added comprehensive unit tests in test_azure_transformation.py to verify nested-to-flat transformation, pass-through of flat tools, and immutability.
- Ensures cross-provider compatibility for tool definitions.
Fixes#19523
* docs: document Claude Code default models and env var overrides
- Update config example with current Claude Code 2.1.x model names
- Add section documenting default models (sonnet/haiku) that Claude Code requests
- Document env var overrides (ANTHROPIC_DEFAULT_SONNET_MODEL, etc.)
- Show how model_name alias can route to any provider (Bedrock, Vertex, etc.)
* Update docs
Removed warning about changing model names in Claude Code versions.
* docs: add 1M context support and improve Claude Code quickstart guide
- Add comprehensive 1M context window documentation
- Document [1m] suffix usage and shell escaping requirements
- Clarify that LiteLLM config should NOT include [1m] in model names
- Add standalone claude_code_1m_context.md guide
- Improve model selection documentation with environment variables
- Add section on default models used by Claude Code v2.1.14
- Add troubleshooting for 1M context issues
- Reorganize to emphasize environment variables approach
Addresses GitHub issue #14444
* docs: reorder model selection options - prioritize --model over env vars
- Move command line/session model selection to Option 1 (most reliable)
- Move environment variables to Option 2
- Add note that env vars may be cached from previous session
- Emphasize that --model always uses exact model specified
* docs: reorganize 1M context section - separate command line from env vars
- Split 1M context examples into two clear sections
- Show command line usage first (--model and /model)
- Show environment variables as alternative approach
- Improves readability and emphasizes most reliable method
* docs: remove misleading default models section from website tutorial
- Remove 'Default Models Used by Claude Code' section (misleading)
- Remove claim that config must match exact default model names
- Update config comment to be more general
- Add claude-opus-4-5-20251101 to example config
- Keep authentication section as-is
* docs: correct model selection in website tutorial
- Remove incorrect claim that Claude Code automatically uses proxy models
- Add explicit model selection examples with --model and /model
- Show environment variables as alternative approach
- Remove misleading comment about 'multiple configured'
* docs: add 1M context section to website tutorial
- Add section on using [1m] suffix for 1 million token context
- Include warning about shell escaping (quotes required)
- Explain how Claude Code handles [1m] internally
- Add /context verification command
- Note that LiteLLM config should NOT include [1m]
* docs: add tip about using .env for API keys
- Add note that ANTHROPIC_API_KEY can be stored in .env file
- Clarifies alternative to exporting environment variables
* Consolidated change
* fix(prompt_security): update message processing to persist sanitized files and filter for API calls
* fix per krrishdholakia suggestion
Forward static_headers from /mcp-rest/test/* routes into the MCP client so headers are present during session.initialize() and tool discovery.
Also add a shared merge_mcp_headers() helper to keep header precedence consistent and ensure OpenAPI-to-MCP generated tools include static_headers.
Tests:
- pytest tests/test_litellm/proxy/_experimental/mcp_server/test_rest_endpoints.py
- pytest tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py -k register_openapi_tools_includes_static_headers
Fixes#19341
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* feat: add GMI Cloud provider support
Add GMI Cloud as an OpenAI-compatible provider with:
- Provider configuration in providers.json
- Documentation page with usage examples
- Model pricing for 16 models (Claude, GPT, DeepSeek, Gemini, etc.)
- Sidebar entry for docs navigation
* Add gmi_cloud to provider_endpoints_support.json
Add provider entry to pass CI validation check that ensures all
providers in openai_like/providers.json are documented.
* Fix provider key: gmi_cloud -> gmi
Match the provider key with providers.json
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Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
