* fix: Avoid attaching tool calls when a call_id already exists
* fix: Prevent MCP responses from reviving past tool calls via previous_response_id
* test: Parametrize MCP streaming test to cover OpenAI and Anthropic models
* test: Fail MCP streaming test when LiteLLM logs errors during follow-up calls
* test: Let MCP tool-execution mock accept new kwargs for streaming tests
* chore: fix lint error
* docs: Add Google Workload Identity Federation (WIF) documentation to Vertex AI (#19320)
- Added new section documenting WIF support for Vertex AI authentication
- Included SDK and Proxy configuration examples
- Added sample WIF credentials file format for AWS federation
- Mentioned LLM Credentials UI as an alternative for credential management
- Added link to Google Cloud WIF documentation
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix(bedrock): deduplicate tool calls in assistant history (#15178)
* fix(types): add missing Set import to factory.py
---------
Co-authored-by: Yuta Saito <uc4w6c@bma.biglobe.ne.jp>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: YutaSaito <36355491+uc4w6c@users.noreply.github.com>
* feat(gemini): add opt-in support for responseJsonSchema
Add support for Gemini's native responseJsonSchema parameter which uses
standard JSON Schema format instead of OpenAPI-style responseSchema.
Benefits of responseJsonSchema (Gemini 2.0+ only):
- Standard JSON Schema format (lowercase types)
- Supports additionalProperties for stricter validation
- Better compatibility with Pydantic's model_json_schema()
- No propertyOrdering required
Usage:
```python
response_format={
"type": "json_schema",
"json_schema": {"schema": {...}},
"use_json_schema": True # opt-in
}
```
This is backwards compatible - existing code continues to use
responseSchema by default.
Closes#16340
* docs: add documentation for use_json_schema parameter
Document the new use_json_schema option for Gemini 2.0+ models
in the JSON Mode documentation.
* refactor(gemini): use responseJsonSchema by default for Gemini 2.0+
Remove opt-in flag `use_json_schema` and automatically detect model version:
- Gemini 2.0+: uses responseJsonSchema (standard JSON Schema, supports additionalProperties)
- Gemini 1.5: uses responseSchema (OpenAPI format, legacy)
This follows LiteLLM's philosophy of abstracting provider differences -
users write the same code regardless of model version.
* test(vertex): update json_schema tests to accept both responseSchema formats
Gemini 2.x+ uses responseJsonSchema while Gemini 1.x uses responseSchema.
Update tests to accept both formats since litellm now auto-selects based
on model version.
* feat(azure): add support for Azure OpenAI v1 API
When api_version is 'v1', 'latest', or 'preview', use the standard
OpenAI client instead of AzureOpenAI client with base_url pointing
to /openai/v1/ endpoint.
This follows Microsoft's documentation for the new v1 API format:
https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#api-specs
Changes:
- Add OpenAI/AsyncOpenAI imports to common_utils.py and azure.py
- Modify get_azure_openai_client() to detect v1 API versions and
create appropriate client type
- Update isinstance checks and type hints to accept both client types
- Add unit tests for v1 API client creation
* fix(azure): fix MyPy type errors for v1 API support
- Add type: ignore for AsyncOpenAI constructor
- Update type hints in files/handler.py and batches/handler.py
- Add OpenAI/AsyncOpenAI to Union types for client parameters
- Update isinstance checks to include OpenAI/AsyncOpenAI
* fix(azure): update type hints in files and batches handlers for v1 API
Update async method signatures to accept Union[AsyncAzureOpenAI, AsyncOpenAI]
to fix mypy errors when using v1 API client.
When using http:// api_base (converted to ws://), the websockets library
throws "ssl argument is incompatible with a ws:// URI". Only pass SSL
context for secure wss:// connections.
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Replace copy.deepcopy with model_dump + model_validate in streaming
iterator logging to handle Pydantic ValidatorIterator objects that
cannot be pickled when tool_choice uses allowed_tools mode.
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* fix(agentcore): simplify agentcore streaming
* fix(agentcore): move CustomStreamWrapper import to module level
The deferred imports inside streaming methods caused initialization delays
during health check requests, leading to timeouts in ECS deployments.
- Move CustomStreamWrapper import to module-level (line 19)
- Remove deferred imports from get_sync_custom_stream_wrapper (line 588)
- Remove deferred import from get_async_custom_stream_wrapper (line 747)
- Remove from TYPE_CHECKING block to use actual import
This ensures the import happens at module load time rather than during
first request processing, preventing health check endpoint blocking.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* test(agentcore): ensure sync response
* chore: upgrade boto3 to 1.40.76 in pyproject.toml
* chore: added taplo.toml
* fix(types): correct annotation type hint for MyPy compatibility
Update _convert_annotations_to_chat_format return type from
Dict[str, Any] to ChatCompletionAnnotation TypedDict to match
the Message class's expected type signature.
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Benedikt Óskarsson <bensi94@hotmail.com>
Fixes#19225
Add prompt_cache_key and other missing OpenAI Chat Completions params
to DEFAULT_CHAT_COMPLETION_PARAM_VALUES so drop_params: true works.
Also fix additional_drop_params to filter extra params for all providers,
not just OpenAI/Azure.
