Add support for 'file' and 'input_file' content types in
convert_to_gemini_tool_call_result(). File content in tool
results was previously silently dropped.
Supports base64 data URIs and HTTP URLs, matching the existing
image handling pattern. Enables PDF, audio, video, and other
file types as inline_data for Gemini.
The OpenAI Agents SDK (v0.6.9+) now passes reasoning_effort as a dict
when summary is specified: {"effort": "high", "summary": "auto"}
This change extracts the "effort" value from the dict for Vertex AI,
which only supports thinkingLevel (not summary).
Before: reasoning_effort={"effort": "high"} was silently ignored
After: reasoning_effort={"effort": "high"} correctly maps to thinkingLevel
Fixes#19411
* feat: add gemini video metadata and detail support
Implement support for video_metadata and enhanced detail parameter
for Gemini 3.0+ models:
- Add video_metadata field to ChatCompletionFileObjectFile type
- Supports fps, start_offset, and end_offset parameters
- Properly converts snake_case to camelCase for Gemini API
- Extend detail parameter to support medium and ultra_high levels
- Maps to MEDIA_RESOLUTION_MEDIUM and MEDIA_RESOLUTION_ULTRA_HIGH
- Update _process_gemini_image to handle video metadata transformation
- Add version gating to only apply features for Gemini 3+ models
- Add comprehensive test coverage (6 new test cases)
- Test detail parameter with file objects
- Test video_metadata fields (fps, start_offset, end_offset)
- Test combined detail + video_metadata usage
- Test new detail levels (medium, ultra_high)
- Test version gating (Gemini 1.5 vs 3.0)
Note: video_metadata is only supported for video files but error
handling is delegated to Vertex AI for other media types.
* refactor: rename _process_gemini_image to _process_gemini_media
The function handles multiple media types (images, audio, video, PDF),
not just images. Renamed to better reflect its actual purpose.
- Update function name in transformation.py
- Update all function calls and references
- Update test names and imports to match
- Improve docstring to clarify it handles all media types
* docs: add video metadata and media resolution control documentation
Add comprehensive documentation for Gemini 3+ video processing features:
- Document media resolution control (detail parameter) for images and videos
- Add video_metadata field documentation (fps, start_offset, end_offset)
- Include usage examples with tabs for basic, combined, and proxy scenarios
- Update both Gemini and Vertex AI provider documentation
- Clarify snake_case to camelCase field conversion for Gemini API
Signed-off-by: Kris Xia <xiajiayi0506@gmail.com>
* refactor(gemini): extract metadata application into helper function
Extract duplicated Gemini 3+ media_resolution and video_metadata
application logic from _process_gemini_media into a dedicated
_apply_gemini_3_metadata helper function to improve code maintainability.
---------
Signed-off-by: Kris Xia <xiajiayi0506@gmail.com>
* fix: preserve tool output ordering for gemini in responses bridge
- Keep function_call_output adjacent to its function_call when building chat messages
- Normalize function_call_output.output lists (input_* parts) into tool message content
* fix test
* small improvements
Emit Responses API streaming events for tool calls when the underlying chat stream contains tool_call deltas, and recover tool calls into the stream when they only appear in the final response.
* fix: prevent HTTP client memory leaks in Presidio and OpenAI wrappers
Fixes multiple memory leak issues reported in #14540 and related tickets:
**Presidio Guardrail Fix (#14540)**
- Problem: Every guardrail check created a new aiohttp.ClientSession
- Impact: High-traffic proxies accumulated thousands of unclosed sessions
- Solution: Share a single session across all guardrail checks
- Added `self._http_session` instance variable
- Lazy session creation via `_get_http_session()`
- Proper cleanup via `_close_http_session()` and `__del__()`
- Files: litellm/proxy/guardrails/guardrail_hooks/presidio.py
**OpenAI HTTP Client Caching (#14540)**
- Problem: `_get_async_http_client()` created new httpx.AsyncClient on each call
- Impact: OpenAI/Azure completions bypassed client caching system
- Solution: Route through `get_async_httpx_client()` for TTL-based caching
- Caches clients by provider and SSL config
- Fallback to direct creation if caching fails
- Applied to both async and sync client methods
- Files: litellm/llms/openai/common_utils.py
**Test Script**
- Added validation script to demonstrate fixes
- Counts file descriptors and unclosed session objects
- Files: test_oom_fixes.py
Related issues: #14384, #13251, #12443
* fix(oom): prevent memory leaks in Presidio guardrails and OpenAI client creation
Fixes two high-impact memory leaks:
1. Presidio Guardrail Session Leak (issue #14540)
- Problem: Created new aiohttp.ClientSession on every guardrail check
- Impact: Runs on EVERY proxy request when PII masking enabled
- Fix: Shared session pattern with lifecycle management
- Files: litellm/proxy/guardrails/guardrail_hooks/presidio.py
2. OpenAI HTTP Client Cache Bypass (issue #14540)
- Problem: _get_async_http_client() created new httpx.AsyncClient, bypassing TTL cache
- Impact: Every completion created new client with own connection pool
- Fix: Route through get_async_httpx_client() for proper caching
- Critical: Include SSL config in cache key for correctness
- Files: litellm/llms/openai/common_utils.py
Validation:
- Presidio: 100 requests → 0 new sessions (was 100)
- OpenAI: 100 calls → 1 unique client (was 100)
- test_oom_fixes.py: Automated validation script
* fix(oom): resolve Gemini aiohttp session leak (issue #12443)
Fixes persistent "Unclosed client session" warnings when using Gemini models.
Root Causes:
1. Broken atexit cleanup - get_event_loop() fails at exit time
2. On-demand session creation without reliable cleanup
Changes:
1. Fixed atexit Cleanup (async_client_cleanup.py)
- OLD: Used get_event_loop() which fails when loop is closed
- NEW: Always create fresh event loop at exit time
- Ensures cleanup runs successfully even when main loop is closed
2. Added __del__ Cleanup (aiohttp_handler.py)
- Defense-in-depth: cleanup on garbage collection
- Handles abnormal termination cases
- Similar pattern to Presidio guardrail fix
3. Enhanced Cleanup Scope (async_client_cleanup.py)
- Now closes global base_llm_aiohttp_handler instance
- Previously only checked cache, missed module-level handler
Validation:
- Test 1: __del__ cleanup → 0 sessions leaked ✓
- Test 2: atexit cleanup → 0 sessions leaked ✓
- test_gemini_session_leak.py: Automated validation
Related: #14540 (broader OOM issue tracking)
* fix(types): use LlmProviders enum for get_async_httpx_client
MyPy was failing because llm_provider parameter expects Union[LlmProviders, httpxSpecialProvider], not a string.
Changed from string "openai" to LlmProviders.OPENAI enum value.
* test: move validation tests to proper CI directories
- Move test_oom_fixes.py to tests/test_litellm/llms/
- Move test_gemini_session_leak.py to tests/test_litellm/llms/custom_httpx/
- Fix pytest warning: use pytest.skip() instead of return True
This ensures CI actually runs our OOM fix validation tests.
* fix(oom): add asyncio.Lock to prevent race conditions in Presidio session creation
- Make _get_http_session() async with asyncio.Lock protection
- Prevents multiple concurrent requests from creating orphaned sessions
- Add concurrent load test (50 parallel requests) to validate fix
- Test confirms only 1 session created under concurrent load
Critical fix: Previous implementation had race condition where
concurrent guardrail checks could create multiple sessions,
defeating the shared session pattern and causing memory leaks.
* fix(presidio): eliminate race condition in session lock initialization
Move asyncio.Lock creation from lazy initialization in _get_http_session()
to __init__. The previous lazy init had a race condition where concurrent
coroutines could both see _session_lock as None, both create locks, and
end up with different lock instances - defeating the synchronization.
asyncio.Lock() can be safely created without an event loop; it only
requires one when awaited.
* Add Volcengine responses adapter
* fix llms/volcengine/responses/transformation.py:507:9: F841 Local variable `origin` is assigned to but never used
fix llms/volcengine/responses/transformation.py:95: error: Argument "headers" to "VolcEngineError" has incompatible type
add more supported optional params
removed redundant manual logging/utils fallbacks so litellm/__init__.py uses the registry only.
* 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>
