* fix: prevent duplicate spend logs in Responses API for non-OpenAI providers
Fixes#15740
This fixes a logging duplication bug where using kwargs.pop() removed
the litellm_logging_obj before passing kwargs to internal acompletion()
calls, causing duplicate spend log entries for providers without native
Responses API support (Anthropic, Gemini, etc).
By changing from pop() to get(), the logging object is preserved and
reused across the internal completion call, preventing duplicate entries
and maintaining correct cost tracking.
* test: add test for logging object preservation in responses API
Verify that litellm_logging_obj is preserved in kwargs when calling
responses(), ensuring no duplicate spend log entries are created.
- Change variable name in litellm/__init__.py from configured_cold_storage_logger to cold_storage_custom_logger
- Update all references across the codebase to use the new variable name
- This fixes silent failure of cold storage logging due to variable name mismatch
- Configuration files use cold_storage_custom_logger, code should match
Files updated:
- litellm/__init__.py
- litellm/litellm_core_utils/litellm_logging.py
- litellm/proxy/spend_tracking/cold_storage_handler.py
- litellm/responses/litellm_completion_transformation/session_handler.py
- tests/test_litellm/litellm_core_utils/test_litellm_logging.py
- tests/test_litellm/responses/litellm_completion_transformation/test_session_handler.py
* fix: pass extra_headers parameter through responses API transformation chain
Ensure extra_headers parameter is properly forwarded from the responses() function
through the transformation handler and config to maintain header propagation in
litellm_completion_request dict.
* Add tests
* fix proxy config
* fix(responses api): fix streaming ID consistency and tool format handling (#12640)
* fix(responses): ensure streaming chunk IDs use consistent encoding format
Fixes streaming ID inconsistency where streaming responses used raw provider IDs
while non-streaming responses used properly encoded IDs with provider context.
Changes:
- Updated LiteLLMCompletionStreamingIterator to accept provider context
- Added _encode_chunk_id() method using same logic as non-streaming responses
- Modified chunk transformation to encode all streaming item_ids with resp_ prefix
- Updated handlers to pass custom_llm_provider and litellm_metadata to streaming iterator
Impact:
- Streaming chunk IDs now format: resp_<base64_encoded_provider_context>
- Enables session continuity when using streaming response IDs as previous_response_id
- Allows provider detection and load balancing with streaming responses
- Maintains backward compatibility with existing streaming functionality
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(types): add explicit Optional[str] type annotation for model_id
This resolves MyPy type checking error where model_id could be None
but wasn't explicitly typed as Optional[str].
* fix(types): handle None case for litellm_metadata access
Prevents 'Item None has no attribute get' error by checking for None
before accessing litellm_metadata dictionary.
* test: add comprehensive tests for streaming ID consistency
Adds unit and E2E tests to verify streaming chunk IDs are properly encoded
with consistent format across streaming responses.
## Tests Added
### Unit Test (test_reasoning_content_transformation.py)
- `test_streaming_chunk_id_encoding()`: Validates the `_encode_chunk_id()` method
correctly encodes chunk IDs with `resp_` prefix and provider context
### E2E Tests (test_e2e_openai_responses_api.py)
- `test_streaming_id_consistency_across_chunks()`: Tests that all streaming chunk IDs
are properly encoded across multiple chunks in a real streaming response
- `test_streaming_response_id_as_previous_response_id()`: Tests the core use case -
using streaming response IDs for session continuity with `previous_response_id`
## Key Testing Approach
- Uses **Gemini** (non-OpenAI model) to test the transformation logic rather than
OpenAI passthrough, since the streaming ID consistency issue occurs when LiteLLM
transforms responses rather than just passing through to native OpenAI responses API
- Tests validate that streaming chunk IDs now use same encoding as non-streaming responses
- Verifies session continuity works with streaming responses
Addresses @ishaan-jaff's request for unit tests covering the streaming ID consistency fix.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(lint): remove unused imports in transformation.py
Removes unused imports to fix CI linting errors:
- GenericResponseOutputItem
- OutputFunctionToolCall
* test: remove E2E tests from openai_endpoints_tests
Remove streaming ID consistency E2E tests as requested by @ishaan-jaff.
Keep only the mock/unit test in test_reasoning_content_transformation.py
* revert: remove streaming chunk ID encoding to original behavior
This reverts the streaming chunk ID encoding changes to understand the original issue better.
Original behavior was:
- Streaming chunks: raw provider IDs
- Streaming final response: raw IDs (PROBLEM!)
- Non-streaming final response: encoded IDs (correct)
The real issue: streaming final response IDs were not encoded, breaking session continuity.
* fix(responses): encode streaming final response IDs to match OpenAI behavior
Fixes streaming ID inconsistency to match OpenAI's Responses API behavior:
- Streaming chunks: raw message IDs (like OpenAI's msg_xxx)
- Final response: encoded IDs (like OpenAI's resp_xxx)
This enables session continuity by ensuring streaming final response IDs
have the same encoded format as non-streaming responses, allowing them
to be used as previous_response_id in follow-up requests.
Changes:
- Add custom_llm_provider and litellm_metadata to LiteLLMCompletionStreamingIterator
- Update handlers to pass provider context to streaming iterator
- Apply _update_responses_api_response_id_with_model_id to final streaming response
- Keep streaming chunks as raw IDs to match OpenAI format
Impact:
- Session continuity works with streaming responses
- Load balancing can detect provider from streaming final response IDs
- Format matches OpenAI's Responses API exactly
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* test: update unit test to match correct OpenAI-compatible behavior
Updates the unit test to verify streaming chunk IDs are raw (not encoded)
to match OpenAI's responses API format:
- Streaming chunks: raw message IDs (like msg_xxx)
- Final response: encoded IDs (like resp_xxx)
This reflects the correct behavior implemented in the fix.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* cleanup
* TestBaseResponsesAPIStreamingIterator
---------
Co-authored-by: Javier de la Torre <jatorre@carto.com>
Co-authored-by: Claude <noreply@anthropic.com>
* add _transform_responses_api_function_call_to_chat_completion_message
* test_responses_api_with_tool_calls
* TestFunctionCallTransformation
* fixes for responses API testing google ai studio
* TestGoogleAIStudioResponsesAPITest
* test_responses_api_with_tool_calls
* test_responses_api_with_tool_calls
* test_basic_openai_responses_streaming_delete_endpoint
* Add _transform_input_image_item_to_image_item() to handle image transformations
* Update content transformation to process input_image items from Responses API format to Chat Completions format
Add reasoning content support when Responses API falls
back to completions API, enabling reasoning content for
all LLM providers (Anthropic, Vertex AI, etc.) since
OpenAI is currently the only native Responses API
provider.
* Add ReasoningSummaryTextDeltaEvent for streaming
reasoning deltas
* Update streaming iterator to detect and transform
reasoning content
* Extract reasoning content as separate output items in
transformations
* Support reasoning content alongside regular message
content
Closes https://github.com/BerriAI/litellm/issues/11302
* Handle file content type transformation in responses api (#11310)
* Handle file content type transformation in responses api
* change to use input_file
* -
* TestLiteLLMCompletionResponsesConfig
* test: TestLiteLLMCompletionResponsesConfig
* fix: fix linting
---------
Co-authored-by: Jayme Gordon <jayme_gordon@icloud.com>
