* 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>
* feat(main.py): use asyncio.sleep for mock_Timeout=true on async request
adds unit testing to ensure proxy does not fail if specific Openai requests hang (e.g. recent o1 outage)
* fix(streaming_handler.py): fix deepseek r1 return reasoning content on streaming
Fixes https://github.com/BerriAI/litellm/issues/7942
* Revert "fix(streaming_handler.py): fix deepseek r1 return reasoning content on streaming"
This reverts commit 7a052a64e3.
* fix(deepseek-r-1): return reasoning_content as a top-level param
ensures compatibility with existing tools that use it
* fix: fix linting error
* fix(base_utils.py): supported nested json schema passed in for anthropic calls
* refactor(base_utils.py): refactor ref parsing to prevent infinite loop
* test(test_openai_endpoints.py): refactor anthropic test to use bedrock
* fix(langfuse_prompt_management.py): add unit test for sync langfuse calls
Resolves https://github.com/BerriAI/litellm/issues/7938#issuecomment-2613293757
* feat(router.py): add retry headers to response
makes it easy to add testing to ensure model-specific retries are respected
* fix(add_retry_headers.py): clarify attempted retries vs. max retries
* test(test_fallbacks.py): add test for checking if max retries set for model is respected
* test(test_fallbacks.py): assert values for attempted retries and max retries are as expected
* fix(utils.py): return timeout in litellm proxy response headers
* test(test_fallbacks.py): add test to assert model specific timeout used on timeout error
* test: add bad model with timeout to proxy
* fix: fix linting error
* fix(router.py): fix get model list from model alias
* test: loosen test restriction - account for other events on proxy
* feat(health_check.py): set upperbound for api when making health check call
prevent bad model from health check to hang and cause pod restarts
* fix(health_check.py): cleanup task once completed
* fix(constants.py): bump default health check timeout to 1min
* docs(health.md): add 'health_check_timeout' to health docs on litellm
* build(proxy_server_config.yaml): add bad model to health check
this fixes a bug in usage-based-routing-v2 which was caused b/c of how the result was being returned from dual cache async_batch_get_cache. it also adds unit testing for that function (and it's sync equivalent)
