- Update test_anthropic_via_responses_api expected_events to include
CONTENT_PART_ADDED between OUTPUT_ITEM_ADDED and OUTPUT_TEXT_DELTA
- Add TestEnsureOutputItemContentPartAdded with 3 mock tests:
message item emits content_part.added, reasoning item does not,
and the event is only emitted once
- aresponses() now pops prompt_id from kwargs after the async hook runs
and passes merged_optional_params via _async_prompt_merged_params.
responses() checks for this internal kwarg first and skips the sync
hook entirely when present — eliminating double-merge of template
messages.
- merged_optional_params from async_get_chat_completion_prompt is no
longer discarded (_); it flows through to local_vars in responses().
- Async tests now assert get_chat_completion_prompt.assert_not_called()
to directly detect any double-execution regression.
Made-with: Cursor
- Fix async path: call async_get_chat_completion_prompt in aresponses()
before executor dispatch, mirroring acompletion() in main.py. Discard
merged_optional_params in async path (sync responses() handles them
via local_vars), avoiding TypeError from duplicate kwargs in partial().
- Fix provider re-resolution: replace "/" in model heuristic with
model != original_model comparison so bare model names are handled.
- Add 3 async tests covering hook invocation, optional param
propagation, and non-message item filtering in aresponses().
Made-with: Cursor
The isolate_litellm_state conftest fixture saved/restored litellm.callbacks
but never cleared it before each test, unlike the other callback lists. It
also didn't handle litellm.model_fallbacks. Leaked callbacks and fallback
config caused mocked tests to route through Router/fallback paths, hitting
real APIs with mock keys.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GLOBAL_LOGGING_WORKER.flush() calls queue.join() which blocks until all
items are task_done(). In CI with pytest-asyncio, each test gets a fresh
event loop so the worker reinitializes its queue - items from a previous
test never get task_done(), causing an infinite hang.
Fix: wrap flush() with asyncio.wait_for(..., timeout=10.0).
The `if hasattr(...)` guards in test_acompletion_with_mcp_adds_metadata_to_streaming
and test_acompletion_with_mcp_streaming_metadata_in_correct_chunks could silently skip
the provider_specific_fields assertions if chunks lacked choices/delta. Replace with
unconditional `assert hasattr(...)` so failures surface immediately.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Fixes test failures that occur during parallel test execution (pytest -n 4)
due to module reloading issues with conftest.py reloading litellm.
Changes:
- Add module reload fixtures to ensure fresh references after conftest reloads
- Use patch.object and string-based patches instead of direct attribute assignment
- Use class name comparison instead of isinstance for reloaded modules
- Handle case where litellm is missing from sys.modules during parallel runs
- Move stream consumption inside patch contexts to avoid real API calls
- Mock litellm.acompletion instead of low-level HTTP handlers
- Add skipif decorator for enterprise-only test classes
Affected test files:
- test_container_integration.py
- test_responses_background_cost.py
- test_huggingface_embedding_handler.py
- test_vertex_ai_rerank_integration.py
- test_volcengine_responses_transformation.py
- test_pillar_guardrails.py
- test_litellm_pre_call_utils.py
- test_proxy_server.py
- test_converse_transformation.py
- test_chat_completions_handler.py
- test_aresponses_api_with_mcp.py
- test_anthropic_experimental_pass_through_messages_handler.py
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: preserve metadata for custom callbacks on codex/responses path (#21204)
- Use metadata or litellm_metadata when calling update_environment_variables
in responses/main.py so metadata is not overwritten by None on the
bridge path (completion -> responses API).
- Add tests for metadata in custom callback for codex models and for
litellm_metadata in aresponses().
Co-authored-by: Cursor <cursoragent@cursor.com>
* Update tests/test_litellm/responses/test_metadata_codex_callback.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
1. test_acompletion_with_mcp_streaming_metadata_in_correct_chunks:
- Moved stream consumption inside patch context to avoid real API calls
- The previous implementation had assertions outside the `with patch(...)`
block, causing real OpenAI API calls when consuming the stream
2. TestCheckResponsesCost tests:
- Added skip condition when litellm_enterprise module is not available
- These tests import from litellm_enterprise.proxy.common_utils.check_responses_cost
which is only available in the enterprise version
* fix(vertex_ai): forward extra_body to completion transformation handler
The responses() function accepted extra_body as a named parameter but
did not pass it to response_api_handler when responses_api_provider_config
was None (completion transformation path), silently dropping it.
Also adds deep-merge support for extra_body in Vertex AI Gemini
transformation, so dict values like generationConfig are merged rather
than replaced.
* refactor(vertex_ai): extract _merge_extra_body to fix PLR0915 lint
Move the extra_body merge loop into a helper function to keep
_transform_request_body under the 50-statement limit.
When transforming chat completion responses to Responses API format,
image_tokens from completion_tokens_details was not being included
in output_tokens_details. This affected Vertex AI/Gemini models that
return image token counts in candidatesTokensDetails with modality="IMAGE".
The fix adds image_tokens handling alongside existing reasoning_tokens
and text_tokens transformation.
When using the responses API with provider-specific params (aws_*, vertex_*)
without explicitly passing custom_llm_provider, the code crashed with:
AttributeError: 'NoneType' object has no attribute 'startswith'
Root cause: local_vars was captured via locals() before get_llm_provider()
detected the provider from the model string (e.g., "bedrock/..."), so
custom_llm_provider remained None when processing provider-specific params.
Fix: Update local_vars["custom_llm_provider"] after get_llm_provider() call
so the detected provider is available for param processing.
Affected provider-specific params:
- aws_* (aws_region_name, aws_access_key_id, etc.) for Bedrock/SageMaker
- vertex_* (vertex_project, vertex_location, etc.) for Vertex AI
* 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.
Calculate `total_tokens` in usage data in Response manually if:
- `total_tokens` is missing
- `total_tokens` can be calculated from input and output tokens
Run the test for this feature with:
`poetry run pytest tests/test_litellm/responses/test_responses_utils.py -k "test_transform_response_api_usage_calculates_total_from_input_and_output_tokens_if_available" -v`
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