diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index 73e74c228ba..4935c65f407 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -44,8 +44,9 @@ class LiteLLMMessagesToCompletionTransformationHandler: For OpenAI models, Chat Completions typically does not return reasoning text (only token accounting). To return a thinking-like content block in the - Anthropic response format, we route the request through OpenAI's Responses API - and request a reasoning summary. + Anthropic response format, we route the request through OpenAI's Responses API. + If the user provides a `summary` field in the thinking dict, it is passed + through to the OpenAI reasoning params (opt-in per OpenAI spec). """ custom_llm_provider = completion_kwargs.get("custom_llm_provider") if custom_llm_provider is None: @@ -77,18 +78,20 @@ class LiteLLMMessagesToCompletionTransformationHandler: completion_kwargs["model"] = f"responses/{model}" reasoning_effort = completion_kwargs.get("reasoning_effort") + summary = thinking.get("summary") if isinstance(reasoning_effort, str) and reasoning_effort: - completion_kwargs["reasoning_effort"] = { - "effort": reasoning_effort, - "summary": "detailed", - } + reasoning_dict: Dict[str, Any] = {"effort": reasoning_effort} + if summary: + reasoning_dict["summary"] = summary + completion_kwargs["reasoning_effort"] = reasoning_dict elif isinstance(reasoning_effort, dict): if ( - "summary" not in reasoning_effort + summary + and "summary" not in reasoning_effort and "generate_summary" not in reasoning_effort ): updated_reasoning_effort = dict(reasoning_effort) - updated_reasoning_effort["summary"] = "detailed" + updated_reasoning_effort["summary"] = summary completion_kwargs["reasoning_effort"] = updated_reasoning_effort @staticmethod diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index a7362a94312..b0138ecbf5d 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -693,6 +693,9 @@ class LiteLLMAnthropicMessagesAdapter: thinking ) if reasoning_effort: + summary = thinking.get("summary") if isinstance(thinking, dict) else None + if summary: + return {"reasoning_effort": {"effort": reasoning_effort, "summary": summary}} return {"reasoning_effort": reasoning_effort} return {} @@ -924,7 +927,11 @@ class LiteLLMAnthropicMessagesAdapter: cast(Dict[str, Any], thinking) ) if reasoning_effort: - new_kwargs["reasoning_effort"] = reasoning_effort + summary = thinking.get("summary") if isinstance(thinking, dict) else None + if summary: + new_kwargs["reasoning_effort"] = {"effort": reasoning_effort, "summary": summary} + else: + new_kwargs["reasoning_effort"] = reasoning_effort ## CONVERT OUTPUT_FORMAT to RESPONSE_FORMAT if "output_format" in anthropic_message_request: diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py index 935babe4380..497809b05ff 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -241,7 +241,11 @@ class LiteLLMAnthropicToResponsesAPIAdapter: effort = "low" else: effort = "minimal" - return {"effort": effort, "summary": "detailed"} + result: Dict[str, Any] = {"effort": effort} + summary = thinking.get("summary") + if summary: + result["summary"] = summary + return result def translate_request( self, diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py index 636e84fe796..7864c5f59ad 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py @@ -180,7 +180,7 @@ def test_openai_model_with_thinking_converts_to_reasoning(): assert "reasoning" in call_kwargs, "reasoning should be passed to litellm.responses" # budget_tokens=1024 -> effort="minimal" (< 2000 threshold) - expected_reasoning = {"effort": "minimal", "summary": "detailed"} + expected_reasoning = {"effort": "minimal"} assert call_kwargs["reasoning"] == expected_reasoning, ( f"reasoning should be {expected_reasoning} for budget_tokens=1024, " f"got {call_kwargs.get('reasoning')}" @@ -213,12 +213,135 @@ class TestThinkingParameterTransformation: from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( LiteLLMAnthropicMessagesAdapter, ) - + thinking = {"type": "enabled", "budget_tokens": 1024} result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model( thinking=thinking, model="openai/gpt-5.2", ) - + assert result == {"reasoning_effort": "minimal"} assert "thinking" not in result + + +class TestThinkingSummaryPreservation: + """Tests for issue #20998: thinking.summary must be preserved when routing to OpenAI Responses API.""" + + def test_thinking_summary_concise_preserved_for_openai(self): + """User-provided summary='concise' should not be replaced with 'detailed'.""" + from litellm.llms.anthropic.experimental_pass_through.adapters.handler import ( + LiteLLMMessagesToCompletionTransformationHandler, + ) + + thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"} + completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "medium"} + LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed( + completion_kwargs, thinking=thinking + ) + assert completion_kwargs["reasoning_effort"] == {"effort": "medium", "summary": "concise"} + + def test_thinking_summary_auto_preserved_for_openai(self): + """User-provided summary='auto' should be preserved.""" + from litellm.llms.anthropic.experimental_pass_through.adapters.handler import ( + LiteLLMMessagesToCompletionTransformationHandler, + ) + + thinking = {"type": "enabled", "budget_tokens": 10000, "summary": "auto"} + completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "high"} + LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed( + completion_kwargs, thinking=thinking + ) + assert completion_kwargs["reasoning_effort"] == {"effort": "high", "summary": "auto"} + + def test_thinking_without_summary_does_not_inject_summary(self): + """When no summary is provided, no summary should be injected (opt-in per OpenAI spec).""" + from litellm.llms.anthropic.experimental_pass_through.adapters.handler import ( + LiteLLMMessagesToCompletionTransformationHandler, + ) + + thinking = {"type": "enabled", "budget_tokens": 5000} + completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "medium"} + LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed( + completion_kwargs, thinking=thinking + ) + assert completion_kwargs["reasoning_effort"] == {"effort": "medium"} + assert "summary" not in completion_kwargs["reasoning_effort"] + + def test_openai_model_with_thinking_summary_end_to_end(self): + """End-to-end: anthropic_messages_handler should preserve thinking.summary for OpenAI models. + + OpenAI models are routed to litellm.responses(), so we verify the + reasoning dict passed to it contains the user's summary value. + """ + from litellm.llms.anthropic.experimental_pass_through.messages.handler import ( + anthropic_messages_handler, + ) + + with patch("litellm.responses", return_value="test-response") as mock_responses: + try: + anthropic_messages_handler( + max_tokens=1024, + messages=[{"role": "user", "content": "What is 2+2?"}], + model="openai/gpt-5.2", + api_key="test-api-key", + thinking={ + "type": "enabled", + "budget_tokens": 5000, + "summary": "concise", + }, + ) + except Exception: + pass + + mock_responses.assert_called_once() + call_kwargs = mock_responses.call_args.kwargs + reasoning = call_kwargs["reasoning"] + assert reasoning["summary"] == "concise", \ + f"Expected summary='concise', got summary='{reasoning.get('summary')}'" + + def test_responses_adapter_preserves_summary(self): + """translate_thinking_to_reasoning should include summary when user provides it.""" + from litellm.llms.anthropic.experimental_pass_through.responses_adapters.transformation import ( + LiteLLMAnthropicToResponsesAPIAdapter, + ) + + thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"} + result = LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(thinking) + assert result == {"effort": "medium", "summary": "concise"} + + def test_responses_adapter_no_summary_when_not_provided(self): + """translate_thinking_to_reasoning should not include summary when not provided.""" + from litellm.llms.anthropic.experimental_pass_through.responses_adapters.transformation import ( + LiteLLMAnthropicToResponsesAPIAdapter, + ) + + thinking = {"type": "enabled", "budget_tokens": 5000} + result = LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(thinking) + assert result == {"effort": "medium"} + assert "summary" not in result + + def test_translate_thinking_for_model_preserves_summary(self): + """translate_thinking_for_model should include summary in reasoning_effort dict when user provides it.""" + from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( + LiteLLMAnthropicMessagesAdapter, + ) + + thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"} + result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model( + thinking=thinking, + model="openai/gpt-5.2", + ) + assert result == {"reasoning_effort": {"effort": "medium", "summary": "concise"}} + + def test_translate_thinking_for_model_no_summary_when_not_provided(self): + """translate_thinking_for_model should return plain string reasoning_effort when no summary provided.""" + from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( + LiteLLMAnthropicMessagesAdapter, + ) + + thinking = {"type": "enabled", "budget_tokens": 5000} + result = LiteLLMAnthropicMessagesAdapter.translate_thinking_for_model( + thinking=thinking, + model="openai/gpt-5.2", + ) + assert result == {"reasoning_effort": "medium"}