From e50677bbb6652e2dd9cf4ef2e8fe7532a886144e Mon Sep 17 00:00:00 2001 From: Vigilans Date: Mon, 13 Apr 2026 17:39:15 +0800 Subject: [PATCH] fix(adapter): map output_config.effort to reasoning_effort (#25079) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Anthropic's adaptive thinking (thinking.type="adaptive") and output_config.effort were silently dropped when translating to OpenAI format, resulting in no reasoning_effort on the outgoing request. Adapter changes (format translation): - adapters/transformation.py: add "adaptive" branch to translate_anthropic_thinking_to_reasoning_effort(); pass through output_config.effort as-is in _translate_thinking_to_openai(); add "output_config" to translatable_anthropic_params - adapters/handler.py: extract output_config from extra_kwargs into request_data so it reaches the translation layer - responses_adapters/transformation.py: add "adaptive" branch and output_config param to translate_thinking_to_reasoning() Handler changes (model-aware normalization): - utils.py: add normalize_reasoning_effort_value() that uses get_model_info() to map "max" → "xhigh"/"high" and "minimal" → "minimal"/"low" based on model capabilities - adapters/handler.py: call normalization before responses routing - responses_adapters/handler.py: call normalization after translation Relates to BerriAI/litellm#25079 --- .../adapters/handler.py | 52 +++++++++++++++++++ .../adapters/transformation.py | 12 +++++ .../responses_adapters/handler.py | 13 +++++ .../responses_adapters/transformation.py | 40 ++++++++++---- .../experimental_pass_through/utils.py | 44 ++++++++++++++++ 5 files changed, 151 insertions(+), 10 deletions(-) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index 897ca3bf893..d16f5afb45c 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -106,6 +106,44 @@ class LiteLLMMessagesToCompletionTransformationHandler: updated_reasoning_effort["summary"] = effective_summary completion_kwargs["reasoning_effort"] = updated_reasoning_effort + @staticmethod + def _normalize_reasoning_effort( + completion_kwargs: Dict[str, Any], + ) -> None: + """ + Normalize reasoning_effort values based on target model capabilities. + + Handles both string ("max") and dict ({"effort": "max", "summary": ...}) + formats. Uses model registry to check supports_xhigh/supports_minimal. + """ + from litellm.llms.anthropic.experimental_pass_through.utils import ( + normalize_reasoning_effort_value, + ) + + reasoning_effort = completion_kwargs.get("reasoning_effort") + if reasoning_effort is None: + return + + model = cast(str, completion_kwargs.get("model", "")) + custom_llm_provider = completion_kwargs.get("custom_llm_provider") + + if isinstance(reasoning_effort, str): + normalized = normalize_reasoning_effort_value( + reasoning_effort, model=model, custom_llm_provider=custom_llm_provider + ) + if normalized != reasoning_effort: + completion_kwargs["reasoning_effort"] = normalized + elif isinstance(reasoning_effort, dict) and "effort" in reasoning_effort: + effort = reasoning_effort["effort"] + normalized = normalize_reasoning_effort_value( + effort, model=model, custom_llm_provider=custom_llm_provider + ) + if normalized != effort: + completion_kwargs["reasoning_effort"] = { + **reasoning_effort, + "effort": normalized, + } + @staticmethod def _prepare_completion_kwargs( *, @@ -163,6 +201,12 @@ class LiteLLMMessagesToCompletionTransformationHandler: if output_format: request_data["output_format"] = output_format + # Extract output_config from extra_kwargs so the translator can use it + # (e.g. output_config.effort for adaptive thinking → reasoning_effort) + extra_kwargs = extra_kwargs or {} + if "output_config" in extra_kwargs: + request_data["output_config"] = extra_kwargs["output_config"] + ( openai_request, tool_name_mapping, @@ -202,6 +246,14 @@ class LiteLLMMessagesToCompletionTransformationHandler: ): completion_kwargs[key] = value + # Normalize reasoning_effort based on model capabilities + # (e.g. "max" → "xhigh"/"high", "minimal" → "low" if unsupported) + # Must run BEFORE _route_openai_thinking, which prepends "responses/" + # to the model name and would break get_model_info() lookups. + LiteLLMMessagesToCompletionTransformationHandler._normalize_reasoning_effort( + completion_kwargs + ) + LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed( completion_kwargs, thinking=thinking, diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 072ae7c3bbe..e5d2b4ce782 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -317,6 +317,7 @@ class LiteLLMAnthropicMessagesAdapter: "tools", "thinking", "output_format", + "output_config", ] def _is_web_search_tool(self, tool: Dict[str, Any]) -> bool: @@ -694,6 +695,11 @@ class LiteLLMAnthropicMessagesAdapter: return "low" else: return "minimal" + elif thinking_type == "adaptive": + # Adaptive thinking: effort is controlled by output_config.effort, + # not budget_tokens. Return a default; caller should override with + # output_config.effort when available. + return "medium" return None @@ -1041,6 +1047,12 @@ class LiteLLMAnthropicMessagesAdapter: if not reasoning_effort: return + # For adaptive thinking, override with output_config.effort if available + if isinstance(thinking, dict) and thinking.get("type") == "adaptive": + output_config = anthropic_message_request.get("output_config") + if isinstance(output_config, dict) and output_config.get("effort"): + reasoning_effort = output_config["effort"] + summary = thinking.get("summary") if isinstance(thinking, dict) else None auto_summary = is_reasoning_auto_summary_enabled() if summary: diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py index 198ebe1ff8c..01ab2596827 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py @@ -72,6 +72,19 @@ def _build_responses_kwargs( anthropic_request = AnthropicMessagesRequest(**request_data) # type: ignore[typeddict-item] responses_kwargs = _ADAPTER.translate_request(anthropic_request) + # Normalize reasoning effort based on model capabilities + # (e.g. "max" → "xhigh"/"high", "minimal" → "low" if unsupported) + reasoning = responses_kwargs.get("reasoning") + if isinstance(reasoning, dict) and "effort" in reasoning: + from litellm.llms.anthropic.experimental_pass_through.utils import ( + normalize_reasoning_effort_value, + ) + + effort = reasoning["effort"] + normalized = normalize_reasoning_effort_value(effort, model=model) + if normalized != effort: + responses_kwargs["reasoning"] = {**reasoning, "effort": normalized} + if stream: responses_kwargs["stream"] = True 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 913470e7088..2badc2a3276 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -251,25 +251,41 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_thinking_to_reasoning( - thinking: Dict[str, Any] + thinking: Dict[str, Any], + output_config: Optional[Dict[str, Any]] = None, ) -> Optional[Dict[str, Any]]: """ Convert Anthropic thinking param to Responses API reasoning param. thinking.budget_tokens maps to reasoning effort: >= 10000 -> high, >= 5000 -> medium, >= 2000 -> low, < 2000 -> minimal + + For adaptive thinking, uses output_config.effort if available, + otherwise defaults to medium. """ - if not isinstance(thinking, dict) or thinking.get("type") != "enabled": + if not isinstance(thinking, dict): return None - budget = thinking.get("budget_tokens", 0) - if budget >= 10000: - effort = "high" - elif budget >= 5000: + + thinking_type = thinking.get("type") + + if thinking_type == "adaptive": + # Use output_config.effort if available effort = "medium" - elif budget >= 2000: - effort = "low" + if isinstance(output_config, dict) and output_config.get("effort"): + effort = output_config["effort"] + elif thinking_type == "enabled": + budget = thinking.get("budget_tokens", 0) + if budget >= 10000: + effort = "high" + elif budget >= 5000: + effort = "medium" + elif budget >= 2000: + effort = "low" + else: + effort = "minimal" else: - effort = "minimal" + return None + auto_summary = is_reasoning_auto_summary_enabled() result: Dict[str, Any] = {"effort": effort} summary = thinking.get("summary") @@ -346,7 +362,11 @@ class LiteLLMAnthropicToResponsesAPIAdapter: # thinking -> reasoning thinking = anthropic_request.get("thinking") if isinstance(thinking, dict): - reasoning = self.translate_thinking_to_reasoning(thinking) + output_config = anthropic_request.get("output_config") + reasoning = self.translate_thinking_to_reasoning( + thinking, + output_config=cast(Optional[Dict[str, Any]], output_config), + ) if reasoning: responses_kwargs["reasoning"] = reasoning diff --git a/litellm/llms/anthropic/experimental_pass_through/utils.py b/litellm/llms/anthropic/experimental_pass_through/utils.py index 6c1db6017b2..d975bee0bc2 100644 --- a/litellm/llms/anthropic/experimental_pass_through/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/utils.py @@ -1,4 +1,5 @@ import os +from typing import Optional import litellm @@ -9,3 +10,46 @@ def is_reasoning_auto_summary_enabled() -> bool: litellm.reasoning_auto_summary or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" ) + + +def normalize_reasoning_effort_value( + effort: str, + model: str, + custom_llm_provider: Optional[str] = None, +) -> str: + """ + Normalize a reasoning effort value based on model capabilities. + + Degradation chains: + - "max" → max / xhigh / high + - "xhigh" → xhigh / high + - "minimal" → minimal / low + - other values pass through unchanged + """ + if effort not in ("max", "xhigh", "minimal"): + return effort + + from litellm.utils import get_model_info + + try: + model_info = get_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + except Exception: + model_info = {} + + if effort == "max": + if model_info.get("supports_max_reasoning_effort"): + return "max" + if model_info.get("supports_xhigh_reasoning_effort"): + return "xhigh" + return "high" + elif effort == "xhigh": + if model_info.get("supports_xhigh_reasoning_effort"): + return "xhigh" + return "high" + elif effort == "minimal": + if model_info.get("supports_minimal_reasoning_effort"): + return "minimal" + return "low" + return "medium"