diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index f1443edf455..98d98fe1a24 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -3834,6 +3834,7 @@ PassThroughEndpointLoggingResultValues = Union[ EmbeddingResponse, VideoObject, StandardPassThroughResponseObject, + ResponsesAPIResponse, ] diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py index 6dd1f8548eb..9f353226dd0 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py @@ -5,7 +5,7 @@ Handles cost tracking and logging for OpenAI passthrough endpoints, specifically """ from datetime import datetime -from typing import List, Optional, Union +from typing import List, Optional, Tuple, Union from urllib.parse import urlparse import httpx @@ -18,6 +18,7 @@ from litellm.litellm_core_utils.litellm_logging import ( ) from litellm.llms.openai.openai import OpenAIConfig from litellm.llms.openai.openai import OpenAIConfig as OpenAIConfigType +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.proxy._types import PassThroughEndpointLoggingTypedDict from litellm.proxy.pass_through_endpoints.llm_provider_handlers.base_passthrough_logging_handler import ( BasePassthroughLoggingHandler, @@ -29,6 +30,7 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( EndpointType, PassthroughStandardLoggingPayload, ) +from litellm.types.llms.openai import ResponsesAPIResponse from litellm.types.utils import ImageResponse, LlmProviders, PassthroughCallTypes from litellm.utils import ModelResponse, TextCompletionResponse @@ -236,6 +238,42 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): ) return 0.0 + @staticmethod + def _build_responses_api_response_and_cost( + model: str, + httpx_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + custom_llm_provider: str, + ) -> Tuple[ResponsesAPIResponse, float]: + """Transform a Responses API raw response into a ResponsesAPIResponse + and compute its cost. + + The Responses API has a different on-the-wire shape from chat + completions (`output: [...]` instead of `choices: [...]`), so the + chat-completions `transform_response` raises KeyError 'choices' on + a Responses payload. Use the dedicated Responses-API transformer + (`OpenAIResponsesAPIConfig.transform_response_api_response`) here. + + Returns (litellm_model_response, response_cost) — symmetric with the + chat-completions branch which produces the same two values inline, + and analogous to the image branches' `_calculate_image_*_cost` helpers + (which return cost only because the image-response object is trivial + to build inline; the Responses payload needs a real transformer). + """ + responses_config = OpenAIResponsesAPIConfig() + litellm_model_response = responses_config.transform_response_api_response( + model=model, + raw_response=httpx_response, + logging_obj=logging_obj, + ) + response_cost = litellm.completion_cost( + completion_response=litellm_model_response, + model=model, + custom_llm_provider=custom_llm_provider, + call_type="responses", + ) + return litellm_model_response, response_cost + @staticmethod def openai_passthrough_handler( # noqa: PLR0915 httpx_response: httpx.Response, @@ -301,7 +339,12 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): try: response_cost = 0.0 litellm_model_response: Optional[ - Union[ModelResponse, TextCompletionResponse, ImageResponse] + Union[ + ModelResponse, + TextCompletionResponse, + ImageResponse, + ResponsesAPIResponse, + ] ] = None handler_instance = OpenAIPassthroughLoggingHandler() @@ -384,29 +427,18 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): litellm_model_response._hidden_params = {} litellm_model_response._hidden_params["response_cost"] = response_cost elif is_responses: - # Handle responses API cost calculation - provider_config = handler_instance.get_provider_config(model=model) - existing_litellm_params = kwargs.get("litellm_params", {}) or {} - litellm_model_response = provider_config.transform_response( - raw_response=httpx_response, - model_response=litellm.ModelResponse(), + # Responses-API cost tracking — see + # `_build_responses_api_response_and_cost` for why this needs + # a dedicated transformer (the chat-completions transform + # crashes on the Responses payload shape). + ( + litellm_model_response, + response_cost, + ) = OpenAIPassthroughLoggingHandler._build_responses_api_response_and_cost( model=model, - messages=request_body.get("messages", []), + httpx_response=httpx_response, logging_obj=logging_obj, - optional_params=request_body.get("optional_params", {}), - api_key="", - request_data=request_body, - encoding=litellm.encoding, - json_mode=False, - litellm_params=existing_litellm_params, - ) - - # Calculate cost using LiteLLM's cost calculator with responses call type - response_cost = litellm.completion_cost( - completion_response=litellm_model_response, - model=model, custom_llm_provider=custom_llm_provider, - call_type="responses", ) # Update kwargs with cost information diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index af1d39da020..46043d10a06 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -458,7 +458,16 @@ class PassThroughEndpointLogging: return False def _is_supported_openai_endpoint(self, url_route: str) -> bool: - """Check if the OpenAI endpoint is supported by the passthrough logging handler.""" + """Check if the OpenAI endpoint is supported by the passthrough logging handler. + + The Responses API route is included because + `openai_passthrough_handler` has a dedicated `elif is_responses:` + branch that knows how to extract usage + cost from the + Responses-API on-the-wire shape. Without including it here, the + outer dispatch filters Responses calls out before reaching the + handler — the inner branch is then unreachable and Responses + calls land in `LiteLLM_SpendLogs` with zero tokens / zero spend. + """ from .llm_provider_handlers.openai_passthrough_logging_handler import ( OpenAIPassthroughLoggingHandler, ) @@ -469,6 +478,7 @@ class PassThroughEndpointLogging: url_route ) or OpenAIPassthroughLoggingHandler.is_openai_image_editing_route(url_route) + or OpenAIPassthroughLoggingHandler.is_openai_responses_route(url_route) ) def _set_cost_per_request( diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py index 3c6af3e528a..401ea2ef589 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py @@ -683,36 +683,43 @@ class TestOpenAIPassthroughLoggingHandler: "litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload" ) @patch( - "litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler.OpenAIPassthroughLoggingHandler.get_provider_config" + "litellm.llms.openai.responses.transformation.OpenAIResponsesAPIConfig.transform_response_api_response" ) def test_responses_api_cost_tracking( - self, mock_get_provider_config, mock_get_standard_logging, mock_completion_cost + self, + mock_transform_responses, + mock_get_standard_logging, + mock_completion_cost, ): - """Test cost tracking for responses API route""" + """Test cost tracking for responses API route. + + Mocks the Responses-API transformer (the dedicated one this branch + of the handler dispatches into post-fix) so we can assert the + downstream cost-calculation contract without depending on the + real transformer's full behavior. + """ # Arrange mock_completion_cost.return_value = 0.000050 mock_get_standard_logging.return_value = {"test": "logging_payload"} - # Mock the provider config's transform_response to return a valid ModelResponse - from litellm import ModelResponse + # Mock the Responses transformer's return — a ResponsesAPIResponse + # carrying the usage fields downstream cost-calc expects. + from litellm.types.llms.openai import ResponsesAPIResponse - mock_model_response = ModelResponse( + mock_responses_api_response = ResponsesAPIResponse.model_construct( id="resp_abc123", + object="response", + created_at=1677652288, model="gpt-4o-2024-08-06", - choices=[ - { - "message": { - "role": "assistant", - "content": "Hello! How can I help you today?", - } - } - ], - usage={"prompt_tokens": 20, "completion_tokens": 15, "total_tokens": 35}, + status="completed", + output=[], + usage={ + "input_tokens": 20, + "output_tokens": 15, + "total_tokens": 35, + }, ) - - mock_provider_config = MagicMock() - mock_provider_config.transform_response.return_value = mock_model_response - mock_get_provider_config.return_value = mock_provider_config + mock_transform_responses.return_value = mock_responses_api_response # Mock responses API response mock_responses_response = { @@ -768,6 +775,109 @@ class TestOpenAIPassthroughLoggingHandler: assert mock_logging_obj.model_call_details["model"] == "gpt-4o" assert mock_logging_obj.model_call_details["custom_llm_provider"] == "openai" + @patch("litellm.completion_cost") + @patch( + "litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload" + ) + def test_responses_api_uses_responses_transformer_not_chat_completions( + self, mock_get_standard_logging, mock_completion_cost + ): + """Regression test for the Responses-API cost-tracking dispatch bug. + + BUG: the `elif is_responses:` branch in `openai_passthrough_handler` + was calling `OpenAIConfig.transform_response` (the chat-completions + transformer) on a Responses API payload. Chat-completions + transform_response expects `choices: [...]` in the raw response; + the Responses API uses `output: [...]` and `usage.input_tokens` / + `usage.output_tokens` (not `prompt_tokens` / `completion_tokens`). + The result was a KeyError 'choices' inside + `convert_to_model_response_object`, swallowed by the surrounding + try/except, and the SpendLogs row was written with zero tokens + and zero spend. + + FIX: use the dedicated `OpenAIResponsesAPIConfig.transform_response_api_response` + for the Responses branch. + + This test exercises the REAL transformer (no mocked + `get_provider_config`) so that running it against the un-fixed + handler raises and running it against the fixed handler succeeds. + """ + mock_completion_cost.return_value = 0.000050 + mock_get_standard_logging.return_value = {"test": "logging_payload"} + + # A real-shaped Azure / OpenAI Responses API payload — NO `choices`, + # uses `output` and `usage.input_tokens` / `usage.output_tokens`. + responses_api_body = { + "id": "resp_abc123", + "object": "response", + "created_at": 1677652288, + "model": "gpt-4o-2024-08-06", + "status": "completed", + "output": [ + { + "type": "message", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "Hello!", + } + ], + } + ], + "usage": { + "input_tokens": 20, + "output_tokens": 15, + "total_tokens": 35, + }, + } + + mock_httpx_response = self._create_mock_httpx_response(responses_api_body) + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + "custom_llm_provider": "openai", + } + + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=responses_api_body, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/responses", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gpt-4o", "input": "Tell me about AI"}, + **kwargs, + ) + + # Pre-fix this assertion fails — the handler swallows the + # KeyError raised by the chat-completions transformer and falls + # back to the passthrough_chat_handler which yields a different + # response_cost value. Post-fix, the Responses transformer + # succeeds and we get the mocked 0.000050. + assert result is not None + assert result["kwargs"]["response_cost"] == 0.000050 + assert result["kwargs"]["model"] == "gpt-4o" + + # `completion_cost` must be called with the responses call type + # and a `ResponsesAPIResponse` (not a `ModelResponse`). + mock_completion_cost.assert_called_once() + call_kwargs = mock_completion_cost.call_args[1] + assert call_kwargs["call_type"] == "responses" + + from litellm.types.llms.openai import ResponsesAPIResponse + + assert isinstance(call_kwargs["completion_response"], ResponsesAPIResponse), ( + "completion_response must be a ResponsesAPIResponse; passing a " + "chat-completions ModelResponse means the Responses transformer " + "isn't being used and we're back in the bug." + ) + class TestOpenAIPassthroughIntegration: """Integration tests for OpenAI passthrough cost tracking""" @@ -872,6 +982,126 @@ class TestOpenAIPassthroughIntegration: ) assert self.handler.is_openai_route("") == False + def test_is_supported_openai_endpoint_includes_responses_api(self): + """Regression test for the outer dispatch gate. + + `_is_supported_openai_endpoint` is the gate that decides whether the + OpenAI handler runs for a given URL. Before this gate accepted the + Responses API, calls to `/v1/responses` would fail the gate and the + handler's `elif is_responses:` branch was unreachable in the live + success-handler pipeline — every Responses-API call landed in + `LiteLLM_SpendLogs` with zero tokens / zero spend even though the + handler had a Responses branch internally. + + This test exercises the dispatch decision directly so future + refactors of `_is_supported_openai_endpoint` can't silently + remove Responses from the OR-chain without a test failure. + """ + # Responses must be supported on api.openai.com and openai.azure.com. + assert ( + self.handler._is_supported_openai_endpoint( + "https://api.openai.com/v1/responses" + ) + is True + ) + assert ( + self.handler._is_supported_openai_endpoint( + "https://openai.azure.com/v1/responses" + ) + is True + ) + # The other supported endpoints stay supported (no regression). + assert ( + self.handler._is_supported_openai_endpoint( + "https://api.openai.com/v1/chat/completions" + ) + is True + ) + assert ( + self.handler._is_supported_openai_endpoint( + "https://api.openai.com/v1/images/generations" + ) + is True + ) + assert ( + self.handler._is_supported_openai_endpoint( + "https://api.openai.com/v1/images/edits" + ) + is True + ) + # Unsupported OpenAI endpoints (e.g. /v1/models) still return False. + assert ( + self.handler._is_supported_openai_endpoint( + "https://api.openai.com/v1/models" + ) + is False + ) + + @patch( + "litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler.OpenAIPassthroughLoggingHandler.openai_passthrough_handler" + ) + @pytest.mark.asyncio + async def test_success_handler_dispatches_responses_api_to_openai_handler( + self, mock_openai_handler + ): + """End-to-end dispatch test for the Responses API path. + + Pre-fix: `_is_supported_openai_endpoint` returned False for + `/v1/responses` URLs, so the OpenAI handler was never called. + This test would fail (mock never invoked) on the un-fixed + success_handler — passes only when the dispatch gate accepts + Responses URLs. + """ + mock_openai_handler.return_value = { + "result": {"id": "resp_abc123"}, + "kwargs": { + "response_cost": 0.0001, + "model": "gpt-4o", + "custom_llm_provider": "openai", + }, + } + + mock_httpx_response = MagicMock(spec=httpx.Response) + mock_httpx_response.text = ( + '{"id": "resp_abc123", "object": "response", ' + '"output": [], "usage": {"input_tokens": 5, "output_tokens": 3}}' + ) + + mock_logging_obj = AsyncMock() + mock_logging_obj.model_call_details = {} + mock_logging_obj.async_success_handler = AsyncMock() + + passthrough_payload = PassthroughStandardLoggingPayload( + url="https://api.openai.com/v1/responses", + request_body={"model": "gpt-4o", "input": "Hello"}, + request_method="POST", + ) + + await self.handler.pass_through_async_success_handler( + httpx_response=mock_httpx_response, + response_body={ + "id": "resp_abc123", + "object": "response", + "output": [], + "usage": {"input_tokens": 5, "output_tokens": 3}, + }, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/responses", + result="", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + request_body={"model": "gpt-4o", "input": "Hello"}, + passthrough_logging_payload=passthrough_payload, + ) + + # The OpenAI handler MUST have been invoked. Pre-fix the dispatch + # gate filtered Responses URLs out and the mock was never called. + mock_openai_handler.assert_called_once() + # And we can verify it was dispatched with the Responses URL. + call_kwargs = mock_openai_handler.call_args.kwargs + assert call_kwargs["url_route"] == "https://api.openai.com/v1/responses" + @patch( "litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler.OpenAIPassthroughLoggingHandler.openai_passthrough_handler" )