""" Test for response_api_endpoints/endpoints.py """ import unittest from collections.abc import Mapping from typing import Any, Final, Literal from unittest.mock import AsyncMock, MagicMock, patch import httpx import pytest import respx from fastapi.testclient import TestClient from httpx import Response import litellm from litellm.proxy.proxy_server import app from litellm.types.llms.openai import ResponsesAPIResponse @pytest.mark.asyncio @pytest.mark.parametrize( "path,error_kind", [ ("/v1/responses", "rate_limit"), ("/v1/responses", "numeric_rate_limit"), ("/v1/responses", "server_error"), ("/v1/responses", "response_failed"), ("/v1/responses", "cyber_policy"), ("/cursor/chat/completions", "server_error"), ("/v1/chat/completions", "server_error"), ], ) async def test_streaming_upstream_errors_keep_the_client_protocol( monkeypatch: pytest.MonkeyPatch, path: str, error_kind: Literal["rate_limit", "numeric_rate_limit", "server_error", "response_failed", "cyber_policy"], ) -> None: import litellm.proxy.proxy_server as ps from litellm.proxy.auth.user_api_key_auth import user_api_key_auth model: Final = "gpt-6-astra" message: Final = "Upstream cannot complete this response" code: Final = { "rate_limit": "rate_limit_exceeded", "numeric_rate_limit": "429", "server_error": "server_error", "response_failed": "server_error", "cyber_policy": "cyber_policy", }[error_kind] error: Final = {"message": message, "code": code, "type": None, "param": "input"} response: Final = {"id": "resp_upstream", "object": "response", "created_at": 1, "status": "in_progress", "model": model, "output": [], "parallel_tool_calls": True, "tool_choice": "auto", "tools": []} created: Final = {"type": "response.created", "sequence_number": 0, "response": response} tool_added: Final = {"type": "response.output_item.added", "sequence_number": 1, "output_index": 0, "item": {"type": "function_call", "id": "fc_partial", "call_id": "call_partial", "name": "read_file", "arguments": "", "status": "in_progress"}} tool_delta: Final = {"type": "response.function_call_arguments.delta", "sequence_number": 2, "item_id": "fc_partial", "output_index": 0, "delta": '{"path":"partial'} failed: Final = ( {"type": "response.failed", "sequence_number": 9, "response": {**response, "status": "failed", "error": error}} if error_kind in ("response_failed", "cyber_policy") else {"type": "error", "error": error} ) chat: Final = {"id": "chatcmpl_partial", "object": "chat.completion.chunk", "created": 1, "model": model, "choices": [{"index": 0, "delta": {"content": "partial"}, "finish_reason": None}]} is_chat: Final = path == "/v1/chat/completions" partial: Final = path != "/v1/responses" or error_kind in ("numeric_rate_limit", "response_failed", "cyber_policy") response_events: Final = (created, tool_added, tool_delta, failed) if partial else (failed,) upstream_events: Final = (chat, {"error": error}) if is_chat else response_events wire: Final = "".join("data: " + json.dumps(event) + "\n\n" for event in upstream_events) upstream_url: Final = "https://streaming.example/v1" router: Final = litellm.Router( model_list=[{"model_name": model, "litellm_params": { "model": "openai/" + model, "api_base": upstream_url, "api_key": "fixture-key"}}], num_retries=0, ) monkeypatch.setattr(ps, "llm_router", router) monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) monkeypatch.setitem(app.dependency_overrides, user_api_key_auth, _auth_override) with respx.mock as transport: transport.post(upstream_url + ("/chat/completions" if is_chat else "/responses")).respond( 200, content=wire, headers={"Content-Type": "text/event-stream"} ) async with httpx.AsyncClient(transport=httpx.ASGITransport(app), base_url="http://testserver") as client: result: Final = await client.post( path, json={ "model": model, "stream": True, **({"messages": [{"role": "user", "content": "hello"}]} if is_chat else {"input": "hello"}), }, ) frames: Final = tuple(frame for frame in result.text.split("\n\n") if "data: " in frame) events: Final = tuple( json.loads(next(line[6:] for line in frame.splitlines() if line.startswith("data: "))) for frame in frames if "data: [DONE]" not in frame ) assert result.status_code == 200, result.text assert message in result.text if path == "/v1/responses": assert frames[-1] == "data: [DONE]", result.text assert frames[-2].startswith("event: response.failed\n"), result.text if partial: assert [event["type"] for event in events] == [ "response.created", "response.output_item.added", "response.function_call_arguments.delta", "response.failed", ] assert events[2]["delta"] == tool_delta["delta"] assert events[-1]["sequence_number"] == events[-2]["sequence_number"] + 1 assert events[-1]["response"]["id"] == events[0]["response"]["id"] else: assert [event["type"] for event in events] == ["response.failed"] assert events[0]["sequence_number"] == 0 assert events[0]["response"]["id"].startswith("resp_") assert events[-1]["response"]["status"] == "failed" assert events[-1]["response"]["error"]["code"] == { "rate_limit": "rate_limit_exceeded", "numeric_rate_limit": "rate_limit_exceeded", "server_error": "server_error", "response_failed": "server_error", "cyber_policy": "cyber_policy", }[error_kind] assert events[-1]["response"]["error"]["message"] == message else: assert events[0]["object"] == "chat.completion.chunk", result.text assert "response.failed" not in result.text assert "error" in events[-1] @pytest.mark.asyncio async def test_responses_api_background_polling_rejects_missing_input(): from fastapi import Response as FastAPIResponse from starlette.requests import Request from litellm.proxy._types import ProxyException, UserAPIKeyAuth from litellm.proxy.response_api_endpoints.endpoints import responses_api processor = MagicMock() async def return_exception(*, e: Exception, **kwargs: object) -> Exception: return e processor._handle_llm_api_exception = AsyncMock(side_effect=return_exception) processor.common_processing_pre_call_logic = AsyncMock(return_value=({"model": "gpt-4o"}, MagicMock())) async def receive(): return { "type": "http.request", "body": b'{"model":"gpt-4o","background":true}', "more_body": False, } request = Request( { "type": "http", "method": "POST", "path": "/v1/responses", "headers": [(b"content-type", b"application/json")], }, receive, ) with ( patch( # test-quality-ok: endpoint constructs the processor directly "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing", return_value=processor, ), patch( # test-quality-ok: polling decision is imported inside the endpoint "litellm.proxy.response_polling.polling_handler.should_use_polling_for_request", return_value=True, ), patch( # test-quality-ok: background task is imported inside the endpoint "litellm.proxy.response_polling.background_streaming.background_streaming_task", new_callable=AsyncMock, ) as mock_background_streaming_task, patch( # test-quality-ok: polling handler is imported inside the endpoint "litellm.proxy.response_polling.polling_handler.ResponsePollingHandler.create_initial_state", new_callable=AsyncMock, ) as mock_create_initial_state, ): with pytest.raises(ProxyException) as exc_info: await responses_api( request=request, fastapi_response=FastAPIResponse(), user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"), ) assert exc_info.value.code == "400" assert exc_info.value.param == "input" processor.common_processing_pre_call_logic.assert_awaited_once() mock_background_streaming_task.assert_not_called() mock_create_initial_state.assert_not_awaited() @pytest.mark.asyncio async def test_responses_api_background_polling_accepts_input_from_prompt_template(): from fastapi import Response as FastAPIResponse from starlette.requests import Request from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.response_api_endpoints.endpoints import responses_api processor = MagicMock() processor.common_processing_pre_call_logic = AsyncMock( return_value=({"model": "gpt-4o", "input": "hello from prompt"}, MagicMock()) ) initial_state = MagicMock() async def receive(): return { "type": "http.request", "body": b'{"model":"gpt-4o","prompt_id":"greeting","background":true}', "more_body": False, } request = Request( { "type": "http", "method": "POST", "path": "/v1/responses", "headers": [(b"content-type", b"application/json")], }, receive, ) with ( patch( # test-quality-ok: endpoint constructs the processor directly "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing", return_value=processor, ), patch( # test-quality-ok: polling decision is imported inside the endpoint "litellm.proxy.response_polling.polling_handler.should_use_polling_for_request", return_value=True, ), patch( # test-quality-ok: background task is imported inside the endpoint "litellm.proxy.response_polling.background_streaming.background_streaming_task", new_callable=AsyncMock, ), patch( # test-quality-ok: avoid scheduling a background task in this unit test "litellm.proxy.response_api_endpoints.endpoints.asyncio.create_task", ), patch( # test-quality-ok: polling handler is imported inside the endpoint "litellm.proxy.response_polling.polling_handler.ResponsePollingHandler.create_initial_state", new_callable=AsyncMock, ) as mock_create_initial_state, ): mock_create_initial_state.return_value = initial_state result = await responses_api( request=request, fastapi_response=FastAPIResponse(), user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"), ) assert result is initial_state processor.common_processing_pre_call_logic.assert_awaited_once() mock_create_initial_state.assert_awaited_once() request_data = mock_create_initial_state.await_args.kwargs["request_data"] assert request_data["input"] == "hello from prompt" class TestResponsesAPIEndpoints(unittest.TestCase): @pytest.mark.asyncio @patch("litellm.proxy.proxy_server.llm_router") @patch("litellm.proxy.proxy_server.user_api_key_auth") async def test_openai_v1_responses_route(self, mock_auth, mock_router): """ Test that /openai/v1/responses endpoint is correctly registered and accessible. """ mock_auth.return_value = MagicMock( token="test_token", user_id="test_user", team_id=None, ) mock_router.aresponses = AsyncMock( return_value={ "id": "resp_abc123", "object": "realtime.response", "status": "completed", "output": [ { "type": "message", "role": "assistant", "content": [{"type": "text", "text": "Test response"}], } ], } ) client = TestClient(app) test_data = {"model": "gpt-4o", "input": "Tell me about AI"} response = client.post( "/openai/v1/responses", json=test_data, headers={"Authorization": "Bearer sk-1234"}, ) assert response.status_code in [200, 401, 500] @pytest.mark.asyncio @patch("litellm.proxy.proxy_server.llm_router") @patch("litellm.proxy.proxy_server.user_api_key_auth") async def test_cursor_chat_completions_route(self, mock_auth, mock_router): """ Test that /cursor/chat/completions endpoint: 1. Accepts Responses API input format 2. Returns chat completions format response 3. Transforms streaming responses correctly """ from litellm.types.llms.openai import ResponsesAPIResponse from litellm.types.utils import ResponseOutputMessage, ResponseOutputText mock_auth.return_value = MagicMock( token="test_token", user_id="test_user", team_id=None, ) # Mock a Responses API response mock_responses_response = ResponsesAPIResponse( id="resp_cursor123", created_at=1234567890, model="gpt-4o", object="response", output=[ ResponseOutputMessage( type="message", role="assistant", content=[ResponseOutputText(type="output_text", text="Hello from Cursor!")], ) ], ) mock_router.aresponses = AsyncMock(return_value=mock_responses_response) client = TestClient(app) # Test with Responses API input format (what Cursor sends) test_data = { "model": "gpt-4o", "input": [{"role": "user", "content": "Hello"}], } response = client.post( "/cursor/chat/completions", json=test_data, headers={"Authorization": "Bearer sk-1234"}, ) # Should return 200 (or 401/500 if auth fails) assert response.status_code in [200, 401, 500] # If successful, verify it returns chat completions format if response.status_code == 200: response_data = response.json() # Should have chat completion structure assert "choices" in response_data or "id" in response_data # Should not have Responses API structure assert "output" not in response_data or "status" not in response_data @pytest.mark.asyncio @patch("litellm.proxy.proxy_server.llm_router") @patch("litellm.proxy.proxy_server.user_api_key_auth") async def test_responses_api_key_spend_header_includes_response_cost(self, mock_auth, mock_router): """ Test that x-litellm-key-spend header includes the current request's response_cost for /v1/responses endpoint. This ensures the spend header reflects updated spend including the current request, even though spend tracking updates happen asynchronously after the response. """ from litellm.types.llms.openai import ResponsesAPIResponse from litellm.types.utils import ResponseOutputMessage, ResponseOutputText # Create mock user API key with initial spend mock_user_api_key_dict = MagicMock() mock_user_api_key_dict.token = "test_token" mock_user_api_key_dict.user_id = "test_user" mock_user_api_key_dict.team_id = None mock_user_api_key_dict.spend = 0.001 # Initial spend: $0.001 mock_user_api_key_dict.tpm_limit = None mock_user_api_key_dict.rpm_limit = None mock_user_api_key_dict.max_budget = None mock_user_api_key_dict.allowed_model_region = None mock_user_api_key_dict.api_key = "sk-test-key" mock_user_api_key_dict.metadata = {} mock_auth.return_value = mock_user_api_key_dict # Mock response with hidden_params containing response_cost mock_response = ResponsesAPIResponse( id="resp_test123", created_at=1234567890, model="gpt-4o", object="response", output=[ ResponseOutputMessage( type="message", role="assistant", content=[ResponseOutputText(type="output_text", text="Test response")], ) ], ) # Add hidden_params with response_cost to the mock response mock_response._hidden_params = { "response_cost": 0.0005, # Current request cost: $0.0005 "model_id": "test-model-id", } mock_router.aresponses = AsyncMock(return_value=mock_response) client = TestClient(app) test_data = {"model": "gpt-4o", "input": "Tell me about AI"} response = client.post( "/v1/responses", json=test_data, headers={"Authorization": "Bearer sk-test-key"}, ) # Verify the response was successful assert response.status_code == 200 # Verify x-litellm-key-spend header includes current request cost assert "x-litellm-key-spend" in response.headers key_spend_value = float(response.headers["x-litellm-key-spend"]) expected_spend = 0.001 + 0.0005 # Initial spend + current request cost assert key_spend_value == pytest.approx(expected_spend, abs=1e-10) # Verify x-litellm-response-cost header is present assert "x-litellm-response-cost" in response.headers response_cost_value = float(response.headers["x-litellm-response-cost"]) assert response_cost_value == pytest.approx(0.0005, abs=1e-10) import json class TestManagedResponsesWSFirstMessage: @pytest.mark.asyncio async def test_first_message_processed_before_loop(self): """ ManagedResponsesWebSocketHandler must process first_message before entering its receive loop. Regression for clients that connect without ?model= (e.g. Codex) and send model inside the first response.create event. """ from litellm.responses.streaming_iterator import ManagedResponsesWebSocketHandler first = json.dumps( { "type": "response.create", "model": "gpt-4o-mini", "store": False, "input": [ { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "hi"}], } ], } ) ws = MagicMock() ws.receive_text = AsyncMock(side_effect=Exception("disconnect")) ws.send_text = AsyncMock() processed: list = [] async def fake_process(msg: str) -> None: processed.append(msg) handler = ManagedResponsesWebSocketHandler( websocket=ws, model="gpt-4o-mini", logging_obj=MagicMock(), first_message=first, ) handler._process_response_create = fake_process # type: ignore[method-assign] await handler.run() assert processed == [first] @pytest.mark.asyncio async def test_no_first_message_falls_through_to_loop(self): """When first_message is None, run() goes straight to receive_text().""" from litellm.responses.streaming_iterator import ManagedResponsesWebSocketHandler subsequent = json.dumps({"type": "response.create", "model": "gpt-4o-mini"}) ws = MagicMock() ws.receive_text = AsyncMock(side_effect=[subsequent, Exception("disconnect")]) ws.send_text = AsyncMock() processed: list = [] async def fake_process(msg: str) -> None: processed.append(msg) handler = ManagedResponsesWebSocketHandler( websocket=ws, model="gpt-4o-mini", logging_obj=MagicMock(), first_message=None, ) handler._process_response_create = fake_process # type: ignore[method-assign] await handler.run() assert processed == [subsequent] class TestResponsesWSStreamingFirstMessage: @pytest.mark.asyncio async def test_client_to_backend_replays_first_message(self): """ ResponsesWebSocketStreaming.client_to_backend must send first_message to the backend before entering the receive loop. """ from litellm.responses.streaming_iterator import ResponsesWebSocketStreaming first = json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []}) ws = MagicMock() ws.receive_text = AsyncMock(side_effect=Exception("disconnect")) backend_ws = MagicMock() backend_ws.send = AsyncMock() streaming = ResponsesWebSocketStreaming( websocket=ws, backend_ws=backend_ws, logging_obj=MagicMock(), first_message=first, ) await streaming.client_to_backend() backend_ws.send.assert_awaited_once_with(first) class TestWSSessionCostTracking: @pytest.mark.asyncio async def test_router_budget_limiter_skips_aresponses_websocket_call_type(self): """ RouterBudgetLimiting.async_log_success_event must not raise when call_type='_aresponses_websocket', even when standard_logging_object is None. Per-turn costs are tracked by individual aresponses calls inside the session; the outer session wrapper fires with result=None. """ from litellm.router_strategy.budget_limiter import RouterBudgetLimiting limiter = RouterBudgetLimiting.__new__(RouterBudgetLimiting) kwargs = { "call_type": "_aresponses_websocket", "standard_logging_object": None, "litellm_params": {"custom_llm_provider": "vertex_ai"}, } await limiter.async_log_success_event( kwargs=kwargs, response_obj=None, start_time=None, end_time=None, ) @pytest.mark.asyncio async def test_router_budget_limiter_skips_arealtime_call_type(self): """Same guard applies to _arealtime WS session wrappers.""" from litellm.router_strategy.budget_limiter import RouterBudgetLimiting limiter = RouterBudgetLimiting.__new__(RouterBudgetLimiting) kwargs = { "call_type": "_arealtime", "standard_logging_object": None, "litellm_params": {"custom_llm_provider": "openai"}, } await limiter.async_log_success_event( kwargs=kwargs, response_obj=None, start_time=None, end_time=None, ) class TestWSModelExtraction: """Test _extract_model_from_first_ws_event for flat and nested frame formats.""" def test_flat_format_extracts_model(self): from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) event = {"type": "response.create", "model": "gpt-4o", "input": "hello"} assert _extract_model_from_first_ws_event(event) == "gpt-4o" def test_nested_format_extracts_model(self): from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) event = {"type": "response.create", "response": {"model": "gpt-4o", "input": "hello"}} assert _extract_model_from_first_ws_event(event) == "gpt-4o" def test_nested_format_takes_precedence_over_flat(self): from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) event = { "type": "response.create", "model": "flat-model", "response": {"model": "nested-model"}, } assert _extract_model_from_first_ws_event(event) == "nested-model" def test_no_model_returns_none(self): from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) event = {"type": "response.create", "input": "hello"} assert _extract_model_from_first_ws_event(event) is None def test_non_object_returns_none(self): from litellm.proxy.response_api_endpoints.endpoints import ( _extract_model_from_first_ws_event, ) assert _extract_model_from_first_ws_event([]) is None class TestResponsesWSFirstFrameValidation: @pytest.mark.asyncio async def test_rejects_non_response_create_first_frame(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(return_value=json.dumps({"type": "session.update", "model": "gpt-4o"})) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None ws.send_text.assert_awaited_once() ws.close.assert_awaited_once_with(code=1008, reason="Invalid first message") error_payload = json.loads(ws.send_text.await_args.args[0]) assert error_payload["error"]["message"] == "First message must be a response.create JSON object." @pytest.mark.asyncio async def test_rejects_non_object_json_first_frame(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(return_value=json.dumps(["gpt-4o"])) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None ws.send_text.assert_awaited_once() ws.close.assert_awaited_once_with(code=1008, reason="Invalid first message") @pytest.mark.asyncio async def test_client_disconnect_first_frame_does_not_close(self): from fastapi import WebSocketDisconnect from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(side_effect=WebSocketDisconnect(code=1006)) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None ws.close.assert_not_awaited() ws.send_text.assert_not_awaited() @pytest.mark.asyncio async def test_server_error_first_frame_closes_with_internal_error(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(side_effect=RuntimeError("boom")) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None ws.close.assert_awaited_once_with(code=1011, reason="Internal server error") class TestResponsesWSFirstFrameModelAuth: @pytest.mark.asyncio async def test_endpoint_enforces_auth_after_model_from_first_frame(self): from litellm.proxy.response_api_endpoints.endpoints import ( responses_websocket_endpoint, ) ws = MagicMock() ws.headers = {} ws.query_params = {} ws.scope = {"headers": []} ws.url = "ws://testserver/v1/responses" ws.accept = AsyncMock() ws.receive_text = AsyncMock( return_value=json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []}) ) ws.close = AsyncMock() processor = MagicMock() processor.common_processing_pre_call_logic = AsyncMock(return_value=({"model": "gpt-4o-mini"}, MagicMock())) async def fake_llm_call(): return None with ( patch( "litellm.proxy.response_api_endpoints.endpoints._enforce_responses_ws_first_frame_model_auth", new_callable=AsyncMock, ) as mock_model_auth, patch( "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing", return_value=processor, ), patch( "litellm.proxy.route_llm_request.route_request", new_callable=AsyncMock, return_value=fake_llm_call(), ), ): await responses_websocket_endpoint( websocket=ws, model=None, user_api_key_dict=MagicMock(), ) mock_model_auth.assert_awaited_once() @pytest.mark.asyncio @pytest.mark.parametrize("nested", [False, True]) @pytest.mark.parametrize("query_model", [None, "gpt-4o-mini"]) async def test_endpoint_routes_on_first_frame_input_and_previous_response_id( self, nested: bool, query_model: str | None ): from litellm.proxy.response_api_endpoints.endpoints import ( responses_websocket_endpoint, ) replayed_input = [{"type": "reasoning", "id": "encitem_abc", "encrypted_content": "litellm_enc:abc;blob"}] payload = {"model": "gpt-4o-mini", "input": replayed_input, "previous_response_id": "resp_prev"} first_frame = {"type": "response.create", "response": payload} if nested else {"type": "response.create", **payload} raw_first_frame = json.dumps(first_frame) ws = MagicMock() ws.headers = {} ws.query_params = {} ws.scope = {"headers": []} ws.url = "ws://testserver/v1/responses" ws.accept = AsyncMock() ws.receive_text = AsyncMock(return_value=raw_first_frame) ws.close = AsyncMock() processor = MagicMock() processor.common_processing_pre_call_logic = AsyncMock( return_value=({"model": "gpt-4o-mini", "litellm_metadata": {}}, MagicMock()) ) async def fake_llm_call(): return None with ( patch( # test-quality-ok: first-frame model auth needs a live router and key table and has its own tests below "litellm.proxy.response_api_endpoints.endpoints._enforce_responses_ws_first_frame_model_auth", new_callable=AsyncMock, ), patch( # test-quality-ok: the pre-call processor needs a live proxy; the payload it hands to routing is what is under test "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing", return_value=processor, ), patch( # test-quality-ok: routing is the seam where the first frame's input and previous_response_id become observable "litellm.proxy.route_llm_request.route_request", new_callable=AsyncMock, return_value=fake_llm_call(), ) as mock_route_request, ): await responses_websocket_endpoint( websocket=ws, model=query_model, user_api_key_dict=MagicMock(), ) ws.receive_text.assert_awaited_once() routed = mock_route_request.await_args.kwargs["data"] assert routed["model"] == "gpt-4o-mini" assert routed["input"] == replayed_input assert routed["previous_response_id"] == "resp_prev" assert processor.common_processing_pre_call_logic.await_args.kwargs["model"] == "gpt-4o-mini" assert mock_route_request.await_args.kwargs["route_type"] == "_aresponses_websocket" ws.close.assert_not_awaited() @pytest.mark.asyncio @pytest.mark.parametrize("provider_rejected", [True, False]) async def test_endpoint_books_a_provider_rejected_connection_as_a_failed_request(self, provider_rejected: bool): from litellm.proxy.response_api_endpoints.endpoints import ( responses_websocket_endpoint, ) ws = MagicMock() ws.headers = {} ws.query_params = {} ws.scope = {"headers": []} ws.url = "ws://testserver/v1/responses" ws.accept = AsyncMock() ws.receive_text = AsyncMock( return_value=json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []}) ) ws.close = AsyncMock() processor = MagicMock() processor.common_processing_pre_call_logic = AsyncMock( return_value=({"model": "gpt-4o-mini", "litellm_metadata": {}}, MagicMock()) ) failure = litellm.BadRequestError( message="invalid_encrypted_content", model="gpt-4o-mini", llm_provider="openai" ) async def fake_llm_call(): return failure if provider_rejected else None proxy_logging_obj = MagicMock() proxy_logging_obj.post_call_failure_hook = AsyncMock(return_value=None) user_api_key_dict = MagicMock() with ( patch( # test-quality-ok: first-frame model auth needs a live router and key table and has its own tests above "litellm.proxy.response_api_endpoints.endpoints._enforce_responses_ws_first_frame_model_auth", new_callable=AsyncMock, ), patch( # test-quality-ok: the pre-call processor needs a live proxy; what the endpoint does with the relay's outcome is under test "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing", return_value=processor, ), patch( # test-quality-ok: routing is the seam that hands back the relay's outcome "litellm.proxy.route_llm_request.route_request", new_callable=AsyncMock, return_value=fake_llm_call(), ), patch( # test-quality-ok: the failure hook is the proxy's only path to a failed spend log row "litellm.proxy.proxy_server.proxy_logging_obj", proxy_logging_obj, ), ): await responses_websocket_endpoint( websocket=ws, model=None, user_api_key_dict=user_api_key_dict, ) ws.close.assert_not_awaited() if not provider_rejected: proxy_logging_obj.post_call_failure_hook.assert_not_awaited() return proxy_logging_obj.post_call_failure_hook.assert_awaited_once() booked = proxy_logging_obj.post_call_failure_hook.await_args.kwargs assert booked["original_exception"] is failure assert booked["user_api_key_dict"] is user_api_key_dict assert booked["request_data"]["model"] == "gpt-4o-mini" @pytest.mark.asyncio async def test_endpoint_sends_an_error_frame_when_routing_rejects_the_connection(self): from litellm.proxy.response_api_endpoints.endpoints import ( responses_websocket_endpoint, ) ws = MagicMock() ws.headers = {} ws.query_params = {} ws.scope = {"headers": []} ws.url = "ws://testserver/v1/responses" ws.accept = AsyncMock() ws.receive_text = AsyncMock( return_value=json.dumps({"type": "response.create", "model": "gpt-4o-mini", "input": []}) ) ws.send_text = AsyncMock() ws.close = AsyncMock() processor = MagicMock() processor.common_processing_pre_call_logic = AsyncMock( return_value=({"model": "gpt-4o-mini", "litellm_metadata": {}}, MagicMock()) ) rejection = litellm.RateLimitError( message="origin deployment is cooling down", model="gpt-4o-mini", llm_provider="openai" ) proxy_logging_obj = MagicMock() proxy_logging_obj.post_call_failure_hook = AsyncMock(return_value=None) user_api_key_dict = MagicMock() with ( patch( # test-quality-ok: first-frame model auth needs a live router and key table and has its own tests above "litellm.proxy.response_api_endpoints.endpoints._enforce_responses_ws_first_frame_model_auth", new_callable=AsyncMock, ), patch( # test-quality-ok: the pre-call processor needs a live proxy; what the endpoint tells the client is under test "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing", return_value=processor, ), patch( # test-quality-ok: routing is the seam that raises the affinity rejection "litellm.proxy.route_llm_request.route_request", new_callable=AsyncMock, side_effect=rejection, ), patch( # test-quality-ok: the failure hook is the proxy's only path to a failed spend log row "litellm.proxy.proxy_server.proxy_logging_obj", proxy_logging_obj, ), ): await responses_websocket_endpoint( websocket=ws, model=None, user_api_key_dict=user_api_key_dict, ) frame = json.loads(ws.send_text.await_args.args[0]) assert frame["type"] == "error" assert frame["status"] == 429 assert frame["error"]["type"] == "rate_limit_exceeded" assert "cooling down" in frame["error"]["message"] ws.close.assert_awaited_once_with(code=1011, reason="Internal server error") booked = proxy_logging_obj.post_call_failure_hook.await_args.kwargs assert booked["original_exception"] is rejection assert booked["user_api_key_dict"] is user_api_key_dict assert booked["request_data"]["model"] == "gpt-4o-mini" @pytest.mark.asyncio async def test_reruns_model_auth_for_first_frame_model(self): from starlette.requests import Request from litellm.proxy.response_api_endpoints.endpoints import ( _enforce_responses_ws_first_frame_model_auth, ) request = Request({"type": "http", "method": "POST", "path": "/v1/responses", "headers": []}) user_api_key_dict = MagicMock() llm_router = MagicMock() with ( patch( "litellm.proxy.auth.user_api_key_auth._enforce_key_and_fallback_model_access", new_callable=AsyncMock, ) as mock_key_check, patch( "litellm.proxy.auth.user_api_key_auth._run_centralized_common_checks", new_callable=AsyncMock, ) as mock_common_checks, patch( "litellm.proxy.proxy_server.llm_model_list", [], ), patch("litellm.proxy.proxy_server.master_key", "sk-test"), patch("litellm.proxy.proxy_server.user_custom_auth", None), patch("litellm.proxy.proxy_server.general_settings", {}), ): await _enforce_responses_ws_first_frame_model_auth( request=request, model="gpt-4o-mini", user_api_key_dict=user_api_key_dict, llm_router=llm_router, ) mock_key_check.assert_awaited_once_with( valid_token=user_api_key_dict, request_data={"model": "gpt-4o-mini"}, route="/v1/responses", request=request, llm_model_list=[], llm_router=llm_router, ) mock_common_checks.assert_awaited_once_with( user_api_key_auth_obj=user_api_key_dict, request=request, request_data={"model": "gpt-4o-mini"}, route="/v1/responses", ) class TestReadWSModelFromFirstFrameErrors: @pytest.mark.asyncio async def test_timeout_closes_without_error_frame(self): import asyncio from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(side_effect=asyncio.TimeoutError()) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None ws.send_text.assert_not_awaited() ws.close.assert_awaited_once_with(code=1008, reason="Timed out waiting for first message") @pytest.mark.asyncio async def test_invalid_json_sends_error_and_closes(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(return_value="this is not json") ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None payload = json.loads(ws.send_text.await_args.args[0]) assert payload["error"]["message"] == "First message is not valid JSON." ws.close.assert_awaited_once_with(code=1008, reason="Invalid JSON in first message") @pytest.mark.asyncio async def test_missing_model_sends_error_and_closes(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) ws = MagicMock() ws.receive_text = AsyncMock(return_value=json.dumps({"type": "response.create", "input": []})) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result is None payload = json.loads(ws.send_text.await_args.args[0]) assert "No model provided" in payload["error"]["message"] ws.close.assert_awaited_once_with(code=1008, reason="No model provided") @pytest.mark.asyncio async def test_valid_first_frame_returns_model_and_raw(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) raw = json.dumps({"type": "response.create", "model": "gpt-4o", "input": []}) ws = MagicMock() ws.receive_text = AsyncMock(return_value=raw) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws) assert result == ("gpt-4o", raw) ws.send_text.assert_not_awaited() ws.close.assert_not_awaited() @pytest.mark.asyncio async def test_query_model_wins_over_first_frame_model(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) raw = json.dumps({"type": "response.create", "model": "gpt-4o", "input": []}) ws = MagicMock() ws.receive_text = AsyncMock(return_value=raw) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws, query_model="reasoning-group") assert result == ("reasoning-group", raw) ws.close.assert_not_awaited() @pytest.mark.asyncio async def test_query_model_satisfies_a_first_frame_without_model(self): from litellm.proxy.response_api_endpoints.endpoints import ( _read_ws_model_from_first_frame, ) raw = json.dumps({"type": "response.create", "input": []}) ws = MagicMock() ws.receive_text = AsyncMock(return_value=raw) ws.send_text = AsyncMock() ws.close = AsyncMock() result = await _read_ws_model_from_first_frame(ws, query_model="reasoning-group") assert result == ("reasoning-group", raw) ws.send_text.assert_not_awaited() ws.close.assert_not_awaited() class TestManagedResponsesSameProvider: def _handler(self, model, custom_llm_provider=None): from litellm.responses.streaming_iterator import ( ManagedResponsesWebSocketHandler, ) return ManagedResponsesWebSocketHandler( websocket=MagicMock(), model=model, logging_obj=MagicMock(), custom_llm_provider=custom_llm_provider, ) def test_none_model_treated_as_same_provider(self): assert self._handler("openai/gpt-4o")._same_provider(None) is True def test_identical_model_is_same_provider(self): assert self._handler("openai/gpt-4o")._same_provider("openai/gpt-4o") is True def test_same_provider_different_model(self): assert self._handler("gpt-4o")._same_provider("gpt-4o-mini") is True def test_different_provider_is_not_same(self): assert self._handler("gpt-4o")._same_provider("vertex_ai/gemini-2.0-flash") is False def test_inject_credentials_keeps_provider_for_same_provider_model(self): handler = self._handler("gpt-4o", custom_llm_provider="openai") call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="gpt-4o-mini") assert call_kwargs["custom_llm_provider"] == "openai" def test_inject_credentials_drops_provider_for_cross_provider_model(self): handler = self._handler("gpt-4o", custom_llm_provider="openai") call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="vertex_ai/gemini-2.0-flash") assert "custom_llm_provider" not in call_kwargs def test_unresolvable_connection_model_falls_back_to_custom_provider(self): handler = self._handler("my-custom-deployment", custom_llm_provider="openai") assert handler._same_provider("gpt-4o-mini") is True call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="gpt-4o-mini") assert call_kwargs["custom_llm_provider"] == "openai" def test_unresolvable_connection_model_still_drops_cross_provider(self): handler = self._handler("my-custom-deployment", custom_llm_provider="openai") call_kwargs: dict = {} handler._inject_credentials(call_kwargs, model="vertex_ai/gemini-2.0-flash") assert "custom_llm_provider" not in call_kwargs def _auth_override(): from litellm.proxy._types import UserAPIKeyAuth return UserAPIKeyAuth(api_key="sk-test-cursor", user_id="cursor-user") def test_cursor_chat_completions_messages_body_uses_chat_pipeline(): """A genuine chat-completions body (``messages`` present; what Cursor sends for models whose BYOK it already fixed) must run through the standard chat pipeline untouched: multi-turn tool history (assistant tool_calls + role="tool" results) and nested chat-format tool defs are valid there, while blindly renaming ``messages`` to ``input`` (the pre-fix behavior) produced items the Responses API rejects. Asserts acompletion is called with the exact messages and aresponses is never touched.""" from litellm.proxy.auth.user_api_key_auth import user_api_key_auth import litellm.proxy.proxy_server as ps messages = [ {"role": "user", "content": "read a file"}, { "role": "assistant", "content": None, "tool_calls": [ { "id": "call_hist1", "type": "function", "function": {"name": "read_file", "arguments": '{"path": "a.py"}'}, } ], }, {"role": "tool", "tool_call_id": "call_hist1", "content": "file contents"}, {"role": "user", "content": "now summarize"}, ] mock_router = MagicMock() mock_router.acompletion = AsyncMock( return_value=litellm.ModelResponse( id="chatcmpl-cursor-1", choices=[ { "index": 0, "message": {"role": "assistant", "content": "summary"}, "finish_reason": "stop", } ], model="gpt-4o", ) ) mock_router.aresponses = AsyncMock() mock_router.get_available_deployment = MagicMock(return_value=None) app.dependency_overrides[user_api_key_auth] = _auth_override try: with patch.object(ps, "llm_router", mock_router): client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "gpt-4o", "messages": messages, "tools": [ { "type": "function", "function": {"name": "read_file", "parameters": {"type": "object"}}, } ], }, headers={"Authorization": "Bearer sk-test-cursor"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200, response.text body = response.json() assert body["choices"][0]["message"]["content"] == "summary" assert "output" not in body mock_router.acompletion.assert_called_once() called_kwargs = mock_router.acompletion.call_args.kwargs assert called_kwargs["messages"] == messages assert "input" not in called_kwargs mock_router.aresponses.assert_not_called() def test_cursor_chat_completions_input_body_uses_responses_pipeline_and_strips_stream_options(): """A Responses-shaped body (``input``, no ``messages``; what Cursor agent mode sends) must run through the Responses pipeline with chat-completions output, and ``stream_options`` (chat-completions-only; Cursor sends include_usage) must be stripped before the Responses call since OpenAI's Responses API rejects it. Stripping must not mutate the dict _read_request_body returned: that can be the request-scope cached parsed body itself, and removing a key from it corrupts the cache's key snapshot so any later _read_request_body caller (spend tracking, logging hooks) silently gets an empty body; a follow-up read must still see the full original body.""" import asyncio from openai.types.responses import ResponseOutputMessage, ResponseOutputText from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_utils.http_parsing_utils import ( _read_request_body as real_read_request_body, ) from litellm.types.llms.openai import ResponsesAPIResponse import litellm.proxy.proxy_server as ps captured_requests = [] async def capturing_read_request_body(request): captured_requests.append(request) return await real_read_request_body(request=request) mock_router = MagicMock() mock_router.aresponses = AsyncMock( return_value=ResponsesAPIResponse( id="resp_cursor_agent1", created_at=1234567890, model="gpt-4o", object="response", output=[ ResponseOutputMessage( id="msg_agent1", type="message", role="assistant", status="completed", content=[ResponseOutputText(type="output_text", text="agent reply", annotations=[])], ) ], ) ) mock_router.acompletion = AsyncMock() app.dependency_overrides[user_api_key_auth] = _auth_override try: with ( patch.object(ps, "llm_router", mock_router), patch( "litellm.proxy.response_api_endpoints.endpoints._read_request_body", side_effect=capturing_read_request_body, ), ): client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "gpt-4o", "input": [{"role": "user", "content": "hello"}], "stream_options": {"include_usage": True}, }, headers={"Authorization": "Bearer sk-test-cursor"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200, response.text body = response.json() assert body["choices"][0]["message"]["content"] == "agent reply" assert "output" not in body mock_router.aresponses.assert_called_once() called_kwargs = mock_router.aresponses.call_args.kwargs assert "stream_options" not in called_kwargs mock_router.acompletion.assert_not_called() assert captured_requests followup_body = asyncio.run(real_read_request_body(request=captured_requests[0])) assert followup_body.get("stream_options") == {"include_usage": True} assert followup_body.get("input") == [{"role": "user", "content": "hello"}] def test_cursor_models_route_delegates_to_model_list(): """Clients pointed at /cursor as an OpenAI-compatible base URL resolve and verify keys via GET {base}/models (the OpenAI SDK contract). Without a dedicated route those requests fall through to the Cursor Cloud Agents passthrough and 401 for lack of a Cursor API key, so BYOK verification fails before any chat request is sent. Both /cursor/models and /cursor/v1/models must serve the standard model list instead.""" from litellm.proxy.auth.user_api_key_auth import user_api_key_auth import litellm.proxy.proxy_server as ps model_payload = {"data": [{"id": "gpt-5.6", "object": "model"}], "object": "list"} app.dependency_overrides[user_api_key_auth] = _auth_override try: with patch.object(ps, "model_list", AsyncMock(return_value=model_payload)) as mock_model_list: client = TestClient(app) for path in ("/cursor/models", "/cursor/v1/models"): response = client.get(path, headers={"Authorization": "Bearer sk-test-cursor"}) assert response.status_code == 200, f"{path}: {response.text}" assert response.json() == model_payload assert mock_model_list.call_count == 2 finally: app.dependency_overrides.pop(user_api_key_auth, None) class TestNestFlatChatTools: def test_flat_custom_tool_is_nested(self): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope result = _convert_tool_envelope( {"type": "custom", "name": "ApplyPatch", "description": "V4A patch", "format": {"type": "text"}}, to_chat=True, ) assert result == { "type": "custom", "custom": {"name": "ApplyPatch", "description": "V4A patch", "format": {"type": "text"}}, } def test_flat_function_tool_is_nested(self): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope result = _convert_tool_envelope( {"type": "function", "name": "read_file", "description": "d", "parameters": {"type": "object"}}, to_chat=True, ) assert result == { "type": "function", "function": {"name": "read_file", "description": "d", "parameters": {"type": "object"}}, } def test_already_nested_and_unrecognized_tools_pass_through_unchanged(self): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope tools = [ {"type": "custom", "custom": {"name": "already_nested"}}, {"type": "function", "function": {"name": "f", "parameters": {}}}, {"type": "web_search"}, {"type": "custom"}, {"name": "typeless"}, {}, "junk", None, 42, ] assert [_convert_tool_envelope(tool, to_chat=True) for tool in tools] == tools class TestCursorMessagesArmToolNormalization: @pytest.mark.asyncio async def test_flat_custom_tool_nested_before_chat_completion_delegation(self): from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy._types import UserAPIKeyAuth seen = {} async def fake_chat_completion(request, fastapi_response, model, user_api_key_dict): from litellm.proxy.common_utils.http_parsing_utils import _read_request_body seen["body"] = await _read_request_body(request=request) return {"id": "chatcmpl-fake", "object": "chat.completion", "choices": []} app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-1234") try: with patch("litellm.proxy.proxy_server.chat_completion", new=fake_chat_completion): client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "gpt-5.6", "messages": [{"role": "user", "content": "use ApplyPatch"}], "tools": [ { "type": "function", "function": {"name": "read_file", "parameters": {"type": "object"}}, }, { "type": "custom", "name": "ApplyPatch", "description": "V4A patch", "format": { "type": "grammar", "definition": "start: patch", "syntax": "lark", }, }, ], "tool_choice": {"type": "custom", "name": "ApplyPatch"}, }, headers={"Authorization": "Bearer sk-1234"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200 assert seen["body"]["tools"] == [ {"type": "function", "function": {"name": "read_file", "parameters": {"type": "object"}}}, { "type": "custom", "custom": { "name": "ApplyPatch", "description": "V4A patch", "format": { "type": "grammar", "grammar": {"definition": "start: patch", "syntax": "lark"}, }, }, }, ] assert seen["body"]["tool_choice"] == {"type": "custom", "custom": {"name": "ApplyPatch"}} assert seen["body"]["messages"] == [{"role": "user", "content": "use ApplyPatch"}] @pytest.mark.asyncio async def test_messages_body_without_flat_tools_leaves_parsed_body_cache_untouched(self): from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy._types import UserAPIKeyAuth seen = {} async def fake_chat_completion(request, fastapi_response, model, user_api_key_dict): from litellm.proxy.common_utils.http_parsing_utils import _read_request_body seen["body"] = await _read_request_body(request=request) return {"id": "chatcmpl-fake", "object": "chat.completion", "choices": []} body = { "model": "gpt-5.6", "messages": [{"role": "user", "content": "hi"}], "tools": [{"type": "function", "function": {"name": "f", "parameters": {}}}], } app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-1234") try: with patch("litellm.proxy.proxy_server.chat_completion", new=fake_chat_completion): client = TestClient(app) response = client.post( "/cursor/chat/completions", json=body, headers={"Authorization": "Bearer sk-1234"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200 assert seen["body"]["tools"] == body["tools"] assert seen["body"]["messages"] == body["messages"] class TestToolEnvelopeConversionMatrix: """ Cursor mixes Responses API shapes into chat bodies PER LEVEL, independently (live-captured: a pre-nested custom envelope carrying a flat grammar format). Tool definitions and tool_choice share one envelope rule, so every cell of direction x envelope x format must land on that direction's canonical shape. """ FLAT_GRAMMAR = {"type": "grammar", "definition": "start: patch", "syntax": "lark"} NESTED_GRAMMAR = {"type": "grammar", "grammar": {"definition": "start: patch", "syntax": "lark"}} TEXT = {"type": "text"} @pytest.mark.parametrize("to_chat", [True, False]) @pytest.mark.parametrize("envelope", ["flat", "nested"]) @pytest.mark.parametrize("format_shape", ["absent", "text", "flat_grammar", "nested_grammar"]) def test_every_direction_envelope_and_format_lands_canonical(self, to_chat, envelope, format_shape): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope format_value = { "absent": None, "text": self.TEXT, "flat_grammar": self.FLAT_GRAMMAR, "nested_grammar": self.NESTED_GRAMMAR, }[format_shape] payload = {"name": "ApplyPatch", "description": "V4A patch"} if format_value is not None: payload["format"] = format_value tool = {"type": "custom", "custom": payload} if envelope == "nested" else {"type": "custom", **payload} canonical_payload = {"name": "ApplyPatch", "description": "V4A patch"} if format_shape in ("flat_grammar", "nested_grammar"): canonical_payload["format"] = self.NESTED_GRAMMAR if to_chat else self.FLAT_GRAMMAR elif format_shape == "text": canonical_payload["format"] = self.TEXT expected = ( {"type": "custom", "custom": canonical_payload} if to_chat else {"type": "custom", **canonical_payload} ) assert _convert_tool_envelope(tool, to_chat=to_chat) == expected def test_nested_envelope_with_flat_grammar_matches_live_cursor_capture(self): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope cursor_tool = {"type": "custom", "custom": {"name": "ApplyPatch", "format": self.FLAT_GRAMMAR}} assert _convert_tool_envelope(cursor_tool, to_chat=True) == { "type": "custom", "custom": {"name": "ApplyPatch", "format": self.NESTED_GRAMMAR}, } @pytest.mark.parametrize("to_chat", [True, False]) def test_conversion_is_idempotent(self, to_chat): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope once = _convert_tool_envelope({"type": "custom", "name": "A", "format": self.FLAT_GRAMMAR}, to_chat=to_chat) assert _convert_tool_envelope(once, to_chat=to_chat) == once def test_nested_function_tool_flattens_and_flat_passes_through(self): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope nested = {"type": "function", "function": {"name": "read_file", "parameters": {"type": "object"}}} flat = {"type": "function", "name": "read_file", "parameters": {"type": "object"}} assert _convert_tool_envelope(nested, to_chat=False) == flat assert _convert_tool_envelope(flat, to_chat=False) == flat @pytest.mark.parametrize("to_chat", [True, False]) def test_unrecognized_entries_pass_through(self, to_chat): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope entries = [{"type": "web_search"}, {"type": "custom"}, "junk", None, {}, 42, {"type": "auto"}] assert [_convert_tool_envelope(entry, to_chat=to_chat) for entry in entries] == entries @pytest.mark.parametrize("to_chat", [True, False]) def test_empty_nested_envelope_falls_back_to_top_level_payload(self, to_chat): """An empty nested envelope must not shadow payload fields that sit at the top level; treating the empty dict as the sole payload source dropped the name.""" from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope hybrid = {"type": "custom", "custom": {}, "name": "ApplyPatch", "format": self.TEXT} expected_payload = {"name": "ApplyPatch", "format": self.TEXT} expected = {"type": "custom", "custom": expected_payload} if to_chat else {"type": "custom", **expected_payload} assert _convert_tool_envelope(hybrid, to_chat=to_chat) == expected def test_nested_payload_wins_over_stray_top_level_fields(self): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope tool = {"type": "custom", "custom": {"name": "NestedName"}, "name": "TopName"} assert _convert_tool_envelope(tool, to_chat=False) == {"type": "custom", "name": "NestedName"} @pytest.mark.parametrize("to_chat", [True, False]) def test_nameless_envelope_passes_through_unchanged(self, to_chat): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope nameless = {"type": "custom", "custom": {}, "description": "no name anywhere"} assert _convert_tool_envelope(nameless, to_chat=to_chat) == nameless class TestToolChoiceSharesTheToolEnvelopeRule: """ tool_choice carries the same {"type": T, T: {...}} chat envelope as a tool definition, so it converts through the same function in both directions. OpenAI requires the nested key on chat (SDK ChatCompletionNamedToolChoiceParam and ChatCompletionNamedToolChoiceCustomParam both mark it Required). """ @pytest.mark.parametrize("choice_type", ["custom", "function"]) def test_flat_tool_choice_is_nested_for_chat(self, choice_type): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope assert _convert_tool_envelope({"type": choice_type, "name": "ApplyPatch"}, to_chat=True) == { "type": choice_type, choice_type: {"name": "ApplyPatch"}, } @pytest.mark.parametrize("choice_type", ["custom", "function"]) def test_nested_tool_choice_is_flattened_for_responses(self, choice_type): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope assert _convert_tool_envelope({"type": choice_type, choice_type: {"name": "ApplyPatch"}}, to_chat=False) == { "type": choice_type, "name": "ApplyPatch", } @pytest.mark.parametrize("to_chat", [True, False]) def test_sentinel_and_malformed_tool_choice_pass_through(self, to_chat): from litellm.proxy.response_api_endpoints.endpoints import _convert_tool_envelope for unchanged in ("auto", "required", "none", None, {"type": "auto"}, 42): assert _convert_tool_envelope(unchanged, to_chat=to_chat) == unchanged class TestNormalizeToolDialectCoversBothFields: """ The regression that motivated one normalizer: tools were converted while tool_choice was left flat, so OpenAI rejected the request. Both fields move together in a single call, on both arms. """ @pytest.mark.parametrize("to_chat", [True, False]) def test_tools_and_tool_choice_convert_together(self, to_chat): from litellm.proxy.response_api_endpoints.endpoints import _normalize_tool_dialect flat = {"type": "custom", "name": "ApplyPatch"} nested = {"type": "custom", "custom": {"name": "ApplyPatch"}} source = flat if to_chat else nested expected = nested if to_chat else flat out = _normalize_tool_dialect({"messages": [], "tools": [source], "tool_choice": source}, to_chat=to_chat) assert out["tools"] == [expected] assert out["tool_choice"] == expected def test_body_needing_no_conversion_is_returned_by_identity(self): from litellm.proxy.response_api_endpoints.endpoints import _normalize_tool_dialect data = {"messages": [], "tools": [{"type": "function", "function": {"name": "f"}}], "tool_choice": "auto"} assert _normalize_tool_dialect(data, to_chat=True) is data def test_absent_tool_fields_are_not_invented(self): from litellm.proxy.response_api_endpoints.endpoints import _normalize_tool_dialect data = {"messages": [{"role": "user", "content": "hi"}]} result = _normalize_tool_dialect(data, to_chat=True) assert result == data assert "tools" not in result and "tool_choice" not in result class TestCursorInputArmFlattening: @pytest.mark.asyncio async def test_nested_chat_shapes_in_input_body_reach_aresponses_flattened(self): from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from openai.types.responses import ResponseOutputMessage, ResponseOutputText from litellm.types.llms.openai import ResponsesAPIResponse mock_response = ResponsesAPIResponse( id="resp_flat123", created_at=1234567890, model="gpt-5.6", object="response", output=[ ResponseOutputMessage( id="msg_flat123", type="message", role="assistant", status="completed", content=[ResponseOutputText(type="output_text", text="ok", annotations=[])], ) ], ) app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-1234") try: with patch("litellm.proxy.proxy_server.llm_router") as mock_router: mock_router.aresponses = AsyncMock(return_value=mock_response) client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "gpt-5.6", "input": [{"role": "user", "content": "use ApplyPatch"}], "tools": [ { "type": "custom", "custom": { "name": "ApplyPatch", "format": { "type": "grammar", "grammar": {"definition": "start: patch", "syntax": "lark"}, }, }, }, {"type": "function", "name": "read_file", "parameters": {"type": "object"}}, ], "tool_choice": {"type": "custom", "custom": {"name": "ApplyPatch"}}, }, headers={"Authorization": "Bearer sk-1234"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200 call_kwargs = mock_router.aresponses.call_args.kwargs assert call_kwargs["tools"] == [ { "type": "custom", "name": "ApplyPatch", "format": {"type": "grammar", "definition": "start: patch", "syntax": "lark"}, }, {"type": "function", "name": "read_file", "parameters": {"type": "object"}}, ] assert call_kwargs["tool_choice"] == {"type": "custom", "name": "ApplyPatch"} class TestChatCompletionsBodyDetection: def test_routing_matrix(self): from litellm.proxy.response_api_endpoints.endpoints import _is_chat_completions_body assert _is_chat_completions_body({"messages": [{"role": "user", "content": "hi"}]}) is True assert _is_chat_completions_body({"messages": [{"role": "user", "content": "hi"}], "input": []}) is True assert _is_chat_completions_body({"messages": None, "input": [{"role": "user", "content": "hi"}]}) is False assert _is_chat_completions_body({"messages": [], "input": [{"role": "user", "content": "hi"}]}) is False assert _is_chat_completions_body({"messages": None}) is True assert _is_chat_completions_body({"messages": []}) is True assert _is_chat_completions_body({"input": [{"role": "user", "content": "hi"}]}) is False assert _is_chat_completions_body({}) is False @pytest.mark.asyncio async def test_null_messages_stub_with_input_reaches_responses_arm(self): from openai.types.responses import ResponseOutputMessage, ResponseOutputText from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.types.llms.openai import ResponsesAPIResponse mock_response = ResponsesAPIResponse( id="resp_stub1", created_at=1234567890, model="gpt-5.6", object="response", output=[ ResponseOutputMessage( id="msg_stub1", type="message", role="assistant", status="completed", content=[ResponseOutputText(type="output_text", text="ok", annotations=[])], ) ], ) app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-1234") try: with patch("litellm.proxy.proxy_server.llm_router") as mock_router: mock_router.aresponses = AsyncMock(return_value=mock_response) client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "gpt-5.6", "messages": None, "input": [{"role": "user", "content": "hello"}], }, headers={"Authorization": "Bearer sk-1234"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200 assert mock_router.aresponses.call_args is not None assert mock_router.aresponses.call_args.kwargs["input"] == [{"role": "user", "content": "hello"}] class TestParseCursorModelVariant: @pytest.mark.parametrize( "model,expected_base,expected_effort", [ ("claude-opus-5-thinking-high", "claude-opus-5", "high"), ("claude-opus-5-thinking-xhigh-fast", "claude-opus-5", "xhigh"), ("gemini-3.0-pro-thinking-low", "gemini-3.0-pro", "low"), ("claude-opus-5-fast", "claude-opus-5", None), ("gpt-5.6-sol", "gpt-5.6-sol", None), ("foo-thinking-ultra-fast", "foo-thinking-ultra", None), ("gpt-5.6-thinking-max", "gpt-5.6", "max"), ("foo-thinking-mega-fast", "foo-thinking-mega", None), ("-thinking-high", "-thinking-high", None), ], ) def test_parse_matrix(self, model, expected_base, expected_effort): from litellm.proxy.response_api_endpoints.endpoints import _parse_cursor_model_variant variant = _parse_cursor_model_variant(model) assert variant.base_model == expected_base assert variant.reasoning_effort == expected_effort class TestResolveCursorModelVariant: @pytest.fixture(scope="class") def wildcard_router(self): from litellm import Router return Router( model_list=[ {"model_name": "anthropic/*", "litellm_params": {"model": "anthropic/*", "api_key": "fake"}}, {"model_name": "openai/*", "litellm_params": {"model": "openai/*", "api_key": "fake"}}, { "model_name": "explicit-alias-thinking-high", "litellm_params": {"model": "anthropic/claude-opus-5", "api_key": "fake"}, }, ] ) def test_chat_body_suffix_stripped_into_reasoning_effort(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = { "model": "claude-opus-5-thinking-xhigh-fast", "messages": [{"role": "user", "content": "hi"}], } resolved = _resolve_cursor_model_variant(body, wildcard_router) assert resolved["model"] == "claude-opus-5" assert resolved["reasoning_effort"] == "xhigh" assert resolved["messages"] == body["messages"] assert body["model"] == "claude-opus-5-thinking-xhigh-fast" def test_responses_body_suffix_stripped_into_reasoning_dict(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = {"model": "claude-opus-5-thinking-high", "input": [{"role": "user", "content": "hi"}]} resolved = _resolve_cursor_model_variant(body, wildcard_router) assert resolved["model"] == "claude-opus-5" assert resolved["reasoning"] == {"effort": "high"} def test_responses_body_merges_effort_into_existing_reasoning(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = { "model": "claude-opus-5-thinking-high", "input": [{"role": "user", "content": "hi"}], "reasoning": {"summary": "auto"}, } resolved = _resolve_cursor_model_variant(body, wildcard_router) assert resolved["model"] == "claude-opus-5" assert resolved["reasoning"] == {"summary": "auto", "effort": "high"} def test_existing_reasoning_effort_wins_but_model_still_rewritten(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant chat_body = { "model": "claude-opus-5-thinking-high", "messages": [{"role": "user", "content": "hi"}], "reasoning_effort": "low", } resolved_chat = _resolve_cursor_model_variant(chat_body, wildcard_router) assert resolved_chat["model"] == "claude-opus-5" assert resolved_chat["reasoning_effort"] == "low" responses_body = { "model": "claude-opus-5-thinking-high", "input": [{"role": "user", "content": "hi"}], "reasoning": {"effort": "low"}, } resolved_responses = _resolve_cursor_model_variant(responses_body, wildcard_router) assert resolved_responses["model"] == "claude-opus-5" assert resolved_responses["reasoning"] == {"effort": "low"} def test_fast_only_suffix_strips_without_reasoning(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = {"model": "claude-opus-5-fast", "messages": [{"role": "user", "content": "hi"}]} resolved = _resolve_cursor_model_variant(body, wildcard_router) assert resolved["model"] == "claude-opus-5" assert "reasoning_effort" not in resolved def test_explicitly_configured_suffixed_name_untouched(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = {"model": "explicit-alias-thinking-high", "messages": [{"role": "user", "content": "hi"}]} assert _resolve_cursor_model_variant(body, wildcard_router) is body def test_provider_inferable_bare_name_untouched(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "hi"}]} assert _resolve_cursor_model_variant(body, wildcard_router) is body def test_unservable_base_untouched(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = {"model": "totally-unknown-thinking-high", "messages": [{"role": "user", "content": "hi"}]} assert _resolve_cursor_model_variant(body, wildcard_router) is body def test_no_router_untouched(self): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant body = {"model": "claude-opus-5-thinking-high", "messages": [{"role": "user", "content": "hi"}]} assert _resolve_cursor_model_variant(body, None) is body def test_missing_or_non_string_model_untouched(self, wildcard_router): from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant no_model = {"messages": [{"role": "user", "content": "hi"}]} assert _resolve_cursor_model_variant(no_model, wildcard_router) is no_model null_model = {"model": None, "messages": [{"role": "user", "content": "hi"}]} assert _resolve_cursor_model_variant(null_model, wildcard_router) is null_model def _router_serving_only(base_model: str) -> MagicMock: mock_router = MagicMock() mock_router.model_names = set() mock_router.model_group_alias = {} mock_router.team_public_model_names = frozenset() mock_router.is_recognized_model.side_effect = lambda model: ( model in mock_router.model_names or model in mock_router.model_group_alias ) mock_router.router_general_settings.pass_through_all_models = False mock_router.default_deployment = None mock_router.pattern_router.patterns = {base_model: ["anthropic/*"]} mock_router.pattern_router.get_pattern.side_effect = lambda model: ( [{"model_name": "anthropic/*"}] if model == base_model else None ) return mock_router class TestCursorModelSuffixResolutionEndToEnd: @pytest.mark.asyncio async def test_chat_arm_rewrites_suffixed_model_before_delegation(self): from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth seen = {} async def fake_chat_completion(request, fastapi_response, model, user_api_key_dict): from litellm.proxy.common_utils.http_parsing_utils import _read_request_body seen["body"] = await _read_request_body(request=request) return {"id": "chatcmpl-fake", "object": "chat.completion", "choices": []} app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-1234") try: with ( patch("litellm.proxy.proxy_server.llm_router", new=_router_serving_only("claude-opus-5")), patch("litellm.proxy.proxy_server.chat_completion", new=fake_chat_completion), ): client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "claude-opus-5-thinking-xhigh-fast", "messages": [{"role": "user", "content": "hi"}], }, headers={"Authorization": "Bearer sk-1234"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200 assert seen["body"]["model"] == "claude-opus-5" assert seen["body"]["reasoning_effort"] == "xhigh" assert seen["body"]["messages"] == [{"role": "user", "content": "hi"}] @pytest.mark.asyncio async def test_responses_arm_rewrites_suffixed_model_before_routing(self): from openai.types.responses import ResponseOutputMessage, ResponseOutputText from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.types.llms.openai import ResponsesAPIResponse mock_response = ResponsesAPIResponse( id="resp_suffix1", created_at=1234567890, model="claude-opus-5", object="response", output=[ ResponseOutputMessage( id="msg_suffix1", type="message", role="assistant", status="completed", content=[ResponseOutputText(type="output_text", text="ok", annotations=[])], ) ], ) mock_router = _router_serving_only("claude-opus-5") mock_router.aresponses = AsyncMock(return_value=mock_response) app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-1234") try: with patch("litellm.proxy.proxy_server.llm_router", new=mock_router): client = TestClient(app) response = client.post( "/cursor/chat/completions", json={ "model": "claude-opus-5-thinking-high", "input": [{"role": "user", "content": "hello"}], }, headers={"Authorization": "Bearer sk-1234"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200 assert mock_router.aresponses.call_args is not None assert mock_router.aresponses.call_args.kwargs["model"] == "claude-opus-5" assert mock_router.aresponses.call_args.kwargs["reasoning"] == {"effort": "high"} def _cursor_budget_auth_env(base_model: str, spend: float): from litellm import Router from litellm.caching.dual_cache import DualCache from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.hooks.model_max_budget_limiter import ( VIRTUAL_KEY_SPEND_CACHE_KEY_PREFIX, _PROXY_VirtualKeyModelMaxBudgetLimiter, ) valid_token = UserAPIKeyAuth( api_key="sk-cursor-budget-test", token="hashed-cursor-budget-token", model_max_budget={base_model: {"budget_limit": 0.00001, "time_period": "1d"}}, ) limiter = _PROXY_VirtualKeyModelMaxBudgetLimiter(dual_cache=DualCache()) limiter.dual_cache.in_memory_cache.set_cache( key=f"{VIRTUAL_KEY_SPEND_CACHE_KEY_PREFIX}:{valid_token.token}:{base_model}:1d", value=spend, ) router = Router( model_list=[{"model_name": "anthropic/*", "litellm_params": {"model": "anthropic/*", "api_key": "fake"}}] ) mock_proxy_logging_obj = MagicMock() mock_proxy_logging_obj.post_call_failure_hook = AsyncMock(return_value=None) proxy_server_attrs = { "prisma_client": MagicMock(), "user_api_key_cache": DualCache(), "proxy_logging_obj": mock_proxy_logging_obj, "master_key": "sk-master-key", "general_settings": {}, "llm_model_list": [], "llm_router": router, "open_telemetry_logger": None, "model_max_budget_limiter": limiter, "user_custom_auth": None, "jwt_handler": None, "litellm_proxy_admin_name": "admin", } return valid_token, proxy_server_attrs def _post_cursor_with_real_auth(valid_token, proxy_server_attrs, request_model: str): with ( patch.multiple("litellm.proxy.proxy_server", **proxy_server_attrs), patch( "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, return_value=valid_token, ), ): client = TestClient(app) return client.post( "/cursor/chat/completions", json={"model": request_model, "input": [{"role": "user", "content": "hi"}]}, headers={"Authorization": "Bearer sk-cursor-budget-test"}, ) class TestCursorVariantPerModelBudgetEnforcement: """Regression tests for the per-model budget bypass on /cursor/chat/completions. user_api_key_auth enforced key model_max_budget against the raw request model, but _resolve_cursor_model_variant only rewrote minted aliases like -thinking- to inside the handler, after auth had already run. A key whose budget for was exhausted could keep calling through any unconfigured alias. The variant must now be resolved in a route-level dependency that runs before user_api_key_auth, so these tests exercise the real dependency chain (real auth, real budget limiter) through TestClient and fail if that ordering ever breaks.""" def test_minted_alias_rejected_when_base_model_budget_exhausted(self): valid_token, attrs = _cursor_budget_auth_env(base_model="claude-opus-5", spend=1.0) response = _post_cursor_with_real_auth(valid_token, attrs, request_model="claude-opus-5-thinking-high") assert response.status_code == 422, response.text error = response.json()["error"] assert error["type"] == "budget_exceeded" assert "exceeded budget for model=claude-opus-5" in error["message"] def test_alias_rejection_matches_base_model_rejection(self): valid_token, attrs = _cursor_budget_auth_env(base_model="claude-opus-5", spend=1.0) base_response = _post_cursor_with_real_auth(valid_token, attrs, request_model="claude-opus-5") alias_response = _post_cursor_with_real_auth(valid_token, attrs, request_model="claude-opus-5-fast") assert base_response.status_code == 422, base_response.text assert alias_response.status_code == 422, alias_response.text assert alias_response.json() == base_response.json() class TestCursorVariantResolvedBeforeAuth: """The route-level resolver dependency must rewrite the parsed body before user_api_key_auth reads it, so every auth check (model access, key and end-user model budgets, rate limits) sees the base model, and names the router already serves must reach auth untouched.""" def _run_with_recording_auth(self, mock_router, request_model: str): from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_utils.http_parsing_utils import _read_request_body from fastapi import Request bodies_seen_by_auth = [] async def recording_auth(request: Request) -> UserAPIKeyAuth: bodies_seen_by_auth.append(await _read_request_body(request=request)) return UserAPIKeyAuth(api_key="sk-test-cursor") async def fake_chat_completion(request, fastapi_response, model, user_api_key_dict): return {"id": "chatcmpl-fake", "object": "chat.completion", "choices": []} app.dependency_overrides[user_api_key_auth] = recording_auth try: with ( patch("litellm.proxy.proxy_server.llm_router", new=mock_router), patch("litellm.proxy.proxy_server.chat_completion", new=fake_chat_completion), ): client = TestClient(app) response = client.post( "/cursor/chat/completions", json={"model": request_model, "messages": [{"role": "user", "content": "hi"}]}, headers={"Authorization": "Bearer sk-test-cursor"}, ) finally: app.dependency_overrides.pop(user_api_key_auth, None) assert response.status_code == 200, response.text assert len(bodies_seen_by_auth) == 1 return bodies_seen_by_auth[0] def test_auth_sees_base_model_for_minted_alias(self): auth_body = self._run_with_recording_auth( mock_router=_router_serving_only("claude-opus-5"), request_model="claude-opus-5-thinking-xhigh-fast", ) assert auth_body["model"] == "claude-opus-5" assert auth_body["reasoning_effort"] == "xhigh" def test_auth_sees_servable_model_name_untouched(self): mock_router = _router_serving_only("claude-opus-5") mock_router.model_names = {"claude-opus-5-thinking-high"} auth_body = self._run_with_recording_auth( mock_router=mock_router, request_model="claude-opus-5-thinking-high", ) assert auth_body["model"] == "claude-opus-5-thinking-high" assert "reasoning_effort" not in auth_body class TestCursorGateRecognizesRoutingGroups: def test_group_name_variant_is_not_mangled(self): from litellm import Router from litellm.proxy.response_api_endpoints.endpoints import _resolve_cursor_model_variant router = Router( model_list=[{"model_name": "member-fast", "litellm_params": {"model": "openai/gpt-4o", "api_key": "fake"}}], routing_groups=[ {"group_name": "grouped-thinking-high", "models": ["member-fast"], "routing_strategy": "simple-shuffle"} ], ) body = {"model": "grouped-thinking-high", "messages": [{"role": "user", "content": "hi"}]} resolved = _resolve_cursor_model_variant(body, router) assert resolved["model"] == "grouped-thinking-high" assert "reasoning_effort" not in resolved BLOCK_MESSAGE = "Content flagged by policy, response withheld" def _post_blocked_responses( original_response: ResponsesAPIResponse | litellm.ModelResponse | None, payload: Mapping[str, object] | None = None, ) -> httpx.Response: from litellm.integrations.custom_guardrail import ModifyResponseException from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth exc = ModifyResponseException( message=BLOCK_MESSAGE, model="gpt-4o-mini", request_data={"model": "gpt-4o-mini", "input": "hi"}, guardrail_name="zero-usage-regression", original_response=original_response, ) mock_proxy_logging = MagicMock() mock_proxy_logging.post_call_failure_hook = AsyncMock() app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( api_key="sk-test", request_route="/v1/responses" ) body = {"model": "gpt-4o-mini", "input": "Write a haiku about token accounting"} if payload: body.update(payload) try: with ( patch( "litellm.proxy.response_api_endpoints.endpoints.ProxyBaseLLMRequestProcessing.base_process_llm_request", new=AsyncMock(side_effect=exc), ), patch("litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging), ): client = TestClient(app) return client.post("/v1/responses", json=body, headers={"Authorization": "Bearer sk-1234"}) finally: app.dependency_overrides.pop(user_api_key_auth, None) def _assert_blocked_output_item(item: Mapping[str, object], text: str) -> None: assert item["type"] == "message" assert item["id"].startswith("msg_") assert item["role"] == "assistant" assert item["status"] == "completed" assert item["content"][0]["type"] == "output_text" assert item["content"][0]["text"] == text def _sse_data_frames(text: str) -> list[str]: return [line.removeprefix("data: ").strip() for line in text.splitlines() if line.startswith("data: ")] class TestGuardrailBlockedResponsesUsage: """Regression tests for https://github.com/BerriAI/litellm/issues/36880. The ModifyResponseException handler in responses_api hardcoded the synthetic blocked reply's usage to zeros, discarding the real token counts the blocked upstream call consumed. The blocked reply must carry the usage from e.original_response, exactly like /v1/chat/completions already does.""" def _post_blocked_responses(self, original_response): return _post_blocked_responses(original_response) def test_post_call_block_reports_real_upstream_usage(self): from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse original = ResponsesAPIResponse( id="resp_upstream", created_at=1, model="gpt-4o-mini", object="response", output=[], status="completed", usage=ResponseAPIUsage(input_tokens=14, output_tokens=20, total_tokens=34), ) response = self._post_blocked_responses(original) assert response.status_code == 200, response.text body = response.json() assert body["output"][0]["content"][0]["text"] == "Content flagged by policy, response withheld" assert body["usage"]["input_tokens"] == 14 assert body["usage"]["output_tokens"] == 20 assert body["usage"]["total_tokens"] == 34 def test_post_call_block_maps_bridged_chat_usage(self): original = litellm.ModelResponse() original.usage = litellm.Usage(prompt_tokens=14, completion_tokens=18, total_tokens=32) response = self._post_blocked_responses(original) assert response.status_code == 200, response.text usage = response.json()["usage"] assert usage["input_tokens"] == 14 assert usage["output_tokens"] == 18 assert usage["total_tokens"] == 32 def test_pre_call_block_reports_zero_usage(self): response = self._post_blocked_responses(None) assert response.status_code == 200, response.text usage = response.json()["usage"] assert usage["input_tokens"] == 0 assert usage["output_tokens"] == 0 assert usage["total_tokens"] == 0 class TestResponsesInputTokens: """Regression tests for POST /v1/responses/input_tokens. The docs promise OpenAI-format token counting on the proxy, but the route was never registered, so the POST fell through to the GET/DELETE-only /v1/responses/{response_id} route and returned 405.""" def _post_input_tokens( self, body: dict[str, Any], path: str = "/v1/responses/input_tokens", counter: AsyncMock | None = None, ) -> tuple[Response, AsyncMock]: from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.response_api_endpoints.endpoints import _proxy_token_counter from litellm.types.utils import TokenCountResponse token_counter_mock = ( counter if counter is not None else AsyncMock( return_value=TokenCountResponse( total_tokens=13, request_model=body.get("model", ""), model_used=body.get("model", ""), tokenizer_type="openai_api", ) ) ) app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth(api_key="sk-test", request_route=path) app.dependency_overrides[_proxy_token_counter] = lambda: token_counter_mock try: client = TestClient(app) response = client.post(path, json=body, headers={"Authorization": "Bearer sk-1234"}) return response, token_counter_mock finally: app.dependency_overrides.pop(user_api_key_auth, None) app.dependency_overrides.pop(_proxy_token_counter, None) def test_string_input_returns_openai_input_tokens_shape(self): response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "Hello, how are you?"}) assert response.status_code == 200, response.text assert response.json() == {"object": "response.input_tokens", "input_tokens": 13} counter.assert_awaited_once() assert counter.call_args.kwargs["call_endpoint"] is True token_request = counter.call_args.kwargs["request"] assert token_request.model == "gpt-4o" assert token_request.messages == [{"role": "user", "content": "Hello, how are you?"}] def test_every_route_alias_is_registered(self): for path in ("/v1/responses/input_tokens", "/responses/input_tokens", "/openai/v1/responses/input_tokens"): response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, path=path) assert response.status_code == 200, f"{path}: {response.status_code} {response.text}" def test_input_items_instructions_and_tools_are_forwarded(self): tools = [ { "type": "function", "name": "get_weather", "description": "Get weather for a city", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}, } ] response, counter = self._post_input_tokens( { "model": "gpt-4o", "input": [{"role": "user", "content": "What is the weather in Paris?"}], "instructions": "You are terse.", "tools": tools, } ) assert response.status_code == 200, response.text token_request = counter.call_args.kwargs["request"] assert token_request.messages == [ {"role": "system", "content": "You are terse."}, {"role": "user", "content": "What is the weather in Paris?"}, ] assert token_request.tools == tools def test_missing_model_returns_openai_400(self): response, counter = self._post_input_tokens({"input": "Hello"}) assert response.status_code == 400, response.text assert response.json() == { "error": { "message": "Missing required parameter: 'model'.", "type": "invalid_request_error", "param": "model", "code": "missing_required_parameter", } } counter.assert_not_awaited() def test_missing_input_returns_openai_400(self): response, counter = self._post_input_tokens({"model": "gpt-4o"}) assert response.status_code == 400, response.text assert response.json() == { "error": { "message": "Missing required parameter: 'input'.", "type": "invalid_request_error", "param": "input", "code": "missing_required_parameter", } } counter.assert_not_awaited() @pytest.mark.parametrize("empty_input", ["", []]) def test_empty_input_returns_openai_400(self, empty_input): response, counter = self._post_input_tokens({"model": "gpt-4o", "input": empty_input}) assert response.status_code == 400, response.text assert response.json() == { "error": { "message": """One of "input" or "previous_response_id" or 'prompt' or 'conversation' must be provided.""", "type": "invalid_request_error", "param": None, "code": "missing_required_parameter", } } counter.assert_not_awaited() def test_invalid_tools_returns_openai_400(self): response, counter = self._post_input_tokens({"model": "gpt-4o", "input": "hi", "tools": "not-a-list"}) assert response.status_code == 400, response.text error = response.json()["error"] assert error["type"] == "invalid_request_error" counter.assert_not_awaited() def test_provider_error_maps_status_code(self): from litellm.proxy._types import ProxyException failing_counter = AsyncMock( side_effect=ProxyException( message="rate limited", type="token_counting_error", param="model", code="429", ) ) response, _ = self._post_input_tokens({"model": "gpt-4o", "input": "hi"}, counter=failing_counter) assert response.status_code == 429, response.text assert response.json()["error"]["message"] == "rate limited" class TestGuardrailBlockedResponsesShape: """A pre_call block raises ModifyResponseException before any provider call. The reply must satisfy the Responses API contract the request selected: stream=true answers SSE ending in one response.completed whose output[0] is a completed assistant message item with output_text content, and a plain POST answers JSON with the same item, both with the usage the blocked call consumed (zero for pre_call).""" def test_non_stream_block_is_a_completed_assistant_message(self): response = _post_blocked_responses(None) assert response.status_code == 200, response.text assert response.headers["content-type"].startswith("application/json") body = response.json() _assert_blocked_output_item(body["output"][0], BLOCK_MESSAGE) assert body["usage"]["total_tokens"] == 0 def test_stream_block_answers_sse_with_completed_event(self): response = _post_blocked_responses(None, payload={"stream": True}) assert response.status_code == 200, response.text assert response.headers["content-type"].startswith("text/event-stream") frames = _sse_data_frames(response.text) assert frames[-1] == "[DONE]" events = [json.loads(frame) for frame in frames[:-1]] types = [event["type"] for event in events] assert "response.created" in types completed = [event for event in events if event["type"] == "response.completed"] assert len(completed) == 1 completed_response = completed[0]["response"] _assert_blocked_output_item(completed_response["output"][0], BLOCK_MESSAGE) assert completed_response["usage"]["total_tokens"] == 0 delta_text = "".join(event["delta"] for event in events if event["type"] == "response.output_text.delta") assert delta_text == BLOCK_MESSAGE def test_stream_block_keeps_upstream_usage(self): from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse original = ResponsesAPIResponse( id="resp_upstream", created_at=1, model="gpt-4o-mini", object="response", output=[], status="completed", usage=ResponseAPIUsage(input_tokens=14, output_tokens=20, total_tokens=34), ) response = _post_blocked_responses(original, payload={"stream": True}) assert response.status_code == 200, response.text frames = _sse_data_frames(response.text) completed = [json.loads(frame) for frame in frames[:-1] if json.loads(frame)["type"] == "response.completed"] usage = completed[0]["response"]["usage"] assert usage["input_tokens"] == 14 assert usage["output_tokens"] == 20 assert usage["total_tokens"] == 34 def test_responses_routes_document_response_models_in_openapi_schema(): from typing import cast from fastapi import FastAPI from litellm.proxy.response_api_endpoints.endpoints import router def as_object(value: object) -> dict[str, object]: assert isinstance(value, dict) return cast(dict[str, object], value) openapi_app = FastAPI() openapi_app.include_router(router) openapi: Final = cast(dict[str, object], openapi_app.openapi()) def ok_200_properties(path: str, method: str) -> dict[str, object]: operation: Final = as_object(as_object(as_object(openapi)["paths"])[path])[method] schema: Final = as_object( as_object( as_object(as_object(as_object(as_object(operation)["responses"])["200"])["content"])["application/json"] )["schema"] ) ref: Final = schema["$ref"] assert isinstance(ref, str) component: Final = ref.rsplit("/", 1)[-1] return as_object( as_object(as_object(as_object(as_object(openapi)["components"])["schemas"])[component])["properties"] ) assert "output" in ok_200_properties("/v1/responses", "post") assert "output" in ok_200_properties("/v1/responses/{response_id}", "get") assert "deleted" in ok_200_properties("/v1/responses/{response_id}", "delete") assert "data" in ok_200_properties("/v1/responses/{response_id}/input_items", "get")