diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index f54eeca5178..914b0d6888e 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -414,6 +414,11 @@ def _resolve_vertex_location_for_cost( return VertexBase.get_vertex_region(configured_location, model) +def _provider_response_id(source: object) -> str | None: + candidate: Final = source.get("id") if isinstance(source, dict) else getattr(source, "id", None) + return candidate if isinstance(candidate, str) and candidate else None + + class Logging(LiteLLMLoggingBaseClass): global \ supabaseClient, \ @@ -3832,11 +3837,12 @@ class Logging(LiteLLMLoggingBaseClass): if isinstance(result, ResponsesAPIResponse): return self._translate_responses_api_response_to_model_response(result) + provider_response_id: Final = _provider_response_id(result) httpx_response: Final = self.model_call_details.get("httpx_response", None) if httpx_response and isinstance(httpx_response, httpx.Response): result = litellm.AnthropicConfig().transform_response( raw_response=httpx_response, - model_response=litellm.ModelResponse(), + model_response=litellm.ModelResponse(id=provider_response_id), model=self.model, messages=[], logging_obj=self, @@ -3859,7 +3865,7 @@ class Logging(LiteLLMLoggingBaseClass): status_code=200, headers={}, ), - model_response=litellm.ModelResponse(), + model_response=litellm.ModelResponse(id=provider_response_id), json_mode=None, speed=self.optional_params.get("speed") if self.optional_params else None, ) @@ -3882,7 +3888,7 @@ class Logging(LiteLLMLoggingBaseClass): return LiteLLMResponsesTransformationHandler().transform_response( model=self.model, raw_response=result, - model_response=litellm.ModelResponse(), + model_response=litellm.ModelResponse(id=_provider_response_id(result)), logging_obj=self, request_data={}, messages=[], @@ -3897,7 +3903,7 @@ class Logging(LiteLLMLoggingBaseClass): "usage-only ModelResponse to keep the spend_logs row.", str(e), ) - model_response: Final = litellm.ModelResponse() + model_response: Final = litellm.ModelResponse(id=_provider_response_id(result)) model_response.model = self.model usage: Final = getattr(result, "usage", None) if usage is not None and ResponseAPILoggingUtils._is_response_api_usage(usage): diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py index a36a365f39a..0acc7b1b584 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py @@ -107,6 +107,7 @@ class AnthropicPassthroughLoggingHandler: start_time=start_time, end_time=end_time, logging_obj=logging_obj, + response_id=optional_str(response_body.get("id")), ) return { @@ -148,8 +149,9 @@ class AnthropicPassthroughLoggingHandler: return model @staticmethod - def _extract_model_from_anthropic_chunks( + def _extract_message_start_field( all_chunks: Sequence[str | bytes], + field: str, ) -> str | None: for raw in all_chunks: text = raw.decode("utf-8") if isinstance(raw, bytes) else raw @@ -163,11 +165,23 @@ class AnthropicPassthroughLoggingHandler: if not isinstance(data, dict): continue if data.get("type") == "message_start": - model = (data.get("message") or {}).get("model") - if model: - return model + value = (data.get("message") or {}).get(field) + if isinstance(value, str) and value: + return value return None + @staticmethod + def _extract_model_from_anthropic_chunks( + all_chunks: Sequence[str | bytes], + ) -> str | None: + return AnthropicPassthroughLoggingHandler._extract_message_start_field(all_chunks, "model") + + @staticmethod + def _extract_response_id_from_anthropic_chunks( + all_chunks: Sequence[str | bytes], + ) -> str | None: + return AnthropicPassthroughLoggingHandler._extract_message_start_field(all_chunks, "id") + @staticmethod def _stream_was_interrupted( all_chunks: Sequence[str | bytes], @@ -251,6 +265,7 @@ class AnthropicPassthroughLoggingHandler: start_time: datetime, end_time: datetime, logging_obj: LiteLLMLoggingObj, + response_id: str | None = None, ): """ Create the standard logging object for Anthropic passthrough @@ -312,8 +327,7 @@ class AnthropicPassthroughLoggingHandler: json.dumps(kwargs, indent=4, default=str), ) - # set litellm_call_id to logging response object - litellm_model_response.id = logging_obj.litellm_call_id + litellm_model_response.id = response_id or logging_obj.litellm_call_id litellm_model_response.model = model logging_obj.model_call_details["model"] = model if not logging_obj.model_call_details.get("custom_llm_provider"): @@ -413,6 +427,7 @@ class AnthropicPassthroughLoggingHandler: start_time=start_time, end_time=end_time, logging_obj=litellm_logging_obj, + response_id=AnthropicPassthroughLoggingHandler._extract_response_id_from_anthropic_chunks(all_chunks), ) return { diff --git a/tests/e2e/llm_translation/passthrough_client.py b/tests/e2e/llm_translation/passthrough_client.py index 20a8592db20..a56d3dc077e 100644 --- a/tests/e2e/llm_translation/passthrough_client.py +++ b/tests/e2e/llm_translation/passthrough_client.py @@ -168,6 +168,35 @@ def completed_responses_object(result: StreamingResponse) -> ResponsesObject | N return completed[-1] if completed else None +class AnthropicMessageObject(BaseModel): + id: str + + +class AnthropicStreamEvent(BaseModel): + """One SSE frame of a native Anthropic stream. Only `message_start` carries the + message, so it stays optional and the deltas validate as themselves.""" + + type: str + message: AnthropicMessageObject | None = None + + +def anthropic_message_id(result: StreamingResponse) -> str | None: + """The `msg_...` id the caller was served, which is what the spend row is keyed by + on this route: off the `message_start` frame when streaming, off the body when not.""" + if not result.is_streaming: + return AnthropicMessageObject.model_validate_json(result.body).id + events = ( + AnthropicStreamEvent.model_validate_json(payload) + for payload in result.stream_events + ) + started = tuple( + event.message + for event in events + if event.type == "message_start" and event.message is not None + ) + return started[0].id if started else None + + class OpenAIResponsesBody(BaseModel): model: str input: str diff --git a/tests/e2e/llm_translation/test_passthrough_e2e.py b/tests/e2e/llm_translation/test_passthrough_e2e.py index 7e6a8b25155..50ea8f4b4df 100644 --- a/tests/e2e/llm_translation/test_passthrough_e2e.py +++ b/tests/e2e/llm_translation/test_passthrough_e2e.py @@ -2,7 +2,8 @@ Each test sends a NATIVE provider request through the proxy's passthrough route and verifies the proxy still logged a costed SpendLogs row -(call_type="pass_through_endpoint"), correlated by the x-litellm-call-id header. +(call_type="pass_through_endpoint"), correlated by the id the caller was served: +the x-litellm-call-id header on gemini, the `msg_...` message id on anthropic. Covered: gemini ("gemini-2.5-flash") + anthropic ("claude-haiku-4-5"), streaming + non-streaming, plus native tool calls. See LLM_TRANSLATION_COVERAGE_MATRIX.md. @@ -14,7 +15,7 @@ A passthrough call returning non-2xx fails hard (never a skip); once it returns import pytest from e2e_config import CHEAP_OPENAI_MODEL, unique_marker -from e2e_http import StreamingResponse, require_successful_call, unwrap +from e2e_http import require_successful_call, unwrap from lifecycle import ResourceManager from models import KeyGenerateBody, SpendLogRow from passthrough_client import ( @@ -24,6 +25,7 @@ from passthrough_client import ( JsonSchema, JsonSchemaProperty, PassthroughClient, + anthropic_message_id, completed_responses_object, ) @@ -33,18 +35,18 @@ REALTIME_MODEL = "gpt-realtime-2" pytestmark = pytest.mark.e2e -def _fetch_cost_breakdown(client: PassthroughClient, result: StreamingResponse) -> SpendLogRow: +def _fetch_cost_breakdown(client: PassthroughClient, request_id: str | None) -> SpendLogRow: """The passthrough call's logged row, polled until it carries a cost. Asserts (not skips) that a 2xx passthrough call produced a costed row - the whole point of passthrough spend tracking. """ - assert result.call_id, "passthrough response had no x-litellm-call-id header" + assert request_id, "passthrough response carried no id to correlate its spend row by" rows = client.proxy.poll_logs_for_request_id( - result.call_id, + request_id, predicate=lambda rs: (rs[0].spend or 0) > 0, ) - assert rows, f"no SpendLogs row for passthrough call_id {result.call_id}" + assert rows, f"no SpendLogs row for passthrough request_id {request_id}" row = rows[0] assert row.call_type == "pass_through_endpoint" assert (row.spend or 0) > 0, f"passthrough call was not costed: {row}" @@ -64,7 +66,7 @@ def test_gemini_passthrough_nonstreaming_logs_cost( ) require_successful_call(result) - row = _fetch_cost_breakdown(client, result) + row = _fetch_cost_breakdown(client, result.call_id) assert row.custom_llm_provider == "gemini" assert "gemini" in (row.model or "") assert tag in (row.request_tags or []), f"tags not logged: {row.request_tags}" @@ -107,7 +109,7 @@ def test_gemini_passthrough_streaming_logs_cost( require_successful_call(result) assert result.chunks > 0, "streaming passthrough produced no events" - row = _fetch_cost_breakdown(client, result) + row = _fetch_cost_breakdown(client, result.call_id) assert row.custom_llm_provider == "gemini" @@ -137,7 +139,7 @@ def test_gemini_passthrough_tool_call_logs_cost( require_successful_call(result) assert "functionCall" in result.body, "gemini did not emit a tool call" - row = _fetch_cost_breakdown(client, result) + row = _fetch_cost_breakdown(client, result.call_id) assert row.custom_llm_provider == "gemini" @@ -150,7 +152,7 @@ def test_anthropic_passthrough_nonstreaming_logs_cost( result = client.anthropic_message(scoped_key, "claude-haiku-4-5", "Say hello") require_successful_call(result) - row = _fetch_cost_breakdown(client, result) + row = _fetch_cost_breakdown(client, anthropic_message_id(result)) assert row.custom_llm_provider == "anthropic" assert "claude" in (row.model or "") @@ -164,7 +166,7 @@ def test_anthropic_passthrough_streaming_logs_cost( require_successful_call(result) assert result.chunks > 0, "streaming passthrough produced no events" - row = _fetch_cost_breakdown(client, result) + row = _fetch_cost_breakdown(client, anthropic_message_id(result)) assert row.custom_llm_provider == "anthropic" @@ -190,7 +192,7 @@ def test_anthropic_passthrough_tool_call_logs_cost( require_successful_call(result) assert "tool_use" in result.body, "anthropic did not emit a tool call" - row = _fetch_cost_breakdown(client, result) + row = _fetch_cost_breakdown(client, anthropic_message_id(result)) assert row.custom_llm_provider == "anthropic" diff --git a/tests/pass_through_tests/test_anthropic_passthrough.py b/tests/pass_through_tests/test_anthropic_passthrough.py index d42e06937dc..0452b171f9e 100644 --- a/tests/pass_through_tests/test_anthropic_passthrough.py +++ b/tests/pass_through_tests/test_anthropic_passthrough.py @@ -50,9 +50,9 @@ async def test_anthropic_basic_completion_with_headers(): anthropic_api_output_tokens = ( reported_usage.get("output_tokens", None) if reported_usage else None ) - litellm_call_id = response_headers.get("x-litellm-call-id") + anthropic_message_id = response_json.get("id") - print(f"LiteLLM Call ID: {litellm_call_id}") + print(f"Anthropic message ID: {anthropic_message_id}") # Wait for spend to be logged await asyncio.sleep(15) @@ -64,7 +64,7 @@ async def test_anthropic_basic_completion_with_headers(): print(f"Attempt {attempt + 1}/{max_retries} to check spend logs") async with session.get( - f"http://0.0.0.0:4000/spend/logs?request_id={litellm_call_id}", + f"http://0.0.0.0:4000/spend/logs?request_id={anthropic_message_id}", headers={"Authorization": "Bearer sk-1234"}, ) as spend_response: print("text spend response") @@ -84,25 +84,25 @@ async def test_anthropic_basic_completion_with_headers(): print("Waiting 10 seconds before retry...") await asyncio.sleep(10) - # Spend data might be unavailable (auth error, slow DB write, etc.) - if ( - spend_data is None - or not isinstance(spend_data, list) - or len(spend_data) == 0 - or not isinstance(spend_data[0], dict) - or "request_id" not in spend_data[0] - ): - print(f"Spend data not available or is error response: {spend_data}") - print("Skipping spend assertions (DB write may be slow in CI)") + if not isinstance(spend_data, list): + print(f"Spend endpoint answered with an error response: {spend_data}") + print("Skipping spend assertions (spend logs unreachable in CI)") return + assert spend_data, ( + f"GET /spend/logs?request_id={anthropic_message_id} found no row for the id " + "the caller received" + ) + log_entry = spend_data[0] # Basic existence checks assert isinstance(log_entry, dict), "Log entry should be a dictionary" # Request metadata assertions - assert log_entry["request_id"] == litellm_call_id, "Request ID should match" + assert ( + log_entry["request_id"] == anthropic_message_id + ), "Request ID should be the message id the caller received" assert ( log_entry["call_type"] == "pass_through_endpoint" ), "Call type should be pass_through_endpoint" @@ -182,8 +182,6 @@ async def test_anthropic_streaming_with_headers(): assert response.status == 200, "Response should be successful" response_headers = response.headers print(f"Response headers: {response_headers}") - litellm_call_id = response_headers.get("x-litellm-call-id") - print(f"LiteLLM Call ID: {litellm_call_id}") collected_output = [] async for line in response.content: @@ -194,13 +192,18 @@ async def test_anthropic_streaming_with_headers(): print("Collected output:", "".join(collected_output)) anthropic_api_usage_chunks = [] + anthropic_message_id = None for chunk in collected_output: chunk_json = json.loads(chunk) + if chunk_json.get("type") == "message_start": + anthropic_message_id = chunk_json.get("message", {}).get("id") if "usage" in chunk_json: anthropic_api_usage_chunks.append(chunk_json["usage"]) elif "message" in chunk_json and "usage" in chunk_json["message"]: anthropic_api_usage_chunks.append(chunk_json["message"]["usage"]) + print(f"Anthropic message ID: {anthropic_message_id}") + print( "anthropic_api_usage_chunks", json.dumps(anthropic_api_usage_chunks, indent=4, default=str), @@ -232,7 +235,7 @@ async def test_anthropic_streaming_with_headers(): print(f"Attempt {attempt + 1}/{max_retries} to check spend logs") async with session.get( - f"http://0.0.0.0:4000/spend/logs?request_id={litellm_call_id}", + f"http://0.0.0.0:4000/spend/logs?request_id={anthropic_message_id}", headers={"Authorization": "Bearer sk-1234"}, ) as spend_response: spend_data = await spend_response.json() @@ -250,25 +253,25 @@ async def test_anthropic_streaming_with_headers(): print("Waiting 10 seconds before retry...") await asyncio.sleep(10) - # Spend data might be unavailable (auth error, slow DB write, etc.) - if ( - spend_data is None - or not isinstance(spend_data, list) - or len(spend_data) == 0 - or not isinstance(spend_data[0], dict) - or "request_id" not in spend_data[0] - ): - print(f"Spend data not available or is error response: {spend_data}") - print("Skipping spend assertions (DB write may be slow in CI)") + if not isinstance(spend_data, list): + print(f"Spend endpoint answered with an error response: {spend_data}") + print("Skipping spend assertions (spend logs unreachable in CI)") return + assert spend_data, ( + f"GET /spend/logs?request_id={anthropic_message_id} found no row for the id " + "the caller received" + ) + log_entry = spend_data[0] # Basic existence checks assert isinstance(log_entry, dict), "Log entry should be a dictionary" # Request metadata assertions - assert log_entry["request_id"] == litellm_call_id, "Request ID should match" + assert ( + log_entry["request_id"] == anthropic_message_id + ), "Request ID should be the message id the caller received" assert ( log_entry["call_type"] == "pass_through_endpoint" ), "Call type should be pass_through_endpoint" diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py index 323930eee60..173c9adab0d 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py @@ -3956,3 +3956,207 @@ def test_caller_forged_router_metadata_is_discarded(bucket): ) metadata = json.loads(payload["metadata"]) assert metadata["router_metadata"] is None + + +ANTHROPIC_MESSAGES_RESPONSE: Final = { + "id": "msg_01Lit6806NonStreaming", + "type": "message", + "role": "assistant", + "model": "claude-haiku-4-5", + "content": [{"type": "text", "text": "epsilon"}], + "stop_reason": "end_turn", + "stop_sequence": None, + "usage": {"input_tokens": 14, "output_tokens": 4}, +} + +ANTHROPIC_MESSAGES_SSE_CHUNKS: Final = ( + 'event: message_start\ndata: {"type":"message_start","message":{"id":"msg_01Lit6806Streaming",' + '"type":"message","role":"assistant","model":"claude-haiku-4-5","content":[],' + '"usage":{"input_tokens":14,"output_tokens":1}}}\n\n', + 'event: content_block_start\ndata: {"type":"content_block_start","index":0,' + '"content_block":{"type":"text","text":""}}\n\n', + 'event: content_block_delta\ndata: {"type":"content_block_delta","index":0,' + '"delta":{"type":"text_delta","text":"epsilon"}}\n\n', + 'event: content_block_stop\ndata: {"type":"content_block_stop","index":0}\n\n', + 'event: message_delta\ndata: {"type":"message_delta","delta":{"stop_reason":"end_turn"},' + '"usage":{"output_tokens":4}}\n\n', + "event: message_stop\ndata: {\"type\":\"message_stop\"}\n\n", +) + + +def _anthropic_messages_logging_obj(*, stream: bool) -> Any: + from litellm.litellm_core_utils.litellm_logging import Logging + + logging_obj = Logging( + model="claude-haiku-4-5", + messages=[{"role": "user", "content": "hi"}], + stream=stream, + call_type="anthropic_messages", + start_time=datetime.datetime.now(timezone.utc), + litellm_call_id="6806cafe-0000-4000-8000-000000000001", + function_id="1234", + ) + logging_obj.optional_params = {} + logging_obj.model_call_details["custom_llm_provider"] = "anthropic" + return logging_obj + + +def _spend_log_request_id(response_obj: Any, kwargs: dict) -> str: + payload = get_logging_payload( + kwargs=kwargs, + response_obj=response_obj, + start_time=datetime.datetime.now(timezone.utc), + end_time=datetime.datetime.now(timezone.utc), + ) + return payload["request_id"] + + +def test_spend_log_request_id_is_the_message_id_a_non_streaming_messages_caller_received(): + """ + POST /v1/messages hands the caller `id: msg_...`, the only request id they ever see, so + GET /spend/logs?request_id=msg_... has to find the row. + """ + logging_obj = _anthropic_messages_logging_obj(stream=False) + + logged_response = logging_obj._handle_anthropic_messages_response_logging( + result=ANTHROPIC_MESSAGES_RESPONSE + ) + + assert logged_response.id == "msg_01Lit6806NonStreaming" + assert ( + _spend_log_request_id( + response_obj=logged_response, + kwargs={ + "call_type": "anthropic_messages", + "model": "claude-haiku-4-5", + "litellm_call_id": "6806cafe-0000-4000-8000-000000000001", + "litellm_params": {"metadata": {"user_api_key": "test-key"}}, + }, + ) + == "msg_01Lit6806NonStreaming" + ) + + +def test_spend_log_request_id_is_the_message_id_a_streaming_messages_caller_received(): + """ + The streaming leg of /v1/messages logs through the Anthropic passthrough handler, which used + to stamp litellm_call_id over the msg_ id carried by the message_start event. + """ + from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import ( + AnthropicPassthroughLoggingHandler, + ) + from litellm.types.passthrough_endpoints.pass_through_endpoints import EndpointType + + logging_obj = _anthropic_messages_logging_obj(stream=True) + logging_obj.model_call_details["stream"] = True + + logged = AnthropicPassthroughLoggingHandler._handle_logging_anthropic_collected_chunks( + litellm_logging_obj=logging_obj, + passthrough_success_handler_obj=MagicMock(), + url_route="/v1/messages", + request_body={"model": "claude-haiku-4-5"}, + endpoint_type=EndpointType.ANTHROPIC, + start_time=datetime.datetime.now(timezone.utc), + all_chunks=list(ANTHROPIC_MESSAGES_SSE_CHUNKS), + end_time=datetime.datetime.now(timezone.utc), + ) + + assert logged["result"].id == "msg_01Lit6806Streaming" + assert ( + _spend_log_request_id( + response_obj=logged["result"], + kwargs={ + **logged["kwargs"], + "call_type": "anthropic_messages", + "litellm_call_id": "6806cafe-0000-4000-8000-000000000001", + "litellm_params": {"metadata": {"user_api_key": "test-key"}}, + }, + ) + == "msg_01Lit6806Streaming" + ) + + +def test_spend_log_request_id_still_falls_back_to_litellm_call_id_without_a_provider_id(): + """ + Anthropic-compatible upstreams that omit `id` must keep landing on litellm_call_id rather + than on a fresh chatcmpl- uuid nobody can look up. + """ + logging_obj = _anthropic_messages_logging_obj(stream=True) + logging_obj.model_call_details["stream"] = True + + from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import ( + AnthropicPassthroughLoggingHandler, + ) + + AnthropicPassthroughLoggingHandler._create_anthropic_response_logging_payload( + litellm_model_response=litellm.ModelResponse(id="chatcmpl-generated"), + model="claude-haiku-4-5", + kwargs={}, + start_time=datetime.datetime.now(timezone.utc), + end_time=datetime.datetime.now(timezone.utc), + logging_obj=logging_obj, + ) + assert logging_obj.model_call_details["complete_streaming_response"].id == ( + "6806cafe-0000-4000-8000-000000000001" + ) + + +def test_spend_log_request_id_for_chat_completions_is_untouched(): + """ + /v1/chat/completions callers look their rows up by the chatcmpl- id in the response body. + """ + assert ( + _spend_log_request_id( + response_obj=litellm.ModelResponse(id="chatcmpl-EJvWIw3DAhuKYuwp3jJI4Pnhp2vjv", choices=[]), + kwargs={ + "call_type": "acompletion", + "model": "gpt-5.6", + "litellm_call_id": "6806cafe-0000-4000-8000-000000000002", + "litellm_params": {"metadata": {"user_api_key": "test-key"}}, + }, + ) + == "chatcmpl-EJvWIw3DAhuKYuwp3jJI4Pnhp2vjv" + ) + + +def test_spend_log_request_id_is_the_response_id_a_bridged_messages_caller_received(): + """ + /v1/messages against a non-Anthropic model answers with the Responses id the caller then + looks their row up by, so the row must not fall back to a fresh chatcmpl- uuid. + """ + from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse + + logging_obj = _anthropic_messages_logging_obj(stream=False) + bridged_response = ResponsesAPIResponse( + id="resp_01Lit6806Bridged", + object="response", + created_at=1767225600, + model="gpt-5.6", + status="completed", + output=[ + { + "id": "msg_bridged_output", + "type": "message", + "role": "assistant", + "status": "completed", + "content": [{"type": "output_text", "text": "delta", "annotations": []}], + } + ], + usage=ResponseAPIUsage(input_tokens=13, output_tokens=5, total_tokens=18), + ) + + logged_response = logging_obj._handle_anthropic_messages_response_logging(result=bridged_response) + + assert logged_response.id == "resp_01Lit6806Bridged" + assert ( + _spend_log_request_id( + response_obj=logged_response, + kwargs={ + "call_type": "anthropic_messages", + "model": "gpt-5.6", + "litellm_call_id": "6806cafe-0000-4000-8000-000000000003", + "litellm_params": {"metadata": {"user_api_key": "test-key"}}, + }, + ) + == "resp_01Lit6806Bridged" + )