e8d5922ff9
* fix(core): handle ResponseIncompleteEvent in OpenAI Responses API streaming
When reasoning models (gpt-5.x) exhaust their max_output_tokens budget
on chain-of-thought reasoning, OpenAI emits a ResponseIncompleteEvent
instead of ResponseCompletedEvent. This was previously unhandled, causing
final_response to remain None — which meant get_content() and
get_tool_call_objects() returned empty results, silently dropping the
partial response.
Now ResponseIncompleteEvent is handled identically to
ResponseCompletedEvent (extracting partial content, usage stats, and
token details), with an additional warning log indicating the incomplete
reason.
* fix(core): propagate finish_reason for Responses API incomplete events
- Guard usage extraction against None usage payload in
ResponseIncompleteEvent handler
- Add _finish_reason override to LettaLLMAdapter so streaming adapters
can explicitly set finish_reason without a chat_completions_response
- Map incomplete_details.reason="max_output_tokens" to
finish_reason="length" in SimpleLLMStreamAdapter, matching the Chat
Completions API convention
- This allows the agent loop's _decide_continuation to correctly return
stop_reason="max_tokens_exceeded" instead of "end_turn" when the model
exhausts its output token budget on reasoning
* fix(core): handle empty content parts in incomplete ResponseOutputMessage
When a model hits max_output_tokens after starting a ResponseOutputMessage
but before producing any content parts, the message has content=[]. This
previously raised ValueError("Got 0 content parts, expected 1"). Now it
logs a warning and skips the empty message, allowing reasoning-only
incomplete responses to be processed cleanly.
* fix(core): map all incomplete reasons to finish_reason, not just max_output_tokens
Handle content_filter and any future unknown incomplete reasons from the
Responses API instead of silently leaving finish_reason as None.
285 lines
13 KiB
Python
285 lines
13 KiB
Python
from typing import AsyncGenerator, List
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from letta.adapters.letta_llm_stream_adapter import LettaLLMStreamAdapter
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from letta.errors import LLMError
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from letta.log import get_logger
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logger = get_logger(__name__)
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from letta.helpers.datetime_helpers import get_utc_timestamp_ns
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from letta.interfaces.anthropic_parallel_tool_call_streaming_interface import SimpleAnthropicStreamingInterface
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from letta.interfaces.gemini_streaming_interface import SimpleGeminiStreamingInterface
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from letta.interfaces.openai_streaming_interface import SimpleOpenAIResponsesStreamingInterface, SimpleOpenAIStreamingInterface
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from letta.otel.tracing import log_attributes, safe_json_dumps, trace_method
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from letta.schemas.enums import ProviderType
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from letta.schemas.letta_message import LettaMessage
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from letta.schemas.letta_message_content import LettaMessageContentUnion
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from letta.schemas.provider_trace import ProviderTrace
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from letta.schemas.user import User
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from letta.server.rest_api.streaming_response import get_cancellation_event_for_run
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from letta.settings import settings
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from letta.utils import safe_create_task
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class SimpleLLMStreamAdapter(LettaLLMStreamAdapter):
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"""
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Adapter for handling streaming LLM requests with immediate token yielding.
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This adapter supports real-time streaming of tokens from the LLM, providing
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minimal time-to-first-token (TTFT) latency. It uses specialized streaming
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interfaces for different providers (OpenAI, Anthropic) to handle their
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specific streaming formats.
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"""
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def _extract_tool_calls(self) -> list:
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"""extract tool calls from interface, trying parallel API first then single API"""
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# try multi-call api if available
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if hasattr(self.interface, "get_tool_call_objects"):
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try:
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calls = self.interface.get_tool_call_objects()
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if calls:
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return calls
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except Exception:
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pass
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# fallback to single-call api
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try:
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single = self.interface.get_tool_call_object()
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return [single] if single else []
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except Exception:
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return []
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async def invoke_llm(
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self,
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request_data: dict,
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messages: list,
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tools: list,
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use_assistant_message: bool, # NOTE: not used
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requires_approval_tools: list[str] = [],
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step_id: str | None = None,
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actor: User | None = None,
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) -> AsyncGenerator[LettaMessage, None]:
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"""
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Execute a streaming LLM request and yield tokens/chunks as they arrive.
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This adapter:
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1. Makes a streaming request to the LLM
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2. Yields chunks immediately for minimal TTFT
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3. Accumulates response data through the streaming interface
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4. Updates all instance variables after streaming completes
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"""
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# Store request data
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self.request_data = request_data
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# Track request start time for latency calculation
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request_start_ns = get_utc_timestamp_ns()
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# Get cancellation event for this run to enable graceful cancellation (before branching)
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cancellation_event = get_cancellation_event_for_run(self.run_id) if self.run_id else None
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# Instantiate streaming interface
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if self.llm_config.model_endpoint_type in [ProviderType.anthropic, ProviderType.bedrock, ProviderType.minimax]:
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# NOTE: different
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self.interface = SimpleAnthropicStreamingInterface(
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requires_approval_tools=requires_approval_tools,
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run_id=self.run_id,
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step_id=step_id,
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)
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elif self.llm_config.model_endpoint_type in [
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ProviderType.openai,
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ProviderType.deepseek,
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ProviderType.openrouter,
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ProviderType.zai,
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ProviderType.chatgpt_oauth,
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]:
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# Decide interface based on payload shape
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use_responses = "input" in request_data and "messages" not in request_data
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# No support for Responses API proxy
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is_proxy = self.llm_config.provider_name == "lmstudio_openai"
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# ChatGPT OAuth always uses Responses API format
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if self.llm_config.model_endpoint_type == ProviderType.chatgpt_oauth:
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use_responses = True
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is_proxy = False
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if use_responses and not is_proxy:
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self.interface = SimpleOpenAIResponsesStreamingInterface(
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is_openai_proxy=False,
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messages=messages,
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tools=tools,
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requires_approval_tools=requires_approval_tools,
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run_id=self.run_id,
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step_id=step_id,
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cancellation_event=cancellation_event,
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)
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else:
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self.interface = SimpleOpenAIStreamingInterface(
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is_openai_proxy=self.llm_config.provider_name == "lmstudio_openai",
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messages=messages,
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tools=tools,
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requires_approval_tools=requires_approval_tools,
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model=self.llm_config.model,
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run_id=self.run_id,
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step_id=step_id,
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cancellation_event=cancellation_event,
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)
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elif self.llm_config.model_endpoint_type in [ProviderType.google_ai, ProviderType.google_vertex]:
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self.interface = SimpleGeminiStreamingInterface(
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requires_approval_tools=requires_approval_tools,
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run_id=self.run_id,
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step_id=step_id,
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cancellation_event=cancellation_event,
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)
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else:
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raise ValueError(f"Streaming not supported for provider {self.llm_config.model_endpoint_type}")
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# Start the streaming request (map provider errors to common LLMError types)
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try:
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# Gemini uses async generator pattern (no await) to maintain connection lifecycle
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# Other providers return awaitables that resolve to iterators
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if self.llm_config.model_endpoint_type in [ProviderType.google_ai, ProviderType.google_vertex]:
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stream = self.llm_client.stream_async(request_data, self.llm_config)
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else:
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stream = await self.llm_client.stream_async(request_data, self.llm_config)
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except Exception as e:
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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latency_ms = int((self.llm_request_finish_timestamp_ns - request_start_ns) / 1_000_000)
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await self.llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json=None,
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llm_config=self.llm_config,
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latency_ms=latency_ms,
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error_msg=str(e),
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error_type=type(e).__name__,
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)
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if isinstance(e, LLMError):
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raise
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raise self.llm_client.handle_llm_error(e, llm_config=self.llm_config)
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# Process the stream and yield chunks immediately for TTFT
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try:
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async for chunk in self.interface.process(stream): # TODO: add ttft span
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# Yield each chunk immediately as it arrives
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yield chunk
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except Exception as e:
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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latency_ms = int((self.llm_request_finish_timestamp_ns - request_start_ns) / 1_000_000)
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await self.llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json=None,
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llm_config=self.llm_config,
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latency_ms=latency_ms,
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error_msg=str(e),
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error_type=type(e).__name__,
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)
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if isinstance(e, LLMError):
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raise
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raise self.llm_client.handle_llm_error(e, llm_config=self.llm_config)
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# After streaming completes, extract the accumulated data
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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# extract tool calls from interface (supports both single and parallel calls)
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self.tool_calls = self._extract_tool_calls()
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# preserve legacy single-call field for existing consumers
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self.tool_call = self.tool_calls[-1] if self.tool_calls else None
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# Extract reasoning content from the interface
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# TODO this should probably just be called "content"?
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# self.reasoning_content = self.interface.get_reasoning_content()
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# Extract all content parts
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self.content: List[LettaMessageContentUnion] = self.interface.get_content()
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# Extract usage statistics from the interface
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# Each interface implements get_usage_statistics() with provider-specific logic
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self.usage = self.interface.get_usage_statistics()
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self.usage.step_count = 1
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# Store any additional data from the interface
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self.message_id = self.interface.letta_message_id
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# Populate finish_reason for downstream continuation logic.
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# In Responses streaming, max_output_tokens is expressed via incomplete_details.reason.
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if hasattr(self.interface, "final_response") and self.interface.final_response is not None:
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resp = self.interface.final_response
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incomplete_details = getattr(resp, "incomplete_details", None)
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incomplete_reason = getattr(incomplete_details, "reason", None) if incomplete_details else None
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if incomplete_reason == "max_output_tokens":
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self._finish_reason = "length"
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elif incomplete_reason == "content_filter":
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self._finish_reason = "content_filter"
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elif incomplete_reason is not None:
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# Unknown incomplete reason — preserve it as-is for diagnostics
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self._finish_reason = incomplete_reason
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elif getattr(resp, "status", None) == "completed":
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self._finish_reason = "stop"
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# Log request and response data
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self.log_provider_trace(step_id=step_id, actor=actor)
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@trace_method
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def log_provider_trace(self, step_id: str | None, actor: User | None) -> None:
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"""
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Log provider trace data for telemetry purposes in a fire-and-forget manner.
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Creates an async task to log the request/response data without blocking
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the main execution flow. For streaming adapters, this includes the final
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tool call and reasoning content collected during streaming.
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Args:
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step_id: The step ID associated with this request for logging purposes
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actor: The user associated with this request for logging purposes
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"""
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if step_id is None or actor is None:
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return
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response_json = {
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"content": {
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"tool_call": self.tool_call.model_dump_json() if self.tool_call else None,
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# "reasoning": [content.model_dump_json() for content in self.reasoning_content],
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# NOTE: different
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# TODO potentially split this into both content and reasoning?
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"content": [content.model_dump_json() for content in self.content],
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},
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"id": self.interface.message_id,
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"model": self.interface.model,
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"role": "assistant",
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# "stop_reason": "",
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# "stop_sequence": None,
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"type": "message",
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# Use raw_usage if available for transparent provider trace logging, else fallback
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"usage": self.interface.raw_usage
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if hasattr(self.interface, "raw_usage") and self.interface.raw_usage
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else {
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"input_tokens": self.usage.prompt_tokens,
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"output_tokens": self.usage.completion_tokens,
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},
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}
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log_attributes(
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{
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"request_data": safe_json_dumps(self.request_data),
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"response_data": safe_json_dumps(response_json),
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}
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)
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if settings.track_provider_trace:
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safe_create_task(
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self.telemetry_manager.create_provider_trace_async(
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actor=actor,
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provider_trace=ProviderTrace(
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request_json=self.request_data,
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response_json=response_json,
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step_id=step_id,
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agent_id=self.agent_id,
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agent_tags=self.agent_tags,
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run_id=self.run_id,
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call_type=self.call_type,
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org_id=self.org_id,
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user_id=self.user_id,
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llm_config=self.llm_config.model_dump() if self.llm_config else None,
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),
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),
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label="create_provider_trace",
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)
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