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Resumed streamed run emits `tool_output` for the approved call without a tool name (and no `tool_called`)

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#5,343 1 comentario 0 reacciones 0 asignados Ver en GitHub

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Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python
Área
ai-infra-agents

Línea de trabajo

Start with the repro script: run it to see that the resumed stream yields a tool_output item whose ToolCallOutputItem has only call_id, output and type. Find where Runner.run_streamed handles a resumed RunState and where the approved call's output item is created. Choosing between adding a tool name to ToolCallOutputItem and emitting tool_called on resume is a design decision to settle with maintainers first. Done means a resumed stream lets a fresh consumer identify the tool behind each output, with a test covering the approval resume path.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Please read this first
  • Have you read the docs? Yes
  • Have you searched for related issues? Yes, found none covering stream events across an approval resume.
Describe the bug

When a streamed run pauses on a needs_approval tool and is resumed from RunState, the resumed stream emits tool_output for the approved call but no tool_called. ToolCallOutputItem carries only call_id, output and type, with no tool name.

A stream consumer that starts fresh on resume, which is the usual case when the pause outlives the process (a human approves minutes later, on another worker), can't tell which tool produced the output without separately persisting a call_id → name map from the first run.

Debug information
  • Agents SDK version: 0.23.1 (also 0.22.3)
  • Python version: 3.12
  • Model and model provider: not involved (fake model below, no network)
  • Does the issue reproduce with the latest Agents SDK release? Yes
  • Does the issue occur consistently or intermittently? Consistently
Repro steps
import asyncio
from collections.abc import AsyncIterator
from typing import Any

from openai.types.responses import (
    Response, ResponseCompletedEvent, ResponseFunctionToolCall, ResponseOutputMessage, ResponseOutputText,
)
from agents import Agent, ModelResponse, Runner, Usage, function_tool
from agents.models.interface import Model


def _resp(output: list[Any]) -> Response:
    return Response(id="r", created_at=0, model="fake", object="response", output=output,
                    parallel_tool_calls=False, tool_choice="auto", tools=[])


class FakeModel(Model):
    def __init__(self) -> None:
        self.turns = [
            [ResponseFunctionToolCall(type="function_call", call_id="call_1", name="delete_file",
                                      arguments='{"path": "a.txt"}', id="fc_1")],
            [ResponseOutputMessage(id="m", type="message", role="assistant", status="completed",
                                   content=[ResponseOutputText(type="output_text", text="done", annotations=[])])],
        ]

    def _next(self) -> list[Any]:
        return self.turns.pop(0)

    async def get_response(self, *a: Any, **k: Any) -> ModelResponse:
        return ModelResponse(output=self._next(), usage=Usage(), response_id="r")

    async def stream_response(self, *a: Any, **k: Any) -> AsyncIterator[Any]:
        yield ResponseCompletedEvent(type="response.completed", response=_resp(self._next()), sequence_number=0)


@function_tool(needs_approval=True)
def delete_file(path: str) -> str:
    return f"deleted {path}"


async def main() -> None:
    agent = Agent(name="a", model=FakeModel(), tools=[delete_file])
    first = Runner.run_streamed(agent, "delete a.txt")
    async for e in first.stream_events():
        if e.type == "run_item_stream_event":
            print("run 1:", e.name)
    state = first.to_state()
    state.approve(first.interruptions[0])
    resumed = Runner.run_streamed(agent, state)
    async for e in resumed.stream_events():
        if e.type == "run_item_stream_event":
            item = e.item
            print("resume:", e.name, type(item).__name__,
                  "raw_item keys:", sorted(item.raw_item.keys()) if isinstance(item.raw_item, dict) else type(item.raw_item).__name__)


asyncio.run(main())

Output:

run 1: tool_called
resume: tool_output ToolCallOutputItem raw_item keys: ['call_id', 'output', 'type']
resume: message_output_created MessageOutputItem raw_item keys: ResponseOutputMessage
Expected behavior

The resumed stream identifies the tool behind each output. Either:

  • add the tool name to ToolCallOutputItem (e.g. tool_name), which also helps outside resume, since function-call output items never carry a name; or
  • emit tool_called for the approved call when the run resumes.
Lenguaje dominante
Python
Estrellas
29.9k
Forks
4.8k
Merge medio
1 d 3 min
PR fusionados (30 d)
213

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