Runner

Runner

Source code in src/agents/run.py

class Runner:
    @classmethod
    async def run(
        cls,
        starting_agent: Agent[TContext],
        input: str | list[TResponseInputItem],
        *,
        context: TContext | None = None,
        max_turns: int = DEFAULT_MAX_TURNS,
        hooks: RunHooks[TContext] | None = None,
        run_config: RunConfig | None = None,
    ) -> RunResult:
        """Run a workflow starting at the given agent. The agent will run in a loop until a final
        output is generated. The loop runs like so:
        1. The agent is invoked with the given input.
        2. If there is a final output (i.e. the agent produces something of type
            `agent.output_type`, the loop terminates.
        3. If there's a handoff, we run the loop again, with the new agent.
        4. Else, we run tool calls (if any), and re-run the loop.

        In two cases, the agent may raise an exception:
        1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
        2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

        Note that only the first agent's input guardrails are run.

        Args:
            starting_agent: The starting agent to run.
            input: The initial input to the agent. You can pass a single string for a user message,
                or a list of input items.
            context: The context to run the agent with.
            max_turns: The maximum number of turns to run the agent for. A turn is defined as one
                AI invocation (including any tool calls that might occur).
            hooks: An object that receives callbacks on various lifecycle events.
            run_config: Global settings for the entire agent run.

        Returns:
            A run result containing all the inputs, guardrail results and the output of the last
            agent. Agents may perform handoffs, so we don't know the specific type of the output.
        """
        if hooks is None:
            hooks = RunHooks[Any]()
        if run_config is None:
            run_config = RunConfig()

        with TraceCtxManager(
            workflow_name=run_config.workflow_name,
            trace_id=run_config.trace_id,
            group_id=run_config.group_id,
            metadata=run_config.trace_metadata,
            disabled=run_config.tracing_disabled,
        ):
            current_turn = 0
            original_input: str | list[TResponseInputItem] = copy.deepcopy(input)
            generated_items: list[RunItem] = []
            model_responses: list[ModelResponse] = []

            context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
                context=context,  # type: ignore
            )

            input_guardrail_results: list[InputGuardrailResult] = []

            current_span: Span[AgentSpanData] | None = None
            current_agent = starting_agent
            should_run_agent_start_hooks = True

            try:
                while True:
                    # Start an agent span if we don't have one. This span is ended if the current
                    # agent changes, or if the agent loop ends.
                    if current_span is None:
                        handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)]
                        tool_names = [t.name for t in current_agent.tools]
                        if output_schema := cls._get_output_schema(current_agent):
                            output_type_name = output_schema.output_type_name()
                        else:
                            output_type_name = "str"

                        current_span = agent_span(
                            name=current_agent.name,
                            handoffs=handoff_names,
                            tools=tool_names,
                            output_type=output_type_name,
                        )
                        current_span.start(mark_as_current=True)

                    current_turn += 1
                    if current_turn > max_turns:
                        _utils.attach_error_to_span(
                            current_span,
                            SpanError(
                                message="Max turns exceeded",
                                data={"max_turns": max_turns},
                            ),
                        )
                        raise MaxTurnsExceeded(f"Max turns ({max_turns}) exceeded")

                    logger.debug(
                        f"Running agent {current_agent.name} (turn {current_turn})",
                    )

                    if current_turn == 1:
                        input_guardrail_results, turn_result = await asyncio.gather(
                            cls._run_input_guardrails(
                                starting_agent,
                                starting_agent.input_guardrails
                                + (run_config.input_guardrails or []),
                                copy.deepcopy(input),
                                context_wrapper,
                            ),
                            cls._run_single_turn(
                                agent=current_agent,
                                original_input=original_input,
                                generated_items=generated_items,
                                hooks=hooks,
                                context_wrapper=context_wrapper,
                                run_config=run_config,
                                should_run_agent_start_hooks=should_run_agent_start_hooks,
                            ),
                        )
                    else:
                        turn_result = await cls._run_single_turn(
                            agent=current_agent,
                            original_input=original_input,
                            generated_items=generated_items,
                            hooks=hooks,
                            context_wrapper=context_wrapper,
                            run_config=run_config,
                            should_run_agent_start_hooks=should_run_agent_start_hooks,
                        )
                    should_run_agent_start_hooks = False

                    model_responses.append(turn_result.model_response)
                    original_input = turn_result.original_input
                    generated_items = turn_result.generated_items

                    if isinstance(turn_result.next_step, NextStepFinalOutput):
                        output_guardrail_results = await cls._run_output_guardrails(
                            current_agent.output_guardrails + (run_config.output_guardrails or []),
                            current_agent,
                            turn_result.next_step.output,
                            context_wrapper,
                        )
                        return RunResult(
                            input=original_input,
                            new_items=generated_items,
                            raw_responses=model_responses,
                            final_output=turn_result.next_step.output,
                            _last_agent=current_agent,
                            input_guardrail_results=input_guardrail_results,
                            output_guardrail_results=output_guardrail_results,
                        )
                    elif isinstance(turn_result.next_step, NextStepHandoff):
                        current_agent = cast(Agent[TContext], turn_result.next_step.new_agent)
                        current_span.finish(reset_current=True)
                        current_span = None
                        should_run_agent_start_hooks = True
                    elif isinstance(turn_result.next_step, NextStepRunAgain):
                        pass
                    else:
                        raise AgentsException(
                            f"Unknown next step type: {type(turn_result.next_step)}"
                        )
            finally:
                if current_span:
                    current_span.finish(reset_current=True)

    @classmethod
    def run_sync(
        cls,
        starting_agent: Agent[TContext],
        input: str | list[TResponseInputItem],
        *,
        context: TContext | None = None,
        max_turns: int = DEFAULT_MAX_TURNS,
        hooks: RunHooks[TContext] | None = None,
        run_config: RunConfig | None = None,
    ) -> RunResult:
        """Run a workflow synchronously, starting at the given agent. Note that this just wraps the
        `run` method, so it will not work if there's already an event loop (e.g. inside an async
        function, or in a Jupyter notebook or async context like FastAPI). For those cases, use
        the `run` method instead.

        The agent will run in a loop until a final output is generated. The loop runs like so:
        1. The agent is invoked with the given input.
        2. If there is a final output (i.e. the agent produces something of type
            `agent.output_type`, the loop terminates.
        3. If there's a handoff, we run the loop again, with the new agent.
        4. Else, we run tool calls (if any), and re-run the loop.

        In two cases, the agent may raise an exception:
        1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
        2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

        Note that only the first agent's input guardrails are run.

        Args:
            starting_agent: The starting agent to run.
            input: The initial input to the agent. You can pass a single string for a user message,
                or a list of input items.
            context: The context to run the agent with.
            max_turns: The maximum number of turns to run the agent for. A turn is defined as one
                AI invocation (including any tool calls that might occur).
            hooks: An object that receives callbacks on various lifecycle events.
            run_config: Global settings for the entire agent run.

        Returns:
            A run result containing all the inputs, guardrail results and the output of the last
            agent. Agents may perform handoffs, so we don't know the specific type of the output.
        """
        return asyncio.get_event_loop().run_until_complete(
            cls.run(
                starting_agent,
                input,
                context=context,
                max_turns=max_turns,
                hooks=hooks,
                run_config=run_config,
            )
        )

    @classmethod
    def run_streamed(
        cls,
        starting_agent: Agent[TContext],
        input: str | list[TResponseInputItem],
        context: TContext | None = None,
        max_turns: int = DEFAULT_MAX_TURNS,
        hooks: RunHooks[TContext] | None = None,
        run_config: RunConfig | None = None,
    ) -> RunResultStreaming:
        """Run a workflow starting at the given agent in streaming mode. The returned result object
        contains a method you can use to stream semantic events as they are generated.

        The agent will run in a loop until a final output is generated. The loop runs like so:
        1. The agent is invoked with the given input.
        2. If there is a final output (i.e. the agent produces something of type
            `agent.output_type`, the loop terminates.
        3. If there's a handoff, we run the loop again, with the new agent.
        4. Else, we run tool calls (if any), and re-run the loop.

        In two cases, the agent may raise an exception:
        1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
        2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

        Note that only the first agent's input guardrails are run.

        Args:
            starting_agent: The starting agent to run.
            input: The initial input to the agent. You can pass a single string for a user message,
                or a list of input items.
            context: The context to run the agent with.
            max_turns: The maximum number of turns to run the agent for. A turn is defined as one
                AI invocation (including any tool calls that might occur).
            hooks: An object that receives callbacks on various lifecycle events.
            run_config: Global settings for the entire agent run.

        Returns:
            A result object that contains data about the run, as well as a method to stream events.
        """
        if hooks is None:
            hooks = RunHooks[Any]()
        if run_config is None:
            run_config = RunConfig()

        # If there's already a trace, we don't create a new one. In addition, we can't end the
        # trace here, because the actual work is done in `stream_events` and this method ends
        # before that.
        new_trace = (
            None
            if get_current_trace()
            else trace(
                workflow_name=run_config.workflow_name,
                trace_id=run_config.trace_id,
                group_id=run_config.group_id,
                metadata=run_config.trace_metadata,
                disabled=run_config.tracing_disabled,
            )
        )
        # Need to start the trace here, because the current trace contextvar is captured at
        # asyncio.create_task time
        if new_trace:
            new_trace.start(mark_as_current=True)

        output_schema = cls._get_output_schema(starting_agent)
        context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
            context=context  # type: ignore
        )

        streamed_result = RunResultStreaming(
            input=copy.deepcopy(input),
            new_items=[],
            current_agent=starting_agent,
            raw_responses=[],
            final_output=None,
            is_complete=False,
            current_turn=0,
            max_turns=max_turns,
            input_guardrail_results=[],
            output_guardrail_results=[],
            _current_agent_output_schema=output_schema,
            _trace=new_trace,
        )

        # Kick off the actual agent loop in the background and return the streamed result object.
        streamed_result._run_impl_task = asyncio.create_task(
            cls._run_streamed_impl(
                starting_input=input,
                streamed_result=streamed_result,
                starting_agent=starting_agent,
                max_turns=max_turns,
                hooks=hooks,
                context_wrapper=context_wrapper,
                run_config=run_config,
            )
        )
        return streamed_result

    @classmethod
    async def _run_input_guardrails_with_queue(
        cls,
        agent: Agent[Any],
        guardrails: list[InputGuardrail[TContext]],
        input: str | list[TResponseInputItem],
        context: RunContextWrapper[TContext],
        streamed_result: RunResultStreaming,
        parent_span: Span[Any],
    ):
        queue = streamed_result._input_guardrail_queue

        # We'll run the guardrails and push them onto the queue as they complete
        guardrail_tasks = [
            asyncio.create_task(
                RunImpl.run_single_input_guardrail(agent, guardrail, input, context)
            )
            for guardrail in guardrails
        ]
        guardrail_results = []
        try:
            for done in asyncio.as_completed(guardrail_tasks):
                result = await done
                if result.output.tripwire_triggered:
                    _utils.attach_error_to_span(
                        parent_span,
                        SpanError(
                            message="Guardrail tripwire triggered",
                            data={
                                "guardrail": result.guardrail.get_name(),
                                "type": "input_guardrail",
                            },
                        ),
                    )
                queue.put_nowait(result)
                guardrail_results.append(result)
        except Exception:
            for t in guardrail_tasks:
                t.cancel()
            raise

        streamed_result.input_guardrail_results = guardrail_results

    @classmethod
    async def _run_streamed_impl(
        cls,
        starting_input: str | list[TResponseInputItem],
        streamed_result: RunResultStreaming,
        starting_agent: Agent[TContext],
        max_turns: int,
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
    ):
        current_span: Span[AgentSpanData] | None = None
        current_agent = starting_agent
        current_turn = 0
        should_run_agent_start_hooks = True

        streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent))

        try:
            while True:
                if streamed_result.is_complete:
                    break

                # Start an agent span if we don't have one. This span is ended if the current
                # agent changes, or if the agent loop ends.
                if current_span is None:
                    handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)]
                    tool_names = [t.name for t in current_agent.tools]
                    if output_schema := cls._get_output_schema(current_agent):
                        output_type_name = output_schema.output_type_name()
                    else:
                        output_type_name = "str"

                    current_span = agent_span(
                        name=current_agent.name,
                        handoffs=handoff_names,
                        tools=tool_names,
                        output_type=output_type_name,
                    )
                    current_span.start(mark_as_current=True)

                current_turn += 1
                streamed_result.current_turn = current_turn

                if current_turn > max_turns:
                    _utils.attach_error_to_span(
                        current_span,
                        SpanError(
                            message="Max turns exceeded",
                            data={"max_turns": max_turns},
                        ),
                    )
                    streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
                    break

                if current_turn == 1:
                    # Run the input guardrails in the background and put the results on the queue
                    streamed_result._input_guardrails_task = asyncio.create_task(
                        cls._run_input_guardrails_with_queue(
                            starting_agent,
                            starting_agent.input_guardrails + (run_config.input_guardrails or []),
                            copy.deepcopy(ItemHelpers.input_to_new_input_list(starting_input)),
                            context_wrapper,
                            streamed_result,
                            current_span,
                        )
                    )
                try:
                    turn_result = await cls._run_single_turn_streamed(
                        streamed_result,
                        current_agent,
                        hooks,
                        context_wrapper,
                        run_config,
                        should_run_agent_start_hooks,
                    )
                    should_run_agent_start_hooks = False

                    streamed_result.raw_responses = streamed_result.raw_responses + [
                        turn_result.model_response
                    ]
                    streamed_result.input = turn_result.original_input
                    streamed_result.new_items = turn_result.generated_items

                    if isinstance(turn_result.next_step, NextStepHandoff):
                        current_agent = turn_result.next_step.new_agent
                        current_span.finish(reset_current=True)
                        current_span = None
                        should_run_agent_start_hooks = True
                        streamed_result._event_queue.put_nowait(
                            AgentUpdatedStreamEvent(new_agent=current_agent)
                        )
                    elif isinstance(turn_result.next_step, NextStepFinalOutput):
                        streamed_result._output_guardrails_task = asyncio.create_task(
                            cls._run_output_guardrails(
                                current_agent.output_guardrails
                                + (run_config.output_guardrails or []),
                                current_agent,
                                turn_result.next_step.output,
                                context_wrapper,
                            )
                        )

                        try:
                            output_guardrail_results = await streamed_result._output_guardrails_task
                        except Exception:
                            # Exceptions will be checked in the stream_events loop
                            output_guardrail_results = []

                        streamed_result.output_guardrail_results = output_guardrail_results
                        streamed_result.final_output = turn_result.next_step.output
                        streamed_result.is_complete = True
                        streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
                    elif isinstance(turn_result.next_step, NextStepRunAgain):
                        pass
                except Exception as e:
                    if current_span:
                        _utils.attach_error_to_span(
                            current_span,
                            SpanError(
                                message="Error in agent run",
                                data={"error": str(e)},
                            ),
                        )
                    streamed_result.is_complete = True
                    streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
                    raise

            streamed_result.is_complete = True
        finally:
            if current_span:
                current_span.finish(reset_current=True)

    @classmethod
    async def _run_single_turn_streamed(
        cls,
        streamed_result: RunResultStreaming,
        agent: Agent[TContext],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
        should_run_agent_start_hooks: bool,
    ) -> SingleStepResult:
        if should_run_agent_start_hooks:
            await asyncio.gather(
                hooks.on_agent_start(context_wrapper, agent),
                (
                    agent.hooks.on_start(context_wrapper, agent)
                    if agent.hooks
                    else _utils.noop_coroutine()
                ),
            )

        output_schema = cls._get_output_schema(agent)

        streamed_result.current_agent = agent
        streamed_result._current_agent_output_schema = output_schema

        system_prompt = await agent.get_system_prompt(context_wrapper)

        handoffs = cls._get_handoffs(agent)

        model = cls._get_model(agent, run_config)
        model_settings = agent.model_settings.resolve(run_config.model_settings)
        final_response: ModelResponse | None = None

        input = ItemHelpers.input_to_new_input_list(streamed_result.input)
        input.extend([item.to_input_item() for item in streamed_result.new_items])

        # 1. Stream the output events
        async for event in model.stream_response(
            system_prompt,
            input,
            model_settings,
            agent.tools,
            output_schema,
            handoffs,
            get_model_tracing_impl(
                run_config.tracing_disabled, run_config.trace_include_sensitive_data
            ),
        ):
            if isinstance(event, ResponseCompletedEvent):
                usage = (
                    Usage(
                        requests=1,
                        input_tokens=event.response.usage.input_tokens,
                        output_tokens=event.response.usage.output_tokens,
                        total_tokens=event.response.usage.total_tokens,
                    )
                    if event.response.usage
                    else Usage()
                )
                final_response = ModelResponse(
                    output=event.response.output,
                    usage=usage,
                    referenceable_id=event.response.id,
                )

            streamed_result._event_queue.put_nowait(RawResponsesStreamEvent(data=event))

        # 2. At this point, the streaming is complete for this turn of the agent loop.
        if not final_response:
            raise ModelBehaviorError("Model did not produce a final response!")

        # 3. Now, we can process the turn as we do in the non-streaming case
        single_step_result = await cls._get_single_step_result_from_response(
            agent=agent,
            original_input=streamed_result.input,
            pre_step_items=streamed_result.new_items,
            new_response=final_response,
            output_schema=output_schema,
            handoffs=handoffs,
            hooks=hooks,
            context_wrapper=context_wrapper,
            run_config=run_config,
        )

        RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue)
        return single_step_result

    @classmethod
    async def _run_single_turn(
        cls,
        *,
        agent: Agent[TContext],
        original_input: str | list[TResponseInputItem],
        generated_items: list[RunItem],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
        should_run_agent_start_hooks: bool,
    ) -> SingleStepResult:
        # Ensure we run the hooks before anything else
        if should_run_agent_start_hooks:
            await asyncio.gather(
                hooks.on_agent_start(context_wrapper, agent),
                (
                    agent.hooks.on_start(context_wrapper, agent)
                    if agent.hooks
                    else _utils.noop_coroutine()
                ),
            )

        system_prompt = await agent.get_system_prompt(context_wrapper)

        output_schema = cls._get_output_schema(agent)
        handoffs = cls._get_handoffs(agent)
        input = ItemHelpers.input_to_new_input_list(original_input)
        input.extend([generated_item.to_input_item() for generated_item in generated_items])

        new_response = await cls._get_new_response(
            agent,
            system_prompt,
            input,
            output_schema,
            handoffs,
            context_wrapper,
            run_config,
        )

        return await cls._get_single_step_result_from_response(
            agent=agent,
            original_input=original_input,
            pre_step_items=generated_items,
            new_response=new_response,
            output_schema=output_schema,
            handoffs=handoffs,
            hooks=hooks,
            context_wrapper=context_wrapper,
            run_config=run_config,
        )

    @classmethod
    async def _get_single_step_result_from_response(
        cls,
        *,
        agent: Agent[TContext],
        original_input: str | list[TResponseInputItem],
        pre_step_items: list[RunItem],
        new_response: ModelResponse,
        output_schema: AgentOutputSchema | None,
        handoffs: list[Handoff],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
    ) -> SingleStepResult:
        processed_response = RunImpl.process_model_response(
            agent=agent,
            response=new_response,
            output_schema=output_schema,
            handoffs=handoffs,
        )
        return await RunImpl.execute_tools_and_side_effects(
            agent=agent,
            original_input=original_input,
            pre_step_items=pre_step_items,
            new_response=new_response,
            processed_response=processed_response,
            output_schema=output_schema,
            hooks=hooks,
            context_wrapper=context_wrapper,
            run_config=run_config,
        )

    @classmethod
    async def _run_input_guardrails(
        cls,
        agent: Agent[Any],
        guardrails: list[InputGuardrail[TContext]],
        input: str | list[TResponseInputItem],
        context: RunContextWrapper[TContext],
    ) -> list[InputGuardrailResult]:
        if not guardrails:
            return []

        guardrail_tasks = [
            asyncio.create_task(
                RunImpl.run_single_input_guardrail(agent, guardrail, input, context)
            )
            for guardrail in guardrails
        ]

        guardrail_results = []

        for done in asyncio.as_completed(guardrail_tasks):
            result = await done
            if result.output.tripwire_triggered:
                # Cancel all guardrail tasks if a tripwire is triggered.
                for t in guardrail_tasks:
                    t.cancel()
                _utils.attach_error_to_current_span(
                    SpanError(
                        message="Guardrail tripwire triggered",
                        data={"guardrail": result.guardrail.get_name()},
                    )
                )
                raise InputGuardrailTripwireTriggered(result)
            else:
                guardrail_results.append(result)

        return guardrail_results

    @classmethod
    async def _run_output_guardrails(
        cls,
        guardrails: list[OutputGuardrail[TContext]],
        agent: Agent[TContext],
        agent_output: Any,
        context: RunContextWrapper[TContext],
    ) -> list[OutputGuardrailResult]:
        if not guardrails:
            return []

        guardrail_tasks = [
            asyncio.create_task(
                RunImpl.run_single_output_guardrail(guardrail, agent, agent_output, context)
            )
            for guardrail in guardrails
        ]

        guardrail_results = []

        for done in asyncio.as_completed(guardrail_tasks):
            result = await done
            if result.output.tripwire_triggered:
                # Cancel all guardrail tasks if a tripwire is triggered.
                for t in guardrail_tasks:
                    t.cancel()
                _utils.attach_error_to_current_span(
                    SpanError(
                        message="Guardrail tripwire triggered",
                        data={"guardrail": result.guardrail.get_name()},
                    )
                )
                raise OutputGuardrailTripwireTriggered(result)
            else:
                guardrail_results.append(result)

        return guardrail_results

    @classmethod
    async def _get_new_response(
        cls,
        agent: Agent[TContext],
        system_prompt: str | None,
        input: list[TResponseInputItem],
        output_schema: AgentOutputSchema | None,
        handoffs: list[Handoff],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
    ) -> ModelResponse:
        model = cls._get_model(agent, run_config)
        model_settings = agent.model_settings.resolve(run_config.model_settings)
        new_response = await model.get_response(
            system_instructions=system_prompt,
            input=input,
            model_settings=model_settings,
            tools=agent.tools,
            output_schema=output_schema,
            handoffs=handoffs,
            tracing=get_model_tracing_impl(
                run_config.tracing_disabled, run_config.trace_include_sensitive_data
            ),
        )

        context_wrapper.usage.add(new_response.usage)

        return new_response

    @classmethod
    def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchema | None:
        if agent.output_type is None or agent.output_type is str:
            return None

        return AgentOutputSchema(agent.output_type)

    @classmethod
    def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]:
        handoffs = []
        for handoff_item in agent.handoffs:
            if isinstance(handoff_item, Handoff):
                handoffs.append(handoff_item)
            elif isinstance(handoff_item, Agent):
                handoffs.append(handoff(handoff_item))
        return handoffs

    @classmethod
    def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model:
        if isinstance(run_config.model, Model):
            return run_config.model
        elif isinstance(run_config.model, str):
            return run_config.model_provider.get_model(run_config.model)
        elif isinstance(agent.model, Model):
            return agent.model

        return run_config.model_provider.get_model(agent.model)

run async classmethod

run(
    starting_agent: Agent[TContext],
    input: str | list[TResponseInputItem],
    *,
    context: TContext | None = None,
    max_turns: int = DEFAULT_MAX_TURNS,
    hooks: RunHooks[TContext] | None = None,
    run_config: RunConfig | None = None,
) -> RunResult

Run a workflow starting at the given agent. The agent will run in a loop until a final output is generated. The loop runs like so: 1. The agent is invoked with the given input. 2. If there is a final output (i.e. the agent produces something of type agent.output_type, the loop terminates. 3. If there's a handoff, we run the loop again, with the new agent. 4. Else, we run tool calls (if any), and re-run the loop.

In two cases, the agent may raise an exception: 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

Note that only the first agent's input guardrails are run.

Parameters:

Name	Type	Description	Default
starting_agent	Agent[TContext]	The starting agent to run.	required
input	str | list[TResponseInputItem]	The initial input to the agent. You can pass a single string for a user message, or a list of input items.	required
context	TContext | None	The context to run the agent with.	None
max_turns	int	The maximum number of turns to run the agent for. A turn is defined as one AI invocation (including any tool calls that might occur).	DEFAULT_MAX_TURNS
hooks	RunHooks[TContext] | None	An object that receives callbacks on various lifecycle events.	None
run_config	RunConfig | None	Global settings for the entire agent run.	None

Returns:

Type	Description
RunResult	A run result containing all the inputs, guardrail results and the output of the last
RunResult	agent. Agents may perform handoffs, so we don't know the specific type of the output.

Source code in src/agents/run.py

@classmethod
async def run(
    cls,
    starting_agent: Agent[TContext],
    input: str | list[TResponseInputItem],
    *,
    context: TContext | None = None,
    max_turns: int = DEFAULT_MAX_TURNS,
    hooks: RunHooks[TContext] | None = None,
    run_config: RunConfig | None = None,
) -> RunResult:
    """Run a workflow starting at the given agent. The agent will run in a loop until a final
    output is generated. The loop runs like so:
    1. The agent is invoked with the given input.
    2. If there is a final output (i.e. the agent produces something of type
        `agent.output_type`, the loop terminates.
    3. If there's a handoff, we run the loop again, with the new agent.
    4. Else, we run tool calls (if any), and re-run the loop.

    In two cases, the agent may raise an exception:
    1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
    2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

    Note that only the first agent's input guardrails are run.

    Args:
        starting_agent: The starting agent to run.
        input: The initial input to the agent. You can pass a single string for a user message,
            or a list of input items.
        context: The context to run the agent with.
        max_turns: The maximum number of turns to run the agent for. A turn is defined as one
            AI invocation (including any tool calls that might occur).
        hooks: An object that receives callbacks on various lifecycle events.
        run_config: Global settings for the entire agent run.

    Returns:
        A run result containing all the inputs, guardrail results and the output of the last
        agent. Agents may perform handoffs, so we don't know the specific type of the output.
    """
    if hooks is None:
        hooks = RunHooks[Any]()
    if run_config is None:
        run_config = RunConfig()

    with TraceCtxManager(
        workflow_name=run_config.workflow_name,
        trace_id=run_config.trace_id,
        group_id=run_config.group_id,
        metadata=run_config.trace_metadata,
        disabled=run_config.tracing_disabled,
    ):
        current_turn = 0
        original_input: str | list[TResponseInputItem] = copy.deepcopy(input)
        generated_items: list[RunItem] = []
        model_responses: list[ModelResponse] = []

        context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
            context=context,  # type: ignore
        )

        input_guardrail_results: list[InputGuardrailResult] = []

        current_span: Span[AgentSpanData] | None = None
        current_agent = starting_agent
        should_run_agent_start_hooks = True

        try:
            while True:
                # Start an agent span if we don't have one. This span is ended if the current
                # agent changes, or if the agent loop ends.
                if current_span is None:
                    handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)]
                    tool_names = [t.name for t in current_agent.tools]
                    if output_schema := cls._get_output_schema(current_agent):
                        output_type_name = output_schema.output_type_name()
                    else:
                        output_type_name = "str"

                    current_span = agent_span(
                        name=current_agent.name,
                        handoffs=handoff_names,
                        tools=tool_names,
                        output_type=output_type_name,
                    )
                    current_span.start(mark_as_current=True)

                current_turn += 1
                if current_turn > max_turns:
                    _utils.attach_error_to_span(
                        current_span,
                        SpanError(
                            message="Max turns exceeded",
                            data={"max_turns": max_turns},
                        ),
                    )
                    raise MaxTurnsExceeded(f"Max turns ({max_turns}) exceeded")

                logger.debug(
                    f"Running agent {current_agent.name} (turn {current_turn})",
                )

                if current_turn == 1:
                    input_guardrail_results, turn_result = await asyncio.gather(
                        cls._run_input_guardrails(
                            starting_agent,
                            starting_agent.input_guardrails
                            + (run_config.input_guardrails or []),
                            copy.deepcopy(input),
                            context_wrapper,
                        ),
                        cls._run_single_turn(
                            agent=current_agent,
                            original_input=original_input,
                            generated_items=generated_items,
                            hooks=hooks,
                            context_wrapper=context_wrapper,
                            run_config=run_config,
                            should_run_agent_start_hooks=should_run_agent_start_hooks,
                        ),
                    )
                else:
                    turn_result = await cls._run_single_turn(
                        agent=current_agent,
                        original_input=original_input,
                        generated_items=generated_items,
                        hooks=hooks,
                        context_wrapper=context_wrapper,
                        run_config=run_config,
                        should_run_agent_start_hooks=should_run_agent_start_hooks,
                    )
                should_run_agent_start_hooks = False

                model_responses.append(turn_result.model_response)
                original_input = turn_result.original_input
                generated_items = turn_result.generated_items

                if isinstance(turn_result.next_step, NextStepFinalOutput):
                    output_guardrail_results = await cls._run_output_guardrails(
                        current_agent.output_guardrails + (run_config.output_guardrails or []),
                        current_agent,
                        turn_result.next_step.output,
                        context_wrapper,
                    )
                    return RunResult(
                        input=original_input,
                        new_items=generated_items,
                        raw_responses=model_responses,
                        final_output=turn_result.next_step.output,
                        _last_agent=current_agent,
                        input_guardrail_results=input_guardrail_results,
                        output_guardrail_results=output_guardrail_results,
                    )
                elif isinstance(turn_result.next_step, NextStepHandoff):
                    current_agent = cast(Agent[TContext], turn_result.next_step.new_agent)
                    current_span.finish(reset_current=True)
                    current_span = None
                    should_run_agent_start_hooks = True
                elif isinstance(turn_result.next_step, NextStepRunAgain):
                    pass
                else:
                    raise AgentsException(
                        f"Unknown next step type: {type(turn_result.next_step)}"
                    )
        finally:
            if current_span:
                current_span.finish(reset_current=True)

run_sync classmethod

run_sync(
    starting_agent: Agent[TContext],
    input: str | list[TResponseInputItem],
    *,
    context: TContext | None = None,
    max_turns: int = DEFAULT_MAX_TURNS,
    hooks: RunHooks[TContext] | None = None,
    run_config: RunConfig | None = None,
) -> RunResult

Run a workflow synchronously, starting at the given agent. Note that this just wraps the run method, so it will not work if there's already an event loop (e.g. inside an async function, or in a Jupyter notebook or async context like FastAPI). For those cases, use the run method instead.

The agent will run in a loop until a final output is generated. The loop runs like so: 1. The agent is invoked with the given input. 2. If there is a final output (i.e. the agent produces something of type agent.output_type, the loop terminates. 3. If there's a handoff, we run the loop again, with the new agent. 4. Else, we run tool calls (if any), and re-run the loop.

In two cases, the agent may raise an exception: 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

Note that only the first agent's input guardrails are run.

Parameters:

Name	Type	Description	Default
starting_agent	Agent[TContext]	The starting agent to run.	required
input	str | list[TResponseInputItem]	The initial input to the agent. You can pass a single string for a user message, or a list of input items.	required
context	TContext | None	The context to run the agent with.	None
max_turns	int	The maximum number of turns to run the agent for. A turn is defined as one AI invocation (including any tool calls that might occur).	DEFAULT_MAX_TURNS
hooks	RunHooks[TContext] | None	An object that receives callbacks on various lifecycle events.	None
run_config	RunConfig | None	Global settings for the entire agent run.	None

Returns:

Type	Description
RunResult	A run result containing all the inputs, guardrail results and the output of the last
RunResult	agent. Agents may perform handoffs, so we don't know the specific type of the output.

Source code in src/agents/run.py

@classmethod
def run_sync(
    cls,
    starting_agent: Agent[TContext],
    input: str | list[TResponseInputItem],
    *,
    context: TContext | None = None,
    max_turns: int = DEFAULT_MAX_TURNS,
    hooks: RunHooks[TContext] | None = None,
    run_config: RunConfig | None = None,
) -> RunResult:
    """Run a workflow synchronously, starting at the given agent. Note that this just wraps the
    `run` method, so it will not work if there's already an event loop (e.g. inside an async
    function, or in a Jupyter notebook or async context like FastAPI). For those cases, use
    the `run` method instead.

    The agent will run in a loop until a final output is generated. The loop runs like so:
    1. The agent is invoked with the given input.
    2. If there is a final output (i.e. the agent produces something of type
        `agent.output_type`, the loop terminates.
    3. If there's a handoff, we run the loop again, with the new agent.
    4. Else, we run tool calls (if any), and re-run the loop.

    In two cases, the agent may raise an exception:
    1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
    2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

    Note that only the first agent's input guardrails are run.

    Args:
        starting_agent: The starting agent to run.
        input: The initial input to the agent. You can pass a single string for a user message,
            or a list of input items.
        context: The context to run the agent with.
        max_turns: The maximum number of turns to run the agent for. A turn is defined as one
            AI invocation (including any tool calls that might occur).
        hooks: An object that receives callbacks on various lifecycle events.
        run_config: Global settings for the entire agent run.

    Returns:
        A run result containing all the inputs, guardrail results and the output of the last
        agent. Agents may perform handoffs, so we don't know the specific type of the output.
    """
    return asyncio.get_event_loop().run_until_complete(
        cls.run(
            starting_agent,
            input,
            context=context,
            max_turns=max_turns,
            hooks=hooks,
            run_config=run_config,
        )
    )

run_streamed classmethod

run_streamed(
    starting_agent: Agent[TContext],
    input: str | list[TResponseInputItem],
    context: TContext | None = None,
    max_turns: int = DEFAULT_MAX_TURNS,
    hooks: RunHooks[TContext] | None = None,
    run_config: RunConfig | None = None,
) -> RunResultStreaming

Run a workflow starting at the given agent in streaming mode. The returned result object contains a method you can use to stream semantic events as they are generated.

The agent will run in a loop until a final output is generated. The loop runs like so: 1. The agent is invoked with the given input. 2. If there is a final output (i.e. the agent produces something of type agent.output_type, the loop terminates. 3. If there's a handoff, we run the loop again, with the new agent. 4. Else, we run tool calls (if any), and re-run the loop.

In two cases, the agent may raise an exception: 1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised. 2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

Note that only the first agent's input guardrails are run.

Parameters:

Name	Type	Description	Default
starting_agent	Agent[TContext]	The starting agent to run.	required
input	str | list[TResponseInputItem]	The initial input to the agent. You can pass a single string for a user message, or a list of input items.	required
context	TContext | None	The context to run the agent with.	None
max_turns	int	The maximum number of turns to run the agent for. A turn is defined as one AI invocation (including any tool calls that might occur).	DEFAULT_MAX_TURNS
hooks	RunHooks[TContext] | None	An object that receives callbacks on various lifecycle events.	None
run_config	RunConfig | None	Global settings for the entire agent run.	None

Returns:

Type	Description
RunResultStreaming	A result object that contains data about the run, as well as a method to stream events.

Source code in src/agents/run.py

@classmethod
def run_streamed(
    cls,
    starting_agent: Agent[TContext],
    input: str | list[TResponseInputItem],
    context: TContext | None = None,
    max_turns: int = DEFAULT_MAX_TURNS,
    hooks: RunHooks[TContext] | None = None,
    run_config: RunConfig | None = None,
) -> RunResultStreaming:
    """Run a workflow starting at the given agent in streaming mode. The returned result object
    contains a method you can use to stream semantic events as they are generated.

    The agent will run in a loop until a final output is generated. The loop runs like so:
    1. The agent is invoked with the given input.
    2. If there is a final output (i.e. the agent produces something of type
        `agent.output_type`, the loop terminates.
    3. If there's a handoff, we run the loop again, with the new agent.
    4. Else, we run tool calls (if any), and re-run the loop.

    In two cases, the agent may raise an exception:
    1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
    2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

    Note that only the first agent's input guardrails are run.

    Args:
        starting_agent: The starting agent to run.
        input: The initial input to the agent. You can pass a single string for a user message,
            or a list of input items.
        context: The context to run the agent with.
        max_turns: The maximum number of turns to run the agent for. A turn is defined as one
            AI invocation (including any tool calls that might occur).
        hooks: An object that receives callbacks on various lifecycle events.
        run_config: Global settings for the entire agent run.

    Returns:
        A result object that contains data about the run, as well as a method to stream events.
    """
    if hooks is None:
        hooks = RunHooks[Any]()
    if run_config is None:
        run_config = RunConfig()

    # If there's already a trace, we don't create a new one. In addition, we can't end the
    # trace here, because the actual work is done in `stream_events` and this method ends
    # before that.
    new_trace = (
        None
        if get_current_trace()
        else trace(
            workflow_name=run_config.workflow_name,
            trace_id=run_config.trace_id,
            group_id=run_config.group_id,
            metadata=run_config.trace_metadata,
            disabled=run_config.tracing_disabled,
        )
    )
    # Need to start the trace here, because the current trace contextvar is captured at
    # asyncio.create_task time
    if new_trace:
        new_trace.start(mark_as_current=True)

    output_schema = cls._get_output_schema(starting_agent)
    context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
        context=context  # type: ignore
    )

    streamed_result = RunResultStreaming(
        input=copy.deepcopy(input),
        new_items=[],
        current_agent=starting_agent,
        raw_responses=[],
        final_output=None,
        is_complete=False,
        current_turn=0,
        max_turns=max_turns,
        input_guardrail_results=[],
        output_guardrail_results=[],
        _current_agent_output_schema=output_schema,
        _trace=new_trace,
    )

    # Kick off the actual agent loop in the background and return the streamed result object.
    streamed_result._run_impl_task = asyncio.create_task(
        cls._run_streamed_impl(
            starting_input=input,
            streamed_result=streamed_result,
            starting_agent=starting_agent,
            max_turns=max_turns,
            hooks=hooks,
            context_wrapper=context_wrapper,
            run_config=run_config,
        )
    )
    return streamed_result

RunConfig dataclass

Configures settings for the entire agent run.

Source code in src/agents/run.py

@dataclass
class RunConfig:
    """Configures settings for the entire agent run."""

    model: str | Model | None = None
    """The model to use for the entire agent run. If set, will override the model set on every
    agent. The model_provider passed in below must be able to resolve this model name.
    """

    model_provider: ModelProvider = field(default_factory=OpenAIProvider)
    """The model provider to use when looking up string model names. Defaults to OpenAI."""

    model_settings: ModelSettings | None = None
    """Configure global model settings. Any non-null values will override the agent-specific model
    settings.
    """

    handoff_input_filter: HandoffInputFilter | None = None
    """A global input filter to apply to all handoffs. If `Handoff.input_filter` is set, then that
    will take precedence. The input filter allows you to edit the inputs that are sent to the new
    agent. See the documentation in `Handoff.input_filter` for more details.
    """

    input_guardrails: list[InputGuardrail[Any]] | None = None
    """A list of input guardrails to run on the initial run input."""

    output_guardrails: list[OutputGuardrail[Any]] | None = None
    """A list of output guardrails to run on the final output of the run."""

    tracing_disabled: bool = False
    """Whether tracing is disabled for the agent run. If disabled, we will not trace the agent run.
    """

    trace_include_sensitive_data: bool = True
    """Whether we include potentially sensitive data (for example: inputs/outputs of tool calls or
    LLM generations) in traces. If False, we'll still create spans for these events, but the
    sensitive data will not be included.
    """

    workflow_name: str = "Agent workflow"
    """The name of the run, used for tracing. Should be a logical name for the run, like
    "Code generation workflow" or "Customer support agent".
    """

    trace_id: str | None = None
    """A custom trace ID to use for tracing. If not provided, we will generate a new trace ID."""

    group_id: str | None = None
    """
    A grouping identifier to use for tracing, to link multiple traces from the same conversation
    or process. For example, you might use a chat thread ID.
    """

    trace_metadata: dict[str, Any] | None = None
    """
    An optional dictionary of additional metadata to include with the trace.
    """

model class-attribute instance-attribute

model: str | Model | None = None

The model to use for the entire agent run. If set, will override the model set on every agent. The model_provider passed in below must be able to resolve this model name.

model_provider class-attribute instance-attribute

model_provider: ModelProvider = field(
    default_factory=OpenAIProvider
)

The model provider to use when looking up string model names. Defaults to OpenAI.

model_settings class-attribute instance-attribute

model_settings: ModelSettings | None = None

Configure global model settings. Any non-null values will override the agent-specific model settings.

handoff_input_filter class-attribute instance-attribute

handoff_input_filter: HandoffInputFilter | None = None

A global input filter to apply to all handoffs. If Handoff.input_filter is set, then that will take precedence. The input filter allows you to edit the inputs that are sent to the new agent. See the documentation in Handoff.input_filter for more details.

input_guardrails class-attribute instance-attribute

input_guardrails: list[InputGuardrail[Any]] | None = None

A list of input guardrails to run on the initial run input.

output_guardrails class-attribute instance-attribute

output_guardrails: list[OutputGuardrail[Any]] | None = None

A list of output guardrails to run on the final output of the run.

tracing_disabled class-attribute instance-attribute

tracing_disabled: bool = False

Whether tracing is disabled for the agent run. If disabled, we will not trace the agent run.

trace_include_sensitive_data class-attribute instance-attribute

trace_include_sensitive_data: bool = True

Whether we include potentially sensitive data (for example: inputs/outputs of tool calls or LLM generations) in traces. If False, we'll still create spans for these events, but the sensitive data will not be included.

workflow_name class-attribute instance-attribute

workflow_name: str = 'Agent workflow'

The name of the run, used for tracing. Should be a logical name for the run, like "Code generation workflow" or "Customer support agent".

trace_id class-attribute instance-attribute

trace_id: str | None = None

A custom trace ID to use for tracing. If not provided, we will generate a new trace ID.

group_id class-attribute instance-attribute

group_id: str | None = None

A grouping identifier to use for tracing, to link multiple traces from the same conversation or process. For example, you might use a chat thread ID.

trace_metadata class-attribute instance-attribute

trace_metadata: dict[str, Any] | None = None

An optional dictionary of additional metadata to include with the trace.