_run_impl.py

from __future__ import annotations

import asyncio
import dataclasses
import inspect
from collections.abc import Awaitable
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, cast

from openai.types.responses import (
    ResponseComputerToolCall,
    ResponseFileSearchToolCall,
    ResponseFunctionToolCall,
    ResponseFunctionWebSearch,
    ResponseOutputMessage,
)
from openai.types.responses.response_computer_tool_call import (
    ActionClick,
    ActionDoubleClick,
    ActionDrag,
    ActionKeypress,
    ActionMove,
    ActionScreenshot,
    ActionScroll,
    ActionType,
    ActionWait,
)
from openai.types.responses.response_input_param import ComputerCallOutput
from openai.types.responses.response_reasoning_item import ResponseReasoningItem

from .agent import Agent, ToolsToFinalOutputResult
from .agent_output import AgentOutputSchema
from .computer import AsyncComputer, Computer
from .exceptions import AgentsException, ModelBehaviorError, UserError
from .guardrail import (
    FactCheckingGuardrail,
    FactCheckingGuardrailResult,
    InputGuardrail,
    InputGuardrailResult,
    OutputGuardrail,
    OutputGuardrailResult,
)
from .handoffs import Handoff, HandoffInputData
from .items import (
    HandoffCallItem,
    HandoffOutputItem,
    ItemHelpers,
    MessageOutputItem,
    ModelResponse,
    ReasoningItem,
    RunItem,
    ToolCallItem,
    ToolCallOutputItem,
    TResponseInputItem,
)
from .lifecycle import RunHooks
from .logger import logger
from .model_settings import ModelSettings
from .models.interface import ModelTracing
from .run_context import RunContextWrapper, TContext
from .stream_events import RunItemStreamEvent, StreamEvent
from .tool import ComputerTool, FunctionTool, FunctionToolResult, Tool
from .tracing import (
    SpanError,
    Trace,
    function_span,
    get_current_trace,
    guardrail_span,
    handoff_span,
    trace,
)
from .util import _coro, _error_tracing

if TYPE_CHECKING:
    from .run import RunConfig


class QueueCompleteSentinel:
    pass


QUEUE_COMPLETE_SENTINEL = QueueCompleteSentinel()

_NOT_FINAL_OUTPUT = ToolsToFinalOutputResult(is_final_output=False, final_output=None)


@dataclass
class AgentToolUseTracker:
    agent_to_tools: list[tuple[Agent, list[str]]] = field(default_factory=list)
    """Tuple of (agent, list of tools used). Can't use a dict because agents aren't hashable."""

    def add_tool_use(self, agent: Agent[Any], tool_names: list[str]) -> None:
        existing_data = next((item for item in self.agent_to_tools if item[0] == agent), None)
        if existing_data:
            existing_data[1].extend(tool_names)
        else:
            self.agent_to_tools.append((agent, tool_names))

    def has_used_tools(self, agent: Agent[Any]) -> bool:
        existing_data = next((item for item in self.agent_to_tools if item[0] == agent), None)
        return existing_data is not None and len(existing_data[1]) > 0


@dataclass
class ToolRunHandoff:
    handoff: Handoff
    tool_call: ResponseFunctionToolCall


@dataclass
class ToolRunFunction:
    tool_call: ResponseFunctionToolCall
    function_tool: FunctionTool


@dataclass
class ToolRunComputerAction:
    tool_call: ResponseComputerToolCall
    computer_tool: ComputerTool


@dataclass
class ProcessedResponse:
    new_items: list[RunItem]
    handoffs: list[ToolRunHandoff]
    functions: list[ToolRunFunction]
    computer_actions: list[ToolRunComputerAction]
    tools_used: list[str]  # Names of all tools used, including hosted tools

    def has_tools_to_run(self) -> bool:
        # Handoffs, functions and computer actions need local processing
        # Hosted tools have already run, so there's nothing to do.
        return any(
            [
                self.handoffs,
                self.functions,
                self.computer_actions,
            ]
        )


@dataclass
class NextStepHandoff:
    new_agent: Agent[Any]


@dataclass
class NextStepFinalOutput:
    output: Any


@dataclass
class NextStepRunAgain:
    pass


@dataclass
class SingleStepResult:
    original_input: str | list[TResponseInputItem]
    """The input items i.e. the items before run() was called. May be mutated by handoff input
    filters."""

    model_response: ModelResponse
    """The model response for the current step."""

    pre_step_items: list[RunItem]
    """Items generated before the current step."""

    new_step_items: list[RunItem]
    """Items generated during this current step."""

    next_step: NextStepHandoff | NextStepFinalOutput | NextStepRunAgain
    """The next step to take."""

    @property
    def generated_items(self) -> list[RunItem]:
        """Items generated during the agent run (i.e. everything generated after
        `original_input`)."""
        return self.pre_step_items + self.new_step_items


def get_model_tracing_impl(
    tracing_disabled: bool, trace_include_sensitive_data: bool
) -> ModelTracing:
    if tracing_disabled:
        return ModelTracing.DISABLED
    elif trace_include_sensitive_data:
        return ModelTracing.ENABLED
    else:
        return ModelTracing.ENABLED_WITHOUT_DATA


class RunImpl:
    @classmethod
    async def execute_tools_and_side_effects(
        cls,
        *,
        agent: Agent[TContext],
        # The original input to the Runner
        original_input: str | list[TResponseInputItem],
        # Everything generated by Runner since the original input, but before the current step
        pre_step_items: list[RunItem],
        new_response: ModelResponse,
        processed_response: ProcessedResponse,
        output_schema: AgentOutputSchema | None,
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
    ) -> SingleStepResult:
        # Make a copy of the generated items
        pre_step_items = list(pre_step_items)

        new_step_items: list[RunItem] = []
        new_step_items.extend(processed_response.new_items)

        # First, lets run the tool calls - function tools and computer actions
        function_results, computer_results = await asyncio.gather(
            cls.execute_function_tool_calls(
                agent=agent,
                tool_runs=processed_response.functions,
                hooks=hooks,
                context_wrapper=context_wrapper,
                config=run_config,
            ),
            cls.execute_computer_actions(
                agent=agent,
                actions=processed_response.computer_actions,
                hooks=hooks,
                context_wrapper=context_wrapper,
                config=run_config,
            ),
        )
        new_step_items.extend([result.run_item for result in function_results])
        new_step_items.extend(computer_results)

        # Second, check if there are any handoffs
        if run_handoffs := processed_response.handoffs:
            return await cls.execute_handoffs(
                agent=agent,
                original_input=original_input,
                pre_step_items=pre_step_items,
                new_step_items=new_step_items,
                new_response=new_response,
                run_handoffs=run_handoffs,
                hooks=hooks,
                context_wrapper=context_wrapper,
                run_config=run_config,
            )

        # Third, we'll check if the tool use should result in a final output
        check_tool_use = await cls._check_for_final_output_from_tools(
            agent=agent,
            tool_results=function_results,
            context_wrapper=context_wrapper,
            config=run_config,
        )

        if check_tool_use.is_final_output:
            # If the output type is str, then let's just stringify it
            if not agent.output_type or agent.output_type is str:
                check_tool_use.final_output = str(check_tool_use.final_output)

            if check_tool_use.final_output is None:
                logger.error(
                    "Model returned a final output of None. Not raising an error because we assume"
                    "you know what you're doing."
                )

            return await cls.execute_final_output(
                agent=agent,
                original_input=original_input,
                new_response=new_response,
                pre_step_items=pre_step_items,
                new_step_items=new_step_items,
                final_output=check_tool_use.final_output,
                hooks=hooks,
                context_wrapper=context_wrapper,
            )

        # Now we can check if the model also produced a final output
        message_items = [item for item in new_step_items if isinstance(item, MessageOutputItem)]

        # We'll use the last content output as the final output
        potential_final_output_text = (
            ItemHelpers.extract_last_text(message_items[-1].raw_item) if message_items else None
        )

        # There are two possibilities that lead to a final output:
        # 1. Structured output schema => always leads to a final output
        # 2. Plain text output schema => only leads to a final output if there are no tool calls
        if output_schema and not output_schema.is_plain_text() and potential_final_output_text:
            final_output = output_schema.validate_json(potential_final_output_text)
            return await cls.execute_final_output(
                agent=agent,
                original_input=original_input,
                new_response=new_response,
                pre_step_items=pre_step_items,
                new_step_items=new_step_items,
                final_output=final_output,
                hooks=hooks,
                context_wrapper=context_wrapper,
            )
        elif (
            not output_schema or output_schema.is_plain_text()
        ) and not processed_response.has_tools_to_run():
            return await cls.execute_final_output(
                agent=agent,
                original_input=original_input,
                new_response=new_response,
                pre_step_items=pre_step_items,
                new_step_items=new_step_items,
                final_output=potential_final_output_text or "",
                hooks=hooks,
                context_wrapper=context_wrapper,
            )
        else:
            # If there's no final output, we can just run again
            return SingleStepResult(
                original_input=original_input,
                model_response=new_response,
                pre_step_items=pre_step_items,
                new_step_items=new_step_items,
                next_step=NextStepRunAgain(),
            )

    @classmethod
    def maybe_reset_tool_choice(
        cls, agent: Agent[Any], tool_use_tracker: AgentToolUseTracker, model_settings: ModelSettings
    ) -> ModelSettings:
        """Resets tool choice to None if the agent has used tools and the agent's reset_tool_choice
        flag is True."""

        if agent.reset_tool_choice is True and tool_use_tracker.has_used_tools(agent):
            return dataclasses.replace(model_settings, tool_choice=None)

        return model_settings

    @classmethod
    def process_model_response(
        cls,
        *,
        agent: Agent[Any],
        all_tools: list[Tool],
        response: ModelResponse,
        output_schema: AgentOutputSchema | None,
        handoffs: list[Handoff],
    ) -> ProcessedResponse:
        items: list[RunItem] = []

        run_handoffs = []
        functions = []
        computer_actions = []
        tools_used: list[str] = []
        handoff_map = {handoff.tool_name: handoff for handoff in handoffs}
        function_map = {tool.name: tool for tool in all_tools if isinstance(tool, FunctionTool)}
        computer_tool = next((tool for tool in all_tools if isinstance(tool, ComputerTool)), None)

        for output in response.output:
            if isinstance(output, ResponseOutputMessage):
                items.append(MessageOutputItem(raw_item=output, agent=agent))
            elif isinstance(output, ResponseFileSearchToolCall):
                items.append(ToolCallItem(raw_item=output, agent=agent))
                tools_used.append("file_search")
            elif isinstance(output, ResponseFunctionWebSearch):
                items.append(ToolCallItem(raw_item=output, agent=agent))
                tools_used.append("web_search")
            elif isinstance(output, ResponseReasoningItem):
                items.append(ReasoningItem(raw_item=output, agent=agent))
            elif isinstance(output, ResponseComputerToolCall):
                items.append(ToolCallItem(raw_item=output, agent=agent))
                tools_used.append("computer_use")
                if not computer_tool:
                    _error_tracing.attach_error_to_current_span(
                        SpanError(
                            message="Computer tool not found",
                            data={},
                        )
                    )
                    raise ModelBehaviorError(
                        "Model produced computer action without a computer tool."
                    )
                computer_actions.append(
                    ToolRunComputerAction(tool_call=output, computer_tool=computer_tool)
                )
            elif not isinstance(output, ResponseFunctionToolCall):
                logger.warning(f"Unexpected output type, ignoring: {type(output)}")
                continue

            # At this point we know it's a function tool call
            if not isinstance(output, ResponseFunctionToolCall):
                continue

            tools_used.append(output.name)

            # Handoffs
            if output.name in handoff_map:
                items.append(HandoffCallItem(raw_item=output, agent=agent))
                handoff = ToolRunHandoff(
                    tool_call=output,
                    handoff=handoff_map[output.name],
                )
                run_handoffs.append(handoff)
            # Regular function tool call
            else:
                if output.name not in function_map:
                    _error_tracing.attach_error_to_current_span(
                        SpanError(
                            message="Tool not found",
                            data={"tool_name": output.name},
                        )
                    )
                    raise ModelBehaviorError(f"Tool {output.name} not found in agent {agent.name}")
                items.append(ToolCallItem(raw_item=output, agent=agent))
                functions.append(
                    ToolRunFunction(
                        tool_call=output,
                        function_tool=function_map[output.name],
                    )
                )

        return ProcessedResponse(
            new_items=items,
            handoffs=run_handoffs,
            functions=functions,
            computer_actions=computer_actions,
            tools_used=tools_used,
        )

    @classmethod
    async def execute_function_tool_calls(
        cls,
        *,
        agent: Agent[TContext],
        tool_runs: list[ToolRunFunction],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        config: RunConfig,
    ) -> list[FunctionToolResult]:
        async def run_single_tool(
            func_tool: FunctionTool, tool_call: ResponseFunctionToolCall
        ) -> Any:
            with function_span(func_tool.name) as span_fn:
                if config.trace_include_sensitive_data:
                    span_fn.span_data.input = tool_call.arguments
                try:
                    _, _, result = await asyncio.gather(
                        hooks.on_tool_start(context_wrapper, agent, func_tool),
                        (
                            agent.hooks.on_tool_start(context_wrapper, agent, func_tool)
                            if agent.hooks
                            else _coro.noop_coroutine()
                        ),
                        func_tool.on_invoke_tool(context_wrapper, tool_call.arguments),
                    )

                    await asyncio.gather(
                        hooks.on_tool_end(context_wrapper, agent, func_tool, result),
                        (
                            agent.hooks.on_tool_end(context_wrapper, agent, func_tool, result)
                            if agent.hooks
                            else _coro.noop_coroutine()
                        ),
                    )
                except Exception as e:
                    _error_tracing.attach_error_to_current_span(
                        SpanError(
                            message="Error running tool",
                            data={"tool_name": func_tool.name, "error": str(e)},
                        )
                    )
                    if isinstance(e, AgentsException):
                        raise e
                    raise UserError(f"Error running tool {func_tool.name}: {e}") from e

                if config.trace_include_sensitive_data:
                    span_fn.span_data.output = result
            return result

        tasks = []
        for tool_run in tool_runs:
            function_tool = tool_run.function_tool
            tasks.append(run_single_tool(function_tool, tool_run.tool_call))

        results = await asyncio.gather(*tasks)

        return [
            FunctionToolResult(
                tool=tool_run.function_tool,
                output=result,
                run_item=ToolCallOutputItem(
                    output=result,
                    raw_item=ItemHelpers.tool_call_output_item(tool_run.tool_call, str(result)),
                    agent=agent,
                ),
            )
            for tool_run, result in zip(tool_runs, results)
        ]

    @classmethod
    async def execute_computer_actions(
        cls,
        *,
        agent: Agent[TContext],
        actions: list[ToolRunComputerAction],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        config: RunConfig,
    ) -> list[RunItem]:
        results: list[RunItem] = []
        # Need to run these serially, because each action can affect the computer state
        for action in actions:
            results.append(
                await ComputerAction.execute(
                    agent=agent,
                    action=action,
                    hooks=hooks,
                    context_wrapper=context_wrapper,
                    config=config,
                )
            )

        return results

    @classmethod
    async def execute_handoffs(
        cls,
        *,
        agent: Agent[TContext],
        original_input: str | list[TResponseInputItem],
        pre_step_items: list[RunItem],
        new_step_items: list[RunItem],
        new_response: ModelResponse,
        run_handoffs: list[ToolRunHandoff],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
    ) -> SingleStepResult:
        # If there is more than one handoff, add tool responses that reject those handoffs
        multiple_handoffs = len(run_handoffs) > 1
        if multiple_handoffs:
            output_message = "Multiple handoffs detected, ignoring this one."
            new_step_items.extend(
                [
                    ToolCallOutputItem(
                        output=output_message,
                        raw_item=ItemHelpers.tool_call_output_item(
                            handoff.tool_call, output_message
                        ),
                        agent=agent,
                    )
                    for handoff in run_handoffs[1:]
                ]
            )

        actual_handoff = run_handoffs[0]
        with handoff_span(from_agent=agent.name) as span_handoff:
            handoff = actual_handoff.handoff
            new_agent: Agent[Any] = await handoff.on_invoke_handoff(
                context_wrapper, actual_handoff.tool_call.arguments
            )
            span_handoff.span_data.to_agent = new_agent.name
            if multiple_handoffs:
                requested_agents = [handoff.handoff.agent_name for handoff in run_handoffs]
                span_handoff.set_error(
                    SpanError(
                        message="Multiple handoffs requested",
                        data={
                            "requested_agents": requested_agents,
                        },
                    )
                )

            # Append a tool output item for the handoff
            new_step_items.append(
                HandoffOutputItem(
                    agent=agent,
                    raw_item=ItemHelpers.tool_call_output_item(
                        actual_handoff.tool_call,
                        handoff.get_transfer_message(new_agent),
                    ),
                    source_agent=agent,
                    target_agent=new_agent,
                )
            )

            # Execute handoff hooks
            await asyncio.gather(
                hooks.on_handoff(
                    context=context_wrapper,
                    from_agent=agent,
                    to_agent=new_agent,
                ),
                (
                    agent.hooks.on_handoff(
                        context_wrapper,
                        agent=new_agent,
                        source=agent,
                    )
                    if agent.hooks
                    else _coro.noop_coroutine()
                ),
            )

            # If there's an input filter, filter the input for the next agent
            input_filter = handoff.input_filter or (
                run_config.handoff_input_filter if run_config else None
            )
            if input_filter:
                logger.debug("Filtering inputs for handoff")
                handoff_input_data = HandoffInputData(
                    input_history=tuple(original_input)
                    if isinstance(original_input, list)
                    else original_input,
                    pre_handoff_items=tuple(pre_step_items),
                    new_items=tuple(new_step_items),
                )
                if not callable(input_filter):
                    _error_tracing.attach_error_to_span(
                        span_handoff,
                        SpanError(
                            message="Invalid input filter",
                            data={"details": "not callable()"},
                        ),
                    )
                    raise UserError(f"Invalid input filter: {input_filter}")
                filtered = input_filter(handoff_input_data)
                if not isinstance(filtered, HandoffInputData):
                    _error_tracing.attach_error_to_span(
                        span_handoff,
                        SpanError(
                            message="Invalid input filter result",
                            data={"details": "not a HandoffInputData"},
                        ),
                    )
                    raise UserError(f"Invalid input filter result: {filtered}")

                original_input = (
                    filtered.input_history
                    if isinstance(filtered.input_history, str)
                    else list(filtered.input_history)
                )
                pre_step_items = list(filtered.pre_handoff_items)
                new_step_items = list(filtered.new_items)

        return SingleStepResult(
            original_input=original_input,
            model_response=new_response,
            pre_step_items=pre_step_items,
            new_step_items=new_step_items,
            next_step=NextStepHandoff(new_agent),
        )

    @classmethod
    async def execute_final_output(
        cls,
        *,
        agent: Agent[TContext],
        original_input: str | list[TResponseInputItem],
        new_response: ModelResponse,
        pre_step_items: list[RunItem],
        new_step_items: list[RunItem],
        final_output: Any,
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
    ) -> SingleStepResult:
        # Run the on_end hooks
        await cls.run_final_output_hooks(agent, hooks, context_wrapper, final_output)

        return SingleStepResult(
            original_input=original_input,
            model_response=new_response,
            pre_step_items=pre_step_items,
            new_step_items=new_step_items,
            next_step=NextStepFinalOutput(final_output),
        )

    @classmethod
    async def run_final_output_hooks(
        cls,
        agent: Agent[TContext],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        final_output: Any,
    ):
        await asyncio.gather(
            hooks.on_agent_end(context_wrapper, agent, final_output),
            agent.hooks.on_end(context_wrapper, agent, final_output)
            if agent.hooks
            else _coro.noop_coroutine(),
        )

    @classmethod
    async def run_single_input_guardrail(
        cls,
        agent: Agent[Any],
        guardrail: InputGuardrail[TContext],
        input: str | list[TResponseInputItem],
        context: RunContextWrapper[TContext],
    ) -> InputGuardrailResult:
        with guardrail_span(guardrail.get_name()) as span_guardrail:
            result = await guardrail.run(agent, input, context)
            span_guardrail.span_data.triggered = result.output.tripwire_triggered
            return result

    @classmethod
    async def run_single_output_guardrail(
        cls,
        guardrail: OutputGuardrail[TContext],
        agent: Agent[Any],
        agent_output: Any,
        context: RunContextWrapper[TContext],
    ) -> OutputGuardrailResult:
        with guardrail_span(guardrail.get_name()) as span_guardrail:
            result = await guardrail.run(agent=agent, agent_output=agent_output, context=context)
            span_guardrail.span_data.triggered = result.output.tripwire_triggered
            return result

    @classmethod
    async def run_single_fact_checking_guardrail(
        cls,
        guardrail: FactCheckingGuardrail[TContext],
        agent: Agent[Any],
        agent_output: Any,
        context: RunContextWrapper[TContext],
        agent_input: Any,
    ) -> FactCheckingGuardrailResult:
        with guardrail_span(guardrail.get_name()) as span_guardrail:
            result = await guardrail.run(
                agent=agent, agent_output=agent_output, context=context, agent_input=agent_input
            )
            span_guardrail.span_data.triggered = result.output.tripwire_triggered
            return result

    @classmethod
    def stream_step_result_to_queue(
        cls,
        step_result: SingleStepResult,
        queue: asyncio.Queue[StreamEvent | QueueCompleteSentinel],
    ):
        for item in step_result.new_step_items:
            if isinstance(item, MessageOutputItem):
                event = RunItemStreamEvent(item=item, name="message_output_created")
            elif isinstance(item, HandoffCallItem):
                event = RunItemStreamEvent(item=item, name="handoff_requested")
            elif isinstance(item, HandoffOutputItem):
                event = RunItemStreamEvent(item=item, name="handoff_occured")
            elif isinstance(item, ToolCallItem):
                event = RunItemStreamEvent(item=item, name="tool_called")
            elif isinstance(item, ToolCallOutputItem):
                event = RunItemStreamEvent(item=item, name="tool_output")
            elif isinstance(item, ReasoningItem):
                event = RunItemStreamEvent(item=item, name="reasoning_item_created")
            else:
                logger.warning(f"Unexpected item type: {type(item)}")
                event = None

            if event:
                queue.put_nowait(event)

    @classmethod
    async def _check_for_final_output_from_tools(
        cls,
        *,
        agent: Agent[TContext],
        tool_results: list[FunctionToolResult],
        context_wrapper: RunContextWrapper[TContext],
        config: RunConfig,
    ) -> ToolsToFinalOutputResult:
        """Returns (i, final_output)."""
        if not tool_results:
            return _NOT_FINAL_OUTPUT

        if agent.tool_use_behavior == "run_llm_again":
            return _NOT_FINAL_OUTPUT
        elif agent.tool_use_behavior == "stop_on_first_tool":
            return ToolsToFinalOutputResult(
                is_final_output=True, final_output=tool_results[0].output
            )
        elif isinstance(agent.tool_use_behavior, dict):
            names = agent.tool_use_behavior.get("stop_at_tool_names", [])
            for tool_result in tool_results:
                if tool_result.tool.name in names:
                    return ToolsToFinalOutputResult(
                        is_final_output=True, final_output=tool_result.output
                    )
            return ToolsToFinalOutputResult(is_final_output=False, final_output=None)
        elif callable(agent.tool_use_behavior):
            if inspect.iscoroutinefunction(agent.tool_use_behavior):
                return await cast(
                    Awaitable[ToolsToFinalOutputResult],
                    agent.tool_use_behavior(context_wrapper, tool_results),
                )
            else:
                return cast(
                    ToolsToFinalOutputResult, agent.tool_use_behavior(context_wrapper, tool_results)
                )

        logger.error(f"Invalid tool_use_behavior: {agent.tool_use_behavior}")
        raise UserError(f"Invalid tool_use_behavior: {agent.tool_use_behavior}")


class TraceCtxManager:
    """Creates a trace only if there is no current trace, and manages the trace lifecycle."""

    def __init__(
        self,
        workflow_name: str,
        trace_id: str | None,
        group_id: str | None,
        metadata: dict[str, Any] | None,
        disabled: bool,
    ):
        self.trace: Trace | None = None
        self.workflow_name = workflow_name
        self.trace_id = trace_id
        self.group_id = group_id
        self.metadata = metadata
        self.disabled = disabled

    def __enter__(self) -> TraceCtxManager:
        current_trace = get_current_trace()
        if not current_trace:
            self.trace = trace(
                workflow_name=self.workflow_name,
                trace_id=self.trace_id,
                group_id=self.group_id,
                metadata=self.metadata,
                disabled=self.disabled,
            )
            self.trace.start(mark_as_current=True)

        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.trace:
            self.trace.finish(reset_current=True)


class ComputerAction:
    @classmethod
    async def execute(
        cls,
        *,
        agent: Agent[TContext],
        action: ToolRunComputerAction,
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        config: RunConfig,
    ) -> RunItem:
        output_func = (
            cls._get_screenshot_async(action.computer_tool.computer, action.tool_call)
            if isinstance(action.computer_tool.computer, AsyncComputer)
            else cls._get_screenshot_sync(action.computer_tool.computer, action.tool_call)
        )

        _, _, output = await asyncio.gather(
            hooks.on_tool_start(context_wrapper, agent, action.computer_tool),
            (
                agent.hooks.on_tool_start(context_wrapper, agent, action.computer_tool)
                if agent.hooks
                else _coro.noop_coroutine()
            ),
            output_func,
        )

        await asyncio.gather(
            hooks.on_tool_end(context_wrapper, agent, action.computer_tool, output),
            (
                agent.hooks.on_tool_end(context_wrapper, agent, action.computer_tool, output)
                if agent.hooks
                else _coro.noop_coroutine()
            ),
        )

        # TODO: don't send a screenshot every single time, use references
        image_url = f"data:image/png;base64,{output}"
        return ToolCallOutputItem(
            agent=agent,
            output=image_url,
            raw_item=ComputerCallOutput(
                call_id=action.tool_call.call_id,
                output={
                    "type": "computer_screenshot",
                    "image_url": image_url,
                },
                type="computer_call_output",
            ),
        )

    @classmethod
    async def _get_screenshot_sync(
        cls,
        computer: Computer,
        tool_call: ResponseComputerToolCall,
    ) -> str:
        action = tool_call.action
        if isinstance(action, ActionClick):
            computer.click(action.x, action.y, action.button)
        elif isinstance(action, ActionDoubleClick):
            computer.double_click(action.x, action.y)
        elif isinstance(action, ActionDrag):
            computer.drag([(p.x, p.y) for p in action.path])
        elif isinstance(action, ActionKeypress):
            computer.keypress(action.keys)
        elif isinstance(action, ActionMove):
            computer.move(action.x, action.y)
        elif isinstance(action, ActionScreenshot):
            computer.screenshot()
        elif isinstance(action, ActionScroll):
            computer.scroll(action.x, action.y, action.scroll_x, action.scroll_y)
        elif isinstance(action, ActionType):
            computer.type(action.text)
        elif isinstance(action, ActionWait):
            computer.wait()

        return computer.screenshot()

    @classmethod
    async def _get_screenshot_async(
        cls,
        computer: AsyncComputer,
        tool_call: ResponseComputerToolCall,
    ) -> str:
        action = tool_call.action
        if isinstance(action, ActionClick):
            await computer.click(action.x, action.y, action.button)
        elif isinstance(action, ActionDoubleClick):
            await computer.double_click(action.x, action.y)
        elif isinstance(action, ActionDrag):
            await computer.drag([(p.x, p.y) for p in action.path])
        elif isinstance(action, ActionKeypress):
            await computer.keypress(action.keys)
        elif isinstance(action, ActionMove):
            await computer.move(action.x, action.y)
        elif isinstance(action, ActionScreenshot):
            await computer.screenshot()
        elif isinstance(action, ActionScroll):
            await computer.scroll(action.x, action.y, action.scroll_x, action.scroll_y)
        elif isinstance(action, ActionType):
            await computer.type(action.text)
        elif isinstance(action, ActionWait):
            await computer.wait()

        return await computer.screenshot()