Agents

ToolsToFinalOutputFunction module-attribute

ToolsToFinalOutputFunction: TypeAlias = Callable[
    [RunContextWrapper[TContext], list[FunctionToolResult]],
    MaybeAwaitable[ToolsToFinalOutputResult],
]

A function that takes a run context and a list of tool results, and returns a ToolsToFinalOutputResult.

ToolsToFinalOutputResult dataclass

ソースコード位置： src/agents/agent.py

@dataclass
class ToolsToFinalOutputResult:
    is_final_output: bool
    """Whether this is the final output. If False, the LLM will run again and receive the tool call
    output.
    """

    final_output: Any | None = None
    """The final output. Can be None if `is_final_output` is False, otherwise must match the
    `output_type` of the agent.
    """

is_final_output instance-attribute

is_final_output: bool

Whether this is the final output. If False, the LLM will run again and receive the tool call output.

final_output class-attribute instance-attribute

final_output: Any | None = None

The final output. Can be None if is_final_output is False, otherwise must match the output_type of the agent.

AgentToolStreamEvent

Bases: TypedDict

Streaming event emitted when an agent is invoked as a tool.

ソースコード位置： src/agents/agent.py

class AgentToolStreamEvent(TypedDict):
    """Streaming event emitted when an agent is invoked as a tool."""

    event: StreamEvent
    """The streaming event from the nested agent run."""

    agent: Agent[Any]
    """The nested agent emitting the event."""

    tool_call: ResponseFunctionToolCall | None
    """The originating tool call, if available."""

event instance-attribute

event: StreamEvent

The streaming event from the nested agent run.

agent instance-attribute

agent: Agent[Any]

The nested agent emitting the event.

tool_call instance-attribute

tool_call: ResponseFunctionToolCall | None

The originating tool call, if available.

StopAtTools

Bases: TypedDict

ソースコード位置： src/agents/agent.py

class StopAtTools(TypedDict):
    stop_at_tool_names: list[str]
    """A list of tool names, any of which will stop the agent from running further."""

stop_at_tool_names instance-attribute

stop_at_tool_names: list[str]

A list of tool names, any of which will stop the agent from running further.

MCPConfig

Bases: TypedDict

Configuration for MCP servers.

ソースコード位置： src/agents/agent.py

class MCPConfig(TypedDict):
    """Configuration for MCP servers."""

    convert_schemas_to_strict: NotRequired[bool]
    """If True, we will attempt to convert the MCP schemas to strict-mode schemas. This is a
    best-effort conversion, so some schemas may not be convertible. Defaults to False.
    """

    failure_error_function: NotRequired[ToolErrorFunction | None]
    """Optional function to convert MCP tool failures into model-visible messages. If explicitly
    set to None, tool errors will be raised instead. If unset, defaults to
    default_tool_error_function.
    """

    include_server_in_tool_names: NotRequired[bool]
    """If True, local MCP tools are exposed with server-prefixed public names to avoid name
    collisions across multiple MCP servers. Defaults to False.
    """

convert_schemas_to_strict instance-attribute

convert_schemas_to_strict: NotRequired[bool]

If True, we will attempt to convert the MCP schemas to strict-mode schemas. This is a best-effort conversion, so some schemas may not be convertible. Defaults to False.

failure_error_function instance-attribute

failure_error_function: NotRequired[
    ToolErrorFunction | None
]

Optional function to convert MCP tool failures into model-visible messages. If explicitly set to None, tool errors will be raised instead. If unset, defaults to default_tool_error_function.

include_server_in_tool_names instance-attribute

include_server_in_tool_names: NotRequired[bool]

If True, local MCP tools are exposed with server-prefixed public names to avoid name collisions across multiple MCP servers. Defaults to False.

AgentBase dataclass

Bases: Generic[TContext]

Base class for Agent and RealtimeAgent.

ソースコード位置： src/agents/agent.py

@dataclass
class AgentBase(Generic[TContext]):
    """Base class for `Agent` and `RealtimeAgent`."""

    name: str
    """The name of the agent."""

    handoff_description: str | None = None
    """A description of the agent. This is used when the agent is used as a handoff, so that an
    LLM knows what it does and when to invoke it.
    """

    tools: list[Tool] = field(default_factory=list)
    """A list of tools that the agent can use."""

    mcp_servers: list[MCPServer] = field(default_factory=list)
    """A list of [Model Context Protocol](https://modelcontextprotocol.io/) servers that
    the agent can use. Every time the agent runs, it will include tools from these servers in the
    list of available tools.

    NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call
    `server.connect()` before passing it to the agent, and `server.cleanup()` when the server is no
    longer needed. Consider using `MCPServerManager` from `agents.mcp` to keep connect/cleanup
    in the same task.
    """

    mcp_config: MCPConfig = field(default_factory=lambda: MCPConfig())
    """Configuration for MCP servers."""

    async def _get_mcp_tool_reserved_names(
        self, run_context: RunContextWrapper[TContext]
    ) -> set[str]:
        reserved_tool_names = {tool.name for tool in self.tools if isinstance(tool, FunctionTool)}

        async def _check_handoff_enabled(handoff_obj: Handoff[Any, Any]) -> bool:
            attr = handoff_obj.is_enabled
            if isinstance(attr, bool):
                return attr
            res = attr(run_context, self)
            if inspect.isawaitable(res):
                return bool(await res)
            return bool(res)

        for handoff_item in getattr(self, "handoffs", ()):
            if isinstance(handoff_item, Handoff):
                if await _check_handoff_enabled(handoff_item):
                    reserved_tool_names.add(handoff_item.tool_name)
            elif isinstance(handoff_item, AgentBase):
                reserved_tool_names.add(Handoff.default_tool_name(handoff_item))
        return reserved_tool_names

    async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]:
        """Fetches the available tools from the MCP servers."""
        convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False)
        failure_error_function = self.mcp_config.get(
            "failure_error_function", default_tool_error_function
        )
        include_server_in_tool_names = self.mcp_config.get("include_server_in_tool_names", False)
        reserved_tool_names = (
            await self._get_mcp_tool_reserved_names(run_context)
            if include_server_in_tool_names
            else None
        )
        return await MCPUtil.get_all_function_tools(
            self.mcp_servers,
            convert_schemas_to_strict,
            run_context,
            self,
            failure_error_function=failure_error_function,
            include_server_in_tool_names=include_server_in_tool_names,
            reserved_tool_names=reserved_tool_names,
        )

    async def get_all_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]:
        """All agent tools, including MCP tools and function tools."""
        mcp_tools = await self.get_mcp_tools(run_context)

        async def _check_tool_enabled(tool: Tool) -> bool:
            if not isinstance(tool, FunctionTool):
                return True

            attr = tool.is_enabled
            if isinstance(attr, bool):
                return attr
            res = attr(run_context, self)
            if inspect.isawaitable(res):
                return bool(await res)
            return bool(res)

        results = await gather_with_cancel(*(_check_tool_enabled(t) for t in self.tools))
        enabled: list[Tool] = [t for t, ok in zip(self.tools, results, strict=False) if ok]
        all_tools: list[Tool] = prune_orphaned_tool_search_tools([*mcp_tools, *enabled])
        _validate_codex_tool_name_collisions(all_tools)
        return all_tools

name instance-attribute

name: str

The name of the agent.

handoff_description class-attribute instance-attribute

handoff_description: str | None = None

A description of the agent. This is used when the agent is used as a handoff, so that an LLM knows what it does and when to invoke it.

tools class-attribute instance-attribute

tools: list[Tool] = field(default_factory=list)

A list of tools that the agent can use.

mcp_servers class-attribute instance-attribute

mcp_servers: list[MCPServer] = field(default_factory=list)

A list of Model Context Protocol servers that the agent can use. Every time the agent runs, it will include tools from these servers in the list of available tools.

NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call server.connect() before passing it to the agent, and server.cleanup() when the server is no longer needed. Consider using MCPServerManager from agents.mcp to keep connect/cleanup in the same task.

mcp_config class-attribute instance-attribute

mcp_config: MCPConfig = field(
    default_factory=lambda: MCPConfig()
)

Configuration for MCP servers.

get_mcp_tools async

get_mcp_tools(
    run_context: RunContextWrapper[TContext],
) -> list[Tool]

Fetches the available tools from the MCP servers.

ソースコード位置： src/agents/agent.py

async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]:
    """Fetches the available tools from the MCP servers."""
    convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False)
    failure_error_function = self.mcp_config.get(
        "failure_error_function", default_tool_error_function
    )
    include_server_in_tool_names = self.mcp_config.get("include_server_in_tool_names", False)
    reserved_tool_names = (
        await self._get_mcp_tool_reserved_names(run_context)
        if include_server_in_tool_names
        else None
    )
    return await MCPUtil.get_all_function_tools(
        self.mcp_servers,
        convert_schemas_to_strict,
        run_context,
        self,
        failure_error_function=failure_error_function,
        include_server_in_tool_names=include_server_in_tool_names,
        reserved_tool_names=reserved_tool_names,
    )

get_all_tools async

get_all_tools(
    run_context: RunContextWrapper[TContext],
) -> list[Tool]

All agent tools, including MCP tools and function tools.

ソースコード位置： src/agents/agent.py

async def get_all_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]:
    """All agent tools, including MCP tools and function tools."""
    mcp_tools = await self.get_mcp_tools(run_context)

    async def _check_tool_enabled(tool: Tool) -> bool:
        if not isinstance(tool, FunctionTool):
            return True

        attr = tool.is_enabled
        if isinstance(attr, bool):
            return attr
        res = attr(run_context, self)
        if inspect.isawaitable(res):
            return bool(await res)
        return bool(res)

    results = await gather_with_cancel(*(_check_tool_enabled(t) for t in self.tools))
    enabled: list[Tool] = [t for t, ok in zip(self.tools, results, strict=False) if ok]
    all_tools: list[Tool] = prune_orphaned_tool_search_tools([*mcp_tools, *enabled])
    _validate_codex_tool_name_collisions(all_tools)
    return all_tools

Agent dataclass

Bases: AgentBase, Generic[TContext]

An agent is an AI model configured with instructions, tools, guardrails, handoffs and more.

We strongly recommend passing instructions, which is the "system prompt" for the agent. In addition, you can pass handoff_description, which is a human-readable description of the agent, used when the agent is used inside tools/handoffs.

Agents are generic on the context type. The context is a (mutable) object you create. It is passed to tool functions, handoffs, guardrails, etc.

See AgentBase for base parameters that are shared with RealtimeAgents.

ソースコード位置： src/agents/agent.py

@dataclass
class Agent(AgentBase, Generic[TContext]):
    """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more.

    We strongly recommend passing `instructions`, which is the "system prompt" for the agent. In
    addition, you can pass `handoff_description`, which is a human-readable description of the
    agent, used when the agent is used inside tools/handoffs.

    Agents are generic on the context type. The context is a (mutable) object you create. It is
    passed to tool functions, handoffs, guardrails, etc.

    See `AgentBase` for base parameters that are shared with `RealtimeAgent`s.
    """

    instructions: (
        str
        | Callable[
            [RunContextWrapper[TContext], Agent[TContext]],
            MaybeAwaitable[str],
        ]
        | None
    ) = None
    """The instructions for the agent. Will be used as the "system prompt" when this agent is
    invoked. Describes what the agent should do, and how it responds.

    Can either be a string, or a function that dynamically generates instructions for the agent. If
    you provide a function, it will be called with the context and the agent instance. It must
    return a string.
    """

    prompt: Prompt | DynamicPromptFunction | None = None
    """A prompt object (or a function that returns a Prompt). Prompts allow you to dynamically
    configure the instructions, tools and other config for an agent outside of your code. Only
    usable with OpenAI models, using the Responses API.
    """

    handoffs: list[Agent[Any] | Handoff[TContext, Any]] = field(default_factory=list)
    """Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs,
    and the agent can choose to delegate to them if relevant. Allows for separation of concerns and
    modularity.
    """

    model: str | Model | None = None
    """The model implementation to use when invoking the LLM.

    By default, if not set, the agent will use the default model configured in
    `agents.models.get_default_model()` (currently "gpt-5.4-mini").
    """

    model_settings: ModelSettings = field(default_factory=get_default_model_settings)
    """Configures model-specific tuning parameters (e.g. temperature, top_p).

    Accepts a ``ModelSettings`` instance or a dictionary containing its fields.
    """

    input_guardrails: list[InputGuardrail[TContext]] = field(default_factory=list)
    """A list of checks that run in parallel to the agent's execution, before generating a
    response. Runs only if the agent is the first agent in the chain.
    """

    output_guardrails: list[OutputGuardrail[TContext]] = field(default_factory=list)
    """A list of checks that run on the final output of the agent, after generating a response.
    Runs only if the agent produces a final output.
    """

    output_type: type[Any] | AgentOutputSchemaBase | None = None
    """The type of the output object. If not provided, the output will be `str`. In most cases,
    you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc).
    You can customize this in two ways:
    1. If you want non-strict schemas, pass `AgentOutputSchema(MyClass, strict_json_schema=False)`.
    2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema)
       creation, subclass and pass an `AgentOutputSchemaBase` subclass.
    """

    hooks: AgentHooks[TContext] | None = None
    """A class that receives callbacks on various lifecycle events for this agent.
    """

    tool_use_behavior: (
        Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools | ToolsToFinalOutputFunction
    ) = "run_llm_again"
    """
    This lets you configure how tool use is handled.
    - "run_llm_again": The default behavior. Tools are run, and then the LLM receives the results
        and gets to respond.
    - "stop_on_first_tool": The output from the first tool call is treated as the final result.
        In other words, it isn’t sent back to the LLM for further processing but is used directly
        as the final output.
    - A StopAtTools object: The agent will stop running if any of the tools listed in
        `stop_at_tool_names` is called.
        The final output will be the output of the first matching tool call.
        The LLM does not process the result of the tool call.
    - A function: If you pass a function, it will be called with the run context and the list of
      tool results. It must return a `ToolsToFinalOutputResult`, which determines whether the tool
      calls result in a final output.

      NOTE: This configuration is specific to FunctionTools. Hosted tools, such as file search,
      web search, etc. are always processed by the LLM.
    """

    reset_tool_choice: bool = True
    """Whether to reset the tool choice to the default value after a tool has been called. Defaults
    to True. This ensures that the agent doesn't enter an infinite loop of tool usage."""

    if TYPE_CHECKING:

        def __init__(
            self,
            name: str,
            handoff_description: str | None = None,
            tools: list[Tool] = ...,
            mcp_servers: list[MCPServer] = ...,
            mcp_config: MCPConfig = ...,
            instructions: (
                str
                | Callable[
                    [RunContextWrapper[TContext], Agent[TContext]],
                    MaybeAwaitable[str],
                ]
                | None
            ) = None,
            prompt: Prompt | DynamicPromptFunction | None = None,
            handoffs: list[Agent[Any] | Handoff[TContext, Any]] = ...,
            model: str | Model | None = None,
            model_settings: ModelSettings | dict[str, Any] = ...,
            input_guardrails: list[InputGuardrail[TContext]] = ...,
            output_guardrails: list[OutputGuardrail[TContext]] = ...,
            output_type: type[Any] | AgentOutputSchemaBase | None = None,
            hooks: AgentHooks[TContext] | None = None,
            tool_use_behavior: (
                Literal["run_llm_again", "stop_on_first_tool"]
                | StopAtTools
                | ToolsToFinalOutputFunction
            ) = "run_llm_again",
            reset_tool_choice: bool = True,
        ) -> None: ...

    def __post_init__(self):
        from typing import get_origin

        if not isinstance(self.name, str):
            raise TypeError(f"Agent name must be a string, got {type(self.name).__name__}")

        if self.handoff_description is not None and not isinstance(self.handoff_description, str):
            raise TypeError(
                f"Agent handoff_description must be a string or None, "
                f"got {type(self.handoff_description).__name__}"
            )

        if not isinstance(self.tools, list):
            raise TypeError(f"Agent tools must be a list, got {type(self.tools).__name__}")

        if not isinstance(self.mcp_servers, list):
            raise TypeError(
                f"Agent mcp_servers must be a list, got {type(self.mcp_servers).__name__}"
            )

        if not isinstance(self.mcp_config, dict):
            raise TypeError(
                f"Agent mcp_config must be a dict, got {type(self.mcp_config).__name__}"
            )

        if (
            self.instructions is not None
            and not isinstance(self.instructions, str)
            and not callable(self.instructions)
        ):
            raise TypeError(
                f"Agent instructions must be a string, callable, or None, "
                f"got {type(self.instructions).__name__}"
            )

        if (
            self.prompt is not None
            and not callable(self.prompt)
            and not hasattr(self.prompt, "get")
        ):
            raise TypeError(
                f"Agent prompt must be a Prompt, DynamicPromptFunction, or None, "
                f"got {type(self.prompt).__name__}"
            )

        if not isinstance(self.handoffs, list):
            raise TypeError(f"Agent handoffs must be a list, got {type(self.handoffs).__name__}")

        if self.model is not None and not isinstance(self.model, str):
            from .models.interface import Model

            if not isinstance(self.model, Model):
                raise TypeError(
                    f"Agent model must be a string, Model, or None, got {type(self.model).__name__}"
                )

        self.model_settings = _coerce_model_settings(
            self.model_settings,
            parameter_name="Agent model_settings",
            model_settings_type=_declared_model_settings_type(type(self), "model_settings"),
        )

        if self.model is not None and self.model_settings == get_default_model_settings():
            self.model_settings = _initial_model_settings_for_model(self.model)

        if not isinstance(self.input_guardrails, list):
            raise TypeError(
                f"Agent input_guardrails must be a list, got {type(self.input_guardrails).__name__}"
            )

        if not isinstance(self.output_guardrails, list):
            raise TypeError(
                f"Agent output_guardrails must be a list, "
                f"got {type(self.output_guardrails).__name__}"
            )

        if self.output_type is not None:
            from .agent_output import AgentOutputSchemaBase

            if not (
                isinstance(self.output_type, type | AgentOutputSchemaBase)
                or get_origin(self.output_type) is not None
            ):
                raise TypeError(
                    f"Agent output_type must be a type, AgentOutputSchemaBase, or None, "
                    f"got {type(self.output_type).__name__}"
                )

        if self.hooks is not None:
            from .lifecycle import AgentHooksBase

            if not isinstance(self.hooks, AgentHooksBase):
                raise TypeError(
                    f"Agent hooks must be an AgentHooks instance or None, "
                    f"got {type(self.hooks).__name__}"
                )

        if (
            not (
                isinstance(self.tool_use_behavior, str)
                and self.tool_use_behavior in ["run_llm_again", "stop_on_first_tool"]
            )
            and not isinstance(self.tool_use_behavior, dict)
            and not callable(self.tool_use_behavior)
        ):
            raise TypeError(
                f"Agent tool_use_behavior must be 'run_llm_again', 'stop_on_first_tool', "
                f"StopAtTools dict, or callable, got {type(self.tool_use_behavior).__name__}"
            )

        if not isinstance(self.reset_tool_choice, bool):
            raise TypeError(
                f"Agent reset_tool_choice must be a boolean, "
                f"got {type(self.reset_tool_choice).__name__}"
            )

    def clone(self, **kwargs: Any) -> Agent[TContext]:
        """Make a copy of the agent, with the given arguments changed.
        Notes:
            - Uses `dataclasses.replace`, which performs a **shallow copy**.
            - Mutable attributes like `tools` and `handoffs` are shallow-copied:
              new list objects are created only if overridden, but their contents
              (tool functions and handoff objects) are shared with the original.
            - To modify these independently, pass new lists when calling `clone()`.
        Example:
            ```python
            new_agent = agent.clone(instructions="New instructions")
            ```
        """
        if (
            "model" in kwargs
            and "model_settings" not in kwargs
            and _model_settings_match_implicit_model_defaults(self.model, self.model_settings)
        ):
            kwargs["model_settings"] = _initial_model_settings_for_model(kwargs["model"])
        if "model_settings" in kwargs:
            kwargs["model_settings"] = _coerce_model_settings(
                kwargs["model_settings"],
                parameter_name="Agent model_settings",
                model_settings_type=type(self.model_settings),
                inherited_model_settings=self.model_settings,
            )
        return dataclasses.replace(self, **kwargs)

    def as_tool(
        self,
        tool_name: str | None,
        tool_description: str | None,
        custom_output_extractor: (
            Callable[[RunResult | RunResultStreaming], Awaitable[str]] | None
        ) = None,
        is_enabled: bool
        | Callable[[RunContextWrapper[Any], AgentBase[Any]], MaybeAwaitable[bool]] = True,
        on_stream: Callable[[AgentToolStreamEvent], MaybeAwaitable[None]] | None = None,
        run_config: RunConfig | dict[str, Any] | None = None,
        max_turns: int | None = None,
        hooks: RunHooks[TContext] | None = None,
        previous_response_id: str | None = None,
        conversation_id: str | None = None,
        session: Session | None = None,
        failure_error_function: ToolErrorFunction | None = default_tool_error_function,
        needs_approval: bool
        | Callable[[RunContextWrapper[Any], dict[str, Any], str], Awaitable[bool]] = False,
        parameters: type[Any] | None = None,
        input_builder: StructuredToolInputBuilder | None = None,
        include_input_schema: bool = False,
    ) -> FunctionTool:
        """Transform this agent into a tool, callable by other agents.

        This is different from handoffs in two ways:
        1. In handoffs, the new agent receives the conversation history. In this tool, the new agent
           receives generated input.
        2. In handoffs, the new agent takes over the conversation. In this tool, the new agent is
           called as a tool, and the conversation is continued by the original agent.

        Args:
            tool_name: The name of the tool. If not provided, the agent's name will be used.
            tool_description: The description of the tool, which should indicate what it does and
                when to use it.
            custom_output_extractor: A function that extracts the output from the agent. If not
                provided, the last message from the agent will be used. Nested run results expose
                `agent_tool_invocation` metadata when this agent is invoked via `as_tool()`.
            is_enabled: Whether the tool is enabled. Can be a bool or a callable that takes the run
                context and agent and returns whether the tool is enabled. Disabled tools are hidden
                from the LLM at runtime.
            on_stream: Optional callback (sync or async) to receive streaming events from the nested
                agent run. The callback receives an `AgentToolStreamEvent` containing the nested
                agent, the originating tool call (when available), and each stream event. When
                provided, the nested agent is executed in streaming mode.
            failure_error_function: If provided, generate an error message when the tool (agent) run
                fails. The message is sent to the LLM. If None, the exception is raised instead.
            needs_approval: Bool or callable to decide if this agent tool should pause for approval.
            parameters: Structured input type for the tool arguments (dataclass or Pydantic model).
            input_builder: Optional function to build the nested agent input from structured data.
            include_input_schema: Whether to include the full JSON schema in structured input.
        """

        if run_config is not None:
            from .run_config import _coerce_run_config

            run_config = _coerce_run_config(run_config)

        def _is_supported_parameters(value: Any) -> bool:
            if not isinstance(value, type):
                return False
            if dataclasses.is_dataclass(value):
                return True
            return issubclass(value, BaseModel)

        tool_name_resolved = tool_name or _transforms.transform_string_function_style(self.name)
        tool_description_resolved = tool_description or ""
        has_custom_parameters = parameters is not None
        include_schema = bool(include_input_schema and has_custom_parameters)
        should_capture_tool_input = bool(
            has_custom_parameters or include_schema or input_builder is not None
        )

        if parameters is None:
            params_adapter = TypeAdapter(AgentAsToolInput)
            params_schema = ensure_strict_json_schema(params_adapter.json_schema())
        else:
            if not _is_supported_parameters(parameters):
                raise TypeError("Agent tool parameters must be a dataclass or Pydantic model type.")
            params_adapter = TypeAdapter(parameters)
            params_schema = ensure_strict_json_schema(params_adapter.json_schema())

        schema_info = build_structured_input_schema_info(
            params_schema,
            include_json_schema=include_schema,
        )

        def _normalize_tool_input(parsed: Any, tool_name: str) -> Any:
            # Prefer JSON mode so structured params (datetime/UUID/Decimal, etc.) serialize cleanly.
            try:
                return params_adapter.dump_python(parsed, mode="json")
            except Exception as exc:
                raise ModelBehaviorError(
                    f"Failed to serialize structured tool input for {tool_name}: {exc}"
                ) from exc

        async def _run_agent_impl(context: ToolContext, input_json: str) -> Any:
            from .run import DEFAULT_MAX_TURNS, Runner
            from .tool_context import ToolContext

            tool_name = (
                context.tool_name if isinstance(context, ToolContext) else tool_name_resolved
            )
            json_data = _parse_function_tool_json_input(
                tool_name=tool_name,
                input_json=input_json,
            )
            _log_function_tool_invocation(tool_name=tool_name, input_json=input_json)

            try:
                parsed_params = params_adapter.validate_python(json_data)
            except ValidationError as exc:
                raise ModelBehaviorError(f"Invalid JSON input for tool {tool_name}: {exc}") from exc

            params_data = _normalize_tool_input(parsed_params, tool_name)
            resolved_input = await resolve_agent_tool_input(
                params=params_data,
                schema_info=schema_info if should_capture_tool_input else None,
                input_builder=input_builder,
            )
            if not isinstance(resolved_input, str) and not isinstance(resolved_input, list):
                raise ModelBehaviorError("Agent tool called with invalid input")

            resolved_max_turns = max_turns if max_turns is not None else DEFAULT_MAX_TURNS
            resolved_run_config = run_config
            if resolved_run_config is None and isinstance(context, ToolContext):
                resolved_run_config = context.run_config
            tool_state_scope_id = get_agent_tool_state_scope(context)
            if isinstance(context, ToolContext):
                # Use a fresh ToolContext to avoid sharing approval state with parent runs.
                nested_context = ToolContext(
                    context=context.context,
                    usage=context.usage,
                    tool_name=context.tool_name,
                    tool_call_id=context.tool_call_id,
                    tool_arguments=context.tool_arguments,
                    tool_call=context.tool_call,
                    tool_namespace=context.tool_namespace,
                    agent=context.agent,
                    run_config=resolved_run_config,
                )
                set_agent_tool_state_scope(nested_context, tool_state_scope_id)
                if should_capture_tool_input:
                    nested_context.tool_input = params_data
            elif isinstance(context, RunContextWrapper):
                if should_capture_tool_input:
                    nested_context = RunContextWrapper(context=context.context)
                    set_agent_tool_state_scope(nested_context, tool_state_scope_id)
                    nested_context.tool_input = params_data
                else:
                    nested_context = context.context
            else:
                if should_capture_tool_input:
                    nested_context = RunContextWrapper(context=context)
                    set_agent_tool_state_scope(nested_context, tool_state_scope_id)
                    nested_context.tool_input = params_data
                else:
                    nested_context = context
            run_result: RunResult | RunResultStreaming | None = None
            resume_state: RunState | None = None
            should_record_run_result = True

            def _nested_approvals_status(
                interruptions: list[ToolApprovalItem],
            ) -> Literal["approved", "pending", "rejected"]:
                has_pending = False
                has_decision = False
                for interruption in interruptions:
                    call_id = interruption.call_id
                    if not call_id:
                        has_pending = True
                        continue
                    tool_namespace = RunContextWrapper._resolve_tool_namespace(interruption)
                    status = context.get_approval_status(
                        interruption.tool_name or "",
                        call_id,
                        tool_namespace=tool_namespace,
                        existing_pending=interruption,
                    )
                    if status is False:
                        return "rejected"
                    if status is True:
                        has_decision = True
                    if status is None:
                        has_pending = True
                if has_decision:
                    return "approved"
                if has_pending:
                    return "pending"
                return "approved"

            def _apply_nested_approvals(
                nested_context: RunContextWrapper[Any],
                parent_context: RunContextWrapper[Any],
                interruptions: list[ToolApprovalItem],
            ) -> None:
                def _find_mirrored_approval_record(
                    interruption: ToolApprovalItem,
                    *,
                    approved: bool,
                ) -> Any | None:
                    candidate_keys = list(RunContextWrapper._resolve_approval_keys(interruption))
                    for candidate_key in get_function_tool_approval_keys(
                        tool_name=RunContextWrapper._resolve_tool_name(interruption),
                        tool_namespace=RunContextWrapper._resolve_tool_namespace(interruption),
                        tool_lookup_key=RunContextWrapper._resolve_tool_lookup_key(interruption),
                        include_legacy_deferred_key=True,
                    ):
                        if candidate_key not in candidate_keys:
                            candidate_keys.append(candidate_key)
                    fallback: Any | None = None
                    for candidate_key in candidate_keys:
                        candidate = parent_context._approvals.get(candidate_key)
                        if candidate is None:
                            continue
                        if approved and candidate.approved is True:
                            return candidate
                        if not approved and candidate.rejected is True:
                            return candidate
                        if fallback is None:
                            fallback = candidate
                    return fallback

                for interruption in interruptions:
                    call_id = interruption.call_id
                    if not call_id:
                        continue
                    tool_name = RunContextWrapper._resolve_tool_name(interruption)
                    tool_namespace = RunContextWrapper._resolve_tool_namespace(interruption)
                    approval_key = RunContextWrapper._resolve_approval_key(interruption)
                    status = parent_context.get_approval_status(
                        tool_name,
                        call_id,
                        tool_namespace=tool_namespace,
                        existing_pending=interruption,
                    )
                    if status is None:
                        continue
                    approval_record = parent_context._approvals.get(approval_key)
                    if approval_record is None:
                        approval_record = _find_mirrored_approval_record(
                            interruption,
                            approved=status,
                        )
                    if status is True:
                        always_approve = bool(approval_record and approval_record.approved is True)
                        nested_context.approve_tool(
                            interruption,
                            always_approve=always_approve,
                        )
                    else:
                        always_reject = bool(approval_record and approval_record.rejected is True)
                        nested_context.reject_tool(
                            interruption,
                            always_reject=always_reject,
                        )

            if isinstance(context, ToolContext) and context.tool_call is not None:
                pending_run_result = peek_agent_tool_run_result(
                    context.tool_call,
                    scope_id=tool_state_scope_id,
                )
                if pending_run_result and getattr(pending_run_result, "interruptions", None):
                    status = _nested_approvals_status(pending_run_result.interruptions)
                    if status == "pending":
                        run_result = pending_run_result
                        should_record_run_result = False
                    elif status in ("approved", "rejected"):
                        resume_state = pending_run_result.to_state()
                        if resume_state._context is not None:
                            # Apply only explicit parent approvals to the nested resumed run.
                            _apply_nested_approvals(
                                resume_state._context,
                                context,
                                pending_run_result.interruptions,
                            )
                        consume_agent_tool_run_result(
                            context.tool_call,
                            scope_id=tool_state_scope_id,
                        )

            if run_result is None:
                if on_stream is not None:
                    stream_handler = on_stream
                    run_result_streaming = Runner.run_streamed(
                        starting_agent=cast(Agent[Any], self),
                        input=resume_state or resolved_input,
                        context=None if resume_state is not None else cast(Any, nested_context),
                        run_config=resolved_run_config,
                        max_turns=resolved_max_turns,
                        hooks=hooks,
                        previous_response_id=None
                        if resume_state is not None
                        else previous_response_id,
                        conversation_id=None if resume_state is not None else conversation_id,
                        session=session,
                    )
                    # Dispatch callbacks in the background so slow handlers do not block
                    # event consumption.
                    event_queue: asyncio.Queue[AgentToolStreamEvent | None] = asyncio.Queue()

                    async def _run_handler(payload: AgentToolStreamEvent) -> None:
                        """Execute the user callback while capturing exceptions."""
                        try:
                            maybe_result = stream_handler(payload)
                            if inspect.isawaitable(maybe_result):
                                await maybe_result
                        except Exception as exc:

                            def diagnostic_extra() -> dict[str, object]:
                                return {"agent_name": self.name}

                            log_model_and_tool_action_error(
                                logger,
                                "Error while handling an agent tool on_stream event",
                                exc,
                                diagnostic_extra=diagnostic_extra,
                            )

                    async def dispatch_stream_events() -> None:
                        while True:
                            payload = await event_queue.get()
                            is_sentinel = payload is None  # None marks the end of the stream.
                            try:
                                if payload is not None:
                                    await _run_handler(payload)
                            finally:
                                event_queue.task_done()

                            if is_sentinel:
                                break

                    dispatch_task = asyncio.create_task(dispatch_stream_events())
                    stream_iteration_cancelled = False

                    try:
                        from .stream_events import AgentUpdatedStreamEvent

                        current_agent = run_result_streaming.current_agent
                        try:
                            async for event in run_result_streaming.stream_events():
                                if isinstance(event, AgentUpdatedStreamEvent):
                                    current_agent = event.new_agent

                                payload: AgentToolStreamEvent = {
                                    "event": event,
                                    "agent": current_agent,
                                    "tool_call": context.tool_call,
                                }
                                await event_queue.put(payload)
                        except asyncio.CancelledError:
                            stream_iteration_cancelled = True
                            raise
                    finally:
                        if stream_iteration_cancelled:
                            dispatch_task.cancel()
                            try:
                                await dispatch_task
                            except asyncio.CancelledError:
                                pass
                        else:
                            await event_queue.put(None)
                            await event_queue.join()
                            await dispatch_task
                    run_result = run_result_streaming
                else:
                    run_result = await Runner.run(
                        starting_agent=cast(Agent[Any], self),
                        input=resume_state or resolved_input,
                        context=None if resume_state is not None else cast(Any, nested_context),
                        run_config=resolved_run_config,
                        max_turns=resolved_max_turns,
                        hooks=hooks,
                        previous_response_id=None
                        if resume_state is not None
                        else previous_response_id,
                        conversation_id=None if resume_state is not None else conversation_id,
                        session=session,
                    )
            assert run_result is not None

            # Store the run result by tool call identity so nested interruptions can be read later.
            interruptions = getattr(run_result, "interruptions", None)
            if isinstance(context, ToolContext) and context.tool_call is not None and interruptions:
                if should_record_run_result:
                    record_agent_tool_run_result(
                        context.tool_call,
                        run_result,
                        scope_id=tool_state_scope_id,
                    )

            if custom_output_extractor is not None:
                return await custom_output_extractor(run_result)

            if run_result.final_output is not None and (
                not isinstance(run_result.final_output, str) or run_result.final_output != ""
            ):
                return run_result.final_output

            from .items import ItemHelpers, MessageOutputItem, ToolCallOutputItem

            for item in reversed(run_result.new_items):
                if isinstance(item, MessageOutputItem):
                    text_output = ItemHelpers.text_message_output(item)
                    if text_output:
                        return text_output

                if (
                    isinstance(item, ToolCallOutputItem)
                    and isinstance(item.output, str)
                    and item.output
                ):
                    return item.output

            return run_result.final_output

        run_agent_tool = _build_wrapped_function_tool(
            name=tool_name_resolved,
            description=tool_description_resolved,
            params_json_schema=params_schema,
            invoke_tool_impl=_run_agent_impl,
            on_handled_error=_build_handled_function_tool_error_handler(
                span_message="Error running tool (non-fatal)",
                span_message_for_json_decode_error="Error running tool",
                log_label="Tool",
            ),
            failure_error_function=failure_error_function,
            strict_json_schema=True,
            is_enabled=is_enabled,
            needs_approval=needs_approval,
            tool_origin=ToolOrigin(
                type=ToolOriginType.AGENT_AS_TOOL,
                agent_name=self.name,
                agent_tool_name=tool_name_resolved,
            ),
        )
        run_agent_tool._is_agent_tool = True
        run_agent_tool._agent_instance = self

        return run_agent_tool

    async def get_system_prompt(self, run_context: RunContextWrapper[TContext]) -> str | None:
        if isinstance(self.instructions, str):
            return self.instructions
        elif callable(self.instructions):
            # Inspect the signature of the instructions function
            sig = inspect.signature(self.instructions)
            params = list(sig.parameters.values())

            # Enforce exactly 2 parameters
            if len(params) != 2:
                raise TypeError(
                    f"'instructions' callable must accept exactly 2 arguments (context, agent), "
                    f"but got {len(params)}: {[p.name for p in params]}"
                )

            # Call once, then await if needed. Callable instances with async
            # ``__call__`` are not coroutine functions, so checking
            # ``iscoroutinefunction(self.instructions)`` would skip the await.
            result = self.instructions(run_context, self)
            if inspect.isawaitable(result):
                return await result
            return result

        elif self.instructions is not None:
            logger.error(
                "Instructions must be a string or a callable function, got %s",
                type(self.instructions).__name__,
            )

        return None

    async def get_prompt(
        self, run_context: RunContextWrapper[TContext]
    ) -> ResponsePromptParam | None:
        """Get the prompt for the agent."""
        from ._public_agent import get_public_agent

        return await PromptUtil.to_model_input(
            self.prompt,
            run_context,
            cast(Agent[TContext], get_public_agent(self)),
        )

instructions class-attribute instance-attribute

instructions: (
    str
    | Callable[
        [RunContextWrapper[TContext], Agent[TContext]],
        MaybeAwaitable[str],
    ]
    | None
) = None

The instructions for the agent. Will be used as the "system prompt" when this agent is invoked. Describes what the agent should do, and how it responds.

Can either be a string, or a function that dynamically generates instructions for the agent. If you provide a function, it will be called with the context and the agent instance. It must return a string.

prompt class-attribute instance-attribute

prompt: Prompt | DynamicPromptFunction | None = None

A prompt object (or a function that returns a Prompt). Prompts allow you to dynamically configure the instructions, tools and other config for an agent outside of your code. Only usable with OpenAI models, using the Responses API.

handoffs class-attribute instance-attribute

handoffs: list[Agent[Any] | Handoff[TContext, Any]] = field(
    default_factory=list
)

Handoffs are sub-agents that the agent can delegate to. You can provide a list of handoffs, and the agent can choose to delegate to them if relevant. Allows for separation of concerns and modularity.

model class-attribute instance-attribute

model: str | Model | None = None

The model implementation to use when invoking the LLM.

By default, if not set, the agent will use the default model configured in agents.models.get_default_model() (currently "gpt-5.4-mini").

model_settings class-attribute instance-attribute

model_settings: ModelSettings = field(
    default_factory=get_default_model_settings
)

Configures model-specific tuning parameters (e.g. temperature, top_p).

Accepts a ModelSettings instance or a dictionary containing its fields.

input_guardrails class-attribute instance-attribute

input_guardrails: list[InputGuardrail[TContext]] = field(
    default_factory=list
)

A list of checks that run in parallel to the agent's execution, before generating a response. Runs only if the agent is the first agent in the chain.

output_guardrails class-attribute instance-attribute

output_guardrails: list[OutputGuardrail[TContext]] = field(
    default_factory=list
)

A list of checks that run on the final output of the agent, after generating a response. Runs only if the agent produces a final output.

output_type class-attribute instance-attribute

output_type: type[Any] | AgentOutputSchemaBase | None = None

The type of the output object. If not provided, the output will be str. In most cases, you should pass a regular Python type (e.g. a dataclass, Pydantic model, TypedDict, etc). You can customize this in two ways: 1. If you want non-strict schemas, pass AgentOutputSchema(MyClass, strict_json_schema=False). 2. If you want to use a custom JSON schema (i.e. without using the SDK's automatic schema) creation, subclass and pass an AgentOutputSchemaBase subclass.

hooks class-attribute instance-attribute

hooks: AgentHooks[TContext] | None = None

A class that receives callbacks on various lifecycle events for this agent.

tool_use_behavior class-attribute instance-attribute

tool_use_behavior: (
    Literal["run_llm_again", "stop_on_first_tool"]
    | StopAtTools
    | ToolsToFinalOutputFunction
) = "run_llm_again"

This lets you configure how tool use is handled. - "run_llm_again": The default behavior. Tools are run, and then the LLM receives the results and gets to respond. - "stop_on_first_tool": The output from the first tool call is treated as the final result. In other words, it isn’t sent back to the LLM for further processing but is used directly as the final output. - A StopAtTools object: The agent will stop running if any of the tools listed in stop_at_tool_names is called. The final output will be the output of the first matching tool call. The LLM does not process the result of the tool call. - A function: If you pass a function, it will be called with the run context and the list of tool results. It must return a ToolsToFinalOutputResult, which determines whether the tool calls result in a final output.

NOTE: This configuration is specific to FunctionTools. Hosted tools, such as file search, web search, etc. are always processed by the LLM.

reset_tool_choice class-attribute instance-attribute

reset_tool_choice: bool = True

Whether to reset the tool choice to the default value after a tool has been called. Defaults to True. This ensures that the agent doesn't enter an infinite loop of tool usage.

name instance-attribute

name: str

The name of the agent.

handoff_description class-attribute instance-attribute

handoff_description: str | None = None

A description of the agent. This is used when the agent is used as a handoff, so that an LLM knows what it does and when to invoke it.

tools class-attribute instance-attribute

tools: list[Tool] = field(default_factory=list)

A list of tools that the agent can use.

mcp_servers class-attribute instance-attribute

mcp_servers: list[MCPServer] = field(default_factory=list)

A list of Model Context Protocol servers that the agent can use. Every time the agent runs, it will include tools from these servers in the list of available tools.

NOTE: You are expected to manage the lifecycle of these servers. Specifically, you must call server.connect() before passing it to the agent, and server.cleanup() when the server is no longer needed. Consider using MCPServerManager from agents.mcp to keep connect/cleanup in the same task.

mcp_config class-attribute instance-attribute

mcp_config: MCPConfig = field(
    default_factory=lambda: MCPConfig()
)

Configuration for MCP servers.

clone

clone(**kwargs: Any) -> Agent[TContext]

Make a copy of the agent, with the given arguments changed. Notes: - Uses dataclasses.replace, which performs a shallow copy. - Mutable attributes like tools and handoffs are shallow-copied: new list objects are created only if overridden, but their contents (tool functions and handoff objects) are shared with the original. - To modify these independently, pass new lists when calling clone(). Example:

new_agent = agent.clone(instructions="New instructions")

ソースコード位置： src/agents/agent.py

def clone(self, **kwargs: Any) -> Agent[TContext]:
    """Make a copy of the agent, with the given arguments changed.
    Notes:
        - Uses `dataclasses.replace`, which performs a **shallow copy**.
        - Mutable attributes like `tools` and `handoffs` are shallow-copied:
          new list objects are created only if overridden, but their contents
          (tool functions and handoff objects) are shared with the original.
        - To modify these independently, pass new lists when calling `clone()`.
    Example:
        ```python
        new_agent = agent.clone(instructions="New instructions")
        ```
    """
    if (
        "model" in kwargs
        and "model_settings" not in kwargs
        and _model_settings_match_implicit_model_defaults(self.model, self.model_settings)
    ):
        kwargs["model_settings"] = _initial_model_settings_for_model(kwargs["model"])
    if "model_settings" in kwargs:
        kwargs["model_settings"] = _coerce_model_settings(
            kwargs["model_settings"],
            parameter_name="Agent model_settings",
            model_settings_type=type(self.model_settings),
            inherited_model_settings=self.model_settings,
        )
    return dataclasses.replace(self, **kwargs)

as_tool

as_tool(
    tool_name: str | None,
    tool_description: str | None,
    custom_output_extractor: Callable[
        [RunResult | RunResultStreaming], Awaitable[str]
    ]
    | None = None,
    is_enabled: bool
    | Callable[
        [RunContextWrapper[Any], AgentBase[Any]],
        MaybeAwaitable[bool],
    ] = True,
    on_stream: Callable[
        [AgentToolStreamEvent], MaybeAwaitable[None]
    ]
    | None = None,
    run_config: RunConfig | dict[str, Any] | None = None,
    max_turns: int | None = None,
    hooks: RunHooks[TContext] | None = None,
    previous_response_id: str | None = None,
    conversation_id: str | None = None,
    session: Session | None = None,
    failure_error_function: ToolErrorFunction
    | None = default_tool_error_function,
    needs_approval: bool
    | Callable[
        [RunContextWrapper[Any], dict[str, Any], str],
        Awaitable[bool],
    ] = False,
    parameters: type[Any] | None = None,
    input_builder: StructuredToolInputBuilder | None = None,
    include_input_schema: bool = False,
) -> FunctionTool

Transform this agent into a tool, callable by other agents.

This is different from handoffs in two ways: 1. In handoffs, the new agent receives the conversation history. In this tool, the new agent receives generated input. 2. In handoffs, the new agent takes over the conversation. In this tool, the new agent is called as a tool, and the conversation is continued by the original agent.

引数：

名前	タイプ	デスクリプション	デフォルト
tool_name	str | None	The name of the tool. If not provided, the agent's name will be used.	必須
tool_description	str | None	The description of the tool, which should indicate what it does and when to use it.	必須
custom_output_extractor	Callable[[RunResult | RunResultStreaming], Awaitable[str]] | None	A function that extracts the output from the agent. If not provided, the last message from the agent will be used. Nested run results expose agent_tool_invocation metadata when this agent is invoked via as_tool().	None
is_enabled	bool | Callable[[RunContextWrapper[Any], AgentBase[Any]], MaybeAwaitable[bool]]	Whether the tool is enabled. Can be a bool or a callable that takes the run context and agent and returns whether the tool is enabled. Disabled tools are hidden from the LLM at runtime.	True
on_stream	Callable[[AgentToolStreamEvent], MaybeAwaitable[None]] | None	Optional callback (sync or async) to receive streaming events from the nested agent run. The callback receives an AgentToolStreamEvent containing the nested agent, the originating tool call (when available), and each stream event. When provided, the nested agent is executed in streaming mode.	None
failure_error_function	ToolErrorFunction | None	If provided, generate an error message when the tool (agent) run fails. The message is sent to the LLM. If None, the exception is raised instead.	default_tool_error_function
needs_approval	bool | Callable[[RunContextWrapper[Any], dict[str, Any], str], Awaitable[bool]]	Bool or callable to decide if this agent tool should pause for approval.	False
parameters	type[Any] | None	Structured input type for the tool arguments (dataclass or Pydantic model).	None
input_builder	StructuredToolInputBuilder | None	Optional function to build the nested agent input from structured data.	None
include_input_schema	bool	Whether to include the full JSON schema in structured input.	False

ソースコード位置： src/agents/agent.py

def as_tool(
    self,
    tool_name: str | None,
    tool_description: str | None,
    custom_output_extractor: (
        Callable[[RunResult | RunResultStreaming], Awaitable[str]] | None
    ) = None,
    is_enabled: bool
    | Callable[[RunContextWrapper[Any], AgentBase[Any]], MaybeAwaitable[bool]] = True,
    on_stream: Callable[[AgentToolStreamEvent], MaybeAwaitable[None]] | None = None,
    run_config: RunConfig | dict[str, Any] | None = None,
    max_turns: int | None = None,
    hooks: RunHooks[TContext] | None = None,
    previous_response_id: str | None = None,
    conversation_id: str | None = None,
    session: Session | None = None,
    failure_error_function: ToolErrorFunction | None = default_tool_error_function,
    needs_approval: bool
    | Callable[[RunContextWrapper[Any], dict[str, Any], str], Awaitable[bool]] = False,
    parameters: type[Any] | None = None,
    input_builder: StructuredToolInputBuilder | None = None,
    include_input_schema: bool = False,
) -> FunctionTool:
    """Transform this agent into a tool, callable by other agents.

    This is different from handoffs in two ways:
    1. In handoffs, the new agent receives the conversation history. In this tool, the new agent
       receives generated input.
    2. In handoffs, the new agent takes over the conversation. In this tool, the new agent is
       called as a tool, and the conversation is continued by the original agent.

    Args:
        tool_name: The name of the tool. If not provided, the agent's name will be used.
        tool_description: The description of the tool, which should indicate what it does and
            when to use it.
        custom_output_extractor: A function that extracts the output from the agent. If not
            provided, the last message from the agent will be used. Nested run results expose
            `agent_tool_invocation` metadata when this agent is invoked via `as_tool()`.
        is_enabled: Whether the tool is enabled. Can be a bool or a callable that takes the run
            context and agent and returns whether the tool is enabled. Disabled tools are hidden
            from the LLM at runtime.
        on_stream: Optional callback (sync or async) to receive streaming events from the nested
            agent run. The callback receives an `AgentToolStreamEvent` containing the nested
            agent, the originating tool call (when available), and each stream event. When
            provided, the nested agent is executed in streaming mode.
        failure_error_function: If provided, generate an error message when the tool (agent) run
            fails. The message is sent to the LLM. If None, the exception is raised instead.
        needs_approval: Bool or callable to decide if this agent tool should pause for approval.
        parameters: Structured input type for the tool arguments (dataclass or Pydantic model).
        input_builder: Optional function to build the nested agent input from structured data.
        include_input_schema: Whether to include the full JSON schema in structured input.
    """

    if run_config is not None:
        from .run_config import _coerce_run_config

        run_config = _coerce_run_config(run_config)

    def _is_supported_parameters(value: Any) -> bool:
        if not isinstance(value, type):
            return False
        if dataclasses.is_dataclass(value):
            return True
        return issubclass(value, BaseModel)

    tool_name_resolved = tool_name or _transforms.transform_string_function_style(self.name)
    tool_description_resolved = tool_description or ""
    has_custom_parameters = parameters is not None
    include_schema = bool(include_input_schema and has_custom_parameters)
    should_capture_tool_input = bool(
        has_custom_parameters or include_schema or input_builder is not None
    )

    if parameters is None:
        params_adapter = TypeAdapter(AgentAsToolInput)
        params_schema = ensure_strict_json_schema(params_adapter.json_schema())
    else:
        if not _is_supported_parameters(parameters):
            raise TypeError("Agent tool parameters must be a dataclass or Pydantic model type.")
        params_adapter = TypeAdapter(parameters)
        params_schema = ensure_strict_json_schema(params_adapter.json_schema())

    schema_info = build_structured_input_schema_info(
        params_schema,
        include_json_schema=include_schema,
    )

    def _normalize_tool_input(parsed: Any, tool_name: str) -> Any:
        # Prefer JSON mode so structured params (datetime/UUID/Decimal, etc.) serialize cleanly.
        try:
            return params_adapter.dump_python(parsed, mode="json")
        except Exception as exc:
            raise ModelBehaviorError(
                f"Failed to serialize structured tool input for {tool_name}: {exc}"
            ) from exc

    async def _run_agent_impl(context: ToolContext, input_json: str) -> Any:
        from .run import DEFAULT_MAX_TURNS, Runner
        from .tool_context import ToolContext

        tool_name = (
            context.tool_name if isinstance(context, ToolContext) else tool_name_resolved
        )
        json_data = _parse_function_tool_json_input(
            tool_name=tool_name,
            input_json=input_json,
        )
        _log_function_tool_invocation(tool_name=tool_name, input_json=input_json)

        try:
            parsed_params = params_adapter.validate_python(json_data)
        except ValidationError as exc:
            raise ModelBehaviorError(f"Invalid JSON input for tool {tool_name}: {exc}") from exc

        params_data = _normalize_tool_input(parsed_params, tool_name)
        resolved_input = await resolve_agent_tool_input(
            params=params_data,
            schema_info=schema_info if should_capture_tool_input else None,
            input_builder=input_builder,
        )
        if not isinstance(resolved_input, str) and not isinstance(resolved_input, list):
            raise ModelBehaviorError("Agent tool called with invalid input")

        resolved_max_turns = max_turns if max_turns is not None else DEFAULT_MAX_TURNS
        resolved_run_config = run_config
        if resolved_run_config is None and isinstance(context, ToolContext):
            resolved_run_config = context.run_config
        tool_state_scope_id = get_agent_tool_state_scope(context)
        if isinstance(context, ToolContext):
            # Use a fresh ToolContext to avoid sharing approval state with parent runs.
            nested_context = ToolContext(
                context=context.context,
                usage=context.usage,
                tool_name=context.tool_name,
                tool_call_id=context.tool_call_id,
                tool_arguments=context.tool_arguments,
                tool_call=context.tool_call,
                tool_namespace=context.tool_namespace,
                agent=context.agent,
                run_config=resolved_run_config,
            )
            set_agent_tool_state_scope(nested_context, tool_state_scope_id)
            if should_capture_tool_input:
                nested_context.tool_input = params_data
        elif isinstance(context, RunContextWrapper):
            if should_capture_tool_input:
                nested_context = RunContextWrapper(context=context.context)
                set_agent_tool_state_scope(nested_context, tool_state_scope_id)
                nested_context.tool_input = params_data
            else:
                nested_context = context.context
        else:
            if should_capture_tool_input:
                nested_context = RunContextWrapper(context=context)
                set_agent_tool_state_scope(nested_context, tool_state_scope_id)
                nested_context.tool_input = params_data
            else:
                nested_context = context
        run_result: RunResult | RunResultStreaming | None = None
        resume_state: RunState | None = None
        should_record_run_result = True

        def _nested_approvals_status(
            interruptions: list[ToolApprovalItem],
        ) -> Literal["approved", "pending", "rejected"]:
            has_pending = False
            has_decision = False
            for interruption in interruptions:
                call_id = interruption.call_id
                if not call_id:
                    has_pending = True
                    continue
                tool_namespace = RunContextWrapper._resolve_tool_namespace(interruption)
                status = context.get_approval_status(
                    interruption.tool_name or "",
                    call_id,
                    tool_namespace=tool_namespace,
                    existing_pending=interruption,
                )
                if status is False:
                    return "rejected"
                if status is True:
                    has_decision = True
                if status is None:
                    has_pending = True
            if has_decision:
                return "approved"
            if has_pending:
                return "pending"
            return "approved"

        def _apply_nested_approvals(
            nested_context: RunContextWrapper[Any],
            parent_context: RunContextWrapper[Any],
            interruptions: list[ToolApprovalItem],
        ) -> None:
            def _find_mirrored_approval_record(
                interruption: ToolApprovalItem,
                *,
                approved: bool,
            ) -> Any | None:
                candidate_keys = list(RunContextWrapper._resolve_approval_keys(interruption))
                for candidate_key in get_function_tool_approval_keys(
                    tool_name=RunContextWrapper._resolve_tool_name(interruption),
                    tool_namespace=RunContextWrapper._resolve_tool_namespace(interruption),
                    tool_lookup_key=RunContextWrapper._resolve_tool_lookup_key(interruption),
                    include_legacy_deferred_key=True,
                ):
                    if candidate_key not in candidate_keys:
                        candidate_keys.append(candidate_key)
                fallback: Any | None = None
                for candidate_key in candidate_keys:
                    candidate = parent_context._approvals.get(candidate_key)
                    if candidate is None:
                        continue
                    if approved and candidate.approved is True:
                        return candidate
                    if not approved and candidate.rejected is True:
                        return candidate
                    if fallback is None:
                        fallback = candidate
                return fallback

            for interruption in interruptions:
                call_id = interruption.call_id
                if not call_id:
                    continue
                tool_name = RunContextWrapper._resolve_tool_name(interruption)
                tool_namespace = RunContextWrapper._resolve_tool_namespace(interruption)
                approval_key = RunContextWrapper._resolve_approval_key(interruption)
                status = parent_context.get_approval_status(
                    tool_name,
                    call_id,
                    tool_namespace=tool_namespace,
                    existing_pending=interruption,
                )
                if status is None:
                    continue
                approval_record = parent_context._approvals.get(approval_key)
                if approval_record is None:
                    approval_record = _find_mirrored_approval_record(
                        interruption,
                        approved=status,
                    )
                if status is True:
                    always_approve = bool(approval_record and approval_record.approved is True)
                    nested_context.approve_tool(
                        interruption,
                        always_approve=always_approve,
                    )
                else:
                    always_reject = bool(approval_record and approval_record.rejected is True)
                    nested_context.reject_tool(
                        interruption,
                        always_reject=always_reject,
                    )

        if isinstance(context, ToolContext) and context.tool_call is not None:
            pending_run_result = peek_agent_tool_run_result(
                context.tool_call,
                scope_id=tool_state_scope_id,
            )
            if pending_run_result and getattr(pending_run_result, "interruptions", None):
                status = _nested_approvals_status(pending_run_result.interruptions)
                if status == "pending":
                    run_result = pending_run_result
                    should_record_run_result = False
                elif status in ("approved", "rejected"):
                    resume_state = pending_run_result.to_state()
                    if resume_state._context is not None:
                        # Apply only explicit parent approvals to the nested resumed run.
                        _apply_nested_approvals(
                            resume_state._context,
                            context,
                            pending_run_result.interruptions,
                        )
                    consume_agent_tool_run_result(
                        context.tool_call,
                        scope_id=tool_state_scope_id,
                    )

        if run_result is None:
            if on_stream is not None:
                stream_handler = on_stream
                run_result_streaming = Runner.run_streamed(
                    starting_agent=cast(Agent[Any], self),
                    input=resume_state or resolved_input,
                    context=None if resume_state is not None else cast(Any, nested_context),
                    run_config=resolved_run_config,
                    max_turns=resolved_max_turns,
                    hooks=hooks,
                    previous_response_id=None
                    if resume_state is not None
                    else previous_response_id,
                    conversation_id=None if resume_state is not None else conversation_id,
                    session=session,
                )
                # Dispatch callbacks in the background so slow handlers do not block
                # event consumption.
                event_queue: asyncio.Queue[AgentToolStreamEvent | None] = asyncio.Queue()

                async def _run_handler(payload: AgentToolStreamEvent) -> None:
                    """Execute the user callback while capturing exceptions."""
                    try:
                        maybe_result = stream_handler(payload)
                        if inspect.isawaitable(maybe_result):
                            await maybe_result
                    except Exception as exc:

                        def diagnostic_extra() -> dict[str, object]:
                            return {"agent_name": self.name}

                        log_model_and_tool_action_error(
                            logger,
                            "Error while handling an agent tool on_stream event",
                            exc,
                            diagnostic_extra=diagnostic_extra,
                        )

                async def dispatch_stream_events() -> None:
                    while True:
                        payload = await event_queue.get()
                        is_sentinel = payload is None  # None marks the end of the stream.
                        try:
                            if payload is not None:
                                await _run_handler(payload)
                        finally:
                            event_queue.task_done()

                        if is_sentinel:
                            break

                dispatch_task = asyncio.create_task(dispatch_stream_events())
                stream_iteration_cancelled = False

                try:
                    from .stream_events import AgentUpdatedStreamEvent

                    current_agent = run_result_streaming.current_agent
                    try:
                        async for event in run_result_streaming.stream_events():
                            if isinstance(event, AgentUpdatedStreamEvent):
                                current_agent = event.new_agent

                            payload: AgentToolStreamEvent = {
                                "event": event,
                                "agent": current_agent,
                                "tool_call": context.tool_call,
                            }
                            await event_queue.put(payload)
                    except asyncio.CancelledError:
                        stream_iteration_cancelled = True
                        raise
                finally:
                    if stream_iteration_cancelled:
                        dispatch_task.cancel()
                        try:
                            await dispatch_task
                        except asyncio.CancelledError:
                            pass
                    else:
                        await event_queue.put(None)
                        await event_queue.join()
                        await dispatch_task
                run_result = run_result_streaming
            else:
                run_result = await Runner.run(
                    starting_agent=cast(Agent[Any], self),
                    input=resume_state or resolved_input,
                    context=None if resume_state is not None else cast(Any, nested_context),
                    run_config=resolved_run_config,
                    max_turns=resolved_max_turns,
                    hooks=hooks,
                    previous_response_id=None
                    if resume_state is not None
                    else previous_response_id,
                    conversation_id=None if resume_state is not None else conversation_id,
                    session=session,
                )
        assert run_result is not None

        # Store the run result by tool call identity so nested interruptions can be read later.
        interruptions = getattr(run_result, "interruptions", None)
        if isinstance(context, ToolContext) and context.tool_call is not None and interruptions:
            if should_record_run_result:
                record_agent_tool_run_result(
                    context.tool_call,
                    run_result,
                    scope_id=tool_state_scope_id,
                )

        if custom_output_extractor is not None:
            return await custom_output_extractor(run_result)

        if run_result.final_output is not None and (
            not isinstance(run_result.final_output, str) or run_result.final_output != ""
        ):
            return run_result.final_output

        from .items import ItemHelpers, MessageOutputItem, ToolCallOutputItem

        for item in reversed(run_result.new_items):
            if isinstance(item, MessageOutputItem):
                text_output = ItemHelpers.text_message_output(item)
                if text_output:
                    return text_output

            if (
                isinstance(item, ToolCallOutputItem)
                and isinstance(item.output, str)
                and item.output
            ):
                return item.output

        return run_result.final_output

    run_agent_tool = _build_wrapped_function_tool(
        name=tool_name_resolved,
        description=tool_description_resolved,
        params_json_schema=params_schema,
        invoke_tool_impl=_run_agent_impl,
        on_handled_error=_build_handled_function_tool_error_handler(
            span_message="Error running tool (non-fatal)",
            span_message_for_json_decode_error="Error running tool",
            log_label="Tool",
        ),
        failure_error_function=failure_error_function,
        strict_json_schema=True,
        is_enabled=is_enabled,
        needs_approval=needs_approval,
        tool_origin=ToolOrigin(
            type=ToolOriginType.AGENT_AS_TOOL,
            agent_name=self.name,
            agent_tool_name=tool_name_resolved,
        ),
    )
    run_agent_tool._is_agent_tool = True
    run_agent_tool._agent_instance = self

    return run_agent_tool

get_prompt async

get_prompt(
    run_context: RunContextWrapper[TContext],
) -> ResponsePromptParam | None

Get the prompt for the agent.

ソースコード位置： src/agents/agent.py

async def get_prompt(
    self, run_context: RunContextWrapper[TContext]
) -> ResponsePromptParam | None:
    """Get the prompt for the agent."""
    from ._public_agent import get_public_agent

    return await PromptUtil.to_model_input(
        self.prompt,
        run_context,
        cast(Agent[TContext], get_public_agent(self)),
    )

get_mcp_tools async

get_mcp_tools(
    run_context: RunContextWrapper[TContext],
) -> list[Tool]

Fetches the available tools from the MCP servers.

ソースコード位置： src/agents/agent.py

async def get_mcp_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]:
    """Fetches the available tools from the MCP servers."""
    convert_schemas_to_strict = self.mcp_config.get("convert_schemas_to_strict", False)
    failure_error_function = self.mcp_config.get(
        "failure_error_function", default_tool_error_function
    )
    include_server_in_tool_names = self.mcp_config.get("include_server_in_tool_names", False)
    reserved_tool_names = (
        await self._get_mcp_tool_reserved_names(run_context)
        if include_server_in_tool_names
        else None
    )
    return await MCPUtil.get_all_function_tools(
        self.mcp_servers,
        convert_schemas_to_strict,
        run_context,
        self,
        failure_error_function=failure_error_function,
        include_server_in_tool_names=include_server_in_tool_names,
        reserved_tool_names=reserved_tool_names,
    )

get_all_tools async

get_all_tools(
    run_context: RunContextWrapper[TContext],
) -> list[Tool]

All agent tools, including MCP tools and function tools.

ソースコード位置： src/agents/agent.py

async def get_all_tools(self, run_context: RunContextWrapper[TContext]) -> list[Tool]:
    """All agent tools, including MCP tools and function tools."""
    mcp_tools = await self.get_mcp_tools(run_context)

    async def _check_tool_enabled(tool: Tool) -> bool:
        if not isinstance(tool, FunctionTool):
            return True

        attr = tool.is_enabled
        if isinstance(attr, bool):
            return attr
        res = attr(run_context, self)
        if inspect.isawaitable(res):
            return bool(await res)
        return bool(res)

    results = await gather_with_cancel(*(_check_tool_enabled(t) for t in self.tools))
    enabled: list[Tool] = [t for t, ok in zip(self.tools, results, strict=False) if ok]
    all_tools: list[Tool] = prune_orphaned_tool_search_tools([*mcp_tools, *enabled])
    _validate_codex_tool_name_collisions(all_tools)
    return all_tools