Handoffs

HandoffInputFilter module-attribute

HandoffInputFilter: TypeAlias = Callable[
    [HandoffInputData], MaybeAwaitable[HandoffInputData]
]

A function that filters the input data passed to the next agent.

HandoffHistoryMapper module-attribute

HandoffHistoryMapper: TypeAlias = Callable[
    [list[TResponseInputItem]], list[TResponseInputItem]
]

A function that maps the previous transcript to the nested summary payload.

HandoffInputData dataclass

Source code in src/agents/handoffs/__init__.py

@dataclass(frozen=True)
class HandoffInputData:
    input_history: str | tuple[TResponseInputItem, ...]
    """
    The input history before `Runner.run()` was called.
    """

    pre_handoff_items: tuple[RunItem, ...]
    """
    The items generated before the agent turn where the handoff was invoked.
    """

    new_items: tuple[RunItem, ...]
    """
    The new items generated during the current agent turn, including the item that triggered the
    handoff and the tool output message representing the response from the handoff output.
    """

    run_context: RunContextWrapper[Any] | None = None
    """
    The run context at the time the handoff was invoked. Note that, since this property was added
    later on, it is optional for backwards compatibility.
    """

    def clone(self, **kwargs: Any) -> HandoffInputData:
        """
        Make a copy of the handoff input data, with the given arguments changed. For example, you
        could do:

        ```
        new_handoff_input_data = handoff_input_data.clone(new_items=())
        ```
        """

        return dataclasses_replace(self, **kwargs)

input_history instance-attribute

input_history: str | tuple[TResponseInputItem, ...]

The input history before Runner.run() was called.

pre_handoff_items instance-attribute

pre_handoff_items: tuple[RunItem, ...]

The items generated before the agent turn where the handoff was invoked.

new_items instance-attribute

new_items: tuple[RunItem, ...]

The new items generated during the current agent turn, including the item that triggered the handoff and the tool output message representing the response from the handoff output.

run_context class-attribute instance-attribute

run_context: RunContextWrapper[Any] | None = None

The run context at the time the handoff was invoked. Note that, since this property was added later on, it is optional for backwards compatibility.

clone

clone(**kwargs: Any) -> HandoffInputData

Make a copy of the handoff input data, with the given arguments changed. For example, you could do:

new_handoff_input_data = handoff_input_data.clone(new_items=())

Source code in src/agents/handoffs/__init__.py

def clone(self, **kwargs: Any) -> HandoffInputData:
    """
    Make a copy of the handoff input data, with the given arguments changed. For example, you
    could do:

    ```
    new_handoff_input_data = handoff_input_data.clone(new_items=())
    ```
    """

    return dataclasses_replace(self, **kwargs)

Handoff dataclass

Bases: Generic[TContext, TAgent]

A handoff is when an agent delegates a task to another agent.

For example, in a customer support scenario you might have a "triage agent" that determines which agent should handle the user's request, and sub-agents that specialize in different areas like billing, account management, etc.

Source code in src/agents/handoffs/__init__.py

@dataclass
class Handoff(Generic[TContext, TAgent]):
    """A handoff is when an agent delegates a task to another agent.

    For example, in a customer support scenario you might have a "triage agent" that determines
    which agent should handle the user's request, and sub-agents that specialize in different areas
    like billing, account management, etc.
    """

    tool_name: str
    """The name of the tool that represents the handoff."""

    tool_description: str
    """The description of the tool that represents the handoff."""

    input_json_schema: dict[str, Any]
    """The JSON schema for the handoff input. Can be empty if the handoff does not take an input."""

    on_invoke_handoff: Callable[[RunContextWrapper[Any], str], Awaitable[TAgent]]
    """The function that invokes the handoff.

    The parameters passed are: (1) the handoff run context, (2) the arguments from the LLM as a
    JSON string (or an empty string if ``input_json_schema`` is empty). Must return an agent.
    """

    agent_name: str
    """The name of the agent that is being handed off to."""

    input_filter: HandoffInputFilter | None = None
    """A function that filters the inputs that are passed to the next agent.

    By default, the new agent sees the entire conversation history. In some cases, you may want to
    filter inputs (for example, to remove older inputs or remove tools from existing inputs). The
    function receives the entire conversation history so far, including the input item that
    triggered the handoff and a tool call output item representing the handoff tool's output. You
    are free to modify the input history or new items as you see fit. The next agent that runs will
    receive ``handoff_input_data.all_items``. IMPORTANT: in streaming mode, we will not stream
    anything as a result of this function. The items generated before will already have been
    streamed.
    """

    nest_handoff_history: bool | None = None
    """Override the run-level ``nest_handoff_history`` behavior for this handoff only."""

    strict_json_schema: bool = True
    """Whether the input JSON schema is in strict mode. We strongly recommend setting this to True
    because it increases the likelihood of correct JSON input."""

    is_enabled: bool | Callable[[RunContextWrapper[Any], AgentBase[Any]], MaybeAwaitable[bool]] = (
        True
    )
    """Whether the handoff is enabled.

    Either a bool or a callable that takes the run context and agent and returns whether the
    handoff is enabled. You can use this to dynamically enable or disable a handoff based on your
    context or state.
    """

    def get_transfer_message(self, agent: AgentBase[Any]) -> str:
        return json.dumps({"assistant": agent.name})

    @classmethod
    def default_tool_name(cls, agent: AgentBase[Any]) -> str:
        return _transforms.transform_string_function_style(f"transfer_to_{agent.name}")

    @classmethod
    def default_tool_description(cls, agent: AgentBase[Any]) -> str:
        return (
            f"Handoff to the {agent.name} agent to handle the request. "
            f"{agent.handoff_description or ''}"
        )

tool_name instance-attribute

tool_name: str

The name of the tool that represents the handoff.

tool_description instance-attribute

tool_description: str

The description of the tool that represents the handoff.

input_json_schema instance-attribute

input_json_schema: dict[str, Any]

The JSON schema for the handoff input. Can be empty if the handoff does not take an input.

on_invoke_handoff instance-attribute

on_invoke_handoff: Callable[
    [RunContextWrapper[Any], str], Awaitable[TAgent]
]

The function that invokes the handoff.

The parameters passed are: (1) the handoff run context, (2) the arguments from the LLM as a JSON string (or an empty string if input_json_schema is empty). Must return an agent.

agent_name instance-attribute

agent_name: str

The name of the agent that is being handed off to.

input_filter class-attribute instance-attribute

input_filter: HandoffInputFilter | None = None

A function that filters the inputs that are passed to the next agent.

By default, the new agent sees the entire conversation history. In some cases, you may want to filter inputs (for example, to remove older inputs or remove tools from existing inputs). The function receives the entire conversation history so far, including the input item that triggered the handoff and a tool call output item representing the handoff tool's output. You are free to modify the input history or new items as you see fit. The next agent that runs will receive handoff_input_data.all_items. IMPORTANT: in streaming mode, we will not stream anything as a result of this function. The items generated before will already have been streamed.

nest_handoff_history class-attribute instance-attribute

nest_handoff_history: bool | None = None

Override the run-level nest_handoff_history behavior for this handoff only.

strict_json_schema class-attribute instance-attribute

strict_json_schema: bool = True

Whether the input JSON schema is in strict mode. We strongly recommend setting this to True because it increases the likelihood of correct JSON input.

is_enabled class-attribute instance-attribute

is_enabled: (
    bool
    | Callable[
        [RunContextWrapper[Any], AgentBase[Any]],
        MaybeAwaitable[bool],
    ]
) = True

Whether the handoff is enabled.

Either a bool or a callable that takes the run context and agent and returns whether the handoff is enabled. You can use this to dynamically enable or disable a handoff based on your context or state.

default_handoff_history_mapper

default_handoff_history_mapper(
    transcript: list[TResponseInputItem],
) -> list[TResponseInputItem]

Return a single assistant message summarizing the transcript.

Source code in src/agents/handoffs/history.py

def default_handoff_history_mapper(
    transcript: list[TResponseInputItem],
) -> list[TResponseInputItem]:
    """Return a single assistant message summarizing the transcript."""

    summary_message = _build_summary_message(transcript)
    return [summary_message]

get_conversation_history_wrappers

get_conversation_history_wrappers() -> tuple[str, str]

Return the current start/end markers used for the nested conversation summary.

Source code in src/agents/handoffs/history.py

def get_conversation_history_wrappers() -> tuple[str, str]:
    """Return the current start/end markers used for the nested conversation summary."""

    return (_conversation_history_start, _conversation_history_end)

nest_handoff_history

nest_handoff_history(
    handoff_input_data: HandoffInputData,
    *,
    history_mapper: HandoffHistoryMapper | None = None,
) -> HandoffInputData

Summarize the previous transcript for the next agent.

Source code in src/agents/handoffs/history.py

def nest_handoff_history(
    handoff_input_data: HandoffInputData,
    *,
    history_mapper: HandoffHistoryMapper | None = None,
) -> HandoffInputData:
    """Summarize the previous transcript for the next agent."""

    normalized_history = _normalize_input_history(handoff_input_data.input_history)
    flattened_history = _flatten_nested_history_messages(normalized_history)
    pre_items_as_inputs = [
        _run_item_to_plain_input(item) for item in handoff_input_data.pre_handoff_items
    ]
    new_items_as_inputs = [_run_item_to_plain_input(item) for item in handoff_input_data.new_items]
    transcript = flattened_history + pre_items_as_inputs + new_items_as_inputs

    mapper = history_mapper or default_handoff_history_mapper
    history_items = mapper(transcript)
    filtered_pre_items = tuple(
        item
        for item in handoff_input_data.pre_handoff_items
        if _get_run_item_role(item) != "assistant"
    )

    return handoff_input_data.clone(
        input_history=tuple(deepcopy(item) for item in history_items),
        pre_handoff_items=filtered_pre_items,
    )

reset_conversation_history_wrappers

reset_conversation_history_wrappers() -> None

Restore the default <CONVERSATION HISTORY> markers.

Source code in src/agents/handoffs/history.py

def reset_conversation_history_wrappers() -> None:
    """Restore the default ``<CONVERSATION HISTORY>`` markers."""

    global _conversation_history_start, _conversation_history_end
    _conversation_history_start = _DEFAULT_CONVERSATION_HISTORY_START
    _conversation_history_end = _DEFAULT_CONVERSATION_HISTORY_END

set_conversation_history_wrappers

set_conversation_history_wrappers(
    *, start: str | None = None, end: str | None = None
) -> None

Override the markers that wrap the generated conversation summary.

Pass None to leave either side unchanged.

Source code in src/agents/handoffs/history.py

def set_conversation_history_wrappers(
    *,
    start: str | None = None,
    end: str | None = None,
) -> None:
    """Override the markers that wrap the generated conversation summary.

    Pass ``None`` to leave either side unchanged.
    """

    global _conversation_history_start, _conversation_history_end
    if start is not None:
        _conversation_history_start = start
    if end is not None:
        _conversation_history_end = end

handoff

handoff(
    agent: Agent[TContext],
    *,
    tool_name_override: str | None = None,
    tool_description_override: str | None = None,
    input_filter: Callable[
        [HandoffInputData], HandoffInputData
    ]
    | None = None,
    nest_handoff_history: bool | None = None,
    is_enabled: bool
    | Callable[
        [RunContextWrapper[Any], Agent[Any]],
        MaybeAwaitable[bool],
    ] = True,
) -> Handoff[TContext, Agent[TContext]]

handoff(
    agent: Agent[TContext],
    *,
    on_handoff: OnHandoffWithInput[THandoffInput],
    input_type: type[THandoffInput],
    tool_description_override: str | None = None,
    tool_name_override: str | None = None,
    input_filter: Callable[
        [HandoffInputData], HandoffInputData
    ]
    | None = None,
    nest_handoff_history: bool | None = None,
    is_enabled: bool
    | Callable[
        [RunContextWrapper[Any], Agent[Any]],
        MaybeAwaitable[bool],
    ] = True,
) -> Handoff[TContext, Agent[TContext]]

handoff(
    agent: Agent[TContext],
    *,
    on_handoff: OnHandoffWithoutInput,
    tool_description_override: str | None = None,
    tool_name_override: str | None = None,
    input_filter: Callable[
        [HandoffInputData], HandoffInputData
    ]
    | None = None,
    nest_handoff_history: bool | None = None,
    is_enabled: bool
    | Callable[
        [RunContextWrapper[Any], Agent[Any]],
        MaybeAwaitable[bool],
    ] = True,
) -> Handoff[TContext, Agent[TContext]]

handoff(
    agent: Agent[TContext],
    tool_name_override: str | None = None,
    tool_description_override: str | None = None,
    on_handoff: OnHandoffWithInput[THandoffInput]
    | OnHandoffWithoutInput
    | None = None,
    input_type: type[THandoffInput] | None = None,
    input_filter: Callable[
        [HandoffInputData], HandoffInputData
    ]
    | None = None,
    nest_handoff_history: bool | None = None,
    is_enabled: bool
    | Callable[
        [RunContextWrapper[Any], Agent[TContext]],
        MaybeAwaitable[bool],
    ] = True,
) -> Handoff[TContext, Agent[TContext]]

Create a handoff from an agent.

Parameters:

Name	Type	Description	Default
agent	Agent[TContext]	The agent to handoff to.	required
tool_name_override	str | None	Optional override for the name of the tool that represents the handoff.	None
tool_description_override	str | None	Optional override for the description of the tool that represents the handoff.	None
on_handoff	OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None	A function that runs when the handoff is invoked.	None
input_type	type[THandoffInput] | None	The type of the input to the handoff. If provided, the input will be validated against this type. Only relevant if you pass a function that takes an input.	None
input_filter	Callable[[HandoffInputData], HandoffInputData] | None	A function that filters the inputs that are passed to the next agent.	None
nest_handoff_history	bool | None	Optional override for the RunConfig-level nest_handoff_history flag. If None we fall back to the run's configuration.	None
is_enabled	bool | Callable[[RunContextWrapper[Any], Agent[TContext]], MaybeAwaitable[bool]]	Whether the handoff is enabled. Can be a bool or a callable that takes the run context and agent and returns whether the handoff is enabled. Disabled handoffs are hidden from the LLM at runtime.	True

Source code in src/agents/handoffs/__init__.py

def handoff(
    agent: Agent[TContext],
    tool_name_override: str | None = None,
    tool_description_override: str | None = None,
    on_handoff: OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None = None,
    input_type: type[THandoffInput] | None = None,
    input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None,
    nest_handoff_history: bool | None = None,
    is_enabled: bool
    | Callable[[RunContextWrapper[Any], Agent[TContext]], MaybeAwaitable[bool]] = True,
) -> Handoff[TContext, Agent[TContext]]:
    """Create a handoff from an agent.

    Args:
        agent: The agent to handoff to.
        tool_name_override: Optional override for the name of the tool that represents the handoff.
        tool_description_override: Optional override for the description of the tool that
            represents the handoff.
        on_handoff: A function that runs when the handoff is invoked.
        input_type: The type of the input to the handoff. If provided, the input will be validated
            against this type. Only relevant if you pass a function that takes an input.
        input_filter: A function that filters the inputs that are passed to the next agent.
        nest_handoff_history: Optional override for the RunConfig-level ``nest_handoff_history``
            flag. If ``None`` we fall back to the run's configuration.
        is_enabled: Whether the handoff is enabled. Can be a bool or a callable that takes the run
            context and agent and returns whether the handoff is enabled. Disabled handoffs are
            hidden from the LLM at runtime.
    """

    assert (on_handoff and input_type) or not (on_handoff and input_type), (
        "You must provide either both on_handoff and input_type, or neither"
    )
    type_adapter: TypeAdapter[Any] | None
    if input_type is not None:
        assert callable(on_handoff), "on_handoff must be callable"
        sig = inspect.signature(on_handoff)
        if len(sig.parameters) != 2:
            raise UserError("on_handoff must take two arguments: context and input")

        type_adapter = TypeAdapter(input_type)
        input_json_schema = type_adapter.json_schema()
    else:
        type_adapter = None
        input_json_schema = {}
        if on_handoff is not None:
            sig = inspect.signature(on_handoff)
            if len(sig.parameters) != 1:
                raise UserError("on_handoff must take one argument: context")

    async def _invoke_handoff(
        ctx: RunContextWrapper[Any], input_json: str | None = None
    ) -> Agent[TContext]:
        if input_type is not None and type_adapter is not None:
            if input_json is None:
                _error_tracing.attach_error_to_current_span(
                    SpanError(
                        message="Handoff function expected non-null input, but got None",
                        data={"details": "input_json is None"},
                    )
                )
                raise ModelBehaviorError("Handoff function expected non-null input, but got None")

            validated_input = _json.validate_json(
                json_str=input_json,
                type_adapter=type_adapter,
                partial=False,
            )
            input_func = cast(OnHandoffWithInput[THandoffInput], on_handoff)
            if inspect.iscoroutinefunction(input_func):
                await input_func(ctx, validated_input)
            else:
                input_func(ctx, validated_input)
        elif on_handoff is not None:
            no_input_func = cast(OnHandoffWithoutInput, on_handoff)
            if inspect.iscoroutinefunction(no_input_func):
                await no_input_func(ctx)
            else:
                no_input_func(ctx)

        return agent

    tool_name = tool_name_override or Handoff.default_tool_name(agent)
    tool_description = tool_description_override or Handoff.default_tool_description(agent)

    # Always ensure the input JSON schema is in strict mode. If needed, we can make this
    # configurable in the future.
    input_json_schema = ensure_strict_json_schema(input_json_schema)

    async def _is_enabled(ctx: RunContextWrapper[Any], agent_base: AgentBase[Any]) -> bool:
        from ..agent import Agent

        assert callable(is_enabled), "is_enabled must be callable here"
        assert isinstance(agent_base, Agent), "Can't handoff to a non-Agent"
        result = is_enabled(ctx, agent_base)
        if inspect.isawaitable(result):
            return await result
        return bool(result)

    return Handoff(
        tool_name=tool_name,
        tool_description=tool_description,
        input_json_schema=input_json_schema,
        on_invoke_handoff=_invoke_handoff,
        input_filter=input_filter,
        nest_handoff_history=nest_handoff_history,
        agent_name=agent.name,
        is_enabled=_is_enabled if callable(is_enabled) else is_enabled,
    )