autogen_agentchat.teams#

This module provides implementation of various pre-defined multi-agent teams. Each team inherits from the BaseGroupChat class.

class BaseGroupChat(participants: List[ChatAgent], group_chat_manager_name: str, group_chat_manager_class: type[SequentialRoutedAgent], termination_condition: TerminationCondition | None = None, max_turns: int | None = None, runtime: AgentRuntime | None = None, custom_message_types: List[type[BaseAgentEvent | BaseChatMessage]] | None = None)[source]#

Bases: Team, ABC, ComponentBase[BaseModel]

The base class for group chat teams.

To implement a group chat team, first create a subclass of BaseGroupChatManager and then create a subclass of BaseGroupChat that uses the group chat manager.

component_type: ClassVar[ComponentType] = 'team'#: The logical type of the component.

async load_state(state: Mapping[str, Any]) → None[source]#

Load an external state and overwrite the current state of the group chat team.

The state is loaded by calling the agent_load_state() method on each participant and the group chat manager with their internal agent ID. See save_state() for the expected format of the state.

async pause() → None[source]#

Pause its participants when the team is running by calling their on_pause() method via direct RPC calls.

Attention

This is an experimental feature introduced in v0.4.9 and may subject to change or removal in the future.

The team must be initialized before it can be paused.

Different from termination, pausing the team does not cause the run() or run_stream() method to return. It calls the on_pause() method on each participant, and if the participant does not implement the method, it will be a no-op.

Note

It is the responsibility of the agent class to handle the pause and ensure that the agent can be resumed later. Make sure to implement the on_pause() method in your agent class for custom pause behavior. By default, the agent will not do anything when called.

Raises:: RuntimeError – If the team has not been initialized. Exceptions from the participants when calling their implementations of on_pause are propagated to this method and raised.

async reset() → None[source]#

Reset the team and its participants to their initial state.

The team must be stopped before it can be reset.

Raises:: RuntimeError – If the team has not been initialized or is currently running.

Example using the RoundRobinGroupChat team:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent("Assistant1", model_client=model_client)
    agent2 = AssistantAgent("Assistant2", model_client=model_client)
    termination = MaxMessageTermination(3)
    team = RoundRobinGroupChat([agent1, agent2], termination_condition=termination)
    stream = team.run_stream(task="Count from 1 to 10, respond one at a time.")
    async for message in stream:
        print(message)

    # Reset the team.
    await team.reset()
    stream = team.run_stream(task="Count from 1 to 10, respond one at a time.")
    async for message in stream:
        print(message)


asyncio.run(main())

async resume() → None[source]#

Resume its participants when the team is running and paused by calling their on_resume() method via direct RPC calls.

Attention

This is an experimental feature introduced in v0.4.9 and may subject to change or removal in the future.

The team must be initialized before it can be resumed.

Different from termination and restart with a new task, resuming the team does not cause the run() or run_stream() method to return. It calls the on_resume() method on each participant, and if the participant does not implement the method, it will be a no-op.

Note

It is the responsibility of the agent class to handle the resume and ensure that the agent continues from where it was paused. Make sure to implement the on_resume() method in your agent class for custom resume behavior.

Raises:: RuntimeError – If the team has not been initialized. Exceptions from the participants when calling their implementations of on_resume method are propagated to this method and raised.

async run(*, task: str | BaseChatMessage | Sequence[BaseChatMessage] | None = None, cancellation_token: CancellationToken | None = None) → TaskResult[source]#

Run the team and return the result. The base implementation uses run_stream() to run the team and then returns the final result. Once the team is stopped, the termination condition is reset.

Parameters:

task (str | BaseChatMessage | Sequence[BaseChatMessage] | None) – The task to run the team with. Can be a string, a single BaseChatMessage , or a list of BaseChatMessage.
cancellation_token (CancellationToken | None) – The cancellation token to kill the task immediately. Setting the cancellation token potentially put the team in an inconsistent state, and it may not reset the termination condition. To gracefully stop the team, use ExternalTermination instead.

Returns:

result – The result of the task as TaskResult. The result contains the messages produced by the team and the stop reason.

Example using the RoundRobinGroupChat team:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent("Assistant1", model_client=model_client)
    agent2 = AssistantAgent("Assistant2", model_client=model_client)
    termination = MaxMessageTermination(3)
    team = RoundRobinGroupChat([agent1, agent2], termination_condition=termination)

    result = await team.run(task="Count from 1 to 10, respond one at a time.")
    print(result)

    # Run the team again without a task to continue the previous task.
    result = await team.run()
    print(result)


asyncio.run(main())

Example using the CancellationToken to cancel the task:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_core import CancellationToken
from autogen_ext.models.openai import OpenAIChatCompletionClient


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent("Assistant1", model_client=model_client)
    agent2 = AssistantAgent("Assistant2", model_client=model_client)
    termination = MaxMessageTermination(3)
    team = RoundRobinGroupChat([agent1, agent2], termination_condition=termination)

    cancellation_token = CancellationToken()

    # Create a task to run the team in the background.
    run_task = asyncio.create_task(
        team.run(
            task="Count from 1 to 10, respond one at a time.",
            cancellation_token=cancellation_token,
        )
    )

    # Wait for 1 second and then cancel the task.
    await asyncio.sleep(1)
    cancellation_token.cancel()

    # This will raise a cancellation error.
    await run_task


asyncio.run(main())

Run the team and produces a stream of messages and the final result of the type TaskResult as the last item in the stream. Once the team is stopped, the termination condition is reset.

Note

If an agent produces ModelClientStreamingChunkEvent, the message will be yielded in the stream but it will not be included in the messages.

Parameters:

task (str | BaseChatMessage | Sequence[BaseChatMessage] | None) – The task to run the team with. Can be a string, a single BaseChatMessage , or a list of BaseChatMessage.
cancellation_token (CancellationToken | None) – The cancellation token to kill the task immediately. Setting the cancellation token potentially put the team in an inconsistent state, and it may not reset the termination condition. To gracefully stop the team, use ExternalTermination instead.

Returns:

stream – an AsyncGenerator that yields BaseAgentEvent, BaseChatMessage, and the final result TaskResult as the last item in the stream.

Example using the RoundRobinGroupChat team:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent("Assistant1", model_client=model_client)
    agent2 = AssistantAgent("Assistant2", model_client=model_client)
    termination = MaxMessageTermination(3)
    team = RoundRobinGroupChat([agent1, agent2], termination_condition=termination)

    stream = team.run_stream(task="Count from 1 to 10, respond one at a time.")
    async for message in stream:
        print(message)

    # Run the team again without a task to continue the previous task.
    stream = team.run_stream()
    async for message in stream:
        print(message)


asyncio.run(main())

Example using the CancellationToken to cancel the task:

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.ui import Console
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_core import CancellationToken
from autogen_ext.models.openai import OpenAIChatCompletionClient


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent("Assistant1", model_client=model_client)
    agent2 = AssistantAgent("Assistant2", model_client=model_client)
    termination = MaxMessageTermination(3)
    team = RoundRobinGroupChat([agent1, agent2], termination_condition=termination)

    cancellation_token = CancellationToken()

    # Create a task to run the team in the background.
    run_task = asyncio.create_task(
        Console(
            team.run_stream(
                task="Count from 1 to 10, respond one at a time.",
                cancellation_token=cancellation_token,
            )
        )
    )

    # Wait for 1 second and then cancel the task.
    await asyncio.sleep(1)
    cancellation_token.cancel()

    # This will raise a cancellation error.
    await run_task


asyncio.run(main())

async save_state() → Mapping[str, Any][source]#

Save the state of the group chat team.

The state is saved by calling the agent_save_state() method on each participant and the group chat manager with their internal agent ID. The state is returned as a nested dictionary: a dictionary with key agent_states, which is a dictionary the agent names as keys and the state as values.

{
    "agent_states": {
        "agent1": ...,
        "agent2": ...,
        "RoundRobinGroupChatManager": ...
    }
}

Note

Starting v0.4.9, the state is using the agent name as the key instead of the agent ID, and the team_id field is removed from the state. This is to allow the state to be portable across different teams and runtimes. States saved with the old format may not be compatible with the new format in the future.

Caution

When calling save_state() on a team while it is running, the state may not be consistent and may result in an unexpected state. It is recommended to call this method when the team is not running or after it is stopped.

class MagenticOneGroupChat(participants: List[ChatAgent], model_client: ChatCompletionClient, *, termination_condition: TerminationCondition | None = None, max_turns: int | None = 20, runtime: AgentRuntime | None = None, max_stalls: int = 3, final_answer_prompt: str = ORCHESTRATOR_FINAL_ANSWER_PROMPT)[source]#

Bases: BaseGroupChat, Component[MagenticOneGroupChatConfig]

A team that runs a group chat with participants managed by the MagenticOneOrchestrator.

The orchestrator handles the conversation flow, ensuring that the task is completed efficiently by managing the participants’ interactions.

The orchestrator is based on the Magentic-One architecture, which is a generalist multi-agent system for solving complex tasks (see references below).

Parameters:

participants (List[ChatAgent]) – The participants in the group chat.
model_client (ChatCompletionClient) – The model client used for generating responses.
termination_condition (TerminationCondition, optional) – The termination condition for the group chat. Defaults to None. Without a termination condition, the group chat will run based on the orchestrator logic or until the maximum number of turns is reached.
max_turns (int, optional) – The maximum number of turns in the group chat before stopping. Defaults to 20.
max_stalls (int, optional) – The maximum number of stalls allowed before re-planning. Defaults to 3.
final_answer_prompt (str, optional) – The LLM prompt used to generate the final answer or response from the team’s transcript. A default (sensible for GPT-4o class models) is provided.

Raises:

ValueError – In orchestration logic if progress ledger does not have required keys or if next speaker is not valid.

Examples:

MagenticOneGroupChat with one assistant agent:

import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import MagenticOneGroupChat
from autogen_agentchat.ui import Console


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    assistant = AssistantAgent(
        "Assistant",
        model_client=model_client,
    )
    team = MagenticOneGroupChat([assistant], model_client=model_client)
    await Console(team.run_stream(task="Provide a different proof to Fermat last theorem"))


asyncio.run(main())

References

If you use the MagenticOneGroupChat in your work, please cite the following paper:

@article{fourney2024magentic,
    title={Magentic-one: A generalist multi-agent system for solving complex tasks},
    author={Fourney, Adam and Bansal, Gagan and Mozannar, Hussein and Tan, Cheng and Salinas, Eduardo and Niedtner, Friederike and Proebsting, Grace and Bassman, Griffin and Gerrits, Jack and Alber, Jacob and others},
    journal={arXiv preprint arXiv:2411.04468},
    year={2024}
}

classmethod _from_config(config: MagenticOneGroupChatConfig) → Self[source]#

Create a new instance of the component from a configuration object.

Parameters:: config (T) – The configuration object.
Returns:: Self – The new instance of the component.

_to_config() → MagenticOneGroupChatConfig[source]#

Dump the configuration that would be requite to create a new instance of a component matching the configuration of this instance.

Returns:: T – The configuration of the component.

component_config_schema#: alias of MagenticOneGroupChatConfig

component_provider_override: ClassVar[str | None] = 'autogen_agentchat.teams.MagenticOneGroupChat'#: Override the provider string for the component. This should be used to prevent internal module names being a part of the module name.

class RoundRobinGroupChat(participants: List[ChatAgent], termination_condition: TerminationCondition | None = None, max_turns: int | None = None, runtime: AgentRuntime | None = None, custom_message_types: List[type[BaseAgentEvent | BaseChatMessage]] | None = None)[source]#

Bases: BaseGroupChat, Component[RoundRobinGroupChatConfig]

A team that runs a group chat with participants taking turns in a round-robin fashion to publish a message to all.

If a single participant is in the team, the participant will be the only speaker.

Parameters:

participants (List[BaseChatAgent]) – The participants in the group chat.
termination_condition (TerminationCondition, optional) – The termination condition for the group chat. Defaults to None. Without a termination condition, the group chat will run indefinitely.
max_turns (int, optional) – The maximum number of turns in the group chat before stopping. Defaults to None, meaning no limit.

Raises:

ValueError – If no participants are provided or if participant names are not unique.

Examples:

A team with one participant with tools:

import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.ui import Console


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    async def get_weather(location: str) -> str:
        return f"The weather in {location} is sunny."

    assistant = AssistantAgent(
        "Assistant",
        model_client=model_client,
        tools=[get_weather],
    )
    termination = TextMentionTermination("TERMINATE")
    team = RoundRobinGroupChat([assistant], termination_condition=termination)
    await Console(team.run_stream(task="What's the weather in New York?"))


asyncio.run(main())

A team with multiple participants:

import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.ui import Console


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent("Assistant1", model_client=model_client)
    agent2 = AssistantAgent("Assistant2", model_client=model_client)
    termination = TextMentionTermination("TERMINATE")
    team = RoundRobinGroupChat([agent1, agent2], termination_condition=termination)
    await Console(team.run_stream(task="Tell me some jokes."))


asyncio.run(main())

classmethod _from_config(config: RoundRobinGroupChatConfig) → Self[source]#

Create a new instance of the component from a configuration object.

Parameters:: config (T) – The configuration object.
Returns:: Self – The new instance of the component.

_to_config() → RoundRobinGroupChatConfig[source]#

Dump the configuration that would be requite to create a new instance of a component matching the configuration of this instance.

Returns:: T – The configuration of the component.

component_config_schema#: alias of RoundRobinGroupChatConfig

component_provider_override: ClassVar[str | None] = 'autogen_agentchat.teams.RoundRobinGroupChat'#: Override the provider string for the component. This should be used to prevent internal module names being a part of the module name.

class SelectorGroupChat(participants: List[ChatAgent], model_client: ChatCompletionClient, *, termination_condition: TerminationCondition | None = None, max_turns: int | None = None, runtime: AgentRuntime | None = None, selector_prompt: str = 'You are in a role play game. The following roles are available:\n{roles}.\nRead the following conversation. Then select the next role from {participants} to play. Only return the role.\n\n{history}\n\nRead the above conversation. Then select the next role from {participants} to play. Only return the role.\n', allow_repeated_speaker: bool = False, max_selector_attempts: int = 3, selector_func: Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], str | None] | Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], Awaitable[str | None]] | None = None, candidate_func: Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], List[str]] | Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], Awaitable[List[str]]] | None = None, custom_message_types: List[type[BaseAgentEvent | BaseChatMessage]] | None = None)[source]#

Bases: BaseGroupChat, Component[SelectorGroupChatConfig]

A group chat team that have participants takes turn to publish a message to all, using a ChatCompletion model to select the next speaker after each message.

Parameters:

participants (List[ChatAgent]) – The participants in the group chat, must have unique names and at least two participants.
model_client (ChatCompletionClient) – The ChatCompletion model client used to select the next speaker.
termination_condition (TerminationCondition, optional) – The termination condition for the group chat. Defaults to None. Without a termination condition, the group chat will run indefinitely.
max_turns (int, optional) – The maximum number of turns in the group chat before stopping. Defaults to None, meaning no limit.
selector_prompt (str, optional) – The prompt template to use for selecting the next speaker. Available fields: ‘{roles}’, ‘{participants}’, and ‘{history}’.
allow_repeated_speaker (bool, optional) – Whether to include the previous speaker in the list of candidates to be selected for the next turn. Defaults to False. The model may still select the previous speaker – a warning will be logged if this happens.
max_selector_attempts (int, optional) – The maximum number of attempts to select a speaker using the model. Defaults to 3. If the model fails to select a speaker after the maximum number of attempts, the previous speaker will be used if available, otherwise the first participant will be used.
selector_func (Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], str | None], Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], Awaitable[str | None]], optional) – A custom selector function that takes the conversation history and returns the name of the next speaker. If provided, this function will be used to override the model to select the next speaker. If the function returns None, the model will be used to select the next speaker.
candidate_func (Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], List[str]], Callable[[Sequence[BaseAgentEvent | BaseChatMessage]], Awaitable[List[str]]], optional) – A custom function that takes the conversation history and returns a filtered list of candidates for the next speaker selection using model. If the function returns an empty list or None, SelectorGroupChat will raise a ValueError. This function is only used if selector_func is not set. The allow_repeated_speaker will be ignored if set.

Raises:

ValueError – If the number of participants is less than two or if the selector prompt is invalid.

Examples:

A team with multiple participants:

import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.ui import Console


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    async def lookup_hotel(location: str) -> str:
        return f"Here are some hotels in {location}: hotel1, hotel2, hotel3."

    async def lookup_flight(origin: str, destination: str) -> str:
        return f"Here are some flights from {origin} to {destination}: flight1, flight2, flight3."

    async def book_trip() -> str:
        return "Your trip is booked!"

    travel_advisor = AssistantAgent(
        "Travel_Advisor",
        model_client,
        tools=[book_trip],
        description="Helps with travel planning.",
    )
    hotel_agent = AssistantAgent(
        "Hotel_Agent",
        model_client,
        tools=[lookup_hotel],
        description="Helps with hotel booking.",
    )
    flight_agent = AssistantAgent(
        "Flight_Agent",
        model_client,
        tools=[lookup_flight],
        description="Helps with flight booking.",
    )
    termination = TextMentionTermination("TERMINATE")
    team = SelectorGroupChat(
        [travel_advisor, hotel_agent, flight_agent],
        model_client=model_client,
        termination_condition=termination,
    )
    await Console(team.run_stream(task="Book a 3-day trip to new york."))


asyncio.run(main())

A team with a custom selector function:

import asyncio
from typing import Sequence
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.ui import Console
from autogen_agentchat.messages import BaseAgentEvent, BaseChatMessage


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    def check_calculation(x: int, y: int, answer: int) -> str:
        if x + y == answer:
            return "Correct!"
        else:
            return "Incorrect!"

    agent1 = AssistantAgent(
        "Agent1",
        model_client,
        description="For calculation",
        system_message="Calculate the sum of two numbers",
    )
    agent2 = AssistantAgent(
        "Agent2",
        model_client,
        tools=[check_calculation],
        description="For checking calculation",
        system_message="Check the answer and respond with 'Correct!' or 'Incorrect!'",
    )

    def selector_func(messages: Sequence[BaseAgentEvent | BaseChatMessage]) -> str | None:
        if len(messages) == 1 or messages[-1].to_text() == "Incorrect!":
            return "Agent1"
        if messages[-1].source == "Agent1":
            return "Agent2"
        return None

    termination = TextMentionTermination("Correct!")
    team = SelectorGroupChat(
        [agent1, agent2],
        model_client=model_client,
        selector_func=selector_func,
        termination_condition=termination,
    )

    await Console(team.run_stream(task="What is 1 + 1?"))


asyncio.run(main())

classmethod _from_config(config: SelectorGroupChatConfig) → Self[source]#

Create a new instance of the component from a configuration object.

Parameters:: config (T) – The configuration object.
Returns:: Self – The new instance of the component.

_to_config() → SelectorGroupChatConfig[source]#

Dump the configuration that would be requite to create a new instance of a component matching the configuration of this instance.

Returns:: T – The configuration of the component.

component_config_schema#: alias of SelectorGroupChatConfig

component_provider_override: ClassVar[str | None] = 'autogen_agentchat.teams.SelectorGroupChat'#: Override the provider string for the component. This should be used to prevent internal module names being a part of the module name.

class Swarm(participants: List[ChatAgent], termination_condition: TerminationCondition | None = None, max_turns: int | None = None, runtime: AgentRuntime | None = None, custom_message_types: List[type[BaseAgentEvent | BaseChatMessage]] | None = None)[source]#

Bases: BaseGroupChat, Component[SwarmConfig]

A group chat team that selects the next speaker based on handoff message only.

The first participant in the list of participants is the initial speaker. The next speaker is selected based on the HandoffMessage message sent by the current speaker. If no handoff message is sent, the current speaker continues to be the speaker.

Parameters:

participants (List[ChatAgent]) – The agents participating in the group chat. The first agent in the list is the initial speaker.
termination_condition (TerminationCondition, optional) – The termination condition for the group chat. Defaults to None. Without a termination condition, the group chat will run indefinitely.
max_turns (int, optional) – The maximum number of turns in the group chat before stopping. Defaults to None, meaning no limit.

Basic example:

import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import Swarm
from autogen_agentchat.conditions import MaxMessageTermination


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent1 = AssistantAgent(
        "Alice",
        model_client=model_client,
        handoffs=["Bob"],
        system_message="You are Alice and you only answer questions about yourself.",
    )
    agent2 = AssistantAgent(
        "Bob", model_client=model_client, system_message="You are Bob and your birthday is on 1st January."
    )

    termination = MaxMessageTermination(3)
    team = Swarm([agent1, agent2], termination_condition=termination)

    stream = team.run_stream(task="What is bob's birthday?")
    async for message in stream:
        print(message)


asyncio.run(main())

Using the HandoffTermination for human-in-the-loop handoff:

import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import Swarm
from autogen_agentchat.conditions import HandoffTermination, MaxMessageTermination
from autogen_agentchat.ui import Console
from autogen_agentchat.messages import HandoffMessage


async def main() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")

    agent = AssistantAgent(
        "Alice",
        model_client=model_client,
        handoffs=["user"],
        system_message="You are Alice and you only answer questions about yourself, ask the user for help if needed.",
    )
    termination = HandoffTermination(target="user") | MaxMessageTermination(3)
    team = Swarm([agent], termination_condition=termination)

    # Start the conversation.
    await Console(team.run_stream(task="What is bob's birthday?"))

    # Resume with user feedback.
    await Console(
        team.run_stream(
            task=HandoffMessage(source="user", target="Alice", content="Bob's birthday is on 1st January.")
        )
    )


asyncio.run(main())

classmethod _from_config(config: SwarmConfig) → Swarm[source]#

Create a new instance of the component from a configuration object.

Parameters:: config (T) – The configuration object.
Returns:: Self – The new instance of the component.

_to_config() → SwarmConfig[source]#

Dump the configuration that would be requite to create a new instance of a component matching the configuration of this instance.

Returns:: T – The configuration of the component.

component_config_schema#: alias of SwarmConfig

component_provider_override: ClassVar[str | None] = 'autogen_agentchat.teams.Swarm'#: Override the provider string for the component. This should be used to prevent internal module names being a part of the module name.