多智能体¶

单个智能体如果需要在多个领域进行专业化或管理许多工具，可能会感到吃力。为了解决这个问题，您可以将您的智能体分解为更小、独立的智能体，并将它们组合成一个多智能体系统。

在多智能体系统中，智能体需要相互通信。它们通过移交进行通信——移交是一种原语，描述将控制权移交给哪个智能体以及发送给该智能体的有效载荷。

两种最流行的多智能体架构是：

监督者——由一个中心监督者智能体协调各个智能体。监督者控制所有通信流和任务委托，根据当前上下文和任务需求决定调用哪个智能体。
群集——智能体根据其专业化动态地相互移交控制权。系统会记住上次活动的智能体，确保在后续交互中，对话与该智能体继续进行。

监督者¶

Supervisor

使用 langgraph-supervisor 库创建监督者多智能体系统

pip install langgraph-supervisor

API 参考：ChatOpenAI | create_react_agent | create_supervisor

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langgraph_supervisor import create_supervisor

def book_hotel(hotel_name: str):
    """Book a hotel"""
    return f"Successfully booked a stay at {hotel_name}."

def book_flight(from_airport: str, to_airport: str):
    """Book a flight"""
    return f"Successfully booked a flight from {from_airport} to {to_airport}."

flight_assistant = create_react_agent(
    model="openai:gpt-4o",
    tools=[book_flight],
    prompt="You are a flight booking assistant",
    name="flight_assistant"
)

hotel_assistant = create_react_agent(
    model="openai:gpt-4o",
    tools=[book_hotel],
    prompt="You are a hotel booking assistant",
    name="hotel_assistant"
)

supervisor = create_supervisor(
    agents=[flight_assistant, hotel_assistant],
    model=ChatOpenAI(model="gpt-4o"),
    prompt=(
        "You manage a hotel booking assistant and a"
        "flight booking assistant. Assign work to them."
    )
).compile()

for chunk in supervisor.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
            }
        ]
    }
):
    print(chunk)
    print("\n")

群集¶

Swarm

使用 langgraph-swarm 库创建群集多智能体系统

pip install langgraph-swarm

API 参考：create_react_agent | create_swarm | create_handoff_tool

from langgraph.prebuilt import create_react_agent
from langgraph_swarm import create_swarm, create_handoff_tool

transfer_to_hotel_assistant = create_handoff_tool(
    agent_name="hotel_assistant",
    description="Transfer user to the hotel-booking assistant.",
)
transfer_to_flight_assistant = create_handoff_tool(
    agent_name="flight_assistant",
    description="Transfer user to the flight-booking assistant.",
)

flight_assistant = create_react_agent(
    model="anthropic:claude-3-5-sonnet-latest",
    tools=[book_flight, transfer_to_hotel_assistant],
    prompt="You are a flight booking assistant",
    name="flight_assistant"
)
hotel_assistant = create_react_agent(
    model="anthropic:claude-3-5-sonnet-latest",
    tools=[book_hotel, transfer_to_flight_assistant],
    prompt="You are a hotel booking assistant",
    name="hotel_assistant"
)

swarm = create_swarm(
    agents=[flight_assistant, hotel_assistant],
    default_active_agent="flight_assistant"
).compile()

for chunk in swarm.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
            }
        ]
    }
):
    print(chunk)
    print("\n")

移交¶

多智能体交互中一个常见的模式是**移交**，即一个智能体将控制权“移交”给另一个智能体。移交允许您指定：

目的地：要导航到的目标智能体
有效载荷：传递给该智能体的信息

langgraph-supervisor（监督者将控制权移交给单个智能体）和 langgraph-swarm（单个智能体可以将控制权移交给其他智能体）都使用了这一点。

要使用 create_react_agent 实现移交，您需要：

创建一个特殊工具，可以控制权转移到另一个智能体

def transfer_to_bob():
    """Transfer to bob."""
    return Command(
        # name of the agent (node) to go to
        goto="bob",
        # data to send to the agent
        update={"messages": [...]},
        # indicate to LangGraph that we need to navigate to
        # agent node in a parent graph
        graph=Command.PARENT,
    )

创建可以使用移交工具的单个智能体

flight_assistant = create_react_agent(
    ..., tools=[book_flight, transfer_to_hotel_assistant]
)
hotel_assistant = create_react_agent(
    ..., tools=[book_hotel, transfer_to_flight_assistant]
)

定义一个包含单个智能体作为节点的父图

from langgraph.graph import StateGraph, MessagesState
multi_agent_graph = (
    StateGraph(MessagesState)
    .add_node(flight_assistant)
    .add_node(hotel_assistant)
    ...
)

综合来看，以下是如何实现一个包含两个智能体（一个航班预订助手和一个酒店预订助手）的简单多智能体系统：

from typing import Annotated
from langchain_core.tools import tool, InjectedToolCallId
from langgraph.prebuilt import create_react_agent, InjectedState
from langgraph.graph import StateGraph, START, MessagesState
from langgraph.types import Command

def create_handoff_tool(*, agent_name: str, description: str | None = None):
    name = f"transfer_to_{agent_name}"
    description = description or f"Transfer to {agent_name}"

    @tool(name, description=description)
    def handoff_tool(
        state: Annotated[MessagesState, InjectedState], # (1)!
        tool_call_id: Annotated[str, InjectedToolCallId],
    ) -> Command:
        tool_message = {
            "role": "tool",
            "content": f"Successfully transferred to {agent_name}",
            "name": name,
            "tool_call_id": tool_call_id,
        }
        return Command(  # (2)!
            goto=agent_name,  # (3)!
            update={"messages": state["messages"] + [tool_message]},  # (4)!
            graph=Command.PARENT,  # (5)!
        )
    return handoff_tool

# Handoffs
transfer_to_hotel_assistant = create_handoff_tool(
    agent_name="hotel_assistant",
    description="Transfer user to the hotel-booking assistant.",
)
transfer_to_flight_assistant = create_handoff_tool(
    agent_name="flight_assistant",
    description="Transfer user to the flight-booking assistant.",
)

# Simple agent tools
def book_hotel(hotel_name: str):
    """Book a hotel"""
    return f"Successfully booked a stay at {hotel_name}."

def book_flight(from_airport: str, to_airport: str):
    """Book a flight"""
    return f"Successfully booked a flight from {from_airport} to {to_airport}."

# Define agents
flight_assistant = create_react_agent(
    model="anthropic:claude-3-5-sonnet-latest",
    tools=[book_flight, transfer_to_hotel_assistant],
    prompt="You are a flight booking assistant",
    name="flight_assistant"
)
hotel_assistant = create_react_agent(
    model="anthropic:claude-3-5-sonnet-latest",
    tools=[book_hotel, transfer_to_flight_assistant],
    prompt="You are a hotel booking assistant",
    name="hotel_assistant"
)

# Define multi-agent graph
multi_agent_graph = (
    StateGraph(MessagesState)
    .add_node(flight_assistant)
    .add_node(hotel_assistant)
    .add_edge(START, "flight_assistant")
    .compile()
)

# Run the multi-agent graph
for chunk in multi_agent_graph.stream(
    {
        "messages": [
            {
                "role": "user",
                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
            }
        ]
    }
):
    print(chunk)
    print("\n")

访问智能体的状态
Command 原语允许将状态更新和节点转换指定为一个单一操作，这对于实现移交很有用。
要移交控制权的智能体或节点的名称。
获取智能体的消息，并在移交过程中将它们**添加到**父级的**状态**中。下一个智能体将看到父级状态。
向 LangGraph 指示我们需要导航到**父级**多智能体图中的智能体节点。

注意

此移交实现假定：

每个智能体接收多智能体系统中整体消息历史（跨所有智能体）作为其输入
每个智能体将其内部消息历史输出到多智能体系统的整体消息历史

请查看 LangGraph 监督者和群集文档，了解如何自定义移交。

多智能体¶

监督者¶

群集¶

移交¶

评论