多代理¶
如果单个智能体需要在多个领域进行专业化或管理许多工具，它可能会面临困难。为了解决这个问题，您可以将您的智能体分解成更小、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，智能体之间需要相互通信。它们通过交接（handoffs）来实现这一点——这是一种描述将控制权交给哪个智能体以及向该智能体发送什么负载的原始操作。
两种最流行的多智能体架构是
- 主管（supervisor）——单个智能体由一个中央主管智能体协调。主管控制所有通信流和任务委派，根据当前上下文和任务要求决定调用哪个智能体。
- 集群（swarm）——智能体根据其专业特长动态地相互交接控制权。系统会记住最后活跃的智能体，确保在后续交互中，对话从该智能体继续。
主管型¶
使用 langgraph-supervisor 库来创建主管式多智能体系统
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")
集群¶
使用 langgraph-swarm 库来创建集群式多智能体系统
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")
交接¶
多智能体交互中的一个常见模式是 交接（handoffs），即一个智能体将控制权交接给另一个智能体。交接允许您指定
- 目标（destination）：要导航到的目标智能体
- 负载：要传递给该代理的信息
这既被 langgraph-supervisor（主管将控制权交接给单个智能体）也用于 langgraph-swarm（单个智能体可以将控制权交接给其他智能体）。
要使用 create_react_agent 实现交接，您需要
-
创建一个特殊的工具，可以将控制权转移给不同的智能体
-
创建可以访问交接工具的单个智能体
-
定义一个将单个智能体作为节点的父图
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：
API 参考：tool | InjectedToolCallId | create_react_agent | InjectedState | StateGraph | START | Command
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")
- 访问智能体状态
Command原始操作允许将状态更新和节点转换指定为单个操作，这对于实现交接非常有用。- 要移交到的代理或节点的名称。
- 获取智能体的消息并将其添加到父级的状态中，作为交接的一部分。下一个智能体将看到父级状态。
- 指示 LangGraph 我们需要导航到父级多代理图中的代理节点。