如何构建多智能体网络（函数式 API）¶
在本操作指南中，我们将演示如何实现一个 多智能体网络 架构，其中每个智能体都可以与其他每个智能体通信（多对多连接），并且可以决定接下来调用哪个智能体。我们将使用 函数式 API —— 单个智能体将被定义为任务，而智能体移交将在主 entrypoint() 中定义
from langgraph.func import entrypoint
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
# Define a tool to signal intent to hand off to a different agent
@tool(return_direct=True)
def transfer_to_hotel_advisor():
"""Ask hotel advisor agent for help."""
return "Successfully transferred to hotel advisor"
# define an agent
travel_advisor_tools = [transfer_to_hotel_advisor, ...]
travel_advisor = create_react_agent(model, travel_advisor_tools)
# define a task that calls an agent
@task
def call_travel_advisor(messages):
response = travel_advisor.invoke({"messages": messages})
return response["messages"]
# define the multi-agent network workflow
@entrypoint()
def workflow(messages):
call_active_agent = call_travel_advisor
while True:
agent_messages = call_active_agent(messages).result()
messages = messages + agent_messages
call_active_agent = get_next_agent(messages)
return messages
API 参考： entrypoint | create_react_agent | tool
设置¶
首先，让我们安装所需的软件包
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")
为 LangGraph 开发设置 LangSmith
注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用程序——阅读 此处 了解更多关于如何开始的信息。
旅行智能体示例¶
在本示例中，我们将构建一个可以相互通信的旅行助手智能体团队。
我们将创建 2 个智能体
travel_advisor：可以帮助提供旅行目的地建议。可以向hotel_advisor寻求帮助。hotel_advisor：可以帮助提供酒店建议。可以向travel_advisor寻求帮助。
这是一个完全连接的网络 - 每个智能体都可以与任何其他智能体对话。
首先，让我们创建一些智能体将要使用的工具
import random
from typing_extensions import Literal
from langchain_core.tools import tool
@tool
def get_travel_recommendations():
"""Get recommendation for travel destinations"""
return random.choice(["aruba", "turks and caicos"])
@tool
def get_hotel_recommendations(location: Literal["aruba", "turks and caicos"]):
"""Get hotel recommendations for a given destination."""
return {
"aruba": [
"The Ritz-Carlton, Aruba (Palm Beach)"
"Bucuti & Tara Beach Resort (Eagle Beach)"
],
"turks and caicos": ["Grace Bay Club", "COMO Parrot Cay"],
}[location]
@tool(return_direct=True)
def transfer_to_hotel_advisor():
"""Ask hotel advisor agent for help."""
return "Successfully transferred to hotel advisor"
@tool(return_direct=True)
def transfer_to_travel_advisor():
"""Ask travel advisor agent for help."""
return "Successfully transferred to travel advisor"
API 参考： tool
转移工具
您可能已经注意到，我们在转移工具中使用了 @tool(return_direct=True)。这样做是为了让单个智能体（例如，travel_advisor）在调用这些工具后可以提前退出 ReAct 循环。这是期望的行为，因为我们希望检测到智能体何时调用此工具，并立即将控制权移交给不同的智能体。
注意：这旨在与预构建的 create_react_agent 一起使用——如果您正在构建自定义智能体，请确保手动添加逻辑来处理标记为 return_direct 的工具的提前退出。
现在让我们定义我们的智能体任务，并将它们组合成一个多智能体网络工作流程
from langchain_core.messages import AIMessage
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
from langgraph.graph import add_messages
from langgraph.func import entrypoint, task
model = ChatAnthropic(model="claude-3-5-sonnet-latest")
# Define travel advisor ReAct agent
travel_advisor_tools = [
get_travel_recommendations,
transfer_to_hotel_advisor,
]
travel_advisor = create_react_agent(
model,
travel_advisor_tools,
state_modifier=(
"You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). "
"If you need hotel recommendations, ask 'hotel_advisor' for help. "
"You MUST include human-readable response before transferring to another agent."
),
)
@task
def call_travel_advisor(messages):
# You can also add additional logic like changing the input to the agent / output from the agent, etc.
# NOTE: we're invoking the ReAct agent with the full history of messages in the state
response = travel_advisor.invoke({"messages": messages})
return response["messages"]
# Define hotel advisor ReAct agent
hotel_advisor_tools = [get_hotel_recommendations, transfer_to_travel_advisor]
hotel_advisor = create_react_agent(
model,
hotel_advisor_tools,
state_modifier=(
"You are a hotel expert that can provide hotel recommendations for a given destination. "
"If you need help picking travel destinations, ask 'travel_advisor' for help."
"You MUST include human-readable response before transferring to another agent."
),
)
@task
def call_hotel_advisor(messages):
response = hotel_advisor.invoke({"messages": messages})
return response["messages"]
@entrypoint()
def workflow(messages):
messages = add_messages([], messages)
call_active_agent = call_travel_advisor
while True:
agent_messages = call_active_agent(messages).result()
messages = add_messages(messages, agent_messages)
ai_msg = next(m for m in reversed(agent_messages) if isinstance(m, AIMessage))
if not ai_msg.tool_calls:
break
tool_call = ai_msg.tool_calls[-1]
if tool_call["name"] == "transfer_to_travel_advisor":
call_active_agent = call_travel_advisor
elif tool_call["name"] == "transfer_to_hotel_advisor":
call_active_agent = call_hotel_advisor
else:
raise ValueError(f"Expected transfer tool, got '{tool_call['name']}'")
return messages
API 参考： AIMessage | ChatAnthropic | create_react_agent | add_messages | entrypoint | task
最后，让我们定义一个帮助程序来渲染智能体输出
from langchain_core.messages import convert_to_messages
def pretty_print_messages(update):
if isinstance(update, tuple):
ns, update = update
# skip parent graph updates in the printouts
if len(ns) == 0:
return
graph_id = ns[-1].split(":")[0]
print(f"Update from subgraph {graph_id}:")
print("\n")
for node_name, node_update in update.items():
print(f"Update from node {node_name}:")
print("\n")
for m in convert_to_messages(node_update["messages"]):
m.pretty_print()
print("\n")
API 参考： convert_to_messages
让我们使用与原始多智能体系统相同的输入来测试一下
for chunk in workflow.stream(
[
{
"role": "user",
"content": "i wanna go somewhere warm in the caribbean. pick one destination and give me hotel recommendations",
}
],
subgraphs=True,
):
pretty_print_messages(chunk)
Update from subgraph call_travel_advisor:
Update from node agent:
==================================[1m Ai Message [0m==================================
[{'text': "I'll help you find a warm Caribbean destination and then get some hotel recommendations for you.\n\nLet me first get some destination recommendations for the Caribbean region.", 'type': 'text'}, {'id': 'toolu_015vT8PkPq1VXvjrDvSpWUwJ', 'input': {}, 'name': 'get_travel_recommendations', 'type': 'tool_use'}]
Tool Calls:
get_travel_recommendations (toolu_015vT8PkPq1VXvjrDvSpWUwJ)
Call ID: toolu_015vT8PkPq1VXvjrDvSpWUwJ
Args:
Update from subgraph call_travel_advisor:
Update from node tools:
=================================[1m Tool Message [0m=================================
Name: get_travel_recommendations
turks and caicos
Update from subgraph call_travel_advisor:
Update from node agent:
==================================[1m Ai Message [0m==================================
[{'text': "Based on the recommendation, I suggest Turks and Caicos! This beautiful British Overseas Territory is known for its stunning white-sand beaches, crystal-clear turquoise waters, and year-round warm weather. Grace Bay Beach in Providenciales is consistently ranked among the world's best beaches. The islands offer excellent snorkeling, diving, and water sports opportunities, plus a relaxed Caribbean atmosphere.\n\nNow, let me connect you with our hotel advisor to get some specific hotel recommendations for Turks and Caicos.", 'type': 'text'}, {'id': 'toolu_01JY7pNNWFuaWoe9ymxFYiPV', 'input': {}, 'name': 'transfer_to_hotel_advisor', 'type': 'tool_use'}]
Tool Calls:
transfer_to_hotel_advisor (toolu_01JY7pNNWFuaWoe9ymxFYiPV)
Call ID: toolu_01JY7pNNWFuaWoe9ymxFYiPV
Args:
Update from subgraph call_travel_advisor:
Update from node tools:
=================================[1m Tool Message [0m=================================
Name: transfer_to_hotel_advisor
Successfully transferred to hotel advisor
Update from subgraph call_hotel_advisor:
Update from node agent:
==================================[1m Ai Message [0m==================================
[{'text': 'Let me get some hotel recommendations for Turks and Caicos:', 'type': 'text'}, {'id': 'toolu_0129ELa7jFocn16bowaGNapg', 'input': {'location': 'turks and caicos'}, 'name': 'get_hotel_recommendations', 'type': 'tool_use'}]
Tool Calls:
get_hotel_recommendations (toolu_0129ELa7jFocn16bowaGNapg)
Call ID: toolu_0129ELa7jFocn16bowaGNapg
Args:
location: turks and caicos
Update from subgraph call_hotel_advisor:
Update from node tools:
=================================[1m Tool Message [0m=================================
Name: get_hotel_recommendations
["Grace Bay Club", "COMO Parrot Cay"]
Update from subgraph call_hotel_advisor:
Update from node agent:
==================================[1m Ai Message [0m==================================
Here are two excellent hotel options in Turks and Caicos:
1. Grace Bay Club: This luxury resort is located on the world-famous Grace Bay Beach. It offers all-oceanfront suites, exceptional dining options, and personalized service. The resort features adult-only and family-friendly sections, making it perfect for any type of traveler.
2. COMO Parrot Cay: This exclusive private island resort offers the ultimate luxury escape. It's known for its pristine beach, world-class spa, and holistic wellness programs. The resort provides an intimate, secluded experience with top-notch amenities and service.
Would you like more specific information about either of these properties or would you like to explore hotels in another destination?
travel_advisor 选择了一个目的地，然后决定调用 hotel_advisor 以获取更多信息！