如何构建一个多智能体网络（功能API）¶
在本指南中，我们将演示如何实现一个多代理网络架构，其中每个代理都可以与其他所有代理通信（多对多连接），并且可以决定下一个调用哪个代理。我们将使用函数式API——单个代理将被定义为任务，代理交接将在主入口点中定义：
from langgraph.func import entrypoint
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
# 定义一个工具，用于向不同的代理传递意图
@tool(return_direct=True)
def transfer_to_hotel_advisor():
"""请求酒店顾问代理的帮助。"""
return "成功转接到酒店顾问"
# 定义一个代理
travel_advisor_tools = [transfer_to_hotel_advisor, ...]
travel_advisor = create_react_agent(model, travel_advisor_tools)
# 定义一个调用代理的任务
@task
def call_travel_advisor(messages):
response = travel_advisor.invoke({"messages": messages})
return response["messages"]
# 定义多代理网络的工作流程
@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 Reference: entrypoint | create_react_agent | tool
设置环境¶
首先，让我们安装所需的包
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")
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旅行代理示例¶
在这个示例中，我们将建立一个旅行助手代理团队，这些代理能够相互交流。
我们将创建两个代理：
travel_advisor：可以提供旅行目的地推荐。可以向hotel_advisor寻求帮助。hotel_advisor：可以提供酒店推荐。可以向travel_advisor寻求帮助。
这是一个完全连接的网络——每个代理都可以与其他任何代理通信。
首先，让我们创建一些代理将要使用的工具：
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 Reference: tool
传输工具
你可能已经注意到我们在传输工具中使用了@tool(return_direct=True)。这样做的目的是为了让单个代理（例如，travel_advisor）在调用这些工具时能够提前退出ReAct循环。这是期望的行为，因为我们希望在代理调用此工具时立即检测到并立即将控制权移交给另一个代理。
注意：这意在与预构建的create_react_agent一起使用——如果你正在构建一个自定义代理，请确保手动添加处理标记为return_direct的工具的提前退出逻辑。
现在让我们定义代理任务并将它们组合成一个单一的多代理网络工作流：
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 Reference: AIMessage | ChatAnthropic | create_react_agent | add_messages | entrypoint | task
最后，让我们定义一个辅助函数来渲染代理输出：
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 Reference: convert_to_messages
让我们使用与原来的多智能体系统相同的输入来进行测试：
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 获取更多信息！