当有了工具列表和模型后，就可以通过create_react_agent这个LangGraph框架中预构建的方法来创建自治循环代理（ReAct）的工作流，其必要的参数如下：

• model： 支持工具调用的LangChain聊天模型。
• tools： 工具列表、ToolExecutor 或 ToolNode 实例。
• state_schema：图的状态模式。必须有messages和is_last_step键。默认为定义这两个键的Agent State。

ReAct案例

from dotenv import dotenv_values
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from typing import Union, Optional
from pydantic import BaseModel, Field
import requests, json, asyncio
from langgraph.prebuilt import create_react_agent


env_vars = dotenv_values('.env')
OPENAI_KEY = env_vars['OPENAI_API_KEY'] 
OPENAI_BASE_URL = env_vars['OPENAI_API_BASE'] 
SERPER_KEY = env_vars['SERPER_KEY'] 
WEATHER_KEY = env_vars['WEATHER_KEY']


## 第一个工具
class WeatherLoc(BaseModel):
    location: str = Field(description="The location name of the city")


@tool(args_schema=WeatherLoc)
def get_weather(location):
    """
    Function to query current weather.
    :param loc: Required parameter, of type string, representing the specific city name for the weather query. \
    Note that for cities in China, the corresponding English city name should be used. For example, to query the weather for Beijing, \
    the loc parameter should be input as 'Beijing'.
    :return: The result of the OpenWeather API query for current weather, with the specific URL request address being: https://api.openweathermap.org/data/2.5/weather. \
    The return type is a JSON-formatted object after parsing, represented as a string, containing all important weather information.
    """
    # Step 1.构建请求
    url = "https://api.openweathermap.org/data/2.5/weather"

    # Step 2.设置查询参数
    params = {
        "q": location,               
        "appid": WEATHER_KEY,    
        "units": "metric",      
        "lang":"zh_cn"        
    }

    # Step 3.发送GET请求
    response = requests.get(url, params=params)
    
    # Step 4.解析响应
    data = response.json()
    return json.dumps(data)


# 第二个工具
class SearchQuery(BaseModel):
    query: str = Field(description="Questions for networking queries")


@tool(args_schema = SearchQuery)
def fetch_real_time_info(query):
    """Get real-time Internet information"""
    url = "https://google.serper.dev/search"
    payload = json.dumps({
      "q": query,
      "num": 1,
    })
    headers = {
      'X-API-KEY': SERPER_KEY,
      'Content-Type': 'application/json'
    }
    
    response = requests.post(url, headers=headers, data=payload)
    data = json.loads(response.text)
    if 'organic' in data:
        return json.dumps(data['organic'],  ensure_ascii=False) 
    else:
        return json.dumps({"error": "No organic results found"},  ensure_ascii=False)  


llm = ChatOpenAI(model="gpt-4o-mini", api_key=OPENAI_KEY,base_url=OPENAI_BASE_URL)  
tools = [fetch_real_time_info, get_weather]
graph = create_react_agent(llm, tools=tools)


# 可以自动处理成 HumanMessage 的消息格式
finan_response = graph.invoke({"messages":["what is labubu"]})
print(569, finan_response)  
# finan_response["messages"][-1].content

以上案例中使用了两个外部工具[fetch_real_time_info, get_weather], 图的生成也只用了一行代码create_react_agent(llm, tools=tools) ,确实比之前两个案例简单直接了很多。