发布时间：2026年4月10日 ｜ 阅读时长：约10分钟

 传统方式：硬编码多步流程
def traditional_approach():
     Step 1: 调用天气API
    weather_data = call_weather_api("北京")
    weather_type = parse_weather(weather_data)
    
     Step 2: 根据天气筛选餐厅
    if weather_type == "晴":
        restaurants = call_restaurant_api("北京", cuisine="中餐")
    else:
        restaurants = call_restaurant_api("北京", cuisine="中餐", indoor=True)
    
     Step 3: 返回结果
    return format_result(weather_data, restaurants)

 ReAct循环的伪代码实现
def react_loop(user_goal):
    context = f"Goal: {user_goal}"
    max_iterations = 10
    
    for _ in range(max_iterations):
         Step 1: 思考 (Thought)
        thought = llm_reason(f"基于当前上下文，决定下一步行动。上下文：{context}")
        
         Step 2: 行动 (Action)
        if thought.type == "final_answer":
            return thought.answer
        else:
            observation = execute_tool(thought.tool_name, thought.params)
        
         Step 3: 观察 (Observation)
        context = f"{context}\nAction: {thought}\nObservation: {observation}"

 安装依赖
 pip install openai requests

import json
import requests
from typing import Dict, List, Any

 定义可用的工具列表
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "获取指定城市的当前天气信息",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "城市名称，如'北京'"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

 工具的具体实现
def get_weather(city: str) -> str:
    """调用天气API获取天气信息"""
     模拟API调用（实际使用时可替换为真实API）
     此处演示用模拟数据
    weather_db = {
        "北京": "晴天，25°C，空气质量良好",
        "上海": "多云，22°C，东风2级",
        "广州": "小雨，28°C，湿度85%"
    }
    return weather_db.get(city, f"暂时无法获取{city}的天气信息")

class SimpleAgent:
    def __init__(self, api_key: str, model: str = "gpt-4"):
        self.api_key = api_key
        self.model = model
        self.conversation_history = []   短期记忆
        self.tools_map = {
            "get_weather": get_weather
        }
    
    def execute_tool(self, tool_name: str, arguments: Dict) -> str:
        """执行工具调用"""
        if tool_name in self.tools_map:
            return self.tools_map[tool_name](arguments)
        return f"错误：未找到工具 {tool_name}"
    
    def chat(self, user_input: str) -> str:
        """Agent的主入口"""
         将用户输入加入历史
        self.conversation_history.append({
            "role": "user",
            "content": user_input
        })
        
         调用LLM进行推理
        response = self._call_llm_with_tools()
        
         判断是否需要调用工具
        if self._needs_tool_call(response):
            tool_calls = response["tool_calls"]
            for tool_call in tool_calls:
                tool_name = tool_call["function"]["name"]
                arguments = json.loads(tool_call["function"]["arguments"])
                
                 执行工具
                tool_result = self.execute_tool(tool_name, arguments)
                
                 将工具结果加入上下文
                self.conversation_history.append({
                    "role": "tool",
                    "tool_call_id": tool_call["id"],
                    "content": tool_result
                })
            
             再次调用LLM生成最终回答
            final_response = self._call_llm()
            return final_response
        
        return response.get("content", "")
    
     辅助方法（实际实现需调用真实LLM API）
    def _call_llm_with_tools(self):
        """调用LLM API，传入工具定义"""
        pass   实际实现见下方完整示例

 完整示例（使用OpenAI兼容API）
from openai import OpenAI

client = OpenAI(api_key="your-api-key")

messages = [
    {"role": "system", "content": "你是一个智能助手，可以帮助用户查询天气等信息。"},
    {"role": "user", "content": "帮我查一下北京的天气"}
]

response = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=TOOLS,   传入工具定义
    tool_choice="auto"
)

 解析并执行工具调用
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        if tool_call.function.name == "get_weather":
            args = json.loads(tool_call.function.arguments)
            weather = get_weather(args["city"])
            print(f"查询结果：{weather}")