Agent 是什么
一、定义
Agent = 大模型 + 工具 + 循环决策
二、Agent 流程
用户提问 → 模型思考 → 决定要不要用工具 → 执行工具 → 看结果 → 再思考 → ... → 最终回答三、Agent 的完整架构（五个组件）
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Agent 主循环
LLM
大脑/决策者
Tools
手/能力
Memory
记忆
Prompt
规则/人设
外部世界
API/数据库/文件
| 组件 | 作用 |
|---|---|
| LLM | 理解任务、决定下一步 |
| Tools | 执行具体动作 |
| Memory | 记住对话历史、长期知识 |
| Prompt | 定义角色、规则、输出格式 |
| Loop | 串联以上组件，循环直到完成 |
四、ReAct 是什么？
ReAct = Reason（推理）+ Act（行动），目前最常见的 Agent 模式。
Thought（思考）: 用户问北京天气，我需要查天气工具
Action（行动）: 调用 get_weather(city="北京")
Observation（观察）: 工具返回 "晴，25℃"
Thought: 我已经有数据了，可以回答了
Action: 输出最终答案给用户五、Tool Loop（工具循环）
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普通文字回复
超过最大轮数
用户输入任务
把 messages + tools 发给模型
模型返回什么?
执行对应工具函数
把结果以 role:tool 追加到 messages
任务完成，返回给用户
强制终止，提示失败
六、完整示例
py
def run_agent(client, model, user_message) -> str:
messages = [
{
"role": "system",
"content": "你是数学助手。涉及计算时必须调用 calculate 工具，不要自己心算。",
},
{"role": "user", "content": user_message},
]
for turn in range(MAX_TURNS):
response = client.chat.completions.create(
model=model, messages=messages, tools=TOOLS
)
message = response.choices[0].message
if message.tool_calls:
messages.append(message)
for tool_call in message.tool_calls:
tool_result = dispatch_tool(
tool_call,
)
messages.append(
{
"role": "tool",
"content": tool_result,
"tool_call_id": tool_call.id,
}
)
else:
return message.content
return "对话达到最大轮数，请重新开始。"