OpenClaw AI 智能代理搭建与配置指南

第四部分：创建第一个 AI 代理

4.1 基础代理创建

from openclaw import Agent
from openclaw.memory import ShortTermMemory
from openclaw.tools import ToolSet

secretary_crab = Agent(
    name="秘书小蟹",
    llm_config={
        "provider": "openai",
        "model": "gpt-3.5-turbo",
        "temperature": 0.3
    },
    memory=ShortTermMemory(capacity=10),
    tools=ToolSet(["file_operator", "calendar"])
)

response = secretary_crab.run("帮我整理今天的日程，并创建一个会议纪要文件")
print("小蟹说:", response)

第五部分：高级功能

5.1 多代理协作系统

from openclaw import Agent, AgentTeam

researcher_crab = Agent(
    name="研究员小蟹",
    role="研究员",
    llm_config={"model": "gpt-4"},
    tools=["web_search", "file_operator"]
)
writer_crab = Agent(
    name="作家小蟹",
    role="写作者",
    llm_config={"model": "gpt-3.5-turbo"},
    tools=["text_editor"]
)

team = AgentTeam([researcher_crab, writer_crab])
result = team.collaborate("研究人工智能的最新发展，并写一篇总结报告")

5.2 长期记忆系统

from openclaw.memory import LongTermMemory, VectorMemory

crab_memory = LongTermMemory(
    storage_type="vector",
    db_path="./crab_memory.db"
)

crab_memory.remember(key="user_preference", value="用户喜欢简洁的回答，讨厌技术术语")
memory = crab_memory.recall("user_preference")

第六部分：部署和监控

6.1 API 服务部署

from fastapi import FastAPI
from openclaw import Agent

app = FastAPI()
crab = Agent(name="API 螃蟹")

@app.post("/ask")
async def ask_crab(question: str):
    response = crab.run(question)
    return {"answer": response}

@app.get("/status")
async def check_status():
    return {
        "status": "healthy",
        "tasks_completed": crab.stats.total_tasks,
        "memory_usage": crab.memory.usage
    }

6.2 监控系统

import logging
from datetime import datetime

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("CrabMonitor")

class CrabMonitor:
    def __init__(self, crab):
        self.crab = crab
        self.start_time = datetime.now()

    def log_activity(self, task):
        logger.info(f"螃蟹活动日志：时间={datetime.now()}, 任务={task}")

第七部分：实战案例 - 智能助手

完整示例：个人 AI 秘书

from openclaw import Agent
from openclaw.tools import EmailTool, CalendarTool, WeatherTool, ReminderTool
from openclaw.memory import HybridMemory

class PersonalAssistant:
    def __init__(self, user_name):
        self.user_name = user_name
        self.assistant = Agent(
            name=f"{user_name}的秘书小蟹",
            personality={"style": "友好专业", "language": "中文", "formality": "中等"},
            tools=[EmailTool(), CalendarTool(), WeatherTool(), ReminderTool()],
            memory=HybridMemory(short_term_capacity=20, long_term_enabled=True)
        )

    def start_day(self):
        weather = self.assistant.use_tool("weather", action="get_forecast")
        schedule = self.assistant.use_tool("calendar", action="get_today_events")
        report = self.assistant.run(f"根据以下信息，生成早安报告：今日天气：{weather}, 今日日程：{schedule}")
        return report

    def handle_email(self, email_content):
        response = self.assistant.run(f"帮我处理这封邮件：{email_content}")
        return response

if __name__ == "__main__":
    my_assistant = PersonalAssistant("小明")
    morning_report = my_assistant.start_day()
    print(morning_report)

第八部分：性能优化

8.1 缓存机制

from openclaw.cache import SemanticCache

cache = SemanticCache(capacity=100, similarity=0.85)

@cache.cache_result
async def get_answer(question):
    return await crab.run(question)

8.2 并行处理

import asyncio

async def parallel_tasks():
    tasks = [
        crab.run("查天气"),
        crab.run("读邮件"),
        crab.run("写报告")
    ]
    results = await asyncio.gather(*tasks)
    return results

常见问题解答

Q1: 运行太慢怎么办？ A: 使用更小的模型 (gpt-3.5)，开启缓存，并行处理。

Q2: 经常出错？ A: 调低 temperature(0.1 以下)，增加验证步骤，使用更强大的模型。

Q3: 内存占用太高？ A: 限制记忆容量，使用轻量级存储，定期清理缓存。

总结

搭建 OpenClaw AI 代理流程包括：准备环境、安装配置、添加工具、部署运行。通过合理配置 LLM 与工具，可实现自动化任务处理与多代理协作。