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building-ai-agent-on-cloudflare

building-ai-agent-on-cloudflare

github.com/cloudflareAgent 工作流5.4k45 次安装今天更新
代码仓库

关于 building-ai-agent-on-cloudflare

使用 Cloudflare 的 Agents SDK 构建有状态 AI Agent 的技能,面向开发者。它涵盖持久化状态、实时连接、定时任务和客户端集成。

主要功能

  1. 01跨连接持久保存状态
  2. 02通过 WebSocket 和 HTTP 通信
  3. 03安排定时及周期任务
  4. 04使用 SQLite 存储
  5. 05提供 React 和 JavaScript 客户端示例

适用场景

  1. 01构建有状态聊天 Agent
  2. 02断开后恢复会话
  3. 03安排 Agent 后台任务
  4. 04为 Agent 添加持久化数据

安装

复制下面的命令,在终端中运行。

$npx skills add https://github.com/cloudflare/cloudflare-docs --skill building-ai-agent-on-cloudflare

安装后在 Claude Code 中输入/building-ai-agent-on-cloudflare确认已加载。

包含的文件

除 SKILL.md 外,安装时一起下载的参考资料和脚本。

references/
agent-patterns.md
examples.md
state-patterns.md
troubleshooting.md

详细介绍

构建 Cloudflare Agents

使用 Cloudflare 的 Agents SDK 构建 AI 驱动的 Agent,支持持久化状态、实时通信和工具集成。

适用场景

  • 用户想构建 AI Agent 或聊天机器人
  • 用户需要有状态的实时 AI 交互
  • 用户询问 Cloudflare Agents SDK
  • 用户想安排定时任务或后台 AI 工作
  • 用户需要基于 WebSocket 的 AI 通信

前置条件

  • 已启用 Workers 的 Cloudflare 账户
  • Node.js 18+ 和 npm/pnpm/yarn
  • Wrangler CLI(npm install -g wrangler)

快速开始

npm create cloudflare@latest -- my-agent --template=cloudflare/agents-starter
cd my-agent
npm start

Agent 运行于 http://localhost:8787

核心概念

什么是 Agent?

Agent 是一种有状态、持久化的 AI 服务,可以:

  • 在请求和重新连接之间保持状态
  • 通过 WebSocket 或 HTTP 通信
  • 通过 Durable Objects 在 Cloudflare 边缘运行
  • 安排任务并调用工具
  • 水平扩展(每位用户/每个会话都有自己的实例)
Agent 生命周期
Client connects → Agent.onConnect() → Agent processes messages
                                    → Agent.onMessage()
                                    → Agent.setState() (persists + syncs)
Client disconnects → State persists → Client reconnects → State restored

Agent 基本结构

import { Agent, Connection } from "agents";

interface Env {
  AI: Ai;  // Workers AI binding
}

interface State {
  messages: Array<{ role: string; content: string }>;
  preferences: Record<string, string>;
}

export class MyAgent extends Agent<Env, State> {
  // Initial state for new instances
  initialState: State = {
    messages: [],
    preferences: {},
  };

  // Called when agent starts or resumes
  async onStart() {
    console.log("Agent started with state:", this.state);
  }

  // Handle WebSocket connections
  async onConnect(connection: Connection) {
    connection.send(JSON.stringify({
      type: "welcome",
      history: this.state.messages,
    }));
  }

  // Handle incoming messages
  async onMessage(connection: Connection, message: string) {
    const data = JSON.parse(message);

    if (data.type === "chat") {
      await this.handleChat(connection, data.content);
    }
  }

  // Handle disconnections
  async onClose(connection: Connection) {
    console.log("Client disconnected");
  }

  // React to state changes
  onStateUpdate(state: State, source: string) {
    console.log("State updated by:", source);
  }

  private async handleChat(connection: Connection, userMessage: string) {
    // Add user message to history
    const messages = [
      ...this.state.messages,
      { role: "user", content: userMessage },
    ];

    // Call AI
    const response = await this.env.AI.run("@cf/meta/llama-3-8b-instruct", {
      messages,
    });

    // Update state (persists and syncs to all clients)
    this.setState({
      ...this.state,
      messages: [
        ...messages,
        { role: "assistant", content: response.response },
      ],
    });

    // Send response
    connection.send(JSON.stringify({
      type: "response",
      content: response.response,
    }));
  }
}

入口点配置

// src/index.ts
import { routeAgentRequest } from "agents";
import { MyAgent } from "./agent";

export default {
  async fetch(request: Request, env: Env) {
    // routeAgentRequest handles routing to /agents/:class/:name
    return (
      (await routeAgentRequest(request, env)) ||
      new Response("Not found", { status: 404 })
    );
  },
};

export { MyAgent };

客户端通过以下地址连接:wss://my-agent.workers.dev/agents/MyAgent/session-id

Wrangler 配置

name = "my-agent"
main = "src/index.ts"
compatibility_date = "2024-12-01"

[ai]
binding = "AI"

[durable_objects]
bindings = [{ name = "AGENT", class_name = "MyAgent" }]

[[migrations]]
tag = "v1"
new_classes = ["MyAgent"]

状态管理

读取状态
// Current state is always available
const currentMessages = this.state.messages;
const userPrefs = this.state.preferences;
更新状态
// setState persists AND syncs to all connected clients
this.setState({
  ...this.state,
  messages: [...this.state.messages, newMessage],
});

// Partial updates work too
this.setState({
  preferences: { ...this.state.preferences, theme: "dark" },
});
SQL 存储

对于复杂查询,请使用内嵌的 SQLite 数据库:

// Create tables
await this.sql`
  CREATE TABLE IF NOT EXISTS documents (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    title TEXT NOT NULL,
    content TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
  )
`;

// Insert
await this.sql`
  INSERT INTO documents (title, content)
  VALUES (${title}, ${content})
`;

// Query
const docs = await this.sql`
  SELECT * FROM documents WHERE title LIKE ${`%${search}%`}
`;

定时任务

Agent 可以安排未来的工作:

async onMessage(connection: Connection, message: string) {
  const data = JSON.parse(message);

  if (data.type === "schedule_reminder") {
    // Schedule task for 1 hour from now
    const { id } = await this.schedule(3600, "sendReminder", {
      message: data.reminderText,
      userId: data.userId,
    });

    connection.send(JSON.stringify({ type: "scheduled", taskId: id }));
  }
}

// Called when scheduled task fires
async sendReminder(data: { message: string; userId: string }) {
  // Send notification, email, etc.
  console.log(`Reminder for ${data.userId}: ${data.message}`);

  // Can also update state
  this.setState({
    ...this.state,
    lastReminder: new Date().toISOString(),
  });
}
调度选项
// Delay in seconds
await this.schedule(60, "taskMethod", { data });

// Specific date
await this.schedule(new Date("2025-01-01T00:00:00Z"), "taskMethod", { data });

// Cron expression (recurring)
await this.schedule("0 9 * * *", "dailyTask", {});  // 9 AM daily
await this.schedule("*/5 * * * *", "everyFiveMinutes", {});  // Every 5 min

// Manage schedules
const schedules = await this.getSchedules();
await this.cancelSchedule(taskId);

聊天 Agent(AI 驱动)

对于以聊天为核心的 Agent,可扩展 AIChatAgent:

import { AIChatAgent } from "agents/ai-chat-agent";

export class ChatBot extends AIChatAgent<Env> {
  // Called for each user message
  async onChatMessage(message: string) {
    const response = await this.env.AI.run("@cf/meta/llama-3-8b-instruct", {
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        ...this.messages,  // Automatic history management
        { role: "user", content: message },
      ],
      stream: true,
    });

    // Stream response back to client
    return response;
  }
}

包含以下功能:

  • 自动记录消息历史
  • 可恢复的流式传输(断开连接后仍可继续)
  • 内置 saveMessages(),用于持久化

客户端集成

React Hook
import { useAgent } from "agents/react";

function Chat() {
  const { state, send, connected } = useAgent({
    agent: "my-agent",
    name: userId,  // Agent instance ID
  });

  const sendMessage = (text: string) => {
    send(JSON.stringify({ type: "chat", content: text }));
  };

  return (
    <div>
      {state.messages.map((msg, i) => (
        <div key={i}>{msg.role}: {msg.content}</div>
      ))}
      <input onKeyDown={(e) => e.key === "Enter" && sendMessage(e.target.value)} />
    </div>
  );
}
原生 JavaScript
const ws = new WebSocket("wss://my-agent.workers.dev/agents/MyAgent/user123");

ws.onopen = () => {
  console.log("Connected to agent");
};

ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  console.log("Received:", data);
};

ws.send(JSON.stringify({ type: "chat", content: "Hello!" }));

常见模式

常见问题请参阅 references/agent-patterns.md:

  • 工具调用和函数执行
  • 多 Agent 编排
  • RAG(检索增强生成)
  • 人在回路工作流

部署

# Deploy
npx wrangler deploy

# View logs
wrangler tail

# Test endpoint
curl https://my-agent.workers.dev/agents/MyAgent/test-user

故障排查

常见问题请参阅 references/troubleshooting.md。

参考资料

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