MCPInstall

building-ai-agent-on-cloudflare

Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling".

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/cloudflare/cloudflare-docs --skill building-ai-agent-on-cloudflare
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads building-ai-agent-on-cloudflare automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Building Cloudflare Agents

Creates AI-powered agents using Cloudflare's Agents SDK with persistent state, real-time communication, and tool integration.

When to Use

  • User wants to build an AI agent or chatbot
  • User needs stateful, real-time AI interactions
  • User asks about the Cloudflare Agents SDK
  • User wants scheduled tasks or background AI work
  • User needs WebSocket-based AI communication

Prerequisites

  • Cloudflare account with Workers enabled
  • Node.js 18+ and npm/pnpm/yarn
  • Wrangler CLI (npm install -g wrangler)

Quick Start

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

Agent runs at http://localhost:8787

Core Concepts

What is an Agent?

An Agent is a stateful, persistent AI service that:

  • Maintains state across requests and reconnections
  • Communicates via WebSockets or HTTP
  • Runs on Cloudflare's edge via Durable Objects
  • Can schedule tasks and call tools
  • Scales horizontally (each user/session gets own instance)

Agent Lifecycle

Client connects → Agent.onConnect() → Agent processes messages
                                    → Agent.onMessage()
                                    → Agent.setState() (persists + syncs)
Client disconnects → State persists → Client reconnects → State restored

Basic Agent Structure

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,
    }));
  }
}

Entry Point Configuration

// 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 };

Clients connect via: wss://my-agent.workers.dev/agents/MyAgent/session-id

Wrangler Configuration

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"]

State Management

Reading State

// Current state is always available
const currentMessages = this.state.messages;
const userPrefs = this.state.preferences;

Updating State

// 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 Storage

For complex queries, use the embedded SQLite database:

// 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}%`}
`;

Scheduled Tasks

Agents can schedule future work:

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(),
  });
}

Schedule Options

// 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);

Chat Agent (AI-Powered)

For chat-focused agents, extend 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;
  }
}

Features included:

  • Automatic message history
  • Resumable streaming (survives disconnects)
  • Built-in saveMessages() for persistence

Client Integration

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>
  );
}

Vanilla 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!" }));

Common Patterns

See references/agent-patterns.md for:

  • Tool calling and function execution
  • Multi-agent orchestration
  • RAG (Retrieval Augmented Generation)
  • Human-in-the-loop workflows

Deployment

# Deploy
npx wrangler deploy

# View logs
wrangler tail

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

Troubleshooting

See references/troubleshooting.md for common issues.

References

  • references/examples.md — Official templates and production examples
  • references/agent-patterns.md — Advanced patterns
  • references/state-patterns.md — State management strategies
  • references/troubleshooting.md — Error solutions

FAQ

What does the building-ai-agent-on-cloudflare skill do?

Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling".

How do I install building-ai-agent-on-cloudflare?

Run npx skills add https://github.com/cloudflare/cloudflare-docs --skill building-ai-agent-on-cloudflare in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/building-ai-agent-on-cloudflare/ (personal) or .claude/skills/building-ai-agent-on-cloudflare/ (project).

Which AI agents can use building-ai-agent-on-cloudflare?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is building-ai-agent-on-cloudflare safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains building-ai-agent-on-cloudflare?

It is published by cloudflare in cloudflare/cloudflare-docs, and it is an official skill from the maker of the technology it covers. It has 45 recorded installs.

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