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".
View on GitHubSelect agents to install to:
npx add-skill https://github.com/cloudflare/skills/blob/main/skills/building-ai-agent-on-cloudflare/SKILL.md -a claude-code --skill building-ai-agent-on-cloudflareInstallation paths:
.claude/skills/building-ai-agent-on-cloudflare/# 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
```bash
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
```typescript
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: "wel