DevOps & Infra
building-ai-agent-on-cloudflare - Claude MCP Skill
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".
SEO Guide: Enhance your AI agent with the building-ai-agent-on-cloudflare tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to builds ai agents on cloudflare using the agents sdk with state management, real-time websockets, sch... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# 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: "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
```typescript
// 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
```toml
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
```typescript
// Current state is always available
const currentMessages = this.state.messages;
const userPrefs = this.state.preferences;
```
### Updating State
```typescript
// 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:
```typescript
// 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:
```typescript
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
```typescript
// 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`:
```typescript
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
```tsx
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
```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](references/agent-patterns.md) for:
- Tool calling and function execution
- Multi-agent orchestration
- RAG (Retrieval Augmented Generation)
- Human-in-the-loop workflows
## Deployment
```bash
# 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](references/troubleshooting.md) for common issues.
## References
- [references/examples.md](references/examples.md) — Official templates and production examples
- [references/agent-patterns.md](references/agent-patterns.md) — Advanced patterns
- [references/state-patterns.md](references/state-patterns.md) — State management strategies
- [references/troubleshooting.md](references/troubleshooting.md) — Error solutionsSignals
Information
- Repository
- cloudflare/cloudflare-docs
- Author
- cloudflare
- Last Sync
- 3/3/2026
- Repo Updated
- 3/3/2026
- Created
- 2/6/2026
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