building-ai-agent-on-cloudflare
Installation
Summary
Stateful AI agents on Cloudflare Workers with WebSocket communication, persistent state, and scheduled tasks.
- Build agents using the Agents SDK that maintain state across client reconnections and scale via Durable Objects
- Support real-time WebSocket communication, HTTP endpoints, and automatic message history for chat-focused agents
- Schedule background tasks with delay, specific dates, or cron expressions; query state with embedded SQLite
- Extend
AgentorAIChatAgentbase classes, deploy with Wrangler, and integrate clients via React hooks or vanilla JavaScript
SKILL.md
Building Cloudflare Agents
Your knowledge of the Agents SDK may be outdated. Prefer retrieval over pre-training for any agent-building task.
Retrieval Sources
| Source | How to retrieve | Use for |
|---|---|---|
| Agents SDK docs | https://github.com/cloudflare/agents/tree/main/docs |
SDK API, state, routing, scheduling |
| Cloudflare Agents docs | https://developers.cloudflare.com/agents/ |
Platform integration, deployment |
| Workers docs | Search tool or https://developers.cloudflare.com/workers/ |
Runtime APIs, bindings, config |
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": "MyAgent", "class_name": "MyAgent" }]
},
"migrations": [{ "tag": "v1", "new_sqlite_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 "@cloudflare/ai-chat";
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