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: "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], }); ``` ### 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 ) `; // 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") { 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 }) { console.log(`Reminder for ${data.userId}: ${data.message}`); } ``` ### 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 // 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> { 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, { role: "user", content: message }, ], stream: true, }); return response; } } ``` ## Client Integration ### React Hook ```tsx import { useAgent } from "agents/react"; function Chat() { const { state, send, connected } = useAgent({ agent: "my-agent", name: userId, }); 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> ))} </div> ); } ``` ### Vanilla JavaScript ```javascript const ws = new WebSocket("wss://my-agent.workers.dev/agents/MyAgent/user123"); 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 solutions @ref(agent-patterns.md) @ref(examples.md) @ref(state-patterns.md) @ref(troubleshooting.md)