cloudflare-agents

name	cloudflare-agents
version	1.0.0
description	Cloudflare Agents SDK - Build and deploy AI-powered agents on Cloudflare's edge with real-time WebSocket communication, persistent state, SQL storage, task scheduling, MCP integration, and human-in-the-loop workflows.

Cloudflare Agents SDK Skill

The Cloudflare Agents SDK enables you to build and deploy AI-powered agents that run on Cloudflare's global edge network. Agents can autonomously perform tasks, communicate with clients in real-time via WebSockets, call AI models, persist state, schedule tasks, run asynchronous workflows, browse the web, query data from databases, support human-in-the-loop interactions, and act as MCP (Model Context Protocol) servers or clients.

Core Value: Agents deploy on Cloudflare's Durable Objects - stateful micro-servers that can scale to tens of millions and run close to users for low-latency interactivity.

When to Use This Skill

This skill should be triggered when:

• Building AI agents that need persistent state and real-time communication
• Creating chatbots or conversational AI interfaces on Cloudflare Workers
• Implementing WebSocket-based real-time applications
• Deploying agents with embedded SQL databases (SQLite)
• Scheduling tasks with delays, dates, or cron expressions
• Building human-in-the-loop approval workflows for AI tool execution
• Creating or connecting to MCP (Model Context Protocol) servers
• Deploying stateful AI applications on edge infrastructure
• Building multi-user collaborative AI applications
• Integrating with Vercel AI SDK on Cloudflare Workers

Quick Reference

Installation

# Add to existing Workers project
npm i agents

# Or create new project from starter template
npm create cloudflare@latest agents-starter -- --template=cloudflare/agents-starter

# Navigate and run
cd agents-starter
mv .env.local .env
npm install
npm run dev

Package Imports

// Core Agent classes
import { Agent } from "agents";
import { AIChatAgent } from "agents/ai-chat-agent";
import { McpAgent } from "agents/mcp";

// Client-side
import { AgentClient } from "agents/client";

// React hooks
import { useAgent } from "agents/react";
import { useAgentChat } from "agents/ai-react";

// Routing
import { routeAgentRequest, getAgentByName } from "agents";

Basic Agent Setup

import { Agent, routeAgentRequest } from "agents";

// Define your Agent class
export class MyAgent extends Agent<Env, State> {
  // Optional: Set initial state
  initialState = {
    counter: 0,
    messages: [],
  };

  // Called when agent starts or resumes
  async onStart() {
    console.log("Agent started");
  }

  // Handle HTTP requests
  async onRequest(request: Request): Promise<Response> {
    return new Response("Hello from Agent!");
  }

  // Handle WebSocket connections
  async onConnect(connection: Connection, ctx: ConnectionContext) {
    console.log(`Client connected: ${connection.id}`);
  }

  // Handle WebSocket messages
  async onMessage(: , : ) {
     data = .(message  );
    connection.(.({ : data }));
  }

  
  () {
    .(, state);
  }
}


  {
   () {
     (request, env);
  },
};

Configuration (wrangler.jsonc)

{
  "name": "my-agent",
  "main": "src/index.ts",
  "compatibility_date": "2025-01-01",
  "compatibility_flags": ["nodejs_compat"],
  "observability": {
    "enabled": true
  },
  "durable_objects": {
    "bindings": [
      {
        "name": "MY_AGENT",
        "class_name": "MyAgent"
      }
    ]
  },
  "migrations": [
    {
      "tag": "v1",
      "new_sqlite_classes": [
    
  

Agent API Reference

Agent Class

The base class for all agents, providing lifecycle hooks and capabilities.

class Agent<Env, State> {
  // Properties
  env: Env;                    // Environment bindings
  state: State;                // Current state (read-only)
  initialState?: State;        // Optional default state

  // Lifecycle hooks
  onStart(): void | Promise<void>;
  onRequest(request: Request): Response | Promise<Response>;
  onConnect(connection: Connection, ctx: ConnectionContext): void;
  onMessage(connection: Connection, message: WSMessage): void;
  onError(connection: Connection, error: Error): void;
  onClose(connection: Connection, code: number, reason: string, wasClean: ): ;
  (: , :  | ): ;

  
  (: <>): ;

  
  sql: ;

  
  (:  |  | , : , ?: ): ;
  (: ):  | ;
  (?: ): [];
  (: ): ;

  
  (: , : , ...): ;
  (: ): ;
  (): [];
}

AIChatAgent Class

Extended Agent class for building chat interfaces with AI models.

import { AIChatAgent } from "agents/ai-chat-agent";
import { createOpenAI } from "@ai-sdk/openai";
import { streamText, createDataStreamResponse } from "ai";

export class ChatAgent extends AIChatAgent<Env> {
  async onChatMessage(onFinish: (result: any) => void) {
    return createDataStreamResponse({
      execute: async (dataStream) => {
        const openai = createOpenAI({
          apiKey: this.env.OPENAI_API_KEY,
        });

        const stream = streamText({
          model: openai("gpt-4o"),
          messages: this.messages,  // Built-in message history
          onFinish,
        });

        stream.mergeIntoDataStream(dataStream);
      },
    });
  }
}

Features:

• Automatic resumable streaming (reconnects resume from last position)
• Works across browser tabs and devices
• Built-in message history via this.messages

State Management

Server-Side State

interface GameState {
  players: string[];
  score: number;
  status: "waiting" | "playing" | "finished";
}

class GameAgent extends Agent<Env, GameState> {
  initialState: GameState = {
    players: [],
    score: 0,
    status: "waiting",
  };

  async addPlayer(name: string) {
    this.setState({
      ...this.state,
      players: [...this.state.players, name],
    });
  }

  async startGame() {
    this.setState({
      ...this.state,
      status: "playing",
    });
  }

  onStateUpdate(state: GameState, source: "server" | Connection) {
    // Triggered on every state change
    
    .(, state);
  }
}

Client-Side Synchronization (React)

import { useAgent } from "agents/react";
import { useState, useEffect } from "react";

function GameInterface() {
  const [state, setState] = useState({ players: [], score: 0 });

  const agent = useAgent({
    agent: "game-agent",
    name: "room-123",  // Unique instance identifier
    onStateUpdate: (newState) => setState(newState),
  });

  const addPlayer = (name: string) => {
    agent.setState({
      ...state,
      players: [...state.players, name],
    });
  };

  return (
    <div>
      <h2>Players: {state.players.join(", ")}</h2>
      <button onClick={() => addPlayer("Player")}>Join</button>
    </div>
  );
}

State Characteristics:

• Persisted across Agent restarts
• Thread-safe for concurrent updates
• Zero-latency (colocated with Agent)
• JSON-serializable data
• Automatic sync to all connected clients

SQL Database

Each Agent has an embedded SQLite database with zero-latency access.

class DataAgent extends Agent<Env, State> {
  // Basic query
  async getUser(userId: string) {
    const [user] = this.sql`SELECT * FROM users WHERE id = ${userId}`;
    return user;
  }

  // Typed query
  async getUsers(): User[] {
    return this.sql<User>`SELECT * FROM users ORDER BY created_at DESC`;
  }

  // Insert data
  async createUser(name: string, email: string) {
    this.sql`INSERT INTO users (name, email, created_at)
             VALUES (${name}, ${email}, ${new Date().toISOString()})`;
  }

  // Transaction-like operations
  async transferCredits(from: string, to: string, amount: number) {
    .;
    .;
  }
}

Database Limits:

• Up to 1GB per Agent instance
• Each task/row up to 2MB
• Immediate read-after-write consistency

Task Scheduling

Schedule tasks for future execution using delays, dates, or cron expressions.

class SchedulerAgent extends Agent<Env, State> {
  async scheduleReminder(userId: string, message: string, when: Date) {
    // Schedule with Date
    const taskId = this.schedule(when, "sendReminder", { userId, message });
    return taskId;
  }

  async scheduleDelay(seconds: number) {
    // Schedule with delay in seconds
    this.schedule(seconds, "delayedTask", { data: "payload" });
  }

  async scheduleCron() {
    // Schedule with cron expression (runs daily at 9am UTC)
    this.schedule("0 9 * * *", "dailyReport", {});
  }

  // Callback methods (must match callback string)
  async sendReminder(data: { userId: string; message: string }) {
    // Send notification to user
    console.log();
  }

   () {
    .(, data);
  }

   () {
    
  }

  
   () {
     all = .();
     pending = .({ :  });
     crons = .({ :  });
     { all, pending, crons };
  }

  
   () {
    .(taskId);
  }
}

Schedule Types:

• "scheduled" - One-time at specific Date
• "delayed" - After N seconds
• "cron" - Recurring via cron expression

WebSocket Communication

Server-Side

class ChatRoomAgent extends Agent<Env, State> {
  onConnect(connection: Connection, ctx: ConnectionContext) {
    // Access request headers, cookies, URL
    const url = new URL(ctx.request.url);
    const token = url.searchParams.get("token");

    // Validate and accept connection
    if (!this.validateToken(token)) {
      connection.close(4001, "Unauthorized");
      return;
    }

    // Store connection-specific state
    connection.setState({ userId: token });

    // Send welcome message
    connection.send(JSON.stringify({ type: "welcome", state: this.state }));
  }

  onMessage(connection: Connection, message: WSMessage) {
    const data = JSON.(message  );

     (data.) {
       :
        .(connection, data.);
        ;
       :
        .(connection);
        ;
    }
  }

  () {
    .();
  }

  () {
    .();
  }

   () {
    
    
  }
}

Client-Side (Vanilla JS)

import { AgentClient } from "agents/client";

const client = new AgentClient({
  agent: "chat-room-agent",
  name: "room-general",
});

client.addEventListener("message", (event) => {
  const data = JSON.parse(event.data);
  console.log("Received:", data);
});

client.addEventListener("open", () => {
  client.send(JSON.stringify({ type: "chat", content: "Hello!" }));
});

client.addEventListener("close", (event) => {
  console.log("Disconnected:", event.reason);
});

Client-Side (React)

import { useAgent } from "agents/react";

function ChatRoom() {
  const [messages, setMessages] = useState([]);

  const agent = useAgent({
    agent: "chat-room-agent",
    name: "room-general",
    onMessage: (message) => {
      const data = JSON.parse(message.data);
      setMessages((prev) => [...prev, data]);
    },
    onOpen: () => console.log("Connected"),
    onClose: () => console.log("Disconnected"),
  });

  const sendMessage = (content: string) => {
    agent.send(JSON.stringify({ type: "chat", content }));
  };

  return (
    <div>
      {messages.map((msg, i) => (
        <div key={i}>{msg.content}</>
      ))}
       {
        if (e.key === "Enter") sendMessage(e.target.value);
      }} />
    
  );
}

AI Chat with useAgentChat

Build complete chat interfaces with the useAgentChat hook.

import { useAgent } from "agents/react";
import { useAgentChat } from "agents/ai-react";

function AIChat() {
  const agent = useAgent({
    agent: "chat-agent",
    name: "session-123",
  });

  const {
    messages,
    input,
    handleInputChange,
    handleSubmit,
    isLoading,
    clearHistory,
  } = useAgentChat({
    agent,
    maxSteps: 10,  // Max tool invocations per message
  });

  return (
    <div className="chat-container">
      <div className="messages">
        {messages.map((msg) => (
          <div key={msg.id} className={msg.role}>
            {msg.content}
          </div>
        ))}
      </div>

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Type a message..."
          disabled=
        />
        
          Send
        
      

      Clear History
    
  );
}

Human-in-the-Loop

Implement approval workflows for sensitive AI tool executions.

Server-Side

import { AIChatAgent } from "agents/ai-chat-agent";
import { streamText, tool } from "ai";

// Tools requiring confirmation
const toolsRequiringConfirmation = ["send_email", "make_payment", "delete_data"];

export class ApprovalAgent extends AIChatAgent<Env> {
  async onChatMessage(onFinish) {
    return createDataStreamResponse({
      execute: async (dataStream) => {
        const stream = streamText({
          model: openai("gpt-4o"),
          messages: this.messages,
          tools: {
            send_email: tool({
              description: "Send an email to a user",
              parameters: z.object({
                to: z.string(),
                subject: z.string(),
                body: z.string(),
              }),
              // No execute = requires confirmation
            }),
            : ({
              : ,
              : z.({ : z.() }),
              :  ({ city }) => {
                
                 { : , :  };
              },
            }),
          },
          onFinish,
        });
        stream.(dataStream);
      },
    });
  }
}

Client-Side (React)

import { useAgentChat } from "agents/ai-react";

function ApprovalChat() {
  const { messages, handleSubmit, addToolResult } = useAgentChat({
    agent,
    maxSteps: 10,
  });

  const handleApprove = async (toolCallId: string, args: any) => {
    // Execute the tool and return result
    const result = await executeToolOnServer(toolCallId, args);
    addToolResult({ toolCallId, result });
  };

  const handleReject = (toolCallId: string) => {
    addToolResult({ toolCallId, result: "User rejected this action" });
  };

  return (
    <div>
      {messages.map((msg) => (
        <div key={msg.id}>
          {msg.content}

          {/* Show approval UI for pending tool calls */}
          {msg.toolInvocations?.map((tool) => (
            tool.state === "call" && (
              <div key={tool.toolCallId} className="approval-prompt">
                <p>Approve {tool.toolName}?</p>
                {JSON.stringify(tool.args, null, 2)}
                 handleApprove(tool.toolCallId, tool.args)}>
                  Approve
                
                 handleReject(tool.toolCallId)}>
                  Reject
                
              
            )
          ))}
        
      ))}
    
  );
}

Best Practices:

• Only require confirmation for meaningful consequences (payments, emails, data changes)
• Show complete context including all tool arguments
• Implement timeouts for auto-rejection
• Log all approval decisions for audit

MCP (Model Context Protocol)

As MCP Server

Expose your Agent as an MCP server for AI assistants.

import { McpAgent } from "agents/mcp";
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";

export class MyMcpServer extends McpAgent<Env> {
  server = new McpServer({
    name: "my-mcp-server",
    version: "1.0.0",
  });

  async init() {
    // Register tools
    this.server.tool(
      "search_database",
      "Search the database for records",
      { query: z.string() },
      async (args) => {
        const results = this.sql`SELECT * FROM records WHERE name LIKE ${args.query}`;
        return { content: [{ type: "text", text: JSON.stringify(results) }] };
      }
    );

    // Register resources
    this.server.resource(
      "database://records",
      "Database records",
       () => {
         records = .;
         { : [{ : , : .(records) }] };
      }
    );
  }
}

As MCP Client

Connect to external MCP servers from your Agent.

class AgentWithMcp extends Agent<Env, State> {
  async onStart() {
    // Connect to MCP server
    await this.addMcpServer(
      "weather-service",
      "https://weather-mcp.example.com",
      this.env.MCP_CALLBACK_HOST,
      "/agents"
    );
  }

  async getWeather(city: string) {
    // MCP tools are automatically available
    // Tools are namespaced by server ID
    const servers = this.getMcpServers();
    // Use tools via AI model integration
  }

  async cleanup() {
    await this.removeMcpServer("weather-service");
  }
}

MCP Features:

• Hibernation support (preserves state during sleep)
• HTTP streamable transport (recommended)
• Automatic tool namespacing
• OAuth authorization support
• Elicitation (user input during tool execution)

Routing & Calling Agents

Automatic Routing

import { routeAgentRequest } from "agents";

export default {
  async fetch(request: Request, env: Env) {
    // Routes /agents/:agent/:name automatically
    // e.g., /agents/my-agent/user-123
    return routeAgentRequest(request, env);
  },
};

Manual Routing

import { getAgentByName } from "agents";

export default {
  async fetch(request: Request, env: Env) {
    const url = new URL(request.url);

    if (url.pathname.startsWith("/custom/")) {
      const agentName = url.pathname.split("/")[2];
      const agent = getAgentByName(env.MY_AGENT, agentName);
      return agent.fetch(request);
    }

    return new Response("Not found", { status: 404 });
  },
};

Direct Method Invocation (RPC)

import { getAgentByName } from "agents";

export default {
  async fetch(request: Request, env: Env) {
    const agent = getAgentByName(env.MY_AGENT, "user-123");

    // Call methods directly (no HTTP serialization)
    const response = await agent.chat("Hello!");
    const state = await agent.getState();

    return Response.json({ response, state });
  },
};

Authentication

Via routeAgentRequest Hooks

export default {
  async fetch(request: Request, env: Env) {
    return routeAgentRequest(request, env, {
      onBeforeConnect: async (request) => {
        const token = request.headers.get("Authorization");
        if (!validateToken(token)) {
          return new Response("Unauthorized", { status: 401 });
        }
        return null; // Continue to agent
      },
      onBeforeRequest: async (request) => {
        // Similar validation for HTTP requests
      },
    });
  },
};

Via Middleware (Hono)

import { Hono } from "hono";
import { jwt } from "hono/jwt";
import { getAgentByName } from "agents";

const app = new Hono();

app.use("/agents/*", jwt({ secret: process.env.JWT_SECRET }));

app.all("/agents/:agent/:name/*", async (c) => {
  const user = c.get("jwtPayload");
  const agent = getAgentByName(c.env.MY_AGENT, c.req.param("name"));
  return agent.fetch(c.req.raw);
});

export default app;

Deployment

# Deploy to Cloudflare
npm run deploy
# or
wrangler deploy

# Set secrets
wrangler secret put OPENAI_API_KEY
wrangler secret put JWT_SECRET

# View logs
wrangler tail

Related Cloudflare Services

Service	Integration
Workers AI	Serverless GPU-powered models via @cloudflare/ai
AI Gateway	Caching, rate limiting, model fallbacks
Vectorize	Vector database for RAG and semantic search
Workflows	Stateful execution with automatic retries
D1	Additional SQL database storage
R2	Object storage for large files
KV	Key-value storage for config/cache

Reference Files

This skill includes comprehensive documentation in references/:

• api-reference.md - Complete Agent class API documentation
• state-sql.md - State management and SQL database guide
• mcp-integration.md - Model Context Protocol server/client guide
• examples.md - Full application examples

Resources

• Cloudflare Agents Documentation
• Agents API Reference
• GitHub: cloudflare/agents
• GitHub: cloudflare/agents-starter
• npm: @cloudflare/agents
• MCP Documentation
• Human-in-the-Loop Guide
• Schedule Tasks
• Store and Sync State
• Blog: Making Cloudflare the Best Platform for AI Agents

Notes

• Agents run on Durable Objects (stateful micro-servers)
• Each Agent instance has its own isolated state and SQLite database
• WebSocket hibernation preserves state during inactivity
• Supports AI SDK v6 with resumable streaming
• MCP servers can be stateful applications (not just API wrappers)
• Global uniqueness: same name = same instance across requests
• Licensed under Apache 2.0

Version History

• 1.0.0 (2026-01-08): Initial release
  • Core Agent and AIChatAgent documentation
  • State management and SQL database API
  • WebSocket communication patterns
  • Task scheduling (delay, Date, cron)
  • Human-in-the-loop workflows
  • MCP server and client integration
  • React hooks (useAgent, useAgentChat)
  • Authentication patterns
  • Deployment configuration