Cloudflare Agents

Cloudflare Agents SDK

Status: Production Ready ✅ Last Updated: 2025-11-23 Dependencies: cloudflare-worker-base (recommended) Latest Versions: agents@0.2.23 (Nov 13, 2025), @modelcontextprotocol/sdk@latest Production Tested: Cloudflare's own MCP servers (https://github.com/cloudflare/mcp-server-cloudflare)

Recent Updates (2025):

• Sept 2025: AI SDK v5 compatibility, automatic message migration
• April 2025: MCP support (MCPAgent class), import { context } from agents
• March 2025: Package rename (agents-sdk → agents)

What is Cloudflare Agents?

The Cloudflare Agents SDK enables building AI-powered autonomous agents that run on Cloudflare Workers + Durable Objects. Agents can:

• Communicate in real-time via WebSockets and Server-Sent Events
• Persist state with built-in SQLite database (up to 1GB per agent)
• Schedule tasks using delays, specific dates, or cron expressions
• Run workflows by triggering asynchronous Cloudflare Workflows
• Browse the web using Browser Rendering API + Puppeteer
• Implement RAG with Vectorize vector database + Workers AI embeddings
• Build MCP servers implementing the Model Context Protocol
• Support human-in-the-loop patterns for review and approval
• Scale to millions of independent agent instances globally

Each agent instance is a globally unique, stateful micro-server that can run for seconds, minutes, or hours.

Do You Need Agents SDK?

STOP: Before using Agents SDK, ask yourself if you actually need it.

Use JUST Vercel AI SDK (Simpler) When:

• ✅ Building a basic chat interface
• ✅ Server-Sent Events (SSE) streaming is sufficient (one-way: server → client)
• ✅ No persistent agent state needed (or you manage it separately with D1/KV)
• ✅ Single-user, single-conversation scenarios
• ✅ Just need AI responses, no complex workflows or scheduling

This covers 80% of chat applications. For these cases, use Vercel AI SDK directly on Workers - it's simpler, requires less infrastructure, and handles streaming automatically.

Example (no Agents SDK needed):

// worker.ts - Simple chat with AI SDK only
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

export default {
  async fetch(request: Request, env: Env) {
    const { messages } = await request.json();

    const result = streamText({
      model: openai('gpt-4o-mini'),
      messages
    });

    return result.toTextStreamResponse(); // Automatic SSE streaming
  }
}

// client.tsx - React with built-in hooks
import { useChat } from 'ai/react';

function ChatPage() {
  const { messages, input, handleSubmit } = useChat({ api: '/api/chat' });
  // Done. No Agents SDK needed.
}

Result: 100 lines of code instead of 500. No Durable Objects setup, no WebSocket complexity, no migrations.

Use Agents SDK When You Need:

• ✅ WebSocket connections (true bidirectional real-time communication)
• ✅ Durable Objects (globally unique, stateful agent instances)
• ✅ Built-in state persistence (SQLite storage up to 1GB per agent)
• ✅ Multi-agent coordination (agents calling and communicating with each other)
• ✅ Scheduled tasks (delays, cron expressions, recurring jobs)
• ✅ Human-in-the-loop workflows (approval gates, review processes)
• ✅ Long-running agents (background processing, autonomous workflows)
• ✅ MCP servers with stateful tool execution

This is ~20% of applications - when you need the infrastructure that Agents SDK provides.

Key Understanding: What Agents SDK IS vs IS NOT

Agents SDK IS:

• 🏗️ Infrastructure layer for WebSocket connections, Durable Objects, and state management
• 🔧 Framework for building stateful, autonomous agents
• 📦 Wrapper around Durable Objects with lifecycle methods

Agents SDK IS NOT:

• ❌ AI inference provider (you bring your own: AI SDK, Workers AI, OpenAI, etc.)
• ❌ Streaming response handler (use AI SDK for automatic parsing)
• ❌ LLM integration (that's a separate concern)

Think of it this way:

• Agents SDK = The building (WebSockets, state, rooms)
• AI SDK / Workers AI = The AI brain (inference, reasoning, responses)

You can use them together (recommended for most cases), or use Workers AI directly (if you're willing to handle manual SSE parsing).

Decision Flowchart

Building an AI application?
│
├─ Need WebSocket bidirectional communication? ───────┐
│  (Client sends while server streams, agent-initiated messages)
│
├─ Need Durable Objects stateful instances? ──────────┤
│  (Globally unique agents with persistent memory)
│
├─ Need multi-agent coordination? ────────────────────┤
│  (Agents calling/messaging other agents)
│
├─ Need scheduled tasks or cron jobs? ────────────────┤
│  (Delayed execution, recurring tasks)
│
├─ Need human-in-the-loop workflows? ─────────────────┤
│  (Approval gates, review processes)
│
└─ If ALL above are NO ─────────────────────────────→ Use AI SDK directly
                                                       (Much simpler approach)

   If ANY above are YES ────────────────────────────→ Use Agents SDK + AI SDK
                                                       (More infrastructure, more power)

Architecture Comparison

Still Not Sure?

Start with AI SDK. You can always migrate to Agents SDK later if you discover you need WebSockets or Durable Objects. It's easier to add infrastructure later than to remove it.

For most developers: If you're building a chat interface and don't have specific requirements for WebSockets, multi-agent coordination, or scheduled tasks, use AI SDK directly. You'll ship faster and with less complexity.

Proceed with Agents SDK only if you've identified a specific need for its infrastructure capabilities.

Quick Start (10 Minutes)

1. Scaffold Project with Template

npm create cloudflare@latest my-agent -- \
  --template=cloudflare/agents-starter \
  --ts \
  --git \
  --deploy false

What this creates:

• Complete Agent project structure
• TypeScript configuration
• wrangler.jsonc with Durable Objects bindings
• Example chat agent implementation
• React client with useAgent hook

2. Or Add to Existing Worker

cd my-existing-worker
npm install agents

Then create an Agent class:

// src/index.ts
import { Agent, AgentNamespace } from "agents";

export class MyAgent extends Agent {
  async onRequest(request: Request): Promise<Response> {
    return new Response("Hello from Agent!");
  }
}

export default MyAgent;

3. Configure Durable Objects Binding

Create or update wrangler.jsonc:

{
  "$schema": "node_modules/wrangler/config-schema.json",
  "name": "my-agent",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-21",
  "compatibility_flags": ["nodejs_compat"],
  "durable_objects": {
    "bindings": [
      {
        "name": "MyAgent",        // MUST match class name
        "class_name": "MyAgent"   // MUST match exported class
      }
    ]
  },
  "migrations": [
    {
      "tag": "v1",
      "new_sqlite_classes": ["MyAgent"]  // CRITICAL: Enables SQLite storage
    }
  ]
}

CRITICAL Configuration Rules:

• ✅ name and class_name MUST be identical
• ✅ new_sqlite_classes MUST be in first migration (cannot add later)
• ✅ Agent class MUST be exported (or binding will fail)
• ✅ Migration tags CANNOT be reused (each migration needs unique tag)

4. Deploy

npx wrangler@latest deploy

Your agent is now running at: https://my-agent.<subdomain>.workers.dev

Architecture Overview: How the Pieces Fit Together

Understanding what each tool does prevents confusion and helps you choose the right combination.

The Stack

┌─────────────────────────────────────────────────────────┐
│                    Your Application                      │
│                                                          │
│  ┌────────────────┐         ┌──────────────────────┐   │
│  │  Agents SDK    │         │   AI Inference       │   │
│  │  (Infra Layer) │   +     │   (Brain Layer)      │   │
│  │                │         │                      │   │
│  │ • WebSockets   │         │  Choose ONE:         │   │
│  │ • Durable Objs │         │  • Vercel AI SDK ✅   │   │
│  │ • State (SQL)  │         │  • Workers AI ⚠️      │   │
│  │ • Scheduling   │         │  • OpenAI Direct     │   │
│  │ • Multi-agent  │         │  • Anthropic Direct  │   │
│  └────────────────┘         └──────────────────────┘   │
│         ↓                             ↓                │
│  Manages connections          Generates responses      │
│  and state                    and handles streaming    │
└─────────────────────────────────────────────────────────┘
                          ↓
              Cloudflare Workers + Durable Objects

What Each Tool Provides

1. Agents SDK (This Skill)

Purpose: Infrastructure for stateful, real-time agents

Provides:

• ✅ WebSocket connection management (bidirectional real-time)
• ✅ Durable Objects wrapper (globally unique agent instances)
• ✅ Built-in state persistence (SQLite up to 1GB)
• ✅ Lifecycle methods (onStart, onConnect, onMessage, onClose)
• ✅ Task scheduling (this.schedule() with cron/delays)
• ✅ Multi-agent coordination (routeAgentRequest())
• ✅ Client libraries (useAgent, AgentClient, agentFetch)

Does NOT Provide:

• ❌ AI inference (no LLM calls)
• ❌ Streaming response parsing (bring your own)
• ❌ Provider integrations (OpenAI, Anthropic, etc.)

Think of it as: The building and infrastructure (rooms, doors, plumbing) but NOT the residents (AI).

2. Vercel AI SDK (Recommended for AI)

Purpose: AI inference with automatic streaming

Provides:

• ✅ Automatic streaming response handling (SSE parsing done for you)
• ✅ Multi-provider support (OpenAI, Anthropic, Google, etc.)
• ✅ React hooks (useChat, useCompletion, useAssistant)
• ✅ Unified API across providers
• ✅ Tool calling / function calling
• ✅ Works on Cloudflare Workers ✅

Example:

import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

const result = streamText({
  model: openai('gpt-4o-mini'),
  messages: [...]
});

// Returns SSE stream - no manual parsing needed
return result.toTextStreamResponse();

When to use with Agents SDK:

• ✅ Most chat applications
• ✅ When you want React hooks
• ✅ When you use multiple AI providers
• ✅ When you want clean, abstracted AI calls

Combine with Agents SDK:

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

export class MyAgent extends AIChatAgent<Env> {
  async onChatMessage(onFinish) {
    // Agents SDK provides: WebSocket, state, this.messages
    // AI SDK provides: Automatic streaming, provider abstraction

    return streamText({
      model: openai('gpt-4o-mini'),
      messages: this.messages  // Managed by Agents SDK
    }).toTextStreamResponse();
  }
}

3. Workers AI (Alternative for AI)

Purpose: Cloudflare's on-platform AI inference

Provides:

• ✅ Cost-effective inference (included in Workers subscription)
• ✅ No external API keys needed
• ✅ Models: LLaMA 3, Qwen, Mistral, embeddings, etc.
• ✅ Runs on Cloudflare's network (low latency)

Does NOT Provide:

• ❌ Automatic streaming parsing (returns raw SSE format)
• ❌ React hooks
• ❌ Multi-provider abstraction

Manual parsing required:

const response = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
  messages: [...],
  stream: true
});

// Returns raw SSE format - YOU must parse
for await (const chunk of response) {
  const text = new TextDecoder().decode(chunk);  // Uint8Array → string
  if (text.startsWith('data: ')) {              // Check SSE format
    const data = JSON.parse(text.slice(6));     // Parse JSON
    if (data.response) {                        // Extract .response field
      fullResponse += data.response;
    }
  }
}

When to use:

• ✅ Cost is critical (embeddings, high-volume)
• ✅ Need Cloudflare-specific models
• ✅ Willing to handle manual SSE parsing
• ✅ No external dependencies allowed

Trade-off: Save money, spend time on manual parsing.

Recommended Combinations

Option A: Agents SDK + Vercel AI SDK (Recommended ⭐)

Use when: You need WebSockets/state AND want clean AI integration

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

export class ChatAgent extends AIChatAgent<Env> {
  async onChatMessage(onFinish) {
    return streamText({
      model: openai('gpt-4o-mini'),
      messages: this.messages,  // Agents SDK manages history
      onFinish
    }).toTextStreamResponse();
  }
}

Pros:

• ✅ Best developer experience
• ✅ Automatic streaming
• ✅ WebSockets + state from Agents SDK
• ✅ Clean, maintainable code

Cons:

• ⚠️ Requires external API keys
• ⚠️ Additional cost for AI provider

Option B: Agents SDK + Workers AI

Use when: You need WebSockets/state AND cost is critical

import { Agent } from "agents";

export class BudgetAgent extends Agent<Env> {
  async onMessage(connection, message) {
    const response = await this.env.AI.run('@cf/meta/llama-3-8b-instruct', {
      messages: [...],
      stream: true
    });

    // Manual SSE parsing required (see Workers AI section above)
    for await (const chunk of response) {
      // ... manual parsing ...
    }
  }
}

Pros:

• ✅ Cost-effective
• ✅ No external dependencies
• ✅ WebSockets + state from Agents SDK

Cons:

• ❌ Manual SSE parsing complexity
• ❌ Limited model selection
• ❌ More code to maintain

Option C: Just Vercel AI SDK (No Agents)

Use when: You DON'T need WebSockets or Durable Objects

// worker.ts - Simple Workers route
export default {
  async fetch(request: Request, env: Env) {
    const { messages } = await request.json();

    const result = streamText({
      model: openai('gpt-4o-mini'),
      messages
    });

    return result.toTextStreamResponse();
  }
}

// client.tsx - Built-in React hooks
import { useChat } from 'ai/react';

function Chat() {
  const { messages, input, handleSubmit } = useChat({ api: '/api/chat' });
  return <form
}

Pros:

• ✅ Simplest approach
• ✅ Least code
• ✅ Fast to implement
• ✅ Built-in React hooks

Cons:

• ❌ No WebSockets (only SSE)
• ❌ No Durable Objects state
• ❌ No multi-agent coordination

Best for: 80% of chat applications

Decision Matrix

Key Takeaway

Agents SDK is infrastructure, not AI. You combine it with AI inference tools:

• For best DX: Agents SDK + Vercel AI SDK ⭐
• For cost savings: Agents SDK + Workers AI (accept manual parsing)
• For simplicity: Just AI SDK (if you don't need WebSockets/state)

The rest of this skill focuses on Agents SDK (the infrastructure layer). For AI inference patterns, see the ai-sdk-core or cloudflare-workers-ai skills.

Configuration (wrangler.jsonc)

Critical Required Configuration:

{
  "durable_objects": {
    "bindings": [{ "name": "MyAgent", "class_name": "MyAgent" }]
  },
  "migrations": [
    { "tag": "v1", "new_sqlite_classes": ["MyAgent"] }  // MUST be in first migration
  ]
}

Common Optional Bindings: ai, vectorize, browser, workflows, d1_databases, r2_buckets

CRITICAL Migration Rules:

• ✅ new_sqlite_classes MUST be in tag "v1" (cannot add SQLite to existing deployed class)
• ✅ name and class_name MUST match exactly
• ✅ Migrations are atomic (all instances updated simultaneously)
• ✅ Each tag must be unique, cannot edit/remove previous tags

See: https://developers.cloudflare.com/agents/api-reference/configuration/

Core Agent Patterns

Agent Class Basics - Extend Agent<Env, State> with lifecycle methods:

• onStart() - Agent initialization
• onRequest() - Handle HTTP requests
• onConnect/onMessage/onClose() - WebSocket handling
• onStateUpdate() - React to state changes

Key Properties:

• this.env - Environment bindings (AI, DB, etc.)
• this.state - Current agent state (read-only)
• this.setState() - Update persisted state
• this.sql - Built-in SQLite database
• this.name - Agent instance identifier
• this.schedule() - Schedule future tasks

See: Official Agent API docs at https://developers.cloudflare.com/agents/api-reference/agents-api/

WebSockets & Real-Time Communication

Agents support WebSockets for bidirectional real-time communication. Use when you need:

• Client can send messages while server streams
• Agent-initiated messages (notifications, updates)
• Long-lived connections with state

Basic Pattern:

export class ChatAgent extends Agent<Env, State> {
  async onConnect(connection: Connection, ctx: ConnectionContext) {
    // Auth check, add to participants, send welcome
  }

  async onMessage(connection: Connection, message: WSMessage) {
    // Process message, update state, broadcast response
  }
}

SSE Alternative: For one-way server → client streaming (simpler, HTTP-based), use Server-Sent Events instead of WebSockets.

See: https://developers.cloudflare.com/agents/api-reference/websockets/

State Management

Two State Mechanisms:

1. this.setState(newState) - JSON-serializable state (up to 1GB)

   • Automatically persisted, syncs to WebSocket clients
   • Use for: User preferences, session data, small datasets

2. this.sql - Built-in SQLite database (up to 1GB)

   • Tagged template literals prevent SQL injection
   • Use for: Relational data, large datasets, complex queries

State Rules:

• ✅ JSON-serializable only (objects, arrays, primitives, null)
• ✅ Persists across restarts, immediately consistent
• ❌ No functions or circular references
• ❌ 1GB total limit (state + SQL combined)

SQL Pattern:

await this.sql`CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, email TEXT)`
await this.sql`INSERT INTO users (email) VALUES (${userEmail})`  // ← Prepared statement
const users = await this.sql`SELECT * FROM users WHERE email = ${email}`  // ← Returns array

See: https://developers.cloudflare.com/agents/api-reference/store-and-sync-state/

Schedule Tasks

Agents can schedule tasks to run in the future using this.schedule().

Delay (Seconds)

export class MyAgent extends Agent {
  async onRequest(request: Request): Promise<Response> {
    // Schedule task to run in 60 seconds
    const { id } = await this.schedule(60, "checkStatus", { requestId: "123" });

    return Response.json({ scheduledTaskId: id });
  }

  // This method will be called in 60 seconds
  async checkStatus(data: { requestId: string }) {
    console.log('Checking status for request:', data.requestId);
    // Perform check, update state, send notification, etc.
  }
}

Specific Date

export class MyAgent extends Agent {
  async scheduleReminder(reminderDate: string) {
    const date = new Date(reminderDate);

    const { id } = await this.schedule(date, "sendReminder", {
      message: "Time for your appointment!"
    });

    return id;
  }

  async sendReminder(data: { message: string }) {
    console.log('Sending reminder:', data.message);
    // Send email, push notification, etc.
  }
}

Cron Expressions

export class MyAgent extends Agent {
  async setupRecurringTasks() {
    // Every 10 minutes
    await this.schedule("*/10 * * * *", "checkUpdates", {});

    // Every day at 8 AM
    await this.schedule("0 8 * * *", "dailyReport", {});

    // Every Monday at 9 AM
    await this.schedule("0 9 * * 1", "weeklyReport", {});

    // Every hour on the hour
    await this.schedule("0 * * * *", "hourlyCheck", {});
  }

  async checkUpdates(data: any) {
    console.log('Checking for updates...');
  }

  async dailyReport(data: any) {
    console.log('Generating daily report...');
  }

  async weeklyReport(data: any) {
    console.log('Generating weekly report...');
  }

  async hourlyCheck(data: any) {
    console.log('Running hourly check...');
  }
}

Managing Scheduled Tasks

export class MyAgent extends Agent {
  async manageSchedules() {
    // Get all scheduled tasks
    const allTasks = this.getSchedules();
    console.log('Total tasks:', allTasks.length);

    // Get specific task by ID
    const taskId = "some-task-id";
    const task = await this.getSchedule(taskId);

    if (task) {
      console.log('Task:', task.callback, 'at', new Date(task.time));
      console.log('Payload:', task.payload);
      console.log('Type:', task.type);  // "scheduled" | "delayed" | "cron"

      // Cancel the task
      const cancelled = await this.cancelSchedule(taskId);
      console.log('Cancelled:', cancelled);
    }

    // Get tasks in time range
    const upcomingTasks = this.getSchedules({
      timeRange: {
        start: new Date(),
        end: new Date(Date.now() + 24 * 60 * 60 * 1000)  // Next 24 hours
      }
    });

    console.log('Upcoming tasks:', upcomingTasks.length);

    // Filter by type
    const cronTasks = this.getSchedules({ type: "cron" });
    const delayedTasks = this.getSchedules({ type: "delayed" });
  }
}

Scheduling Constraints:

• Each task maps to a SQL database row (max 2 MB per task)
• Total tasks limited by: (task_size * count) + other_state < 1GB
• Cron tasks continue running until explicitly cancelled
• Callback method MUST exist on Agent class (throws error if missing)

CRITICAL ERROR: If callback method doesn't exist:

// ❌ BAD: Method doesn't exist
await this.schedule(60, "nonExistentMethod", {});

// ✅ GOOD: Method exists
await this.schedule(60, "existingMethod", {});

async existingMethod(data: any) {
  // Implementation
}

Run Workflows

Agents can trigger asynchronous Cloudflare Workflows.

Workflow Binding Configuration

wrangler.jsonc:

{
  "workflows": [
    {
      "name": "MY_WORKFLOW",
      "class_name": "MyWorkflow"
    }
  ]
}

If Workflow is in a different script:

{
  "workflows": [
    {
      "name": "EMAIL_WORKFLOW",
      "class_name": "EmailWorkflow",
      "script_name": "email-workflows"  // Different project
    }
  ]
}

Triggering a Workflow

import { Agent } from "agents";
import { WorkflowEntrypoint, WorkflowEvent, WorkflowStep } from "cloudflare:workers";

interface Env {
  MY_WORKFLOW: Workflow;
  MyAgent: AgentNamespace<MyAgent>;
}

export class MyAgent extends Agent<Env> {
  async onRequest(request: Request): Promise<Response> {
    const userId = new URL(request.url).searchParams.get('userId');

    // Trigger a workflow immediately
    const instance = await this.env.MY_WORKFLOW.create({
      id: `user-${userId}`,
      params: { userId, action: "process" }
    });

    // Or schedule a delayed workflow trigger
    await this.schedule(300, "runWorkflow", { userId });

    return Response.json({ workflowId: instance.id });
  }

  async runWorkflow(data: { userId: string }) {
    const instance = await this.env.MY_WORKFLOW.create({
      id: `delayed-${data.userId}`,
      params: data
    });

    // Monitor workflow status periodically
    await this.schedule("*/5 * * * *", "checkWorkflowStatus", { id: instance.id });
  }

  async checkWorkflowStatus(data: { id: string }) {
    // Check workflow status (see Workflows docs for details)
    console.log('Checking workflow:', data.id);
  }
}

// Workflow definition (can be in same or different file/project)
export class MyWorkflow extends WorkflowEntrypoint<Env> {
  async run(event: WorkflowEvent<{ userId: string }>, step: WorkflowStep) {
    // Workflow implementation
    const result = await step.do('process-data', async () => {
      return { processed: true };
    });

    return result;
  }
}

Agents vs Workflows

Best Practice: Use Agents to coordinate multiple Workflows. Agents can trigger, monitor, and respond to Workflow results while maintaining user interaction.

Browse the Web

Agents can use Browser Rendering for web scraping and automation:

Binding: Add "browser": { "binding": "BROWSER" } to wrangler.jsonc Package: @cloudflare/puppeteer Use Case: Web scraping, screenshots, automated browsing within agent workflows

See: cloudflare-browser-rendering skill for complete Puppeteer + Workers integration guide.

Retrieval Augmented Generation (RAG)

Agents can implement RAG using Vectorize (vector database) + Workers AI (embeddings):

Pattern: Ingest docs → generate embeddings → store in Vectorize → query → retrieve context → pass to AI

Bindings:

• "ai": { "binding": "AI" } - Workers AI for embeddings
• "vectorize": { "bindings": [{ "binding": "VECTORIZE", "index_name": "my-vectors" }] } - Vector search

Typical Workflow:

1. Generate embeddings with Workers AI (@cf/baai/bge-base-en-v1.5)
2. Upsert vectors to Vectorize (this.env.VECTORIZE.upsert(vectors))
3. Query similar vectors (this.env.VECTORIZE.query(queryVector, { topK: 5 }))
4. Use retrieved context in AI prompt

See: cloudflare-vectorize skill for complete RAG implementation guide.

Using AI Models

Agents can call AI models using:

• Vercel AI SDK (recommended): Multi-provider, automatic streaming, tool calling
• Workers AI: Cloudflare's on-platform inference (cost-effective, manual parsing)

Architecture Note: Agents SDK provides infrastructure (WebSockets, state, scheduling). AI inference is a separate layer - use AI SDK for the "brain".

See:

• ai-sdk-core skill for complete AI SDK integration patterns
• cloudflare-workers-ai skill for Workers AI streaming parsing

Calling Agents

Two Main Patterns:

1. routeAgentRequest(request, env) - Auto-route via URL pattern /agents/:agent/:name

   • Example: /agents/my-agent/user-123 routes to MyAgent instance "user-123"

2. getAgentByName<Env, T>(env.AgentBinding, instanceName) - Custom routing

   • Returns agent stub for calling methods or passing requests
   • Example: const agent = getAgentByName(env.MyAgent, 'user-${userId}')

Multi-Agent Communication:

export class AgentA extends Agent<Env> {
  async processData(data: any) {
    const agentB = getAgentByName<Env, AgentB>(this.env.AgentB, 'processor-1');
    return await (await agentB).analyze(data);
  }
}

CRITICAL Security: Always authenticate in Worker BEFORE creating/accessing agents. Agents should assume the caller is authorized.

See: https://developers.cloudflare.com/agents/api-reference/calling-agents/

Client APIs

Browser/React Integration:

• AgentClient (from agents/client) - WebSocket client for browser
• agentFetch (from agents/client) - HTTP requests to agents
• useAgent (from agents/react) - React hook for WebSocket connections + state sync
• useAgentChat (from agents/ai-react) - Pre-built chat UI hook

All client libraries automatically handle: WebSocket connections, state synchronization, reconnection logic.

See: https://developers.cloudflare.com/agents/api-reference/client-apis/

Model Context Protocol (MCP)

Build MCP servers using the Agents SDK.

MCP Server Setup

npm install @modelcontextprotocol/sdk agents

Basic MCP Server

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

export class MyMCP extends McpAgent {
  server = new McpServer({ name: "Demo", version: "1.0.0" });

  async init() {
    // Define a tool
    this.server.tool(
      "add",
      "Add two numbers together",
      {
        a: z.number().describe("First number"),
        b: z.number().describe("Second number")
      },
      async ({ a, b }) => ({
        content: [{ type: "text", text: String(a + b) }]
      })
    );
  }
}

Stateful MCP Server

type State = { counter: number };

export class StatefulMCP extends McpAgent<Env, State> {
  server = new McpServer({ name: "Counter", version: "1.0.0" });

  initialState: State = { counter: 0 };

  async init() {
    // Resource
    this.server.resource(
      "counter",
      "mcp://resource/counter",
      (uri) => ({
        contents: [{ uri: uri.href, text: String(this.state.counter) }]
      })
    );

    // Tool
    this.server.tool(
      "increment",
      "Increment the counter",
      { amount: z.number() },
      async ({ amount }) => {
        this.setState({
          ...this.state,
          counter: this.state.counter + amount
        });

        return {
          content: [{
            type: "text",
            text: `Counter is now ${this.state.counter}`
          }]
        };
      }
    );
  }
}

MCP Transport Configuration

import { Hono } from 'hono';

const app = new Hono();

// Modern streamable HTTP transport (recommended)
app.mount('/mcp', MyMCP.serve('/mcp').fetch, { replaceRequest: false });

// Legacy SSE transport (deprecated)
app.mount('/sse', MyMCP.serveSSE('/sse').fetch, { replaceRequest: false });

export default app;

Transport Comparison:

• /mcp: Streamable HTTP (modern, recommended)
• /sse: Server-Sent Events (legacy, deprecated)

MCP with OAuth

import { OAuthProvider } from '@cloudflare/workers-oauth-provider';

export default new OAuthProvider({
  apiHandlers: {
    '/sse': MyMCP.serveSSE('/sse'),
    '/mcp': MyMCP.serve('/mcp')
  },
  // OAuth configuration
  clientId: 'your-client-id',
  clientSecret: 'your-client-secret',
  // ... other OAuth settings
});

Testing MCP Server

# Run MCP inspector
npx @modelcontextprotocol/inspector@latest

# Connect to: http://localhost:8788/mcp

Cloudflare's MCP Servers: See reference for production examples.

Critical Rules

Always Do ✅

1. Export Agent class - Must be exported for binding to work
2. Include new_sqlite_classes in v1 migration - Cannot add SQLite later
3. Match binding name to class name - Prevents "binding not found" errors
4. Authenticate in Worker, not Agent - Security best practice
5. Use tagged template literals for SQL - Prevents SQL injection
6. Handle WebSocket disconnections - State persists, connections don't
7. Verify scheduled task callback exists - Throws error if method missing
8. Use global unique instance names - Same name = same agent globally
9. Check state size limits - Max 1GB total per agent
10. Monitor task payload size - Max 2MB per scheduled task
11. Use workflow bindings correctly - Must be configured in wrangler.jsonc
12. Create Vectorize indexes before inserting - Required for metadata filtering
13. Close browser instances - Prevent resource leaks
14. Use setState() for persistence - Don't just modify this.state
15. Test migrations locally first - Migrations are atomic, can't rollback

Never Do ❌

1. Don't add SQLite to existing deployed class - Must be in first migration
2. Don't gradually deploy migrations - Atomic only
3. Don't skip authentication in Worker - Always auth before agent access
4. Don't construct SQL strings manually - Use tagged templates
5. Don't exceed 1GB state per agent - Hard limit
6. Don't schedule tasks with non-existent callbacks - Runtime error
7. Don't assume same name = different agent - Global uniqueness
8. Don't use SSE for MCP - Deprecated, use /mcp transport
9. Don't forget browser binding - Required for web browsing
10. Don't modify this.state directly - Use setState() instead

Known Issues Prevention

This skill prevents 16+ documented issues:

Issue 1: Migrations Not Atomic

Error: "Cannot gradually deploy migration" Source: https://developers.cloudflare.com/durable-objects/reference/durable-objects-migrations/ Why: Migrations apply to all instances simultaneously Prevention: Deploy migrations independently of code changes, use npx wrangler versions deploy

Issue 2: Missing new_sqlite_classes

Error: "Cannot enable SQLite on existing class" Source: https://developers.cloudflare.com/agents/api-reference/configuration/ Why: SQLite must be enabled in first migration Prevention: Include new_sqlite_classes in tag "v1" migration

Issue 3: Agent Class Not Exported

Error: "Binding not found" or "Cannot access undefined" Source: https://developers.cloudflare.com/agents/api-reference/agents-api/ Why: Durable Objects require exported class Prevention: export class MyAgent extends Agent (with export keyword)

Issue 4: Binding Name Mismatch

Error: "Binding 'X' not found" Source: https://developers.cloudflare.com/agents/api-reference/configuration/ Why: Binding name must match class name exactly Prevention: Ensure name and class_name are identical in wrangler.jsonc

Issue 5: Global Uniqueness Not Understood

Error: Unexpected behavior with agent instances Source: https://developers.cloudflare.com/agents/api-reference/agents-api/ Why: Same name always returns same agent instance globally Prevention: Use unique identifiers (userId, sessionId) for instance names

Issue 6: WebSocket State Not Persisted

Error: Connection state lost after disconnect Source: https://developers.cloudflare.com/agents/api-reference/websockets/ Why: WebSocket connections don't persist, but agent state does Prevention: Store important data in agent state via setState(), not connection state

Issue 7: Scheduled Task Callback Doesn't Exist

Error: "Method X does not exist on Agent" Source: https://developers.cloudflare.com/agents/api-reference/schedule-tasks/ Why: this.schedule() calls method that isn't defined Prevention: Ensure callback method exists before scheduling

Issue 8: State Size Limit Exceeded

Error: "Maximum database size exceeded" Source: https://developers.cloudflare.com/agents/api-reference/store-and-sync-state/ Why: Agent state + scheduled tasks exceed 1GB Prevention: Monitor state size, use external storage (D1, R2) for large data

Issue 9: Scheduled Task Too Large

Error: "Task payload exceeds 2MB" Source: https://developers.cloudflare.com/agents/api-reference/schedule-tasks/ Why: Each task maps to database row with 2MB limit Prevention: Keep task payloads minimal, store large data in agent state/SQL

Issue 10: Workflow Binding Missing

Error: "Cannot read property 'create' of undefined" Source: https://developers.cloudflare.com/agents/api-reference/run-workflows/ Why: Workflow binding not configured in wrangler.jsonc Prevention: Add workflow binding before using this.env.WORKFLOW

Issue 11: Browser Binding Required

Error: "BROWSER binding undefined" Source: https://developers.cloudflare.com/agents/api-reference/browse-the-web/ Why: Browser Rendering requires explicit binding Prevention: Add "browser": { "binding": "BROWSER" } to wrangler.jsonc

Issue 12: Vectorize Index Not Found

Error: "Index does not exist" Source: https://developers.cloudflare.com/agents/api-reference/rag/ Why: Vectorize index must be created before use Prevention: Run wrangler vectorize create before deploying agent

Issue 13: MCP Transport Confusion

Error: "SSE transport deprecated" Source: https://developers.cloudflare.com/agents/model-context-protocol/transport/ Why: SSE transport is legacy, streamable HTTP is recommended Prevention: Use /mcp endpoint with MyMCP.serve('/mcp'), not /sse

Issue 14: Authentication Bypass

Error: Security vulnerability Source: https://developers.cloudflare.com/agents/api-reference/calling-agents/ Why: Authentication done in Agent instead of Worker Prevention: Always authenticate in Worker before calling getAgentByName()

Issue 15: Instance Naming Errors

Error: Cross-user data leakage Source: https://developers.cloudflare.com/agents/api-reference/calling-agents/ Why: Poor instance naming allows access to wrong agent Prevention: Use namespaced names like user-${userId}, validate ownership

Issue 16: Workers AI Streaming Requires Manual Parsing

Error: "Cannot read property 'response' of undefined" or empty AI responses Source: https://developers.cloudflare.com/workers-ai/platform/streaming/ Why: Workers AI returns streaming responses as Uint8Array in Server-Sent Events (SSE) format, not plain objects Prevention: Use TextDecoder + SSE parsing pattern (see "Workers AI (Alternative for AI)" section above)

The problem - Attempting to access stream chunks directly fails:

const response = await env.AI.run(model, { stream: true });
for await (const chunk of response) {
  console.log(chunk.response);  // ❌ undefined - chunk is Uint8Array, not object
}

The solution - Parse SSE format manually:

const response = await env.AI.run(model, { stream: true });
for await (const chunk of response) {
  const text = new TextDecoder().decode(chunk);  // Step 1: Uint8Array → string
  if (text.startsWith('data: ')) {              // Step 2: Check SSE format
    const jsonStr = text.slice(6).trim();       // Step 3: Extract JSON from "data: {...}"
    if (jsonStr === '[DONE]') break;            // Step 4: Handle termination
    const data = JSON.parse(jsonStr);           // Step 5: Parse JSON
    if (data.response) {                        // Step 6: Extract .response field
      fullResponse += data.response;
    }
  }
}

Better alternative: Use Vercel AI SDK which handles this automatically:

import { streamText } from 'ai';
import { createCloudflare } from '@a

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