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Agents Sdk

OrganizationPopular
cloudflare
agents-sdk

Build, debug, or review Cloudflare Agents SDK applications using the agents package.

Overview

Publishercloudflare
Repositoryskills
Skill nameagents-sdk
Stars
2.8K
Forks
277
Bundled files
19
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 19 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cloudflare on GitHub. Read the source before you install it.

Installation

Install the Agents Sdk AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cloudflare/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/agents-sdk .claude/skills/agents-sdk
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agents Sdk in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agents Sdk on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agents Sdk is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cloudflare Agents SDK

Your knowledge of the Agents SDK may be outdated. Prefer retrieval over pre-training for any Agents SDK task.

Retrieval Sources

Cloudflare docs: https://developers.cloudflare.com/agents/

TopicDocs URLUse for
Getting startedQuick startFirst agent, project setup
Adding to existing projectAdd to existing projectInstall into existing Workers app
ConfigurationConfigurationwrangler.jsonc, bindings, assets, deployment
Agent classAgents APIAgent lifecycle, patterns, pitfalls
StateStore and sync statesetState, validateStateChange, persistence
RoutingRoutingURL patterns, routeAgentRequest
Callable methodsCallable methods@callable, RPC, streaming, timeouts
SchedulingSchedule tasksschedule(), scheduleEvery(), cron
WorkflowsRun workflowsAgentWorkflow, durable multi-step tasks
HTTP/WebSocketsWebSocketsLifecycle hooks, hibernation
Chat agentsChat agentsAIChatAgent, streaming, tools, persistence
Client SDKClient SDKuseAgent, AgentClient, state, RPC, HTTP
Client toolsClient toolsClient-side tools, autoContinueAfterToolResult
Server-driven messagesAutonomous responsessaveMessages, waitUntilStable, server-initiated turns
Resumable streamingChat agentsStream recovery on disconnect
EmailEmailEmail routing, secure reply resolver
MCP clientMCP clientConnecting to MCP servers
MCP serverMCP serverBuilding MCP servers with createMcpHandler
MCP transportsMCP transportsStreamable HTTP, SSE, RPC transport options
Securing MCP serversSecuring MCPOAuth, proxy MCP, hardening
Human-in-the-loopHuman-in-the-loopWorkflow approvals, elicitation, timeout handling
Durable executionDurable executionrunFiber(), stash(), surviving DO eviction
QueueQueueBuilt-in FIFO queue, queue()
RetriesRetriesthis.retry(), backoff/jitter
ObservabilityObservabilityDiagnostics-channel events
Push notificationsPush notificationsWeb Push + VAPID from agents
WebhooksWebhooksReceiving external webhooks
Cross-domain authCross-domain authWebSocket auth, tokens, CORS
Readonly connectionsReadonlyshouldConnectionBeReadonly
VoiceVoiceExperimental STT/TTS, withVoice
Browse the webBrowser toolsExperimental CDP browser automation
ThinkThinkExperimental higher-level chat agent class
MigrationsAI SDK v5, AI SDK v6Upgrading @cloudflare/ai-chat

Capabilities

The Agents SDK provides:

  • Persistent state — SQLite-backed, auto-synced to clients via setState
  • Callable RPC@callable() methods invoked over WebSocket
  • Scheduling — One-time, recurring (scheduleEvery), and cron tasks
  • Workflows — Durable multi-step background processing via AgentWorkflow
  • Durable executionrunFiber() / stash() for work that survives DO eviction
  • Queue — Built-in FIFO queue with retries via queue()
  • Retriesthis.retry() with exponential backoff and jitter
  • MCP integration — Connect to MCP servers or build your own with createMcpHandler
  • Email handling — Receive and reply to emails with secure routing
  • Streaming chatAIChatAgent with resumable streams, message persistence, tools
  • Server-driven messagessaveMessages, waitUntilStable for proactive agent turns
  • React hooksuseAgent, useAgentChat for client apps
  • Observabilitydiagnostics_channel events for state, RPC, schedule, lifecycle
  • Push notifications — Web Push + VAPID delivery from agents
  • Webhooks — Receive and verify external webhooks
  • Voice (experimental) — STT/TTS via @cloudflare/voice
  • Browser tools (experimental) — CDP-powered browsing via agents/browser
  • Think (experimental) — Higher-level chat agent via @cloudflare/think

FIRST: Verify Installation

bash
npm ls agents  # Should show agents package

If not installed:

bash
npm install agents

For chat agents:

bash
npm install agents @cloudflare/ai-chat ai @ai-sdk/react

Wrangler Configuration

jsonc
{
  "compatibility_flags": ["nodejs_compat"],
  "durable_objects": {
    "bindings": [{ "name": "MyAgent", "class_name": "MyAgent" }]
  },
  "migrations": [{ "tag": "v1", "new_sqlite_classes": ["MyAgent"] }]
}

Gotchas:

  • Do NOT enable experimentalDecorators in tsconfig (breaks @callable)
  • Never edit old migrations — always add new tags
  • Each agent class needs its own DO binding + migration entry
  • Add "ai": { "binding": "AI" } for Workers AI

Agent Class

typescript
import { Agent, routeAgentRequest, callable } from "agents";

type State = { count: number };

export class Counter extends Agent<Env, State> {
  initialState = { count: 0 };

  validateStateChange(nextState: State, source: Connection | "server") {
    if (nextState.count < 0) throw new Error("Count cannot be negative");
  }

  onStateUpdate(state: State, source: Connection | "server") {
    console.log("State updated:", state);
  }

  @callable()
  increment() {
    this.setState({ count: this.state.count + 1 });
    return this.state.count;
  }
}

export default {
  fetch: (req, env) => routeAgentRequest(req, env) ?? new Response("Not found", { status: 404 })
};

Routing

Requests route to /agents/{agent-name}/{instance-name}:

ClassURL
Counter/agents/counter/user-123
ChatRoom/agents/chat-room/lobby

Client: useAgent({ agent: "Counter", name: "user-123" })

Custom routing: use getAgentByName(env.MyAgent, "instance-id") then agent.fetch(request).

Core APIs

TaskAPI
Read statethis.state.count
Write statethis.setState({ count: 1 })
SQL querythis.sql`SELECT * FROM users WHERE id = ${id}`
Schedule (delay)await this.schedule(60, "task", payload)
Schedule (cron)await this.schedule("0 * * * *", "task", payload)
Schedule (interval)await this.scheduleEvery(30, "poll")
RPC method@callable() myMethod() { ... }
Streaming RPC@callable({ streaming: true }) stream(res) { ... }
Start workflowawait this.runWorkflow("ProcessingWorkflow", params)
Durable fiberawait this.runFiber("name", async (ctx) => { ... })
Enqueue workthis.queue("handler", payload)
Retry with backoffawait this.retry(fn, { maxAttempts: 5 })
Broadcast to clientsthis.broadcast(message)
Get connectionsthis.getConnections(tag?)

React Client

Read client-sdk.md for client selection and current connection examples. For chat UI and tools, also read streaming-chat.md.

References

Core

Chat & Streaming

Background Processing

Integrations

Experimental

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agents Sdk AI skill do?

Build, debug, or review Cloudflare Agents SDK applications using the agents package.

Why use Agents Sdk on TypingMind?

Because you install it once and use it with any model. Agents Sdk is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agents Sdk in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cloudflare/skills/tree/main/skills/agents-sdk. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agents Sdk?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agents Sdk?

As many as you like. As long as a model supports skills, you can use Agents Sdk with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agents Sdk AI skill free?

Yes. It is published on GitHub by cloudflare under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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