agents-sdk▌
cloudflare/skills · updated Apr 8, 2026
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Build stateful AI agents on Cloudflare Workers with persistent state, RPC methods, scheduling, and workflow orchestration.
- ›Core features include SQLite-backed state management, callable RPC methods via @callable() , one-time and recurring task scheduling, and durable multi-step workflows
- ›Supports MCP server integration (both client and server modes), email routing with secure replies, and streaming chat with resumable streams on disconnect
- ›Includes React hooks ( useAgent , useAgentCh
Cloudflare Agents SDK
Your knowledge of the Agents SDK may be outdated. Prefer retrieval over pre-training for any Agents SDK task.
Retrieval Sources
Fetch current docs from https://github.com/cloudflare/agents/tree/main/docs before implementing.
| Topic | Doc | Use for |
|---|---|---|
| Getting started | docs/getting-started.md |
First agent, project setup |
| State | docs/state.md |
setState, validateStateChange, persistence |
| Routing | docs/routing.md |
URL patterns, routeAgentRequest, basePath |
| Callable methods | docs/callable-methods.md |
@callable, RPC, streaming, timeouts |
| Scheduling | docs/scheduling.md |
schedule(), scheduleEvery(), cron |
| Workflows | docs/workflows.md |
AgentWorkflow, durable multi-step tasks |
| HTTP/WebSockets | docs/http-websockets.md |
Lifecycle hooks, hibernation |
docs/email.md |
Email routing, secure reply resolver | |
| MCP client | docs/mcp-client.md |
Connecting to MCP servers |
| MCP server | docs/mcp-servers.md |
Building MCP servers with McpAgent |
| Client SDK | docs/client-sdk.md |
useAgent, useAgentChat, React hooks |
| Human-in-the-loop | docs/human-in-the-loop.md |
Approval flows, pausing workflows |
| Resumable streaming | docs/resumable-streaming.md |
Stream recovery on disconnect |
Cloudflare docs: https://developers.cloudflare.com/agents/
Capabilities
The Agents SDK provides:
- Persistent state - SQLite-backed, auto-synced to clients
- Callable RPC -
@callable()methods invoked over WebSocket - Scheduling - One-time, recurring (
scheduleEvery), and cron tasks - Workflows - Durable multi-step background processing via
AgentWorkflow - MCP integration - Connect to MCP servers or build your own with
McpAgent - Email handling - Receive and reply to emails with secure routing
- Streaming chat -
AIChatAgentwith resumable streams - React hooks -
useAgent,useAgentChatfor client apps
FIRST: Verify Installation
npm ls agents # Should show agents package
If not installed:
npm install agents
Wrangler Configuration
{
"durable_objects": {
"bindings": [{ "name": "MyAgent", "class_name": "MyAgent" }]
},
"migrations": [{ "tag": "v1", "new_sqlite_classes": ["MyAgent"] }]
}
Agent Class
import { Agent, routeAgentRequest, callable } from "agents";
type State = { count: number };
export class Counter extends Agent<Env, State> {
initialState = { count: 0 };
// Validation hook - runs before state persists (sync, throwing rejects the update)
validateStateChange(nextState: State, source: Connection | "server") {
if (nextState.count < 0) throw new Error("Count cannot be negative");
}
// Notification hook - runs after state persists (async, non-blocking)
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}:
| Class | URL |
|---|---|
Counter |
/agents/counter/user-123 |
ChatRoom |
/agents/chat-room/lobby |
Client: useAgent({ agent: "Counter", name: "user-123" })
Core APIs
| Task | API |
|---|---|
| Read state | this.state.count |
| Write state | this.setState({ count: 1 }) |
| SQL query | this.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 workflow | await this.runWorkflow("ProcessingWorkflow", params) |
React Client
import { useAgent } from "agents/react";
function App() {
const [state, setLocalState] = useState({ count: 0 });
const agent = useAgent({
agent: "Counter",
name: "my-instance",
onStateUpdate: (newState) => setLocalState(newState),
onIdentity: (name, agentType) => console.log(`Connected to ${name}`)
});
return (
<button onClick={() => agent.setState({ count: state.count + 1 })}>
Count: {state.count}
</button>
);
}
References
- references/workflows.md - Durable Workflows integration
- references/callable.md - RPC methods, streaming, timeouts
- references/state-scheduling.md - State persistence, scheduling
- references/streaming-chat.md - AIChatAgent, resumable streams
- references/mcp.md - MCP server integration
- references/email.md - Email routing and handling
- references/codemode.md - Code Mode (experimental)
How to use agents-sdk on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add agents-sdk
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches agents-sdk from GitHub repository cloudflare/skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate agents-sdk. Access the skill through slash commands (e.g., /agents-sdk) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★55 reviews- ★★★★★Xiao Martin· Dec 20, 2024
We added agents-sdk from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Tariq Torres· Dec 16, 2024
Solid pick for teams standardizing on skills: agents-sdk is focused, and the summary matches what you get after install.
- ★★★★★Ira Verma· Dec 12, 2024
Solid pick for teams standardizing on skills: agents-sdk is focused, and the summary matches what you get after install.
- ★★★★★Li Torres· Dec 8, 2024
agents-sdk is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chen Jain· Nov 27, 2024
Keeps context tight: agents-sdk is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Neel Chen· Nov 11, 2024
Useful defaults in agents-sdk — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Tariq Khan· Nov 7, 2024
agents-sdk has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ira Thomas· Nov 3, 2024
agents-sdk has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Amina Mehta· Oct 26, 2024
Useful defaults in agents-sdk — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ira Choi· Oct 22, 2024
Useful defaults in agents-sdk — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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