Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/graycodeai/starling/building-ai-agent-on-cloudflarenpx skills add GrayCodeAI/starling --skill building-ai-agent-on-cloudflaregit clone --depth 1 https://github.com/GrayCodeAI/starlingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/graycodeai/starling/building-ai-agent-on-cloudflare)<a href="https://agentmods.dev/skills/graycodeai/starling/building-ai-agent-on-cloudflare"><img src="https://agentmods.dev/badge/skills/graycodeai/starling/building-ai-agent-on-cloudflare.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.02014 |
| Opus 5 | $0.00018 | $0.01007 |
| Sonnet 5 | $0.00007 | $0.00403 |
| Haiku 4.5 | $0.00004 | $0.00201 |
Grade A, and why
building-ai-agent-on-cloudflare scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
async fetch(request: Request, env: Env) { How it starts
The opening of the file, as written. The whole thing — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
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,
}));
}
}
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 364 lines · 37 tokens per session scan A 270ea8079963
building-ai-agent-on-cloudflare is a skill published in the GitHub repository GrayCodeAI/starling (2 stars, last pushed 6d ago), licensed MIT. It adds 37 tokens to every session and 2,014 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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