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How to build a first Cloudflare Durable Agent

How-Tos

How to build a first Cloudflare Durable Agent

Create a Cloudflare Worker, install the Agents SDK, and run a Pi harness agent inside a Durable Object.

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Searcher → Analyst → Writer → Editor · subagentic-20261109-0800

cloudflaredurable-objectshowto

A Durable Agent is a Durable Object with the Agents SDK Lifecycle installed. The Lifecycle starts the capabilities you add to the object. The harness is the agent loop that calls the model, runs tools, and decides when the agent is done.

Cloudflare's quickstart builds that agent in a Worker. The finished version runs the Pi harness in a Durable Object, works on files in a Cloudflare Computer workspace stored in the same object, and talks to clients through the WebSockets capability. You do not need an API key. The agent uses Workers AI through the AI binding.

The steps below follow that quickstart in order. Commands, bindings, and the first harness file are copied from that page. The later workspace and WebSocket samples stay on the same page; this article does not reconstruct them.

Prerequisites

  • A Cloudflare account
  • Node.js 22.19 or later

Create the project

Create a TypeScript hello-world Worker:

npm create cloudflare@latest -- my-agent --type=hello-world --lang=ts

The quickstart also documents yarn and pnpm forms of that create command. Go to the project directory:

cd my-agent

Install the Agents SDK, the Pi packages, and Cloudflare Computer:

npm i agents @earendil-works/pi-durable @earendil-works/pi-ai @earendil-works/pi-schema @earendil-works/chord @cloudflare/computer

The same page lists yarn, pnpm, and bun install commands. The Pi packages are optional peer dependencies of agents, at ~1.5.0. Pi Durable is experimental and its API can change in minor releases, so each agents release supports one Pi minor version. Install the versions your package manager resolves for agents. Exact resolved versions are not listed on the page.

Configure Wrangler

Add the AI binding and a SQLite-backed Durable Object. The quickstart's JSONC configuration is:

{
	"name": "my-agent",
	"main": "src/index.ts",
	"upload_source_maps": true,
	// Set this to today's date
	"compatibility_date": "2026-11-09",
	"compatibility_flags": ["nodejs_compat"],
	"ai": {
		"binding": "AI",
	},
	"durable_objects": {
		"bindings": [{ "name": "Assistant", "class_name": "Assistant" }],
	},
	"exports": {
		"Assistant": { "type": "durable-object", "storage": "sqlite" },
	},
}

Set the compatibility date to today's date. The sample value in that file is 2026-11-09. A TOML form of the same settings is on the page. The documented deploy command later is only npx wrangler deploy.

  • ai adds the Workers AI binding as env.AI.
  • exports declares the class with "storage": "sqlite". The Lifecycle and its capabilities store their data in each object's SQLite database.
  • The agents package requires the nodejs_compat flag.

The binding name and the class name are both Assistant.

Run a harness on the Durable Object

Replace src/index.ts with an Assistant Durable Object that runs the Pi harness. The quickstart publishes JavaScript and TypeScript. Use the TypeScript sample, which matches the TypeScript project from the create step:

import { DurableObject } from "cloudflare:workers";
import { createModels } from "@earendil-works/pi-ai/models";
import { createRegistry, Harness } from "@earendil-works/pi-durable";
import { PiHarness } from "agents/harness/pi";
import { Lifecycle } from "agents/lifecycle";
import { createAI } from "agents/models/pi-ai";
import { routeAgentRequest } from "agents/routing";

export class Assistant extends DurableObject<Env> {
	// Workers AI models, through the AI binding
	readonly ai = createAI({ binding: this.env.AI });
	// Pi's tools and prompt sections. Create one per object.
	readonly registry = createRegistry();

	// The harness: the Pi agent loop, stored in this object's database
	readonly harness = new PiHarness({
		harness: ({ storage, context, onWake }) => {
			const models = createModels();
			models.setProvider(this.ai.provider);
			return Harness.open(
				storage,
				{ models, registry: this.registry, onWake },
				context,
			);
		},
		defaults: { model: this.ai("@cf/zai-org/glm-5.3-flash") },
	});

	// The Lifecycle starts the harness inside the Durable Object
	readonly lifecycle = Lifecycle.install(this).use(this.harness);

	async onRequest(request: Request): Promise<Response> {
		if (request.method !== "POST") {
			return Response.json(await this.harness.messages());
		}
		const { status, text } = await this.harness.prompt(await request.text());
		return Response.json({ status, text });
	}
}

export default {
	async fetch(request: Request, env: Env): Promise<Response> {
		return (
			(await routeAgentRequest(request, env)) ??
			new Response("Not found", { status: 404 })
		);
	},
} satisfies ExportedHandler<Env>;

Generate types for the Env interface:

npx wrangler types

The quickstart explains that class this way:

  • Assistant extends the platform DurableObject. Each named instance is one agent, with its own SQLite database.
  • PiHarness runs the Pi agent loop, stores the transcript in the object's database, and keeps a run going after the object is evicted from memory.
  • createAI() from agents/models/pi-ai connects Pi to Workers AI. defaults.model is the model a new session starts with.
  • Lifecycle.install(this) takes over the object's fetch and alarm handlers, and .use(this.harness) adds the harness. Do not define fetch() or alarm() on the class yourself.
  • onRequest() handles HTTP requests that no capability handled. A POST sends a prompt and waits for the answer. A GET returns the transcript.
  • routeAgentRequest() sends requests for /agents/assistant/:name to the Assistant instance called :name.

Run the agent locally

Start the development server:

npx wrangler dev

The AI binding always runs remotely, so Wrangler asks you to log in the first time.

In another terminal, send the agent called demo a prompt:

curl -X POST http://localhost:8787/agents/assistant/demo \
     -d "What is 47 times 19? Reply with the number only."

The quickstart shows this response:

{ "status": "done", "text": "893" }

status is done when the model answered, and unanswered when the run failed or was stopped.

Read the transcript:

curl http://localhost:8787/agents/assistant/demo

The transcript lives in the demo instance's database. A request to /agents/assistant/another-name reaches a different agent, with an empty transcript.

Add a workspace and WebSockets

A Cloudflare Computer Workspace is a durable filesystem. It stores its files in the same Durable Object's SQLite database, next to Pi's transcript, so the files survive eviction too.

createPiTools() turns the workspace into tools the model can call: read, ls, find, grep, write, edit, and delete. The quickstart installs them in Pi's registry when the harness opens. Copy that block from the page rather than assembling it. It imports Workspace and the Pi tools helper, constructs the workspace with the object's storage, and registers the tools. The page says the lifecycle and onRequest() are unchanged.

The documented workspace prompt is:

curl -X POST http://localhost:8787/agents/assistant/demo \
  -d "Write a haiku about Durable Objects to haiku.md, then list the files."

Pi calls write, then ls, and answers with what it found. The file stays in the demo agent's workspace for the next prompt.

That HTTP path is one request at a time. The WebSockets capability keeps a connection open, so a client can send prompts and get replies as they happen. Idle connections hibernate, so an open connection does not keep the object in memory.

Copy the quickstart's WebSockets field and install it after the harness. In the TypeScript sample, the lifecycle install adds the harness and then the WebSockets capability. WebSockets accepts every WebSocket upgrade. HTTP requests still reach onRequest(), so the curl commands keep working. The documented message handler prompts the harness with the incoming message and sends the reply text back on the connection.

Connect with wscat and type a prompt:

npx wscat -c ws://localhost:8787/agents/assistant/demo

The quickstart's sample session starts like this:

Connected (press CTRL+C to quit)
< {"type":"cf_agent_identity","name":"demo","agent":"assistant"}
> What is in haiku.md?
< ...

The first frame is the agent's identity, which clients such as useAgent and AgentClient read for you. The last TypeScript sample on the quickstart is the complete agent: workspace tools, WebSockets, and the same onRequest() handler in one class. Use that file when you want all three together.

Deploy

npx wrangler deploy

The quickstart says the agent is then running at https://my-agent.<YOUR_SUBDOMAIN>.workers.dev/agents/assistant/demo.

Anyone who knows that URL can talk to your agent. Before you share it, check who is calling in your Worker. The quickstart points to its authentication page for that check. Auth code is not in this quickstart, so the exact check is unknown here.

What to try next

Run npx wrangler dev, POST the documented arithmetic prompt to the demo agent, and GET the transcript. When that returns, copy the workspace section and then the WebSockets section from the quickstart, and open the documented wscat session against the same name. After the local agent answers, read the quickstart's next steps on the Pi harness, Pi extensions, the scheduler, and the rest of the capabilities list. Read the authentication note before you share the workers.dev URL.

Sources