Workers AI Features

Generated apps can call Workers AI from the App Durable Object through this.env.AI. The platform provides this binding automatically in Dynamic Worker previews and native Cloudflare deployments. Do not add an ai section to the generated app's wrangler.json.

Use only AI.run(model, inputs, options?). Preview uses a restricted proxy with this method, while deployed Workers receive the native Workers AI binding. Methods such as AI.gateway(), AI.models(), AI.autorag(), and AI.toMarkdown() are not portable between preview and deployment.

Type and usage

import { DurableObject } from "cloudflare:workers";

interface Env {
  AI: Ai;
}

export class App extends DurableObject<Env> {
  async fetch(request: Request): Promise<Response> {
    const { prompt } = await request.json<{ prompt: string }>();
    const result = await this.env.AI.run("@cf/meta/llama-4-scout-17b-16e-instruct", {
      messages: [
        { role: "system", content: "Answer clearly and concisely." },
        { role: "user", content: prompt },
      ],
    });
    return Response.json(result);
  }
}

Keep AI calls in the server-side App class. Browser code should call an app API route; it must never receive model credentials or call the binding directly.

Common tasks

Text generation

Use @cf/meta/llama-4-scout-17b-16e-instruct with either prompt or messages:

const result = await this.env.AI.run("@cf/meta/llama-4-scout-17b-16e-instruct", {
  messages: [{ role: "user", content: userPrompt }],
  max_tokens: 512,
});

Embeddings

Use @cf/baai/bge-base-en-v1.5. The response contains embedding vectors in data:

const result = await this.env.AI.run("@cf/baai/bge-base-en-v1.5", {
  text: ["first document", "second document"],
});

Image classification

Use @cf/microsoft/resnet-50 with image bytes converted to a number array: