Get started - Workers AI local dev

In this guide, you will get started with Workers AI, experiment with a large language model (LLM), and deploy your first AI powered app on the Workers platform.

​​ Before you begin

Setup your local development environment, if this is your first time developing with Wrangler.

​​ 1. Create a Workers project

Create a new project named hello-ai by running:

$ npm create cloudflare@latest

$ yarn create cloudflare

When setting up your hello-ai Worker, answer the setup questions as follows:

• Enter hello-ai for the directory to create in
• Choose "Hello World" script for the type of application
• Select yes to using TypeScript
• Select yes to using Git
• Select no to deploying

This will create a new hello-ai directory. Your new hello-ai directory will include:

• A "Hello World" Worker at src/index.ts
• A wrangler.toml configuration file.

Navigate to your app directory:

$ cd hello-ai

​​ 2. Connect your Worker to Workers AI

You must create a binding for your Worker to connect to Workers AI. Bindings allow your Workers to access resources or services, like Workers AI, on the Cloudflare developer platform. You create bindings by updating your wrangler.toml file.

To bind Workers AI to your Worker, add the following to the end of your wrangler.toml file:

wrangler.toml
[ai]
binding = "AI" # i.e. available in your Worker on env.AI

You can also bind Workers AI to a Pages Function. For more information, refer to Functions Bindings.

​​ 3. Install the Workers AI client library

$ npm install --save-dev @cloudflare/ai

$ yarn add --dev @cloudflare/ai

​​ 4. Run an inference task in your Worker

Now we are ready to run an inference task in our our worker. In this case, we will use an LLM, like lambda-2, to answer a questions.

Go to your hello-ai and update the index.ts with the following code:

"src/index.ts"
import { Ai } from '@cloudflare/ai'

export interface Env {
  // If you set another name in wrangler.toml as the value for 'binding',
  // replace "AI" with the variable name you defined.
  AI: any;
}

export default {
  async fetch(request: Request, env: Env) {
    const ai = new Ai(env.AI);

    const response = await ai.run('@cf/meta/llama-2-7b-chat-int8', {
        prompt: "What is the origin of the phrase Hello, World"
      }
    );

    return new Response(JSON.stringify(response));
  },
};

After configuring your Worker, you can test your project locally before you deploy globally.

​​ 5. Develop locally with Wrangler

While in your project directory, test Workers AI locally by running. Note, you will be prompted to login at this time:

$ npx wrangler dev --remote

When you run npx wrangler dev, Wrangler will give you a URL (most likely localhost:8787) to review your Worker. After you visit the URL Wrangler provides, you will see this message:

{
  "result": {
    "response": "Hello, World first appeared in 1974 at Bell Labs when Brian Kernighan included it in the C programming language example. It became widely used as a basic test program due to simplicity and clarity. It represents an inviting greeting from a program to the world."
  }
}

​​ 6. Deploy your AI Worker

Before deploying your AI Worker globally, log in with your Cloudflare account by running:

$ npx wrangler login

You will be directed to a web page asking you to log in to the Cloudflare dashboard. After you have logged in, you will be asked if Wrangler can make changes to your Cloudflare account. Scroll down and select Allow to continue.

Finally, deploy your Worker to make your project accessible on the Internet. To deploy your Worker, run:

$ npx wrangler deploy
# Outputs: https://hello-ai.<YOUR_SUBDOMAIN>.workers.dev

You can now visit the URL to run your AI Worker.

By finishing this tutorial, you have created a Worker, connected it to Workers AI, and ran an inference tasks from your model.

​​ Next steps

If you have any feature requests or notice any bugs, share your feedback directly with the Cloudflare team by joining the Cloudflare Developers community on Discord Open external link .