---
description: Use Workers AI with the Vercel AI SDK for streaming text generation, tool calls, and structured output.
title: Vercel AI SDK
image: https://developers.cloudflare.com/og-docs.png
---

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# Vercel AI SDK

Last updated Aug 24, 2026|Copy as Markdown|[View as Markdown](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/configuration/ai-sdk/index.md)|[Agent setup](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/agent-setup/)

Workers AI can be used with the [Vercel AI SDK ↗](https://sdk.vercel.ai/) for JavaScript and TypeScript codebases.

## Setup

Install the [workers-ai-provider provider ↗](https://sdk.vercel.ai/providers/community-providers/cloudflare-workers-ai):

npmyarnpnpmbun

```
npm i workers-ai-provider
```

```
yarn add workers-ai-provider
```

```
pnpm add workers-ai-provider
```

```
bun add workers-ai-provider
```

Then, add an AI binding in your Workers project Wrangler file:

```toml
[ai]
binding = "AI"
```

## Models

The AI SDK can be configured to work with [any AI model](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/models/).

```js
import { createWorkersAI } from "workers-ai-provider";

const workersai = createWorkersAI({ binding: env.AI });

// Choose any model: https://developers.cloudflare.com/workers-ai/models/
const model = workersai("@cf/meta/llama-3.1-8b-instruct", {});
```

## Generate Text

Once you have selected your model, you can generate text from a given prompt.

```js
import { createWorkersAI } from 'workers-ai-provider';
import { generateText } from 'ai';

type Env = {
  AI: Ai;
};

export default {
  async fetch(_: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const result = await generateText({
      model: workersai('@cf/meta/llama-2-7b-chat-int8'),
      prompt: 'Write a 50-word essay about hello world.',
    });

    return new Response(result.text);
  },
};
```

## Stream Text

For longer responses, consider streaming responses to provide as the generation completes.

```js
import { createWorkersAI } from 'workers-ai-provider';
import { streamText } from 'ai';

type Env = {
  AI: Ai;
};

export default {
  async fetch(_: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const result = streamText({
      model: workersai('@cf/meta/llama-2-7b-chat-int8'),
      prompt: 'Write a 50-word essay about hello world.',
    });

    return result.toTextStreamResponse({
      headers: {
        // add these headers to ensure that the
        // response is chunked and streamed
        'Content-Type': 'text/x-unknown',
        'content-encoding': 'identity',
        'transfer-encoding': 'chunked',
      },
    });
  },
};
```

## Generate Structured Objects

You can provide a Zod schema to generate a structured JSON response.

```js
import { createWorkersAI } from 'workers-ai-provider';
import { generateObject } from 'ai';
import { z } from 'zod';

type Env = {
  AI: Ai;
};

export default {
  async fetch(_: Request, env: Env) {
    const workersai = createWorkersAI({ binding: env.AI });
    const result = await generateObject({
      model: workersai('@cf/meta/llama-3.1-8b-instruct'),
      prompt: 'Generate a Lasagna recipe',
      schema: z.object({
        recipe: z.object({
          ingredients: z.array(z.string()),
          description: z.string(),
        }),
      }),
    });

    return Response.json(result.object);
  },
};
```

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