> ## Documentation Index
> Fetch the complete documentation index at: https://docs.literalai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Vercel AI SDK

This integration allows you to very simply add observability and monitoring to your LLM application based on [Vercel's AI SDK](https://sdk.vercel.ai/docs/introduction). The instrumentation is available for the 4 main methods of the Vercel AI SDK: `streamText`, `generateText`, `streamObject` and `generateObject`.

<Tip>The Vercel AI SDK integration already support LLM tracing. You should not use it in conjunction with other LLM provider integrations such as [OpenAI](/integrations/openai).</Tip>

```typescript theme={null}
import { LiteralClient } from '@literalai/client';
import { openai } from '@ai-sdk/openai';

// Here we use the wrapped `streamText` from the Literal AI client. This will allow us to automatically capture
// both the response from the LLM API and the other informations present in the response (latency, token counts, etc...).
const streamText = literalAiClient.instrumentation.vercel.streamText;

export async function POST(req: Request) {
    const { messages } = await req.json();
  
  // The wrapped version of `streamText` accepts the same parameters as the original function.
  const result = streamText({
    model: openai('gpt-4o'),
    messages,
  });

  return result.toDataStreamResponse();
}
```

## With Threads and Runs

In most cases, you will want to keep track of the different generations from your application by grouping them into [Threads or Runs](/guides/logs). This is especially useful when you want to understand the context in which a generation was made, or when you want to compare different generations.

```typescript TypeScript theme={null}

const generateText = literalAiClient.instrumentation.vercel.generateText;

export async function POST(req: Request) {
const streamText =
  literalAiClient.instrumentation.vercel.streamText;

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = await literalAiClient
    .thread({ name: "Example" })
    .wrap(async () => {
      return streamText({
        model: openai("gpt-4o"),
        messages,
      });
    });

  return result.toDataStreamResponse();
}

}
```

## With Metadata, Tags and Step IDs

Using our Vercel AI SDK integration, you can pass metadata, tags and a step ID at the generation level.
These values will be automatically added to the generation when it is logged on Literal AI.

```typescript theme={null}
import { v4 as uuidv4 } from 'uuid';

const literalaiStepId = uuidv4();

const generateText = literalAiClient.instrumentation.vercel.generateText;

const { text } = await generateText({
  model: openai('gpt-4o-mini'),
  prompt: question,
  literalaiStepId,
  literalaiTags: ['tag1', 'tag2'],
  literalaiMetadata: { otherKey: 'otherValue' }
});
```

## Cookbooks

You can find more involved examples in our Cookbooks repository :

* [This chatbot](https://github.com/Chainlit/literalai-cookbooks/blob/main/typescript/nextjs-openai) uses Vercel AI SDK's `useChat` hook in the frontend
* [This example](https://github.com/Chainlit/literalai-cookbooks/blob/main/typescript/vercel-ai-sdk) uses the Vercel AI SDK integration in the backend