Vercel AI SDK

This integration allows you to very simply add observability and monitoring to your LLM application based on Vercel’s AI SDK. The instrumentation is available for the two main methods of the Vercel AI SDK: generateText and streamText.

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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. 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.

export async function POST(req: Request) {
  await literalClient.thread({ name: 'Example' }).wrap(async () => {
    const { text } = await generateText({
      model: openai('gpt-3.5-turbo'),
      prompt: question,
    });

    return { text };
  });
}

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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.

import { v4 as uuidv4 } from 'uuid';

const literalaiStepId = uuidv4();

const { text } = await generateText({
  model: openai('gpt-3.5-turbo'),
  prompt: question,
  literalaiStepId,
  literalaiTags: ['tag1', 'tag2'],
  literalaiMetadata: { otherKey: 'otherValue' }
});

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Cookbooks

You can find more involved examples in our Cookbooks repository :

• This chatbot uses Vercel AI SDK’s useChat hook in the frontend
• This example uses the Vercel AI SDK integration in the backend