# Laminar observability

[Laminar](https://laminar.sh) is the open-source platform for tracing and evaluating AI applications.

Laminar features:

- [Tracing compatible with AI SDK and more](https://docs.lmnr.ai/tracing/introduction),
- [Evaluations](https://docs.lmnr.ai/evaluations/introduction),
- [Browser agent observability](https://docs.lmnr.ai/tracing/browser-agent-observability)

<Note>
  A version of this guide is available in [Laminar's
  docs](https://docs.lmnr.ai/tracing/integrations/vercel-ai-sdk).
</Note>

## Setup

Laminar's tracing is based on OpenTelemetry. It supports AI SDK [telemetry](/docs/ai-sdk-core/telemetry).

### Installation

To start with Laminar's tracing, first [install](https://docs.lmnr.ai/installation) the `@lmnr-ai/lmnr` package.

<Tabs items={['pnpm', 'npm', 'yarn', 'bun']}>
  <Tab>
    <Snippet text="pnpm add @lmnr-ai/lmnr" dark />
  </Tab>
  <Tab>
    <Snippet text="npm install @lmnr-ai/lmnr" dark />
  </Tab>
  <Tab>
    <Snippet text="yarn add @lmnr-ai/lmnr" dark />
  </Tab>

  <Tab>
    <Snippet text="bun add @lmnr-ai/lmnr" dark />
  </Tab>
</Tabs>

### Get your project API key and set in the environment

Then, either sign up on [Laminar](https://laminar.sh) or self-host an instance ([github](https://github.com/lmnr-ai/lmnr)) and create a new project.

In the project settings, create and copy the API key.

In your .env

```bash
LMNR_PROJECT_API_KEY=...
```

## Next.js

### Initialize tracing

In Next.js, Laminar initialization should be done in `instrumentation.{ts,js}`:

```javascript
export async function register() {
  // prevent this from running in the edge runtime
  if (process.env.NEXT_RUNTIME === 'nodejs') {
    const { Laminar } = await import('@lmnr-ai/lmnr');
    Laminar.initialize({
      projectApiKey: process.env.LMNR_PROJECT_API_KEY,
    });
  }
}
```

### Add @lmnr-ai/lmnr to your next.config

In your `next.config.js` (`.ts` / `.mjs`), add the following lines:

```javascript
const nextConfig = {
  serverExternalPackages: ['@lmnr-ai/lmnr'],
};

export default nextConfig;
```

This is because Laminar depends on OpenTelemetry, which uses some Node.js-specific functionality, and we need to inform Next.js about it. Learn more in the [Next.js docs](https://nextjs.org/docs/app/api-reference/config/next-config-js/serverExternalPackages).

### Tracing AI SDK calls

Then, when you call AI SDK functions in any of your API routes, add the Laminar tracer to the `experimental_telemetry` option.

```javascript highlight="3,8-11"
import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';
import { getTracer } from '@lmnr-ai/lmnr';

const { text } = await generateText({
  model: openai('gpt-4o-mini'),
  prompt: 'What is Laminar flow?',
  experimental_telemetry: {
    isEnabled: true,
    tracer: getTracer(),
  },
});
```

This will create spans for `ai.generateText`. Laminar collects and displays the following information:

- LLM call input and output
- Start and end time
- Duration / latency
- Provider and model used
- Input and output tokens
- Input and output price
- Additional metadata and span attributes

### Older versions of Next.js

If you are using 13.4 ≤ Next.js < 15, you will also need to enable the experimental instrumentation hook. Place the following in your `next.config.js`:

```javascript
module.exports = {
  experimental: {
    instrumentationHook: true,
  },
};
```

For more information, see Laminar's [Next.js guide](https://docs.lmnr.ai/tracing/nextjs) and Next.js [instrumentation docs](https://nextjs.org/docs/app/api-reference/file-conventions/instrumentation). You can also learn how to enable all traces for Next.js in the docs.

### Usage with `@vercel/otel`

Laminar can live alongside `@vercel/otel` and trace AI SDK calls. The default Laminar setup will ensure that

- regular Next.js traces are sent via `@vercel/otel` to your Telemetry backend configured with Vercel,
- AI SDK and other LLM or browser agent traces are sent via Laminar.

```javascript
import { registerOTel } from '@vercel/otel';

export async function register() {
  registerOTel('my-service-name');
  if (process.env.NEXT_RUNTIME === 'nodejs') {
    const { Laminar } = await import('@lmnr-ai/lmnr');
    // Make sure to initialize Laminar **after** `@registerOTel`
    Laminar.initialize({
      projectApiKey: process.env.LMNR_PROJECT_API_KEY,
    });
  }
}
```

For an advanced configuration that allows you to trace all Next.js traces via Laminar, see an example [repo](https://github.com/lmnr-ai/lmnr-ts/tree/main/examples/nextjs).

### Usage with `@sentry/node`

Laminar can live alongside `@sentry/node` and trace AI SDK calls. Make sure to initialize Laminar **after** `Sentry.init`.

This will ensure that

- Whatever is instrumented by Sentry is sent to your Sentry backend,
- AI SDK and other LLM or browser agent traces are sent via Laminar.

```javascript
export async function register() {
  if (process.env.NEXT_RUNTIME === 'nodejs') {
    const Sentry = await import('@sentry/node');
    const { Laminar } = await import('@lmnr-ai/lmnr');

    Sentry.init({
      dsn: process.env.SENTRY_DSN,
    });

    // Make sure to initialize Laminar **after** `Sentry.init`
    Laminar.initialize({
      projectApiKey: process.env.LMNR_PROJECT_API_KEY,
    });
  }
}
```

## Node.js

### Initialize tracing

Then, initialize tracing in your application:

```javascript
import { Laminar } from '@lmnr-ai/lmnr';

Laminar.initialize();
```

This must be done once in your application, as early as possible, but _after_ other tracing libraries (e.g. `@sentry/node`) are initialized.

Read more in Laminar [docs](https://docs.lmnr.ai/tracing/introduction).

### Tracing AI SDK calls

Then, when you call AI SDK functions in any of your API routes, add the Laminar tracer to the `experimental_telemetry` option.

```javascript highlight="3,8-11"
import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';
import { getTracer } from '@lmnr-ai/lmnr';

const { text } = await generateText({
  model: openai('gpt-4o-mini'),
  prompt: 'What is Laminar flow?',
  experimental_telemetry: {
    isEnabled: true,
    tracer: getTracer(),
  },
});
```

This will create spans for `ai.generateText`. Laminar collects and displays the following information:

- LLM call input and output
- Start and end time
- Duration / latency
- Provider and model used
- Input and output tokens
- Input and output price
- Additional metadata and span attributes

### Usage with `@sentry/node`

Laminar can work with `@sentry/node` to trace AI SDK calls. Make sure to initialize Laminar **after** `Sentry.init`:

```javascript
const Sentry = await import('@sentry/node');
const { Laminar } = await import('@lmnr-ai/lmnr');

Sentry.init({
  dsn: process.env.SENTRY_DSN,
});

Laminar.initialize({
  projectApiKey: process.env.LMNR_PROJECT_API_KEY,
});
```

This will ensure that

- Whatever is instrumented by Sentry is sent to your Sentry backend,
- AI SDK and other LLM or browser agent traces are sent via Laminar.

The two libraries allow for additional advanced configuration, but the default setup above is recommended.

## Additional configuration

### Span name

If you want to override the default span name, you can set the `functionId` inside the `telemetry` option.

```javascript
import { getTracer } from '@lmnr-ai/lmnr';

const { text } = await generateText({
  model: openai('gpt-4.1-nano'),
  prompt: `Write a poem about Laminar flow.`,
  experimental_telemetry: {
    isEnabled: true,
    tracer: getTracer(),
    functionId: 'poem-writer',
  },
});
```

### Nested spans

If you want to trace not just the AI SDK calls, but also other functions in your application, you can use Laminar's `observe` wrapper.

```javascript highlight="3"
import { getTracer, observe } from '@lmnr-ai/lmnr';

const result = await observe({ name: 'my-function' }, async () => {
  // ... some work
  await generateText({
    //...
  });
  // ... some work
});
```

This will create a span with the name "my-function" and trace the function call. Inside it, you will see the nested `ai.generateText` spans.

To trace input arguments of the function that you wrap in `observe`, pass them to the wrapper as additional arguments. The return value of the function will be returned from the wrapper and traced as the span's output.

```javascript
const result = await observe(
  { name: 'poem writer' },
  async (topic: string, mood: string) => {
    const { text } = await generateText({
      model: openai('gpt-4.1-nano'),
      prompt: `Write a poem about ${topic} in ${mood} mood.`,
    });
    return text;
  },
  'Laminar flow',
  'happy',
);
```

### Metadata

In Laminar, metadata is set on the trace level. Metadata contains key-value pairs and can be used to filter traces.

```javascript
import { getTracer } from '@lmnr-ai/lmnr';

const { text } = await generateText({
  model: openai('gpt-4.1-nano'),
  prompt: `Write a poem about Laminar flow.`,
  experimental_telemetry: {
    isEnabled: true,
    tracer: getTracer(),
    metadata: {
      'my-key': 'my-value',
      'another-key': 'another-value',
    },
  },
});
```

This is converted to Laminar's [metadata](https://docs.lmnr.ai/tracing/structure/metadata) and stored in the trace.

### Tags

One of the reserved metadata keys is `tags`. It can be used to add [tags](https://docs.lmnr.ai/tracing/structure/tags) to the span.

Tags can subsequently be used to filter traces in Laminar.

```javascript
import { getTracer } from '@lmnr-ai/lmnr';

const { text } = await generateText({
  model: openai('gpt-4.1-nano'),
  prompt: `Write a poem about Laminar flow.`,
  experimental_telemetry: {
    isEnabled: true,
    tracer: getTracer(),
    metadata: {
      tags: ['fallback-model', 'api-handler'],
    },
  },
});
```

### Session ID and User ID

Traces in Laminar can be grouped into [sessions](https://docs.lmnr.ai/tracing/structure/session) or by [user ID](https://docs.lmnr.ai/tracing/structure/user-id). These are also
reserved metadata keys.

```javascript
import { getTracer } from '@lmnr-ai/lmnr';

const { text } = await generateText({
  model: openai('gpt-4.1-nano'),
  prompt: `Write a poem about Laminar flow.`,
  experimental_telemetry: {
    isEnabled: true,
    tracer: getTracer(),
    metadata: {
      sessionId: 'session-123',
      userId: 'user-123',
    },
  },
});
```


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