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

# LLM observability with Laminar and OpenLLMetry

## Introduction to Laminar

Laminar is an [open-source platform](https://github.com/lmnr-ai/lmnr) for tracing and evaluating AI applications.

Laminar is fully compatible with OpenTelemetry, so you can use OpenLLMetry to trace your applications on Laminar.

Laminar's OpenTelemetry backend supports both gRPC and HTTP trace exporters.

The recommended setup is to use gRPC, as it's more efficient. You will need to create a gRPC exporter and pass it to the Traceloop SDK.

### (Recommended) gRPC setup

<Steps>
  <Step title="Install dependencies">
    ```bash theme={null}
    pip install traceloop-sdk openai
    ```
  </Step>

  <Step title="Set up environment variables">
    To get your API key, either sign up on [Laminar](https://lmnr.ai) and get it from the project settings,
    or spin up [Laminar](https://github.com/lmnr-ai/lmnr) locally.

    ```python theme={null}
    import os
    os.environ["LMNR_PROJECT_API_KEY"] = "<YOUR_LMNR_PROJECT_API_KEY>"
    os.environ["LMNR_BASE_URL"] = "https://api.lmnr.ai:8443"
    ```
  </Step>

  <Step title="Initialize the OpenTelemetry gRPC exporter">
    ```python theme={null}
    import os
    from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
        OTLPSpanExporter,
    )

    exporter = OTLPSpanExporter(
        endpoint=os.environ["LMNR_BASE_URL"],
        # IMPORTANT: note that "authorization" must be lowercase
        headers={
            "authorization": f"Bearer {os.environ['LMNR_PROJECT_API_KEY']}"
        }
    )
    ```
  </Step>

  <Step title="Initialize the Traceloop SDK">
    ```python theme={null}
    from traceloop.sdk import Traceloop
    Traceloop.init(exporter=exporter)
    ```
  </Step>

  <Step title="Run your application">
    ```python theme={null}
    from openai import OpenAI
    openai_client = OpenAI()

    chat_completion = openai_client.chat.completions.create(
        messages=[
            {
              "role": "user",
              "content": "What is Laminar flow?",
            }
        ],
        model="gpt-4.1-nano",
    )

    print(chat_completion)
    ```
  </Step>

  <Step title="Example trace in Laminar">
    Example trace in Laminar. ([Direct link](https://www.lmnr.ai/shared/traces/af09c6ee-ec63-1cce-674c-86bd43d62683))
  </Step>
</Steps>

### (Alternative) HTTP quick setup

Laminar's backend also supports accepting traces over HTTP, so for a minimal configuration change you can do:

```bash theme={null}
TRACELOOP_BASE_URL="https://api.lmnr.ai"
TRACELOOP_HEADERS="Authorization=<YOUR_LMNR_PROJECT_API_KEY>"
```

and skip step 3 (exporter setup) above.

For more information check out the [Laminar docs](https://docs.lmnr.ai/).