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

# OpenLIT

> LLM Observability with OpenLIT Platform and OpenLIT

To send OpenTelemetry metrics and traces generated by OpenLIT from your AI Application to the OpenLIT Platform, follow the below steps.

### 1. Get your Credentials

If you haven't deployed the OpenLIT Platform yet, follow the [Installation Guide](/latest/openlit/installation) to set it up.

**Common OpenLIT Platform endpoints:**

* **Kubernetes cluster**: `http://openlit.openlit.svc.cluster.local:4318`
* **Local development**: `http://localhost:4318` (using port-forward)
* **External/Ingress**: Your configured external endpoint

### 2. Instrument your application

<Tabs>
  <Tab title="SDK">
    **For direct integration into your Python applications:**

    <Tabs>
      <Tab title="Function Arguments">
        ```python theme={null}
        import openlit

        openlit.init(
          otlp_endpoint="http://localhost:4318"
        )
        ```

        Replace `http://localhost:4318` with your OpenLIT Platform endpoint:

        * **Local development**: `http://localhost:4318`
        * **Kubernetes cluster**: `http://openlit.openlit.svc.cluster.local:4318`
        * **External**: Your configured external endpoint
      </Tab>

      <Tab title="Environment Variables">
        ```python theme={null}
        import openlit

        openlit.init()
        ```

        Set these environment variables:

        ```shell theme={null}
        export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318"
        export OTEL_SERVICE_NAME="my-ai-service"
        export OTEL_DEPLOYMENT_ENVIRONMENT="production"
        ```

        Replace `http://localhost:4318` with your OpenLIT Platform endpoint.
      </Tab>
    </Tabs>

    Refer to the OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
  </Tab>

  <Tab title="CLI">
    **For zero-code auto-instrumentation via command line:**

    <Tabs>
      <Tab title="CLI Arguments">
        ```shell theme={null}
        # Using CLI arguments
        openlit-instrument \
          --otlp-endpoint "YOUR_OPENLIT_PLATFORM_ENDPOINT" \
          --service-name "my-ai-service" \
          --deployment-environment "production" \
          python app.py
        ```

        Replace:

        1. `YOUR_OPENLIT_PLATFORM_ENDPOINT` with your OpenLIT Platform endpoint from Step 1.
      </Tab>

      <Tab title="Environment Variables">
        ```shell theme={null}
        # Set environment variables (takes precedence over CLI args)
        export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OPENLIT_PLATFORM_ENDPOINT"
        export OTEL_SERVICE_NAME="my-ai-service"
        export OTEL_DEPLOYMENT_ENVIRONMENT="production"

        # Run your application
        openlit-instrument python app.py
        ```

        Replace:

        1. `YOUR_OPENLIT_PLATFORM_ENDPOINT` with your OpenLIT Platform endpoint from Step 1.
      </Tab>
    </Tabs>

    Refer to the OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
  </Tab>
</Tabs>

### 3. Access OpenLIT Platform Dashboard

Once your LLM application is instrumented, you can explore the comprehensive observability data in the OpenLIT Platform:

**Access the Dashboard**:

```bash theme={null}
# Get the external service details
kubectl get svc -n openlit openlit

# For local access via port-forwarding:
kubectl port-forward -n openlit svc/openlit 3000:3000
# Then visit: http://localhost:3000
```

**What You'll See**:

1. **LLM Observability Dashboard**: Comprehensive view of your AI applications including:
   * **Real-time Metrics**: Request rates, latency, and error rates
   * **Cost Tracking**: Token usage and cost breakdown by model and application
   * **Performance Analytics**: Response times, throughput, and model performance
   * **Trace Visualization**: Detailed execution flow with full request/response context
2. **Vector Database Analytics**: Monitor your vector database operations and performance
3. **GPU Monitoring**: Track GPU utilization and performance metrics (if enabled)
4. **Custom Dashboards**: Create tailored views for your specific monitoring needs

Your OpenLIT-instrumented AI applications will appear automatically in the OpenLIT Platform with comprehensive observability including LLM costs, token usage, model performance, distributed tracing, and business intelligence - all in a single, self-hosted platform designed specifically for AI workloads.