> ## 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.

# Murnitur

> LLM Observability with Murnitur and OpenLIT

To send OpenTelemetry traces generated by OpenLIT from your AI Application to Murnitur.ai, follow the below steps.

### 1. Get your Credentials

1. **Visit Murnitur.ai**: Go to [Murnitur.ai](https://murnitur.ai) to create your account
2. **Generate API Key**: Navigate to your dashboard and generate your API key
3. **Save your credentials**:
   * **API Key**: Your Murnitur trace token for authentication
   * **Endpoint**: `https://middleware.murnitur.ai`

<Note>
  Murnitur.ai is optimized for trace data. The integration automatically disables metrics to focus on trace observability.
</Note>

### 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="https://middleware.murnitur.ai",
          otlp_headers="x-murnix-trace-token=YOUR_MURNITUR_API_KEY",
          disable_metrics=True  # Murnitur focuses on trace data
        )
        ```

        Replace:

        1. `YOUR_MURNITUR_API_KEY` with your Murnitur API key from Step 1.
      </Tab>

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

        openlit.init()
        ```

        Set these environment variables:

        ```shell theme={null}
        export OTEL_EXPORTER_OTLP_ENDPOINT="https://middleware.murnitur.ai"
        export OTEL_EXPORTER_OTLP_HEADERS="x-murnix-trace-token=YOUR_MURNITUR_API_KEY"
        export OTEL_SERVICE_NAME="my-ai-service"
        export OTEL_DEPLOYMENT_ENVIRONMENT="production"
        export OPENLIT_DISABLE_METRICS="true"
        ```

        Replace:

        1. `YOUR_MURNITUR_API_KEY` with your Murnitur API key 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>

  <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 "https://middleware.murnitur.ai" \
          --otlp-headers "x-murnix-trace-token=YOUR_MURNITUR_API_KEY" \
          --service-name "my-ai-service" \
          --deployment-environment "production" \
          --disable-metrics \
          python app.py
        ```

        Replace:

        1. `YOUR_MURNITUR_API_KEY` with your Murnitur API key from Step 1.
      </Tab>

      <Tab title="Environment Variables">
        ```shell theme={null}
        # Set environment variables (takes precedence over CLI args)
        export OTEL_EXPORTER_OTLP_ENDPOINT="https://middleware.murnitur.ai"
        export OTEL_EXPORTER_OTLP_HEADERS="x-murnix-trace-token=YOUR_MURNITUR_API_KEY"
        export OTEL_SERVICE_NAME="my-ai-service"
        export OTEL_DEPLOYMENT_ENVIRONMENT="production"
        export OPENLIT_DISABLE_METRICS="true"

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

        Replace:

        1. `YOUR_MURNITUR_API_KEY` with your Murnitur API key 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. Visualize in Murnitur.ai

Once your LLM application is instrumented, you can explore the telemetry data in Murnitur.ai:

1. **Navigate to Murnitur.ai**: Go to your [Murnitur.ai Dashboard](https://murnitur.ai)
2. **Explore Traces**: View your AI application traces including:
   * **LLM request traces** with detailed timing and execution flow
   * **Model performance** analytics and latency metrics
   * **Request/response data** for debugging and optimization
   * **Token usage** and cost tracking information
   * **Complete trace hierarchy** showing the full request lifecycle
3. **Trace Analytics**: Analyze patterns, performance bottlenecks, and usage trends
4. **Performance Monitoring**: Monitor latency, throughput, and error rates
5. **Debugging Tools**: Use detailed trace data to debug and optimize your AI applications

Your OpenLIT-instrumented AI applications will appear automatically in Murnitur.ai with comprehensive trace observability focused on LLM performance, execution flow, and detailed debugging capabilities optimized for AI workloads.