> ## 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.

# Highlight.io

> LLM Observability with Highlight.io and OpenLIT

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

### 1. Get your Credentials

1. **Sign in to your Highlight.io account**
2. **Navigate to Project Settings**:
   * Go to your project dashboard
   * Click on **Settings** → **Project Settings**
3. **Get your Project ID**:
   * Copy your **Project ID** from the settings page
   * This will be used in the OTLP endpoint URL
4. **Generate API Key** (if needed):
   * Navigate to **API Keys** section
   * Generate a new API key for OpenTelemetry ingestion
   * Copy the API key for authentication

### 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://otel.highlight.io:4318/v1/traces", 
          otlp_headers="x-highlight-project=YOUR_PROJECT_ID"
        )
        ```

        Replace:

        1. `YOUR_PROJECT_ID` with your Highlight.io Project ID from Step 1.
           * Example: `x-highlight-project=1jdkoe52`
      </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://otel.highlight.io:4318/v1/traces"
        export OTEL_EXPORTER_OTLP_HEADERS="x-highlight-project=YOUR_PROJECT_ID"
        ```

        Replace:

        1. `YOUR_PROJECT_ID` with your Highlight.io Project ID from Step 1.
           * Example: `x-highlight-project=1jdkoe52`
      </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://otel.highlight.io:4318/v1/traces" \
          --otlp-headers "x-highlight-project=YOUR_PROJECT_ID" \
          --service-name "my-ai-service" \
          --deployment-environment "production" \
          python app.py
        ```

        Replace:

        1. `YOUR_PROJECT_ID` with your Highlight.io Project ID from Step 1.
           * Example: `x-highlight-project=1jdkoe52`
      </Tab>

      <Tab title="Environment Variables">
        ```shell theme={null}
        # Set environment variables (takes precedence over CLI args)
        export OTEL_EXPORTER_OTLP_ENDPOINT="https://otel.highlight.io:4318/v1/traces"
        export OTEL_EXPORTER_OTLP_HEADERS="x-highlight-project=YOUR_PROJECT_ID"
        export OTEL_SERVICE_NAME="my-ai-service"
        export OTEL_DEPLOYMENT_ENVIRONMENT="production"

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

        Replace:

        1. `YOUR_PROJECT_ID` with your Highlight.io Project ID from Step 1.
           * Example: `x-highlight-project=1jdkoe52`
      </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 Highlight.io

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

1. **Navigate to Traces**: Go to your Highlight.io project dashboard and click on **Traces**
2. **Explore AI Operations**: View your AI application traces including:
   * LLM request traces with detailed timing
   * Token usage and cost information
   * Vector database operations
   * Model performance analytics
   * Request/response payloads (if enabled)
3. **Session Monitoring**: Link traces to user sessions for full-stack observability
4. **Error Tracking**: Monitor and debug AI application errors and exceptions
5. **Performance Analysis**: Analyze latency, throughput, and resource usage

Your OpenLIT-instrumented AI applications will appear automatically in Highlight.io with comprehensive observability including LLM costs, token usage, model performance, and integration with your existing application monitoring.