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

# Online Experiments

> Tag traces with experiment IDs and variants to analyze A/B test results in HoneyHive

Use trace enrichment to tag requests with experiment information, then analyze results using HoneyHive's charting tools.

This approach works with any feature flag system (Statsig, LaunchDarkly, custom) - just pass the experiment ID and variant to your traces.

## How to Run Online Experiments

**Prerequisites:** HoneyHive tracing set up per the [Quickstart](/v2/introduction/tracing-quickstart).

<Steps>
  <Step title="Tag traces with experiment metadata and capture feedback">
    When a request is part of an experiment, enrich the span with the experiment ID, variant, and user feedback:

    ```python theme={null}
    import os
    from honeyhive import HoneyHiveTracer, trace

    tracer = HoneyHiveTracer.init(
        api_key=os.getenv("HH_API_KEY"),
        project=os.getenv("HH_PROJECT")
    )

    @trace
    def generate_response(user_input: str, experiment_id: str, variant: str):
        # Tag this span with experiment info
        tracer.enrich_span(metadata={
            "experiment_id": experiment_id,
            "variant": variant  # e.g., "control" or "treatment"
        })
        
        # Your LLM logic here...
        response = call_llm(user_input)
        
        # Later, when user provides feedback, attach it to this span
        tracer.enrich_span(feedback={"liked": user_liked_response})
        
        return response
    ```

    <Tip>
      The `feedback` object accepts any structure. Use boolean for thumbs up/down (`liked: true`), numbers for ratings (`score: 4`), or strings for comments.
    </Tip>
  </Step>

  <Step title="Analyze results in HoneyHive">
    1. Go to **Discover** to build charts
    2. Select **All Events** to analyze at the span level
    3. **Filter** by `metadata.experiment_id` to isolate experiment data
    4. Set metric to your feedback field (e.g., `feedback.liked`)
    5. **Group by** `metadata.variant` to compare control vs treatment

    This shows you how each variant performs on your chosen metric.
  </Step>
</Steps>

## Learn More

<CardGroup cols={2}>
  <Card title="Enriching Traces" icon="tag" href="/v2/tracing/enrich-traces">
    All enrichment patterns and options
  </Card>

  <Card title="User Feedback" icon="message" href="/v2/tracing/setting-user-feedback">
    Feedback schema and best practices
  </Card>
</CardGroup>