# Instrument LlamaIndex

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**When to use:** LlamaIndex pipeline (RAG, query engines) and you want traces in Stratix.

## Approach 1 — SDK adapter (when available)

The Stratix SDK ships LlamaIndex adapter coverage as an optional extras group:

```bash
pip install 'layerlens[frameworks-llamaindex]'
```

Wire the LayerLens callback into your `CallbackManager`. For canonical patterns (auto-instrumentation primitives), see the [`samples/instrument/`](https://github.com/layerlens/sdk-python/tree/main/samples/instrument) directory and the LangChain instrumented sample (same pattern).

## Approach 2 — manual trace upload from your pipeline

Capture per-query input + output, upload as a trace file:

```python
from layerlens import Stratix
client = Stratix()

# Run your LlamaIndex query
response = query_engine.query("What did the report say about Q3?")

# Build a trace record (any flexible Dict[str, Any] for `data`)
trace_record = {
 "input": "What did the report say about Q3?",
 "output": str(response),
 "spans": [
 {"name": "retrieval", "chunks": [n.node.text for n in response.source_nodes]},
 {"name": "synthesis", "text": str(response)},
 ],
}

# Write to JSONL and upload (≤50 MB per file)
import json
from pathlib import Path
Path("trace.jsonl").write_text(json.dumps(trace_record))
result = client.traces.upload("trace.jsonl")

# Score with a faithfulness judge
client.trace_evaluations.create(trace_id=result.trace_ids[0], judge_id=FAITHFULNESS_JUDGE_ID)
```

## See also

* [Workflow: Instrument](/6.-build-wire-your-code/workflow.md)
* [Integrations](/6.-build-wire-your-code/migration.md)
* [SDK reference: traces](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/traces.md)
* [SDK adapter samples](https://github.com/layerlens/stratix-python/tree/main/samples/instrument)


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