> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/concepts/uploading-data/logging-traces.md).

# Logging traces (LlamaIndex, Langchain, etc.)

Users can log traces from their real-time (production) applications by invoking the Catalyst Tracer from their SDK. This allows Catalyst to read inferences in real-time and log them for instant evaluations. This way, users can avoid downloading and uploading in batches.

The Catalyst Tracer can be enabled with the following commands:

```python
from ragaai_catalyst.tracers import Tracer
from ragaai_catalyst import (
    RagaAICatalyst,
    init_tracing
)

# Initialize Catalyst Object
catalyst = RagaAICatalyst(
    access_key = "RNb1evdFRTKjb6I7bfhy",
    secret_key = "HmFFbkNOoub7mIXAD3bhXJWdTF283dlBh6K1Ev3z",
    base_url = "https://dev-ragaai.deephealthos.com/api"
)

# Start Tracing (Place this at the top of your code)
tracer = Tracer(
    project_name="your-project-name",
    dataset_name="your-dataset-name",
    metadata={"key1": "value1", "key2": "value2"},
    tracer_type="langchain"
)
init_tracing(catalyst=catalyst, tracer=tracer)
```

* If successful, you can view the logged dataset by navigating to Project\_Name -> Dataset\_Name
* Metrics can be run on logged datasets in the same fashion as for CSV uploads.


---

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