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

# Observability

> Built-in tracing, execution timelines and monitoring

Every Tensorlake function call is automatically traced. You get execution timelines, logs, metrics, and error details without configuring any observability infrastructure.

## Execution Timelines

When a request flows through your application, Tensorlake records every function call in an execution timeline. You can see:

* **Function call sequence** — which functions ran and in what order
* **Timing** — how long each function took, including cold start time
* **Dependencies** — which function calls ran in parallel vs. sequentially
* **Status** — success, failure, or retry for each function call

This is available in the [Tensorlake Dashboard](https://cloud.tensorlake.ai/applications) for every application request.

## Structured Logging

Use Python's standard `print()` or `logging` module inside your functions. Logs are captured automatically and associated with the specific function call and request.

```python theme={null}
from tensorlake.applications import function
import logging

logger = logging.getLogger(__name__)

@function()
def process_data(data: str) -> str:
    logger.info(f"Processing {len(data)} characters")
    result = transform(data)
    logger.info(f"Transformation complete, output size: {len(result)}")
    return result
```

Logs are available in the dashboard and through the [Logging guide](/applications/guides/logging) for configuration details.

## Learn More

<CardGroup cols={2}>
  <Card title="Logging" icon="file-lines" href="/applications/guides/logging">
    Structured logging configuration.
  </Card>
</CardGroup>