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

# Python

> Expose process, runtime, and custom Prometheus metrics from a Python service running on LocalOps, and visualize them with a community Grafana dashboard.

A complete walkthrough of instrumenting a Python service using the official
[`prometheus_client`](https://github.com/prometheus/client_python) library.

See the [overview](/environment/services/instrument-service) for the general approach.

## 1. Add the dependency

```bash theme={null}
pip install prometheus-client
```

Or in `requirements.txt`:

```
prometheus-client>=0.20.0
```

## 2. Register custom metrics and expose `/metrics`

`start_http_server` spins up a dedicated HTTP server on the given port that serves `/metrics`. The default registry
already includes process metrics (CPU, memory, open FDs) and Python runtime metrics (GC stats, threads).

```python theme={null}
import time
from prometheus_client import Counter, start_http_server

emails_sent = Counter(
    'emails_sent_total',
    'Total number of emails sent',
    ['status'],
)

if __name__ == '__main__':
    start_http_server(9090)
    while True:
        emails_sent.labels(status='success').inc()
        time.sleep(1)
```

<Tip>
  For ASGI apps (FastAPI, Starlette), use [`prometheus-fastapi-instrumentator`](https://github.com/trallnag/prometheus-fastapi-instrumentator)
  to mount `/metrics` on your main app port. For Django, use [`django-prometheus`](https://github.com/korfuri/django-prometheus).
  For Flask, use [`prometheus-flask-exporter`](https://github.com/rycus86/prometheus_flask_exporter).
</Tip>

## 3. Declare the metrics endpoint in `ops.json`

```json theme={null}
{
    "metrics": {
        "endpoint": "/metrics",
        "interval": 15,
        "port": 9090
    }
}
```

## 4. Visualize with a community dashboard

Python doesn't have a single canonical community dashboard like JVM or Node.js, but the metrics exposed by the default
registry (`process_*` and `python_*`) are well-supported by generic process dashboards.

| Dashboard                                                         | ID     | Notes                                                                                                                    |
| ----------------------------------------------------------------- | ------ | ------------------------------------------------------------------------------------------------------------------------ |
| [Prometheus Stats](https://grafana.com/grafana/dashboards/2)      | `2`    | Generic dashboard for `process_*` metrics (CPU, memory, FDs) that work for any Python service using `prometheus_client`. |
| [Node Exporter Full](https://grafana.com/grafana/dashboards/1860) | `1860` | Host-level metrics if you also want OS-level context for your Python service.                                            |

For a Python-app-specific dashboard with panels for requests/sec, latency percentiles, error rates, app uptime, and
interpreter version, see [pilosus/prometheus-client-python-app-grafana-dashboard](https://github.com/pilosus/prometheus-client-python-app-grafana-dashboard)
on GitHub — it's a JSON dashboard you can import directly.

You can also browse [grafana.com/grafana/dashboards](https://grafana.com/grafana/dashboards/?search=python) for more
Python-related community dashboards.

To import: in Grafana, go to **Dashboards → New → Import**, enter the dashboard ID (or upload the JSON), and select
your LocalOps Prometheus data source.