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

# Python (bitfrost)

> One line of Python auto-instruments openai, anthropic, litellm, and smolagents, and streams every LLM call to Voight over OpenTelemetry.

[`bitfrost`](https://github.com/Voightxyz/bitfrost) is Voight's OpenTelemetry bridge for Python. It turns the OTel GenAI spans your LLM libraries already emit into Voight events: model calls, prompts, tokens, cost, latency, errors, and the trace tree that links them.

It is also a standalone tool: without a Voight key it captures to your terminal, a JSONL file, or SQLite, with zero network calls. Point it at Voight when you want the dashboard.

## Install

```bash theme={null}
pip install bitfrost
```

Python 3.10+.

## Quick start

One line at your app's entrypoint:

```python theme={null}
import bitfrost
from bitfrost.backends.voight import VoightBackend

bitfrost.quickstart(agent="my-app", backend=VoightBackend())
```

`quickstart` auto-detects and instruments every supported library it finds: `openai`, `anthropic`, `litellm`, and `smolagents`. Your normal LLM calls start streaming to the dashboard, no other code changes.

`VoightBackend` reads your API key from the `VOIGHT_KEY` environment variable:

```bash theme={null}
export VOIGHT_KEY="vk_..."
```

Create the key in the dashboard under Settings, or from the "Add app" guide.

## What lands in Voight

Each LLM call becomes one event carrying:

* the model and provider, resolved from the span's `gen_ai.*` attributes (both the v1.27 and v1.32+ semantic-convention generations are understood)
* prompt messages and the response text, subject to the privacy level
* token counts (input, output, total): the dashboard's token and cost KPIs read these directly
* duration, outcome, and the error message when a call fails
* the span's own timestamp, so batched or delayed sends keep an honest timeline
* the OpenTelemetry trace context (`traceId`, `spanId`, `parentSpanId`), so multi-step requests arrive as a tree, not a flat list

## Privacy

Three levels, applied before anything leaves the process: `standard` (default), `minimal` (drops prompt and response content), and `off`. PII scrubbing (12 patterns plus Luhn) runs on outbound content.

```python theme={null}
bitfrost.quickstart(agent="my-app", backend=VoightBackend(), privacy="minimal")
```

## No supported library?

If none of the four libraries is installed, `quickstart` still installs the tracer and warns you. Any manually created OpenTelemetry span with `gen_ai.*` attributes is captured, so custom stacks and other instrumentations keep working.

## Next

* [AI Apps overview](/ai-apps/overview): what the dashboard shows
* [Tracing](/ai-apps/tracing): how events group into traces
* [bitfrost on GitHub](https://github.com/Voightxyz/bitfrost): full README, CLI, TUI, and the local offline dashboard