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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.

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Install

Python 3.10+.

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Quick start

One line at your app’s entrypoint:
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:
Create the key in the dashboard under Settings, or from the “Add app” guide.

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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

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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.

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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.

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