Most LLM observability tools are SaaS — your prompts leave your machine and you pay per event. Lookspan is the opposite: one command, runs locally, your data never leaves your box, infra cost zero.
npx lookspan # → http://127.0.0.1:3100
It ingests spans/traces from your agents into a local SQLite database and shows them in a real-time dashboard:
- a timeline (waterfall) of where time goes, plus a conversation transcript of each prompt/response
- cost tracking per span and trace, latency p50/p95/p99
- alerts on errors / cost / latency thresholds
It is MCP-native, with drop-in wrappers for the OpenAI and Anthropic SDKs (observeOpenAI / observeAnthropic) and an OpenTelemetry receiver — point any OTel exporter at it, no Lookspan SDK required.
Newer additions:
- Replay a captured prompt against another model and diff cost / latency / output
- LLM-as-judge scoring of a trace
- Datasets to run a whole test set in batch and compare runs (model A vs B)
Local-first by design: binds to 127.0.0.1, redacts secret-looking values server-side, and your prompts/outputs never leave your machine.
MIT, TypeScript.
npx lookspan
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