LLM-as-judge

Lookspan can have a model judge a captured response — scoring it 0–1 against a metric — and store the result as a score on the trace. Combined with Replay, this turns observation into a measurable feedback loop.

Provide a key

Judging calls a provider, so it needs an in-memory API key (same as Replay):

LOOKSPAN_OPENAI_API_KEY=sk-... npx lookspan
#   ...or LOOKSPAN_ANTHROPIC_API_KEY / --openai-key / --anthropic-key

Judge a trace

In the dashboard, use the Replay & judge panel on a trace. Or via the API:

# Score the response 0–1 with an LLM judge (stored as an "llm-judge" score)
curl -X POST localhost:3100/api/traces/<id>/judge \
  -H 'content-type: application/json' \
  -d '{"metric":"correctness"}'

The judge request body accepts { metric?, model?, provider?, rubric? }:

• metric — what you’re scoring (e.g. correctness, helpfulness).
• model / provider — which model acts as judge (defaults apply).
• rubric — an optional custom rubric to steer the judge.

The score is persisted on the trace with source llm-judge and shows up in the dashboard alongside any other scores.

Attach scores by hand or from an assertion

You don’t need a model to record a score. Any evaluation — an assertion in your test suite, a human rating — can be attached directly:

curl -X POST localhost:3100/api/traces/<id>/scores \
  -H 'content-type: application/json' \
  -d '{"name":"correctness","value":1,"comment":"matched expected","source":"assertion"}'

The body is { name, value, comment?, source? }.

Scale it up

To judge many prompts at once, build a dataset and run it with judge: true — each item is replayed and scored, with aggregate score per run.