Scoring: judge how well your AI actually performed

Scoring lets you attach a named quality signal to a function run, a step, or an experiment variant. It shines for AI evals: run an LLM-as-a-judge over a result and write the verdict back as a score with inngest.score({ name, value }), or defer the judge entirely so a slow model call never blocks the run that produced the output.

Key features:

• Score right at the execution layer — Scores land on the run itself alongside execution metadata. inngest.score() writes immediately, and step.score("id", { name, value }) records the score as a durable, memoized step.
• Defer expensive scorers — createScorer() turns an LLM-as-a-judge or any other eval into a deferred function. Trigger it with defer() from your run and the score is written off the critical path.
• Score variants from anywhere — inngest.score.experiment() credits a score to the experiment variant that produced a result, even when the signal arrives much later.

Available now in beta in the Inngest TypeScript SDK.