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. A score is just a named number or boolean, so it tracks ordinary product signals too, from click-through and saves to conversions and error rates.

Reach for it whenever you need to judge how well code ran, not just whether it succeeded: LLM-as-a-judge evals, deterministic guardrails (length, format, refusal checks), engagement signals, and human ratings. Scores are how you turn an experiment into a winner.

• Score right at the execution layer. Scores land on the run itself, alongside the rest of the execution metadata Inngest already captures, not in a separate metrics pipeline. 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 for you, off the critical path, without blocking the result a user sees.
• 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 in a separate run: a click, a rating, or a judge's verdict.

Scoring is available now in beta in the Inngest TypeScript SDK. Install the latest with npm install inngest@latest. inngest.score() lives on the client. step.score() needs scoreMiddleware(), which, along with createScorer, is imported from inngest/experimental.

• Score a function run
• Build a deferred scorer
• inngest.score() reference