Jev is now available in LangSmith Evals

Jev, a System One model from TypeSafe AI, is now available in LangSmith as a judge for evaluating agent behavior through fast, typed decisions rather than generated text. Unlike deterministic code-based evaluators and slower, less consistent LLM-as-a-judge approaches, Jev assesses states such as agent traces using yes/no probability, multiple-choice, and ordered-score questions, making it suited to high-volume, narrowly defined criteria such as PII leakage, user intent, and frustration. TypeSafe claims Jev is substantially cheaper and faster than comparable LLM classification methods and can evaluate multiple criteria in parallel; a cited test against GPT-5.6 variants and Claude Sonnet 4.6 found that Jev matched human decisions, had much lower variance, averaged 0.44 seconds per call, and cost $0.00035 per call. LangSmith users can configure Jev by adding a TypeSafe API key, selecting the TypeSafe provider and jev-latest model in an evaluator, defining the evaluated state, and adding typed feedback questions, after which results can be filtered, charted, or used to trigger alerts and automations.

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