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Covered in Agents & ProtocolsAI EngineeringModels & Benchmarks, where the background and the sources for this subject live.
Two shipped integrations put Jev inside an agent loop, from Pydantic AI and LangChain. Both leave three things to your code: a ledger of actions already executed, because Jev may re-propose a tool that ran; a per-question projection of state, because accuracy falls as irrelevant context grows; and the confidence threshold that escalates to a language model.
5 min read
TypeSafe publishes four use cases for Jev and two rate limits: 250,000 tokens per second and 1,200 requests a minute. Below 12,500 tokens per request the request limit binds first, so a small-state workload reaches a fraction of the token ceiling. Map-reducing a petabyte stays out of reach.
4 min read
Jev posts state and a map of questions to one endpoint and returns typed answers carrying probabilities. TypeSafe claims 0% type errors, which is a guarantee about schema conformance rather than about correctness. A noul of 0.95 is a well-formed number that can still be wrong about the thing it scored.
3 min read
Jev answers noul, choice and score questions. Choice caps at 255 options and score at 10 levels, and both return a confidence derived from their probability distribution. Noul returns no confidence field at all, because the probability it hands back already describes the uncertainty. Modelling the problem means picking among those three.
4 min read