Building a Harness with Jev (LangChain)

No, it is TypeSafe AI's own report on classification tasks, repeated as an attributed claim. The post's own demonstration is one urgency Noul returning 0.999 for a support message, with no accuracy comparison against an LLM judge, so treat the speedup as a vendor ceiling.

Yes for the classifier. State can be text, structured data or LangChain messages, which is what makes it callable from a node or middleware hook with the context the agent already has. What comes back are classification results rather than a chat response.

Routing middleware lets Jev assess a request and select a model by criteria you define, sending straightforward work to a fast cheap model and hard debugging to a capable one. Treat it as unstable: the import shown sits in an experimental namespace, and the entry copy notes the middleware beyond the classifier may change between releases.

No, and the post says so directly. Jev is not a drop-in replacement because it generates no text; the split it proposes is an LLM for open-ended reasoning and generation, with Jev for the fast structured decisions along the way.

Install langchain-typesafe, set TYPESAFE_API_KEY, then create a TypeSafeClassifier and call .invoke() with a state plus a questions dict; read typed results such as response.nouls["urgent"].noul instead of chat text.

TypeSafeClassifier, which you pass your state and questions to and which returns classification results rather than a chat response.

In middleware or tools, for model routing and fast structured decisions, while an LLM handles open-ended reasoning and generation; the state can be text, structured data or LangChain messages.