This LangChain share is quite interesting.

@verysmallwoods VerySmallWoods · Sep 19, 2026 · case #048689 · listed via Jev Hub

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Shared a LangChain post on using Jev to build an agent harness with a classifier that is faster and cheaper than comparable LLMs.

The original post, translated

This LangChain share is quite interesting.😁 I think, very soon, every coding agent will have classifiers. It introduces how to use Jev to build a harness for agents. Jev is a model just released by @typesafeai, it only makes classification decisions, official data shows it is 200 times faster and 400 times cheaper than similar LLMs. Agent loop

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LangChain 的这份分享有点意思喔。😁 我想，很快，各家 coding agent 都会有分类器出现了。它介绍了怎么用 Jev 给 agent 搭 harness。Jev 是 @typesafeai 刚发布的模型，只做分类决策，官方数据显示，比同类 LLM 快 200 倍、便宜 400 倍。 Agent loop

The post above is a machine translation from zh; the untranslated text is in the fold-out.

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Where this case fits

Filed under agents & workflows, coding & developer tools. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the agents & workflows page.

🤖 Agents & workflows 🛠️ Coding & developer tools