I built jevpipe as a wrapper for TypeSafe's Jev System 1 model.

Jev answers yes/no, picks from a few choices or gives a score, with probabilities, and it's fast and cheap. See https://docs.typesafe.ai/introduction for more details on Jev. It performs on par with small LLMs while being faster and cheaper (independent study: https://www.ayautomate.com/blog/jev-vs-llm-benchmark), and in my benchmark it was better calibrated than DeepSeek V4.1 Flash (see below).

I wanted agents to be able to use such a System 1 in the middle of a task without writing a script each time. So jevpipe is a small CLI that can be used like grep or other Unix tools: pipe lines or files in and get classifications out.

Example:

  cat abstracts.jsonl | jevpipe filter "Does this paper report a randomized trial?"
  cat abstracts.jsonl | jevpipe map -q '{"design": {"type": "choicsign?", "criteria": {"rct": "randomized trial", "observational":"observational study", "review": "review or meta-analysis", "other": "anything else"}}}' | jq -r .answers.design.choice | sort | uniq -c

filter prints the lines that pass, map returns typed answers as JS basically program their own System 1 for anything and apply it to a large chunk of data for "gut feeling" decisions.

I also added an agent skill for jevpipe that teaches the agent when and how to use it.

If you are interested in technical details, I also did a benchmark because that seemed to be missing from the recent evaluations. But it is by no means coding only or just a search tool. https://github.com/fabianboth/jevpipe/blob/main/bench/README...

It needs an OpenRouter key. I'm not affiliated with TypeSafe.

very nice. I finally made typesafe.ai account and put down $5 whole dollars.