Kungie/gut

Try it in your browser → The site runs gut's local model in the page: no key, no server.
Your code keeps running into questions that aren't logic: *Is this comment spam? Which team owns
this ticket? How urgent is it? Is the agent's task done?* Until now there were three answers:
- **Regex and keyword rules** — free and instant, and wrong the moment someone phrases it differently.
- **A frontier LLM** — understands anything, at seconds and cents a call, with prose to parse.
- **Train a classifier** — cheap to run, once you have the labelled data, the pipeline and the week.
There is a fourth: **a small model made for exactly these questions.** TypeSafe AI's
Jev answers typed questions directly — a probability for yes, a
distribution over options, a score on a scale — with nothing to generate or parse, billed on input
only. gut is built around it, and makes it a line of code:
No prompt, no parsing, no threshold — and no model named at the call site.
Three questions
It knows when it doesn't know
A regex never hesitates, and neither does an LLM. gut can:
Say how careful to be in words — lean="yes", stakes="high" — and gut works out the thresholds.
A thousand subjects, one line
Jev gets concurrent requests, a local model batched passes, and nothing already cached is asked
twice. @gut.semantic does the same for several questions about one subject.
Jev first, any model
Jev is the model gut is designed around. It is not the only one: the model is configuration, and
the same line runs unchanged on any of these.
Or several at once. Cascade asks the cheapest model first and passes on only what it is unsure of:
Every answer is a model's own probabilities, never parsed from text, and decision.model names the model that gave it. Your own model can be a backend too: here is how.
Install
The package on PyPI is gutfeel (gut was taken); the import is plain import gut. No model at hand? gut.FakeBackend(answers={"is spam": 0.97}) answers from fixtures, for tests.
From a shell, and for agents
The big model thinks; the small one decides, fast. An agent with gut judges a thousand files,
commits or search results in one command instead of reading each one itself:
Docs