gut

Judgment calls as one line of Python — built for Jev, and running on any small model.

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:

import gut

gut.configure(backend=gut.JevBackend()) # or just set TYPESAFE_API_KEY

if gut.likely(comment, "is spam"): hide(comment)

No prompt, no parsing, no threshold — and no model named at the call site.

Three questions

gut.likely(ticket, "is a bug report")                      # yes / no

gut.classify(ticket, Team) # which one — an Enum gut.rate(ticket, ["can wait", "this week", "right now"]) # how much

It knows when it doesn't know

A regex never hesitates, and neither does an LLM. gut can:

match gut.likely(email, "the customer threatens to cancel", ask_human=True):

case gut.YES: escalate(email) case gut.NO: auto_reply(email) case gut.UNSURE: send_to_a_person(email)

Say how careful to be in words — lean="yes", stakes="high" — and gut works out the thresholds.

A thousand subjects, one line

spam = gut.each(comments).likely("is spam")     # one decision per comment, in order

teams = gut.each(tickets).classify(Team)

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.

gut.configure(backend=gut.JevBackend())                            # TypeSafe AI's Jev

gut.configure(backend=gut.JevBackend.openrouter()) # Jev, through OpenRouter gut.configure(backend=gut.JevBackend.ollaya("winnow:e4b")) # open decision model, Ollaya gut.configure(backend=gut.ZeroShotBackend()) # NLI model, on your CPU gut.configure(backend=gut.TransformersBackend("Qwen/Qwen3-0.6B")) # small LLM, on your machine gut.configure(backend=gut.OpenAICompatibleBackend( # Ollama, vLLM, llama.cpp "qwen2.5:1.5b", base_url="http://localhost:11434/v1")) gut.configure(backend=gut.OpenAICompatibleBackend("gpt-4.1-nano")) # OpenAI gut.configure(backend=gut.OpenAIDecisionsBackend()) # OpenAI's Decisions API

Or several at once. Cascade asks the cheapest model first and passes on only what it is unsure of:

gut.configure(backend=gut.Cascade(

gut.ZeroShotBackend(), # free and local: settles the obvious gut.JevBackend(), # sees only what the first could not ))

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

pip install "gutfeel[jev]"           # + JevBackend: TypeSafe, OpenRouter or Ollaya

pip install "gutfeel[local]" # + ZeroShotBackend and TransformersBackend (PyTorch) pip install gutfeel # core: any OpenAI-compatible server; FakeBackend for tests pip install gutfeel-mcp # + the MCP server, gutfeel-mcp

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:

git ls-files | gut filter "retries failed requests" --read-files --max-cost 0.50

claude mcp add gut --env TYPESAFE_API_KEY=your-key -- uvx gutfeel-mcp # MCP Registry: io.github.Kungie/gut npx skills add Kungie/gut --skill gut # teaches a coding agent when to reach for it

Docs

[The documentation](https://gutpy.dev/docs/), one page per idea: Getting started · Backends · Knowing when it doesn't know · Asking everything at once · Async · Exact costs · Caching and observability · Command line · MCP server · Honest limitations. examples/ runs the same code on every backend.

Status and license

Pre-1.0, Apache-2.0. Every code block in these docs runs in the test suite · contributing