deepanwadhwa/OpenDecision

Zero-shot decision engine built on the MoritzLaurer ModernBERT-large NLI checkpoint, around 400M parameters, pulled from Hugging Face on first use. Exposes Choice, Noul and Score over natural-language criteria rather than structured question objects, and states that its confidence figure measures concentration in the returned distribution, not the chance the answer is correct.

OpenDecision plays Doom at skill 5 — Image from github.com

deepanwadhwa/OpenDecision on GitHub

Stars: 56
Forks: 3
Language: Python
License: Apache-2.0
Last push: 21 Sep 2026

Read from GitHub on 24 Sep 2026. Counts change daily.

How it uses Jev

response = client.system_one(
    state="I was charged twice and need this fixed before my meeting.",
    questions={
        "department": Choice(
            instructions="Which department should handle this?",
            criteria={
                "billing": "Payments and refunds",
                "technical": "Software problems",
                "sales": "Purchasing questions",
            },
        ),
        "urgent": Noul(
            instructions="This request is time-sensitive.",
        ),
        "frustration": Score(

View in examples/m3_typesafe_sdk_demo.py

Open on GitHub

PrimitivesChoice Score Noul

DSPy: programming, not prompting, language models

Declares typed input/output Signatures for LLM modules and optimizes the underlying prompts and weights against a metric. Its Signature abstraction is the closest widely-used open equivalent of Jev's typed-question interface. Star count is GitHub's rounded display figure.

stanfordnlp38k starsOpen

AlternativeStructured extraction+3

trycua/cua

Cua's own family of small System 1 models for computer-use decisions, separate from TypeSafe's Jev. CUA-S1-FORMS is the first release, with weights and a dataset on Hugging Face.

Cua (@trycua)26k starsOpen

Alternative

NandhaKishorM/laya

Decision model from Convai Innovations that fully fine-tunes an encoder and adds a typed-decision head: a 421M English checkpoint on ModernBERT-large and a 322M multilingual one on mmBERT-base, with a router picking between them by detected language. Returns choice, score and noul answers, and reports expected calibration error of 0.081 after temperature fitting, down from 0.466 as shipped.

NandhaKishorM21k starsOpen

Alternative

Outlines: structured outputs for LLMs

Guarantees valid structured output during generation by constraining decoding to a grammar or schema. The mechanism HN commenters repeatedly cited as already covering Jev's cannot-produce-a-type-error guarantee on ordinary LLMs. Star count is GitHub's rounded display figure.

dottxt-ai16k starsOpen

AlternativeStructured extraction+2

Instructor: reliable JSON from any LLM

Pydantic-based library that extracts typed, validated structured data from LLMs across providers with automatic retries. The established way teams get Jev-style typed answers today, at LLM latency and cost. Star count is GitHub's rounded display figure.

567-labs14k starsOpen

AlternativeStructured extraction+1

jaredpalmer/kev

A family of small, self-hosted decision models built on Qwen3.5 at 0.8B, 4B and 9B, with training code and an API that matches TypeSafe's System One so TypeSafe's own Python SDK can point at a local server.

jaredpalmer6.3k starsOpen

Alternative

Menu

deepanwadhwa/OpenDecision