Topics / #jev

Jev projects on GitHub

As of , the most-starred Jev projects on GitHub are laya (33.2K stars), QuantDinger (12.9K stars) and kev (9.3K stars). GitHub.Locker tracks 26 Jev repositories with 75.2K stars combined. The fastest-growing on October 16 was laya, with +130 stars in a day.

26Projects
75.2KStars in total
+404Stars in a day

Hot right now

Most stars gained in a day · Oct 16

The all-time top

26 projects · by stars

  1. #1 · NandhaKishorM/laya

    laya

    Laya is a non-autoregressive decision engine: instead of generating text, it answers typed questions — multiple choice, a numeric score, or yes/no — about any text in a single forward pass, across 100+ languages. A built-in router detects the language and picks the right checkpoint per request.

    33.2K
  2. #2 · OpenByteInc/QuantDinger

    QuantDinger

    QuantDinger is an open-source, self-hosted AI Trading OS: it lets you research trading ideas with AI, write Python strategies on a Strategy API, backtest them server-side, and run paper or live trading across crypto exchanges and traditional brokers, with web, mobile, API, and MCP access.

    12.9K
  3. #3 · jaredpalmer/kev

    kev

    Kev is an open family of small decision models (0.8B to 27B parameters) built on Qwen3.5/3.8 that answer yes/no, multiple-choice and rating questions about text with calibrated probabilities, acting as a self-hosted drop-in replacement for TypeSafe's hosted Jev model.

    9.3K
  1. 4

    jevgrep

    2.8Kdzhng/jevgrep · TypeScript

    jevgrep (the jg CLI) lets coding agents find relevant code by describing what it does in a question, instead of searching for exact text or paths. It uses an engine called Jev to judge relevance across folders, files, and declarations and returns reading leads plus verbatim source excerpts to stdout.

  2. 5

    awesome-jev

    2.4Kyibie/awesome-jev · Python

    A curated list of public projects, integrations, and discussions built on Jev — TypeSafe AI's System One model for typed decisions. It tracks who is actually building with it and which decision patterns transfer across industries.

  3. 6

    agent-beacon

    1.8KAsymptote-Labs/agent-beacon · Go

    An open-source memory layer for AI coding agents: it captures session history across Claude Code, Cursor, Codex, OpenCode and 20+ other harnesses, then turns useful workflows, corrections and debugging patterns into knowledge future agents can reuse.

  4. 7

    AnyJev

    1.4Knokia-applied-research/AnyJev · Python

    A Python library that turns any open LLM into a Jev-style decision model: ask a typed question, get back a choice with a probability you can threshold — no generation, no parsing, no fine-tuning.

  5. 8

    jev

    1.3Kfeder-cr/jev · C++

    jevos-1b is a free, open-source yes/no decision engine that runs locally on your laptop's CPU, returning the probability that the answer is 'yes' for a given text plus question in about 50-220 ms, as a self-hosted alternative to the cloud-based Jev service.

  6. 9

    reticle

    1.2Kreticlehq/reticle · TypeScript

    Reticle lets a coding AI agent verify its own work by driving the real running app and reading what actually happened — network calls, app state, console — instead of just assuming the code works, returning a pass, fail, or 'unknown' verdict with the exact file:line to fix.

  7. 10

    dscode

    997qiz029/dscode · JavaScript

    DSCODE is a terminal coding agent for macOS built on DeepSeek Harness: a persistent shell session that the TUI, CLI and your own scripts all share, with tasks handed between agent sessions and an independent reviewer checking code changes before a turn ends.

  8. 11

    awesome-jev

    975heyjunpenn/awesome-jev · Astro

    A verified, community-maintained catalog of open-source projects built with Jev — TypeSafe AI's System One model for typed decisions inside software. The README snapshot covers 832 unique public GitHub repositories.

  9. 12

    TypeLLM

    956TypeLLM/TypeLLM · Python

    TypeLLM adds type-safe, schema-guaranteed output generation to existing open autoregressive LLMs, without changing their weights or architecture, letting models still think freely while returning typed JSON Schema-constrained answers.

  10. 13

    von

    916wfzyx/von · Python

    Von is an open-source, non-autoregressive decision model (395M parameters, 1.5 GB) that makes calibrated discrete, probabilistic, and ordinal decisions in a single forward pass in under 25 ms — a local, free alternative to the proprietary TypeSafe Jev.

  11. 14

    Valen

    843Liuziyu77/Valen · Jupyter Notebook

    Valen (万澜) is a multimodal decision model that adds visual perception to System One-style decision-making: it scores candidate actions against text, images and video using a task instruction, instead of generating free-form answers.

  12. 15

    foreman

    771thruwire/foreman · Python

    Foreman is a supervisor that watches a coding agent (like Codex or OpenCode) while it works on a ticket or bug report, using a fast decision model called Jev to independently judge whether the implementation is complete, needs verification, or needs a human.

  13. 16

    awesome-jev-projects

    684logicrw/awesome-jev-projects · JavaScript

    Awesome Jev is a curated radar of open-source projects in the Jev ecosystem — project discovery through short, plain-language write-ups, so you can size a project up before spending time on it.

  14. 17

    awesome-jev

    601kydlikebtc/awesome-jev · Python

    A curated index of 1207 public resources for Jev, TypeSafe AI's System One decision model, organised by the decision each resource illustrates. It's an independent catalog, not the product or an SDK.

  15. 18

    jev-experiments

    406dabit3/jev-experiments · TypeScript

    A monorepo of latency-focused demo apps built around TypeSafe / Jev, each of them written by the Devin AI coding agent.

  16. 19

    agent-jev

    388malevrigns/agent-jev · Python

    AgentJev-0.6B is a tiny 'System One' decision model for AI agent loops: you feed it unstructured state (diffs, traces, logs) plus structured boolean, choice, or score questions, and it returns calibrated probability distributions in one ~50ms forward pass without decoding any text.

  17. 20

    awesome-jev-live

    338wh000wh000/awesome-jev-live · Python

    Awesome Jev Live is an evidence-graded index of projects built around Jev, TypeSafe AI's System One model — 836 entries covering SDKs, MCP tools, agents, apps and open models, published in 20 languages.

  18. 21

    jev-align

    309sutro-sh/jev-align · Python

    An experimental CLI from Sutro for building calibrated AI Functions on TypeSafe's Jev: it finds uncertain examples in your dataset, has you label them, and uses GEPA to keep improving the function.

  19. 22

    awesome-jev

    238Amal-David/awesome-jev · Python

    A curated awesome-list of demos, projects, SDKs and skills built with Jev — 44 reviewed picks with source links, plus a curated gallery of demos from X and YouTube.

  20. 23

    jevmem

    127Avinash-jetwani/jevmem · TypeScript

    jevmem saves the decisions, rules, and failed approaches from your Claude Code (and other coding agent) chats into a JEVMEM.md file in your repo, and brings the relevant ones back in later sessions so context survives a fresh chat.

  21. 24

    agent-router

    115nidhi-singh02/agent-router · TypeScript

    Agent Router is a CLI that looks at your AI coding-agent subscriptions, filters them by quota and policy rules, asks a ranking service for the best pick, and then launches the chosen agent (Cursor, Claude Code, Codex, or OpenCode) in a Herdr pane with your task.

  22. 25

    Bud-Decision-Studio

    110BudEcosystem/Bud-Decision-Studio · HTML

    Bud Decision Studio is a cross-platform desktop app and local serving system for 'decision models' (Jev-like 'System One' models): you give it a situation and typed questions, and it returns a calibrated probability for every allowed answer instead of generated text.

  23. 26

    jevals

    105openlayer-ai/jevals · Python

    jevals is a Python library for evaluating and guardrailing AI agent traces using 'Jev' decision models instead of a text-generating LLM judge. It bundles every check for a trace into one request that costs a fraction of a cent and returns in a few hundred milliseconds, so you can eval every trace and even run checks inside the agent loop.

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