Workflow engines, schedulers and batch jobs that use Jev to choose a branch, retry or escalation step.
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The native CLI supports an interactive shell, scripted requests and embedding, with skills, MCP servers and subagents. Jev receives review policy, context and the pending action, returning a clear or caution decision for the permission pipeline.
The pipeline develops a learning brief, mathematics, visual plan and Manim scene, then reviews the rendered result. Jev evaluates candidate artifacts and Astra's text audits through typed Score, Noul and Choice answers; it does not receive the images.
The model answers choice, Boolean and ordered-score questions from states and candidates without decoding answer tokens. NanoJev is a separate local model inspired by Jev; recorded comparisons include Jev and untuned Qwen on the same game interfaces.
RAG-Fusion generates query variants and merges retrieval rankings with Reciprocal Rank Fusion to find material a single query may miss. Jev is an optional reranker in the evaluation pipeline; its experiments require JEV_API_KEY. The original demo remains vector-only. Compare retrieval strategies on NFCorpus with the evaluation harness; the hybrid pipeline combines BM25, vector search, fusion and reranking.
Awesome Jev is heyjunpenn’s community catalog of Jev projects, organized into SDKs, agents, applications, research and other categories. Entries explain what Jev decides and track implementation evidence, reproduction status and metric provenance. Browse by category, then use the evidence labels and upstream links to inspect a project’s actual decision workflow.
Jev Review scans Git diffs or complete codebases for correctness, security, reliability, compatibility and test coverage. Jev selects evidence, classifies mechanisms, scores severity and, when needed, uses Choice to select a reviewer. Code applies thresholds and workflow policy. Use Node.js 24+, Git and a TypeSafe key to generate JSON reports or inspect saved reviews locally.
The project controls a Minecraft Java 1.16.5 player through Mineflayer, using a separate model for planning and Jev for player actions. Configure the model relay and local runtime paths, install dependencies, start the vanilla server, and launch the documented nether-agent workflow.
The curated list organizes Jev providers, SDKs, agent workflows, context tools, games and evaluations with source and limitation notes. Read the Choice, Score and Noul introduction, then follow the relevant workflow category and pinned research notes to source code.
System One Connector exposes typed decisions to coding agents through its evaluate command and MCP integration. It connects to Jev or compatible self-hosted models such as CLM and Laya. Define Choice, Noul or Score questions; agent setup and batch evaluation are provided.
A source-backed Jev project directory with a reusable Jev-only GitHub review workflow.
The ai agent workflow tool that scales from small, simple projects to large, complex codebases.
Jev Browser — indexed browser automation. Jev chooses the control, Playwright acts. A CodexQA skill.
Talk to your Mac. Local whisper.cpp + one Jev (TypeSafe) call per command + macOS automation.
Browser automation where an LLM plans and Jev (Typesafe System One) decides. Library, CLI and MCP server.
Give an agent real hands and eyes: undetectable, DOM-independent GUI automation. A $4 USB-HID microcontroller for hands, a vision grounding model for eyes, any LLM for a brain.
Classify first. Read selectively. A portable agent plugin and MCP tool for batch text classification.
Open-source macOS AI computer use and native GUI automation on Apple silicon. Jev + OmniParser CoreML + Apple Vision OCR. Bring your own OpenRouter, Vercel AI Gateway, or TypesafeAI token.
Fast, typed browser automation powered by Jev and Playwright
Async LangGraph workflow that gets a typed Jev Choice (invoice or general) and routes each inbound email to the matching handler.
Typed decisions from the shell: an unofficial stdlib-Python CLI and Agent Skill for TypeSafe's Jev model, via the TypeSafe API (default) or OpenRouter. Yes/no, choice and ordinal scores with calibrated probabilities, semantic grep and batch mode.
SLO-aware LLM inference router with Jev decisions, live queue metrics, counterfactual evaluation, and reproducible latency/cost benchmarks
Jev 决策模型驱动的 UI 自动化测试框架：不生成「下一步做什么」的文字，而是在候选元素里直接打分；一句话写用例，pytest 执行，Allure 出报告，每个操作决策中位 458 ms（UI test automation driven by the Jev decision model — scored decisions, not generated prose; ~458 ms per decision）
A Kotlin Android SDK for UI automation powered by TypeSafe Jev, with an accessibility runtime and sample app.
Jev for every agentic development workflow — the System One decision model wired into whatever harness an agent codes in: OpenCode today, Claude Code and Hermes adapters next.