JevEmbed — security grade SAFE, quality 67/100

Security audit verdict: SAFE · quality 67/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by HITsz-TMG · LLM Plugin · ★ 64

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is JevEmbed safe to install? View the security audit →

About JevEmbed

JevEmbed Meet JevEmbed — turn embeddings into decisions. Choose, score, and judge with your choice of embedding model. JevEmbed is a Python framework for embedding-based Choice, Score, and Noul decisions. It provides a Python API, CLI, and optional HTTP server using Jev-style request and response schemas. JevEmbed is an independent implementation. News September 24, 2026: JevEmbed now supports CLM-v0.1-8B for Choice, Score, and Noul decisions. See the CLM setup guide. September 24, 2026: JevEmbed-Data is available for fine-tuning, with 1.67 million labeled Choice, Score, and Noul questions. Installation Use Python 3.10–3.12 for the tested model stack. Run these commands from the project root: installs dependencies for local inference, the HTTP client, and the HTTP server. It automatically applies the version constraints in ; no separate installation is needed. Contributors can additionally install for testing and packaging. Pur

embeddingsjevjev-apillmstructured-decisionssystem-onetyped-decisions

Quick Facts

Stars64
Forks3
LanguagePython
CategoryLLM Plugin
LicenseApache-2.0
Quality Score66.7436081087048/100
Open Issues2
Last Updated2026-09-30
Created2026-09-22
Platformspython
Est. Tokens~20k

JevEmbed alternative? Top 6 similar tools

Looking for a JevEmbed alternative? If you're comparing JevEmbed with other llm plugin tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • open-alternative-jev by ikermoel · ⭐ 60

    Open-source alternative to TypeSafe's Jev: a System One style model layer that gives typed, calibrated decisio

  • awesome-jev by OmniJev · ⭐ 65

    🔥🔥 Papers, open reproductions and independent evaluations behind System One models and Jev.

  • stuntd by bladedevoff · ⭐ 64

    Local proxy that learns your app's typed LLM decisions and answers them with a Laya head. Jev and OpenAI compa

  • RSI-Jev by Shanghua-Gao · ⭐ 57

    Typed-decision models (noul / choice / score) trained by a self-improving loop of AI agents — checkpoints, the

  • awesome-jev-typesafe by valentynkit · ⭐ 188

    Typed decisions with TypeSafe's Jev, the first System One model

  • awesome-jev by kraayenjon · ⭐ 171

    A curated list of Jev use cases, projects, SDKs, and resources. Jev is TypeSafe AI's System One model for fast

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Frequently Asked Questions

What is JevEmbed?

JevEmbed is Meet JevEmbed — turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.. It is categorized as a LLM Plugin with 64 GitHub stars.

What programming language is JevEmbed written in?

JevEmbed is primarily written in Python. It covers topics such as embeddings, jev, jev-api.

How do I install or use JevEmbed?

You can find installation instructions and usage details in the JevEmbed GitHub repository at github.com/HITsz-TMG/JevEmbed. The project has 64 stars and 3 forks, indicating an active community.

What license does JevEmbed use?

JevEmbed is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to JevEmbed?

The top alternatives to JevEmbed on Agent Skills Hub include open-alternative-jev, awesome-jev, stuntd. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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