von: The open-source System One decision model

by wfzyx · AI Tool · ★ 922

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

How to install von

pip install von
uv add von

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

decision-modeljevmachine-learningpythonrlcdsystem-onetypesafe

Quick Facts

Stars922
Forks74
LanguagePython
CategoryAI Tool
LicenseApache-2.0
Quality Score64/100
Last Updated2026-10-19
Created2026-09-18
Platformspython
Est. Tokens~15k

About von

🪨 Von () The Open-Source System One Decision Model. Sub-15ms decisions. 14MB footprint. Zero cloud latency. Zero API keys. Zero VC tax. Named in homage to John von Neumann and Ludwig von Mises. Why Von? TypeSafe AI raised $40M to charge $0.042/1M tokens for what amounts to a smart statement behind a closed-source cloud API waitlist. Von is 100% open-source and runs locally on your machine.

von alternative? Top 6 similar tools

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

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  • openJev-verdict-2.0 by Heman10x-NGU · ⭐ 305

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  • awesome-jev by kraayenjon · ⭐ 204

    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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    Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue upda

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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