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 anarchy-ai · Agent Tool · ★ 489
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LLM-VM safe to install? View the security audit →
🤖 Anarchy Labs' LLM-VM 🤖 An Open-Source AGI Server for Open-Source LLMs 🛑 DEVELOPMENT ON PAUSE 🛑 This is Anarchy's effort to build 🏗️ an open generalized artificial intelligence 🤖 through the LLM-VM: a way to give your LLMs superpowers 🦸 and superspeed 🚄. You can find detailed instructions to try it live here: anarchy.ai This project is in BETA. Expect continuous improvement and development. Table of Contents Table of Contents About What Why Features and Roadmap Quick Start and Installation Requirements Installation Generating Completions Running LLMs Locally Supported Models
| Stars | 489 |
| Forks | 140 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.0085588868489/100 |
| Open Issues | 131 |
| Last Updated | 2024-05-14 |
| Created | 2023-05-19 |
| Platforms | python |
| Est. Tokens | ~118k |
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LLM-VM is irresponsible innovation. Try now at https://chat.dev/. It is categorized as a Agent Tool with 489 GitHub stars.
LLM-VM is primarily written in Python. It covers topics such as artificial-intelligence, deep-learning, distillation.
You can find installation instructions and usage details in the LLM-VM GitHub repository at github.com/anarchy-ai/LLM-VM. The project has 489 stars and 140 forks, indicating an active community.
LLM-VM is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to LLM-VM on Agent Skills Hub include free-ai-resources-x, LLM-Tools, AITreasureBox. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: