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 riccardomusmeci · Agent Tool · ★ 467
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mlx-llm safe to install? View the security audit →
mlx-llm Large Language Models (LLMs) applications and tools running on Apple Silicon in real-time with Apple MLX. Go to the entire Youtube Video. How to install 🔨 Models 🧠 Currently, out-of-the-box supported models are: To create a model with pre-trained weights from HuggingFace: You can also load a new version of pre-trained weights for a
| Stars | 467 |
| Forks | 28 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 63.0133906437189/100 |
| Open Issues | 1 |
| Last Updated | 2025-01-29 |
| Created | 2023-12-07 |
| Platforms | python |
| Est. Tokens | ~127k |
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mlx-llm is Large Language Models (LLMs) applications and tools running on Apple Silicon in real-time with Apple MLX.. It is categorized as a Agent Tool with 467 GitHub stars.
mlx-llm is primarily written in Python. It covers topics such as llama, llm, mistral.
You can find installation instructions and usage details in the mlx-llm GitHub repository at github.com/riccardomusmeci/mlx-llm. The project has 467 stars and 28 forks, indicating an active community.
The top alternatives to mlx-llm on Agent Skills Hub include ovo-local-llm, Risuai, WorkflowAI. 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: