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 ddalcu · Agent Tool · ★ 1.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mlx-serve safe to install? View the security audit →
mlx-serve — run any LLM on your Mac OpenAI- and Anthropic-compatible local inference for Apple Silicon — MLX and GGUF — faster than LM Studio on the same file. No Python. No cloud. No Electron. ddalcu.github.io/mlx-serve · Download MLX Core.app · Changelog ★ If mlx-serve saves you from spinning up another Electron app, star the repo — it genuinely helps people fin
| Stars | 1,259 |
| Forks | 115 |
| Language | Zig |
| Category | Agent Tool |
| Quality Score | 71.1837400706557/100 |
| Open Issues | 44 |
| Last Updated | 2026-09-14 |
| Created | 2026-02-17 |
| Platforms | claude-code |
| Est. Tokens | ~23k |
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mlx-serve is Native LLM inference server for Apple Silicon. OpenAI + Anthropic API compatible. No Python. Includes MLX Core macOS app with chat, agent mode, and tool calling.. It is categorized as a Agent Tool with 1.3k GitHub stars.
mlx-serve is primarily written in Zig. It covers topics such as agent, anthropic-api, apple-silicon.
You can find installation instructions and usage details in the mlx-serve GitHub repository at github.com/ddalcu/mlx-serve. The project has 1.3k stars and 115 forks, indicating an active community.
The top alternatives to mlx-serve on Agent Skills Hub include Rapid-MLX, vllm-mlx, claude-code-local. 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: