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 madroidmaq · Agent Tool · ★ 734
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mlx-omni-server safe to install? View the security audit →
MLX Omni Server Local AI inference server optimized for Apple Silicon MLX Omni Server provides dual API compatibility with both OpenAI and Anthropic APIs, enabling seamless local inference on Apple Silicon using the MLX framework. Installation • Quick Start • Documentation • Contributing ✨ Features 🚀 Apple Silicon Optimized - Built on MLX framework for M1/M2/M3/M4 chips 🔌 Dual API Support - Compatible with both OpenAI and Anthropic APIs 🎯 Complete AI Suite - Chat, audio processing, image generation, embeddings ⚡ High Performance - Local inference with hardware acceleration 🔐 Privacy-First - All processing happens locally on your machine 🛠 Drop-in Replacement - Works with existing OpenAI and Anthropic SDKs 🚀 Installation ⚡ Quick Start Start the server: Choose your p
| Stars | 734 |
| Forks | 90 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.1450274928993/100 |
| Open Issues | 25 |
| Last Updated | 2026-05-09 |
| Created | 2024-11-05 |
| Platforms | cli, python |
| Est. Tokens | ~371k |
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mlx-omni-server is MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements OpenAI-compatible API endpoints, enabling seamless. It is categorized as a Agent Tool with 734 GitHub stars.
mlx-omni-server is primarily written in Python. It covers topics such as function-calling, genai, mlx.
You can find installation instructions and usage details in the mlx-omni-server GitHub repository at github.com/madroidmaq/mlx-omni-server. The project has 734 stars and 90 forks, indicating an active community.
mlx-omni-server is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mlx-omni-server on Agent Skills Hub include openai-function-calling-tools, vllm-mlx, sgr-agent-core. 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.
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