local-ai-mac — security grade SAFE, quality 57/100

Security audit verdict: SAFE · quality 57/100

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 dmitryryabkov · Agent Tool · ★ 70

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

🔒 Is local-ai-mac safe to install? View the security audit →

About local-ai-mac

Running (and coding with) Local AI on a Mac This is a comprehensive guide on how to run AI models and coding agents locally on a Mac. I gathered various bits and pieces of information scattered across the internet into a single resource. If you have a spare Mac lying around, you can turn it into an “LLM server” and use it instead of paying for OpenAI, Claude, etc. You’ll need a decent amount of RAM for this to work. Around 32GB is where things start getting interesting. I’m currently running 30B-class models with the same 128K context window as OpenAI Codex on an M3 MacBook Pro with 36GB of RAM — all locally. ⚠️ This guide assumes familiarity with LLM concepts, inference engines, and basic system configuration. It is a good starting point for anyone who is building local AI systems or is experimenting with agent workflows. Why? Because it works — and surprisingly well. But more importantly: there are cost and privacy benefits of running AI locally. The architecture of Apple’s M-series chips suits this type of workload. AI models benefit from GPUs with access to large amounts of memory.

agentic-aiagentic-workflowagentic-workflowsapple-siliconclaude-codellama-cppllamacpplm-studiolocal-ailocal-llm

Quick Facts

Stars70
Forks5
CategoryAgent Tool
LicenseMIT
Quality Score57.1620626204335/100
Open Issues1
Last Updated2026-06-13
Created2026-04-02
Platformsclaude-code
Est. Tokens~23k

Compatible Skills

These tools work well together with local-ai-mac for enhanced workflows:

  • houtini-lm — semantic(0.27)+complementary+rare_topics+similar_pop+shared_platform (54%)

local-ai-mac alternative? Top 6 similar tools

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

  • maclocal-api by scouzi1966 · ⭐ 338

    'afm' command cli: macOS server and single prompt mode that exposes Apple's Foundation and MLX Models and othe

  • SAM by SyntheticAutonomicMind · ⭐ 131

    Synthetic Autonomic Mind - An AI assistant for everyone.

  • ovo-local-llm by ovoment · ⭐ 105

    A private Claude-Code-style coding agent for Apple Silicon — run chat, code, and local model workflows on-devi

  • lilbee by tobocop2 · ⭐ 53

    The whole local AI stack in one executable: it runs and manages local AI models across every GPU, and it's a s

  • open-jet by L-Forster · ⭐ 50

    A terminal coding agent, and a Python SDK for embedding on-device models in your own apps.

  • AgenticGoKit by AgenticGoKit · ⭐ 169

    Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agno

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Frequently Asked Questions

What is local-ai-mac?

local-ai-mac is Practical guide to running LLMs locally on Apple Silicon, with a focus on architecture, memory scaling, and agent workflows.. It is categorized as a Agent Tool with 70 GitHub stars.

How do I install or use local-ai-mac?

You can find installation instructions and usage details in the local-ai-mac GitHub repository at github.com/dmitryryabkov/local-ai-mac. The project has 70 stars and 5 forks, indicating an active community.

What license does local-ai-mac use?

local-ai-mac is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to local-ai-mac?

The top alternatives to local-ai-mac on Agent Skills Hub include maclocal-api, SAM, ovo-local-llm. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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