
oMLX turns an Apple Silicon Mac into a local AI server built for coding agents. The problem it solves is specific. Claude Code, Cursor and Codex keep reprocessing the same long prompt every time a tool call changes the context, and on a local machine that's painfully slow. oMLX saves that work to RAM and SSD, so anything the agent has already processed comes back from disk instead. It speaks the OpenAI and Anthropic APIs on localhost:8000, starts from a menu bar app, and has a dashboard for loading models and setting up agents in one click. It's free and open source (Apache 2.0), installed from a DMG or Homebrew. You need macOS 15 or later, an M-series chip and at least 16 GB of RAM, though they recommend 64 GB or more for daily coding. It reuses models you've already downloaded through Hugging Face or LM Studio. If you just want a local chatbot, stick with Ollama.
/admin for models, chat, bench, pinning, TTL.Local models have been fine for chat for a while. For agents they've been painful, because an agent changes its prompt dozens of times a session and a basic server reprocesses the whole thing every time. oMLX is built around that exact problem rather than around a nice chat window. It promises Claude Code and Cursor sessions that start in 5 seconds instead of 90, and its README backs that up with the details: paged caching, SSD offload, continuous batching and native support for Anthropic's messages API.
That's a different job from Lizard, which hosts what the agent has written, and from BrowserOS neo, which gives the agent a browser. oMLX gives the agent a model that runs on your desk. If you don't have Apple Silicon, if 64 GB of RAM is out of reach, or if you're happy paying Anthropic for usage, skip it. You can also skip the multi-Mac cluster docs, which are experimental.
Install it if you already keep MLX models around and you're tired of watching Claude Code spin while your local backend acts like every message is the first. Its 21,000 GitHub stars show people are excited, but stars won't tell you how hot your laptop gets. Time a session against the backend you use today, on your own hardware, before moving real work onto it.