# Rapid-MLX

> Apache-2.0 local LLM server for Apple Silicon — OpenAI + Anthropic APIs, tool-call parsing tuned for coding agents

**Category:** AI Agents  
**Pricing:** open-source  
**URL:** https://rapidmlx.com  
**GitHub:** https://github.com/raullenchai/Rapid-MLX  
**Added:** 2026-10-07

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## The Hook

Rapid-MLX runs local models on a Mac through MLX and exposes OpenAI (/v1/chat/completions, /v1/responses) and Anthropic Messages (/v1/messages) endpoints, so Claude Code, Codex-style agents and anything OpenAI-compatible can point at your own machine instead of a cloud API.

## What's Good

Apache-2.0 (raullenchai/Rapid-MLX). Free path: brew install rapid-mlx (in homebrew-core) or pip install, pull a model, start the server. Focus is reliable tool calling: 27 parser modules with auto-detect fallback, plus continuous batching, a radix prefix cache saved across restarts, and quantized KV cache. The README publishes method and raw data: about 1.5x faster decode than mlx-lm at the median of 18 coding tasks on an M4 Pro Mac mini, up to about 4x on whole-file edits thanks to speculative decoding. A macOS desktop app exists if you want a GUI. ★~3.9k.

## The Catch

Mac and Apple Silicon only. Speed claims come from the project itself on specific Qwen models; the README notes a long-prompt agent turn was close to a tie and a dense 12B model was no faster than Ollama. Model licenses are separate, so check the model card before commercial use. Young (first commit February 2026) and moving fast. Vs Ollama (cross-platform, bigger ecosystem) and oMLX: pick Rapid-MLX when the job is agent tool calls on a Mac.

## Verdict

The one to try if you run coding agents against local models on a Mac and tool calls keep breaking — free and measured, but Mac-only and benchmarks are the project's own.