# 使用 Homebrew 安装 rapid-mlx

查看 rapid-mlx 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

## 安装

```sh
sudo av install brew:rapid-mlx
```

其他安装命令:

### macOS

- Homebrew (100%):

```sh
brew install rapid-mlx
```

  证据: local Homebrew formula metadata

## 软件包事实

- **软件包键:** brew:rapid-mlx
- **软件包管理器:** Homebrew
- **版本:** 0.11.9
- **来源摘要:** Fast local AI engine for Apple Silicon with an OpenAI-compatible API
- **主页:** <https://github.com/raullenchai/Rapid-MLX>
- **最后更新:** 2026-08-03T09:11:37Z
- **已生成:** 2026-08-03T19:37:03+00:00

## 可执行文件

- rapid-mlx (别名)
- rapid-mlx-bench (别名)
- rapid-mlx-chat (别名)
- rmlx (别名)
- vllm-mlx (别名)
- vllm-mlx-bench (别名)
- vllm-mlx-chat (别名)

## 安装行为

- Bottle: 不可用

## 版本和新鲜度

- 页面生成时间: 2026-08-03
- 管理器版本: 0.11.9
## 项目历史与用法

Rapid-MLX is a local inference engine and OpenAI-compatible HTTP server built on Apple's MLX stack for Apple Silicon Macs. It packages model serving, interactive chat, benchmarking, model management, and agent/IDE integration behind the `rapid-mlx` CLI.

### 项目历史

Development began in early 2026, with the official repository created in February and tagged releases available from March. The package was formerly exposed through `vllm-mlx` command names; those entry points remain as compatibility aliases while current documentation directs new users to `rapid-mlx`.

### 采用历史

Rapid-MLX developed a fast release cadence and first-class integrations for coding agents and Python frameworks. By the 0.10 series it was distributed from PyPI and Homebrew core, while its documentation described dozens of model families, an alias catalog, and reproducible community hardware benchmarks.

### 使用方式

A typical workflow is `rapid-mlx serve <model-alias>` followed by pointing an OpenAI-compatible client at `http://localhost:8000/v1`. Companion commands provide a terminal chat REPL, downloads and cache management, diagnostics, benchmarks, and setup templates for tools such as Codex, Claude Code, Aider, and Cursor.

### 为什么软件包爱好者会关心

Rapid-MLX packages the Apple-Silicon-specific MLX ecosystem into a familiar server-and-CLI interface, reducing model selection to stable aliases and preserving compatibility with the broad OpenAI client ecosystem. Its Homebrew, pip, uv, and self-contained installer paths make local LLM serving accessible without maintaining a bespoke Python environment.

### 时间线

- 2026-02-25: Official GitHub repository created.
- 2026-03-20: v0.3.0 published, the oldest retained GitHub release.
- 2026: Command branding moved from vllm-mlx to rapid-mlx while compatibility entry points were retained.
- 2026: Version 0.10.12 entered Homebrew core according to the official README.

### Related projects

- Rapid-MLX builds on Apple MLX and MLX-LM concepts, consumes Hugging Face model repositories, offers vLLM-style flags, and acts as a local backend for OpenAI-compatible clients and coding agents.

### 来源

- <https://github.com/raullenchai/Rapid-MLX>
- <https://rapidmlx.com/docs/>
- <https://rapidmlx.com/docs/cli>
- <https://rapidmlx.com/docs/getting-started>


## 安全说明

没有找到 rapid-mlx 的匹配本地密钥处理 manifest。Nucleus 软件包元数据仍在此发布，以便未来覆盖拥有稳定的软件包 URL。



## Combined YAML source

View the package source record on GitHub. [combined/rapid-mlx.yml](https://github.com/mxcl/pkgdb/blob/main/combined/rapid-mlx.yml)


## 来源

- pkg.so package database
- curated package history
- pkgdb category and tag curation
- cross-ecosystem install command graph