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brew

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

其他安装命令

macOS

Homebrew已验证 · 100%
brew install rapid-mlx

local Homebrew formula metadata

概览

软件包摘要

Fast local AI engine for Apple Silicon with an OpenAI-compatible API

命令和别名

  • rapid-mlx
  • rapid-mlx-bench
  • rapid-mlx-chat
  • vllm-mlx
  • vllm-mlx-bench
  • vllm-mlx-chat

历史

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.

安全态势

尚未找到受保护工具覆盖

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。

安装行为

  • formula 元数据中未记录 Homebrew post-install 钩子。
  • Homebrew bottle 元数据适用于 3 个平台目标。
  • 安装时包含 8 个运行时依赖。
  • 构建元数据列出 3 个构建依赖。

建议审查

在无人值守的代理使用前,请检查该工具是否读取明文凭据、写入远程状态、发布制品或调用插件。

可执行文件

已安装的可执行文件

命令类型暴露范围备注
rapid-mlxcli全局可执行文件
rapid-mlx-benchcli全局可执行文件
rapid-mlx-chatcli全局可执行文件
vllm-mlxcli全局可执行文件
vllm-mlx-benchcli全局可执行文件
vllm-mlx-chatcli全局可执行文件

新鲜度

版本和新鲜度

这些信号区分页生成时间、软件包管理器活动和上游发布比较。只有存在证据 URL 和可比较版本时,才会提示版本落后。

页面生成时间2026-07-25
管理器版本0.10.18
管理器更新时间2026-07-25
本地数据OK
上游not checked
检测到的最新版本未检测到

https://github.com/raullenchai/Rapid-MLX

安装元数据

软件包元数据

软件包键brew:rapid-mlx
版本0.10.18
软件包管理器Homebrew
软件包管理器页面https://formulae.brew.sh/formula/rapid-mlx
主页https://github.com/raullenchai/Rapid-MLX
仓库https://github.com/raullenchai/Rapid-MLX
上游文档https://rapidmlx.com/docs
许可证Apache-2.0
源码归档https://files.pythonhosted.org/packages/6f/14/863070b4f8087540aa29219fa75dd74ac27ec484701ecb3a9c7d68fd1e8e/rapid_mlx-0.10.18.tar.gz
最后更新2026-07-25T04:02:37Z
Pulsenew
依赖certifi, libyaml, mlx, numpy, openssl@3, pydantic, python@3.14, rpds-py
构建依赖cmake, pkgconf, rust
Bottle可用 (于 arm64_sequoia, arm64_sonoma, arm64_tahoe)
Homebrew post-install未定义
服务未声明

注册表事实

源数据库详情

Source DatabaseHomebrew formula API
Taphomebrew/core
Full Namerapid-mlx
Version Scheme0
Revision0
Requirements
  • macos
Bottle Stable Root URLhttps://ghcr.io/v2/homebrew/core
Deprecatedno
Disabledno
Keg Onlyno
URL Keys
  • stable

来源线索

由仓库数据生成

此页面由 av-webscripts/generate-pkg-sqlite.py 生成的私有软件包 SQLite 工件提供。

使用的来源

  • Nucleus package database
  • av.db category and tag curation
  • cross-ecosystem install command graph
  • curated package history
  • package relationship graph
  • package version freshness
  • package-page enrichment