by raullenchai
Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server and Mac app for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Release-gated with Claude Code, Codex CLI, Aider, Hermes and DeepSeek Harness.
# Add to your Claude Code skills
git clone https://github.com/raullenchai/Rapid-MLXLast scanned: 5/6/2026
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Rapid-MLX is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by raullenchai. Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server and Mac app for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Release-gated with Claude Code, Codex CLI, Aider, Hermes and DeepSeek Harness. It has 3,899 GitHub stars.
Yes. Rapid-MLX passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/raullenchai/Rapid-MLX" and add it to your Claude Code skills directory (see the Installation section above).
Rapid-MLX is primarily written in Python. It is open-source under raullenchai on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Rapid-MLX against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Feature facts only, taken mostly from each project's own documentation as of 2026-09-26 (sources below). For measured speed, see the benchmark linked above.
| Rapid-MLX | oMLX | Ollama | LM Studio | mlx-lm (mlx_lm.server) |
|
|---|---|---|---|---|---|
| License | Apache-2.0 | Apache-2.0 | MIT | Closed-source app; its MLX engine and lms CLI are MIT |
MIT |
| Inference engine on a Mac | MLX | MLX | GGML/GGUF engine, plus an MLX engine for safetensors models | llama.cpp (GGUF) and MLX engines | MLX |
| Local API | OpenAI (/v1/chat/completions, /v1/responses) and Anthropic Messages (/v1/messages) |
OpenAI and Anthropic Messages | Ollama API, OpenAI-compatible, Anthropic-compatible (subset) | OpenAI-compatible, Anthropic-compatible, LM Studio REST API | OpenAI-style chat API |
| GUI | Rapid-MLX Desktop (macOS app) | macOS menu-bar app and web admin panel | Desktop app (macOS, Windows) | Desktop app | None (CLI / Python) |
| Concurrent requests | Continuous batching | Continuous batching (mlx-lm BatchGenerator) |
Parallel requests per model (OLLAMA_NUM_PARALLEL, default 1) |
Continuous batching (llama.cpp engine; MLX engine since 0.4.2) | Continuous batching (BatchGenerator); one request at a time with a quantized KV cache |
| Prompt / KV cache | Radix prefix cache in memory (with state snapshots for hybrid models), saved to disk on shutdown and restored at startup; quantized KV cache | Tiered KV cache: in-memory hot tier plus SSD cold tier, reused across restarts | Reuses the previous request's prompt KV cache; KV cache quantization (OLLAMA_KV_CACHE_TYPE) |
— | In-memory prompt cache; quantized KV cache (--kv-bits) |
| Tool-call parsing | 27 parser modules, including an auto-detect fallback (rapid_mlx/tool_parsers) |
mlx-lm formats plus the auto-detected families listed in its README | Tool calling | Tool use via the OpenAI-compatible API | Tool calls for models whose tokenizer declares tool-calling support |
Sources: oMLX README ·
Ollama OpenAI and Anthropic compatibility, FAQ, MLX engine ·
LM Studio Anthropic compatibility, parallel requests, 0.4.2 changelog, app terms, mlx-engine ·
mlx-lm server docs and server.py.
Ollama prompt-cache reuse is from our benchmark notes.
— = not verified for this table. Spot an error? Please open an issue.
Measured speed: the 3.0× above is 82.9 vs 27.2 tok/s aggregate decode (32 GB M2 Pro Mac mini, Rapid-MLX 0.12.11 vs Ollama 0.32.7). Including prefill, whole-batch throughput was 1.6×; single-stream decode was about 1.5×; a dense 12B model was no faster; and llama.cpp-family engines prefilled cold prompts faster (method and raw data).
Head-to-head with oMLX and Ollama on the same Macs (18 GB and 48 GB) — decode, agent sessions, restart, concurrency, and where Rapid-MLX is slower, with raw data: rapidmlx.com/compare.
Use Rapid-MLX as a local backend for agents, apps, or your own code. If a client accepts an OpenAI- or Anthropic-compatible endpoint, it can usually use Rapid-MLX without an adapter.
Five Tier-1 agents are exercised end-to-end on real weights before release. See the tested compatibility matrix fo