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One workspace all your AI agents can read and write. Encrypted client-side, model-agnostic, gone 24h after the last edit.
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@raullenchaiDevelopment
Plugin
rapid-mlx
The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider.
Plugin Signal73/100Solid
maintained100
capable23
documented100
adopted76
Install
$ npx -y skills add raullenchai/Rapid-MLX --agent claude-code
What's inside
Skill(1 skill)
README
Works with your AI stack
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 for exact API coverage and setup status.
Install
Desktop — macOS (Apple Silicon)
The easiest way to chat locally, manage models, and use vision, files, voice, and image generation from one app.
- Download Rapid-MLX Desktop
- Browse signed Desktop releases
- Requires an M-series Mac; Windows and Linux desktop builds are not available yet
CLI and server — macOS (Apple Silicon)
# Homebrew — prebuilt bottle from homebrew-core
brew install rapid-mlx
# Or the guided installer — detects RAM and recommends a starter model
curl -fsSL https://rapidmlx.com/install.sh | bash
Both install the same rapid-mlx CLI. Prefer uv or pip, or want to verify
the installer before running it? See alternative install methods
and install security.
The guided installer prefers a runnable model already cached on this Mac when
it fits the RAM tier. Otherwise its quick first-chat download is
lfm2.5-1b-4bit below 16 GB and qwen3.5-4b-4bit at 16 GB or above; larger
quality picks remain available through rapid-mlx recipe and the model picker.
Quick Start (60 seconds)
1. Chat with a model right now:
rapid-mlx chat
Defaults to qwen3.5-4b-4bit. First run downloads the weights (~3 GB) with a progress bar and drops you into a REPL. Type /help for slash commands, /exit to quit.
2. Or serve it for use from other apps:
rapid-mlx serve qwen3.5-4b-4bit
Starts an OpenAI-compatible HTTP server bound to http://localhost:8000. Point any client that supports a local custom endpoint (Aider, LangChain, OpenCode, PydanticAI, your own scripts) at http://localhost:8000/v1; Claude Code / Anthropic SDK uses http://localhost:8000 (the Anthropic messages route lives at /v1/messages under the same host).
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"default","messages":[{"role":"user","content":"Say hello"}]}'
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
print(client.chat.completions.create(
model="default",
messages=[{"role": "user", "content": "Say hello"}],
).choices[0].message.content)
3. Or wire up your coding agent — one command:
rapid-mlx launch claude-code
With a server running (step 2), this patches Claude Code's local config (~/.claude/settings.json) to route at http://localhost:8000 — no manual env vars, no editing JSON by hand. You get a fully local Claude Code: $0 per token, nothing leaves your Mac. Swap in cline or continue-dev for the other IDE clients, or run rapid-mlx launch list to see what's detected on this machine.
Cursor: Cursor currently routes BYOK requests through its own servers, so its servers cannot reach a Rapid-MLX endpoint on
localhost. Rapid-MLX therefore does not generate a Cursor localhost config. If you intentionally expose the server through a public HTTPS tunnel, setRAPID_MLX_API_KEY=your-secretfor bothrapid-mlx serve ...andrapid-mlx launch cursor --server-url https://your-public-host. This is no longer a fully local connection; never expose an unauthenticated server. Rapid-MLX rejects explicit local/private addresses but cannot verify reachability from Cursor's network, whose DNS view may differ from your Mac.
Vision / audio / video / diffusion models? Base install is text-only (~460 MB). Vision, audio (TTS, STT, voice cloning), video generation, embeddings, and DFlash speculative decoding ship as opt-in extras. → Optional extras
Not into the terminal? Rapid-MLX Desktop bundles the same engine inside a one-click Mac app.
Image generation
Generate images locally from the Desktop Images tab or the OpenAI-compatible Images API. Install the image runtime when using the CLI:
pip install 'rapid-mlx[image]'
rapid-mlx serve flux2-klein-4b
curl http://localhost:8000/v1/images/generations \
-H 'Content-Type: application/json' \
-d '{"model":"flux2-klein-4b","prompt":"A red sailboat on an alpine lake","size":"1024x1024","seed":42}'
Edit an existing PNG or JPEG with the edit-specialized model:
In one terminal:
rapid-mlx serve qwen-image-edit
In another terminal:
curl http://localhost:8000/v1/images/edits \
-F image=@input.png \
-F model=qwen-image-edit \
-F 'prompt=Change the sky to a warm sunset' \
-F response_format=b64_json
flux2-klein-4b is the recommended starting point. Download sizes are the
currently cataloged model payloads; minimum unified memory includes practical
headroom for macOS and the serving process. Omit steps to use the validated
family default shown below.
| Alias | Best fit | Modes | Download | Minimum unified memory | Default steps |
|---|---|---|---|---|---|
flux2-klein-4b | Recommended; fast everyday images | Generate + edit | 4.3 GiB | 12 GB | 4 |
bonsai-image-4b-2bit | Smallest download | Generate | 3.6 GiB | 12 GB | 4 |
z-image-turbo | Photorealistic images | Generate | 5.5 GiB | 16 GB | 8 |
flux-schnell | FLUX.1 compatibility | Generate | 9.0 GiB | 16 GB | 4 |
sdxl-base | SDXL compatibility | Generate | 6.5 GiB | 16 GB | 30 |
flux2-klein-4b-bf16 | Explicit full-precision Klein path | Generate + edit | 14.9 GiB | 32 GB | 4 |
hidream-o1-dev | Complex compositions | Generate | 16.4 GiB | 32 GB | 28 |
sd35-large-4bit | Stable Diffusion 3.5 | Generate | 15.3 GiB | 32 GB | 28 |
qwen-image | Text inside images | Generate | 28.9 GiB | 64 GB | 20 |
qwen-image-edit | Precise instruction edits and text changes | Edit | 34.9 GiB | 96 GB | 20 |
At 1024×1024 with the four-step Klein default, measured warm generation was
about 9.2 seconds per image on an M3 Ultra. On a 32 GB M2 Pro, the q4 path
measured a 52.7-second median; explicitly selecting
flux2-klein-4b-bf16 reduced that median to 41.8 seconds (1.26× throughput).
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rapid-mlx is a Claude Code plugin with 1 hand-picked skill for development work, indexed on Flowy. Install it with the command on its page. It includes perfup. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
- What is rapid-mlx?
- The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider.
- How do I install rapid-mlx?
- Run these in Claude Code: npx -y skills add raullenchai/Rapid-MLX --agent claude-code. Then prompt normally.
- Does rapid-mlx auto-invoke its skills?
- Not yet. It is indexed on Flowy as a plain plugin. Request auto-invocation on its page and Flowy will route its skills for you as you prompt.
- Is rapid-mlx free?
- Yes. Flowy is free and open source, with nothing gated. You can read every skill in full before you install.
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