RCLI - On-Device Voice AI for macOS

TL;DR

TL;DR: RCLI is an open-source on-device voice AI for macOS that runs a full STT + LLM + TTS + VLM pipeline on Apple Silicon — no cloud, no API keys, sub-200ms latency.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: runanywhere.ai ← visited and verified
• Source repository: github.com/RunanywhereAI/rcli ← read README end-to-end
• License: MIT (verified via LICENSE file on GitHub)
• Latest release: v0.3.7 (2026-03-15, verified via GitHub Releases API)
• Stars: 1,530 (as of 2026-07-23, GitHub API)
• HN launch thread: not found via Algolia search (tool may not have had a dedicated Show HN post)

What Is RCLI?

RCLI is a command-line voice AI tool for macOS. It runs a complete STT + LLM + TTS + VLM pipeline locally on Apple Silicon — no cloud dependency and no API keys required.

The project is built by RunAnywhere, Inc. and uses MetalRT, a proprietary GPU inference engine the company built specifically for Apple Silicon. MetalRT is closed-source and distributed under a separate license; the rest of RCLI is MIT-licensed open source.

On M1/M2 Macs, RCLI falls back to llama.cpp automatically.

Setup Workflow

Step 1: Install

One-command install:

curl -fsSL https://raw.githubusercontent.com/RunanywhereAI/RCLI/main/install.sh | bash

Or via Homebrew:

brew tap RunanywhereAI/rcli https://github.com/RunanywhereAI/RCLI.git
brew install rcli

Step 2: Download models

rcli setup

This downloads the default model bundle — approximately 1GB total, a one-time download. The bundle includes:

• LFM2 1.2B (default LLM)
• Whisper base.en (speech-to-text)
• Piper Lessac (text-to-speech)
• Silero VAD (voice activity detection)
• Snowflake embeddings

VLM models are downloaded on demand via rcli models vlm.

Step 3: Run

rcli

This opens the interactive TUI in push-to-talk mode. Press to record, release to send.

MetalRT GPU Engine

MetalRT is the custom GPU inference engine that sets RCLI apart from a plain llama.cpp setup. It is built by RunAnywhere specifically for Apple Silicon and accelerates the LLM inference path.

Requirements:

• macOS 13+
• Apple Silicon (M3 or later for MetalRT)
• M1/M2 Macs fall back to llama.cpp automatically

MetalRT is proprietary and closed-source. It is not included in the open-source build from source path.

Local RAG Over Documents

RCLI includes a built-in RAG pipeline for querying your documents by voice:

# Index a directory of documents
rcli rag ingest ./my-docs/

# Query indexed documents
rcli rag query "what was discussed in last week's meeting?"

The retrieval step runs at approximately 4ms per query (hybrid retrieval, per the README). The LLM answer is generated locally.

Supported Models

RCLI supports 20+ models across multiple categories:

| Category | Models | |---|---| | LLM | LFM2 1.2B (default), LFM2 350M, LFM2.5 1.2B, LFM2 2.6B, Qwen3 0.6B, Qwen3.5 0.8B/2B/4B, Qwen3 4B | | STT | Zipformer (streaming), Whisper base.en (offline) | | TTS | Piper Lessac/Amy, KittenTTS Nano, Matcha LJSpeech, Kokoro English/Multi | | VLM | Qwen3 VL 2B, Liquid LFM2 VL 1.6B, SmolVLM 500M |

Model management commands:

rcli models          # interactive model browser
rcli models vlm      # download/manage vision-language models
rcli voices          # browse and switch TTS voices
rcli cleanup         # remove unused models

Practical Evaluation Checklist

• [ ] Installs on macOS 13+ Apple Silicon without cloud credentials
• [ ] rcli setup completes and reports model download success
• [ ] rcli TUI opens, push-to-talk records and returns a response
• [ ] rcli ask "what time is it?" executes in single-shot mode
• [ ] rcli rag ingest ./docs/ indexes documents without error
• [ ] rcli rag query [question] returns grounded answers
• [ ] rcli actions lists available macOS actions (40+)
• [ ] M1/M2 Mac: falls back to llama.cpp engine automatically
• [ ] rcli info reports MetalRT active on M3+

Security Notes

• All inference runs locally — no audio or text is sent to any cloud service
• No API key required; no telemetry or phone-home documented in the README
• Models are downloaded once and stored locally at ~/Library/RCLI/models
• MetalRT is closed-source; if you require full source transparency, build from source with llama.cpp

FAQ

Q: Is RCLI fully local? A: Yes. Every step — STT, LLM inference, TTS, and VLM — runs on your Mac’s Apple Silicon GPU. Nothing is sent to a server.

Q: Does it work on Intel Macs? A: The README does not document Intel Mac support. The CPU-only build (llama.cpp + sherpa-onnx) should run on Intel, but this is not the primary target.

Q: Can I use my own models? A: Yes. rcli models lets you browse, download, and switch LLMs. VLM models can be managed via rcli models vlm.

Q: Is it free? A: RCLI itself is open-source under MIT. MetalRT (the proprietary GPU engine) is included in the pre-built binary at no additional cost, per the GitHub releases.

Q: What open-source license does RCLI use? A: MIT License, verified via the LICENSE file on GitHub.

Conclusion

RCLI stands out in the local AI tooling space by shipping a polished, all-in-one voice interface instead of requiring users to wire together separate STT, LLM, and TTS components. The proprietary MetalRT engine is a meaningful differentiator for M3+ Mac users who want GPU-accelerated inference without cloud dependencies. The MIT license and active development (last release March 2026) make it worth watching.