RCLI
RCLI is a macOS voice assistant that runs entirely on-device using Apple Silicon, combining speech-to-text, LLM inference, text-to-speech, and document Q&A (RAG) without cloud calls. It includes a proprietary GPU engine (MetalRT) for M3+ chips and falls back to llama.cpp on M1/M2, with 40 built-in macOS automation actions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | RunanywhereAI/RCLI |
| Owner | RunanywhereAI |
| Primary language | C++ |
| License | MIT — OSI-approved |
| Stars | 1.5k |
| Forks | 83 |
| Open issues | 12 |
| Latest release | v0.3.7 (2026-03-15) |
| Last updated | 2026-03-16 |
| Source | https://github.com/RunanywhereAI/RCLI |
What RCLI is
Built in C++, RCLI implements a concurrent STT + LLM + TTS pipeline using Metal GPU acceleration. It supports multiple LLM/STT/TTS model families (Qwen3, LFM2, Whisper, Kokoro), includes hybrid BM25+vector RAG with ~4ms latency, on-device VLM analysis (camera/screen), and tool-calling for macOS automation via AppleScript and shell commands.
Get the RCLI source
Clone the repository and explore it locally.
git clone https://github.com/RunanywhereAI/RCLI.gitcd RCLI# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires M3 or later Apple Silicon for MetalRT; M1/M2 auto-falls back to llama.cpp with 2–5x slower inference. Verify target Mac model before rollout.
- Models (~1GB) download during `rcli setup` one-time. Plan for initial network bandwidth and disk space on air-gapped Macs; offline model management is manual.
- macOS actions use AppleScript and shell commands; 40 actions are hard-coded. Extending actions requires modifying source code or contributes to the repo.
- RAG ingests local files only (PDF, DOCX, TXT); no built-in sync with cloud storage (OneDrive, S3, etc.). Document updates require manual re-ingest.
- No user/session management, audit logging, or multi-user access control. Suitable only for single-user or development teams, not regulated environments.
When to avoid it — and what to weigh
- Requires Linux or Windows Deployment — RCLI is macOS-only (Apple Silicon required). No Windows, Linux, or Intel Mac support. Non-Mac teams must use alternatives.
- Need Real-Time Multi-User Concurrency — RCLI is a single-user CLI/TUI. No multi-seat licensing, server mode, or API gateway for distributed teams. Not suitable for call centers or shared systems.
- Require Vendor Long-Term Stability Guarantees — Project is ~2 weeks old (created 2026-03-04), backed by a private company (RunAnywhere, Inc.). No production SLA, no long-term support roadmap documented, no enterprise contracts.
- MetalRT Dependency for Production Performance — MetalRT (proprietary) is required for <200ms latency and 550 tok/s throughput on M3+. Its license is non-standard and unavailable for public review; falls back to llama.cpp on M1/M2, reducing performance significantly.
License & commercial use
RCLI core is MIT-licensed (permissive, allows commercial use). MetalRT GPU engine is proprietary (non-standard license, requires review with legal). Vision models (Qwen3 VL, LFM2 VL, SmolVLM) licenses not specified; llama.cpp is Apache 2.0.
RCLI source code (MIT) permits commercial use. However, MetalRT binary is proprietary and its commercial use terms are unclear; licensing inquiries directed to [email protected]. Recommend legal review before shipping MetalRT-dependent features in a commercial product.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Possible |
| Assessment confidence | Medium |
RCLI runs entirely on-device; no remote exfiltration of audio or documents. However, no formal security audit disclosed. Considerations: voice data is not encrypted at rest, AppleScript/shell execution could be exploited if LLM output is not validated, RAG index is plaintext, and no user authentication. Air-gapped Macs mitigate cloud risk but not local privilege escalation.
Alternatives to consider
Apple Siri + Shortcuts + Local ML
Native, supported, but limited tool-calling, weaker NLU, no RAG, and tightly coupled to Apple ecosystem. No fine-tuned LLM control.
Ollama (llama.cpp wrapper) + Whisper + Piper
Open-source, cross-platform alternative to MetalRT. Slower on M3+ but no proprietary license risk. Requires manual orchestration of STT/TTS/LLM pipelines.
Open WebUI + Local LLM (self-hosted on Intel/Mac)
Browser-based, multi-user, supports RAG and model swapping. Slower inference, more deployment overhead, but vendor-agnostic and fully open-source.
Build on RCLI with DEV.co software developers
Test RCLI's on-device voice pipeline on Apple Silicon M3+ for proof-of-concept. Review MetalRT licensing and production readiness (2-week-old project) with your legal and engineering teams before commercial rollout.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
RCLI FAQ
Can I use RCLI on M1 or M2 Macs?
Is MetalRT open-source?
Can I extend the 40 macOS actions?
Does RCLI support multi-user or server deployment?
Work with a software development agency
Adopting RCLI is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate rag frameworks software in production.
Evaluate RCLI for Your macOS Privacy Needs
Test RCLI's on-device voice pipeline on Apple Silicon M3+ for proof-of-concept. Review MetalRT licensing and production readiness (2-week-old project) with your legal and engineering teams before commercial rollout.