OpenClaw Voice: DIY Hardware for Speech-to-Speech Smart Home Control
A developer repurposed the Home Assistant Voice PE ($59 speaker puck) into a dedicated hardware voice interface for OpenClaw, achieving sub-second speech-to-speech smart home control with voiceprint-based access verification. The entire stack is open-sourced on GitHub.
Key Features
- True speech-to-speech: OpenAI Realtime handles conversations with sub-second latency. You can interrupt it mid-sentence.
- Instant memory recall: The bridge greps
MEMORY.md+ dailies + person-files directly — no agent turn. Ask "What's Grandma's number?" and get an answer in ~1 second. - Self-improving lookups: If grep misses, it escalates to a full OpenClaw turn (scans iMessage, etc.), writes the answer to a person-file. Next time it's sub-second.
- Long-running tasks that announce back: "Research flight prices to London for October" — task handed to OpenClaw, answer spoken back in the room where the request originated. Room-tagged, with details texted. If the turn runs long, you get a "still working on that" notification instead of a timeout.
- Phone call execution: Using a separate openclaw-voice-call-realtime plugin (Twilio + OpenAI Realtime), OpenClaw can place actual phone calls. Example: "Call the pharmacy and ask if my prescription is ready" — call is made, conversation transcribed, result spoken back in the kitchen.
- Voice memory: "Remember that…" with speaker-gated writes. Voiceprints work with guided enrollment, runs natively in C++ on-device.
- Custom wake word: Community trainer; "Hey Leonard" ships as default, with a dropdown to switch.
- False-wake flywheel: Double-tap the button logs a false positive, fed into weekly retraining to improve the model.
- Announce endpoint: Bearer-authed POST — any script or cron job can speak: "Leave in fifteen minutes for the school run."
Integration Contract
The bridge pattern is minimal — one URL, two POST shapes ({'question'} and {'recall'}) plus the announce endpoint. Agent-agnostic but tested against OpenClaw. Docs: agent-integration.md
Design Rule
The Voice PE has no knowledge of its own — all escalations run as OpenClaw turns in the same workspace (MEMORY.md, TOOLS.md, same scripts). Teach OpenClaw once in chat, and the voice path knows it immediately.
Cost
Light household use is tens of cents per day.
📖 Read the full source: r/openclaw
👀 See Also
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Adam: An Embeddable Cross-Platform AI Agent Library in C
Adam is a C library providing a complete agent loop with tool calling, memory, voice, and both cloud/local LLM support, designed to be embedded into any application.
Karpathy's autoresearch project: AI agents run overnight LLM training experiments
Andrej Karpathy released a minimal autoresearch project where an AI agent edits train.py, runs 5-minute nanochat training experiments, checks if val_bpb improved, and repeats overnight on a single GPU.