I made OpenClaw voice - dedicated hardware for your openclaw.

I've spent the last while turning the Home Assistant Voice PE ($59 speaker puck) into a magical proper voice interface for OpenClaw, and it's crossed the line from "demo" to "my family uses this all day." Its really pretty darn cool. It recognizes me or my wife / or guests for access verification using voice prints - Give it a whirl I've Open-sourced the whole stack: https://github.com/TristanBrotherton/voicepe-realtime OpenAI Realtime handles the conversation (true speech-to-speech, sub-second smart home control, you can interrupt it mid-sentence), and OpenClaw is the brain behind it — its memory, its hands, and its phone. Instant recall from OpenClaw's memory. "What's Grandma's number?" answers in about a second. The trick: no agent turn at all. OpenClaw's memory is markdown, so the bridge just greps MEMORY.md + the dailies + person-files and hands matching lines to the realtime model. Anything OpenClaw knows, the house knows, instantly. Deep lookups that self-improve. If the grep misses, it escalates to a real OpenClaw turn — which scans iMessage history or whatever sources it has, answers, and writes what it found to a person-file. So every deep lookup promotes that fact to the instant tier. Ask twice, and the second time is sub-second. Long-running tasks that find you. "Research flight prices to London for October." It says "I'll look into it and report back," hands the task to OpenClaw, and you walk away. OpenClaw browses for as long as it takes — then the answer is announced out loud in the room you asked from (the request is room-tagged), with details texted via whatever channels you run. If the turn runs long you get an honest "still working on that" instead of a timeout, and the report-back is enforced by the bridge, not model goodwill. I have it setup to text me if i don't respond. The phone handoff. This one gets the biggest reaction from guests: "Call the pharmacy and ask if my prescription is ready" With my openclaw-voice-call-realtime plugin (https://github.com/TristanBrotherton/openclaw-voice-call-realtime — Twilio + OpenAI Realtime, in-call tools, transcripts), OpenClaw places an actual phone call, has the conversation, and the answer is spoken back in your kitchen when the call ends. Voice in, errand run, voice out. A voice for your automations. The announce endpoint is just a bearer-authed POST, so OpenClaw cron jobs can speak: "leave in fifteen minutes for the school run." Any script on your LAN can too. Teach once, works everywhere. This was the design rule that made it click: the Voice PE has no knowledge of its own. Voice escalations run as OpenClaw turns in the same workspace — same MEMORY.md, same TOOLS.md, same scripts. Teach OpenClaw something in chat and the voice path knows it immediately. No second brain to maintain. Beyond the OpenClaw stuff: custom wake word trained on my voices (community trainer, "Hey Leonard" ships as default, dropdown to switch), local speaker recognition with guided voice enrollment runs natively in C++ on device (it knows who's talking and addresses you by name — timers announce to whoever set them), "remember that…" voice memory with speaker-gated writes, false-wake flagging feeding a weekly retrain flywheel, and a pile of production hardening (session refresh before OpenAI's 60-min cap, echo guards, etc.). The integration contract is deliberately tiny — one URL, two POST shapes ({"question"} and {"recall"}) plus the announce endpoint — so it's agent-agnostic if you run something else, but OpenClaw is what it's built around and tested against. Docs cover the whole bridge pattern: https://github.com/TristanBrotherton/voicepe-realtime/blob/main/docs/agent-integration.md Happy to answer anything — wake word training, the voiceprint stuff, costs (light household use is tens of cents a day), or the bridge internals. I've tried to put a real focus on UX, so once setup it really starts to feel like a human you can rely on. If the wake word picks up a false positive, hit the button twice and its logged as a fail, and when a retraining session runs it will improve the model. Everything gets better with time...