I replaced the chat window for my local AI agent with a face

A developer built Ghost Vessel, a monitor-resident avatar that replaces the chat interface for a local LLM agent. The avatar uses pre-rendered clips and emotion beats from the model's output to create a video-call-style interaction, avoiding live inference to keep GPU resources for the LLM. The project is open-source under MIT and includes adapters for Hermes and OpenClaw agents.

I run a local LLM agent Hermes on my own machine. The problem was never the model — it was the interface . I had a Telegram tab open all day just to talk to it: type a command, wait, read a wall of text back, scroll. It felt like texting a very capable stranger. So I built Ghost Vessel — a monitor-resident, video-call-style avatar that fronts the agent. The name is the whole idea: the Here's what actually turned out to be interesting to build. The core idea is an output contract . Instead of treating the agent's reply as text to print, I split every reply into three planes: Emotion beats are inline tags the model emits in-band with its answer: working — the avatar puts on glasses and takes notes while a task runs confirm deploy to prod? — pops a human-in-the-loop approve/cancel, and the agent blocks on your keypress happy / concerned / … — fine-grained facial expressionsSo "run the build, and if it passes, deploy" becomes a little performance : it looks busy while working, shows you the log as a card, then leans in and asks before the irreversible step. The text you'd have skim-read becomes something you glance at. The obvious way to animate a face is live inference. I didn't want that — the GPU is busy running the actual model. Instead the avatar is ~30 pre-rendered clips , and the emotion beats just select and blend between them blink-aligned seamless idle loops, a head-pose "settle gate" so an expression only reveals when the head is frontal . The avatar's runtime cost is basically video playback. Your GPU stays 100% on your LLM. The tradeoff: no real-time lip-sync. I decided a believable talking mouth loop + expressive face reads as "a person on a call" far more than perfect phoneme-matching does, and it costs nothing at runtime. That single decision collapsed most of the hard engineering. The agent integration is the part I was most unsure would work. It turned out clean: the app registers as a connector . The agent's gateway dials out to a local WebSocket the app hosts, and exchanges frames — so from the agent's side, the avatar is just "another channel," indistinguishable from Telegram. Adapters for Hermes and OpenClaw are included, plus a demo responder so it runs with zero setup. And the chat pane serves the agent's live slash-command menu — type / and you get the same 52 commands you'd see in the messenger, passed straight through. The engine is MIT and usable on its own: clone it, drop a folder of clips named by emotion happy.mp4 , working.mp4 , idle.mp4 , … , point it at your agent. Avatars are pure-data bundles — no code runs when you install one — so you can build your own; the full reproducible method is in the repo. The part I'd most like feedback on is the emotion-beat output format — the contract that turns LLM text into a UI performance. Has anyone else built output contracts to drive an interface from model output? What broke, and what did models reliably get right vs. wrong? I'd genuinely like to compare notes.