It gives OpenClaw its own isolated Docker workstation so it doesn't touch your local file and passwords, then records what it does — screen, console, network — and uses AI to analyze the recordings.

The idea came from a simple problem: you hire openclaw work for you, you provide a secure seat and monitor everything with AI camera.

The concern from my side is, chat logs tell what the openclaw said it did, but not what it actually did. Especially when it's running autonomously — installing packages, writing files, making network calls — inside a container you can't see.

CameraClaw sits between you and the sandbox. It captures screen activity through KasmVNC, streams the agent's console in real time, and optionally sends screen changes to a VLM for analysis. Everything gets saved as a timeline you can scrub through later.

It runs standalone —

./deploy.sh to build the sandbox, npm start for the dashboard, open localhost:3100. Also plugs into the DeepCamera / Aegis-AI ecosystem as a skill, but doesn't need any of that.

MIT licensed. Happy to answer questions about the architecture or the recording pipeline.