A real-time HUD (heads-up display) for OpenClaw power users
Claw HUD has been released as a real-time heads-up display for OpenClaw power users, offering live monitoring of critical gateway metrics. This new tool provides a comprehensive, at-a-glance overview of an OpenClaw instance's operational state, including active sessions, loaded skills, agent context usage, network traffic, scheduled cron jobs, overall gateway health, and associated costs. It addresses a key need for enhanced visibility into the dynamic processes of an agentic AI system.
Technically, Claw HUD operates as a read-only observer, connecting to the OpenClaw gateway via its WebSocket protocol v3. It offers both a terminal user interface (TUI) built with Ink and React, and a web dashboard powered by Next.js and Tailwind, receiving live updates via Server-Sent Events (SSE). The tool meticulously adheres to the OpenClaw gateway's strict client schema, declaring itself as `openclaw-control-ui` with `client.mode: ui`, and leverages existing device identity files for authentication. This design ensures seamless integration and secure monitoring within the OpenClaw environment.
This release significantly enhances the operational transparency for OpenClaw deployments, providing a crucial observability layer previously unavailable. By offering real-time insights into agent sessions, resource consumption, and gateway performance, Claw HUD empowers developers to debug complex multi-agent systems more effectively and optimize resource allocation. It bridges a gap in the OpenClaw ecosystem, moving beyond basic CLI interactions to offer a persistent, dynamic view of agentic activity, which is vital for robust agent development and deployment.
This signal is particularly strong for OpenClaw developers and operators who manage active agent deployments. Developers will find it invaluable for debugging agent behavior, understanding skill invocation patterns, and monitoring context window usage in real-time. Operators can leverage the HUD for proactive monitoring of gateway health, traffic, and cost, ensuring stable and efficient operation of their agent infrastructure. Researchers exploring agentic AI will also benefit from the live insights into agent execution dynamics, aiding in the analysis of complex agent interactions.