command-center

Monitor AI agent activity, usage, costs, and system health via a dashboard.

241|59|Updated Jan 29, 2026

Search Tags:#monitoring #ai #agents #dashboard #real-time #nodejs

Ready to run

npx skills add https://github.com/jontsai/openclaw-command-center --skill command-center-jontsai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: command-center
Source: https://github.com/jontsai/openclaw-command-center/tree/main
Command: npx skills add https://github.com/jontsai/openclaw-command-center --skill command-center-jontsai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

OpenClaw Command Center solves the problem of fragmented visibility by providing a centralized, real-time dashboard for monitoring AI agent sessions, LLM usage, costs, and system vitals.

Core Features & Use Cases

Real-time session monitoring: view all active AI sessions with live updates.
LLM usage and cost insights: track token usage, costs, and budgets across agents.
System health and diagnostics: monitor CPU, memory, disk, and overall service status.
Multi-profile/workspace support: run isolated dashboards for different OpenClaw setups.
Quick start-friendly deployment: drop-in skill that starts a local server.

Quick Start

Install the skill and start the dashboard by running node lib/server.js.

Frequently Asked Questions about command-center

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I monitor AI agent activity and costs in real-time?▼

You can monitor real-time AI agent activity, usage, and costs by starting a centralized dashboard server that loads local workspace configuration and exposes REST and SSE endpoints for live updates.

What is the best way to track LLM token usage and system health across deployments?▼

Tracking LLM token usage and system health requires a centralized dashboard that monitors active sessions, token costs, CPU, memory, and disk vitals across multiple isolated workspace profiles.

Do I need Node.js to run a real-time AI agent dashboard?▼

Yes, you need Node.js 18 or higher along with a local OpenClaw workspace to initialize and run the real-time AI agent monitoring dashboard server effectively.

Can I use REST and SSE endpoints to integrate AI monitoring data into my own automation?▼

Yes, the monitoring dashboard exposes REST and SSE endpoints specifically designed for integration and automation, allowing you to pull live AI agent session data and system diagnostics programmatically.

How do I start a local server for monitoring multiple OpenClaw workspaces?▼

You can start the local monitoring server by installing the skill and running node lib/server.js, which loads configuration from the local workspace to provide isolated dashboards for different setups.

What are the limitations of using a drop-in dashboard for AI agent monitoring?▼

The dashboard's monitoring scope is limited to local OpenClaw deployments, meaning it requires a local workspace configuration to initialize and cannot monitor remote or disconnected agent environments.