command-center

You can monitor OpenClaw AI agent sessions and token costs in real-time by using a dashboard that aggregates active conversations, per-model token usage, and system vitals into a single view. This provides live operational awareness across workspaces.

The best way to track LLM token usage and fuel costs is to use a dashboard that provides per-model token visibility and cost auditing. This consolidates fuel gauges and system vitals for software engineers managing AI workloads.

To secure sensitive data during live AI agent demonstrations, use privacy controls and access configuration features. These tools protect sensitive information and secure dashboards in diverse operational environments.

Yes, you can view cron jobs and memory state insights for OpenClaw workloads. The monitoring dashboard provides operational awareness by displaying scheduled jobs, Cerebro topics, and memory or state insights alongside active sessions.

You need a Node.js environment to run the AI agent monitoring dashboard by starting the server with node. The skill also requires a SKILL.md file with name and description frontmatter to load deterministic monitoring tasks.

Your AI agent monitoring dashboard shows high token costs by aggregating per-model token usage across all active sessions. Use the cost auditing and LLM fuel gauge features to identify which models or conversations consume the most tokens.