Agent Observability Dashboard
Skill by orosha-ai
Why use this skill?
Monitor OpenClaw agent performance, trace tool chains, and analyze metrics with the Agent Observability Dashboard. Optimize your agents today.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/orosha-ai/agent-observability-dashboardWhat This Skill Does
The Agent Observability Dashboard by orosha-ai provides a robust, centralized monitoring ecosystem for your OpenClaw agents. It serves as a unified command center, aggregating fragmented telemetry data into actionable insights. The dashboard tracks critical performance indicators like tool latency, success rates, and token consumption, while offering deep-dive trace visualization for complex multi-step tool chains. By maintaining a historical log of agent interactions, it allows developers to compare performance across different sessions and identify bottlenecks or regressions in real-time. Whether you are debugging a failed tool call or auditing API costs, this tool provides the granular data necessary to ensure your agents remain reliable and cost-effective in production environments.
Installation
To integrate this observability suite into your OpenClaw environment, ensure your workspace is configured to access the OpenClaw skill registry. Execute the following command in your terminal:
clawhub install openclaw/skills/skills/orosha-ai/agent-observability-dashboard
Once installed, you can initialize the dashboard server using python3 scripts/observability.py --dashboard to launch the browser-based visualization interface.
Use Cases
This skill is essential for teams moving beyond prototyping. Use it to:
- Debugging & Troubleshooting: Visualize decision flows to identify exactly where a multi-step agent chain fails.
- Cost Management: Monitor token usage and API expenditures to optimize prompt efficiency.
- Performance Tuning: Track latency for external API calls and tune agent logic for faster response times.
- Regression Testing: Compare performance metrics across different agent versions or prompt iterations.
- Production Alerting: Set automated thresholds for latency or error rates to receive instant notifications if agent health degrades.
Example Prompts
- "Open the observability dashboard and show me the error rate for my main agent over the last 24 hours."
- "I need to analyze the trace logs for session agent:main:12345 to see why the memory_write tool failed."
- "Export the last week of latency and token usage metrics to a CSV file for my quarterly performance report."
Tips & Limitations
- Retention: By default, logs are stored locally. Ensure you have sufficient disk space for long-term trace storage.
- Alerting: While this tool supports threshold-based alerts, ensure your environment's notification system is configured to receive these triggers.
- Security: Since this dashboard contains detailed execution traces, ensure that access to the dashboard is restricted to authorized personnel, as traces may include sensitive parameter data passed to tools.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-orosha-ai-agent-observability-dashboard": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read, file-write
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