A unified observability suite providing production-grade metrics, traces, and performance insights for OpenClaw agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-observability-dashboard
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-observability-dashboard using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Observability Dashboard?
The Agent Observability Dashboard is a comprehensive monitoring solution designed specifically for developers building with Openclaw Skills. It bridges the gap between raw agent logs and actionable insights by centralizing metrics such as latency, success rates, and token usage into a single, localized interface. By providing a production-grade visibility layer, it ensures that developers can debug complex tool chains and optimize agent decision flows without relying on external enterprise platforms.
This tool emphasizes a local-only approach, storing all telemetry in SQLite or InfluxDB to maintain data privacy. Whether you are managing a single autonomous agent or a fleet of specialized workers, this addition to your library of Openclaw Skills offers the transparency needed to track regressions and improve the overall quality of AI-driven workflows.
Agent Observability Dashboard Use Cases
- Debugging multi-step tool calls and identifying bottlenecks in agent decision chains.
- Monitoring real-time token usage and API costs across various Openclaw Skills.
- Comparing performance and success rates between different agent versions or sessions.
- Setting automated alerts to notify developers when latency or error counts exceed specific thresholds.
- Exporting session traces and performance reports for stakeholder review or audit logs.
How Agent Observability Dashboard Works
- The system auto-captures data from agent logs through an instrumentation layer tailored for Openclaw Skills.
- Metrics and session traces are ingested into a local time-series store, such as SQLite or InfluxDB.
- The analytics engine processes raw data to identify trends, throughput, and potential hallucinations.
- Developers interact with the data via a Flask-based web dashboard or a robust CLI for manual reporting.
- Detailed traces visualize every tool call, parameter, and latency point to reconstruct agent logic.
Agent Observability Dashboard Setup
To integrate this into your environment and start monitoring your Openclaw Skills, follow these steps:
# Clone the repository
git clone https://github.com/orosha-ai/agent-observability-dashboard
# Install the required Python dependencies
pip install flask pandas influxdb-client
# Launch the web-based dashboard
python3 scripts/observability.py --dashboard
Access the UI at http://localhost:5000 to view real-time session data.
Agent Observability Dashboard Data Schema & Taxonomy
The dashboard organizes data into structured schemas to ensure compatibility across different Openclaw Skills:
| Component | Data Type | Description |
|---|---|---|
| Session ID | String | Unique identifier for the agent session (e.g., agent:main:12345). |
| Metrics | Time-series | Latency (ms), Token Count, API Cost (USD), and Success Status. |
| Traces | JSON Object | Array of tool calls including timestamps, parameters, and error details. |
| Reports | CSV/MD/JSON | Aggregated performance summaries over specific periods (e.g., 24h). |
Agent Observability Dashboard Advanced Features
- Cross-agent aggregation to compare different Openclaw Skills side-by-side.
- Customizable alert thresholds for proactive monitoring of production-level agents.
- Support for InfluxDB to handle high-frequency telemetry in complex environments.
- Automated hallucination detection and correction tracking for quality assurance.
- Local-only data promise ensures all telemetry remains within your own infrastructure.
SKILL.md
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