ClawMetry for OpenClaw

Product Introduction

1. Definition: ClawMetry is an open-source observability dashboard (technical category: AIOps/MLOps) designed explicitly for monitoring OpenClaw AI agents. It provides real-time telemetry for autonomous AI systems.
2. Core Value Proposition: ClawMetry solves the critical gap in monitoring AI agent performance by offering zero-configuration, real-time insights into token costs, sub-agent coordination, and system health—enabling cost control and operational transparency for AI deployments.

Main Features

1. Real-Time Token Cost Tracking: Monitors LLM token consumption per agent interaction using OpenTelemetry instrumentation. Calculates cumulative costs across providers (e.g., OpenAI, Anthropic) and alerts on budget thresholds via WebSocket push.
2. Sub-Agent Activity Visualization: Maps hierarchical agent workflows via dynamic DAG (Directed Acyclic Graph) diagrams. Toggles between granular views of active/inactive sub-agents and their communication latency using React Flow and WebGL rendering.
3. Cron Job Monitoring: Audits scheduled AI tasks with execution timelines, success/failure rates, and log outputs. Integrates with OpenClaw’s scheduler API to visualize job dependencies and auto-retry patterns.
4. Memory Change Diffing: Tracks state mutations in agent memory using binary patching algorithms. Highlights semantic deltas between memory snapshots for debugging logic errors.
5. Session History Playback: Indexes agent interactions with timestamped transcripts and allows rewinding to specific reasoning steps via SQLite-backed session storage.

Problems Solved

1. Pain Point: AI agents operate as "black boxes" with opaque costs, unpredictable sub-agent failures, and untraceable memory changes—leading to budget overruns and undiagnosed errors.
2. Target Audience:
   • AI Engineers deploying OpenClaw agents in production
   • DevOps teams managing LLM cost governance
   • Research scientists debugging multi-agent systems
   • Startups scaling autonomous AI workflows
3. Use Cases:
   • Auditing token expenditure per customer to optimize pricing
   • Diagnosing cron job failures in automated AI reporting systems
   • Validating memory integrity during critical operations (e.g., financial reasoning)
   • Demonstrating compliance via auditable session histories

Unique Advantages

1. Differentiation: Unlike generic tools (e.g., Grafana), ClawMetry requires no manual metric instrumentation, auto-discovers OpenClaw components, and visualizes AI-specific data (e.g., token burn rates, reasoning chains). Competitors lack native OpenClaw integration.
2. Key Innovation: Patented live flow visualizer that renders agent decision paths as interactive graphs using delta-streaming WebSockets, eliminating the need for complex Prometheus/Grafana setups.

Frequently Asked Questions (FAQ)

1. How does ClawMetry install without configuration?
   ClawMetry uses OpenClaw’s native hooks to auto-instrument agents via pip install clawmetry, injecting monitoring via Python’s sys.monitoring API—no code changes required.
2. Can ClawMetry monitor agents on Raspberry Pi?
   Yes, its lightweight collector (<10MB RAM) supports ARM architectures and headless Linux deployments, including edge devices.
3. Does ClawMetry support multi-cloud LLM cost tracking?
   Yes, it aggregates token costs across 20+ LLM providers (e.g., Azure, AWS Bedrock) and calculates real-time spend per agent.
4. How secure is ClawMetry for sensitive AI workflows?
   All data stays on-premises; no external telemetry. Optional AES-256 encryption for session histories and RBAC controls.
5. What visualization libraries power ClawMetry’s dashboards?
   Built with React/Victory for charts and Cytoscape for flow diagrams, optimized for 100ms real-time updates via WebSockets.