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ClawMetry for OpenClaw Review

ClawMetry for OpenClaw is a real-time observability dashboard designed to monitor and govern AI agents, particularly those built with OpenClaw and other runtimes.

shipped Feb 19, 2026freemium
Domain rating25Traffic rank#3AI-readablepartial
ClawMetry for OpenClaw — product screenshot

Why it matters

1ClawMetry for OpenClaw has over 545,000 installs across more than 127 countries.
2It provides end-to-end encrypted remote monitoring, introduced in March 2026.
3The platform supports multiple AI agent runtimes, including OpenClaw, NVIDIA NemoClaw, Claude Code, and Codex.
4ClawMetry tracks token usage and costs per call, per session, and per model.

Stork’s verdict on ClawMetry for OpenClaw

ClawMetry delivers granular real-time observability for agents, but it's primarily for agentic workflows, not broader LLM apps.

ClawMetry for OpenClaw reviewed by Stork AI · stork.ai/en/clawmetry-for-openclaw

Specs

API Available

Yes, public API

overview

What is ClawMetry for OpenClaw?

ClawMetry for OpenClaw is an AI agent observability tool developed by ClawMetry that enables developers, researchers, and operations teams to monitor and govern AI agents in real-time. It provides a comprehensive view of agent activity, costs, and performance, aiming to bring transparency to complex AI workflows.

ClawMetry offers 'X-ray vision' into an AI agent's runtime, mapping directly to OpenClaw's three-layer architecture. Its primary function is to provide real-time insights into what AI agents are doing, how much they are costing, what might be stuck, and what actions require approval. Key functionalities include real-time monitoring of sub-agent activity (files read, commands executed, tools called, thought processes), cost tracking (token usage and costs per call, session, and model), and debugging through full session history with timelines. It also monitors system health (cron jobs, service uptime, disk usage) and offers governance features like end-to-end encrypted client-side encryption, secret redaction, and a tamper-evident audit log. ClawMetry supports multiple runtimes, including OpenClaw, NVIDIA NemoClaw, Claude Code, Codex, Cursor, and Aider.

features

Key Features of ClawMetry for OpenClaw

ClawMetry for OpenClaw provides a suite of features designed for comprehensive AI agent monitoring and governance, ensuring transparency and control over complex AI workflows.

  • Real-time observability dashboard for AI agent activity, costs, and performance.
  • End-to-end encrypted remote monitoring via cloud sync layer and native Mac menu bar app (introduced March 22, 2026).
  • Token cost tracking per call, per session, and per model to manage budgets and identify inefficiencies.
  • Full session history with timelines and tool calls for post-mortem analysis and debugging of multi-agent interactions.
  • System health monitoring, including cron jobs, service uptime, disk usage, and active sub-agents.
  • Governance and security features: client-side encryption, secret redaction at ingest, tamper-evident audit logs, and approval for risky actions.
  • Multi-runtime observability supporting OpenClaw, NVIDIA NemoClaw, Claude Code, Codex, Cursor, and Aider.
  • Agent Builder feature for describing, building, hosting, and monitoring AI agents.
  • Runaway loop detection to prevent budget overruns and resource waste.
  • Local-first architecture with zero-configuration installation via pip install clawmetry.

use cases

Who Should Use ClawMetry for OpenClaw?

ClawMetry for OpenClaw is primarily designed for individuals and teams engaged in the development, deployment, and management of AI agents, particularly those utilizing the OpenClaw framework or other supported runtimes.

  • Developers building with AI agents: To gain real-time visibility into agent actions, debug behaviors, and optimize performance.
  • Teams needing real-time visibility into AI agent operations: To monitor sub-agent activity, tool calls, and thought processes across complex workflows.
  • Users wanting to track token usage and costs: To manage budgets, identify expensive operations, and prevent runaway loops.
  • Organizations requiring governance for AI agents: To ensure data privacy with E2E encryption, maintain audit logs, and approve potentially risky agent actions.
  • Anyone needing to debug or understand AI agent behavior: To analyze full session histories and timelines for post-mortem analysis and continuous improvement.

how to use

How to Use ClawMetry for OpenClaw

ClawMetry for OpenClaw is designed for straightforward installation and immediate use, providing real-time observability for AI agents with minimal setup.

  • 1Installation: Execute curl -fsSL https://clawmetry.com/install.sh | bash in your terminal for cross-platform setup.
  • 2Agent Integration: Integrate ClawMetry into your OpenClaw or other supported AI agent runtimes to begin data collection.
  • 3Dashboard Access: Access the real-time observability dashboard via a web browser or the native Mac menu bar app.
  • 4Monitor Activity: View sub-agent activities, tool calls, and thought processes as they occur.
  • 5Track Costs: Monitor token usage and associated costs per call, session, and model.
  • 6Analyze & Debug: Utilize session histories and timelines for debugging and performance optimization.

pricing

ClawMetry for OpenClaw Pricing & Plans

ClawMetry for OpenClaw operates on a freemium model, offering a free tier with core functionalities for monitoring AI agents. Specific details regarding paid tiers or advanced features are not publicly detailed beyond the freemium offering.

  • Freemium: Free access to core real-time observability features for AI agents.

Pros

  • +Provides real-time, granular observability into AI agent activity, including sub-agent actions and thought processes.
  • +Offers end-to-end encrypted remote monitoring, enhancing data privacy and accessibility (introduced March 2026).
  • +Tracks token usage and costs per call, session, and model, aiding in budget management and efficiency.
  • +Supports multiple AI agent runtimes, including OpenClaw, NVIDIA NemoClaw, Claude Code, and Codex.
  • +Features a zero-configuration installation (pip install clawmetry) and cross-platform compatibility (macOS, Linux, Windows, Raspberry Pi).
  • +Includes governance features like secret redaction, tamper-evident audit logs, and approval for risky actions.

Cons

  • −Specific pricing details for advanced or enterprise tiers beyond the freemium model are not extensively detailed.
  • −While supporting multiple runtimes, its deepest integration and 'X-ray vision' are specifically tailored to OpenClaw's architecture.
  • −The 'Agent Builder' feature, while useful, may not offer the same level of customization as direct code development for complex agents.
  • −As an observability tool, it complements rather than replaces agent coordination platforms like JARVIS Mission Control.

Similar Tools

ClawMetry for OpenClaw vs Competitors

ClawMetry for OpenClaw differentiates itself by offering a specialized, real-time observability solution purpose-built for AI agents, contrasting with more general-purpose ML observability platforms.

1
LangfuseOn Stork

Provides comprehensive tracing, monitoring, and debugging for LLM applications and agents, with built-in evaluation capabilities and a prompt playground.

While offering a robust dashboard for traces and evaluations, Langfuse is observability-first, meaning automated failure clustering or advanced metric recommendations might require more manual setup compared to ClawMetry's potentially more integrated agent-specific analytics.

2
Arize Phoenix↗

An open-source tool for visual debugging and evaluation of LLM, RAG, and agent applications, strong in built-in evaluation metrics and drift detection.

Phoenix provides a strong foundation for tracing and evaluation with a visual interface, but as a more general ML observability tool expanding into GenAI, it might require more configuration to achieve the highly specialized, real-time agent-specific insights that ClawMetry offers for OpenClaw.

3
MLflowOn Stork

A comprehensive MLOps platform that includes experiment tracking, model management, and an interactive UI for logging and visualizing ML runs, adaptable for AI agent observability.

MLflow is a broader MLOps platform, so while it offers powerful tracking and visualization, setting up real-time, agent-specific observability dashboards might require more manual integration and custom development compared to ClawMetry's out-of-the-box solution for OpenClaw agents.

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