ClawMetry addresses a common pain point in AI agent development: how to get immediate, rich observability into the behavior and cost of OpenClaw AI agents without tedious setup or instrumentation. It provides a zero-configuration, real-time dashboard that auto-discovers the workspace, sessions, logs, and scheduled tasks (crons), offering a developer a live snapshot of what their AI agents are doing.
What ClawMetry does and how it works
ClawMetry is a Python application built on Flask, designed to run locally or inside Docker. It connects to OpenClaw AI agents running on the same machine by auto-detecting their workspace environment. This means no configuration files or manual setup are needed — the dashboard figures out where agent logs and sessions live and starts monitoring immediately.
The primary feature is a live animated flow diagram that visualizes messages traveling through the AI system: incoming messages from 19 supported messaging channels, routing through the brain (the agent’s core processing), interactions with tools, and responses sent back through channels. This flow is updated in real-time, giving an immediate sense of the agent’s activity and message processing pipeline.
Alongside the flow visualization, ClawMetry tracks token usage and cost metrics, breaking them down daily, weekly, and monthly. This helps developers stay aware of API usage and the operational expense of running AI agents. It also monitors sessions and cron jobs, providing color-coded log streaming for fast error spotting, memory file browsing to inspect agent state, and chat-bubble style transcript replays for easier debugging of conversations.
Enterprise-grade features include alerting mechanisms based on budget caps and error rate thresholds. Alerts can route to Slack, Discord, or PagerDuty, ensuring teams can respond to issues promptly. There’s also an approval gating system that requires explicit permission for destructive actions such as deleting data, force pushing changes, or mutating databases and installed packages — a safety mechanism rarely found in open-source AI agent tooling.
Why ClawMetry stands out technically
The technical strength of ClawMetry lies in its real-time, zero-config observability combined with operational safety features. The live flow diagram is not just a static dashboard but an animated representation of message routing through channels, brain, and tools. This kind of visualization is rare in AI agent observability and provides a clear window into the inner workings of an otherwise opaque system.
Under the hood, ClawMetry uses Flask as the backend server, which serves the dashboard on localhost port 8900 by default. The project supports Python 3.8+ and installs its dependencies via pip. This lightweight stack keeps the footprint minimal and accessible to a wide range of developers.
The auto-discovery of workspace, logs, sessions, and cron jobs is a significant DX improvement. Instead of manual paths or config files, it inspects the environment to find relevant artifacts to monitor. This reduces friction and encourages regular use.
The approval gating system is a thoughtful addition for production safety. Many AI agent tools assume trust and do not guard against accidental destructive operations triggered by agents. ClawMetry requires explicit approval for critical actions, preventing costly mistakes.
The alerting integration with common team communication tools and incident management platforms makes it suitable for team environments and enterprises.
The tradeoffs are clear: ClawMetry is designed for local or Docker deployment with mounted volumes, so cloud-native or highly distributed environments might require additional adaptation. Its focus on OpenClaw agents limits its applicability to that ecosystem but provides deep, tailored observability within it.
Quick start
Getting started with ClawMetry is straightforward thanks to the documented installation options.
One-liner installation (recommended):
curl -sSL https://raw.githubusercontent.com/vivekchand/clawmetry/main/install.sh | bash
pip installation:
pip install clawmetry
clawmetry
From source:
git clone https://github.com/vivekchand/clawmetry.git
cd clawmetry && pip install flask && python3 dashboard.py
Docker deployment:
ClawMetry supports running inside Docker containers with mounted workspace volumes to access logs and data.
The documentation recommends running ClawMetry’s sync daemon on the host machine rather than inside sandbox environments to avoid network policy restrictions.
Verdict
ClawMetry is a practical tool for developers working with OpenClaw AI agents who want immediate, zero-config insight into agent operations. Its real-time animated flow visualization provides a unique perspective on message routing that can help diagnose complex multi-agent workflows.
The operational safety features like approval gating and alerting make it suitable for teams managing mission-critical or budget-sensitive AI deployments. However, its tight coupling with OpenClaw and local/Docker-based deployment model means it is less suited for cloud-native or multi-cloud distributed architectures out of the box.
Overall, ClawMetry is worth exploring if you are invested in the OpenClaw ecosystem and want a no-fuss observability dashboard that emphasizes developer experience, cost awareness, and operation safety. The codebase appears pragmatic and accessible, and the quick start options lower the barrier to adoption. For other AI agent frameworks, the concepts here might inspire similar observability features but direct reuse would require adaptation.
# Running ClawMetry after pip install
pip install clawmetry
clawmetry
This minimal experience gets you started monitoring your OpenClaw agents in real time with no config needed.
The project is a solid example of how focused observability tooling can improve AI agent development and operational confidence without the overhead of complex setup or heavy dependencies.
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→ GitHub Repo: vivekchand/clawmetry ⭐ 337 · Python