ClawMetry

Real-time observability dashboard for OpenClaw AI agents

1.2K followers

Real-time observability dashboard for OpenClaw AI agents

1.2K followers

ClawMetry is a free, open-source observability dashboard for OpenClaw AI agents. Think Grafana, but purpose-built for AI. One command install (pip install clawmetry), zero config. Monitor token costs, sub-agent activity, cron jobs, memory changes, and session history. All in real-time with a beautiful live flow visualization. Works on macOS, Linux, Windows, even Raspberry Pi

This is the 3rd launch from ClawMetry. View more

ClawMetry for NVIDIA NemoClaw

Launched this week

Know what's happening inside your NemoClaw sandboxes

Full observability inside NVIDIA NemoClaw sandboxes. One command on the host, every sandbox gets covered. See every thought, tool call, and token cost in real time. Brain activity, flow visualization, memory monitoring. All E2E encrypted. 95K+ installs. 100+ countries. Open source (MIT). Cloud sync: $5/sandbox/month.

ClawMetry for NVIDIA NemoClaw gallery image

Free Options

Launch tags:Open Source•Developer Tools•Artificial Intelligence

Launch Team / Built With

New VS Code Extensions by Kilo Code — Run Parallel AI Agents in VS Code. Free & Open Source

Run Parallel AI Agents in VS Code. Free & Open Source

Promoted

Bababot

Congarts to the launch.

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3d ago

ClawMetry

Maker

@emma_watson21 Thank you Emma!

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7h ago

jared.so

Interesting product! Congrats on the launch!

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3d ago

ClawMetry

Maker

@maks_bilski Thank you Maks! Appreciate the support 🦞

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7h ago

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Previous ClawMetry Launches

ClawMetry CloudSee your OpenClaw agents' costs, activity & memory live

Launched on March 18th, 2026

ClawMetry for OpenClawReal-time observability dashboard for OpenClaw AI agents

Launched on February 18th, 2026

Forum Threads

p/clawmetry

•

6d ago

What's the one thing you wish you could see inside your AI agent's brain?

I've been building ClawMetry for past 5 weeks. 90k+ installs across 100+ countries.
The observability features I built first were the ones I personally needed: a live execution graph (Flow tab), full decision transcripts (Brain tab), token cost tracking per session, and visibility into sub-agent spawns.
But I keep hearing variations of the same thing: "I don't really know what my agents are doing." And everyone means something slightly different by that.
For some it's costs. For some it's timing (why did this take 4 minutes?). For some it's trust (did the agent actually do what I think it did?). For some it's failures (where exactly did it break?).
So I want to ask you directly:
If you're running AI agents today -- what's the one thing missing from your observability setup? What would make you feel like you actually understand what's happening inside your agents?
Options I'm thinking about next:
- Alerting (get notified when an agent fails or goes over budget)
- Cost per task breakdown (not just per session)
- Agent run comparisons (before/after a prompt change)
- Memory snapshots (what did the agent "know" at each decision point)
Drop your answer below. The next feature I build will be heavily influenced by this thread.
(ClawMetry is free to try locally: pip install clawmetry. Cloud: app.clawmetry.com, $5/node/month, 7-day free trial.)

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