ClawMetry

Real-time observability & governance for AI agents

845 followers

Real-time observability & governance for AI agents

845 followers

ClawMetry is a observability & governance layer for 21+ AI agent runtimes. It observes Claude Code, Codex, Cursor, Antigravity, Grok, Goose, OpenCode, OpenClaw, Hermes, PicoClaw, NanoClaw etc. Think Arize, but purpose-built for AI Agent runtimes. 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

Framer AI AgentsDesign and publish professional sites with AI

Promoted

ClawMetry Launches

ClawMetry for NVIDIA NemoClawKnow what's happening inside your NemoClaw sandboxes

Launched on April 1st, 2026

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

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3mo ago

ClawMetry x OpenInfer: run OpenClaw agents on any hardware and watch every turn

If you use ClawMetry to watch your OpenClaw agents, here is something new. We just shipped an integration with OpenInfer, which routes each inference workload to the most efficient hardware across its partner cloud.

The combination means you can run an agent across a mix of GPUs and CPUs and still see exactly what ran, where it ran, and what it cost, per session and per sub-agent. No code changes, still read-only.

p/clawmetry

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5mo 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.)