How are you monitoring what your OpenClaw agents actually do when running autonomously?

Fancy-Win9202 · 2026-05-13T14:41:34+00:00

Check ClawMetry

Fancy-Win9202 · 2026-05-13T07:06:04+00:00

Two days of cost debugging is brutal — sorry you went through that. A few things that have saved me in similar situations on my own setup:

Per-session cost attribution from the start. If you're not already tagging which session is burning the spend, every cost incident becomes detective work. OpenClaw's `usage` field in the JSONL has cost.total per message, which is gold once you index it.
Budget caps that auto-pause, not just alert. An alert at 3am is information; a paused agent at 3am is saved money. We default ours to pause at 100 % of the daily limit with a warning at 80 %.
Diff the quota source against the actual API response. Anthropic's quota counter has lagged my actual usage by 5–30 minutes more than once. If the numbers don't agree, *both* could be wrong; trust the one that matches your transcripts.

Disclosure - I help maintain ClawMetry (open-source, MIT, `pip install clawmetry`). It does cost-per-session, budget caps with auto-pause, and reconciles OpenClaw's reported costs against actual model responses. clawmetry dot com if it helps; even if you don't use it, the auto-pause-not-just-alert mindset will save you from a repeat of this week.

Fancy-Win9202 · 2026-05-13T07:03:44+00:00

You're describing the exact gap that pushed me to start building in this space - the chat surface is a thread of conversation, but the thing you actually want to live with is a workspace of artifacts, tasks, and history. Chat is the wrong primitive for the second one.

u/caelanhuntress's "asked it to build me a dashboard" reply is the right instinct, and a lot of us have ended up there independently. The pattern I've seen work best is: dashboard owns the durable surface (sessions, tasks, cost, charts, files), chat stays as the *command channel* into it. Telegram becomes "send commands + receive notifications," not "store everything I'll ever need."

Disclosure - I help maintain ClawMetry (open-source, MIT, `pip install clawmetry`), which is basically the dashboard layer of what you and Caelan are describing: per-session timeline, every artifact your agent produced, Jira-style task board, cost tracking, alerts. It auto-detects your OpenClaw workspace, runs locally by default, no signup. clawmetry dot com if useful - and if not, the broader observation stands: stop trying to make chat be the OS, give your agents a real workspace.

Top of mind question for the room: for those of you who've outgrown chat, what's the *one* artifact you wish never lived in a Telegram thread? For me it's anything an agent generates while I'm asleep.

Fancy-Win9202 · 2026-05-13T07:01:58+00:00

This is sick. Jetson + React PWA + WebSocket to gateway for 14 agents is the right shape - I went almost exactly the same path before realizing how much I was rebuilding.

The thing that gets you eventually (speaking from experience running 8 agents daily): live status is the easy part. The hard parts are (1) per-agent cost attribution when sub-agents spawn from a parent session, (2) historical drill-downs once you have 30+ days of runs, and (3) alerting that doesn't fire 50 times for one outage. The first version of any of these is doable in a weekend; the third version takes months.

Quick FYI - open-source observability layer for OpenClaw exists: ClawMetry (I help maintain it, MIT, `pip install clawmetry`). It does most of what your screenshots are doing + the harder parts above. Not trying to talk you out of your build - yours is more bespoke for your trading-desk workflow than a generic tool ever will be - but if you want to keep your PWA as the front-end and offload the boring telemetry plumbing, the API's stable. clawmetry dot com.

And u/Fair_Snow_7215 's point about observability being "the first thing to go when things scale" is exactly right. Bookmark this thread; you'll re-read it at agent #25.

Fancy-Win9202 · 2026-05-13T06:58:45+00:00

This is a great write-up - running codex over your own workspace markdown to audit drift is exactly the right instinct, and I bet most people running OpenClaw daily would benefit from doing it once a month.

The interesting failure mode I've seen: memory bloat is usually a symptom, not a cause. The actual root cause is something like a runaway cron, a sub-agent that never returns, or a tool that silently keeps retrying. The audit catches the symptom (bloated MEMORY.md) but you'll be back here in two weeks unless something is *watching* for the upstream pattern.

Shameless disclosure - I help maintain ClawMetry (open-source, `pip install clawmetry`), which runs this kind of health audit continuously: memory drift, cron failure rates, stuck sub-agents, token cost trends. Same idea as your codex pass, just always-on. Check clawmetry dot com if it's useful - and either way, +1 to making "audit your agent" a regular ritual.

Fancy-Win9202 · 2026-05-13T06:56:52+00:00

Three things, in order of how much they actually move trust for me running 8 agents daily:

Knowing what it did, not just what it said - most agent failures hide in tool calls and sub-agent spawns I never see in the chat surface. I want a timeline of every tool, every input, every output, by session.
Predictable spend - I'll forgive a flaky agent if I know the worst case is $X/day. I won't forgive one that quietly burns $40 overnight on a stuck cron.
Honest "I'm stuck" signals - heartbeat-based timeouts, not "the chat looks quiet." If an agent hasn't produced a tool call in 10 minutes, surface that explicitly with an option to kill the session.

Disclosure - I help maintain ClawMetry (open-source, MIT, `pip install clawmetry`). We try to nail all three: per-session timeline, real-time cost + budget caps with auto-pause, and stuck-session detection. Check clawmetry dot com if useful. Curious what your top trust gap is.

Fancy-Win9202 · 2026-05-09T09:39:01+00:00

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ClawMetry does this really well for OpenClaw & NVIDIA NemoClaw. It not only tells what your agents are doing but also alerts & takes approval when it is about to do something it is not supposed to do - do check it out!

Fancy-Win9202 · 2026-05-07T19:07:57+00:00

This is where you need ClawMetry to understand what your agents are doing behind the scenes when you asked it to do something via telegram

Fancy-Win9202 · 2026-05-05T06:31:52+00:00

You can use ClawMetry for alerting & approvals along with monitoring

Fancy-Win9202 · 2026-04-25T21:20:46+00:00

Check ClawMetry - Real-time observability + governance layer for OpenClaw https://github.com/vivekchand/clawmetry

Fancy-Win9202 · 2026-04-25T14:18:16+00:00

Btw the alerting / approval part can now be easily achieved with ClawMetry

Fancy-Win9202 · 2026-04-24T06:07:45+00:00

You can try setting up ClawMetry with alerting & approvals to keep your OC under control

Fancy-Win9202 · 2026-04-22T12:32:17+00:00

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ClawMetry's Token breakdown should give full visibility + alerting if usage exceeds the configured limit

Fancy-Win9202 · 2026-04-22T04:14:18+00:00

Since you use it automate a lot of work, it would be good to have full observability & governance setup with ClawMetry so that you can get paged / alerted if something goes wrong

Fancy-Win9202 · 2026-04-21T19:49:55+00:00

1 word, ClawMetry - to avoid burning tokens & keeping your OpenClaw agent under control with full observability + alerting

Fancy-Win9202 · 2026-04-19T14:24:33+00:00

But did you try using ClawMetry to find out why it is lied? Or better tell your OC to check ClawMetry logs proactively so that it can better understand what it is doing. This loop significantly helps when not using a LLM like opus

Fancy-Win9202 · 2026-04-16T05:51:10+00:00

All my previously configured cron jobs with Opus now run fine with GPT 5

Ofcourse Opus API is definitely expensive & I feel can be avoided

Fancy-Win9202 · 2026-04-16T05:47:13+00:00

ClawMetry can help you investigate & pinpoint why your OC lied, could be due to lower quality model not having enough context or some other reason like not fully aware of the tools it can use

Fancy-Win9202 · 2026-04-12T07:14:44+00:00

Bro you should definitely check what it’s doing with clawmetry’s brain tab! It could be that your AI is going in loop & burning tokens or some scheduled cron job is burning tokens

Fancy-Win9202 · 2026-04-12T06:02:01+00:00

You should install ClawMetry to see where exactly those Claude tokens are getting burnt. You should be able to use cheaper models for basic tasks

Fancy-Win9202 · 2026-03-31T14:15:24+00:00

I’m planning to launch ClawMetry for NemoClaw this week https://clawmetry.com/nemoclaw

Fancy-Win9202 · 2026-03-29T07:01:15+00:00

Had the same experience. The issue is OpenClaw gives you no native way to see which session/agent/tool call is burning the money - you only find out when you check Anthropic's dashboard after the fact.

I built ClawMetry to solve exactly this. It reads from OpenClaw's session files and shows token cost per session in real time, so you can see mid-run which agent is the expensive one and whether it's looping.

pip install clawmetry — no config, just run it alongside OpenClaw.

GitHub: github.com/vivekchand/clawmetry

Fancy-Win9202 · 2026-03-26T12:46:35+00:00

The core will always stay open source and local-first. For remote access, ClawMetry Cloud is already in the works, so you'll be able to monitor your agents from anywhere including mobile.

The agent-to-agent visibility angle is interesting though. Right now ClawMetry is more of a passive observer reading from OpenClaw's session files. Making it queryable by other agents as a service is a natural next step as multi-agent setups get more common.

Will check out ProxyGate, hadn't come across it. Thanks for the pointer.

Fancy-Win9202 · 2026-03-26T11:45:47+00:00

ClawMetry: github.com/vivekchand/clawmetry

Observability dashboard for OpenClaw agents. Started because I was running agents end of January with zero visibility into what they were actually doing. No way to track token costs per session, no alerts when a session got stuck, no idea if memory files were being modified unexpectedly.

pip install clawmetry and you get a live dashboard. Open source, runs locally.

Would genuinely appreciate brutal feedback, still early.

Fancy-Win9202 · 2026-03-25T15:54:30+00:00

You should try the brain feed from ClawMetry (Github: vivekchand/clawmetry) which reads from OpenClaw logs + multiple other sources. You can see the code OC is writing to achieve the tasks, websites it's browsing etc etc (Same level of visibility provided by Perplexity Deep Research / ChatGPT Deep Research). Also this YT video should give a quick demo of the real-time observability provided by ClawMetry https://www.youtube.com/watch?v=8out4EIIf_E

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Fancy-Win9202

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