token-monitor
Local-first desktop widget for tracking token usage, costs, and limits across 38+ AI coding tools, including Claude Code, Codex, Cursor, OpenCode, and OpenClaw, with multi-device sync.
Runssource: GitHubhomepageJavaScriptMITcommit fce070c789ae
JavaScript, MIT licensed. The project labels itself: ai, ai tools, antigravity, claude code, codex, cursor, deepseek and deepseek harness.
token-monitor runs. An Argusic agent installed it in 18 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
Token Monitor installed, linted, and all 4056 tests pass. The Electron app, headless agent, and Node hub all start and operate correctly. Real Codex token usage data was collected from this container's existing sessions.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 18 minutes
- Cold machine to finish
- 7 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- Model tokens used
- 46,462
- Exact commit tested
- fce070c789ae
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- How the result was proved
- runs before 2026-09-07 did not record the command used to check the result
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
"resetCredits": null,
"region": ""
}
]
}
}
The agent:once dry-run works perfectly - it collected actual Argusic Agent usage from the container…
Let me compile the results.
tokens used
46,462
The agent:once dry-run works perfectly - it collected actual Argusic Agent usage from the container…
Let me compile the results.Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Took 18 minutes to install, slower than the median of the 36 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 37 ai projects Argusic has installed and timed, token-monitor was the 32nd fastest to reach a running state, and 30 of 37 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
aiai-toolsantigravityclaude-codecodexcursordeepseekdeepseek-harnessdshhermes-agentlinuxllmlocal-firstmacosopenclawopencodeself-hostedtoken-trackertoken-usagewindows
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/token-monitor)Questions
- Does token-monitor run?
- Yes. token-monitor runs. Argusic installed and launched it on a clean machine in 18 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test token-monitor?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit fce070c789ae. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test token-monitor?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does token-monitor take to install?
- 18 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does token-monitor need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing token-monitor?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does token-monitor compare with the alternatives?
- Of the 37 ai projects Argusic has installed and timed, token-monitor was the 32nd fastest to reach a running state, and 30 of 37 reached one at all.
- Where is the evidence for token-monitor?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.