Hey PH! I'm Vimox - I build AI products by day and burn an embarrassing amount of tokens doing it. TokenFlow started as a weekend question: which of my agents was actually eating my budget? Every tool I use - Claude Code, Codex, Cursor, Cline, OpenCode — writes its own logs in its own format, and none of them agree on what a cached token even is. So I built the thing I wanted: one local engine that reads all of those logs, normalizes them properly (cache read/write/input as separate, mutually exclusive buckets; streaming re-reports collapsed; null never coerced to zero), versions its price table with cited sources, and shows the result in a dashboard plus a native macOS menu bar app with forecasts and anomaly alerts. Three principles I refused to compromise on: - Nothing leaves your machine. No accounts, no API keys, no telemetry. - Zero runtime dependencies. If it can't be audited, it doesn't ship. - Unpriced models show as "unknown" with a fix-it action - never a silent $0. It's MIT, free, and installs in one line: brew install --cask tokenflow (or npm i -g @vimoxshah/tokenflow) Today's version has 12 dashboard views, 10 log adapters, and 145 tests running on macOS/Linux/Windows across Node 22/24. What would make it genuinely useful for you? I'll be in the comments all day - Windows tray app and a team-rollup mode are already on my list depending on what you ask for.

Tokenflow

See where your AI tokens actually go

See where your AI tokens actually go