★ CodeBurn

CodeBurn, a free, open-source, local-first tool from developer Thiago Perrotta, tracks AI coding token usage and cost across 41 tools and agents including Claude Code, Pi, Codex, and Cursor, broken down by model, project, and task. It reads session files already on disk, requiring no wrapper, proxy, or API key, and offers a TUI, web, desktop, and macOS menubar interface. A 30-day yield analysis showed $594.85 (31%) of $1,915.61 in AI spend was productive, $611.66 (32%) abandoned, and $709.10 (37%) ambiguous across 714 sessions.

♠ Previously https://perrotta.dev/2025/09/claude-ccusage/ . CodeBurn https://github.com/getagentseal/codeburn shows Gen AI / LLM code usage: where did all the money CodeBurn is a free, open-source, local-first tool that tracks AI coding token usage and cost across 41 tools and agents Claude Code, Pi, Codex, Cursor, and more , broken down by model, project, and task. It reads the session files already on disk. No wrapper, proxy, or API key is needed, and nothing leaves the machine great . It’s a TUI. Installation methods: % brew install codeburn OR % npx codeburn The same data is also available via web and desktop interfaces. On macOS, menubar downloads the native app to ~/Applications and launches it: % codeburn menubar Resolving CodeBurn Menubar v0.9.20... Downloading CodeBurnMenubar-v0.9.20.zip... Verifying checksum... Unpacking... Verifying app bundle... Launching CodeBurn Menubar... Ready. /Users/thiago.perrotta/Applications/CodeBurnMenubar.app It shows the current spend in the menu bar. Clicking it opens local breakdowns by agent, model, and activity, plus trends, forecasts, and exports. It refreshes every 30 seconds by default and backs off on battery. It tracks spend calculated from local sessions, not provider usage windows or reset countdowns. Codexbar https://perrotta.dev/2026/05/codexbar/ does the latter. I find that the CLI TUI is enough. A few subcommands: yield correlates AI sessions with nearby Git commits. It classifies the money spent as productive, reverted, abandoned, or ambiguous: % codeburn yield -p 30days Analyzing yield for Last 30 Days... Productive: $594.85 31% - 117 sessions shipped to main Reverted: $0.00 0% - 0 sessions were reverted Abandoned: $611.66 32% - 377 sessions never committed Ambiguous: $709.10 37% - 220 sessions lost commits to concurrent sessions Attribution: timestamp-window based heuristic Total: $1915.61 - 714 sessions It’s an experimental timestamp-based heuristic, but a neat answer to “did all those tokens produce code that shipped?” report opens the interactive dashboard for a given period, with token and cost breakdowns by tool, model, project, and task: % codeburn report --provider pi -p month ∎ — § — Reply via email mailto:serendipity@perrotta.dev?subject=Reply to: CodeBurn