CodeBurn

What it does

CodeBurn is a free, open-source tracker for AI coding costs. It reads the session files your tools already write - Claude Code, Cursor, Codex, Copilot, 40 in all, and shows every token and dollar by the task, model, project, and pull request that used it. The Optimize tab finds waste like cache bloat or retry tax, applies the fix, and tracks what it actually saved. Everything runs on your machine: no account, no uploads. MIT-licensed and genuinely free, used by 150k+ developers across the world.

CodeBurn is a free, local-first tool that tracks AI coding token usage and cost across 42 tools (Claude Code, Cursor, Codex, Copilot, Gemini and more). Break spend down by model, project, and task. A privacy-first ccusage alternative. 100% local, open source.

CodeBurn is a member of Anthropic's Claude Startups program. You used to find out at the end of the month. Now the number sits next to your clock all day, above every window, and one click opens the whole bill. The day's spend sits next to your clock. No dashboard to open, no tab to find. Plan usage metered live for every agent, with time remaining on each window. Per agent totals from today to lifetime, local or combined across your machines. Spend split by activity: coding, debugging, feature work, with turns and 1-shot rates. Native Swift. Reads local session files only. Nothing leaves your Mac. On Linux, the same number lives in your GNOME top bar. The bill stops being one number and…from codeburn.app

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  CodeBurn – Analyze Claude Code token usage by taskApr 2026 · github.com · ▲112

  Built this after realizing I was spending ~$1400/week on Claude Code with almost no visibility into what was actually consuming tokens. Tools like ccusage give a cost breakdown per model and per day, but I wanted to understand usage at the task level. CodeBurn reads the JSONL session transcripts that Claude Code stores locally (~/.claude/projects/) and classifies each turn into 13 categories based on tool usage patterns (no LLM calls involved). One surprising result: about 56% of my spend was on conversation turns with no tool usage. Actual coding (edits/writes) was…

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