Claude Usage Tracker

What it does

No single place shows your total Claude spend across tools. If you use Cursor, Claude Code CLI, Windsurf, Cline, or others — each burns tokens separately and you're flying blind. Claude Usage Tracker auto-detects 9+ tools, scans local session data, and shows everything in one dashboard: daily costs, model breakdowns, heatmaps, session logs, and monthly projections. Native macOS app or browser mode for other OS device. No cloud, no accounts, no telemetry. 100% free and open source (MIT).

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  Claude Code usage tracking by LangWatch Jul 2026 · langwatch.ai · ▲353

  See what your Claude Code sessions actually cost

• CC
  Claude Code Usage Monitor – real-time tracker to dodge usage cut-offs2025 · github.com · ▲245

  I kept slamming into Claude Code limits mid-session and couldn’t find a quick way to see how close I was getting, so I hacked together a tiny local tracker. Streams your prompt + completion usage in real time Predicts whether you’ll hit the cap before the session ends Runs 100 % locally (no auth, no server) Presets for Pro, Max × 5, Max × 20 — tweak a JSON if your plan’s different GitHub: https://github.com/Maciek-roboblog/Claude-Code-Usage-Monitor It’s already spared me a few “why did my run just stop?” moments, but it’s still rough around the edges. Feedback, bug…

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  ClaudeUsageBarJan 2026 · ▲401

  Know exactly how much Claude you have left at a glance

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  UsagebarJan 2026 · ▲172

  Track Claude Code Usage from your Menu Bar

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  Diet Claude12d ago · dietclaude.com · ▲423

  Never get blindsided by Claude's usage limits again

• CA
  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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