tokenpayback 0.3.0

Your AI activity ledger — reads every coding agent's local sessions (Claude Code, Codex, Hermes, OpenClaw, OpenHuman, Cursor) and shows what your tokens did.

tokenpayback

Your AI activity ledger — every agent, every activity, 100% local.
One CLI. Reads every coding agent on your machine. Tells you what your tokens did.

🌐 Live demo · 📦 Install · 🤖 Supported agents · 🔒 Privacy

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The problem

You're paying for Claude Code + Codex + maybe Cursor, Hermes, OpenClaw, OpenHuman. Each agent keeps its own session log on your disk. No tool tells you what those tokens did across all of them.

Cost dashboards show you how many tokens you burned. None of them tell you:

• Was this a new feature or a brainstorm?
• Did the agent finally fix the bug or just dance around it?
• How much of the spend went into shipping code vs. answering questions vs. organizing your life?

tokenpayback reads every agent's local data, classifies every session via LLM, and shows you the answer. It runs on your machine. Data never leaves.

This week — what your $264 of AI tokens did
─────────────────────────────────────────────
🚢 Code shipped         14 PRs · 3,120 lines
🐛 Bug fixed             3 sessions
🧹 Code cleaned          2 sessions
⚙️  Infra changed         5 sessions
📚 Info gathered         6 sessions
💡 Ideas explored        4 sessions
🎯 Life shipped          2 sessions (resumes, video drafts)
❓ Question answered     11 sessions

Every category gets credit. Asking a question and getting an answer is value. Sketching out an idea is value. Code is just one shape of value.

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Supported agents

tokenpayback auto-detects which of these you have installed and reads each one:

Agent	Local path	Status
Claude Code	~/.claude/projects/	✅ Full support
Codex CLI	~/.codex/sessions/	✅ Full support
Hermes (Nous Research)	~/.hermes/	🟡 Beta — SQLite reader
OpenClaw 🦞	~/.openclaw/ or ~/Library/Application Support/OpenClaw/	🟡 Beta — auto-detect
OpenHuman (tinyhumans.ai)	~/.openhuman/	🟡 Beta — SQLite reader
Cursor	~/Library/Application Support/Cursor/User/	🟡 Beta — composer data

Each agent has its own parser file in tokenpayback/parsers/. Adding a new agent = one file. PRs welcome.

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Install

Requires Python 3.9+.

pipx install tokenpayback     # recommended (isolated env)
# or
pip install --user tokenpayback

You'll need an LLM API key for session classification — set ONE:

export ANTHROPIC_API_KEY=sk-ant-...        # recommended — you probably already have one
export OPENAI_API_KEY=sk-...
export LITELLM_API_KEY=...
export LITELLM_BASE_URL=https://your-proxy/v1
export LITELLM_MODEL=gpt-4o-mini

Skip classification entirely with tokenpayback --no-classify (still shows cost & agent activity).

Run

tokenpayback                  # scan all agents + classify + open dashboard in browser
tokenpayback scan             # just scan & write data
tokenpayback serve            # serve existing data on local port

First run takes ~60 seconds (LLM classifies each session). Subsequent runs use cache.

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Categories are personalized — not hardcoded

The first time you run tokenpayback, the LLM looks at a sample of your real sessions and induces categories that fit how YOU use AI. An engineer's taxonomy will look very different from a creator's, a founder's, or a data scientist's.

tokenpayback                    # first run auto-generates ~/.tokenpayback/taxonomy.yaml
tokenpayback taxonomy show      # see what it came up with
tokenpayback taxonomy edit      # rename categories, change baselines, add new ones
tokenpayback taxonomy regen     # re-discover from scratch

Example: an engineer might end up with something like:

categories:
  - id: ship-feature
    icon: 🚢
    label: Ship feature
    description: Completing a new product feature end-to-end with commits
    baseline_usd: 80
    per_pr_usd: 700
    per_line_usd: 0.30
  - id: cf-worker-debug
    icon: 🛠
    label: CF Worker debugging
    description: Diagnosing issues in Cloudflare Worker deploys
    baseline_usd: 60
  ...

A content creator might end up with tiktok-edit, research, voiceover-prep, etc. Everyone's dashboard speaks their own language.

No baseline is $0 by default. Asking a question and getting an answer IS value. The point is to make the assumptions visible, not to hide them behind a SaaS pricing model.

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Privacy

• ❌ No tracking, no analytics, no phone-home
• ❌ No account, no email, no sign-up
• ❌ Your session data NEVER leaves your machine
• ✅ The only outbound calls: (1) your chosen LLM for classification, (2) Anthropic/OpenAI usage APIs only if you opt in, (3) GitHub API only if you opt in
• ✅ Open source. Read every line.

The LLM classification step sends a one-paragraph summary of each session (first prompt, tool call counts, sample bash commands) — not full prompts or code. Skip it entirely with --no-classify.

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How it works

┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐
│ ~/.claude/      │  │ ~/.codex/       │  │ ~/.hermes/      │  │ ~/Library/.../  │
│ projects/       │  │ sessions/       │  │ *.db (SQLite)   │  │ Cursor/User/    │
│ *.jsonl         │  │ *.jsonl         │  │                 │  │ state.vscdb     │
└────────┬────────┘  └────────┬────────┘  └────────┬────────┘  └────────┬────────┘
         │                    │                    │                    │
         └────────────────────┴─────┬──────────────┴────────────────────┘
                                    │
                            tokenpayback/parsers
                                    │
                                    ▼
                       ┌─────────────────────────┐
                       │  normalized Session[]   │
                       │   agent, project,       │
                       │   tokens, tool_counts,  │
                       │   est_cost_usd...       │
                       └────────────┬────────────┘
                                    │
                                    ▼  LLM classifier
                       ┌─────────────────────────┐
                       │   + category, summary,  │
                       │     value_signal        │
                       └────────────┬────────────┘
                                    │
                       ┌────────────┴───────────┐
                       │                         │
                       ▼                         ▼
              activity ledger            engineering ROI
              (every category)           (GitHub PR/commits)
                       │                         │
                       └────────────┬────────────┘
                                    ▼
                       local dashboard (localhost)

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What it's not

• Not a SaaS. No cloud, no signup, nothing to sell you.
• Not a tracker. It cares about your spend, not your activity in aggregate.
• Not an attribution oracle. Value heuristics are estimates. We're transparent about it.
• Not a replacement for evals. Use Braintrust / Langfuse / Inspect for output quality.

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Roadmap

For v0.3:

• Sankey diagram: from agent → category → outcome
• Time-series of how your category mix shifts week-over-week
• Per-tool cost breakdown (which Bash patterns cost you the most?)
• Native Mac app via Tauri or pywebview (no more "open browser" feel)
• LLM-graded value (replace flat baselines with case-by-case judgment)

PRs welcome. Open an issue first for anything non-trivial.

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Contributing

Add support for a new agent:

git clone https://github.com/gongyibob-ctrl/tokenpayback.git
cd tokenpayback
python3 -m venv .venv && .venv/bin/pip install -e .
# Create tokenpayback/parsers/<your_agent>.py — subclass BaseParser
# Register in tokenpayback/parsers/__init__.py ALL_PARSERS
# Test: .venv/bin/tokenpayback scan

Each parser is ~50 lines. See parsers/claude_code.py as the reference.

Code style: small modules, no premature abstraction, transparent heuristics.

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Why "tokenpayback"?

Because the question isn't "how many tokens did I burn?" — every tool answers that. The question is "did those tokens come back as something?" — and "something" doesn't have to be code. A clear answer, a written note, a fixed bug, a planned weekend — those count too.

Built by @gongyibob-ctrl. Made in a weekend, open sourced because it shouldn't have to be a startup.

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License

MIT. Use it, fork it, sell improvements built on it.