Install via ClawHub: clawhub install tokenmeter

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Track your AI API usage and costs across all providers — locally, privately.

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You're using Claude Code, Cursor, ChatGPT, Azure OpenAI, and a dozen other AI tools. Your monthly bill is a mystery until it arrives. Sound familiar?

tokenmeter solves this by:

• 📊 Tracking token usage across OpenAI, Anthropic, Azure OpenAI, and Google
• 💰 Calculating real-time cost estimates based on current pricing
• 🔒 Running 100% locally — your data never leaves your machine
• 📈 Showing trends and breakdowns by model, day, and application

pip install tokenmeter
# or
pipx install tokenmeter

# Log a usage event manually
tokenmeter log --provider anthropic --model claude-sonnet-4 --input 1500 --output 500

# Import from Claude Code's usage file
tokenmeter import claude-code

# Show today's summary
tokenmeter summary

# Show cost breakdown
tokenmeter costs --period week

# Interactive dashboard
tokenmeter dashboard

• Anthropic (Claude 3.5, 4, Opus, Sonnet, Haiku)
• OpenAI (GPT-4, GPT-4o, o1, o3)
• Azure OpenAI (all deployed models)
• Google (Gemini Pro, Ultra, Flash)

╭─────────────────── tokenmeter ───────────────────╮
│  TODAY  $122.42  (396.9K tokens)                 │
│  WEEK  $1142.22  (3.4M tokens)                   │
╰──────────────────────────────────────────────────╯

Provider   Input   Output  Cache R  Cache W  Total    Cost
───────────────────────────────────────────────────────────
Anthropic  12.2K   384.7K  116.4M   13.1M    396.9K   $122.42

Cache R and Cache W show prompt caching usage:

• Cache Write: Tokens stored in cache (paid once, slightly more expensive)
• Cache Read: Tokens reused from cache (90% cheaper than regular input)

This reveals the true value of OpenClaw/Claude's prompt caching. In this example:

• 116.4M cache reads saved ~$350 vs sending as regular input
• Cache reads are the #1 cost saver for heavy users

• Claude Code usage logs
• OpenAI API response headers
• Custom webhook endpoint for proxy integration

• Daily/weekly/monthly trends
• Cost by model breakdown
• Input vs output token ratios
• Cache token tracking (reads + writes)
• Peak usage hours

tokenmeter tracks prompt caching usage from OpenClaw and Claude:

What is prompt caching?

• Instead of sending your entire context every turn, Claude stores it in cache
• You pay slightly more to WRITE to cache once
• Then pay 90% LESS to READ from cache on subsequent turns

Real-world example:

Without caching: 1 billion tokens × $3/M = $3,000
With caching: 1 billion tokens × $0.30/M = $300
Savings: $2,700

tokenmeter shows both cache reads and writes so you can see exactly how much you're saving.

# Set up pricing (auto-fetched, but customizable)
tokenmeter config --show

# Set budget alerts
tokenmeter alert --daily 5.00 --weekly 25.00

1. Manual logging: Use tokenmeter log after API calls
2. Proxy mode: Run tokenmeter proxy to intercept and log all LLM traffic
3. Import mode: Pull from existing usage logs (Claude Code, etc.)

All data stored in ~/.tokenmeter/usage.db (SQLite).

Each usage record contains:

• provider: The actual API provider (who you're paying)

  • Example: anthropic when using Claude via API
  • Example: openai when using GPT-4

• app: The tool/session that made the request (for organizational purposes)

  • Example: clawdbot - requests from your OpenClaw bot
  • Example: claude-code - requests from Claude Code CLI
  • Example: Custom label you set with --app flag

Common confusion: After migrating from "Clawdbot" to "OpenClaw", you may see sessions labeled app=clawdbot but with provider=anthropic. This is correct — the app label persists from the original session name, while provider shows who's actually billing you.

Note: .openclaw and .clawdbot directories may point to the same data (symlink). Both are imported as app=clawdbot for historical sessions.

• Zero telemetry — nothing sent anywhere
• Local storage only — SQLite database on your machine
• No API keys stored — we only track usage, not credentials
• Open source — audit the code yourself

• VS Code extension
• Prometheus metrics export
• Slack/Discord alerts
• Team usage aggregation (self-hosted)

Found a bug? Have an idea? Open an issue or submit a PR!

If this tool saved you time, please ⭐ star this repo — it helps others find it.

MIT — use it, fork it, improve it.

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Built during a 5 AM coding session because AI bills are getting out of hand. 🌅