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Llm Token Tracker

Token usage tracker for OpenAI and Claude APIs with MCP support, real-time session tracking, and accurate pricing for 2025 models

developer-toolsapiaillm
By wn01011
32Updated 7 months agoTypeScriptMIT

Installation

npx -y llm-token-tracker

Configuration

{
  "mcpServers": {
    "llm-token-tracker": {
      "command": "npx",
      "args": ["-y", "llm-token-tracker"]
    }
  }
}

How to use

  1. Run the installation command above (if needed)
  2. Open your Claude Code settings file (~/.claude/settings.json)
  3. Add the configuration to the mcpServers section
  4. Restart Claude Code to apply changes

LLM Token Tracker 🧮

Token usage tracker for OpenAI, Claude, and Gemini APIs with MCP (Model Context Protocol) support. Pass accurate API costs to your users.

npm version License: MIT

<a href="https://glama.ai/mcp/servers/@wn01011/llm-token-tracker"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@wn01011/llm-token-tracker/badge" alt="llm-token-tracker MCP server" /> </a>

✨ Features

  • šŸŽÆ Simple Integration - One line to wrap your API client
  • šŸ“Š Automatic Tracking - No manual token counting
  • šŸ’° Accurate Pricing - Up-to-date pricing for all models (2025)
  • šŸ”„ Multiple Providers - OpenAI, Claude, and Gemini support
  • šŸ“ˆ User Management - Track usage per user/session
  • 🌐 Currency Support - USD and KRW
  • šŸ¤– MCP Server - Use directly in Claude Desktop!
  • šŸ†• Intuitive Session Tracking - Real-time usage with progress bars

šŸ“¦ Installation

npm install llm-token-tracker

šŸš€ Quick Start

Option 1: Use as Library

const { TokenTracker } = require('llm-token-tracker');
// or import { TokenTracker } from 'llm-token-tracker';

// Initialize tracker
const tracker = new TokenTracker({
  currency: 'USD' // or 'KRW'
});

// Example: Manual tracking
const trackingId = tracker.startTracking('user-123');

// ... your API call here ...

tracker.endTracking(trackingId, {
  provider: 'openai', // or 'anthropic' or 'gemini'
  model: 'gpt-3.5-turbo',
  inputTokens: 100,
  outputTokens: 50,
  totalTokens: 150
});

// Get user's usage
const usage = tracker.getUserUsage('user-123');
console.log(`Total cost: $${usage.totalCost}`);

šŸ”§ With Real APIs

To use with actual OpenAI/Anthropic APIs:

const OpenAI = require('openai');
const { TokenTracker } = require('llm-token-tracker');

const tracker = new TokenTracker();
const openai = tracker.wrap(new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
}));

// Use normally - tracking happens automatically
const response = await openai.chat.completions.create({
  model: "gpt-3.5-turbo",
  messages: [{ role: "user", content: "Hello!" }]
});

console.log(response._tokenUsage);
// { tokens: 125, cost: 0.0002, model: "gpt-3.5-turbo" }

Option 2: Use as MCP Server

Add to Claude Desktop settings (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "token-tracker": {
      "command": "npx",
      "args": ["llm-token-tracker"]
    }
  }
}

Then in Claude:

  • "Calculate current session usage" - See current session usage with intuitive format
  • "Calculate current conversation cost" - Get cost breakdown with input/output tokens
  • "Track my API usage"
  • "Compare costs between GPT-4 and Claude"
  • "Show my total spending today"

Available MCP Tools

  1. get_current_session - šŸ†• Get current session usage (RECOMMENDED)

    • Returns: Used/Remaining tokens, Input/Output breakdown, Cost, Progress bar
    • Default user_id: current-session
    • Default budget: 190,000 tokens
    • Perfect for real-time conversation tracking!
  2. track_usage - Track token usage for an AI API call

    • Parameters: provider, model, input_tokens, output_tokens, user_id
  3. get_usage - Get usage summary for specific user or all users

  4. compare_costs - Compare costs between different models

  5. clear_usage - Clear usage data for a user

Example MCP Output

šŸ’° Current Session
━━━━━━━━━━━━━━━━━━━━━━
šŸ“Š Used: 62,830 tokens (33.1%)
✨ Remaining: 127,170 tokens
[ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘ā–‘]

šŸ“„ Input: 55,000 tokens
šŸ“¤ Output: 7,830 tokens
šŸ’µ Cost: $0.2825
━━━━━━━━━━━━━━━━━━━━━━

šŸ“‹ Model Breakdown:
  • anthropic/claude-sonnet-4.5: 62,830 tokens ($0.2825)

šŸ“Š Supported Models & Pricing (Updated 2025)

OpenAI (2025)

ModelInput (per 1K tokens)Output (per 1K tokens)Notes
GPT-5 Series
GPT-5$0.00125$0.010Latest flagship model
GPT-5 Mini$0.00025$0.0010Compact version
GPT-4.1 Series
GPT-4.1$0.0020$0.008Advanced reasoning
GPT-4.1 Mini$0.00015$0.0006Cost-effective
GPT-4o Series
GPT-4o$0.0025$0.010Multimodal
GPT-4o Mini$0.00015$0.0006Fast & cheap
o1 Reasoning Series
o1$0.015$0.060Advanced reasoning
o1 Mini$0.0011$0.0044Efficient reasoning
o1 Pro$0.015$0.060Pro reasoning
Legacy Models
GPT-4 Turbo$0.01$0.03
GPT-4$0.03$0.06
GPT-3.5 Turbo$0.0005$0.0015Most affordable
Media Models
DALL-E 3$0.040 per image-Image generation
Whisper$0.006 per minute-Speech-to-text

Anthropic (2025)

ModelInput (per 1K tokens)Output (per 1K tokens)Notes
Claude 4 Series
Claude Opus 4.1$0.015$0.075Most powerful
Claude Opus 4$0.015$0.075Flagship model
Claude Sonnet 4.5$0.003$0.015Best for coding
Claude Sonnet 4$0.003$0.015Balanced
Claude 3 Series
Claude 3.5 Sonnet$0.003$0.015
Claude 3.5 Haiku$0.00025$0.00125Fastest
Claude 3 Opus$0.015$0.075
Claude 3 Sonnet$0.003$0.015
Claude 3 Haiku$0.00025$0.00125Most affordable

Google Gemini (2025)

ModelInput (per 1K tokens)Output (per 1K tokens)Notes
Gemini 2.0 Series
Gemini 2.0 Flash (Exp)FreeFreeExperimental preview
Gemini 2.0 Flash ThinkingFreeFreeReasoning preview
Gemini 1.5 Series
Gemini 1.5 Pro$0.00125$0.005Most capable
Gemini 1.5 Flash$0.000075$0.0003Fast & efficient
Gemini 1.5 Flash-8B$0.0000375$0.00015Ultra-fast
Gemini 1.0 Series
Gemini 1.0 Pro$0.0005$0.0015Legacy model
Gemini 1.0 Pro Vision$0.00025$0.0005Multimodal
Gemini Ultra$0.002$0.006Premium tier

Note: Prices shown are per 1,000 tokens. Batch API offers 50% discount. Prompt caching can reduce costs by up to 90%.

šŸŽÆ Examples

Run the example:

npm run example

Check examples/basic-usage.js for detailed usage patterns.

šŸ“ API Reference

new TokenTracker(config)

  • config.currency: 'USD' or 'KRW' (default: 'USD')
  • config.webhookUrl: Optional webhook for usage notifications

tracker.wrap(client)

Wrap an OpenAI or Anthropic client for automatic tracking.

tracker.forUser(userId)

Create a user-specific tracker instance.

tracker.startTracking(userId?, sessionId?)

Start manual tracking session. Returns tracking ID.

tracker.endTracking(trackingId, usage)

End tracking and record usage.

tracker.getUserUsage(userId)

Get total usage for a user.

tracker.getAllUsersUsage()

Get usage summary for all users.

šŸ›  Development

# Install dependencies
npm install

# Build TypeScript
npm run build

# Watch mode
npm run dev

# Run examples
npm run example

šŸ“„ License

MIT

šŸ¤ Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

šŸ› Issues

For bugs and feature requests, please create an issue.

šŸ“¦ What's New in v2.4.0

  • šŸŽ‰ Gemini API Support - Full integration with Google's Gemini models
  • šŸ’Ž Gemini 2.0 Support - Free preview models included
  • šŸ“Š Enhanced Pricing - Up-to-date Gemini 1.5 and 2.0 pricing
  • šŸ”§ Auto-detection - Automatic Gemini client wrapping
  • šŸ’° Cost Comparison - Compare Gemini with OpenAI and Claude

šŸ“¦ What's New in v2.3.0

  • šŸ’± Real-time exchange rates - Automatic USD to KRW conversion
  • 🌐 Uses exchangerate-api.com for accurate rates
  • šŸ’¾ 24-hour caching to minimize API calls
  • šŸ“Š New get_exchange_rate tool to check current rates
  • šŸ”„ Background auto-updates with fallback support

What's New in v2.2.0

  • šŸ—„ļø File-based persistence - Session data survives server restarts
  • šŸ’¾ Automatic saving to ~/.llm-token-tracker/sessions.json
  • šŸ”„ Works for both npm and local installations
  • šŸ“Š Historical data tracking across sessions
  • šŸŽÆ Zero configuration required - just works!

What's New in v2.1.0

  • šŸ†• Added get_current_session tool for intuitive session tracking
  • šŸ“Š Real-time progress bars and visual indicators
  • šŸ’° Enhanced cost breakdown with input/output token separation
  • šŸŽØ Improved formatting with thousands separators
  • šŸ”§ Better default user_id handling (current-session)

Built with ā¤ļø for developers who need transparent AI API billing.

View source on GitHub