llm-usage-tracker 0.1.0

Drop-in token + cost tracker for OpenAI / LiteLLM / Gemini with caching awareness

llm-usage-tracker

A drop-in token and cost tracker for popular LLM libraries with caching awareness. Automatically tracks usage across multiple LLM providers by monkey-patching their API calls.

Features

• Zero-config tracking: Just import and start tracking
• Multi-provider support: OpenAI, LiteLLM, Google Gemini
• Cost calculation: Automatic cost computation based on current pricing
• Session management: Track usage across different sessions
• Caching awareness: Handles cached responses appropriately

Supported Libraries

• OpenAI v1 (client + module-level)
• OpenAI v0 (legacy ChatCompletion.create)
• LiteLLM (optional)
• Google google-generativeai (optional)

Installation

pip install -e .

# With optional dependencies:
pip install -e .[litellm]  # For LiteLLM support
pip install -e .[gemini]   # For Google Gemini support
pip install -e .[all]      # All optional dependencies

Quick Start

from llm_usage_tracker import setup_patch, print_usage_costs

# Enable tracking
setup_patch()

# Your existing OpenAI/LiteLLM/Gemini code works unchanged
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Print usage summary
print_usage_costs()

Usage with Sessions

from llm_usage_tracker import UsageSession

with UsageSession("my-session") as session:
    # Your LLM calls here
    pass

# Get session costs
costs = session.compute_costs()
print(f"Total cost: ${costs['total_cost']:.4f}")

License

MIT