tokenator 0.1.11

Token usage tracking wrapper for LLMs

Tokenator : Track and analyze LLM token usage and cost

Have you ever wondered about :

• How many tokens does your AI agent consume?
• How much does it cost to do run a complex AI workflow with multiple LLM providers?
• How much money/tokens did you spend today on developing with LLMs?

Afraid not, tokenator is here! With tokenator's easy to use API, you can start tracking LLM usage in a matter of minutes.

Get started with just 3 lines of code!

Installation

pip install tokenator

Usage

OpenAI

from openai import OpenAI
from tokenator import tokenator_openai

openai_client = OpenAI(api_key="your-api-key")

# Wrap it with Tokenator
client = tokenator_openai(openai_client)

# Use it exactly like the OpenAI client
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Works with AsyncOpenAI and streaming=True as well! Note : When streaming, don't forget to add stream_options={"include_usage": True} to the create() call!

Cost Analysis

from tokenator import usage

# Get usage for different time periods
usage.last_hour()
usage.last_day()
usage.last_week()
usage.last_month()

# Custom date range
usage.between("2024-03-01", "2024-03-15")

# Get usage for different LLM providers
usage.last_day("openai")
usage.last_day("anthropic")
usage.last_day("google")

Example usage object

print(cost.last_hour().model_dump_json(indent=4))

{
    "total_cost": 0.0004,
    "total_tokens": 79,
    "prompt_tokens": 52,
    "completion_tokens": 27,
    "providers": [
        {
            "total_cost": 0.0004,
            "total_tokens": 79,
            "prompt_tokens": 52,
            "completion_tokens": 27,
            "provider": "openai",
            "models": [
                {
                    "total_cost": 0.0004,
                    "total_tokens": 79,
                    "prompt_tokens": 52,
                    "completion_tokens": 27,
                    "model": "gpt-4o-2024-08-06"
                }
            ]
        }
    ]
}

Features

• Drop-in replacement for OpenAI, Anthropic client
• Automatic token usage tracking
• Cost analysis for different time periods
• SQLite storage with zero configuration
• Thread-safe operations
• Minimal memory footprint
• Minimal latency footprint

Anthropic

from anthropic import Anthropic, AsyncAnthropic
from tokenator import tokenator_anthropic

anthropic_client = AsyncAnthropic(api_key="your-api-key")

# Wrap it with Tokenator
client = tokenator_anthropic(anthropic_client)

# Use it exactly like the Anthropic client
response = await client.messages.create(
    model="claude-3-5-haiku-20241022",
    messages=[{"role": "user", "content": "hello how are you"}],
    max_tokens=20,
)

print(response)

print(usage.last_execution().model_dump_json(indent=4))
"""
{
    "total_cost": 0.0001,
    "total_tokens": 23,
    "prompt_tokens": 10,
    "completion_tokens": 13,
    "providers": [
        {
            "total_cost": 0.0001,
            "total_tokens": 23,
            "prompt_tokens": 10,
            "completion_tokens": 13,
            "provider": "anthropic",
            "models": [
                {
                    "total_cost": 0.0004,
                    "total_tokens": 79,
                    "prompt_tokens": 52,
                    "completion_tokens": 27,
                    "model": "claude-3-5-haiku-20241022"
                }
            ]
        }
    ]
}
"""

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Most importantly, none of your data is ever sent to any server.

License

MIT