Have you ever wondered :
- How many tokens does your AI agent consume?
- How much does it cost to run a complex AI workflow with multiple LLM providers?
- Which LLM is more cost effective for my use case?
- How much money/tokens did you spend today on developing with LLMs?
Afraid not, tokenator is here! With tokenator's easy to use functions, you can start tracking LLM usage in a matter of minutes.
Get started with just 3 lines of code!
Tokenator supports the official SDKs from openai, anthropic and google-genai(the new one). LLM providers which use the openai SDK like perplexity, deepseek and xAI are also supported.
pip install tokenatorfrom 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!
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")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"
}
]
}
]
}Want more code, example use cases and ideas? Check out our amazing cookbooks!
- 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
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"
}
]
}
]
}
"""from google import genai
from tokenator import tokenator_gemini
gemini_client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
# Wrap it with Tokenator
client = tokenator_gemini(gemini_client)
# Use it exactly like the google-genai client
response = models.generate_content(
model="gemini-2.0-flash",
contents="hello how are you",
)
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": "gemini",
"models": [
{
"total_cost": 0.0004,
"total_tokens": 79,
"prompt_tokens": 52,
"completion_tokens": 27,
"model": "gemini-2.0-flash"
}
]
}
]
}
"""You can use xAI models through the openai SDK and track usage using provider parameter in tokenator.
from openai import OpenAI
from tokenator import tokenator_openai
xai_client = OpenAI(
api_key=os.getenv("XAI_API_KEY"),
base_url="https://api.x.ai/v1"
)
# Wrap it with Tokenator
client = tokenator_openai(xai_client, db_path=temp_db, provider="xai")
# Use it exactly like the OpenAI client but with xAI models
response = client.chat.completions.create(
model="grok-2-latest",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response)
print(usage.last_execution())Today, a variety of AI companies have made their APIs compatible to the openai SDK.
You can track usage of any such AI models using tokenator's provider parameter.
For example, let's see how we can track usage of perplexity tokens.
from openai import OpenAI
from tokenator import tokenator_openai
perplexity_client = OpenAI(
api_key=os.getenv("PERPLEXITY_API_KEY"),
base_url="https://api.perplexity.ai"
)
# Wrap it with Tokenator
client = tokenator_openai(perplexity_client, db_path=temp_db, provider="perplexity")
# Use it exactly like the OpenAI client but with perplexity models
response = client.chat.completions.create(
model="sonar",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response)
print(usage.last_execution())
print(usage.provider("perplexity"))Most importantly, none of your data is ever sent to any server.
MIT