tokenbudget 0.1.2

Lightweight token tracking, cost management, and budget enforcement for LLM API calls

TokenBudget

Stop bleeding money on LLM API calls.

A lightweight Python library for tracking tokens, managing costs, and enforcing budgets across all major LLM providers.

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The Problem

You're building with LLMs and:

• Costs spiral out of control with no visibility
• No idea which API calls are eating your budget
• Production bills that make you cry
• Clunky observability platforms that require external services

There's no simple pip install library that just works.

The Solution

from tokenbudget import TokenTracker, budget

tracker = TokenTracker()
client = tracker.wrap_openai(openai.OpenAI())

# Every call is tracked automatically
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

print(tracker.usage)
# Usage(total_tokens=25, total_cost_usd=0.000375, calls=1)

That's it. No platforms, no external services, no configuration.

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Features

Token Tracking - Automatic tracking for OpenAI, Anthropic, Google Cost Calculation - Built-in pricing database (always up-to-date) Budget Enforcement - Decorators to prevent overspending Response Caching - Save money with zero-cost cached responses Usage Reports - Beautiful tables + CSV/JSON exports Multi-Provider - One tracker for all your LLM calls Thread-Safe - Works seamlessly in concurrent applications Async Support - Works with async clients out of the box

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Installation

# Basic installation
pip install tokenbudget

# With OpenAI support
pip install tokenbudget[openai]

# With Anthropic support
pip install tokenbudget[anthropic]

# With everything
pip install tokenbudget[all]

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Quick Examples

1. Basic Tracking

from tokenbudget import TokenTracker
import openai

tracker = TokenTracker()
client = tracker.wrap_openai(openai.OpenAI())

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is 2+2?"}]
)

print(f"Tokens: {tracker.usage.total_tokens}")
print(f"Cost: ${tracker.usage.total_cost_usd:.6f}")

2. Budget Enforcement

from tokenbudget import budget, BudgetExceeded

@budget(max_cost_usd=1.00, max_tokens=50000)
def my_llm_pipeline(data):
    # All LLM calls inside are tracked
    # Raises BudgetExceeded if limit is hit
    result = process_with_llm(data)
    return result

# Or as context manager
with budget(max_cost_usd=0.50) as ctx:
    response = client.chat.completions.create(...)
    print(f"Remaining: ${ctx.remaining_budget:.4f}")

3. Multi-Provider Tracking

import openai
import anthropic
from tokenbudget import TokenTracker

tracker = TokenTracker()

# Track OpenAI
openai_client = tracker.wrap_openai(openai.OpenAI())
openai_client.chat.completions.create(model="gpt-4o", ...)

# Track Anthropic
anthropic_client = tracker.wrap_anthropic(anthropic.Anthropic())
anthropic_client.messages.create(model="claude-sonnet-4-5", ...)

# Combined reporting
print(f"Total cost: ${tracker.total_cost_usd:.4f}")
print(tracker.usage_by_provider)

4. Response Caching

tracker = TokenTracker(cache="memory")
client = tracker.wrap_openai(openai.OpenAI())

# First call - costs money
response1 = client.chat.completions.create(model="gpt-4o", ...)

# Identical call - FREE (cached)
response2 = client.chat.completions.create(model="gpt-4o", ...)

stats = tracker.cache_stats
print(f"Saved: ${stats.saved_cost_usd:.4f}")

5. Usage Reports

from tokenbudget import generate_table_report

print(generate_table_report(tracker))

# Output:
# ┌─────────────────────────────────────┐
# │ TokenBudget Usage Report            │
# ├─────────────────────────────────────┤
# │ Provider   │ Calls │ Tokens │ Cost  │
# │ openai     │   15  │ 12.3k  │ $0.24 │
# │ anthropic  │    8  │  8.1k  │ $0.18 │
# ├─────────────────────────────────────┤
# │ Total      │   23  │ 20.4k  │ $0.42 │
# └─────────────────────────────────────┘

# Export to CSV/JSON
tracker.export_csv("usage.csv")
tracker.export_json("usage.json")

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Supported Models

OpenAI gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo, o1, o1-mini, o3-mini

Anthropic claude-opus-4-5, claude-sonnet-4-5, claude-haiku-4-5, claude-3-5-sonnet, claude-3-opus

Google gemini-2.0-flash, gemini-2.0-pro, gemini-1.5-pro, gemini-1.5-flash

Need a custom model? Easy:

from tokenbudget import register_model

register_model(
    "my-custom-model",
    input_per_1k=0.001,
    output_per_1k=0.002,
    provider="custom"
)

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API Reference

TokenTracker

tracker = TokenTracker(cache=None)  # cache: "memory", "disk", or None

Methods:

• wrap_openai(client) - Wrap OpenAI client
• wrap_anthropic(client) - Wrap Anthropic client
• track(model, prompt_tokens, completion_tokens, provider) - Manual tracking
• reset() - Reset all statistics

Properties:

• usage - Overall usage stats
• usage_by_provider - Per-provider breakdown
• total_cost_usd - Total cost across all calls
• cache_stats - Cache hit/miss statistics

Budget Enforcement

@budget(max_cost_usd=None, max_tokens=None, tracker=None)
def my_function():
    ...

# Or as context manager
with budget(max_cost_usd=1.0) as ctx:
    ...
    print(ctx.remaining_budget)
    print(ctx.current_usage)

Exceptions:

• BudgetExceeded - Cost limit exceeded
• TokenLimitReached - Token limit exceeded

Pricing

from tokenbudget import get_price, register_model, calculate_cost

# Get model pricing
price = get_price("gpt-4o")
print(price.input_per_1k, price.output_per_1k)

# Calculate cost
cost = calculate_cost("gpt-4o", input_tokens=1000, output_tokens=500)

# Register custom model
register_model("my-model", input_per_1k=0.001, output_per_1k=0.002)

Reports

from tokenbudget import generate_table_report, export_csv, export_json

# Pretty table
print(generate_table_report(tracker))

# Export
export_csv(tracker, "usage.csv")
export_json(tracker, "usage.json")

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Custom Providers

Support any LLM provider:

from tokenbudget.providers.custom import CustomProvider

custom = CustomProvider(
    tracker=tracker,
    provider_name="my-llm-service",
    extract_model=lambda r: r["model"],
    extract_prompt_tokens=lambda r: r["usage"]["input"],
    extract_completion_tokens=lambda r: r["usage"]["output"],
)

# Track your custom response
custom.track(api_response)

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Contributing

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

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License

MIT License - see LICENSE file for details.

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Author

Built by a developer tired of surprise LLM bills.

If this saved you money, consider starring the repo.