llm-toll 0.2.0

Lightweight drop-in Python decorator to track costs, monitor token usage, and enforce budget/rate limits for LLM API calls

llm-toll

A lightweight, drop-in Python decorator to track costs, monitor token usage, and enforce budget and rate limits for LLM API calls.

Overview

llm_toll is a developer tool designed for local prototyping and small-scale production scripts. By simply wrapping a function with @track_costs, developers can automatically log token usage, calculate the exact cost of the run in USD, and halt execution if a hard-coded budget or API rate limit is breached.

Features

• Drop-In Decorator — Minimal code intrusion. Just add @track_costs above any function making an LLM call.
• Multi-Provider Support — Built-in pricing matrices for OpenAI, Anthropic, Gemini, and general OpenAI-compatible endpoints.
• Hard Budget Caps — Prevents functions from executing if the cumulative cost exceeds a defined threshold.
• Rate Limiting — Local enforcement of RPM and TPM to prevent HTTP 429 errors.
• Local Persistence — SQLite-backed usage tracking across multiple script runs and days.
• Cost Reporting — Clean, color-coded terminal summary of cost per call and total session cost.

Quick Start

Installation

pip install llm-toll
# or, with uv
uv add llm-toll

Basic Usage (Auto-detect)

For users utilizing standard SDKs, the decorator infers the model and token count from the response object.

from llm_toll import track_costs

@track_costs(project="my_scraper", max_budget=2.00, reset="monthly")
def generate_summary(text):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": text}]
    )
    return response  # Decorator parses the usage from this object

Advanced Usage (Rate Limits & Explicit Models)

For custom setups or raw API requests, users can explicitly state the model and rate limits.

from llm_toll import track_costs

@track_costs(
    model="claude-sonnet-4-20250514",
    rate_limit="50/min",
    tpm_limit="40000/min",
    extract_usage=lambda res: (res['in_tokens'], res['out_tokens'])
)
def custom_anthropic_call(prompt):
    # custom logic here
    pass

Streaming Support

The decorator automatically detects streaming responses (generators). Cost is tracked after the stream is fully consumed.

from llm_toll import track_costs

@track_costs(project="my_app", max_budget=5.00)
def stream_response(text):
    return client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": text}],
        stream=True,
        stream_options={"include_usage": True},  # recommended for accurate counts
    )

for chunk in stream_response("Hello"):
    print(chunk.choices[0].delta.content, end="")
# Cost is logged automatically after the stream completes

Note: For accurate token counts with OpenAI streaming, pass stream_options={"include_usage": True}. Without it, output tokens are estimated using a character-based heuristic.

Supported Providers

Provider	SDK Auto-Parsing	Streaming Support	Custom Model Overrides
OpenAI	Yes (openai client)	Yes (chunk calculation)	Yes
Anthropic	Yes (anthropic client)	Yes	Yes
Google Gemini	Yes (google-genai client)	Yes	Yes
Local/Ollama	No (Cost is $0)	N/A	Rate limiting only

Error Handling

from llm_toll.exceptions import BudgetExceededError, LocalRateLimitError

try:
    result = generate_summary("some text")
except BudgetExceededError as e:
    print(f"Budget exceeded: {e}")
except LocalRateLimitError as e:
    print(f"Rate limit hit: {e}")

Development

# Install dependencies
uv sync --extra dev

# Run tests
uv run pytest

# Lint & format
uv run ruff check .
uv run ruff format .

# Type check
uv run mypy src/llm_toll

License

MIT License — see LICENSE for details.