Reading
A Python library for monitoring and controlling LLM API costs, supporting multi-provider cost tracking, budget allocation and enforcement, real-time monitoring, and expenditure forecasting to help teams effectively manage AI call costs.
Section 01
A Python library for monitoring and controlling LLM API costs, supporting multi-provider cost tracking, budget allocation and enforcement, real-time monitoring, and expenditure forecasting to help teams effectively manage AI call costs.
Section 02
Section 03
Token Budget Agent is a Python library specifically designed for cost management of Large Language Model (LLM) APIs. With the widespread adoption of advanced models like GPT-4, Claude, and Gemini, AI API call costs have become a significant expense for many teams and enterprises. Token Budget Agent was created to provide a comprehensive solution to help users track, control, and forecast LLM API usage costs.
This library supports multiple mainstream AI providers including OpenAI, Anthropic, Google, Mistral, and Meta, and allows users to customize models and pricing rules. Both individual developers and enterprise teams can use this tool to establish effective cost control mechanisms.
Section 04
Token Budget Agent offers a rich set of functional modules covering all aspects of cost management.
Section 05
The library has built-in pricing data for mainstream LLM providers, including GPT-4 series, Claude 3 series, Gemini series, Llama series, and Mistral series. Users can accurately calculate the cost of each API call without manually maintaining price lists.
Cost tracking supports multiple dimensions such as by provider, by model, and by project, allowing users to clearly understand fund flows. This fine-grained tracking capability is crucial for identifying high-cost call sources and optimizing resource allocation.
Section 06
Token Budget Agent allows setting multi-level budget rules:
Budget rules support multiple enforcement actions, including logging only, issuing warnings, triggering callback functions, or directly blocking requests. This flexible configuration allows users to choose the appropriate risk control level based on business needs.
Section 07
The library provides multiple real-time monitoring methods, including context manager and decorator support. Developers can easily wrap existing code to automatically record token usage for each API call without modifying business logic.
The real-time monitoring feature allows teams to instantly grasp the current expenditure status, detect abnormal usage patterns in time, and prevent budget overruns.
Section 08
Based on historical usage data, Token Budget Agent can predict expenditure trends over a future period. This feature is very valuable for financial planning and capacity planning, helping teams prepare for budget adjustments in advance.
The forecasting algorithm considers recent changes in consumption rates and provides expenditure forecasts for different time spans such as 30 days and 90 days.
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