Monitors and analyzes LLM token usage and cache effectiveness to optimize costs in multi-agent AI systems.
The LLM Cache & Cost Tracking skill provides comprehensive observability for AI applications by integrating Langfuse to monitor token usage, financial expenses, and cache performance. It enables developers to track hierarchical cost rollups across complex multi-agent workflows, attribute expenses to specific agents, and measure the ROI of semantic and prompt caching strategies. By providing real-time insights into cache hit rates and total financial savings, this skill helps teams manage their LLM budget effectively and ensure production-ready cost efficiency through detailed metadata attribution and historical reporting.
主要功能
01Detailed cost-by-agent reporting and historical queries
02Automatic token and cost tracking via Langfuse decorators
0369 GitHub stars
04Real-time cache effectiveness and hit-rate monitoring
05Support for L1, L2, and provider-native prompt caching analysis
06Hierarchical trace grouping for multi-agent cost attribution
使用场景
01Monitoring production LLM budgets and identifying high-cost prompt patterns
02Attributing LLM spend to specific agents or users in a multi-agent application
03Validating the financial impact of implementing semantic or prompt caching
