LangChain Performance Tuning

About

This skill provides comprehensive strategies and implementation patterns to speed up LangChain-based AI applications. It helps developers move beyond simple prototypes to production-ready systems by implementing response caching (In-memory, SQLite, Redis), optimizing batch and async processing, and enabling token-aware prompt truncation. It also includes tools for benchmarking current performance and intelligent model routing to balance cost, quality, and speed effectively, ensuring your LLM pipelines are both fast and cost-efficient.

Key Features

• Performance benchmarking and latency measurement tools
• Token-aware prompt optimization and model routing strategies
• Optimized batch and async processing for high throughput
• Streaming response implementation for improved perceived latency
• Multi-level response caching (In-memory, SQLite, Redis)
• 983 GitHub stars

Use Cases

• Reducing API response times in customer-facing LLM applications
• Processing large datasets efficiently using batching and parallelism
• Minimizing costs and latency by routing simpler tasks to smaller models