LangChain in Production: Best Practices, Pitfalls, and Performance Optimization

LangChain is powerful but requires careful configuration for production. Key best practices: 1) Use LCEL (LangChain Expression Language) for all chains - provides built-in async support, streaming, retries, and better observability. 2) Implement caching: InMemoryCache for development, RedisCache for production. Cache both LLM calls (same prompt = same response) and embeddings. Can reduce costs 40-60% for repetitive queries. 3) Streaming responses: use .astream() for real-time token delivery - critical for UX in chat applications. 4) Observability with LangSmith: wrap chains with tracing to see every LLM call, token usage, latency, and errors. Essential for debugging complex chains. 5) Error handling: implement retry logic with exponential backoff, fallback to cheaper/smaller models on rate limits, timeout handling. 6) Token management: validate input length before API calls, implement truncation strategy for context overflow. 7) Async everything: use async/await throughout, avoid blocking sync calls in async context. Common anti-patterns: creating new LLM instances per request (expensive), not using connection pooling for vector stores, missing error boundaries in chains, building chains without streaming support.