LangChain is the go-to framework for LLM applications. Moving from prototype to production requires robust architecture and error handling.
Why LangChain?
Unified interface for LLM providers, prompt management, chains, and RAG. Modular design for chatbots to multi-agent systems.
Handles provider abstraction, prompts, output parsing, memory, retrieval — you focus on app logic.
Production Architecture
1. Provider Fallback Chain
Primary (GPT-4) → Secondary (Claude) → Tertiary (Gemini). Laurel Jar uses this for 99.9%+ availability.
2. RAG Implementation
LangChain's document loaders, splitters, vector stores. Configure chunk size and retrieval per data type.
3. Caching
Cache identical queries. Supports in-memory, Redis, SQLite. Semantic caching for similar queries.
60%
Cost reduction with caching
3x
Faster responses
Best Practices
- Structured output: PydanticOutputParser for typed responses
- Retry logic: Exponential backoff with jitter
- Prompt versioning: Version-control with code
- Testing: Unit and integration tests with real LLMs
- Monitoring: LangSmith for tracing
💡
Use LCEL. Declarative chain composition with streaming, async, batch support. Easier to test and deploy.
Laurel Jar builds production LangChain apps with enterprise reliability.