LangChain abstracts LLM application patterns into reusable components. Chains combine operations sequentially. Agents make dynamic decisions. Memory persists conversation context. Understanding these patterns accelerates development.
Core Components
Models wrap different LLM providers. Prompts template dynamic inputs. Chains sequence multiple operations. Agents select tools based on inputs. Memory maintains conversation state.
- Use chains for sequential LLM operations
- Implement agents for dynamic tool selection
- Add memory for conversational applications
- Create custom tools extending agent capabilities
- Use retrieval chains for RAG applications
When to Use LangChain
LangChain accelerates prototyping and common patterns. Complex applications may outgrow abstractions. Evaluate whether abstractions help or hinder your specific use case.