langchain-architecture

Build multi-step LLM workflows in LangGraph by using StateGraph to orchestrate agent actions, managing application state robustly with Pydantic or TypedDict for reliable data handling across nodes.

Create a ReAct agent using LangChain 1.x to integrate tool access, like database searches, and implement memory systems to maintain conversation history across multiple turns effectively.

State management for custom agents in LangChain works by defining structured state schemas with Pydantic or TypedDict, allowing LangGraph to pass and update context persistently across workflow steps.

You can use LangGraph to orchestrate a multi-agent system for customer support, enabling agents to access a knowledge base, perform database actions via tools, and manage complex user interactions.

You need LangGraph for RAG applications when orchestrating cyclical agent logic, multi-step retrieval, and complex state transitions, whereas standard LangChain handles simpler linear retrieval workflows.

Production deployment considerations for LangChain LLM applications include managing agent state persistence, scaling multi-step workflow orchestration, integrating external tools securely, and maintaining reliable memory systems.