Both are from the same company. But they solve very different problems. Here's when to use each — based on building real AI agents for Indian businesses.
In practice — we use both together. LangChain for components (tools, retrievers, prompts) and LangGraph for the agent orchestration layer. LangGraph is the newer, more powerful choice for building production AI agents.
LangChain is the original AI application framework — a comprehensive library for building LLM-powered applications. It provides chains, agents, retrievers, memory, and 100+ integrations out of the box. It's the standard starting point for most AI developers.
LangGraph is a newer library built on top of LangChain specifically for creating stateful, multi-actor AI agent applications. It models agents as graph nodes with explicit state management — making complex agent flows predictable and controllable in production.
For a real estate agency's WhatsApp chatbot — we use LangChain. It's a single-agent system that retrieves property info from a vector database and responds to queries. LangChain's RAG components handle this cleanly without the complexity of LangGraph.
For a sales AI agent that researches leads, drafts personalised outreach emails, schedules follow-ups, and updates the CRM — we use LangGraph. This agent has multiple states (research, write, schedule, update), needs to remember context across steps, and must handle errors gracefully at each stage. LangGraph's stateful graph model makes this reliable in production.
For a document processing agent that reads legal documents, extracts clauses, flags risks, and generates a summary report — we use LangGraph with LangChain components. LangGraph orchestrates the multi-step process; LangChain provides the document loaders and retrievers.