What is LangChain vs LangGraph?

The Best Approach: Use Both

When to Choose Each

- Shipping quickly with standard agent patterns

- Agents fit the default loop (model → tools → response)

- You want high-level abstractions over low-level control

Choose LangGraph when:
- You need stateful, cyclic multi-agent systems

- Building long-running business process automation

- You need human-in-the-loop for sensitive workflows

- Your application requires complex state management

Use Cases
- LangChain: RAG pipelines, translation, document processing, straightforward task automation

- LangGraph: Multi-turn conversations, long-running workflows, complex decision-making, multi-agent coordination

Detailed Comparison

- LangChain: Linear orchestration with standard tool-calling loop

- LangGraph: Stateful graph-based execution with cycles and branching

Loop Support
- LangChain: Limited support via built-in agent loop

- LangGraph: Native support for loops and cyclic reasoning

Conditional Branching
- LangChain: Implemented via RunnableMap

- LangGraph: Native support for conditional edges

LangChain is a high-level framework for quickly building AI agents with standard patterns, while LangGraph is a lower-level orchestration framework for stateful, custom multi-agent systems with loops, persistence, and human-in-the-loop control. The best part is they are complementary LangChain agents are built on LangGraph, so you can start high-level and drop down for more control when needed.