LangChain vs LangGraph Config
Understand the key differences, performance implications, and scaling paths for your AI stack.
Choosing between LangChain and LangGraph depends heavily on your architecture, model requirements, and scale.
Comparison Overview
While LangChain excels in certain scenarios, LangGraph is often preferred for different use cases. Understanding their fundamental design is critical for your AI stack.
When to use LangChain
- When you need strict local control.
- If your team is already deeply familiar with its ecosystem.
When to use LangGraph
- When you want managed infrastructure or scaling.
- If you are starting a completely greenfield AI project.