Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Aug 13·5 min read
LangChain vs LangGraph: 4 Key Differences and When to Use Each
TL;DR
LangChain and LangGraph are two frameworks designed for building agentic workflows, each with distinct features. Understanding their differences helps developers choose the right tool for their specific needs.
✦ Why It Matters
Evaluate your project needs today to choose between LangChain and LangGraph effectively.
Key Takeaways
- Use LangChain for linear, predictable workflows.
- Opt for LangGraph when loops and state management are needed.
- Implement dynamic interrupts for human approval in LangGraph.
- Utilize checkpointers in LangGraph to resume workflows after failures.
How It Works
LangChain operates as a pipeline where components are chained together in a linear fashion, while LangGraph uses a graph structure with nodes and edges to allow for complex interactions and state management. This enables developers to create workflows that can loop back and forth, maintaining context and state throughout the process.