⚡ TL;DR — 30-Second Verdict
Use LangChain for standard LLM chains, RAG pipelines, and simple agents. Use LangGraph when you need complex multi-step agents with persistent state, branching logic, human-in-the-loop, or multi-agent coordination. Most serious agentic applications should start with LangChain and graduate to LangGraph when they need stateful orchestration. They work best together.
Quick Comparison
| Feature | LangChain | LangGraph |
|---|---|---|
| Purpose | General LLM app framework | Stateful agentic workflows |
| Architecture | Chains, LCEL pipelines | Graph nodes + edges + state |
| State management | Limited (session-level) | First-class persistent state |
| Human-in-the-loop | Basic interrupt support | Native breakpoints + resume |
| Multi-agent | Basic agent routing | Full supervisor/subgraph patterns |
| Learning curve | Moderate | Steeper (graph mental model) |
| Maturity | Mature, stable API | Newer, rapidly evolving |
What Is LangChain?
LangChain is the most widely used LLM application framework, which means the most tutorials, community answers, and third-party integrations. That said, the abstraction layer can feel excessive for simple use cases. My recommendation: use LangChain when you need its integrations (150+ vector stores, document loaders, tools) or when team familiarity matters. For simple chains, LangGraph or even raw API calls are often cleaner.
— AI Nav Editorial Team on LangChain
→ Read the full LangChain review
What Is LangGraph?
LangGraph has found solid traction with 10k+ GitHub stars, indicating real-world adoption beyond early adopters. A useful framework for automating multi-step tasks that would otherwise require manual coordination. Set realistic expectations: autonomous agents work well on well-defined tasks with clear success criteria, and struggle with ambiguous goals. Always run with budget limits set.
— AI Nav Editorial Team on LangGraph
→ Read the full LangGraph review