LangGraph vs LlamaIndex
One is built for agent control flow, the other for connecting agents to your data. They overlap less than the name comparisons suggest.
People compare LangGraph and LlamaIndex as if they are rivals, but they started from different problems. LangGraph is about orchestrating an agent's control flow. LlamaIndex is about retrieval: connecting an agent to your documents, databases, and knowledge bases. Many teams use both.
What each one is great at
LangGraph's strength is the loop: state, branching, checkpoints, and human approval. If the hard part of your agent is deciding what to do next and surviving across steps, LangGraph is built for that. LlamaIndex's strength is the data: parsing documents, chunking, embedding, indexing, and retrieving the right context. If the hard part of your agent is reading your own data well, LlamaIndex leads.
Where they overlap
Both now ship an agent layer. LlamaIndex has an event-driven Workflows API that can orchestrate agents, and LangGraph can call retrieval tools. So you can build a retrieval agent in either one. The question is which hard part dominates your project.
Using them together
A common production pattern is LlamaIndex for the retrieval pipeline and LangGraph for the agent orchestration on top. LlamaIndex fetches and ranks the right context; LangGraph decides what the agent does with it, when to ask a human, and how to persist the run. They are complementary more than competitive.
How to choose
If your project is mostly question-answering over your own documents, start with LlamaIndex. If it is a multi-step agent that takes actions and needs control and persistence, start with LangGraph. If it is both, expect to use both, and let the retrieval needs and the control-flow needs each pick their own tool.