LangChain vs LlamaIndex
Anyone building a RAG app or an LLM agent hits both of these fast. LangChain is the broad toolkit. It gives you chains, agents, tools, memory, and integrations for building most kinds of LLM apps. LlamaIndex is narrower on purpose. It is built around getting your data indexed, retrieved, and queried well.
| Dimension | LangChain | LlamaIndex |
|---|---|---|
| What it is | A broad framework for LLM apps and agents | A framework focused on data and retrieval for RAG |
| License | Open source, with a commercial platform available | Open source, with a commercial platform available |
| Center of gravity | Chains, agents, tools, orchestration | Indexing, retrieval, query engines |
| Languages | Python and JavaScript/TypeScript | Python and TypeScript |
| Integrations | Very large catalog of models, stores, and tools | Strong on data loaders and vector stores |
| Agents and orchestration | First-class, with LangGraph for graph-based flows | Present, but retrieval comes first |
| RAG ergonomics | Flexible, with more wiring to do yourself | Opinionated, with less code for good retrieval |
| Best for | Multi-step apps and agent workflows | Search and Q&A over your own documents |
You are building something bigger than retrieval. An agent that calls tools, a multi-step workflow, or an app that talks to many models and services. LangChain gives you the orchestration pieces and the widest integration catalog. LangGraph handles stateful, branching flows when a simple chain runs out of room.
Your core problem is retrieval quality over your own data, and you want good defaults without wiring everything by hand. LlamaIndex gets you from documents to a working query engine quickly. Data loaders and indexing choices are the main event here.
For a plain \"answer questions over my documents\" build, I reach for LlamaIndex first. The retrieval defaults are stronger and you write less glue. For an agent or a multi-step app with tools and branching, I reach for LangChain, and LangGraph once state gets real. The common mistake is treating this as either/or. The two overlap heavily, and people happily use LlamaIndex for the retrieval layer inside a LangChain app. Do not agonize over the framework. What decides whether your RAG is good is your chunking, your retrieval, and your evals. Neither library saves you from getting those right.