LangChain and LlamaIndex answer different questions about agent architecture. LangChain is orchestration-first: you build chains and agents that call tools, and data is one kind of tool among many. It is the right pick when your value comes from multi-step reasoning, API chaining, or decision-making logic. LlamaIndex is data-first: you build a data layer first, then agents query it. It is the right pick when your value comes from your own documents, your extraction quality, or your hybrid retrieval logic.
Pick LangChain if most of your complexity is orchestration. You are wiring together multiple APIs, building decision trees, or composing tool calls in non-trivial ways. Pick LlamaIndex if most of your complexity is the data layer. You have a large corpus of internal documents, you need extraction quality, or you are shipping a search product where data handling is the moat. The hard answer is that they are not interchangeable. Pick based on where the business value actually lives.
In practice, many teams run both. Use LangChain to orchestrate high-level workflows. Use LlamaIndex as the data engine inside a LangChain agent. The two compose cleanly when you treat LlamaIndex as a query engine that LangChain calls as a tool. If you are just prototyping, start with LangChain because the learning curve is more gradual. If you know you own a large data corpus, start with LlamaIndex because the ingestion patterns will pay dividends. The transition is smooth enough that the initial pick does not lock you in.