The short version
LangChain and LlamaIndex overlap, but their centers of gravity differ. LangChain is an agent framework: you compose a model, tools, a prompt and middleware, and its agents run on LangGraph. LlamaIndex is a data framework for agents: it is strongest at turning messy documents into something a model can search and cite accurately.
Ask where your project is hard. If it is retrieval quality, start with LlamaIndex. If it is tool orchestration and control, start with LangChain.
Where LangChain wins
Agents and tools. LangChain’s create_agent gives you a configurable agent loop with tool calling, structured output and model choice. Because it runs on LangGraph, you get durable execution, persistence and human-in-the-loop when you need them.
Middleware. Guardrails, retries, model routing and custom tool policies plug in as middleware instead of being written into every agent. That keeps production behavior consistent.
TypeScript. LangChain maintains both Python and TypeScript libraries. For a team building in Node.js or Next.js, this is often decisive.
Tracing and evaluation. LangSmith gives step-by-step traces and evaluation datasets, which is how you catch regressions when prompts or models change.
Where LlamaIndex wins
Retrieval quality. LlamaIndex ships many index types, query engines, rerankers and retrieval patterns. Trying hybrid search, hierarchical retrieval or sub-question decomposition is a configuration change, not a rewrite.
Document parsing. LlamaParse, LlamaIndex’s hosted document platform, handles scans, complex tables, forms and slide decks that break basic PDF loaders. Bad parsing is the most common reason RAG assistants give wrong answers, so this matters.
Workflows over data. Event-driven Workflows combine agents, connectors and tools with branching, retries and human review, which suits document-heavy processes like AI document data extraction.
Fast experiments. The high-level API gets a working query engine running in a few lines, and the lower-level API lets you replace any component later.
What we recommend
For a RAG knowledge assistant over complex documents, we usually reach for LlamaIndex for ingestion and retrieval. For agents that act across business systems, we use LangChain or LangGraph, often calling a LlamaIndex retriever as one tool. For simple assistants, we sometimes use neither and call the model SDK directly.
Before choosing a framework, decide whether retrieval or fine-tuning fits the problem (RAG vs fine-tuning) and budget with the cost of a RAG knowledge assistant.