LlamaIndex and LangChain are two open-source Python frameworks for building retrieval-augmented generation (RAG). LlamaIndex is a data framework…
See the core focus that shapes how each framework handles RAG.
LlamaIndex is a data framework: its center of gravity is getting your documents in, indexing them well, and answering questions over them. Loaders, indices, retrievers, and query engines are first-class, so a solid RAG pipeline is a few lines.
LangChain is a general orchestration framework: chains sequence steps, agents decide steps at run time, and a large integration ecosystem connects models, tools, and stores. RAG is one thing it does among many, so it gives you more flexibility and more moving parts.
Compare the two frameworks on the criteria that matter for a RAG build.
Both can build excellent RAG, and their feature sets overlap heavily. The difference is emphasis: how quickly you get retrieval working, versus how much orchestration you can layer on around it.
Criterion | LlamaIndex | LangChain -------------------|---------------------|-------------------- Primary focus | data / retrieval | orchestration RAG setup speed | very fast | fast, more wiring Indexing options | rich, first-class | via integrations Agents / workflows | growing | extensive Ecosystem breadth | retrieval-centric | very broad Best fit | search / Q&A apps | multi-step agents
Read across each row: LlamaIndex leads on indexing and fast retrieval setup; LangChain leads on agents, workflows, and ecosystem breadth. Neither is strictly better — the right pick follows how retrieval-heavy versus orchestration-heavy your app is.
Turn the comparison into a clear decision rule.
The rule: if the app is mostly retrieval — search, document Q&A, a knowledge base over your data — start with LlamaIndex, because it gets high-quality retrieval working with the least effort. If the app is a multi-step or agentic workflow where RAG is one tool among tool calls, branching, and memory, reach for LangChain (often with LangGraph for control flow).
When unsure, prototype the retrieval in LlamaIndex first; you can always wrap it in a larger LangChain workflow later.
Use the two together and steer clear of common RAG mistakes either way.
The two are not mutually exclusive. A common pattern uses LlamaIndex for ingestion, indexing, and retrieval, then exposes its query engine as a tool inside a LangChain (or LangGraph) agent that handles the broader workflow. Choose per layer, not per project.
Most RAG quality problems are framework-independent: poor chunking, a weak embedding model, no reranking, and no evaluation. Picking LlamaIndex or LangChain will not fix bad retrieval. Get chunking, embeddings, and an eval loop right first, then let the framework choice follow your orchestration needs.
LlamaIndex and LangChain both build RAG. LlamaIndex is a data framework that makes ingestion, indexing, and retrieval fast and first-class; LangChain is an orchestration framework whose chains and agents suit complex workflows where RAG is one part. Rule: retrieval-centric app to LlamaIndex, multi-step agentic app to LangChain, and combine them by exposing LlamaIndex retrieval as a tool in a LangChain agent. Most RAG quality issues are framework-independent.
Take a RAG app idea — say an internal-docs assistant that also files tickets. Split it into a retrieval layer and an orchestration layer, choose LlamaIndex or LangChain for each, and write the one-line rule you used to decide.
What is the main difference between LlamaIndex and LangChain for RAG?
LlamaIndex specializes in ingest-index-query for retrieval, while LangChain provides general orchestration (chains, agents, integrations) with RAG as one capability.
When should I choose LlamaIndex for a RAG app?
LlamaIndex's retrieval focus makes it the quickest path for search and Q&A over your documents.
When is LangChain the better fit?
LangChain's orchestration strengths (often with LangGraph) suit complex workflows where retrieval is one part of a larger agent.
Can LlamaIndex and LangChain be used together?
They compose per layer: LlamaIndex handles ingestion and retrieval, LangChain handles the broader workflow, so you can pick the best of each.