LangChain vs LlamaIndex: The Quick Decision

The five-second answer: LlamaIndex if your application is fundamentally "answer questions over my data" (RAG); LangChain if it's a broader LLM application — agents, tool use, multi-step workflows — where retrieval is one component among several. Both can do both; the difference is what each is *optimized* for and how much you fight the framework.

This is the decision-first version. For the full deep dive with code on the RAG use case specifically, see LangChain vs LlamaIndex for RAG applications.

Mental models

LlamaIndex is a *data framework*. Its core abstractions — documents, nodes, indexes, retrievers, query engines — all orbit one job: ingest your data and answer questions over it well. Loading 300+ data sources (LlamaHub), chunking, embedding, retrieval strategies, and response synthesis are first-class and tuned.

LangChain is an *orchestration framework*. Its core abstractions — runnables, chains, tools, and (via LangGraph) stateful graphs — orbit composing LLM calls with everything else. Retrieval exists, but it's one module in a much wider toolbox alongside agents, memory, and integrations with practically everything.

Decision table

Your projectPickWhy

Chat over internal docs / knowledge baseLlamaIndexThe exact center of its design; best retrieval ergonomics out of the box Agent that calls APIs, with some retrievalLangChain (+ LangGraph)Agent loops, tool calling, and state machines are its home turf Complex RAG (multi-index routing, rerankers, citation tracking)LlamaIndexAdvanced retrieval modules are deeper than LangChain's equivalents Multi-step workflow with branching/human-in-the-loopLangGraphExplicit state graphs beat chained abstractions for this Both heavy RAG *and* complex orchestrationBoth — LlamaIndex as the retriever inside a LangChain/LangGraph appOfficial integration exists; common in production Simple app, few LLM callsNeitherProvider SDKs directly; frameworks earn their keep only at complexity

That last row is real advice, not a joke: a straightforward "retrieve top-k, stuff into prompt" flow is ~50 lines against raw SDKs plus a vector client, and you'll understand every line. Frameworks pay off when you need their *depth* — swap-in rerankers, eval hooks, observability — not for hello-world RAG.

What experienced teams actually report

LlamaIndex feels focused; LangChain feels vast. LlamaIndex's surface area maps cleanly to the RAG pipeline. LangChain's breadth means more to learn and historically more abstraction churn — much improved since LCEL/LangGraph stabilized, but the gap in "time to mental model" persists.

Ecosystem pull: LangChain's integration catalog and its observability/eval stack (LangSmith — see our evaluation workflow guide) are the strongest gravity wells; teams often choose LangChain less for the core library than for the surrounding tooling.

Migration is cheap at the edges, expensive at the core. Both wrap the same models and vector stores (Chroma vs Qdrant etc.), so swapping the retrieval layer later is days, not months — don't agonize over the choice for a prototype.

FAQ

Is LlamaIndex only Python? Primarily Python with an official TypeScript port; LangChain has first-class Python and JS. All-TypeScript Next.js teams should also weigh Vercel AI SDK vs LangChain.js.

Does the model choice matter more than the framework? For answer quality — usually yes. Frameworks shape developer experience; the model caps quality. Current options in the model library.

Haystack? The third serious option, strongest in enterprise/on-prem pipelines — covered in LangChain vs LlamaIndex vs Haystack.

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*Last updated: June 2026.*