The framing of "LangChain vs LlamaIndex" is slightly misleading, because these two started as answers to different questions and have been growing toward each other ever since. Choosing well means understanding what each was built to do first, because that's still what each does best.
Here's how we decide when we start a project.
They come from opposite ends of the problem
LlamaIndex began as a data framework. Its core question is: you have a pile of documents, how do you get the right pieces of them in front of a language model? Everything good about it radiates out from that — ingestion, chunking, indexing, and retrieval strategies that go well beyond naive vector search. If your problem is fundamentally "answer questions over my data," LlamaIndex was designed for exactly that shape.
LangChain began as an orchestration framework. Its core question is: how do you chain model calls, tools and logic into an application? Its strength is composition — agents that call tools, multi-step chains, and increasingly LangGraph for stateful, branching agent workflows with real control flow. If your problem is "an agent that does things," LangChain has the richer machinery for it.
That origin difference still predicts which one feels natural for a given task, even though both have since grown into the other's territory.
Where they overlap now
LlamaIndex has added agents and tool-calling. LangChain has extensive retrieval and RAG support. So for a straightforward RAG chatbot, either one will get you there, and the choice comes down to which model of the world you'd rather work in.
The honest truth: for a simple retrieve-then-answer application, the framework is not what determines whether it's good. Your data quality, chunking strategy and retrieval relevance matter far more than which library wraps them. Teams agonise over this decision and then ship a mediocre bot because the source documents were messy. Spend the energy there.
When LlamaIndex is the better fit
- Retrieval is the hard part. You need more than top-k vector search — hierarchical indexes, summary indexes, hybrid keyword-plus-vector retrieval, or query routing across multiple sources.
- The application is document-centric. Search, question-answering, or knowledge-base assistants where the value is in getting the right context.
- You want strong retrieval with less orchestration boilerplate. For pure RAG, it tends to get you to a good result with fewer moving parts.
When LangChain is the better fit
- The agent does more than answer. It calls APIs, updates records, runs multi-step processes, or coordinates several tools.
- You need real control flow. LangGraph gives you stateful graphs with branching, loops, retries and human-in-the-loop checkpoints — the things that separate a demo agent from one that survives production.
- Retrieval is one component, not the whole app. RAG is a step inside a larger workflow rather than the point of it.
Using both is common, and fine
This is the part the "vs" framing hides: plenty of production systems use LlamaIndex for the retrieval layer and LangChain (or LangGraph) for the orchestration around it. LlamaIndex finds the right context; LangChain decides what to do with it and what happens next. They interoperate, and treating them as mutually exclusive is a false choice.
If you're building something with a genuinely hard retrieval problem and genuinely complex agent logic, using each for its strength is often the right architecture rather than a compromise.
The decision table
| If this is true | Lean toward |
|---|---|
| The core job is Q&A over your documents | LlamaIndex |
| Retrieval needs to be sophisticated | LlamaIndex |
| The agent takes actions and calls tools | LangChain |
| You need branching, state or human checkpoints | LangChain / LangGraph |
| Hard retrieval and complex orchestration | Both, each for its strength |
| It's a simple RAG bot and you'll ship either | Whichever your team knows |
What actually determines success
Neither framework will save a system built on bad foundations. The things that decide whether an AI application works in production are mostly framework-independent: the quality of your source data, whether your retrieval surfaces the relevant context rather than merely similar text, whether you have an evaluation harness to tell if a change helped, and whether you've handled the failure cases. Pick the framework that fits the shape of your problem, then spend your real effort on those.
We build production RAG systems and agents in both LangChain and LlamaIndex — and we'll tell you when the framework is the least important decision on the table. Tell us what you're building.
Related: LangChain development services · LLM integration & RAG development · Why AI agent prototypes fail in production