LangChain vs LlamaIndex: What’s the Difference
LangChain is a general LLM-application framework; LlamaIndex is focused specifically on data ingestion and retrieval — here’s when each one actually fits
LangChain is the general-purpose framework for building LLM applications; LlamaIndex specializes specifically in getting your own data ready for retrieval. They overlap in the RAG space, but they weren’t built to solve the same problem.
LangChain: broad, general-purpose
LangChain is built around chains, agents, and tool use, with over 700 integrations across models, vector stores, and external APIs. It’s the more general framework — if you’re building something with multi-step reasoning, tool calls, or an agent that needs to decide what to do next, LangChain’s abstractions are built for that breadth.
LlamaIndex: focused on your data
LlamaIndex is built around one problem specifically: getting your own data — documents, databases, APIs — into a form an LLM can retrieve from effectively. Its indexing strategies, data connectors, and query engines are more purpose-built for retrieval quality than LangChain’s more general document-loading utilities.
Where the actual overlap is
Both can build a basic RAG pipeline — load documents, chunk them, embed them, retrieve on query, generate an answer. The difference shows up as requirements get more specific: if retrieval quality itself is the hard part of your project (which chunking strategy, which index structure, how to handle structured vs. unstructured data), LlamaIndex’s specialization pays off. If the hard part is everything around retrieval — multi-step agent logic, tool orchestration, conversation state — LangChain’s breadth is the better fit.
Using both together
A common real pattern: LlamaIndex handles ingestion and retrieval, LangChain handles the surrounding agent or chain logic that consumes what LlamaIndex retrieves. Neither framework requires you to pick one exclusively.
The practical pick
- Retrieval quality over your own data is the main challenge → LlamaIndex.
- Building a general agent or multi-step chain, retrieval is one part of a bigger system → LangChain.
- Both matter → use them together — LlamaIndex for ingestion/retrieval, LangChain for the rest.
Frequently Asked Questions
Is LlamaIndex a competitor to LangChain?
Not really — they overlap but have different centers of gravity. LangChain is a general framework for chains, agents, and tool use across 700+ integrations. LlamaIndex is focused specifically on data ingestion, indexing, and retrieval for RAG. Many projects use both together.
Which is better for building a RAG pipeline?
LlamaIndex, if retrieval quality is the main challenge — it has more purpose-built indexing strategies and data connectors for that specific job. LangChain can also build RAG pipelines, but it’s a general-purpose toolkit rather than a retrieval specialist.
Which is better for building an agent that uses tools?
LangChain — it has broader agent tooling, more integrations, and is the more general framework for orchestrating multi-step LLM workflows beyond just retrieval.
Can you use LangChain and LlamaIndex together?
Yes, and it’s a common pattern — using LlamaIndex for the ingestion/retrieval layer and LangChain for the surrounding agent or chain logic that consumes the retrieved context.