LangChain vs LlamaIndex: Which Should You Use?

LangChain and LlamaIndex are two of the most widely used frameworks for building applications on large language models, and they are often compared as alternatives. In practice they emphasize different things, and understanding that difference is the key to choosing well. This article compares the two and explains when each fits.

Different Centers of Gravity

The clearest way to understand the two frameworks is by what each was built around. LlamaIndex was created with retrieval at its center, focusing on ingesting documents and answering questions from them. It keeps the indexing and query flow tight, offering many ways to load data and a range of indexing strategies such as vector, keyword, and summary indexes.

LangChain, by contrast, leans into orchestration. It provides abstractions for chaining language-model calls, using tools, managing memory, and building agents, and its LangGraph extension adds stateful, graph-based workflows. Where LlamaIndex optimizes the retrieval layer, LangChain aims to coordinate many components into larger systems.

Strengths of Each

LlamaIndex shines when retrieval quality is the main concern. For applications such as document question answering, internal knowledge bases, and search over large collections, its document loaders and indexing options make it efficient to build effective retrieval-augmented generation, often with less code. It is more opinionated, which trades some fine-grained control for ease of use.

LangChain shines when retrieval is just one part of a larger agentic system. If your application needs multi-step reasoning, tool use, decision-making, and stateful coordination, especially across multiple agents, LangChain and LangGraph provide the structure for it. The flexibility comes with a steeper learning curve and often more code for an equivalent retrieval task.

Ecosystem and Code

LangChain has a very large ecosystem, with extensive integrations and a big community, which can make it easier to find an existing connector or example for a given need. LlamaIndex also has a strong ecosystem, particularly rich in data connectors aimed at ingesting many document sources.

On code volume, building a basic retrieval pipeline tends to require less code in LlamaIndex because the framework handles more of the retrieval details by default. LangChain's modularity gives you more control but usually asks you to wire more pieces together yourself.

How to Choose, and Using Both

If your project is primarily about retrieving information from documents and you want strong results quickly, LlamaIndex is a natural fit. If your project is an agent system where retrieval is one capability among several, and you need orchestration, memory, and multi-agent coordination, LangChain and LangGraph are the better match.

Importantly, the two are not mutually exclusive. Many production systems use both, with LlamaIndex serving as the retrieval layer and LangGraph as the orchestration layer. Rather than treating the choice as either-or, consider which framework should own which responsibility in your architecture.