LangChain vs LlamaIndex

LangChain and LlamaIndex are two of the most popular frameworks for building applications on large language models (LLMs). Their feature sets overlap heavily today, but each grew from a different center of gravity, and that origin still shapes where each one shines.

LangChain began as a general framework for chaining LLM calls, tools, and logic into applications. LlamaIndex began as a toolkit for connecting LLMs to data, with deep features for ingesting, indexing, and retrieving documents, the core of retrieval-augmented generation (RAG).

Key Differences

LangChain is broad. It offers abstractions for prompts, chains, agents, memory, and an extensive catalog of integrations with models, vector stores, and external tools. Its companion projects extend it further: LangGraph for building stateful, multi-step agent workflows as graphs, and LangSmith for tracing, evaluation, and observability. This breadth makes it powerful for complex, multi-step systems, though the layered abstractions can feel heavy for simple tasks.

LlamaIndex is focused. It provides a smooth path from raw data to a high-quality retrieval pipeline: a rich set of data connectors, flexible indexing strategies, and query engines tuned for accurate document question answering. For a team whose main need is "answer questions over my documents," LlamaIndex often gets there with less ceremony. It does include agent and tool features, but they are not its primary emphasis.

The frameworks are not mutually exclusive; many projects use LlamaIndex for retrieval inside a LangChain-orchestrated application.

When to Choose LangChain

Choose LangChain when your application is more than retrieval: multi-step agents, tool use, branching logic, and orchestration across many services. Its large integration catalog reduces glue code, and LangGraph plus LangSmith give you structured control and observability for production agents. It is the better fit when the system's complexity lives in the workflow rather than the data layer.

When to Choose LlamaIndex

Choose LlamaIndex when RAG is the heart of the application. Its connectors and indexing strategies make ingestion and retrieval straightforward, and its query engines are tuned for answer quality over documents. With fewer abstractions to learn for that use case, it is often the faster route to a solid document question-answering system.

Practical Considerations

Both frameworks move fast and add abstractions quickly, which can be a double-edged sword: convenient at first, but a source of hidden complexity and debugging difficulty as systems grow. A common lesson is to keep the core logic understandable and avoid leaning on framework magic where a few lines of direct code would be clearer and easier to maintain. Invest early in observability, since multi-step LLM systems are hard to debug without tracing each call, prompt, and retrieval. Whichever you pick, treat retrieval quality and evaluation as first-class concerns, because the framework only orchestrates; answer quality still comes from good chunking, embeddings, reranking, and prompts. Many teams use both together rather than choosing one exclusively.

Verdict

Think of it as orchestration versus retrieval. Reach for LangChain when the hard part is coordinating agents, tools, and multi-step logic. Reach for LlamaIndex when the hard part is turning your data into accurate retrieval. Because they compose well, a common pattern is to let LlamaIndex own the retrieval layer and LangChain own the surrounding orchestration. Evaluate both against your actual data and workflow before committing.