LangChain vs LlamaIndex: Which Should You Choose?

LangChain and LlamaIndex both help you build LLM apps. The difference is emphasis: LangChain spans chains, agents, and integrations broadly; LlamaIndex started as “connect your data to LLMs” and still shines on ingestion, indexing, and query engines.

Choose LlamaIndex when

• You are building RAG over heterogeneous documents (PDFs, Notion, SQL).
• You want opinionated index types (vector, tree, keyword hybrid) with less glue code.
• Your team’s main pain is retrieval quality and data connectors.

Choose LangChain when

• You need multi-step agents with many third-party tools out of the box.
• You are orchestrating multiple models, retrievers, and human-in-the-loop steps.
• You already use LangGraph or LangSmith for tracing in production.

You can mix both

Some teams index with LlamaIndex and orchestrate agents with LangChain. Do not let framework loyalty block shipping—wrap whichever layer you use behind your own interfaces so you can swap later.

What actually determines success

Chunking strategy, metadata filters, eval datasets, and observability matter more than the logo on the import. Read fine-tuning vs RAG before assuming you need either framework’s heaviest features.

For agent loops, see building your first AI agent.