LangChain vs LlamaIndex: Which Should You Use for Your AI Agent?

When teams ask us which framework to use for their AI agent project, the honest answer is: it depends. LangChain and LlamaIndex both solve real problems, but they solve slightly different ones. Let's break it down.

LangChain: Best for Agent Orchestration

LangChain shines when you need to build agents that use tools, make decisions, and chain together multiple steps. If your use case involves an agent that can search the web, query a database, send an email, and synthesise the results — LangChain's tool-use and agent abstractions are excellent. LangGraph (its stateful agent extension) adds memory and cycles, making it ideal for complex multi-step workflows.

LlamaIndex: Best for RAG and Knowledge Retrieval

LlamaIndex is purpose-built for retrieval-augmented generation. If your primary need is ingesting documents (PDFs, Notion pages, databases) and querying them with an LLM, LlamaIndex's indexing and retrieval pipeline is significantly more mature and flexible than LangChain's equivalent. Its query engines, routers, and sub-question decomposers are best-in-class for knowledge-intensive applications.

When to Use Both Together

The frameworks are not mutually exclusive. A common production pattern is to use LlamaIndex for the retrieval layer (indexing your company knowledge base) and LangChain to orchestrate the agent that calls that retrieval as one of many available tools. This gives you the best of both worlds.

Our Recommendation

For most business automation agents we build at Digipeasy, we default to LangChain + LangGraph for orchestration with LlamaIndex handling any document retrieval requirements. For pure document Q&A products without complex agent behaviour, LlamaIndex alone is often cleaner and faster to build.