LangChain vs LlamaIndex: Which to Use When

Orchestration versus data

LangChain is strongest at composing steps: prompt → model → parser pipelines, routing, tools, agents and (with LangGraph) stateful workflows. LlamaIndex is strongest at getting your data into the model: many data loaders, flexible chunking into nodes, several index types, retrievers, rerankers and query engines tuned for question answering over documents. They overlap (both do RAG and agents) and can be mixed, for example a LlamaIndex retriever used inside a LangChain chain. Pick the one whose abstractions match your main problem, keep your own code thin around it, and avoid mixing two frameworks without a reason.

A rough decision guide

Starting points only; both libraries keep adding features.

Main need                                         Lean toward
Chat app: prompts, tools, routing, streaming      LangChain (+ LangGraph for state)
Q&A over many files with rich indexing options     LlamaIndex
Multi-step agent with branching and human review   LangGraph
Simple one-call app                                provider SDK directly
Unsure                                             prototype the same 10 questions in both and compare

Quick check: Which is LlamaIndex strongest at?

• Managing GPUs
• Training base models
• Rendering web pages
• Loading, indexing and querying your own data