[[LLamaIndex]] and [[LangChain]] are similar enough that simple [[LLM engineering]] applications can effectively use either. More robust applications can use both. LlamaIndex is best for - Simple, out-of-the-box data ingestion and chunking - Native support for structured data, Pandas, graphs, SQL, etc. - Fast prototyping of RAG with minimal boilerplate - Advanced querying tools like sub-question decomposition, table summarization, etc. LangChain is better for - Fine-grained control over chains, tools, routing - More flexibility for multi-agent systems and tool use - Integration with advanced tools (e.g. code interpreters, browsers, actions) - Declarative control flows using LangGraph Here's a comparison of key features. |Feature / Step|LangChain|LlamaIndex| |---|---|---| |**Document loading**|`langchain.document_loaders`|`llama_index.readers`| |**Chunking**|Manual (`TextSplitter`)|Built-in semantic splitters| |**Vector store**|Plug-and-play (Chroma, etc)|Plug-and-play (or built-in)| |**Embedding models**|Customizable|Also customizable| |**Query interface**|`RetrievalQA`, `RAGChain`|`QueryEngine`, `ChatEngine`| |**Graph traversal**|Use LangGraph manually|Built-in **KGQueryEngine**| |**Structured connectors**|Weak|Strong (Pandas, SQL, Graphs)| |**Ease of RAG setup**|Medium|Easy| |**Multi-agent orchestration**|Supported via LangGraph|Not native (requires LangChain)|