LlamaIndex
LLM data framework and RAG engine
Highlights
- 200+ data connectors
- Built-in advanced RAG strategies
- LlamaParse document parser
Use cases
- Enterprise knowledge-base Q&A
- Intelligent document retrieval
- Complex RAG strategy apps
Review
LlamaIndex positions itself as the bridge between LLMs and private data, standardizing the full pipeline of document loading, splitting, indexing, retrieval, and generation. It is one of the mainstream choices for building RAG applications. Compared with LangChain's general-framework positioning, it is more specialized in data connectivity and retrieval quality.
Core capabilities include 200+ data connectors (Notion, Confluence, SQL, PDF, etc.), multiple index types (vector, tree, keyword, knowledge graph), query engines and routers, advanced RAG strategies (sub-questions, recursive, fusion re-ranking), plus the LlamaParse document parser and LlamaCloud hosted service.
The highlight is high coverage of data source integrations and advanced RAG patterns, with substantial engineering around retrieval quality. The downside is that feature overlap with LangChain keeps growing, some advanced APIs are nested through multiple layers, debugging requires reading several layers of source, and occasional breaking changes ship between versions.
Use cases: enterprise knowledge-base Q&A, intelligent document retrieval, applications needing complex RAG strategies. For simple demos LangChain is lighter; for data-heavy scenarios LlamaIndex is smoother.