LlamaIndex
RAG-specialized framework for data ingestion, indexing, and retrieval for LLMs. Connectors for 160+ data sources. More focused on data pipelines than LangChain, which is broader.
Strengths
- 160+ data connectors: PDF, Notion, Slack, SQL, APIs, local files
- Chunking and indexing optimized for RAG, better retrieval out of the box
- LlamaParse: intelligent parsing of complex documents (tables, images, PDFs)
- LlamaCloud: managed RAG in production without custom infra
- Simpler than LangChain for pure RAG use case, less abstraction overhead
Limitations
- Less versatile than LangChain, focused on RAG, not general orchestration
- LlamaParse and LlamaCloud are paid, premium features aren't free
- TypeScript ecosystem less mature than Python, JS SDK is catching up
- Dense and sometimes confusing documentation, many concepts to absorb
Best for
- RAG on enterprise documents: wikis, Confluence, SharePoint, Google Drive
- Chatbots on technical docs with tables, images, and complex PDFs
- Data ingestion pipelines to feed a vector store (Pinecone, pgvector)