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Use LlamaIndex when your application is primarily about data retrieval — RAG, document Q&A, knowledge base search. Its indexing and query engine abstractions are more sophisticated. Use LangChain when you need broad integration with tools, agents, and general LLM orchestration. Many production systems use both: LlamaIndex for the data layer, LangChain for the application layer.
Not for basic use. The open-source framework handles standard documents well with community loaders. LlamaParse is valuable for complex documents (PDFs with tables, charts, multi-column layouts) where standard parsers fail. LlamaCloud's managed indices are useful for production deployments that want managed infrastructure.
Start with VectorStoreIndex for most use cases — it's the most versatile and well-supported. Use TreeIndex when you need document summarization. KeywordTableIndex for exact keyword matching. KnowledgeGraphIndex for relationship-based queries. In practice, 90% of applications use VectorStoreIndex. Combine indices with ComposableGraph when you need multiple strategies.
LlamaIndex supports incremental updates through document management: you can insert, delete, and update documents in indices without full re-indexing. Each document has a doc_id for tracking. The refresh mechanism detects changed documents and updates only affected embeddings. For production, combine this with a document tracking system for your data sources.
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Last verified March 2026