LangChain vs LlamaIndex for Production RAG in 2026: When to Use Which

Pros

• Purpose-built RAG abstractions: SimpleDirectoryReader, VectorStoreIndex, QueryEngine
• Best-in-class document parsing: PDFs, tables, code, images with LlamaParse
• Built-in evaluation framework (RAGAs-compatible) for RAG quality measurement
• Property graph index for knowledge graph construction
• Tight integration with 50+ vector stores out of the box

Cons

• Less flexible for non-RAG workflows (complex agents, tool chains)
• Smaller community than LangChain
• API changes between major versions can break pipelines
• LlamaCloud adds cost for managed data pipelines

Pros

• Largest ecosystem: 600+ integrations with tools, APIs, vector stores, LLMs
• LangGraph: best-in-class multi-agent workflow orchestration
• LCEL (LangChain Expression Language) for declarative chain composition
• LangSmith: production observability, tracing, and evaluation platform
• Best community: most StackOverflow answers, tutorials, and examples

Cons

• RAG abstractions are more generic — less optimised than LlamaIndex for document retrieval
• Heavier abstraction layer can obscure what's happening under the hood
• Rapid breaking changes in early versions (0.x era) burned many teams
• More complex setup for simple RAG vs LlamaIndex's higher-level APIs