LlamaIndex vs LangChain

LlamaIndex vs LangChain compares two Python toolkits for connecting large language models (LLMs) to private data. LlamaIndex (formerly GPT Index) focuses on retrieval-augmented generation pipelines: it offers graph-based indexes, automatic chunking, and query engines that choose between vector, keyword, or SQL retrieval at runtime. It comes with scoring dashboards and a single high-level API — index.query— perfect for data-centric teams that need fast ingestion and search without deep hint plumbing. LangChain provides a broader, Lego-style framework: loaders, embeddings, vector stores, chains, agents, and memory elements all fit together so developers can build not just RAGs, but multi-tool agents, streaming applications, and trackable cost-of-observability. LlamaIndex delivers reasonable production speed for search-intensive use cases; LangChain provides fine-grained control, broader integration, and easier model swapping (GPT-4o today, Claude tomorrow). Many developers combine the two: LlamaIndex handles intelligent indexing, while LangChain orchestrates hints, agents, and UI endpoints, bringing together the best of both worlds for enterprise AI.