LangChain vs LlamaIndex — Which One Should You Use for RAG?

Description

LangChain and LlamaIndex are the leading frameworks for building Retrieval-Augmented Generation (RAG) apps, but they focus on different parts of the problem. In this video, we show exactly what each framework does, the tradeoffs, and how to pick (or combine) them for your RAG projects. What is RAG? Retrieval-Augmented Generation (RAG) is a critical concept where you feed a Large Language Model (LLM) with relevant external context retrieved from an index or database before generating an answer. This architecture helps fix knowledge gaps, reduces hallucinations, and enables up-to-date answers using private data. Both LangChain and LlamaIndex aim to help with the typical RAG architecture, which includes data ingestion, chunking, embedding, vector store/indexing, retrieval, synthesis, and orchestration. Key Differences and Focus: LangChain is an opinionated, modular framework focused broadly on building chains, agents, and end-to-end LLM applications, with RAG implemented as one pattern among many. It provides flexible building blocks, making it extremely modular for wiring up any model, vector DB, or tool. LangChain excels in agentic and multi-step workflows, tool use, external APIs, and orchestration. Key primitives include DocumentLoaders, TextSplitters, VectorStores (like FAISS, Pinecone, Weaviate), Retrievers, and Chains/Agents. LlamaIndex is a framework centered specifically on indexing, structuring, and retrieving documents for LLMs, treating indexing as a first-class citizen. LlamaIndex is purpose-built for high-quality document-centric RAG, offering fast ingestion and retrieval. It provides rich index types (vector index, tree index, composable graph indexes) that help structure knowledge and synthesize long documents. It includes good defaults for chunking, citations, and metadata handling, making it production-friendly. How to Choose (or Combine): • If your primary need is document ingestion, quality retrieval, and a production knowledge base, you should start with LlamaIndex. • If you need agents, custom tool orchestration, or multi-step workflows, you should start with LangChain. • The pragmatic answer often involves a combined approach: use LlamaIndex for ingestion and index management, and then use LangChain for the orchestration/agents that call that index as a retriever. We demonstrate the minimal RAG setup for both frameworks in Python pseudo-code to show how quickly a document-centric RAG can be made with LlamaIndex and how components are wired together in LangChain. #LangChain #LlamaIndex #RAG #RetrievalAugmentedGeneration #LLM #AIFrameworks #LangChainTutorial #LlamaIndexTutorial #VectorStore #Indexing #LLMOrchestration #Agents #Chains #Embeddings #Python #AIDevelopment #KnowledgeBase #SemanticSearch #MultiStepWorkflows #FAISS #Pinecone #Weaviate #LangSmith #DocumentSearch #AIComparison