LangChain vs LangGraph vs LlamaIndex.

Published 2026-09-21 in AI Agents

LangChain vs LangGraph vs LlamaIndex: Which One Should You Use? 🤖 Building an AI application with LLMs? Three popular frameworks often come up: LangChain, LangGraph, and LlamaIndex. But they solve different problems. 🔹 LangChain: Application Layer Best for building LLM applications using prompts, tools, APIs, memory, and different model integrations. 🔹 LangGraph: Orchestration Layer Useful when your AI system needs complex workflows, multiple agents, state management, loops, routing, or human-in-the-loop processes. 🔹 LlamaIndex: Data Layer Focused on connecting LLMs with your private data through ingestion, indexing, retrieval, and RAG. Simple way to remember: 📚 LlamaIndex → Connect & retrieve data ⚙️ LangChain → Build application logic 🔄 LangGraph → Orchestrate complex workflows And the interesting part? You don't always have to choose just one. A production AI system can combine all three: LlamaIndex → Data & RAG ⬇️ LangChain → Application logic & tools ⬇️ LangGraph → Agents, state & workflow orchestration The right choice depends on what you're building: a simple chatbot, a RAG application, or a complex multi-agent AI system. The right choice depends on what you’re building: a…

More AI Agents articles · All collections · Practice challenges