LangChain vs LangGraph | Build Production-Ready AI Agents (With Code)

Description

In this video, we explain LangChain vs LangGraph with real-world examples and Python code. LangChain helps you build LLM applications like RAG pipelines, tool calling, and prompt chains. LangGraph helps you build reliable AI agents with state, loops, retries, conditional routing, and human-in-the-loop workflows. This video is ideal for: Developers building AI agents Engineers working on RAG systems Anyone moving from simple LLM chains to production workflows 📄 Complete Code + Explanation (PDF Download): 👉 https://drive.google.com/file/d/1AVuOX5WaRtz-a-fXXwG9xRKPJ-zkvceQ/view?usp=drive_link ⏱️ Chapters (SEO + Retention Optimized) 00:00 LangChain vs LangGraph – Overview 00:30 What is LangChain (with Code) 02:30 RAG & Linear Chains 03:45 Problems with AI Agents Using Chains 04:45 What is LangGraph 05:30 LangGraph State & Nodes 06:30 Agent Workflow with Code 07:45 Conditional Routing & Loops 08:45 Real-World AI Agent Example 09:30 LangChain vs LangGraph Summary 10:00 Code Link & Closing 🏷️ Tags langchain, langgraph, langchain vs langgraph, ai agents, agentic ai, llm agents, langchain tutorial, langgraph tutorial, rag, retrieval augmented generation, python ai, openai agents, generative ai, ai workflow, ai orchestration, llm frameworks, ai automation, production ai 🔖 Hashtags Top 3 (most important): #LangChain #LangGraph #AIAgents Extended: #AgenticAI #RAG #Python #GenerativeAI #LLM #AITools #SoftwareEngineering