Apr 5
Loading video player...
Are you using LangChain when you should be using LangGraph? 🤔 In this video, we break down LangChain vs LangGraph in just 5 minutes so you can clearly understand which one to use while building AI applications. If you're working with LLMs, AI agents, or RAG systems, this distinction is critical. 🚀 What you’ll learn: * What LangChain is and where it works best (linear workflows) * What LangGraph is and why it’s powerful (multi-agent systems) * Key differences between LangChain and LangGraph * When to use LangChain vs LangGraph in real-world AI systems LangChain is great for: ✔️ Prompt chaining ✔️ Retrieval & summarization ✔️ Simple LLM pipelines LangGraph is built for: ✔️ Multi-agent systems ✔️ Stateful workflows ✔️ Loops, branching & dynamic decisions This video is perfect for: * AI Engineers * Backend Developers * Anyone learning LLMs, RAG, or AI system design 💬 Comment below: When would you choose LangGraph over LangChain? If this helped, subscribe for more no-fluff AI content 🚀 #LangChain #LangGraph #AIEngineering #AIAgents #LLM #GenAI #RAG #AIForDevelopers