Multi-Turn Chatbot with Memory in LangGraph 🤖

Welcome to the LangGraph Series! 🌙 In this session, we’re exploring multi‑turn conversations in LangGraph using a message list as state — enabling your LLM to see the entire conversation history. 🤖⚙️ If you want to build AI agents that can remember past interactions and respond contextually like ChatGPT, this is a must‑learn concept. By the end of this lesson, you’ll understand: ✅ What multi‑turn conversation means in LangGraph ✅ How to store messages as state ✅ How LLMs access the full chat history ✅ How to build memory-aware AI agents ✅ How context improves response quality ✅ Real‑world use cases like chatbots and assistants ✅ Common mistakes when handling conversation state With hands‑on examples and clear explanations, you’ll gain the confidence to build context-aware, memory-driven AI applications using LangGraph. 🚀 💬 Don’t forget to Like, Subscribe, and Comment what you learned today — your support keeps these coding lessons going strong! ✨ Before you sleep, make sure you’ve learned something new. ✨ #LangGraph #AI #AIAgents #LLM #Chatbots #ArtificialIntelligence #Python #AIEngineering #AgenticAI #ConversationalAI #CodeBeforeYouSleep #YashJain