Welcome to the ultimate Agentic AI using LangGraph playlist! In this series, we’ll take you from the fundamentals of Agentic AI — understanding how it differs from generative AI and tool-augmented chatbots — all the way to building powerful, production-ready systems using LangGraph. You’ll learn core concepts like state management, prompt chaining, orchestrator-worker patterns, conditional edges, tool calling, and multi-agent setups. Along the way, we’ll explore advanced workflows including RAG, agentic RAG, memory, persistence, observability, and deployment strategies. Whether you're a beginner or looking to master Agentic AI frameworks, this playlist will equip you to design, build, and debug sophisticated AI agent systems with confidence!
Curated by: CampusX (28 videos)
In this video, we continue the Agentic AI using LangGraph series by learning how to implement short-term memory in LLM-based systems. The video explains why LLMs are stateless, how short-term memory works using conversation history, and how to implement it in LangGraph using checkpointers and thread IDs. You’ll then learn why in-memory storage is not suitable for production and how to persist conversation state using PostgreSQL with Docker. Finally, the video covers the context window problem in LLMs and teaches practical techniques like trimming, deletion, and summarization to handle long conversations safely and reliably in real-world agentic AI systems. Code -https://github.com/campusx-official/memory-in-llms Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in ⌚Chapters⌚ 00:00 - Introduction and video overview 00:01:38 - Recap: LLMs are stateless and memory fundamentals 00:03:33 - Short-term memory concept and checkpointers in LangGraph 00:04:46 - Implementing short-term memory with threads and in-memory storage 00:09:22 - Limitations of in-memory memory storage 00:11:16 - Need for persistence in production systems 00:12:38 - Setting up PostgreSQL using Docker 00:15:54 - Implementing persistent memory with PostgreSQL checkpointer 00:17:36 - Verifying persistence after application restart 00:19:30 - Context window and context overflow problem 00:21:38 - Trimming strategy to control token limits 00:31:19 - Limitations of trimming 00:32:03 - Summarization concept for long conversations 00:35:24 - Deletion and its role in summarization 00:40:12 - Implementing summarization workflow in LangGraph 00:49:26 - Testing summarization behavior in practice 00:52:08 - Final recap and key takeaways