How To Implement Short Term Memory Using LangGraph

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/m... Notes: https://learnwith.campusx.in/s/store/... 📱 Grow with us: CampusX' LinkedIn:   / campusx-official   CampusX on Instagram for daily tips:   / campusx.official   My LinkedIn:   / nitish-singh-03412789   Discord:   / discord   E-mail us at [email protected] ⌚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