Agentic AI Core Components: Memory Systems - Part 2

Memory is one of the most critical but misunderstood components of Agentic AI systems. In this video, we break down how memory actually works inside modern AI agents and how it enables systems to learn from experience without retraining the model. In the second part, we implement a working memory system using: - LangGraph - LangChain - OpenAI embeddings - ChatGPT models - FAISS vector stores We build an agent that: 1- Retrieves semantic and episodic memories 2- Assembles short-term context 3- Generates plans and actions 4- Reflects on outcomes 5- Stores lessons back into memory The system then uses those lessons to improve future decisions. No stubs are used — the notebook makes real API calls to embeddings and ChatGPT. Get the notebook here: https://github.com/hussien824/Agentic-AI-Tutorials/blob/main/memory_systems_agentic_ai_explained.ipynb