Part 3: Building Graph Memory for AI Agents with LangGraph & Neo4j | Step-by-Step Tutorial

Resources: GitHub repo: https://github.com/homayounsrp/React_Agent/tree/AgentMemory LangGraph docs: https://blog.langchain.dev/memory-for-agents/In this video I’ll show you how I built a context-aware AI agent with LangGraph and Neo4j that truly never forgets. Watch my full LangGraph–Neo4j integration, see how I added persistent agent memory using a knowledge graph, and explore the AI memory architecture powering this stateful chatbot. Whether you need a hands-on LangGraph tutorial or a deep dive into Neo4j tutorial best practices, this walkthrough covers everything to create scalable, long-term AI memory in your own projects. 🔍 What You’ll Learn Agent Memory: Schema design for storing and retrieving conversation context with LangGraph Memory Management: Techniques to update, prune, and scale your knowledge graph agent Vector Memory Agent: Encoding, embeddings, and semantic search for instant recall Graph Database AI: Saving memory as nodes & edges in Neo4j LangGraph Pipelines: Building connectors, custom nodes, and graph-powered RAG Scalable AI Memory: Performance tips as your dataset grows End-to-End Agent: Live demo of an AI agent that adapts over time 🛠️ Key Features Demonstrated Context-aware agent referencing past messages with precision Real-time AI agent demo showcasing memory “in action” LangGraph + Neo4j integration code snippets for production Best practices for high-throughput, persistent AI memory 📅 Chapters 00:00 – Introduction & Overview 00:01:02 – Agent System Design 00:01:18 – File Structure 00:01:42 – Long-Term Memory Database Connection 00:02:12 – Long-Term vs. Short-Term Memory 00:04:12 – Streamlit Chatbot UI 👍 If you find this LangGraph tutorial helpful, hit Like and Subscribe for more Neo4j tutorial content. Drop your questions about agent memory, stateful chatbots, or knowledge graph AI below—and let’s push the boundaries of conversational AI together! Tags: langgraph tutorial, langgraph agent, langgraph js, langgraph project, langgraph memory, langgraph context, langgraph vector, langchain tutorial, langchain agent, ai agent tutorial, neo4j tutorial, knowledge graph AI, vector database chatbot, stateful chatbot, persistent memory agent, context aware chatbot, graph embeddings, streamlit chatbot, python chatbot