LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - Context Engineering for AI Agents Using LangGraph: Episodic Memory | Step-by-Step

*Resources:* - GitHub: https://github.com/homayounsrp/context-engineering-episodic-memory - The credit for this knowledge goes to this course: https://learn.deeplearning.ai/courses/long-term-agentic-memory-with-langgraph/lesson/ovv0p/introduction *Previous Video:* - Part 8: Context Engineering: The Core Concept | Non-Technical Explanation: https://www.youtube.com/watch?v=LmVDUChGvXw&t=71s - Part 9: Context Engineering : Semantic Memory | Step-by-Step: https://www.youtube.com/watch?v=2FA4U6dmrLE In this practical LangGraph tutorial, I’ll walk you through exactly how to build intelligent, context-aware AI agents using Model Context Protocol (MCP), LangGraph, and an advanced episodic memory system. If you're ready to master context engineering—the hottest skill in AI right now—this video is your ultimate guide. Context engineering determines how your AI agent remembers and uses information, transforming a basic chatbot into a proactive assistant. Unlike prompt engineering, which briefly guides immediate responses, context engineering establishes structured guard rails to consistently guide your agent’s memory, interactions, and decisions. These guard rails ensure your AI remains reliable, effective, and aligned with your goals. This video specifically covers how to leverage LangGraph MCP integration to set clear guard rails for managing agent interactions. MCP serves as a structured backbone, ensuring goals, inputs, outputs, and intermediate states are preserved clearly across your agent's workflow. These guard rails guide the AI agent’s memory, keeping responses coherent and meaningful, even over long conversations. Using LangGraph, you'll orchestrate structured, multi-step workflows for AI agents. LangGraph agents, when combined with MCP’s guard rails, consistently maintain context, preventing confusion or drifting conversations. The result? A powerful, proactive agent capable of remembering user-specific details and learning continuously from past interactions. You'll also learn how to implement episodic memory—another set of powerful guard rails—allowing your agent to recall detailed past interactions. Episodic memory gives your AI agents the ability to retain personalized context and remember events, making responses significantly more relevant. These memory systems serve as additional guard rails, ensuring agents never lose critical context. Additionally, I’ll demonstrate integrating LangMem’s semantic memory into your LangGraph agents. Semantic memory offers your agents structured guard rails that ensure critical information remains easily accessible, enhancing the accuracy and relevance of responses. Your agent can persistently store and quickly retrieve important past interactions, maintaining consistency throughout sessions. This tutorial clearly explains the difference between context engineering and prompt engineering, emphasizing why context engineering’s structured guard rails dramatically outperform traditional methods. Prompt engineering may influence one-off interactions, but only context engineering consistently guides agent interactions over time through strong, effective guard rails. Throughout the video, you'll follow a practical, hands-on demo. Starting with MCP integration, setting strong guard rails for context management, orchestrating complex agent workflows with LangGraph, and enhancing memory through episodic and semantic systems—this tutorial gives you everything needed to build intelligent, context-aware AI agents immediately. The demo isn't theoretical—it's real-world. You’ll clearly see guard rails in action, ensuring the agent remembers user details, maintains context, and evolves intelligently. These structured guard rails directly improve user experiences by enabling agents to respond proactively and intelligently to user needs. By mastering context engineering and setting structured guard rails using MCP and LangGraph, you'll build AI agents that scale effectively, stay reliable, and continuously improve. Whether you’re new to AI or an experienced developer looking to level up, this tutorial provides essential context engineering skills, making your agents smarter, more consistent, and truly context-aware. ----- Timestamps: 0:00 - Intro 0:47- System Design 1:38- Implementation 6:08 - Demo 7:44 - Outro LangGraph,MCP,Model Context Protocol,LangGraph tutorial,build AI agent,LangGraph MCP integration,AI agents,LangGraph agents,MCP integration,Using MCP with LangGraph agents,Context Engineering for Agents,Context Engineering — The Hottest Skill in AI Right Now,Context Engineering is the New Vibe Coding (Learn this Now),Context Engineering vs. Prompt Engineering: Guiding LLM Agents,Context Engineering: What It Is and Why It Matters