Part 6: Building AI AGENT using Semantic Kernel | Comparison with LangGraph + Step-by-Step Tutorial

GitHub Repo: https://github.com/homayounsrp/semantic_kernel In this video, I walk you through how I developed an AI agent using Microsoft’s Semantic Kernel and connected it to a Neo4j knowledge graph! PLUS, I compare it head-to-head with LangGraph, another popular framework for building LLM-powered agents. Whether you’re an AI enthusiast, knowledge graph nerd, or looking to supercharge your applications with contextual reasoning, this video has something for you. I break down my full development process—from designing the agent’s architecture to integrating cutting-edge LLMs (Large Language Models) and leveraging the power of graph databases. And if you’re wondering which framework to use, I share my hands-on experience with both Semantic Kernel and LangGraph. What you’ll learn: Overview of Semantic Kernel: How it enables intelligent orchestration and memory for your AI agents. Neo4j Integration: Connecting the AI agent to a rich knowledge graph for dynamic, context-aware answers. Semantic Kernel vs. LangGraph: Direct comparison of features, strengths, weaknesses, and use cases. Real-world Applications: How to use this stack for recommendations, question answering, semantic search, and more. Demo & Code Walkthrough: Step-by-step coding, configuration, and deployment. Tips & Gotchas: What I learned, pitfalls to avoid, and resources to get you started. 🔗 Key Links: Semantic Kernel Docs LangGraph Docs Neo4j Knowledge Graphs Full Source Code (GitHub) My Blog/Website Timestamps: 0:00 - Introduction 1:01 - What is Semantic Kernel? 1:24 - Semantic Kernel Main Components 3:15 - LangGraph or Semantic Kernel? 3:15 - Production Readiness: LangGraph or Semantic Kernel? 5:14 - Scalability: LangGraph or Semantic Kernel? 6:27 - Maintenance: LangGraph or Semantic Kernel? 7:34 - Streaming: LangGraph or Semantic Kernel? 10:45 - Semantic Kernel vs. LangGraph: Pros & Cons 9:38 - Implementation 16:42 - Outro