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This is a complete, end-to-end masterclass on building and benchmarking AI agentic systems, from raw Python code to today’s most powerful AI agent frameworks. 📂 Full source code and implementations available on github: 👉https://github.com/samugit83/TheGradi... We’ll build a fully functional, production-grade multi-agent architecture from absolute scratch, then recreate the exact same system using six of the most talked-about frameworks in the AI ecosystem: 🧩 LangChain + LangGraph 🤖 OpenAI Agent SDK 👥 CrewAI 🦙 LlamaIndex 🧠 Microsoft AutoGen ⚡ Microsoft Semantic Kernel Each implementation is dissected, analyzed, and benchmarked to reveal what each framework truly offers — and what it hides beneath its abstractions. You’ll learn not just how to use them, but when to use them… and when you’re better off going framework-free. We’ll implement and evaluate all the core components of a real production-ready agent system, including: 🔀 Multi-Agent Orchestration — Routing, coordination, and role management between specialized agents. 🧠 State Management — Maintaining stable context across tasks and sessions. 🧩 Memory Systems — From short-term caching to persistent, long-term memory. ⚙️ Tool Integration — Connecting agents with APIs, functions, and real-world tools. 🌐 MCP Server Integration — Testing interoperability with Model Context Protocol servers. 💻 Coder Agent & Execution Pipelines — Code generation, testing, and self-correction loops. 🧾 Structured JSON Output — Producing clean, machine-readable responses. 💰 Token Usage Tracking — Monitoring cost, performance, and efficiency. 🛡️ Guardrails & Safety Systems — Ensuring reliability and constraint compliance in production. Each feature is benchmarked across frameworks using six key real-world metrics that actually matter to developers: Abstraction Grade Level – How much control you retain vs. how much is hidden. Code Readability & Simplicity – Can your team maintain it months later? Setup Complexity – How heavy is the boilerplate and configuration overhead? Developer Experience (DX) – Is it intuitive and pleasant to build with? Documentation & Clarity – How easy is it to find and follow the right guidance? Flexibility & Customization – Can it adapt to complex, evolving requirements? At the end, we’ll compile all results into a comprehensive framework scorecard, showing you which frameworks deliver real value and where pure Python still reigns supreme. Everything in this tutorial — including all 7 complete implementations — is available on GitHub, ready for you to clone, test, and extend. Whether you’re an AI engineer, researcher, or developer exploring agentic architectures, this video will transform how you understand and evaluate AI frameworks, helping you build smarter, faster, and more maintainable systems. 💥 By the end, you’ll know exactly when to rely on frameworks — and when to trust your own code. 👍 If You Found This Tutorial Helpful, Please: Like this video 👍 Subscribe for weekly deep dives into knowledge graphs, AI, and machine learning 🤖 Hit the 🔔 Bell Icon to get notified whenever new tutorials drop! 💬 Questions or Feedback? Drop a comment below to share your results, ask questions, or suggest future topics. I love hearing about your applications of knowledge graphs and advanced AI techniques! 🎓 About the Instructor: I'm Samuele Giampieri, an AI engineer passionate about bridging cutting-edge research with practical applications. My expertise spans knowledge graphs, NLP, and AI-driven retrieval systems, and I enjoy creating resources that empower innovation. 🔗 Connect with Me: GitHub: https://github.com/samugit83 LinkedIn: / samuele-giampieri-b1b67597 Website: https://www.devergolabs.com #AIagents #LangChain #OpenAI #AutoGen #SemanticKernel #CrewAI #LlamaIndex #PythonAI #AIframeworks #AgenticSystems #AIDevelopment #AIBenchmark #MachineLearning #AICoding #AIEngineering