Welcome to the ultimate Agentic AI using LangGraph playlist! In this series, we’ll take you from the fundamentals of Agentic AI — understanding how it differs from generative AI and tool-augmented chatbots — all the way to building powerful, production-ready systems using LangGraph. You’ll learn core concepts like state management, prompt chaining, orchestrator-worker patterns, conditional edges, tool calling, and multi-agent setups. Along the way, we’ll explore advanced workflows including RAG, agentic RAG, memory, persistence, observability, and deployment strategies. Whether you're a beginner or looking to master Agentic AI frameworks, this playlist will equip you to design, build, and debug sophisticated AI agent systems with confidence!
Curated by: CampusX (28 videos)
Code - https://github.com/campusx-official/langgraph-tutorials In this video, we dive deep into how to build and use subgraphs in LangGraph—a critical concept for designing scalable, modular, and multi-agent GenAI systems. We start by understanding what subgraphs are at a conceptual level and why they become essential as GenAI workflows grow in complexity. You’ll see how large agentic systems can be broken down into smaller, independent agents, each represented as a subgraph with its own logic, tools, memory, and state. The video then covers: Why subgraphs are important for multi-agent architectures How subgraphs improve modularity, reusability, and maintainability LangGraph-specific benefits like failure isolation, state separation, and observability The two ways to implement subgraphs in LangGraph: Invoking a subgraph from inside a node (isolated state) Adding a subgraph directly as a node (shared state) Finally, we walk through a practical coding example where a parent graph generates an answer and a subgraph handles translation, showing both implementation patterns side by side. If you’re building agentic AI systems, multi-agent workflows, or production-grade GenAI applications with LangGraph, this video will give you a strong foundation to design cleaner and more scalable architectures. 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in ⌚Chapters⌚ 00:00:00 - Introduction: Subgraphs in Agentic AI and LangGraph 00:01:06 - What Are Subgraphs? (Conceptual Understanding) 00:02:19 - Why Subgraphs Are Needed in Complex GenAI Workflows 00:05:57 - Subgraphs for Multi-Agent Architectures 00:07:04 - Core Benefits: Modularity, Reusability, Maintainability 00:08:09 - LangGraph-Specific Benefits of Subgraphs 00:11:08 - Ways to Implement Subgraphs in LangGraph 00:13:40 - Practical Example: Translation Workflow with Subgraphs 00:19:11 - Two Subgraph Mechanisms Compared (Isolated vs Shared State) 00:21:26 - Best Practices, Persistence, and Observability 00:22:36 - Wrap-up