Building AI Agents with LangGraph, RAG, and Memory

Modern AI applications require more than simple prompt-and-response interactions; they demand stateful logic, contextual retrieval, and the ability to use external tools. This text-based course guides you step by step through creating sophisticated conversational systems using LangGraph, RAG architectures, and memory persistence. You will gain the conceptual understanding and practical coding skills needed to orchestrate autonomous agents, integrate external data sources via vector embeddings, and manage complex conversation histories. What you'll learn: - Understand the core terminology of agentic architectures, state machines, and LangGraph graphs - Implement Retrieval-Augmented Generation (RAG) pipelines to ground model answers in custom data - Configure conversational memory to maintain context across multi-turn interactions - Connect external tools and APIs for dynamic function execution - Apply multimodal processing patterns to handle text and image inputs within agent workflows - Design resilient control flows with conditional routing and human-in-the-loop patterns The course begins with foundational concepts and essential terminology before progressing into modular, step-by-step written tutorials and clean code implementations. This course is designed for developers, data practitioners, and tech enthusiasts looking for a structured, beginner-friendly introduction to building robust AI agents without requiring prior agentic framework experience. Start reading today to build smarter, tool-enabled AI systems from the ground up.