Build a Multi-Agent Research Assistant with LangGraph

AI engineering is rapidly shifting from single-prompt applications to complex, autonomous multi-agent systems. If you have basic Python skills and want to build intelligent applications that can research, reason, and collaborate, this is your starting point. This course guides you through the foundational concepts of agentic AI. You will learn how to design a multi-agent research assistant that breaks down complex queries, retrieves accurate information, and synthesizes data into coherent responses. By reading through structured explanations and hands-on code snippets, you will transition from traditional programming to modern AI orchestration. What you'll learn: • Understand the core terminology, definitions, and architecture of multi-agent AI systems. • Build stateful, distributed AI applications using LangGraph and Python. • Implement foundational Retrieval-Augmented Generation (RAG) patterns to ground agent responses in factual data. • Design specialized agents that collaborate to research, summarize, and format information. • Apply modern Python practices like type hints and dataclasses for robust AI engineering. • Manage agent state, routing, and conversational memory effectively. The journey begins with a clear breakdown of key AI terminology and foundational definitions before moving into practical implementation. You will explore step-by-step written exercises that gradually combine single agents into a powerful, collaborative research system. Designed for Python developers and beginners eager to explore AI engineering, this course requires no prior machine learning experience. Start building your own autonomous AI assistants today.