Building AI Agents with LangGraph and CrewAI

AI is shifting from passive chat interfaces to active, autonomous agents that can plan, use tools, and collaborate to solve complex tasks. Understanding how to build these agentic workflows is one of the most valuable skills in modern software development. This text-based course guides you through the foundational concepts of AI agents and multi-agent orchestration. You will learn how to design, build, and manage stateful agents that can execute multi-step plans, integrate external tools, and work together to automate real-world processes. What you'll learn: - Understand the core architectures of AI agents, including planning, memory, and tool execution. - Build stateful, multi-agent workflows using LangGraph to manage complex decision-making loops. - Configure cooperative agent teams using CrewAI to automate multi-step business processes. - Implement tool calling and function execution to connect your agents with external APIs and data sources. - Apply state management and human-in-the-loop patterns to ensure agent reliability and safety. - Design robust prompts and system instructions tailored for autonomous agent behavior. You will start by exploring the basic definitions and architecture of single agents before moving on to advanced multi-agent coordination. Through written explanations and practical code walkthroughs, you will progress from basic tool-use scripts to fully orchestrated agent teams. This course is designed for beginner to intermediate Python developers who want to enter the field of AI engineering. No prior experience with AI agents or LLM frameworks is required, though a basic understanding of Python is recommended. Start building your first autonomous AI agent team today.