Agentic AI with LangGraph, CrewAI, AG2, and BeeAI

This course provides a practical, framework-agnostic introduction to building advanced multi-agent AI systems. You’ll learn how agentic design patterns and orchestration techniques create modular, maintainable, and intelligent systems capable of solving complex tasks. Through guided instruction and hands-on labs, you’ll explore four major frameworks—LangGraph, CrewAI, BeeAI, and AG2—and understand their strengths, differences, and design philosophies. You’ll start by using LangGraph to implement foundational workflow patterns, including sequential agents, conditional routing, memory-aware flows, and parallel branches. These patterns serve as building blocks for more complex, adaptive agent systems. You’ll apply agent orchestration in CrewAI, creating multi-agent workflows using agents, tasks, tools, and structured output schemas. You’ll extend applications with custom tool integrations and learn how CrewAI handles coordination and governance in multi-agent scenarios. You will also explore BeeAI’s workflow-oriented architecture and AG2’s multi-agent conversation engine, building applications that rely on structured messaging, role-based collaboration, and coordinated problem solving. By completing the course, you’ll gain the skills to architect flexible, efficient, and production-ready agentic systems, choosing the right framework and workflow patterns for each application. 38:T111e,

• Explain how agentic frameworks support modular and scalable AI system design
• Apply LangGraph workflow patterns such as sequential flows, routing, and parallelization
• Construct multi-agent applications using CrewAI with tasks, structured outputs, and tool integrations
• Create agents and workflows with BeeAI and design multi-agent conversations using AG2
• Implement orchestration strategies that coordinate multiple agents to solve complex tasks
• Select appropriate frameworks and design patterns to optimize performance and maintainability in AI projects

• Working knowledge of Python programming, LangChain, and LangGraph, along with an understanding of how agentic AI systems work.