Multi-Agent AI Development with LangGraph and CrewAI

The next evolution of artificial intelligence is moving beyond simple chat prompts toward autonomous agents that can plan, use tools, and work in teams. This course provides a clear path for anyone looking to build intelligent systems that don't just generate text, but execute complex workflows independently. You will transition from understanding basic LLM interactions to designing sophisticated multi-agent architectures. By reading through conceptual breakdowns and code-based examples, you will learn how to orchestrate multiple AI entities, each with specialized roles, to solve problems that a single model cannot handle alone. What you'll learn: - Understand the core principles of agentic design and autonomous reasoning - Build role-based agent teams using the CrewAI framework - Design complex, stateful AI workflows and cyclic graphs with LangGraph - Implement multi-agent conversation patterns using AutoGen (AG2) - Apply agentic Retrieval-Augmented Generation (RAG) to connect agents with live data - Practice memory management techniques to maintain context across agent interactions - Configure tools and functions that allow agents to interact with the physical world The course begins with essential terminology and the foundational logic of autonomous agents before moving into the specific syntax and patterns of leading industry frameworks. You will explore how to select the right tool for specific orchestration needs and how to handle error correction within agent loops. This course is designed for beginners and developers who want to stay at the forefront of AI development. No prior experience with agentic frameworks is required. Start building your first autonomous AI workforce today.