AI Agents with LangGraph leverages a graph representation to define relationships and communication between autonomous AI agents. Each node represents an agent or tool, enabling task decomposition, prompt customization, and dynamic action routing. The framework integrates seamlessly with popular LLMs and supports custom tool functions, memory stores, and logging for debugging. Developers can prototype complex workflows, automate multi-step processes, and experiment with collaborative agent interactions in just a few lines of Python code.