Graphs are everywhere, representing connections in everything from social networks to molecules. But getting AI to understand and analyze these complex structures has been a challenge. Traditional methods either lack flexibility or struggle with complex reasoning. Now, researchers have developed GraphTeam, a groundbreaking approach that uses a team of cooperating LLMs to tackle graph analysis. Imagine a group of specialized AI agents, each with its own expertise: a question analyst refining the problem, a research assistant digging up relevant information, a coding expert writing the programs, a reasoning guru tackling tough logic puzzles, and a formatting specialist polishing the final answer. That's GraphTeam in a nutshell. This collaborative system draws inspiration from how humans solve problems, mimicking our ability to break down complex tasks, consult resources, try different approaches, and refine solutions. Tested on six challenging graph analysis benchmarks, GraphTeam consistently outperformed existing methods, boasting an average 25.85% accuracy boost. It excelled in tasks ranging from basic graph theory to the deployment of complex graph neural networks (GNNs). While GNNs remain a particularly tough nut to crack, GraphTeam’s innovative multi-agent collaboration offers a promising new path forward. This research opens doors for AI to analyze and interpret complex relational data more effectively, paving the way for advancements in fields like drug discovery, social network analysis, and urban planning.