SELA is an innovative system that enhances automated machine learning (AutoML) by integrating Monte Carlo Tree Search (MCTS) with LLM-based agents. Traditional AutoML methods often produce low-diversity and suboptimal code, limiting their effectiveness in model selection and integration. SELA represents pipeline configurations as trees, enabling agents to intelligently explore the solution space and iteratively refine strategies based on experimental feedback.