💡 The paper introduces EvoScientist, a multi-agent framework designed to enhance scientific discovery by learning from past interactions. The problem with current AI scientist systems is that they rely on static pipelines and fail to adapt based on accumulated interaction histories, leading to overlooked research directions, repeated failed experiments, and pursuit of infeasible ideas. To address this, EvoScientist uses three specialized agents: a Researcher Agent for idea generation, an Engineer Agent for experiment implementation, and an Evolution Manager Agent that distills insights from prior interactions into reusable knowledge. The framework also includes two persistent memory modules: an ideation memory that summarizes feasible research directions and records unsuccessful ones, and an experimentation memory that captures effective data processing and model training strategies. These modules enable the agents to retrieve relevant prior strategies, improving idea quality and code execution success rates over time. The results show that EvoScientist outperforms seven state-of-the-art systems in scientific idea generation, achieving higher novelty, feasibility, relevance, and clarity, and also improves code execution success rates through multi-agent evolution, demonstrating the effectiveness of persistent memory for end-to-end scientific discovery. Overall, the paper contributes a novel framework that enables AI scientists to learn from their past interactions and adapt their research strategies, leading to more effective and efficient scientific discovery.