Systematic reviews and mapping studies are critical to synthesize research, identify gaps, and guide future work, but are often labor-intensive and time-consuming. Existing tools provide partial support for specific steps, leaving much of the process manual and error-prone. We present ProfOlaf, a semi-automated tool designed to streamline systematic reviews while maintaining methodological rigor. ProfOlaf supports iterative snowballing for article collection with human-in-the-loop filtering and uses large language models to help select articles, extract key topics, and answer queries about the content of articles. By combining automation with guided manual effort, ProfOlaf enhances the efficiency, quality, and reproducibility of systematic reviews across research fields. ProfOlaf can be used both as a CLI tool and in web application format. A video demonstrating ProfOlaf is available at: https://youtu.be/R-gY4dJlN3s

翻译：系统性综述与图谱研究对于综合研究成果、识别研究空白及指导未来工作至关重要，但通常劳动密集且耗时。现有工具仅对特定步骤提供部分支持，使得大部分流程仍需手动操作且易出错。本文提出ProfOlaf，一种旨在精简系统性综述流程同时保持方法学严谨性的半自动化工具。ProfOlaf支持采用人机协同过滤的迭代滚雪球式文献收集，并利用大语言模型辅助文献筛选、提取关键主题及回答关于文献内容的查询。通过将自动化与引导式人工操作相结合，ProfOlaf提升了跨研究领域系统性综述的效率、质量与可复现性。该工具支持命令行界面与网络应用两种使用模式。演示视频详见：https://youtu.be/R-gY4dJlN3s