Automatic question answering (QA) facilitates extraction and identification of relevant knowledge in large data sources that would otherwise be hard to find for humans. Furthermore, numerous other natural language processing (NLP) tasks can be formulated within the scope of a QA framework, which positions QA as one of the most prominent NLP tasks. Due to the rapid progress in the field, the researchers are confronted with situations where state-of-the-art models are outdated just a few months after they have been published. In this project we aim to provide researchers with an extensible QA platform to explore, compare and combine state-of-the-art QA approaches, as well as to aid developing novel approaches by standardizing access to available data and model sources.