We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods on the same model, thus enabling the fair comparison of different repair techniques. In this paper, we evaluate AIREPAIR with five recent repair methods on popular deep-learning datasets and models. Our evaluation confirms the utility of AIREPAIR, by comparing and analyzing the results from different repair techniques. A demonstration is available at https://youtu.be/UkKw5neeWhw. These files are the trained models for AIREPAIR to repair. You can download and play them with AIREPAIR. The code is available at: https://github.com/theyoucheng/AIRepair.