Automated neural architecture search (NAS) methods have been demonstrated as a powerful tool to facilitate neural architecture design. However, the broad applicability of NAS has been restrained due to the difficulty of designing task-specific search spaces and the necessity and verbosity to implement every NAS component from scratch when switching to another search space. In this work, we propose ModularNAS, a framework that implements essential components of NAS in a modularized and unified manner. It enables automatic search space generation for customized use cases while reusing predefined search strategies, with little extra work needed for each case. We conduct extensive experiments to verify the improved model performance on various tasks by reusing supported NAS components over customized search spaces. We have also shown that targeting existing architectures, ModularNAS can find superior ones concerning accuracy and deployment efficiency, such as latency and FLOPS. The source code of our framework can be found at https://github.com/huawei-noah/vega/tree/master/vega/algorithms/nas/modnas.