NASLib is a modular and flexible framework created with the aim of providing a common codebase to the community to facilitate research on Neural Architecture Search (NAS). It offers high-level abstractions for designing and reusing search spaces, interfaces to benchmarks and evaluation pipelines, enabling the implementation and extension of state-of-the-art NAS methods with a few lines of code. The modularized nature of NASLib allows researchers to easily innovate on individual components (e.g., define a new search space while reusing an optimizer and evaluation pipeline, or propose a new optimizer with existing search spaces). It is designed to be modular, extensible and easy to use. NASLib was developed by the AutoML Freiburg group and with the help of the NAS community, we are constantly adding new search spaces, optimizers and benchmarks to the library. Please reach out to firstname.lastname@example.org for any questions or potential collaborations.