TextAttack provides researchers and practitioners with tools to evaluate NLP model robustness by running adversarial attacks that test model vulnerabilities. It includes a library of attack algorithms and transformation techniques for generating adversarial examples. The framework also supports data augmentation to improve model generalization and offers command-line utilities for training NLP models.