  Continual Learning is an important and challenging problem in machine
learning, where models must adapt to a continuous stream of new data without
forgetting previously acquired knowledge. While existing frameworks are built
on PyTorch, the rising popularity of JAX might lead to divergent codebases,
ultimately hindering reproducibility and progress. To address this problem, we
introduce SequeL, a flexible and extensible library for Continual Learning that
supports both PyTorch and JAX frameworks. SequeL provides a unified interface
for a wide range of Continual Learning algorithms, including
regularization-based approaches, replay-based approaches, and hybrid
approaches. The library is designed towards modularity and simplicity, making
the API suitable for both researchers and practitioners. We release
SequeL\footnote{\url{https://github.com/nik-dim/sequel}} as an open-source
library, enabling researchers and developers to easily experiment and extend
the library for their own purposes.
