PINA is an open-source Python library that provides an intuitive interface for the approximated resolution of Ordinary Differential Equations and Partial Differential Equations using a deep learning paradigm, in particular via PINNs. The gain of popularity for PINNs in recent years, and the evolution of open-source frameworks, such as TensorFlow, Keras, and PyTorch, led to the development of several libraries, whose focus is the exploitation of PINNs to approximately solve ODEs and PDEs. We here mention some PyTorch-based libraries, NeuroDiffEq (Chen et al., 2020), IDRLNet (Peng et al., 2021), NVIDIA Modulus (NVIDIA Modulus, 2023), and some TensorFlow-based libraries, such as DeepXDE (Lu et al., 2021), TensorDiffEq (McClenny et al., 2021), SciANN (Haghighat & Juanes, 2021) (which is both TensorFlow and Keras-based), PyDEns (Koryagin et al., 2019), Elvet (Araz et al., 2021), NVIDIA SimNet (Hennigh et al., 2021). Among all these frameworks, PINA wants to emerge for its easiness of usage, allowing the users to quickly formulate the problem at hand and solve it, resulting in an intuitive framework designed by researchers for researchers.