Neutrino telescopes, an extension of traditional multiwavelength astronomy, provide a complementary view of the universe using neutrinos. Differences in detector geometry and detection medium means that improvements to reconstruction techniques made at one experiment is not readily applicable to another. Recently, deep learning has shown to improve prediction speed, accuracy and offer indifference to detector geometry and detection medium, providing a unique opportunity for collaboration. This work introduces GraphNeT 2.0, an open-source, detector-agnostic deep learning library for neutrino telescopes and related experiments. GraphNeT enables inter-experimental collaboration on the use and development of advanced methods based on major deep learning paradigms like transformers, normalizing flows, graph neural networks and more.