  GraphNeT is an open-source python framework aimed at providing high quality,
user friendly, end-to-end functionality to perform reconstruction tasks at
neutrino telescopes using graph neural networks (GNNs). GraphNeT makes it fast
and easy to train complex models that can provide event reconstruction with
state-of-the-art performance, for arbitrary detector configurations, with
inference times that are orders of magnitude faster than traditional
reconstruction techniques. GNNs from GraphNeT are flexible enough to be applied
to data from all neutrino telescopes, including future projects such as IceCube
extensions or P-ONE. This means that GNN-based reconstruction can be used to
provide state-of-the-art performance on most reconstruction tasks in neutrino
telescopes, at real-time event rates, across experiments and physics analyses,
with vast potential impact for neutrino and astro-particle physics.
