Graph-Learn (formerly AliGraph) is a distributed framework designed for the development and application of large-scale graph neural networks. It abstracts a set of programming paradigms suitable for common graph neural network models from the practical problems of large-scale graph training, and has been successfully applied to many scenarios such as search recommendation, network security, knowledge graph, etc. within Alibaba. Graph-Learn provides both Python and C++ interfaces for graph sampling operations, and provides a gremlin-like GSL (Graph Sampling Language) interface. For upper layer graph learning models, Graph-Learn provides a set of paradigms and processes for model development. It is compatible with TensorFlow and PyTorch, and provides data layer, model layer interfaces and rich model examples.