TensorFlow GNN is an open-source project on GitHub with about 1,547 stars, most recently updated in 2026. TensorFlow GNN is Google’s library for building graph neural networks on the TensorFlow platform. It provides graph tensor representations, convolution layers and scalable training pipelines for graphs ranging from small molecules to web-scale networks, and integrates with the wider TensorFlow ecosystem.
Published on GitHub under the Apache-2.0 license, TensorFlow GNN has attracted a community of about 1,547 stars, which places it among the more visible open-source AI projects in its niche. Development was still active as of 2026, based on the repository’s latest updates. Because the full source code is public, teams can audit how it works, adapt it to their own stack, and self-host it rather than depending on a closed hosted product.
The repository is tagged with topics including deep-learning, gnn, machine-learning, tensorflow, which gives a quick picture of where it fits in the open-source AI ecosystem. New users can explore the examples and documentation in the repository to understand the main workflows before integrating it into their own projects, and the public issue tracker shows what the maintainers are actively working on and what the community is asking for.
TensorFlow GNN is Apache-2.0 open source from the TensorFlow team and free to use.
TensorFlow GNN is best suited for TensorFlow developers and researchers building graph learning models for structured, relational data. Anyone evaluating it should start with the GitHub repository, where the documentation, examples and issue tracker show exactly what the current version can do.