Graphistry aligns with the way that you work, allowing you to bring next-generation visualization and analysis into your native environment. Easily add visual analytics to your existing dashboards to extend your existing tools and workflow. Data scientists can work with Graphistry directly from within their favorite data science notebooks, and developers can leverage the developer API to build a powerful visual front-end to any application. Work the way that you want, and we’ll bring the visualization.
Call the PyGraphistry library directly from Jupyter, SageMaker, Databricks, Colab, and other popular Python data science environments. The Pandas-based API makes it easy to load data from CSVs, Spark, SQL, graph databases, and more. Directly load your own data tables and graphs. If you have custom analytics, you can annotate the data with their output, and explore the result with Graphistry.
Graph-App-Kit is an open source project combining the graph intelligence powers of Graphistry with the dashboarding ease of Streamlit. It combines patterns the Graphistry team has reused across many graph projects as teams go from code-heavy Jupyter notebook experiments to deploying streamlined analyst tools. Whether building your first graph app, trying an idea, or wanting to check a reference, this project aims to simplify that process. Graph-App-Kit covers pieces like: Easy code editing and deployment, optional air-gapped self-hosting, a project structure ready for teams, built-in authentication, no need for custom JS/CSS, batteries-included data + library dependencies, and fast loading & visualization of large graphs.