--- id: gravis version: "0.1.0" license: Apache License, Version 2.0 license_treatment: permissive maintenance: abandoned --- # gravis — Interactive graph visualizations with Python and HTML/CSS/JS. License: permissive · Maintenance: abandoned · Downloads: 82.7K/mo ## What it is and what it does Gravis is a Python library for creating interactive graph visualizations that render as standalone HTML files. It takes graph data (nodes and edges) and generates a web-based visualization you can open in any browser to explore the structure interactively. The library is designed for developers and researchers who need to visualize networks, hierarchies, or relationship diagrams without building a full web application. The package depends only on setuptools and produces pure HTML/CSS/JavaScript output, making it portable and easy to share. However, it has been abandoned since its initial 0.1.0 release in December 2021 and receives no maintenance, so there will be no bug fixes, feature additions, or compatibility updates for newer Python versions or browser standards. Use it for: - Visualize knowledge graphs or semantic networks for research and documentation. - Explore social network structures or organizational hierarchies interactively in a browser. - Generate interactive diagrams of software dependencies or system architectures. - Create educational visualizations of algorithms or data structures for teaching. - Export graph data from Python analysis pipelines as shareable HTML reports. ## Worth the install? AI-flagged interpretation of the facts above — verify before relying. Gravis generates interactive graph visualizations as HTML/CSS/JavaScript output that can be viewed in a web browser, letting you explore network structures and relationships visually. Yes, but with caution. Gravis is worth installing if you need a lightweight way to turn Python graph data into interactive HTML visualizations and you don't require ongoing maintenance or support. The permissive Apache License, Version 2.0 and low install friction make it accessible. However, its abandoned status since 2021-12-08 means no updates for Python version compatibility, browser standards, or bug fixes—use it only for one-off visualizations or internal tools where stagnation is acceptable. ## Install pip install gravis uv add gravis poetry add gravis ## Installing gravis Before you install: Installation is straightforward with low friction—a pure Python wheel with only setuptools as a runtime dependency. However, the package has been abandoned since its single release on 2021-12-08, with no maintenance activity in years, so expect no bug fixes or updates. License in practice: Apache License 2.0 is permissive, allowing commercial and private use with minimal restrictions. You may use, modify, and distribute gravis freely as long as you include a copy of the license and state any changes. Quickstart: pip install gravis import gravis # Create and visualize a simple graph graph = gravis.Graph() graph.add_node('A') graph.add_node('B') graph.add_edge('A', 'B') gravis.render(graph, output_file='graph.html') Requires Python 3.5 or later; output is HTML/CSS/JS intended for viewing in a web browser. Verify before relying: - Whether the package handles large graphs efficiently or has known performance limits. - What graph layout algorithms are available and how customizable the visualization styling is. - Whether the HTML output works with modern browser versions and what dependencies it expects client-side. - What specific graph data structures and methods the API provides beyond basic node/edge operations. ## Package facts - License: Apache License, Version 2.0 (permissive) - Python support: supports_current - Install friction: low - Maintenance: abandoned - Downloads: 82.7K/month (top 15,000 on PyPI) - Known vulnerabilities: none known ## Tags interactive graph visualization, network diagram HTML, graph rendering Python, visual graph explorer, network structure display, graph layout visualization, interactive network browser, graph-visualization, interactive-html, abandoned [View on SkillFed](https://skillfed.io/packages/gravis) · [View on PyPI](https://pypi.org/project/gravis/)