About Awesome Dataviz
The open directory of data visualization tools, built in the open since 2015.
Awesome Dataviz began as a GitHub “awesome list” of open-source data visualization frameworks, libraries and software, created by Fabio Souto and now maintained by Javier Luraschi with 93 contributors, sponsored by Hal9. This website turns that list into a searchable directory with live data about every tool.
How tools are selected
Every tool on this site is one line in the README of the repository. Anyone can propose a tool with a pull request; maintainers check that it is a working data visualization tool, that the description is short and neutral, and that it is not a duplicate. Open-source tools are preferred, and spam or projects whose functionality cannot be verified are declined. The website is rebuilt from the README automatically, so the list and the site never disagree.
Where the numbers come from
- GitHub stars, forks, open issues, license, language, topics and releases come from the GitHub API for the 283 tools with a public GitHub repository (GitLab for a few others).
- Commits count commits to the repository's default branch in each of the last 12 complete calendar months.
- Contributors is the number of GitHub accounts with commits, as reported by the GitHub API.
- Packages and downloads come from npm, PyPI, CRAN, crates.io, the Go module proxy and RubyGems. A package is only shown when the registry links back to the same repository, or a maintainer confirmed it, so an install command never points at an unrelated package with the same name. 225 packages are currently verified. Weekly downloads come from the npm downloads API, pypistats.org and the CRAN logs.
- Listed since is the date an entry first appeared in the README, from its git history.
Everything is refreshed daily. When an API is unavailable, the previous day's value is kept rather than dropped.
Maintenance status
- Active: at least one commit in the last 90 days.
- Maintained: at least one commit in the last 12 months.
- Inactive: no commits for more than a year. Mature tools can be inactive and still work well.
- Archived: the repository is archived and read-only.
How rankings work
Lists are ordered by GitHub stars, the most widely available signal of how many people know and use a project. Stars measure attention rather than quality, so each page also shows recent activity, contributors and package downloads, and every tool page links to alternatives and comparisons.
Use the data
The directory is available as JSON, through an MCP server for AI assistants, and as plain text in llms.txt. Content is licensed under CC BY 4.0: reuse it with attribution to Awesome Dataviz.
Contact
Found a mistake or a missing tool? Open an issue or submit a pull request.