Awesome Dataviz — Every data visualization tool, in one place | LaunchDaily
Awesome Dataviz
Every data visualization tool, in one place
data visualization
charting libraries
developer tools
open source
directory
dashboards
Awesome Dataviz is a free, open, community-curated directory of 291 data visualization tools and libraries across 23 categories, covering charting libraries, mapping toolkits, graph and network visualizers, dashboards, and diagrams-as-code for JavaScript, Python, R, and many other languages. Every entry is ranked with live GitHub, npm, PyPI, and CRAN data refreshed daily from public APIs.
Each tool is ranked with combined GitHub stars (2.4M tracked in total), commit activity, and npm/PyPI/CRAN data, refreshed daily from public APIs; 171 of tracked repositories (60%) showed activity in the last 90 days.
Landscape visualization
An interactive dot plot places every tool by GitHub stars on a log scale, grouped by category, with hover details and click-through to each tool.
Rankings and discovery views
Multiple ways to explore: most popular by GitHub stars (D3.js 113.8K, Mermaid 90.5K, Grafana 77.1K, Apache Superset 75K), most active this year by commits (Kibana 21K, Lightwind/Lightdash 12.2K, Grafana 11.3K), recently added tools, and an Atom feed for new entries.
Topic-based browsing
Curated topics including maps & geospatial, graph & network visualization, 3D & scientific, terminal charts, financial & stock charts, Jupyter & notebook visualization, GPU-accelerated & WebGL, large datasets, time series & real-time charts, ML & AI visualization, and grammar of graphics libraries.
AI agent access via MCP and APIs
The full directory is available to AI assistants through an MCP server (connectable with a single 'claude mcp add' command), a JSON API, and llms.txt, so agents can recommend visualization tools from current data instead of training memory.
Full-text search with popular shortcuts
A directory-wide search with quick links to popular segments like JS charting, Python, React, JS maps, R, and JS graphs & networks.
How It Works
1
Search or browse the directory
Use the search bar or browse by one of 23 language/technology categories (e.g., Python data visualization, R packages, open-source dashboards & BI, diagrams as code).
2
Compare using live metrics
Scan the landscape chart, popularity rankings, and activity rankings to gauge how widely adopted and actively maintained each tool is, based on daily-refreshed GitHub, npm, PyPI, and CRAN data.
3
Open and evaluate tools
Click any dot or listing to open the tool's details; follow the Atom feed or 'recently added' section to keep up with new entries.
4
Connect via MCP (optional)
Point any MCP client at https://awesomedataviz.com/mcp so an AI assistant can query the directory and recommend tools from current data.
Pros & Cons
Pros
Free and open: an open, community-curated directory with no cost to use.
Broad coverage: 291 tools spanning 23 categories and virtually every major language and platform, including niche ones like terminal charts and diagrams-as-code.
Objective, data-driven rankings based on live GitHub, npm, PyPI, and CRAN metrics rather than editorial opinion.
Daily refreshes from public APIs keep star counts and activity data current (as of Oct 4, 2026 in the site's data).
Machine-readable access via MCP server, JSON API, and llms.txt, useful for AI-assisted tool selection.
Who It's For
Best for
Developers choosing a charting, mapping, or graph visualization library for a specific language or framework.
Data engineers and analysts comparing open-source dashboards and BI tools like Grafana, Apache Superset, and Metabase.
Engineers looking for niche visualization categories such as terminal charts, diagrams as code, GPU/WebGL rendering, or ML model visualizati
AI agents and assistants that need up-to-date, structured recommendations of visualization tools via MCP or JSON APIs.
Not ideal for
Teams needing a hands-on trial, sandbox, or side-by-side feature comparison within the directory itself — it links out rather than hosting t
Use Cases
Picking a charting library for a specific stack — JavaScript, React, Python, R, C++, Go, Rust, Julia, .NET, Ruby, JVM, or mobile frameworks like Flutter, Androi
Comparing open-source dashboards and BI platforms such as Grafana, Apache Superset, Metabase, and Kibana by popularity and commit activity.
Finding specialized tools: mapping and geospatial libraries (Leaflet, Cesium, Deck.gl), graph/network visualizers (xyflow, G6, Sigma.js), diagrams as code (Merm
Letting an AI coding assistant recommend current, well-maintained visualization libraries via the MCP server or JSON API.
FAQ
What is Awesome Dataviz?
It's an open, community-curated directory of 291 data visualization tools and libraries across 23 categories — charting libraries, mapping toolkits, graph visualizers, dashboards, diagrams-as-code and more — ranked with live GitHub, npm, PyPI and CRAN data refreshed daily.
How much does it cost?
The site presents itself as an open, community-curated directory and doesn't mention any paid plans; the directory, its rankings, MCP server, JSON API, and llms.txt are freely accessible.
Is Awesome Dataviz open source?
It describes itself as an 'open, community-curated directory,' and its data comes from public APIs; the site doesn't explicitly state where its source code is hosted.
Multiple discovery modes: search, category browsing, topic pages, popularity and activity rankings, an interactive landscape chart, and an Atom feed for new tools.
Cons
It is a directory/link aggregator, not a tool itself — it helps you choose a visualization library but doesn't chart anything.
Rankings lean heavily on GitHub stars and commit counts, which measure popularity and activity but not quality, documentation, or fit for your use case.
Coverage is uneven by ecosystem (48 Python tools versus 3–7 for many other languages), so smaller ecosystems have fewer options listed.
No visible editorial reviews, benchmarks, or comparison of features between tools — evaluation beyond the metrics is up to you.
Community-curated, so the catalog's completeness and freshness of individual tool descriptions depend on contributor activity.
Buyers seeking commercial, paid, or vendor-supported visualization products with reviews and procurement guidance; the focus is open-source,
Non-technical users who want a single recommended tool without comparing libraries and metrics.
JavaScript (charting, maps, graphs/networks, React), Python, R, C++, Go, Julia, Java/Kotlin/Scala, C#/.NET, Ruby, Rust, plus mobile (Android, iOS, Flutter, React Native), and cross-cutting categories like dashboards & BI, machine learning visualization, and diagrams as code.
How is the popularity data kept current?
GitHub, npm, PyPI and CRAN metrics are pulled from public APIs and refreshed daily; the site shows its last refresh date and reports how many tracked repositories were active in the last 90 days.
Can AI assistants use the directory?
Yes — the whole directory is exposed via an MCP server at https://awesomedataviz.com/mcp (connect with 'claude mcp add --transport http awesome-dataviz https://awesomedataviz.com/mcp'), plus a JSON API and llms.txt, so assistants recommend tools from current data instead of memory.
How do I keep up with new tools?
Follow the 'Recently added' section or subscribe to the site's Atom feed to see newly listed tools, such as sankeydiagram.net and newly added entries like D3.js, Grafana, and Streamlit.
