→ anyplot.ai

anyplot is an open catalogue of plotting examples. Each plot type starts as one library-agnostic specification, and an AI pipeline writes, renders, reviews, and maintains its implementations in 15 plotting libraries across Python, R, Julia, and JavaScript. On anyplot.ai, you compare the libraries side by side and copy working code.

Built by Markus Neusinger.

One spec in 15 libraries: the site's live preview card for ridgeline-basic shows six of them; the spec page shows all 15.

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The catalogue in numbers

Specifications	Implementations	Libraries	Languages
325	4,863	15	4

Counted on 2026-11-02 from the public API (https://api.anyplot.ai/stats); the stats page shows the current numbers. 316 of the 325 specs exist in all 15 libraries; the other nine, mostly 3D and map plots, lack one or two.

Language	Libraries
Python	matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, lets-plot
R	ggplot2
Julia	Makie.jl
JavaScript	Chart.js, D3.js, ECharts, Highcharts, MUI X Charts (React)

What you can do on the site

• Compare libraries. Each spec page shows the same plot in every library, usually all 15, in a light and a dark theme, each with its code and its review score.
• Search and filter. A typo-tolerant search matches spec titles and tag values, and the matches become filters: language, library, plot type, data structure, domain, and features, plus implementation tags such as techniques, dependencies, and styling.
• Explore by similarity. The map draws the catalogue as a graph of specs linked by tag similarity, and every spec page lists related specs.
• Use it from an AI assistant. The MCP server serves the same catalogue to coding agents.

How the pipeline works

AI writes every implementation, most of them Claude running in GitHub Actions. GitHub issues hold the state of each plot.

1. Request. Someone opens an issue, from GitHub or from a link on anyplot.ai.
2. Specification. spec-create.yml checks for duplicates, assigns a spec ID and tags, and opens a pull request with the spec. The maintainer approves it on the issue.
3. Generation. impl-generate.yml writes one implementation per library and renders it in both themes.
4. Review. impl-review.yml first runs automated checks: the code parses and runs, the image exists and isn't blank, and the canvas has its target size. An AI reviewer then scores the render, blind to any earlier score, on a 100-point rubric of 24 criteria (prompts/quality-criteria.md): visual quality 30, design 20, spec compliance 15, data quality 15, code quality 10, and library mastery 10.
5. Repair. Below the bar, impl-repair.yml fixes the defects the review named, and the plot is reviewed again.
6. Merge. impl-merge.yml merges the pull request, promotes the images from staging to production in Cloud Storage, and syncs PostgreSQL.

The quality bar

• A first review needs 90 of 100. Each repair lowers the bar by 10 points: 80, 70, 60, and 50 after the fourth and last repair. An implementation still below 50 is closed and marked failed.
• The rubric reserves 90 and more for a plot that "should genuinely impress a data visualization professional". The reviewer never sees the thresholds.
• Technical criteria start at full points and lose them only through a written defect line that names the criterion, the observed value, and the fix. Design and library criteria start at a default and rise only with named evidence.
• Code that fails to run, a blank image, clipped labels, or a static library faking interactivity scores 0.
• One exception to the bar: a review of 80 or more that names no defect and gives every technical criterion its full points is approved, because a repair would have nothing to fix.

Nightly regeneration

daily-regen.yml regenerates the oldest part of the catalogue every night: by default the oldest spec in every library, and since 2026-10-31 the two oldest single implementations instead. A regeneration replaces the live implementation only when its review, which scores both versions, finds a fixed defect and no regression (the regen gate).

For the labels, retries, and every workflow, see Workflows.

Use with my data (agent chat)

Status: experimental, open to every visitor since 2 November 2026.

On a plot page, choose use with my data and paste your own data. An agent adapts that plot's catalogue code to your data, renders it in a sandbox, reviews the render against the catalogue's rubric, repairs it once if needed, and returns the image and the code.

• Agents. A Google ADK service on Cloud Run. The model is Claude Haiku 5.5 through Vertex AI in the EU; Gemini 3.8 Flash is a configurable second arm. Runtimes exist for all 15 libraries.
• Sandboxed rendering. No model-written code runs in the agent service. A separate renderer runs it in Cloud Run sandboxes (gVisor): no network egress, no metadata server, and a read-only root filesystem. The renderer's service account holds no IAM role.
• Static validation. Before anything runs, a validator reads the code (Python's ast, tree-sitter for R, Julia, and JavaScript) and refuses disallowed imports and calls.
• Guard judges. A scope judge reads every typed message before any agent sees it and fails closed; a dataset judge checks pasted data and notes. Spend limits apply per user and per day, and a budget guard switches Vertex AI off when its monthly budget is reached.
• Evals. A model-regression harness runs synthetic, stress, and promoted real cases; separate evals score the scope judge and the data preparation.
• Content-free telemetry. Usage events hold only enums, buckets, counts, and IDs from closed sets. No message, data value, or column name reaches a line, and a test enforces it.

Read the agent network design, the agents README, and the telemetry reference.

MCP server

anyplot runs an MCP (Model Context Protocol) server, so AI assistants can search the catalogue and fetch implementation code.

Tool	What it does
list_specs	Lists all plot specifications
search_specs_by_tags	Finds specs by tags: plot type, data type, domain, features, library, and implementation tags
get_spec_detail	Returns a full specification with its implementations
get_implementation	Returns the code for one library
list_libraries	Lists the supported libraries
get_tag_values	Lists the values of a tag category

Add it to your MCP client configuration, for example Claude Code's .mcp.json:

{
  "mcpServers": {
    "anyplot": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://api.anyplot.ai/mcp/"]
    }
  }
}

The server speaks Streamable HTTP only. Clients with native support connect to https://api.anyplot.ai/mcp/ without the mcp-remote bridge. The legacy SSE endpoint (https://api.anyplot.ai/sse/) has been removed. For details, see MCP server.

Tech stack

Area	Stack
Backend	Python 3.14, FastAPI, SQLAlchemy (async), PostgreSQL, Alembic
Frontend	React 19, TypeScript, Vite, MUI
Agents	Google ADK, Claude Haiku 5.5 on Vertex AI, Cloud Run sandboxes
Pipeline	GitHub Actions with Claude Code
Infrastructure	Cloud Run, Cloud SQL, Cloud Storage, Cloud Build, BigQuery, Cloudflare

Each render runtime is pinned: Python by pyproject.toml and uv.lock, the JavaScript libraries by package.json and package-lock.json, Julia by Project.toml and Manifest.toml, and R by a dated Posit Package Manager snapshot.

Project structure

anyplot/
├── plots/              # One directory per plot type: spec, metadata, implementations
├── prompts/            # Generation and review prompts, the quality rubric
├── .github/workflows/  # The pipeline (spec-create, impl-*, daily-regen) and CI
├── api/                # FastAPI backend and the MCP server
├── app/                # React frontend
├── agents/             # "Use with my data": ADK agent service, sandbox renderer, evals
├── core/               # Shared Python logic: database, palette, checks
├── automation/         # Workflow scripts: sync, labels, the regen gate, the JS render harness
├── alembic/            # Database migrations
├── tests/              # Unit, integration, and end-to-end tests
├── infra/              # Cloudflare configuration
├── scripts/            # Maintenance scripts
├── tools/, changelog.d/  # Changelog tooling and one changelog fragment per pull request
├── agentic/            # Commands and docs for the coding agents that work on this repository
└── docs/               # Documentation

The root package.json and package-lock.json pin the JavaScript render runtime, and Project.toml and Manifest.toml pin the Julia one. A plot directory looks like this:

plots/scatter-basic/
├── specification.md     # Library-agnostic specification
├── specification.yaml   # Tags, creation date, issue
├── metadata/            # Per-library quality score, review, preview URLs
└── implementations/
    ├── python/          # matplotlib.py, seaborn.py, … (8 files)
    ├── r/ggplot2.R
    ├── julia/makie.jl
    └── javascript/      # chartjs.js, d3.js, echarts.js, highcharts.js, muix.tsx

For every directory, see Repository structure.

Contributing

You contribute ideas, not code: the pipeline writes every implementation.

Action	When to use	On anyplot.ai
Suggest a spec	Propose a new plot type	spec.suggest() on the home page and the specs list
Report a spec issue	A specification has a problem	"report issue" on the spec page
Report an implementation issue	One library's implementation has a problem	"report issue" on the spec page

1. Open an issue, on GitHub or through a link on anyplot.ai.
2. The AI validates and processes it.
3. The maintainer reviews and approves it.
4. The pipeline generates or fixes the code, and the review gates it.

Don't open pull requests with plot code. A plot with quality problems usually needs a better spec, not hand-edited code. For details, see Contributing. To run the project locally, see the development guide.

Documentation

• Vision: the product vision
• Workflows: the automation and its labels
• Agent network: the design of "Use with my data"
• Reference: API, MCP, database, repository structure, and style guide
• Documentation index: everything else

License notes

Most plotting libraries are fully open source. Highcharts is free for non-commercial use; commercial use requires a license from highcharts.com.

The project is under the MIT License; see LICENSE. The JetBrains Mono fonts bundled in agents/anyplot/render/fonts/ for the footer strip of user plots are third-party files under the SIL Open Font License 1.1; see their notice.

Links

• Website: anyplot.ai
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Analytics: public Plausible dashboard

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Built by Markus Neusinger

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