→ pyplots.ai

library-agnostic, ai-powered python plotting examples.

pyplots is an AI-powered platform for Python data visualization that automatically discovers, generates, tests, and maintains plotting examples. Browse hundreds of plots across all major Python libraries - matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, and lets-plot.

Community-driven, AI-maintained - Propose plot ideas via GitHub Issues, AI generates the code, automated quality checks ensure excellence. Zero manual coding required.

• AI-generated code - All implementations automatically generated and maintained by AI
• Compare libraries - View matplotlib, seaborn, plotly side-by-side for the same plot
• Always current - AI agents continuously update examples with latest library versions
• Natural language search - Find plots by asking "show correlation between variables"
• AI quality review - Claude evaluates every plot against quality standards (score ≥ 50 required)
• Open source - Community proposes ideas via Issues, AI generates the code

Specification-first design: Every plot starts as a Markdown spec (library-agnostic), then AI generates implementations for all 9 supported libraries.

plots/scatter-basic/ ├── specification.md # Library-agnostic specification ├── specification.yaml # Tags, created, issue, suggested ├── metadata/ # Per-library metadata (quality scores, preview URLs) │ ├── matplotlib.yaml │ └── ... └── implementations/ ├── matplotlib.py ├── seaborn.py ├── plotly.py └── ... (6 more) 

Issue-based workflow: GitHub Issues as state machine for plot lifecycle. Status tracked via live-updating table (no sub-issues). Each library generates in parallel, creating PRs to a feature branch.

AI quality review: Claude evaluates generated plots. Score ≥ 90 → immediate merge. Score < 90 → repair loop (max 3 attempts). After 3 attempts: ≥ 50 → merge, < 50 → failed.

See docs/reference/ for details.

Backend: FastAPI • PostgreSQL • SQLAlchemy • Python 3.10+

Frontend: React 19 • Vite • TypeScript • MUI

Plotting: matplotlib • seaborn • plotly • bokeh • altair • plotnine • pygal • highcharts • lets-plot

Infrastructure: Google Cloud Run • Cloud SQL • Cloud Storage

Automation: GitHub Actions

AI: Claude (code generation + quality review)

pyplots provides an MCP (Model Context Protocol) server for AI assistants to search plot specifications and fetch implementation code.

Available Tools:

• list_specs - List all plot specifications
• search_specs_by_tags - Search by plot type, domain, features, library
• get_spec_detail - Get full specification with all implementations
• get_implementation - Get code for a specific library
• list_libraries - List supported plotting libraries
• get_tag_values - Get available tag values by category

Add to your MCP client configuration (e.g., Claude Code .mcp.json):

SSE Transport (recommended, wider compatibility):

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

Streamable HTTP Transport (modern, bidirectional):

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

Most plotting libraries are fully open source. Note these exceptions:

• Highcharts: Free for non-commercial use. Commercial use requires a license from highcharts.com

pyplots/ ├── plots/ # Plot specs + metadata + implementations ├── prompts/ # AI agent prompts ├── api/ # FastAPI backend ├── app/ # React frontend ├── core/ # Shared business logic ├── automation/ # Workflow scripts (sync, labels) ├── tests/ # Test suite (unit, integration, e2e) ├── alembic/ # Database migrations ├── docs/ # Documentation └── .github/workflows/ # GitHub Actions 

For details, see Repository Structure

• Vision - Product vision and mission
• Contributing - How to add/improve specs and implementations
• Workflows - Automation flows and label system
• Reference - API, database, repository structure

We welcome contributions! All code is AI-generated - you propose ideas, AI implements them.

Three ways to contribute (from pyplots.ai or GitHub):

How it works:

1. You create Issue (or click link on pyplots.ai)
2. AI validates and processes your input
3. Maintainer reviews and approves
4. AI generates/fixes the code
5. Automated quality review ensures excellence

Important: Don't submit code directly! If a plot has quality issues, it means the spec needs improvement, not the code.

See contributing.md for details.

See Development Guide for local setup instructions.

MIT License - see LICENSE file for details.

• Website: pyplots.ai
• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Stats: Public Analytics