flint-chart README

The project's README from github.com/microsoft/flint-chart. As one markdown file: README.md.

Flint: A Visualization Language for the AI Era

npm: flint-chart npm: flint-chart-mcp arXiv: 2607.20775 CI License: MIT

Please visit: Flint Project Site | Visual Themes | MCP Server Guide | 中文主页

Flint is a visualization intermediate language that lets AI agents turn simple, human-editable chart specs into expressive, polished visualizations. Rather than requiring agents to hardcode verbose parameters for scales, axes, spacing, labels, and layout, Flint derives those decisions from a high-level semantic specification, the data, and an optional visual theme (e.g., New York Times, Economist, Swiss, and McKinsey). A compact spec compiles to native Vega-Lite, ECharts, Chart.js, Plotly specs, or editable Excel charts.

This repo contains two main components:

Try it in your agent

Try Flint with your agent using our hosted MCP server. Clients that support remote HTTP MCP servers include GitHub Copilot in VS Code (MCP: Add Server → HTTP) and Claude (Customize → Connectors → Add custom connector). Point either client at:

https://flint.data-formulator.ai/mcp

Then ask your agent to find some public data and visualize it, for example:

Go to Yahoo Finance, find today's percentage change for the Magnificent Seven
tech stocks, and compare their performance with Flint in Economist style.

For a local MCP server (recommended for large datasets), check the MCP Server Guide for details.

A wall of charts produced by Flint across its supported visualization backends.

Features

Updates

See the changelog for complete release notes.

Flint compiling a compact chart spec into a Vega-Lite spec and rendered heatmap visualization.
Flint turns compact chart specs into backend-native specs and rendered visualizations.

Install

# Use Flint in your JavaScript/TypeScript codebase
npm install flint-chart

# For agents and MCP clients
npx -y flint-chart-mcp

Python package: to be released. The current Python port is a source-only preview in this repo.

Use Flint As A Library

Every backend accepts the same ChartAssemblyInput and returns the target library's native spec object.

import { assembleVegaLite } from 'flint-chart';

const spec = assembleVegaLite({
  data: { values: myData },
  semantic_types: { weight: 'Quantity', mpg: 'Quantity', origin: 'Country' },
  chart_spec: {
    chartType: 'Scatter Plot',
    encodings: { x: { field: 'weight' }, y: { field: 'mpg' }, color: { field: 'origin' } },
    baseSize: { width: 400, height: 300 },
  },
});
// → a ready-to-render Vega-Lite spec

Swap the backend without changing the input shape:

import { assembleECharts, assembleChartjs, assemblePlotly, assembleExcel } from 'flint-chart';

const echartsOption = assembleECharts(input);
const chartjsConfig = assembleChartjs(input);
const plotlyFigure = assemblePlotly(input);
const excelArtifact = assembleExcel(input);

Apply Visual Themes

theme_spec sits beside chart_spec: the chart spec defines what the chart means, while the theme defines how that meaning is presented. A theme can guide layout, labels, legends, axes, mark geometry, typography, and color as one coherent visual system.

Use one of Flint's ten built-in presets:

const themedSpec = assembleVegaLite({
  ...input,
  theme_spec: 'economist',
});

Or inherit a preset and override only the decisions that belong to your brand:

const brandedSpec = assembleVegaLite({
  ...input,
  theme_spec: {
    extends: 'economist',
    id: 'our-brand',
    ink: {
      series: { single: '#6b3fa0' },
    },
  },
});

Nested objects merge; arrays and scalar values replace the inherited value. ThemeSpec currently affects Vega-Lite output. Compare all presets on the theme explorer. See Using themes for the complete custom and inherited-theme reference.

Twelve charts rendered with Flint's Economist theme.
Economist — compact editorial graphics with a strong red accent.

Twelve charts rendered with Flint's Swiss theme.
Swiss — typographic structure, restrained color, and a clear visual grid.

Twelve charts rendered with Flint's Pop theme.
Pop — bold color, emphatic marks, and playful graphic contrast.

See the API reference, backend references for Vega-Lite, ECharts, Chart.js, Plotly, and Excel, plus the live editor for more library examples.

Use Flint As An MCP Server

Install flint-chart-mcp as a Model Context Protocol server when you want an agent to create charts in the same conversation where the question starts. It can open an interactive chart view, return static PNG/SVG output, or produce backend-native chart specs.

For setup, start with the Flint MCP project page. It includes client configuration, usage examples, and links to deeper references.

Agent chat showing Flint Chart as an MCP App with a grouped bar chart preview and chart options.

MCP calls let agents embed rows directly as data.values, or read local JSON, CSV, or TSV files by data.url. For agent workflows without MCP, use the standalone agent skill.

Repository overview

flint-chart/
├── packages/
│   ├── flint-js/            npm package `flint-chart` (TypeScript)
│   │   └── src/
│   │       ├── core/        semantics, themes, layout, decisions, shared types
│   │       ├── chart-types/ shared chart definitions and template metadata
│   │       ├── vegalite/    Vega-Lite backend
│   │       ├── echarts/     ECharts backend
│   │       ├── chartjs/     Chart.js backend
│   │       ├── plotly/      Plotly backend
│   │       ├── excel/       native Excel backend
│   │       ├── gallery/     gallery assembly and generated references
│   │       └── test-data/   fixtures and stress-test generators
│   ├── flint-mcp/           MCP server, MCP App UI, assets, and tests
│   └── flint-py/            Python port preview (package to be released)
├── site/                    Vite + React project site, gallery, editor, and docs
├── agent-skills/            chart- and theme-authoring skills for agents
├── agents/                  agent and MCP server configuration
├── shared/test-data/        JSON fixtures shared across JS and Python
├── scripts/                 reference generation and theme audit tooling
├── docs/                    user, API, backend, and architecture documentation
└── design-docs/             design proposals and implementation research

Documentation

The project site is the main entry point for examples, the live editor, and concept docs. For source-level references, start with the API reference, the theme guide, the Flint MCP project page, or the Development guide. See the changelog for notable changes in each release.


Contributing

Contributions are welcome! See .github/CONTRIBUTING.md and the Development guide.

git clone https://github.com/microsoft/flint-chart
cd flint-chart
npm install            # root workspaces: packages/flint-js + flint-mcp + site

npm run typecheck      # typecheck packages/flint-js + packages/flint-mcp
npm run test           # Vitest (packages/flint-js + packages/flint-mcp)
npm run build          # build packages/flint-js + packages/flint-mcp
npm run site           # demo site (gallery + editor) at http://localhost:5274/

Node 18+ is required. The demo site aliases flint-chart to packages/flint-js/src, so library edits hot-reload in the gallery and editor without rebuilding dist/.

We especially welcome contributions that add new chart templates or new rendering backends.

This project has adopted the Microsoft Open Source Code of Conduct. See SECURITY.md to report vulnerabilities.

Contributor

Flint is built by Microsoft Research in collaboration with the IDEAS Lab, Renmin University of China. We welcome you to join us — see Contributing to get involved.

A research paper describing Flint is coming soon.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos is subject to those third parties' policies.

License

MIT © Microsoft Corporation

flint-chart (microsoft/flint-chart)

Searching Database…

What is this?Flint is a visualization intermediate language that lets AI agents turn simple, human-editable chart specs into polished, expressive charts instead of hand-coding scales, axes, and layout.
Is this a chart library or an MCP server?Both: the repo ships flint-chart, a JS/TS library that compiles Flint specs into Vega-Lite, ECharts, Chart.js, Plotly, or Excel-native output, and flint-chart-mcp, an MCP server that lets agents create, validate, and render those charts from a chat or coding environment.
How do I start using Flint with my agent?Point an MCP-capable client (GitHub Copilot in VS Code or Claude's custom connector) at Flint's hosted MCP server, or run flint-chart-mcp locally for large datasets per the MCP Server Guide; to use it as a plain library, import flint-chart and pass a ChartAssemblyInput to the backend you want.
Do I need anything installed before using the JS library?Node 18+ is required to build and run the project.
Is the Python version ready to use yet?No, the Python package is not released yet — the current Python port is only a source-only preview in the repo, and ThemeSpec currently only affects Vega-Lite output.
Who is Flint built for?It's aimed at AI agents and the developers building agent workflows that need to generate reliable, good-looking charts without writing verbose charting-library parameters by hand.
Which chart backends can Flint compile to?One Flint spec compiles to backend-native output for Vega-Lite, ECharts, Chart.js, Plotly, or native, editable Excel charts.
What do Flint's visual themes do?A ThemeSpec defines layout, labels, legends, axes, mark geometry, typography and color as one coherent system; you can use one of ten built-in presets (e.g. New York Times, Economist, Swiss, McKinsey) or inherit and override just the parts you need.
Why not just hardcode chart library parameters?Flint derives scale, axis, spacing, and layout decisions from a high-level semantic spec (70+ semantic types like Rank, Price, Country) plus the data and an optional theme, so agents don't have to manually tune verbose chart parameters.
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