Provides an MCP server and machine-readable schemas for generating and validating React-based chart components, supporting everything from standard line charts to complex network graphs and geographic visualizations.
Supports the translation of Vega-Lite specifications into Semiotic configurations, enabling AI agents to convert standardized chart definitions into React-based visualizations.

A React data visualization library designed for AI-assisted development.
Simple charts in 5 lines. Network graphs, streaming data, and coordinated dashboards when you need them. Structured schemas and an MCP server so AI coding assistants generate correct chart code on the first try.
import { LineChart } from "semiotic"
<LineChart
data={salesData}
xAccessor="month"
yAccessor="revenue"
/>Why Semiotic
Semiotic is a data visualization library for React that combines broad chart coverage with first-class AI tooling. It handles the chart types that most libraries skip — network graphs, streaming data, statistical distributions, coordinated views — and ships with machine-readable schemas so LLMs can generate correct code without examples.
Built for AI-assisted development
Semiotic ships with everything an AI coding assistant needs to generate correct visualizations without trial and error:
semiotic/ai— a single import with all 37 chart components, optimized for LLM code generationai/schema.json— machine-readable prop schemas for every componentnpx semiotic-mcp— an MCP server for tool-based chart rendering in any MCP clientnpx semiotic-ai --doctor— validate component + props JSON from the command line with typo suggestions and anti-pattern detectiondiagnoseConfig(component, props)— programmatic anti-pattern detector with 12 checks and actionable fixesCLAUDE.md— instruction files auto-synced for Claude, Cursor, Copilot, Windsurf, and Clinellms.txt— machine-readable documentation following the emerging standard
Every chart includes a built-in error boundary, dev-mode validation
warnings with typo suggestions, and accessibility features (canvas
aria-label, keyboard-navigable legends, aria-live tooltips, SVG
<title>/<desc>) so AI-generated code fails gracefully with
actionable diagnostics instead of a blank screen.
Beyond standard charts
Network visualization. Force-directed graphs, Sankey diagrams, chord diagrams, tree layouts, treemaps, circle packing, and orbit diagrams — all as React components with the same prop API as LineChart.
Streaming data. Realtime charts render on canvas at 60fps with a ref-based push API. Built-in decay, pulse, and staleness encoding for monitoring dashboards.
Coordinated views. LinkedCharts provides hover cross-highlighting,
brush cross-filtering, and selection synchronization across any combination
of chart types — zero wiring.
Geographic visualization. Choropleth maps, proportional symbol maps, flow maps with animated particles, and distance cartograms — all canvas-rendered with d3-geo projections, zoom/pan, tile basemaps, and drag-rotate globe spinning.
Statistical summaries. Box plots, violin plots, swarm plots, histograms, LOESS smoothing, forecast with confidence envelopes, and anomaly detection. Marginal distribution graphics on scatterplot axes with a single prop.
Start simple, go deep
Layer | For | Example |
Charts | Common visualizations with sensible defaults |
|
Frames | Full control over rendering, interaction, and layout |
|
Every Chart component accepts a frameProps prop to access the underlying
Frame API without leaving the simpler interface.
Serialization and interop
Charts serialize to JSON and back: toConfig, fromConfig, toURL,
copyConfig, configToJSX. Have Vega-Lite specs? fromVegaLite(spec)
translates them to Semiotic configs — works with configToJSX() for
full round-trip from notebooks and AI-generated specs.
When to use something else
Need a standard bar or line chart for a dashboard you'll never need to customize beyond colors and labels? Recharts has a larger ecosystem and more community examples. Need GPU-accelerated rendering for millions of data points? Apache ECharts handles that scale.
Semiotic is for projects that outgrow those libraries — when you need network graphs alongside time series, streaming data alongside static snapshots, or coordinated views across chart types.
Install
npm install semioticRequires React 18.1+ or React 19.
Quick Examples
Coordinated Dashboard
Hover one chart, highlight the same data in another — zero wiring:
import { LinkedCharts, Scatterplot, BarChart } from "semiotic"
<LinkedCharts>
<Scatterplot
data={data} xAccessor="age" yAccessor="income" colorBy="region"
linkedHover={{ name: "hl", fields: ["region"] }}
selection={{ name: "hl" }}
/>
<BarChart
data={summary} categoryAccessor="region" valueAccessor="total"
selection={{ name: "hl" }}
/>
</LinkedCharts>Streaming Metrics with Decay
Live data fades old points, flashes new ones, flags stale feeds:
import { RealtimeLineChart } from "semiotic"
const chartRef = useRef()
chartRef.current.push({ time: Date.now(), value: cpuLoad })
<RealtimeLineChart
ref={chartRef}
timeAccessor="time"
valueAccessor="value"
decay={{ type: "exponential", halfLife: 100 }}
staleness={{ threshold: 5000, showBadge: true }}
/>Network Graphs
Force-directed graphs and Sankey diagrams — same API as LineChart:
import { ForceDirectedGraph, SankeyDiagram } from "semiotic"
<ForceDirectedGraph
nodes={people} edges={friendships}
colorBy="team" nodeSize={8} showLabels
/>
<SankeyDiagram
edges={budgetFlows}
sourceAccessor="from" targetAccessor="to" valueAccessor="amount"
/>Geographic Visualization
Choropleth maps, flow maps, and distance cartograms with canvas rendering, zoom/pan, tile basemaps, and animated particles:
import { ChoroplethMap, FlowMap, DistanceCartogram } from "semiotic/geo"
<ChoroplethMap
areas={geoJsonFeatures} valueAccessor="gdp"
colorScheme="viridis" projection="equalEarth" zoomable tooltip
/>
<FlowMap
nodes={airports} flows={routes} valueAccessor="passengers"
showParticles particleStyle={{ color: "source", speedMultiplier: 1.5 }}
/>
<DistanceCartogram
points={cities} center="rome" costAccessor="travelDays"
showRings costLabel="days" lines={routes}
/>Streaming System Monitor
Live service topology with threshold alerting and click-to-inspect:
import { StreamNetworkFrame, ChartContainer, DetailsPanel, LinkedCharts } from "semiotic"
const chartRef = useRef()
chartRef.current.push({ source: "API", target: "Orders", value: 15 })
<LinkedCharts>
<ChartContainer title="System Monitor" status="live"
detailsPanel={
<DetailsPanel position="right" trigger="click">
{(datum) => <div>{datum.id}: {datum.value} req/s</div>}
</DetailsPanel>
}>
<StreamNetworkFrame ref={chartRef} chartType="sankey"
showParticles particleStyle={{ proportionalSpeed: true }}
thresholds={{ metric: n => n.value, warning: 100, critical: 250 }}
/>
</ChartContainer>
</LinkedCharts>Standard Charts
Line, bar, scatter, area — all the basics, with sensible defaults:
import { LineChart, BarChart } from "semiotic"
<LineChart
data={salesData}
xAccessor="month" yAccessor="revenue"
curve="monotoneX" showPoints
/>
<BarChart
data={categoryData}
categoryAccessor="department" valueAccessor="sales"
orientation="horizontal" colorBy="region"
/>All Chart Components
Category | Components |
XY |
|
Categorical |
|
Network |
|
Geo |
|
Realtime |
|
Coordination |
|
Layout |
|
Frames |
|
Vega-Lite Translation
Paste a Vega-Lite spec, get a Semiotic chart:
import { fromVegaLite } from "semiotic/data"
import { configToJSX, fromConfig } from "semiotic"
const config = fromVegaLite({
mark: "bar",
data: { values: [{ a: "A", b: 28 }, { a: "B", b: 55 }] },
encoding: {
x: { field: "a", type: "nominal" },
y: { field: "b", type: "quantitative" },
},
})
// Render directly
const { componentName, props } = fromConfig(config)
// → componentName: "BarChart", props: { data, categoryAccessor: "a", valueAccessor: "b" }
// Or generate JSX code
configToJSX(config)
// → <BarChart data={[...]} categoryAccessor="a" valueAccessor="b" />Supports bar, line, area, point, rect, arc, tick marks with encoding translation for color, size, aggregation, and binning.
Smaller Bundles
Import only what you need:
import { LineChart } from "semiotic/xy" // ~156 KB
import { BarChart } from "semiotic/ordinal" // ~124 KB
import { ForceDirectedGraph } from "semiotic/network" // ~123 KB
import { ChoroplethMap } from "semiotic/geo" // ~102 KB (+ d3-geo peer)
import { LineChart } from "semiotic/ai" // ~397 KB (all HOCs)Granular entry points export only v3 Stream Frames and HOC charts — no legacy utilities or backwards-compatibility shims.
TypeScript
Built with strict: true. Full type definitions ship with the package.
Generics for type-safe accessors:
interface Sale { month: number; revenue: number }
<LineChart<Sale>
data={sales}
xAccessor="month" // TS validates this is keyof Sale
yAccessor="revenue"
/>Server-Side Rendering
All chart components render SVG automatically in server environments — no special imports or configuration needed:
// Works in Next.js App Router, Remix, Astro — same component, same props
import { LineChart } from "semiotic"
// Server: renders <svg> with path/circle/rect elements
// Client: renders <canvas> with SVG overlay for axes
<LineChart data={data} xAccessor="date" yAccessor="value" />For standalone SVG generation (email, OG images, PDF), use the server entry point:
import { renderToStaticSVG } from "semiotic/server"
const svg = renderToStaticSVG("xy", {
lines: [{ coordinates: data }],
xAccessor: "date",
yAccessor: "value",
size: [600, 400],
})MCP Server
Semiotic ships with an MCP server that lets AI coding assistants render charts, diagnose configuration problems, discover schemas, and get chart recommendations via tool calls.
Setup
Add to your MCP client config (e.g. claude_desktop_config.json for Claude Desktop):
{
"mcpServers": {
"semiotic": {
"command": "npx",
"args": ["semiotic-mcp"]
}
}
}No API keys or authentication required. The server runs locally via stdio.
Tools
Tool | Description |
| Render a Semiotic chart to static SVG. Supports the components returned by |
| Return the prop schema for a specific component. Pass |
| Recommend chart types for a data sample. Pass |
| Check a chart configuration for common problems — empty data, bad dimensions, missing accessors, wrong data shape, and more. Returns a human-readable diagnostic report with actionable fixes. |
| Generate a pre-filled GitHub issue URL for bug reports or feature requests. Pass |
Example: get schema for a component
Tool: getSchema
Args: { "component": "LineChart" }
→ Returns: { "name": "LineChart", "description": "...", "parameters": { "properties": { "data": ..., "xAccessor": ..., ... } } }Example: suggest a chart for your data
Tool: suggestChart
Args: {
"data": [
{ "month": "Jan", "revenue": 120, "region": "East" },
{ "month": "Feb", "revenue": 180, "region": "West" }
]
}
→ Returns:
1. BarChart (high confidence) — categorical field (region) with values (revenue)
2. StackedBarChart (medium confidence) — two categorical fields (month, region)
3. DonutChart (medium confidence) — 2 categories — proportional compositionExample: render a chart
Tool: renderChart
Args: {
"component": "BarChart",
"props": {
"data": [
{ "category": "Q1", "revenue": 120 },
{ "category": "Q2", "revenue": 180 },
{ "category": "Q3", "revenue": 150 }
],
"categoryAccessor": "category",
"valueAccessor": "revenue"
}
}
→ Returns: <svg>...</svg>Example: diagnose a broken config
Tool: diagnoseConfig
Args: { "component": "LineChart", "props": { "data": [] } }
→ Returns: ✗ [EMPTY_DATA] data is an empty array — Fix: provide at least one data pointExample: report an issue
Tool: reportIssue
Args: {
"title": "Bug: BarChart tooltip shows undefined for custom accessor",
"body": "When using valueAccessor='amount', tooltip displays 'undefined'.\n\ndiagnoseConfig output: ✓ no issues detected.",
"labels": ["bug"]
}
→ Returns: Open this URL to submit the issue: https://github.com/nteract/semiotic/issues/new?...CLI alternative
For quick validation without an MCP client:
npx semiotic-ai --doctor # validate component + props JSON
npx semiotic-ai --schema # dump all chart schemas
npx semiotic-ai --compact # compact schema (fewer tokens)Documentation
Charts — all 37 chart types with live examples
Frames — full Frame API reference
Features — axes, annotations, tooltips, styling, Vega-Lite translator
Cookbook — advanced patterns and recipes
Playground — interactive prop exploration
Upgrading
Migration Guide — upgrading from v1.x or v2.x
Changelog — full release history
Contributing
See CONTRIBUTING.md. Our community follows the nteract Code of Conduct.
Acknowledgments
Development of this library owes a lot to Susie Lu, Jason Reid, James Womack, Matt Herman, Shelby Sturgis, and Tristan Reid.
The Sankey layout engine is based on sankey-plus
by Tom Shanley, which improved on his earlier
d3-sankey-circular with better cycle detection, hierarchical arc stacking,
and dynamic extent adjustment.
Semiotic icon based on an icon by Andre Schauer.