@statelyai/graph

A TypeScript graph library for JSON-serializable graph IR. Use it to validate, analyze, transform, and round-trip directed, undirected, hierarchical, port-aware, and visual graphs across tools.

Made from our experience at stately.ai, where we build visual tools for complex systems.

Install

npm install @statelyai/graph

Optional peers are only needed for specific adapters:

Highlights

• Plain JSON graphs with no runtime wrappers required; omitted data defaults to null
• Standalone functions with a consistent get*/gen*/is*/add* naming model
• Directed, undirected, hierarchical, and visual graph support
• Ports for node-editor and dataflow-style graphs
• Algorithms for traversal, paths, centrality, communities, connectivity, isomorphism, ordering, MST, and walks
• Diff/patch utilities for graph state changes
• Multi-format conversion via package subpaths, with fidelity claims tested against fixtures
• Small, fast test suite with broad format coverage

Quick Start

Graphs are plain JSON-serializable objects. All operations are standalone functions — no classes, no DOM, no rendering engine.

import {
  createGraph,
  addNode,
  addEdge,
  getShortestPath,
} from '@statelyai/graph';

const graph = createGraph({
  nodes: [
    { id: 'a', label: 'Start' },
    { id: 'b', label: 'Middle' },
    { id: 'c', label: 'End' },
  ],
  edges: [
    { id: 'e1', sourceId: 'a', targetId: 'b' },
    { id: 'e2', sourceId: 'b', targetId: 'c' },
  ],
});

// Mutate in place
addNode(graph, { id: 'd', label: 'Shortcut' });
addEdge(graph, { id: 'e3', sourceId: 'a', targetId: 'd' });

// Algorithms work on the plain object
const path = getShortestPath(graph, { from: 'a', to: 'c' });

Graph Manipulation

Look up, add, delete, and update nodes and edges. Query neighbors, predecessors, successors, degree, and more.

import {
  getNode,
  deleteNode,
  getNeighbors,
  getSources,
} from '@statelyai/graph';

const node = getNode(graph, 'a'); // lookup by id
deleteNode(graph, 'd'); // removes node + connected edges
const neighbors = getNeighbors(graph, 'a'); // adjacent nodes
const roots = getSources(graph); // nodes with no incoming edges

Batch operations (addEntities, deleteEntities, updateEntities) let you apply multiple changes at once.

Hierarchy

Nodes support parent-child relationships for compound/nested graphs. Query children, ancestors, descendants, depth, and least common ancestor. Use flatten() to decompose into a flat leaf-node graph.

import { createGraph, getChildren, getLCA, flatten } from '@statelyai/graph';

const graph = createGraph({
  nodes: [
    { id: 'a' },
    { id: 'b', initialNodeId: 'b1' },
    { id: 'b1', parentId: 'b' },
    { id: 'b2', parentId: 'b' },
    { id: 'c' },
  ],
  edges: [
    { id: 'e1', sourceId: 'a', targetId: 'b' }, // resolves to a -> b1
    { id: 'e2', sourceId: 'b1', targetId: 'b2' },
    { id: 'e3', sourceId: 'b', targetId: 'c' }, // expands from all leaves of b
  ],
});

const children = getChildren(graph, 'b'); // [b1, b2]
const flat = flatten(graph); // only leaf nodes, edges resolved

Ports

Ports are optional named connection points on nodes. They are useful for flow-based systems, node editors, and dataflow graphs where edges need to target a specific input or output.

import { createGraph, getEdgesByPort, getPorts } from '@statelyai/graph';

const graph = createGraph({
  nodes: [
    {
      id: 'fetch',
      ports: [{ name: 'result', direction: 'out' }],
    },
    {
      id: 'render',
      ports: [{ name: 'input', direction: 'in' }],
    },
  ],
  edges: [
    {
      id: 'e1',
      sourceId: 'fetch',
      sourcePort: 'result',
      targetId: 'render',
      targetPort: 'input',
    },
  ],
});

getPorts(graph, 'fetch'); // [{ name: 'result', ... }]
getEdgesByPort(graph, 'render', 'input'); // [e1]

Schema Validation

Use the @statelyai/graph/schemas subpath when you want runtime validation or JSON Schema generation. validateGraph() combines shape checks with graph invariants such as duplicate ids, dangling edges, missing parents, missing initial nodes, duplicate ports, invalid port references, and parent cycles.

import { GraphSchema, isGraph, validateGraph } from '@statelyai/graph/schemas';

const unknownValue: unknown = JSON.parse(input);

if (isGraph(unknownValue)) {
  // fully typed Graph
} else {
  console.error(validateGraph(unknownValue));
}

const parsed = GraphSchema.parse(unknownValue);

Algorithms

Includes traversal (BFS, DFS, preorder/postorder), pathfinding (shortest path, simple paths, all-pairs shortest paths, A*), centrality/link analysis (degree, closeness, betweenness, PageRank, HITS, eigenvector), community detection (label propagation, Girvan-Newman, greedy modularity, modularity scoring), cycle detection, connected/strongly-connected components, bridges, articulation points, biconnected components, isomorphism, topological sort, minimum spanning tree, and more. Many algorithms have lazy generator variants (gen*) for early exit.

import {
  bfs,
  dfs,
  hasPath,
  isAcyclic,
  getShortestPath,
  getCycles,
  getTopologicalSort,
  getConnectedComponents,
  getMinimumSpanningTree,
  getPageRank,
  getLabelPropagationCommunities,
  genGirvanNewmanCommunities,
  getBridges,
  isIsomorphic,
} from '@statelyai/graph';

for (const node of bfs(graph, 'a')) {
  /* breadth-first */
}
for (const node of dfs(graph, 'a')) {
  /* depth-first */
}

hasPath(graph, 'a', 'c'); // reachability
isAcyclic(graph); // cycle check
getShortestPath(graph, { from: 'a', to: 'c' }); // single shortest path
getTopologicalSort(graph); // topological order (or null)
getConnectedComponents(graph); // connected components
getMinimumSpanningTree(graph, { weight: (e) => e.data?.weight ?? 1 }); // MST
getPageRank(graph); // link analysis scores
getLabelPropagationCommunities(graph); // community detection
[...genGirvanNewmanCommunities(graph)]; // lazy community splits
getBridges(graph); // bridge edges
isIsomorphic(graph, otherGraph); // structural equivalence

Diff & Walks

Beyond classic graph algorithms, the library also includes utilities for evolving and exploring graph state:

• getDiff(), getPatches(), applyPatches() for graph change tracking
• genRandomWalk(), genWeightedRandomWalk(), and coverage helpers for model-based testing and simulation
• getSubgraph() and reverseGraph() for structural transforms

Visual Graphs

createVisualGraph() guarantees x, y, width, height on all nodes and edges (default 0).

import { createVisualGraph } from '@statelyai/graph';

const diagram = createVisualGraph({
  direction: 'right',
  nodes: [
    { id: 'a', x: 0, y: 0, width: 120, height: 60, shape: 'rectangle' },
    { id: 'b', x: 200, y: 0, width: 120, height: 60, shape: 'ellipse' },
  ],
  edges: [{ id: 'e1', sourceId: 'a', targetId: 'b', width: 100, height: 100 }],
});

Format Conversion

Import and export graphs to many formats. Converters are available as subpath imports.

import { toDOT } from '@statelyai/graph/dot';
import { fromGEXF } from '@statelyai/graph/gexf';
import { toCytoscapeJSON } from '@statelyai/graph/cytoscape';
import { toD3Graph } from '@statelyai/graph/d3';

const dot = toDOT(graph); // Graphviz DOT
const cytoData = toCytoscapeJSON(graph); // Cytoscape.js JSON
const d3Data = toD3Graph(graph); // D3.js { nodes, links }
const imported = fromGEXF(gexfXmlString); // GEXF (Gephi)

Supported formats: Cytoscape.js JSON, D3.js JSON, D2, JSON Graph Format, GEXF, GraphML, GML, TGF, DOT, Mermaid (flowchart, state, sequence, class, ER, mindmap, block, Ishikawa), ELK, xyflow, adjacency list, and edge list.

Each bidirectional format also has a converter object:

import { cytoscapeConverter } from '@statelyai/graph/cytoscape';

const cyto = cytoscapeConverter.to(graph);
const back = cytoscapeConverter.from(cyto);

Round-trip fidelity may use adapter-specific graph, node, and edge data metadata when the target format does not have a native field for a source concept. A partial round-trip entry means the adapter still drops meaningful source information instead of preserving it as metadata.

Format Support

Some formats have optional peer dependencies: fast-xml-parser (GEXF, GraphML) and dotparser (DOT). All other formats are dependency-free.

Format-specific docs live alongside the source:

• Adjacency list
• Cytoscape
• D3
• D2
• DOT
• Edge list
• ELK
• GEXF
• GML
• GraphML
• JGF
• Mermaid
• TGF
• xyflow
• Converter helpers

Examples

The repo includes runnable examples under examples/:

• Flow-based math shows ports, topological ordering, and value propagation.
• Async workflow models an n8n/Zapier-style workflow with ports and dependency-aware execution.

Development

pnpm install
pnpm verify
pnpm bench

See CONTRIBUTING.md for contributor conventions, format-module checklist, and release notes guidance.

Why this library?

Graph file formats define how to store graphs. Visualization libraries define how to render them. This library is the trusted interchange and analysis layer in between: plain JSON objects in, validation, algorithms, transforms, diffing, and format-preserving conversion out.

GEXF file → fromGEXF() → Graph → run algorithms, mutate → toCytoscapeJSON() → render

Your Graph is a plain object that survives JSON.stringify, structuredClone, postMessage, and localStorage without adapters.

A canonical graph is a deterministic projection of a graph for comparison, hashing, snapshots, or caches. A future pure helper would return a new graph with stable node/edge ordering and normalized optional fields. A hash would be a digest of that canonical JSON. A summary would be a small structural report, for example node count, edge count, roots, sinks, component count, compound depth, port count, and whether the graph is acyclic. A pure sortGraph() would return a sorted copy and never mutate the input.

License

MIT