GitHub - statelyai/graph: Universal utilities for working with graphs

A TypeScript graph library built on plain JSON objects. Supports directed/undirected graphs, hierarchical nodes, graph algorithms, visual properties, and serialization to DOT, GraphML, Mermaid, and more.

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:

Package	Needed for
`fast-xml-parser`	`@statelyai/graph/gexf`, `@statelyai/graph/graphml`
`dotparser`	`@statelyai/graph/dot` parsing
`cytoscape`	Cytoscape integration tests and consumer typing
`d3-force`	D3 force integration tests and consumer typing
`elkjs`	`@statelyai/graph/elk`
`zod`	`@statelyai/graph/schemas`

Highlights

Plain JSON graphs with no runtime wrappers required
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
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]

Algorithms

Includes traversal (BFS, DFS), pathfinding (shortest path, simple paths, all-pairs shortest paths), 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, JSON Graph Format, GEXF, GraphML, GML, TGF, DOT, Mermaid (flowchart, state, sequence, class, ER, mindmap, block), 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);

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:

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

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 computational layer in between: plain JSON objects in, algorithms and mutations, plain JSON objects 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.

License

MIT