@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:

Package	Needed for
fast-xml-parser	@statelyai/graph/gexf, @statelyai/graph/graphml
dotparser	@statelyai/graph/dot parsing
zod	@statelyai/graph/schemas
elkjs	@statelyai/graph/elk, @statelyai/graph/layout/elk
@dagrejs/dagre	@statelyai/graph/layout/dagre
@hpcc-js/wasm-graphviz	@statelyai/graph/layout/graphviz
d3-force	@statelyai/graph/layout/d3-force
graphology, graphology-layout-forceatlas2	@statelyai/graph/layout/forceatlas2
d3-hierarchy	@statelyai/graph/layout/d3-hierarchy
webcola	@statelyai/graph/layout/webcola
cytoscape	@statelyai/graph/layout/cytoscape, Cytoscape format typing

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, flow/cuts, matching, cores, isomorphism, ordering, MST, and walks
• Pluggable layout over eight external engines (ELK, Graphviz, dagre, d3-force, ForceAtlas2, tidy tree, WebCola, cytoscape) — pure functions, optional peers
• 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.

updateNode/updateEdge accept any config field. Optional fields (position, size, shape, color, style, edge weight/mode/ports) can be unset by passing null; undefined leaves them unchanged:

updateNode(graph, 'a', { x: 100, color: 'red' }); // set
updateEdge(graph, 'e1', { weight: 2, mode: 'undirected' });
updateNode(graph, 'a', { color: null }); // unset

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

For structural invariant checking without zod, the core export getGraphIssues(graph) returns machine-readable issues (duplicate ids, dangling edge endpoints, missing parents, parent cycles, missing initial nodes, duplicate or invalid port references) — the recommended gate for untrusted or imported graphs:

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

const issues = getGraphIssues(importedGraph);
if (issues.length > 0) {
  console.error(issues.map((issue) => issue.message));
}

Use the @statelyai/graph/schemas subpath when you want full runtime shape validation or JSON Schema generation. validateGraph() combines zod shape checks with the same graph invariants.

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*, bidirectional Dijkstra), centrality/link analysis (degree, closeness, betweenness, PageRank, HITS, eigenvector, Katz), community detection (Louvain, label propagation, Girvan-Newman, greedy modularity, modularity scoring), flow & cuts (getMaxFlow, getMinCut), bipartite analysis (isBipartite, Hopcroft–Karp getMaximumBipartiteMatching), k-cores (getCoreNumbers, getKCore), cycle detection, connected/strongly-connected components, bridges, articulation points, biconnected components, dominator trees, transitive reduction, isomorphism, topological sort, minimum spanning tree, and seeded graph generators (createCompleteGraph, createGridGraph, createRandomGraph). Many algorithms have lazy generator variants (gen*) for early exit. See docs/algorithms.md for the full reference.

Hot algorithm loops (centrality, components) run on an internal compressed-sparse-row snapshot — cached and invalidated transparently like the rest of the index — so they stay fast on large graphs without changing the plain-JSON model. Algorithm results are differential-tested against graphology on seeded random graphs.

import {
  bfs,
  dfs,
  hasPath,
  isAcyclic,
  getShortestPath,
  getCycles,
  getTopologicalSort,
  getConnectedComponents,
  getMinimumSpanningTree,
  getPageRank,
  getLouvainCommunities,
  getLabelPropagationCommunities,
  genGirvanNewmanCommunities,
  getBridges,
  getMaxFlow,
  getDominatorTree,
  getTransitiveReduction,
  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, { getWeight: (e) => e.weight ?? 1 }); // MST
getPageRank(graph); // link analysis scores
getLouvainCommunities(graph); // community detection (Louvain)
getLabelPropagationCommunities(graph); // community detection
[...genGirvanNewmanCommunities(graph)]; // lazy community splits
getBridges(graph); // bridge edges
getMaxFlow(graph, { from: 'a', to: 'c' }); // max flow + min cut
getDominatorTree(graph, { from: 'a' }); // immediate dominators
getTransitiveReduction(graph); // minimal equivalent DAG
isIsomorphic(graph, otherGraph); // structural equivalence

Layout

Plug-and-play layout over external engines — pure functions in, positioned VisualGraph out. No layout algorithms of our own; each adapter is a subpath with an optional peer dependency. The hierarchical engines (ELK, dagre, Graphviz) also produce routed edge points and computed edge-label rects; the physics/tree/cytoscape engines position nodes only.

import { getElkLayout } from '@statelyai/graph/layout/elk'; // elkjs
import { getDagreLayout } from '@statelyai/graph/layout/dagre'; // @dagrejs/dagre
import { getGraphvizLayout } from '@statelyai/graph/layout/graphviz'; // @hpcc-js/wasm-graphviz (8 engines)
import { genForceLayout } from '@statelyai/graph/layout/d3-force'; // d3-force
import { getForceAtlas2Layout } from '@statelyai/graph/layout/forceatlas2'; // graphology FA2
import { getTidyTreeLayout } from '@statelyai/graph/layout/d3-hierarchy'; // d3-hierarchy
import { getColaLayout } from '@statelyai/graph/layout/webcola'; // webcola (constraints)
import { getCytoscapeLayout } from '@statelyai/graph/layout/cytoscape'; // cytoscape ecosystem
import { applyLayoutFrame, getLayoutBounds, centerGraph } from '@statelyai/graph/layout';

const laidOut = await getElkLayout(graph, {
  measure: (node) => measureText(node.label), // text measurement stays yours
  constraints: { layer: (node) => node.data?.tier }, // portable layer constraint
});

// Physics layouts are generators — one tick per frame, cancel by stopping
for (const frame of genForceLayout(graph, { seed: 42 })) {
  applyLayoutFrame(graph, frame);
  render(graph);
}

Edge x/y/width/height are canonically the edge-label rect; routes live in edge.points (routing says how to interpret them). Layouts are plain JSON — tween between engines with genLayoutTransition, or diff them with getPatches. See docs/layout.md and docs/layout-transitions.md.

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

Format	Hierarchy	Ports	Visual	Round-trip	Notes
adjacency-list	none	none	none	partial	Connectivity only; edge metadata is lost.
cytoscape	full	full	full	full	Graph/node/edge metadata (incl. per-edge mode) round-trips through element data.
d3	full	full	full	full	Graph/node/edge metadata (incl. per-edge mode) round-trips through the loose JSON shape.
d2	full	full	full	full	Hierarchy, ports, styles, and connector modes round-trip; nested vars sub-blocks are dropped.
dot	partial	partial	partial	partial	Edge port ids round-trip, but :port:compass mapping is still incomplete.
edge-list	none	none	none	partial	Endpoints only.
elk	full	full	full	full	Metadata round-trips through reserved layout options; port ids are emitted as nodeId__portName (document-unique, as ELK requires).
gexf	full	full	full	full	Custom attributes preserve metadata; bidirectional maps to directed.
gml	full	full	full	full	Metadata round-trips through direct and JSON fields; per-edge/graph mode via a dialect key.
graphml	full	full	partial	full	Emit is own-dialect (<data> fields, flat); import handles both dialects incl. standard nested <graph>, native <port> elements, and sourceport/targetport attributes. Multi-graph files import the first graph.
jgf	full	full	full	full	Metadata (incl. per-edge/graph mode) round-trips through metadata objects.
tgf	none	none	none	partial	Minimal ids and labels only.
xyflow	full	full	full	full	Metadata (incl. weight, ports, per-edge mode) round-trips through reserved data fields; parents are ordered before children for React Flow.
mermaid/block	partial	none	partial	partial	Syntax-driven, not port-aware.
mermaid/class	none	none	none	partial	Class syntax is stored conservatively.
mermaid/er	none	none	none	partial	Focuses on entities and cardinality.
mermaid/flowchart	partial	none	partial	partial	linkStyle indices are fragile.
mermaid/ishikawa	full	none	none	partial	Preserves hierarchy, not fishbone layout.
mermaid/mindmap	full	none	partial	partial	Icon syntax is not fully re-emitted.
mermaid/sequence	partial	none	none	partial	Actor links and menu syntax are incomplete.
mermaid/state	full	none	partial	partial	Isolated states and labels now emit (labels via the description form); initialNodeId round-trips as [*] -->.

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

Guides

• Layout guide — the adapter contract, all eight engines, constraints, sizing, web workers
• Layout transitions — tween between engines; layouts are just data
• Algorithms reference — every algorithm with complexity and semantics notes
• Benchmarks — measured against graphology, ngraph, graphlib, and cytoscape
• Migrating from graphlib
• React Flow + ELK pipeline — measured nodes, worker layout, live re-layout

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