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react-flow-architecture

Architectural guidance for building node-based UIs with React Flow. Use when designing flow-based applications, making decisions about state management, integration patterns, or evaluating whether React Flow fits a use case.

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React Flow Architecture

When to Use React Flow

Good Fit

  • Visual programming interfaces
  • Workflow builders and automation tools
  • Diagram editors (flowcharts, org charts)
  • Data pipeline visualization
  • Mind mapping tools
  • Node-based audio/video editors
  • Decision tree builders
  • State machine designers

Consider Alternatives

  • Simple static diagrams (use SVG or canvas directly)
  • Heavy real-time collaboration (may need custom sync layer)
  • 3D visualizations (use Three.js, react-three-fiber)
  • Graph analysis with 10k+ nodes (use WebGL-based solutions like Sigma.js)

Decision workflow (gates)

Run this sequence before locking the stack or sprinting implementation. Skip only for throwaway prototypes.

  1. Name the interactions — List the top user actions (e.g. drag, connect, delete, group). Pass: Each action maps to a concrete React Flow callback you will implement (onNodesChange, onConnect, …).

  2. Classify scale — Estimate peak nodes (visible canvas or document total). Pass: Your range matches a row in Node Count Guidelines and you accept the listed strategy (e.g. onlyRenderVisibleElements when that row implies it).

  3. Place state — Choose local hooks, an external store, or Redux/other. Pass: One sentence states where persistence, undo, or cross-surface sync will live, or explicitly “not needed yet.”

  4. Re-check alternatives — If the use case matches Consider Alternatives, Pass: One sentence explains why React Flow still fits or which listed alternative you chose instead.

Architecture Patterns

Package Structure (xyflow)

@xyflow/system (vanilla TypeScript)
├── Core algorithms (edge paths, bounds, viewport)
├── xypanzoom (d3-based pan/zoom)
├── xydrag, xyhandle, xyminimap, xyresizer
└── Shared types

@xyflow/react (depends on @xyflow/system)
├── React components and hooks
├── Zustand store for state management
└── Framework-specific integrations

@xyflow/svelte (depends on @xyflow/system)
└── Svelte components and stores

Implication: Core logic is framework-agnostic. When contributing or debugging, check if issue is in @xyflow/system or framework-specific package.

State Management Approaches

1. Local State (Simple Apps)

// useNodesState/useEdgesState for prototyping
const [nodes, setNodes, onNodesChange] = useNodesState(initialNodes);
const [edges, setEdges, onEdgesChange] = useEdgesState(initialEdges);

Pros: Simple, minimal boilerplate Cons: State isolated to component tree

2. External Store (Production)

// Zustand store example
import { create } from 'zustand';

interface FlowStore {
  nodes: Node[];
  edges: Edge[];
  setNodes: (nodes: Node[]) => void;
  onNodesChange: OnNodesChange;
}

const useFlowStore = create<FlowStore>((set, get) => ({
  nodes: initialNodes,
  edges: initialEdges,
  setNodes: (nodes) => set({ nodes }),
  onNodesChange: (changes) => {
    set({ nodes: applyNodeChanges(changes, get().nodes) });
  },
}));

// In component
function Flow() {
  const { nodes, edges, onNodesChange } = useFlowStore();
  return <ReactFlow nodes={nodes} onNodesChange={onNodesChange} />;
}

Pros: State accessible anywhere, easier persistence/sync Cons: More setup, need careful selector optimization

3. Redux/Other State Libraries

// Connect via selectors
const nodes = useSelector(selectNodes);
const dispatch = useDispatch();

const onNodesChange = useCallback((changes: NodeChange[]) => {
  dispatch(nodesChanged(changes));
}, [dispatch]);

Data Flow Architecture

User Input → Change Event → Reducer/Handler → State Update → Re-render
     ↓
[Drag node] → onNodesChange → applyNodeChanges → setNodes → ReactFlow
     ↓
[Connect]   → onConnect → addEdge → setEdges → ReactFlow
     ↓
[Delete]    → onNodesDelete → deleteElements → setNodes/setEdges → ReactFlow

Sub-Flow Pattern (Nested Nodes)

// Parent node containing child nodes
const nodes = [
  {
    id: 'group-1',
    type: 'group',
    position: { x: 0, y: 0 },
    style: { width: 300, height: 200 },
  },
  {
    id: 'child-1',
    parentId: 'group-1',  // Key: parent reference
    extent: 'parent',      // Key: constrain to parent
    position: { x: 10, y: 30 },  // Relative to parent
    data: { label: 'Child' },
  },
];

Considerations:

  • Use extent: 'parent' to constrain dragging
  • Use expandParent: true to auto-expand parent
  • Parent z-index affects child rendering order

Viewport Persistence

// Save viewport state
const { toObject, setViewport } = useReactFlow();

const handleSave = () => {
  const flow = toObject();
  // flow.nodes, flow.edges, flow.viewport
  localStorage.setItem('flow', JSON.stringify(flow));
};

const handleRestore = () => {
  const flow = JSON.parse(localStorage.getItem('flow'));
  setNodes(flow.nodes);
  setEdges(flow.edges);
  setViewport(flow.viewport);
};

Integration Patterns

With Backend/API

// Load from API
useEffect(() => {
  fetch('/api/flow')
    .then(r => r.json())
    .then(({ nodes, edges }) => {
      setNodes(nodes);
      setEdges(edges);
    });
}, []);

// Debounced auto-save
const debouncedSave = useMemo(
  () => debounce((nodes, edges) => {
    fetch('/api/flow', {
      method: 'POST',
      body: JSON.stringify({ nodes, edges }),
    });
  }, 1000),
  []
);

useEffect(() => {
  debouncedSave(nodes, edges);
}, [nodes, edges]);

With Layout Algorithms

import dagre from 'dagre';

function getLayoutedElements(nodes: Node[], edges: Edge[]) {
  const g = new dagre.graphlib.Graph();
  g.setGraph({ rankdir: 'TB' });
  g.setDefaultEdgeLabel(() => ({}));

  nodes.forEach((node) => {
    g.setNode(node.id, { width: 150, height: 50 });
  });

  edges.forEach((edge) => {
    g.setEdge(edge.source, edge.target);
  });

  dagre.layout(g);

  return {
    nodes: nodes.map((node) => {
      const pos = g.node(node.id);
      return { ...node, position: { x: pos.x, y: pos.y } };
    }),
    edges,
  };
}

Performance Scaling

Node Count Guidelines

NodesStrategy
< 100Default settings
100-500Enable onlyRenderVisibleElements
500-1000Simplify custom nodes, reduce DOM elements
> 1000Consider virtualization, WebGL alternatives

Optimization Techniques

<ReactFlow
  // Only render nodes/edges in viewport
  onlyRenderVisibleElements={true}

  // Reduce node border radius (improves intersect calculations)
  nodeExtent={[[-1000, -1000], [1000, 1000]]}

  // Disable features not needed
  elementsSelectable={false}
  panOnDrag={false}
  zoomOnScroll={false}
/>

Trade-offs

Controlled vs Uncontrolled

ControlledUncontrolled
More boilerplateLess code
Full state controlInternal state
Easy persistenceNeed toObject()
Better for complex appsGood for prototypes

Connection Modes

Strict (default)Loose
Source → Target onlyAny handle → any handle
Predictable behaviorMore flexible
Use for data flowsUse for diagrams
<ReactFlow connectionMode={ConnectionMode.Loose} />

Edge Rendering

Default edgesCustom edges
Fast renderingMore control
Limited stylingAny SVG/HTML
Simple use casesComplex labels

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Reviews App Intents code for intent structure, entities, shortcuts, and parameters. Use when reviewing code with import AppIntents, @AppIntent, AppEntity, AppShortcutsProvider, or @Parameter.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

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