🧠 Brainy API Reference

import { Brainy, NounType, VerbType } from '@soulcraft/brainy'

const brain = new Brainy() // Zero config!
await brain.init() // VFS auto-initialized!

// Add data (text auto-embeds!)
const id = await brain.add({
 data: 'The future of AI is here',
 type: NounType.Concept,
 metadata: { category: 'technology' }
})

// Search with Triple Intelligence
const results = await brain.find({
 query: 'artificial intelligence',
 where: { year: { greaterThan: 2020 } },
 connected: { from: id, depth: 2 }
})

// Fork for safe experimentation
const experiment = await brain.fork('test-feature')
await experiment.add({ data: 'test', type: NounType.Document })
await experiment.commit({ message: 'Add test data' })

// Entity versioning
await brain.versions.save(id, { tag: 'v1.0', description: 'Initial version' })
await brain.update(id, { category: 'AI' })
await brain.versions.save(id, { tag: 'v2.0' })

const id = await brain.add({
 data: 'JavaScript is a programming language', // Text or pre-computed vector
 type: NounType.Concept, // Required: Entity type
 metadata: { // Optional metadata
 category: 'programming',
 year: 1995
 }
})

const entity = await brain.get(id)
console.log(entity?.data) // Original data
console.log(entity?.metadata) // Metadata
console.log(entity?.vector) // Embedding vector

await brain.update({
 id: entityId,
 data: 'Updated content', // Optional: new data
 metadata: { updated: true } // Optional: new metadata (merges)
})

await brain.delete(id)

// Simple text search
const results = await brain.find('machine learning')

// Advanced Triple Intelligence query
const results = await brain.find({
 query: 'artificial intelligence', // Vector similarity
 where: { // Metadata filtering
 year: { greaterThan: 2020 },
 category: { oneOf: ['AI', 'ML'] }
 },
 connected: { // Graph traversal
 to: conceptId,
 depth: 2,
 type: VerbType.RelatedTo
 },
 limit: 10
})

// Zero-config hybrid search (just works)
const results = await brain.find({
 query: 'David Smith' // Finds both exact text matches AND semantically similar
})

// Force text-only search (exact keyword matching)
const textResults = await brain.find({
 query: 'exact keyword',
 searchMode: 'text'
})

// Force semantic-only search (vector similarity)
const semanticResults = await brain.find({
 query: 'artificial intelligence concepts',
 searchMode: 'semantic'
})

// Custom hybrid weighting (0 = text only, 1 = semantic only)
const customResults = await brain.find({
 query: 'David Smith',
 hybridAlpha: 0.3 // Favor text matching
})

const results = await brain.find({ query: 'david the warrior' })

// Each result now includes:
results[0].textMatches // ["david", "warrior"] - exact query words found
results[0].textScore // 0.25 - text match quality (0-1)
results[0].semanticScore // 0.87 - semantic similarity (0-1)
results[0].matchSource // 'both' | 'text' | 'semantic'

// Plain text (works as before)
const highlights = await brain.highlight({
 query: "david the warrior",
 text: "David Smith is a brave fighter who battles dragons"
})
// [
// { text: "David", score: 1.0, position: [0, 5], matchType: 'text' },
// { text: "fighter", score: 0.78, position: [25, 32], matchType: 'semantic' },
// { text: "battles", score: 0.72, position: [37, 44], matchType: 'semantic' }
// ]

// Rich-text JSON (auto-detected)
const highlights = await brain.highlight({
 query: "david the warrior",
 text: JSON.stringify(tiptapDocument) // TipTap, Slate, Lexical, Draft.js, Quill
})
// Extracts text from nodes, annotates with contentCategory:
// [
// { text: "David", score: 1.0, matchType: 'text', contentCategory: 'title' },
// { text: "fighter", score: 0.78, matchType: 'semantic', contentCategory: 'content' }
// ]

// HTML input (auto-detected)
const highlights = await brain.highlight({
 query: "warrior",
 text: "<h1>David the Warrior</h1><p>A brave fighter.</p>"
})

// Custom extractor for proprietary formats
const highlights = await brain.highlight({
 query: "function",
 text: sourceCode,
 contentExtractor: (text) => treeSitterParse(text) // Your custom parser
})

// Style differently based on match type and content category
highlights.forEach(h => {
 const style = h.matchType === 'text' ? 'font-weight: bold' : 'background: yellow'
 if (h.contentCategory === 'title') { /* render as heading highlight */ }
 if (h.contentCategory === 'code') { /* render with code styling */ }
 if (h.contentCategory === 'annotation') { /* render as comment/caption */ }
 // Apply style from h.position[0] to h.position[1]
})

brain.defineAggregate({
  name: 'monthly_spending',
  source: {
    type: NounType.Event,
    where: { domain: 'financial', subtype: 'transaction' }
  },
  groupBy: [
    'category',
    { field: 'date', window: 'month' }   // Time-windowed dimension
  ],
  metrics: {
    total:   { op: 'sum', field: 'amount' },
    count:   { op: 'count' },
    average: { op: 'avg', field: 'amount' },
    highest: { op: 'max', field: 'amount' },
    lowest:  { op: 'min', field: 'amount' },
    spread:  { op: 'stddev', field: 'amount' }  // Welford's online algorithm
  },
  materialize: true   // Optional: write results as NounType.Measurement entities
})

brain.removeAggregate('monthly_spending')

// Simple: query by name
const results = await brain.find({ aggregate: 'monthly_spending' })

// With filtering on group keys
const foodOnly = await brain.find({
  aggregate: 'monthly_spending',
  where: { category: 'food' }
})

// With sorting and pagination
const topCategories = await brain.find({
  aggregate: {
    name: 'monthly_spending',
    orderBy: 'total',
    order: 'desc',
    limit: 10
  }
})

// Combine find-level params (where, orderBy, limit, offset merge automatically)
const recentFood = await brain.find({
  aggregate: 'monthly_spending',
  where: { category: 'food' },
  orderBy: 'total',
  order: 'desc',
  limit: 12
})

{
  id: string,               // Aggregate group ID (or materialized entity ID)
  score: 1.0,               // Always 1.0 for aggregates
  type: NounType.Measurement,
  metadata: {
    __aggregate: 'monthly_spending',  // Source aggregate name
    category: 'food',                 // Group key values
    date: '2024-01',                  // Time window bucket
    total: 342.50,                    // Computed metrics
    count: 28,
    average: 12.23,
    highest: 45.00,
    lowest: 2.50
  },
  entity: Entity              // Full entity structure
}

// Transaction = NounType.Event + financial metadata
await brain.add({
  data: 'Coffee at Blue Bottle',
  type: NounType.Event,
  metadata: {
    domain: 'financial',
    subtype: 'transaction',
    amount: 5.50,
    currency: 'USD',
    category: 'food',
    date: Date.now(),
    merchant: 'Blue Bottle Coffee'
  }
})

// Account = NounType.Collection + financial metadata
await brain.add({
  data: 'Checking Account',
  type: NounType.Collection,
  metadata: {
    domain: 'financial',
    subtype: 'account',
    accountType: 'checking',
    currency: 'USD',
    institution: 'Chase'
  }
})

// Invoice = NounType.Document + financial metadata
await brain.add({
  data: 'Invoice #1234 from Acme Corp',
  type: NounType.Document,
  metadata: {
    domain: 'financial',
    subtype: 'invoice',
    amount: 15000,
    currency: 'USD',
    status: 'pending',
    dueDate: Date.UTC(2024, 2, 15),
    vendor: 'Acme Corp'
  }
})

const relId = await brain.relate({
 from: sourceId,
 to: targetId,
 type: VerbType.RelatedTo,
 data: 'Collaborated on the research paper',  // Optional: content for this edge
 metadata: {    // Optional: structured edge fields
  strength: 0.9,
  role: 'primary author'
 }
})

// Get all relationships FROM an entity
const outgoing = await brain.getRelations({ from: entityId })

// Get all relationships TO an entity
const incoming = await brain.getRelations({ to: entityId })

// Filter by type
const related = await brain.getRelations({
 from: entityId,
 type: VerbType.Contains
})

const result = await brain.addMany({
 items: [
 { data: 'Entity 1', type: NounType.Document },
 { data: 'Entity 2', type: NounType.Concept }
 ]
})

console.log(result.successful) // Array of IDs
console.log(result.failed) // Array of errors

const result = await brain.deleteMany({
 ids: [id1, id2, id3]
})

const result = await brain.updateMany({
 updates: [
 { id: id1, metadata: { updated: true } },
 { id: id2, data: 'New content' }
 ]
})

const ids = await brain.relateMany({
 relations: [
 { from: id1, to: id2, type: VerbType.RelatedTo },
 { from: id1, to: id3, type: VerbType.Contains }
 ]
})

// Create a fork
const experiment = await brain.fork('test-feature')

// Make changes safely in isolation
await experiment.add({ data: 'Test entity', type: NounType.Document })
await experiment.update({ id: someId, metadata: { modified: true } })

// Parent is unaffected!
const parentData = await brain.find({}) // Original data unchanged

await brain.checkout('main')
await brain.checkout('test-feature')

const branches = await brain.listBranches()
// ['main', 'test-feature', 'experiment-2']

const current = await brain.getCurrentBranch()
// 'main'

const commitId = await brain.commit({
 message: 'Add new features',
 author: 'dev@example.com',
 metadata: { ticket: 'PROJ-123' }
})

await brain.deleteBranch('old-experiment')

const history = await brain.getHistory({
 branch: 'main',
 limit: 10
})

// Get commit ID from history
const commits = await brain.getHistory({ limit: 1 })
const commitId = commits[0].id

// Create snapshot (lazy-loading, no eager data loading)
const snapshot = await brain.asOf(commitId, {
 cacheSize: 10000 // LRU cache size (default: 10000)
})

// Query historical state - full Triple Intelligence works!
const results = await snapshot.find({
 query: 'AI research',
 where: { category: 'technology' }
})

// Get historical relationships
const related = await snapshot.getRelated(entityId, { depth: 2 })

// MUST close when done to free memory
await snapshot.close()

// Save version with tag
const version = await brain.versions.save('user-123', {
 tag: 'v1.0',
 description: 'Initial user profile',
 metadata: { author: 'dev@example.com' }
})

console.log(version.version) // 1
console.log(version.contentHash) // SHA-256 hash
console.log(version.createdAt) // Timestamp

const versions = await brain.versions.list('user-123', {
 limit: 10,
 offset: 0
})

versions.forEach(v => {
 console.log(`Version ${v.version}: ${v.tag} - ${v.description}`)
})

// Restore by version number
await brain.versions.restore('user-123', 1)

// Restore by tag
await brain.versions.restore('user-123', 'beta')

const diff = await brain.versions.compare('user-123', 1, 2)

console.log(diff.totalChanges) // Total changes
console.log(diff.modified) // Modified fields
console.log(diff.added) // Added fields
console.log(diff.removed) // Removed fields

// Check specific changes
const nameChange = diff.modified.find(c => c.path === 'metadata.name')
console.log(`${nameChange.oldValue} → ${nameChange.newValue}`)

// View old version without changing current state
const v1Content = await brain.versions.getContent('user-123', 1)
console.log(v1Content.metadata.name) // Old name

// Current state unchanged
const current = await brain.get('user-123')
console.log(current.metadata.name) // Current name

// Make a bad change
await brain.update('user-123', { status: 'deleted' })

// Undo immediately
await brain.versions.undo('user-123')

const result = await brain.versions.prune('user-123', {
 keepRecent: 10, // Keep 10 most recent
 keepTagged: true, // Always keep tagged versions
 olderThan: Date.now() - 30 * 24 * 60 * 60 * 1000 // Older than 30 days
})

console.log(`Deleted ${result.deleted}, kept ${result.kept}`)

const latest = await brain.versions.getLatest('user-123')
if (latest) {
 console.log(`Latest: v${latest.version} (${latest.tag})`)
}

const beta = await brain.versions.getVersionByTag('user-123', 'beta')

const count = await brain.versions.count('user-123')
console.log(`${count} versions saved`)

if (await brain.versions.hasVersions('user-123')) {
 console.log('Entity has version history')
}

import { VersioningAugmentation } from '@soulcraft/brainy'

// Configure auto-versioning
const versioning = new VersioningAugmentation({
 enabled: true,
 onUpdate: true, // Version on update()
 onDelete: false, // Don't version on delete
 entities: ['user-*'], // Only version users
 excludeEntities: ['temp-*'],
 excludeTypes: ['temporary'],
 keepRecent: 50, // Auto-prune old versions
 keepTagged: true
})

// Apply augmentation
brain.augment(versioning)

// Now updates auto-create versions
await brain.update('user-123', { name: 'New Name' })

// Version automatically created!
const versions = await brain.versions.list('user-123')
console.log(`Auto-created version: ${versions[0].version}`)

// Save version on main
await brain.versions.save('doc-1', { tag: 'main-v1' })

// Fork and create version
const feature = await brain.fork('feature')
await feature.update('doc-1', { content: 'Feature update' })
await feature.versions.save('doc-1', { tag: 'feature-v1' })

// Versions are isolated
const mainVersions = await brain.versions.list('doc-1')
const featureVersions = await feature.versions.list('doc-1')

console.log(mainVersions.length !== featureVersions.length) // true

_version:{entityId}:{versionNum}:{branch} // Version metadata
_version_blob:{contentHash} // Content blob (deduplicated)

// Create entity
await brain.add({
 data: 'User profile',
 id: 'user-123',
 type: 'user',
 metadata: { name: 'Alice', email: 'alice@example.com' }
})

// Save v1
await brain.versions.save('user-123', { tag: 'v1.0' })

// Make changes
await brain.update('user-123', { name: 'Alice Smith' })

// Save v2
await brain.versions.save('user-123', { tag: 'v2.0' })

// Compare versions
const diff = await brain.versions.compare('user-123', 1, 2)

// Restore to v1 if needed
await brain.versions.restore('user-123', 'v1.0')

// Development workflow
await brain.update('app-config', { version: '1.0.0-alpha' })
await brain.versions.save('app-config', { tag: 'alpha' })

await brain.update('app-config', { version: '1.0.0-beta' })
await brain.versions.save('app-config', { tag: 'beta' })

await brain.update('app-config', { version: '1.0.0' })
await brain.versions.save('app-config', { tag: 'release' })

// Rollback to beta if issues found
await brain.versions.restore('app-config', 'beta')

// Track all changes
const versioning = new VersioningAugmentation({
 enabled: true,
 onUpdate: true,
 entities: ['audit-*'],
 keepRecent: 100 // Keep 100 versions for audit
})

brain.augment(versioning)

// All updates now tracked
await brain.update('audit-record-1', { status: 'modified' })
await brain.update('audit-record-1', { status: 'approved' })

// View complete history
const versions = await brain.versions.list('audit-record-1')
versions.forEach(v => {
 console.log(`${v.createdAt}: ${v.description}`)
})

// VFS files can be versioned with actual blob content
await brain.vfs.writeFile('/docs/readme.md', 'Version 1 content')

// Get the file's entity ID
const stat = await brain.vfs.stat('/docs/readme.md')

// Save version 1
await brain.versions.save(stat.entityId, { tag: 'v1', description: 'Initial draft' })

// Modify the file
await brain.vfs.writeFile('/docs/readme.md', 'Version 2 - updated content')

// Save version 2
await brain.versions.save(stat.entityId, { tag: 'v2', description: 'Updated docs' })

// Compare versions - content is DIFFERENT
const v1 = await brain.versions.getContent(stat.entityId, 1)
const v2 = await brain.versions.getContent(stat.entityId, 2)
console.log(v1.data !== v2.data) // true

// Restore to v1 - writes content back to blob storage
await brain.versions.restore(stat.entityId, 'v1')

// File is now back to v1
const content = await brain.vfs.readFile('/docs/readme.md')
console.log(content.toString()) // 'Version 1 content'

// Exclude VFS entities from semantic search
const semanticOnly = await brain.find({
 query: 'artificial intelligence',
 where: {
 isVFSEntity: { notEquals: true } // Only semantic entities
 }
})

// Or filter to ONLY VFS entities
const vfsOnly = await brain.find({
 where: {
 isVFSEntity: { equals: true } // Only VFS files/folders
 }
})

// Check if an entity is a VFS entity
if (entity.metadata.isVFSEntity === true) {
 console.log('This is a VFS file or folder')
}

const content = await brain.vfs.readFile('/docs/README.md')
console.log(content.toString())

await brain.vfs.writeFile('/docs/README.md', 'New content', {
 encoding: 'utf-8'
})

await brain.vfs.unlink('/docs/old-file.md')

await brain.vfs.mkdir('/projects/new-app', { recursive: true })

const files = await brain.vfs.readdir('/projects')

// With file types
const entries = await brain.vfs.readdir('/projects', { withFileTypes: true })
entries.forEach(entry => {
 console.log(entry.name, entry.isDirectory() ? 'DIR' : 'FILE')
})

await brain.vfs.rmdir('/old-project', { recursive: true })

const stats = await brain.vfs.stat('/docs/README.md')
console.log(stats.size) // File size
console.log(stats.mtime) // Modified time
console.log(stats.isDirectory()) // Is directory?

const results = await brain.vfs.search('React components with hooks', {
 path: '/src',
 limit: 10
})

const similar = await brain.vfs.findSimilar('/src/App.tsx', {
 limit: 5,
 threshold: 0.7
})

const tree = await brain.vfs.getTreeStructure('/projects', {
 maxDepth: 3
})

const files = await brain.vfs.getDescendants('/src', {
 filter: (entity) => entity.name.endsWith('.tsx')
})

const meta = await brain.vfs.getMetadata('/src/App.tsx')
console.log(meta.todos) // Extracted TODOs
console.log(meta.tags) // Tags

const rels = await brain.vfs.getRelationships('/src/App.tsx')
// Returns: imports, references, dependencies

const todos = await brain.vfs.getTodos('/src/App.tsx')

const allTodos = await brain.vfs.getAllTodos('/src')

const stats = await brain.vfs.getProjectStats('/projects/my-app')
console.log(stats.fileCount)
console.log(stats.totalSize)
console.log(stats.fileTypes) // Breakdown by extension

const tsxFiles = await brain.vfs.searchEntities({
 type: 'file',
 extension: '.tsx'
})

// Simple similarity score
const score = await brain.neural().similar(
 'renewable energy',
 'sustainable power'
) // 0.87

// Detailed result
const result = await brain.neural().similar('text1', 'text2', {
 detailed: true
})
console.log(result.score)
console.log(result.explanation)

const clusters = await brain.neural().clusters({
 algorithm: 'kmeans',
 k: 5,
 minSize: 3
})

clusters.forEach(cluster => {
 console.log(cluster.label)
 console.log(cluster.items)
 console.log(cluster.centroid)
})

const neighbors = await brain.neural().neighbors(entityId, {
 k: 10,
 threshold: 0.7
})

const outliers = await brain.neural().outliers(0.3)
// Returns entity IDs that are outliers

const vizData = await brain.neural().visualize({
 maxNodes: 100,
 dimensions: 3,
 algorithm: 'force',
 includeEdges: true
})
// Use with D3.js, Cytoscape, GraphML tools

const clusters = await brain.neural().clusterFast({
 k: 10,
 maxIterations: 50
})

const clusters = await brain.neural().clusterLarge({
 k: 20,
 batchSize: 1000
})

// CSV import
await brain.import('data.csv', {
 format: 'csv',
 createEntities: true
})

// Excel import
await brain.import('sales.xlsx', {
 format: 'excel',
 sheets: ['Q1', 'Q2']
})

// PDF import
await brain.import('research.pdf', {
 format: 'pdf',
 extractTables: true
})

// URL import
await brain.import('https://api.example.com/data.json')

// Export to file
await brain.export('/path/to/backup.brainy')

// Create instant snapshot using COW fork
await brain.fork('backup-2025-01-19')

// Time-travel to specific commit
const snapshot = await brain.asOf(commitId)
const entities = await snapshot.find({ limit: 100 })

const brain = new Brainy({
 // Storage configuration
 storage: {
 type: 'memory', // memory | opfs | filesystem | s3 | r2 | gcs | azure
 path: './brainy-data', // For filesystem storage
 compression: true, // Enable gzip compression (60-80% savings)

 // Cloud storage configs (see Storage Adapters section)
 s3Storage: { ... },
 r2Storage: { ... },
 gcsStorage: { ... },
 azureStorage: { ... }
 },

 // HNSW vector index config
 hnsw: {
 M: 16, // Connections per layer
 efConstruction: 200, // Construction quality
 efSearch: 100, // Search quality
 typeAware: true // Enable type-aware indexing
 },

 // Model configuration (embedded in WASM - zero config needed)
 // Model: all-MiniLM-L6-v2 (384 dimensions)
 // Device: CPU via WASM (works everywhere)

 // Cache configuration
 cache: {
 enabled: true,
 maxSize: 10000,
 ttl: 3600000 // 1 hour in ms
 }
})

await brain.init() // Required! VFS auto-initialized

const brain = new Brainy({
 storage: { type: 'memory' }
})

const brain = new Brainy({
 storage: { type: 'opfs' }
})

const brain = new Brainy({
 storage: {
 type: 'filesystem',
 path: './brainy-data',
 compression: true // 60-80% space savings
 }
})

const brain = new Brainy({
 storage: {
 type: 's3',
 s3Storage: {
 bucketName: 'my-brainy-data',
 region: 'us-east-1',
 accessKeyId: process.env.AWS_ACCESS_KEY_ID,
 secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY
 }
 }
})

// Enable Intelligent-Tiering for 96% cost savings
await brain.storage.enableIntelligentTiering('entities/', 'auto-tier')

const brain = new Brainy({
 storage: {
 type: 'r2',
 r2Storage: {
 accountId: process.env.CF_ACCOUNT_ID,
 bucketName: 'my-brainy-data',
 accessKeyId: process.env.CF_ACCESS_KEY_ID,
 secretAccessKey: process.env.CF_SECRET_ACCESS_KEY
 }
 }
})

const brain = new Brainy({
 storage: {
 type: 'gcs',
 gcsStorage: {
 bucketName: 'my-brainy-data',
 projectId: process.env.GCP_PROJECT_ID,
 keyFilename: './gcp-key.json'
 }
 }
})

// Enable auto-tiering
await brain.storage.enableAutoclass({
 terminalStorageClass: 'ARCHIVE'
})

const brain = new Brainy({
 storage: {
 type: 'azure',
 azureStorage: {
 accountName: process.env.AZURE_STORAGE_ACCOUNT,
 accountKey: process.env.AZURE_STORAGE_KEY,
 containerName: 'brainy-data'
 }
 }
})

await brain.clear()

const count = await brain.getNounCount()

const count = await brain.getVerbCount()

const vector = await brain.embed('Hello world')
// 384-dimensional vector
console.log(vector.length) // 384

const embeddings = await brain.embedBatch([
 'Machine learning is fascinating',
 'Deep neural networks',
 'Natural language processing'
])
console.log(embeddings.length) // 3
console.log(embeddings[0].length) // 384

const score = await brain.similarity(
 'The cat sat on the mat',
 'A feline was resting on the rug'
)
console.log(score) // ~0.85 (high semantic similarity)

// Get all connected entities
const neighbors = await brain.neighbors(entityId)

// Get outgoing connections only
const outgoing = await brain.neighbors(entityId, {
 direction: 'outgoing',
 limit: 10
})

// Multi-hop traversal
const extended = await brain.neighbors(entityId, {
 depth: 2,
 direction: 'both'
})

// Find all duplicates
const duplicates = await brain.findDuplicates()

for (const group of duplicates) {
 console.log('Original:', group.entity.id)
 for (const dup of group.duplicates) {
 console.log(` Duplicate: ${dup.entity.id} (${dup.similarity.toFixed(2)})`)
 }
}

// Find person duplicates with higher threshold
const personDupes = await brain.findDuplicates({
 type: NounType.PERSON,
 threshold: 0.9,
 limit: 50
})

const stats = await brain.indexStats()
console.log(`Entities: ${stats.entities}`)
console.log(`Vectors: ${stats.vectors}`)
console.log(`Relationships: ${stats.relationships}`)
console.log(`Memory: ${(stats.memoryUsage.total / 1024 / 1024).toFixed(1)}MB`)
console.log(`Fields: ${stats.metadataFields.join(', ')}`)

// Find all clusters
const clusters = await brain.cluster()

for (const cluster of clusters) {
 console.log(`${cluster.clusterId}: ${cluster.entities.length} entities`)
}

// Find document clusters with centroids
const docClusters = await brain.cluster({
 type: NounType.Document,
 threshold: 0.85,
 minClusterSize: 3,
 includeCentroid: true
})

const stats = brain.getStats()
console.log(stats.entityCount)
console.log(stats.relationshipCount)
console.log(stats.cacheHitRate)

const brain = new Brainy(config)
await brain.init() // Required! VFS auto-initialized here

await brain.shutdown() // Graceful shutdown, flush caches

// Create
const id = await brain.add({
 data: 'Quantum computing breakthrough',
 type: NounType.Concept,
 metadata: { category: 'tech', year: 2024 }
})

// Read
const entity = await brain.get(id)

// Update
await brain.update({
 id,
 metadata: { updated: true }
})

// Delete
await brain.delete(id)

// Create entities
const ai = await brain.add({
 data: 'Artificial Intelligence',
 type: NounType.Concept
})

const ml = await brain.add({
 data: 'Machine Learning',
 type: NounType.Concept
})

// Create relationship
await brain.relate({
 from: ml,
 to: ai,
 type: VerbType.IsA
})

// Traverse graph
const results = await brain.find({
 connected: { from: ai, depth: 2 }
})

const results = await brain.find({
 query: 'modern frontend frameworks', // 🔍 Vector
 where: { // 📊 Document
 year: { greaterThan: 2020 },
 category: { oneOf: ['framework', 'library'] }
 },
 connected: { // 🕸️ Graph
 to: reactId,
 depth: 2,
 type: VerbType.BuiltOn
 },
 limit: 10
})

// Fork for experimentation
const experiment = await brain.fork('test-migration')

// Make changes in isolation
await experiment.add({
 data: 'New feature',
 type: NounType.Document
})

// Commit your work
await experiment.commit({
 message: 'Add new feature',
 author: 'dev@example.com'
})

// Switch to experimental branch to make it active
await brain.checkout('test-migration')

// Write files
await brain.vfs.writeFile('/docs/README.md', 'Project documentation')
await brain.vfs.mkdir('/src/components', { recursive: true })

// Read files
const content = await brain.vfs.readFile('/docs/README.md')

// Semantic search
const reactFiles = await brain.vfs.search('React components with hooks', {
 path: '/src'
})

// Get tree structure (safe, prevents infinite recursion)
const tree = await brain.vfs.getTreeStructure('/projects', {
 maxDepth: 3
})