docs: flagship README for the 8.0 GA; GA version is 8.0.1
Rewrites the README around the feature-showcase structure: write->index->query table, runnable quick start (subtype-correct on the 8.0 strict default, real VerbTypes), feature tour with per-pillar code, the scale-up path to @soulcraft/cor, and measured-only performance claims with benchmark citations. GA retargets to 8.0.1: npm permanently retired the 8.0.0 version number after a January development-cycle publish/unpublish, so the first stable 8.x release is 8.0.1. RELEASES.md documents this; the phantom 8.0.0 CHANGELOG entry is removed (the release run regenerates it as 8.0.1).
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# Brainy
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<p align="center">
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<img src="https://raw.githubusercontent.com/soulcraftlabs/brainy/main/brainy.png" alt="Brainy Logo" width="200">
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<img src="https://raw.githubusercontent.com/soulcraftlabs/brainy/main/brainy.png" alt="Brainy" width="180">
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</p>
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[](https://www.npmjs.com/package/@soulcraft/brainy)
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[](https://www.npmjs.com/package/@soulcraft/brainy)
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[](https://soulcraft.com/docs)
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[](LICENSE)
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[](https://www.typescriptlang.org/)
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<h1 align="center">Brainy</h1>
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**Three database paradigms. One API. Zero configuration.**
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<p align="center">
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<b>Three database paradigms. One API. Zero configuration.</b><br>
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The in-process knowledge database for TypeScript — vector search, graph traversal,<br>
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and metadata filtering unified in a single query.
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</p>
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Built because we were tired of stitching together Pinecone + Neo4j + MongoDB and spending weeks on configuration before writing a single line of business logic. Brainy unifies vector search, graph traversal, and metadata filtering so you don't have to choose.
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<p align="center">
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<a href="https://www.npmjs.com/package/@soulcraft/brainy"><img src="https://img.shields.io/npm/v/@soulcraft/brainy.svg" alt="npm version"></a>
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<a href="https://www.npmjs.com/package/@soulcraft/brainy"><img src="https://img.shields.io/npm/dm/@soulcraft/brainy.svg" alt="npm downloads"></a>
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<a href="https://github.com/soulcraftlabs/brainy/actions/workflows/ci.yml"><img src="https://github.com/soulcraftlabs/brainy/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
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<a href="https://soulcraft.com/docs"><img src="https://img.shields.io/badge/docs-soulcraft.com-blue.svg" alt="Documentation"></a>
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<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="MIT License"></a>
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<a href="https://www.typescriptlang.org/"><img src="https://img.shields.io/badge/%3C%2F%3E-TypeScript-%230074c1.svg" alt="TypeScript"></a>
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</p>
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**New here?** → **[What is Brainy? — plain-language overview, no jargon](docs/eli5.md)**
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<p align="center">
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<a href="#quick-start">Quick start</a> ·
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<a href="#one-query-three-engines">One query</a> ·
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<a href="#feature-tour">Features</a> ·
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<a href="#from-laptop-to-hundreds-of-millions">Scale with Cor</a> ·
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<a href="#documentation">Docs</a>
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</p>
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---
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## Install
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Built because we were tired of stitching a vector store to a graph database to a document store — and spending weeks on plumbing before writing a line of business logic. Brainy indexes every fact **three ways at once** and lets one call query them together:
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| You write | Brainy indexes it as | You query it with |
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|---|---|---|
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| `data: 'Ada wrote the first program'` | a **384-dim vector** (local embedding — no API key) | `find({ query: 'computing pioneers' })` |
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| `metadata: { field: 'CS', year: 1843 }` | **structured fields** (O(1) exact, O(log n) range) | `find({ where: { year: { lessThan: 1900 } } })` |
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| `relate({ from: ada, to: babbage })` | a **typed, directed graph edge** | `find({ connected: { to: babbage, depth: 2 } })` |
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It runs **inside your process** — no server, no Docker, nothing to operate — and persists to plain files you can snapshot with a hard link.
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**New here?** → **[What is Brainy? — plain-language overview, no jargon](docs/eli5.md)**
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## Quick start
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```bash
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bun add @soulcraft/brainy # fastest — recommended
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npm install @soulcraft/brainy # Node.js — fully supported
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bun add @soulcraft/brainy # Bun ≥ 1.1 — recommended
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npm install @soulcraft/brainy # Node.js ≥ 22
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```
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## Quick Start
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```javascript
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import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
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const brain = new Brainy()
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const brain = new Brainy() // in-memory; one line swaps to disk
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await brain.init()
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// Add knowledge — text auto-embeds, metadata auto-indexes
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const reactId = await brain.add({
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// Text auto-embeds locally; metadata auto-indexes
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const react = await brain.add({
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data: 'React is a JavaScript library for building user interfaces',
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type: NounType.Concept,
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subtype: 'library',
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metadata: { category: 'frontend', year: 2013 }
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})
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const nextId = await brain.add({
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data: 'Next.js framework for React with server-side rendering',
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const next = await brain.add({
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data: 'Next.js is a React framework with server-side rendering',
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type: NounType.Concept,
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metadata: { category: 'framework', year: 2016 }
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subtype: 'framework',
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metadata: { category: 'frontend', year: 2016 }
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})
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// Create a relationship
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await brain.relate({ from: nextId, to: reactId, type: VerbType.BuiltOn })
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// Query all three paradigms at once
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const results = await brain.find({
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query: 'modern frontend frameworks', // Vector similarity
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where: { year: { greaterThan: 2015 } }, // Metadata filtering
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connected: { to: reactId, depth: 2 } // Graph traversal
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})
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await brain.relate({ from: next, to: react, type: VerbType.DependsOn, subtype: 'runtime' })
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```
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**[Full API Reference](docs/api/README.md)** | **[soulcraft.com/docs](https://soulcraft.com/docs)**
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---
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## Three Indexes, One Query
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Every piece of knowledge lives in three indexes simultaneously:
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- **`data`** → **Vector index** — Content for semantic search. Strings auto-embed into 384-dim vectors. Queried with `find({ query: '...' })`.
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- **`metadata`** → **Metadata index** — Structured fields for filtering. O(1) lookups. Queried with `find({ where: { ... } })`.
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- **`relate()`** → **Graph index** — Typed, directed relationships between entities. Traversed with `find({ connected: { ... } })`.
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```javascript
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// Data → vector index (semantic search)
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const articleId = await brain.add({
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data: 'A deep dive into transformer architectures',
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type: NounType.Document,
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metadata: { author: 'Dr. Chen', year: 2024, tags: ['AI'] } // → metadata index
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})
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// Relationships → graph index
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await brain.relate({ from: authorId, to: articleId, type: VerbType.Authored })
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// Query all three at once
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brain.find({
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query: 'attention mechanisms', // Vector similarity
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where: { year: { greaterThan: 2023 } }, // Metadata filter
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connected: { from: authorId, depth: 1 } // Graph traversal
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})
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```
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**[Data Model Reference](docs/DATA_MODEL.md)** | **[Query Operators](docs/QUERY_OPERATORS.md)**
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---
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## Features
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### Triple Intelligence
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Vector search + graph traversal + metadata filtering in every query. No stitching services together — one `find()` call combines all three.
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## One query, three engines
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```javascript
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const results = await brain.find({
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query: 'machine learning',
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where: { department: 'engineering', level: 'senior' },
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connected: { from: teamLeadId, via: VerbType.WorksWith, depth: 2 }
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query: 'modern frontend frameworks', // vector — what it means
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where: { year: { greaterThan: 2015 } }, // metadata — what it is
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connected: { to: react, depth: 2 } // graph — what it touches
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})
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```
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### Hybrid Search
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Every clause is optional; any combination composes. Under the hood Brainy plans the query across an HNSW vector index, a roaring-bitmap field index, and an adjacency graph index — and re-validates every result against your predicate before returning it, so a corrupt index can never hand you a wrong answer.
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Automatically combines keyword (text) and semantic (vector) search. No configuration needed.
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## Feature tour
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### The database is a value
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Pin it, rewind it, fork it. Snapshot isolation without a server.
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```javascript
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await brain.find({ query: 'David Smith' }) // Auto: text + semantic
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await brain.find({ query: 'AI concepts', searchMode: 'semantic' }) // Semantic only
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await brain.find({ query: 'exact id', searchMode: 'text' }) // Text only
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const db = brain.now() // pin current state — O(1)
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await brain.transact([ // atomic all-or-nothing, CAS-guarded
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{ op: 'update', id: order, metadata: { status: 'paid' } },
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{ op: 'relate', from: invoice, to: order, type: VerbType.References, subtype: 'billing' }
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], { ifAtGeneration: db.generation })
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await db.get(order) // still 'pending' — pinned forever
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await brain.get(order) // 'paid' — live
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const lastWeek = await brain.asOf(Date.now() - 7 * 86_400_000) // full query surface, past state
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const whatIf = await db.with([{ op: 'remove', id: order }]) // speculative — never touches disk
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await brain.now().persist('/backups/today') // instant hard-link snapshot
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```
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### Query Operators
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**[Consistency model](docs/concepts/consistency-model.md)** · **[Snapshots & time travel](docs/guides/snapshots-and-time-travel.md)**
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Filter metadata with equality, comparison, array, existence, pattern, and logical operators:
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### Local embeddings — no API keys
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Strings embed on-device with a bundled MiniLM model (WASM). Semantic search works offline, in CI, and on air-gapped machines, at zero cost per call. Hybrid keyword + semantic ranking is the default:
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```javascript
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await brain.find({
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where: {
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status: 'active', // Exact match
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score: { greaterThan: 90 }, // Comparison
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tags: { contains: 'ai' }, // Array
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anyOf: [{ role: 'admin' }, { role: 'owner' }] // Logical OR
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}
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})
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await brain.find({ query: 'David Smith' }) // auto: text + semantic
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await brain.find({ query: 'AI concepts', searchMode: 'semantic' }) // semantic only
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```
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**[Query Operators Reference](docs/QUERY_OPERATORS.md)** — all operators with indexed/in-memory matrix
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### A typed graph, not a bag of edges
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### Graph Relationships
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Typed, directed edges between entities. Traverse connections at any depth.
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42 entity types × 127 relationship types form a shared vocabulary for any domain — healthcare (`Patient → diagnoses → Condition`), finance (`Account → transfers → Transaction`), yours. Your own taxonomy layers on with `subtype`, enforced at write time:
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```javascript
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await brain.relate({ from: personId, to: projectId, type: VerbType.WorksOn })
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await brain.add({ data: 'Avery Brooks', type: NounType.Person, subtype: 'employee' })
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const results = await brain.find({
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connected: { from: personId, via: VerbType.WorksOn, depth: 3 }
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})
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brain.counts.bySubtype(NounType.Person) // O(1) — { employee: 12, customer: 847 }
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brain.requireSubtype(NounType.Person, { values: ['employee', 'customer'], required: true })
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```
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### Database as a Value
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**[Type system](docs/architecture/noun-verb-taxonomy.md)** · **[Subtypes & facets](docs/guides/subtypes-and-facets.md)**
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The whole database, pinned as an immutable value. Snapshot isolation, time travel, atomic transactions, instant hard-link snapshots.
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### Graph analytics built in
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```javascript
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const db = brain.now() // Pin current state — O(1)
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// Atomic multi-write transaction (all-or-nothing, with CAS)
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await brain.transact([
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{ op: 'update', id: orderId, metadata: { status: 'paid' } },
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{ op: 'relate', from: invoiceId, to: orderId, type: VerbType.References, subtype: 'billing' }
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], { meta: { author: 'billing-service' }, ifAtGeneration: db.generation })
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await db.get(orderId) // Still 'pending' — pinned, forever
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await brain.get(orderId) // 'paid' — live
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// Time travel: full query surface at any past state
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const yesterday = await brain.asOf(new Date(Date.now() - 86_400_000))
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const past = await yesterday.find({ query: 'unpaid orders' })
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// What-if: speculative writes, nothing touches disk
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const whatIf = await db.with([{ op: 'remove', id: orderId }])
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// Instant backup: hard-link snapshot, opens read-only with Brainy.load()
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await brain.now().persist('/backups/today')
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await brain.graph.rank() // which entities matter most (centrality)
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await brain.graph.communities() // natural clusters
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await brain.graph.path(a, b) // how two things connect
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await brain.graph.subgraph([seed], { depth: 2 }) // bounded neighborhood → { nodes, edges }
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await brain.graph.export() // whole graph, one O(N+E) streaming pass
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```
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**[Consistency Model](docs/concepts/consistency-model.md)** | **[Snapshots & Time Travel](docs/guides/snapshots-and-time-travel.md)**
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### Write-time aggregations
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### Virtual Filesystem
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`SUM` / `COUNT` / `AVG` / `MIN` / `MAX` with `GROUP BY` and time windows, maintained incrementally on every write — reads are O(1) lookups, not scans. **[Aggregation guide](docs/guides/aggregation.md)**
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File operations with semantic search built in.
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```javascript
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const vfs = brain.vfs
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await vfs.writeFile('/docs/readme.md', 'Project documentation')
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const content = await vfs.readFile('/docs/readme.md')
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const tree = await vfs.getTreeStructure('/docs', { maxDepth: 3 })
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// Semantic file search
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const matches = await vfs.search('React components with hooks')
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```
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|
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**[VFS Quick Start](docs/vfs/QUICK_START.md)** | **[Common Patterns](docs/vfs/COMMON_PATTERNS.md)**
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|
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### Import Anything
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CSV, Excel, PDF, URLs — auto-detected format, auto-classified entities.
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### Import anything
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|
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```javascript
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await brain.import('customers.csv')
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await brain.import('sales-data.xlsx') // all sheets processed
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await brain.import('research-paper.pdf') // tables extracted automatically
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await brain.import('sales.xlsx') // every sheet
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await brain.import('research-paper.pdf') // tables extracted
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await brain.import('https://api.example.com/data.json')
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await brain.import('./handbook.md', { vfsPath: '/imports/handbook' }) // preserve in VFS
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```
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|
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**[Import Guide](docs/guides/import-anything.md)**
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Entities auto-classify on the way in; `brain.extractEntities(text)` exposes the same NER ensemble directly. **[Import guide](docs/guides/import-anything.md)**
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### Entity Extraction
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|
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AI-powered named entity recognition with 4-signal ensemble scoring.
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### A filesystem that understands content
|
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|
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```javascript
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const entities = await brain.extractEntities('John Smith founded Acme Corp in New York')
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// [
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// { text: 'John Smith', type: NounType.Person, confidence: 0.95 },
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// { text: 'Acme Corp', type: NounType.Organization, confidence: 0.92 },
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// { text: 'New York', type: NounType.Location, confidence: 0.88 }
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// ]
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await brain.vfs.writeFile('/docs/readme.md', 'Project documentation')
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await brain.vfs.search('React components with hooks') // semantic file search
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```
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|
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**[Neural Extraction Guide](docs/neural-extraction.md)**
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**[VFS quick start](docs/vfs/QUICK_START.md)**
|
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|
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### Plugin System
|
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### Operations-grade by default
|
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|
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Optional native acceleration via `@soulcraft/cortex` — SIMD distance calculations, CRoaring bitmaps, Candle ML embeddings.
|
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- **Single-writer, many-reader** — an exclusive lock protects the data directory; `Brainy.openReadOnly()` and the `brainy inspect` CLI examine a live brain from another process, safely.
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- **Self-upgrading data files** — a 7.x brain opens under 8.x and migrates itself behind an observable lock (`getIndexStatus().migration`), with an automatic pre-upgrade backup. No migration scripts.
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- **No silent wrong answers** — cold-open guards self-heal or throw typed errors (`MetadataIndexNotReadyError`, `GraphIndexNotReadyError`); they never return `[]` for data that exists.
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|
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**[Multi-process model](docs/concepts/multi-process.md)** · **[Inspection guide](docs/guides/inspection.md)**
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|
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## From laptop to hundreds of millions
|
||||
|
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Brainy's TypeScript engines take you a long way. When you outgrow them, add the native engine — **the API doesn't change**:
|
||||
|
||||
```bash
|
||||
npm install @soulcraft/cor
|
||||
```
|
||||
|
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```javascript
|
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const brain = new Brainy({ plugins: ['@soulcraft/cortex'] })
|
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await brain.init()
|
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const brain = new Brainy({ storage: { type: 'filesystem', path: './data' } })
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await brain.init() // Cor auto-detected — same code, native engines underneath
|
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```
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|
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Plugins are opt-in. Brainy never auto-imports packages unless listed in `plugins`.
|
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[`@soulcraft/cor`](https://www.npmjs.com/package/@soulcraft/cor) (Brainy 8.x ↔ Cor 3.x, version-matched) registers Rust implementations behind every provider seam: SIMD distance kernels, memory-mapped storage, a disk-native vector index that doesn't need your dataset in RAM, durable LSM field/graph indexes that serve cold opens instantly, and native aggregation. Recall@10 measured **0.99 / 0.96 / 0.96 at 1M / 10M / 100M vectors** in Cor's release gate.
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||||
|
||||
**[Plugin Documentation](docs/PLUGINS.md)**
|
||||
Open core, commercial accelerator: Brainy is MIT and complete on its own; Cor is licensed and funds both.
|
||||
|
||||
---
|
||||
## Performance
|
||||
|
||||
## Type System
|
||||
- JS distance kernels: **~6× faster cosine, ~1.4× euclidean** than 7.x (measured: [`tests/benchmarks/distance-microbench.mjs`](tests/benchmarks/distance-microbench.mjs), 384-dim, median of 41).
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||||
- Whole-graph reads are single **O(N + E)** cursor walks — a consumer-measured 19k-edge export dropped from ~27 s of per-node calls to one scan.
|
||||
- Full numbers and capacity planning: **[docs/PERFORMANCE.md](docs/PERFORMANCE.md)** · **[docs/SCALING.md](docs/SCALING.md)**
|
||||
|
||||
42 noun types and 127 verb types form a universal knowledge protocol:
|
||||
## Use cases
|
||||
|
||||
```
|
||||
42 Nouns × 127 Verbs = 5,334 base relationship combinations
|
||||
```
|
||||
|
||||
Model any domain — healthcare (`Patient → diagnoses → Condition`), finance (`Account → transfers → Transaction`), education (`Student → completes → Course`), or your own.
|
||||
|
||||
### Subtypes — sub-classification within a NounType *or* VerbType
|
||||
|
||||
Both noun types and verb types are intentionally coarse. Use the top-level `subtype` field to sub-classify entities AND relationships within a type — flat string, no hierarchy, your choice of vocabulary:
|
||||
|
||||
```javascript
|
||||
// Nouns: sub-classify entities
|
||||
await brain.add({
|
||||
data: 'Avery Brooks — runs the AI lab',
|
||||
type: NounType.Person,
|
||||
subtype: 'employee' // 'customer', 'vendor', 'contractor', …
|
||||
})
|
||||
|
||||
// Verbs: sub-classify relationships
|
||||
await brain.relate({
|
||||
from: ceoId,
|
||||
to: vpId,
|
||||
type: VerbType.ReportsTo,
|
||||
subtype: 'direct' // 'dotted-line', 'matrix', …
|
||||
})
|
||||
|
||||
// Filter on the fast path — column-store hit, not metadata fallback:
|
||||
const employees = await brain.find({ type: NounType.Person, subtype: 'employee' })
|
||||
const directReports = await brain.related({ from: ceoId, subtype: 'direct' })
|
||||
|
||||
// O(1) counts via the persisted rollups:
|
||||
brain.counts.bySubtype(NounType.Person)
|
||||
// → { employee: 12, customer: 847, vendor: 34 }
|
||||
|
||||
brain.counts.byRelationshipSubtype(VerbType.ReportsTo)
|
||||
// → { direct: 12, 'dotted-line': 3 }
|
||||
```
|
||||
|
||||
**Enforce the pairing.** Register a vocabulary per type or turn on brain-wide strict mode to ensure every entity AND relationship has both `type` AND `subtype`:
|
||||
|
||||
```javascript
|
||||
// Per-type rule with a closed vocabulary
|
||||
brain.requireSubtype(NounType.Person, { values: ['employee', 'customer'], required: true })
|
||||
|
||||
// 8.0 default: every write requires a subtype. Exempt genuine catch-all types…
|
||||
const brain = new Brainy({ requireSubtype: { except: [NounType.Thing] } })
|
||||
|
||||
// …or opt out while migrating pre-8.0 data, then audit and back-fill:
|
||||
const legacy = new Brainy({ requireSubtype: false })
|
||||
await legacy.audit() // gaps, grouped by type
|
||||
await legacy.fillSubtypes({ [NounType.Person]: 'unspecified' }) // close them
|
||||
```
|
||||
|
||||
For other facets you want counted (`status`, `source`, `role`), register them with `brain.trackField(name)`. Renaming an existing convention to `subtype`? Use `brain.migrateField({from, to, entityKind: 'both'})` to walk nouns AND verbs in one pass. Full guide: **[Subtypes & Facets](docs/guides/subtypes-and-facets.md)**.
|
||||
|
||||
**[Noun-Verb Taxonomy](docs/architecture/noun-verb-taxonomy.md)** | **[Stage 3 Canonical Reference](docs/STAGE3-CANONICAL-TAXONOMY.md)**
|
||||
|
||||
---
|
||||
|
||||
## Storage: Memory and Filesystem
|
||||
|
||||
The same API at every scale. Change one config line to go from prototype to production.
|
||||
|
||||
### Development — Zero Config
|
||||
|
||||
```javascript
|
||||
const brain = new Brainy()
|
||||
```
|
||||
|
||||
### Production — Filesystem (gzip compression on by default)
|
||||
|
||||
```javascript
|
||||
const brain = new Brainy({
|
||||
storage: { type: 'filesystem', path: './data' }
|
||||
})
|
||||
```
|
||||
|
||||
### Backups and Portability — Snapshots
|
||||
|
||||
```javascript
|
||||
const db = brain.now()
|
||||
await db.persist('/backups/2026-06-11') // instant hard-link snapshot
|
||||
await db.release()
|
||||
|
||||
const snapshot = await Brainy.load('/backups/2026-06-11') // read-only Db
|
||||
const hits = await snapshot.find({ query: 'quarterly invoices' })
|
||||
await snapshot.release()
|
||||
```
|
||||
|
||||
A snapshot directory is self-contained — copy it to another machine, open it with `Brainy.load()`, or restore it wholesale with `brain.restore(path, { confirm: true })`.
|
||||
|
||||
Performance benchmarks and capacity planning in **[docs/PERFORMANCE.md](docs/PERFORMANCE.md)**.
|
||||
|
||||
**[Capacity Planning](docs/operations/capacity-planning.md)**
|
||||
|
||||
---
|
||||
|
||||
## Use Cases
|
||||
|
||||
- **AI agents** — Persistent memory with semantic recall and relationship tracking
|
||||
- **Knowledge bases** — Auto-linking, semantic search, relationship-aware navigation
|
||||
- **Semantic search** — Find by meaning across codebases, documents, or media
|
||||
- **Enterprise knowledge** — CRM, product catalogs, institutional memory
|
||||
- **Interactive experiences** — Game worlds, NPCs, and characters that remember
|
||||
- **Content platforms** — Similarity-based discovery, intelligent tagging
|
||||
|
||||
---
|
||||
**AI agent memory** — persistent semantic recall with relationship tracking · **Knowledge bases** — auto-linking and meaning-aware navigation · **Semantic search** over codebases, documents, media · **Enterprise data** — CRM, catalogs, institutional memory · **Games & simulations** — worlds and characters that remember.
|
||||
|
||||
## Documentation
|
||||
|
||||
### Start Here
|
||||
|
||||
- **[Brainy explained simply](docs/eli5.md)** — Plain-language overview, no jargon, no code
|
||||
|
||||
### Core
|
||||
|
||||
- **[API Reference](docs/api/README.md)** — Every method with parameters, returns, and examples
|
||||
- **[Data Model](docs/DATA_MODEL.md)** — Entity structure, data vs metadata
|
||||
- **[Query Operators](docs/QUERY_OPERATORS.md)** — All BFO operators with examples
|
||||
- **[Find System](docs/FIND_SYSTEM.md)** — Natural language find() and hybrid search
|
||||
|
||||
### Architecture
|
||||
|
||||
- **[Architecture Overview](docs/architecture/overview.md)** — System design and components
|
||||
- **[Triple Intelligence](docs/architecture/triple-intelligence.md)** — Vector + graph + metadata unified query
|
||||
- **[Noun-Verb Taxonomy](docs/architecture/noun-verb-taxonomy.md)** — Universal type system
|
||||
- **[Data Storage Architecture](docs/architecture/data-storage-architecture.md)** — Type-aware indexing and HNSW
|
||||
|
||||
### Virtual Filesystem
|
||||
|
||||
- **[VFS Quick Start](docs/vfs/QUICK_START.md)** — Build file explorers that never crash
|
||||
- **[VFS Core](docs/vfs/VFS_CORE.md)** — Full VFS API reference
|
||||
- **[Semantic VFS](docs/vfs/SEMANTIC_VFS.md)** — AI-powered file navigation
|
||||
|
||||
### Guides
|
||||
|
||||
- **[Import Anything](docs/guides/import-anything.md)** — CSV, Excel, PDF, URLs
|
||||
- **[Framework Integration](docs/guides/framework-integration.md)** — React, Vue, Angular, Svelte
|
||||
- **[Natural Language Queries](docs/guides/natural-language.md)** — Master the find() method
|
||||
|
||||
### Operations
|
||||
|
||||
- **[Capacity Planning](docs/operations/capacity-planning.md)** — Memory, storage, and scaling
|
||||
- **[Performance](docs/PERFORMANCE.md)** — Benchmarks and architecture details
|
||||
|
||||
---
|
||||
| Start | Core | Going deeper |
|
||||
|---|---|---|
|
||||
| [Brainy explained simply](docs/eli5.md) | [API reference](docs/api/README.md) | [Architecture overview](docs/architecture/overview.md) |
|
||||
| [Installation](docs/guides/installation.md) | [Data model](docs/DATA_MODEL.md) | [Consistency model](docs/concepts/consistency-model.md) |
|
||||
| [Natural-language queries](docs/guides/natural-language.md) | [Query operators](docs/QUERY_OPERATORS.md) | [Multi-process model](docs/concepts/multi-process.md) |
|
||||
| | [Find system](docs/FIND_SYSTEM.md) | [Scaling](docs/SCALING.md) |
|
||||
|
||||
## Requirements
|
||||
|
||||
**Bun 1.1+** (recommended) or **Node.js 22 LTS**
|
||||
**Bun ≥ 1.1** (recommended) or **Node.js ≥ 22**. Brainy 8.x is server-only; the 7.x line remains on npm for browser use.
|
||||
|
||||
```bash
|
||||
bun add @soulcraft/brainy # Bun — best performance
|
||||
npm install @soulcraft/brainy # Node.js — fully supported
|
||||
```
|
||||
## Contributing & license
|
||||
|
||||
> Brainy 8.0 is server-only. Browser support (OPFS storage, Web Workers, in-browser WASM embeddings) was removed in 8.0 — the 7.x line remains available on npm if you need it.
|
||||
|
||||
## Single-Writer Model
|
||||
|
||||
Brainy is **single-writer, many-reader** on filesystem storage. One writer
|
||||
holds an exclusive lock on the data directory; any number of readers can
|
||||
inspect it concurrently. Opening a second writer throws with the PID of the
|
||||
existing one.
|
||||
|
||||
```typescript
|
||||
// Live application — writer mode is the default
|
||||
const brain = new Brainy({ storage: { type: 'filesystem', path: '/data/brain' } })
|
||||
await brain.init()
|
||||
|
||||
// Out-of-band diagnostics from a separate process — safe to run while the
|
||||
// writer is live
|
||||
const reader = await Brainy.openReadOnly({
|
||||
storage: { type: 'filesystem', path: '/data/brain' }
|
||||
})
|
||||
await reader.requestFlush({ timeoutMs: 5000 })
|
||||
const stats = await reader.stats()
|
||||
```
|
||||
|
||||
For incident debugging, use the `brainy inspect` CLI:
|
||||
|
||||
```bash
|
||||
brainy inspect stats /data/brain
|
||||
brainy inspect find /data/brain --where '{"entityType":"booking"}'
|
||||
brainy inspect explain /data/brain --where '{"entityType":"booking"}'
|
||||
brainy inspect health /data/brain
|
||||
```
|
||||
|
||||
See [the multi-process model](docs/concepts/multi-process.md) and the
|
||||
[inspection guide](docs/guides/inspection.md) for the full story, including
|
||||
stale-lock detection and the cross-process flush RPC.
|
||||
|
||||
## Contributing
|
||||
|
||||
We welcome contributions! See **[CONTRIBUTING.md](CONTRIBUTING.md)** for guidelines.
|
||||
|
||||
## License
|
||||
|
||||
MIT © Brainy Contributors
|
||||
Contributions welcome — see **[CONTRIBUTING.md](CONTRIBUTING.md)**. MIT © Brainy Contributors.
|
||||
|
|
|
|||
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