David Snelling a99b1e83c4 chore: rename to @soulcraftlabs/brainy for Open Brainy on The Source

Prepares the repo for its new home at soulcraftlabs/open-brainy ahead
of the Forgejo transfer: package name, publish registry, release
script, and every install/import reference across docs, src, tests,
examples, and integrations now point at @soulcraftlabs/brainy on
The Source. The npmjs storefront leg and byte-identity pair
verification are stripped from the release script — The Source is
now the only publish target. README gains an Open Brainy explainer
and a registry note for consumers.

@soulcraft/brainy 10.4.2 was the last release under the old name.

2026-08-27 17:07:09 -07:00

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Quick Start

Get Brainy running in under a minute.

1. Install

npm install @soulcraftlabs/brainy

2. Initialize

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

const brain = new Brainy()
await brain.init()

That's it. Brainy auto-configures storage, loads the embedding model, and builds the indexes.

3. Add Knowledge

// Text is automatically embedded into 384-dim vectors
const reactId: string = await brain.add({
  data: 'React is a JavaScript library for building user interfaces',
  type: NounType.Concept,
  subtype: 'library',                                  // Sub-classification within Concept
  metadata: { category: 'frontend', year: 2013 }
})

const nextId: string = await brain.add({
  data: 'Next.js framework for React with server-side rendering',
  type: NounType.Concept,
  subtype: 'framework',
  metadata: { category: 'framework', year: 2016 }
})

type is one of Brainy's 42 stable NounTypes. subtype is your free-form sub-classification within that type — flat string, no hierarchy, indexed on the fast path. See Subtypes & Facets for the full guide.

4. Create Relationships

// Typed graph relationships
await brain.relate({
  from: nextId,
  to: reactId,
  type: VerbType.DependsOn
})

5. Query with Triple Intelligence

import type { Result } from '@soulcraftlabs/brainy'

// All three search paradigms in one call
const results: Result[] = await brain.find({
  query: 'modern frontend frameworks',    // Vector similarity search
  where: { year: { greaterThan: 2015 } }, // Metadata filtering
  connected: { to: reactId, depth: 2 }   // Graph traversal
})

console.log(results[0].data)   // 'Next.js framework for React...'
console.log(results[0].score)  // 0.94

What Just Happened

Every entity you add() lives in three indexes simultaneously:

Index	What it stores	Query with
Vector	384-dim embedding of `data`	`find({ query: '...' })`
Metadata	All `metadata` fields	`find({ where: { ... } })`
Graph	Typed relationships from `relate()`	`find({ connected: { ... } })`

find() queries all three in parallel and fuses the results.

Natural Language Queries

Brainy understands 220+ natural language patterns:

// These all work without any configuration
await brain.find({ query: 'recent documents about machine learning' })
await brain.find({ query: 'articles created this week' })
await brain.find({ query: 'people who work at Anthropic' })

Next Steps

Triple Intelligence — understand how the query engine works
The Find System — advanced queries, operators, and graph traversal
API Reference — complete method documentation
Storage Adapters — filesystem, memory

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