The embedded database for local-first JavaScript apps.
Documents + vector search built in Rust — browser, Node.js, and React Native. No cloud. No compromise.

Documentation · Web Demo · Mobile Demo · Web Guide · Node.js Guide · React Native Guide

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Most JavaScript apps require three separate tools to handle structured queries, vector similarity search, and offline-first storage — each with its own API, each requiring a server. TalaDB replaces all three with a single embedded database that runs entirely on the user's device, across every JavaScript runtime.

Why TalaDB?

	TalaDB	RxDB / Dexie	Expo SQLite	LanceDB
Runs in browser	✓	✓	✗	✗
React Native	✓	✗	✓	✗
On-device vector search	✓	✗	✗	✓
Unified API across runtimes	✓	✗	✗	✗
No cloud required	✓	✓	✓	✗
Rust core	✓	✗	✗	✓

The only embedded database with vector search that runs on all three JS runtimes with a single API.

The same Rust core powers all three runtimes:

Runtime	Package	Mechanism
Browser	@taladb/web	wasm-bindgen + OPFS via SharedWorker
Node.js	@taladb/node	napi-rs native module
React Native	@taladb/react-native	JSI HostObject (C FFI via cbindgen)

Application code uses the unified taladb package with a single TypeScript API on every platform.

Highlights

• Vector search — on-device similarity search (cosine, dot, euclidean) with optional metadata pre-filter; pairs naturally with on-device AI models (transformers.js, ONNX Web)
• Hybrid queries — combine a regular document filter with vector ranking in one call: find the 5 most semantically similar english-language support articles without two round-trips
• MongoDB-like API — familiar filter and update DSL, fully typed with TypeScript generics
• ACID transactions — powered by redb, a pure-Rust B-tree storage engine
• Live queries — subscribe to a filter and receive snapshots after every write, no polling

+ encryption at rest, full-text search, schema migrations, snapshot export/import, CLI tools.

Usage

Install

# Browser
pnpm add taladb @taladb/web

# Node.js
pnpm add taladb @taladb/node

# React Native
pnpm add taladb @taladb/react-native

Quick start

import { openDB } from 'taladb'

const db = await openDB('myapp.db')  // OPFS in browser, file on Node.js / React Native

As a document database

interface Article {
  _id?: string
  title: string
  category: string
  locale: string
  publishedAt: number
}

const articles = db.collection<Article>('articles')

// Insert
const id = await articles.insert({
  title: 'How to reset your password',
  category: 'support',
  locale: 'en',
  publishedAt: Date.now(),
})

// Query with filters
const results = await articles.find({
  category: 'support',
  locale: 'en',
  publishedAt: { $gte: Date.now() - 86_400_000 },
})

// Update
await articles.updateOne({ _id: id }, { $set: { title: 'Reset your password' } })

// Delete
await articles.deleteOne({ _id: id })

// Secondary index for fast lookups
await articles.createIndex('category')
await articles.createIndex('publishedAt')

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As a vector database

import { pipeline } from '@xenova/transformers'

// Any on-device embedding model works
const embedder = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2')
const embed = async (text: string) => {
  const out = await embedder(text, { pooling: 'mean', normalize: true })
  return Array.from(out.data) as number[]
}

// 1. Create the vector index once (backfills existing documents automatically)
await articles.createVectorIndex('embedding', { dimensions: 384 })

// 2. Insert documents with their embeddings
await articles.insert({
  title: 'How to reset your password',
  category: 'support',
  locale: 'en',
  publishedAt: Date.now(),
  embedding: await embed('How to reset your password'),
})

// 3. Semantic search — find the 5 most similar articles
const query = await embed('forgot my login credentials')
const results = await articles.findNearest('embedding', query, 5)

results.forEach(({ document, score }) => {
  console.log(score.toFixed(3), document.title)
})
// 0.941  How to reset your password
// 0.887  Account recovery options
// 0.823  Two-factor authentication setup

---

Hybrid search — filter then rank

Filter by metadata first, then rank by vector similarity. One call, no extra round-trips.

// "Find the 5 most relevant english support articles for this query"
const results = await articles.findNearest('embedding', query, 5, {
  category: 'support',
  locale: 'en',
})

// Works across all runtimes — browser, React Native, Node.js
// Data never leaves the device

---

Live queries

// Subscribe to changes — callback fires after every matching write
const unsub = articles.subscribe({ category: 'support' }, (docs) => {
  console.log('support articles updated:', docs.length)
})

// Stop listening
unsub()

Documentation

Full documentation is at taladb.dev.

Section	Link
Introduction & architecture	/introduction
Core concepts	/concepts
Feature overview	/features
Web (Browser / WASM) guide	/guide/web
Node.js guide	/guide/node
React Native guide	/guide/react-native
Collection API	/api/collection
Filters	/api/filters
Updates	/api/updates
Migrations	/api/migrations
Encryption	/api/encryption
Live queries	/api/live-queries

Development

Prerequisites

• Rust stable 1.75+
• wasm-pack — for browser builds
• Node.js 18+ and pnpm 9+
• @napi-rs/cli — for Node.js native module builds

Running tests

# Rust unit + integration tests
cargo test --workspace

# TypeScript tests
pnpm --filter taladb test

# Browser WASM tests (requires Chrome)
wasm-pack test packages/@taladb/web --headless --chrome

Building

# Browser WASM
pnpm --filter @taladb/web build

# Node.js native module
pnpm --filter @taladb/node build

# TypeScript package
pnpm --filter taladb build

# All packages
pnpm build

Local docs

pnpm docs:dev     # dev server at http://localhost:5173
pnpm docs:build   # production build
pnpm docs:preview # preview production build

Contributing

TalaDB is maintained by Dennis. Bug reports, PRs, and feedback are all welcome.

1. Fork the repo and create a branch: git checkout -b feat/my-feature
2. Make your changes and add tests
3. Run cargo test --workspace and pnpm --filter taladb test
4. Open a pull request with a clear description

Open an issue before large features or architectural changes. See CONTRIBUTING.md for the full development workflow.

Reach out: dennis@thinkgrid.dev

Support TalaDB

TalaDB is free and open-source, maintained by one person. If it saves you time, sponsoring on GitHub directly funds continued development: new runtimes, query operators, and performance work.

License

MIT © thinkgrid-labs

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Documents + vectors, on-device. No cloud. · taladb.dev