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.

Performance

Measured with the reproducible suites in scripts/ (pnpm bench:web for the browser, pnpm bench for Node.js) on a 2018 MacBook Pro (Intel i5-8259U, 8 GB) — deliberately modest hardware; treat these as a floor. TalaDB v0.9.0, file-backed / OPFS, medians after warmup.

Browser (WASM + OPFS) — the flagship runtime, measured in headless Chrome (every timing includes the worker round-trip):

Operation	Scale	Result
findOne by _id	100k docs	100 µs
find on indexed field	100k docs	300 µs
Bulk ingest (insertMany)	batches of 5k	~57k docs/s
findNearest (384-dim, exact k-NN)	10k vectors	35 ms
findNearest (384-dim, exact k-NN)	50k vectors	170 ms
Hybrid: indexed filter + vector rank	50k vectors	162 ms

Node.js (native) — file-backed, fsync-durable per write:

Operation	Scale	Result
findOne by _id	100k docs	25 µs
find on indexed field	100k docs	169 µs
find, two-sided range ($gte+$lt)	100k docs	1.4 ms
Bulk ingest (insertMany)	batches of 5k	~36k docs/s
findNearest (384-dim, exact k-NN)	10k / 100k vectors	18 ms / 198 ms
Hybrid: indexed filter + vector rank	100k vectors	346 ms

Vector search is exact by default — no approximation, no recall trade-off — with an optional HNSW index on Node.js (93 ms → 15 ms at 50k vectors). The v0.9.0 scan rewrite roughly halved flat vector search and turned two-sided range queries into a single bounded index scan (~463 ms → 1.4 ms). Browser vector search currently trails native by ~2× purely for lack of WASM simd128 — a measured SIMD build closes the gap and is next up. Full tables, methodology, and tuning notes: taladb.dev/benchmarks.

Usage

Install

Every app installs the unified taladb package plus one runtime binding for its platform. Everything else is optional.

Web app (browser)

pnpm add taladb @taladb/web          # required
pnpm add @taladb/react               # optional — React hooks (useFind, useFindOne, …)

Mobile app (React Native / Expo)

pnpm add taladb @taladb/react-native # required
pnpm add @taladb/react               # optional — the same hooks work in React Native

Node.js (server / scripts)

pnpm add taladb @taladb/node                 # required
pnpm add @taladb/sync-mongodb mongodb        # optional — sync to MongoDB (server-side only)

Package	Web	Mobile (RN)	Node	Role
taladb	✅ required	✅ required	✅ required	Unified API
@taladb/web	✅ required	—	—	Browser WASM binding
@taladb/react-native	—	✅ required	—	React Native (JSI) binding
@taladb/node	—	—	✅ required	Node.js native binding
@taladb/react	⭕ optional	⭕ optional	—	React / React Native hooks
@taladb/sync-mongodb	—	—	⭕ optional	MongoDB sync (server-side only)
@taladb/cloudflare	—	—	⭕ optional	Cloudflare Workers deploy target

Bidirectional HTTP sync (HttpSyncAdapter) ships inside taladb — no extra install. Database sync adapters like @taladb/sync-mongodb hold DB credentials and run server-side only; a web or mobile app syncs through your own API, never a direct database connection.

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

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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

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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
Benchmarks	/benchmarks
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

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