React Native AI

A collection of on-device AI primitives for React Native with first-class Vercel AI SDK support. Run AI models directly on users' devices for privacy-preserving, low-latency inference without server costs.

Features

• 🚀 Instant AI - Use built-in system models immediately without downloads
• 🔒 Privacy-first - All processing happens on-device, data stays local
• 🎯 Vercel AI SDK compatible - Drop-in replacement with familiar APIs
• 🎨 Complete toolkit - Text generation, embeddings, transcription, speech synthesis

AI SDK Compatibility

DevTools

The AI SDK Profiler plugin captures OpenTelemetry spans from Vercel AI SDK requests and surfaces them in Rozenite DevTools. DevTools are runtime agnostic, so they work with on-device and remote runtimes.

npm install @react-native-ai/dev-tools

Rozenite must be installed and enabled in your app. See the Rozenite getting started guide.

The Expo demo app in apps/expo-example includes native-development Rozenite wiring for this plugin. Run the example app, open React Native DevTools, and select AI SDK Profiler to inspect chat spans emitted by the Vercel AI SDK.

If the AI SDK Profiler panel is visible but remains empty after sending a chat message, close that React Native DevTools window and open a fresh one. In practice, a stale debugger session can keep the Rozenite panel mounted without receiving the current app's telemetry stream.

Available Providers

Apple

Native integration with Apple's on-device AI capabilities. Built-in - no model downloads required, uses system models.

• Text Generation - Apple Foundation Models for chat and completion
• Embeddings - NLContextualEmbedding for 512-dimensional semantic vectors
• Transcription - SpeechAnalyzer for fast, accurate speech-to-text
• Speech Synthesis - AVSpeechSynthesizer for natural text-to-speech with system voices

Installation

npm install @react-native-ai/apple

No additional linking needed, works immediately on iOS devices (autolinked).

Usage

import { apple } from '@react-native-ai/apple'
import {
  generateText,
  embed,
  experimental_transcribe as transcribe,
  experimental_generateSpeech as speech,
} from 'ai'

// Text generation with Apple Intelligence
const { text } = await generateText({
  model: apple(),
  prompt: 'Explain quantum computing',
})

// Generate embeddings
const { embedding } = await embed({
  model: apple.textEmbeddingModel(),
  value: 'Hello world',
})

// Transcribe audio
const { text } = await transcribe({
  model: apple.transcriptionModel(),
  audio: audioBuffer,
})

// Text-to-speech
const { audio } = await speech({
  model: apple.speechModel(),
  text: 'Hello from Apple!',
})

Availability

See the Apple documentation for detailed setup and usage guides.

Llama

Run any GGUF model on-device using llama.rn. Requires download - models are downloaded from HuggingFace.

Supported Features

Installation

npm install @react-native-ai/llama llama.rn react-native-blob-util

Usage

import { llama } from '@react-native-ai/llama'
import { generateText, streamText } from 'ai'

// Create model instance (Model ID format: "owner/repo/filename.gguf")
const model = llama.languageModel(
  'ggml-org/SmolLM3-3B-GGUF/SmolLM3-Q4_K_M.gguf'
)

// Download from HuggingFace (with progress)
await model.download((progress) => {
  console.log(`Downloading: ${progress.percentage}%`)
})

// Initialize model (loads into memory)
await model.prepare()

// Generate text
const { text } = await generateText({
  model,
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Write a haiku about coding.' },
  ],
})

// Cleanup when done
await model.unload()

Model Compatibility

Any GGUF model from HuggingFace can be used. Use the format owner/repo/filename.gguf as the model ID. Popular choices include:

• ggml-org/SmolLM3-3B-GGUF/SmolLM3-Q4_K_M.gguf
• bartowski/Llama-3.2-3B-Instruct-GGUF/Llama-3.2-3B-Instruct-Q4_K_M.gguf
• Qwen/Qwen2.5-1.5B-Instruct-GGUF/qwen2.5-1.5b-instruct-q4_k_m.gguf

📚 View full Llama documentation →

MLC

Run popular open-source LLMs directly on-device using MLC LLM's optimized runtime. Requires download - models must be downloaded before use.

Installation

npm install @react-native-ai/mlc

Requires the "Increased Memory Limit" capability in Xcode. See the getting started guide for setup instructions.

Usage

import { mlc } from '@react-native-ai/mlc'
import { generateText } from 'ai'

// Create model instance
const model = mlc.languageModel('Llama-3.2-3B-Instruct')

// Download and prepare model (one-time setup)
await model.download()
await model.prepare()

// Generate response with Llama via MLC engine
const { text } = await generateText({
  model,
  prompt: 'Explain quantum computing',
})

Available Models

[!NOTE] MLC requires iOS devices with sufficient memory (1-8GB depending on model). The prebuilt runtime supports the models listed above. For other models or custom configurations, you'll need to recompile the MLC runtime from source.

Documentation

Comprehensive guides and API references are available at react-native-ai.dev.

Contributing

Read the contribution guidelines before contributing.

Agent skills

This repository provides agent skills to help you integrate and use the packages. You can easily install them with:

npx skills add https://github.com/callstackincubator/react-native-ai --skill react-native-ai-skills

or manually by copying the skills/ directory in your .cursor/ directory.

Made with ❤️ at Callstack

react-native-ai is an open source project and will always remain free to use. If you think it's cool, please star it 🌟.

Callstack is a group of React and React Native geeks, contact us at hello@callstack.com if you need any help with these or just want to say hi!

Made with create-react-native-library