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.

• 🚀 Instant AI - Use built-in system models immediately without downloads
• 🔒 Privacy-first - Inference runs on-device, so data stays local. Cloud models are opt-in
• 🎯 Vercel AI SDK compatible - Use generateText, streamText, embed and other AI SDK functions with local models
• 🎨 Beyond text - Embeddings, transcription, speech synthesis, image and video generation, and vision models

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.

If the AI SDK Profiler panel stays empty after you send a message, close the React Native DevTools window and open a new one. A stale debugger session can keep the panel mounted without receiving telemetry.

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

npm install @react-native-ai/apple

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

import { apple } from '@react-native-ai/apple'
import {
  generateText,
  embed,
  transcribe,
  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.embeddingModel(),
  value: 'Hello world',
})

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

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

See the Apple documentation for detailed setup and usage guides.

Run Gemini Nano on-device on Android through Google's ML Kit GenAI libraries. Built-in - Gemini Nano is provided by the system through AICore, so there are no model files to bundle and no API key.

• Text Generation - generateText and streamText with on-device Gemini Nano, with images and thinking
• Tool Calling - AI SDK tools with Gemini Nano 4's function calling
• Transcription - Speech to text with ML Kit speech recognition
• Embeddings - On-device text embeddings

npm install @react-native-ai/android

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

const { availability } = await android.getModelInfo()
// When downloadable, the first request downloads Gemini Nano on its own
if (availability === 'available' || availability === 'downloadable') {
  const { text } = await generateText({
    model: android(),
    prompt: 'Explain quantum computing',
  })
}

See the Android documentation for device checks, tool calling, transcription and embeddings.

Run models exported with Apple's coreai-models on Apple's Core AI runtime, on iOS 27 devices. Your app downloads or bundles the models, and every task has a hosted export to start from, except raw function calls, which run your own .aimodel.

• Text Generation - Qwen3 and other LLM exports, with structured output, tool calling and reasoning
• Image Prompts - Qwen3-VL answers questions about an image
• Image and Video Generation - FLUX.2 diffusion pipelines, and Wan 2.1 video on macOS (it does not fit in iPhone memory yet)
• Transcription - Parakeet TDT, for files and live audio
• Embeddings - CLIP text and image embeddings in one space
• Vision - Object detection, segmentation, video tracking, super-resolution, classification and depth
• Any .aimodel - Raw function calls with tensors and images

npm install @react-native-ai/core-ai @react-native-ai/apple ai

import { coreAI } from '@react-native-ai/core-ai'
import { generateImage, streamText, transcribe } from 'ai'

// Text generation with an exported Qwen3
const result = streamText({
  model: coreAI.languageModel({
    id: 'qwen3-0.6b',
    source: qwen3,
  }),
  prompt: 'Explain Core AI in one sentence.',
})

for await (const delta of result.textStream) {
  console.log(delta)
}

// Image generation with FLUX.2 klein
const { image } = await generateImage({
  model: coreAI.imageModel({
    id: 'flux2-klein-4b',
    source: flux,
  }),
  prompt: 'a red apple on a wooden table',
})

// Transcription with Parakeet TDT
const { text } = await transcribe({
  model: coreAI.transcriptionModel({
    id: 'parakeet',
    source: parakeet,
  }),
  audio: recording,
})

See the Core AI documentation for native setup, every task and the hosted models.

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

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

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

// Download from HuggingFace (Model ID format: "owner/repo/filename.gguf")
const modelPath = await downloadModel(
  'bartowski/Qwen_Qwen3.5-2B-GGUF/Qwen_Qwen3.5-2B-Q4_K_M.gguf',
  (progress) => console.log(`Downloading: ${progress.percentage}%`)
)

// Create model instance
const model = llama.languageModel(modelPath)

// 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()

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

• bartowski/Qwen_Qwen3.5-2B-GGUF/Qwen_Qwen3.5-2B-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 →

@react-native-ai/json-ui lets a tool-calling model build React Native UI. The model calls tools that add, update, and delete nodes in a JSON spec, and GenerativeUIView renders it. It's designed for small on-device models with limited context.

npm install @react-native-ai/json-ui

See the JSON UI documentation.

Guides and API reference are available at react-native-ai.dev.

Read the contribution guidelines before contributing.

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 into your agent's skills directory (for example .cursor/skills/).

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 [email protected] if you need any help with these or just want to say hi!

Made with create-react-native-library