Software Mansion's best practices for on-device AI in React Native using React Native ExecuTorch. Covers LLMs (chat, tool calling, structured output), computer vision (classification, object detection, OCR, segmentation, style transfer, embeddings, text-to-image), speech processing (speech-to-text, text-to-speech, voice activity detection), VisionCamera real-time frame processing, model loading and resource management. Trigger on: 'react-native-executorch', 'ExecuTorch', 'on-device AI', 'on-device ML', 'local AI', 'offline AI', 'useLLM', 'useClassification', 'useObjectDetection', 'useOCR', 'useVerticalOCR', 'useStyleTransfer', 'useTextToImage', 'useImageEmbeddings', 'useImageSegmentation', 'useSpeechToText', 'useTextToSpeech', 'useVAD', 'useTextEmbeddings', 'useTokenizer', 'useExecutorchModule', 'ResourceFetcher', 'real-time frame processing', 'runOnFrame', 'tool calling', 'structured output', or any request to run AI/ML models locally in a React Native app.
On-Device AI
Software Mansion's production patterns for on-device AI in React Native using React Native ExecuTorch.
Load at most one reference file per question. For hook API signatures, model constants, and configuration options, webfetch the relevant page from the official docs at https://docs.swmansion.com/react-native-executorch/docs/.
Decision Tree
Pick the right hook based on the AI task.
What AI task does the feature need?
│
├── Text generation, chatbot, or reasoning?
│ └── useLLM → see llm.md
│ ├── Text-only chat → standard useLLM
│ ├── Tool calling → configure with toolsConfig
│ └── Structured JSON output → getStructuredOutputPrompt
│
├── Understanding images?
│ ├── What's in this image? → useClassification → see vision.md
│ ├── Where are objects? → useObjectDetection → see vision.md
│ ├── Read text from image? → useOCR / useVerticalOCR → see vision.md
│ ├── Segment regions? → useImageSegmentation → see vision.md
│ ├── Apply artistic style? → useStyleTransfer → see vision.md
│ ├── Generate image from text? → useTextToImage → see vision.md
│ └── Embed image as vector? → useImageEmbeddings → see vision.md
│
├── Speech or audio processing?
│ ├── Transcribe speech → useSpeechToText → see speech.md
│ ├── Synthesize speech → useTextToSpeech → see speech.md
│ └── Detect speech segments → useVAD → see speech.md
│
├── Text utilities?
│ ├── Convert text to vectors → useTextEmbeddings → see vision.md
│ └── Count tokens → useTokenizer
│
├── Real-time camera processing?
│ └── runOnFrame with VisionCamera v5 → see vision.md
│
└── Custom model (.pte)?
└── useExecutorchModule → see setup.md
Critical Rules
Always check isReady before calling forward or generate. Hooks load models asynchronously. Calling inference methods before the model is ready throws ModuleNotLoaded.
Interrupt LLM generation before unmounting the component. Unmounting while isGenerating is true causes a crash. Call llm.interrupt() and wait for isGenerating to become false before navigating away.
Use quantized models on mobile. Full-precision models consume too much memory for most devices. React Native ExecuTorch ships quantized variants for all supported models.
Audio for speech-to-text must be 16kHz mono. Mismatched sample rates produce garbled transcriptions silently.
Audio from text-to-speech is 24kHz. Create the AudioContext with { sampleRate: 24000 } for playback.
Set pixelFormat: 'rgb' for VisionCamera frame processing. The default yuv format produces incorrect results with ExecuTorch vision models.
References
File
When to read
llm.md
LLM chat (functional and managed), tool calling, structured output, token batching, context window configuration, model selection, generation config
Speech-to-text (batch and streaming transcription), text-to-speech (batch and streaming synthesis), voice activity detection, audio format requirements
setup.md
Installation, model loading strategies (bundled, remote, local), ResourceFetcher download management, error handling with RnExecutorchError, custom models with useExecutorchModule, Metro config for .pte files
