PocketPal AI

A private AI assistant that runs entirely on your phone.

Chat with language models, give them a voice, and let them use tools — all on-device. No account, no cloud, no internet required.

pocketpal.dev · Get the app · Leaderboard · PalsHub · Discussions

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Why PocketPal AI?

Most AI apps are a thin window onto someone else's server — every message you type gets shipped off, logged, and analyzed somewhere you can't see. PocketPal flips that around: the AI lives on your phone, and your conversations never leave it.

• 🔒 Private by default — every prompt, response, and document stays on your device. Nothing is uploaded or stored on external servers.
• ✈️ Works offline — download a model once and it just works, with no connection and no account. On a plane, on a trail, anywhere.
• 📱 Runs on hardware you already own — real language models, voices, and tools, tuned to make the most of your phone's CPU, GPU, and NPU.
• 🆓 Free and open source — no subscription, no "pro" tier to unlock the AI. MIT-licensed and built in the open.

Privacy note: The only data that ever leaves your device is what you explicitly choose to share — benchmark results (if you opt into the leaderboard) and feedback you submit through the app.

Contents

• Features
• Get the app
• How it works
• Using the app
• For developers
• Contributing
• Roadmap
• Community & support
• License

Features

• 🧠 On-device chat — run GGUF language models (Gemma, Qwen, Phi, Llama, and more) fully offline.
• 🗣️ Text-to-speech — give your assistant a voice with on-device neural TTS (Kokoro and other engines), no cloud calls.
• 🎭 Pals — create personalized assistants with their own model, system prompt, and personality (Assistant and Roleplay types).
• 🛍️ PalsHub — discover and install community Pals, including premium ones via in-app checkout.
• 🛠️ Talents & tools — let capable Pals call built-in tools (calculator, date/time, rich HTML rendering) inside a tool-use loop.
• 📥 Hugging Face integration — search and download GGUF models, including gated ones, directly from the HF Hub with your access token.
• 📊 Benchmarking — measure tokens/sec and memory, and optionally compare on the AI Phone Leaderboard.
• ⚡ Hardware acceleration — CPU, GPU (Metal on iOS, OpenCL/Adreno on Android), and NPU (Qualcomm Hexagon) inference paths, with graceful fallback.
• 🌍 Localized — available in 11 languages, on phones and tablets, including full iPad support.

Get the app

Platform
iOS / iPadOS
Android

Three steps to your first chat:

1. Install PocketPal from the App Store or Google Play.
2. Download a model — tap the menu (☰) → Models, pick one that fits your phone, and download (or add one from Hugging Face).
3. Load it and start chatting — that's it, you're running AI fully offline.

How it works

You don't need to know any of this to use PocketPal — but if you're curious how a phone runs real AI offline, here's the short version.

PocketPal is a four-layer stack, from the silicon up to the chat UI. Each layer has one job, and the dependency direction is strictly top-down — the JS app talks to native bridges, bridges talk to inference engines, engines target hardware backends.

Layer	What runs here
UI & Tool Use	The React Native app (UI via React Native Paper, state via MobX, chat history in WatermelonDB). The AgentRunner drives each chat turn — streaming tokens, dispatching Talents (tools) when the model calls them, and feeding results back for follow-up reasoning. Pals are configurable personas; PalsHub is the in-app marketplace for sharing and buying them.
Bridging	Native modules that connect JavaScript to the engines. llama.rn bridges LLM inference over JSI; react-native-speech and onnxruntime-react-native bridge text-to-speech.
Engine	The inference engines. llama.cpp runs language models in the quantized GGUF format. ONNX Runtime runs TTS voice models in the ONNX format.
Hardware	Where the math actually happens. PocketPal targets CPU (universal fallback), GPU (Metal on iOS, OpenCL on Qualcomm Adreno for Android), and NPU (Qualcomm Hexagon) — falling back gracefully and offloading partial layers when a full backend isn't available.

Using the app

📥 Download & load a model

1. Open the app and tap the Menu (☰), then go to Models.
2. Pick a model from the list and tap Download, or tap + to add one from Hugging Face or local storage.
3. From Hugging Face, search GGUF models and choose a quantization that fits your device's memory and storage — download now or bookmark for later.
4. After downloading, tap Load (or use the chevron icon left of the chat input to load right from the chat screen).

💬 Chat

1. Make sure a model is loaded.
2. Open the Chat page and start talking.
3. The screen stays awake during inference and deactivates when idle.
4. Copy a full response with the copy icon, or long-press a paragraph to copy just that.
5. Edit any of your messages with a long-press — the AI regenerates from your change. Hit retry for a fresh answer, optionally with a different model.

🎭 Pals & PalsHub

Create personalized assistants: - Assistant Pal — pick a default model, set a system prompt (write it yourself or have the app generate one), and customize the chat input color. - Roleplay Pal — everything above, plus location, the AI's role, and other contextual parameters.

Switch personas with the Pal picker on the chat page. Browse PalsHub in-app to discover community Pals, including premium ones via in-app checkout (US iOS & Android).

Creating a cocktail-recipe assistant

📊 Benchmark your device

1. Open the Benchmark page.
2. Run performance tests to compare speed and efficiency across models.
3. Review tokens/sec and memory usage.
4. Optionally share your results to the AI Phone Leaderboard.

🔑 Set up a Hugging Face token (for gated models)

1. Create an access token in your Hugging Face account (docs).
2. In PocketPal, go to Settings → Set Token, paste it, and save.

💌 Send feedback

Go to App Info → "Sharing your thoughts", type your feedback — feature requests, suggestions, anything — and submit.

For developers

PocketPal is a standard React Native app. If you can build a React Native project, you can build PocketPal.

Prerequisites

• Node.js — version is pinned in .nvmrc (currently 22.21.0); run nvm use to match it. Older Node will fail the engines check.
• Yarn 1 (Classic) — packageManager is pinned to yarn@1.22.22.
• Xcode + CocoaPods, and Ruby + Bundler (for iOS / Fastlane tooling).
• Android Studio + Android SDK/NDK.

See the React Native environment setup for platform details.

Clone, install & run

git clone https://github.com/a-ghorbani/pocketpal-ai
cd pocketpal-ai

nvm use                       # match the pinned Node version
yarn install                  # install JS dependencies
(cd ios && pod install)       # iOS only

yarn start                    # Metro bundler
yarn ios                      # build + run on iOS simulator
yarn android                  # build + run on Android emulator

Core on-device chat works without any backend keys; only PalsHub/auth features need additional configuration.

Native-change rule: if you change package.json, a native module, ios/, android/, the Podfile, or build.gradle, re-run pod install and rebuild both platforms — a JS reload won't pick up native changes.

Quality gates

yarn lint           # ESLint
yarn typecheck      # tsc --noEmit
yarn test           # Jest
yarn l10n:validate  # validate locale JSON (placeholders, integrity)

Run yarn lint && yarn typecheck && yarn test before opening a PR. Commits are validated by Commitlint (Conventional Commits) via a Husky hook.

Repository layout

src/
├── screens/        # Chat, Models, Pals, Benchmark, Settings, About, …
├── components/     # Reusable UI
├── store/          # MobX stores (Model, ChatSession, Pal, TTS, HF, Benchmark, …)
├── services/
│   ├── agent/      # AgentRunner — the chat / tool loop
│   ├── talents/    # Tool engines + registries
│   ├── tts/        # TTS engines (kokoro, kitten, supertonic, system)
│   ├── palshub/    # PalsHub marketplace integration
│   └── downloads/  # Model download manager
├── database/       # WatermelonDB schema, models, migrations
├── repositories/   # Data-access layer over the DB
├── locales/        # i18n JSON + lazy loader (index.ts is the registry)
└── hooks/  api/  theme/  utils/  config/  specs/

Tech stack

Versions are pinned in package.json; the highlights:

Area	Choice
Framework	React Native 0.82.1, React 19.1.1 (New Architecture)
Language	TypeScript 5.0.4
UI	React Native Paper 5.14.5, React Navigation
State	MobX 6 (mobx, mobx-react, mobx-persist-store)
Persistence	WatermelonDB (chat history), AsyncStorage (settings), Keychain (secrets)
LLM	llama.rn 0.12.4 → llama.cpp · GGUF
TTS	react-native-speech 2.3.1 + onnxruntime-react-native 1.23.2 · ONNX
Tooling	Yarn 1 (Classic), ESLint, Prettier, Jest, Husky + Commitlint

Extending PocketPal

A Talent is a tool the model can call mid-conversation. Engines are registered in a TalentRegistry, exposed to the model as tool schemas; the AgentRunner detects a call, runs the engine, and returns the result for the next turn.

Talent	Engine	Does
calculate	CalculateEngine	Arithmetic / expression evaluation
datetime	DatetimeEngine	Current date / time
render_html	RenderHtmlEngine	Renders model-produced HTML in chat

Good first contributions: - A new Talent — implement a TalentEngine and register it in src/services/talents/. - A new TTS engine — add it under src/services/tts/engines/. - A new locale — add a JSON file in src/locales/ (or translate on Weblate).

Contributing

Contributions are welcome — bug reports, fixes, features, translations, and docs all help.

1. Fork and branch: git checkout -b feature/your-feature-name
2. Make your changes; run on a device/emulator (yarn ios / yarn android). Re-run pod install + rebuild if you touched nat