PhoneClaw is a local AI agent framework for phones and edge devices. It runs Gemma 4 E2B / E4B via LiteRT and MiniCPM-V 4.6 on device, uses native mobile Skills for Calendar, Reminders, Contacts, Clipboard, Health data, image understanding, voice, and LiveLand, and lets you explicitly choose Web Search, webpage reading, or LAN-based Mac Gateway inference when a task needs it.

PhoneClaw (this project) is unrelated to the Android automation repo "rohanarun/phoneclaw" or the "phoneclaw" GitHub organization. Android app-store search results may also show a separate app with the same name.

Demo

Latest Updates

2026-06-23

• Added LiveLand: use PhoneClaw from Dynamic Island. The local AI listens, understands your request, runs the task, and shows the result right there
• Launch LiveLand from Home Screen widgets, Lock Screen widgets, Shortcuts, Control Center widgets, or inside the app. It also works on non-iOS 27 systems
• Task progress is easier to follow: PhoneClaw now shows when it received your command, is understanding it, is querying or executing, is preparing the result, and is done
• More natural phone tasks: ask for health data, create reminders, schedule calendar events, find contacts, translate text, and more through natural language
• Fixed a LiveLand widget launch issue that could incorrectly show "download model first" or "model loading" even after the model was already installed
• Safer actions: unclear times, titles, contacts, deletes, and bulk operations ask for clarification or confirmation before running
• LiveLand runs on local models by default, with a fully offline local path and local execution free of per-call token billing
• TestFlight link: https://testflight.apple.com/join/YuUSwq78

2026-06-08

• Added the PhoneClaw Gateway Mac client: keep it running on your Mac, advertise it over Bonjour, then pair from the iPhone to use Mac-side Ollama, Codex CLI, or Antigravity CLI as a remote inference source
• Added a Mac Remote page in iPhone settings: discover Macs on the same LAN, approve pairing on the Mac, choose a Mac-side model, then use it from the normal chat screen
• Remote models are only used after you explicitly pair a Mac and select one. With Ollama, inference stays on your Mac; with CLI or other upstream providers, data handling follows that provider's behavior
• How to use: download PhoneClawGateway-macOS-v0.1.1.zip, unzip it, open PhoneClawGateway.app, allow Local Network permission, then pair from Mac Remote on the iPhone; see Use the Mac client for remote inference

2026-06-05

• PhoneClaw is live on TestFlight — Join TestFlight
• Rebuilt the overall Agent framework: improved Skill result semantics, Skill routing, tool-call chains, contextual follow-ups, and multi-step task handling, so the model can more reliably choose whether to answer directly, call tools, or continue from the previous result while reducing accidental tool triggers, repeated calls, and off-track follow-ups
• Added Health range reports: summarize steps, distance, active energy, heart rate, sleep, workouts, weight, and HRV across a selected time range, then generate a local health summary, trends, and key metric explanations

Update history

2026-06-01

• PhoneClaw is live on TestFlight — Join TestFlight
• Added Calendar read support: query today's, tomorrow's, this week's, and next 7 days' schedule, with busyness and free-time analysis
• Improved Web Search and long-answer browsing: realtime information can be summarized, and history remains scrollable while the model is responding

2026-05-17

• PhoneClaw is live on TestFlight — Join TestFlight
• A private local Agent running on iPhone, performing inference and Skill calls entirely on-device with a fully offline local path

2026-05-12

• Released v1.4.0 — Download
• Added MiniCPM-V 4.6 multimodal model — image Q&A and real-time camera recognition in LIVE mode
• Fixed several known issues in LIVE mode

2026-05-07

• Added MTP speculative decoding toggle (experimental — only speeds up Gemma 4 E4B with short replies).

2026-04-25

• Released v1.3.1 — Download
• Added English LIVE mode
• Fixed some known bugs
• Released v1.3.0 — Download
• Added English localization — the app automatically switches based on system language.
• Refactored the download module: resumable downloads, background downloads, and automatic fastest-mirror selection based on current network conditions.

2026-04-23

• Released v1.2.2 — added the ability to choose between GPU or CPU inference backend directly from the settings page; CPU is now the default to fit within Sideloadly-signed memory limits.
• ⚠️ Sideloadly-signed IPA usage note: due to the memory cap of sideload-signing, the E4B model only works on CPU (GPU will fail). We recommend using the E2B model — it's fully featured and more stable under the cap.
• 💡 If you can, build from source with Xcode: Xcode-signed builds aren't subject to the sideload memory cap — you can run E2B / E4B with GPU enabled for best performance. Download

2026-04-20

• Released an unsigned IPA — sign and install to iPhone via Sideloadly. Download

2026-04-18

• Added in-app download for LIVE mode voice models — download directly from the settings page and start using LIVE mode

2026-04-17

• Added LIVE Mode: a new real-time voice interaction mode with natural conversation flow and barge-in interruption
• LIVE Mode supports camera input: the model can recognize and understand the environment, objects, and scenes captured by the camera in real time, enabling multimodal "see and speak" interaction

2026-04-10

• Added Health Skill: read HealthKit data including today's/yesterday's steps, weekly step trends, walking distance, active calories, resting heart rate, last night's sleep, weekly sleep summary, and recent workouts — 9 tools total, all data processed locally
• Improved multi-turn response speed: cross-turn KV cache reuse reduces time-to-first-token by ~3.5x for consecutive queries within the same skill
• The 9 health tools are automatically selected by the model based on user intent

2026-04-09

• Ongoing framework and infrastructure work. Major improvement to the multi-turn agent framework: the Router now correctly preserves skill context across turns, and even small models can reliably complete multi-turn tool calls.

2026-04-08

• Model downloads now include a ModelScope mirror for faster Gemma 4 downloads in mainland China
• Major rework of memory management: the inference budget is now dynamically derived from actual available memory, with improved long prompt, long answer, and multi-turn tool-call context handling

2026-04-07

• Added voice input, with on-device audio analysis and recognition
• Added Thinking Mode, available from the top-right corner in chat
• Added chat history, with support for new sessions, switching, and deletion
• Improved memory management and inference budgeting for long answers, multimodal requests, and model switching

2026-04-06

• The default install flow is back to a shell app, with models downloaded on-device as needed
• The settings page now includes model download, permission status, and bilingual display names
• Contacts, reminders, calendar, device info, and clipboard flows have received a round of stability fixes

Core Features

Private Local Agent: Run inference and Skill calls directly on device. Use natural language to work with Calendar, Reminders, Contacts, Clipboard, Health data, and other local tasks.

Mac Remote Inference: Optionally pair with a Mac on the same LAN through PhoneClaw Gateway and use Mac-side Ollama, Codex CLI, or Antigravity CLI models while keeping the native iPhone chat and Skill experience.

Image Understanding and LIVE Vision: Ask questions about photos from the camera or photo picker, or enable the camera in LIVE mode so the model can understand the scene in real time.

Personal Data Analysis: Read local schedules, Health data, contacts, reminders, and clipboard content to generate summaries, availability analysis, and next-step suggestions. Personal data is processed on-device by default.

Realtime Information: When explicitly requested, search public webpages, fetch readable webpage text, and summarize live information into an actionable answer.

Voice Interaction: Supports voice input and LIVE real-time conversation for hands-free questions, notes, and actions.

Technical and Experience Features

File-Driven Skill System: Each capability is defined by a single Markdown file (SKILL.md). Adding or modifying a skill can happen through the Skill file. Skills are language-agnostic — anyone can write and share them.

Model Management and Resumable Downloads: Gemma main models and LIVE voice models can be downloaded, canceled, resumed, and retried directly on iPhone, or bundled into the app at build time.

Fully Offline First with Clear Data Flow: Inference and local Skill calls run on-device by default. Conversations, images, and personal data stay on iPhone. Web Search, webpage reading, and paired Mac remote inference are explicit user-triggered capabilities; Mac remote inference sends the current request to your paired Mac, and any further upstream access depends on the provider selected in the Mac client.

Mobile Memory Optimization: Includes model switching, system prompt editing, cache cleanup, and history trimming tuned for phone-scale on-device inference limits.

Bilingual Experience: Choose Auto, Chinese, or English in settings. The UI, default system prompt, built-in Skills, tool results, and permission text switch together.

Technical Notes

• On-device Gemma on iPhone
• PhoneClaw Skill System
• iOS Memory and Context Limits
• Promotion Kit

Built-in Skill Examples

Calendar: Create calendar events, query schedules, and analyze busyness or free time using natural language.

"Schedule a meeting at Hightech Park tomorrow at 2pm"

"What is on my calendar today?"

"How busy am I this week?"

Reminders: Set time-based reminders that fire a system push notification exactly on schedule.

"Remind me tonight at 8 to send the file to my boss"

Contacts: Search, save, update, or delete contacts with name, phone, company, email, and notes. Automatically deduped by phone number.

"Save Wang's number 13812345678, he's from Bytedance"

"Check Sarah Lee's phone number"

Clipboard: Read and write the system clipboard. Useful as a data relay in multi-step tasks.

"Copy that text to the clipboard"

Translate: Translate between any pair of languages, with automatic source detection.

"Translate that last line into Japanese"

Health Data: Read HealthKit steps, distance, calories, heart rate, sleep, and workout records after user authorization. All data stays on-device.

"How many steps did I take today?"

"How did I sleep last night?"

"How are my steps this week?"

"What's my resting heart rate?"

Web Search: When explicitly requested, search public webpages or read a URL, then summarize realtime information into an answer.

"Search the web for today's AI news"

"Read and summarize this page: https://example.com"

Requirements

• macOS + Xcode 16 or later
• iOS 17.0 or later
• CocoaPods
• A real device with a developer account (Apple ID)

Model recommendation:

Quick Start

Recommended install: TestFlight. After installing, download a model in Model Settings, then enable the Skills you need.

Building from source requires macOS + Xcode 16, iOS 17+, CocoaPods, a real device, and an Apple ID.

1. Clone the repository

git clone https://github.com/kellyvv/phoneclaw.git
cd phoneclaw

2. Install dependencies

pod install

3. Optional: pre-download a model locally

The default recommended flow is now:

1. Install the app shell to the iPhone from Xcode
2. Open the app
3. Go to Model Settings
4. Download Gemma 4 E2B or Gemma 4 E4B directly on the phone

You only need the Models/ directory on your Mac if you want to bundle a model inside the app itself.

Gemma 4 now runs on LiteRT-LM: each model is a single .litertlm file in place of an MLX weight directory. Install the Hugging Face CLI first:

brew install hf
# or
pip install -U "huggingface_hub"

E2B only (recommended):

mkdir -p ./Models
hf download litert-community/gemma-4-E2B-it-litert-lm gemma-4-E2B-it.litertlm --local-dir ./Models

E4B only:

mkdir -p ./Models
hf download litert-community/gemma-4-E4B-it-litert-lm gemma-4-E4B-it.litertlm --local-dir ./Models

Both models:

mkdir -p ./Models
hf download litert-community/gemma-4-E2B-it-litert-lm gemma-4-E2B-it.litertlm --local-dir ./Models
hf download litert-community/gemma-4-E4B-it-litert-lm gemma-4-E4B-it.litertlm --local-dir ./Models

Expected files after download:

Models/
├── gemma-4-E2B-it.litertlm
└── gemma-4-E4B-it.litertlm

Models/ is listed in .gitignore and stays outside commits. Approximate file sizes: E2B ~2.4 GB, E4B ~3.4 GB. In mainland China, set HF_ENDPOINT=https://hf-mirror.com to use the mirror, or download the same file from the ModelScope mirror.

LIVE Mode (voice interaction) additional models

If you want to use LIVE mode with voice recognition and synthesis, download the ASR and TTS models:

# ASR — Chinese streaming speech recognition (zipformer, int8, ~160MB)
hf download csukuangfj/sherpa-onnx-streaming-zipformer-zh-int8-2025-06-30 \
  --local-dir ./Models/sherpa-asr-zh \
  --exclude "test_wavs/*" "*.md" ".gitattributes"

# TTS — Chinese text-to-speech (keqing, ~125MB)
hf download csukuangfj/vits-zh-hf-keqing \
  --local-dir ./Models/vits-zh-hf-keqing \
  --exclude "*.py" "*.sh" ".gitattributes"

After downloading, add Models/sherpa-asr-zh and Models/vits-zh-hf-keqing as folder references to Copy Bundle Resources in Xcode. LIVE mode can also use system speech as a fallback.

4. Open the workspace

open PhoneClaw.xcworkspace

Always open .xcworkspace.

5. Configure signing and run

1. In Xcode, select the PhoneClaw target
2. Open Signing & Capabilities
3. Set your Team
4. Change the Bundle Identifier to a unique value
5. Connect your iPhone and press ⌘R

On first install, if prompted to trust the developer certificate: Settings → General → VPN & Device Management → Trust

6. First use

After opening the app:

• Top-right puzzle icon: Skill management
• Top-right slider icon: Model settings / system prompt / permissions
• If you installed a shell-only app, tap Download in the model settings page first

Download a model first, then enable Calendar, Reminders, and Contacts in the permissions page, then try:

Remind me tonight at 8 to send the file
Save Wang's phone number 13812345678
Translate that last line into English

7. Use the Mac client for remote inference

The Mac client turns a Mac on the same LAN into an optional remote inference source for the phone runtime. PhoneClaw still uses its chat UI and Skill system, while model inference requests are sent to your paired Mac.

Option A: download the release (recommended)

1. Download PhoneClawGateway-macOS-v0.1.1.zip
2. Unzip it and open PhoneClawGateway.app
3. On first launch, allow the macOS Local Network permission prompt
4. If macOS blocks first launch, open Finder, right-click PhoneClawGateway.app, then choose Open

Gateway listens on port 18080 by default and advertises the _phoneclaw-llm._tcp Bonjour service.

Option B: build from source

cd MacGateway
bash build-app.sh
open PhoneClawGateway.app

The source-built app also needs macOS Local Network permission.

Configure the Mac runtime source

1. Open PhoneClawGateway.app
2. Choose a runtime source in the main window: Ollama, Codex CLI, or Antigravity CLI
3. If you use Ollama, install and start Ollama first, then pull a model, for example:

ollama pull gemma3:4b

4. Return to Gateway, scan, and confirm that the model appears in the list

Pair from the iPhone

1. Make sure the iPhone and Mac are on the same LAN
2. Open PhoneClaw → top-right slider → Mac Remote
3. Tap the Mac, then approve the request in the Mac client
4. After pairing, choose a Mac-side model and return to chat

Remote models appear under the Remote section in the model picker. For Mac discovery, check that the Mac client is running, Local Network permission is allowed, both devices are on the same Wi-Fi, and the macOS firewall allows PhoneClawGateway.app to accept LAN connections.

Default Install Flow and Model Bundling

Option A — Shell app + on-device model download

This is now the default recommended setup.

Advantages:

1. Much smaller install size from Xcode
2. Faster first-time app installation from the Mac
3. Users can choose E2B or E4B directly on the phone

By default, the project builds a lightweight app while model files download on device or get copied explicitly during custom builds.

Option B — E2B only

1. Keep Models/gemma-4-E2B-it.litertlm, remove Models/gemma-4-E4B-it.litertlm
2. In Xcode's Project Navigator, delete the unused model file reference and choose Remove Reference
3. In PhoneClaw > Build Phases > Copy Bundle Resources, make sure gemma-4-E2B-it.litertlm is included as a single file resource
4. Edit allModels in LLM/Models/PredefinedModels.swift to only include the models actually shipped (otherwise the settings page will show options that don't exist)

Option C — Both E2B and E4B

Download both models:

brew install hf
mkdir -p ./Models
hf download litert-community/gemma-4-E2B-it-litert-lm gemma-4-E2B-it.litertlm --local-dir ./Models
hf download litert-community/gemma-4-E4B-it-litert-lm gemma-4-E4B-it.litertlm --local-dir ./Models

Then add both .litertlm files into Xcode's Copy Bundle Resources.

Adding Custom Skills

Create a SKILL.md file in the app's data directory and hot-reload in-app:

Application Support/PhoneClaw/skills/<skill-id>/SKILL.md

---
name: MySkill
name-zh: My Skill
description: What this skill does
version: "1.0.0"
icon: star
disabled: false
type: device          # device = native API; content = prompt-only; network = public internet access

triggers:
  - schedule planning

allowed-tools:
  - my-tool-name

examples:
  - query: "How a user might phrase it"
    scenario: "What scenario triggers this"
---

# Skill Instructions

Tell the model when to call tools, how to structure arguments, and when to answer directly.

The type field controls routing: device calls native iOS APIs, content is prompt-only text processing, and network is for explicit live web search or webpage reading. If this skill needs to call native APIs or network tools, register the tool in Tools/ToolRegistry.swift (and add a handler under Tools/Handlers/). The framework validates allowed-tools against the registry at startup, so any typo will surface immediately in the console.

FAQ

How do I trigger permission dialogs after install? Permission dialogs appear when a Skill reaches the related system API call. After a previous denial, re-enable the permission in system Settings.

Why does the model fail to load after switching? Verify that the model file name matches allModels in LLM/Models/PredefinedModels.swift, that the model has finished downloading on-device if you are using the shell-only install flow, or that it was actually included in the app bundle if you are shipping it built-in, and that the device has enough memory.

Why does creating a reminder fail? The latest code first attempts to reuse an existing writable reminder list. If none is found, it tries to automatically create a PhoneClaw list. If that also fails, the system reminder source itself is likely read-only.

How do I make the iPhone discover the Mac client? Make sure PhoneClawGateway.app is running and macOS has allowed Local Network permission. The iPhone and Mac must be on the same LAN. If discovery still fails, check that the macOS firewall allows the app to accept incoming connections, or rebuild the app with bash MacGateway/build-app.sh and open the generated app again.

Roadmap

1. More iOS native APIs

• File and directory access
• Image picking, description, and Q&A
• Photo library reading, organization, and search
• Notes
• Reminder due-time alerts
• General local notifications
• Maps and location
• URL webpage reading and context passing
• Safari / URL Scheme handoff to external apps
• Contacts search, create, update, and delete
• Calendar creation, schedule reading, busyness, and free-time analysis
• Reminder creation
• Read-only HealthKit analysis

2. More Skills

Continue breaking capabilities into focused Skills with clear model, tool, and permission responsibilities. Directions worth adding:

• File management
• Image understanding
• Photo organization
• Schedule creation, querying, and busyness analysis
• Personal information management: contacts, calendar, reminders, clipboard, and Health data
• Local knowledge base search
• Voice input / text-to-speech
• Web Search / webpage reading
• Translation

3. More local models

Beyond the main chat model, suitable additions include:

• Vision / multimodal model
• OCR model
• Speech recognition model
• Speech synthesis model
• Embedding / Reranker model
• A smaller tool argument extraction model
• A stronger planning model or multi-model pipeline

This moves PhoneClaw from "one big model doing everything" toward "multiple local models working together."

4. Cross-app automation

PhoneClaw uses the cross-app capabilities that iOS actually provides:

• App Intents / Shortcuts (LiveLand / LIVE launch intents shipped; task-level intents planned)
• URL Scheme / Deep Link
• Share Sheet extensions
• Clipboard relay
• System reminder notifications
• System notification wake-up and cross-app orchestration

A realistic goal: pass content between apps, open a specific app to a specific screen, and compress multi-step operations into a single natural language command.

5. External hardware and visual input

• LIVE camera real-time recognition
• External video input
• Screen understanding
• External hardware integration

Explore connecting external video input and screen understanding with local models, so PhoneClaw goes beyond answering questions in isolation and develops stronger real-world perception and scheduling capabilities.

References

• Hugging Face CLI documentation
• Hugging Face download guide
• Gemma 4 E2B LiteRT model
• Gemma 4 E4B LiteRT model
• Gemma 4 E2B (ModelScope mirror)
• Gemma 4 E4B (ModelScope mirror)
• MiniCPM-V 4.6 model
• OpenBMB MiniCPM-V iOS Demo

License

Apache 2.0