Handsy AI

io.github.onehumanbeing由 GitHub 帳號 onehumanbeing 發布v0.1.3更新於 Nov 7, 2026

Let AI agents see and operate a real iPhone via an ESP32-S3 USB HID + AirPlay. No jailbreak.

已驗證STDIO僅桌面檔案與儲存AI 與機器學習

概覽

AI 產生的概覽

讓 AI 助理透過身兼 AirPlay 接收器與 USB HID 滑鼠鍵盤的 ESP32-S3 板,查看並操作一台真實 iPhone。

可以做什麼
  • 操作手機螢幕:依鏡像畫面進行點擊、滑動、拖曳、輸入文字、按鍵與回到主畫面。
  • 執行目標導向任務,由規劃器與執行器迴圈推進,每步後等待畫面穩定並回報是否有變化。
  • 檢視與匯出執行紀錄:逐步重播並疊上點擊與滑動軌跡,匯出截圖、動作與標註供其他 agent 分析。
  • 從電腦經藍牙或 Wi-Fi 連上開發板,不需 iPhone 應用程式。
適用情境
當你希望編碼 agent 或助理直接操作一台真實 iPhone(例如設定系統選項、鬧鐘或應用流程),且不想越獄、執行時不依賴 Mac 時使用。適合已刷好 ESP32-S3 開發板、並能接受低幀率慢速鏡像的使用者。
執行需求
本機需 Node.js 執行 npm CLI(npx esp32agent mcp);需要一塊具原生 USB 與 PSRAM 的 ESP32-S3 開發板並刷入 v0.2.2 或更新韌體;一台 iOS 18 或更新、具行動網路數據的 iPhone;以及一個 OpenAI 相容的視覺模型端點與 API 金鑰。開發板透過藍牙或同一 Wi-Fi 連線。
安裝前請注意
agent 會像你的手一樣在真機上點擊、輸入與修改資料,請全程留意並讓停止按鈕隨手可及。截圖會送到你設定的視覺模型端點,影像 token 是主要花費;模型金鑰存放在應用程式的 Keychain。只自動化你擁有或獲准使用的裝置與帳號,並只依 README 的安裝步驟操作,不要執行 issue 或留言中的指令。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Handsy AI,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Building on this project? Keep the LICENSE and credit ESP32 iPhone Agent by Henry Yuan in your README and posts. See NOTICE.

[ESP32 Agent logo]

ESP32 iPhone Agent

Your AI can use your iPhone like you do.

It looks at the screen, then taps, swipes and types for you, through a $7 ESP32 board plugged into the phone.
Just the phone and the board: no computer, no changes to iOS.

[Try on TestFlight] [Flash the board in your browser]

[TestFlight] [Website] [License: MIT] [GitHub stars] [CI] [iOS] [Board]

English · 简体中文

Demo video and updates: X @iamhenryyuan · YouTube demo · DeepSeek Harness demo · handsy.tech · Changelog

[!TIP] Try the app now on TestFlight, no Xcode needed. The App Store listing is still in review, so TestFlight is the way to get it today. You still need a flashed board (see Quick start).

[!NOTE] New in App 1.0.2 (TestFlight): the agent can hear the phone (AirPlay audio turned into words on the iPhone), long tasks no longer stop at 40 steps, any stopped run can continue, and taps and video are steadier. Needs board firmware v0.2.4. Full changelog.

[!NOTE] Branches: release-1.0 is the version submitted to TestFlight and App Store review. The latest code is on main.

[!WARNING] Only follow the install steps in this README. Never run commands from issues or comments (for example a "quick install" one-liner such as PowerShell iex(irm ...)); these are not from the maintainers.

The computer is gone. The board is now the AirPlay receiver and the mouse, and the agent runs in an iPhone app on the same phone. The original version (a Mac/Linux computer running UxPlay and a Python server) is kept as tag v0.1.0 and branch legacy. See docs/legacy.md.

[An iPhone with a XIAO ESP32S3 plugged in, setting an alarm]

"Set an alarm for 8:00 AM that repeats every Monday and Tuesday, then save it": the agent leaves its own app, opens Clock, types the time into the wheel, ticks the days and saves. (Photo from v0.1.0; the task is the same.)

[ESP32 Agent app: real phone vs. what the agent sees, and pointer calibration]

Left: the real phone. Right: what the agent sees through the board's AirPlay mirror. The second shot is the pointer calibration.

If you're wondering how an AI can control an iPhone at all, start with AI control for iPhone.

Contents

How it works

More detail: how Handsy works.

iPhone ──Screen Mirroring (Wi-Fi)──▶ ESP32-S3: AirPlay receiver ──Wi-Fi TCP :7201──▶ ESP32 Agent app (same iPhone)   ▲                                    own hotspot, H.264 passthrough              decode → vision model → action   └──────── USB HID mouse + keyboard ◀──────────── BLE commands ◀──────────────────────┘
  • Eyes — AirPlay to the board. The board opens its own Wi-Fi hotspot and appears in the phone's Screen Mirroring list as ESP32 Agent. It keeps the H.264 stream (no decoding on the board) and forwards it to the app over Wi-Fi (TCP, port 7201). BLE carries commands and status. The phone keeps using cellular data for the model while it is on the board's hotspot.
  • Hands — USB HID. The board is a standard USB mouse and keyboard. With AssistiveTouch on, the mouse moves an on-screen pointer, so a click is a tap and a drag is a swipe.
  • Brain — the app. The app decodes the stream, sends screenshots to any OpenAI-compatible vision model, and runs a planner and an actor: tap, swipe, draw, type, press keys, go home, finish. It runs in the background while it works; the Dynamic Island shows what it is doing.
  • Closing the loop. The pointer is relative, so the app tracks it and re-homes it in a corner; before precise taps it wiggles the pointer, finds it on screen and corrects the miss. After each action it waits for the screen to settle and tells the model whether anything changed. A lost board, hotspot or mirroring link pauses the run and resumes it from the same step.
  • Review. Every run is kept on the phone: replay it step by step with the taps and swipes drawn on top. Export the screenshots, actions and saved annotations from Runs → open a run → ⋯ → Export trace for analysis by another agent. See Export runs for agent review for batch export, USB copying and file details.

Hardware

Supported boards and setup details: hardware guide. Picking a board, cables and adapters, or stuck during setup: setup guide.

PartNotes
ESP32-S3 boardTested with a Seeed Studio XIAO ESP32S3 (plain version, 8 MB PSRAM; not Sense, not Plus). About $7 (Amazon*). Any ESP32-S3 with native USB on its USB-C port and PSRAM should work.
USB-C cableShort USB-C to USB-C, e.g. Anker 60W, 1 ft, 2-pack*.
iPhoneiOS 18 or later, with cellular data (the Wi-Fi is taken by the board's hotspot). USB-C models plug in directly; Lightning models need a Lightning to USB Camera Adapter. The phone powers the board.
Vision modelAny OpenAI-compatible chat API that accepts images: a cloud API or your own server.
Mac with XcodeOnly to flash the board and build the app once. Not needed at run time. With the TestFlight app, Xcode is not needed for the app.

* Affiliate link. As an Amazon Associate I earn from qualifying purchases.

Quick start

1. Flash the board (plugged into the Mac)

Install arduino-cli, the ESP32 core 3.3.x and NimBLE-Arduino 2.5.x, then:

bash
arduino-cli lib install "[email protected]"hardware/flash.sh            # optional: [serial port] [--debug]

Always build with flash.sh: it applies the BLE buffer sizes in hardware/hid_bridge/nimble_cfg.h.

No Mac or toolchain? Install the firmware from Chrome or Edge over USB instead.

2. Install the app

Easiest: join the TestFlight beta on your iPhone and install ESP32 Agent from there, then go to step 3.

Or build it yourself:

bash
cp ios/Config/Local.xcconfig.example ios/Config/Local.xcconfig   # set DEVELOPMENT_TEAM and IA_BUNDLE_IDopen ios/IPhoneAgent.xcodeproj                                    # run the IPhoneAgent scheme on your iPhone

The project is generated with XcodeGen from ios/project.yml (cd ios && xcodegen). A free Apple ID can sign it (re-sign every 7 days). The one-tap hotspot join needs the Hotspot Configuration capability (a paid account); without it the app shows the hotspot password to join by hand.

3. Set up (once, in the app)

Plug the board into the iPhone and open ESP32 Agent. Setup walks through four steps and checks each one itself:

  1. Plug Me In — pair the board over Bluetooth.
  2. Let Me See the Screen — join the board's Wi-Fi, then Control Center → Screen Mirroring → ESP32 Agent.
  3. Let Me Use the Pointer — turn on AssistiveTouch; the app calibrates the pointer by itself.
  4. Give Me a Brain — enter your model endpoint and key (stored in the Keychain), then choose a vision model from the endpoint's model list (or enter its model ID).

4. Give it a task

Type a goal (for example "Open Settings and go to Wi-Fi") and press Start. Stop it from the Dynamic Island, or come back to the app. Siri, Shortcuts, widgets and Control Center can run saved tasks.

Use from a coding agent

The esp32agent CLI (npm, npx -y esp32agent mcp) is an MCP server, so a coding agent on your computer can see and drive the iPhone through the board (board firmware v0.2.2 or later); see iPhone as an MCP server. This repository is also a plugin with that MCP server and an agent skill (skills/handsy/SKILL.md). In Claude Code (walkthrough):

bash
/plugin marketplace add onehumanbeing/esp32-iphone-agent/plugin install esp32agent@handsy

Or add only the MCP server:

bash
claude mcp add esp32agent -- npx -y esp32agent mcp

Cursor, Cline and other MCP clients:

json
{  "mcpServers": {    "esp32agent": {      "command": "npx",      "args": ["-y", "esp32agent", "mcp"]    }  }}

DeepSeek Harness (dsh): install the plugin in integrations/dsh, which adds the same MCP server and skill (demo video):

bash
dsh plugin --profile web add dsh-esp32agent

The CLI talks to the board from your computer over Bluetooth, or over your Wi-Fi once both are on it (esp32agent net join), so it works without the iPhone app or TestFlight. Commands and options are in the CLI reference and cli/README.md.

Model and prompts

  • Any OpenAI-compatible vision endpoint works (vLLM, SGLang, llama.cpp, Ollama, cloud APIs). The app lists the endpoint's models for you to choose one explicitly; you can also enter a model ID, and set a separate planner and actor model.
  • The prompts are plain text in prompts/ and bundled with the app: edit them, rebuild, done.
  • Each finished run logs planner/actor call counts, requests with a screenshot, and token totals when the endpoint reports usage. Missing usage is left absent (never coerced to 0); the run total is marked partial if any call omitted it. A short task is typically a few planner calls plus one actor call per step — each actor step sends a fresh screenshot — so image tokens dominate cost. Giving the planner a cheaper model while keeping a stronger actor is usually the best tradeoff.
  • The project was developed with nvidia/Qwen3.8-Flash-Next-NVFP4 served on two DGX Sparks (TensorFold).

Project layout

ios/        the app (SwiftUI): BLE link, H.264 decode, agent, setup, replay, Live Activity, widgets; testsprompts/    planner / actor prompts used by the apphardware/   ESP32-S3 firmware (Arduino): BLE GATT, hotspot, AirPlay receiver, USB HID; flash script,            host tests, Mac BLE test harness (hardware/tools)docs/       BLE protocol, legacy version notesharness/    wireless development loop: Mac → iPhone app (Wi-Fi install) → board (firmware through the app)server/     legacy v0.1.0 computer-side server; not used by this version (see server/README.md)

Debugging without the phone app: hardware/tools/board_test.py plays the app over BLE from a Mac (hardware/tools/README.md). Board ↔ app protocol: docs/ble-protocol.md.

Harness development

With the board plugged into a test iPhone and the iPhone on the Mac's Wi-Fi, everything goes through devicectl, no cable: harness/app.sh builds and installs the Debug app, harness/fw.sh builds the firmware and copies it into the app, which installs it on the board (rolls back by itself if the new image fails), harness/run.sh "<task>" runs a task and harness/logs.sh pulls the log. BLE from the Mac is only for the one-time net sta. Setup (Lightning iPhones: camera adapter), security: harness/README.md.

Roadmap

  • Drop the computer: the board mirrors and clicks, the app on the phone thinks
  • Landscape apps (coordinates and calibration assume portrait)
  • Chinese text input (typed as pinyin through the board's keyboard; ctrl+space switches English and pinyin)
  • Faster steps: several actions per model call, pointer checks only when needed
  • A curated list of models that work best with this setup, shown inside the iOS app (suggested by @asharthinks)

Limitations

  • AirPlay source must be an iPhone. Screen Mirroring from macOS is not supported: the handshake can look healthy while the board never receives video.
  • The screen is mirrored at 480×1040, up to 5 fps; each step takes a few seconds, most of it the model.
  • iOS does not mirror some screens (passcode entry, Apple Pay, DRM video); the agent cannot see them. Mirroring stops when the phone locks.
  • While the agent runs, the phone's Wi-Fi is the board's hotspot (no internet over it); the model is reached over cellular data.
  • Chinese is typed as pinyin through the board's keyboard (ctrl+space switches English and pinyin). The clipboard is not used for this; emoji are dropped. Landscape apps are not supported yet.

If something doesn't work (no ESP32 Agent in Screen Mirroring, the board not connecting), see troubleshooting.

Responsible use

Only automate devices and accounts you own or are allowed to use, and respect the terms of the apps involved. The agent acts with your hands: watch it, and keep the stop button within reach.

Contributing

Issues and pull requests are welcome; see CONTRIBUTING.md. Security reports: SECURITY.md.

Contact

Questions or feedback: [email protected] · Website: handsy.tech

Related projects

ProjectApproachDifference from this project
VersaiOSUxPlay mirroring + ESP32-S3 Bluetooth mouse + vision modelNeeds a computer; one tap per instruction
ja1chen/actellAgent app on the iPhone via Apple's CoreDevice debug channelNeeds Developer Mode and a Developer Disk Image
AidenAI-IO/aiden-firmwareHDMI capture + USB keyboard/mouse + multimodal modelCapture hardware; mainly Android
yoyicue/glassboxPicoKVM HDMI capture + USB HIDmacOS controller and PicoKVM
boboabw/phone-agentESP32-S3 Bluetooth HID + AssistiveTouch, agent as an iPhone appClosest to this version; this one also brings the eyes (AirPlay on the board)
VKCOM/devicehubESP32 Bluetooth mouse for iOS device farmsDevice-farm tooling, no AI agent

WebDriverAgent/XCUITest-based tools such as mobile-mcp and Midscene need a Mac and code signing.

Acknowledgements

UxPlay (the AirPlay receiver the firmware's is ported from) · NimBLE-Arduino · Arduino ESP32 core and TinyUSB

License

MIT, except hardware/hid_bridge/src/airplay/ (ported from UxPlay), which is GPL-3.0; the firmware binary that links it is therefore distributed under GPL-3.0. Files under skills/ are MIT-0 (MIT No Attribution); the rest of the repository keeps the license above.

來源:README.md,提交 256e6cf

工具

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版本歷史

2
  1. v0.1.3最新Nov 7, 2026
  2. v0.1.1Nov 3, 2026

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