esp32-iphone-agent

What's new

• It hears the phone. The board forwards AirPlay audio and the iPhone turns it into words, so listening exercises and voice prompts work. How it works.

• Free Jev line. Jev takes the plain steps in a fraction of a second and leaves the rest to the large model; the agent loop is rebuilt around one model call per step.

• Long tasks. No 40-step limit; any stopped run can continue. Every run can be replayed step by step on the phone.

Get it on TestFlight (App Store listing in review). Needs board firmware v0.2.4. Full changelog.

Related MCP server: iphone-use

Why a board

Most iPhone agents run on a computer. Before the first task you install Xcode, turn on Developer Mode, pair the phone over USB and type commands into a terminal, and the computer has to stay on for every task after that. Fine for a developer, a wall for everyone else. Here the board does the computer's job, so the phone is all you need.

The honest costs: you buy a $7 board, each step takes a few seconds (most of it the model), and the phone uses cellular data while it runs because the board's hotspot takes the Wi-Fi.

Not a developer? Start here

1. Get the board. A Seeed XIAO ESP32S3, about $7: what to buy.

2. Flash it in Chrome or Edge. No tools to install: handsy.tech/install.

3. Install the app and follow its four setup steps. TestFlight · setup guide with pictures.

Stuck? See troubleshooting or mail support@handsy.tech.

How it works

New to the idea? Start with AI control for iPhone, then 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.

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

The first version (v0.1.0) ran on a Mac/Linux computer with UxPlay and a Python server. It is kept as tag v0.1.0 and branch legacy; see docs/legacy.md.

Hardware

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

* 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:

arduino-cli lib install "NimBLE-Arduino@2.5.1"
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:

cp ios/Config/Local.xcconfig.example ios/Config/Local.xcconfig   # set DEVELOPMENT_TEAM and IA_BUNDLE_ID
open 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):

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

Or add only the MCP server:

claude mcp add esp32agent -- npx -y esp32agent mcp

Cursor, Cline and other MCP clients:

{
  "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):

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.

• Settings → Model → Advanced → Extra body JSON is merged into each chat request. DeepSeek's API (api.deepseek.com) thinks at high effort unless told otherwise, so the app sends {"thinking":{"type":"disabled"}} there unless that JSON already contains thinking; use {"thinking":{"type":"enabled"},"reasoning_effort":"low"} to keep a little reasoning.

• 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; tests
prompts/    planner / actor prompts used by the app
hardware/   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 notes
harness/    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.

Phone out of reach (e.g. on cellular): the optional, off-by-default remote debug log uploads the app's text log to a server you run.

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. Start from the latest main and open pull requests against main. Before opening a pull request, update your branch from the latest main (fetch and rebase, or merge in the latest main). Security reports: SECURITY.md.

The latest code is on main; release-1.0 is the version submitted to TestFlight and App Store review, and is no longer the branch for contributions.

Contact

Questions or feedback: support@handsy.tech · Website: handsy.tech

Related projects

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

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

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.

Contributors

Thanks to everyone who has helped build ESP32 iPhone Agent: @cy41 · @shinjiyu · @Yi-111-a · @buddyh · @djs-91 · @EliotAndres · @lukas-1997 · @mvanhorn. Pull requests start from the latest main.

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