io.github.Aelindra/mobile-agent-harness
Android device automation for AI agents: MCP server, CLI, and plugin runtime over adb.
التثبيت
خطوات التثبيت موجودة في التوثيق أدناه. التوثيق
التوثيق
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المحتويات
mobile-agent-harness
Android device automation for AI agents. MCP server, CLI, and a hot-reloadable plugin runtime on top of a self-healing uiautomator2 bridge. No device-side app required.
中文文档:README.zh-CN.md
This is the base runtime only. Plugins live in the community catalog: awesome-mobile-agent-harness-plugin. App business knowledge for AI lives in app-lore, which this repo's knowledge/ loader reads directly.
Install
git clone https://github.com/Aelindra/mobile-agent-harness
cd mobile-agent-harness
pip install -e . # add ".[ocr]" for vision.ocr / ui2.*
Requires Python 3.9+ and adb on PATH with USB debugging enabled.
Quick start
- Point the bridge at your device:
export AGENT_SERIAL_DEFAULT=192.168.1.23:5555 # or "emulator-5555"
- List the tool surface (works without a device) and run the offline tests:
python -m core.cli tools
python tests/test_runtime.py
- Call a tool:
python -m core.cli call ui.snapshot
python -m core.cli call ui.click --args '{"selector":"text@^Network & internet$","pkg":"com.android.settings"}'
- Attach to any MCP client:
{
"mcpServers": {
"phone": {
"command": "python",
"args": ["-m", "core.cli", "mcp"],
"cwd": "/path/to/mobile-agent-harness",
"env": { "AGENT_SERIAL_DEFAULT": "192.168.1.23:5555" }
}
}
}
All tools return a {"ok": bool, "error"?, ...} envelope. ok=false is a business result (e.g. error_type=selector_not_found) — adapt instead of retrying.
Tools
| Group | Tools |
|---|---|
| Observation | ui.snapshot ui.dump ui.find ui2.state ui2.screen_evidence ui2.timeline ui2.find_templates vision.screenshot vision.ocr vision.diff |
| Action | ui.click ui.click_handle ui.text_handle ui.hold_read ui.set_text ui.scroll ui.back ui.home input.tap input.swipe input.key |
| App | app.launch app.current app.probe app.list app.stop app.wait_idle |
| Shell & data | shell.run state.prefs state.db file.push file.pull net.http |
| Events | events.tail events.wait events.capture events.record_start events.record_stop |
| Knowledge & tasks | knowledge.list knowledge.guide ui2.check_states ui2.orient task.begin task.note task.end template.add sys.capabilities |
ui.snapshot returns a compact accessibility-tree listing with element handles (e0, e1, ...); ui.click_handle acts on a handle with drift detection. ui2.state fuses the accessibility tree with OCR for canvas-drawn UIs. ui2.screen_evidence collects measurable screen features (overlay level, motion, color, layout density) without interpreting them. app.probe returns deterministic side signals (package, version, orientation); ui2.timeline captures N frames of lightweight evidence in time order; ui2.orient probes context, routes to matching knowledge packs, and evaluates their state discriminators — returning matched states or ranked hypotheses with the missing evidence listed.
Selectors
Selectors resolve against the live accessibility tree. Coordinates are not used.
# ui.click selector examples
"id@d98 && pkg@com.example.app" # resource-id, short form completed with pkg@
"text@^Settings$ && pkg@com.example.app"
"class@android.widget.EditText && [clickable=true]"
"desc@^Search$" # content-desc regex
Harnesses and plugins
A harness file declares per-app flows as selector steps with pre/postconditions; distill drafts one by exploring an app, lint checks selector quality, and failed postconditions mark tools stale for re-distillation. See harnesses/com.android.settings/harness.json5 for the built-in example.
Plugins drop into plugins/ and register tools or capability providers at load time; registrations are reversible and hot-reloaded on file change. See the Plugin development guide in README.zh-CN.md and knowledge/README.md for the knowledge-pack format.
For business context on apps where model priors are unreliable, point agents at app-lore — a community catalog of structural app surveys in the open Agent Skills format, split per feature module for complex apps. Mobile scenarios in particular are private-domain and underrepresented in training data, so agents benefit from loading the relevant survey before operating an unfamiliar app. The local knowledge/ directory remains this repo's runtime layer (packs, icon templates, state discriminators).
Configuration
| Variable | Description | Example |
|---|---|---|
AGENT_SERIAL_DEFAULT | Device serial used when --serial/ANDROID_SERIAL absent | 192.168.1.23:5555 |
AGENT_SERIAL_USB | USB serial fallback | 0123456789abcdef |
MAH_FRAME_STREAM | 1 enables the H.264 frame stream for observation tools | 1 |
MAH_VISION_MODE | Vision enhancement: off / client / endpoint | client |
MAH_VLM_URL / MAH_VLM_MODEL | External vision endpoint (OpenAI-compatible) | http://127.0.0.1:11434/v1/chat/completions |
How it compares
- vs mobile-mcp / agent-device: they are generic device toolsets; this project adds the layer above transport — per-app knowledge as files (harnesses), a hot-reloadable plugin runtime with capability seams, and an explicit adb/root privilege model.
- vs droidrun: droidrun requires a device-side accessibility app; this project is zero-install over adb.
- Root support is declarative: if the device already has
su, the shell seam gains a root provider gated by a command allowlist. This project does not provide root.
Known limitations
Messaging, banking, and e-commerce apps run active anti-automation risk control; the adb tier is unreliable on them. Target use is development, testing, and your own device workflows.
Contributing
Plugins are contributed to awesome-mobile-agent-harness-plugin; app business knowledge to app-lore — see their contributing guides.
License
MIT.
الأسئلة الشائعة
ما هو io.github.Aelindra/mobile-agent-harness؟
Android device automation for AI agents: MCP server, CLI, and plugin runtime over adb.
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