ИИ-агенты
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Write coding-agent rules once, then apply them to every AI coding tool you use.
★ 2,9 тыс. звёздMIT
Смотреть руководство →AppAgent — проект с открытым исходным кодом из категории «ИИ-агенты» от TencentQQGYLab (TencentQQGYLab/AppAgent на GitHub). У него 6 900 звёзд на GitHub и 759 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 19 марта 2025.
AppAgent — Tencent's multimodal agent that learns smartphone apps by exploration, then operates them.
Prepare an Android device the agent can control, the target app, and your multimodal model credentials; richer app documentation leads to higher task success, per the project's tips.
Clone TencentQQGYLab/AppAgent from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Clone the repo and install its requirements, then configure the agent with your model settings and device connection.
Run the exploration phase on one app so AppAgent builds its documentation, inspect that documentation for accuracy, then give it a deployment task.
The agent explores an app — autonomously or guided by your demonstrations — and generates documentation of what the app can do.
With the learned documentation, the agent then executes user tasks inside the app.
Built on LLM-based multimodal perception, reading screens as a smartphone user would.
The project's own guidance: the more extensive and accurate the app documentation, the higher the task-completion likelihood — and you can inspect and correct it.
You can demonstrate app functions yourself to teach the agent, instead of relying only on autonomous exploration.
Ships with the paper's evaluation setup and results, as a CHI 2025 system.
Get the source from the official GitHub repository for AppAgent.
git clone https://github.com/TencentQQGYLab/AppAgent.git
cd AppAgentInstall the Python requirements after cloning, as the repo's quick start prerequisites describe.
pip install -r requirements.txt # see README quick start prerequisitesConfigure the agent, run exploration on your target app to build its documentation, then run a deployment task.
# Follow README steps 2-4: configure the agent, run the exploration phase, then the deployment phaseTencentQQGYLab (Tencent), through the TencentQQGYLab/AppAgent repository, with the project page at appagent-official.github.io.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
It is driven by GPT-4V-class multimodal models, so you need your own model API access and pay for that usage; the framework code is free under MIT.
Both drive phones from language. AppAgent's distinctive bet is its two-phase design — explore and document an app first, then deploy — from a CHI research system, whereas general mobile-agent frameworks focus on ready-to-use CLI/API operation across apps.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — run the exploration phase on one app so AppAgent builds its documentation, inspect that documentation for accuracy, then give it a deployment task.
Write coding-agent rules once, then apply them to every AI coding tool you use.
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