AgentStudio
AgentStudio is **a trinity of environments, tools, and benchmarks** for general virtual agents to interact with any computer software. AgentStudio targets the desiderata for robust, general, and open-ended virtual agents by providing:
1. **A lightweight, interactive environment** with highly **generic observation and action spaces**, e.g., video observations and GUI/API actions
2. **Tools for creating online benchmark tasks, annotating GUI elements, and labeling actions in videos**
3. **Online benchmark tasks** that evaluate both GUI interactions and function calling with **auto-evaluation** and language feedback
4. **Three benchmark datasets**: GroundUI, IDMBench, and CriticBench, for fundamental agent abilities, including GUI grounding, learning from videos, and success detection
Comparisons with existing work:
## News
- **Oct 3, 2024**: Released the arXiv paper v2 and a full version of AgentStudio, including comprehensive documentation, complete tasks, and datasets!!
- **Aug 18, 2024**: Major update to clean up the codebase and datasets.
- **Mar 30, 2024**: Released the beta version of AgentStudio.
## Installation
Install requirements:
```bash
apt-get install gnome-screenshot xclip xdotool # If using Ubuntu 22.04
conda create --name agent-studio python=3.11 -y
conda activate agent-studio
pip install -e '.[client]'
```
All confidential API keys should be stored in `agent_studio/config/api_key.json`, e.g., OpenAI API key, Claude API key, Gemini API key, etc. We have provided an example config in `agent_studio/config/api_key_template.json`.
## AgentStudio Overall Benchmark Tasks
AgentStudio provides the most generic observation and action spaces, which significantly expands the task space, allowing for developing and evaluating agents in real-world settings. We introduce a benchmark suite consisting of 205 tasks. These tasks span API usages such as terminal and Gmail and GUI software like VS Code. Please find more in [eval_online_benchmarks/README.md](eval_online_benchmarks/README.md). The task-related files are available at our project page.
## AgentStudio Datasets Decompose Agent Abilities
To gain deeper insights into agent capabilities beyond the overall performance measured by online benchmark tasks, we develop three datasets using AgentStudio: GroundUI, IDMBench, and CriticBench. These datasets target general UI grounding, learning from videos, and success detection. More details are provided in [eval_agent_desiderata/README.md](eval_agent_desiderata/README.md). All data are available at our project page.
## AgentStudio Tools
To facilitate the development and evaluation of agents within the AgentStudio environment, we provide three tools for:
- Benchmark task creation and validation
- Step-level GUI element annotation
- Trajectory-level video-action recording and refinement
These tools, combined with the realistic environment of AgentStudio, contribute to the generation of rich, structured data for training and evaluating agents. Please refer to [docs/annotate_ground_ui.md](docs/annotate_ground_ui.md) for the GUI annotation tool, [agent_studio/recorder/README.md](agent_studio/recorder/README.md) for the video-action annotation tool, and [eval_online_benchmarks/README.md](eval_online_benchmarks/README.md) for the task creation/validation.
## Contributing
Contributions and feedback from everyone on how to make this into a better tool are more than welcome. Please check out [CONTRIBUTING.md](CONTRIBUTING.md) for how to get involved.
## Acknowledgement
We would like to thank the following projects for their inspiration and contributions to the open-source community: [Open Interpreter](https://github.com/KillianLucas/open-interpreter), [WebArena](https://github.com/web-arena-x/webarena), [Cradle](https://baai-agents.github.io/Cradle/), [Synapse](https://ltzheng.github.io/Synapse/), [SeeClick](https://github.com/njucckevin/SeeClick), [ScreenAgent](https://github.com/niuzaisheng/ScreenAgent), [OSWorld](https://github.com/xlang-ai/OSWorld), etc.
## Citation
If you find AgentStudio useful, please cite our paper:
```bibtex
@article{zheng2024agentstudio,
title={AgentStudio: A Toolkit for Building General Virtual Agents},
author={Longtao Zheng and Zhiyuan Huang and Zhenghai Xue and Xinrun Wang and Bo An and Shuicheng Yan},
journal={arXiv preprint arXiv:2403.17918},
year={2024}
}
```