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Spider2-V

🌏 Website • 📝 Paper • 🔍 Data • 📸 Task Viewer


Tasks

📢 Updates

🏗️ Installation

💾 On Your Desktop or Server

The executable environment~(desktop Ubuntu 22.04 LTS) is based on our previous work OSWord. Please install the pip dependencies and virtual machine first.

  1. First, clone this repository and cd into it. Then, install the dependencies listed in requirements.txt. It is recommended that you use the latest version of conda to manage the environment, but you can also choose to manually install the dependencies. Please ensure that the version of Python is >= 3.11.
# Clone the Spider2-V repository
git clone https://github.com/xlang-ai/Spider2-V

# Change directory into the cloned repository
cd Spider2-V

# Optional: Create a Conda environment for Spider2-V
conda create -n spider2v python=3.11
conda activate spider2v

# Install required dependencies
pip install -r requirements.txt
  1. Install VMware Workstation Pro (for systems with Apple Chips, you should install VMware Fusion) and append the vmrun command into system path. The installation process can refer to How to install VMware Worksation Pro. Verify the successful installation by running the following:
vmrun -T ws list

If the installation along with the environment variable set is successful, you will see the message showing the current running virtual machines.

Note: We will also support using VirtualBox in the near future if you have issues with VMware Pro.

  1. Install other dependencies like Playwright.
playwright install chromium

All set! Now, you/agent can interact with the environment via the Quick Start below.

🚀 Quick Start

Run the following minimal example to interact with the environment:

  • It will automatically download the prepared virtual machine from Huggingface (for arm64 and for x86) into ./vm_data, configure the environment, and take one snapshot called init_state for you.
from desktop_env.envs.desktop_env import DesktopEnv

# feel free to change the example!
# task instruction: Help me materialize the asset top10_story_ids in this dagster project in the UI. Do NOT materialize other assets.
example_path = 'evlauation_examples/examples/dagster/22ef9058-6188-422a-9c12-e6934e4ed936/22ef9058-6188-422a-9c12-e6934e4ed936.json'
with open(example_path, 'r') as infile:
    example = json.load(infile)

env = DesktopEnv(action_space="pyautogui")

obs = env.reset(task_config=example)
print(f'Task instruction: {example["instruction"]}')
obs, reward, done, info = env.step("pyautogui.rightClick()")
input('Now, you can finish the task in the virtual machine manually and Press ENTER to evaluate ...')
score = env.evaluate()
print(f'Evaluation score: {float(score):.1f}')
env.close()

You will see all the logs of the system running normally, including the successful creation of the environment, completion of setup, and successful execution of a rightClick action on the screen, which means you are ready to continue.

🧪 Experiments

A quick glance on the success rates of state-of-the-art VLM agents.

VLM Abstract Verbose Account Non-Account Overall
Claude-3-Opus 5.3 10.9 5.9 9.3 8.1
Gemini-Pro-1.5 6.1 12.1 8.8 9.3 9.1
GPT-4o 11.3 16.2 10.6 15.6 13.8
GPT-4V 11.3 16.6 11.2 15.4 14.0

🕸️ Different Data Splits

The entire task set contains $494$ examples (evaluation_examples/test_all.json) covering $20$ applications. There are different categories to split the entire task set into subsets, depending on:

  • whether the task instruction is verbose or abstract (evaluation_examples/test_verbose.json and evaluation_examples/test_abstract.json)
  • whether a real account is needed (evaluation_examples/test_account.json and evaluation_examples/test_non_account.json)
  • etc.

Note that, verbose instruction means we explicitly tell the agent how to complete the task step-by-step. If you want to test examples which require real accounts (e.g., Snowflake and Google BigQuery), please register relevant accounts and fill credentials into template files under folder evaluation_examples/settings first (see Account Guideline for more step-by-step details).

With respect to the task format, see Task Format.

👷 Human Validation

If you want to check more examples manually in the virtual machine on a GUI screen, which includes:

  1. reseting the environment;
  2. completing the task in the virtual machine by yourself;
  3. invoking the customized evaluation function after completion, you can run the following interactive script:
  • if the --path_to_vm argument is not specified, the script will automatically find available virtual machines under folder ./vm_data. If not found, it will download our prepared VM from Huggingface (for arm64 and for x86) into ./vm_data and use it
  • if the --snapshot argument is not specified, it will use snapshot with name init_state by default
  • if the --example argument is not specified, it will test on one example evaluation_examples/test_one.json by default
python run_spider2v_human.py --snapshot init_state --example evaluation_examples/test_non_account.json

💻 Agent Baselines

If you wish to run the baseline agent used in our paper, you can execute the following command as an example under the GPT4-o pyautogui+SoM+ExecutionFeedback+RAG setting:

Set OPENAI_API_KEY environment variable with your API key:

export OPENAI_API_KEY='changme'
python run_spider2v_agent.py --snapshot init_state \
    --model gpt-4o-2024-05-13 \
    --headless \
    --action_space pyautogui \
    --observation_space som \
    --execution_feedback \
    --rag \
    --result_dir ./results \
    --example evaluation_examples/test_non_account.json

The results, which include screenshots, a11y trees, actions, and video recordings of the agent's task completion, will be saved in the ./results directory.

If you want to use other models, you can revise the code file mm_agents/agent.py.

❓ FAQ

What is the username and password for the virtual machines?

The username and password for the virtual machines are as follows:

  • Ubuntu: user / password

How to tackle task examples requiring accounts?

See Account Guideline

How can I configure a proxy for the VM if I'm behind a GFW?

See Proxy Guideline.

Where to download the documents for retrieval?

The preprocessed documents can be downloaded from GoogleDrive. See Documents for details.

What are the running times and costs?

Setting Expected Time Average Prompt Tokens Average Completion Tokens Average Cost
GPT-4o 8.7m 0.32M 1.6K $1.64
GPT-4V 9.1m 0.32M 1.8K $3.29
  • action_space = pyautogui, observation_space = SoM
  • history_trajectory_length = 3, max_steps = 15, RAG = True
  • prompt tokens include image pixels. Calculated per task in June 2024.

📄 Citation

If you find this benchmark useful, please consider citing our work:

@misc{Spider2-V,
      title={Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?}, 
      author={Ruisheng Cao and Fangyu Lei and Haoyuan Wu and Jixuan Chen and Yeqiao Fu and Hongcheng Gao and Xinzhuang Xiong and Hanchong Zhang and Yuchen Mao and Wenjing Hu and Tianbao Xie and Hongshen Xu and Danyang Zhang and Sida Wang and Ruoxi Sun and Pengcheng Yin and Caiming Xiong and Ansong Ni and Qian Liu and Victor Zhong and Lu Chen and Kai Yu and Tao Yu},
      year={2024},
      eprint={2407.10956},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.10956}, 
}

@misc{OSWorld,
      title={OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments}, 
      author={Tianbao Xie and Danyang Zhang and Jixuan Chen and Xiaochuan Li and Siheng Zhao and Ruisheng Cao and Toh Jing Hua and Zhoujun Cheng and Dongchan Shin and Fangyu Lei and Yitao Liu and Yiheng Xu and Shuyan Zhou and Silvio Savarese and Caiming Xiong and Victor Zhong and Tao Yu},
      year={2024},
      eprint={2404.07972},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}