OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real
  Computer Environments
Authors: name: Tianbao Xie, name: Danyang Zhang, name: Jixuan Chen, name: Xiaochuan Li, name: Siheng Zhao, name: Ruisheng Cao, name: Toh Jing Hua, name: Zhoujun Cheng, name: Dongchan Shin, name: Fangyu Lei, name: Yitao Liu, name: Yiheng Xu, name: Shuyan Zhou, name: Silvio Savarese, name: Caiming Xiong, name: Victor Zhong, name: Tao Yu

Autonomous agents that accomplish complex computer tasks with minimal human
interventions have the potential to transform human-computer interaction,
significantly enhancing accessibility and productivity. However, existing
benchmarks either lack an interactive environment or are limited to
environments specific to certain applications or domains, failing to reflect
the diverse and complex nature of real-world computer use, thereby limiting the
scope of tasks and agent scalability. To address this issue, we introduce
OSWorld, the first-of-its-kind scalable, real computer environment for
multimodal agents, supporting task setup, execution-based evaluation, and
interactive learning across various operating systems such as Ubuntu, Windows,
and macOS. OSWorld can serve as a unified, integrated computer environment for
assessing open-ended computer tasks that involve arbitrary applications.
Building upon OSWorld, we create a benchmark of 369 computer tasks involving
real web and desktop apps in open domains, OS file I/O, and workflows spanning
multiple applications. Each task example is derived from real-world computer
use cases and includes a detailed initial state setup configuration and a
custom execution-based evaluation script for reliable, reproducible evaluation.
Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWorld
reveals significant deficiencies in their ability to serve as computer
assistants. While humans can accomplish over 72.36% of the tasks, the best
model achieves only 12.24% success, primarily struggling with GUI grounding and
operational knowledge. Comprehensive analysis using OSWorld provides valuable
insights for developing multimodal generalist agents that were not possible
with previous benchmarks. Our code, environment, baseline models, and data are
publicly available at https://os-world.github.io.