WebArena: A Realistic Web Environment for Building Autonomous Agents

TL;DR

WebArena provides a realistic, web-based environment for developing and benchmarking autonomous agents performing complex, real-world internet tasks, revealing current AI limitations and guiding future improvements.

Contribution

The paper introduces WebArena, a highly realistic web environment with benchmark tasks for evaluating autonomous agents' ability to perform internet-based tasks.

Findings

GPT-4-based agents achieve only 14.41% success rate

Humans outperform agents with 78.24% success rate

WebArena exposes challenges in current AI capabilities for web tasks

Abstract

With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and designed to emulate tasks that humans routinely perform on the internet. We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%. These results highlight the need for further development of robust agents, that current state-of-the-art large language models are far from perfect performance in these real-life tasks, and that WebArena can be used to measure such progress.