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TIGER-AI-Lab/ClawBench

⭐ 942 Python repository created 2026-04-10

ClawBench is a comprehensive, open-source benchmark designed to evaluate the capability of AI browser agents in performing real-world, everyday online tasks. It features a diverse set of tasks (153 in V1, 130 in V2) across 144 live websites, covering approximately 15 life categories such as booking travel, ordering food, applying for jobs, and managing email. The benchmark measures end-to-end task success through a sophisticated 5-layer recording pipeline and utilizes an agentic evaluator that compares each run against human references. This approach provides a robust and realistic assessment of how well AI agents can navigate and interact with dynamic web environments to achieve specific goals. The project highlights that even the best agents currently complete only about one-third of the tasks, underscoring the challenges and areas for improvement in AI agent development. ClawBench provides a standardized framework for developers and researchers to test their AI agents, understand their limitations, and drive progress in the field of agentic AI. It also offers a sister project, HarnessBench, which focuses on fixing the base model and varying the harness, providing an orthogonal axis for evaluation. The project is designed for easy setup and execution, including a one-line quick start command and support for Docker-isolated harnesses. Overall, ClawBench serves as a crucial tool for both academic research and industry development in the rapidly evolving domain of AI agents, providing a quantitative measure of performance on practical, browser-based tasks.

https://github.com/TIGER-AI-Lab/ClawBench

agent-evaluationai-agent-benchmarkllm-evaluationbenchmarkbrowser-agentweb-agentonline-taskseveryday-tasksevaluationdatasetagentic-ai

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