CRAB Benchmark

CRAB: Cross-environment Agent Benchmark

CRAB (Cross-environment Agent Benchmark) is a pioneering framework designed for evaluating Multimodal Language Model (MLM) agents. It offers an end-to-end solution to build, operate, and assess agents in diverse environments.

Key Features

• Cross-environment Support: Seamlessly operates across platforms like Ubuntu and Android, ensuring adaptability.
• Graph Evaluator: Provides detailed performance analysis beyond binary success rates, identifying strengths and weaknesses.
• Task Generation: Automates complex task creation using graph-based methods, mimicking real-world scenarios.
• Ease of Use: Simplified integration with Python-based operations and declarative programming for reproducibility.

Use Cases

• Benchmarking MLM agents for GUI automation and task execution.
• Testing agent performance in varied settings with 120 tasks in CRAB Benchmark-v0.
• Enhancing agent capabilities through detailed feedback and leaderboard comparisons.

Unique Selling Points

• First-of-its-kind benchmark supporting multiple environments with fine-grained evaluation.
• Open-source framework with accessible code and benchmark data on GitHub.
• Comprehensive testing with leading models like GPT-4o, Gemini, and Claude, showcased via demo videos.