It is a general-purpose agent benchmark framework designed for Multimodal Language Model (MLM) agents, providing an end-to-end, easy-to-use system to build agents, operate environments, and create benchmarks for evaluation.
It is a general-purpose agent benchmark framework designed for Multimodal Language Model (MLM) agents, providing an end-to-end, easy-to-use system to build agents, operate environments, and create benchmarks for evaluation. CRAB features three key components: cross-environment support, a graph evaluator, and task generation. The framework enables the development and testing of MLM agents across multiple environments, such as Ubuntu and Android, and supports various communication settings. CRAB Benchmark-v0, developed using this framework, includes 120 tasks across these two environments, tested with six different MLMs under three distinct communication settings.
The results are based on CRAB Benchmark v0, released on October 18, 2024, which evaluates agents on tasks like opening apps, summarizing messages, and performing actions across devices. For example, tasks include opening Slack in Ubuntu, summarizing messages, and sending them via Android’s Messages app, or checking incomplete tasks in Android’s Tasks app and performing them. Another task involves summarizing schedules in Android’s Calendar app and creating a markdown file in Ubuntu using Terminal and Vim. These tasks are executed under settings like OpenAI GPT-4o with single or multi-agent configurations.
CRAB is compared with existing GUI agents and benchmarks, highlighting its unique features such as cross-environment support and task generation. The framework is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, allowing users to borrow its source code with proper attribution. Demo videos, though edited for better viewing, reflect actual execution times with tens of seconds of waiting between steps. CRAB aims to advance the evaluation and development of MLM agents through its comprehensive and flexible benchmarking capabilities.
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