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What is CRAB?
CRAB is a comprehensive framework designed to facilitate the development, operation, and evaluation of Multimodal Language Model (MLM) agents. It features cross-environment support, a graph evaluator for detailed performance analysis, and automated task generation to simulate real-world scenarios.
The framework stands out by supporting multiple environments, allowing agents to adapt across different interfaces. CRAB offers fine-grained evaluation with graph evaluator, and uses a graph-based method for task generation which combines multiple sub-tasks. The system's architecture ensures ease of use, enabling the addition of new environments with minimal Python coding, and experiment reproducibility through a declarative programming paradigm.
Features
- Cross-environments: Supports multiple environments, ensuring agents adapt across different interfaces.
- Graph evaluator: Provides fine-grained evaluation, and detailed analysis of agent performance.
- Task Generation: Automates task creation using a graph-based method.
- Easy-to-use: Adding a new environment requires only a few lines of Python code.
Use Cases
- Evaluating the performance of Multimodal Language Models.
- Developing and testing agents in diverse operating environments (Ubuntu and Android).
- Creating dynamic tasks that mimic real-world scenarios for agent training.
- Analyzing agent strengths and weaknesses through detailed performance metrics.
- Reproducing experimental environments for consistent benchmarking.
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