CRAB: Cross-environment Agent Benchmark

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Category:AI Agent Development Frameworks

Overview

An open-source framework for building and benchmarking environments tailored for large language model (LLM) agents across multiple platforms.

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CRAB (Cross-environment Agent Benchmark) is an open-source framework developed by CAMEL-AI for constructing and evaluating environments designed for large language model (LLM) agents. It supports the creation of cross-platform environments, enabling deployment across in-memory systems, Docker-hosted environments, virtual machines, or distributed physical machines. CRAB introduces a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction, facilitating comprehensive assessment of agent performance across diverse settings.

Autonomy level

83%

Reasoning: CRAB enables high-level autonomous operation through its multi-agent architecture supporting simultaneous device control and task decomposition via graph evaluators. While requiring initial task setup by humans (autonomy limitation), its demonstrated 38% completion rate for GPT-4o on novel cross-platform workflows shows substantial independence in ...

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Some of the use cases of CRAB: Cross-environment Agent Benchmark:

Developing and benchmarking LLM agents across multiple environments.
Evaluating agent performance with fine-grained, graph-based metrics.
Constructing tasks and evaluators efficiently for comprehensive agent assessment.
Facilitating cross-platform deployment of AI agents in diverse settings.
Advancing research in multimodal language model agents and their applications.

Pricing model:

free

Code access:

open-source

Popularity level: 75%