CRAB: Cross-environment Agent Benchmark
Overview
An open-source framework for building and benchmarking environments tailored for large language model (LLM) agents across multiple platforms.
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.
AI Agent Store research
What the evidence says about CRAB: Cross-environment Agent Benchmark
CRAB: Cross-environment Agent Benchmark is best understood as an agent benchmark framework rather than an end-user agent. CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents.
Last reviewed July 30, 2026
Verified capabilities
- Agent application development
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents.[1]
Where it fits best
- Developing and benchmarking LLM agents across multiple environments.[1]
Sources and research method (1)
We record only claims tied to public sources checked by our team or listing workflow. Counts above are derived directly from this profile, not a subjective rating.
- CRAB: Cross-environment Agent Benchmark for Multimodal Language Model AgentsOfficial site · checked 2026-07-30
Autonomy level
83%
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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.
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Popularity level: 75%
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