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.
Product overview
Capabilities, pricing and fit
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.
Information as of July 30, 2026
What it can do
- 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 (1)
- CRAB: Cross-environment Agent Benchmark for Multimodal Language Model AgentsOfficial site · checked 2026-07-30
Autonomy level
83/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
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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