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🦀️ CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents. https://crab.camel-ai.org/
| Date | Stars |
|---|---|
| 2026-07-31 | 424 |
| 2026-08-02 | 424 |
| 2026-08-06 | 424 |
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# 🦀 CRAB: Cross-platform Agent Benchmark for Multimodal Embodied Language Model Agents
[![arXiv][arxiv-image]][arxiv-url]
[![Slack][slack-image]][slack-url]
[![Discord][discord-image]][discord-url]
[![Wechat][wechat-image]][wechat-url]
[![Twitter][twitter-image]][twitter-url]
<p align="center">
<a href="https://camel-ai.github.io/crab/">Documentation</a> |
<a href="https://crab.camel-ai.org/">Website & Demos</a> |
<a href="https://www.camel-ai.org/post/crab">Blog</a> |
<a href="https://dandansamax.github.io/posts/crab-paper/">Chinese Blog</a> |
<a href="https://www.camel-ai.org/">CAMEL-AI</a>
</p>
<p align="center">
<img src='https://raw.githubusercontent.com/camel-ai/crab/main/assets/CRAB_logo1.png' width=800>
</p>
## Overview
CRAB is a framework for building LLM agent benchmark environments in a Python-centric way.
#### Key Features
🌐 Cross-platform and Multi-environment
* Create build agent environments that support various deployment options including in-memory, Docker-hosted, virtual machines, or distributed physical machines, provided they are accessible via Python functions.
* Let the agent access all the environments in the same time through a unified interface.
⚙ ️Easy-to-use Configuration
* Add a new action by simply adding a `@action` decorator on a Python function.
* Define the environment by integrating several actions together.
📐 Novel Benchmarking Suite
* Define tasks and the corresponding evaluators in an intuitive Python-native way.
* Introduce a novel graph evaluator method providing fine-grained metrics.
## Installation
#### Prerequisites
- Python 3.10 or newer
```bash
pip install crab-framework[client]
```
## Experiment on CRAB-Benchmark-v0
All datasets and experiment code are in [crab-benchmark-v0](./crab-benchmark-v0/) directory. Please carefully read the [benchmark tutorial](./crab-benchmark-v0/README.md) before using our benchmark.
## Examples
#### Run template environment with openai agent
```bash
export OPENAI_API_KEY=<your api key>
python examples/single_env.py
python examples/multi_env.py
```
## Demo Video
[](https://www.youtube.com/watch?v=PNqrHNQlU6I&ab_channel=CamelAI)
## Cite
Please cite [our paper](https://arxiv.org/abs/2407.01511) if you use anything related in your work:
```
@misc{xu2024crab,
title={CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents},
author={Tianqi Xu and Linyao Chen and Dai-Jie Wu and Yanjun Chen and Zecheng Zhang and Xiang Yao and Zhiqiang Xie and Yongchao Chen and Shilong Liu and Bochen Qian and Philip Torr and Bernard Ghanem and Guohao Li},
year={2024},
eprint={2407.01511},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.01511},
}
```
## Community
Join us ([*Discord*](https://discord.camel-ai.org/) or [*WeChat*](https://ghli.org/camel/wechat.png)) in pushing the boundaries of finding the scaling laws of agents.
- **WeChat Community:** Scan the QR code below to join our WeChat community.
<div align="center">
<img src="assets/wechatgroup.jpeg" alt="WeChat QR Code" width="50%">
</div>
<br>
[slack-url]: https://join.slack.com/t/camel-kwr1314/shared_invite/zt-1vy8u9lbo-ZQmhIAyWSEfSwLCl2r2eKA
[slack-image]: https://img.shields.io/badge/Slack-CAMEL--AI-blueviolet?logo=slack
[discord-url]: https://discord.gg/CNcNpquyDc
[discord-image]: https://img.shields.io/badge/Discord-CAMEL--AI-7289da?logo=discord&logoColor=white&color=7289da
[wechat-url]: https://ghli.org/camel/wechat.png
[wechat-image]: https://img.shields.io/badge/WeChat-CamelAIOrg-brightgreen?logo=wechat&logoColor=white
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[arxiv-image]: https://img.shieExcerpt of 4,089 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5269cab5266214b2, topic:large-language-models
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