🏝️ OASIS is a scalable, open-source social media simulator that integrates large language models with rule-based agents to realistically mimic the behavior of up to one million users on platforms like Twitter and Reddit. It's designed to facilitate the study of complex social phenomena such as information spread, group polarization, and herd behavior, offering a versatile tool for exploring diverse social dynamics and user interactions in digital environments.
OASIS supports simulations of up to one million agents, enabling studies of social media dynamics at a scale comparable to real-world platforms.
Adapts to real-time changes in social networks and content, mirroring the fluid dynamics of platforms like Twitter and Reddit for authentic simulation experiences.
Agents can perform 23 actions, such as following, commenting, and reposting, allowing for rich, multi-faceted interactions.
Features interest-based and hot-score-based recommendation algorithms, simulating how users discover content and interact within social media platforms.
oasis_introduction.mp4
For more showcaes:
- Can 1,000,000 AI agents simulate social media? →Watch demo
git clone https://github.com/camel-ai/oasis.git
cd oasisPlease choose one of the following methods to set up your environment. You only need to follow one of these methods.
- Option 1: Using Conda (Linux & macOS & windows)
conda create --name oasis python=3.10
conda activate oasis- Option 2: Using venv (Linux & macOS)
python -m venv oasis-venv
source oasis-venv/bin/activate- Option 3: Using venv (Windows)
python -m venv oasis-venv
oasis-venv\Scripts\activatepip install --upgrade pip setuptools
pip install -e . # This will install dependencies as specified in pyproject.tomlTip
For more detailed instructions and additional configuration options, check out the installation section.
To discover how to create profiles for large-scale users, as well as how to visualize and analyze social simulation data once your experiment concludes, please refer to More Tutorials for detailed guidance.
We welcome community contributions! Join us in building these exciting features.
- Support OPENAI Embedding model for Twhin-Bert Recommendation System. - 📆 March 25, 2025
- Updated social media links and QR codes in the README! Join OASIS & CAMEL on WeChat, X, Reddit, and Discord. - 📆 March 24, 2025
- Add multi-threading support to speed up LLM inference by 13x - 📆 March 4, 2025
- Slightly refactoring the database to add Quote Action and modify Repost Action - 📆 January 13, 2025
- Added the demo video and oasis's star history in the README - 📆 January 5, 2025
- Introduced an Electronic Mall on the Reddit platform - 📆 December 5, 2024
- OASIS initially released on arXiv - 📆 November 19, 2024
- OASIS GitHub repository initially launched - 📆 November 19, 2024
We greatly appreciate your interest in contributing to our open-source initiative. To ensure a smooth collaboration and the success of contributions, we adhere to a set of contributing guidelines similar to those established by CAMEL. For a comprehensive understanding of the steps involved in contributing to our project, please refer to the CAMEL contributing guidelines. 🤝🚀
An essential part of contributing involves not only submitting new features with accompanying tests (and, ideally, examples) but also ensuring that these contributions pass our automated pytest suite. This approach helps us maintain the project's quality and reliability by verifying compatibility and functionality.
If you're keen on exploring new research opportunities or discoveries with our platform and wish to dive deeper or suggest new features, we're here to talk. Feel free to get in touch for more details at [email protected].
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Join us (Discord or WeChat) in pushing the boundaries of finding the scaling laws of agents.
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Join WechatGroup for further discussions!
@misc{yang2024oasisopenagentsocial,
title={OASIS: Open Agent Social Interaction Simulations with One Million Agents},
author={Ziyi Yang and Zaibin Zhang and Zirui Zheng and Yuxian Jiang and Ziyue Gan and Zhiyu Wang and Zijian Ling and Jinsong Chen and Martz Ma and Bowen Dong and Prateek Gupta and Shuyue Hu and Zhenfei Yin and Guohao Li and Xu Jia and Lijun Wang and Bernard Ghanem and Huchuan Lu and Chaochao Lu and Wanli Ouyang and Yu Qiao and Philip Torr and Jing Shao},
year={2024},
eprint={2411.11581},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.11581},
}
We would like to thank Douglas for designing the logo of our project.
The source code is licensed under Apache 2.0.
