[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72153":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":36,"readmeContent":37,"aiSummary":38,"trendingCount":16,"starSnapshotCount":16,"syncStatus":39,"lastSyncTime":40,"discoverSource":41},72153,"oasis","camel-ai\u002Foasis","camel-ai","🏝️ OASIS: Open Agent Social Interaction Simulations with One Million Agents. ","https:\u002F\u002Fdocs.oasis.camel-ai.org\u002F",null,"Python",4763,577,30,42,0,24,61,214,72,30.29,"Apache License 2.0",false,"main",true,[27,28,29,30,31,32,33,34,35],"agent-based-framework","agent-based-simulation","ai-societies","deep-learning","large-language-models","large-scale","llm-agents","multi-agent-systems","natural-language-processing","2026-06-12 02:02:59","\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.camel-ai.org\u002F\">\n    \u003Cimg src=\"assets\u002Fbanner.png\" alt=banner>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003C\u002Fbr>\n\n\u003Cdiv align=\"center\">\n\n\u003Ch1> OASIS: Open Agent Social Interaction Simulations with One Million Agents\n\u003C\u002Fh1>\n\n[![Documentation][docs-image]][docs-url]\n[![Discord][discord-image]][discord-url]\n[![X][x-image]][x-url]\n[![Reddit][reddit-image]][reddit-url]\n[![Wechat][wechat-image]][wechat-url]\n[![Wechat][oasis-image]][oasis-url]\n[![Hugging Face][huggingface-image]][huggingface-url]\n[![Star][star-image]][star-url]\n[![Package License][package-license-image]][package-license-url]\n\n\u003Ch4 align=\"center\">\n\n[Community](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Fcamel#community) |\n[Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.11581) |\n[Examples](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Ftree\u002Fmain\u002Fexamples) |\n[Dataset](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fecho-yiyiyi\u002Foasis-dataset) |\n[Citation](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis#-citation) |\n[Contributing](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis#-contributing-to-oasis) |\n[CAMEL-AI](https:\u002F\u002Fwww.camel-ai.org\u002F)\n\n\u003C\u002Fh4>\n\n\u003C\u002Fdiv>\n\n\u003Cbr>\n\n\u003Cp align=\"left\">\n  \u003Cimg src='assets\u002Fintro.png'>\n\n🏝️ OASIS is a scalable, open-source social media simulator that incorporates large language model 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.\n\n\u003C\u002Fp>\n\n\u003Cbr>\n\n\u003Cdiv align=\"center\">\n🌟 Star OASIS on GitHub and be instantly notified of new releases.\n\u003C\u002Fdiv>\n\n\u003Cbr>\n\n\u003Cdiv align=\"center\">\n    \u003Cimg src=\"assets\u002Fstar.gif\" alt=\"Star\" width=\"196\" height=\"52\">\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cbr>\n\n## ✨ Key Features\n\n### 📈 Scalability\n\nOASIS supports simulations of up to ***one million agents***, enabling studies of social media dynamics at a scale comparable to real-world platforms.\n\n### 📲 Dynamic Environments\n\nAdapts to real-time changes in social networks and content, mirroring the fluid dynamics of platforms like **Twitter** and **Reddit** for authentic simulation experiences.\n\n### 👍🏼 Diverse Action Spaces\n\nAgents can perform **23 actions**, such as following, commenting, and reposting, allowing for rich, multi-faceted interactions.\n\n### 🔥 Integrated Recommendation Systems\n\nFeatures **interest-based** and **hot-score-based recommendation algorithms**, simulating how users discover content and interact within social media platforms.\n\n\u003Cbr>\n\n## 📺 Demo Video\n\n### Introducing OASIS: Open Agent Social Interaction Simulations with One Million Agents\n\nhttps:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F3bd2553c-d25d-4d8c-a739-1af51354b15a\n\n\u003Cbr>\n\nFor more showcaes:\n\n- Can 1,000,000 AI agents simulate social media?\n  [→Watch demo](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lprGHqkApus&t=2s)\n\n\u003Cbr>\n\n## 🎯 Usecase\n\n\u003Cdiv align=\"left\">\n    \u003Cimg src=\"assets\u002Fresearch_simulation.png\" alt=usecase1>\n    \u003Cimg src=\"assets\u002Finteraction.png\" alt=usecase2>\n   \u003Ca href=\"http:\u002F\u002Fwww.matrix.eigent.ai\">\n    \u003Cimg src=\"assets\u002Fcontent_creation.png\" alt=usecase3>\n   \u003C\u002Fa>\n    \u003Cimg src=\"assets\u002Fprediction.png\" alt=usecase4>\n\u003C\u002Fdiv>\n\n## ⚙️ Quick Start\n\n1. **Install the OASIS package:**\n\nInstalling OASIS is a breeze thanks to its availability on PyPI. Simply open your terminal and run:\n\n```bash\npip install camel-oasis\n```\n\n2. **Set up your OpenAI API key:**\n\n```bash\n# For Bash shell (Linux, macOS, Git Bash on Windows):\nexport OPENAI_API_KEY=\u003Cinsert your OpenAI API key>\n\n# For Windows Command Prompt:\nset OPENAI_API_KEY=\u003Cinsert your OpenAI API key>\n```\n\n3. **Prepare the agent profile file:**\n\nCreate the profile you want to assign to the agent. As an example, you can download [user_data_36.json](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Fblob\u002Fmain\u002Fdata\u002Freddit\u002Fuser_data_36.json) and place it in your local `.\u002Fdata\u002Freddit` folder.\n\n4. **Run the following Python code:**\n\n```python\nimport asyncio\nimport os\n\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\nimport oasis\nfrom oasis import (ActionType, LLMAction, ManualAction,\n                   generate_reddit_agent_graph)\n\n\nasync def main():\n    # Define the model for the agents\n    openai_model = ModelFactory.create(\n        model_platform=ModelPlatformType.OPENAI,\n        model_type=ModelType.GPT_4O_MINI,\n    )\n\n    # Define the available actions for the agents\n    available_actions = [\n        ActionType.LIKE_POST,\n        ActionType.DISLIKE_POST,\n        ActionType.CREATE_POST,\n        ActionType.CREATE_COMMENT,\n        ActionType.LIKE_COMMENT,\n        ActionType.DISLIKE_COMMENT,\n        ActionType.SEARCH_POSTS,\n        ActionType.SEARCH_USER,\n        ActionType.TREND,\n        ActionType.REFRESH,\n        ActionType.DO_NOTHING,\n        ActionType.FOLLOW,\n        ActionType.MUTE,\n    ]\n\n    agent_graph = await generate_reddit_agent_graph(\n        profile_path=\".\u002Fdata\u002Freddit\u002Fuser_data_36.json\",\n        model=openai_model,\n        available_actions=available_actions,\n    )\n\n    # Define the path to the database\n    db_path = \".\u002Fdata\u002Freddit_simulation.db\"\n\n    # Delete the old database\n    if os.path.exists(db_path):\n        os.remove(db_path)\n\n    # Make the environment\n    env = oasis.make(\n        agent_graph=agent_graph,\n        platform=oasis.DefaultPlatformType.REDDIT,\n        database_path=db_path,\n    )\n\n    # Run the environment\n    await env.reset()\n\n    actions_1 = {}\n    actions_1[env.agent_graph.get_agent(0)] = [\n        ManualAction(action_type=ActionType.CREATE_POST,\n                     action_args={\"content\": \"Hello, world!\"}),\n        ManualAction(action_type=ActionType.CREATE_COMMENT,\n                     action_args={\n                         \"post_id\": \"1\",\n                         \"content\": \"Welcome to the OASIS World!\"\n                     })\n    ]\n    actions_1[env.agent_graph.get_agent(1)] = ManualAction(\n        action_type=ActionType.CREATE_COMMENT,\n        action_args={\n            \"post_id\": \"1\",\n            \"content\": \"I like the OASIS world.\"\n        })\n    await env.step(actions_1)\n\n    actions_2 = {\n        agent: LLMAction()\n        for _, agent in env.agent_graph.get_agents()\n    }\n\n    # Perform the actions\n    await env.step(actions_2)\n\n    # Close the environment\n    await env.close()\n\n\nif __name__ == \"__main__\":\n    asyncio.run(main())\n```\n\n\u003Cbr>\n\n> \\[!TIP\\]\n> For more detailed instructions and additional configuration options, check out the [documentation](https:\u002F\u002Fdocs.oasis.camel-ai.org\u002F).\n\n### 💰 Token Consumption Reference\n\nTo help you estimate costs before running a simulation, here is a measured reference for token consumption:\n\n| Parameter              | Value      |\n| ---------------------- | ---------- |\n| Number of Agents       | 100        |\n| Activation Probability | 1          |\n| Time Steps             | 1          |\n| Input Tokens           | 335,600    |\n| Output Tokens          | 16,750     |\n| Model                  | QWEN_TURBO |\n\n> \\[!NOTE\\]\n> Token usage scales with the number of agents, activation probability, and time steps. Use this reference as a baseline to estimate the cost of larger simulations.\n\nEstimated cost for 1 time step, activation probability 0.1 (Qwen pricing as of 2024-12-14):\n\n| Model     | 100 Agents | 1,000 Agents | 10,000 Agents |\n| --------- | ---------- | ------------ | ------------- |\n| qwen-plus | ¥0.026848  | ¥0.26848     | ¥2.6848       |\n| qwen-max  | ¥0.717     | ¥7.717       | ¥77.17        |\n\n### More Tutorials\n\nTo 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](examples\u002Fexperiment\u002Fuser_generation_visualization.md) for detailed guidance.\n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\"assets\u002Ftutorial.png\" alt=\"Tutorial Overview\">\n\u003C\u002Fdiv>\n\n## 📢 News\n\n### Upcoming Features & Contributions\n\n> We welcome community contributions! Join us in building these exciting features.\n\n- [Support Multi Modal Platform](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Fissues\u002F47)\n\n\u003C!-- - Public release of our dataset on Hugging Face (November 05, 2024) -->\n\n### Latest Updates\n\n📢 Update the camel-ai version to 0.2.78 and update the dataset HuggingFace link.  - 📆 December 4, 2025\n\n- Add the report post action to mark inappropriate content. - 📆 June 8, 2025\n- Add features for creating group chats, sending messages in group chats, and leaving group chats. - 📆 June 6, 2025\n- Support Interview Action for asking agents specific questions and getting answers. - 📆 June 2, 2025\n- Support customization of each agent's models, tools, and prompts; refactor the interface to follow the PettingZoo style. - 📆 May 22, 2025\n- Refactor into the OASIS environment, publish camel-oasis on PyPI, and release the documentation. - 📆 April 24, 2025\n- Support OPENAI Embedding model for Twhin-Bert Recommendation System. - 📆 March 25, 2025\n  ...\n- Slightly refactoring the database to add Quote Action and modify Repost Action - 📆 January 13, 2025\n- Added the demo video and oasis's star history in the README - 📆 January 5, 2025\n- Introduced an Electronic Mall on the Reddit platform - 📆 December 5, 2024\n- OASIS initially released on arXiv - 📆 November 19, 2024\n- OASIS GitHub repository initially launched - 📆 November 19, 2024\n\n## 🔎 Follow-up Research\n\n- [MultiAgent4Collusion](https:\u002F\u002Fgithub.com\u002Frenqibing\u002FMultiAgent4Collusion): multi-agent collusion simulation framework in social systems\n- [CUBE](https:\u002F\u002Fgithub.com\u002Fecho-yiyiyi\u002Fcube): dynamic simulations in customized unity3D-based environments\n- [MultiAgent4Fraud](https:\u002F\u002Fgithub.com\u002Fzheng977\u002FMutiAgent4Fraud): financial fraud risks by collaborative LLM agents on social platforms\n- More to come...\n\nIf your research is based on OASIS, we'd be happy to feature your work here—feel free to reach out or submit a pull request to add it to the [README](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Fblob\u002Fmain\u002FREADME.md)!\n\n## 🥂 Contributing to OASIS🏝️\n\n> 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 OASIS [contributing guidelines](https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md). 🤝🚀\n>\n> 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.\n\n## 📬 Community & Contact\n\nIf 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 camel.ai.team@gmail.com.\n\n\u003Cbr>\n\n- Join us ([*Discord*](https:\u002F\u002Fdiscord.camel-ai.org\u002F) or [*WeChat*](https:\u002F\u002Fghli.org\u002Fcamel\u002Fwechat.png)) in pushing the boundaries of finding the scaling laws of agents.\n\n- Join WechatGroup for further discussions!\n\n\u003Cdiv align=\"\">\n  \u003Cimg src=\"assets\u002Fwechatgroup.png\" alt=\"WeChat Group QR Code\" width=\"600\">\n\u003C\u002Fdiv>\n\n## 🌟 Star History\n\n[![Star History Chart](https:\u002F\u002Fapi.star-history.com\u002Fsvg?repos=camel-ai\u002Foasis&type=Date)](https:\u002F\u002Fstar-history.com\u002F#camel-ai\u002Foasis&Date)\n\n## 🔗 Citation\n\n```\n@misc{yang2024oasisopenagentsocial,\n      title={OASIS: Open Agent Social Interaction Simulations with One Million Agents},\n      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},\n      year={2024},\n      eprint={2411.11581},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      url={https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.11581},\n}\n```\n\n## 🙌 Acknowledgment\n\nWe would like to thank Douglas for designing the logo of our project.\n\n## 🖺 License\n\nThe source code is licensed under Apache 2.0.\n\n[discord-image]: https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb\n[discord-url]: https:\u002F\u002Fdiscord.camel-ai.org\u002F\n[docs-image]: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDocumentation-EB3ECC\n[docs-url]: https:\u002F\u002Fdocs.oasis.camel-ai.org\u002F\n[huggingface-image]: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Hugging%20Face-CAMEL--AI-ffc107?color=ffc107&logoColor=white\n[huggingface-url]: https:\u002F\u002Fhuggingface.co\u002Fcamel-ai\n[oasis-image]: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWeChat-OASISProject-brightgreen?logo=wechat&logoColor=white\n[oasis-url]: .\u002Fassets\u002Fwechatgroup.png\n[package-license-image]: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache_2.0-blue.svg\n[package-license-url]: https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Fblob\u002Fmain\u002Flicenses\u002FLICENSE\n[reddit-image]: https:\u002F\u002Fimg.shields.io\u002Freddit\u002Fsubreddit-subscribers\u002FCamelAI?style=plastic&logo=reddit&label=r%2FCAMEL&labelColor=white\n[reddit-url]: https:\u002F\u002Fwww.reddit.com\u002Fr\u002FCamelAI\u002F\n[star-image]: https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fcamel-ai\u002Foasis?label=stars&logo=github&color=brightgreen\n[star-url]: https:\u002F\u002Fgithub.com\u002Fcamel-ai\u002Foasis\u002Fstargazers\n[wechat-image]: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWeChat-CamelAIOrg-brightgreen?logo=wechat&logoColor=white\n[wechat-url]: .\u002Fassets\u002Fwechat.JPGwechat.jpg\n[x-image]: https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002FCamelAIOrg?style=social\n[x-url]: https:\u002F\u002Fx.com\u002FCamelAIOrg\n","OASIS 是一个可扩展的开源社交媒体模拟器，利用大型语言模型代理来真实地模拟多达一百万用户在类似 Twitter 和 Reddit 平台上的行为。其核心功能包括支持大规模代理（最多一百万个）、动态适应社交网络和内容变化、提供多样化的行动空间（如关注、评论和转发等23种动作），以及集成基于兴趣和热度的推荐系统。这些技术特点使得 OASIS 能够准确再现信息传播、群体极化和从众行为等复杂社会现象。该工具非常适合用于研究数字环境中的社交动态和用户互动，适用于学术研究、数据分析及社交媒体策略制定等多个场景。",2,"2026-06-11 03:40:35","high_star"]