OASIS is a scalable, open-source framework for simulating social media platforms (Twitter, Reddit) using LLM-powered agents. The system models up to one million agents with realistic actions (post, like, follow, comment, report, etc.) and supports configurable recommendation systems, agent graphs, and time scaling. It is designed for research into information spread, misinformation, group polarization, and herd behavior, as evidenced by extensive experiment scripts and real-world datasets included in the repository. The architecture follows a PettingZoo-style environment loop with a Platform, SocialAgent, AgentGraph, and RecSys modules, all built on top of the CAMEL framework. While the core is a reusable simulation library, the repo also contains research data, visualization scripts, and one-off experiment configurations, making it a hybrid of software application and research project.
This repository implements a Chinese-localized version of the Stanford Generative Agents simulation. It creates a virtual town with 25 AI agents powered by LLMs (primarily via Ollama) that autonomously organize events, form relationships, and exhibit human-like behaviors. The system consists of a simulation engine that runs agent interactions over time, a compression tool to generate replay data, and a Flask-based web frontend for visualizing the simulation. Agents maintain memory, generate plans, reflect on experiences, and interact through LLM-driven conversations and actions. The application supports local deployment with configurable LLMs and includes features like breakpoint resumption and conversation logging.