OASIS is a Python framework for simulating social media platforms (Twitter, Reddit) populated by LLM-powered agents. It is designed for computational social science research, specifically studying information spread, opinion dynamics, group polarization, and the impact of platform algorithms. The system is built on top of the CAMEL AI framework for LLM interaction. The architecture follows a modular, event-driven pattern. The core simulation loop is managed by OasisEnv, which coordinates agents (SocialAgent), a platform (Platform), an async message channel (Channel), and a social graph (AgentGraph). Actions are generated by agents (either manually or via LLM calls), passed through the channel, and executed by the platform against a SQLite database. A recommendation system (Recsys) provides personalized content feeds using various strategies. The codebase is a hybrid of a software library and a research project. While the core framework is well-structured with clean abstractions (e.g., Channel, AgentGraph, Recsys), it carries significant research code debt: tight coupling to the fast-moving camel-ai framework, global state in recommendation modules, skipped tests, hardcoded paths in experiments, and mixed sync/async patterns. The extensive YAML-driven experiment configurations and visualization scripts are tightly coupled to specific research questions (misinformation, polarization) and datasets. Maintenance is active in the context of ongoing research, but the codebase shows signs of technical drift (skipped tests, commented-out features, dependency on specific LLM API patterns). It is a powerful tool for researchers but would require significant hardening for production use.