Building an AI social simulation with OASIS

A walkthrough of persona design and multi-agent social simulation

Intro

What would people’s first reaction be if immortality became free and universally available starting today?

AI social simulation lets us explore exactly this kind of question. By giving LLMs distinct personas and placing them on a simulated social platform, we can observe how different agents might react to the same world-changing event.

In this article, I built a simulated Twitter/X feed with 35 AI agents representing people from 35 different countries. The starting point is a UN announcement about free immortality, posted as a seed post into the feed, and the agents react from there.

The full code for this project is available on GitHub.

Tools Used

The simulation is built in Python:

OASIS (CAMEL-AI) is the social simulation framework. It provides a simulated Twitter/X environment complete with a database, a recommendation system, and agent action types such as posting, liking, and following. Each agent in OASIS is backed by an LLM that reads its persona and decides what to do each round.