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View guide →Stanford's Generative Agents simulation - 25 AI characters living in a virtual town
generative_agents is an open-source ai agents project by joonspk-research (joonspk-research/generative_agents on GitHub). It has 22,200 GitHub stars and 3,112 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 5 August 2024.
Read the paper summary and decide whether you want to replay a demo or run a live simulation.
Get the repository, which contains the environment front-end and the simulation server.
Create the utils file with your OpenAI API key, then install the Python requirements.
Start the environment server and the simulation server, then watch or replay the agents' day.
Implements the paper's memory stream, retrieval, reflection and planning loop for each agent.
Runs 25 agents in the Smallville environment ('the Ville') with daily routines and emergent social behavior.
Simulations can be saved, replayed and demoed, so experiments are reproducible.
Author and load agent history files to change what agents remember.
Create your own base simulations with different agents and starting states.
Watch agents move and interact through the web-based environment server.
Get the source code from GitHub and enter the project directory.
git clone https://github.com/joonspk-research/generative_agents.git
cd generative_agentsAfter creating the utils file with your OpenAI API key (README Step 1), install the Python dependencies.
pip install -r requirements.txt # see README for the exact pathThe README walks through starting the environment server, then the simulation server, then running or replaying a simulation.
# Start the environment (frontend) server, then the simulation server
# per README 'Running a Simulation' steps 1-2It is maintained by the paper's authors (Joon Sung Park and collaborators, Stanford) as research code on GitHub.
Yes - the code is Apache-2.0 licensed and free to use under that license.
Yes, for live simulations you need your own OpenAI API key, and a town full of agents making LLM calls can consume meaningful API credit; replaying saved simulations avoids most of that cost.
Compared with agent frameworks like LangChain, this is not a toolkit for building your own agent app - it is a complete, paper-faithful social simulation you run, replay and modify.
Clone the repo, add your OpenAI key in the utils file, install requirements.txt, and follow the README's two-server run instructions.
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