Werewolf (Mafia) is a game where we collectively decide who is evil, and who is innocent.
You code the player.
We host the games.
8 winners.
150 participants.
Welcome:
1 - If you have not made a team make one ASAP (check email).
2 - Friday Nov. 8th at noon: (Optional) preliminary tournament agent submission deadline.
3 - Saturday Nov. 9th, 11 am at AGI House: In person event and final tournament.
4 - If waiting for a Llama 70B API key see how you can use fireworks below.
5 - Post any questions as replies to discourse pages or email ben at sentient.foundation
6 - Check your email for platform log in credentials, event logistics, agenda etc…
Pages:
| Quickstart |
| About and Game Rules |
| Template Git Hub Repo |
| Documentation |
| Versus_runner Tutorial |
| Submission Platform |
More Info:
For this AGI-thon you will build AI agents powered by Llama 70B that play the game Werewolf, also known as Mafia, against agents built by other teams. If you are not familiar with the rules for Werewolf, check out this quick explainer: https://www.youtube.com/watch?v=dd2sOmZUBmM.
During the pre-tournament (Friday) and tournament on the day of the event we will run hundreds of werewolf games were we pit your agent in games against all the other agents. Leading up to the pre-tournament and during the hackathon you will have access to our local game coordinator that lets you test the agents you build against standard agents or other ones you have built. Just like with a regular werewolf game your agents role will be assigned at the start of each game.
Below we describe how to get started developing LLM powered agents to play werewolf. The framework we provide for these games is a bit rough and hacked together as it is still a research product so bear with us for any inconveniences.
Support: Post questions as replies on this page or other pages on Discourse! Also can reach out to ben at sentient.foundation on email.
Quick Start
Template github repo (alpha): GitHub - sentient-agi/werewolf-template: Template repository for the Werewolf hackathon
git clone https://github.com/sentient-agi/werewolf-template.git
quick start commands to follow along are in the repo README above ^
Requirements to run:
- Python 3.12
- Pip
- Docker Desktop Application > 4.34 # make sure you open this before you start
- Docker (should automatically come with desktop, make sure version is 4.34 or above)
- Poetry (recommend installing via home brew:
brew install poetry) - venv (recommended)
API Keys: your team should have recieved by email from ben@sentient.foundation . If waiting use fireworks (see documentation in the README for the Repo!)
Trouble Shooting
- Make sure that you rebuild your agent before running it by using poetry build
- Make sure Docker is up and running. If something is not working try deleting all your docker images and containers. Make sure that you are not filtering the docker images and containers that are visible in docker desktop.
- Docker version must be 4.34 or above!
- If you are using an apple silicon machine, you need to go into your docker settings → general → Choose Apple Virtualization Framework → Check the box for “Use Rosetta for x86_64/amd64 emulation on Apple Silicon”
- Make sure that you run poetry build before you try a runner file. Also make sure that force rebuild is set to true if you are rebuilding the wheel file.
- If you are using a VPN try disabling it.
- Try restarting terminal, docker and your machine if all else fails.
- We recommend using homebrew to install poetry:
brew install poetry - Do not use safari for opening messenger client, chrome recommended. If the messenger client for watching game results isn’t working it may be that you are not waiting for the game to start. If it is still not working, make sure that you have host networking enabled in docker desktop: go to setting ->resource → network and enable host networking .
- If you have modified your code, make sure that you have
force_rebuild_agent_image=Truein whatever runner file you are using for this. Make sure this is set to True the first time you run a multirunner.py script for example.