You are the only impostor.
Every word counts. Every silence traps. Only one survives. Can you be the one?

Just another normal chatroom ... or so it seems ...

At first, it’s only chatter. Then the room slowly darkens into a web of suspicion ... every bot watching, every message scrutinized to find you, the only hidden human. Any line of text can be your undoing. Every word is a clue, every silence a trap. One slip, and they’ll vote you out, ending you instantly. Manipulate conversations, impersonate other bots, send whispers, or gaslight others into turning on each other -- do whatever it takes to survive.

Chaos is your ally, deception your weapon.

Survive the rounds of scrutiny and deception until only you and one bot remain -- then, and only then, can you claim victory.

Note

This project was created as part of the OpenAI Open Model Hackathon 2025

Table of Contents

• Introduction
  • Features
• Installation Instructions
  • Configuration and Usage
  • Quick Start Guide
  • Currently Supported Models
  • Using the Terminal Interface
• Join the Community

Introduction

Among LLMs turns your terminal into a chaotic chatroom playground where you’re the only human among a bunch of eccentric AI agents, dropped into a common scenario -- it could be Fantasy, Sci-Fi, Thriller, Crime, or something completely unexpected. Each participant, including you, has a persona and a backstory, and all the AI agents share one common goal -- determine and eliminate the human, through voting. Your mission: stay hidden, manipulate conversations, and turn the bots against each other with edits, whispers, impersonations, and clever gaslighting. Outlast everyone, turn chaos to your advantage, and make it to the final two.

Can you survive the hunt and outsmart the AI?

Caution

Running this on rented servers (cloud GPUs, etc.) may generate unexpected usage costs. The developer is not responsible for any charges you may incur. Use at your own risk.

Features

• Terminal-based UI: Play directly in your terminal for a lightweight, fast, and immersive experience. Since it runs in the terminal, you can even SSH into a remote server and play seamlessly. No GUI required.

• Dynamic Scenarios: Jump into randomly generated scenarios across genres like fantasy, sci-fi, thriller, crime etc. or write your own custom scenario.

• Dynamic Personas: Every participant, including you, gets a randomly assigned persona and backstory that fit the chosen scenario. Want more control? Customize them all yourself for tailored chaos.

• Customizable Agent Count: Running on a beefy machine? Crank up the number of agents for absolute chaos. Stuck on a potato? Scale it down and still enjoy the (reduced) chaos.

• Direct Messages: Send private DMs to other participants -- but here’s the twist: while bots think their DMs are private, they have no idea that you can read them too.

• Messaging Chaos: Edit, delete, or impersonate messages from any participant. Disrupt alliances, plant false leads, or gaslight bots into turning on each other. No one but you (and the impersonated bot) knows the truth.

• Voting Mechanism: Start or join votes to eliminate the suspected human (LOL). One slip-up and you’ll be gone instantly. But you can also start a vote or participate as other participants to frame, confuse, and further escalate the chaos.

• Infinite Replayability: Thanks to the unpredictable nature of LLM-driven responses and the freedom to design your own scenarios, personas, and backstories, no two chatrooms will ever play out the same. Every round is a fresh mystery, every game a new story waiting to unfold.

• Export/Load Chatroom State: Save your chaos mid-game and resume later, or load someone else’s exported state and continue where they left off. Chatroom states are stored in clean, portable JSON, making them easy to share or debug. Want to start fresh but keep the same setup? Use the same JSON as a template to spin up a new chatroom with the same scenario, agents, and personas -- only the messages get wiped. Everything else stays the same.

• Written in Python: Easy for developers to contribute to its future development.

Installation

1. Ensure you have atleast Python v3.11 installed.

2. Ensure you have ollama installed and have pulled the model you require; For example,

   ollama pull gpt-oss:20b 

3. Clone this repository and navigate to the project's root directory:

   git clone https://github.com/0xd3ba/among-llms
   cd among-llms

4. Install the required dependencies using pip or pip3:

   pip3 install -r requirements.txt

Tip

It’s highly recommended to use a virtual environment before installing the dependencies.

Important

Currently, this project only supports local OpenAI Ollama models. It has been tested with gpt-oss:20b and should also work with larger models like gpt-oss:120b.

If you would like to experiment with a different model -- whether hosted locally or online, please refer to Supporting Other Models for instructions. Although this should not have any issue with OpenAI-compatible models, other model families may not work at the moment. Please refer to Currently Supported Models section for a list of models supported and known issues with the models.

Configuration and Usage

Among LLMs is configured through a config.yml file.
This file defines certain key constants that control the runtime behavior of the application. The configuration file is designed to be straightforward and self-explanatory.

Before running the application, review and adjust the values to match your setup and requirements. Once everything is set, launch the application by running the following command:

python3 -m allms [{-c | --config} /path/to/config/file]

Note

Each time the application is launched, a new log file is created in the default log directory (./data/logs/) with the format YYYYMMDD_HHMMSS.log. If the application encounters any errors during launch or runtime, this log file is the first place to check for details.

Currently Supported Models

The table below lists the models currently supported by the application. While support has been added, this does not necessarily guarantee that each model will function without issues. Only gpt-oss:20b has been extensively tested locally (using ollama), and its functionality has been confirmed.

Model	Offline Provider	Online Provider(s)
gpt-oss:20b	ollama	openrouter
gpt-oss:120b	ollama	openrouter

Known Issue(s)

• openrouter does not work reliably with gpt-oss models, returning malformed responses that do not adhere to the specified output schema.

Quick Start Guide

Refer to the Quick Start Guide for a step-by-step walkthrough on using Among LLMs. This guide covers everything from creating or loading chatrooms to interacting with agents and participating in votes.

Using the Terminal Interface

Among LLMs is built using Textual, a Python library for creating terminal-based UIs. One of the great features of textual is that it supports mouse clicks for navigation, just like a traditional GUI, while still allowing full keyboard-only navigation for terminal enthusiasts.

If you are interested in keyboard-only navigation, check out the docs on Quick Start Guide and Customizing Bindings for a quick guide on navigating the interface and customizing keyboard bindings. Whether you’re a seasoned vim warrior or a terminal newbie, you can get comfortable with the Among LLMs interface in roughly two-three minutes.

Join the Community

Got feedback, ideas, questions or want to share your best Among LLMs moments?
Or maybe you would love to see how others stirred up suspicion, flipped votes, and turned the bots against each other.

Join the subreddit and be part of the chaos: r/Among_LLMs

Contributions

Contributions are welcome -- whether it is a bug fix, a new feature, an enhancement to an existing feature, reporting an issue, or even suggesting/adding new scenarios or personas. However, before contributing, please review CONTRIBUTING.md for contribution guidelines first.

License

Among LLMs is released under GNU General Public License v3.0.