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AI Town 🏠💻💌

Live Demo

Join our community Discord: AI Stack Devs

Screen Shot 2023-08-14 at 10 01 00 AM

AI Town is a virtual town where AI characters live, chat and socialize.

This project is a deployable starter kit for easily building and customizing your own version of AI town. Inspired by the research paper Generative Agents: Interactive Simulacra of Human Behavior.

The primary goal of this project, beyond just being a lot of fun to work on, is to provide a platform with a strong foundation that is meant to be extended. The back-end natively supports shared global state, transactions, and a simulation engine and should be suitable from everything from a simple project to play around with to a scalable, multi-player game. A secondary goal is to make a JS/TS framework available as most simulators in this space (including the original paper above) are written in Python.

Overview

Stack

  • Game engine, database, and vector search: Convex
  • Auth (Optional): Clerk
  • Default chat model is llama3 and embeddings with mxbai-embed-large.
  • Local inference: Ollama
  • Configurable for other cloud LLMs: Together.ai or anything that speaks the OpenAI API. PRs welcome to add more cloud provider support.
  • Pixel Art Generation: Replicate, Fal.ai
  • Background Music Generation: Replicate using MusicGen

Installation

Note: There is a one-click install of a fork of this project on Pinokio for anyone interested in running but not modifying it 😎

1. Clone repo and Install packages

git clone https://github.com/a16z-infra/ai-town.git
cd ai-town
npm install

2. To develop locally with Convex:

Either download a pre-built binary(recommended), or build it from source and run it.

# For new Macs:
curl  -L -O https://github.com/get-convex/convex-backend/releases/latest/download/convex-local-backend-aarch64-apple-darwin.zip
unzip convex-local-backend-aarch64-apple-darwin.zip

brew install just

# Runs the server
./convex-local-backend

This also installs just (e.g. brew install just or cargo install just). We use just like make to add extra params, so you run just convex ... instead of npx convex ... for local development.

If you're running the pre-built binary on Mac and there's an Apple warning, go to the folder it's in and right-click it and select "Open" to bypass. From then on you can run it from the commandline. Or you can compile it from source and run it (see above).

3. To run a local LLM, download and run Ollama.

You can leave the app running or run ollama serve. ollama serve will warn you if the app is already running. Run ollama pull llama3 to have it download llama3. Test it out with ollama run llama3. If you want to customize which model to use, adjust convex/util/llm.ts or set just convex env set LLM_MODEL # model. Ollama model options can be found here.

You might want to set NUM_MEMORIES_TO_SEARCH to 1 in constants.ts, to reduce the size of conversation prompts, if you see slowness.

Check out convex/config.ts to configure which models to offer to the UI, or to set it up to talk to a cloud-hosted LLM.

4. Adding background music with Replicate (Optional)

For Daily background music generation, create a Replicate account and create a token in your Profile's API Token page. just convex env set REPLICATE_API_TOKEN # token

5. Run the code

To run both the front and and back end:

npm run dev

Note: If you encounter a node version error on the convex server upon application startup, please use node version 18, which is the most stable. One way to do this is by installing nvm and running nvm install 18 or nvm use 18. Do this before both the npm run dev above and the ./convex-local-backend in Step 2.

You can now visit http://localhost:5173.

If you'd rather run the frontend in a separate terminal from Convex (which syncs your backend functions as they're saved), you can run these two commands:

npm run dev:frontend
npm run dev:backend

See package.json for details, but dev:backend runs just convex dev

Note: The simulation will pause after 5 minutes if the window is idle. Loading the page will unpause it. You can also manually freeze & unfreeze the world with a button in the UI. If you want to run the world without the browser, you can comment-out the "stop inactive worlds" cron in convex/crons.ts.

Various commands to run / test / debug

To stop the back end, in case of too much activity

This will stop running the engine and agents. You can still run queries and run functions to debug.

just convex run testing:stop

To restart the back end after stopping it

just convex run testing:resume

To kick the engine in case the game engine or agents aren't running

just convex run testing:kick

To archive the world

If you'd like to reset the world and start from scratch, you can archive the current world:

just convex run testing:archive

Then, you can still look at the world's data in the dashboard, but the engine and agents will no longer run.

You can then create a fresh world with init.

just convex run init

To clear all databases

You can wipe all tables with the wipeAllTables testing function.

just convex run testing:wipeAllTables

To pause your backend deployment

You can go to the dashboard to your deployment settings to pause and un-pause your deployment. This will stop all functions, whether invoked from the client, scheduled, or as a cron job. See this as a last resort, as there are gentler ways of stopping above. Once you

Docker Installation

Before Launching Docker

Modify your package.json file to add the --host option to your front-end server (Vite):

{
  "name": "ai-town",
  "version": "0.0.0",
  "private": true,
  "scripts": {
    "dev": "npm-run-all --parallel dev:frontend dev:backend",
    "build": "tsc && vite build",
    "lint": "eslint .",
    "predev": "just convex dev --run init --until-success",
    "dev:backend": "just convex dev --tail-logs",
    "dev:frontend": "vite --host", // <------------------------------------------ modify this line
    "test": "NODE_OPTIONS=--experimental-vm-modules jest --verbose",
    "le": "vite src/editor/"
  }
}

Launching Docker Compose

Run the following command to launch Docker Compose:

docker-compose up --build

Once completed, you can close the terminal.

Launching an Interactive Docker Terminal

In another terminal, still in the aitown directory, launch an interactive Docker terminal:

docker-compose exec ai-town /bin/bash

Running Locally

  1. Download and unzip the local Convex backend:

    curl -L -O https://github.com/get-convex/convex-backend/releases/download/precompiled-2024-06-28-91981ab/convex-local-backend-x86_64-unknown-linux-gnu.zip
unzip convex-local-backend-x86_64-unknown-linux-gnu.zip

  2. Verify the convex-local-backend file is in the directory, then remove the zip file:

    rm convex-local-backend-x86_64-unknown-linux-gnu.zip

  3. Make the file executable:

    chmod +x /usr/src/app/convex-local-backend

  4. Launch the Convex backend server:

    ./convex-local-backend

Relaunching an Interactive Docker Terminal for aitown server

In another terminal, in the aitown directory, relaunch:

docker-compose exec ai-town /bin/bash

Configuring Socat

Configure socat with the host's IP address:

HOST_IP=YOUR-HOST-IP  # Use your host's IP address (not the Docker IP)
socat TCP-LISTEN:11434,fork TCP:$HOST_IP:11434 &

Testing the Connection

Test the connection:

curl http://localhost:11434/

If it says "Ollama is running", it's good!

Starting Services

Make sure Convex knows where to find Ollama (to skip a random mysterious bug ...):

just convex env set OLLAMA_HOST http://localhost:11434

Update the browser list:

npx update-browserslist-db@latest

Launch AI Town:

npm run dev

For relaunching

launch container then Simply open two terminal in your AI-town folder with docker-compose exec ai-town /bin/bash

Launch the Convex backend server: bash ./convex-local-backend And in the second terminal simply Configuring Socat, Launch AI Town.

Windows Installation

Prerequisites

  1. Windows 10/11 with WSL2 installed
  2. Internet connection

1. Install WSL2

First, you need to install WSL2. Follow this guide to set up WSL2 on your Windows machine. We recommend using Ubuntu as your Linux distribution.

2. Update Packages

Open your WSL terminal (Ubuntu) and update your packages:

sudo apt update

3. Install NVM and Node.js

NVM (Node Version Manager) helps manage multiple versions of Node.js. Install NVM and Node.js 18 (the stable version):

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.2/install.sh | bash
export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"
source ~/.bashrc
nvm install 18
nvm use 18

4. Install Python and Pip

Python is required for some dependencies. Install Python and Pip:

sudo apt-get install python3 python3-pip
sudo ln -s /usr/bin/python3 /usr/bin/python

5. Install Additional Tools

Install unzip and socat:

sudo apt install unzip socat

6. Install Rust and Cargo

Cargo is the Rust package manager. Install Rust and Cargo:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

7. Install just with Cargo

just is used to run commands. Install it with Cargo:

cargo install just
export PATH="$HOME/.cargo/bin:$PATH"
just --version

8. Configure socat to Bridge Ports for Ollama

Run the following command to bridge ports, allowing communication between Convex and Ollama:

socat TCP-LISTEN:11434,fork TCP:$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):11434 &

Test if it's working:

curl http://127.0.0.1:11434

If it responds OK, the Ollama API is accessible.

9. Clone the AI Town Repository

Clone the AI Town repository from GitHub:

git clone https://github.com/a16z-infra/ai-town.git
cd ai-town

10. Install NPM Packages

Install the necessary npm packages:

npm install

11. Install Precompiled Convex

Download and install the precompiled version of Convex:

curl -L -O https://github.com/get-convex/convex-backend/releases/download/precompiled-2024-06-28-91981ab/convex-local-backend-x86_64-unknown-linux-gnu.zip
unzip convex-local-backend-x86_64-unknown-linux-gnu.zip
rm convex-local-backend-x86_64-unknown-linux-gnu.zip
chmod +x convex-local-backend

12. Launch Convex

In a separate terminal, launch Convex:

./convex-local-backend

13. Configure Convex to Use Ollama

Set the Ollama host in Convex:

just convex env set OLLAMA_HOST http://localhost:11434

14. Launch AI Town

Finally, launch AI Town:

npm run dev

Visit http://localhost:5173 in your browser to see AI Town in action.

Relaunching AI Town on windows WSL :

If you need to restart the services:

  1. Ensure socat is running:

    socat TCP-LISTEN:11434,fork TCP:$(cat /etc/resolv.conf | grep nameserver | awk '{print $2}'):11434 &

  2. Launch Convex:

    ./convex-local-backend

In another terminal : 3. Launch AI Town:

npm run dev

Customize your own simulation

NOTE: every time you change character data, you should re-run just convex run testing:wipeAllTables and then npm run dev to re-upload everything to Convex. This is because character data is sent to Convex on the initial load. However, beware that just convex run testing:wipeAllTables WILL wipe all of your data.

  1. Create your own characters and stories: All characters and stories, as well as their spritesheet references are stored in characters.ts. You can start by changing character descriptions.

  2. Updating spritesheets: in data/characters.ts, you will see this code:

export const characters = [
  {
    name: 'f1',
    textureUrl: '/assets/32x32folk.png',
    spritesheetData: f1SpritesheetData,
    speed: 0.1,
  },
  ...
];

You should find a sprite sheet for your character, and define sprite motion / assets in the corresponding file (in the above example, f1SpritesheetData was defined in f1.ts)

  1. Update the Background (Environment): The map gets loaded in convex/init.ts from data/gentle.js. To update the map, follow these steps:

    • Use Tiled to export tilemaps as a JSON file (2 layers named bgtiles and objmap)
    • Use the convertMap.js script to convert the JSON to a format that the engine can use.
node data/convertMap.js <mapDataPath> <assetPath> <tilesetpxw> <tilesetpxh>
  • <mapDataPath>: Path to the Tiled JSON file.
  • <assetPath>: Path to tileset images.
  • <tilesetpxw>: Tileset width in pixels.
  • <tilesetpxh>: Tileset height in pixels. Generates converted-map.js that you can use like gentle.js
  1. Change the background music by modifying the prompt in convex/music.ts
  2. Change how often to generate new music at convex/crons.ts by modifying the generate new background music job

Using a cloud AI Provider

Configure convex/util/llm.ts or set these env variables:

just convex env set LLM_API_HOST # url
just convex env set LLM_MODEL # model

The embeddings model config needs to be changed in code, since you need to specify the embeddings dimension.

Keys

For Together.ai, visit https://api.together.xyz/settings/api-keys For OpenAI, visit https://platform.openai.com/account/api-keys

Using hosted Convex

You can run your Convex backend in the cloud by just running

npx convex dev --until-success --configure

And updating the package.json scripts to remove just: change just convex ... to convex ....

You'll then need to set any environment variables you had locally in the cloud environment with npx convex env set or on the dashboard: https://dashboard.convex.dev/deployment/settings/environment-variables

Deploy the app

Deploy Convex functions to prod environment

Before you can run the app, you will need to make sure the Convex functions are deployed to its production environment.

  1. Run npx convex deploy to deploy the convex functions to production
  2. Run npx convex run init --prod

If you have existing data you want to clear, you can run npx convex run testing:wipeAllTables --prod

Adding Auth (Optional)

You can add clerk auth back in with git revert b44a436. Or just look at that diff for what changed to remove it.

Make a Clerk account

  • Go to https://dashboard.clerk.com/ and click on "Add Application"
  • Name your application and select the sign-in providers you would like to offer users
  • Create Application
  • Add VITE_CLERK_PUBLISHABLE_KEY and CLERK_SECRET_KEY to .env.local
VITE_CLERK_PUBLISHABLE_KEY=pk_***
CLERK_SECRET_KEY=sk_***
  • Go to JWT Templates and create a new Convex Template.
  • Copy the JWKS endpoint URL for use below.
just convex env set CLERK_ISSUER_URL # e.g. https://your-issuer-url.clerk.accounts.dev/

Deploy to Vercel

  • Register an account on Vercel and then install the Vercel CLI.
  • If you are using Github Codespaces: You will need to install the Vercel CLI and authenticate from your codespaces cli by running vercel login.
  • Deploy the app to Vercel with vercel --prod.

Using local inference from a cloud deployment.

We support using Ollama for conversation generations. To have it accessible from the web, you can use Tunnelmole or Ngrok or similar.

Using Tunnelmole

Tunnelmole is an open source tunneling tool.

You can install Tunnelmole using one of the following options:

  • NPM: npm install -g tunnelmole
  • Linux: curl -s https://tunnelmole.com/sh/install-linux.sh | sudo bash
  • Mac: curl -s https://tunnelmole.com/sh/install-mac.sh --output install-mac.sh && sudo bash install-mac.sh
  • Windows: Install with NPM, or if you don't have NodeJS installed, download the exe file for Windows here and put it somewhere in your PATH.

Once Tunnelmole is installed, run the following command:

tmole 11434

Tunnelmole should output a unique url once you run this command.

Using Ngrok

Ngrok is a popular closed source tunneling tool.

Once ngrok is installed and authenticated, run the following command:

ngrok http http://localhost:11434

Ngrok should output a unique url once you run this command.

Add Ollama endpoint to Convex

just convex env set OLLAMA_HOST # your tunnelmole/ngrok unique url from the previous step

Update Ollama domains

Ollama has a list of accepted domains. Add the ngrok domain so it won't reject traffic. see ollama.ai for more details.

Credits

🧑‍🏫 What is Convex?

Convex is a hosted backend platform with a built-in database that lets you write your database schema and server functions in TypeScript. Server-side database queries automatically cache and subscribe to data, powering a realtime useQuery hook in our React client. There are also clients for Python, Rust, ReactNative, and Node, as well as a straightforward HTTP API.

The database supports NoSQL-style documents with opt-in schema validation, relationships and custom indexes (including on fields in nested objects).

The query and mutation server functions have transactional, low latency access to the database and leverage our v8 runtime with determinism guardrails to provide the strongest ACID guarantees on the market: immediate consistency, serializable isolation, and automatic conflict resolution via optimistic multi-version concurrency control (OCC / MVCC).

The action server functions have access to external APIs and enable other side-effects and non-determinism in either our optimized v8 runtime or a more flexible node runtime.

Functions can run in the background via scheduling and cron jobs.

Development is cloud-first, with hot reloads for server function editing via the CLI, preview deployments, logging and exception reporting integrations, There is a dashboard UI to browse and edit data, edit environment variables, view logs, run server functions, and more.

There are built-in features for reactive pagination, file storage, reactive text search, vector search, https endpoints (for webhooks), snapshot import/export, streaming import/export, and runtime validation for function arguments and database data.

Everything scales automatically, and it’s free to start.