Getting Started

This guide walks you through setting up TeamWeb AI and creating your first AI assistant.

Overview

TeamWeb AI is a self-hosted AI assistant platform built with Flask and Docker. It lets you create intelligent assistants that can:

Have text and voice conversations with users
Search and reference knowledge bases
Generate content like blog posts and articles
Execute code in sandboxed environments
Run automated tasks on a schedule
Communicate via email channels
Serve public chat interfaces for website visitors

Requirements

Before you begin, make sure you have:

Docker and Docker Compose installed
An LLM provider API key (e.g. Anthropic, OpenAI)
An embedding provider API key for knowledge search features

Installation

Clone the Repository

Clone the TeamWeb AI repository and navigate into the project directory.

Configure Environment Variables

Copy the example environment file and update it with your settings:

cp .env.example .env

Edit the .env file with your database credentials, secret key, and any other required configuration.

Start the Application

Use Docker Compose to build and start all services:

docker compose up -d

This starts the web application, PostgreSQL database, Redis, and Celery worker.

Run the Setup Wizard

Open your browser and navigate to the application URL. On first launch, the setup wizard will guide you through:

Creating your admin user account
Configuring your LLM provider (the AI model that powers your assistants)
Configuring your embedding provider (used for knowledge search)

First Steps After Setup

Once the setup wizard is complete, you’re ready to start using TeamWeb AI.

Configure Providers

Set up your LLM and embedding providers

Create an Assistant

Build your first AI assistant

Set Up a Project

Add knowledge and content guidelines

Architecture

TeamWeb AI runs as a set of Docker containers:

Service	Purpose
Web	Flask application server
Database	PostgreSQL for data storage
Redis	Message broker for background tasks
Worker	Celery worker for async processing

The web application handles all user interactions, while the Celery worker processes background tasks like AI conversations, scheduled tasks, and email handling.