Welcome to the big-AGI Installation Guide - Whether you're a developer eager to explore, a system integrator, or an enterprise looking for a white-label solution, this comprehensive guide ensures a smooth setup process for your own instance of big-AGI and related products.
Try big-AGI - You don't need to install anything if you want to play with big-AGI
and have your API keys to various model services. You can access our free instance on big-AGI.com.
The free instance runs the latest main-stable
branch from this repository.
If you want to change the code, have a deeper configuration, add your own models, or run your own instance, follow the steps below.
Prerequisites:
- Node.js and npm installed on your machine.
Steps:
- Clone the big-AGI repository:
git clone https://github.com/enricoros/big-AGI.git cd big-AGI
- Install dependencies:
npm install
- Run the development server:
Your big-AGI instance is now running at
npm run dev
http://localhost:3000
.
The production build is optimized for performance and follows the same steps 1 and 2 as for local development.
- Build the production version:
# .. repeat the steps above up to `npm install`, then: npm run build
- Start the production server:
Your big-AGI production instance is on
next start --port 3000
http://localhost:3000
.
Want to pre-enable models, customize the interface, or deploy with username/password or alter code to your needs? Check out the Customizations Guide for detailed instructions.
To deploy big-AGI on a public server, you have several options. Choose the one that best fits your needs.
Install big-AGI on Vercel with just a few clicks.
Create your GitHub fork, create a Vercel project over that fork, and deploy it. Or press the button below for convenience.
Deploy on Cloudflare's global network by installing big-AGI on Cloudflare Pages. Check out the Cloudflare Installation Guide for step-by-step instructions.
Containerize your big-AGI installation using Docker for portability and scalability. Our Docker Deployment Guide will walk you through the process, or follow the steps below for a quick start.
- (optional) Build the Docker image - if you do not want to use the pre-built Docker images:
docker build -t big-agi .
- Run the Docker container with either:
Access your big-AGI instance at
# 2A. if you built the image yourself: docker run -d -p 3000:3000 big-agi # 2B. or use the pre-built image: docker run -d -p 3000:3000 ghcr.io/enricoros/big-agi # 2C. or use docker-compose: docker-compose up
http://localhost:3000
.
Follow the instructions found on Midori AI Subsystem Site for your host OS. After completing the setup process, install the Big-AGI docker backend to the Midori AI Subsystem.
For businesses seeking a fully-managed, scalable solution, consider our managed installations. Enjoy all the features of big-AGI without the hassle of infrastructure management. hello@big-agi.com to learn more.
Join our vibrant community of developers, researchers, and AI enthusiasts. Share your projects, get help, and collaborate with others.
For any questions or inquiries, please don't hesitate to reach out to our team.