Getting Started with AutoGPT: Self-Hosting Guide¶

Introduction¶

This guide will help you setup the server and builder for the project.

Warning

DO NOT FOLLOW ANY OUTSIDE TUTORIALS AS THEY WILL LIKELY BE OUT OF DATE

Prerequisites¶

To setup the server, you need to have the following installed:

Checking if you have Node.js & NPM installed¶

We use Node.js to run our frontend application.

If you need assistance installing Node.js:
https://nodejs.org/en/download/

NPM is included with Node.js, but if you need assistance installing NPM: https://docs.npmjs.com/downloading-and-installing-node-js-and-npm

You can check if you have Node.js & NPM installed by running the following command:

node -v
npm -v

Once you have Node.js installed, you can proceed to the next step.

Checking if you have Docker & Docker Compose installed¶

Docker containerizes applications, while Docker Compose orchestrates multi-container Docker applications.

If you need assistance installing docker: https://docs.docker.com/desktop/

Docker-compose is included in Docker Desktop, but if you need assistance installing docker compose: https://docs.docker.com/compose/install/

You can check if you have Docker installed by running the following command:

docker -v
docker compose -v

Once you have Docker and Docker Compose installed, you can proceed to the next step.

Raspberry Pi 5 Specific Notes

On Raspberry Pi 5 with Raspberry Pi OS, the default 16K page size will cause issues with the supabase-vector container (expected: 4K).
To fix this, edit /boot/firmware/config.txt and add:

kernel=kernel8.img

Then reboot. You can check your page size with:

getconf PAGESIZE

16384 means 16K (incorrect), and 4096 means 4K (correct). After adjusting, docker compose up -d --build should work normally.
See supabase/supabase #33816 for additional context.

Quick Setup with Auto Setup Script (Recommended)¶

If you're self-hosting AutoGPT locally, we recommend using our official setup script to simplify the process. This will install dependencies (like Docker), pull the latest code, and launch the app with minimal effort.

For macOS/Linux:

curl -fsSL https://setup.agpt.co/install.sh -o install.sh && bash install.sh

For Windows (PowerShell):

powershell -c "iwr https://setup.agpt.co/install.bat -o install.bat; ./install.bat"

This method is ideal if you're setting up for development or testing and want to skip manual configuration.

Manual Setup¶

Cloning the Repository¶

The first step is cloning the AutoGPT repository to your computer. To do this, open a terminal window in a folder on your computer and run:

git clone https://github.com/Significant-Gravitas/AutoGPT.git

If you get stuck, follow this guide.

Once that's complete you can continue the setup process.

Running the AutoGPT Platform¶

To run the platform, follow these steps:

Navigate to the autogpt_platform directory inside the AutoGPT folder:
```
 cd AutoGPT/autogpt_platform
```
Copy the .env.default file to .env in autogpt_platform:

 cp .env.default .env

This command will copy the .env.default file to .env in the autogpt_platform directory. You can modify the .env file to add your own environment variables.

Run the platform services:
```
 docker compose up -d --build
```
This command will start all the necessary backend services defined in the docker-compose.yml file in detached mode.

🛠️ Using the Makefile for Common Tasks¶

The repository includes a Makefile with helpful commands to streamline setup and development. You may use make commands as an alternative to calling Docker or scripts directly.

Most-used Makefile commands¶

Inside the autogpt_platform directory, you can use:

Command	What it Does
`make start-core`	Start just the core services (Supabase, Redis, RabbitMQ) in background
`make stop-core`	Stop the core services
`make logs-core`	Tail the logs for core services
`make format`	Format & lint backend (Python) and frontend (TypeScript) code
`make migrate`	Run backend database migrations
`make run-backend`	Run the backend FastAPI server
`make run-frontend`	Run the frontend Next.js development server

Example usage:

make start-core
make run-backend
make run-frontend

You can always check available Makefile recipes by running:

make help

(or just inspecting the Makefile in the repo root).

Checking if the application is running¶

You can check if the server is running by visiting http://localhost:3000 in your browser.

Notes:

By default the application for different services run on the following ports:

Frontend UI Server: 3000 Backend Websocket Server: 8001 Execution API Rest Server: 8006

Additional Notes¶

You may want to change your encryption key in the .env file in the autogpt_platform/backend directory.

To generate a new encryption key, run the following command in python:

from cryptography.fernet import Fernet;Fernet.generate_key().decode()

Or run the following command in the autogpt_platform/backend directory:

poetry run cli gen-encrypt-key

Then, replace the existing key in the autogpt_platform/backend/.env file with the new one.

📌 Windows Installation Note¶

When installing Docker on Windows, it is highly recommended to select WSL 2 instead of Hyper-V. Using Hyper-V can cause compatibility issues with Supabase, leading to the supabase-db container being marked as unhealthy.

Steps to enable WSL 2 for Docker:¶

Install WSL 2.
Ensure that your Docker settings use WSL 2 as the default backend:
Open Docker Desktop.
Navigate to Settings > General.
Check Use the WSL 2 based engine.
Restart Docker Desktop.

Already Installed Docker with Hyper-V?¶

If you initially installed Docker with Hyper-V, you don’t need to reinstall it. You can switch to WSL 2 by following these steps: 1. Open Docker Desktop. 2. Go to Settings > General. 3. Enable Use the WSL 2 based engine. 4. Restart Docker.

🚨 Warning: Enabling WSL 2 may erase your existing containers and build history. If you have important containers, consider backing them up before switching.

For more details, refer to Docker's official documentation.

Development¶

Frontend Development¶

Running the frontend locally¶

To run the frontend locally, you need to have Node.js and PNPM installed on your machine.

Install Node.js to manage dependencies and run the frontend application.

Install PNPM to manage the frontend dependencies.

Run the service dependencies (backend, database, message queues, etc.):

docker compose --profile local up deps_backend --build --detach

Go to the autogpt_platform/frontend directory:

cd frontend

Install the dependencies:

pnpm install

Generate the API client:

pnpm generate:api-client

Run the frontend application:

pnpm dev

Formatting & Linting¶

Auto formatter and linter are set up in the project. To run them:

Format the code:

pnpm format

Lint the code:

pnpm lint

Or for both frontend and backend, from the root:

make format

Testing¶

To run the tests, you can use the following command:

pnpm test

Backend Development¶

Running the backend locally¶

To run the backend locally, you need to have Python 3.10 or higher installed on your machine.

Install Poetry to manage dependencies and virtual environments.

Run the backend dependencies (database, message queues, etc.):

docker compose --profile local up deps --build --detach

Or equivalently with Makefile:

make start-core

Go to the autogpt_platform/backend directory:

cd backend

Install the dependencies:

poetry install --with dev

Run the backend server:

poetry run app

Or from within autogpt_platform:

make run-backend

Formatting & Linting¶

Auto formatter and linter are set up in the project. To run them:

Format the code:

poetry run format

Lint the code:

poetry run lint

Or format both frontend and backend at once:

make format

Testing¶

To run the tests:

poetry run pytest -s

Adding a New Agent Block¶

To add a new agent block, you need to create a new class that inherits from Block and provides the following information: * All the block code should live in the blocks (backend.blocks) module. * input_schema: the schema of the input data, represented by a Pydantic object. * output_schema: the schema of the output data, represented by a Pydantic object. * run method: the main logic of the block. * test_input & test_output: the sample input and output data for the block, which will be used to auto-test the block. * You can mock the functions declared in the block using the test_mock field for your unit tests. * Once you finish creating the block, you can test it by running poetry run pytest backend/blocks/test/test_block.py -s. * Create a Pull Request to the dev branch of the repository with your changes so you can share it with the community :)