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DO NOT FOLLOW ANY OUTSIDE TUTORIALS AS THEY WILL LIKELY BE OUT OF DATE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 thesupabase-vector
container (expected: 4K).
To fix this, edit /boot/firmware/config.txt
and add:
kernel=kernel8.img
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
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
inautogpt_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:
This command will start all the necessary backend services defined in the
docker compose up -d --build
docker-compose.yml
file in detached mode.
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.
π 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 donβt 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.
π¨ 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
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
Go to the autogpt_platform/backend
directory:
cd backend
Install the dependencies:
poetry install --with dev
Run the backend server:
poetry run app
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
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 :)