AutoGPT Installation and Features

In this post, you can read all you need to start playing around with AutoGPT open-source library.

🚀 Features

  • 🌐 Internet access for searches and information gathering
  • 💾 Long-Term and Short-Term memory management
  • 🧠 GPT-4 instances for text generation
  • 🔗 Access to popular websites and platforms
  • 🗃️ File storage and summarization with GPT-3.5

📋 Requirements

Optional:

  • ElevenLabs Key (If you want the AI to speak)

💾 Installation

To install Auto-GPT, follow these steps:

  1. Make sure you have all the requirements above, if not, install/get them.

The following commands should be executed in a CMD, Bash or Powershell window. To do this, go to a folder on your computer, click in the folder path at the top and type CMD, then press enter.

  1. Clone the repository: For this step you need Git installed, but you can just download the zip file instead by clicking the button at the top of this page ☝️
git clone https://github.com/Torantulino/Auto-GPT.git
  1. Navigate to the project directory: (Type this into your CMD window, you’re aiming to navigate the CMD window to the repository you just downloaded)
cd 'Auto-GPT'
  1. Install the required dependencies: (Again, type this into your CMD window)
pip install -r requirements.txt
  1. Rename .env.template to .env and fill in your OPENAI_API_KEY. If you plan to use Speech Mode, fill in your ELEVEN_LABS_API_KEY as well.
  • Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
  • Obtain your ElevenLabs API key from: https://elevenlabs.io. You can view your xi-api-key using the “Profile” tab on the website.
  • If you want to use GPT on an Azure instance, set USE_AZURE to True and then:
    • Rename azure.yaml.template to azure.yaml and provide the relevant azure_api_baseazure_api_version and all of the deployment ids for the relevant models in the azure_model_map section:
      • fast_llm_model_deployment_id – your gpt-3.5-turbo or gpt-4 deployment id
      • smart_llm_model_deployment_id – your gpt-4 deployment id
      • embedding_model_deployment_id – your text-embedding-ada-002 v2 deployment id
    • Please specify all of these values as double quoted strings
    • details can be found here: https://pypi.org/project/openai/ in the Microsoft Azure Endpoints section and here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line for the embedding model.

🔧 Usage

  1. Run the main.py Python script in your terminal: (Type this into your CMD window)
python scripts/main.py
  1. After each of AUTO-GPT’s actions, type “NEXT COMMAND” to authorise them to continue.
  2. To exit the program, type “exit” and press Enter.

Logs

You will find activity and error logs in the folder ./logs

To output debug logs:

python scripts/main.py --debug

🗣️ Speech Mode

Use this to use TTS for Auto-GPT

python scripts/main.py --speak

🔍 Google API Keys Configuration

This section is optional, use the official google api if you are having issues with error 429 when running a google search. To use the google_official_search command, you need to set up your Google API keys in your environment variables.

  1. Go to the Google Cloud Console.
  2. If you don’t already have an account, create one and log in.
  3. Create a new project by clicking on the “Select a Project” dropdown at the top of the page and clicking “New Project”. Give it a name and click “Create”.
  4. Go to the APIs & Services Dashboard and click “Enable APIs and Services”. Search for “Custom Search API” and click on it, then click “Enable”.
  5. Go to the Credentials page and click “Create Credentials”. Choose “API Key”.
  6. Copy the API key and set it as an environment variable named GOOGLE_API_KEY on your machine. See setting up environment variables below.
  7. Go to the Custom Search Engine page and click “Add”.
  8. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
  9. Once you’ve created your search engine, click on “Control Panel” and then “Basics”. Copy the “Search engine ID” and set it as an environment variable named CUSTOM_SEARCH_ENGINE_ID on your machine. See setting up environment variables below.

Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches.

Setting up environment variables

For Windows Users:

setx GOOGLE_API_KEY "YOUR_GOOGLE_API_KEY"
setx CUSTOM_SEARCH_ENGINE_ID "YOUR_CUSTOM_SEARCH_ENGINE_ID"

For macOS and Linux users:

export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
export CUSTOM_SEARCH_ENGINE_ID="YOUR_CUSTOM_SEARCH_ENGINE_ID"

Redis Setup

Install docker desktop.

Run:

docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest

See https://hub.docker.com/r/redis/redis-stack-server for setting a password and additional configuration.

Set the following environment variables:

MEMORY_BACKEND=redis
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=

Note that this is not intended to be run facing the internet and is not secure, do not expose redis to the internet without a password or at all really.

You can optionally set

WIPE_REDIS_ON_START=False

To persist memory stored in Redis.

You can specify the memory index for redis using the following:

MEMORY_INDEX=whatever

🌲 Pinecone API Key Setup

Pinecone enables the storage of vast amounts of vector-based memory, allowing for only relevant memories to be loaded for the agent at any given time.

  1. Go to pinecone and make an account if you don’t already have one.
  2. Choose the Starter plan to avoid being charged.
  3. Find your API key and region under the default project in the left sidebar.

Setting up environment variables

Simply set them in the .env file.

Alternatively, you can set them from the command line (advanced):

For Windows Users:

setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp

For macOS and Linux users:

export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp

Setting Your Cache Type

By default Auto-GPT is going to use LocalCache instead of redis or Pinecone.

To switch to either, change the MEMORY_BACKEND env variable to the value that you want:

local (default) uses a local JSON cache file pinecone uses the Pinecone.io account you configured in your ENV settings redis will use the redis cache that you configured

View Memory Usage

  1. View memory usage by using the --debug flag 🙂

💀 Continuous Mode ⚠️

Run the AI without user authorisation, 100% automated. Continuous mode is not recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise. Use at your own risk.

  1. Run the main.py Python script in your terminal:
python scripts/main.py --continuous

  1. To exit the program, press Ctrl + C

GPT3.5 ONLY Mode

If you don’t have access to the GPT4 api, this mode will allow you to use Auto-GPT!

python scripts/main.py --gpt3only

It is recommended to use a virtual machine for tasks that require high security measures to prevent any potential harm to the main computer’s system and data.

🖼 Image Generation

By default, Auto-GPT uses DALL-e for image generation. To use Stable Diffusion, a HuggingFace API Token is required.

Once you have a token, set these variables in your .env:

IMAGE_PROVIDER=sd
HUGGINGFACE_API_TOKEN="YOUR_HUGGINGFACE_API_TOKEN"

⚠️ Limitations

This experiment aims to showcase the potential of GPT-4 but comes with some limitations:

  1. Not a polished application or product, just an experiment
  2. May not perform well in complex, real-world business scenarios. In fact, if it actually does, please share your results!
  3. Quite expensive to run, so set and monitor your API key limits with OpenAI!

Run tests

To run tests, run the following command:

python -m unittest discover tests

To run tests and see coverage, run the following command:

coverage run -m unittest discover tests

Run linter

This project uses flake8 for linting. To run the linter, run the following command:

flake8 scripts/ tests/

# Or, if you want to run flake8 with the same configuration as the CI:
flake8 scripts/ tests/ --select E303,W293,W291,W292,E305