self-ai-operating-computer 2.0.12

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Self-Operating Computer Framework

A framework to enable multimodal models to operate a computer.

Version v2.0.5 (Latest) Release Summary

New Features:

• Interactive Model Selection: When running operate without specifying a model, a welcome screen is displayed, followed by an interactive menu to select your desired model.
• Dynamic API Key Prompting: The application now intelligently prompts for required API keys (e.g., OpenAI, Google, Anthropic) only when a model requiring that key is selected and the key is not found in your environment variables or .env file.
• Custom System Prompt: Users can now provide a custom system prompt from a file or an environment variable (CUSTOM_SYSTEM_PROMPT). If the environment variable is set, the option to load from it will be hidden.

Improvements:

• Expanded Google Gemini Support: Added full support for gemini-2.5-pro and gemini-2.5-flash models.
• Enhanced Ollama Integration: Improved handling for Ollama models, including setting http://localhost:11434 as the default host and providing more informative error messages when Ollama models are not found.
• Gemma 3n Model Support: Integrated support for gemma3n, gemma3n:e2b, and gemma3n:e4b models via Ollama.
• Robust Error Handling: Improved error handling for API calls to prevent unexpected fallbacks and provide clearer error messages.

Bug Fixes:

• Resolved an issue where the application would incorrectly prompt for an OpenAI API key when a Google Gemini model was selected.
• Fixed an issue where the application would attempt to use an incorrect model name for gemini-2.5-flash-lite (which is not a valid model name).
• Addressed the "Extra data" JSON parsing error when receiving responses from Gemini models.

Using the same inputs and outputs as a human operator, the model views the screen and decides on a series of mouse and keyboard actions to reach an objective. Released Nov 2023, the Self-Operating Computer Framework was one of the first examples of usiself-ai-operating-computerng a multimodal model to view the screen and operate a computer.

Key Features

• Compatibility: Designed for various multimodal models.
• Expanded Model Support: Now integrated with the latest OpenAI o3, o4-mini, GPT-4.1, GPT-4.1 mini, GPT-4.1 nano, Gemini 2.5 Pro, Gemini 2.5 Flash, and Gemma 3n models (including e2b and e4b variants), alongside existing support for GPT-4o, Claude 3, Qwen-VL, and LLaVa.
• Enhanced Ollama Integration: Improved handling for Ollama models, including default host configuration and more informative error messages.
• Future Plans: Support for additional models.

Demo

https://github.com/malah-code/self-ai-operating-computer/assets/42594239/9e8abc96-c76a-46fb-9b13-03678b3c67e0

Run Self-Operating Computer

Run from PyPI

To run the application by installing it from PyPI, follow these steps:

1. Install the project:

   pip install self-ai-operating-computer
   

2. Run the project:

   operate
   

Run from Source Code

To run the application from your local copy (after making changes), follow these steps:

1. Uninstall previous installations (if any):

   pip uninstall self-ai-operating-computer
   

   Confirm uninstallation when prompted.

2. Install in editable mode: Navigate to the project's root directory (where setup.py is located) and run:

   pip install -e .
   

   This links your local source code to your Python environment, so changes are immediately reflected.

3. Run the project:

   operate
   

   If you run operate without any arguments, a welcome screen will be displayed, followed by an interactive model selection menu. If a model requires an API key that is not found in your environment variables or .env file, you will be prompted to enter it.

Once you select a model, you will be prompted to provide a custom system prompt. This can be loaded from a file or an environment variable.

4. Enter your OpenAI Key: If you don't have one, you can obtain an OpenAI key here. If you need you change your key at a later point, run vim .env to open the .env and replace the old key.

5. Give Terminal app the required permissions: As a last step, the Terminal app will ask for permission for "Screen Recording" and "Accessibility" in the "Security & Privacy" page of Mac's "System Preferences".

Using operate Modes

OpenAI models

The default model for the project is gpt-4o which you can use by simply typing operate. To try running OpenAI's new o1 model, use the command below.

operate -m o1-with-ocr

To experiment with OpenAI's latest gpt-4.1 model, run:

operate -m gpt-4.1

To experiment with OpenAI's latest gpt-4.1 mini model, run:

operate -m gpt-4.1-mini

To experiment with OpenAI's latest gpt-4.1 nano model, run:

operate -m gpt-4.1-nano

To experiment with OpenAI's latest o3 model, run:

operate -m o3

To experiment with OpenAI's latest o4-mini model, run:

operate -m o4-mini

Multimodal Models -m

Try Google's gemini-1.5-pro-latest, gemini-2.5-pro, or gemini-2.5-flash by following the instructions below. Start operate with the Gemini model

operate -m gemini-2.5-pro

Enter your Google AI Studio API key when terminal prompts you for it If you don't have one, you can obtain a key here after setting up your Google AI Studio account. You may also need authorize credentials for a desktop application. It took me a bit of time to get it working, if anyone knows a simpler way, please make a PR.

Try Claude -m claude-3

Use Claude 3 with Vision to see how it stacks up to GPT-4-Vision at operating a computer. Navigate to the Claude dashboard to get an API key and run the command below to try it.

operate -m claude-3

Try qwen -m qwen-vl

Use Qwen-vl with Vision to see how it stacks up to GPT-4-Vision at operating a computer. Navigate to the Qwen dashboard to get an API key and run the command below to try it.

operate -m qwen-vl

Try LLaVa Hosted Through Ollama -m llava

If you wish to experiment with the Self-Operating Computer Framework using LLaVA on your own machine, you can with Ollama!

First, install Ollama on your machine from https://ollama.ai/download.

Once Ollama is installed, pull the LLaVA model:

ollama pull llava

This will download the model on your machine which takes approximately 5 GB of storage.

When Ollama has finished pulling LLaVA, start the server:

ollama serve

That's it! Now start operate and select the LLaVA model:

operate -m llava

Important: Error rates when using LLaVA are very high. This is simply intended to be a base to build off of as local multimodal models improve over time.

Try Gemma 3n Hosted Through Ollama -m gemma3n

If you wish to experiment with the Self-Operating Computer Framework using Gemma 3n on your own machine, you can with Ollama!
Note: Ollama currently only supports MacOS and Linux. Windows now in Preview

First, install Ollama on your machine from https://ollama.ai/download.

Once Ollama is installed, pull the Gemma 3n model:

ollama pull gemma3n

This will download the model on your machine. You can also pull specific versions like ollama pull gemma3n:e2b or ollama pull gemma3n:e4b.

When Ollama has finished pulling Gemma 3n, start the server:

ollama serve

That's it! Now start operate and select the Gemma 3n model. You can specify gemma3n, gemma3n:e2b, or gemma3n:e4b:

operate -m gemma3n:e2b

Learn more about Ollama at its GitHub Repository

Voice Mode --voice

The framework supports voice inputs for the objective. Try voice by following the instructions below. Clone the repo to a directory on your computer:

git clone https://github.com/malah-code/self-ai-operating-computer.git

Cd into directory:

cd self-ai-operating-computer

Install the additional requirements-audio.txt

pip install -r requirements-audio.txt

Install device requirements For mac users:

brew install portaudio

For Linux users:

sudo apt install portaudio19-dev python3-pyaudio

Run with voice mode

operate --voice

Optical Character Recognition Mode -m gpt-4-with-ocr

The Self-Operating Computer Framework now integrates Optical Character Recognition (OCR) capabilities with the gpt-4-with-ocr mode. This mode gives GPT-4 a hash map of clickable elements by coordinates. GPT-4 can decide to click elements by text and then the code references the hash map to get the coordinates for that element GPT-4 wanted to click.

Based on recent tests, OCR performs better than som and vanilla GPT-4 so we made it the default for the project. To use the OCR mode you can simply write:

operate or operate -m gpt-4-with-ocr will also work.

Set-of-Mark Prompting -m gpt-4-with-som

The Self-Operating Computer Framework now supports Set-of-Mark (SoM) Prompting with the gpt-4-with-som command. This new visual prompting method enhances the visual grounding capabilities of large multimodal models.

Learn more about SoM Prompting in the detailed arXiv paper: here.

For this initial version, a simple YOLOv8 model is trained for button detection, and the best.pt file is included under model/weights/. Users are encouraged to swap in their best.pt file to evaluate performance improvements. If your model outperforms the existing one, please contribute by creating a pull request (PR).

Start operate with the SoM model

operate -m gpt-4-with-som

Contributions are Welcomed!:

If you want to contribute yourself, see CONTRIBUTING.md.

Feedback

For any input on improving this project, feel free to reach out to Josh on Twitter.

Join Our Discord Community

For real-time discussions and community support, join our Discord server.

• If you're already a member, join the discussion in #self-ai-operating-computer.
• If you're new, first join our Discord Server.

Follow HyperWriteAI for More Updates

Stay updated with the latest developments:

• Follow HyperWriteAI on Twitter.
• Follow HyperWriteAI on LinkedIn.

Compatibility

• This project is compatible with Mac OS, Windows, and Linux (with X server installed).

OpenAI Rate Limiting Note

The gpt-4o model is required. To unlock access to this model, your account needs to spend at least $5 in API credits. Pre-paying for these credits will unlock access if you haven't already spent the minimum $5.
Learn more here

Supported Models Summary

Here's a summary of all currently supported models and how to run them:

• OpenAI GPT-4o (Default):

  operate
  

• OpenAI o1-with-ocr:

  operate -m o1-with-ocr
  

• OpenAI o3:

  operate -m o3
  

• OpenAI o4-mini:

  operate -m o4-mini
  

• OpenAI gpt-4.1:

  operate -m gpt-4.1
  

• OpenAI gpt-4.1 mini:

  operate -m gpt-4.1-mini
  

• OpenAI gpt-4.1 nano:

  operate -m gpt-4.1-nano
  

• OpenAI gpt-4-with-ocr:

  operate -m gpt-4-with-ocr
  

• OpenAI gpt-4-with-som:

  operate -m gpt-4-with-som
  

• Google Gemini 1.5 Pro (latest):

  operate -m gemini-1.5-pro-latest
  

• Google Gemini 2.5 Pro:

  operate -m gemini-2.5-pro
  

• Google Gemini 2.5 Flash:

  operate -m gemini-2.5-flash
  

• Anthropic Claude 3:

  operate -m claude-3
  

• Qwen-VL:

  operate -m qwen-vl
  

• LLaVa (via Ollama):

  operate -m llava
  

• Gemma 3n (via Ollama):

  operate -m gemma3n
  

• Gemma 3n:e2b (via Ollama):

  operate -m gemma3n:e2b
  

• Gemma 3n:e4b (via Ollama):

  operate -m gemma3n:e4b
  

Release Notes

Version 0.0.X (Latest)

New Features:

• Interactive Model Selection: When running operate without specifying a model, a welcome screen is displayed, followed by an interactive menu to select your desired model.
• Dynamic API Key Prompting: The application now intelligently prompts for required API keys (e.g., OpenAI, Google, Anthropic) only when a model requiring that key is selected and the key is not found in your environment variables or .env file.
• Custom System Prompt: Users can now provide a custom system prompt from a file or an environment variable (CUSTOM_SYSTEM_PROMPT). If the environment variable is set, the option to load from it will be hidden.

Improvements:

• Expanded Google Gemini Support: Added full support for gemini-2.5-pro and gemini-2.5-flash models.
• Enhanced Ollama Integration: Improved handling for Ollama models, including setting http://localhost:11434 as the default host and providing more informative error messages when Ollama models are not found.
• Gemma 3n Model Support: Integrated support for gemma3n, gemma3n:e2b, and gemma3n:e4b models via Ollama.
• Robust Error Handling: Improved error handling for API calls to prevent unexpected fallbacks and provide clearer error messages.

Bug Fixes:

• Resolved an issue where the application would incorrectly prompt for an OpenAI API key when a Google Gemini model was selected.
• Fixed an issue where the application would attempt to use an incorrect model name for gemini-2.5-flash-lite (which is not a valid model name).
• Addressed the "Extra data" JSON parsing error when receiving responses from Gemini models.