OpenAdapt: AI-First Process Automation with Large Multimodal Models (LMMs).

OpenAdapt is the open source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs).

Enormous volumes of mental labor are wasted on repetitive GUI workflows.

Foundation Models (e.g. GPT-4, ACT-1) are powerful automation tools.

OpenAdapt connects Foundation Models to GUIs:

Early demo: https://www.loom.com/share/9d77eb7028f34f7f87c6661fb758d1c0 (more coming soon!)

(Slides)

Welcome to OpenAdapt! This Python library implements AI-First Process Automation with the power of Large Multimodal Modals (LMMs) by:

Recording screenshots and associated user input
Aggregating and visualizing user input and recordings for development
Converting screenshots and user input into tokenized format
Generating synthetic input via transformer model completions
Generating task trees by analyzing recordings (work-in-progress)
Replaying synthetic input to complete tasks (work-in-progress)

The goal is similar to that of Robotic Process Automation, except that we use Large Multimodal Models instead of conventional RPA tools.

The direction is adjacent to Adept.ai, with some key differences:

OpenAdapt is model agnostic
OpenAdapt generates prompts automatically (auto-prompted, not user-prompted)
OpenAdapt works with all types of desktop GUIs, including virtualized (e.g. Citrix) and web
OpenAdapt is open source (MIT license)

Features

State-of-the-art GUI understanding via Segment Anything in High Quality:

Industry leading privacy (PII/PHI scrubbing) via AWS Comprehend, Microsoft Presidio and Private AI:

Decentralized and secure data distribution via Magic Wormhole:

Detailed performance monitoring via pympler and tracemalloc:

And much more!

🚀 Open Contract Positions at OpenAdapt.AI

We are thrilled to open new contract positions for developers passionate about pushing boundaries in technology. If you're ready to make a significant impact, consider the following roles:

Frontend Developer

Responsibilities: Develop and test key features such as process visualization, demo booking, app store, and blog integration.
Skills: Proficiency in modern frontend technologies and a knack for UI/UX design.

Machine Learning Engineer

Role: Implement and refine process replay strategies using state-of-the-art LLMs/LMMs. Extract dynamic process descriptions from extensive process recordings.
Skills: Strong background in machine learning, experience with LLMs/LMMs, and problem-solving aptitude.

Software Engineer

Focus: Enhance memory optimization techniques during process recording and replay. Develop sophisticated tools for process observation and productivity measurement.
Skills: Expertise in software optimization, memory management, and analytics.

Technical Writer

Focus: Maintaining OpenAdapt repositories
Skills: Passion for writing and/or documentation

🔍 How to Apply

Step 1: Submit an empty Pull Request to OpenAdapt or OpenAdapt.web. Format your PR title as [Proposal] <your title here>
Step 2: Include a brief, informal outline of your approach in the PR description. Feel free to add any questions you might have.
Need Clarifications? Reach out to us on Discord.

We're looking forward to your contributions. Let's build the future 🚀

Install

Installation Method	Recommended for	Ease of Use
Scripted	Non-technical users	Streamlines the installation process for users unfamiliar with setup steps
Manual	Technical Users	Allows for more control and customization during the installation process

Installation Scripts

Windows

Press Windows Key, type "powershell", and press Enter

Copy and paste the following command into the terminal, and press Enter (If Prompted for User Account Control, click 'Yes'):

 Start-Process powershell -Verb RunAs -ArgumentList '-NoExit', '-ExecutionPolicy', 'Bypass', '-Command', "iwr -UseBasicParsing -Uri 'https://raw.githubusercontent.com/OpenAdaptAI/OpenAdapt/main/install/install_openadapt.ps1' | Invoke-Expression"

MacOS

Download and install Git and Python 3.10
Press Command+Space, type "terminal", and press Enter

Copy and paste the following command into the terminal, and press Enter:

 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/OpenAdaptAI/OpenAdapt/HEAD/install/install_openadapt.sh)"

Manual Setup

Prerequisite:

Python 3.10
Git
Tesseract (for OCR)

For the setup of any/all of the above dependencies, follow the steps SETUP.md.

Install with Poetry :

git clone https://github.com/OpenAdaptAI/OpenAdapt.git
cd OpenAdapt
pip3 install poetry
poetry install
poetry shell
alembic upgrade head
pytest

Permissions

See how to set up system permissions on macOS here.

Run

Record

Create a new recording by running the following command:

python -m openadapt.record "testing out openadapt"

Wait until all three event writers have started:

| INFO     | __mp_main__:write_events:230 - event_type='screen' starting
| INFO     | __mp_main__:write_events:230 - event_type='action' starting
| INFO     | __mp_main__:write_events:230 - event_type='window' starting

Type a few words into the terminal and move your mouse around the screen to generate some events, then stop the recording by pressing CTRL+C.

Current limitations:

recording should be short (i.e. under a minute), as they are somewhat memory intensive, and there is currently an open issue describing a possible memory leak
the only touchpad and trackpad gestures currently supported are pointing the cursor and left or right clicking, as described in this open issue

Visualize

Visualize the latest recording you created by running the following command:

python -m openadapt.visualize

This will open a scrollable window that looks something like this:

For an alternative visualization, run:

python -m openadapt.deprecated.visualize

This will open up a tab in your browser that looks something like this:

Playback

You can play back the recording using the following command:

python -m openadapt.replay NaiveReplayStrategy

Other replay strategies include:

StatefulReplayStrategy: Proof-of-concept which uses the OpenAI GPT-4 API with prompts constructed via OS-level window data.

See https://github.com/OpenAdaptAI/OpenAdapt/tree/main/openadapt/strategies for a complete list. More ReplayStrategies coming soon! (see Contributing).

Contributing

Problem Statement

Our goal is to automate the task described and demonstrated in a Recording. That is, given a new Screenshot, we want to generate the appropriate ActionEvent(s) based on the previously recorded ActionEvents in order to accomplish the task specified in the Recording.task_description, while accounting for differences in screen resolution, window size, application behavior, etc.

If it's not clear what ActionEvent is appropriate for the given Screenshot, (e.g. if the GUI application is behaving in a way we haven't seen before), we can ask the user to take over temporarily to demonstrate the appropriate course of action.

Data Model

The data model consists of the following entities:

Recording: Contains information about the screen dimensions, platform, and other metadata.
ActionEvent: Represents a user action event such as a mouse click or key press. Each ActionEvent has an associated Screenshot taken immediately before the event occurred. ActionEvents are aggregated to remove unnecessary events (see visualize.)
Screenshot: Contains the PNG data of a screenshot taken during the recording.
WindowEvent: Represents a window event such as a change in window title, position, or size.

API

You can assume that you have access to the following functions:

create_recording("doing taxes"): Creates a recording.
get_latest_recording(): Gets the latest recording.
get_events(recording): Returns a list of ActionEvent objects for the given recording.

See GitBook Documentation for more.

Instructions

Join us on Discord. Then:

Fork this repository and clone it to your local machine.
Get OpenAdapt up and running by following the instructions under Setup.
Look through the list of open issues at https://github.com/OpenAdaptAI/OpenAdapt/issues and once you find one you would like to address, indicate your interest with a comment.
Implement a solution to the issue you selected. Write unit tests for your implementation.
Submit a Pull Request (PR) to this repository. Note: submitting a PR before your implementation is complete (e.g. with high level documentation and/or implementation stubs) is encouraged, as it provides us with the opportunity to provide early feedback and iterate on the approach.

Evaluation Criteria

Your submission will be evaluated based on the following criteria:

Functionality : Your implementation should correctly generate the new ActionEvent objects that can be replayed in order to accomplish the task in the original recording.
Code Quality : Your code should be well-structured, clean, and easy to understand.
Scalability : Your solution should be efficient and scale well with large datasets.
Testing : Your tests should cover various edge cases and scenarios to ensure the correctness of your implementation.

Submission

Commit your changes to your forked repository.
Create a pull request to the original repository with your changes.
In your pull request, include a brief summary of your approach, any assumptions you made, and how you integrated external libraries.
Bonus: interacting with ChatGPT and/or other language transformer models in order to generate code and/or evaluate design decisions is encouraged. If you choose to do so, please include the full transcript.

Troubleshooting

MacOS: if you encounter system alert messages or find issues when making and replaying recordings, make sure to set up permissions accordingly.

In summary (from https://stackoverflow.com/a/69673312):

Settings -> Security & Privacy
Click on the Privacy tab
Scroll and click on the Accessibility Row
Click +
Navigate to /System/Applications/Utilities/ (or wherever Terminal.app is installed)
Click okay.

Developing

Generate migration (after editing a model)

alembic revision --autogenerate -m "<msg>"

Pre-commit Hooks

To ensure code quality and consistency, OpenAdapt uses pre-commit hooks. These hooks will be executed automatically before each commit to perform various checks and validations on your codebase.

The following pre-commit hooks are used in OpenAdapt:

check-yaml: Validates the syntax and structure of YAML files.
end-of-file-fixer: Ensures that files end with a newline character.
trailing-whitespace: Detects and removes trailing whitespace at the end of lines.
black: Formats Python code to adhere to the Black code style. Notably, the --preview feature is used.
isort: Sorts Python import statements in a consistent and standardized manner.

To set up the pre-commit hooks, follow these steps:

Navigate to the root directory of your OpenAdapt repository.
Run the following command to install the hooks:

pre-commit install

Now, the pre-commit hooks are installed and will run automatically before each commit. They will enforce code quality standards and prevent committing code that doesn't pass the defined checks.

Status Checks

When you submit a PR, the "Python CI" workflow is triggered for code consistency. It follows organized steps to review your code:

Python Black Check : This step verifies code formatting using Python Black style, with the --preview flag for style.
Flake8 Review : Next, Flake8 tool thoroughly checks code structure, including flake8-annotations and flake8-docstrings. Though GitHub Actions automates checks, it's wise to locally run flake8 . before finalizing changes for quicker issue spotting and resolution.

Submitting an Issue

Please submit any issues to https://github.com/OpenAdaptAI/OpenAdapt/issues with the following information:

Problem description (please include any relevant console output and/or screenshots)
Steps to reproduce (please help others to help you!)

juvu/OpenAdapt