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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:

<img width="1499" alt="image" src="https://github.com/OpenAdaptAI/OpenAdapt/assets/774615/c811654e-3450-42cd-91ee-935378e3a858"> <img width="1511" alt="image" src="https://github.com/OpenAdaptAI/OpenAdapt/assets/774615/82814cdb-f0d5-4a6b-9d44-a4628fca1590">

Early demos (more coming soon!):

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

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:

  1. OpenAdapt is model agnostic.
  2. OpenAdapt generates prompts automatically by learning from human demonstration (auto-prompted, not user-prompted). This means that agents are grounded in existing processes, which mitigates hallucinations and ensures successful task completion.
  3. OpenAdapt works with all types of desktop GUIs, including virtualized (e.g. Citrix) and web.
  4. OpenAdapt is open source (MIT license).

Install

<br/>
Installation MethodRecommended forEase of Use
ScriptedNon-technical usersStreamlines the installation process for users unfamiliar with setup steps
ManualTechnical UsersAllows for more control and customization during the installation process
<br/>

Installation Scripts

Windows

MacOS

<br/>

Manual Setup

Prerequisite:

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

<br/>

Install with Poetry :

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

Permissions

See how to set up system permissions on macOS here.

Usage

Shell

Run this in every new terminal window once (while inside the OpenAdapt root directory) before running any openadapt commands below:

poetry shell

You should see the something like this:

% poetry shell
Using python3.10 (3.10.13)
...
(openadapt-py3.10) %

Notice the environment prefix (openadapt-py3.10).

Tray

Run the following command to start the system tray icon and launch the web dashboard:

python -m openadapt.entrypoint

This command will print the config, update the database to the latest migration, start the system tray icon and launch the web dashboard.

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:

Visualize

Quickly visualize the latest recording you created by running the following command:

python -m openadapt.visualize

This will generate an HTML file and open a tab in your browser that looks something like this:

image

For a more powerful dashboard, run:

python -m openadapt.app.dashboard.run

This will start a web server locally, and then open a tab in your browser that looks something like this:

image

Playback

You can play back the recording using the following command:

python -m openadapt.replay NaiveReplayStrategy

Other replay strategies include:

The (*) prefix indicates strategies which accept an "instructions" parameter that is used to modify the recording, e.g.:

python -m openadapt.replay VanillaReplayStrategy --instructions "calculate 9-8"

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

Browser integration

To record browser events in Google Chrome (required by the BrowserReplayStrategy), follow these steps:

  1. Go to your Chrome extensions page by entering chrome://extensions in your address bar.

  2. Enable Developer mode (located at the top right).

  3. Click Load unpacked (located at the top left).

  4. Select the chrome_extension directory in the OpenAdapt repo.

  5. Make sure the Chrome extension is enabled (the switch to the right of the OpenAdapt extension widget is turned on).

  6. Set the RECORD_BROWSER_EVENTS flag to true in openadapt/data/config.json.

Features

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

image

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

image

Decentralized and secure data distribution via Magic Wormhole:

image

Detailed performance monitoring via pympler and tracemalloc:

image

System Tray Icon and Client GUI App (work-in-progress)

<img width="661" alt="image" src="https://github.com/OpenAdaptAI/OpenAdapt/assets/774615/601b3a9f-ff16-45e0-a302-39257b06e382">

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

Machine Learning Engineer

Software Engineer

Technical Writer

🔍 How to Apply

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

Contributing

Replay 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 and narrated by the user in AudioInfo.words_with_timestamps, 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:

  1. Recording: Contains information about the screen dimensions, platform, and other metadata.
  2. 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.)
  3. Screenshot: Contains the PNG data of a screenshot taken during the recording.
  4. 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:

See GitBook Documentation for more.

Instructions

Join us on Discord. Then:

  1. Fork this repository and clone it to your local machine.
  2. Get OpenAdapt up and running by following the instructions under Setup.
  3. 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.
  4. Implement a solution to the issue you selected. Write unit tests for your implementation.
  5. 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:

  1. Functionality : Your implementation should correctly generate the new ActionEvent objects that can be replayed in order to accomplish the task in the original recording.

  2. Code Quality : Your code should be well-structured, clean, and easy to understand.

  3. Scalability : Your solution should be efficient and scale well with large datasets.

  4. Testing : Your tests should cover various edge cases and scenarios to ensure the correctness of your implementation.

Submission

  1. Commit your changes to your forked repository.

  2. Create a pull request to the original repository with your changes.

  3. In your pull request, include a brief summary of your approach, any assumptions you made, and how you integrated external libraries.

  4. 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.

MacOS System Alerts

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

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

Developing

Generate migration (after editing a model)

From inside the openadapt directory (containing alembic.ini):

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:

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

  1. Navigate to the root directory of your OpenAdapt repository.

  2. 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:

  1. Python Black Check : This step verifies code formatting using Python Black style, with the --preview flag for style.

  2. 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: