Read our Architecture document
Join the Discussion on the Request for Comments
See also:
- https://github.com/OpenAdaptAI/SoM
- https://github.com/OpenAdaptAI/pynput
- https://github.com/OpenAdaptAI/atomacos
OpenAdapt is the open source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs).
Early demos (more coming soon!):
- https://twitter.com/abrichr/status/1784307190062342237
- https://www.loom.com/share/9d77eb7028f34f7f87c6661fb758d1c0
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 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.
- OpenAdapt works with all types of desktop GUIs, including virtualized (e.g. Citrix) and web.
- OpenAdapt is open source (MIT license).
| 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 |
- 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"
- 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)"
Prerequisite:
- Python 3.10
- Git
- Tesseract (for OCR)
- nvm (node version manager)
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
poetry run postinstall
cd openadapt && alembic upgrade head && cd ..
pytest
See how to set up system permissions on macOS here.
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).
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.
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
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:
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:
For a desktop app-based visualization, run:
python -m openadapt.app.visualize
This will open a scrollable window that looks something like this:
You can play back the recording using the following command:
python -m openadapt.replay NaiveReplayStrategy
Other replay strategies include:
StatefulReplayStrategy: Early proof-of-concept which uses the OpenAI GPT-4 API with prompts constructed via OS-level window data.- (*)
VisualReplayStrategy: Uses Fast Segment Anything Model (FastSAM) to segment active window. - (*)
VanillaReplayStrategy: Assumes the model is capable of directly reasoning on states and actions accurately. With future frontier models, we hope that this script will suddenly work a lot better.
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).
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:
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:
- 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.
- 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.
- 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.
- Focus: Maintaining OpenAdapt repositories
- Skills: Passion for writing and/or documentation
- 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 🚀
Notable Works-in-progress (incomplete, see https://github.com/OpenAdaptAI/OpenAdapt/pulls and https://github.com/OpenAdaptAI/OpenAdapt/issues/ for more)
- Video Recording Hardware Acceleration (help wanted)
- Audio Narration (help wanted)
- Chrome Extension (help wanted)
- Gemini Vision (help wanted)
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.
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. EachActionEventhas an associatedScreenshottaken 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.
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 ofActionEventobjects for the given recording.
See GitBook Documentation for more.
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.
Your submission will be evaluated based on the following criteria:
-
Functionality : Your implementation should correctly generate the new
ActionEventobjects 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.
-
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.
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.
From inside the openadapt directory (containing alembic.ini):
alembic revision --autogenerate -m "<msg>"
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
--previewfeature 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.
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
--previewflag 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.
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!)
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