Open-source tool to seamlessly connect multiple AI models in repeatable flow.
While a live demo is available for convenience, for the best experience we recommend running the application directly on your local machine.
AI Flow is an open source, user-friendly UI application that empowers you to seamlessly connect multiple AI models together, specifically leveraging the capabilities of ChatGPT. This unique tool paves the way to creating interactive networks of different AI models, fostering a multi-perspective response to various prompts.
Imagine setting up a simple workflow where:
- One node generates a poem
- One node gives a review of the poem
- One node creates a prompt for the cover of the poem
- One node makes the cover with Stable Diffusion
If we want to change the idea of the poem, we can edit the first node, and keep the same flow.
This flow logic can then be applied to different inputs, yielding varied results for similar subject matter.
AI Flow is particularly useful for content creation. By enabling different GPT models to adopt different roles, AI Flow orchestrates the creation and evaluation of digital content, from social media threads to blog articles.
Our primary goal with AI Flow is to democratize access to AI interactions, enabling anyone to create and manage their own AI networks right from their personal computer. In addition, we value your autonomy and control over costs. With AI Flow, you utilize your own OpenAI keys, giving you full authority over your budget. Importantly, the software runs locally on your machine, assuring you of privacy and control over your data and operations.
In a nutshell, AI Flow provides a visual platform for crafting and managing AI-driven workflows, thereby facilitating diverse and dynamic AI interactions.
- 🎨 It offers a drag-and-drop interface to design these workflows
- 📊 Monitors their execution in real-time
- 🗂️ AI models can be conveniently managed and organized
- 💾 Workflows can be exported or imported for sharing or backup purposes
By combining those features we can create different flows.
For a quick local setup, grab the .exe from the repository's releases section.
Before getting started, make sure you have the following dependencies installed on your system:
- Clone the repository:
git clone https://github.com/DahnM20/ai-flow.git - Change to the project directory:
cd ai-flow
- Go to the UI directory:
cd packages/ui - Install dependencies:
npm install
- Go to the backend directory:
cd packages/backend - Install Python dependencies:
poetry install
- Launch poetry shell :
poetry shell - Install the windows requirements in the poetry shell :
pip install -r requirements_windows.txt
- Start the backend server:
cd backend && poetry run python server.py - Start the ui application:
cd ui && npm start - Open your browser and navigate to
http://localhost:3000 - Use the drag-and-drop interface to design your AI workflow
- Connect AI models and define data flow between them
- Click "Run" to execute the AI workflow
- Monitor the execution progress and results in real-time
- Go to the docker directory:
cd ./docker - Update the .yml if needed for the PORTS
- Launch
docker-compose upordocker-compose up -d - Open your browser and navigate to
http://localhost:80 - Use
docker-compose stopwhen you want to stop the app.
Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.