PromptMage

simplifies the process of creating and managing LLM workflows as a self-hosted solution.

"PromptMage" is designed to offer an intuitive interface that simplifies the process of creating and managing LLM workflows as a self-hosted solution. It facilitates prompt testing and comparison, and it incorporates version control features to help users track the development of their prompts. Suitable for both small teams and large enterprises, "PromptMage" seeks to improve productivity and foster the practical use of LLM technology.

The approach with "PromptMage" is to provide a pragmatic solution that bridges the current gap in LLM workflow management. We aim to empower developers, researchers, and organizations by making LLM technology more accessible and manageable, thereby supporting the next wave of AI innovations.

• Integrate the prompt playground into your workflow for fast iteration
• Prompts as first-class citizens with version control and collaboration features
• Manual and automatic testing and validation of prompts
• Easy sharing of results with domain experts and stakeholders
• build-in, automatically created API with fastAPI for easy integration and deployment
• Type-hint everything for automatic inference and validation magic

To install promptmage, run the following command:

pip install promptmage 

To use promptmage, run the following command:

promptmage run <path-to-flow>

This will start the promptmage server and run the flow at the given path. You can now access the promptmage interface at http://localhost:8000/gui/.

Have a look at the examples in the examples folder to see how to use promptmage in your application or workflow.

You can find an usage example with docker here: Docker example.

To develop PromptMage, check out the DEVELOPMENT.md file.

Contributing

We welcome contributions from the community! If you're interested in improving PromptMage, you can contribute in the following ways:

• Reporting Bugs: Submit an issue in our repository, providing a detailed description of the problem and steps to reproduce it.
• Feature Requests: Have ideas on how to make FlowForge better? We'd love to hear from you! Please submit an issue, detailing your suggestions.
• Pull Requests: Contributions via pull requests are highly appreciated. Please ensure your code adheres to the coding standards of the project, and submit a pull request with a clear description of your changes.

This project is licensed under the MIT License - see the LICENSE.md file for details.

For any inquiries or further information, feel free to reach out at [email protected].

This project was supported by