IkigAI-Agent is a minimal viable product (MVP) designed to discover purpose for AI agents within the Autodidactic AI framework. The goal of this project is to provide a simple yet effective way for AI agents to understand their roles and responsibilities, aligning them with their "ikigai" or reason for being.
While the initial focus of IkigAI-Agent is on AI agents within the Autodidactic AI framework, we envision that the concepts and tools developed here could also be valuable for human agents and other AI systems in the future. By exploring the concept of "ikigai" in the context of AI, we aim to contribute to the broader conversation on AI ethics, alignment, and purpose-driven design.
- Discovering purpose: IkigAI-Agent helps AI agents identify their core purpose and align their actions accordingly.
- Framework integration: IkigAI-Agent is designed to integrate seamlessly with the Autodidactic AI framework.
- Scalable design: IkigAI-Agent is built with scalability in mind, allowing it to be extended to support more complex use cases in the future.
To get started with IkigAI-Agent, please follow the installation and usage instructions provided in the documentation.
We welcome contributions to IkigAI-Agent! Whether you're interested in improving the code, adding new features, or providing feedback, your input is valuable to us. Please see the CONTRIBUTING.md file for guidelines on how to contribute to this project.
IkigAI-Agent is released under the MIT License. For more information, please see the LICENSE file.
For questions, comments, or suggestions, please reach out to the IkigAI-Agent team at the OpenAgile Solutions website: [https://www.openagilesolutions.com/]
We would like to thank the Autodidactic AI community for their support and contributions to this project.
- Clone this repository.
- Rename the
.env_templatefile to.envand fill in the required values. - Install the dependencies:
pip install -r requirements.txt(if you have a requirements file). - Run your project.
For more examples and use cases, visit our blog.
- Clone this repository.
- Rename the
.env_templatefile to.envand fill in the required values. - Install the dependencies:
pip install -r requirements.txt(if you have a requirements file). - Run your project.
For more examples and use cases, visit our blog.