This repo hosts a package humemai, a human-like memory systems that are modeled with knowledge knoweldge graphs (KGs). At the moment they are nothing but a Python list of quadruples, but soon it'll be a better object type so that they can be compatible with graph databases, e.g., RDFLib, GraphDB, Neo4j, etc. There have been both academic papers and applications that have used this package.

• "A Machine With Human-Like Memory Systems".
• "A Machine with Short-Term, Episodic, and Semantic Memory Systems".
• "Capturing Dynamic Knowledge Graphs with Human-like Memory Systems by Reinforcement Learning".

Click on this link to see the HTML rendered docstrings

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Run make test && make style && make quality in the root repo directory, to ensure code quality.
4. Commit your Changes (git commit -m 'Add some AmazingFeature')
5. Push to the Branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

MIT