Worked on the topoteretes/cognee repository to enhance knowledge graph generation by implementing robust node handling in Python. Focused on backend development and data modeling, the work enforced that each Knowledge Graph node prompt includes a required 'name' field, introducing a fallback mechanism to ensure compatibility with local models. Additionally, a default initialization for the node 'name' field was added to the schema, preventing errors when the field is omitted. These changes improved the stability and scalability of knowledge graph generation across different configurations, addressing both feature development and bug fixes while leveraging AI integration and strong Python programming skills.