Non-functional Requirements

• Tools or components that improve knowledge graph quality (e.g., structure, accuracy, interpretability, or query-ability) in the context of their utility for neuro-symbolic AI systems.
• Modular and reusable code that can be easily adopted by other developers or integrated into broader systems.
• Open data formats (e.g., JSON, MeTTa S-expressions, or common graph formats).
• Reasonable performance for prototyping (e.g., works on medium-sized graphs with hundreds of thousands of nodes/edges on standard hardware).
• Developer documentation and example usage.

• Support for graph enrichment features like typed edges, edge weights, timestamps, or confidence scores.
• Evaluation or benchmarking tools to assess graph quality, utility, or reasoning effectiveness.
• Partial or planned support for larger-scale graphs (e.g., >10M nodes/edges).
• APIs or scripts for batch processing and integration with other tools (e.g., extract-transform-load workflows).

• Integration with the MeTTa language or compatibility with MORK (e.g., exporting/importing graph data, or using MORK as a backend).
• Interactive or semantic query tools for exploring or testing knowledge graphs.
• Streaming or dynamic update support (add/remove/refine nodes/edges on the fly).
• Tools for formatting graphs for LLM consumption or hybrid symbolic-LLM workflows.
• Containerized deployment, REST interface, or CLI for easy testing and reuse.