Knowledge Graph Kernel for Agent Reasoning

KGK is a modular knowledge graph kernel for AI agents. It connects facts, sources, and context in a graph structure so knowledge remains traceable rather than detached from where it came from.

Complex AI reasoning and retrieval need more than a flat collection of documents or embeddings. Systems need a way to represent connected information while retaining source awareness and contextual relationships.

This project explores a kernel-level approach to organizing knowledge for agents that need to retrieve, reason across, and inspect connected context.

A modular knowledge graph kernel for agent-oriented systems

Structures for connecting facts, sources, and contextual relationships

A traceability-focused foundation for retrieval and reasoning workflows

Open-source implementation for technical review and extension

Built with Neo4j, GraphQL, and SQL. The work centers on knowledge graphs, data modeling, retrieval, and complex reasoning support.