Memory Graph Library for AI Agents

A memory-management library for AI agents that represents information as connected graph data. The project is designed to make agent context more structured, navigable, and useful across longer-running tasks.

Agent systems can lose context when information is stored as isolated snippets or transient chat history. Important relationships between facts, entities, and prior activity become difficult to preserve and retrieve.

This project explores graph-based memory as a foundation for AI systems that need to keep and connect context over time.

A memory graph library for storing and connecting agent-relevant information

Graph-based structures for representing relationships across context

A foundation for more deliberate memory retrieval in AI workflows

Integration-oriented components suited to agent applications

Built with Neo4j, GraphQL, and Docker. The work centers on AI memory management, graph data modeling, and infrastructure for agent systems.