ontology

Build a knowledge graph for AI agent memory by creating entities, properties, and relations. This approach structures project, person, and task details to enable persistent, verifiable cross-skill data sharing and querying.

Structured data validation works by enforcing schema constraints and type checking during entity creation. This ensures knowledge graph integrity by preventing invalid properties or relations from being stored in the agent memory.

You can use a typed schema to plan multi-step actions as graph transformations. The schema links multi-step actions to existing entities, allowing agents to execute sequential tasks while maintaining structured data consistency.

The best way to link project entities with team members and tasks is to use a structured knowledge graph. It models these elements as typed entities and relations, enabling complex queries and consistent cross-skill data sharing.

You need PyYAML installed to manage structured agent memory using this approach. It serves as the underlying dependency required to parse and validate the schema configurations for the knowledge graph.