Core objectiveBy the end of this path, you will understand how to bridge Large Language Models
with structured knowledge graphs, giving your agents persistent, stateful
memory.
Learning path
Follow these steps in order to build your foundation in the Worlds Ecosystem.World memory fundamentals
Understand the concept of world memory and how it differs from traditional RAG. Learn why stateful environments are essential for intelligent agents.Start module
Symbolic graph architecture
Learn how to structure knowledge using Subject-Predicate-Object triples. Master the atomic unit of knowledge in the Worlds graph.Start module
Logical reasoning with SPARQL
Master the basics of querying your world memory with symbolic logic. Learn to extract exact facts and follow logical relationships.Start module
Neuro-symbolic agent integration
Connect your AI agent to its memory using neuro-symbolic tool-calling. Build an agent that can read and write to its own world model.Start module
Additional resources
Once you complete the core lessons, explore these guides to deepen your expertise.Knowledge graphs
Master the neuro-symbolic infrastructure of item graphs.
Semantic search
Implement contextual retrieval across the global item store.
API reference
Browse the functional endpoints of the Worlds API.