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Standard retrieval-augmented generation (RAG) often struggles with evolving facts and complex relational queries. Graph RAG in Worlds addresses these limitations by maintaining a stateful, structured memory.

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Limits of stateless RAG

In traditional RAG, you chunk and embed text for retrieval based on semantic similarity. This works for static information but fails to capture relationship dynamics or state changes.

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The evolving fact

  1. Monday: “I am working on Project Apollo.”
  2. Wednesday: “I am pausing Apollo to focus on Project Hermes.”
  3. Friday: “What am I working on?”
A traditional RAG system often retrieves both chunks, forcing the LLM to resolve the contradiction through heuristics. As your data grows, retrieval becomes increasingly noisy.

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Stateful memory

Worlds maintains a living knowledge graph. Instead of storing raw text, it extracts meaning as triples (subject → predicate → object). When facts change, Worlds updates the specific relationships in the graph. This allows you to resolve contradictions at the data layer rather than relying on probabilistic LLM reasoning.

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RAG vs Worlds

RAG vs Graph RAG
FeatureTraditional RAGWorlds, functioning as world memory
SearchSemantic similarityHybrid, combining semantic and relational
StateStatelessStateful, resolving contradictions
InferenceHallucination-proneDeterministic reasoning
StructureUnstructured chunksKnowledge primitives, namely items and triples

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Implementing graph RAG

To implement graph RAG with Worlds, follow the ingestion pipeline to transform your unstructured data into a queryable graph.