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Autonomous agents write new facts into the graph to evolve the world. Worlds supports multiple strategies for ingesting and synchronizing knowledge within a stateful context.

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State mutations

A world mutates its state using RDF patches, which are additions and deletions of facts. Because Worlds is a chronological, append-only ledger, these patches permanently preserve historical truth.

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Import quads

The mutation path on the hosted API is the import endpoint. SPARQL on the data plane is read-only; to change state, send quads to import:
The import above asserts that wazoo:worlds is a schema:SoftwareApplication. Set contentType to text/plain to ingest unstructured text chunks instead. Unlike traditional databases that overwrite records, Worlds uses RDF patches to keep a chronological record of changes.

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Mutation and state

Every update to a world is a transaction that appends new facts to the ledger. This ensures agents can always query past states and see how information has changed.

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RDF patches

An RDF patch is a structured set of additions and deletions. When you update a relationship, the engine performs a patch:
  1. Deletion: Remove the outdated triple.
  2. Addition: Insert the new, verified triple.
This process is atomic and verifiable.

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Update strategies

Worlds supports multiple methods for mutating state, depending on the required precision and automation level.

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Feedback ingestion

Intentional agency requires a bridge between human preferences and graph state. RLHF enables this by treating feedback as a first-class mutation.

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Capturing rewards

When a user or supervisor provides feedback (e.g., a thumbs up or a specific correction), the API records this as a preference item connected to the original fact.

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Recursive learning

As preferences accumulate, the world’s probability landscape is reshaped. Historical reward signals boost retrieval results, which creates a recursive loop where the system learns which triples are most useful or truthful for the agent’s context. This historical truth remains in the ledger, so this preference data can guide future queries. Learn more about preference-aware retrieval.