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After hybrid search disambiguates a starting item, agents traverse its connected facts using SPARQL (SPARQL Protocol and RDF Query Language). This is the W3C standard for knowledge graphs.

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SPARQL reasoning

SPARQL matches patterns against facts, follows relationships, and returns exact results without probabilistic guessing.

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Symbolic logic

Given triples such as user:person wazoo:worksFor wazoo:organization, query for the organization:
TypeScript
import { Worlds } from "@wazoo/worlds-sdk";

const worlds = new Worlds({
  apiKey: process.env.WORLDS_API_KEY,
});

// Execute a SPARQL query to retrieve facts
const response = await worlds.sparql(
  "my-world-id",
  `PREFIX wazoo: <https://wazoo.dev/#>

  SELECT ?wazoo WHERE {
    <https://etok.me/#person> wazoo:worksFor ?wazoo .
  }`,
);

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Neuro-symbolic reasoning

Worlds fuses semantic discovery with symbolic logic. Hybrid search disambiguates the natural language intent into a specific starting item, while SPARQL executes the deterministic traversal across verified facts.
ContextHybrid searchSPARQL
DimensionSemantic discoveryDeterministic reasoning
GoalItem disambiguationFactual traversal
LogicProbabilisticSymbolic
The Worlds API provides an external source of truth that an agent can deterministically verify. In practice, an LLM accesses this source of truth via tool calling, allowing it to ground its responses in verified facts rather than probabilistic weights.

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High-stakes context

Standard RAG struggles with evolving facts and complex relational queries. Worlds maintains a living knowledge graph where contradictions are resolved at the data layer.

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The evolving fact

Consider a scenario where information changes rapidly:
  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?”
Traditional RAG retrieves both contradictory chunks, forcing the LLM to guess. Worlds updates the specific graph relationship, ensuring the agent only retrieves the current state.

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Grounding agents in ontologies

By using discover-schema, agents retrieve the world’s ontology before attempting to query it.
  1. Discovery: Agent retrieves the world’s ontology.
  2. Mapping: Agent maps intent to specific RDF classes and predicates.
  3. Querying: Agent executes precise SPARQL queries instead of depending solely on vector similarity.

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Preference-aware retrieval

While standard SPARQL is strictly deterministic, retrieval can be further aligned with human intent through feedback. When a query results in multiple valid paths, the engine uses the reshaped probability landscape to prefer the path that has historically yielded high-reward results. This transformation moves the system from deterministic reasoning to intentional agency. Learn more about the theory in the Architecture deep-dive.