> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wazoo.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Triplestores (Fuseki, GraphDB, Blazegraph, Oxigraph)

> Worlds vs Traditional RDF Triplestores: context engine vs enterprise SPARQL engines.

## At a glance

|                   | Worlds                                               | Traditional Triplestores (Jena, GraphDB, Oxigraph) |
| :---------------- | :--------------------------------------------------- | :------------------------------------------------- |
| Primary role      | Multi-modal AI context engine & RDF fact ledger      | Dedicated RDF graph database & SPARQL endpoint     |
| Data model        | Append-only RDF quad ledger + hybrid vector index    | RDF triples/quads with OWL/RDFS reasoning          |
| Retrieval         | Reciprocal Rank Fusion (SPARQL + Vector + Full-text) | Standard W3C SPARQL 1.1 Query & Update             |
| Embedding search  | Native vector index integrated into RRF retrieval    | Varies (plugin-based or external vector stores)    |
| Storage Substrate | LibSQL/Turso, Postgres, or Cloud                     | Custom disk structures / RocksDB / Java storage    |

## Overlap and shared capabilities

Worlds and traditional triplestores (such as
[Apache Jena Fuseki](https://jena.apache.org/documentation/fuseki2/),
[Ontotext GraphDB](https://graphdb.ontotext.com/),
[Blazegraph](https://github.com/blazegraph/database), and
[Oxigraph](https://github.com/oxigraph/oxigraph)) share a foundation: W3C
open-standard RDF data models and SPARQL query support. Both allow querying
deterministic graph relationships using standard subject-predicate-object
triples.

The difference lies in target architecture:

* Traditional triplestores are standalone graph databases optimized for heavy
  Semantic Web reasoning (OWL/RDFS), enterprise ontologies, and pure SPARQL 1.1
  endpoints.
* Worlds is built specifically as an AI context engine. It fuses SPARQL graph
  traversal with native vector embeddings and full-text search using Reciprocal
  Rank Fusion (RRF), running on lightweight storage backends like LibSQL/Turso
  and Postgres.

## When Worlds fits

* You need hybrid retrieval combining SPARQL graph filters with vector semantic
  search for AI agents.
* You want lightweight, edge-ready storage adapters (LibSQL, Postgres) without
  managing a Java JVM cluster.
* You require append-only quad ledgers with built-in provenance tracking for
  agent context.

## When Traditional Triplestores fit

* You require complex OWL/RDFS enterprise reasoning and heavy semantic inference
  rules.
* You need a standard, standalone SPARQL endpoint for legacy Semantic Web
  toolchains.
* You are running dedicated graph database clusters (e.g. GraphDB Enterprise or
  Blazegraph).