> ## 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.

# Worlds vs TrustGraph

> Worlds vs TrustGraph: managed curated graphs against a self-hosted agent platform.

## At a glance

|                 | Worlds                 | [TrustGraph](https://trustgraph.ai)    |
| :-------------- | :--------------------- | :------------------------------------- |
| Data model      | RDF triples            | RDF triples (holonic context graph)    |
| How facts enter | Curated import         | Automated extraction plus manual load  |
| Query           | SPARQL + hybrid search | SPARQL + GraphRAG, Document RAG, NL    |
| Provenance      | Fact-level ledger      | Extraction traces, audit logs          |
| Hosting         | Managed data plane     | You run it (Docker, Kubernetes, cloud) |
| Scope           | Context engine         | Full agent platform                    |

## The philosophical difference

Both platforms build on the same standards: RDF triples and SPARQL queries. That
is where the similarity ends.

[TrustGraph](https://trustgraph.ai) is a self-hosted agent intelligence
platform. It ingests documents, automatically constructs a holonic context graph
through entity and relation extraction, and wraps that graph with GraphRAG,
ontology RAG, agent runtimes, flows, MCP integration, and model serving. You
operate the stack, choose the graph and vector stores, and keep everything
inside your infrastructure.

Worlds is a managed context engine and deliberately narrow. Facts enter by
curation, not extraction, and land in an append-only ledger where every fact
carries a traceable path to its source. Retrieval is deterministic by
construction: the graph either contains the fact or it does not, and SPARQL can
prove which. You bring the LLM and the embedding provider; Worlds stores and
retrieves.

The practical difference is who decides what a fact is. TrustGraph's platform
extracts context at scale and makes it explainable. Worlds asserts curated facts
and makes them auditable. The extraction pipeline suits breadth; the curated
ledger suits defensibility.

## When Worlds fits

* You want the RDF and SPARQL model without operating graph infrastructure.
* Facts must be curated and reviewed rather than inferred by a model.
* Agents need [hybrid search](/worlds/search) and SPARQL behind scoped tokens,
  with edge adapters for local and edge deployment.

## When Worlds does not fit

* You need automated knowledge graph construction from documents at scale.
* You want an all-in-one platform: agents, flows, MCP, model serving, and
  monitoring in one self-hosted stack.
* Your requirements mandate running the entire pipeline on-premise.

## Coexistence

Both platforms speak RDF. Export a world to N-Triples and load it into
TrustGraph (`tg-load-turtle`), or bring an extracted graph into Worlds when the
facts need curation and versioning. The serialization interoperates even when
the pipelines do not.