At a glance
The philosophical difference
Both platforms build on the same standards: RDF triples and SPARQL queries. That is where the similarity ends. TrustGraph 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 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.