RDF and SPARQL engines
RDF and SPARQL engines such as Oxigraph, QLever, and Tentris concentrate on standards compliance, query execution, compact deployment, or specialized indexing. This comparison keeps those systems together rather than mixing them into the managed enterprise-platform matrix.
Where this category excels
Focused implementations with clear storage and query-engine boundaries.
Strong specialization in standards compliance, query performance, or compact deployment.
Useful components for applications that do not need a broader operational platform.
Where Legra extends the model
The graph engine sits inside sovereign, encrypted, versioned workspaces rather than defining the entire product boundary.
Hosts, custodians, gateways, workers, and licensors separate execution, transport, durable custody, external I/O, and authority.
Tasks, journals, agreements, files, provenance, pricing, and settlement stay connected to the graph data they concern.
Questions to use in an evaluation
Do you need a focused RDF query engine or infrastructure for governed cross-party work?
Will deployment remain centralized, or must data and authority move between sovereign operators?
Must query results connect to branches, tasks, evidence, rights, and economic exchange?
Beyond database features
Legra also organizes the work that uses and changes the graph, the evidence needed to review that work, and the economic relationships that move data and value between parties. That operational layer is what turns graph infrastructure into data logistics infrastructure.
Legra keeps each piece of work together with the changes it made, the evidence behind it, and any costs incurred. That history stays private to the workspace and cannot be quietly rewritten.
Full provenance connects signed commits and exact deltas to task and transaction lineage, actors, delegated authority, imports, and every retained rule and premise behind derived facts.
The admitted total composes independently priced data value, compute, storage, custody, transport, external I/O, rights, and operator services instead of treating bytes as the value of data.
Every resource-consuming operation follows one path: measure, price, authorize, execute, record, and settle. Detailed evidence stays with the workspace instead of one central billing ledger.
Product-level technical deep dive
This matrix includes only products in the same directly comparable family. Adjacent product types belong in their own guide instead of making one oversized, apples-and-oranges scorecard.
C++ SPARQL engine that scales to a trillion triples on a single commodity machine
| Feature | Legra | Oxigraph | QLever | Tentris |
|---|---|---|---|---|
| Architecture | ||||
| Peer-to-peer distribution | ||||
| No central server required | ||||
| Data Model & Query | ||||
| RDF / Linked Data native | ||||
| Full-text search | ||||
| Query Languages | ||||
| RDF 1.2 | ||||
| SPARQL 1.1 | ||||
| SPARQL 1.2 | ||||
| RDF-star (RDF*) | ||||
| openCypher | ||||
| ISO GQL | ||||
| SQL | ||||
| Graph Store Protocol | ||||
| GraphQL | ||||
| Proprietary query language | ||||
| Versioning & Branching | ||||
| Git-style branching | ||||
| Immutable commits | ||||
| Delta / diff queries | ||||
| Security & Encryption | ||||
| Decentralized identity (DIDs) | ||||
| planned | ||||
| Enterprise & Operations | ||||
| High-availability clustering | ||||
| OWL / RDFS reasoning engine | ||||
| Datalog reasoning | ||||
| SWRL / RIF rules | ||||
| LPG reasoning | ||||
| SHACL / ShEx validation | ||||
| Managed cloud offering | ||||
| Visual graph explorer / GUI tools | ||||
| Production-ready today | ||||
| AI & Vector | ||||
| Built-in vector search | ||||
| Versioned vector indexes | ||||
| Encrypted vector search | ||||
| Data Model Breadth | ||||
| Property graph / LPG support | ||||
| ACID transactions | ||||
| Immutable transaction history | ||||
| Indexing & Performance | ||||
| Ad-hoc index creation | ||||
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