Labelled property graph platforms
Property-graph ergonomics should not require a separate knowledge world.
LPG platforms make graph traversal and application development approachable through labelled nodes, relationships, and languages such as Cypher and GQL. Legra preserves that interface while connecting it to RDF meaning, inference, governed workspaces, provenance, and distributed data logistics.
Axis coverage
The six axes of the complete logistics loop. What each axis asks.
Compare the mechanisms.
Labelled property graph platforms
Accessible graph models for developers and application teams.
Traversal-oriented querying and strong graph-application ecosystems.
Operational graph workloads, algorithms, and visualization tools.
Legra
Cypher and GQL can benefit from ontology-backed meaning and Datalog-grade inference.
Branches, commits, validation, search, tasks, and provenance share one versioned workspace state.
LPG applications can participate in sovereign multi-party agreements and Data Product exchange.
Questions to use in an evaluation
Ask these in the room.
- 01
Will the graph remain an application database, or become shared organizational knowledge?
- 02
Do business concepts need portable semantics beyond one database schema?
- 03
Must agents and workflows operate on reviewed branches with durable evidence?
Graph features are only the substrate.
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.
A complete history of the work
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.
- Work by people, AI agents, and automated services appears in one traceable record.
- See what is happening now, then return to the same history later for review.
Layered provenance
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.
- Evidence answers who or what acted, on whose authority, from which inputs, and with which result.
- Commit, activity, fact-origin, and derivation evidence remains attached to the workspace and its history.
Composable transaction pricing
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.
- Charges, credits, refunds, penalties, and commissions contribute to the final amount.
- Zero is an explicit price through the same accounting path, including self-operated work.
Multidirectional token settlement
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.
- Tokens can flow to data owners, infrastructure and service Operators, and the NetworkOperator treasury.
- The same evidence supports internal chargeback and external settlement; subscriptions supply token packages rather than feature gates.
Product-level technical deep dive
Labelled property graph platforms: product matrix.
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.
ArangoDB
Multi-model database with native graph, document, and key/value support
LadybugDB
Embedded columnar graph database — 'DuckDB for graphs'
GraphQLite
SQLite extension adding graph database capabilities via Cypher
| Feature | Legra | ArangoDB | LadybugDB | GraphQLite |
|---|---|---|---|---|
| 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) | ||||
| 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 | ||||