Technology & security
Operate serious graph-based work without assembling another fragile stack.
Legra brings graph storage, SPARQL, Cypher, GQL, RDFS, OWL-RL, Datalog, validation, branching, task execution, provenance, content addressing, encryption, and peer-to-peer communication into one operating model. Technology teams can run it locally, across organizational infrastructure, or through Operators without giving one vendor authority over every workspace.
What changes
Use RDF and labelled-property-graph interfaces over one graph infrastructure.
Bind queries, validation, inference, search, task execution, and generated artefacts to versioned workspace state.
Keep workspace data encrypted and distribute custody separately from the authority to interpret it.
Give humans and agents the same explicit stories, permissions, budgets, review paths, and evidence model.
Connect existing code and services through APIs and Story implementations instead of rewriting everything as graph data.
Deploy nodes on your own hardware and use network services when activity crosses economic or organizational boundaries.
Inspect the security, architecture, and operational trade-offs directly rather than relying on a generic platform claim.
One graph, several interfaces
RDF semantics and LPG ergonomics do not need separate islands. Legra is designed so SPARQL, Cypher, GQL, Datalog reasoning, validation, vector search, and full-text search participate in one branch-sensitive knowledge context.
Authority and custody are different
Nodes that retain encrypted blocks do not automatically gain the right or keys to read them. Identity, workspace authority, policy, signed history, and task evidence remain explicit across the network.
AI work is ordinary governed work
Agents act through the same Use Cases, Stories, task trees, branches, budgets, tools, and review mechanisms as other participants. Model providers remain replaceable; the durable context and evidence stay in the workspace.
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