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What you'll see

A full VectorMBE instance, pre-seeded and ready

Entity graph

Browse a pre-loaded systems graph with nodes, edges, and OWL-classified entities. Inspect attributes, follow links, and visualize topology in 3D.

OWL reasoning

Run SPARQL-like queries over the ontology. Trace subclass relationships, check satisfiability, and see how formal axioms constrain the model.

Vector search

Search by meaning, not keyword. Vector embeddings rank similar designs, requirements, and telemetry by semantic proximity.

Formal constraints

Evaluate hard constraints that override probabilistic suggestions. See pass/fail results with severity levels and audit trails.

Safety assessment generation

Automatically synthesize hazard analyses, fault trees, and FMEA entries from your model graph. VectorMBE reasons over failure modes, propagation paths, and mitigation coverage to produce auditable safety artifacts.

Architecture reasoning

Ask architectural questions in natural language and receive answers grounded in your actual model. VectorMBE evaluates design decisions, surfaces constraint violations, and explains the rationale behind derived allocations and interface definitions.

Live demo

Hosted instance, now live

The hosted VectorMBE demo is live at vectormbe.vectorstreamsystems.com. GitHub Sponsors get immediate access — sign up below to request a login link.

GitHub Sponsors get priority access. Sponsors are added to the demo within 24 hours.

Run locally

Prefer to run VectorMBE on your own machine? Build the Rust backend and the React UI from source.

Show terminal instructions
bash
# Terminal 1 — start the backend
cd VectorMBE
export LD_LIBRARY_PATH="target/release/build/torch-sys-*/out/libtorch/libtorch/lib:$LD_LIBRARY_PATH"
VECTORMBE_REQUIRE_TORCH_GPU=false ./target/release/vectormbed

# Terminal 2 — start the UI
cd VectorMBE/ui
npm run dev

# Open the local UI in your browser

Requires Rust, Node 22, and a LibTorch setup. Or use Docker: docker-compose up --build in the VectorMBE repo.