Memory an agent can reproduce and replay.
AI agents forget, contradict themselves, and can't show their work. Neruva is a deterministic memory and grounding substrate — a hyperdimensional knowledge graph, hybrid semantic + BM25 retrieval, corrections that stay corrected, citations on every answer, and a snapshot/replay audit of exactly what the agent knew.
Built and run across my own projects, and published as MCP SDKs (npm + PyPI). Deep R&D I chose not to commercialize — shown here as evidence of the engineering.The same principle, applied to memory
Determinism, provenance, and an audit you can replay.
Deterministic recall
The same query returns the same result, every time — retrieval math lives in a deterministic substrate, not a black box. No silent drift between runs.
Enforce-deny corrections
A correction is enforced deterministically: once you tell the system a fact is wrong, it stays corrected — not re-litigated by a model on the next pass.
Provenance & citations
Every answer can carry its source — file, page, confidence — so an agent cites where a claim came from instead of asserting it unsourced.
Snapshot / replay audit
Memory state can be snapshotted and replayed; GDPR atomic forget removes a record cleanly. You can audit exactly what the agent knew, and when.
UNDER THE HOOD
Not a RAG wrapper
The interesting part is the substrate. It is vector-symbolic / hyperdimensional computing, real information-retrieval engineering, and a distributed system — every non-trivial path gated by a benchmark before it ships.
A hyperdimensional knowledge graph
Facts are stored as vector-symbolic structures — D=8192 bipolar hypervectors bound, permuted, and bundled into a relation-sharded graph engine, with BLAS rank-1 updates that add a fact without ever materializing the outer-product matrix.
91.4% recall over 10k facts (in-harness)Hybrid retrieval, validated
Semantic recall fused with a vectorized BM25 implementation (one sparse matmul, validated to 2.3e-5 against the textbook Robertson reference), combined by reciprocal-rank fusion and a regime-adaptive router.
BM25 matches reference to 2.3e-5A real distributed substrate
A hosted FastAPI service on Cloud Run with a per-tenant engine cache and GCS-backed persistence that survives scale-to-zero recycles — deterministic across replicas (a documented hash-salt bug fixed with stable bucketing).
api.neruva.io · hostedPolyglot, published, enforced
Published as MCP SDKs on npm and PyPI, kept in sync across TypeScript and Python, plus a Rust PreToolUse hook that enforce-denies an action when a prior mistake recalls above threshold — so an agent stops repeating its own scars.
npm + PyPI · TS / Python / RustWhy a deterministic substrate
The split is deliberate: the server is a deterministic substrate — storage, retrieval math, provenance, counts — while meaning and judgment stay with the agent. That boundary is what makes recall reproducible and auditable, the same trait that makes VLA's arithmetic and Cairn's trust decisions verifiable. And nothing in the retrieval path ships on intuition — a ranking change has to clear a recall@k or LongMemEval gate first.
Built and running
The memory layer behind all my work — designed, built, and benchmarked solo. Deep systems R&D, not a commercial product.