RuVector — A Self-Learning, Agentic Operating System

The self-learning, self-optimizing vector database — with graph intelligence, local AI, and PostgreSQL built in.

Created by rUv and powering Cognitum, a 🏅 CES 2026 Innovation Awards Honoree — the world's first Agentic Chip designed to be always running for AI agents. Tens of thousands of agents, near-zero power, learns from every signal. Learn more →

npx ruvector

Most vector databases store your data and search it — the same way, every time.

RuVector is fundamentally different. It watches how you use it and gets smarter: search results improve automatically, the system tunes itself to your workload, and it runs AI models right on your hardware — no cloud APIs, no per-query bills, GPUs optional, CPUs preferred. It drops into PostgreSQL, runs in browsers, and ships as a single file.

Open source. ❤️ Free forever.

User Query → [SONA Engine] → Model Response → User Feedback
                  ↑                                 │
                  └─────── Learning Signal ─────────┘
                         (< 1ms adaptation)

<details> <summary>🔍 RuVector vs Typical Vector Databases (20 differences)</summary>

| | RuVector | Typical Vector DB | |---|---|---| | Self-Learning & Optimization | | | | Search quality | 🧠 GNN learns from every query — results improve over time | Static — same results every time | | Self-optimizing | ⚡ SONA auto-tunes routing, ranking, and compression to your workload | Manual tuning required | | 46 attention mechanisms | 🎯 Flash, linear, graph, hyperbolic, mincut-gated (cuts compute 50%) | Basic similarity only | | Transfer learning | 🔄 Knowledge transfers across domains — new tasks bootstrap from past learning | Start from scratch each time | | Graph & Relationships | | | | Graph queries | 🔗 Full Cypher engine — MATCH (a)-[:KNOWS]->(b) like Neo4j | Flat list of results | | Graph transformers | 🔬 8 verified modules: physics, bio, manifold, temporal, economic | No graph support | | Hyperedges | 🕸️ Connect 3+ nodes at once — model group relationships natively | Pairwise only | | AI & Compute | | | | Local LLMs | 🤖 Run models on your hardware — Metal, CUDA, WebGPU, no API costs | Cloud API required (pay per call) | | Sublinear solvers | 📐 O(log n) PageRank, spectral methods, sparse linear systems | Not available | | Graph sparsifier | 🕸️ Keeps a small shadow graph that tracks the full graph's structure in real time | Not available | | Genomics | 🧬 Variant calling, protein translation, HNSW k-mer search in 12 ms | Not available | | Quantum coherence | ⚛️ Error correction via dynamic min-cut optimization | Not available | | Database & Platform | | | | PostgreSQL | 🐘 230+ SQL functions — drop into your existing database, pgvector replacement | Separate service to manage | | Deploy anywhere | 🌐 One file — servers, browsers, phones, IoT, bare metal, WASM (58 KB) | Cloud server required | | Cognitive containers | 🚀 Single .rvf file boots as a service in 125 ms — includes vectors, models, kernel | Configure a cluster | | Live updates | ⚡ Update vectors and graph connections instantly, no downtime | Rebuild index or wait | | Operations | | | | Tamper-proof audit | 🔐 Cryptographic witness chain records every operation automatically | Manual logging | | Branch your data | 🌿 Git-like COW branching — 1M vectors, 100 edits = ~2.5 MB branch | Copy everything | | Scale out | 📈 Raft consensus, multi-master replication, auto-sharding | Paid tiers, per-vector pricing | | Post-quantum crypto | 🛡️ ML-DSA-65 and Ed25519 signatures on every segment | Not available | | Cost | 💰 Free forever — open source (MIT) | Per-query or per-vector pricing |

</details> <details> <summary>📋 See Full Capabilities (75 features across 10 categories)</summary>

Core Vector Database | # | Capability | What It Does | |---|------------|--------------| | 1 | Store vectors | Embeddings from OpenAI, Cohere, local ONNX with HNSW indexing and SIMD acceleration | | 2 | Query with Cypher | Graph queries like Neo4j — MATCH (a)-[:SIMILAR]->(b) with hyperedges | | 3 | The index learns | GNN layers make search results improve over time — every query teaches the system | | 4 | Hyperbolic HNSW | Hierarchy-aware search in Poincare ball space — better for trees and taxonomies | | 5 | Compress automatically | 2-32x memory reduction with adaptive tiered compression and temporal tensor reuse | | 6 | Metadata filtering | Filter search results by any field before scanning vectors — fast hybrid queries | | 7 | Collections | Multi-tenant, schema-managed collections — isolate data per customer or project | | 8 | Snapshots | Point-in-time backups — restore your database to any previous state |

Distributed Systems | # | Capability | What It Does | |---|------------|--------------| | 9 | Raft consensus | Leader election and log replication — nodes agree on state even when some fail | | 10 | Multi-master replication | Vector clocks, conflict resolution, geo-distributed sync across data centers | | 11 | Cluster management | Horizontal scaling with consistent hashing — add nodes without rebalancing everything | | 12 | Delta consensus | Track behavioral changes across distributed nodes with CRDTs and causal ordering | | 13 | Burst scaling | 10-50x capacity scaling for traffic spikes — absorb load then scale back down | | 14 | Auto-sharding | Automatic data partitioning across nodes based on access patterns |

AI & Machine Learning | # | Capability | What It Does | |---|------------|--------------| | 15 | Run LLMs locally | Load GGUF models and run inference on your hardware — Metal, CUDA, ANE, WebGPU | | 16 | RuvLTRA models | Pre-trained GGUF for routing and embeddings in under 10 ms | | 17 | SONA learning | Self-Optimizing Neural Architecture — LoRA fine-tuning + EWC++ memory preservation | | 18 | 46 attention mechanisms | Flash, linear, graph, hyperbolic, mincut-gated (cuts compute 50%) | | 19 | Semantic routing | Route AI requests to the right model or handler using FastGRNN neural inference | | 20 | Sparse inference | PowerInfer-style engine — only activate the neurons you need, 2-10x faster on edge | | 21 | Tiny Dancer | Production-grade agent routing with FastGRNN — lightweight alternative to full LLM | | 22 | Domain expansion | Cross-domain transfer learning — new tasks bootstrap from past learning automatically | | 23 | Advanced math | Optimal transport, Sinkhorn distances, KL divergence, spectral clustering | | 24 | Coherence measurement | Measure signal quality and compare attention mechanisms objectively | | 25 | CNN image embeddings | MobileNet-V3 with SIMD/Winograd/INT8 — <5ms image embeddings, WASM-ready, zero deps |

Graph Transformers (8 verified modules) | # | Capability | What It Does | |---|------------|--------------| | 25 | Proof-gated mutation | Every write to graph state requires a formal proof — bugs cannot corrupt data | | 26 | Sublinear attention | O(n log n) via LSH bucketing, PPR sampling, and spectral sparsification | | 27 | Physics-informed layers | Hamiltonian dynamics, gauge equivariant message passing — energy conserved by construction | | 28 | Biological layers | Spiking attention, Hebbian/STDP learning, dendritic branching | | 29 | Self-organizing layers | Morphogenetic fields, reaction-diffusion growth — graphs that restructure themselves | | 30 | Verified training | Training certificates, delta-apply rollback — bad gradient steps auto-reversed | | 31 | Manifold geometry